{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# <font color='green'>UCI Audit Risk Project Part 1: Classification\n",
"### MIS 6V99.003 Machine Learning \n",
"#### Abhishek Patil \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Table of Contents:\n",
"1. [Data Pre-processing](#Data-Pre-processing)\n",
" * [Importing Datasets](#Importing-Datasets)\n",
" * [Exploring Data](#Exploring-Data)\n",
" * [Merging Dataframes](#Merging-Dataframes)\n",
" * [Data Cleaning](#Data-Cleaning)\n",
" \n",
" \n",
"2. [Correlation Matrix](#Correlation-Matrix)\n",
"\n",
"\n",
"3. [Classification](#Classification)\n",
" * [Classification: Train-Test Split](#Classification:-Train-Test-Split)\n",
" * [Classification: Feature Scaling](#Classification:-Feature-Scaling)\n",
" * [Classification Models](#Classification:-Models)\n",
" * [Voting Classifiers](#Voting-Classifiers)\n",
" * [Hard Voting Classifier 1](#Hard-Voting-Classifier-1)\n",
" * [Hard Voting Classifier 2](#Hard-Voting-Classifier-2)\n",
" * [Soft Voting Classifier 1](#Soft-Voting-Classifier-1)\n",
" * [Soft Voting Classifier 2](#Soft-Voting-Classifier-2)\n",
" * [Bagging](#Bagging)\n",
" * [KNN with Bagging](#KNN-with-Bagging)\n",
" * [Logistic Regression with Bagging](#Logistic-Regression-with-Bagging)\n",
" * [Pasting](#Pasting)\n",
" * [Decision Tree with Pasting](#Decision-Tree-with-Pasting)\n",
" * [Linear SVC with Pasting](#Linear-SVC-with-Pasting)\n",
" * [Adaboost](#Adaboost)\n",
" * [Decision Tree with Adaboost](#Decision-Tree-with-Adaboost)\n",
" * [Logistic Regression with Adaboost](#Logistic-Regression-with-Adaboost)\n",
" * [Gradient Boosting Classifier](#Gradient-Boosting-Classifier)\n",
" * [Voting, Boosting, Pasting and Bagging: Generating a Report table](#Voting,-Boosting,-Pasting-and-Bagging:-Generating-a-Report-table)\n",
" * [Principal Component Analysis](#Principal-Component-Analysis)\n",
" * [KNN Classifier with PCA](#KNN-Classifier-with-PCA)\n",
" * [SVM Classifier with PCA](#SVM-Classifier-with-PCA)\n",
" * [Logistic Regression with PCA](#Logistic-Regression-with-PCA)\n",
" * [Decision Tree Classifier with PCA](#Decision-Tree-Classifier-with-PCA)\n",
" * [Generating a Report table: PCA](#Generating-a-Report-table:-PCA)\n",
" * [Neural Network Model](#Neural-Network-Model) \n",
" \n",
" \n",
"4. [Model Selection](#Model-Selection) \n",
" \n",
" \n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Pre-processing"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#Importing all the required libraries\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"import pandas as pd\n",
"import numpy as np\n",
"pd.set_option('display.max_columns', 50)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importing Datasets"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"audit_risk = pd.read_csv(\"audit_risk.csv\")\n",
"trial = pd.read_csv(\"trial.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exploring Data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sector_score</th>\n",
" <th>PARA_A</th>\n",
" <th>Score_A</th>\n",
" <th>Risk_A</th>\n",
" <th>PARA_B</th>\n",
" <th>Score_B</th>\n",
" <th>Risk_B</th>\n",
" <th>TOTAL</th>\n",
" <th>numbers</th>\n",
" <th>Score_B.1</th>\n",
" <th>Risk_C</th>\n",
" <th>Money_Value</th>\n",
" <th>Score_MV</th>\n",
" <th>Risk_D</th>\n",
" <th>District_Loss</th>\n",
" <th>PROB</th>\n",
" <th>RiSk_E</th>\n",
" <th>History</th>\n",
" <th>Prob</th>\n",
" <th>Risk_F</th>\n",
" <th>Score</th>\n",
" <th>Inherent_Risk</th>\n",
" <th>CONTROL_RISK</th>\n",
" <th>Detection_Risk</th>\n",
" <th>Audit_Risk</th>\n",
" <th>Risk</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>775.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.0</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>20.184536</td>\n",
" <td>2.450194</td>\n",
" <td>0.351289</td>\n",
" <td>1.351029</td>\n",
" <td>10.799988</td>\n",
" <td>0.313144</td>\n",
" <td>6.334008</td>\n",
" <td>13.218481</td>\n",
" <td>5.067655</td>\n",
" <td>0.223711</td>\n",
" <td>1.152964</td>\n",
" <td>14.137631</td>\n",
" <td>0.290979</td>\n",
" <td>8.265434</td>\n",
" <td>2.505155</td>\n",
" <td>0.206186</td>\n",
" <td>0.519072</td>\n",
" <td>0.104381</td>\n",
" <td>0.216753</td>\n",
" <td>0.053608</td>\n",
" <td>2.702577</td>\n",
" <td>17.680612</td>\n",
" <td>0.572680</td>\n",
" <td>0.5</td>\n",
" <td>7.168158</td>\n",
" <td>0.393041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>24.319017</td>\n",
" <td>5.678870</td>\n",
" <td>0.174055</td>\n",
" <td>3.440447</td>\n",
" <td>50.083624</td>\n",
" <td>0.169804</td>\n",
" <td>30.072845</td>\n",
" <td>51.312829</td>\n",
" <td>0.264449</td>\n",
" <td>0.080352</td>\n",
" <td>0.537417</td>\n",
" <td>66.606519</td>\n",
" <td>0.159745</td>\n",
" <td>39.970849</td>\n",
" <td>1.228678</td>\n",
" <td>0.037508</td>\n",
" <td>0.290312</td>\n",
" <td>0.531031</td>\n",
" <td>0.067987</td>\n",
" <td>0.305835</td>\n",
" <td>0.858923</td>\n",
" <td>54.740244</td>\n",
" <td>0.444581</td>\n",
" <td>0.0</td>\n",
" <td>38.667494</td>\n",
" <td>0.488741</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.850000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>5.000000</td>\n",
" <td>0.200000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.400000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.400000</td>\n",
" <td>0.400000</td>\n",
" <td>0.5</td>\n",
" <td>0.280000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.370000</td>\n",
" <td>0.210000</td>\n",
" <td>0.200000</td>\n",
" <td>0.042000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>0.537500</td>\n",
" <td>5.000000</td>\n",
" <td>0.200000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.400000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.583500</td>\n",
" <td>0.400000</td>\n",
" <td>0.5</td>\n",
" <td>0.316700</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3.890000</td>\n",
" <td>0.875000</td>\n",
" <td>0.200000</td>\n",
" <td>0.175000</td>\n",
" <td>0.405000</td>\n",
" <td>0.200000</td>\n",
" <td>0.081000</td>\n",
" <td>1.370000</td>\n",
" <td>5.000000</td>\n",
" <td>0.200000</td>\n",
" <td>1.000000</td>\n",
" <td>0.090000</td>\n",
" <td>0.200000</td>\n",
" <td>0.018000</td>\n",
" <td>2.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.400000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>2.400000</td>\n",
" <td>2.214000</td>\n",
" <td>0.400000</td>\n",
" <td>0.5</td>\n",
" <td>0.555600</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>55.570000</td>\n",
" <td>2.480000</td>\n",
" <td>0.600000</td>\n",
" <td>1.488000</td>\n",
" <td>4.160000</td>\n",
" <td>0.400000</td>\n",
" <td>1.840500</td>\n",
" <td>7.707500</td>\n",
" <td>5.000000</td>\n",
" <td>0.200000</td>\n",
" <td>1.000000</td>\n",
" <td>5.595000</td>\n",
" <td>0.400000</td>\n",
" <td>2.235000</td>\n",
" <td>2.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.400000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>3.250000</td>\n",
" <td>10.663500</td>\n",
" <td>0.400000</td>\n",
" <td>0.5</td>\n",
" <td>3.249900</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>59.850000</td>\n",
" <td>85.000000</td>\n",
" <td>0.600000</td>\n",
" <td>51.000000</td>\n",
" <td>1264.630000</td>\n",
" <td>0.600000</td>\n",
" <td>758.778000</td>\n",
" <td>1268.910000</td>\n",
" <td>9.000000</td>\n",
" <td>0.600000</td>\n",
" <td>5.400000</td>\n",
" <td>935.030000</td>\n",
" <td>0.600000</td>\n",
" <td>561.018000</td>\n",
" <td>6.000000</td>\n",
" <td>0.600000</td>\n",
" <td>2.400000</td>\n",
" <td>9.000000</td>\n",
" <td>0.600000</td>\n",
" <td>5.400000</td>\n",
" <td>5.200000</td>\n",
" <td>801.262000</td>\n",
" <td>5.800000</td>\n",
" <td>0.5</td>\n",
" <td>961.514400</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sector_score PARA_A Score_A Risk_A PARA_B \\\n",
"count 776.000000 776.000000 776.000000 776.000000 776.000000 \n",
"mean 20.184536 2.450194 0.351289 1.351029 10.799988 \n",
"std 24.319017 5.678870 0.174055 3.440447 50.083624 \n",
"min 1.850000 0.000000 0.200000 0.000000 0.000000 \n",
"25% 2.370000 0.210000 0.200000 0.042000 0.000000 \n",
"50% 3.890000 0.875000 0.200000 0.175000 0.405000 \n",
"75% 55.570000 2.480000 0.600000 1.488000 4.160000 \n",
"max 59.850000 85.000000 0.600000 51.000000 1264.630000 \n",
"\n",
" Score_B Risk_B TOTAL numbers Score_B.1 \\\n",
"count 776.000000 776.000000 776.000000 776.000000 776.000000 \n",
"mean 0.313144 6.334008 13.218481 5.067655 0.223711 \n",
"std 0.169804 30.072845 51.312829 0.264449 0.080352 \n",
"min 0.200000 0.000000 0.000000 5.000000 0.200000 \n",
"25% 0.200000 0.000000 0.537500 5.000000 0.200000 \n",
"50% 0.200000 0.081000 1.370000 5.000000 0.200000 \n",
"75% 0.400000 1.840500 7.707500 5.000000 0.200000 \n",
"max 0.600000 758.778000 1268.910000 9.000000 0.600000 \n",
"\n",
" Risk_C Money_Value Score_MV Risk_D District_Loss \\\n",
"count 776.000000 775.000000 776.000000 776.000000 776.000000 \n",
"mean 1.152964 14.137631 0.290979 8.265434 2.505155 \n",
"std 0.537417 66.606519 0.159745 39.970849 1.228678 \n",
"min 1.000000 0.000000 0.200000 0.000000 2.000000 \n",
"25% 1.000000 0.000000 0.200000 0.000000 2.000000 \n",
"50% 1.000000 0.090000 0.200000 0.018000 2.000000 \n",
"75% 1.000000 5.595000 0.400000 2.235000 2.000000 \n",
"max 5.400000 935.030000 0.600000 561.018000 6.000000 \n",
"\n",
" PROB RiSk_E History Prob Risk_F Score \\\n",
"count 776.000000 776.000000 776.000000 776.000000 776.000000 776.000000 \n",
"mean 0.206186 0.519072 0.104381 0.216753 0.053608 2.702577 \n",
"std 0.037508 0.290312 0.531031 0.067987 0.305835 0.858923 \n",
"min 0.200000 0.400000 0.000000 0.200000 0.000000 2.000000 \n",
"25% 0.200000 0.400000 0.000000 0.200000 0.000000 2.000000 \n",
"50% 0.200000 0.400000 0.000000 0.200000 0.000000 2.400000 \n",
"75% 0.200000 0.400000 0.000000 0.200000 0.000000 3.250000 \n",
"max 0.600000 2.400000 9.000000 0.600000 5.400000 5.200000 \n",
"\n",
" Inherent_Risk CONTROL_RISK Detection_Risk Audit_Risk Risk \n",
"count 776.000000 776.000000 776.0 776.000000 776.000000 \n",
"mean 17.680612 0.572680 0.5 7.168158 0.393041 \n",
"std 54.740244 0.444581 0.0 38.667494 0.488741 \n",
"min 1.400000 0.400000 0.5 0.280000 0.000000 \n",
"25% 1.583500 0.400000 0.5 0.316700 0.000000 \n",
"50% 2.214000 0.400000 0.5 0.555600 0.000000 \n",
"75% 10.663500 0.400000 0.5 3.249900 1.000000 \n",
"max 801.262000 5.800000 0.5 961.514400 1.000000 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"audit_risk.describe()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sector_score</th>\n",
" <th>PARA_A</th>\n",
" <th>SCORE_A</th>\n",
" <th>PARA_B</th>\n",
" <th>SCORE_B</th>\n",
" <th>TOTAL</th>\n",
" <th>numbers</th>\n",
" <th>Marks</th>\n",
" <th>Money_Value</th>\n",
" <th>MONEY_Marks</th>\n",
" <th>District</th>\n",
" <th>Loss</th>\n",
" <th>LOSS_SCORE</th>\n",
" <th>History</th>\n",
" <th>History_score</th>\n",
" <th>Score</th>\n",
" <th>Risk</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>775.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" <td>776.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>20.184536</td>\n",
" <td>2.450194</td>\n",
" <td>3.512887</td>\n",
" <td>10.799988</td>\n",
" <td>3.131443</td>\n",
" <td>13.218481</td>\n",
" <td>5.067655</td>\n",
" <td>2.237113</td>\n",
" <td>14.137631</td>\n",
" <td>2.909794</td>\n",
" <td>2.505155</td>\n",
" <td>0.029639</td>\n",
" <td>2.061856</td>\n",
" <td>0.104381</td>\n",
" <td>2.167526</td>\n",
" <td>2.702577</td>\n",
" <td>0.626289</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>24.319017</td>\n",
" <td>5.678870</td>\n",
" <td>1.740549</td>\n",
" <td>50.083624</td>\n",
" <td>1.698042</td>\n",
" <td>51.312829</td>\n",
" <td>0.264449</td>\n",
" <td>0.803517</td>\n",
" <td>66.606519</td>\n",
" <td>1.597452</td>\n",
" <td>1.228678</td>\n",
" <td>0.184280</td>\n",
" <td>0.375080</td>\n",
" <td>0.531031</td>\n",
" <td>0.679869</td>\n",
" <td>0.858923</td>\n",
" <td>0.484100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.850000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>5.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.370000</td>\n",
" <td>0.210000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.537500</td>\n",
" <td>5.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3.890000</td>\n",
" <td>0.875000</td>\n",
" <td>2.000000</td>\n",
" <td>0.405000</td>\n",
" <td>2.000000</td>\n",
" <td>1.370000</td>\n",
" <td>5.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.090000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.400000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>55.570000</td>\n",
" <td>2.480000</td>\n",
" <td>6.000000</td>\n",
" <td>4.160000</td>\n",
" <td>4.000000</td>\n",
" <td>7.707500</td>\n",
" <td>5.000000</td>\n",
" <td>2.000000</td>\n",
" <td>5.595000</td>\n",
" <td>4.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>3.250000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>59.850000</td>\n",
" <td>85.000000</td>\n",
" <td>6.000000</td>\n",
" <td>1264.630000</td>\n",
" <td>6.000000</td>\n",
" <td>1268.910000</td>\n",
" <td>9.000000</td>\n",
" <td>6.000000</td>\n",
" <td>935.030000</td>\n",
" <td>6.000000</td>\n",
" <td>6.000000</td>\n",
" <td>2.000000</td>\n",
" <td>6.000000</td>\n",
" <td>9.000000</td>\n",
" <td>6.000000</td>\n",
" <td>5.200000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sector_score PARA_A SCORE_A PARA_B SCORE_B \\\n",
"count 776.000000 776.000000 776.000000 776.000000 776.000000 \n",
"mean 20.184536 2.450194 3.512887 10.799988 3.131443 \n",
"std 24.319017 5.678870 1.740549 50.083624 1.698042 \n",
"min 1.850000 0.000000 2.000000 0.000000 2.000000 \n",
"25% 2.370000 0.210000 2.000000 0.000000 2.000000 \n",
"50% 3.890000 0.875000 2.000000 0.405000 2.000000 \n",
"75% 55.570000 2.480000 6.000000 4.160000 4.000000 \n",
"max 59.850000 85.000000 6.000000 1264.630000 6.000000 \n",
"\n",
" TOTAL numbers Marks Money_Value MONEY_Marks \\\n",
"count 776.000000 776.000000 776.000000 775.000000 776.000000 \n",
"mean 13.218481 5.067655 2.237113 14.137631 2.909794 \n",
"std 51.312829 0.264449 0.803517 66.606519 1.597452 \n",
"min 0.000000 5.000000 2.000000 0.000000 2.000000 \n",
"25% 0.537500 5.000000 2.000000 0.000000 2.000000 \n",
"50% 1.370000 5.000000 2.000000 0.090000 2.000000 \n",
"75% 7.707500 5.000000 2.000000 5.595000 4.000000 \n",
"max 1268.910000 9.000000 6.000000 935.030000 6.000000 \n",
"\n",
" District Loss LOSS_SCORE History History_score \\\n",
"count 776.000000 776.000000 776.000000 776.000000 776.000000 \n",
"mean 2.505155 0.029639 2.061856 0.104381 2.167526 \n",
"std 1.228678 0.184280 0.375080 0.531031 0.679869 \n",
"min 2.000000 0.000000 2.000000 0.000000 2.000000 \n",
"25% 2.000000 0.000000 2.000000 0.000000 2.000000 \n",
"50% 2.000000 0.000000 2.000000 0.000000 2.000000 \n",
"75% 2.000000 0.000000 2.000000 0.000000 2.000000 \n",
"max 6.000000 2.000000 6.000000 9.000000 6.000000 \n",
"\n",
" Score Risk \n",
"count 776.000000 776.000000 \n",
"mean 2.702577 0.626289 \n",
"std 0.858923 0.484100 \n",
"min 2.000000 0.000000 \n",
"25% 2.000000 0.000000 \n",
"50% 2.400000 1.000000 \n",
"75% 3.250000 1.000000 \n",
"max 5.200000 1.000000 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trial.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most columns from the 2 dataframes have similar feature names and also similar description. Some columns are in all capital letters, some have values in multiples of 10. The only columns in **'trial'** that are entirely differrent are **'Loss'** and **'Risk'**."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#Renaming columns\n",
"trial.columns = ['Sector_score','LOCATION_ID', 'PARA_A', 'Score_A', 'PARA_B',\n",
" 'Score_B', 'TOTAL', 'numbers', 'Marks',\n",
" 'Money_Value', 'MONEY_Marks', 'District',\n",
" 'Loss', 'LOSS_SCORE', 'History', 'History_score', 'Score', 'Risk_trial' ]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"trial['Score_A'] = trial['Score_A']/10\n",
"trial['Score_B'] = trial['Score_B']/10"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Merging Dataframes"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['History', 'LOCATION_ID', 'Money_Value', 'PARA_A', 'PARA_B',\n",
" 'Score', 'Score_A', 'Score_B', 'Sector_score', 'TOTAL', 'numbers'],\n",
" dtype=object)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"same_columns = np.intersect1d(audit_risk.columns, trial.columns)\n",
"same_columns"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Sector_score', 'LOCATION_ID', 'PARA_A', 'Score_A', 'Risk_A', 'PARA_B',\n",
" 'Score_B', 'Risk_B', 'TOTAL', 'numbers', 'Score_B.1', 'Risk_C',\n",
" 'Money_Value', 'Score_MV', 'Risk_D', 'District_Loss', 'PROB', 'RiSk_E',\n",
" 'History', 'Prob', 'Risk_F', 'Score', 'Inherent_Risk', 'CONTROL_RISK',\n",
" 'Detection_Risk', 'Audit_Risk', 'Risk', 'Marks', 'MONEY_Marks',\n",
" 'District', 'Loss', 'LOSS_SCORE', 'History_score', 'Risk_trial'],\n",
" dtype='object')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Merge two Dataframes on common columns using outer join\n",
"merged_df = pd.merge(audit_risk, trial, how='outer', on = ['History', 'LOCATION_ID', 'Money_Value', 'PARA_A', 'PARA_B',\n",
" 'Score', 'Score_A', 'Score_B', 'Sector_score', 'TOTAL', 'numbers'])\n",
"merged_df.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The dataframes have been succesfully merged."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Cleaning"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"df = merged_df.drop(['Risk_trial'], axis = 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, I deleted the 'Risk_trial' column which as originally 'Risk' from 'trial.csv' as it had some values that were different from the 'Risk' column from 'audit_risk.csv'. \n",
"\n",
"The paper provided by professor states that the values of 'Audit_Risk' being greater or equal to 1 are classified as 1 and 0 otherwise. \n",
"\n",
"This condition is being satisfied by the 'Risk' column in 'audit_risk.csv' and not by the 'Risk' column in 'trial.csv'."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 810 entries, 0 to 809\n",
"Data columns (total 33 columns):\n",
"Sector_score 810 non-null float64\n",
"LOCATION_ID 810 non-null object\n",
"PARA_A 810 non-null float64\n",
"Score_A 810 non-null float64\n",
"Risk_A 810 non-null float64\n",
"PARA_B 810 non-null float64\n",
"Score_B 810 non-null float64\n",
"Risk_B 810 non-null float64\n",
"TOTAL 810 non-null float64\n",
"numbers 810 non-null float64\n",
"Score_B.1 810 non-null float64\n",
"Risk_C 810 non-null float64\n",
"Money_Value 809 non-null float64\n",
"Score_MV 810 non-null float64\n",
"Risk_D 810 non-null float64\n",
"District_Loss 810 non-null int64\n",
"PROB 810 non-null float64\n",
"RiSk_E 810 non-null float64\n",
"History 810 non-null int64\n",
"Prob 810 non-null float64\n",
"Risk_F 810 non-null float64\n",
"Score 810 non-null float64\n",
"Inherent_Risk 810 non-null float64\n",
"CONTROL_RISK 810 non-null float64\n",
"Detection_Risk 810 non-null float64\n",
"Audit_Risk 810 non-null float64\n",
"Risk 810 non-null int64\n",
"Marks 810 non-null int64\n",
"MONEY_Marks 810 non-null int64\n",
"District 810 non-null int64\n",
"Loss 810 non-null int64\n",
"LOSS_SCORE 810 non-null int64\n",
"History_score 810 non-null int64\n",
"dtypes: float64(23), int64(9), object(1)\n",
"memory usage: 215.2+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Money_Value has 1 missing value."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"#Replacing the missing value by the median of the column\n",
"df['Money_Value'] = df['Money_Value'].fillna(df['Money_Value'].median())"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sector_score</th>\n",
" <th>PARA_A</th>\n",
" <th>Score_A</th>\n",
" <th>Risk_A</th>\n",
" <th>PARA_B</th>\n",
" <th>Score_B</th>\n",
" <th>Risk_B</th>\n",
" <th>TOTAL</th>\n",
" <th>numbers</th>\n",
" <th>Score_B.1</th>\n",
" <th>Risk_C</th>\n",
" <th>Money_Value</th>\n",
" <th>Score_MV</th>\n",
" <th>Risk_D</th>\n",
" <th>District_Loss</th>\n",
" <th>PROB</th>\n",
" <th>RiSk_E</th>\n",
" <th>History</th>\n",
" <th>Prob</th>\n",
" <th>Risk_F</th>\n",
" <th>Score</th>\n",
" <th>Inherent_Risk</th>\n",
" <th>CONTROL_RISK</th>\n",
" <th>Detection_Risk</th>\n",
" <th>Audit_Risk</th>\n",
" <th>Risk</th>\n",
" <th>Marks</th>\n",
" <th>MONEY_Marks</th>\n",
" <th>District</th>\n",
" <th>Loss</th>\n",
" <th>LOSS_SCORE</th>\n",
" <th>History_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.0</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" <td>810.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>21.406716</td>\n",
" <td>2.348556</td>\n",
" <td>0.344938</td>\n",
" <td>1.294561</td>\n",
" <td>10.347643</td>\n",
" <td>0.308395</td>\n",
" <td>6.068334</td>\n",
" <td>12.665829</td>\n",
" <td>5.064815</td>\n",
" <td>0.222716</td>\n",
" <td>1.146543</td>\n",
" <td>13.526807</td>\n",
" <td>0.287160</td>\n",
" <td>7.918490</td>\n",
" <td>2.493827</td>\n",
" <td>0.205926</td>\n",
" <td>0.516049</td>\n",
" <td>0.100000</td>\n",
" <td>0.216049</td>\n",
" <td>0.051358</td>\n",
" <td>2.674074</td>\n",
" <td>16.999643</td>\n",
" <td>0.567407</td>\n",
" <td>0.5</td>\n",
" <td>6.881682</td>\n",
" <td>0.379012</td>\n",
" <td>2.227160</td>\n",
" <td>2.871605</td>\n",
" <td>2.493827</td>\n",
" <td>0.028395</td>\n",
" <td>2.059259</td>\n",
" <td>2.160494</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>24.760476</td>\n",
" <td>5.579501</td>\n",
" <td>0.173042</td>\n",
" <td>3.378180</td>\n",
" <td>49.067564</td>\n",
" <td>0.167741</td>\n",
" <td>29.461510</td>\n",
" <td>50.292444</td>\n",
" <td>0.259188</td>\n",
" <td>0.078789</td>\n",
" <td>0.526897</td>\n",
" <td>65.213233</td>\n",
" <td>0.157414</td>\n",
" <td>39.157042</td>\n",
" <td>1.219166</td>\n",
" <td>0.036732</td>\n",
" <td>0.287118</td>\n",
" <td>0.520174</td>\n",
" <td>0.066628</td>\n",
" <td>0.299533</td>\n",
" <td>0.851869</td>\n",
" <td>53.676423</td>\n",
" <td>0.437573</td>\n",
" <td>0.0</td>\n",
" <td>37.871031</td>\n",
" <td>0.485441</td>\n",
" <td>0.787889</td>\n",
" <td>1.574145</td>\n",
" <td>1.219166</td>\n",
" <td>0.180464</td>\n",
" <td>0.367323</td>\n",
" <td>0.666277</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.850000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>5.000000</td>\n",
" <td>0.200000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.400000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.400000</td>\n",
" <td>0.400000</td>\n",
" <td>0.5</td>\n",
" <td>0.280000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.370000</td>\n",
" <td>0.070000</td>\n",
" <td>0.200000</td>\n",
" <td>0.014000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>0.430000</td>\n",
" <td>5.000000</td>\n",
" <td>0.200000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.400000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.564500</td>\n",
" <td>0.400000</td>\n",
" <td>0.5</td>\n",
" <td>0.312900</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3.890000</td>\n",
" <td>0.820000</td>\n",
" <td>0.200000</td>\n",
" <td>0.164000</td>\n",
" <td>0.335000</td>\n",
" <td>0.200000</td>\n",
" <td>0.067000</td>\n",
" <td>1.290000</td>\n",
" <td>5.000000</td>\n",
" <td>0.200000</td>\n",
" <td>1.000000</td>\n",
" <td>0.050000</td>\n",
" <td>0.200000</td>\n",
" <td>0.010000</td>\n",
" <td>2.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.400000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
" <td>2.200000</td>\n",
" <td>2.116000</td>\n",
" <td>0.400000</td>\n",
" <td>0.5</td>\n",
" <td>0.470800</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>55.570000</td>\n",
" <td>2.397500</td>\n",
" <td>0.600000</td>\n",
" <td>1.438500</td>\n",
" <td>2.985000</td>\n",
" <td>0.400000</td>\n",
" <td>1.347000</td>\n",
" <td>7.337500</td>\n",
" <td>5.000000</td>\n",
" <td>0.200000</td>\n",
" <td>1.000000</td>\n",
" <td>4.862500</td>\n",
" <td>0.200000</td>\n",
" <td>0.972500</td>\n",
" <td>2.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.400000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.600000</td>\n",
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" <td>935.030000</td>\n",
" <td>0.600000</td>\n",
" <td>561.018000</td>\n",
" <td>6.000000</td>\n",
" <td>0.600000</td>\n",
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" <td>9.000000</td>\n",
" <td>0.600000</td>\n",
" <td>5.400000</td>\n",
" <td>5.200000</td>\n",
" <td>801.262000</td>\n",
" <td>5.800000</td>\n",
" <td>0.5</td>\n",
" <td>961.514400</td>\n",
" <td>1.000000</td>\n",
" <td>6.000000</td>\n",
" <td>6.000000</td>\n",
" <td>6.000000</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sector_score PARA_A Score_A Risk_A PARA_B \\\n",
"count 810.000000 810.000000 810.000000 810.000000 810.000000 \n",
"mean 21.406716 2.348556 0.344938 1.294561 10.347643 \n",
"std 24.760476 5.579501 0.173042 3.378180 49.067564 \n",
"min 1.850000 0.000000 0.200000 0.000000 0.000000 \n",
"25% 2.370000 0.070000 0.200000 0.014000 0.000000 \n",
"50% 3.890000 0.820000 0.200000 0.164000 0.335000 \n",
"75% 55.570000 2.397500 0.600000 1.438500 2.985000 \n",
"max 59.850000 85.000000 0.600000 51.000000 1264.630000 \n",
"\n",
" Score_B Risk_B TOTAL numbers Score_B.1 \\\n",
"count 810.000000 810.000000 810.000000 810.000000 810.000000 \n",
"mean 0.308395 6.068334 12.665829 5.064815 0.222716 \n",
"std 0.167741 29.461510 50.292444 0.259188 0.078789 \n",
"min 0.200000 0.000000 0.000000 5.000000 0.200000 \n",
"25% 0.200000 0.000000 0.430000 5.000000 0.200000 \n",
"50% 0.200000 0.067000 1.290000 5.000000 0.200000 \n",
"75% 0.400000 1.347000 7.337500 5.000000 0.200000 \n",
"max 0.600000 758.778000 1268.910000 9.000000 0.600000 \n",
"\n",
" Risk_C Money_Value Score_MV Risk_D District_Loss \\\n",
"count 810.000000 810.000000 810.000000 810.000000 810.000000 \n",
"mean 1.146543 13.526807 0.287160 7.918490 2.493827 \n",
"std 0.526897 65.213233 0.157414 39.157042 1.219166 \n",
"min 1.000000 0.000000 0.200000 0.000000 2.000000 \n",
"25% 1.000000 0.000000 0.200000 0.000000 2.000000 \n",
"50% 1.000000 0.050000 0.200000 0.010000 2.000000 \n",
"75% 1.000000 4.862500 0.200000 0.972500 2.000000 \n",
"max 5.400000 935.030000 0.600000 561.018000 6.000000 \n",
"\n",
" PROB RiSk_E History Prob Risk_F Score \\\n",
"count 810.000000 810.000000 810.000000 810.000000 810.000000 810.000000 \n",
"mean 0.205926 0.516049 0.100000 0.216049 0.051358 2.674074 \n",
"std 0.036732 0.287118 0.520174 0.066628 0.299533 0.851869 \n",
"min 0.200000 0.400000 0.000000 0.200000 0.000000 2.000000 \n",
"25% 0.200000 0.400000 0.000000 0.200000 0.000000 2.000000 \n",
"50% 0.200000 0.400000 0.000000 0.200000 0.000000 2.200000 \n",
"75% 0.200000 0.400000 0.000000 0.200000 0.000000 3.200000 \n",
"max 0.600000 2.400000 9.000000 0.600000 5.400000 5.200000 \n",
"\n",
" Inherent_Risk CONTROL_RISK Detection_Risk Audit_Risk Risk \\\n",
"count 810.000000 810.000000 810.0 810.000000 810.000000 \n",
"mean 16.999643 0.567407 0.5 6.881682 0.379012 \n",
"std 53.676423 0.437573 0.0 37.871031 0.485441 \n",
"min 1.400000 0.400000 0.5 0.280000 0.000000 \n",
"25% 1.564500 0.400000 0.5 0.312900 0.000000 \n",
"50% 2.116000 0.400000 0.5 0.470800 0.000000 \n",
"75% 9.210500 0.400000 0.5 2.882700 1.000000 \n",
"max 801.262000 5.800000 0.5 961.514400 1.000000 \n",
"\n",
" Marks MONEY_Marks District Loss LOSS_SCORE \\\n",
"count 810.000000 810.000000 810.000000 810.000000 810.000000 \n",
"mean 2.227160 2.871605 2.493827 0.028395 2.059259 \n",
"std 0.787889 1.574145 1.219166 0.180464 0.367323 \n",
"min 2.000000 2.000000 2.000000 0.000000 2.000000 \n",
"25% 2.000000 2.000000 2.000000 0.000000 2.000000 \n",
"50% 2.000000 2.000000 2.000000 0.000000 2.000000 \n",
"75% 2.000000 2.000000 2.000000 0.000000 2.000000 \n",
"max 6.000000 6.000000 6.000000 2.000000 6.000000 \n",
"\n",
" History_score \n",
"count 810.000000 \n",
"mean 2.160494 \n",
"std 0.666277 \n",
"min 2.000000 \n",
"25% 2.000000 \n",
"50% 2.000000 \n",
"75% 2.000000 \n",
"max 6.000000 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"'Detection_Risk' and 'Risk_F' have the same values throughout the columns. Deleting these columns."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 810 entries, 0 to 809\n",
"Data columns (total 31 columns):\n",
"Sector_score 810 non-null float64\n",
"LOCATION_ID 810 non-null object\n",
"PARA_A 810 non-null float64\n",
"Score_A 810 non-null float64\n",
"Risk_A 810 non-null float64\n",
"PARA_B 810 non-null float64\n",
"Score_B 810 non-null float64\n",
"Risk_B 810 non-null float64\n",
"TOTAL 810 non-null float64\n",
"numbers 810 non-null float64\n",
"Score_B.1 810 non-null float64\n",
"Risk_C 810 non-null float64\n",
"Money_Value 810 non-null float64\n",
"Score_MV 810 non-null float64\n",
"Risk_D 810 non-null float64\n",
"District_Loss 810 non-null int64\n",
"PROB 810 non-null float64\n",
"RiSk_E 810 non-null float64\n",
"History 810 non-null int64\n",
"Prob 810 non-null float64\n",
"Score 810 non-null float64\n",
"Inherent_Risk 810 non-null float64\n",
"CONTROL_RISK 810 non-null float64\n",
"Audit_Risk 810 non-null float64\n",
"Risk 810 non-null int64\n",
"Marks 810 non-null int64\n",
"MONEY_Marks 810 non-null int64\n",
"District 810 non-null int64\n",
"Loss 810 non-null int64\n",
"LOSS_SCORE 810 non-null int64\n",
"History_score 810 non-null int64\n",
"dtypes: float64(21), int64(9), object(1)\n",
"memory usage: 202.5+ KB\n"
]
}
],
"source": [
"df = df.drop(['Detection_Risk', 'Risk_F'], axis = 1) \n",
"df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"LOCATION_ID has object datatype. However there are numerical values in the column. There must be non-numeric values present."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['23', '6', '7', '8', '13', '37', '24', '3', '4', '14', '5', '20',\n",
" '19', '21', '22', '9', '11', '12', '29', '30', '38', '31', '2',\n",
" '32', '16', '33', '15', '36', '34', '18', '25', '39', '27', '35',\n",
" '40', '41', '42', '1', '28', 'LOHARU', 'NUH', 'SAFIDON', '43',\n",
" '44', '17'], dtype=object)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Unique values in LOCATION_ID column\n",
"df[\"LOCATION_ID\"].unique()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"These are the number of non-numeric values in LOCATION_ID: 3\n"
]
}
],
"source": [
"print(\"These are the number of non-numeric values in LOCATION_ID: \", len(df[(df[\"LOCATION_ID\"] == 'LOHARU') | (df[\"LOCATION_ID\"] == 'NUH') | (df[\"LOCATION_ID\"] == 'SAFIDON')]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Deleting the rows with these 3 values as there is no information about the sequencing of the numbers present in the LOCATION_ID column."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Updated number of rows in the dataset: 807\n"
]
}
],
"source": [
"df = df[(df.LOCATION_ID != 'LOHARU')]\n",
"df = df[(df.LOCATION_ID != 'NUH')]\n",
"df = df[(df.LOCATION_ID != 'SAFIDON')]\n",
"df = df.astype(float)\n",
"print(\"Updated number of rows in the dataset: \",len(df))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dropping duplicate values if any. Here the rows which intersected with both dataframes get deleted. "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Updated number of rows in the dataset: 760\n"
]
}
],
"source": [
"df = df.drop_duplicates(keep = 'first')\n",
"print(\"Updated number of rows in the dataset: \",len(df))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sector_score : 13\n",
"LOCATION_ID : 42\n",
"PARA_A : 362\n",
"Score_A : 3\n",
"Risk_A : 362\n",
"PARA_B : 358\n",
"Score_B : 3\n",
"Risk_B : 360\n",
"TOTAL : 470\n",
"numbers : 5\n",
"Score_B.1 : 3\n",
"Risk_C : 5\n",
"Money_Value : 327\n",
"Score_MV : 3\n",
"Risk_D : 327\n",
"District_Loss : 3\n",
"PROB : 3\n",
"RiSk_E : 5\n",
"History : 7\n",
"Prob : 3\n",
"Score : 17\n",
"Inherent_Risk : 583\n",
"CONTROL_RISK : 11\n",
"Audit_Risk : 600\n",
"Risk : 2\n",
"Marks : 3\n",
"MONEY_Marks : 3\n",
"District : 3\n",
"Loss : 3\n",
"LOSS_SCORE : 3\n",
"History_score : 3\n"
]
}
],
"source": [
"#Number of unique values in each columns\n",
"for i in range(0, len(df.columns)):\n",
" print(df.columns[i], \":\", df.iloc[:,i].nunique())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Correlation Matrix"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
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" background-color: #5b92a3;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col30 {\n",
" background-color: #5a91a2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col0 {\n",
" background-color: #76a4b2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col1 {\n",
" background-color: #6599a9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col2 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col3 {\n",
" background-color: #edcdc4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col4 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col5 {\n",
" background-color: #a1c2cc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col6 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col7 {\n",
" background-color: #a0c1cb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col8 {\n",
" background-color: #c8dce2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col9 {\n",
" background-color: #9bbec8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col10 {\n",
" background-color: #a3c3cc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col11 {\n",
" background-color: #a1c2cc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col12 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col13 {\n",
" background-color: #ecf4f7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col14 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col15 {\n",
" background-color: #90b6c1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col16 {\n",
" background-color: #6b9dad;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col17 {\n",
" background-color: #92b8c3;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col18 {\n",
" background-color: #8cb3bf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col19 {\n",
" background-color: #a5c5ce;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col20 {\n",
" background-color: #f6e5e1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col21 {\n",
" background-color: #f7e9e6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col22 {\n",
" background-color: #a3c3cc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col23 {\n",
" background-color: #aac8d1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col24 {\n",
" background-color: #f8ebe7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col25 {\n",
" background-color: #a3c3cc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col26 {\n",
" background-color: #ecf4f7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col27 {\n",
" background-color: #90b6c1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col28 {\n",
" background-color: #6a9cac;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col29 {\n",
" background-color: #6b9dad;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col30 {\n",
" background-color: #a5c5ce;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col0 {\n",
" background-color: #3f7f93;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col1 {\n",
" background-color: #83adba;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col2 {\n",
" background-color: #f4e0db;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col3 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col4 {\n",
" background-color: #f3ddd8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col5 {\n",
" background-color: #bdd5dc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col6 {\n",
" background-color: #edcdc4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col7 {\n",
" background-color: #bdd5dc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col8 {\n",
" background-color: #d3e3e8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col9 {\n",
" background-color: #bfd6dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col10 {\n",
" background-color: #cee0e6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col11 {\n",
" background-color: #cbdee4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col12 {\n",
" background-color: #acc9d2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col13 {\n",
" background-color: #f2dbd5;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col14 {\n",
" background-color: #aac8d1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col15 {\n",
" background-color: #82adba;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col16 {\n",
" background-color: #7ca8b6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col17 {\n",
" background-color: #8cb3bf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col18 {\n",
" background-color: #a1c2cc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col19 {\n",
" background-color: #c3d9df;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col20 {\n",
" background-color: #dc9c8c;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col21 {\n",
" background-color: #dfebef;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col22 {\n",
" background-color: #a9c7d0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col23 {\n",
" background-color: #a4c4cd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col24 {\n",
" background-color: #e3b1a4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col25 {\n",
