kaggle-titanic-dataset/kaggle_titanic_dataset_with_ANN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QJcPRRWPOaDv"
   },
   "source": [
    "# Titanic Machine Learning from Disaster using an ANN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ksyyTNKhOaEA"
   },
   "source": [
    "## About the dataset:\n",
    "This dataset is taken from Kaggle Competition 'Titanic: Machine Learning from Disaster'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_ECjJrjszHqq"
   },
   "source": [
    "**Setting up Google Colab.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 7727,
     "status": "ok",
     "timestamp": 1554212973283,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "EyX89a5Jgws-",
    "outputId": "36314216-2d4d-4960-ba46-2f0e4c5fd6ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found GPU at: /device:GPU:0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf \n",
    "device_name = tf.test.gpu_device_name() \n",
    "if device_name != '/device:GPU:0': raise SystemError('GPU device not found') \n",
    "print('Found GPU at: {}'.format(device_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 129
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 107205,
     "status": "ok",
     "timestamp": 1554210983025,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "uJnsj_IQhGLt",
    "outputId": "79943d23-aefc-4431-df4b-f402b54eda80"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [

      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SYv93LELOaEb"
   },
   "source": [
    "## Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F5BZr0pzOaEf"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.set_printoptions(precision=4)\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ncxpvWZbbQwX"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xhmV6KBaOaE0"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DsgbAoyYOaFL"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1h3EBYXHOaHX"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YNatzJqsOaH9"
   },
   "outputs": [],
   "source": [
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "W6qLZCD4Wbdl"
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Tf2ZVYsihXt8"
   },
   "outputs": [],
   "source": [
    "from keras.wrappers.scikit_learn import KerasClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-4kFj3Q9XVpw"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KtcgKEh7hi7E"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eRKgKtJ-OaIK"
   },
   "source": [
    "## Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 345
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3087,
     "status": "ok",
     "timestamp": 1554213011081,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "XYNn7n_bOaIh",
    "outputId": "1b29d6ae-e8a7-4d6b-8489-39dce041a57f"
   },
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 48,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv('/content/drive/My Drive/Colab Notebooks/Internships/Titanic/data/train.csv')\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "U4u4hdTZOaI2"
   },
   "source": [
    "## Analyzing and modifying the train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 329
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4693,
     "status": "ok",
     "timestamp": 1554213018086,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "6TLrTC4QOaJC",
    "outputId": "ed7a196f-ca4f-42a3-e9f9-980b3dcb77c5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 90
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3250,
     "status": "ok",
     "timestamp": 1554213023860,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "tMDDy2QfOaJ4",
    "outputId": "4510c325-8696-46c7-b287-da545a03d27c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    491\n",
       "1    216\n",
       "2    184\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for uniformity in each column.\n",
    "df_train.Pclass.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 72
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4403,
     "status": "ok",
     "timestamp": 1554213046576,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "YiuVPQJiOaKH",
    "outputId": "e42caa11-e78a-4e9d-81e7-b2f978b7bd60"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3449,
     "status": "ok",
     "timestamp": 1554213051060,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "MkENfdhBOaKU",
    "outputId": "a1db1b45-6475-4711-80c7-878116f1b2d7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 52,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train.Age.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4174,
     "status": "ok",
     "timestamp": 1554213056182,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "C3oHx6AsOaK2",
    "outputId": "0e6aa999-2edd-4c2c-9dd8-e1c89ede2fd1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177"
      ]
     },
     "execution_count": 53,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Age.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QSMiKFQaOaLK"
   },
   "outputs": [],
   "source": [
    "# Filling the missing values in Age with 0 in a new column.\n",
    "df_train['Age_No_NA'] = df_train.Age.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 164
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4639,
     "status": "ok",
     "timestamp": 1554213066308,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "rBy8KT5hOaLS",
    "outputId": "1f4f682c-cf86-4ebe-a9cd-2734a4b1424a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    608\n",
       "1    209\n",
       "2     28\n",
       "4     18\n",
       "3     16\n",
       "8      7\n",
       "5      5\n",
       "Name: SibSp, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.SibSp.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 164
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5333,
     "status": "ok",
     "timestamp": 1554213072839,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "ZyWtc8TuOaLk",
    "outputId": "4677a014-31e9-449c-9b3f-d4373520202a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    678\n",
       "1    118\n",
       "2     80\n",
       "5      5\n",
       "3      5\n",
       "4      4\n",
       "6      1\n",
       "Name: Parch, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Parch.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3725,
     "status": "ok",
     "timestamp": 1554213077621,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "JaWtzkJ9OaMU",
    "outputId": "2c8b2f7c-0de3-4912-a332-de61f3d36ce4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "681"
      ]
     },
     "execution_count": 57,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train.Ticket.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4141,
     "status": "ok",
     "timestamp": 1554213082768,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "1lWiiWyhOaNk",
    "outputId": "3d5cbc4e-faef-4c25-d51d-dffc012bac9b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248"
      ]
     },
     "execution_count": 58,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train.Fare.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2526,
     "status": "ok",
     "timestamp": 1554213092654,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "J7M_IqNbOaN5",
    "outputId": "dc58f11b-e3d3-4d47-b35c-9ea40e9f43f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "148"
      ]
     },
     "execution_count": 59,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train.Cabin.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1671,
     "status": "ok",
     "timestamp": 1554213095546,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "vJTGot16OaO6",
    "outputId": "a93b57f4-7e41-45c6-a979-d763fd4935a3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "687"
      ]
     },
     "execution_count": 60,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Cabin.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 90
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2754,
     "status": "ok",
     "timestamp": 1554213099054,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "cbaY1qQROaPE",
    "outputId": "952fc2f3-7fc7-48ff-f2cd-dd40011b2302"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Embarked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4857,
     "status": "ok",
     "timestamp": 1554213104810,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "pcq-5kPKOaPb",
    "outputId": "5f1d24a7-1526-491a-fe1d-e784b7a7b0ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 62,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Embarked.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XgWJA1EBOaPq"
   },
   "outputs": [],
   "source": [
    "# Dropping the rows with NA value from Embarked column.\n",
    "df_train.drop(df_train[df_train.Embarked.isnull()].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 90
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5017,
     "status": "ok",
     "timestamp": 1554213114937,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "GDvNt1XkOaP2",
    "outputId": "20465547-1ac4-4dc4-9eb8-9a1f3a4cd9ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    174\n",
       "2     16\n",
       "3     12\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[df_train.Cabin.notnull()].Pclass.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 72
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4095,
     "status": "ok",
     "timestamp": 1554213120185,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "iRqQAzPGOaRK",
    "outputId": "0c863a52-c507-4864-9a6b-2ba168b462d2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    340\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Survived.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 348
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4092,
     "status": "ok",
     "timestamp": 1554213125298,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "WcBNXbRaOaRt",
    "outputId": "963b9eba-6532-4fdd-89f5-90d9c53a24c4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 889 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      "PassengerId    889 non-null int64\n",
      "Survived       889 non-null int64\n",
      "Pclass         889 non-null int64\n",
      "Name           889 non-null object\n",
      "Sex            889 non-null object\n",
      "Age            712 non-null float64\n",
      "SibSp          889 non-null int64\n",
      "Parch          889 non-null int64\n",
      "Ticket         889 non-null object\n",
      "Fare           889 non-null float64\n",
      "Cabin          202 non-null object\n",
      "Embarked       889 non-null object\n",
      "Age_No_NA      889 non-null float64\n",
      "dtypes: float64(3), int64(5), object(5)\n",
      "memory usage: 97.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "39qTAeoDOaSC"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Bi05NN7fOaSH"
   },
   "source": [
    "## Preparing a Baseline Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yNi41SHBOaSL"
   },
   "outputs": [],
   "source": [
    "X = df_train.iloc[:, [2,4,6,7,9,11,12]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3162,
     "status": "ok",
     "timestamp": 1554213135604,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "gH9eOyGhOaSQ",
    "outputId": "8f371e1c-49d9-493c-b936-eabc6823411d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 'male', 1, 0, 7.25, 'S', 22.0], dtype=object)"
      ]
     },
     "execution_count": 68,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BfW10qwTOaSf"
   },
   "outputs": [],
   "source": [
    "y = df_train.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Bu_9TpreOaSm"
   },
   "outputs": [],
   "source": [
    "# Label Encoding various features.\n",
    "labelencoder_sex = LabelEncoder()\n",
    "X[:, 1] = labelencoder_sex.fit_transform(X[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lpZb1FhvOaSs"
   },
   "outputs": [],
   "source": [
    "labelencoder_embarked = LabelEncoder()\n",
    "X[:, 5] = labelencoder_embarked.fit_transform(X[:, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Uvc-xtt6OaTH"
   },
   "outputs": [],
   "source": [
    "# Creating a baseline Random Forest Classifier.\n",
    "classifier = RandomForestClassifier(n_estimators=300, random_state=42, oob_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zBeRAIY_OaTk"
   },
   "outputs": [],
   "source": [
    "# Performing K-fold Cross-validation.\n",
    "scores = cross_val_score(estimator=classifier, X=X, y=y, cv=5, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2965,
     "status": "ok",
     "timestamp": 1554213185133,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "dm5e18d8OaUO",
    "outputId": "ea60f2be-40d9-4a5f-e704-15578a54a397"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.7809, 0.8034, 0.8483, 0.7697, 0.8249])"
      ]
     },
     "execution_count": 75,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1974,
     "status": "ok",
     "timestamp": 1554213189953,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "Y6Jziyc7OaUU",
    "outputId": "066fea66-9e8d-49ab-afb1-128f792cda84"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8054211896146766"
      ]
     },
     "execution_count": 76,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1322,
     "status": "ok",
     "timestamp": 1554213194116,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "lGVgYKZOOaUg",
    "outputId": "ed6be155-2be3-40f2-991e-61df1201ae71"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.028642157769790434"
      ]
     },
     "execution_count": 77,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 127
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2840,
     "status": "ok",
     "timestamp": 1554213201780,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "GGKfG2fAOaUu",
    "outputId": "29733068-8b0f-44e1-a6b6-254b9cb6f9da"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', 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=300, n_jobs=None,\n",
       "            oob_score=True, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 78,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the model on whole dataset, for more train samples.\n",
    "classifier.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2074,
     "status": "ok",
     "timestamp": 1554213205050,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "XT5HksTLOaU9",
    "outputId": "f725bcd2-c925-4b01-cae0-f7bef1da5ef0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8076490438695163"
      ]
     },
     "execution_count": 79,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Classifier's score on the samples which were not seen by the Random Forest algorithm.\n",
    "# oob_score works as a prediction on a validation or train set.\n",
    "classifier.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bR8KowR6OaVC"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KDrPGBPTOaVF"
   },
   "source": [
    "## Creating an ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1770,
     "status": "ok",
     "timestamp": 1554214029072,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "1_coRPBhOaVH",
    "outputId": "2b2d4b92-417d-4a79-b094-aaae307b5e5a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 'male', 1, 0, 7.25, 'S', 22.0], dtype=object)"
      ]
     },
     "execution_count": 97,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_train.iloc[:, [2,4,6,7,9,11,12]].values\n",
    "X[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4My0MgA9OaVk"
   },
   "outputs": [],
   "source": [
    "y = df_train.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8MUdXeqiOaWN"
   },
   "outputs": [],
   "source": [
    "labelencoder_sex = LabelEncoder()\n",
    "X[:, 1] = labelencoder_sex.fit_transform(X[:, 1])\n",
    "\n",
    "labelencoder_embarked = LabelEncoder()\n",
    "X[:, 5] = labelencoder_embarked.fit_transform(X[:, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "myPhkZE1OaW0"
   },
   "outputs": [],
   "source": [
    "# One-Hot Encoding categorical features ie. Sex and Embarked.\n",
    "ohe = OneHotEncoder(categorical_features=[1,5])\n",
    "X = ohe.fit_transform(X).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vk6oP4RPOaX6"
   },
   "outputs": [],
   "source": [
    "X = X[:, [1,3,4,5,6,7,8,9]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iPcDssB9OaX-"
   },
   "outputs": [],
   "source": [
    "# Normalizing all the features \n",
    "norm = MinMaxScaler()\n",
    "X = norm.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kamFTZx-OaYL"
   },
   "outputs": [],
   "source": [
    "# Building the ANN Model\n",
    "model = Sequential()\n",