" background-color: #cee0e6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col26 {\n",
" background-color: #f2dbd5;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col27 {\n",
" background-color: #82adba;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col28 {\n",
" background-color: #7aa7b5;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col29 {\n",
" background-color: #7ca8b6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col30 {\n",
" background-color: #c3d9df;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col0 {\n",
" background-color: #74a3b2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col1 {\n",
" background-color: #679baa;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col2 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col3 {\n",
" background-color: #eccac1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col4 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col5 {\n",
" background-color: #a1c2cc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col6 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col7 {\n",
" background-color: #a1c2cc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col8 {\n",
" background-color: #cadde3;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col9 {\n",
" background-color: #9dbfc9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col10 {\n",
" background-color: #a3c3cc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col11 {\n",
" background-color: #a3c3cc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col12 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col13 {\n",
" background-color: #edf5f7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col14 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col15 {\n",
" background-color: #90b6c1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col16 {\n",
" background-color: #6b9dad;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col17 {\n",
" background-color: #92b8c3;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col18 {\n",
" background-color: #8eb4c0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col19 {\n",
" background-color: #a5c5ce;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col20 {\n",
" background-color: #f6e4df;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col21 {\n",
" background-color: #f7e9e6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col22 {\n",
" background-color: #a3c3cc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col23 {\n",
" background-color: #acc9d2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col24 {\n",
" background-color: #f7e9e6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col25 {\n",
" background-color: #a3c3cc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col26 {\n",
" background-color: #edf5f7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col27 {\n",
" background-color: #90b6c1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col28 {\n",
" background-color: #6a9cac;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col29 {\n",
" background-color: #6b9dad;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col30 {\n",
" background-color: #a5c5ce;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col0 {\n",
" background-color: #8cb3bf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col1 {\n",
" background-color: #6a9cac;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col2 {\n",
" background-color: #b3ced6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col3 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col4 {\n",
" background-color: #b7d0d8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col5 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col6 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col7 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col8 {\n",
" background-color: #c3573b;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col9 {\n",
" background-color: #b4cfd7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col10 {\n",
" background-color: #c1d7de;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col11 {\n",
" background-color: #bdd5dc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col12 {\n",
" background-color: #91b7c2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col13 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col14 {\n",
" background-color: #90b6c1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col15 {\n",
" background-color: #81acb9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col16 {\n",
" background-color: #6b9dad;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col17 {\n",
" background-color: #85aebb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col18 {\n",
" background-color: #aac8d1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col19 {\n",
" background-color: #d5e5ea;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col20 {\n",
" background-color: #f8ece9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col21 {\n",
" background-color: #e6b8ac;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col22 {\n",
" background-color: #afcbd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col23 {\n",
" background-color: #cf7761;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col24 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col25 {\n",
" background-color: #c1d7de;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col26 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col27 {\n",
" background-color: #81acb9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col28 {\n",
" background-color: #6a9cac;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col29 {\n",
" background-color: #6b9dad;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col30 {\n",
" background-color: #d5e5ea;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col0 {\n",
" background-color: #76a4b2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col1 {\n",
" background-color: #92b8c3;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col2 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col3 {\n",
" background-color: #e7baaf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col4 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col5 {\n",
" background-color: #e0ecf0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col6 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col7 {\n",
" background-color: #dfebef;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col8 {\n",
" background-color: #edf5f7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col9 {\n",
" background-color: #cbdee4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col10 {\n",
" background-color: #dbe9ed;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col11 {\n",
" background-color: #d7e6eb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col12 {\n",
" background-color: #acc9d2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col13 {\n",
" background-color: #eac3b9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col14 {\n",
" background-color: #aac8d1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col15 {\n",
" background-color: #6196a7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col16 {\n",
" background-color: #7ca8b6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col17 {\n",
" background-color: #6fa0af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col18 {\n",
" background-color: #a9c7d0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col19 {\n",
" background-color: #d3e3e8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col20 {\n",
" background-color: #cb6e56;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col21 {\n",
" background-color: #ecf4f7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col22 {\n",
" background-color: #9bbec8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col23 {\n",
" background-color: #a5c5ce;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col24 {\n",
" background-color: #e2ad9f;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col25 {\n",
" background-color: #dbe9ed;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col26 {\n",
" background-color: #eac3b9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col27 {\n",
" background-color: #6196a7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col28 {\n",
" background-color: #7da9b7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col29 {\n",
" background-color: #7ca8b6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col30 {\n",
" background-color: #d3e3e8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col0 {\n",
" background-color: #8cb3bf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col1 {\n",
" background-color: #6a9cac;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col2 {\n",
" background-color: #b3ced6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col3 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col4 {\n",
" background-color: #b7d0d8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col5 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col6 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col7 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col8 {\n",
" background-color: #c3573b;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col9 {\n",
" background-color: #b4cfd7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col10 {\n",
" background-color: #c1d7de;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col11 {\n",
" background-color: #bcd4db;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col12 {\n",
" background-color: #91b7c2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col13 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col14 {\n",
" background-color: #90b6c1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col15 {\n",
" background-color: #81acb9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col16 {\n",
" background-color: #6b9dad;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col17 {\n",
" background-color: #85aebb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col18 {\n",
" background-color: #aac8d1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col19 {\n",
" background-color: #d5e5ea;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col20 {\n",
" background-color: #f8ece9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col21 {\n",
" background-color: #e6b8ac;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col22 {\n",
" background-color: #afcbd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col23 {\n",
" background-color: #cf7761;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col24 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col25 {\n",
" background-color: #c1d7de;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col26 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col27 {\n",
" background-color: #81acb9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col28 {\n",
" background-color: #6a9cac;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col29 {\n",
" background-color: #6b9dad;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col30 {\n",
" background-color: #d5e5ea;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col0 {\n",
" background-color: #87b0bc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col1 {\n",
" background-color: #6a9cac;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col2 {\n",
" background-color: #d5e5ea;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col3 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col4 {\n",
" background-color: #d7e6eb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col5 {\n",
" background-color: #c3573b;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col6 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col7 {\n",
" background-color: #c3573b;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col8 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col9 {\n",
" background-color: #b8d1d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col10 {\n",
" background-color: #c2d8df;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col11 {\n",
" background-color: #bfd6dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col12 {\n",
" background-color: #a0c1cb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col13 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col14 {\n",
" background-color: #9fc0ca;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col15 {\n",
" background-color: #83adba;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col16 {\n",
" background-color: #6b9dad;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col17 {\n",
" background-color: #88b1bd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col18 {\n",
" background-color: #adcad2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col19 {\n",
" background-color: #d9e7ec;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col20 {\n",
" background-color: #f5e3de;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col21 {\n",
" background-color: #e2ad9f;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col22 {\n",
" background-color: #b2cdd5;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col23 {\n",
" background-color: #cf7761;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col24 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col25 {\n",
" background-color: #c2d8df;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col26 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col27 {\n",
" background-color: #83adba;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col28 {\n",
" background-color: #6b9dad;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col29 {\n",
" background-color: #6b9dad;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col30 {\n",
" background-color: #d9e7ec;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col0 {\n",
" background-color: #87b0bc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col1 {\n",
" background-color: #6a9cac;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col2 {\n",
" background-color: #aac8d1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col3 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col4 {\n",
" background-color: #acc9d2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col5 {\n",
" background-color: #b2cdd5;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col6 {\n",
" background-color: #d9e7ec;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col7 {\n",
" background-color: #b0ccd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col8 {\n",
" background-color: #b8d1d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col9 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col10 {\n",
" background-color: #cc6f58;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col11 {\n",
" background-color: #c76147;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col12 {\n",
" background-color: #a5c5ce;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col13 {\n",
" background-color: #f5e2dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col14 {\n",
" background-color: #a5c5ce;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col15 {\n",
" background-color: #90b6c1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col16 {\n",
" background-color: #699bab;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col17 {\n",
" background-color: #98bcc7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col18 {\n",
" background-color: #aac8d1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col19 {\n",
" background-color: #b2cdd5;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col20 {\n",
" background-color: #efd1ca;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col21 {\n",
" background-color: #cee0e6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col22 {\n",
" background-color: #bcd4db;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col23 {\n",
" background-color: #acc9d2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col24 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col25 {\n",
" background-color: #cc6f58;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col26 {\n",
" background-color: #f5e2dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col27 {\n",
" background-color: #90b6c1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col28 {\n",
" background-color: #5a91a2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col29 {\n",
" background-color: #699bab;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col30 {\n",
" background-color: #b2cdd5;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col0 {\n",
" background-color: #82adba;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col1 {\n",
" background-color: #6397a7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col2 {\n",
" background-color: #adcad2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col3 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col4 {\n",
" background-color: #afcbd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col5 {\n",
" background-color: #b8d1d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col6 {\n",
" background-color: #e3eef1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col7 {\n",
" background-color: #b8d1d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col8 {\n",
" background-color: #bfd6dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col9 {\n",
" background-color: #cc6f58;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col10 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col11 {\n",
" background-color: #c4583c;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col12 {\n",
" background-color: #b2cdd5;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col13 {\n",
" background-color: #f0d3cc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col14 {\n",
" background-color: #b0ccd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col15 {\n",
" background-color: #98bcc7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col16 {\n",
" background-color: #699bab;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col17 {\n",
" background-color: #a0c1cb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col18 {\n",
" background-color: #b2cdd5;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col19 {\n",
" background-color: #bfd6dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col20 {\n",
" background-color: #e9c2b8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col21 {\n",
" background-color: #dae8ec;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col22 {\n",
" background-color: #c7dbe1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col23 {\n",
" background-color: #b9d2d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col24 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col25 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col26 {\n",
" background-color: #f0d3cc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col27 {\n",
" background-color: #98bcc7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col28 {\n",
" background-color: #5c93a4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col29 {\n",
" background-color: #699bab;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col30 {\n",
" background-color: #bfd6dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col0 {\n",
" background-color: #83adba;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col1 {\n",
" background-color: #6397a7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col2 {\n",
" background-color: #adcad2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col3 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col4 {\n",
" background-color: #afcbd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col5 {\n",
" background-color: #b4cfd7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col6 {\n",
" background-color: #e0ecf0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col7 {\n",
" background-color: #b4cfd7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col8 {\n",
" background-color: #bcd4db;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col9 {\n",
" background-color: #c76147;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col10 {\n",
" background-color: #c4583c;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col11 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col12 {\n",
" background-color: #afcbd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col13 {\n",
" background-color: #f0d6cf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col14 {\n",
" background-color: #afcbd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col15 {\n",
" background-color: #97bbc6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col16 {\n",
" background-color: #699bab;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col17 {\n",
" background-color: #9fc0ca;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col18 {\n",
" background-color: #b0ccd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col19 {\n",
" background-color: #bbd3da;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col20 {\n",
" background-color: #ebc6bd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col21 {\n",
" background-color: #d7e6eb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col22 {\n",
" background-color: #c3d9df;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col23 {\n",
" background-color: #b4cfd7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col24 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col25 {\n",
" background-color: #c4583c;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col26 {\n",
" background-color: #f0d6cf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col27 {\n",
" background-color: #97bbc6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col28 {\n",
" background-color: #5b92a3;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col29 {\n",
" background-color: #699bab;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col30 {\n",
" background-color: #bbd3da;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col0 {\n",
" background-color: #90b6c1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col1 {\n",
" background-color: #528c9e;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col2 {\n",
" background-color: #f9edea;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col3 {\n",
" background-color: #e6f0f3;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col4 {\n",
" background-color: #f9edea;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col5 {\n",
" background-color: #94b9c4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col6 {\n",
" background-color: #c1d7de;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col7 {\n",
" background-color: #94b9c4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col8 {\n",
" background-color: #a9c7d0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col9 {\n",
" background-color: #adcad2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col10 {\n",
" background-color: #bdd5dc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col11 {\n",
" background-color: #bbd3da;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col12 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col13 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col14 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col15 {\n",
" background-color: #6d9ead;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col16 {\n",
" background-color: #679baa;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col17 {\n",
" background-color: #74a3b2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col18 {\n",
" background-color: #81acb9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col19 {\n",
" background-color: #91b7c2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col20 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col21 {\n",
" background-color: #d48571;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col22 {\n",
" background-color: #88b1bd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col23 {\n",
" background-color: #d3e3e8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col24 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col25 {\n",
" background-color: #bdd5dc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col26 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col27 {\n",
" background-color: #6d9ead;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col28 {\n",
" background-color: #6498a8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col29 {\n",
" background-color: #679baa;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col30 {\n",
" background-color: #91b7c2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col0 {\n",
" background-color: #5a91a2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col1 {\n",
" background-color: #8cb3bf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col2 {\n",
" background-color: #dae8ec;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col3 {\n",
" background-color: #eed0c8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col4 {\n",
" background-color: #ddeaee;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col5 {\n",
" background-color: #d4e4e9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col6 {\n",
" background-color: #edcdc4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col7 {\n",
" background-color: #d4e4e9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col8 {\n",
" background-color: #e0ecf0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col9 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col10 {\n",
" background-color: #f5e3de;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col11 {\n",
" background-color: #f6e6e2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col12 {\n",
" background-color: #ecf4f7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col13 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col14 {\n",
" background-color: #eaf3f6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col15 {\n",
" background-color: #7fabb8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col16 {\n",
" background-color: #89b2be;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col17 {\n",
" background-color: #8cb3bf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col18 {\n",
" background-color: #b9d2d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col19 {\n",
" background-color: #dbe9ed;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col20 {\n",
" background-color: #d8917f;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col21 {\n",
" background-color: #f7e9e6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col22 {\n",
" background-color: #b9d2d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col23 {\n",
" background-color: #c3d9df;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col24 {\n",
" background-color: #dda090;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col25 {\n",
" background-color: #f5e3de;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col26 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col27 {\n",
" background-color: #7fabb8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col28 {\n",
" background-color: #85aebb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col29 {\n",
" background-color: #89b2be;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col30 {\n",
" background-color: #dbe9ed;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col0 {\n",
" background-color: #91b7c2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col1 {\n",
" background-color: #528c9e;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col2 {\n",
" background-color: #f9edea;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col3 {\n",
" background-color: #e6f0f3;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col4 {\n",
" background-color: #f9edea;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col5 {\n",
" background-color: #94b9c4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col6 {\n",
" background-color: #c1d7de;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col7 {\n",
" background-color: #94b9c4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col8 {\n",
" background-color: #a8c6cf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col9 {\n",
" background-color: #adcad2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col10 {\n",
" background-color: #bdd5dc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col11 {\n",
" background-color: #bbd3da;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col12 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col13 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col14 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col15 {\n",
" background-color: #6d9ead;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col16 {\n",
" background-color: #679baa;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col17 {\n",
" background-color: #74a3b2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col18 {\n",
" background-color: #81acb9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col19 {\n",
" background-color: #91b7c2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col20 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col21 {\n",
" background-color: #d48571;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col22 {\n",
" background-color: #88b1bd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col23 {\n",
" background-color: #d3e3e8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col24 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col25 {\n",
" background-color: #bdd5dc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col26 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col27 {\n",
" background-color: #6d9ead;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col28 {\n",
" background-color: #6498a8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col29 {\n",
" background-color: #679baa;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col30 {\n",
" background-color: #91b7c2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col0 {\n",
" background-color: #92b8c3;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col1 {\n",
" background-color: #448396;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col2 {\n",
" background-color: #a9c7d0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col3 {\n",
" background-color: #c7dbe1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col4 {\n",
" background-color: #aac8d1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col5 {\n",
" background-color: #87b0bc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col6 {\n",
" background-color: #7fabb8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col7 {\n",
" background-color: #87b0bc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col8 {\n",
" background-color: #90b6c1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col9 {\n",
" background-color: #9abdc7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col10 {\n",
" background-color: #a5c5ce;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col11 {\n",
" background-color: #a4c4cd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col12 {\n",
" background-color: #70a1af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col13 {\n",
" background-color: #b0ccd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col14 {\n",
" background-color: #6fa0af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col15 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col16 {\n",
" background-color: #70a1af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col17 {\n",
" background-color: #cb6e56;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col18 {\n",
" background-color: #7ca8b6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col19 {\n",
" background-color: #88b1bd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col20 {\n",
" background-color: #d9e7ec;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col21 {\n",
" background-color: #91b7c2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col22 {\n",
" background-color: #e7bcb1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col23 {\n",
" background-color: #a4c4cd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col24 {\n",
" background-color: #f6e5e1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col25 {\n",
" background-color: #a5c5ce;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col26 {\n",
" background-color: #b0ccd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col27 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col28 {\n",
" background-color: #679baa;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col29 {\n",
" background-color: #70a1af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col30 {\n",
" background-color: #88b1bd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col0 {\n",
" background-color: #98bcc7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col1 {\n",
" background-color: #679baa;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col2 {\n",
" background-color: #90b6c1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col3 {\n",
" background-color: #c8dce2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col4 {\n",
" background-color: #90b6c1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col5 {\n",
" background-color: #79a7b4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col6 {\n",
" background-color: #9fc0ca;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col7 {\n",
" background-color: #78a6b4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col8 {\n",
" background-color: #7fabb8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col9 {\n",
" background-color: #7ca8b6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col10 {\n",
" background-color: #81acb9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col11 {\n",
" background-color: #7fabb8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col12 {\n",
" background-color: #70a1af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col13 {\n",
" background-color: #bfd6dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col14 {\n",
" background-color: #70a1af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col15 {\n",
" background-color: #78a6b4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col16 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col17 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col18 {\n",
" background-color: #8bb3bf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col19 {\n",
" background-color: #9bbec8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col20 {\n",
" background-color: #ccdfe4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col21 {\n",
" background-color: #87b0bc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col22 {\n",
" background-color: #e2edf1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col23 {\n",
" background-color: #78a6b4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col24 {\n",
" background-color: #dae8ec;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col25 {\n",
" background-color: #81acb9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col26 {\n",
" background-color: #bfd6dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col27 {\n",
" background-color: #78a6b4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col28 {\n",
" background-color: #c45a3f;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col29 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col30 {\n",
" background-color: #9bbec8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col0 {\n",
" background-color: #8cb3bf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col1 {\n",
" background-color: #488598;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col2 {\n",
" background-color: #a5c5ce;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col3 {\n",
" background-color: #cadde3;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col4 {\n",
" background-color: #a8c6cf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col5 {\n",
" background-color: #85aebb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col6 {\n",
" background-color: #85aebb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col7 {\n",
" background-color: #85aebb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col8 {\n",
" background-color: #8eb4c0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col9 {\n",
" background-color: #9dbfc9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col10 {\n",
" background-color: #a9c7d0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col11 {\n",
" background-color: #a8c6cf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col12 {\n",
" background-color: #72a1b0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col13 {\n",
" background-color: #b8d1d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col14 {\n",
" background-color: #70a1af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col15 {\n",
" background-color: #cc6f58;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col16 {\n",
" background-color: #ecf4f7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col17 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col18 {\n",
" background-color: #8bb3bf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col19 {\n",
" background-color: #95b9c5;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col20 {\n",
" background-color: #e0ecf0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col21 {\n",
" background-color: #91b7c2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col22 {\n",
" background-color: #dfa495;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col23 {\n",
" background-color: #a4c4cd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col24 {\n",
" background-color: #f5e3de;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col25 {\n",
" background-color: #a9c7d0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col26 {\n",
" background-color: #b8d1d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col27 {\n",
" background-color: #cc6f58;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col28 {\n",
" background-color: #ddeaee;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col29 {\n",
" background-color: #ecf4f7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col30 {\n",
" background-color: #95b9c5;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col0 {\n",
" background-color: #91b7c2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col1 {\n",
" background-color: #4c889a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col2 {\n",
" background-color: #a5c5ce;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col3 {\n",
" background-color: #dfebef;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col4 {\n",
" background-color: #a8c6cf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col5 {\n",
" background-color: #afcbd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col6 {\n",
" background-color: #bfd6dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col7 {\n",
" background-color: #afcbd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col8 {\n",
" background-color: #b7d0d8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col9 {\n",
" background-color: #b3ced6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col10 {\n",
" background-color: #bfd6dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col11 {\n",
" background-color: #bcd4db;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col12 {\n",
" background-color: #81acb9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col13 {\n",
" background-color: #e2edf1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col14 {\n",
" background-color: #81acb9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col15 {\n",
" background-color: #7ca8b6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col16 {\n",
" background-color: #82adba;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col17 {\n",
" background-color: #90b6c1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col18 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col19 {\n",
" background-color: #d17e68;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col20 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col21 {\n",
" background-color: #b2cdd5;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col22 {\n",
" background-color: #dc9c8c;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col23 {\n",
" background-color: #d1e2e7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col24 {\n",
" background-color: #ecf4f7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col25 {\n",
" background-color: #bfd6dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col26 {\n",
" background-color: #e2edf1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col27 {\n",
" background-color: #7ca8b6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col28 {\n",
" background-color: #70a1af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col29 {\n",
" background-color: #82adba;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col30 {\n",
" background-color: #d17e68;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col0 {\n",
" background-color: #8bb3bf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col1 {\n",
" background-color: #578fa1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col2 {\n",
" background-color: #b8d1d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col3 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col4 {\n",
" background-color: #b9d2d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col5 {\n",
" background-color: #d5e5ea;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col6 {\n",
" background-color: #e2edf1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col7 {\n",
" background-color: #d4e4e9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col8 {\n",
" background-color: #ddeaee;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col9 {\n",
" background-color: #b4cfd7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col10 {\n",
" background-color: #c7dbe1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col11 {\n",
" background-color: #c2d8df;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col12 {\n",
" background-color: #8cb3bf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col13 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col14 {\n",
" background-color: #8bb3bf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col15 {\n",
" background-color: #81acb9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col16 {\n",
" background-color: #8eb4c0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col17 {\n",
" background-color: #94b9c4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col18 {\n",
" background-color: #d27f6a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col19 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col20 {\n",
" background-color: #f5e2dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col21 {\n",
" background-color: #d0e1e6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col22 {\n",
" background-color: #e7bcb1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col23 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col24 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col25 {\n",
" background-color: #c7dbe1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col26 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col27 {\n",
" background-color: #81acb9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col28 {\n",
" background-color: #7fabb8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col29 {\n",
" background-color: #8eb4c0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col30 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col0 {\n",
" background-color: #558ea0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col1 {\n",
" background-color: #87b0bc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col2 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col3 {\n",
" background-color: #da9887;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col4 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col5 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col6 {\n",
" background-color: #cc7059;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col7 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col8 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col9 {\n",
" background-color: #f6e5e1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col10 {\n",
" background-color: #efd1ca;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col11 {\n",
" background-color: #f0d6cf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col12 {\n",
" background-color: #cadde3;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col13 {\n",
" background-color: #d89281;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col14 {\n",
" background-color: #c8dce2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col15 {\n",
" background-color: #acc9d2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col16 {\n",
" background-color: #97bbc6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col17 {\n",
" background-color: #b9d2d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col18 {\n",
" background-color: #d4e4e9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col19 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col20 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col21 {\n",
" background-color: #f9eeeb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col22 {\n",
" background-color: #e6f0f3;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col23 {\n",
" background-color: #d3e3e8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col24 {\n",
" background-color: #d58875;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col25 {\n",
" background-color: #efd1ca;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col26 {\n",
" background-color: #d89281;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col27 {\n",
" background-color: #acc9d2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col28 {\n",
" background-color: #92b8c3;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col29 {\n",
" background-color: #97bbc6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col30 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col0 {\n",
" background-color: #82adba;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col1 {\n",
" background-color: #5890a2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col2 {\n",
" background-color: #f6e4df;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col3 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col4 {\n",
" background-color: #f5e3de;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col5 {\n",
" background-color: #e7bbb0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col6 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col7 {\n",
" background-color: #e7bbb0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col8 {\n",
" background-color: #e3afa2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col9 {\n",
" background-color: #cadde3;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col10 {\n",
" background-color: #dae8ec;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col11 {\n",
" background-color: #d7e6eb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col12 {\n",
" background-color: #d58875;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col13 {\n",
" background-color: #f1d8d2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col14 {\n",
" background-color: #d58875;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col15 {\n",
" background-color: #7fabb8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col16 {\n",
" background-color: #6fa0af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col17 {\n",
" background-color: #87b0bc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col18 {\n",
" background-color: #a4c4cd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col19 {\n",
" background-color: #c8dce2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col20 {\n",
" background-color: #f3dcd6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col21 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col22 {\n",
" background-color: #aac8d1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col23 {\n",
" background-color: #dea191;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col24 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col25 {\n",
" background-color: #dae8ec;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col26 {\n",
" background-color: #f1d8d2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col27 {\n",
" background-color: #7fabb8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col28 {\n",
" background-color: #6b9dad;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col29 {\n",
" background-color: #6fa0af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col30 {\n",
" background-color: #c8dce2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col0 {\n",
" background-color: #85aebb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col1 {\n",
" background-color: #3f7f93;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col2 {\n",
" background-color: #b0ccd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col3 {\n",
" background-color: #ddeaee;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col4 {\n",
" background-color: #b0ccd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col5 {\n",
" background-color: #a9c7d0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col6 {\n",
" background-color: #a9c7d0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col7 {\n",
" background-color: #a9c7d0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col8 {\n",
" background-color: #b2cdd5;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col9 {\n",
" background-color: #bcd4db;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col10 {\n",
" background-color: #c8dce2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col11 {\n",
" background-color: #c7dbe1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col12 {\n",
" background-color: #7da9b7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col13 {\n",
" background-color: #d9e7ec;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col14 {\n",
" background-color: #7da9b7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col15 {\n",
" background-color: #e9c1b6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col16 {\n",
" background-color: #d3e3e8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col17 {\n",
" background-color: #dfa596;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col18 {\n",
" background-color: #dd9e8f;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col19 {\n",
" background-color: #e8beb4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col20 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col21 {\n",
" background-color: #afcbd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col22 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col23 {\n",
" background-color: #dbe9ed;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col24 {\n",
" background-color: #f5e2dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col25 {\n",
" background-color: #c8dce2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col26 {\n",
" background-color: #d9e7ec;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col27 {\n",
" background-color: #e9c1b6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col28 {\n",
" background-color: #bdd5dc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col29 {\n",
" background-color: #d3e3e8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col30 {\n",
" background-color: #e8beb4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col0 {\n",
" background-color: #97bbc6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col1 {\n",
" background-color: #4b879a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col2 {\n",
" background-color: #c7dbe1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col3 {\n",
" background-color: #e4eff2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col4 {\n",
" background-color: #c8dce2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col5 {\n",
" background-color: #ce765f;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col6 {\n",
" background-color: #c2d8df;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col7 {\n",
" background-color: #ce765f;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col8 {\n",
" background-color: #ce755e;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col9 {\n",
" background-color: #b9d2d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col10 {\n",
" background-color: #cadde3;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col11 {\n",
" background-color: #c7dbe1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col12 {\n",
" background-color: #d9e7ec;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col13 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col14 {\n",
" background-color: #d7e6eb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col15 {\n",
" background-color: #a9c7d0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col16 {\n",
" background-color: #76a4b2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col17 {\n",
" background-color: #afcbd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col18 {\n",
" background-color: #d5e5ea;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col19 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col20 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col21 {\n",
" background-color: #dc9c8c;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col22 {\n",
" background-color: #e8f1f4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col23 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col24 {\n",
" background-color: #e6f0f3;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col25 {\n",
" background-color: #cadde3;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col26 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col27 {\n",
" background-color: #a9c7d0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col28 {\n",
" background-color: #6b9dad;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col29 {\n",
" background-color: #76a4b2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col30 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col0 {\n",
" background-color: #448396;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col1 {\n",
" background-color: #7da9b7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col2 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col3 {\n",
" background-color: #e3afa2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col4 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col5 {\n",
" background-color: #c1d7de;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col6 {\n",
" background-color: #e7baaf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col7 {\n",
" background-color: #bfd6dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col8 {\n",
" background-color: #d1e2e7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col9 {\n",
" background-color: #d5e5ea;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col10 {\n",
" background-color: #e9f2f5;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col11 {\n",
" background-color: #e4eff2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col12 {\n",
" background-color: #bdd5dc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col13 {\n",
" background-color: #dfa495;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col14 {\n",
" background-color: #bcd4db;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col15 {\n",
" background-color: #edf5f7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col16 {\n",
" background-color: #9abdc7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col17 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col18 {\n",
" background-color: #b7d0d8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col19 {\n",
" background-color: #d0e1e6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col20 {\n",
" background-color: #d68b78;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col21 {\n",
" background-color: #eaf3f6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col22 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col23 {\n",
" background-color: #aac8d1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col24 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col25 {\n",
" background-color: #e9f2f5;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col26 {\n",
" background-color: #dfa495;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col27 {\n",
" background-color: #edf5f7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col28 {\n",
" background-color: #97bbc6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col29 {\n",
" background-color: #9abdc7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col30 {\n",
" background-color: #d0e1e6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col0 {\n",
" background-color: #82adba;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col1 {\n",
" background-color: #6397a7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col2 {\n",
" background-color: #adcad2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col3 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col4 {\n",
" background-color: #afcbd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col5 {\n",
" background-color: #b8d1d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col6 {\n",
" background-color: #e3eef1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col7 {\n",
" background-color: #b8d1d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col8 {\n",
" background-color: #bfd6dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col9 {\n",
" background-color: #cc6f58;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col10 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col11 {\n",
" background-color: #c4583c;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col12 {\n",
" background-color: #b2cdd5;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col13 {\n",
" background-color: #f0d3cc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col14 {\n",
" background-color: #b0ccd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col15 {\n",
" background-color: #98bcc7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col16 {\n",
" background-color: #699bab;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col17 {\n",
" background-color: #a0c1cb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col18 {\n",
" background-color: #b2cdd5;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col19 {\n",
" background-color: #bfd6dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col20 {\n",
" background-color: #e9c2b8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col21 {\n",
" background-color: #dae8ec;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col22 {\n",
" background-color: #c7dbe1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col23 {\n",
" background-color: #b9d2d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col24 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col25 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col26 {\n",
" background-color: #f0d3cc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col27 {\n",
" background-color: #98bcc7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col28 {\n",
" background-color: #5c93a4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col29 {\n",
" background-color: #699bab;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col30 {\n",
" background-color: #bfd6dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col0 {\n",
" background-color: #5a91a2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col1 {\n",
" background-color: #8cb3bf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col2 {\n",
" background-color: #dae8ec;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col3 {\n",
" background-color: #eed0c8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col4 {\n",
" background-color: #ddeaee;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col5 {\n",
" background-color: #d4e4e9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col6 {\n",
" background-color: #edcdc4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col7 {\n",
" background-color: #d4e4e9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col8 {\n",
" background-color: #e0ecf0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col9 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col10 {\n",
" background-color: #f5e3de;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col11 {\n",
" background-color: #f6e6e2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col12 {\n",
" background-color: #ecf4f7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col13 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col14 {\n",
" background-color: #eaf3f6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col15 {\n",
" background-color: #7fabb8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col16 {\n",
" background-color: #89b2be;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col17 {\n",
" background-color: #8cb3bf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col18 {\n",
" background-color: #b9d2d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col19 {\n",
" background-color: #dbe9ed;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col20 {\n",
" background-color: #d8917f;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col21 {\n",
" background-color: #f7e9e6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col22 {\n",
" background-color: #b9d2d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col23 {\n",
" background-color: #c3d9df;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col24 {\n",
" background-color: #dda090;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col25 {\n",
" background-color: #f5e3de;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col26 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col27 {\n",
" background-color: #7fabb8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col28 {\n",
" background-color: #85aebb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col29 {\n",
" background-color: #89b2be;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col30 {\n",
" background-color: #dbe9ed;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col0 {\n",
" background-color: #92b8c3;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col1 {\n",
" background-color: #448396;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col2 {\n",
" background-color: #a9c7d0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col3 {\n",
" background-color: #c7dbe1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col4 {\n",
" background-color: #aac8d1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col5 {\n",
" background-color: #87b0bc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col6 {\n",
" background-color: #7fabb8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col7 {\n",
" background-color: #87b0bc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col8 {\n",
" background-color: #90b6c1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col9 {\n",
" background-color: #9abdc7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col10 {\n",
" background-color: #a5c5ce;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col11 {\n",
" background-color: #a4c4cd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col12 {\n",
" background-color: #70a1af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col13 {\n",
" background-color: #b0ccd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col14 {\n",
" background-color: #6fa0af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col15 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col16 {\n",
" background-color: #70a1af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col17 {\n",
" background-color: #cb6e56;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col18 {\n",
" background-color: #7ca8b6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col19 {\n",
" background-color: #88b1bd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col20 {\n",
" background-color: #d9e7ec;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col21 {\n",
" background-color: #91b7c2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col22 {\n",
" background-color: #e7bcb1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col23 {\n",
" background-color: #a4c4cd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col24 {\n",
" background-color: #f6e5e1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col25 {\n",
" background-color: #a5c5ce;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col26 {\n",
" background-color: #b0ccd4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col27 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col28 {\n",
" background-color: #679baa;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col29 {\n",
" background-color: #70a1af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col30 {\n",
" background-color: #88b1bd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col0 {\n",
" background-color: #9abdc7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col1 {\n",
" background-color: #6a9cac;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col2 {\n",
" background-color: #90b6c1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col3 {\n",
" background-color: #c8dce2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col4 {\n",
" background-color: #90b6c1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col5 {\n",
" background-color: #79a7b4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col6 {\n",
" background-color: #a0c1cb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col7 {\n",
" background-color: #79a7b4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col8 {\n",
" background-color: #81acb9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col9 {\n",
" background-color: #6fa0af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col10 {\n",
" background-color: #76a4b2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col11 {\n",
" background-color: #74a3b2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col12 {\n",
" background-color: #6fa0af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col13 {\n",
" background-color: #bcd4db;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col14 {\n",
" background-color: #6d9ead;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col15 {\n",
" background-color: #70a1af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col16 {\n",
" background-color: #c45a3f;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col17 {\n",
" background-color: #e8f1f4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col18 {\n",
" background-color: #79a7b4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col19 {\n",
" background-color: #8eb4c0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col20 {\n",
" background-color: #cadde3;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col21 {\n",
" background-color: #85aebb;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col22 {\n",
" background-color: #ccdfe4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col23 {\n",
" background-color: #6fa0af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col24 {\n",
" background-color: #d9e7ec;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col25 {\n",
" background-color: #76a4b2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col26 {\n",
" background-color: #bcd4db;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col27 {\n",
" background-color: #70a1af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col28 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col29 {\n",
" background-color: #c45a3f;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col30 {\n",
" background-color: #8eb4c0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col0 {\n",
" background-color: #98bcc7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col1 {\n",
" background-color: #679baa;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col2 {\n",
" background-color: #90b6c1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col3 {\n",
" background-color: #c8dce2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col4 {\n",
" background-color: #90b6c1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col5 {\n",
" background-color: #79a7b4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col6 {\n",
" background-color: #9fc0ca;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col7 {\n",
" background-color: #78a6b4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col8 {\n",
" background-color: #7fabb8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col9 {\n",
" background-color: #7ca8b6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col10 {\n",
" background-color: #81acb9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col11 {\n",
" background-color: #7fabb8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col12 {\n",
" background-color: #70a1af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col13 {\n",
" background-color: #bfd6dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col14 {\n",
" background-color: #70a1af;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col15 {\n",
" background-color: #78a6b4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col16 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col17 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col18 {\n",
" background-color: #8bb3bf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col19 {\n",
" background-color: #9bbec8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col20 {\n",
" background-color: #ccdfe4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col21 {\n",
" background-color: #87b0bc;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col22 {\n",
" background-color: #e2edf1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col23 {\n",
" background-color: #78a6b4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col24 {\n",
" background-color: #dae8ec;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col25 {\n",
" background-color: #81acb9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col26 {\n",
" background-color: #bfd6dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col27 {\n",
" background-color: #78a6b4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col28 {\n",
" background-color: #c45a3f;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col29 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col30 {\n",
" background-color: #9bbec8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col0 {\n",
" background-color: #8bb3bf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col1 {\n",
" background-color: #578fa1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col2 {\n",
" background-color: #b8d1d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col3 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col4 {\n",
" background-color: #b9d2d9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col5 {\n",
" background-color: #d5e5ea;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col6 {\n",
" background-color: #e2edf1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col7 {\n",
" background-color: #d4e4e9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col8 {\n",
" background-color: #ddeaee;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col9 {\n",
" background-color: #b4cfd7;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col10 {\n",
" background-color: #c7dbe1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col11 {\n",
" background-color: #c2d8df;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col12 {\n",
" background-color: #8cb3bf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col13 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col14 {\n",
" background-color: #8bb3bf;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col15 {\n",
" background-color: #81acb9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col16 {\n",
" background-color: #8eb4c0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col17 {\n",
" background-color: #94b9c4;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col18 {\n",
" background-color: #d27f6a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col19 {\n",
" background-color: #c3553a;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col20 {\n",
" background-color: #f5e2dd;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col21 {\n",
" background-color: #d0e1e6;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col22 {\n",
" background-color: #e7bcb1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col23 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col24 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col25 {\n",
" background-color: #c7dbe1;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col26 {\n",
" background-color: #f2f2f2;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col27 {\n",
" background-color: #81acb9;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col28 {\n",
" background-color: #7fabb8;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col29 {\n",
" background-color: #8eb4c0;\n",
" } #T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col30 {\n",
" background-color: #c3553a;\n",
" }</style> \n",
"<table id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2\" > \n",
"<thead> <tr> \n",
" <th class=\"blank level0\" ></th> \n",
" <th class=\"col_heading level0 col0\" >Sector_score</th> \n",
" <th class=\"col_heading level0 col1\" >LOCATION_ID</th> \n",
" <th class=\"col_heading level0 col2\" >PARA_A</th> \n",
" <th class=\"col_heading level0 col3\" >Score_A</th> \n",
" <th class=\"col_heading level0 col4\" >Risk_A</th> \n",
" <th class=\"col_heading level0 col5\" >PARA_B</th> \n",
" <th class=\"col_heading level0 col6\" >Score_B</th> \n",
" <th class=\"col_heading level0 col7\" >Risk_B</th> \n",
" <th class=\"col_heading level0 col8\" >TOTAL</th> \n",
" <th class=\"col_heading level0 col9\" >numbers</th> \n",
" <th class=\"col_heading level0 col10\" >Score_B.1</th> \n",