    "model.add(Dense(16, activation='relu', input_shape=(8,)))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(16, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1, activation='sigmoid'))       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 329
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3003,
     "status": "ok",
     "timestamp": 1554214284840,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "ybzi_qcLY1-_",
    "outputId": "51352f8a-51b2-4269-9bfd-d00336144b17"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 16)                144       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 433\n",
      "Trainable params: 433\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "R8EV0PJsYwe2"
   },
   "outputs": [],
   "source": [
    "# Compiling the model\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 3729
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 16535,
     "status": "ok",
     "timestamp": 1554214848990,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "pHesA82EYwbA",
    "outputId": "2fc69899-0571-4fa5-f4d6-ca1b7ec78510"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 711 samples, validate on 178 samples\n",
      "Epoch 1/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.5069 - acc: 0.7862 - val_loss: 0.4150 - val_acc: 0.8371\n",
      "Epoch 2/100\n",
      "711/711 [==============================] - 0s 185us/step - loss: 0.4941 - acc: 0.7862 - val_loss: 0.4165 - val_acc: 0.8315\n",
      "Epoch 3/100\n",
      "711/711 [==============================] - 0s 189us/step - loss: 0.4877 - acc: 0.7932 - val_loss: 0.4143 - val_acc: 0.8315\n",
      "Epoch 4/100\n",
      "711/711 [==============================] - 0s 200us/step - loss: 0.4892 - acc: 0.7792 - val_loss: 0.4115 - val_acc: 0.8371\n",
      "Epoch 5/100\n",
      "711/711 [==============================] - 0s 188us/step - loss: 0.5014 - acc: 0.7876 - val_loss: 0.4120 - val_acc: 0.8315\n",
      "Epoch 6/100\n",
      "711/711 [==============================] - 0s 170us/step - loss: 0.4849 - acc: 0.7904 - val_loss: 0.4137 - val_acc: 0.8315\n",
      "Epoch 7/100\n",
      "711/711 [==============================] - 0s 192us/step - loss: 0.5085 - acc: 0.7989 - val_loss: 0.4142 - val_acc: 0.8371\n",
      "Epoch 8/100\n",
      "711/711 [==============================] - 0s 200us/step - loss: 0.4781 - acc: 0.7975 - val_loss: 0.4152 - val_acc: 0.8371\n",
      "Epoch 9/100\n",
      "711/711 [==============================] - 0s 201us/step - loss: 0.4901 - acc: 0.7890 - val_loss: 0.4136 - val_acc: 0.8258\n",
      "Epoch 10/100\n",
      "711/711 [==============================] - 0s 188us/step - loss: 0.4826 - acc: 0.7848 - val_loss: 0.4116 - val_acc: 0.8315\n",
      "Epoch 11/100\n",
      "711/711 [==============================] - 0s 176us/step - loss: 0.4916 - acc: 0.7848 - val_loss: 0.4111 - val_acc: 0.8315\n",
      "Epoch 12/100\n",
      "711/711 [==============================] - 0s 183us/step - loss: 0.4744 - acc: 0.7876 - val_loss: 0.4119 - val_acc: 0.8371\n",
      "Epoch 13/100\n",
      "711/711 [==============================] - 0s 198us/step - loss: 0.4855 - acc: 0.8045 - val_loss: 0.4107 - val_acc: 0.8371\n",
      "Epoch 14/100\n",
      "711/711 [==============================] - 0s 182us/step - loss: 0.5042 - acc: 0.7918 - val_loss: 0.4107 - val_acc: 0.8315\n",
      "Epoch 15/100\n",
      "711/711 [==============================] - 0s 187us/step - loss: 0.4813 - acc: 0.7848 - val_loss: 0.4119 - val_acc: 0.8258\n",
      "Epoch 16/100\n",
      "711/711 [==============================] - 0s 200us/step - loss: 0.4714 - acc: 0.7862 - val_loss: 0.4099 - val_acc: 0.8315\n",
      "Epoch 17/100\n",
      "711/711 [==============================] - 0s 169us/step - loss: 0.4898 - acc: 0.7862 - val_loss: 0.4095 - val_acc: 0.8315\n",
      "Epoch 18/100\n",
      "711/711 [==============================] - 0s 171us/step - loss: 0.5066 - acc: 0.7862 - val_loss: 0.4104 - val_acc: 0.8315\n",
      "Epoch 19/100\n",
      "711/711 [==============================] - 0s 191us/step - loss: 0.4947 - acc: 0.7876 - val_loss: 0.4118 - val_acc: 0.8258\n",
      "Epoch 20/100\n",
      "711/711 [==============================] - 0s 178us/step - loss: 0.4981 - acc: 0.7750 - val_loss: 0.4128 - val_acc: 0.8258\n",
      "Epoch 21/100\n",
      "711/711 [==============================] - 0s 191us/step - loss: 0.4809 - acc: 0.7848 - val_loss: 0.4118 - val_acc: 0.8258\n",
      "Epoch 22/100\n",
      "711/711 [==============================] - 0s 186us/step - loss: 0.5020 - acc: 0.7961 - val_loss: 0.4123 - val_acc: 0.8427\n",
      "Epoch 23/100\n",
      "711/711 [==============================] - 0s 183us/step - loss: 0.4919 - acc: 0.7904 - val_loss: 0.4127 - val_acc: 0.8427\n",
      "Epoch 24/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.4879 - acc: 0.7890 - val_loss: 0.4113 - val_acc: 0.8258\n",
      "Epoch 25/100\n",
      "711/711 [==============================] - 0s 188us/step - loss: 0.5092 - acc: 0.7862 - val_loss: 0.4134 - val_acc: 0.8258\n",
      "Epoch 26/100\n",
      "711/711 [==============================] - 0s 185us/step - loss: 0.4939 - acc: 0.7989 - val_loss: 0.4131 - val_acc: 0.8258\n",
      "Epoch 27/100\n",
      "711/711 [==============================] - 0s 183us/step - loss: 0.5045 - acc: 0.7750 - val_loss: 0.4133 - val_acc: 0.8258\n",
      "Epoch 28/100\n",
      "711/711 [==============================] - 0s 182us/step - loss: 0.4849 - acc: 0.7961 - val_loss: 0.4124 - val_acc: 0.8315\n",
      "Epoch 29/100\n",
      "711/711 [==============================] - 0s 183us/step - loss: 0.4880 - acc: 0.7918 - val_loss: 0.4116 - val_acc: 0.8371\n",
      "Epoch 30/100\n",
      "711/711 [==============================] - 0s 171us/step - loss: 0.4923 - acc: 0.7975 - val_loss: 0.4096 - val_acc: 0.8258\n",
      "Epoch 31/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.4880 - acc: 0.8059 - val_loss: 0.4107 - val_acc: 0.8258\n",
      "Epoch 32/100\n",
      "711/711 [==============================] - 0s 205us/step - loss: 0.4935 - acc: 0.7820 - val_loss: 0.4102 - val_acc: 0.8258\n",
      "Epoch 33/100\n",
      "711/711 [==============================] - 0s 171us/step - loss: 0.4998 - acc: 0.7904 - val_loss: 0.4105 - val_acc: 0.8258\n",
      "Epoch 34/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.5046 - acc: 0.7778 - val_loss: 0.4129 - val_acc: 0.8371\n",
      "Epoch 35/100\n",
      "711/711 [==============================] - 0s 181us/step - loss: 0.4652 - acc: 0.7975 - val_loss: 0.4112 - val_acc: 0.8371\n",
      "Epoch 36/100\n",
      "711/711 [==============================] - 0s 194us/step - loss: 0.4848 - acc: 0.7918 - val_loss: 0.4100 - val_acc: 0.8427\n",
      "Epoch 37/100\n",
      "711/711 [==============================] - 0s 183us/step - loss: 0.4686 - acc: 0.7947 - val_loss: 0.4066 - val_acc: 0.8371\n",
      "Epoch 38/100\n",
      "711/711 [==============================] - 0s 199us/step - loss: 0.4733 - acc: 0.8003 - val_loss: 0.4063 - val_acc: 0.8371\n",
      "Epoch 39/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.4699 - acc: 0.7961 - val_loss: 0.4060 - val_acc: 0.8371\n",
      "Epoch 40/100\n",
      "711/711 [==============================] - 0s 189us/step - loss: 0.4835 - acc: 0.7932 - val_loss: 0.4063 - val_acc: 0.8371\n",
      "Epoch 41/100\n",
      "711/711 [==============================] - 0s 169us/step - loss: 0.4818 - acc: 0.7918 - val_loss: 0.4069 - val_acc: 0.8371\n",
      "Epoch 42/100\n",
      "711/711 [==============================] - 0s 171us/step - loss: 0.4865 - acc: 0.7834 - val_loss: 0.4057 - val_acc: 0.8315\n",
      "Epoch 43/100\n",
      "711/711 [==============================] - 0s 185us/step - loss: 0.5097 - acc: 0.7904 - val_loss: 0.4081 - val_acc: 0.8371\n",
      "Epoch 44/100\n",
      "711/711 [==============================] - 0s 167us/step - loss: 0.4753 - acc: 0.7904 - val_loss: 0.4081 - val_acc: 0.8371\n",
      "Epoch 45/100\n",
      "711/711 [==============================] - 0s 173us/step - loss: 0.4865 - acc: 0.7904 - val_loss: 0.4069 - val_acc: 0.8371\n",
      "Epoch 46/100\n",
      "711/711 [==============================] - 0s 167us/step - loss: 0.4878 - acc: 0.7932 - val_loss: 0.4082 - val_acc: 0.8371\n",
      "Epoch 47/100\n",
      "711/711 [==============================] - 0s 196us/step - loss: 0.5108 - acc: 0.7834 - val_loss: 0.4094 - val_acc: 0.8315\n",
      "Epoch 48/100\n",
      "711/711 [==============================] - 0s 182us/step - loss: 0.4722 - acc: 0.7918 - val_loss: 0.4073 - val_acc: 0.8371\n",
      "Epoch 49/100\n",
      "711/711 [==============================] - 0s 172us/step - loss: 0.4763 - acc: 0.7932 - val_loss: 0.4066 - val_acc: 0.8371\n",
      "Epoch 50/100\n",
      "711/711 [==============================] - 0s 174us/step - loss: 0.4787 - acc: 0.7975 - val_loss: 0.4056 - val_acc: 0.8371\n",
      "Epoch 51/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.4905 - acc: 0.7876 - val_loss: 0.4070 - val_acc: 0.8371\n",
      "Epoch 52/100\n",
      "711/711 [==============================] - 0s 172us/step - loss: 0.4784 - acc: 0.7989 - val_loss: 0.4056 - val_acc: 0.8371\n",
      "Epoch 53/100\n",
      "711/711 [==============================] - 0s 170us/step - loss: 0.4799 - acc: 0.7918 - val_loss: 0.4045 - val_acc: 0.8371\n",
      "Epoch 54/100\n",
      "711/711 [==============================] - 0s 172us/step - loss: 0.4871 - acc: 0.7904 - val_loss: 0.4064 - val_acc: 0.8371\n",
      "Epoch 55/100\n",
      "711/711 [==============================] - 0s 201us/step - loss: 0.4891 - acc: 0.7820 - val_loss: 0.4081 - val_acc: 0.8371\n",
      "Epoch 56/100\n",
      "711/711 [==============================] - 0s 169us/step - loss: 0.4882 - acc: 0.7848 - val_loss: 0.4075 - val_acc: 0.8371\n",
      "Epoch 57/100\n",
      "711/711 [==============================] - 0s 170us/step - loss: 0.4900 - acc: 0.7848 - val_loss: 0.4062 - val_acc: 0.8371\n",
      "Epoch 58/100\n",
      "711/711 [==============================] - 0s 185us/step - loss: 0.4861 - acc: 0.7904 - val_loss: 0.4059 - val_acc: 0.8371\n",
      "Epoch 59/100\n",
      "711/711 [==============================] - 0s 171us/step - loss: 0.4935 - acc: 0.7876 - val_loss: 0.4052 - val_acc: 0.8371\n",
      "Epoch 60/100\n",
      "711/711 [==============================] - 0s 179us/step - loss: 0.4753 - acc: 0.7932 - val_loss: 0.4027 - val_acc: 0.8371\n",
      "Epoch 61/100\n",
      "711/711 [==============================] - 0s 176us/step - loss: 0.4966 - acc: 0.7862 - val_loss: 0.4040 - val_acc: 0.8371\n",
      "Epoch 62/100\n",
      "711/711 [==============================] - 0s 172us/step - loss: 0.4780 - acc: 0.8003 - val_loss: 0.4038 - val_acc: 0.8371\n",
      "Epoch 63/100\n",
      "711/711 [==============================] - 0s 209us/step - loss: 0.4699 - acc: 0.8017 - val_loss: 0.4044 - val_acc: 0.8371\n",
      "Epoch 64/100\n",
      "711/711 [==============================] - 0s 167us/step - loss: 0.4739 - acc: 0.8045 - val_loss: 0.4038 - val_acc: 0.8371\n",
      "Epoch 65/100\n",
      "711/711 [==============================] - 0s 192us/step - loss: 0.4772 - acc: 0.8045 - val_loss: 0.4035 - val_acc: 0.8315\n",
      "Epoch 66/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.4737 - acc: 0.8017 - val_loss: 0.4017 - val_acc: 0.8371\n",
      "Epoch 67/100\n",
      "711/711 [==============================] - 0s 184us/step - loss: 0.4775 - acc: 0.8045 - val_loss: 0.4029 - val_acc: 0.8371\n",
      "Epoch 68/100\n",
      "711/711 [==============================] - 0s 169us/step - loss: 0.4852 - acc: 0.7989 - val_loss: 0.4040 - val_acc: 0.8371\n",
      "Epoch 69/100\n",
      "711/711 [==============================] - 0s 172us/step - loss: 0.4872 - acc: 0.7862 - val_loss: 0.4028 - val_acc: 0.8371\n",
      "Epoch 70/100\n",
      "711/711 [==============================] - 0s 168us/step - loss: 0.4643 - acc: 0.7890 - val_loss: 0.4021 - val_acc: 0.8371\n",
      "Epoch 71/100\n",
      "711/711 [==============================] - 0s 198us/step - loss: 0.4660 - acc: 0.7904 - val_loss: 0.4011 - val_acc: 0.8371\n",
      "Epoch 72/100\n",
      "711/711 [==============================] - 0s 171us/step - loss: 0.4798 - acc: 0.8003 - val_loss: 0.4007 - val_acc: 0.8371\n",
      "Epoch 73/100\n",
      "711/711 [==============================] - 0s 175us/step - loss: 0.4825 - acc: 0.7918 - val_loss: 0.4022 - val_acc: 0.8371\n",
      "Epoch 74/100\n",
      "711/711 [==============================] - 0s 185us/step - loss: 0.4721 - acc: 0.8059 - val_loss: 0.4026 - val_acc: 0.8371\n",
      "Epoch 75/100\n",
      "711/711 [==============================] - 0s 173us/step - loss: 0.4690 - acc: 0.7932 - val_loss: 0.4018 - val_acc: 0.8371\n",
      "Epoch 76/100\n",
      "711/711 [==============================] - 0s 182us/step - loss: 0.4914 - acc: 0.8045 - val_loss: 0.4039 - val_acc: 0.8371\n",
      "Epoch 77/100\n",
      "711/711 [==============================] - 0s 172us/step - loss: 0.4895 - acc: 0.7947 - val_loss: 0.4036 - val_acc: 0.8371\n",
      "Epoch 78/100\n",
      "711/711 [==============================] - 0s 188us/step - loss: 0.4695 - acc: 0.8059 - val_loss: 0.4004 - val_acc: 0.8315\n",
      "Epoch 79/100\n",
      "711/711 [==============================] - 0s 202us/step - loss: 0.4805 - acc: 0.7876 - val_loss: 0.4000 - val_acc: 0.8371\n",
      "Epoch 80/100\n",
      "711/711 [==============================] - 0s 168us/step - loss: 0.4770 - acc: 0.8031 - val_loss: 0.4031 - val_acc: 0.8315\n",
      "Epoch 81/100\n",
      "711/711 [==============================] - 0s 167us/step - loss: 0.4723 - acc: 0.7862 - val_loss: 0.4017 - val_acc: 0.8315\n",
      "Epoch 82/100\n",
      "711/711 [==============================] - 0s 170us/step - loss: 0.4690 - acc: 0.7806 - val_loss: 0.3986 - val_acc: 0.8371\n",
      "Epoch 83/100\n",
      "711/711 [==============================] - 0s 185us/step - loss: 0.4875 - acc: 0.7890 - val_loss: 0.3985 - val_acc: 0.8371\n",
      "Epoch 84/100\n",
      "711/711 [==============================] - 0s 170us/step - loss: 0.4852 - acc: 0.7862 - val_loss: 0.3989 - val_acc: 0.8371\n",
      "Epoch 85/100\n",
      "711/711 [==============================] - 0s 194us/step - loss: 0.4787 - acc: 0.7792 - val_loss: 0.4002 - val_acc: 0.8371\n",
      "Epoch 86/100\n",
      "711/711 [==============================] - 0s 174us/step - loss: 0.4804 - acc: 0.7890 - val_loss: 0.3989 - val_acc: 0.8371\n",
      "Epoch 87/100\n",
      "711/711 [==============================] - 0s 202us/step - loss: 0.4656 - acc: 0.8031 - val_loss: 0.4001 - val_acc: 0.8371\n",
      "Epoch 88/100\n",
      "711/711 [==============================] - 0s 172us/step - loss: 0.4773 - acc: 0.7820 - val_loss: 0.3989 - val_acc: 0.8315\n",
      "Epoch 89/100\n",
      "711/711 [==============================] - 0s 187us/step - loss: 0.4937 - acc: 0.7989 - val_loss: 0.4015 - val_acc: 0.8371\n",
      "Epoch 90/100\n",
      "711/711 [==============================] - 0s 189us/step - loss: 0.4769 - acc: 0.8017 - val_loss: 0.4027 - val_acc: 0.8315\n",
      "Epoch 91/100\n",
      "711/711 [==============================] - 0s 166us/step - loss: 0.4676 - acc: 0.7947 - val_loss: 0.4005 - val_acc: 0.8371\n",
      "Epoch 92/100\n",
      "711/711 [==============================] - 0s 179us/step - loss: 0.4691 - acc: 0.7932 - val_loss: 0.3993 - val_acc: 0.8371\n",
      "Epoch 93/100\n",
      "711/711 [==============================] - 0s 169us/step - loss: 0.4861 - acc: 0.7904 - val_loss: 0.3987 - val_acc: 0.8315\n",
      "Epoch 94/100\n",
      "711/711 [==============================] - 0s 185us/step - loss: 0.4804 - acc: 0.8031 - val_loss: 0.4006 - val_acc: 0.8315\n",
      "Epoch 95/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.4845 - acc: 0.7961 - val_loss: 0.3993 - val_acc: 0.8315\n",
      "Epoch 96/100\n",
      "711/711 [==============================] - 0s 172us/step - loss: 0.4513 - acc: 0.8045 - val_loss: 0.3963 - val_acc: 0.8315\n",
      "Epoch 97/100\n",
      "711/711 [==============================] - 0s 173us/step - loss: 0.4795 - acc: 0.7989 - val_loss: 0.3951 - val_acc: 0.8371\n",
      "Epoch 98/100\n",
      "711/711 [==============================] - 0s 171us/step - loss: 0.4824 - acc: 0.7961 - val_loss: 0.3951 - val_acc: 0.8371\n",
      "Epoch 99/100\n",
      "711/711 [==============================] - 0s 173us/step - loss: 0.4918 - acc: 0.7932 - val_loss: 0.3974 - val_acc: 0.8371\n",
      "Epoch 100/100\n",
      "711/711 [==============================] - 0s 170us/step - loss: 0.4725 - acc: 0.8003 - val_loss: 0.3964 - val_acc: 0.8371\n"
     ]
    }
   ],
   "source": [
    "# Fitting the train set to the model\n",
    "history = model.fit(X, y, epochs=100, batch_size=32, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 707
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4524,
     "status": "ok",
     "timestamp": 1554215157151,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "yefWBrR7YwS7",
    "outputId": "34aca0c0-b7d5-4da7-c76d-412d0009f483"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFZCAYAAACizedRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFZCAYAAACizedRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nciYiIjKZ/wdo5G+lDK6cnwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting training and validation accuracy and loss against epochs.\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs = range(1, len(acc) + 1)\n",
    "plt.plot(epochs, acc, 'r', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, 'r', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improving the performance of the ANN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AO_JNKkLbMjh"
   },
   "outputs": [],
   "source": [
    "# Making modifications to the model. Adding a new layer\n",
    "model = Sequential()\n",
    "model.add(Dense(16, activation='relu', input_shape=(8,)))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(16, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(16, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 403
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3047,
     "status": "ok",
     "timestamp": 1554215539226,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "eENKoxH3bMgL",
    "outputId": "80b5963b-5c87-4832-a8af-01e33aeafbd4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_7 (Dense)              (None, 16)                144       \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 705\n",
      "Trainable params: 705\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0hlVAOuJcz_L"
   },
   "outputs": [],
   "source": [
    "# Compiling the model\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 3729
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 21016,
     "status": "ok",
     "timestamp": 1554215585989,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "CD50GRGocz_x",
    "outputId": "2b6b3ada-ca80-41c7-f3bc-cc6c8559530c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 711 samples, validate on 178 samples\n",
      "Epoch 1/100\n",
      "711/711 [==============================] - 1s 874us/step - loss: 0.7348 - acc: 0.4754 - val_loss: 0.6813 - val_acc: 0.6461\n",
      "Epoch 2/100\n",
      "711/711 [==============================] - 0s 201us/step - loss: 0.6948 - acc: 0.5331 - val_loss: 0.6628 - val_acc: 0.6124\n",
      "Epoch 3/100\n",
      "711/711 [==============================] - 0s 185us/step - loss: 0.6890 - acc: 0.5570 - val_loss: 0.6516 - val_acc: 0.6461\n",
      "Epoch 4/100\n",
      "711/711 [==============================] - 0s 185us/step - loss: 0.6854 - acc: 0.5612 - val_loss: 0.6435 - val_acc: 0.6461\n",
      "Epoch 5/100\n",
      "711/711 [==============================] - 0s 187us/step - loss: 0.6784 - acc: 0.5767 - val_loss: 0.6399 - val_acc: 0.6461\n",
      "Epoch 6/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.6712 - acc: 0.5907 - val_loss: 0.6366 - val_acc: 0.6461\n",
      "Epoch 7/100\n",
      "711/711 [==============================] - 0s 187us/step - loss: 0.6648 - acc: 0.5837 - val_loss: 0.6347 - val_acc: 0.6461\n",
      "Epoch 8/100\n",
      "711/711 [==============================] - 0s 191us/step - loss: 0.6633 - acc: 0.5823 - val_loss: 0.6300 - val_acc: 0.6461\n",
      "Epoch 9/100\n",
      "711/711 [==============================] - 0s 207us/step - loss: 0.6671 - acc: 0.6020 - val_loss: 0.6284 - val_acc: 0.6461\n",
      "Epoch 10/100\n",
      "711/711 [==============================] - 0s 224us/step - loss: 0.6571 - acc: 0.6160 - val_loss: 0.6246 - val_acc: 0.6461\n",
      "Epoch 11/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.6544 - acc: 0.6020 - val_loss: 0.6236 - val_acc: 0.6461\n",
      "Epoch 12/100\n",
      "711/711 [==============================] - 0s 206us/step - loss: 0.6496 - acc: 0.6090 - val_loss: 0.6208 - val_acc: 0.6461\n",
      "Epoch 13/100\n",
      "711/711 [==============================] - 0s 214us/step - loss: 0.6553 - acc: 0.6104 - val_loss: 0.6207 - val_acc: 0.6461\n",
      "Epoch 14/100\n",
      "711/711 [==============================] - 0s 219us/step - loss: 0.6548 - acc: 0.6034 - val_loss: 0.6195 - val_acc: 0.6461\n",
      "Epoch 15/100\n",
      "711/711 [==============================] - 0s 227us/step - loss: 0.6603 - acc: 0.6048 - val_loss: 0.6182 - val_acc: 0.6461\n",
      "Epoch 16/100\n",
      "711/711 [==============================] - 0s 224us/step - loss: 0.6368 - acc: 0.6132 - val_loss: 0.6146 - val_acc: 0.6461\n",
      "Epoch 17/100\n",
      "711/711 [==============================] - 0s 204us/step - loss: 0.6412 - acc: 0.6132 - val_loss: 0.6091 - val_acc: 0.6461\n",
      "Epoch 18/100\n",
      "711/711 [==============================] - 0s 237us/step - loss: 0.6664 - acc: 0.6132 - val_loss: 0.6117 - val_acc: 0.6461\n",
      "Epoch 19/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.6359 - acc: 0.6259 - val_loss: 0.6108 - val_acc: 0.6461\n",
      "Epoch 20/100\n",
      "711/711 [==============================] - 0s 214us/step - loss: 0.6502 - acc: 0.6188 - val_loss: 0.6132 - val_acc: 0.6461\n",
      "Epoch 21/100\n",
      "711/711 [==============================] - 0s 217us/step - loss: 0.6326 - acc: 0.6020 - val_loss: 0.6088 - val_acc: 0.6461\n",
      "Epoch 22/100\n",
      "711/711 [==============================] - 0s 218us/step - loss: 0.6416 - acc: 0.6118 - val_loss: 0.6049 - val_acc: 0.6461\n",
      "Epoch 23/100\n",
      "711/711 [==============================] - 0s 244us/step - loss: 0.6328 - acc: 0.6146 - val_loss: 0.6004 - val_acc: 0.6461\n",
      "Epoch 24/100\n",
      "711/711 [==============================] - 0s 218us/step - loss: 0.6432 - acc: 0.6062 - val_loss: 0.6006 - val_acc: 0.6461\n",
      "Epoch 25/100\n",
      "711/711 [==============================] - 0s 206us/step - loss: 0.6510 - acc: 0.6034 - val_loss: 0.6037 - val_acc: 0.6461\n",
      "Epoch 26/100\n",
      "711/711 [==============================] - 0s 205us/step - loss: 0.6241 - acc: 0.6104 - val_loss: 0.5977 - val_acc: 0.6461\n",
      "Epoch 27/100\n",
      "711/711 [==============================] - 0s 215us/step - loss: 0.6218 - acc: 0.6188 - val_loss: 0.5951 - val_acc: 0.6461\n",
      "Epoch 28/100\n",
      "711/711 [==============================] - 0s 214us/step - loss: 0.6214 - acc: 0.6259 - val_loss: 0.5878 - val_acc: 0.6461\n",
      "Epoch 29/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.6321 - acc: 0.6273 - val_loss: 0.5834 - val_acc: 0.6629\n",
      "Epoch 30/100\n",
      "711/711 [==============================] - 0s 236us/step - loss: 0.6234 - acc: 0.6357 - val_loss: 0.5778 - val_acc: 0.7191\n",
      "Epoch 31/100\n",
      "711/711 [==============================] - 0s 208us/step - loss: 0.6237 - acc: 0.6512 - val_loss: 0.5764 - val_acc: 0.7584\n",
      "Epoch 32/100\n",
      "711/711 [==============================] - 0s 204us/step - loss: 0.6106 - acc: 0.6639 - val_loss: 0.5678 - val_acc: 0.7640\n",
      "Epoch 33/100\n",
      "711/711 [==============================] - 0s 215us/step - loss: 0.6053 - acc: 0.6667 - val_loss: 0.5562 - val_acc: 0.7697\n",
      "Epoch 34/100\n",
      "711/711 [==============================] - 0s 236us/step - loss: 0.6113 - acc: 0.6906 - val_loss: 0.5501 - val_acc: 0.7978\n",
      "Epoch 35/100\n",
      "711/711 [==============================] - 0s 210us/step - loss: 0.5931 - acc: 0.7018 - val_loss: 0.5498 - val_acc: 0.7978\n",
      "Epoch 36/100\n",
      "711/711 [==============================] - 0s 222us/step - loss: 0.5996 - acc: 0.6962 - val_loss: 0.5431 - val_acc: 0.7978\n",
      "Epoch 37/100\n",
      "711/711 [==============================] - 0s 215us/step - loss: 0.5949 - acc: 0.6976 - val_loss: 0.5425 - val_acc: 0.7978\n",
      "Epoch 38/100\n",
      "711/711 [==============================] - 0s 209us/step - loss: 0.5700 - acc: 0.7173 - val_loss: 0.5309 - val_acc: 0.8090\n",
      "Epoch 39/100\n",
      "711/711 [==============================] - 0s 229us/step - loss: 0.6073 - acc: 0.7004 - val_loss: 0.5327 - val_acc: 0.7978\n",
      "Epoch 40/100\n",
      "711/711 [==============================] - 0s 209us/step - loss: 0.6147 - acc: 0.6920 - val_loss: 0.5422 - val_acc: 0.7978\n",
      "Epoch 41/100\n",
      "711/711 [==============================] - 0s 214us/step - loss: 0.5902 - acc: 0.6962 - val_loss: 0.5358 - val_acc: 0.8090\n",
      "Epoch 42/100\n",
      "711/711 [==============================] - 0s 217us/step - loss: 0.6016 - acc: 0.7173 - val_loss: 0.5324 - val_acc: 0.7978\n",
      "Epoch 43/100\n",
      "711/711 [==============================] - 0s 236us/step - loss: 0.5916 - acc: 0.7060 - val_loss: 0.5383 - val_acc: 0.7978\n",
      "Epoch 44/100\n",
      "711/711 [==============================] - 0s 215us/step - loss: 0.5753 - acc: 0.7201 - val_loss: 0.5308 - val_acc: 0.8090\n",
      "Epoch 45/100\n",
      "711/711 [==============================] - 0s 211us/step - loss: 0.5566 - acc: 0.7145 - val_loss: 0.5154 - val_acc: 0.8090\n",
      "Epoch 46/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.5662 - acc: 0.7440 - val_loss: 0.5040 - val_acc: 0.8090\n",
      "Epoch 47/100\n",
      "711/711 [==============================] - 0s 217us/step - loss: 0.5681 - acc: 0.7187 - val_loss: 0.5045 - val_acc: 0.8090\n",
      "Epoch 48/100\n",
      "711/711 [==============================] - 0s 211us/step - loss: 0.5757 - acc: 0.7342 - val_loss: 0.5000 - val_acc: 0.8090\n",
      "Epoch 49/100\n",
      "711/711 [==============================] - 0s 208us/step - loss: 0.5501 - acc: 0.7356 - val_loss: 0.5000 - val_acc: 0.8090\n",
      "Epoch 50/100\n",
      "711/711 [==============================] - 0s 212us/step - loss: 0.5693 - acc: 0.7412 - val_loss: 0.5060 - val_acc: 0.8090\n",
      "Epoch 51/100\n",
      "711/711 [==============================] - 0s 212us/step - loss: 0.5712 - acc: 0.7412 - val_loss: 0.5079 - val_acc: 0.8090\n",
      "Epoch 52/100\n",
      "711/711 [==============================] - 0s 221us/step - loss: 0.5372 - acc: 0.7623 - val_loss: 0.4980 - val_acc: 0.8090\n",
      "Epoch 53/100\n",
      "711/711 [==============================] - 0s 222us/step - loss: 0.5584 - acc: 0.7257 - val_loss: 0.4927 - val_acc: 0.8090\n",
      "Epoch 54/100\n",
      "711/711 [==============================] - 0s 209us/step - loss: 0.5419 - acc: 0.7581 - val_loss: 0.4872 - val_acc: 0.8090\n",
      "Epoch 55/100\n",
      "711/711 [==============================] - 0s 222us/step - loss: 0.5460 - acc: 0.7623 - val_loss: 0.4939 - val_acc: 0.8090\n",
      "Epoch 56/100\n",
      "711/711 [==============================] - 0s 234us/step - loss: 0.5418 - acc: 0.7595 - val_loss: 0.4870 - val_acc: 0.8090\n",
      "Epoch 57/100\n",
      "711/711 [==============================] - 0s 201us/step - loss: 0.5426 - acc: 0.7412 - val_loss: 0.4820 - val_acc: 0.8034\n",
      "Epoch 58/100\n",
      "711/711 [==============================] - 0s 210us/step - loss: 0.5689 - acc: 0.7328 - val_loss: 0.4907 - val_acc: 0.8146\n",
      "Epoch 59/100\n",
      "711/711 [==============================] - 0s 219us/step - loss: 0.5120 - acc: 0.7567 - val_loss: 0.4826 - val_acc: 0.8146\n",
      "Epoch 60/100\n",
      "711/711 [==============================] - 0s 221us/step - loss: 0.5390 - acc: 0.7567 - val_loss: 0.4795 - val_acc: 0.8258\n",
      "Epoch 61/100\n",
      "711/711 [==============================] - 0s 226us/step - loss: 0.5279 - acc: 0.7525 - val_loss: 0.4725 - val_acc: 0.8315\n",
      "Epoch 62/100\n",
      "711/711 [==============================] - 0s 220us/step - loss: 0.5564 - acc: 0.7440 - val_loss: 0.4713 - val_acc: 0.8202\n",
      "Epoch 63/100\n",
      "711/711 [==============================] - 0s 230us/step - loss: 0.5412 - acc: 0.7496 - val_loss: 0.4655 - val_acc: 0.8258\n",
      "Epoch 64/100\n",
      "711/711 [==============================] - 0s 211us/step - loss: 0.5402 - acc: 0.7468 - val_loss: 0.4641 - val_acc: 0.8258\n",
      "Epoch 65/100\n",
      "711/711 [==============================] - 0s 213us/step - loss: 0.5363 - acc: 0.7707 - val_loss: 0.4617 - val_acc: 0.8258\n",
      "Epoch 66/100\n",
      "711/711 [==============================] - 0s 232us/step - loss: 0.5554 - acc: 0.7482 - val_loss: 0.4679 - val_acc: 0.8202\n",
      "Epoch 67/100\n",
      "711/711 [==============================] - 0s 220us/step - loss: 0.5507 - acc: 0.7595 - val_loss: 0.4736 - val_acc: 0.8202\n",
      "Epoch 68/100\n",
      "711/711 [==============================] - 0s 210us/step - loss: 0.5320 - acc: 0.7553 - val_loss: 0.4727 - val_acc: 0.8258\n",
      "Epoch 69/100\n",
      "711/711 [==============================] - 0s 246us/step - loss: 0.5454 - acc: 0.7651 - val_loss: 0.4718 - val_acc: 0.8202\n",
      "Epoch 70/100\n",
      "711/711 [==============================] - 0s 225us/step - loss: 0.5149 - acc: 0.7750 - val_loss: 0.4710 - val_acc: 0.8258\n",
      "Epoch 71/100\n",
      "711/711 [==============================] - 0s 208us/step - loss: 0.5379 - acc: 0.7651 - val_loss: 0.4666 - val_acc: 0.8202\n",
      "Epoch 72/100\n",
      "711/711 [==============================] - 0s 209us/step - loss: 0.5304 - acc: 0.7750 - val_loss: 0.4624 - val_acc: 0.8258\n",
      "Epoch 73/100\n",
      "711/711 [==============================] - 0s 213us/step - loss: 0.5283 - acc: 0.7736 - val_loss: 0.4613 - val_acc: 0.8202\n",
      "Epoch 74/100\n",
      "711/711 [==============================] - 0s 213us/step - loss: 0.5252 - acc: 0.7567 - val_loss: 0.4583 - val_acc: 0.8202\n",
      "Epoch 75/100\n",
      "711/711 [==============================] - 0s 209us/step - loss: 0.5114 - acc: 0.7764 - val_loss: 0.4565 - val_acc: 0.8146\n",
      "Epoch 76/100\n",
      "711/711 [==============================] - 0s 224us/step - loss: 0.5242 - acc: 0.7722 - val_loss: 0.4497 - val_acc: 0.8146\n",
      "Epoch 77/100\n",
      "711/711 [==============================] - 0s 209us/step - loss: 0.5254 - acc: 0.7707 - val_loss: 0.4503 - val_acc: 0.8090\n",
      "Epoch 78/100\n",
      "711/711 [==============================] - 0s 222us/step - loss: 0.5304 - acc: 0.7609 - val_loss: 0.4508 - val_acc: 0.8202\n",
      "Epoch 79/100\n",
      "711/711 [==============================] - 0s 215us/step - loss: 0.5363 - acc: 0.7482 - val_loss: 0.4514 - val_acc: 0.8202\n",
      "Epoch 80/100\n",
      "711/711 [==============================] - 0s 215us/step - loss: 0.5235 - acc: 0.7736 - val_loss: 0.4529 - val_acc: 0.8090\n",
      "Epoch 81/100\n",
      "711/711 [==============================] - 0s 215us/step - loss: 0.5265 - acc: 0.7806 - val_loss: 0.4532 - val_acc: 0.8146\n",
      "Epoch 82/100\n",
      "711/711 [==============================] - 0s 217us/step - loss: 0.5229 - acc: 0.7750 - val_loss: 0.4524 - val_acc: 0.8258\n",
      "Epoch 83/100\n",
      "711/711 [==============================] - 0s 238us/step - loss: 0.5070 - acc: 0.7651 - val_loss: 0.4507 - val_acc: 0.8202\n",
      "Epoch 84/100\n",
      "711/711 [==============================] - 0s 205us/step - loss: 0.5172 - acc: 0.7651 - val_loss: 0.4487 - val_acc: 0.8258\n",
      "Epoch 85/100\n",
      "711/711 [==============================] - 0s 214us/step - loss: 0.5245 - acc: 0.7722 - val_loss: 0.4478 - val_acc: 0.8258\n",
      "Epoch 86/100\n",
      "711/711 [==============================] - 0s 211us/step - loss: 0.5151 - acc: 0.7736 - val_loss: 0.4475 - val_acc: 0.8258\n",
      "Epoch 87/100\n",
      "711/711 [==============================] - 0s 222us/step - loss: 0.5044 - acc: 0.7834 - val_loss: 0.4441 - val_acc: 0.8202\n",
      "Epoch 88/100\n",
      "711/711 [==============================] - 0s 229us/step - loss: 0.5137 - acc: 0.7693 - val_loss: 0.4440 - val_acc: 0.8258\n",
      "Epoch 89/100\n",
      "711/711 [==============================] - 0s 233us/step - loss: 0.5120 - acc: 0.7792 - val_loss: 0.4449 - val_acc: 0.8146\n",
      "Epoch 90/100\n",
      "711/711 [==============================] - 0s 208us/step - loss: 0.5213 - acc: 0.7792 - val_loss: 0.4464 - val_acc: 0.8090\n",
      "Epoch 91/100\n",
      "711/711 [==============================] - 0s 207us/step - loss: 0.5198 - acc: 0.7651 - val_loss: 0.4456 - val_acc: 0.8090\n",
      "Epoch 92/100\n",
      "711/711 [==============================] - 0s 201us/step - loss: 0.5277 - acc: 0.7736 - val_loss: 0.4443 - val_acc: 0.8090\n",
      "Epoch 93/100\n",
      "711/711 [==============================] - 0s 209us/step - loss: 0.5213 - acc: 0.7778 - val_loss: 0.4433 - val_acc: 0.8146\n",
      "Epoch 94/100\n",