" <th class=\"col_heading level0 col11\" >Risk_C</th> \n",
" <th class=\"col_heading level0 col12\" >Money_Value</th> \n",
" <th class=\"col_heading level0 col13\" >Score_MV</th> \n",
" <th class=\"col_heading level0 col14\" >Risk_D</th> \n",
" <th class=\"col_heading level0 col15\" >District_Loss</th> \n",
" <th class=\"col_heading level0 col16\" >PROB</th> \n",
" <th class=\"col_heading level0 col17\" >RiSk_E</th> \n",
" <th class=\"col_heading level0 col18\" >History</th> \n",
" <th class=\"col_heading level0 col19\" >Prob</th> \n",
" <th class=\"col_heading level0 col20\" >Score</th> \n",
" <th class=\"col_heading level0 col21\" >Inherent_Risk</th> \n",
" <th class=\"col_heading level0 col22\" >CONTROL_RISK</th> \n",
" <th class=\"col_heading level0 col23\" >Audit_Risk</th> \n",
" <th class=\"col_heading level0 col24\" >Risk</th> \n",
" <th class=\"col_heading level0 col25\" >Marks</th> \n",
" <th class=\"col_heading level0 col26\" >MONEY_Marks</th> \n",
" <th class=\"col_heading level0 col27\" >District</th> \n",
" <th class=\"col_heading level0 col28\" >Loss</th> \n",
" <th class=\"col_heading level0 col29\" >LOSS_SCORE</th> \n",
" <th class=\"col_heading level0 col30\" >History_score</th> \n",
" </tr></thead> \n",
"<tbody> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row0\" class=\"row_heading level0 row0\" >Sector_score</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col0\" class=\"data row0 col0\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col1\" class=\"data row0 col1\" >-0.052</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col2\" class=\"data row0 col2\" >-0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col3\" class=\"data row0 col3\" >-0.42</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col4\" class=\"data row0 col4\" >-0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col5\" class=\"data row0 col5\" >-0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col6\" class=\"data row0 col6\" >-0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col7\" class=\"data row0 col7\" >-0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col8\" class=\"data row0 col8\" >-0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col9\" class=\"data row0 col9\" >-0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col10\" class=\"data row0 col10\" >-0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col11\" class=\"data row0 col11\" >-0.16</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col12\" class=\"data row0 col12\" >-0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col13\" class=\"data row0 col13\" >-0.32</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col14\" class=\"data row0 col14\" >-0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col15\" class=\"data row0 col15\" >-0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col16\" class=\"data row0 col16\" >-0.086</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col17\" class=\"data row0 col17\" >-0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col18\" class=\"data row0 col18\" >-0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col19\" class=\"data row0 col19\" >-0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col20\" class=\"data row0 col20\" >-0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col21\" class=\"data row0 col21\" >-0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col22\" class=\"data row0 col22\" >-0.16</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col23\" class=\"data row0 col23\" >-0.091</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col24\" class=\"data row0 col24\" >-0.39</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col25\" class=\"data row0 col25\" >-0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col26\" class=\"data row0 col26\" >-0.32</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col27\" class=\"data row0 col27\" >-0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col28\" class=\"data row0 col28\" >-0.082</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col29\" class=\"data row0 col29\" >-0.086</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row0_col30\" class=\"data row0 col30\" >-0.14</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row1\" class=\"row_heading level0 row1\" >LOCATION_ID</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col0\" class=\"data row1 col0\" >-0.052</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col1\" class=\"data row1 col1\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col2\" class=\"data row1 col2\" >-0.0067</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col3\" class=\"data row1 col3\" >0.079</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col4\" class=\"data row1 col4\" >-0.0044</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col5\" class=\"data row1 col5\" >0.0066</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col6\" class=\"data row1 col6\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col7\" class=\"data row1 col7\" >0.0062</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col8\" class=\"data row1 col8\" >0.0065</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col9\" class=\"data row1 col9\" >0.0067</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col10\" class=\"data row1 col10\" >-0.018</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col11\" class=\"data row1 col11\" >-0.015</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col12\" class=\"data row1 col12\" >-0.067</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col13\" class=\"data row1 col13\" >0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col14\" class=\"data row1 col14\" >-0.067</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col15\" class=\"data row1 col15\" >-0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col16\" class=\"data row1 col16\" >-0.0034</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col17\" class=\"data row1 col17\" >-0.097</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col18\" class=\"data row1 col18\" >-0.082</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col19\" class=\"data row1 col19\" >-0.054</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col20\" class=\"data row1 col20\" >0.088</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col21\" class=\"data row1 col21\" >-0.047</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col22\" class=\"data row1 col22\" >-0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col23\" class=\"data row1 col23\" >-0.087</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col24\" class=\"data row1 col24\" >0.063</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col25\" class=\"data row1 col25\" >-0.018</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col26\" class=\"data row1 col26\" >0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col27\" class=\"data row1 col27\" >-0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col28\" class=\"data row1 col28\" >0.0064</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col29\" class=\"data row1 col29\" >-0.0034</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row1_col30\" class=\"data row1 col30\" >-0.054</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row2\" class=\"row_heading level0 row2\" >PARA_A</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col0\" class=\"data row2 col0\" >-0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col1\" class=\"data row2 col1\" >-0.0067</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col2\" class=\"data row2 col2\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col3\" class=\"data row2 col3\" >0.49</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col4\" class=\"data row2 col4\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col5\" class=\"data row2 col5\" >0.16</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col6\" class=\"data row2 col6\" >0.36</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col7\" class=\"data row2 col7\" >0.16</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col8\" class=\"data row2 col8\" >0.27</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col9\" class=\"data row2 col9\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col10\" class=\"data row2 col10\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col11\" class=\"data row2 col11\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col12\" class=\"data row2 col12\" >0.45</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col13\" class=\"data row2 col13\" >0.28</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col14\" class=\"data row2 col14\" >0.45</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col15\" class=\"data row2 col15\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col16\" class=\"data row2 col16\" >0.042</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col17\" class=\"data row2 col17\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col18\" class=\"data row2 col18\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col19\" class=\"data row2 col19\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col20\" class=\"data row2 col20\" >0.42</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col21\" class=\"data row2 col21\" >0.48</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col22\" class=\"data row2 col22\" >0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col23\" class=\"data row2 col23\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col24\" class=\"data row2 col24\" >0.38</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col25\" class=\"data row2 col25\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col26\" class=\"data row2 col26\" >0.28</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col27\" class=\"data row2 col27\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col28\" class=\"data row2 col28\" >0.044</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col29\" class=\"data row2 col29\" >0.042</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row2_col30\" class=\"data row2 col30\" >0.17</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row3\" class=\"row_heading level0 row3\" >Score_A</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col0\" class=\"data row3 col0\" >-0.42</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col1\" class=\"data row3 col1\" >0.079</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col2\" class=\"data row3 col2\" >0.49</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col3\" class=\"data row3 col3\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col4\" class=\"data row3 col4\" >0.5</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col5\" class=\"data row3 col5\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col6\" class=\"data row3 col6\" >0.57</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col7\" class=\"data row3 col7\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col8\" class=\"data row3 col8\" >0.3</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col9\" class=\"data row3 col9\" >0.24</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col10\" class=\"data row3 col10\" >0.27</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col11\" class=\"data row3 col11\" >0.26</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col12\" class=\"data row3 col12\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col13\" class=\"data row3 col13\" >0.47</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col14\" class=\"data row3 col14\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col15\" class=\"data row3 col15\" >0.086</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col16\" class=\"data row3 col16\" >0.091</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col17\" class=\"data row3 col17\" >0.1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col18\" class=\"data row3 col18\" >0.18</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col19\" class=\"data row3 col19\" >0.26</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col20\" class=\"data row3 col20\" >0.72</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col21\" class=\"data row3 col21\" >0.32</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col22\" class=\"data row3 col22\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col23\" class=\"data row3 col23\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col24\" class=\"data row3 col24\" >0.62</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col25\" class=\"data row3 col25\" >0.27</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col26\" class=\"data row3 col26\" >0.47</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col27\" class=\"data row3 col27\" >0.086</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col28\" class=\"data row3 col28\" >0.091</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col29\" class=\"data row3 col29\" >0.091</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row3_col30\" class=\"data row3 col30\" >0.26</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row4\" class=\"row_heading level0 row4\" >Risk_A</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col0\" class=\"data row4 col0\" >-0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col1\" class=\"data row4 col1\" >-0.0044</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col2\" class=\"data row4 col2\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col3\" class=\"data row4 col3\" >0.5</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col4\" class=\"data row4 col4\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col5\" class=\"data row4 col5\" >0.16</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col6\" class=\"data row4 col6\" >0.36</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col7\" class=\"data row4 col7\" >0.16</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col8\" class=\"data row4 col8\" >0.27</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col9\" class=\"data row4 col9\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col10\" class=\"data row4 col10\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col11\" class=\"data row4 col11\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col12\" class=\"data row4 col12\" >0.45</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col13\" class=\"data row4 col13\" >0.29</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col14\" class=\"data row4 col14\" >0.45</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col15\" class=\"data row4 col15\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col16\" class=\"data row4 col16\" >0.042</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col17\" class=\"data row4 col17\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col18\" class=\"data row4 col18\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col19\" class=\"data row4 col19\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col20\" class=\"data row4 col20\" >0.43</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col21\" class=\"data row4 col21\" >0.48</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col22\" class=\"data row4 col22\" >0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col23\" class=\"data row4 col23\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col24\" class=\"data row4 col24\" >0.38</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col25\" class=\"data row4 col25\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col26\" class=\"data row4 col26\" >0.29</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col27\" class=\"data row4 col27\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col28\" class=\"data row4 col28\" >0.045</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col29\" class=\"data row4 col29\" >0.042</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row4_col30\" class=\"data row4 col30\" >0.17</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row5\" class=\"row_heading level0 row5\" >PARA_B</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col0\" class=\"data row5 col0\" >-0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col1\" class=\"data row5 col1\" >0.0066</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col2\" class=\"data row5 col2\" >0.16</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col3\" class=\"data row5 col3\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col4\" class=\"data row5 col4\" >0.16</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col5\" class=\"data row5 col5\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col6\" class=\"data row5 col6\" >0.35</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col7\" class=\"data row5 col7\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col8\" class=\"data row5 col8\" >0.99</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col9\" class=\"data row5 col9\" >0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col10\" class=\"data row5 col10\" >0.23</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col11\" class=\"data row5 col11\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col12\" class=\"data row5 col12\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col13\" class=\"data row5 col13\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col14\" class=\"data row5 col14\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col15\" class=\"data row5 col15\" >0.083</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col16\" class=\"data row5 col16\" >0.042</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col17\" class=\"data row5 col17\" >0.079</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col18\" class=\"data row5 col18\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col19\" class=\"data row5 col19\" >0.32</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col20\" class=\"data row5 col20\" >0.4</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col21\" class=\"data row5 col21\" >0.65</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col22\" class=\"data row5 col22\" >0.19</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col23\" class=\"data row5 col23\" >0.89</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col24\" class=\"data row5 col24\" >0.26</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col25\" class=\"data row5 col25\" >0.23</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col26\" class=\"data row5 col26\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col27\" class=\"data row5 col27\" >0.083</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col28\" class=\"data row5 col28\" >0.044</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col29\" class=\"data row5 col29\" >0.042</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row5_col30\" class=\"data row5 col30\" >0.32</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row6\" class=\"row_heading level0 row6\" >Score_B</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col0\" class=\"data row6 col0\" >-0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col1\" class=\"data row6 col1\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col2\" class=\"data row6 col2\" >0.36</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col3\" class=\"data row6 col3\" >0.57</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col4\" class=\"data row6 col4\" >0.36</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col5\" class=\"data row6 col5\" >0.35</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col6\" class=\"data row6 col6\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col7\" class=\"data row6 col7\" >0.35</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col8\" class=\"data row6 col8\" >0.38</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col9\" class=\"data row6 col9\" >0.28</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col10\" class=\"data row6 col10\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col11\" class=\"data row6 col11\" >0.3</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col12\" class=\"data row6 col12\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col13\" class=\"data row6 col13\" >0.56</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col14\" class=\"data row6 col14\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col15\" class=\"data row6 col15\" >-0.0077</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col16\" class=\"data row6 col16\" >0.091</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col17\" class=\"data row6 col17\" >0.012</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col18\" class=\"data row6 col18\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col19\" class=\"data row6 col19\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col20\" class=\"data row6 col20\" >0.9</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col21\" class=\"data row6 col21\" >0.36</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col22\" class=\"data row6 col22\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col23\" class=\"data row6 col23\" >0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col24\" class=\"data row6 col24\" >0.63</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col25\" class=\"data row6 col25\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col26\" class=\"data row6 col26\" >0.56</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col27\" class=\"data row6 col27\" >-0.0077</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col28\" class=\"data row6 col28\" >0.097</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col29\" class=\"data row6 col29\" >0.091</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row6_col30\" class=\"data row6 col30\" >0.31</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row7\" class=\"row_heading level0 row7\" >Risk_B</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col0\" class=\"data row7 col0\" >-0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col1\" class=\"data row7 col1\" >0.0062</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col2\" class=\"data row7 col2\" >0.16</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col3\" class=\"data row7 col3\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col4\" class=\"data row7 col4\" >0.16</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col5\" class=\"data row7 col5\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col6\" class=\"data row7 col6\" >0.35</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col7\" class=\"data row7 col7\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col8\" class=\"data row7 col8\" >0.99</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col9\" class=\"data row7 col9\" >0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col10\" class=\"data row7 col10\" >0.23</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col11\" class=\"data row7 col11\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col12\" class=\"data row7 col12\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col13\" class=\"data row7 col13\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col14\" class=\"data row7 col14\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col15\" class=\"data row7 col15\" >0.083</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col16\" class=\"data row7 col16\" >0.042</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col17\" class=\"data row7 col17\" >0.079</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col18\" class=\"data row7 col18\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col19\" class=\"data row7 col19\" >0.32</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col20\" class=\"data row7 col20\" >0.4</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col21\" class=\"data row7 col21\" >0.65</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col22\" class=\"data row7 col22\" >0.19</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col23\" class=\"data row7 col23\" >0.89</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col24\" class=\"data row7 col24\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col25\" class=\"data row7 col25\" >0.23</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col26\" class=\"data row7 col26\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col27\" class=\"data row7 col27\" >0.083</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col28\" class=\"data row7 col28\" >0.044</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col29\" class=\"data row7 col29\" >0.042</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row7_col30\" class=\"data row7 col30\" >0.32</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row8\" class=\"row_heading level0 row8\" >TOTAL</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col0\" class=\"data row8 col0\" >-0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col1\" class=\"data row8 col1\" >0.0065</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col2\" class=\"data row8 col2\" >0.27</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col3\" class=\"data row8 col3\" >0.3</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col4\" class=\"data row8 col4\" >0.27</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col5\" class=\"data row8 col5\" >0.99</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col6\" class=\"data row8 col6\" >0.38</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col7\" class=\"data row8 col7\" >0.99</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col8\" class=\"data row8 col8\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col9\" class=\"data row8 col9\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col10\" class=\"data row8 col10\" >0.24</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col11\" class=\"data row8 col11\" >0.23</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col12\" class=\"data row8 col12\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col13\" class=\"data row8 col13\" >0.34</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col14\" class=\"data row8 col14\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col15\" class=\"data row8 col15\" >0.093</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col16\" class=\"data row8 col16\" >0.046</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col17\" class=\"data row8 col17\" >0.088</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col18\" class=\"data row8 col18\" >0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col19\" class=\"data row8 col19\" >0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col20\" class=\"data row8 col20\" >0.43</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col21\" class=\"data row8 col21\" >0.69</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col22\" class=\"data row8 col22\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col23\" class=\"data row8 col23\" >0.89</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col24\" class=\"data row8 col24\" >0.29</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col25\" class=\"data row8 col25\" >0.24</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col26\" class=\"data row8 col26\" >0.34</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col27\" class=\"data row8 col27\" >0.093</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col28\" class=\"data row8 col28\" >0.048</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col29\" class=\"data row8 col29\" >0.046</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row8_col30\" class=\"data row8 col30\" >0.33</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row9\" class=\"row_heading level0 row9\" >numbers</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col0\" class=\"data row9 col0\" >-0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col1\" class=\"data row9 col1\" >0.0067</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col2\" class=\"data row9 col2\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col3\" class=\"data row9 col3\" >0.24</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col4\" class=\"data row9 col4\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col5\" class=\"data row9 col5\" >0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col6\" class=\"data row9 col6\" >0.28</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col7\" class=\"data row9 col7\" >0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col8\" class=\"data row9 col8\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col9\" class=\"data row9 col9\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col10\" class=\"data row9 col10\" >0.91</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col11\" class=\"data row9 col11\" >0.96</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col12\" class=\"data row9 col12\" >0.19</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col13\" class=\"data row9 col13\" >0.45</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col14\" class=\"data row9 col14\" >0.19</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col15\" class=\"data row9 col15\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col16\" class=\"data row9 col16\" >0.035</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col17\" class=\"data row9 col17\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col18\" class=\"data row9 col18\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col19\" class=\"data row9 col19\" >0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col20\" class=\"data row9 col20\" >0.5</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col21\" class=\"data row9 col21\" >0.27</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col22\" class=\"data row9 col22\" >0.23</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col23\" class=\"data row9 col23\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col24\" class=\"data row9 col24\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col25\" class=\"data row9 col25\" >0.91</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col26\" class=\"data row9 col26\" >0.45</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col27\" class=\"data row9 col27\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col28\" class=\"data row9 col28\" >-0.0024</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col29\" class=\"data row9 col29\" >0.035</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row9_col30\" class=\"data row9 col30\" >0.21</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row10\" class=\"row_heading level0 row10\" >Score_B.1</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col0\" class=\"data row10 col0\" >-0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col1\" class=\"data row10 col1\" >-0.018</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col2\" class=\"data row10 col2\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col3\" class=\"data row10 col3\" >0.27</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col4\" class=\"data row10 col4\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col5\" class=\"data row10 col5\" >0.23</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col6\" class=\"data row10 col6\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col7\" class=\"data row10 col7\" >0.23</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col8\" class=\"data row10 col8\" >0.24</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col9\" class=\"data row10 col9\" >0.91</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col10\" class=\"data row10 col10\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col11\" class=\"data row10 col11\" >0.99</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col12\" class=\"data row10 col12\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col13\" class=\"data row10 col13\" >0.51</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col14\" class=\"data row10 col14\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col15\" class=\"data row10 col15\" >0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col16\" class=\"data row10 col16\" >0.036</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col17\" class=\"data row10 col17\" >0.16</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col18\" class=\"data row10 col18\" >0.23</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col19\" class=\"data row10 col19\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col20\" class=\"data row10 col20\" >0.57</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col21\" class=\"data row10 col21\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col22\" class=\"data row10 col22\" >0.26</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col23\" class=\"data row10 col23\" >0.26</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col24\" class=\"data row10 col24\" >0.35</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col25\" class=\"data row10 col25\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col26\" class=\"data row10 col26\" >0.51</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col27\" class=\"data row10 col27\" >0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col28\" class=\"data row10 col28\" >0.0038</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col29\" class=\"data row10 col29\" >0.036</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row10_col30\" class=\"data row10 col30\" >0.25</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row11\" class=\"row_heading level0 row11\" >Risk_C</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col0\" class=\"data row11 col0\" >-0.16</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col1\" class=\"data row11 col1\" >-0.015</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col2\" class=\"data row11 col2\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col3\" class=\"data row11 col3\" >0.26</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col4\" class=\"data row11 col4\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col5\" class=\"data row11 col5\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col6\" class=\"data row11 col6\" >0.3</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col7\" class=\"data row11 col7\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col8\" class=\"data row11 col8\" >0.23</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col9\" class=\"data row11 col9\" >0.96</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col10\" class=\"data row11 col10\" >0.99</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col11\" class=\"data row11 col11\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col12\" class=\"data row11 col12\" >0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col13\" class=\"data row11 col13\" >0.49</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col14\" class=\"data row11 col14\" >0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col15\" class=\"data row11 col15\" >0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col16\" class=\"data row11 col16\" >0.035</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col17\" class=\"data row11 col17\" >0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col18\" class=\"data row11 col18\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col19\" class=\"data row11 col19\" >0.24</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col20\" class=\"data row11 col20\" >0.55</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col21\" class=\"data row11 col21\" >0.3</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col22\" class=\"data row11 col22\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col23\" class=\"data row11 col23\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col24\" class=\"data row11 col24\" >0.34</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col25\" class=\"data row11 col25\" >0.99</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col26\" class=\"data row11 col26\" >0.49</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col27\" class=\"data row11 col27\" >0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col28\" class=\"data row11 col28\" >0.0001</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col29\" class=\"data row11 col29\" >0.035</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row11_col30\" class=\"data row11 col30\" >0.24</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row12\" class=\"row_heading level0 row12\" >Money_Value</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col0\" class=\"data row12 col0\" >-0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col1\" class=\"data row12 col1\" >-0.067</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col2\" class=\"data row12 col2\" >0.45</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col3\" class=\"data row12 col3\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col4\" class=\"data row12 col4\" >0.45</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col5\" class=\"data row12 col5\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col6\" class=\"data row12 col6\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col7\" class=\"data row12 col7\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col8\" class=\"data