      "711/711 [==============================] - 0s 208us/step - loss: 0.4980 - acc: 0.7862 - val_loss: 0.4397 - val_acc: 0.8090\n",
      "Epoch 95/100\n",
      "711/711 [==============================] - 0s 204us/step - loss: 0.5112 - acc: 0.7637 - val_loss: 0.4422 - val_acc: 0.8090\n",
      "Epoch 96/100\n",
      "711/711 [==============================] - 0s 242us/step - loss: 0.5053 - acc: 0.7567 - val_loss: 0.4414 - val_acc: 0.8090\n",
      "Epoch 97/100\n",
      "711/711 [==============================] - 0s 218us/step - loss: 0.5083 - acc: 0.7778 - val_loss: 0.4392 - val_acc: 0.8146\n",
      "Epoch 98/100\n",
      "711/711 [==============================] - 0s 201us/step - loss: 0.5178 - acc: 0.7651 - val_loss: 0.4385 - val_acc: 0.8146\n",
      "Epoch 99/100\n",
      "711/711 [==============================] - 0s 201us/step - loss: 0.5010 - acc: 0.7750 - val_loss: 0.4403 - val_acc: 0.8146\n",
      "Epoch 100/100\n",
      "711/711 [==============================] - 0s 206us/step - loss: 0.5025 - acc: 0.7918 - val_loss: 0.4410 - val_acc: 0.8146\n"
     ]
    }
   ],
   "source": [
    "# Fitting the train set to the model\n",
    "history = model.fit(X, y, epochs=100, batch_size=32, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 707
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2936,
     "status": "ok",
     "timestamp": 1554215615333,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "GhX8ptPPbMaN",
    "outputId": "759252c2-559b-42bc-d8b8-3cd5d25bac98"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFZCAYAAACizedRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFZCAYAAACizedRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQgghAsz/A2tzay3RIvKfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting training and validation accuracy and loss against epochs.\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs = range(1, len(acc) + 1)\n",
    "plt.plot(epochs, acc, 'r', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, 'r', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YDGaXUj5ebe-"
   },
   "outputs": [],
   "source": [
    "# Again making modifications to the model. Changing the number of hidden units and removing the \n",
    "# additional layer.\n",
    "model = Sequential()\n",
    "model.add(Dense(32, activation='relu', input_shape=(8,)))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 329
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4066,
     "status": "ok",
     "timestamp": 1554215854983,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "6VN7G-Nxebfk",
    "outputId": "306c50c5-e1e3-45a4-80e8-d39ad63cf0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_11 (Dense)             (None, 32)                288       \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 1,377\n",
      "Trainable params: 1,377\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rNxgB_2aebgC"
   },
   "outputs": [],
   "source": [
    "# Compiling the model\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 3729
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 18241,
     "status": "ok",
     "timestamp": 1554215881077,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "bMqroISNebgU",
    "outputId": "950c89dd-fa43-44a1-89a1-d7355401257a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 711 samples, validate on 178 samples\n",
      "Epoch 1/100\n",
      "711/711 [==============================] - 1s 896us/step - loss: 0.6654 - acc: 0.5781 - val_loss: 0.6043 - val_acc: 0.6461\n",
      "Epoch 2/100\n",
      "711/711 [==============================] - 0s 197us/step - loss: 0.6462 - acc: 0.5907 - val_loss: 0.5812 - val_acc: 0.6461\n",
      "Epoch 3/100\n",
      "711/711 [==============================] - 0s 188us/step - loss: 0.6189 - acc: 0.6371 - val_loss: 0.5701 - val_acc: 0.6461\n",
      "Epoch 4/100\n",
      "711/711 [==============================] - 0s 170us/step - loss: 0.6157 - acc: 0.6540 - val_loss: 0.5558 - val_acc: 0.6742\n",
      "Epoch 5/100\n",
      "711/711 [==============================] - 0s 224us/step - loss: 0.6068 - acc: 0.6582 - val_loss: 0.5464 - val_acc: 0.7584\n",
      "Epoch 6/100\n",
      "711/711 [==============================] - 0s 188us/step - loss: 0.5901 - acc: 0.7004 - val_loss: 0.5359 - val_acc: 0.7584\n",
      "Epoch 7/100\n",
      "711/711 [==============================] - 0s 184us/step - loss: 0.5854 - acc: 0.7131 - val_loss: 0.5222 - val_acc: 0.8090\n",
      "Epoch 8/100\n",
      "711/711 [==============================] - 0s 173us/step - loss: 0.5993 - acc: 0.7159 - val_loss: 0.5146 - val_acc: 0.8146\n",
      "Epoch 9/100\n",
      "711/711 [==============================] - 0s 199us/step - loss: 0.5850 - acc: 0.7271 - val_loss: 0.5048 - val_acc: 0.7978\n",
      "Epoch 10/100\n",
      "711/711 [==============================] - 0s 197us/step - loss: 0.5539 - acc: 0.7384 - val_loss: 0.4964 - val_acc: 0.7753\n",
      "Epoch 11/100\n",
      "711/711 [==============================] - 0s 189us/step - loss: 0.5683 - acc: 0.7300 - val_loss: 0.4898 - val_acc: 0.8034\n",
      "Epoch 12/100\n",
      "711/711 [==============================] - 0s 202us/step - loss: 0.5598 - acc: 0.7539 - val_loss: 0.4813 - val_acc: 0.8034\n",
      "Epoch 13/100\n",
      "711/711 [==============================] - 0s 200us/step - loss: 0.5481 - acc: 0.7609 - val_loss: 0.4751 - val_acc: 0.7978\n",
      "Epoch 14/100\n",
      "711/711 [==============================] - 0s 200us/step - loss: 0.5291 - acc: 0.7609 - val_loss: 0.4731 - val_acc: 0.7921\n",
      "Epoch 15/100\n",
      "711/711 [==============================] - 0s 197us/step - loss: 0.5327 - acc: 0.7764 - val_loss: 0.4672 - val_acc: 0.7921\n",
      "Epoch 16/100\n",
      "711/711 [==============================] - 0s 191us/step - loss: 0.5556 - acc: 0.7525 - val_loss: 0.4685 - val_acc: 0.7921\n",
      "Epoch 17/100\n",
      "711/711 [==============================] - 0s 187us/step - loss: 0.5252 - acc: 0.7609 - val_loss: 0.4654 - val_acc: 0.7978\n",
      "Epoch 18/100\n",
      "711/711 [==============================] - 0s 192us/step - loss: 0.5293 - acc: 0.7679 - val_loss: 0.4610 - val_acc: 0.7921\n",
      "Epoch 19/100\n",
      "711/711 [==============================] - 0s 193us/step - loss: 0.5211 - acc: 0.7553 - val_loss: 0.4570 - val_acc: 0.7978\n",
      "Epoch 20/100\n",
      "711/711 [==============================] - 0s 219us/step - loss: 0.5273 - acc: 0.7595 - val_loss: 0.4547 - val_acc: 0.7978\n",
      "Epoch 21/100\n",
      "711/711 [==============================] - 0s 200us/step - loss: 0.5240 - acc: 0.7806 - val_loss: 0.4514 - val_acc: 0.8034\n",
      "Epoch 22/100\n",
      "711/711 [==============================] - 0s 189us/step - loss: 0.5040 - acc: 0.7651 - val_loss: 0.4501 - val_acc: 0.7978\n",
      "Epoch 23/100\n",
      "711/711 [==============================] - 0s 194us/step - loss: 0.5248 - acc: 0.7750 - val_loss: 0.4481 - val_acc: 0.7978\n",
      "Epoch 24/100\n",
      "711/711 [==============================] - 0s 193us/step - loss: 0.5116 - acc: 0.7876 - val_loss: 0.4452 - val_acc: 0.8034\n",
      "Epoch 25/100\n",
      "711/711 [==============================] - 0s 191us/step - loss: 0.5188 - acc: 0.7862 - val_loss: 0.4445 - val_acc: 0.8146\n",
      "Epoch 26/100\n",
      "711/711 [==============================] - 0s 189us/step - loss: 0.5084 - acc: 0.7820 - val_loss: 0.4406 - val_acc: 0.8371\n",
      "Epoch 27/100\n",
      "711/711 [==============================] - 0s 209us/step - loss: 0.4898 - acc: 0.7792 - val_loss: 0.4388 - val_acc: 0.8258\n",
      "Epoch 28/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.5004 - acc: 0.7876 - val_loss: 0.4373 - val_acc: 0.8202\n",
      "Epoch 29/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.4942 - acc: 0.7947 - val_loss: 0.4359 - val_acc: 0.8202\n",
      "Epoch 30/100\n",
      "711/711 [==============================] - 0s 193us/step - loss: 0.5012 - acc: 0.7890 - val_loss: 0.4344 - val_acc: 0.8258\n",
      "Epoch 31/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.4982 - acc: 0.7961 - val_loss: 0.4341 - val_acc: 0.8258\n",
      "Epoch 32/100\n",
      "711/711 [==============================] - 0s 189us/step - loss: 0.4844 - acc: 0.7947 - val_loss: 0.4318 - val_acc: 0.8258\n",
      "Epoch 33/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.5172 - acc: 0.7890 - val_loss: 0.4338 - val_acc: 0.8202\n",
      "Epoch 34/100\n",
      "711/711 [==============================] - 0s 185us/step - loss: 0.4989 - acc: 0.7693 - val_loss: 0.4352 - val_acc: 0.8258\n",
      "Epoch 35/100\n",
      "711/711 [==============================] - 0s 209us/step - loss: 0.4937 - acc: 0.7975 - val_loss: 0.4305 - val_acc: 0.8202\n",
      "Epoch 36/100\n",
      "711/711 [==============================] - 0s 196us/step - loss: 0.4904 - acc: 0.7862 - val_loss: 0.4268 - val_acc: 0.8371\n",
      "Epoch 37/100\n",
      "711/711 [==============================] - 0s 186us/step - loss: 0.4972 - acc: 0.7975 - val_loss: 0.4278 - val_acc: 0.8258\n",
      "Epoch 38/100\n",
      "711/711 [==============================] - 0s 191us/step - loss: 0.4938 - acc: 0.7890 - val_loss: 0.4256 - val_acc: 0.8258\n",
      "Epoch 39/100\n",
      "711/711 [==============================] - 0s 185us/step - loss: 0.4826 - acc: 0.8003 - val_loss: 0.4240 - val_acc: 0.8258\n",
      "Epoch 40/100\n",
      "711/711 [==============================] - 0s 188us/step - loss: 0.4817 - acc: 0.7989 - val_loss: 0.4239 - val_acc: 0.8483\n",
      "Epoch 41/100\n",
      "711/711 [==============================] - 0s 197us/step - loss: 0.4856 - acc: 0.8059 - val_loss: 0.4226 - val_acc: 0.8371\n",
      "Epoch 42/100\n",
      "711/711 [==============================] - 0s 210us/step - loss: 0.4830 - acc: 0.7848 - val_loss: 0.4209 - val_acc: 0.8315\n",
      "Epoch 43/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.4911 - acc: 0.8045 - val_loss: 0.4238 - val_acc: 0.8315\n",
      "Epoch 44/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.4938 - acc: 0.7806 - val_loss: 0.4246 - val_acc: 0.8258\n",
      "Epoch 45/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.4764 - acc: 0.7862 - val_loss: 0.4197 - val_acc: 0.8315\n",
      "Epoch 46/100\n",
      "711/711 [==============================] - 0s 194us/step - loss: 0.4697 - acc: 0.7947 - val_loss: 0.4178 - val_acc: 0.8427\n",
      "Epoch 47/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.4964 - acc: 0.7904 - val_loss: 0.4172 - val_acc: 0.8371\n",
      "Epoch 48/100\n",
      "711/711 [==============================] - 0s 185us/step - loss: 0.4892 - acc: 0.7961 - val_loss: 0.4161 - val_acc: 0.8315\n",
      "Epoch 49/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.4654 - acc: 0.8031 - val_loss: 0.4150 - val_acc: 0.8371\n",
      "Epoch 50/100\n",
      "711/711 [==============================] - 0s 212us/step - loss: 0.4785 - acc: 0.8045 - val_loss: 0.4190 - val_acc: 0.8371\n",
      "Epoch 51/100\n",
      "711/711 [==============================] - 0s 191us/step - loss: 0.4778 - acc: 0.7947 - val_loss: 0.4174 - val_acc: 0.8315\n",
      "Epoch 52/100\n",
      "711/711 [==============================] - 0s 193us/step - loss: 0.4834 - acc: 0.7947 - val_loss: 0.4156 - val_acc: 0.8315\n",
      "Epoch 53/100\n",
      "711/711 [==============================] - 0s 204us/step - loss: 0.4850 - acc: 0.7961 - val_loss: 0.4139 - val_acc: 0.8315\n",
      "Epoch 54/100\n",
      "711/711 [==============================] - 0s 191us/step - loss: 0.4935 - acc: 0.7904 - val_loss: 0.4144 - val_acc: 0.8315\n",
      "Epoch 55/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.4768 - acc: 0.7904 - val_loss: 0.4164 - val_acc: 0.8315\n",
      "Epoch 56/100\n",
      "711/711 [==============================] - 0s 194us/step - loss: 0.4880 - acc: 0.7876 - val_loss: 0.4145 - val_acc: 0.8315\n",
      "Epoch 57/100\n",
      "711/711 [==============================] - 0s 209us/step - loss: 0.4755 - acc: 0.8045 - val_loss: 0.4141 - val_acc: 0.8315\n",
      "Epoch 58/100\n",
      "711/711 [==============================] - 0s 189us/step - loss: 0.4737 - acc: 0.8017 - val_loss: 0.4119 - val_acc: 0.8315\n",
      "Epoch 59/100\n",
      "711/711 [==============================] - 0s 197us/step - loss: 0.4727 - acc: 0.7989 - val_loss: 0.4108 - val_acc: 0.8371\n",
      "Epoch 60/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.4665 - acc: 0.7932 - val_loss: 0.4127 - val_acc: 0.8371\n",
      "Epoch 61/100\n",
      "711/711 [==============================] - 0s 201us/step - loss: 0.4735 - acc: 0.8003 - val_loss: 0.4098 - val_acc: 0.8315\n",
      "Epoch 62/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.4837 - acc: 0.7890 - val_loss: 0.4102 - val_acc: 0.8371\n",
      "Epoch 63/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.4676 - acc: 0.7961 - val_loss: 0.4092 - val_acc: 0.8371\n",
      "Epoch 64/100\n",
      "711/711 [==============================] - 0s 215us/step - loss: 0.4628 - acc: 0.7975 - val_loss: 0.4104 - val_acc: 0.8315\n",
      "Epoch 65/100\n",
      "711/711 [==============================] - 0s 186us/step - loss: 0.4809 - acc: 0.7947 - val_loss: 0.4091 - val_acc: 0.8371\n",
      "Epoch 66/100\n",
      "711/711 [==============================] - 0s 193us/step - loss: 0.4819 - acc: 0.8017 - val_loss: 0.4124 - val_acc: 0.8371\n",
      "Epoch 67/100\n",
      "711/711 [==============================] - 0s 193us/step - loss: 0.4780 - acc: 0.7904 - val_loss: 0.4090 - val_acc: 0.8315\n",
      "Epoch 68/100\n",
      "711/711 [==============================] - 0s 193us/step - loss: 0.4667 - acc: 0.8045 - val_loss: 0.4094 - val_acc: 0.8315\n",
      "Epoch 69/100\n",
      "711/711 [==============================] - 0s 193us/step - loss: 0.4709 - acc: 0.7890 - val_loss: 0.4109 - val_acc: 0.8427\n",
      "Epoch 70/100\n",
      "711/711 [==============================] - 0s 194us/step - loss: 0.4752 - acc: 0.8031 - val_loss: 0.4102 - val_acc: 0.8315\n",
      "Epoch 71/100\n",
      "711/711 [==============================] - 0s 191us/step - loss: 0.4724 - acc: 0.8045 - val_loss: 0.4089 - val_acc: 0.8315\n",
      "Epoch 72/100\n",
      "711/711 [==============================] - 0s 212us/step - loss: 0.4754 - acc: 0.7904 - val_loss: 0.4094 - val_acc: 0.8315\n",
      "Epoch 73/100\n",
      "711/711 [==============================] - 0s 194us/step - loss: 0.4766 - acc: 0.7904 - val_loss: 0.4091 - val_acc: 0.8315\n",
      "Epoch 74/100\n",
      "711/711 [==============================] - 0s 201us/step - loss: 0.4527 - acc: 0.8045 - val_loss: 0.4058 - val_acc: 0.8315\n",
      "Epoch 75/100\n",
      "711/711 [==============================] - 0s 185us/step - loss: 0.4767 - acc: 0.8129 - val_loss: 0.4056 - val_acc: 0.8427\n",
      "Epoch 76/100\n",
      "711/711 [==============================] - 0s 189us/step - loss: 0.4691 - acc: 0.7975 - val_loss: 0.4062 - val_acc: 0.8427\n",
      "Epoch 77/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.4812 - acc: 0.7975 - val_loss: 0.4049 - val_acc: 0.8315\n",
      "Epoch 78/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.4812 - acc: 0.7918 - val_loss: 0.4052 - val_acc: 0.8315\n",
      "Epoch 79/100\n",
      "711/711 [==============================] - 0s 209us/step - loss: 0.4731 - acc: 0.8045 - val_loss: 0.4061 - val_acc: 0.8315\n",
      "Epoch 80/100\n",
      "711/711 [==============================] - 0s 191us/step - loss: 0.4592 - acc: 0.8017 - val_loss: 0.4061 - val_acc: 0.8315\n",
      "Epoch 81/100\n",
      "711/711 [==============================] - 0s 199us/step - loss: 0.4609 - acc: 0.8017 - val_loss: 0.4042 - val_acc: 0.8315\n",
      "Epoch 82/100\n",
      "711/711 [==============================] - 0s 199us/step - loss: 0.4757 - acc: 0.8017 - val_loss: 0.4049 - val_acc: 0.8315\n",
      "Epoch 83/100\n",
      "711/711 [==============================] - 0s 186us/step - loss: 0.4679 - acc: 0.8073 - val_loss: 0.4035 - val_acc: 0.8315\n",
      "Epoch 84/100\n",
      "711/711 [==============================] - 0s 191us/step - loss: 0.4708 - acc: 0.7989 - val_loss: 0.4054 - val_acc: 0.8315\n",
      "Epoch 85/100\n",
      "711/711 [==============================] - 0s 191us/step - loss: 0.4656 - acc: 0.8003 - val_loss: 0.4046 - val_acc: 0.8315\n",
      "Epoch 86/100\n",
      "711/711 [==============================] - 0s 217us/step - loss: 0.4759 - acc: 0.8003 - val_loss: 0.4039 - val_acc: 0.8315\n",
      "Epoch 87/100\n",
      "711/711 [==============================] - 0s 192us/step - loss: 0.4582 - acc: 0.7961 - val_loss: 0.4037 - val_acc: 0.8427\n",
      "Epoch 88/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.4579 - acc: 0.8087 - val_loss: 0.4011 - val_acc: 0.8315\n",