row12 col8\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col9\" class=\"data row12 col9\" >0.19</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col10\" class=\"data row12 col10\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col11\" class=\"data row12 col11\" >0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col12\" class=\"data row12 col12\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col13\" class=\"data row12 col13\" >0.39</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col14\" class=\"data row12 col14\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col15\" class=\"data row12 col15\" >0.028</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col16\" class=\"data row12 col16\" >0.031</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col17\" class=\"data row12 col17\" >0.032</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col18\" class=\"data row12 col18\" >0.079</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col19\" class=\"data row12 col19\" >0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col20\" class=\"data row12 col20\" >0.29</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col21\" class=\"data row12 col21\" >0.83</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col22\" class=\"data row12 col22\" >0.069</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col23\" class=\"data row12 col23\" >0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col24\" class=\"data row12 col24\" >0.26</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col25\" class=\"data row12 col25\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col26\" class=\"data row12 col26\" >0.39</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col27\" class=\"data row12 col27\" >0.028</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col28\" class=\"data row12 col28\" >0.024</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col29\" class=\"data row12 col29\" >0.031</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row12_col30\" class=\"data row12 col30\" >0.11</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row13\" class=\"row_heading level0 row13\" >Score_MV</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col0\" class=\"data row13 col0\" >-0.32</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col1\" class=\"data row13 col1\" >0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col2\" class=\"data row13 col2\" >0.28</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col3\" class=\"data row13 col3\" >0.47</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col4\" class=\"data row13 col4\" >0.29</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col5\" class=\"data row13 col5\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col6\" class=\"data row13 col6\" >0.56</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col7\" class=\"data row13 col7\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col8\" class=\"data row13 col8\" >0.34</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col9\" class=\"data row13 col9\" >0.45</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col10\" class=\"data row13 col10\" >0.51</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col11\" class=\"data row13 col11\" >0.49</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col12\" class=\"data row13 col12\" >0.39</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col13\" class=\"data row13 col13\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col14\" class=\"data row13 col14\" >0.39</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col15\" class=\"data row13 col15\" >0.079</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col16\" class=\"data row13 col16\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col17\" class=\"data row13 col17\" >0.1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col18\" class=\"data row13 col18\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col19\" class=\"data row13 col19\" >0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col20\" class=\"data row13 col20\" >0.76</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col21\" class=\"data row13 col21\" >0.48</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col22\" class=\"data row13 col22\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col23\" class=\"data row13 col23\" >0.29</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col24\" class=\"data row13 col24\" >0.69</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col25\" class=\"data row13 col25\" >0.51</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col26\" class=\"data row13 col26\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col27\" class=\"data row13 col27\" >0.079</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col28\" class=\"data row13 col28\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col29\" class=\"data row13 col29\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row13_col30\" class=\"data row13 col30\" >0.33</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row14\" class=\"row_heading level0 row14\" >Risk_D</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col0\" class=\"data row14 col0\" >-0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col1\" class=\"data row14 col1\" >-0.067</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col2\" class=\"data row14 col2\" >0.45</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col3\" class=\"data row14 col3\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col4\" class=\"data row14 col4\" >0.45</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col5\" class=\"data row14 col5\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col6\" class=\"data row14 col6\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col7\" class=\"data row14 col7\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col8\" class=\"data row14 col8\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col9\" class=\"data row14 col9\" >0.19</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col10\" class=\"data row14 col10\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col11\" class=\"data row14 col11\" >0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col12\" class=\"data row14 col12\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col13\" class=\"data row14 col13\" >0.39</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col14\" class=\"data row14 col14\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col15\" class=\"data row14 col15\" >0.028</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col16\" class=\"data row14 col16\" >0.031</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col17\" class=\"data row14 col17\" >0.032</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col18\" class=\"data row14 col18\" >0.079</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col19\" class=\"data row14 col19\" >0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col20\" class=\"data row14 col20\" >0.29</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col21\" class=\"data row14 col21\" >0.83</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col22\" class=\"data row14 col22\" >0.069</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col23\" class=\"data row14 col23\" >0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col24\" class=\"data row14 col24\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col25\" class=\"data row14 col25\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col26\" class=\"data row14 col26\" >0.39</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col27\" class=\"data row14 col27\" >0.028</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col28\" class=\"data row14 col28\" >0.024</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col29\" class=\"data row14 col29\" >0.031</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row14_col30\" class=\"data row14 col30\" >0.11</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row15\" class=\"row_heading level0 row15\" >District_Loss</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col0\" class=\"data row15 col0\" >-0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col1\" class=\"data row15 col1\" >-0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col2\" class=\"data row15 col2\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col3\" class=\"data row15 col3\" >0.086</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col4\" class=\"data row15 col4\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col5\" class=\"data row15 col5\" >0.083</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col6\" class=\"data row15 col6\" >-0.0077</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col7\" class=\"data row15 col7\" >0.083</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col8\" class=\"data row15 col8\" >0.093</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col9\" class=\"data row15 col9\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col10\" class=\"data row15 col10\" >0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col11\" class=\"data row15 col11\" >0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col12\" class=\"data row15 col12\" >0.028</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col13\" class=\"data row15 col13\" >0.079</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col14\" class=\"data row15 col14\" >0.028</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col15\" class=\"data row15 col15\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col16\" class=\"data row15 col16\" >0.055</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col17\" class=\"data row15 col17\" >0.91</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col18\" class=\"data row15 col18\" >0.069</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col19\" class=\"data row15 col19\" >0.083</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col20\" class=\"data row15 col20\" >0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col21\" class=\"data row15 col21\" >0.08</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col22\" class=\"data row15 col22\" >0.64</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col23\" class=\"data row15 col23\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col24\" class=\"data row15 col24\" >0.4</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col25\" class=\"data row15 col25\" >0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col26\" class=\"data row15 col26\" >0.079</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col27\" class=\"data row15 col27\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col28\" class=\"data row15 col28\" >0.036</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col29\" class=\"data row15 col29\" >0.055</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row15_col30\" class=\"data row15 col30\" >0.083</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row16\" class=\"row_heading level0 row16\" >PROB</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col0\" class=\"data row16 col0\" >-0.086</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col1\" class=\"data row16 col1\" >-0.0034</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col2\" class=\"data row16 col2\" >0.042</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col3\" class=\"data row16 col3\" >0.091</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col4\" class=\"data row16 col4\" >0.042</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col5\" class=\"data row16 col5\" >0.042</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col6\" class=\"data row16 col6\" >0.091</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col7\" class=\"data row16 col7\" >0.042</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col8\" class=\"data row16 col8\" >0.046</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col9\" class=\"data row16 col9\" >0.035</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col10\" class=\"data row16 col10\" >0.036</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col11\" class=\"data row16 col11\" >0.035</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col12\" class=\"data row16 col12\" >0.031</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col13\" class=\"data row16 col13\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col14\" class=\"data row16 col14\" >0.031</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col15\" class=\"data row16 col15\" >0.055</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col16\" class=\"data row16 col16\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col17\" class=\"data row16 col17\" >0.41</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col18\" class=\"data row16 col18\" >0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col19\" class=\"data row16 col19\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col20\" class=\"data row16 col20\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col21\" class=\"data row16 col21\" >0.052</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col22\" class=\"data row16 col22\" >0.34</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col23\" class=\"data row16 col23\" >0.073</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col24\" class=\"data row16 col24\" >0.18</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col25\" class=\"data row16 col25\" >0.036</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col26\" class=\"data row16 col26\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col27\" class=\"data row16 col27\" >0.055</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col28\" class=\"data row16 col28\" >0.98</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col29\" class=\"data row16 col29\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row16_col30\" class=\"data row16 col30\" >0.14</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row17\" class=\"row_heading level0 row17\" >RiSk_E</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col0\" class=\"data row17 col0\" >-0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col1\" class=\"data row17 col1\" >-0.097</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col2\" class=\"data row17 col2\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col3\" class=\"data row17 col3\" >0.1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col4\" class=\"data row17 col4\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col5\" class=\"data row17 col5\" >0.079</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col6\" class=\"data row17 col6\" >0.012</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col7\" class=\"data row17 col7\" >0.079</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col8\" class=\"data row17 col8\" >0.088</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col9\" class=\"data row17 col9\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col10\" class=\"data row17 col10\" >0.16</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col11\" class=\"data row17 col11\" >0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col12\" class=\"data row17 col12\" >0.032</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col13\" class=\"data row17 col13\" >0.1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col14\" class=\"data row17 col14\" >0.032</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col15\" class=\"data row17 col15\" >0.91</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col16\" class=\"data row17 col16\" >0.41</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col17\" class=\"data row17 col17\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col18\" class=\"data row17 col18\" >0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col19\" class=\"data row17 col19\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col20\" class=\"data row17 col20\" >0.23</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col21\" class=\"data row17 col21\" >0.082</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col22\" class=\"data row17 col22\" >0.73</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col23\" class=\"data row17 col23\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col24\" class=\"data row17 col24\" >0.41</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col25\" class=\"data row17 col25\" >0.16</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col26\" class=\"data row17 col26\" >0.1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col27\" class=\"data row17 col27\" >0.91</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col28\" class=\"data row17 col28\" >0.37</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col29\" class=\"data row17 col29\" >0.41</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row17_col30\" class=\"data row17 col30\" >0.12</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row18\" class=\"row_heading level0 row18\" >History</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col0\" class=\"data row18 col0\" >-0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col1\" class=\"data row18 col1\" >-0.082</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col2\" class=\"data row18 col2\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col3\" class=\"data row18 col3\" >0.18</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col4\" class=\"data row18 col4\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col5\" class=\"data row18 col5\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col6\" class=\"data row18 col6\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col7\" class=\"data row18 col7\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col8\" class=\"data row18 col8\" >0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col9\" class=\"data row18 col9\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col10\" class=\"data row18 col10\" >0.23</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col11\" class=\"data row18 col11\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col12\" class=\"data row18 col12\" >0.079</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col13\" class=\"data row18 col13\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col14\" class=\"data row18 col14\" >0.079</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col15\" class=\"data row18 col15\" >0.069</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col16\" class=\"data row18 col16\" >0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col17\" class=\"data row18 col17\" >0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col18\" class=\"data row18 col18\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col19\" class=\"data row18 col19\" >0.86</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col20\" class=\"data row18 col20\" >0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col21\" class=\"data row18 col21\" >0.18</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col22\" class=\"data row18 col22\" >0.76</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col23\" class=\"data row18 col23\" >0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col24\" class=\"data row18 col24\" >0.24</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col25\" class=\"data row18 col25\" >0.23</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col26\" class=\"data row18 col26\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col27\" class=\"data row18 col27\" >0.069</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col28\" class=\"data row18 col28\" >0.06</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col29\" class=\"data row18 col29\" >0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row18_col30\" class=\"data row18 col30\" >0.86</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row19\" class=\"row_heading level0 row19\" >Prob</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col0\" class=\"data row19 col0\" >-0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col1\" class=\"data row19 col1\" >-0.054</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col2\" class=\"data row19 col2\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col3\" class=\"data row19 col3\" >0.26</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col4\" class=\"data row19 col4\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col5\" class=\"data row19 col5\" >0.32</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col6\" class=\"data row19 col6\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col7\" class=\"data row19 col7\" >0.32</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col8\" class=\"data row19 col8\" >0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col9\" class=\"data row19 col9\" >0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col10\" class=\"data row19 col10\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col11\" class=\"data row19 col11\" >0.24</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col12\" class=\"data row19 col12\" >0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col13\" class=\"data row19 col13\" >0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col14\" class=\"data row19 col14\" >0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col15\" class=\"data row19 col15\" >0.083</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col16\" class=\"data row19 col16\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col17\" class=\"data row19 col17\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col18\" class=\"data row19 col18\" >0.86</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col19\" class=\"data row19 col19\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col20\" class=\"data row19 col20\" >0.44</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col21\" class=\"data row19 col21\" >0.27</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col22\" class=\"data row19 col22\" >0.64</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col23\" class=\"data row19 col23\" >0.43</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col24\" class=\"data row19 col24\" >0.3</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col25\" class=\"data row19 col25\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col26\" class=\"data row19 col26\" >0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col27\" class=\"data row19 col27\" >0.083</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col28\" class=\"data row19 col28\" >0.1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col29\" class=\"data row19 col29\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row19_col30\" class=\"data row19 col30\" >1</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row20\" class=\"row_heading level0 row20\" >Score</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col0\" class=\"data row20 col0\" >-0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col1\" class=\"data row20 col1\" >0.088</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col2\" class=\"data row20 col2\" >0.42</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col3\" class=\"data row20 col3\" >0.72</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col4\" class=\"data row20 col4\" >0.43</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col5\" class=\"data row20 col5\" >0.4</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col6\" class=\"data row20 col6\" >0.9</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col7\" class=\"data row20 col7\" >0.4</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col8\" class=\"data row20 col8\" >0.43</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col9\" class=\"data row20 col9\" >0.5</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col10\" class=\"data row20 col10\" >0.57</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col11\" class=\"data row20 col11\" >0.55</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col12\" class=\"data row20 col12\" >0.29</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col13\" class=\"data row20 col13\" >0.76</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col14\" class=\"data row20 col14\" >0.29</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col15\" class=\"data row20 col15\" >0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col16\" class=\"data row20 col16\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col17\" class=\"data row20 col17\" >0.23</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col18\" class=\"data row20 col18\" >0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col19\" class=\"data row20 col19\" >0.44</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col20\" class=\"data row20 col20\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col21\" class=\"data row20 col21\" >0.46</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col22\" class=\"data row20 col22\" >0.35</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col23\" class=\"data row20 col23\" >0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col24\" class=\"data row20 col24\" >0.78</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col25\" class=\"data row20 col25\" >0.57</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col26\" class=\"data row20 col26\" >0.76</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col27\" class=\"data row20 col27\" >0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col28\" class=\"data row20 col28\" >0.16</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col29\" class=\"data row20 col29\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row20_col30\" class=\"data row20 col30\" >0.44</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row21\" class=\"row_heading level0 row21\" >Inherent_Risk</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col0\" class=\"data row21 col0\" >-0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col1\" class=\"data row21 col1\" >-0.047</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col2\" class=\"data row21 col2\" >0.48</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col3\" class=\"data row21 col3\" >0.32</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col4\" class=\"data row21 col4\" >0.48</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col5\" class=\"data row21 col5\" >0.65</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col6\" class=\"data row21 col6\" >0.36</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col7\" class=\"data row21 col7\" >0.65</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col8\" class=\"data row21 col8\" >0.69</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col9\" class=\"data row21 col9\" >0.27</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col10\" class=\"data row21 col10\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col11\" class=\"data row21 col11\" >0.3</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col12\" class=\"data row21 col12\" >0.83</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col13\" class=\"data row21 col13\" >0.48</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col14\" class=\"data row21 col14\" >0.83</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col15\" class=\"data row21 col15\" >0.08</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col16\" class=\"data row21 col16\" >0.052</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col17\" class=\"data row21 col17\" >0.082</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col18\" class=\"data row21 col18\" >0.18</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col19\" class=\"data row21 col19\" >0.27</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col20\" class=\"data row21 col20\" >0.46</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col21\" class=\"data row21 col21\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col22\" class=\"data row21 col22\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col23\" class=\"data row21 col23\" >0.75</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col24\" class=\"data row21 col24\" >0.36</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col25\" class=\"data row21 col25\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col26\" class=\"data row21 col26\" >0.48</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col27\" class=\"data row21 col27\" >0.08</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col28\" class=\"data row21 col28\" >0.047</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col29\" class=\"data row21 col29\" >0.052</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row21_col30\" class=\"data row21 col30\" >0.27</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row22\" class=\"row_heading level0 row22\" >CONTROL_RISK</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col0\" class=\"data row22 col0\" >-0.16</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col1\" class=\"data row22 col1\" >-0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col2\" class=\"data row22 col2\" >0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col3\" class=\"data row22 col3\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col4\" class=\"data row22 col4\" >0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col5\" class=\"data row22 col5\" >0.19</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col6\" class=\"data row22 col6\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col7\" class=\"data row22 col7\" >0.19</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col8\" class=\"data row22 col8\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col9\" class=\"data row22 col9\" >0.23</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col10\" class=\"data row22 col10\" >0.26</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col11\" class=\"data row22 col11\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col12\" class=\"data row22 col12\" >0.069</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col13\" class=\"data row22 col13\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col14\" class=\"data row22 col14\" >0.069</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col15\" class=\"data row22 col15\" >0.64</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col16\" class=\"data row22 col16\" >0.34</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col17\" class=\"data row22 col17\" >0.73</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col18\" class=\"data row22 col18\" >0.76</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col19\" class=\"data row22 col19\" >0.64</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col20\" class=\"data row22 col20\" >0.35</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col21\" class=\"data row22 col21\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col22\" class=\"data row22 col22\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col23\" class=\"data row22 col23\" >0.36</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col24\" class=\"data row22 col24\" >0.41</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col25\" class=\"data row22 col25\" >0.26</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col26\" class=\"data row22 col26\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col27\" class=\"data row22 col27\" >0.64</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col28\" class=\"data row22 col28\" >0.28</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col29\" class=\"data row22 col29\" >0.34</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row22_col30\" class=\"data row22 col30\" >0.64</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row23\" class=\"row_heading level0 row23\" >Audit_Risk</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col0\" class=\"data row23 col0\" >-0.091</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col1\" class=\"data row23 col1\" >-0.087</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col2\" class=\"data row23 col2\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col3\" class=\"data row23 col3\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col4\" class=\"data row23 col4\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col5\" class=\"data row23 col5\" >0.89</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col6\" class=\"data row23 col6\" >0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col7\" class=\"data row23 col7\" >0.89</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col8\" class=\"data row23 col8\" >0.89</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col9\" class=\"data row23 col9\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col10\" class=\"data row23 col10\" >0.26</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col11\" class=\"data row23 col11\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col12\" class=\"data row23 col12\" >0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col13\" class=\"data row23 col13\" >0.29</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col14\" class=\"data row23 col14\" >0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col15\" class=\"data row23 col15\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col16\" class=\"data row23 col16\" >0.073</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col17\" class=\"data row23 col17\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col18\" class=\"data row23 col18\" >0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col19\" class=\"data row23 col19\" >0.43</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col20\" class=\"data row23 col20\" >0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col21\" class=\"data row23 col21\" >0.75</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col22\" class=\"data row23 col22\" >0.36</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col23\" class=\"data row23 col23\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col24\" class=\"data row23 col24\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col25\" class=\"data row23 col25\" >0.26</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col26\" class=\"data row23 col26\" >0.29</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col27\" class=\"data row23 col27\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col28\" class=\"data row23 col28\" >0.049</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col29\" class=\"data row23 col29\" >0.073</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row23_col30\" class=\"data row23 col30\" >0.43</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row24\" class=\"row_heading level0 row24\" >Risk</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col0\" class=\"data row24 col0\" >-0.39</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col1\" class=\"data row24 col1\" >0.063</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col2\" class=\"data row24 col2\" >0.38</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col3\" class=\"data row24 col3\" >0.62</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col4\" class=\"data row24 col4\" >0.38</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col5\" class=\"data row24 col5\" >0.26</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col6\" class=\"data row24 col6\" >0.63</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col7\" class=\"data row24 col7\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col8\" class=\"data row24 col8\" >0.29</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col9\" class=\"data row24 col9\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col10\" class=\"data row24 col10\" >0.35</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col11\" class=\"data row24 col11\" >0.34</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col12\" class=\"data row24 col12\" >0.26</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col13\" class=\"data row24 col13\" >0.69</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col14\" class=\"data row24 col14\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col15\" class=\"data row24 col15\" >0.4</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col16\" class=\"data row24 col16\" >0.18</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col17\" class=\"data row24 col17\" >0.41</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col18\" class=\"data row24 col18\" >0.24</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col19\" class=\"data row24 col19\" >0.3</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col20\" class=\"data row24 col20\" >0.78</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col21\" class=\"data row24 col21\" >0.36</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col22\" class=\"data row24 col22\" >0.41</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col23\" class=\"data row24 col23\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col24\" class=\"data row24 col24\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col25\" class=\"data row24 col25\" >0.35</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col26\" class=\"data row24 col26\" >0.69</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col27\" class=\"data