      "Epoch 89/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.4620 - acc: 0.8017 - val_loss: 0.4027 - val_acc: 0.8427\n",
      "Epoch 90/100\n",
      "711/711 [==============================] - 0s 188us/step - loss: 0.4577 - acc: 0.8003 - val_loss: 0.4009 - val_acc: 0.8315\n",
      "Epoch 91/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.4716 - acc: 0.8045 - val_loss: 0.4003 - val_acc: 0.8371\n",
      "Epoch 92/100\n",
      "711/711 [==============================] - 0s 186us/step - loss: 0.4644 - acc: 0.8101 - val_loss: 0.4025 - val_acc: 0.8371\n",
      "Epoch 93/100\n",
      "711/711 [==============================] - 0s 189us/step - loss: 0.4656 - acc: 0.8003 - val_loss: 0.4017 - val_acc: 0.8427\n",
      "Epoch 94/100\n",
      "711/711 [==============================] - 0s 204us/step - loss: 0.4534 - acc: 0.8143 - val_loss: 0.4014 - val_acc: 0.8427\n",
      "Epoch 95/100\n",
      "711/711 [==============================] - 0s 188us/step - loss: 0.4674 - acc: 0.7975 - val_loss: 0.4005 - val_acc: 0.8315\n",
      "Epoch 96/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.4687 - acc: 0.7989 - val_loss: 0.4009 - val_acc: 0.8315\n",
      "Epoch 97/100\n",
      "711/711 [==============================] - 0s 191us/step - loss: 0.4651 - acc: 0.7989 - val_loss: 0.4032 - val_acc: 0.8315\n",
      "Epoch 98/100\n",
      "711/711 [==============================] - 0s 205us/step - loss: 0.4745 - acc: 0.8045 - val_loss: 0.4019 - val_acc: 0.8315\n",
      "Epoch 99/100\n",
      "711/711 [==============================] - 0s 196us/step - loss: 0.4653 - acc: 0.8017 - val_loss: 0.4038 - val_acc: 0.8371\n",
      "Epoch 100/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.4590 - acc: 0.8031 - val_loss: 0.4020 - val_acc: 0.8427\n"
     ]
    }
   ],
   "source": [
    "# Fitting the train set to the model\n",
    "history = model.fit(X, y, epochs=100, batch_size=32, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 707
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6833,
     "status": "ok",
     "timestamp": 1554215909274,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "BTb9HWqzebgr",
    "outputId": "059abb8d-7fe1-4001-db0e-d03890e99bb5"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFZCAYAAACizedRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ni4iIRBiFs4iISIRROIuIiESY/w/XmzQB+oBgVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFZCAYAAACizedRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/h8wVXfOkOdCLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting training and validation accuracy and loss against epochs.\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs = range(1, len(acc) + 1)\n",
    "plt.plot(epochs, acc, 'r', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, 'r', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dR5NxvklfRal"
   },
   "outputs": [],
   "source": [
    "# Adding an additional layer to the previous model.\n",
    "model = Sequential()\n",
    "model.add(Dense(32, activation='relu', input_shape=(8,)))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 403
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4132,
     "status": "ok",
     "timestamp": 1554215993838,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "2J8AWmBwfRbE",
    "outputId": "98ee823a-cfc2-435e-d96f-692e5394507a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_14 (Dense)             (None, 32)                288       \n",
      "_________________________________________________________________\n",
      "dropout_10 (Dropout)         (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_15 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dropout_11 (Dropout)         (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_16 (Dense)             (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dropout_12 (Dropout)         (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 2,433\n",
      "Trainable params: 2,433\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "USCID36cfRbh"
   },
   "outputs": [],
   "source": [
    "# Compiling the model\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 3729
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 19226,
     "status": "ok",
     "timestamp": 1554216020628,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "1GpKkE_RfRbv",
    "outputId": "899a44b6-9956-4910-e73e-ca440d84903a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 711 samples, validate on 178 samples\n",
      "Epoch 1/100\n",
      "711/711 [==============================] - 1s 1ms/step - loss: 0.6857 - acc: 0.5387 - val_loss: 0.6607 - val_acc: 0.6517\n",
      "Epoch 2/100\n",
      "711/711 [==============================] - 0s 206us/step - loss: 0.6617 - acc: 0.5949 - val_loss: 0.6348 - val_acc: 0.6461\n",
      "Epoch 3/100\n",
      "711/711 [==============================] - 0s 187us/step - loss: 0.6636 - acc: 0.5977 - val_loss: 0.6207 - val_acc: 0.6461\n",
      "Epoch 4/100\n",
      "711/711 [==============================] - 0s 194us/step - loss: 0.6523 - acc: 0.6090 - val_loss: 0.6121 - val_acc: 0.6461\n",
      "Epoch 5/100\n",
      "711/711 [==============================] - 0s 182us/step - loss: 0.6307 - acc: 0.6498 - val_loss: 0.5929 - val_acc: 0.6573\n",
      "Epoch 6/100\n",
      "711/711 [==============================] - 0s 184us/step - loss: 0.6294 - acc: 0.6414 - val_loss: 0.5711 - val_acc: 0.7472\n",
      "Epoch 7/100\n",
      "711/711 [==============================] - 0s 192us/step - loss: 0.6199 - acc: 0.6414 - val_loss: 0.5571 - val_acc: 0.7978\n",
      "Epoch 8/100\n",
      "711/711 [==============================] - 0s 181us/step - loss: 0.5953 - acc: 0.6920 - val_loss: 0.5455 - val_acc: 0.7978\n",
      "Epoch 9/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.5928 - acc: 0.6906 - val_loss: 0.5288 - val_acc: 0.7921\n",
      "Epoch 10/100\n",
      "711/711 [==============================] - 0s 231us/step - loss: 0.5895 - acc: 0.7243 - val_loss: 0.5154 - val_acc: 0.7978\n",
      "Epoch 11/100\n",
      "711/711 [==============================] - 0s 228us/step - loss: 0.5754 - acc: 0.7454 - val_loss: 0.4998 - val_acc: 0.7978\n",
      "Epoch 12/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.5706 - acc: 0.7215 - val_loss: 0.4903 - val_acc: 0.8034\n",
      "Epoch 13/100\n",
      "711/711 [==============================] - 0s 214us/step - loss: 0.5594 - acc: 0.7525 - val_loss: 0.4823 - val_acc: 0.7978\n",
      "Epoch 14/100\n",
      "711/711 [==============================] - 0s 220us/step - loss: 0.5775 - acc: 0.7482 - val_loss: 0.4777 - val_acc: 0.8034\n",
      "Epoch 15/100\n",
      "711/711 [==============================] - 0s 217us/step - loss: 0.5512 - acc: 0.7553 - val_loss: 0.4694 - val_acc: 0.8034\n",
      "Epoch 16/100\n",
      "711/711 [==============================] - 0s 243us/step - loss: 0.5623 - acc: 0.7454 - val_loss: 0.4697 - val_acc: 0.8034\n",
      "Epoch 17/100\n",
      "711/711 [==============================] - 0s 220us/step - loss: 0.5579 - acc: 0.7553 - val_loss: 0.4622 - val_acc: 0.8034\n",
      "Epoch 18/100\n",
      "711/711 [==============================] - 0s 211us/step - loss: 0.5728 - acc: 0.7370 - val_loss: 0.4683 - val_acc: 0.8034\n",
      "Epoch 19/100\n",
      "711/711 [==============================] - 0s 219us/step - loss: 0.5418 - acc: 0.7623 - val_loss: 0.4639 - val_acc: 0.8034\n",
      "Epoch 20/100\n",
      "711/711 [==============================] - 0s 213us/step - loss: 0.5271 - acc: 0.7665 - val_loss: 0.4543 - val_acc: 0.8202\n",
      "Epoch 21/100\n",
      "711/711 [==============================] - 0s 211us/step - loss: 0.5531 - acc: 0.7567 - val_loss: 0.4509 - val_acc: 0.8202\n",
      "Epoch 22/100\n",
      "711/711 [==============================] - 0s 220us/step - loss: 0.5247 - acc: 0.7778 - val_loss: 0.4459 - val_acc: 0.8258\n",
      "Epoch 23/100\n",
      "711/711 [==============================] - 0s 236us/step - loss: 0.5272 - acc: 0.7722 - val_loss: 0.4456 - val_acc: 0.8202\n",
      "Epoch 24/100\n",
      "711/711 [==============================] - 0s 214us/step - loss: 0.5256 - acc: 0.7750 - val_loss: 0.4439 - val_acc: 0.8202\n",
      "Epoch 25/100\n",
      "711/711 [==============================] - 0s 205us/step - loss: 0.5416 - acc: 0.7454 - val_loss: 0.4492 - val_acc: 0.8202\n",
      "Epoch 26/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.5107 - acc: 0.7918 - val_loss: 0.4423 - val_acc: 0.8202\n",
      "Epoch 27/100\n",
      "711/711 [==============================] - 0s 222us/step - loss: 0.5216 - acc: 0.7736 - val_loss: 0.4400 - val_acc: 0.8258\n",
      "Epoch 28/100\n",
      "711/711 [==============================] - 0s 223us/step - loss: 0.5223 - acc: 0.7778 - val_loss: 0.4362 - val_acc: 0.8258\n",
      "Epoch 29/100\n",
      "711/711 [==============================] - 0s 218us/step - loss: 0.5050 - acc: 0.7679 - val_loss: 0.4342 - val_acc: 0.8315\n",
      "Epoch 30/100\n",
      "711/711 [==============================] - 0s 242us/step - loss: 0.5110 - acc: 0.7778 - val_loss: 0.4346 - val_acc: 0.8258\n",
      "Epoch 31/100\n",
      "711/711 [==============================] - 0s 212us/step - loss: 0.5226 - acc: 0.7806 - val_loss: 0.4364 - val_acc: 0.8258\n",
      "Epoch 32/100\n",
      "711/711 [==============================] - 0s 218us/step - loss: 0.5217 - acc: 0.7820 - val_loss: 0.4353 - val_acc: 0.8315\n",
      "Epoch 33/100\n",
      "711/711 [==============================] - 0s 231us/step - loss: 0.5042 - acc: 0.7904 - val_loss: 0.4350 - val_acc: 0.8258\n",
      "Epoch 34/100\n",
      "711/711 [==============================] - 0s 225us/step - loss: 0.5245 - acc: 0.7764 - val_loss: 0.4340 - val_acc: 0.8315\n",
      "Epoch 35/100\n",
      "711/711 [==============================] - 0s 208us/step - loss: 0.5083 - acc: 0.7834 - val_loss: 0.4292 - val_acc: 0.8315\n",
      "Epoch 36/100\n",
      "711/711 [==============================] - 0s 240us/step - loss: 0.4938 - acc: 0.7862 - val_loss: 0.4294 - val_acc: 0.8315\n",
      "Epoch 37/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.5106 - acc: 0.7961 - val_loss: 0.4299 - val_acc: 0.8315\n",
      "Epoch 38/100\n",
      "711/711 [==============================] - 0s 208us/step - loss: 0.5103 - acc: 0.7806 - val_loss: 0.4319 - val_acc: 0.8315\n",
      "Epoch 39/100\n",
      "711/711 [==============================] - 0s 217us/step - loss: 0.4990 - acc: 0.7876 - val_loss: 0.4286 - val_acc: 0.8315\n",
      "Epoch 40/100\n",
      "711/711 [==============================] - 0s 224us/step - loss: 0.5093 - acc: 0.7862 - val_loss: 0.4345 - val_acc: 0.8315\n",
      "Epoch 41/100\n",
      "711/711 [==============================] - 0s 208us/step - loss: 0.5121 - acc: 0.7820 - val_loss: 0.4293 - val_acc: 0.8315\n",
      "Epoch 42/100\n",
      "711/711 [==============================] - 0s 213us/step - loss: 0.5224 - acc: 0.7834 - val_loss: 0.4375 - val_acc: 0.8371\n",
      "Epoch 43/100\n",
      "711/711 [==============================] - 0s 234us/step - loss: 0.5033 - acc: 0.7918 - val_loss: 0.4326 - val_acc: 0.8371\n",
      "Epoch 44/100\n",
      "711/711 [==============================] - 0s 213us/step - loss: 0.4933 - acc: 0.8003 - val_loss: 0.4274 - val_acc: 0.8371\n",
      "Epoch 45/100\n",
      "711/711 [==============================] - 0s 210us/step - loss: 0.4885 - acc: 0.7989 - val_loss: 0.4285 - val_acc: 0.8315\n",
      "Epoch 46/100\n",
      "711/711 [==============================] - 0s 207us/step - loss: 0.4866 - acc: 0.7918 - val_loss: 0.4197 - val_acc: 0.8315\n",
      "Epoch 47/100\n",
      "711/711 [==============================] - 0s 205us/step - loss: 0.5025 - acc: 0.7975 - val_loss: 0.4198 - val_acc: 0.8371\n",
      "Epoch 48/100\n",
      "711/711 [==============================] - 0s 215us/step - loss: 0.4917 - acc: 0.7975 - val_loss: 0.4225 - val_acc: 0.8371\n",
      "Epoch 49/100\n",
      "711/711 [==============================] - 0s 227us/step - loss: 0.4789 - acc: 0.7961 - val_loss: 0.4272 - val_acc: 0.8371\n",
      "Epoch 50/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.4820 - acc: 0.8003 - val_loss: 0.4217 - val_acc: 0.8371\n",
      "Epoch 51/100\n",
      "711/711 [==============================] - 0s 220us/step - loss: 0.4746 - acc: 0.7947 - val_loss: 0.4190 - val_acc: 0.8371\n",
      "Epoch 52/100\n",
      "711/711 [==============================] - 0s 228us/step - loss: 0.4896 - acc: 0.8003 - val_loss: 0.4222 - val_acc: 0.8371\n",
      "Epoch 53/100\n",
      "711/711 [==============================] - 0s 214us/step - loss: 0.5171 - acc: 0.7975 - val_loss: 0.4255 - val_acc: 0.8371\n",
      "Epoch 54/100\n",
      "711/711 [==============================] - 0s 221us/step - loss: 0.4992 - acc: 0.7848 - val_loss: 0.4226 - val_acc: 0.8315\n",
      "Epoch 55/100\n",
      "711/711 [==============================] - 0s 218us/step - loss: 0.5007 - acc: 0.7947 - val_loss: 0.4302 - val_acc: 0.8315\n",
      "Epoch 56/100\n",
      "711/711 [==============================] - 0s 244us/step - loss: 0.4972 - acc: 0.7961 - val_loss: 0.4245 - val_acc: 0.8371\n",
      "Epoch 57/100\n",
      "711/711 [==============================] - 0s 215us/step - loss: 0.5045 - acc: 0.7876 - val_loss: 0.4197 - val_acc: 0.8371\n",
      "Epoch 58/100\n",
      "711/711 [==============================] - 0s 225us/step - loss: 0.4897 - acc: 0.8073 - val_loss: 0.4236 - val_acc: 0.8371\n",
      "Epoch 59/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.5041 - acc: 0.7904 - val_loss: 0.4226 - val_acc: 0.8315\n",
      "Epoch 60/100\n",
      "711/711 [==============================] - 0s 220us/step - loss: 0.4946 - acc: 0.8087 - val_loss: 0.4200 - val_acc: 0.8315\n",
      "Epoch 61/100\n",
      "711/711 [==============================] - 0s 227us/step - loss: 0.4753 - acc: 0.7932 - val_loss: 0.4154 - val_acc: 0.8371\n",
      "Epoch 62/100\n",
      "711/711 [==============================] - 0s 222us/step - loss: 0.4830 - acc: 0.8017 - val_loss: 0.4176 - val_acc: 0.8371\n",
      "Epoch 63/100\n",
      "711/711 [==============================] - 0s 219us/step - loss: 0.4748 - acc: 0.8003 - val_loss: 0.4134 - val_acc: 0.8371\n",
      "Epoch 64/100\n",
      "711/711 [==============================] - 0s 212us/step - loss: 0.4866 - acc: 0.8115 - val_loss: 0.4149 - val_acc: 0.8371\n",
      "Epoch 65/100\n",
      "711/711 [==============================] - 0s 211us/step - loss: 0.4902 - acc: 0.8031 - val_loss: 0.4168 - val_acc: 0.8371\n",
      "Epoch 66/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.4928 - acc: 0.8045 - val_loss: 0.4214 - val_acc: 0.8371\n",
      "Epoch 67/100\n",
      "711/711 [==============================] - 0s 217us/step - loss: 0.4940 - acc: 0.7989 - val_loss: 0.4216 - val_acc: 0.8427\n",
      "Epoch 68/100\n",
      "711/711 [==============================] - 0s 226us/step - loss: 0.4777 - acc: 0.8087 - val_loss: 0.4191 - val_acc: 0.8371\n",
      "Epoch 69/100\n",
      "711/711 [==============================] - 0s 233us/step - loss: 0.4645 - acc: 0.7975 - val_loss: 0.4157 - val_acc: 0.8371\n",
      "Epoch 70/100\n",
      "711/711 [==============================] - 0s 231us/step - loss: 0.4886 - acc: 0.8017 - val_loss: 0.4160 - val_acc: 0.8371\n",
      "Epoch 71/100\n",
      "711/711 [==============================] - 0s 213us/step - loss: 0.4684 - acc: 0.7918 - val_loss: 0.4154 - val_acc: 0.8371\n",
      "Epoch 72/100\n",
      "711/711 [==============================] - 0s 211us/step - loss: 0.4869 - acc: 0.8031 - val_loss: 0.4164 - val_acc: 0.8427\n",
      "Epoch 73/100\n",
      "711/711 [==============================] - 0s 209us/step - loss: 0.4871 - acc: 0.7961 - val_loss: 0.4179 - val_acc: 0.8371\n",
      "Epoch 74/100\n",
      "711/711 [==============================] - 0s 212us/step - loss: 0.4642 - acc: 0.8073 - val_loss: 0.4143 - val_acc: 0.8427\n",
      "Epoch 75/100\n",
      "711/711 [==============================] - 0s 224us/step - loss: 0.4821 - acc: 0.8017 - val_loss: 0.4108 - val_acc: 0.8371\n",
      "Epoch 76/100\n",
      "711/711 [==============================] - 0s 234us/step - loss: 0.4823 - acc: 0.7989 - val_loss: 0.4137 - val_acc: 0.8427\n",
      "Epoch 77/100\n",
      "711/711 [==============================] - 0s 218us/step - loss: 0.4857 - acc: 0.7904 - val_loss: 0.4140 - val_acc: 0.8427\n",
      "Epoch 78/100\n",
      "711/711 [==============================] - 0s 213us/step - loss: 0.4758 - acc: 0.8045 - val_loss: 0.4118 - val_acc: 0.8315\n",
      "Epoch 79/100\n",
      "711/711 [==============================] - 0s 219us/step - loss: 0.4591 - acc: 0.8003 - val_loss: 0.4065 - val_acc: 0.8427\n",
      "Epoch 80/100\n",
      "711/711 [==============================] - 0s 222us/step - loss: 0.4820 - acc: 0.7975 - val_loss: 0.4060 - val_acc: 0.8427\n",