row24 col27\" >0.4</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col28\" class=\"data row24 col28\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col29\" class=\"data row24 col29\" >0.18</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row24_col30\" class=\"data row24 col30\" >0.3</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row25\" class=\"row_heading level0 row25\" >Marks</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col0\" class=\"data row25 col0\" >-0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col1\" class=\"data row25 col1\" >-0.018</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col2\" class=\"data row25 col2\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col3\" class=\"data row25 col3\" >0.27</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col4\" class=\"data row25 col4\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col5\" class=\"data row25 col5\" >0.23</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col6\" class=\"data row25 col6\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col7\" class=\"data row25 col7\" >0.23</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col8\" class=\"data row25 col8\" >0.24</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col9\" class=\"data row25 col9\" >0.91</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col10\" class=\"data row25 col10\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col11\" class=\"data row25 col11\" >0.99</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col12\" class=\"data row25 col12\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col13\" class=\"data row25 col13\" >0.51</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col14\" class=\"data row25 col14\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col15\" class=\"data row25 col15\" >0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col16\" class=\"data row25 col16\" >0.036</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col17\" class=\"data row25 col17\" >0.16</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col18\" class=\"data row25 col18\" >0.23</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col19\" class=\"data row25 col19\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col20\" class=\"data row25 col20\" >0.57</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col21\" class=\"data row25 col21\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col22\" class=\"data row25 col22\" >0.26</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col23\" class=\"data row25 col23\" >0.26</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col24\" class=\"data row25 col24\" >0.35</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col25\" class=\"data row25 col25\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col26\" class=\"data row25 col26\" >0.51</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col27\" class=\"data row25 col27\" >0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col28\" class=\"data row25 col28\" >0.0038</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col29\" class=\"data row25 col29\" >0.036</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row25_col30\" class=\"data row25 col30\" >0.25</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row26\" class=\"row_heading level0 row26\" >MONEY_Marks</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col0\" class=\"data row26 col0\" >-0.32</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col1\" class=\"data row26 col1\" >0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col2\" class=\"data row26 col2\" >0.28</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col3\" class=\"data row26 col3\" >0.47</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col4\" class=\"data row26 col4\" >0.29</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col5\" class=\"data row26 col5\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col6\" class=\"data row26 col6\" >0.56</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col7\" class=\"data row26 col7\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col8\" class=\"data row26 col8\" >0.34</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col9\" class=\"data row26 col9\" >0.45</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col10\" class=\"data row26 col10\" >0.51</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col11\" class=\"data row26 col11\" >0.49</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col12\" class=\"data row26 col12\" >0.39</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col13\" class=\"data row26 col13\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col14\" class=\"data row26 col14\" >0.39</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col15\" class=\"data row26 col15\" >0.079</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col16\" class=\"data row26 col16\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col17\" class=\"data row26 col17\" >0.1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col18\" class=\"data row26 col18\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col19\" class=\"data row26 col19\" >0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col20\" class=\"data row26 col20\" >0.76</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col21\" class=\"data row26 col21\" >0.48</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col22\" class=\"data row26 col22\" >0.22</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col23\" class=\"data row26 col23\" >0.29</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col24\" class=\"data row26 col24\" >0.69</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col25\" class=\"data row26 col25\" >0.51</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col26\" class=\"data row26 col26\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col27\" class=\"data row26 col27\" >0.079</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col28\" class=\"data row26 col28\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col29\" class=\"data row26 col29\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row26_col30\" class=\"data row26 col30\" >0.33</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row27\" class=\"row_heading level0 row27\" >District</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col0\" class=\"data row27 col0\" >-0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col1\" class=\"data row27 col1\" >-0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col2\" class=\"data row27 col2\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col3\" class=\"data row27 col3\" >0.086</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col4\" class=\"data row27 col4\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col5\" class=\"data row27 col5\" >0.083</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col6\" class=\"data row27 col6\" >-0.0077</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col7\" class=\"data row27 col7\" >0.083</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col8\" class=\"data row27 col8\" >0.093</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col9\" class=\"data row27 col9\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col10\" class=\"data row27 col10\" >0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col11\" class=\"data row27 col11\" >0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col12\" class=\"data row27 col12\" >0.028</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col13\" class=\"data row27 col13\" >0.079</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col14\" class=\"data row27 col14\" >0.028</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col15\" class=\"data row27 col15\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col16\" class=\"data row27 col16\" >0.055</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col17\" class=\"data row27 col17\" >0.91</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col18\" class=\"data row27 col18\" >0.069</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col19\" class=\"data row27 col19\" >0.083</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col20\" class=\"data row27 col20\" >0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col21\" class=\"data row27 col21\" >0.08</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col22\" class=\"data row27 col22\" >0.64</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col23\" class=\"data row27 col23\" >0.2</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col24\" class=\"data row27 col24\" >0.4</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col25\" class=\"data row27 col25\" >0.15</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col26\" class=\"data row27 col26\" >0.079</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col27\" class=\"data row27 col27\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col28\" class=\"data row27 col28\" >0.036</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col29\" class=\"data row27 col29\" >0.055</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row27_col30\" class=\"data row27 col30\" >0.083</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row28\" class=\"row_heading level0 row28\" >Loss</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col0\" class=\"data row28 col0\" >-0.082</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col1\" class=\"data row28 col1\" >0.0064</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col2\" class=\"data row28 col2\" >0.044</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col3\" class=\"data row28 col3\" >0.091</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col4\" class=\"data row28 col4\" >0.045</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col5\" class=\"data row28 col5\" >0.044</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col6\" class=\"data row28 col6\" >0.097</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col7\" class=\"data row28 col7\" >0.044</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col8\" class=\"data row28 col8\" >0.048</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col9\" class=\"data row28 col9\" >-0.0024</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col10\" class=\"data row28 col10\" >0.0038</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col11\" class=\"data row28 col11\" >0.0001</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col12\" class=\"data row28 col12\" >0.024</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col13\" class=\"data row28 col13\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col14\" class=\"data row28 col14\" >0.024</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col15\" class=\"data row28 col15\" >0.036</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col16\" class=\"data row28 col16\" >0.98</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col17\" class=\"data row28 col17\" >0.37</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col18\" class=\"data row28 col18\" >0.06</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col19\" class=\"data row28 col19\" >0.1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col20\" class=\"data row28 col20\" >0.16</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col21\" class=\"data row28 col21\" >0.047</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col22\" class=\"data row28 col22\" >0.28</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col23\" class=\"data row28 col23\" >0.049</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col24\" class=\"data row28 col24\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col25\" class=\"data row28 col25\" >0.0038</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col26\" class=\"data row28 col26\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col27\" class=\"data row28 col27\" >0.036</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col28\" class=\"data row28 col28\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col29\" class=\"data row28 col29\" >0.98</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row28_col30\" class=\"data row28 col30\" >0.1</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row29\" class=\"row_heading level0 row29\" >LOSS_SCORE</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col0\" class=\"data row29 col0\" >-0.086</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col1\" class=\"data row29 col1\" >-0.0034</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col2\" class=\"data row29 col2\" >0.042</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col3\" class=\"data row29 col3\" >0.091</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col4\" class=\"data row29 col4\" >0.042</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col5\" class=\"data row29 col5\" >0.042</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col6\" class=\"data row29 col6\" >0.091</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col7\" class=\"data row29 col7\" >0.042</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col8\" class=\"data row29 col8\" >0.046</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col9\" class=\"data row29 col9\" >0.035</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col10\" class=\"data row29 col10\" >0.036</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col11\" class=\"data row29 col11\" >0.035</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col12\" class=\"data row29 col12\" >0.031</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col13\" class=\"data row29 col13\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col14\" class=\"data row29 col14\" >0.031</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col15\" class=\"data row29 col15\" >0.055</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col16\" class=\"data row29 col16\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col17\" class=\"data row29 col17\" >0.41</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col18\" class=\"data row29 col18\" >0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col19\" class=\"data row29 col19\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col20\" class=\"data row29 col20\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col21\" class=\"data row29 col21\" >0.052</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col22\" class=\"data row29 col22\" >0.34</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col23\" class=\"data row29 col23\" >0.073</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col24\" class=\"data row29 col24\" >0.18</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col25\" class=\"data row29 col25\" >0.036</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col26\" class=\"data row29 col26\" >0.13</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col27\" class=\"data row29 col27\" >0.055</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col28\" class=\"data row29 col28\" >0.98</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col29\" class=\"data row29 col29\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row29_col30\" class=\"data row29 col30\" >0.14</td> \n",
" </tr> <tr> \n",
" <th id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2level0_row30\" class=\"row_heading level0 row30\" >History_score</th> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col0\" class=\"data row30 col0\" >-0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col1\" class=\"data row30 col1\" >-0.054</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col2\" class=\"data row30 col2\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col3\" class=\"data row30 col3\" >0.26</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col4\" class=\"data row30 col4\" >0.17</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col5\" class=\"data row30 col5\" >0.32</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col6\" class=\"data row30 col6\" >0.31</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col7\" class=\"data row30 col7\" >0.32</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col8\" class=\"data row30 col8\" >0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col9\" class=\"data row30 col9\" >0.21</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col10\" class=\"data row30 col10\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col11\" class=\"data row30 col11\" >0.24</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col12\" class=\"data row30 col12\" >0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col13\" class=\"data row30 col13\" >0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col14\" class=\"data row30 col14\" >0.11</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col15\" class=\"data row30 col15\" >0.083</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col16\" class=\"data row30 col16\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col17\" class=\"data row30 col17\" >0.12</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col18\" class=\"data row30 col18\" >0.86</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col19\" class=\"data row30 col19\" >1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col20\" class=\"data row30 col20\" >0.44</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col21\" class=\"data row30 col21\" >0.27</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col22\" class=\"data row30 col22\" >0.64</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col23\" class=\"data row30 col23\" >0.43</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col24\" class=\"data row30 col24\" >0.3</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col25\" class=\"data row30 col25\" >0.25</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col26\" class=\"data row30 col26\" >0.33</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col27\" class=\"data row30 col27\" >0.083</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col28\" class=\"data row30 col28\" >0.1</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col29\" class=\"data row30 col29\" >0.14</td> \n",
" <td id=\"T_da84ec1e_691c_11e9_8be2_9cb6d08ec8d2row30_col30\" class=\"data row30 col30\" >1</td> \n",
" </tr></tbody> \n",
"</table> "
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x20b49beab38>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import seaborn as sns\n",
"corr = df.corr()\n",
"corr.style.background_gradient(cmap='coolwarm')\n",
"# 'RdBu_r' & 'BrBG' are other good diverging colormaps\n",
"cm = sns.diverging_palette(220, 20, sep=20, as_cmap=True) \n",
"corr.style.background_gradient(cmap=cm).set_precision(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, there are some interesting correlations. Deleting some variables that are highly correlated (0.8 and more) with each other to avoid overfitting the models and to avoid multicollinearity."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row2_col7 {\n",
" background-color: #89b2be;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row2_col8 {\n",
" background-color: #8eb4c0;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row2_col9 {\n",
" background-color: #8cb3bf;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row2_col10 {\n",
" background-color: #e4eff2;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row2_col11 {\n",
" background-color: #3f7f93;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col0 {\n",
" background-color: #dfebef;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col1 {\n",
" background-color: #5e94a5;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col2 {\n",
" background-color: #90b6c1;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col3 {\n",
" background-color: #c3553a;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col4 {\n",
" background-color: #3f7f93;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col5 {\n",
" background-color: #408094;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col6 {\n",
" background-color: #7aa7b5;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col7 {\n",
" background-color: #3f7f93;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col8 {\n",
" background-color: #b0ccd4;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col9 {\n",
" background-color: #6498a8;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col10 {\n",
" background-color: #b9d2d9;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col11 {\n",
" background-color: #488598;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col0 {\n",
" background-color: #5b92a3;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col1 {\n",
" background-color: #4c889a;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col2 {\n",
" background-color: #79a7b4;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col3 {\n",
" background-color: #428195;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col4 {\n",
" background-color: #c3553a;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col5 {\n",
" background-color: #468497;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col6 {\n",
" background-color: #6196a7;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col7 {\n",
" background-color: #e5b6aa;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col8 {\n",
" background-color: #7ca8b6;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col9 {\n",
" background-color: #aac8d1;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col10 {\n",
" background-color: #3f7f93;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col11 {\n",
" background-color: #cbdee4;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col0 {\n",
" background-color: #72a1b0;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col1 {\n",
" background-color: #aac8d1;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col2 {\n",
" background-color: #98bcc7;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col3 {\n",
" background-color: #6095a6;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col4 {\n",
" background-color: #6397a7;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col5 {\n",
" background-color: #c3553a;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col6 {\n",
" background-color: #c1d7de;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col7 {\n",
" background-color: #f0d6cf;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col8 {\n",
" background-color: #d7e6eb;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col9 {\n",
" background-color: #79a7b4;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col10 {\n",
" background-color: #a0c1cb;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col11 {\n",
" background-color: #6599a9;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col0 {\n",
" background-color: #d9e7ec;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col1 {\n",
" background-color: #cbdee4;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col2 {\n",
" background-color: #f8ebe7;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col3 {\n",
" background-color: #a5c5ce;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col4 {\n",
" background-color: #8eb4c0;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col5 {\n",
" background-color: #cee0e6;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col6 {\n",
" background-color: #c3553a;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col7 {\n",
" background-color: #b3ced6;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col8 {\n",
" background-color: #b0ccd4;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col9 {\n",
" background-color: #e1ac9e;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col10 {\n",
" background-color: #e3afa2;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col11 {\n",
" background-color: #79a7b4;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col0 {\n",
" background-color: #699bab;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col1 {\n",
" background-color: #76a4b2;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col2 {\n",
" background-color: #9fc0ca;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col3 {\n",
" background-color: #4f8a9c;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col4 {\n",
" background-color: #e4b2a6;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col5 {\n",
" background-color: #f2dbd5;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col6 {\n",
" background-color: #98bcc7;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col7 {\n",
" background-color: #c3553a;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col8 {\n",
" background-color: #bbd3da;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col9 {\n",
" background-color: #adcad2;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col10 {\n",
" background-color: #6fa0af;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col11 {\n",
" background-color: #a9c7d0;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col0 {\n",
" background-color: #85aebb;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col1 {\n",
" background-color: #d17c67;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col2 {\n",
" background-color: #9dbfc9;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col3 {\n",
" background-color: #b8d1d9;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col4 {\n",
" background-color: #82adba;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col5 {\n",
" background-color: #cadde3;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col6 {\n",
" background-color: #8eb4c0;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col7 {\n",
" background-color: #b4cfd7;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col8 {\n",
" background-color: #c3553a;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col9 {\n",
" background-color: #548d9f;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col10 {\n",
" background-color: #8eb4c0;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col11 {\n",
" background-color: #518b9d;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col0 {\n",
" background-color: #c5dae0;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col1 {\n",
" background-color: #92b8c3;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col2 {\n",
" background-color: #c1d7de;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col3 {\n",
" background-color: #98bcc7;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col4 {\n",
" background-color: #d3e3e8;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col5 {\n",
" background-color: #91b7c2;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col6 {\n",
" background-color: #e1aa9d;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col7 {\n",
" background-color: #cbdee4;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col8 {\n",
" background-color: #82adba;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col9 {\n",
" background-color: #c3553a;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col10 {\n",
" background-color: #eccac1;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col11 {\n",
" background-color: #7fabb8;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col0 {\n",
" background-color: #a0c1cb;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col1 {\n",
" background-color: #a9c7d0;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col2 {\n",
" background-color: #f2f2f2;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col3 {\n",
" background-color: #cee0e6;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col4 {\n",
" background-color: #5a91a2;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col5 {\n",
" background-color: #a0c1cb;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col6 {\n",
" background-color: #e5b5a8;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col7 {\n",
" background-color: #7aa7b5;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col8 {\n",
" background-color: #a0c1cb;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col9 {\n",
" background-color: #f0d3cc;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col10 {\n",
" background-color: #c3553a;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col11 {\n",
" background-color: #6a9cac;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col0 {\n",
" background-color: #3f7f93;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col1 {\n",
" background-color: #3f7f93;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col2 {\n",
" background-color: #3f7f93;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col3 {\n",
" background-color: #3f7f93;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col4 {\n",
" background-color: #c3d9df;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col5 {\n",
" background-color: #3f7f93;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col6 {\n",
" background-color: #3f7f93;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col7 {\n",
" background-color: #94b9c4;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col8 {\n",
" background-color: #3f7f93;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col9 {\n",
" background-color: #3f7f93;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col10 {\n",
" background-color: #448396;\n",
" } #T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col11 {\n",
" background-color: #c3553a;\n",
" }</style> \n",
"<table id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2\" > \n",
"<thead> <tr> \n",
" <th class=\"blank level0\" ></th> \n",
" <th class=\"col_heading level0 col0\" >Risk_A</th> \n",
" <th class=\"col_heading level0 col1\" >Risk_B</th> \n",
" <th class=\"col_heading level0 col2\" >Risk_C</th> \n",
" <th class=\"col_heading level0 col3\" >Risk_D</th> \n",
" <th class=\"col_heading level0 col4\" >RiSk_E</th> \n",
" <th class=\"col_heading level0 col5\" >Prob</th> \n",
" <th class=\"col_heading level0 col6\" >Score</th> \n",
" <th class=\"col_heading level0 col7\" >CONTROL_RISK</th> \n",
" <th class=\"col_heading level0 col8\" >Audit_Risk</th> \n",
" <th class=\"col_heading level0 col9\" >Risk</th> \n",
" <th class=\"col_heading level0 col10\" >MONEY_Marks</th> \n",
" <th class=\"col_heading level0 col11\" >Loss</th> \n",
" </tr></thead> \n",
"<tbody> <tr> \n",
" <th id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2level0_row0\" class=\"row_heading level0 row0\" >Risk_A</th> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row0_col0\" class=\"data row0 col0\" >1</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row0_col1\" class=\"data row0 col1\" >0.16</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row0_col2\" class=\"data row0 col2\" >0.14</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row0_col3\" class=\"data row0 col3\" >0.45</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row0_col4\" class=\"data row0 col4\" >0.12</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row0_col5\" class=\"data row0 col5\" >0.17</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row0_col6\" class=\"data row0 col6\" >0.43</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row0_col7\" class=\"data row0 col7\" >0.15</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row0_col8\" class=\"data row0 col8\" >0.22</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row0_col9\" class=\"data row0 col9\" >0.38</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row0_col10\" class=\"data row0 col10\" >0.29</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row0_col11\" class=\"data row0 col11\" >0.045</td> \n",
" </tr> <tr> \n",
" <th id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2level0_row1\" class=\"row_heading level0 row1\" >Risk_B</th> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row1_col0\" class=\"data row1 col0\" >0.16</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row1_col1\" class=\"data row1 col1\" >1</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row1_col2\" class=\"data row1 col2\" >0.22</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row1_col3\" class=\"data row1 col3\" >0.12</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row1_col4\" class=\"data row1 col4\" >0.079</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row1_col5\" class=\"data row1 col5\" >0.32</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row1_col6\" class=\"data row1 col6\" >0.4</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row1_col7\" class=\"data row1 col7\" >0.19</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row1_col8\" class=\"data row1 col8\" >0.89</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row1_col9\" class=\"data row1 col9\" >0.25</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row1_col10\" class=\"data row1 col10\" >0.31</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row1_col11\" class=\"data row1 col11\" >0.044</td> \n",
" </tr> <tr> \n",
" <th id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2level0_row2\" class=\"row_heading level0 row2\" >Risk_C</th> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row2_col0\" class=\"data row2 col0\" >0.14</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row2_col1\" class=\"data row2 col1\" >0.22</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row2_col2\" class=\"data row2 col2\" >1</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row2_col3\" class=\"data row2 col3\" >0.21</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row2_col4\" class=\"data row2 col4\" >0.15</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row2_col5\" class=\"data row2 col5\" >0.24</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row2_col6\" class=\"data row2 col6\" >0.55</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row2_col7\" class=\"data row2 col7\" >0.25</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row2_col8\" class=\"data row2 col8\" >0.25</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row2_col9\" class=\"data row2 col9\" >0.34</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row2_col10\" class=\"data row2 col10\" >0.49</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row2_col11\" class=\"data row2 col11\" >0.0001</td> \n",
" </tr> <tr> \n",
" <th id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2level0_row3\" class=\"row_heading level0 row3\" >Risk_D</th> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col0\" class=\"data row3 col0\" >0.45</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col1\" class=\"data row3 col1\" >0.12</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col2\" class=\"data row3 col2\" >0.21</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col3\" class=\"data row3 col3\" >1</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col4\" class=\"data row3 col4\" >0.032</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col5\" class=\"data row3 col5\" >0.11</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col6\" class=\"data row3 col6\" >0.29</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col7\" class=\"data row3 col7\" >0.069</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col8\" class=\"data row3 col8\" >0.33</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col9\" class=\"data row3 col9\" >0.25</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col10\" class=\"data row3 col10\" >0.39</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row3_col11\" class=\"data row3 col11\" >0.024</td> \n",
" </tr> <tr> \n",
" <th id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2level0_row4\" class=\"row_heading level0 row4\" >RiSk_E</th> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col0\" class=\"data row4 col0\" >0.12</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col1\" class=\"data row4 col1\" >0.079</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col2\" class=\"data row4 col2\" >0.15</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col3\" class=\"data row4 col3\" >0.032</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col4\" class=\"data row4 col4\" >1</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col5\" class=\"data row4 col5\" >0.12</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col6\" class=\"data row4 col6\" >0.23</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col7\" class=\"data row4 col7\" >0.73</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col8\" class=\"data row4 col8\" >0.2</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col9\" class=\"data row4 col9\" >0.41</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col10\" class=\"data row4 col10\" >0.1</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row4_col11\" class=\"data row4 col11\" >0.37</td> \n",
" </tr> <tr> \n",
" <th id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2level0_row5\" class=\"row_heading level0 row5\" >Prob</th> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col0\" class=\"data row5 col0\" >0.17</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col1\" class=\"data row5 col1\" >0.32</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col2\" class=\"data row5 col2\" >0.24</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col3\" class=\"data row5 col3\" >0.11</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col4\" class=\"data row5 col4\" >0.12</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col5\" class=\"data row5 col5\" >1</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col6\" class=\"data row5 col6\" >0.44</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col7\" class=\"data row5 col7\" >0.64</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col8\" class=\"data row5 col8\" >0.43</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col9\" class=\"data row5 col9\" >0.3</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col10\" class=\"data row5 col10\" >0.33</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row5_col11\" class=\"data row5 col11\" >0.1</td> \n",
" </tr> <tr> \n",
" <th id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2level0_row6\" class=\"row_heading level0 row6\" >Score</th> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col0\" class=\"data row6 col0\" >0.43</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col1\" class=\"data row6 col1\" >0.4</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col2\" class=\"data row6 col2\" >0.55</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col3\" class=\"data row6 col3\" >0.29</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col4\" class=\"data row6 col4\" >0.23</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col5\" class=\"data row6 col5\" >0.44</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col6\" class=\"data row6 col6\" >1</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col7\" class=\"data row6 col7\" >0.35</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col8\" class=\"data row6 col8\" >0.33</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col9\" class=\"data row6 col9\" >0.78</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col10\" class=\"data row6 col10\" >0.76</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row6_col11\" class=\"data row6 col11\" >0.16</td> \n",
" </tr> <tr> \n",
" <th id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2level0_row7\" class=\"row_heading level0 row7\" >CONTROL_RISK</th> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col0\" class=\"data row7 col0\" >0.15</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col1\" class=\"data row7 col1\" >0.19</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col2\" class=\"data row7 col2\" >0.25</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col3\" class=\"data row7 col3\" >0.069</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col4\" class=\"data row7 col4\" >0.73</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col5\" class=\"data row7 col5\" >0.64</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col6\" class=\"data row7 col6\" >0.35</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col7\" class=\"data row7 col7\" >1</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col8\" class=\"data row7 col8\" >0.36</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col9\" class=\"data row7 col9\" >0.41</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col10\" class=\"data row7 col10\" >0.22</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row7_col11\" class=\"data row7 col11\" >0.28</td> \n",
" </tr> <tr> \n",
" <th id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2level0_row8\" class=\"row_heading level0 row8\" >Audit_Risk</th> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col0\" class=\"data row8 col0\" >0.22</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col1\" class=\"data row8 col1\" >0.89</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col2\" class=\"data row8 col2\" >0.25</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col3\" class=\"data row8 col3\" >0.33</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col4\" class=\"data row8 col4\" >0.2</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col5\" class=\"data row8 col5\" >0.43</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col6\" class=\"data row8 col6\" >0.33</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col7\" class=\"data row8 col7\" >0.36</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col8\" class=\"data row8 col8\" >1</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col9\" class=\"data row8 col9\" >0.22</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col10\" class=\"data row8 col10\" >0.29</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row8_col11\" class=\"data row8 col11\" >0.049</td> \n",
" </tr> <tr> \n",
" <th id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2level0_row9\" class=\"row_heading level0 row9\" >Risk</th> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col0\" class=\"data row9 col0\" >0.38</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col1\" class=\"data row9 col1\" >0.25</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col2\" class=\"data row9 col2\" >0.34</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col3\" class=\"data row9 col3\" >0.25</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col4\" class=\"data row9 col4\" >0.41</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col5\" class=\"data row9 col5\" >0.3</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col6\" class=\"data row9 col6\" >0.78</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col7\" class=\"data row9 col7\" >0.41</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col8\" class=\"data row9 col8\" >0.22</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col9\" class=\"data row9 col9\" >1</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col10\" class=\"data row9 col10\" >0.69</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row9_col11\" class=\"data row9 col11\" >0.17</td> \n",
" </tr> <tr> \n",
" <th id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2level0_row10\" class=\"row_heading level0 row10\" >MONEY_Marks</th> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col0\" class=\"data row10 col0\" >0.29</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col1\" class=\"data row10 col1\" >0.31</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col2\" class=\"data row10 col2\" >0.49</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col3\" class=\"data row10 col3\" >0.39</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col4\" class=\"data row10 col4\" >0.1</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col5\" class=\"data row10 col5\" >0.33</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col6\" class=\"data row10 col6\" >0.76</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col7\" class=\"data row10 col7\" >0.22</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col8\" class=\"data row10 col8\" >0.29</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col9\" class=\"data row10 col9\" >0.69</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col10\" class=\"data row10 col10\" >1</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row10_col11\" class=\"data row10 col11\" >0.12</td> \n",
" </tr> <tr> \n",
" <th id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2level0_row11\" class=\"row_heading level0 row11\" >Loss</th> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col0\" class=\"data row11 col0\" >0.045</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col1\" class=\"data row11 col1\" >0.044</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col2\" class=\"data row11 col2\" >0.0001</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col3\" class=\"data row11 col3\" >0.024</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col4\" class=\"data row11 col4\" >0.37</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col5\" class=\"data row11 col5\" >0.1</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col6\" class=\"data row11 col6\" >0.16</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col7\" class=\"data row11 col7\" >0.28</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col8\" class=\"data row11 col8\" >0.049</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col9\" class=\"data row11 col9\" >0.17</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col10\" class=\"data row11 col10\" >0.12</td> \n",
" <td id=\"T_dab6a0b0_691c_11e9_a1ba_9cb6d08ec8d2row11_col11\" class=\"data row11 col11\" >1</td> \n",
" </tr></tbody> \n",
"</table> "
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x20b5a869ba8>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Keeping just the columns that are correlated with the target variable and not with other independent variables.\n",
"df = df[['Risk_A', 'Risk_B', 'Risk_C', 'Risk_D','RiSk_E', 'Prob', 'Score', 'CONTROL_RISK',\n",
" 'Audit_Risk', 'Risk', 'MONEY_Marks', 'Loss']]\n",
"corr = df.corr()\n",
"corr.style.background_gradient(cmap='coolwarm')\n",
"# 'RdBu_r' & 'BrBG' are other good diverging colormaps\n",
"cm = sns.diverging_palette(220, 20, sep=20, as_cmap=True) \n",
"corr.style.background_gradient(cmap=cm).set_precision(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Classification"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"#Creating a new dataframe for classification by deleting the Audit_Risk column.\n",
"class_df = df.drop(\"Audit_Risk\", axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"classification_X = class_df.drop([\"Risk\"], axis = 1)\n",
"classification_y = class_df[\"Risk\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Classification: Train-Test Split"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"X_train_org, X_test_org, y_train, y_test = train_test_split(classification_X, classification_y, \n",
" test_size = 0.25, random_state = 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Classification: Feature Scaling"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import MinMaxScaler\n",
"scaler = MinMaxScaler()\n",
"\n",
"X_train = scaler.fit_transform(X_train_org)\n",
"X_test = scaler.transform(X_test_org)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MinMax scaling is used to avoid any feature to dominate the model. MinMax scaling scales all the data in the columns between 0 to 1."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Classification Models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Voting Classifiers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Hard Voting Classifier 1"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"VotingClassifier(estimators=[('lr', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='warn',\n",
" n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
" tol=0.0001, verbose=0, warm_start=False)), ('knn', KNeighb...bf', max_iter=-1, probability=True, random_state=None,\n",
" shrinking=True, tol=0.001, verbose=False))],\n",
" flatten_transform=None, n_jobs=None, voting='hard', weights=None)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.ensemble import VotingClassifier\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.svm import SVC\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"\n",
"lr_hard = LogisticRegression()\n",
"lr_hard.fit(X_train, y_train)\n",
"knn_hard = KNeighborsClassifier(7)\n",
"knn_hard.fit(X_train, y_train)\n",
"svc_hard = SVC(C = 10, probability = True)\n",
"svc_hard.fit(X_train, y_train)\n",
"\n",
"voting_clf_hard = VotingClassifier(estimators=[('lr', lr_hard), ('knn', knn_hard), ('svc', svc_hard)], voting='hard')\n",
"voting_clf_hard.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LogisticRegression 0.9473684210526315\n",
"KNeighborsClassifier 0.9526315789473684\n",
"SVC 0.9526315789473684\n",
"VotingClassifier 0.9473684210526315\n"
]
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"for clf in (lr_hard, knn_hard, svc_hard, voting_clf_hard):\n",
" clf.fit(X_train, y_train)\n",
" y_test_pred = clf.predict(X_test)\n",
" y_train_pred = clf.predict(X_train)\n",
" print(clf.__class__.__name__, accuracy_score(y_test, y_test_pred))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"report_table_1 = ['Hard Voting Classifier 1', '',accuracy_score(y_train, y_train_pred), accuracy_score(y_test, y_test_pred)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Hard Voting Classifier 2"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"VotingClassifier(estimators=[('knn', KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
" weights='uniform')), ('svc', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
" decision_function_shape='ovr', degree=3, gam... min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
" splitter='best'))],\n",
" flatten_transform=None, n_jobs=None, voting='hard', weights=None)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knn_hard = KNeighborsClassifier(3)\n",
"knn_hard.fit(X_train, y_train)\n",
"svc_hard = SVC(kernel='rbf', random_state= 0)\n",
"svc_hard.fit(X_train, y_train)\n",
"dt_hard = DecisionTreeClassifier(max_depth = 5, random_state= 0)\n",
"dt_hard.fit(X_train, y_train)\n",
"\n",
"voting_clf_hard = VotingClassifier(estimators=[('knn', knn_hard), ('svc', svc_hard), ('dt', dt_hard)], voting='hard')\n",
"voting_clf_hard.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KNeighborsClassifier 0.9578947368421052\n",
"SVC 0.9421052631578948\n",
"DecisionTreeClassifier 0.9894736842105263\n",
"VotingClassifier 0.968421052631579\n"
]
}
],
"source": [
"for clf in (knn_hard, svc_hard, dt_hard, voting_clf_hard):\n",
" clf.fit(X_train, y_train)\n",
" y_test_pred = clf.predict(X_test)\n",
" y_train_pred = clf.predict(X_train)\n",
" print(clf.__class__.__name__, accuracy_score(y_test, y_test_pred))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"report_table_2 = ['Hard Voting Classifier 2', '',accuracy_score(y_train, y_train_pred), accuracy_score(y_test, y_test_pred)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Soft Voting Classifier 1"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"VotingClassifier(estimators=[('lr', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='warn',\n",
" n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
" tol=0.0001, verbose=0, warm_start=False)), ('knn', KNeighb...bf', max_iter=-1, probability=True, random_state=None,\n",
" shrinking=True, tol=0.001, verbose=False))],\n",
" flatten_transform=None, n_jobs=None, voting='soft', weights=None)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lr_soft = LogisticRegression()\n",
"lr_soft.fit(X_train, y_train)\n",
"knn_soft = KNeighborsClassifier(7)\n",
"knn_soft.fit(X_train, y_train)\n",
"svc_soft = SVC(C = 10, probability = True)\n",
"svc_soft.fit(X_train, y_train)\n",
"\n",
"voting_clf_soft = VotingClassifier(estimators=[('lr', lr_soft), ('knn', knn_soft), ('svc', svc_soft)], voting='soft')\n",
"voting_clf_soft.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KNeighborsClassifier 0.9526315789473684\n",
"SVC 0.9526315789473684\n",
"LogisticRegression 0.9473684210526315\n",
"VotingClassifier 0.9526315789473684\n"
]
}
],
"source": [
"for clf in (knn_soft, svc_soft, lr_soft, voting_clf_soft):\n",
" clf.fit(X_train, y_train)\n",
" y_test_pred = clf.predict(X_test)\n",
" y_train_pred = clf.predict(X_train)\n",
" print(clf.__class__.__name__, accuracy_score(y_test, y_test_pred))"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"report_table_3 = ['Soft Voting Classifier 1', '',accuracy_score(y_train, y_train_pred), accuracy_score(y_test, y_test_pred)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Soft Voting Classifier 2"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"VotingClassifier(estimators=[('knn', KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
" weights='uniform')), ('svc', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
" decision_function_shape='ovr', degree=3, gam... min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
" splitter='best'))],\n",
" flatten_transform=None, n_jobs=None, voting='soft', weights=None)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knn_soft = KNeighborsClassifier(5)\n",
"knn_soft.fit(X_train, y_train)\n",
"svc_soft = SVC(kernel='rbf', random_state= 0, probability= True)\n",
"svc_soft.fit(X_train, y_train)\n",
"dt_soft = DecisionTreeClassifier(max_depth = 7, random_state= 0)\n",
"dt_soft.fit(X_train_org, y_train)\n",
"\n",
"voting_clf_soft = VotingClassifier(estimators=[('knn', knn_soft), ('svc', svc_soft), ('dt', dt_soft)], voting='soft')\n",
"voting_clf_soft.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KNeighborsClassifier 0.9578947368421052\n",
"SVC 0.9421052631578948\n",
"DecisionTreeClassifier 0.9842105263157894\n",
"VotingClassifier 0.9842105263157894\n"
]
}
],
"source": [
"for clf in (knn_soft, svc_soft, dt_soft, voting_clf_soft):\n",
" clf.fit(X_train, y_train)\n",
" y_test_pred = clf.predict(X_test)\n",
" y_train_pred = clf.predict(X_train)\n",
" print(clf.__class__.__name__, accuracy_score(y_test, y_test_pred))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"report_table_4 = ['Soft Voting Classifier 2', '',accuracy_score(y_train, y_train_pred), accuracy_score(y_test, y_test_pred)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bagging"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## KNN with Bagging"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, error_score='raise-deprecating',\n",
" estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
" weights='uniform'),\n",
" fit_params=None, iid='warn', n_jobs=-1,\n",
" param_grid={'n_neighbors': [3, 5, 7, 11, 15]},\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
" scoring=None, verbose=0)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.ensemble import BaggingClassifier\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.model_selection import GridSearchCV\n",
"knn = KNeighborsClassifier()\n",
"\n",
"knn_param ={'n_neighbors': [3,5,7,11,15]}\n",
"knn_grid = GridSearchCV(knn, knn_param,cv = 5, n_jobs= -1)\n",
"knn_grid.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best Parameters for KNN Classifier: {'n_neighbors': 3}\n"
]
}
],
"source": [
"print(\"Best Parameters for KNN Classifier: \", knn_grid.best_params_)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, error_score='raise-deprecating',\n",
" estimator=BaggingClassifier(base_estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
" weights='uniform'),\n",
" bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
" max_samples=1.0, n_estimators=10, n_jobs=None, oob_score=False,\n",
" random_state=0, verbose=0, warm_start=False),\n",
" fit_params=None, iid='warn', n_jobs=-1,\n",
" param_grid={'n_estimators': [100, 500, 1000], 'max_samples': [0.1, 0.5, 1.0]},\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
" scoring=None, verbose=0)"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knn = KNeighborsClassifier(n_neighbors=3)\n",
"bag = BaggingClassifier(knn, bootstrap=True, random_state = 0)\n",
"#model param\n",
"grid_param = {'n_estimators': [100, 500, 1000],\n",
" 'max_samples': [0.1, 0.5, 1.0]}\n",
"\n",
"#grid model\n",
"bag_knn_grid = GridSearchCV(bag, grid_param, cv = 5, n_jobs = -1, return_train_score= True)\n",
"\n",
"#train grid model\n",
"bag_knn_grid.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best Parameters for Bagging Classifier: {'max_samples': 1.0, 'n_estimators': 100}\n"
]
}
],
"source": [
"print(\"Best Parameters for Bagging Classifier: \", bag_knn_grid.best_params_)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BaggingClassifier(base_estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
" weights='uniform'),\n",
" bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
" max_samples=1.0, n_estimators=100, n_jobs=-1, oob_score=False,\n",
" random_state=0, verbose=0, warm_start=False)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bag = BaggingClassifier(knn, n_estimators=100, max_samples=1.0, n_jobs = -1, bootstrap=True, random_state=0)\n",
"bag.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KNN with Bagging Training Score: 0.9842105263157894\n",
"KNN with Bagging Testing Score: 0.9631578947368421\n"
]
}
],
"source": [
"print(\"KNN with Bagging Training Score: \", bag.score(X_train, y_train))\n",
"print(\"KNN with Bagging Testing Score: \", bag.score(X_test, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"report_table_5 = ['KNN with Bagging', 'n_neighbors: 3, max_samples: 1.0, n_estimators: 100',bag.score(X_train, y_train), bag.score(X_test, y_test)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Logistic Regression with Bagging"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, error_score='raise-deprecating',\n",
" estimator=BaggingClassifier(base_estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='warn',\n",
" n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
" tol=0.0001, verbose=0, warm_start=...n_estimators=10, n_jobs=None, oob_score=False,\n",
" random_state=0, verbose=0, warm_start=False),\n",
" fit_params=None, iid='warn', n_jobs=-1,\n",
" param_grid={'n_estimators': [100, 500, 1000], 'max_samples': [0.1, 0.5, 1.0]},\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
" scoring=None, verbose=0)"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"lr = LogisticRegression()\n",
"bag = BaggingClassifier(lr, bootstrap=True, random_state = 0)\n",
"grid_param = {'n_estimators': [100, 500, 1000],\n",
" 'max_samples': [0.1, 0.5, 1.0]}\n",
"\n",
"#grid model\n",
"bag_lr_grid = GridSearchCV(bag, grid_param, cv = 5, n_jobs = -1, return_train_score= True)\n",
"\n",
"#train grid model\n",
"bag_lr_grid.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best Parameters for Bagging Classifier: {'max_samples': 1.0, 'n_estimators': 500}\n"
]
}
],
"source": [
"print(\"Best Parameters for Bagging Classifier: \", bag_lr_grid.best_params_)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BaggingClassifier(base_estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='warn',\n",
" n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
" tol=0.0001, verbose=0, warm_start=False),\n",
" bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
" max_samples=1.0, n_estimators=500, n_jobs=-1, oob_score=False,\n",
" random_state=0, verbose=0, warm_start=False)"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bag = BaggingClassifier(lr, n_estimators=500, max_samples=1.0, bootstrap=True, n_jobs=-1, random_state=0)\n",
"bag.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Logistic Regression with Bagging Training Score: 0.9526315789473684\n",
"Logistic Regression with Bagging Testing Score: 0.9473684210526315\n"
]
}
],
"source": [
"print(\"Logistic Regression with Bagging Training Score: \", bag.score(X_train, y_train))\n",
"print(\"Logistic Regression with Bagging Testing Score: \", bag.score(X_test, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"report_table_6 = ['Logistic Regression with Bagging', 'max_samples: 1.0, n_estimators: 500',bag.score(X_train, y_train), bag.score(X_test, y_test)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pasting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Decision Tree with Pasting"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, error_score='raise-deprecating',\n",
" estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
" splitter='best'),\n",
" fit_params=None, iid='warn', n_jobs=-1,\n",
" param_grid={'max_depth': [3, 5, 7, 9, 11, 15]},\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
" scoring=None, verbose=0)"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.tree import DecisionTreeClassifier\n",
"\n",
"dt = DecisionTreeClassifier(random_state = 0)\n",
"grid_param = {'max_depth': [3, 5, 7, 9, 11, 15]}\n",
"dt_grid = GridSearchCV(dt, grid_param, cv = 5, n_jobs = -1)\n",
"dt_grid.fit(X_train_org, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best Parameters for Decision Tree Classifier: {'max_depth': 9}\n"
]
}
],
"source": [
"print(\"Best Parameters for Decision Tree Classifier: \", dt_grid.best_params_)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, error_score='raise-deprecating',\n",
" estimator=BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=9,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min...n_estimators=10, n_jobs=None, oob_score=False,\n",
" random_state=0, verbose=0, warm_start=False),\n",
" fit_params=None, iid='warn', n_jobs=-1,\n",
" param_grid={'n_estimators': [100, 500, 1000], 'max_samples': [0.1, 0.5, 1.0]},\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
" scoring=None, verbose=0)"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#base model\n",
"dt = DecisionTreeClassifier(max_depth = 9, random_state=0)\n",
"bag = BaggingClassifier(dt, random_state = 0)\n",
"#model param\n",
"grid_param = {'n_estimators': [100, 500, 1000],\n",
" 'max_samples': [0.1, 0.5, 1.0]}\n",
"\n",
"#grid model\n",
"bag_dt_grid = GridSearchCV(bag, grid_param, cv = 5, n_jobs = -1, return_train_score= True)\n",
"\n",
"#train grid model\n",
"bag_dt_grid.fit(X_train_org, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best Parameters for Bagging Classifier with Pasting: {'max_samples': 1.0, 'n_estimators': 100}\n"
]
}
],
"source": [
"print(\"Best Parameters for Bagging Classifier with Pasting: \", bag_dt_grid.best_params_)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=9,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
" splitter='best'),\n",
" bootstrap=False, bootstrap_features=False, max_features=1.0,\n",
" max_samples=1.0, n_estimators=100, n_jobs=-1, oob_score=False,\n",
" random_state=0, verbose=0, warm_start=False)"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bag = BaggingClassifier(dt, n_estimators=100, max_samples=1.0, n_jobs = -1, bootstrap=False, random_state=0)\n",
"bag.fit(X_train_org, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Decision Tree Classifier with Pasting Training Score: 1.0\n",
"Decision Tree Classifier with Pasting Testing Score: 0.9842105263157894\n"
]
}
],
"source": [
"print(\"Decision Tree Classifier with Pasting Training Score: \", bag.score(X_train_org, y_train))\n",
"print(\"Decision Tree Classifier with Pasting Testing Score: \", bag.score(X_test_org, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"report_table_7 = ['Decision Tree Classifier with Pasting', 'max_depth: 9, max_samples: 1.0, n_estimators: 100',bag.score(X_train_org, y_train), bag.score(X_test_org, y_test)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Linear SVC with Pasting"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, error_score='raise-deprecating',\n",
" estimator=LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
" intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
" multi_class='ovr', penalty='l2', random_state=0, tol=0.0001,\n",
" verbose=0),\n",
" fit_params=None, iid='warn', n_jobs=None,\n",
" param_grid={'C': [1, 10, 100, 1000]}, pre_dispatch='2*n_jobs',\n",
" refit=True, return_train_score='warn', scoring=None, verbose=0)"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.svm import LinearSVC\n",
"svc = LinearSVC(penalty = 'l2', random_state=0)\n",
"grid_param = {'C':[1, 10, 100, 1000]}\n",
"svc_grid = GridSearchCV(svc, grid_param, cv = 5)\n",
"svc_grid.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best Parameters for Linear SVC: {'C': 100}\n"
]
}
],
"source": [
"print(\"Best Parameters for Linear SVC: \", svc_grid.best_params_)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, error_score='raise-deprecating',\n",
" estimator=BaggingClassifier(base_estimator=LinearSVC(C=100, class_weight=None, dual=True, fit_intercept=True,\n",
" intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
" multi_class='ovr', penalty='l2', random_state=0, tol=0.0001,\n",
" verbose=0),\n",
" bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
" max_samples=1.0, n_estimators=10, n_jobs=None, oob_score=False,\n",
" random_state=0, verbose=0, warm_start=False),\n",
" fit_params=None, iid='warn', n_jobs=-1,\n",
" param_grid={'n_estimators': [100, 500, 1000], 'max_samples': [0.1, 0.5, 1.0]},\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
" scoring=None, verbose=0)"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#base model\n",
"svc = LinearSVC(C = 100, penalty = 'l2', random_state=0)\n",
"bag = BaggingClassifier(svc, random_state = 0)\n",
"#model param\n",
"grid_param = {'n_estimators': [100, 500, 1000],\n",
" 'max_samples': [0.1, 0.5, 1.0]}\n",
"\n",
"#grid model\n",
"bag_svc_grid = GridSearchCV(bag, grid_param, cv = 5, n_jobs = -1, return_train_score= True)\n",
"\n",
"#train grid model\n",
"bag_svc_grid.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best Parameters for Bagging Classifier with Pasting: {'max_samples': 0.1, 'n_estimators': 100}\n"
]
}
],
"source": [
"print(\"Best Parameters for Bagging Classifier with Pasting: \", bag_svc_grid.best_params_)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BaggingClassifier(base_estimator=LinearSVC(C=100, class_weight=None, dual=True, fit_intercept=True,\n",
" intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
" multi_class='ovr', penalty='l2', random_state=0, tol=0.0001,\n",
" verbose=0),\n",
" bootstrap=False, bootstrap_features=False, max_features=1.0,\n",
" max_samples=0.1, n_estimators=100, n_jobs=-1, oob_score=False,\n",
" random_state=0, verbose=0, warm_start=False)"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bag = BaggingClassifier(svc, n_estimators= 100, max_samples= 0.1, bootstrap=False, n_jobs=-1, random_state=0)\n",
"bag.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linear SVC with Pasting Training Score: 0.9912280701754386\n",
"Linear SVC with Pasting Testing Score: 0.9631578947368421\n"
]
}
],
"source": [
"print(\"Linear SVC with Pasting Training Score: \", bag.score(X_train, y_train))\n",
"print(\"Linear SVC with Pasting Testing Score: \", bag.score(X_test, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"report_table_8 = ['Linear SVC with Pasting', 'C: 100, max_samples: 0.1, n_estimators: 100',bag.score(X_train, y_train), bag.score(X_test, y_test)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Adaboost "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Decision Tree with Adaboost"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, ``max_depth = 9`` is taken as the best parameter for Decision Tree as it is seen in the above Decision Tree section."
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, error_score='raise-deprecating',\n",
" estimator=AdaBoostClassifier(algorithm='SAMME.R',\n",
" base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=9,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
" splitter='best'),\n",
" learning_rate=1.0, n_estimators=50, random_state=None),\n",
" fit_params=None, iid='warn', n_jobs=-1,\n",
" param_grid={'n_estimators': [100, 500, 1000], 'learning_rate': [0.1, 0.5, 1, 10]},\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
" scoring=None, verbose=0)"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.ensemble import AdaBoostClassifier\n",
"\n",
"dt = DecisionTreeClassifier(max_depth = 9, random_state=0)\n",
"ada = AdaBoostClassifier(dt)\n",
"\n",
"param = {'n_estimators' : [100,500,1000],\n",
" 'learning_rate': [0.1, 0.5, 1, 10]}\n",
"\n",
"ada_grid = GridSearchCV(ada, param, cv=5, n_jobs= -1)\n",
"\n",
"ada_grid.fit(X_train_org, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best Parameters for Adaboost Classifier: {'learning_rate': 10, 'n_estimators': 500}\n"
]
}
],
"source": [
"print(\"Best Parameters for Adaboost Classifier: \", ada_grid.best_params_)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AdaBoostClassifier(algorithm='SAMME.R',\n",
" base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=9,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
" splitter='best'),\n",
" learning_rate=0.1, n_estimators=100, random_state=0)"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ada = AdaBoostClassifier(dt, n_estimators= 100, learning_rate= 0.1, algorithm=\"SAMME.R\", random_state=0)\n",
"ada.fit(X_train_org, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Decision Tree Classifier with Adaboost Training Score: 1.0\n",
"Decision Tree Classifier with Adaboost Testing Score: 0.9789473684210527\n"
]
}
],
"source": [
"print(\"Decision Tree Classifier with Adaboost Training Score: \", ada.score(X_train_org, y_train))\n",
"print(\"Decision Tree Classifier with Adaboost Testing Score: \", ada.score(X_test_org, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"report_table_9 = ['Decision Tree Classifier with Adaboost', 'max_depth = 9, learning_rate = 0.1, n_estimators = 1000',\n",
" ada.score(X_train_org, y_train), ada.score(X_test_org, y_test)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Logistic Regression with Adaboost"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, error_score='raise-deprecating',\n",
" estimator=AdaBoostClassifier(algorithm='SAMME.R',\n",
" base_estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='warn',\n",
" n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
" tol=0.0001, verbose=0, warm_start=False),\n",
" learning_rate=1.0, n_estimators=50, random_state=None),\n",
" fit_params=None, iid='warn', n_jobs=-1,\n",
" param_grid={'n_estimators': [100, 500, 1000], 'learning_rate': [0.1, 0.5, 1]},\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
" scoring=None, verbose=0)"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lr = LogisticRegression()\n",
"ada = AdaBoostClassifier(lr)\n",
"\n",
"param = {'n_estimators' : [100,500,1000],\n",
" 'learning_rate': [0.1, 0.5, 1]}\n",
"\n",
"ada_grid = GridSearchCV(ada, param, cv=5, n_jobs= -1)\n",
"\n",
"ada_grid.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best Parameters for Adaboost Classifier: {'learning_rate': 1, 'n_estimators': 1000}\n"
]
}
],
"source": [
"print(\"Best Parameters for Adaboost Classifier: \", ada_grid.best_params_)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AdaBoostClassifier(algorithm='SAMME.R',\n",
" base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=9,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
" splitter='best'),\n",
" learning_rate=1, n_estimators=1000, random_state=0)"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ada = AdaBoostClassifier(dt, n_estimators= 1000, learning_rate= 1, algorithm=\"SAMME.R\", random_state=0)\n",
"ada.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Logistic Regression with Adaboost Training Score: 1.0\n",
"Logistic Regression with Adaboost Testing Score: 0.9789473684210527\n"
]
}
],
"source": [
"print(\"Logistic Regression with Adaboost Training Score: \", ada.score(X_train, y_train))\n",
"print(\"Logistic Regression with Adaboost Testing Score: \", ada.score(X_test, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"report_table_10 = ['Logistic Regression with Adaboost', 'learning_rate = 1, n_estimators = 1000',\n",
" ada.score(X_train, y_train), ada.score(X_test, y_test)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Gradient Boosting Classifier"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, error_score='raise-deprecating',\n",
" estimator=GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
" learning_rate=0.1, loss='deviance', max_depth=3,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_sampl... subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
" verbose=0, warm_start=False),\n",
" fit_params=None, iid='warn', n_jobs=None,\n",
" param_grid={'max_depth': [4, 5, 7, 9, 11, 15], 'n_estimators': [100, 500, 1000], 'learning_rate': [0.01, 0.1, 0.5, 1.0]},\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
" scoring=None, verbose=0)"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.ensemble import GradientBoostingClassifier\n",
"\n",
"gb = GradientBoostingClassifier(random_state=0)\n",
"\n",
"param = {'max_depth': [4,5,7,9,11,15],\n",
" 'n_estimators': [100, 500, 1000],\n",
" 'learning_rate': [0.01,0.1, 0.5, 1.0]}\n",
"gb_grid = GridSearchCV(gb, param, cv = 5, return_train_score= True)\n",
"gb_grid.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best Parameters for Gradient Boosting Classifier: {'learning_rate': 1.0, 'max_depth': 4, 'n_estimators': 100}\n"
]
}
],
"source": [
"print(\"Best Parameters for Gradient Boosting Classifier: \", gb_grid.best_params_)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
" learning_rate=1.0, loss='deviance', max_depth=4,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=100,\n",
" n_iter_no_change=None, presort='auto', random_state=0,\n",
" subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
" verbose=0, warm_start=False)"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gb = GradientBoostingClassifier(max_depth=4, n_estimators=100, learning_rate=1.0, random_state=0)\n",
"gb.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gradient Boosting Classifier Training Score: 1.0\n",
"Gradient Boosting Classifier Testing Score: 0.9789473684210527\n"
]
}
],
"source": [
"print(\"Gradient Boosting Classifier Training Score: \", gb.score(X_train, y_train))\n",
"print(\"Gradient Boosting Classifier Testing Score: \", gb.score(X_test, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"report_table_11 = ['Gradient Boosting Classifier', 'learning_rate = 1.0, max_depth = 4, n_estimators = 100',\n",
" gb.score(X_train, y_train), gb.score(X_test, y_test)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Voting, Boosting, Pasting and Bagging: Generating a Report table\n",
"For comparing all the models, let's create a table and a plot."