      "Epoch 81/100\n",
      "711/711 [==============================] - 0s 218us/step - loss: 0.4688 - acc: 0.8087 - val_loss: 0.4103 - val_acc: 0.8427\n",
      "Epoch 82/100\n",
      "711/711 [==============================] - 0s 238us/step - loss: 0.4675 - acc: 0.8059 - val_loss: 0.4139 - val_acc: 0.8427\n",
      "Epoch 83/100\n",
      "711/711 [==============================] - 0s 218us/step - loss: 0.4748 - acc: 0.7947 - val_loss: 0.4087 - val_acc: 0.8483\n",
      "Epoch 84/100\n",
      "711/711 [==============================] - 0s 225us/step - loss: 0.4721 - acc: 0.8003 - val_loss: 0.4084 - val_acc: 0.8427\n",
      "Epoch 85/100\n",
      "711/711 [==============================] - 0s 210us/step - loss: 0.4732 - acc: 0.8031 - val_loss: 0.4100 - val_acc: 0.8427\n",
      "Epoch 86/100\n",
      "711/711 [==============================] - 0s 203us/step - loss: 0.4697 - acc: 0.8087 - val_loss: 0.4087 - val_acc: 0.8427\n",
      "Epoch 87/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.4661 - acc: 0.8017 - val_loss: 0.4077 - val_acc: 0.8483\n",
      "Epoch 88/100\n",
      "711/711 [==============================] - 0s 210us/step - loss: 0.4763 - acc: 0.8017 - val_loss: 0.4084 - val_acc: 0.8427\n",
      "Epoch 89/100\n",
      "711/711 [==============================] - 0s 229us/step - loss: 0.4724 - acc: 0.8003 - val_loss: 0.4097 - val_acc: 0.8427\n",
      "Epoch 90/100\n",
      "711/711 [==============================] - 0s 217us/step - loss: 0.4653 - acc: 0.8031 - val_loss: 0.4091 - val_acc: 0.8427\n",
      "Epoch 91/100\n",
      "711/711 [==============================] - 0s 219us/step - loss: 0.4629 - acc: 0.8003 - val_loss: 0.4102 - val_acc: 0.8427\n",
      "Epoch 92/100\n",
      "711/711 [==============================] - 0s 223us/step - loss: 0.4701 - acc: 0.8003 - val_loss: 0.4072 - val_acc: 0.8427\n",
      "Epoch 93/100\n",
      "711/711 [==============================] - 0s 220us/step - loss: 0.4809 - acc: 0.8045 - val_loss: 0.4077 - val_acc: 0.8427\n",
      "Epoch 94/100\n",
      "711/711 [==============================] - 0s 224us/step - loss: 0.4563 - acc: 0.7961 - val_loss: 0.4039 - val_acc: 0.8427\n",
      "Epoch 95/100\n",
      "711/711 [==============================] - 0s 234us/step - loss: 0.4715 - acc: 0.8003 - val_loss: 0.4073 - val_acc: 0.8427\n",
      "Epoch 96/100\n",
      "711/711 [==============================] - 0s 213us/step - loss: 0.4629 - acc: 0.8017 - val_loss: 0.4090 - val_acc: 0.8427\n",
      "Epoch 97/100\n",
      "711/711 [==============================] - 0s 214us/step - loss: 0.4619 - acc: 0.8059 - val_loss: 0.4071 - val_acc: 0.8427\n",
      "Epoch 98/100\n",
      "711/711 [==============================] - 0s 221us/step - loss: 0.4775 - acc: 0.7918 - val_loss: 0.4069 - val_acc: 0.8427\n",
      "Epoch 99/100\n",
      "711/711 [==============================] - 0s 219us/step - loss: 0.4784 - acc: 0.8003 - val_loss: 0.4092 - val_acc: 0.8427\n",
      "Epoch 100/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.4858 - acc: 0.7904 - val_loss: 0.4068 - val_acc: 0.8371\n"
     ]
    }
   ],
   "source": [
    "# Fitting the train set to the model\n",
    "history = model.fit(X, y, epochs=100, batch_size=32, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 707
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3203,
     "status": "ok",
     "timestamp": 1554216033778,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "jkgJ7uMjfRcE",
    "outputId": "7093d69e-455d-47ed-e3a7-51a8190e4fd1"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFZCAYAAACizedRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFZCAYAAACizedRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3WdgVFX+8PHvnZZOSCCh904gQkAg\nRFpISAARC0gQAQUXG4uu4P6RR8QG6ip2V1SwgQqiKLoCoQak9947Sk0lfdp9XkwyEFJmkkwKye/z\nxsy995x75oD8crqiqqqKEEIIISoNTUUXQAghhBB5SXAWQgghKhkJzkIIIUQlI8FZCCGEqGQkOAsh\nhBCVjARnIYQQopKR4CyqtBkzZhAdHU10dDRBQUH069fP/jktLa1YeUVHRxMfH1/kM7Nnz+aHH34o\nTZFd7pFHHmHJkiUuyatNmzZcvnyZVatW8cILL5TqfT/++KP9Z2fq1llTp07lv//9r0vyEqKi6Cq6\nAEKUpVdeecX+c3h4OP/5z3/o2rVrifJasWKFw2cmT55corxvN5GRkURGRpY4/bVr15g7dy4PPvgg\n4FzdClGdSMtZVGujR4/mvffeY+DAgezevZv4+HjGjx9PdHQ04eHhfPXVV/Znc1uN27ZtY8SIEcye\nPZuBAwcSHh7O9u3bgbyttvDwcBYuXMiwYcO46667ePPNN+15zZkzh9DQUB544AG+++47wsPDCyzf\n4sWLGThwIAMGDGDUqFH8/fffACxZsoRJkyYxbdo0oqKiGDRoECdOnADgwoULDB8+nIiICCZPnozF\nYsmX7/r16xkyZEiea0OHDmXDhg1F1kGuJUuW8Mgjjzh835o1axgyZAhRUVHcf//9HDlyBICYmBgu\nXrxIdHQ0RqPRXrcA3377LYMGDSI6Oponn3ySxMREe91++OGHPProo/Tr149HH32UzMzMwv5oATh6\n9CgxMTFER0czdOhQ/vzzTwDS09N5+umnGThwIP379+fFF1/EZDIVel2I8ibBWVR7Bw8e5I8//iAk\nJIRPP/2Uhg0bsmLFCr755htmz57NpUuX8qU5fPgwd9xxB8uXL+ehhx7i008/LTDvHTt2sGjRIn7+\n+WcWLFjA5cuXOXHiBHPnzmXp0qV8//33hbYaExISePXVV/nqq69YuXIljRs3ztNdu2HDBh566CFi\nY2Pp3r0733zzDQDvvPMOoaGhrF69mrFjx7J79+58eYeGhnL58mUuXLgA2ALs5cuX6dmzp9N1kKuw\n95nNZqZOncprr71GbGws4eHhvPXWWwDMmjWLevXqsWLFCgwGgz2vvXv3Mm/ePObPn8+KFSuoX78+\ns2fPtt9fsWIF7733HqtWrSIxMZFVq1YVWi6r1cpzzz3Hww8/zIoVK3j99deZPHkyaWlp/Prrr9So\nUYPly5cTGxuLVqvl5MmThV4XorxJcBbVXp8+fdBobP8rvPjii0yfPh2ARo0aERAQwF9//ZUvjZeX\nFxEREQAEBQVx8eLFAvMeMmQIWq2WOnXqUKtWLS5dusSOHTvo1q0bgYGBuLm58cADDxSYtlatWuza\ntYu6desC0LVrV3swBWjRogUdOnQAoH379vYAunPnTgYNGgRAcHAwzZs3z5e3wWCgX79+rF27FoDV\nq1cTERGBTqdzug5yFfY+nU7H5s2b6dSpU4HlL0hcXBxRUVHUqlULgOHDh7Np0yb7/T59+lCzZk10\nOh2tW7cu8peGv/76i/j4eAYPHgxAx44dqV+/PgcOHMDf3589e/awceNGrFYrr7zyCu3atSv0uhDl\nTcacRbXn6+tr//nAgQP2lqJGo+HatWtYrdZ8aXx8fOw/azSaAp8B8Pb2tv+s1WqxWCxcv349zzvr\n1KlTYFqLxcKHH37I2rVrsVgspKen06xZswLLkJs3QEpKSp731qhRo8D8o6Ki+Pbbbxk7diyrV6/m\nqaeeKlYd5CrqffPnz+eXX37BaDRiNBpRFKXQfAASExMJDAzMk1dCQoLD71xYXj4+PnneWaNGDRIT\nExk8eDApKSl88MEHnD59mnvuuYcXXniBgQMHFnj95ta9EOVBWs5C3OT5558nKiqK2NhYVqxYgZ+f\nn8vf4e3tTUZGhv3z1atXC3xu2bJlrF27lgULFhAbG8ukSZOcyr9GjRp5ZqLnjtneqlevXhw9epSz\nZ89y9uxZevToARS/Dgp73+7du/niiy/49NNPiY2N5fXXX3dY9tq1a5OcnGz/nJycTO3atR2mK0it\nWrVISUnh5rN9kpOT7a3ymJgYFi9ezLJlyzh06BC//vprkdeFKE8SnIW4SUJCAh06dEBRFH755Rcy\nMzPzBFJXCA4OZtu2bSQmJmI0Ggv9xz8hIYEGDRrg7+9PUlISy5cvJz093WH+nTp1so/F7t69m/Pn\nzxf4nMFg4K677uLtt9+mf//+aLVa+3uLUweFvS8xMZFatWpRv359MjMz+eWXX8jIyEBVVXQ6HRkZ\nGZjN5jx59e3bl1WrVpGUlATAwoUL6dOnj8PvXJCGDRtSt25dli1bZi9bfHw8wcHBfPLJJ/z000+A\nreeiYcOGKIpS6HUhypsEZyFu8swzz/D0008zZMgQMjIyGDFiBNOnTy80wJVEcHAw9913H/fddx9j\nxoyhX79+BT539913k5ycTGRkJJMnT+bZZ5/l8uXLeWZ9F+T5559n3bp1RERE8N1339GzZ89Cn42K\nimL16tUMHDjQfq24dVDY+3r16kVgYCARERGMGzeOsWPH4uPjw6RJk2jTpg2+vr6EhYXlGa8PDg5m\nwoQJjBo1iujoaFJTU/nXv/5V5PctjKIovPvuuyxYsICBAwfy+uuv88EHH+Dp6cnQoUNZunQpUVFR\nREdHo9frGTp0aKHXhShvipznLET5U1XV3iKLi4vj/fffl+5TIYSdtJyFKGeJiYn06NGDv//+G1VV\nWb58uX1GsxBCgJMt51mzZrFv3z4URWHatGkEBwcDcOXKFaZMmWJ/7sKFC0yePJno6GimTp3KxYsX\n0Wq1vPHGGzRq1KjsvoUQt5kffviBL7/8EkVRaN68OTNnzrRPVBJCCIfBefv27cybN4/PPvuMU6dO\nMW3aNBYtWpTvObPZzOjRo5k7dy4rV65k//79zJgxg40bN/LTTz/x/vvvl9mXEEIIIaoSh93aW7Zs\nsW+20KJFC1JSUgo8MOCXX34hKioKLy8vtmzZYt93t2fPngXuUCSEEEKIgjkMzvHx8XnWOfr7+3Pt\n2rV8zy1evJhhw4bZ0/j7+9teoNGgKApGo9FVZRZCCCGqtGJPCCuoF3zPnj00b948zy5BjtLcymwu\nfKcfIYQQojpxuH1nYGBgnnNWr169SkBAQJ5n4uLiCA0NzZPm2rVrtG3bFpPJhKqqDre/S0oq3UYP\nAQE+XLuWWqo8hNSjq0g9uobUo2tIPbqGq+sxIMCn0HsOW85hYWHExsYCcOjQIQIDA/O1kA8cOEDb\ntm3zpMk9aWfdunV07969RAUXQgghqiOHLeeQkBCCgoKIiYlBURRmzJjBkiVL8PHxsU/6unbtWp5l\nIIMGDWLz5s2MHDkSg8HgcEcjIYQQQtxQaXYIK21XgXTbuIbUo2tIPbqG1KNrSD26RqXq1hZCCCFE\n+ZLgLIQQQlQyEpyFEEKISkaCsxBCCFHJOJytLYQQQhTXRx+9x7FjR0hMTCArK4v69RtQo4Yvs2a9\n7TDtsmW/4+XlTZ8+BZ91/sEHsxk+PIb69RuUqGwTJ07guef+TfPmLUuUvjxIcBZCCOFy//znvwBb\noD19+hQTJz7rdNpBg4YUef+ZZyaXqmy3AwnOQgghys3u3TtZuHABGRkZTJz4L/bs2UVc3BqsViuh\noWGMGzeBefM+o2bNmjRr1oIlS35EUTScO3eGvn37M27cBHvLd926NaSnp3H+/Dn+/vsvJk2aTGho\nGAsWfM3q1SupX78BZrOZmJhRhIR0zVeWtLQ0Zs58mbS0VMxmM88++zxt2rTl/fff5ujRI1gsFu67\nbxiDBg3h/fff5tSp42RlGe3XylKVDM7aI4fRXL2CqZAuESGEqFaefx7/RT+6NMvsIfeS/vLrJUp7\n6tRJfvhhCQaDgT17dvHf/85Fo9Hw4INDGTHioTzPHj58iO+//xmr1crw4UMYN25CnvtXr17hnXc+\nZOvWzSxd+jNBQR1YsmQxP/zwM+np6cTE3E9MzKgCy7F48Q8EBXXg4Ycf4ejRw3z00bvMmvU2mzdv\n5Mcfl2I2m1m27HeuX09h8+aNrFu3lkuXkli27PcSfe/iqJLB2Wvmyxji1hJ/4gJ4eFR0cYQQQtyk\nZctW9vMW3N3dmThxAlqtluTkZK5fv57n2TZt2uLu7l5oXsHBnQDbmQ5paWn89dcFmjdvgZubO25u\n7rRrF1Ro2qNHDzNmzHgA2rZtz19/XaBGDV8aNWrC1KnP0a9fBNHRgzEYDDRq1IQnn3ySsLC+REcP\nLm0VOFQlg7O1QUMUoxHdiWOYc/7ghBCi2nr7bRL//VJFl8JOr9cDcPnyJRYt+o4vv/wOT09PRo9+\nMN+zWq22yLxuvq+qKqpqO6o4l6IUnlZRlDynJlqtVgBmz/6QY8eOsmrVClas+IP33vuE2bM/5OrV\n8/z44xL7tbJUJZdSmXN+U9IePlTBJRFCCFGY5ORk/Pz88PT05Nixo1y+fBmTyVSqPOvVq8fp06cw\nm80kJSVx9OiRQp9t27Y9e/bsBODgwQM0a9aCS5cusnjxQtq0acvEic+SkpJivxYUFGS/VtaqZMs5\nNzjrDh8iu4LLIoQQomCtWrXGw8OTJ58cR8eOnRg69H5mz36L4OA7Spynv38tIiOj+cc/xtCkSTPa\ntw8qtPX94IMjmTXrFSZNegKr1cpzz/0ftWsHcPDgPtasWYler2fw4Hvs12JiYgANgwffU+LyOatK\nHnyhXE+hdstGGPv0I2XxUlcUr9qQDfJdQ+rRNaQeXaO61eOyZb8TGRmNVqtlzJgY3n33IwID65Q6\n3/I8+KJKtpzVGr5YGjVGJ93aQghR7SQkJDBhwlj0egMDBkS7JDCXtyoZnAHM7drjtnIFSnw8au3a\nFV0cIYQQ5WT06EcYPfqRii5GqVTJCWEA5vYdANAdkdazEEKI20uVDc6Wdu0B0B0+WMElEUIIIYqn\nygZn+3KqI4cruCRCCCFE8VTZ4Gxp0RLVYJBubSGEELedKhuc0euxtGqD7ugRsFgqujRCCFGtPP74\no/k2AJkz52N++GFBgc/v3r2TF1/8NwBTpz6X7/7PPy9i3rzPCn3fyZMnOH/+HAAzZrxAdnZWSYvO\nsGFDyMjIKHF6V6i6wRkwtw9CycxEe+5MRRdFCCGqlcjIKNauXZXnWlzcWiIiBjhM++ab7xb7fevX\nr+XChfMAvPLKG7i5Fb4f9+2gyi6lgpvGnQ8dwlKJD9UWQoiqpn//ATz55HieemoSAEePHiEgIICA\ngEB27NjG3Llz0Ov1+Pj48Oqrb+ZJO3hwf/74Yw07d27nww9n4+9fi1q1atuPgJw582WuXbtKZmYm\n48ZNoG7deixduoT169fi5+fHSy+9wLffLiItLZU33ngVk8mERqNh6tTpKIrCzJkvU79+A06ePEHr\n1m2YOnV6gd/h6tUredL/5z9votN58+qr00lIiMdoNDJ+/ON07dot37UePXqWqv6qdnBunzNj+8gh\njEOGVnBphBCiYjz/PCxa5OXSPIcMMfPyy4VvkOzn50/9+g04fPgg7dt3YO3aVURGRgOQmprKjBmv\nU79+A1577SW2bduCp6dnvjw+++xjpk9/jVatWjNlyiTq129Aaup1unXrwcCBd/P3338xffpUvvxy\nAd27h9K3b3/a5yyjBZg7dw533z2U/v0HsG7dar788nPGj3+cY8eO8Mors/Dz8+e++waRmpqKj0/+\n3bpuTf/xxx8zZMgwUlKS+eSTL0hNTWXLlk2cOnUy37XSqtLd2hb7WmeZsS2EEOUtMjKaNWtsXdub\nNm2gb9/+ANSsWZO33nqdiRMnsGfPLq5fL/ggiUuXLtGqVWsAOnUKAcDHpwZHjhziySfHMXPmy4Wm\nBTh27AidO3cBICSkKydOHAOgQYNG1KpVG41GQ+3aAaSnpzmV/vDhwzRp0pSMjHRee206u3fvICJi\nQIHXSqtKt5ytdepi9fNDK2udhRDV2Ntvw7//nV7u7+3Tpx/ffvslkZFRNGrUmBo1agDwxhuv8fbb\n79O0aTPeffetQtPffPRj7jEQq1at4Pr163zyyVyuX7/OY4+NLqIEN46ENJnMKIotv1sPwij8iIm8\n6TUaDe7u7nz22dccOLCf5ct/Z9OmP5k2bUaB10qjSrecURTM7YLQnj0D6eX/F1MIIaozT08vWrRo\nxbfffmXv0gZIT0+jTp26pKamsnv3rkKPiaxdO4Dz58+iqip79uwCbMdM1qtXH41Gw/r1a+1pFUXB\ncsvKnHbt2rN7t+1IyL17d9G2bbtilf/W9B06dLCf83zHHZ2YMuUFzp49U+C10qrSLWewzdg2bN6I\n7vhRzDndE0IIIcpHZGQ0r78+gxkzXrNfu//+4Tz55HgaNWrMqFFj+PLLz5kw4al8aSdMeIoXX/w/\n6tatZz+8om/fcKZOfY7Dhw8yePA9BAYG8tVXX3DHHZ15//2384xdP/bYE7zxxmv8/vuv6HR6Xnhh\nOmaz2emy35r+nXfeIi3NzGeffcLSpUvQaDQ89NBo6tWrn+9aaVXJIyNv5j7/a3wmTyL1vY/JGjWm\nVO+oDqrb0XJlRerRNaQeXUPq0TXK88jIqt2tje10KgDtzTuFZWejuXypgkokhBBCFK3qB+e2uQdg\nHEJJScbjw3fx79IB/y4d0Mp5z0IIISqhKj/mjLc3liZN0e/cjn+n9mjS01ANBhSTCffvvyX99cJn\nCgohhBAVoUq2nC9fVjhy5MZXM3UKQcnKQvXxIe2l10jYdwxr7dq4//wjGI0VWFIhhBAivyoZnP/9\nbzcGD/Ykd3Z++mtvkLJgEYk7D5A58RnUWrXIeuBBNAkJGFbFVmxhhRBCiFtUyeDs76+SlqZw5ozt\n61nr1sM4YCAYDPZnsmIeBsB90XcVUkYhhBCiMFUyOLdubQXg2LHCv54lqAOmjndgWBWLcvVqeRVN\nCCGEcKhKBuc2bRwHZ4CskaNQLBbb2LMQQghRSVTp4Hz8eNFfL/v+4ah6Pe4LF0Dl2ItFCCGEqJrB\nuUEDFS8v1WHLWfWvhTFqELojh9Ht31tOpRNCCCGKViWDs6LYxp1PndLgaBvVrJGjAHBfKBPDhBBC\nVA5OBedZs2YxYsQIYmJi2L9/f557ly5dYuTIkQwbNoyXXnoJgG3bttGjRw9Gjx7N6NGjee211wrK\ntky1bm3FaFQ4d04p8jljvwgsgXVwW7IYsgs/OFwIIYQoLw6D8/bt2zl37hyLFi1i5syZzJw5M8/9\nN998k3HjxvHTTz+h1Wq5ePEiAN26dWP+/PnMnz+f6dOnl03pi3Bjxra26Ad1OrKHjUCTlIThz7iy\nL5gQQgjhgMPgvGXLFiIiIgBo0aIFKSkppKWlAWC1Wtm1axfh4eEAzJgxg/r165dhcZ3Xpo3tXE9H\nk8IATGF3AaDbu6dMyySEEEI4w2Hkio+Px8/Pz/7Z39+fa9euAZCYmIiXlxdvvPEGI0eOZPbs2fbn\nTp48yRNPPMHIkSPZtGlTGRS9aM6sdc5lCu4MIJPChBBCVArFPvji5uOfVVXlypUrjBkzhgYNGjBh\nwgTi4uJo164dEydOZODAgVy4cIExY8awcuVKDDft0HUrPz9PdDoHXdAO3Hw2pr8/eHjA6dN6AgL0\nDhL6QL16uB3cX+T5mtWF1IFrSD26htSja0g9ukZ51aPD4BwYGEh8fLz989WrVwkICADAz8+P+vXr\n07hxYwBCQ0M5ceIEffv2ZdCgQQA0btyY2rVrc+XKFRo1alToe5KSMkr1RQo6BLtlS0+OHNFw+XIa\nWgdxv0aHYNxWxRJ/+DRqzverjuRQdteQenQNqUfXkHp0DVfXY1GB3mGfb1hYGLGxtsMhDh06RGBg\nIN7e3gDodDoaNWrE2bNn7febNWvGb7/9xrx58wC4du0aCQkJ1KlTp7Tfo9hat7aSlaVw/nzRM7YB\nzMGdANAdkK5tIYQQFcthyzkkJISgoCBiYmJQFIUZM2awZMkSfHx8iIyMZNq0aUydOhVVVWndujXh\n4eFkZGQwZcoU1qxZg8lk4uWXXy6yS7us3LxTWLNmliKfzQ3O+v37MIVHlnnZhBBCiMI4NeY8ZcqU\nPJ/btm1r/7lJkyb88MMPee57e3szZ84cFxSvdG5eThUV5Sg43wGAbv++Mi+XEEIIUZQquUNYrrZt\nnV9OZa3fAGvt2jJjWwghRIWr0sG5cWMVNzfVqeCMomDueAfa8+dQkhLLvnBCCCFEIap0cNbpoEUL\nK8ePa7BaHT9/Y1LYfgdPCiGEEGWnSgdnsE0Ky8hQ+PtvxzO2TbnBeZ90bQshhKg4VT44504Kc6Zr\n2z4pTJZTCSGEqEDVJjg7s42ntXETrDVryoxtIYQQFarKB+eb1zo7pCiYO3ZCd/oUSur1Mi6ZEEII\nUbAqH5ybNbOi06mOj47McaNrWyaFCSGEqBhVPjjr9bYZ28eOabjpzI5C3diMRMadhRBCVIwqH5wB\n2ra1kpamcPq0M3tsy05hQgghKla1CM59+9p2Clu1yvFupZZmLbB6+0jLWQghRIWpFsE5IsIMwMqV\nTmwlrtFg7hiM9sRxSE8v45IJIYQQ+VWL4FynjkpIiIWtW7WkpDh+3hx8B4qq4rb8f2Ap+sAMIYQQ\nwtWqRXAGGDDAjNmssHat49azqVsPAGo89Q9qdWyN93P/xLA6FqdmlAkhhBClVK2CM0BsrOPgbLx7\nKCk//ETmw2MB8FjwDb4PDcfjo/fKtIxCCCEEVKPgHBRkpUEDK2vX6jCbHTysKBj7DyDt3Y9IOHCc\npN9isdasieecjyEzs1zKK4QQovqqNsFZUSAy0kxyssL27c5tSAKAVou5RyhZY8ahiY/H/ecfy66Q\nQgghBNUoOANERRVj1vYtMsdPQNXp8JjzsYw9CyGEKFPVKjiHhVnw9FRZubIYLecc1nr1yb73AXTH\nj2FYu6oMSieEEELYVKvg7O4OffqYOXlS69RuYbfKfHIiAB6ffuLqogkhhBB21So4w42ubWdmbd/K\n3PEOjHf1xrBhHdpDB11dNCGEEAKohsE5IsKCoqglGncGyHziaQDbzG0hhBCiDFS74BwYqBISYmXr\nVi1JScVPb4yIwtyyFW5LFqO5ctn1BRRCCFHtVbvgDDBkiAmLReHLLw3FT6zRkPn40ygmE+7ffuX6\nwgkhhKj2qmVwHjPGhL+/lc8+M3D9evHTZ99zLwC63TtdXDIhhBCimgZnb2946ikTyckKc+cWv/Ws\n+vljqVsP3ZHDZVA6IYQQ1V21DM4A48YZ8fNTmTPHQGpq8dNb2rVHe/FvlJRk1xdOCCFEtVZtg7Ot\n9WwscevZ3LY9ANqjR11dNCGEENVctQ3OAOPHl7z1bG5nC866I4fKoGRCCCGqs2odnL294YknjCQl\nKcybV7zWsyU3OB+VcWchhBCuVa2DM8BjjxmpWVPl008NpKU5n87cqg2qoqA9eqTsCieEEKJaqvbB\n2cfH1r2dlKQUb9cwT08sTZvZWs5ySpUQQggXqvbBGaBfP9t+27t2Fe+0Kku7IDSJiWiuXimLYgkh\nhKimJDgDwcFW9HqVnTuLF5zNbdsBoJX1zkIIIVxIgjO2oyQ7drRy4ICGzEzn08mkMCGEEGVBgnOO\nLl0smM0K+/c733q2r3WWlrMQQggXkuCco0sXCwC7djlfJZbmLVANBmk5CyGEcCkJzjm6ds0NzsUY\nd9brsbRsje7YUbBay6hkQgghqhsJzjkaNVIJCLCWaFKYkpGB5vy5MiqZEEKI6sap4Dxr1ixGjBhB\nTEwM+/fvz3Pv0qVLjBw5kmHDhvHSSy85laYyUhRb6/nSJQ0XLypOpzO3DwKQE6qEEEK4jMPgvH37\nds6dO8eiRYuYOXMmM2fOzHP/zTffZNy4cfz0009otVouXrzoME1l1aWLrWu6OK1nS85yKhl3FkII\n4SoOg/OWLVuIiIgAoEWLFqSkpJCWs8+l1Wpl165dhIeHAzBjxgzq169fZJrK7M47bePOxQnON06n\nkuAshBDCNRwG5/j4ePz8/Oyf/f39uXbtGgCJiYl4eXnxxhtvMHLkSGbPnu0wTWUWHGxBqy3eZiTW\nho2wenlLt7YQQgiXKcZm0jbqTftIq6rKlStXGDNmDA0aNGDChAnExcUVmaYwfn6e6HTFm4x1q4AA\nn1Kmh+BgOHBAS40aPri5OZmwYwc0O3cS4OsGhuKfDV3ZlLYehY3Uo2tIPbqG1KNrlFc9OgzOgYGB\nxMfH2z9fvXqVgIAAAPz8/Khfvz6NGzcGIDQ0lBMnThSZpjBJSRkl+gK5AgJ8uHatmIcyF6BTJzf2\n7DEQF5dOSIhzy6O8W7bBY+tWErfuse8adrtyVT1Wd1KPriH16BpSj67h6nosKtA77NYOCwsjNjYW\ngEOHDhEYGIi3tzcAOp2ORo0acfbsWfv9Zs2aFZmmssvdjEQmhQkhhKgoDlvOISEhBAUFERMTg6Io\nzJgxgyVLluDj40NkZCTTpk1j6tSpqKpK69atCQ8PR6PR5Etzu8i7GYnJqTTmdrblVNojh+G+siqZ\nEEKI6kJRnRkQLgel7SpwVXeDqkL79l54esKuXelOpVHi46nVsRW4uZH+r+fJfGIizg9YVy7S/eUa\nUo+uIfXoGlKPrlGpurWrG0WxrXe+cEHDlSvObUai1q5N6qdzUT298J75Cn69u2NYHVvGJRVCCFFV\nSXAuQEnGnbPvfYDELbvImPAk2vPn8H1oOD7/eAQl9XpZFVMIIUQVJcG5AJ0724Lz/v3Fqx7Vtybp\nr79F0pqNmO7sjvvSJdSMDkd7/FhZFFMIIUQVJcG5AB072pZQFeds55tZ2geR/OsyMp6YiO7EcWpG\n9cPw+6+uLKIQQogqTIJzAWrVUmnY0Mq+fRpKPF1Oryf91Vlc/+JrFFXFd/wYPN9726XlFEIIUTVJ\ncC5Ex44W4uOdnxRWmOyh95NrvVDcAAAgAElEQVQUuw5Lvfp4zn4LJU1mTAohhCiaBOdCBAfndm2X\nvoosbdqSNXIUitGIPm5dqfMTQghRtUlwLkRwcO6ksNLt953LOGAgAG4rl7skPyGEEFWXBOdCuLLl\nDGDuFIIlsI5t/bPF4pI8hRBCVE0SnAtRp45KYKCVAwdc03JGo8EYGYUmPh7d7p2uyVMIIUSVJMG5\nCMHBVv7+W0N8fOkmheW60bW9wiX5CSGEqJokOBchd9z5wAHXVJOxd19UNzcMMu4shBCiCBKci5C7\nGYnLura9vDD26oPuyGE05866Jk8hhBBVjgTnItyYse26apJZ20IIIRyR4FyEhg1V/PxUly2nAjAO\niAbAECvjzkIIIQomwbkIimLbKezsWQ0pKa7J01q/AabgTui3bCzeiVWqipKc5JpCCCGEqNQkODuQ\n27V98KBrW8+KyYR+3Rqn07gt+p5abZuhPXjAZeUQQghROUlwdsDVm5EAGKNyxp1jnR93dluxDMVq\nRb9ti8vKIYQQonKS4OyAq7fxBDAHd8JStx6G2OVozp9znEBV0W+3BWXd8aMuK4cQQojKSYKzA02b\nqvj4qC5b6wyAopDx7BQ011PwjbkfJSGhyMe1p0+iiY+3/XziuOvKIYQQolKS4OyARmObFHbihIb0\ndNflmzXuH2Q8/Qy6kyfwfXg4RWWu37bV/rP2+LGSvVBV8ZjzMbqd20uWXgghRLmR4OyEjh2tqKrC\noUOura706a+QNWwE+l07qTHhETCbC3xOlzPObGnQEO3VKyWata09eADvl6bh9fYbpSmyEEKIciDB\n2Qm548779rlu3BkAjYbU9z/B2Dcct1WxeD//bIGP6bdtwVrDl+y77wFAe7z4XduGtatsrzx7puTl\nFUIIUS4kODuhWzdbcI6L07k+c4OB61/Ox9TxDjy++xbd/r15bitXr6I7fQrTnd2wtG0PgO5E8bu2\nDWtswVl74XyhLXQhhBCVgwRnJzRpotKunYUNG7QuHXfOpXr7kPHvaQC4/7Agzz39dtt4s7l7KOZW\nbQDQHivejG0lJRn9jm22n81mNH9dKG2RhRBClCEJzk6KijKTna2wfn0ZtJ4BY3gE1toBuC1ZDNnZ\n9uu565pN3UOxtG4NgLaYLWf9hjgUiwWrt48tvXRtCyFEpSbB2UlRUbau4NjYsgnO6PVkDY9Bk5SU\n50hJ/Y6tqHo9pk4hqDX9sATWQVfM5VS5XdrZI0YCEpyFEKKyk+DspM6drQQEWFm1SovFUjbvyIoZ\nBYD7wu9sF9LT0e3fh/mOzuDhAYCldRu0588VufQqD1XFsHY11lq1yBr6ACDBWQghKjsJzk7SaGyt\n5/h4Dbt2lU21Wdq1x9SpM4Y1q9BcuYx+zy4UsxlT99Abz7SydW3rTp1wKk/toYNoL1/C2Lc/luYt\nbNckOAshRKUmwbkYyrxrG8iKeRjFasVt8aI84825zK3bAs5vRmJYuxoAY/9I1IAAVE8vCc5CCFHJ\nSXAuht69LXh4qGUanLPvH4ZqMOC+cAH6rZsBMN3Z3X7f0jpnxvatwVlVMSz/AyUpMc9lw9pVqIqC\nsV8EKAqWps1swVlVy+w7CCGEKB0JzsXg4QF9+pg5flzL6dNKmbxDrelH9sC70R0/hv7P9ZhbtUat\nVct+Pzc4624Jzm6LF+I7diQ17x1k36tbSb2OfvtWzJ1D7HlYmjZDyUhHuXatTMovhBCi9CQ4F1N0\ndDl0bY+0TQxTrNY8XdoA1sA6WH1r5ltO5f79fAB0Rw7jO3woSlIi+vVxKGazrdWcw9