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [],
"source": [
"report_table = pd.DataFrame(list(zip(report_table_1,\n",
" report_table_2,\n",
" report_table_3,\n",
" report_table_4,\n",
" report_table_5,\n",
" report_table_6,\n",
" report_table_7,\n",
" report_table_8,\n",
" report_table_9,\n",
" report_table_10,\n",
" report_table_11))).transpose()"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Model Name</th>\n",
" <th>Model Parameter</th>\n",
" <th>Training Score</th>\n",
" <th>Testing Score</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Model Name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Hard Voting Classifier 1</th>\n",
" <td>Hard Voting Classifier 1</td>\n",
" <td></td>\n",
" <td>0.964912</td>\n",
" <td>0.947368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hard Voting Classifier 2</th>\n",
" <td>Hard Voting Classifier 2</td>\n",
" <td></td>\n",
" <td>0.985965</td>\n",
" <td>0.968421</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Soft Voting Classifier 1</th>\n",
" <td>Soft Voting Classifier 1</td>\n",
" <td></td>\n",
" <td>0.966667</td>\n",
" <td>0.952632</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Soft Voting Classifier 2</th>\n",
" <td>Soft Voting Classifier 2</td>\n",
" <td></td>\n",
" <td>0.994737</td>\n",
" <td>0.984211</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KNN with Bagging</th>\n",
" <td>KNN with Bagging</td>\n",
" <td>n_neighbors: 3, max_samples: 1.0, n_estimators...</td>\n",
" <td>0.984211</td>\n",
" <td>0.963158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logistic Regression with Bagging</th>\n",
" <td>Logistic Regression with Bagging</td>\n",
" <td>max_samples: 1.0, n_estimators: 500</td>\n",
" <td>0.952632</td>\n",
" <td>0.947368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Decision Tree Classifier with Pasting</th>\n",
" <td>Decision Tree Classifier with Pasting</td>\n",
" <td>max_depth: 9, max_samples: 1.0, n_estimators: 100</td>\n",
" <td>1</td>\n",
" <td>0.984211</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Linear SVC with Pasting</th>\n",
" <td>Linear SVC with Pasting</td>\n",
" <td>C: 100, max_samples: 0.1, n_estimators: 100</td>\n",
" <td>0.991228</td>\n",
" <td>0.963158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Decision Tree Classifier with Adaboost</th>\n",
" <td>Decision Tree Classifier with Adaboost</td>\n",
" <td>max_depth = 9, learning_rate = 0.1, n_estimato...</td>\n",
" <td>1</td>\n",
" <td>0.978947</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logistic Regression with Adaboost</th>\n",
" <td>Logistic Regression with Adaboost</td>\n",
" <td>learning_rate = 1, n_estimators = 1000</td>\n",
" <td>1</td>\n",
" <td>0.978947</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Model Name \\\n",
"Model Name \n",
"Hard Voting Classifier 1 Hard Voting Classifier 1 \n",
"Hard Voting Classifier 2 Hard Voting Classifier 2 \n",
"Soft Voting Classifier 1 Soft Voting Classifier 1 \n",
"Soft Voting Classifier 2 Soft Voting Classifier 2 \n",
"KNN with Bagging KNN with Bagging \n",
"Logistic Regression with Bagging Logistic Regression with Bagging \n",
"Decision Tree Classifier with Pasting Decision Tree Classifier with Pasting \n",
"Linear SVC with Pasting Linear SVC with Pasting \n",
"Decision Tree Classifier with Adaboost Decision Tree Classifier with Adaboost \n",
"Logistic Regression with Adaboost Logistic Regression with Adaboost \n",
"\n",
" Model Parameter \\\n",
"Model Name \n",
"Hard Voting Classifier 1 \n",
"Hard Voting Classifier 2 \n",
"Soft Voting Classifier 1 \n",
"Soft Voting Classifier 2 \n",
"KNN with Bagging n_neighbors: 3, max_samples: 1.0, n_estimators... \n",
"Logistic Regression with Bagging max_samples: 1.0, n_estimators: 500 \n",
"Decision Tree Classifier with Pasting max_depth: 9, max_samples: 1.0, n_estimators: 100 \n",
"Linear SVC with Pasting C: 100, max_samples: 0.1, n_estimators: 100 \n",
"Decision Tree Classifier with Adaboost max_depth = 9, learning_rate = 0.1, n_estimato... \n",
"Logistic Regression with Adaboost learning_rate = 1, n_estimators = 1000 \n",
"\n",
" Training Score Testing Score \n",
"Model Name \n",
"Hard Voting Classifier 1 0.964912 0.947368 \n",
"Hard Voting Classifier 2 0.985965 0.968421 \n",
"Soft Voting Classifier 1 0.966667 0.952632 \n",
"Soft Voting Classifier 2 0.994737 0.984211 \n",
"KNN with Bagging 0.984211 0.963158 \n",
"Logistic Regression with Bagging 0.952632 0.947368 \n",
"Decision Tree Classifier with Pasting 1 0.984211 \n",
"Linear SVC with Pasting 0.991228 0.963158 \n",
"Decision Tree Classifier with Adaboost 1 0.978947 \n",
"Logistic Regression with Adaboost 1 0.978947 "
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"report_table.columns = ['Model Name', 'Model Parameter', 'Training Score', 'Testing Score']\n",
"report_table.index = report_table['Model Name']\n",
"report_table.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Principal Component Analysis"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA\n",
"\n",
"pca = PCA(n_components=0.95)\n",
"\n",
"X_train_reduced = pca.fit_transform(X_train)\n",
"X_test_reduced = pca.transform(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of PCA components: 6\n"
]
}
],
"source": [
"print(\"Number of PCA components: \", pca.n_components_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## KNN Classifier with PCA"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GridSearchCV(cv=10, error_score='raise-deprecating',\n",
" estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
" weights='uniform'),\n",
" fit_params=None, iid='warn', n_jobs=None,\n",
" param_grid={'n_neighbors': [3, 4, 5, 6, 7, 8, 9, 10, 15]},\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
" scoring='roc_auc', verbose=0)"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knn = KNeighborsClassifier()\n",
"param_grid = {'n_neighbors':[3, 4, 5, 6, 7, 8, 9, 10, 15]}\n",
"\n",
"grid_knn_clf = GridSearchCV(knn, param_grid=param_grid, cv = 10, scoring='roc_auc')\n",
"grid_knn_clf.fit(X_train_reduced, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best Parameters for KNN Classifier with PCA: {'n_neighbors': 8}\n"
]
}
],
"source": [
"print(\"Best Parameters for KNN Classifier with PCA: \", grid_knn_clf.best_params_)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=None, n_neighbors=8, p=2,\n",
" weights='uniform')"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pca_knn = KNeighborsClassifier(n_neighbors=8)\n",
"pca_knn.fit(X_train_reduced, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KNN Classifier with PCA Training Score: 0.9771929824561404\n",
"KNN Classifier with PCA Testing Score: 0.9421052631578948\n"
]
}
],
"source": [
"print(\"KNN Classifier with PCA Training Score: \", pca_knn.score(X_train_reduced, y_train))\n",
"print(\"KNN Classifier with PCA Testing Score: \", pca_knn.score(X_test_reduced, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [],
"source": [
"pca_report_table_1 = ['KNN Classifier with PCA', 'n_neighbors = 8', \n",
" pca_knn.score(X_train_reduced, y_train), pca_knn.score(X_test_reduced, y_test)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## SVM Classifier with PCA"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, error_score='raise-deprecating',\n",
" estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
" decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
" kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
" shrinking=True, tol=0.001, verbose=False),\n",
" fit_params=None, iid='warn', n_jobs=None,\n",
" param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100], 'kernel': ['linear', 'rbf', 'poly']},\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
" scoring='roc_auc', verbose=0)"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svc = SVC()\n",
"param_grid = {'C':[0.001, 0.01, 0.1, 1, 10, 100], 'kernel': ['linear', 'rbf', 'poly']}\n",
"\n",
"grid_svc_clf = GridSearchCV(svc, param_grid, cv = 5, scoring='roc_auc', return_train_score=True)\n",
"grid_svc_clf.fit(X_train_reduced, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best Parameters for LinearSVC with PCA: {'C': 10, 'kernel': 'rbf'}\n"
]
}
],
"source": [
"print(\"Best Parameters for LinearSVC with PCA: \", grid_svc_clf.best_params_)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n",
" decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
" kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
" shrinking=True, tol=0.001, verbose=False)"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pca_svc = SVC(C= 10, kernel= 'rbf')\n",
"pca_svc.fit(X_train_reduced, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SVC with PCA Training Score: 0.9438596491228071\n",
"SVC with PCA Testing Score: 0.9526315789473684\n"
]
}
],
"source": [
"print(\"SVC with PCA Training Score: \", pca_svc.score(X_train_reduced, y_train))\n",
"print(\"SVC with PCA Testing Score: \", pca_svc.score(X_test_reduced, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [],
"source": [
"pca_report_table_2 = ['SVC with PCA', 'C =10, kernel= rbf', \n",
" pca_svc.score(X_train_reduced, y_train), pca_svc.score(X_test_reduced, y_test)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Logistic Regression with PCA"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, error_score='raise-deprecating',\n",
" estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='warn',\n",
" n_jobs=None, penalty='l2', random_state=0, solver='warn',\n",
" tol=0.0001, verbose=0, warm_start=False),\n",
" fit_params=None, iid='warn', n_jobs=None,\n",
" param_grid={'penalty': ['l1', 'l2']}, pre_dispatch='2*n_jobs',\n",
" refit=True, return_train_score=True, scoring='roc_auc', verbose=0)"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pca_lr = LogisticRegression(random_state=0)\n",
"\n",
"param_grid = {'penalty':['l1', 'l2']}\n",
"\n",
"grid_log_clf = GridSearchCV(pca_lr , param_grid, cv = 5, return_train_score=True, scoring='roc_auc')\n",
"grid_log_clf.fit(X_train_reduced, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best Parameters for Logistic Regression with PCA: {'penalty': 'l1'}\n"
]
}
],
"source": [
"print(\"Best Parameters for Logistic Regression with PCA: \", grid_log_clf.best_params_)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='warn',\n",
" n_jobs=None, penalty='l1', random_state=None, solver='warn',\n",
" tol=0.0001, verbose=0, warm_start=False)"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pca_lr = LogisticRegression(penalty= 'l1')\n",
"pca_lr.fit(X_train_reduced, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Logistic Regression with PCA Training Score: 0.9596491228070175\n",
"Logistic Regression with PCA Testing Score: 0.9578947368421052\n"
]
}
],
"source": [
"print(\"Logistic Regression with PCA Training Score: \", pca_lr.score(X_train_reduced, y_train))\n",
"print(\"Logistic Regression with PCA Testing Score: \", pca_lr.score(X_test_reduced, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [],
"source": [
"pca_report_table_3 = ['Logistic Regression with PCA', 'penalty = l1', \n",
" pca_lr.score(X_train_reduced, y_train), pca_lr.score(X_test_reduced, y_test)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Decision Tree Classifier with PCA"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, error_score='raise-deprecating',\n",
" estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
" splitter='best'),\n",
" fit_params=None, iid='warn', n_jobs=-1,\n",
" param_grid={'max_depth': [3, 5, 7, 9, 11, 15]},\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
" scoring=None, verbose=0)"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Base model\n",
"dt = DecisionTreeClassifier(random_state = 0)\n",
"\n",
"#model param\n",
"grid_param = {'max_depth': [3, 5, 7, 9, 11, 15]}\n",
"\n",
"#grid model\n",
"dt_grid = GridSearchCV(dt, grid_param, cv = 5, n_jobs = -1)\n",
"\n",
"#train grid model\n",
"dt_grid.fit(X_train_reduced, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best Parameters for Decision Tree Classifier: {'max_depth': 5}\n"
]
}
],
"source": [
"print(\"Best Parameters for Decision Tree Classifier: \", dt_grid.best_params_)"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=5,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
" splitter='best')"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pca_dt = DecisionTreeClassifier(max_depth= 5, random_state= 0)\n",
"pca_dt.fit(X_train_reduced, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Decision Tree Classifier with PCA Training Score: 0.9964912280701754\n",
"Decision Tree Classifier with PCA Testing Score: 0.9578947368421052\n"
]
}
],
"source": [
"print(\"Decision Tree Classifier with PCA Training Score: \", pca_dt.score(X_train_reduced, y_train))\n",
"print(\"Decision Tree Classifier with PCA Testing Score: \", pca_dt.score(X_test_reduced, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [],
"source": [
"pca_report_table_4 = ['Decision Tree Classifier with PCA', 'max_depth: 5', \n",
" pca_dt.score(X_train_reduced, y_train), pca_dt.score(X_test_reduced, y_test)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generating a Report table: PCA\n",
"For comparing all the models, let's create a table and a plot."
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [],
"source": [
"pca_report_table = pd.DataFrame(list(zip(pca_report_table_1,\n",
" pca_report_table_2,\n",
" pca_report_table_3,\n",
" pca_report_table_4))).transpose()"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
"pca_report_table.columns = ['Model Name', 'Model Parameter', 'Training Score', 'Testing Score']\n",
"pca_report_table.index = pca_report_table['Model Name']"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Model Name</th>\n",
" <th>Model Parameter</th>\n",
" <th>Training Score</th>\n",
" <th>Testing Score</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Model Name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>KNN Classifier with PCA</th>\n",
" <td>KNN Classifier with PCA</td>\n",
" <td>n_neighbors = 8</td>\n",
" <td>0.977193</td>\n",
" <td>0.942105</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SVC with PCA</th>\n",
" <td>SVC with PCA</td>\n",
" <td>C =10, kernel= rbf</td>\n",
" <td>0.94386</td>\n",
" <td>0.952632</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logistic Regression with PCA</th>\n",
" <td>Logistic Regression with PCA</td>\n",
" <td>penalty = l1</td>\n",
" <td>0.959649</td>\n",
" <td>0.957895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Decision Tree Classifier with PCA</th>\n",
" <td>Decision Tree Classifier with PCA</td>\n",
" <td>max_depth: 5</td>\n",
" <td>0.996491</td>\n",
" <td>0.957895</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Model Name \\\n",
"Model Name \n",
"KNN Classifier with PCA KNN Classifier with PCA \n",
"SVC with PCA SVC with PCA \n",
"Logistic Regression with PCA Logistic Regression with PCA \n",
"Decision Tree Classifier with PCA Decision Tree Classifier with PCA \n",
"\n",
" Model Parameter Training Score \\\n",
"Model Name \n",
"KNN Classifier with PCA n_neighbors = 8 0.977193 \n",
"SVC with PCA C =10, kernel= rbf 0.94386 \n",
"Logistic Regression with PCA penalty = l1 0.959649 \n",
"Decision Tree Classifier with PCA max_depth: 5 0.996491 \n",
"\n",
" Testing Score \n",
"Model Name \n",
"KNN Classifier with PCA 0.942105 \n",
"SVC with PCA 0.952632 \n",
"Logistic Regression with PCA 0.957895 \n",
"Decision Tree Classifier with PCA 0.957895 "
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pca_report_table.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Model Name</th>\n",
" <th>Model Name.1</th>\n",
" <th>Model Parameter</th>\n",
" <th>Training Score</th>\n",
" <th>Testing Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>KNN Classifier</td>\n",
" <td>KNN Classifier</td>\n",
" <td>n_neighbors = 7</td>\n",
" <td>0.977193</td>\n",
" <td>0.952632</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>SVC with PCA</td>\n",
" <td>SVC with PCA</td>\n",
" <td>C =100, kernel= linear</td>\n",
" <td>0.987719</td>\n",
" <td>0.963158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Logistic Regression</td>\n",
" <td>Logistic Regression</td>\n",
" <td>penalty = l1</td>\n",
" <td>0.963158</td>\n",
" <td>0.957895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Decision Tree Classifier</td>\n",
" <td>Decision Tree Classifier</td>\n",
" <td>max_depth: 9</td>\n",
" <td>1.000000</td>\n",
" <td>0.989474</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Model Name Model Name.1 Model Parameter \\\n",
"0 KNN Classifier KNN Classifier n_neighbors = 7 \n",
"1 SVC with PCA SVC with PCA C =100, kernel= linear \n",
"2 Logistic Regression Logistic Regression penalty = l1 \n",
"3 Decision Tree Classifier Decision Tree Classifier max_depth: 9 \n",
"\n",
" Training Score Testing Score \n",
"0 0.977193 0.952632 \n",
"1 0.987719 0.963158 \n",
"2 0.963158 0.957895 \n",
"3 1.000000 0.989474 "
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"report_table_without_pca = pd.read_csv('Classification Report Table without PCA.csv')\n",
"report_table_without_pca.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"ax = pca_report_table[['Training Score','Testing Score']].plot(kind='bar',\n",
" title = \"Comparison of Accuracies of Different Models with PCA\", figsize=(8, 8), fontsize = 8)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x20b5d089b70>"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import matplotlib.image as mpimg\n",
"\n",
"fig = plt.figure(figsize=(8,8))\n",
"ax = fig.add_axes([0, 0, 1, 1])\n",
"\n",
"# Hide spines, ticks, etc.\n",
"ax.axis('off')\n",
"\n",
"# Display the image.\n",
"img=mpimg.imread('Classification Report Table without PCA.PNG')\n",
"ax.imshow(img, aspect='auto')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Neural Network Model"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From d:\\software\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Colocations handled automatically by placer.\n",
"WARNING:tensorflow:From d:\\software\\python36\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.cast instead.\n",
"Epoch 1/40\n",
"570/570 [==============================] - 1s 1ms/step - loss: 0.6370 - acc: 0.6228\n",
"Epoch 2/40\n",
"570/570 [==============================] - 0s 47us/step - loss: 0.6178 - acc: 0.6561\n",
"Epoch 3/40\n",
"570/570 [==============================] - 0s 50us/step - loss: 0.5997 - acc: 0.6737\n",
"Epoch 4/40\n",
"570/570 [==============================] - 0s 39us/step - loss: 0.5821 - acc: 0.7263\n",
"Epoch 5/40\n",
"570/570 [==============================] - 0s 55us/step - loss: 0.5659 - acc: 0.7386\n",
"Epoch 6/40\n",
"570/570 [==============================] - 0s 50us/step - loss: 0.5494 - acc: 0.7807\n",
"Epoch 7/40\n",
"570/570 [==============================] - 0s 51us/step - loss: 0.5335 - acc: 0.8053\n",
"Epoch 8/40\n",
"570/570 [==============================] - 0s 55us/step - loss: 0.5181 - acc: 0.8211\n",
"Epoch 9/40\n",
"570/570 [==============================] - 0s 51us/step - loss: 0.5036 - acc: 0.8351\n",
"Epoch 10/40\n",
"570/570 [==============================] - 0s 53us/step - loss: 0.4898 - acc: 0.8386\n",
"Epoch 11/40\n",
"570/570 [==============================] - 0s 55us/step - loss: 0.4755 - acc: 0.8386\n",
"Epoch 12/40\n",
"570/570 [==============================] - 0s 48us/step - loss: 0.4614 - acc: 0.8421\n",
"Epoch 13/40\n",
"570/570 [==============================] - 0s 52us/step - loss: 0.4487 - acc: 0.8456\n",
"Epoch 14/40\n",
"570/570 [==============================] - 0s 54us/step - loss: 0.4360 - acc: 0.8491\n",
"Epoch 15/40\n",
"570/570 [==============================] - 0s 56us/step - loss: 0.4229 - acc: 0.8491\n",
"Epoch 16/40\n",
"570/570 [==============================] - 0s 45us/step - loss: 0.4110 - acc: 0.8491\n",
"Epoch 17/40\n",
"570/570 [==============================] - 0s 49us/step - loss: 0.3993 - acc: 0.8614\n",
"Epoch 18/40\n",
"570/570 [==============================] - 0s 48us/step - loss: 0.3881 - acc: 0.8632\n",
"Epoch 19/40\n",
"570/570 [==============================] - 0s 44us/step - loss: 0.3770 - acc: 0.8667\n",
"Epoch 20/40\n",
"570/570 [==============================] - 0s 52us/step - loss: 0.3662 - acc: 0.8667\n",
"Epoch 21/40\n",
"570/570 [==============================] - 0s 50us/step - loss: 0.3555 - acc: 0.8667\n",
"Epoch 22/40\n",
"570/570 [==============================] - 0s 47us/step - loss: 0.3454 - acc: 0.8667\n",
"Epoch 23/40\n",
"570/570 [==============================] - 0s 51us/step - loss: 0.3360 - acc: 0.8667\n",
"Epoch 24/40\n",
"570/570 [==============================] - 0s 53us/step - loss: 0.3262 - acc: 0.8667\n",
"Epoch 25/40\n",
"570/570 [==============================] - 0s 58us/step - loss: 0.3171 - acc: 0.8667\n",
"Epoch 26/40\n",
"570/570 [==============================] - 0s 51us/step - loss: 0.3083 - acc: 0.8684\n",
"Epoch 27/40\n",
"570/570 [==============================] - 0s 47us/step - loss: 0.3004 - acc: 0.8667\n",
"Epoch 28/40\n",
"570/570 [==============================] - 0s 54us/step - loss: 0.2916 - acc: 0.8667\n",
"Epoch 29/40\n",
"570/570 [==============================] - 0s 50us/step - loss: 0.2840 - acc: 0.8649\n",
"Epoch 30/40\n",
"570/570 [==============================] - 0s 50us/step - loss: 0.2770 - acc: 0.8667\n",
"Epoch 31/40\n",
"570/570 [==============================] - 0s 58us/step - loss: 0.2697 - acc: 0.8667\n",
"Epoch 32/40\n",
"570/570 [==============================] - 0s 43us/step - loss: 0.2633 - acc: 0.8667\n",
"Epoch 33/40\n",
"570/570 [==============================] - 0s 45us/step - loss: 0.2572 - acc: 0.8632\n",
"Epoch 34/40\n",
"570/570 [==============================] - 0s 48us/step - loss: 0.2509 - acc: 0.8632\n",
"Epoch 35/40\n",
"570/570 [==============================] - 0s 52us/step - loss: 0.2450 - acc: 0.8632\n",
"Epoch 36/40\n",
"570/570 [==============================] - 0s 58us/step - loss: 0.2400 - acc: 0.8614\n",
"Epoch 37/40\n",
"570/570 [==============================] - 0s 54us/step - loss: 0.2331 - acc: 0.8614\n",
"Epoch 38/40\n",
"570/570 [==============================] - 0s 45us/step - loss: 0.2279 - acc: 0.8614\n",
"Epoch 39/40\n",
"570/570 [==============================] - 0s 51us/step - loss: 0.2227 - acc: 0.8579\n",
"Epoch 40/40\n",
"570/570 [==============================] - 0s 43us/step - loss: 0.2169 - acc: 0.8614\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x20b65889f28>"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from keras.models import Sequential\n",
"from keras.layers import Dense,MaxPooling1D\n",
"np.random.seed(0)\n",
"\n",
"model = Sequential()\n",
"model.add(Dense(10, input_dim = 10, activation = 'sigmoid'))\n",
"model.add(Dense(1))\n",
"\n",
"model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n",
"\n",
"model.fit(X_train, y_train, epochs = 40, batch_size = 50)"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NN Train Score: 0.8807017543859649\n",
"NN Test Score: 0.8631578947368421\n"
]
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"y_train_pred = model.predict(X_train)\n",
"y_train_pred = np.where(y_train_pred >= 0.5 , 1, 0)\n",
"y_test_pred = model.predict(X_test)\n",
"y_test_pred = np.where(y_test_pred >= 0.5 , 1, 0)\n",
"print(\"NN Train Score: \",accuracy_score(y_train, y_train_pred))\n",
"print(\"NN Test Score: \", accuracy_score(y_test, y_test_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Selection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Amongst all the models Logistic Regression model with PCA seems to be the best model. "
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Logistic Regression with PCA Training Score: 0.9596491228070175\n",
"Logistic Regression with PCA Testing Score: 0.9578947368421052\n"
]
}
],
"source": [
"print(\"Logistic Regression with PCA Training Score: \", pca_lr.score(X_train_reduced, y_train))\n",
"print(\"Logistic Regression with PCA Testing Score: \", pca_lr.score(X_test_reduced, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted value for 1st testing row: 0.0\n",
"Original value for 1st testing row: 0.0\n",
"\n",
"Predicted value for 5th testing row: 1.0\n",
"Original value for 5th testing row: 1.0\n"
]
}
],
"source": [
"y_predicted = pca_lr.predict(X_test_reduced)\n",
"print(\"Predicted value for 1st testing row: \", y_predicted[0])\n",
"print(\"Original value for 1st testing row: \", y_test.values[0])\n",
"print(\"\")\n",
"print(\"Predicted value for 5th testing row: \", y_predicted[4])\n",
"print(\"Original value for 5th testing row: \", y_test.values[4])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The model works pretty well on the test data as well.\n",
"\n",
"**Training and testing score of around 0.95 was achieved using Logistic Regression.** "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
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