K0GSDjzkII\nUZlJcC6miAgLilK2XdumPuFY6taz/dytR96bioKlVWu0p0+B0QiA5vQpDJs3YgzrReaYcegP7sd3\nxH24LV0C2Mabc90IzqfLrPxCCCFKR4JzMQUGqnTpYmXbNi2JiY6fLxGtlswJT2H18sbUp1++2+bW\nbVAsFrRnbAE2d+lV1qgxpP3nXTJHPox+7x7cly7B6u+PuXMXe1ppOQshROUnwbkEoqPNWCwKa9aU\nXes58+lJJJy8gLVe/Xz3LDfP2LZYcF/4ne1gjMH3gEZD2rsfkTVsBADGvv1Be+PADgnOQghR+Ulw\nLoHcJVW//152wRlFyRNUb5a7jafu+FEMcWvQXr5E9n3D7BuVoNWS+uGnpL73MekvvpwnrbVhI1Sd\nToKzEEJUYmUYXaquNm2sBAVZWL1aR0KCQq1a5bssyb7W+cQxdIcOApA1anTeh3Q6skaNyZ9Yp8Pa\nsJEEZyGEqMSk5VxCDz5owmxW+PXX8v/9xtqwEaqnJ/qdOzDELsPcLsi2xaeTLE2boYm/hpKWWoal\nFEIIUVISnEvo/vvNaDQqixfry//lGg3mFq3Qnj+HYjKR9dDDtm5wJ+WOO2vOni3yOd32bRh+X1qa\nkgohhCgBp5p9s2bNYt++fSiKwrRp0wgODrbfCw8Pp27dumhzxkffeecdzp49yzPPPEOrVq0AaN26\nNdOnTy+D4lecOnVU+va1sHatjpMnFVq2LN+ubUvrNugP7EPV68kaFlO8tE2bA7ZJYZYOHQt9zuff\n/0J7/CjxAy6Bm1upyiuEEMJ5DoPz9u3bOXfuHIsWLeLUqVNMmzaNRYsW5Xnmiy++wMvLy/757Nmz\ndOvWjQ8//ND1Ja5Ehg83sXatjsWL9bzwgrFc3527U5gxenCeHcScSuvMjG2zGe2JYyhmsy2It2lb\n4rIKIYQoHofd2lu2bCEiwrbDVIsWLUhJSSEtLa3MC3Y7GDjQjJeXyk8/6bFay/fd2ZHRWJo0JePp\nScVOa2l2o+VcGO3ZMygmk+3nUydLVkghhBAl4rDlHB8fT1BQkP2zv78/165dw9vb235txowZ/P33\n33Tp0oXJkycDcPLkSZ544glSUlKYOHEiYWFhRb7Hz88Tna7gpUPOCgjwKVX6khg+HL7+WuHoUR/6\n9CnHF/frCWfP4FeStF62rmyPi+fxKKDOAgJ8YNN5+2ffKxegAur2dlcRfx+rIqlH15B6dI3yqsdi\nTzVWbznNaNKkSfTq1QtfX1+efvppYmNj6dy5MxMnTmTgwIFcuHCBMWPGsHLlSgwGQ6H5JiVlFL/0\nNwkI8OHatfKffXzPPVq+/tqTzz830r59drm/v6T869SF4ydJvKXOcuvRc8cecgcqMvcdJK0C6vZ2\nVlF/H6saqUfXkHp0DVfXY1GB3mG3dmBgIPHx8fbPV69eJSAgwP753nvvpVatWuh0Onr37s3x48ep\nU6cOgwYNQlEUGjduTO3atbly5Uopv0bl1LOnhQYNrPz2m57MzIoujfOsTZuh+fsC5HRd30p77Kj9\nZ510awshRLlyGJzDwsKIjY0F4NChQwQGBtq7tFNTUxk/fjzGnAMYduzYQatWrfjtt9+YN28eANeu\nXSMhIYE6deqU1XeoUBoNDBtmIi1NYcWK22dPF0vTZigWC5oL5wu8rz1+DNXDA0vjpjLmLIQQ5cxh\nNAkJCSEoKIiYmBgURWHGjBksWbIEHx8fIiMj6d27NyNGjMDNzY327dsTHR1Neno6U6ZMYc2aNZhM\nJl5++eUiu7Rvd8OHm/ngAzd+/VXHffeZK7o4Trl5xra1eYtbblrQnTiGuWVrrHXr4rZ6JUpKMqpv\nzQooqRBCVD9ONfWmTJmS53PbtjeW1YwdO5axY8fmue/t7c2cOXNcULzbQ+vWVho3trJ5sw6LpdAt\nsSsVe3A+c5pbO7Y1F86jZGVhad0Ga0AArF6J9tRJzCFdy7+gQghRDckOYS5y111mUlIUDh26Paq0\nqLXOuuO28WZLm7ZYWtg2ktGePFF+hRNCiGru9ogkt4G77rIAsHHjbdBs5qZdws7lD87aY8cA2wEb\nlhYtbddOl2DcOTMT93mfcVvNlBNCiEpAgrOL5AbnTZtuj0lhqr8/1toB6HbvgluWx+lO2IKzreWc\nE5xPnSr2Ozy+nofPC8/jvmRx6QsshBDViARnF6lbV6VFCytbtmgx3w5zwhQFY3gE2qtX0B3cn+eW\n9vhRVL0eS9NmWOvVR/X0RFeCbm3D+rW2/KRLXAghikWCswuFhZlJS1PYt+/2qFZjxAAADKtX3rio\nqmiPHcPSshXodKAomJu3RHvmFMXaozQ7G/3WzYCDPbyFEELkc3tEkdvE7da1bewbjqrR5A3Of/2F\nJj0Nc+sbM/ItLVqiZGSguXzJ6bz1O7ejZNh2fZPgLIQQxSPB2YV69ry9JoWpNf0w39kd3a4dKIkJ\ntouHDwM3Tr0Cbow7F6N7Wr9hne0dioLm7Jl849pCCCEKJ8HZhQIDVdq0sbB9uxZj+Z4gWWLZEQNQ\nrFYMcbbx4dzgbG6Tt+UMxTudyrAhDlWrxXRXHzTpaSg3bQErhBCiaBKcXSwszEJGhsKePbdH69nY\n/5Zx59yWc6sCWs6nnGs5KynJ6PbsxtzlTswdg21pz5x2VZGFEKLKk+DsYmFhuePOt0dwtgR1wFK3\nHoZ1q8FigcOHUbVaLDdt6VnclrN+00YUqxVjn343bXYiwVkIIZwlwdnFwsJs66hul3FnFAVjxAA0\nCQno9uyCw4exNGsObm72R1TfmrY10U4GZ0POeLOxd78idyITQghRMAnOLubvD0FBFnbs0JKVVdGl\ncU5u17b7D99BcjKWm2Zq57K0aInm/DnIdnxmtX79Oqxe3phDukhwFkKIEpDgXAbuustCdrbCrl23\nR+vZ1LsPql6P+4/fA2Bu0ybfM+aWrVCsVrTnzhaZl+avC+hOncQUdhfo9VgbNkLV6SQ4CyFEMUhw\nLgO3W9e26lMDU4+eKDmt4gJbzs2dG3fW/7keAFPvvrYLOh2WRo1lzFkIIYpBgnMZCA21oNGobNhw\ne2xGAje6toE8G5Dkcnats2H9jfHmXNamzdDEx6OkpbqiqEIIUeVJcC4Dvr5w550Wdu7UcPWqUtHF\ncUruVp4oim3rzlvkXrOfTqWqGFYsw+uV6eg3/Wnb2lNVMWyIwxJYB8vN66Rzxp01Z6RrWwghnCHB\nuYwMHmxGVRViY2+P1rOlVWvM7YIgJAQ8PPLfb9IUVaNBd/IE2uPH8B1xH75jYvD85ANq3jcY/253\n4P3v59DEX7N1aSs3fimxH08p485CCOEUCc5lZNAg27jzH3/cHsEZRSF56TJYvbrg+25uWBs1Rrdn\nF359QzHErcXYN5yUr74jc+TDKAkJeHwzDwBj7nhzDvuMbdmIRAghnCLBuYw0bqzSsaOFP//Ucv16\nRZfGOWpNP6hZs9D75rbtUIxGrA0akvLtQlIW/YJx8BDSPvgvCQdPcP2Tz8mY+CzZQ+/Pk84enM9V\nfMvZsDoW/67BaBzMOhdCiIokwbkMDRpkxmRSWLXqNmk9O5D+8utc//BTEv/cjjF6UJ6ua7y8yB4e\nQ/pLr+brFrc0aQqUUbe20YiSlOj0425LfkJ7/ixu//vN9WURQggXkeBchnK7tpctqxrB2dKiFdkx\no8DdvXgJPT2x1K1XYHDWHj2C28Lv8Hr5RWo8NAy/0BDcv5rrdNa+Y2Ko3aYp/iFB1HhkFJ7vv2Pb\n6awQufcMf8YV7zu4kNfMV/DrF+bUhi5CiOqpakSNSqptWyvNm1tZs0ZHZmaB86yqDUvTZui3b7UF\npJytQbWHDuIXHoZyy3GSHl/PJevRxxzmqTl7BsPa1VhrB6BkZeG27Hfclv2O539mkXDgBGqtWnme\nV1KS7VuQ6rduBqMRDAYXfUPnKEmJeHz2CUpWFroD+zB37Vau7xdC3B6k5VyGFAUGDTKRkaGwfv3t\nsSFJWbE2bWbbYezCefs1j/lfoagq6f+aQtJvscQfO4uxV190Rw6jJCQ4zNN98UIA0l56lYRDJ0nY\ne4TMMeNQzGZb8L2Fbu8eAFSdDiUjA/2uHS76ds5zX/Q9Ss6+rvqd28v9/UKI24ME5zI2eHBu17a+\ngktSsfKdTpWZidtPP2KpU5eM56dh7hGK6uePKbQnAPptW4rOUFVx//EHVE9Psu8eCoqCtX4Dsoc9\naEu/dVO+JPqcLu2s4TG2zzkbppQbVcX9my9RtbZf1HQ7y/+XAyHE7UGCcxnr3NlK3bpWYmN1mM0V\nXZqKY2lmW+usyRl3dvvfUjTXU8ga+TDoboyumHreBYB+y8Yi89Nt24r23FmyB98D3t430ncKQXVz\nQ785f3DW7bYF58ynn0HVaDDkbDVaXvR/rkd36iTZ9w/HGhAoLWchRKEkOJcxjQYGDjSTlKSwZUv1\n7dq+9XQq9+++BbAF55uYQrqiGgwFBtebuS/+wZb+wZG33HDHFNIV3cH9KNdT8tzS7d2NpW49LK3b\nYO7cBd3unSip5bfOzeNr2zrwzEfGY+raDe3Fv9Fc/Lvc3l8ahmX/o8ao4ZCRUdFFEaJakOBcDm50\nbVff+Xc3B2ftqRMYNm/E2KsP1pwWtV0RwdUuMxO3pb9gqd8A01298902hfZEUVXbBLQcmksX0V6+\nhLlzFwCMvfugWCzotxT9S4CraC5dxLD8f5g6BGPu2g1TzkQw3W3SenZf+B1uq2IxbP6zoosiRLUg\nwbkchIZaqFlTZflyHbdMTK42VD9/rL410Z45jfv3CwDIGjWmwGdNPW0zuAsbd3aLXYbmegrZw0aA\nNn9vhKlHGAD6LTcmheV2aZtCbMHZlHMwh35DXMm+UDG5L/gGxWIh65HxoCiY77QFZ/2O2yM4a08e\nB0C/1cFcACGES0hwLgd6PfTta+biRQ0nTlTfKrc0bYb2/DncF36HtWZNsgcNKfA5U2juuHP+GdcA\nbj8W0qWdm/7O7qg6HfrNN8at9Xt3A9hbzqau3VA9PMpn3Nlsxn3BN1i9fci6f7jt/Xd0tpXxdmg5\nm0z24YiCZsELIVyv+kaKctanjwWgWi+psjRrhpKdjebaVduM6UI2MzF17WYLXAVMClOuXMGwbg2m\nziFYWrcp+EVeXpjv6IRu3x5ITwdutJzNd3SyPePmhql7qG3Z1pUrpf9yRTDELkd76SLZI0bemLzm\n4YG5Q0d0B/ZV+s1ItOfOouTMZtTt3Q05S8GEEGVHgnM56dPH9o/b+vXVedz5xvhy1kMFd2kDOcG1\ns21dclpanlvuSxbbuocLaTXnMvUIs6133rUDrFZ0e3djbtkK1ffG3uHGXn0BMGwsu9azbv9evF6f\nAUDm2PF57pm73IliNKLbv7fM3u8K2hO2Lm3V3R3FaLT3Qgghyo4E53LSsKFKy5YWNm7UYjRWdGkq\nhjVnUpipcwiWoA5FPmvqeZdtwtbN3b7Z2bjP/wpVryf73mFFp89dL71lE9pTJ9GkXrd3aduf6d3H\n9kxR484WC57vvInXjP+H57v/wX3eZ7j9tMjxJilZWXi9/jI1o/qhO3WSjCf/iaVtu7zvz5kUpq/k\n651zg3P2kHsB6doWojxU32ZcBejb18LcuVp27dISGmqp6OKUO2OPnlj9/cn453MOnzWF9oSP3kO/\nZSOmvuEAeL01E93JE2SOGZdva8586buHoioK+i2b7GusTZ1D8jxj7hCM1c8Pw4Y4UNW8B3nk0G+I\nw+s/s/Jdzx4QzfUFPxb4bt2uHfDsU3geO4alcVNS3/3Qdsb1rWW0B+ftZBb5bSqW9tQJALIeHov7\n4oUSnIUoB9JyLke5XdtxcdVz3NnavAUJR89ivPseh8+auoeiajT2SWH6zRvx+OQDLE2bkfby6w7T\nq741MQd1RL9rh33W960tZ7RaTGG90f79F9ozpwrMx7DJtnQo9Y13SP7xV1LmfoO5XXsMq1eiuXI5\nf4LMTHxHDYfjx8mY8CSJ67cUGJgBrI2bYA0IrPTLqXQnjqPqdJi6dsPcvAW6HdvBUv1+uRSiPElw\nLkdhYRZ0OrVajzs7S/WpgbnjHeh370S5ehWfiY+DonD9k8/z7AhWFFNoT5TsbNx/Xoyq12MO6pjv\nGWNO4NSvW1tgHvpNG1B1OrJGPISpbzjGe+4jc+x4FKsVt8WL8j3vtnQJmsRE+L//I/31t8DLq/AC\nKoptM5JLF9H8/ZdT36ncqSrak8dt69T1ekw9eqJJvY728KGKLpkQVZoE53Lk7Q1du1rYs0dDUlJF\nl6byM/XoiWI0UvPBe9H+dYGMZ6dgvrN7MdLb1jsrGemYgzoUODvc2D8SAMOalfnuKWmp6Pbuwdwp\nJM8vBNn3PYBqMOC+cAG3Llz3+OoLVI0GnnjCuTLe1LVdGSkJCWiSk7G0bA3Y/kwA9Nuka1uIsiTB\nuZz16WNBVRU2bpTWsyO5+2zrDh/E1DmEjMn/V7z0OYEEsAXYAlgbNcbcth2GjRvybU2p37YFxWLB\neMsuZKqfP9kD70Z3/Fies6N1e3ej37Mb44BoaNLEqTLmbkZSWbu2dTmbj1hatgJsww0gm5EIUdYk\nOJezvn2r97hzcZh62CZ1qR4epH7yhW03l2JQAwIwt8pp8YV0LfQ5Y0QUSlYWhk0b8lzXb7SNN5vC\neuVLkx3zEGDb1jKXu33vbMdnUeeq7JuRaE/aJoPl1qO1aTMsgXVsk8KKsd2dknod739NRLd7Z+Hv\nOnEc5dq10hVYiCpCgnM569TJiq+vbdy5um7l6SzVz5/UDz8l5Zsf7C234jKGR6BqtZi69Sj8mcgo\nAAyrYvNc12/agKrXYyqgK93Ytz+WuvVw++VnyMpCSU7CfcliLE2b2WeXOyV3M5L9+yrl5h65y6js\n9a8omHr0RHv1Cpozp53Ox/M/b+Dx3bd4fvBugfeV6yn4RfbGd9SwYgV9Iaoqp4LzrFmzGDFiBDEx\nMezfvz/PvfDwcB566CFGjx7N6NGjuZKz21JRaaozrRZ69TJz/ryGM2fyL90ReWXnTMQqqfQXXiJp\n/VaszVsU+ozpzu5Ya/hiWLPKHhiU6yno9u/D1OVO8PTMn0irJXt4DJqUZNxW/IH7wu9QsrJsrWZN\n8X7nNYXehWIyYVi1oljp7DIy8Hl6ArptWx0/W0zaW7q1wdajAeQ5WKTIPI4fw2PeZ7Y0G+IoaKG/\nfv06lIwM9Hv3oN8kh2sI4fBfke3bt3Pu3DkWLVrEzJkzmTlzZr5nvvjiC+bPn8/8+fOpU6eOU2mq\ns9ytPOPiZNy5zHl6Fr7NZy6dDmO//mgvnEd77Chg29dbsVrt494FyYoZBYD79/Nx/3oeqrs7WTnd\n3cWR9fBYADzmfV7stABuq1bgvngh3q+8WKL0RdGePIG1dm1UP3/7NVP3nElht653LmgbUlXFe/pU\nFLMZc7sgNOlpBR5o4rbyxi8mHnM+dk3hhbiNOQzOW7ZsISIiAoAWLVqQkpJC2i1bKroiTXVyYytP\nGXeuLIwRA4AbXdv6jbbx54KOpMxladUaU5c7McStRXf6FNlD70f1L3pzlMLyMfbph2HzRrSHDhY7\nvX79Ott/d25He/RIsdMXKjsb7bmzWFrkHVKwtA/C6lMD/dbNKFev4vHFp9SM6kvtJnXwnP1Wnm5p\nw8oVGNatwdinH2kzXrVdW7Mq73usVgxrVmIJrIOpy524rVxh704XotpSHXjxxRfVVatW2T+PHDlS\nPX36tP1zv3791MmTJ6sxMTHq22+/rVqtVodpCmIymR0VpUpp0UJVfXxUNS2toksiVFVV1atXVVVR\nVLV3b9vnzp1V1c1NVTMzi043Z46q2sKRqm7bVvL3L11qy2PChOKls1pVtUmTG2V45pmSl+FWBw/a\n8nzssfz3Bg603dNqb/zXz8/284gRqpqerqpZWba/6Fqtqh46pKoZGarq4aGqQUF589q61ZZu/HhV\n/ekn28+PP+667yHEbajY/arqLZM1Jk2aRK9evfD19eXpp58mNjbWYZqCJCVlOHymKAEBPly7llqq\nPMrTffcZeOcdNz76KIvx400VXRy7260eXcedmiFd0G3aROKO/fjv3Yup512kpJogtfA/HyViMP7e\nPljatCG5aVvIqbti12O33vg3boJmwQISpvw/1Jp+TiXTnD5FrXPnyI4aiH73LvjmGxIm/79CT/wq\nDsP2vfgCaQ2bkXnLd3HvH43P8uWY7uhE9gMPkjX0AVAUfMc9jH7RIkxHj2MKDcPz1Cky/vEE6QGN\nIM1MjbBeuK1eScKew1gbNgLA88ef8QJS7grHGNYf/8ZN0XzzDQnP/B+12zWrpn8fXav6/n/tWq6u\nx4AAn0LvOezWDgwMJD4+3v756tWrBAQE2D/fe++91KpVC51OR+/evTl+/LjDNAIefdSEm5vK558b\nZCfESsIYEYViseD15msoqlrgEqpbqTV8SVq7kZT5Pxa4N7fTtFoyH3kMJSMD9x++c/x8DkPOoR3G\n8EiyRj6MJjkZt/8tLXk5bnJjjXPLfPeyRj9C/Om/SV6xjsx/PIkaGIgaEEDyz7+T+dBo9Pv24Dnn\nY9te6s+/YE9n7J8zfHBT17Zh1UpUgwFTn762enj8SZSsLDy+meeS7yHE7chhcA4LC7O3hg8dOkRg\nYCDeObslpaamMn78eIw5sy937NhBq1atikwjbAICVIYPN3HmjIbYWJkYVhnkLqlyX/ITUPD65oJY\nmzZDrV271O/PGjUa1d0djy8/d3rvakPOeLOxTz8yR9mO4XSf/3WpywI3rXHO2R0sD0VB9S7gt36D\ngbT3PibttTewevuQ9tqbeXoBbt2RTXPpIvoD+zCFhtnzyxr5MNYavrYJcpVweZkQ5cFhVAgJCSEo\nKIiYmBgURWHGjBksWbIEHx8fIiMj6d27NyNGjMDNzY327dsTHR2Noij50oj8Hn/cxIIFBubM0TNo\nkLmii1PtmTsEYwmsg/bqFVR39yI3LikLqp8/WQ88iMd332JYuwpjZHTRCSwW9Bs3YGncBGuz5qAo\nGHv1wfDnerQnT5R4bXgu7cnjqHo91sbO7XZmpyhkPv40mf94Mt+yMmvTZphbtsKwYT1kZ2NYbQvS\nxgE3vqvq7UPWmEfx/Ph9+P57GDK8VN9DiNuRU022KVOm5Pnctm1b+89jx45l7NixDtOI/Nq0sdK/\nv5k1a3Ts2aOhc2drRUc8n44AACAASURBVBepetNoMEYMwOP7+Zju7AFubuVehMxxE/D47ls85n7m\nMDjr9u1Bk5JM5pCh9i71rNGPYPhzPe7zvyb9lVIsYVRVtCdOYGneAnQl7NkpZL23sX8knp/9F/3W\nzfa13dkRUXmeyXzscTzmfIzy6aflEpyVpEQMcWvR7dpB1tjxWFoV0FsgRDmSHcIq2BNP2IYE5swx\nVHBJBIBx0N22/4ZHVMj7LR2DMXUPxbBuje1c6CLkdmmb+vSzX8seeDdWf3/cf/y+4HXHTlKuXkWT\nej3fMipXyB13dlv2O4YNcZhbtba1/G9ird/AdijI7t0oaWUzkUlJvY7Hh+9Rc0gUtdo1p8bj4/D8\n/FM833u7TN4nRHFIcK5gvXtbaN/ewm+/6fjrL9kxrKIZI6NJ+i2WzAlPVlgZ0qdMRdVo8B09osgt\nMvXr16EqCsa7+ty46OZG1oMPoUlIwG3FHyUug30yWBm0IE2hYaienrgv+AYlIwPjLa3mXOau3cBq\nRbd3j2sLoKoYfvsFv55d8X59Brod2zB3uZP0F6ZjrV3btsZdthAVFUyCcwVTFFvr2WJRmDtXWs8V\nTlEw9wgt9iEbrmTq04+0N2ejiY/HN+Z+lJtWPtilp6PfsQ1zxztQa+Xd+CRr9CMAuC10ftb3rXI3\nATGXcty6QG5uGHv1QTHZlqjdPN58M1OXOwHXHqepOXeWGg8Nw/exsWiSk0h//gUSDp8i+Y9VZPzr\neYy9+qC9fMk+GU6IiiLBuRK47z4zgYFW5s/Xk5xc0aURlUHWI+NJf3YKujOn8R39YL7jLA1bN6GY\nTHm6tHNZWrXG1PEODBviUJJLdnC49pQtOJV2UllhjOG2WdvWGr6FHkpi7moLzo66953ltuh7/Ht3\nx23NKoy9+pK0fgsZz7+QZ1c3U04vhP7P9S55pxAlJcG5EnBzs7WeU1MVPvlEWs/CJuOF6WQ9OBL9\nrp3UePzRPMuK9HE3llAVJPuee22HaaxYVqJ36w4eAMowOEdGoer1GKMGFtpLYa1TF5o0Qb9rR+m6\nma1WvGa+Qo1/PoFqcOP6f78g5aelWJrnX79t7GULzgYng7P21Ak058+VvGxCFEKCcyUxfryJevWs\nfP65gcuXZexZAIpC6rsfYezTD7fY5dQKaY/n22+gxMdj2BBnW+5VSKsze8i9ALj99kvx35uWhn77\nVkzBnVB9a5bmGxTK2rARSXFbSHvznaIf7NEDTXw8mrNnSvai9HRqjB+D5wezMTf7/+3dd3hT1RvA\n8e9NblYHtECLgiBLGWWLDBkKMgUVF5QpKggqONmi4EJAcCCKLGX5YwgVQVGQKQoFEUWmgChFRqHQ\nQtukSW5yfn/cthC7S6Apns/z+Dw0uffm5PUmb86uQtK363E+3D3HBWO8N1fCU6Eipp9+yHuuuRCU\nfKALJft0L1zZJCkXMjkHCJsNhg1z4XAoTJkia89SOrOZC599jv3ZF8GtEfzO25RuWAv1wD7cTZrl\nuEynt0pV3LXrYt68EeVCwfpKzD9tQXG7r/qIdc8ttyJCS+R+UFP9x4epEE3bypkzhHW9B8s3K3Hd\n0YKkb9fn3RKQPlfckJSEum9ProcaTp3EePoU6oH9KInnsz3GvPJLSt7fibA2LSh1e11K16xMeKsm\nIDcCkvIgk3MAiY52U62ah4ULTRw9KmvPUrqQEFLHjOPcr/tJeWsi3sgbAHB27Jzraa5779ebtr8t\n2KjtjNW7MvqFi1Sz9L2jCzEoLHjSeEy7f8XRsw8Xlq7I945h7vSmbdMPuTdtqwf2Xfp3DiPKgydP\nwLztJ4x//5W5j7V68ACm33blqyz/pqQkY5v5MYbTpwp1vlR41rlzYMWKa/Z6MjkHEFWFUaP0kdsT\nJlz7BTCkABcSgmPAU5zf/ivnN/xE2mP9cz3ceV960/aqAnyhCIF5wzq8JUpmDsgqUvXrI8xm1F92\nFuy8tDQsX8XgueFGUqZMBXP+W6Mytgk1/5h7cjbu35/57+ySrXIhCfXgAVwtWnHu6AnO7z5Iynh9\nDnVetfJsX+/QH4R1aE3ImJGEjHipwOdLhWeK3Uro8Bfgs8+u2WvK5BxgunTRaNDAw4oVJnbvlv97\npGwYjXhq18lxBa4Mnqq3oNWqjXnThnw3bRuPHsEYdwx3q7sKvzKYP1ksaHXq6cnMnv+d68zr1mK4\nkITzwUfAWLB9071lb0CrXgNT7NbM2m52fGrOv/6S5fmM2r67cZPMx7SoOvrxewuWnM0rvySsQ2vU\nw4fwhoVh/u4bDMf+LtA1pELyeAh+eYT+71Gjcj/Wj+S3f4BRFBgzRl/Z6c03Ze1ZujK5jtrOZgR0\nxm5RGRtUBAJ3o8Yomobp9998Hg9+ZSThdzbLtv/W+sViANIeiS7Ua7pa3olit2PalXONXT2wHxEU\nhOeGGzH9sjNLPNUdsQBot19Kzp6q1RA2G8Z9e3O8ruHkCdQ9uzFt/RHzmm8JHj2Mkv0fRRGCi7Pm\nkvLWJBQh9I1BpKvOuvhzTHt26/dS0+wHYF4NMjkHoJYtPdx1l8bmzSozZhTdYhhS8ee87wEAn20k\n1T27Cb/rDkr0fBi8vuu5mzesA8DV+u5rV8g8ZM533nlpUJhxz+/YZk5HPbCPoNmf+ByvJJ7HvG4N\nWs0oPFG1C/Waec53drsxHjqIVr0G2m23Yzh7BsPJEz6HmHZsRyiKvgxpZsGNaDVrof5xINtauXnV\nCkrXr0n43S0J63oPJft0J2j2DLRbbiVx7Sac9z+I874H8EZEYv3fgnwNLCvZrSv06pX/Ny9lUi5e\nIPit1xBBwaSOGXdNX1sm5wD1zjtplC3r5ZVXrCxeHADNi1Kx5Kl2C1rNKMwb16NcSMI24yPCOt2N\nun8vlvXfY4n54tLBDgemrT+i1YzCW6580RX6X7KsFCYEIa+9giKEvsXmR1N9mu0tK1eguN2FrjUD\nuJu3QBgMOSZn459HUNxutJpRuBvcBoC667Kmbbcb066deGrUzDIdTYuqg+J2Yzz0R5brWtZ8C4Cj\nV19SXxxOyri3uDh1OklrNuK5tXr6QRYc/Z7AcPEC1qWLcn0fSlIi5k0bYNEilPj4fL77wGeb9gHh\nzRtBaupVfZ2gd9/BkHAW+/Mv4b2x3FV9rX+TyTlA3XyzYOlSB2Fhgueft7J6tUzQUuE47+uK4nIR\n1v4uQl4ZhShRkuT3piEsFoLfGJv5BWfa9iNKWlqRbfqRE2/5m/DccCPqzh0gBKaN6zD/sBHXXW1I\nHTYaw4UkbB9PzTze+sVihKLgfPDhQr+mKBmGVq++PoUrmwSQ0d/sqVkLraGenE2X9Ture39HcTj0\n3c3+JbPfOZtBYabYbXjDwkiZMhX7yDE4nh6CM7pXlr2zHY8+gTCbsc3+JEvrh085M5rPhbiitdYD\nitNJ0Ifvoh4+hLrn96v2MsY/D2ObNR1PxZuxDxp81V4nJzI5B7CaNb0sWmTHaoUnn7Tyww8FG9gi\nSXCpaVv96yiuu9pwfuNW0nr1xf7UEIynTur7JnNZk3aAJWcUBe222zGeiccQd4yQ115FKAopY9/E\n8cSTeCLLEjRjOsrZsxj+/ktfQKXFnVdc+3e30Nf/Nm3fluU54wF9pLZWqzZavfoIRfEZFGZK72++\nfDBYhpwGhRlOncQY97e+sEweg/1EZCTOrg+hHjmMadP6HI9T915KXpd3bRRnltWrMCTqy9KqB/fn\ncXTBKfHxmL//jpAXhqC43aSMfTPH9QSuJpmcA9xtt3mZP98BQN++No4ckfOfpYLx3HIrKWNeI3nC\nFC4sjkGULQuA/dkX8ZS9gaCPPsDwz3HM679HBAXri5sEmIym7ZCRL6Ee2Ieze0+9PzkoCPsLQ1Hs\nqQRNfRfr8qUApD1y5at2ZS7l+cOmLM+p+/UaqVYzChFaAs+t1VF3/5a5qphpx3a93Nms4OaJivK5\nRgZT7Fb9nCZ35Kt8GTunBc2cnuMxmTXniAhMP23JcbGU4sS6cF7mv/2WnN1uQocMolT9mpSpcwsl\ne3XDHLsV152tcXW5zz+vUUAyORcDrVp5eP/9NOx2hYkT5QhuqeAcz75A2uMDfGtkISGkvjwWJS2N\n0KcHoP55BFerOws0J/hayRhUZVn/PcJmI3XkmMzn0nr3w3NTBWxzZ2NdOA9hs/nlC9XdpBkiKAjz\nmtVZR2If2I83IhJRpgwAWoPbMKQk67tZCYG6IxZPZFm8N1fKcl0REoqnUmW9VnvZdTNq6O6m+ftx\npNWtr+/9vWFd5i5i/2bcuwdhtcKzz6JoGub0Pu1rSggsSxdltjZcCcNfRzFv2Yy74W0IRcF48IAf\nCgimrT9iXfI/FGcazg6dSB0+mgv/+4ILC5fmuNTr1SaTczHx0EMa9et7+OorE/v2yf9tkn84u/XA\n3aAh5vRam6t1gDVpp9Pq1kOkz7u2D3zGt8naYsE+dCSK04nxxD84O3XO0kdbKDYbrjbtUP88gvGP\ng5kPK8kXMR6PQ6sZlflY5qCwX3/BcDwOY/xpfQpVDl/sWu26GBITMZw6mfmYKXYbwmpFq9cg30W0\np9eerfM/zfqky4V66CBazVrQrRugNwnnix/3szZt2kCJwQMJ73Q3pmxaIQrC9r8FADieGIinchW9\n5uyHspo3bQDg4kezuLhgCfahI/V9xi1FVxmS3/LFhKLAiBH6/OdJkwKvZiMVUwYDKW9MzPwz4Pqb\nMwQF4W7WHM8NN+IY8nyWp9O69UCrqu8y5XzYfxtRONNr4Jf31xoP6LU1rWatzMcyB4Xt2nlZf3PO\nc2K12hn9znqfsHIhCeOBfbgbNipQy4WrY2dEUDDmjVn7nY2HD6G4XHof9623otWoqY/aT0nO9ZrB\nb71G6eo3Y50zM9fBZvll+2xWemGdlOz5MOZv8vkD4d/cbiyLFuItGYazy/14atTCcP48hjNXPgrd\ntHkjwmzG3TR/XQrXgkzOxUibNh4aNfLw7bdy9TDJf7TGTbA/9xKOHr2zbYYNFBfmLSLxh9jsN8tQ\nVZI/nkXqsFF+rf272nVAmM1YLksoGSO1tcvmUGs1oxAWC+qvu3IdDJZ5fOaIbb1P2LQjFkWIfDdp\nZzKZcDdpinroDwzxp32eyhgNnvFazs73oTidmQvNZMe4fx+2D9/DkJRE6KihhHVp79N0rFxIwrz6\na2wzP85X/7XheBzmtd/hrt+AC4tjQDVR4ok++hztAjJ/vwbjmXh9PIHNhlajpl7mK2wuV86exbT3\nd72vPyjoiq7lT/IbvhhRFBg1Sq89y75nyZ9SXx5LygcfF3UxchcSgggLz/FprcFt2IeNKvBynbkR\noSVwtboLdd8eDH8dBS4N5PJcVnPGbEarXQd13x5MP/6gN0/XqZdzWWv7jtg2xab3N+dzMNjlXM31\ntcBNP23xeTzj2lrtuoCenAHMX6/M/kJCEDJmBIrXy8Wp00nr+iCmnTsIv7sFoc8+RVinNpSuXomS\n/XoSMmYkYR3bZDtX+3K2+Z+heL04Hn8Sd6u7SIpZhShZktDnn8E6L5um+FxYF84FIK3Xo8Cl+F/p\noDDzD7nvjV5UZHIuZlq29NC8uca6dSo//yz/90nS1ebqcj9AZu3ZeGA/wmBAu7WGz3HuBrehaBrq\nkcN6H3QuzdPecuXxhoVhTK/dmrZv0695e+Mcz8mJu0VL/Rr/Ts7p184YHe6Jqo2nUmUs36+BtLQs\n1zF/vRLzjz/gbNcBZ3QvkmfO5cLCJfpqZIs/R/3tV7RGjUkdOhL7oMGofx0lrGMbzGtzGGTmdGL9\nfB7e8HCc9z8IgNawEUkr1+AtU4aQV0dhOB6Xr/doOPEP5g3rcDe8LXPVN62GnpyzGxSm/rydsLat\nchwo5/O+N+vJ2d26Tb7Kcq3Ib/diaMQIfdm/jJ2rnE44e1bhn38Uf47jkCQJcHa4B2E0YvlmpT4S\n+8B+PJWr6JuwX0ZLHxQGoOXS3wzoc7dr18X411GUhATU33ah1alXqIFsWt36eENLYPrxh0sPCoG6\nbw+eSpUvXVNR9KZteyoZA6AyORyEjHsZYTKR+vr4zIdd7Ttx/sefSVy1lnOHjpH09Vrsw0eT+vp4\nLs74FMWjUaJPNLap72YZmGVZtQJDQgJpPfr4xMpTvQYp495CcTgIeSV/G0lYFy1E8XpJ693v0nWq\nVNV3LMum5mxbMBfT778RPOHN3C8sBKZNG/CWKZPZ/B8oZHIuhpo21dfe3rJFpWLFECpUCCUqKoSG\nDUOYNk0OFpMkfxKlS+O+owWmX35G3bUTw4UkPLWyrtmdMSgMcu9vzjw+qjaKEFj/twDF5cLdpJCb\nKqgq7mZ3oP51NHN9b8OpkxjOn8+ScDIHuK1Y5jPYK+jjqRiPx+EY8BSeqrf4Xj8kBK1J0yx9/c4H\nHiZp5Xd4byxHyJvjCH3mSb2mkM726SyEouDo90SWIjsficbdpBmW1aswbci5DxxAOXcO28yP8YaW\nIK3rQ5eeMJnwVLsV9eBB34FrQmBKHyBnWbUi1z5p46E/MJ4+pc9pz2Phl2stsEoj5dvYsU7q1PFQ\no4aXVq00unRxU6qUlylTzJw+nXX6Rmoq9O9vZeZMuZGGJBWU8557AQiaoo9sv3ykdgZP5ap409fR\n9tnsIgcZidM2d7Z+TiH6mzO4M/qd02vPmYPBavsmZ63BbXhuqoA1ZhmlGtQiZNRQzKu+Imjqu3gj\nIrG/NLxAr6vVa0Dimk24b7sd67IlhD18H8q5c/quWjt34Lq7Hd5KlbOeqCgkT5iCMBoJGTXMJ6n/\nW/CENzEkJWEfNhJCQnxfv0ZNFHuqT/O4cf8+jPGn8VS8GYCg99/J8drm9NXVXHcFzkYvGWRyLqai\norysX29n7Vo7y5Y5+PTTNF5+2YXdrjB+fNbBYuPGWVi50sSYMVamTZMJWpIKwnVPFwAs69YC+Mxx\nzmQwYB/yPPaBTyPCS+V5zYyBWsZ/jgNc0cps/+53/vdgsMvLeGHhUtK690Rx2LHNmUnJJ/qgOByk\njBmX/Uj4PIiyZUmK+VofQLZ9G+EdWxP09hsApD3WP8fzPFG1cTzxJOpfRwm6bG30y6l7dmOd/yla\n9Ro4nhiY5fmMH0nqZbXjjGllqcNG4a5TD8uKmBz7nk0Z/c0BNhgMZHK+rvTs6aZWLQ+LF/tOtfr+\neyPz5pmpXt1D+fJeXn/dyuzZMkFLUn55byznUxvOruYM4Hj2RVLfmJCva3purY4w6Z9DrWo1RGRk\nocunRdXBGxaGOT05Z+wXrWWzZaanVhTJH37CuX1/kvTFVzge6499wCCc3XsW+vWx2Uj+5FNSXxyO\n8djfWNatxVOxEq42ue8Lbh8+Gm9EJEHvT846OEwIQkYNQxGClDcnginrd5anRtYR2+aNGduetsX+\n4nAUIQh6f3LWF3c6MW/9Ee3W6gG1C1sGmZyvI0YjvPmm3jw0ZowFISAhQeH5562YzYJPPklj+XI7\nkZFeRo+2Mn++TNCSlF8ZU5FEUFD2TbUFZTbjSR/xfcWLXxgMuJu1wBh3DEPcMdS9v+MNC8Nb/qac\nzzGZcN/ZmpSJ75L61qQr73M1GLCPHMPFaTPwlgwjdeiIPKe1iRIlSRn7BorDQYkBj2b+qACwLFuC\naUcszs735VizzZzrnJGcU1Mxbd+Gu049RGQkrk6d0WpGYVm+FMPRP33ONe3cgWK3B9wUqgwyOV9n\nWrTw0KmTm+3bVVauVHnxRQtnzxoYPdpJVJSXKlUEy5c7KF3ay7BhFpYvl1tRSlJ+ODvr/c5ajZp+\nGzyUUbP1x2YjGU3b5rXfYvzrqN6nXQTrQju79eDcoWM4o3vl7/hHovUm8V2/EN6mOSHPPY3x8CGC\nX38VYbWS8tpbOZ7rrVARERSMmr5qm3nrFn1wXcZKdwYDqS8NR/F6Cfpgis+5gdykDTI5X5fGjnVi\nMgmee87Kd9+ZaNFCY9Agd+bz1at7+eILByEheg3b4SjCwkpSMeGtVJmLH35CSj6brfMjrUdvXC3v\nwtW+4xVfK2MxEtvsGShCZBkMdk0V5EeBopA8cy5Ji2Pw1KiFbdFCSjVvhDH+NPYhL+BNH9iVLYMB\nrUYNjEcOgdt9advT1pcGeLm63I9WvQbWLxZjWfy53nwuBOZN6xEmE647WhbyTV5dMjlfh6pUETz5\npBu7XaFECcGHH6Zl+aFfu7aXxx93ce6cgeXLZfO2JOWHs3tPfUMLP3E3b8mF5SsRpUpf8bU8NWri\nLVMGNb35NtDm7ebF3aYtiRt+JPn9j/CUvQHt1urYB2ddR/3ftBq1UNxujEf/xLRxPd6QUN/R8gYD\nqcNfRtE0Sjz7FKVvq02pBrVQd/+mH/evEeCBQibn69SLLzqJjnYza5aD8uWzX5nk8cfdqKpg5kyT\nXLxEkoo7g8GnFljckjMARiNpPftw/rcDJG74KctCL9nxpPc7m9esRj36J+4WrbKszua6937Or/+R\nlNfG6wuxuFwoQuC89/6r8jb8QXY4XqdCQ2Hq1KxL9F3uxhsF99+vsXy5iU2bjLRu7blGpZMk6Wpw\nN2+JdeWXCJMJT/UaeZ8QqIzGfK+RnrGMp23WJ4Bvk/blPHXq4qhTF8dTg0EIlHPnEKWvvMXiapE1\n5/+4QYP0pUBnzJAri0lSceduofc7e26tUaCtJ4uzjDnnxvRduXJKzj4UBVGmTJEMmMsvWXP+j6tX\nz0uTJhobNqgcOmQgIqKoSyRJUmF5qt2CfdBgtAYNi7oo14yIjMRbqpS+XGmVqv6Z5hYAZM1ZYuBA\nfST3jBlyYJgkFWuKQurr43E+8HBRl+TaUZTMpm1XG//t5V3UZHKW6NRJo2JFL198YSIhoahLI0mS\nVDBaLb1p2y2Ts3Q9MRphwAAXaWkKM2YUdWkkSZIKxvHsiyRP/iDP5UKLE5mcJUBflzs0VPDWWxAT\nI4ciSJJUfHhvuJG0vo8F3LaPVyJf72T8+PF0796d6Ohofv/992yPmTJlCn369AFg+/btNG3alD59\n+tCnTx/eeOMN/5VYuipCQ+GTTxyoKgwaZGPsWAuaVtSlkiRJ+m/Ks4q0Y8cOjh07xpIlS/jzzz8Z\nPXo0S5Ys8TnmyJEj/Pzzz5gu2zWkcePGTJ2a/TZgUmBq187Djh1w770epk83s3evgZkz0yhdWq5Q\nIkmSdC3lWXPetm0bbdvqnexVq1blwoULpKSk+BwzYcIEXnjhhatTQumaqlED1qyx07Gjmy1bVDp1\nCiIhIXDnAkqSJF2P8kzOCQkJhIeHZ/5dqlQpzp49m/l3TEwMjRs3pnx53/0wjxw5wqBBg+jRowc/\n/fSTH4ssXW2hoTB3bhrPPOPi778N9Otnxeks+HVcLti82SibxyVJkgqowCN/xGWLMCclJRETE8Nn\nn31GfHx85uOVKlVi8ODBdOrUiePHj9O3b1/Wrl2LOZcVa8LDg1DV/C3XlpOIiNArOl/SZcTxww/h\n3DlYvFhl9OhQ5s3L/4I6QkCfPvD55zBsGEyadBULHKDk/egfMo7+IePoH9cqjnkm58jISBIum/x6\n5swZItKXkYqNjeX8+fP06tULl8tFXFwc48ePZ/To0dxzzz0AVKxYkTJlyhAfH0+FChVyfJ3ERPsV\nvZGIiFDOnk2+omtIWeM4cSIcPhzEggVGKlRw8vzzrnxdZ+ZME59/bgXgvfcEXbrYqVnTe1XKHIjk\n/egfMo7+IePoH/6OY26JPs9m7ebNm7NmzRoA9u3bR2RkJCHpW2x17NiR1atXs3TpUqZNm0ZUVBSj\nR49m5cqVzJkzB4CzZ89y7tw5ypYt64/3Il1jNhvMnevgppu8jB9vYckSldOnFVJSwJtDrv3pJyNj\nx1qIiPDy7rtpaJrC8OGWHI+XJEmSfOVZc27YsCFRUVFER0ejKApjx44lJiaG0NBQ2rXLfsJ3mzZt\nGDp0KOvXr8ftdjNu3Lhcm7SlwFa2rGDBAgddugQxZMilLdwURVC2rKBbNzd9+7qpWFHwzz8KAwZY\nURSYMyeNpk09bNhg5OuvTSxerNKzp+yAliRJyosiRGDs5HulTQWy2cY/covjzp0GFi0ykZyskJKi\n157/+MNIYqKCogjat/dw4oTC3r1GJkxI4/HH9TW7T55UaN48GLMZtm5N/U9MzZL3o3/IOPqHjKN/\nXMtmbbkUlJRvjRp5adTId9i2wwFffaXy2Wdm1qzRb6cePdw89pg785hy5QQjRzp55RUrr79u4YMP\nct9nWpIk6b9OJmfpithsEB2tER2t8euvBn791UjPnu4so7qfeMLNkiUmFi0y0bChh9693fndS12S\nJOk/5/pZiFQqcg0aeHn8cTdWa9bnVBWmTEnDZhMMG2alTZsg1q83EhidKpIkSYFFJmfpmmnQwMvW\nralER7s5eNBAjx5BPPSQjcOH5W0oSZJ0OfmtKF1T5csLpk5NY+NGO23bavz4o8o99wQRGyvbuCVJ\nkjLI5CwViVq1vPzvfw4+/NBBaio88oiNVav+G0MghAC3O+/jJEn675LJWSpS3btrfP65vlVl//5W\nZs825X3SZYQAj+cqFe4qeeklC/XqBbN/v/z4SZKUPfntIBW51q09rFxpp0wZwejRVgYNsnLwYO63\n5rlzClOnmrnttmCqVAmhe3cb06eb2L/fENCDzPbsMbBwoZmEBAO9etk4fVru+CVJUlb/jXZEKeDV\nqeNl9Wo7/frZiIkxERNjomNHN0OGuGjQwMu5cwpnzijExyusWmUiJkbF6VQIChJUqOBl40aVjRv1\n2zkkRBAeLihZUv+vbFnB7bd7aNbMQ40aXgxX+Sep3Q5pOUzlnjjRAkDnzm6++cZEr142vvrKTvqK\nuJIkSYBcIUz6l6KOo9cLa9ca+eADC7/8og8SUxSBEL41zEqVvDzxhIvoaDclS8KpUwqbNxvZvFll\n/34DFy8qJCUppKb6nhceLrjzTo233nISEeH/Wz8xEdq1C0ZVDXz9dQplylx6jZ07DdxzTzDNmmms\nWOFg6FALCxaY79FewgAAF65JREFUadtWY/58vWlf8lXU9+P1QsbRP67lCmEyOUs+AiWOQkBsrJEZ\nM0wkJSlERAgiI/X/6tTxcNddnnzVgDUN4uIUYmONbNumsnWrkePHDTRo4CEmxk5wsH/L/NhjVlav\n1vvNmzXT+OILBxnLyj/8sI0fflBZudJO06Ye3G7o3dvGxo0qffu6eOcdZ7635PyvCJT7sbiTcfQP\nuXyn9J+nKNCsmd4UfSVUFapUEVSpotGzp4YQ8PzzVhYtMjFggK3ANdY//jCwcKGJpk09dO7su4nH\n/PkmVq82cccdGuXKqSxbpvLyyxbeecfJTz8Z+eEHldatNZo21d+TyQRz5ji4994g5s83U6OGl/79\n5TBuSZLkgDDpP0ZRYPLkNFq31li3TmX4cEueA8iEgC1bjPTsaaNly2BmzDDz2GM2xoyx4Erf3vqP\nPwy8+qqFsDDBRx+lMXcuREV5mDfPzGefmZgwQa8+jxzpuzZ5SAgsXOigTBkvr7xiYcuW4jvfOzDa\n4CTp+iBrztJ/TkaNtWvXIBYuNFO2rKBXLzfJyQrJyXDhgsI//xg4flzh+HEDBw4YOHRIT5qNG2v0\n7Onm44/NzJxpZtcuIx995GDgQCsOh8LHHzsoX14QHAzz5zto3z6IkSMtCKHQsaObBg2ybmpdvrzg\n00/TeOghGwMGWFmzxs7NNxevTLdunZGhQ61MnpxG27bFbG6bJAUg2ecs+fgvxTE+XqFz5yDi4nJv\nQLJYBO3bazz9tIvbbtOTa0oKDB1qJSbGhNEo8HgU+vZ1MXmyXjPOiOO2bUYeesiGpils3JhKVFTW\n5Jxh/nwTQ4daqVXLwzff6P3hHg/s3WvgyBED992nYSrYNPBrIi0NWrQIJi7OQFiYYMOGVG66yT9f\nK/+l+/FqknH0D9nnLEnXQNmygqVL7bz/vgWPB0JDBSEhgtBQKFfOS8WKgooVvZQtK7IMPgsJgenT\n07j9dg+vvmqhWjUPr7/uzPIazZp5WLLEwfnzSq6JGaBvXzd79hiYN89M3742SpYU/PSTSmKiPkrs\nn3+cPPecq1DvNSlJ30HMYinU6bmaPdtEXJyBqCgP+/YZGTjQxooV9oD8ISFJxYWsOUs+ZBwL7tQp\nJT2xX3qssHF0ufRR3bGx+u/m8uW9tGrlYfVqFVUV7NyZWuAR5rt3G3jggSAqV/ayapWdoKACFytH\nCQkKTZoEo6qwfXsKw4dbWbHCxJAhTl55pXA/JC4n70f/kHH0j2tZc5YDwiTpCt14o/DbIiJmMyxY\n4ODjjx3Exqawa1cqH3yQRv/+Ls6dM7BgQcGqo3/9pdCjh42UFIU9e4yMGGEt1MCtgwcNzJtnyrK4\nyuTJZpKTFYYOdRIWpm8LWrmylw8/tLB+ffEd3CZJRU0mZ0kKMCVLwsMPa1SpIjLnPQ8Y4CI4WPDR\nR+YcVx/7t7NnFbp3DyIhwcAbb6TRoIGHJUtMLFxYsAS/dKlKhw5BDBtmpX37IPbs0b82Dh/WE3aV\nKl4efVSfAhYaCrNmOTCbBYMHWzl5Uk7clqTCkMlZkoqBUqXgscdcxMcbWLQo7+SakgK9etn4+28D\nL7zgZOBAN7NnOwgPF4waZeG33/L+6DudMHy4hcGDbagqPPCAm4MHjXToEMSUKWbGjbPg8Si8+qoz\nc6EVgLp1vbz2mpNz5wz07Gnj4sXsyzd+vJk33jDz/fdGLlwoSDQk6fon+5wlHzKO/nE14njmjEKj\nRsGUKSPYvj01xwFX58/DwIE2Nm9W6dnTxXvvXVp5bMMGIz162LjpJsG6damEh2d/jRMnFPr3t/HL\nL0Zq1vTw2WcOqlQRbNxo5PnnrZw6pSf3O+7Q+PJLR5aVzYSAESMszJ1rpmVLjUWLLq2Udu6cQq9e\nNnbtutTsrSiCmjW9PPSQxsCBrsxj5f3oHzKO/nEt+5yN48aNG+e3V7oCdvuVDR4JDrZc8TUkGUd/\nuRpxDA7WB2Bt2qRy881e6tTxHf0tBHz5pUrv3jYOHDDSrp3Gxx+nYbys67dyZYEQ8N13JrZvV2ne\nXCMszPd11q7VE/jRo0YeesjNvHkOypa9dH7Pnm7i4w2cPq0wfXoaZctm/X2vKNCmjYf9+w2sX2/i\n778N3HOPxokTCg89ZGPfPiPdurkZO9ZJhQr6ZiT79xvZsEFl1SqV6tX10fJX835MSFB4+20LixaZ\nMpv7Fy40kZysUK+e1yduxZ38XPuHv+MYHJzz9AlZc5Z8yDj6x9WK48mTCrffHsxNNwm2bk3FaNST\n8vHjCiNGWFm/XsVmEwwb5mTQIHe2S5N6PDBokJWvvjJhswmGD9ebvb1eePNNC598YsZiEbz+upN+\n/dw5rvctBHmuBe5wwMMPB/Hzz0Z69HCzebORkycNPPWUi7FjnT5T1JKS4O23Lcyda0IIhQcfdPPR\nRyaMRv/HMSUFHnggiN27fTNwxiYrNWt6GD/eSfPm18eCKvJz7R9y44tCkDeff8g4+sfVjONLL+m7\nWZlMAo8HvN5LGbJVK43Jk9OoVCn3j3VGLXvMGAsJCQbq1vVgNMKvvxqpVs3DzJlp1K6d+7zs/Dp/\nHu69N4jDh/VE+MorToYMybn28dtvBoYPt/Lbb0bKl4eVK1OoUMF/X1NOJ/TsaWPLFpVevVyMHKkP\ntrPZIClJYfx4MwsX6j8QunZ18/rrTm64ISC+JgtNfq79QybnQpA3n3/IOPrH1YzjyZMKQ4ZYsdsV\nVFWgqvoUrAcfdNOtm1agna3On4dx46wsXqx3YEdHu3n77TS/7tYFes1++HArXbu66d5dy/N4jwfe\ne8/MpEkWqlTR52f7Y4tPjweefNLKqlUmOnVyM2dOWratC7/9ZmDUKCu//GIkIsLLp5+m0aTJldei\nL17Um9OrVLm2X7vyc+0fMjkXgrz5/EPG0T+KWxxjY40kJ0O7doHVjDtlSigTJ0KdOh6+/NJOiRL6\n43v3Gpg0ycyZMwbmzNHXM8+LEPro83nzzDRrprFkiQOrNefjvV6YNcvEuHEWDAYYP96ZOWWsIDwe\n2LzZyNKlJlavVnE6YexYJ089lXOXgb8Vt/sxUMnkXAjy5vMPGUf/kHH0jzJlQnn0URcLFugJ9e23\nnXzwgZkvv7w0VP3mm718+aU9x/W8NQ2++UZl+nR9o5KoKA9ffXUp0eflxx+N9O9v5fx5A336uHju\nOReJiQrnzimcP69gs8Ett3ipXNmbOYL+xAl9D/HYWCNr1qicPq13rlep4iU1FeLjDfTr52L8eGeB\ntiwtLHk/+odMzoUgbz7/kHH0DxlH/4iICOX06eTMpugMdet6GD3ayc6dRiZPtlCxopeYGDsVK176\nOktMhKVLTcyaZSYuzoCiCDp00HjnHWe2I8xzExen0K+fjb17cx7CraqCSpW8OJ36bmYZSpQQ6c35\nbho18nLqlELPnjb27zfStq3GzJkOv60wlxN5P/qH3PhCkiQpndEIH3+chscDx44ZePFFF126aJnT\ntYxGmDjRQteuQcyb5+DAAQMrVpjYvNmI261gswn69XMxcKCLqlULVxepWFHw9dd2pkwxc/KkgdKl\nBaVLC0qVEiQnw5EjBg4fNnL4sAGDQdCxo5smTTw0beqhbl2vz5z0cuX0a/Xvb2PdOpX77gtiwYKc\nm+bj4xVOn9Zr6YmJCqmpCnfeqfn8ELkSCQn6tW+91T8DACX/kDVnyYeMo3/IOPpHfuP4wQdm3nrL\nd85onToeunbV998uXTogvuZ8aBqMGqX3gUdEeJk718Htt19KkCkpMG6chfnzzVnOVVV9vvkLL7jy\n1d+eUxxjY4089piVpCSFL75w0KJFYI05CDSyWbsQ5Jehf8g4+oeMo38UJI4zZ5r48ksT7dpp3H+/\nu9C15GtJCJgzx8Qrr1gwGmHy5DSiozViY40MHmwlLs5AjRoe7rzTQ3i4IDxcpA9UM3P0qAGzWdC7\nt5uhQ12UKZPz+80ujosWqQwdasXr1eerlyghWLPGzs03B37ciopMzoUgvwz9Q8bRP2Qc/eO/EsfN\nm40MGGAjKUmhZUuNH380oigweLCLYcNcWfbh1jRYtkxlyhQLx44ZqFPHw9df27HZsr/+5XH0eOD1\n1y1Mn24mLEwwZ46Dv/4yMHSolZo1PXzzjf2q94EXV3LLSEmSpP+QO+/08N13qdxyi4ctW1QqVRKs\nXGlnzJisiRlAVSE6WmPr1lR69HCzZ4+Rl17KeztQlwsef9zK9OlmqlXTX7NlSw99+7p57DEXBw4Y\nGTJEr01nx+uFrVuNTJ1qZutWI1reU9azEAL++MPAgQMGUlJ8Hz982MDcuSYGDrQyZowl201T/ivk\ngDBJkqQAUKWK4Ntv7WzYoNKunZavhWBMJpg0KY0//jCwbJmJBg08DBiQ/Vxstxv697fy3XcmWrbU\n+PRTByVLXnr+zTedHDpk4JtvTIwbJ+jSxU2JEnpzd2KiwpdfqixfbuLEiUt1urAwwd13a3TooHHH\nHR4iI7P/deB260l9zRqVNWtUn9Hs4eGCm27ycvq0wtmzvvXF1atVPvoojWbNCt8XLgRs325kyxYj\nrVp5aNzYk+f88u3bjbz/vpkqVby89JKTUqUK/fKFJpu1JR8yjv4h4+gfMo75c/KkQtu2QSQmKsTE\nOLIks7CwUB54wM033+iJecECB0FBWa9z7pxChw5BxMVl36gaGiq49143LVt62LFDT7YnT146tlIl\nL02aeGjQwENiosLhwwYOHzZw5IgBh0PPiCVK6Am9RAnB8eMGjh/Xp56VLClo3tzDHXd4aNbMQ0yM\nynvvmRECnn1Wb973eODUKYUTJwyEhgrq1895hHlCgsLSpSqff27KXDoWoF49DwMGuOjaVfPZ6hTg\nwAED48dbWLPmUr01LExfq75fPzflysk+5wKTH2L/kHH0DxlH/5BxzL9t24w89JCNsDDB8uUOqlXT\np3BpGjz3XChffAHNm2t8/nn2iTnDiRMKMTEmLlyAixcVLl5UUBTo1EmjXTvNp19bCH21tvXrVbZv\nN/Lzz0YuXvStltpsgipVvDRr5qFjR41mzTxZtjvNaROVHTsMPP20jbg4A0FBArvd96AXX3QyYoTL\n51whYNo0MxMmmHG7FcxmQZcuGm3banzzjcq336p4vQoREV6qVfNis4HVKnC7FdatMyKEQtOmGqNG\nudi928DkyRYuXlS45RYP8+YZqVZNJucCkR9i/5Bx9A8ZR/+QcSyYWbNMvPyyviapouhzsYOCIC7O\nQLNmGv/7n8Pv66ZfzuvV+5N37zZQpozgllu8VKggfHYfK6jkZHjtNQuxsUbKlhWULy8oV87L8uUm\njh0z0KuXi3fe0VdaS02FF16wsmKFiRtu8DJ4sIuHH3b7NEvHxSnMmWNm6VKVc+d8C1azpocxY5y0\nbXup6TshQWHSJDPz55vo2FFh7lyZnAtEfoj9Q8bRP2Qc/UPGsWCEgHnzTMTGGjl9WuHUKQPx8Qp3\n3aUwbVrydTUK+8wZfaW133/X9y5/5RUngwZZ2b/fSOPGGnPmZL/X+OWEgLQ0fWtTl0shMjLnHxLH\njytUrBiCEDI5F4j8EPuHjKN/yDj6h4yjf1yvcUxJgccft7Fp06U+4kcfdfHWW84s/cn+EHBTqcaP\nH0/37t2Jjo7m999/z/aYKVOm0KdPnwKdI0mSJEmFFRICCxc6iI52Y7UKJk9O4513rk5ivtbynEq1\nY8cOjh07xpIlS/jzzz8ZPXo0S5Ys8TnmyJEj/Pzzz5jSe/nzc44kSZIkXSmzGaZOTWPyZK6LpJwh\nz5rztm3baNu2LQBVq1blwoULpFw+cxyYMGECL7zwQoHOkSRJkiR/uZ4SM+QjOSckJBAeHp75d6lS\npTh79mzm3zExMTRu3Jjy5cvn+xxJkiRJknJW4BXCLh8/lpSURExMDJ999hnx8fH5Oicn4eFBqGrO\ne6XmR26d61L+yTj6h4yjf8g4+oeMo39cqzjmmZwjIyNJSEjI/PvMmTNEREQAEBsby/nz5+nVqxcu\nl4u4uDjGjx+f6zk5SUy0F/Y9ANfvaMRrTcbRP2Qc/UPG0T9kHP0joEZrN2/enDVr1gCwb98+IiMj\nCUmfLNexY0dWr17N0qVLmTZtGlFRUYwePTrXcyRJkiRJyl2eNeeGDRsSFRVFdHQ0iqIwduxYYmJi\nCA0NpV27dvk+R5IkSZKk/JGLkEg+ZBz9Q8bRP2Qc/UPG0T8CqllbkiRJkqRrSyZnSZIkSQowMjlL\nkiRJUoCRyVmSJEmSAoxMzpIkSZIUYAJmtLYkSZIkSTpZc5YkSZKkACOTsyRJkiQFGJmcJUmSJCnA\nyOQsSZIkSQFGJmdJkiRJCjAyOUuSJElSgMlzV6riYPz48ezevRtFURg9ejR169Yt6iIVG5MmTeKX\nX35B0zQGDhxInTp1GD58OB6Ph4iICN555x3MZnNRF7NYSEtLo0uXLjz99NM0a9ZMxrEQVq5cyezZ\ns1FVlWeffZbq1avLOBZQamoqI0aM4MKFC7jdbp555hkiIiIYN24cANWrV+e1114r2kIGsEOHDvH0\n00/Tr18/evfuzalTp7K9B1euXMm8efMwGAx069aNRx55xL8FEcXc9u3bxZNPPimEEOLIkSOiW7du\nRVyi4mPbtm2if//+Qgghzp8/L+68804xcuRIsXr1aiGEEFOmTBGff/55URaxWHn33XfFgw8+KJYv\nXy7jWAjnz58X7du3F8nJySI+Pl6MGTNGxrEQFixYICZPniyEEOL06dOiQ4cOonfv3mL37t1CCCFe\nfPFFsWnTpqIsYsBKTU0VvXv3FmPGjBELFiwQQohs78HU1FTRvn17cfHiReFwOETnzp1FYmKiX8tS\n7Ju1t23bRtu2bQGoWrUqFy5cICUlpYhLVTzcfvvtfPDBBwCUKFECh8PB9u3bufvuuwFo3bo127Zt\nK8oiFht//vknR44c4a677gKQcSyEbdu20axZM0JCQoiMjOSNN96QcSyE8PBwkpKSALh48SJhYWGc\nOHEis0VRxjFnZrOZWbNmERkZmflYdvfg7t27qVOnDqGhoVitVho2bMiuXbv8WpZin5wTEhIIDw/P\n/LtUqVKcPXu2CEtUfBiNRoKCggBYtmwZrVq1wuFwZDYbli5dWsYynyZOnMjIkSMz/5ZxLLh//vmH\ntLQ0Bg0aRM+ePdm2bZuMYyF07tyZkydP0q5dO3r37s3w4cMpUaJE5vMyjjlTVRWr1erzWHb3YEJC\nAqVKlco85mrkneuiz/lyQq5GWmDr1q1j2bJlfPrpp7Rv3z7zcRnL/FmxYgX169enQoUK2T4v45h/\nSUlJTJs2jZMnT9K3b1+f2Mk45s9XX31FuXLlmDNnDgcPHuSZZ54hNDQ083kZx8LLKXZXI6bFPjlH\nRkaSkJCQ+feZM2eIiIgowhIVL1u2bOGTTz5h9uzZhIaGEhQURFpaGlarlfj4eJ/mHSl7mzZt4vjx\n42zatInTp09jNptlHAuhdOnSNGjQAFVVqVixIsHBwRiNRhnHAtq1axctWrQAoEaNGjidTjRNy3xe\nxrFgsvssZ5d36tev79fXLfbN2s2bN2fNmjUA7Nu3j8jISEJCQoq4VMVDcnIykyZNYsaMGYSFhQFw\nxx13ZMZz7dq1tGzZsiiLWCy8//77LF++nKVLl/LII4/w9NNPyzgWQosWLYiNjcXr9ZKYmIjdbpdx\nLISbb76Z3bt3A3DixAmCg4OpWrUqO3fuBGQcCyq7e7BevXrs2bOHixcvkpqayq5du2jUqJFfX/e6\n2JVq8uTJ7Ny5E0VRGDt2LDVq1CjqIhULS5Ys4cMPP6Ry5cqZj02YMIExY8bgdDopV64cb7/9NiaT\nqQhLWbx8+OGHlC9fnhYtWjBixAgZxwJavHgxy5YtA+Cpp56iTp06Mo4FlJqayujRozl37hyapvHc\nc88RERHBq6++itfrpV69eowaNaqoixmQ9u7dy8SJEzlx4gSqqlK2bFkmT57MyJEjs9yD3333HXPm\nzEFRFHr37s19993n17JcF8lZkiRJkq4nxb5ZW5IkSZKuNzI5S5IkSVKAkclZkiRJkgKMTM6SJEmS\nFGBkcpYkSZKkACOTsyRJkiQFGJmcJUmSJCnAyOQsSZIkSQHm/6lX1TqOud9TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting training and validation accuracy and loss against epochs.\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs = range(1, len(acc) + 1)\n",
    "plt.plot(epochs, acc, 'r', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, 'r', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vMSMsPjTrlok"
   },
   "outputs": [],
   "source": [
    "# Again making modifications to the model. Changing the number of hidden units and removing the \n",
    "# additional layer.\n",
    "model = Sequential()\n",
    "model.add(Dense(64, activation='relu', input_shape=(8,)))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 329
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1772,
     "status": "ok",
     "timestamp": 1554219187102,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "71FOTni2rlo6",
    "outputId": "55ebd7af-8ec3-4356-ebeb-283f0d864783"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_18 (Dense)             (None, 64)                576       \n",
      "_________________________________________________________________\n",
      "dropout_13 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_19 (Dense)             (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dropout_14 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_20 (Dense)             (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,801\n",
      "Trainable params: 4,801\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s2WxrhzprlpQ"
   },
   "outputs": [],
   "source": [
    "# Compiling the model\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 3729
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 16893,
     "status": "ok",
     "timestamp": 1554219219784,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "yiOd4IHHrlph",
    "outputId": "cfc9568e-8853-40c6-e86c-0f571a7e8855"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 711 samples, validate on 178 samples\n",
      "Epoch 1/100\n",
      "711/711 [==============================] - 1s 1ms/step - loss: 0.6621 - acc: 0.5837 - val_loss: 0.6098 - val_acc: 0.6461\n",
      "Epoch 2/100\n",
      "711/711 [==============================] - 0s 201us/step - loss: 0.6355 - acc: 0.6076 - val_loss: 0.5771 - val_acc: 0.6461\n",
      "Epoch 3/100\n",
      "711/711 [==============================] - 0s 187us/step - loss: 0.6056 - acc: 0.6470 - val_loss: 0.5536 - val_acc: 0.6629\n",
      "Epoch 4/100\n",
      "711/711 [==============================] - 0s 196us/step - loss: 0.5815 - acc: 0.6723 - val_loss: 0.5297 - val_acc: 0.7697\n",
      "Epoch 5/100\n",
      "711/711 [==============================] - 0s 187us/step - loss: 0.5798 - acc: 0.7257 - val_loss: 0.5130 - val_acc: 0.8258\n",
      "Epoch 6/100\n",
      "711/711 [==============================] - 0s 213us/step - loss: 0.5572 - acc: 0.7665 - val_loss: 0.4939 - val_acc: 0.8202\n",
      "Epoch 7/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.5434 - acc: 0.7679 - val_loss: 0.4798 - val_acc: 0.8202\n",
      "Epoch 8/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.5252 - acc: 0.7736 - val_loss: 0.4696 - val_acc: 0.8202\n",
      "Epoch 9/100\n",
      "711/711 [==============================] - 0s 184us/step - loss: 0.5272 - acc: 0.7862 - val_loss: 0.4585 - val_acc: 0.8202\n",
      "Epoch 10/100\n",
      "711/711 [==============================] - 0s 187us/step - loss: 0.5218 - acc: 0.7679 - val_loss: 0.4519 - val_acc: 0.8202\n",
      "Epoch 11/100\n",
      "711/711 [==============================] - 0s 186us/step - loss: 0.5103 - acc: 0.7806 - val_loss: 0.4463 - val_acc: 0.8202\n",
      "Epoch 12/100\n",
      "711/711 [==============================] - 0s 193us/step - loss: 0.5053 - acc: 0.7750 - val_loss: 0.4416 - val_acc: 0.8202\n",
      "Epoch 13/100\n",
      "711/711 [==============================] - 0s 209us/step - loss: 0.5017 - acc: 0.7904 - val_loss: 0.4374 - val_acc: 0.8202\n",
      "Epoch 14/100\n",
      "711/711 [==============================] - 0s 189us/step - loss: 0.5047 - acc: 0.7764 - val_loss: 0.4340 - val_acc: 0.8202\n",
      "Epoch 15/100\n",
      "711/711 [==============================] - 0s 185us/step - loss: 0.4974 - acc: 0.7806 - val_loss: 0.4309 - val_acc: 0.8202\n",
      "Epoch 16/100\n",
      "711/711 [==============================] - 0s 191us/step - loss: 0.4877 - acc: 0.7890 - val_loss: 0.4303 - val_acc: 0.8202\n",
      "Epoch 17/100\n",
      "711/711 [==============================] - 0s 193us/step - loss: 0.4917 - acc: 0.7904 - val_loss: 0.4252 - val_acc: 0.8202\n",
      "Epoch 18/100\n",
      "711/711 [==============================] - 0s 191us/step - loss: 0.4754 - acc: 0.7961 - val_loss: 0.4213 - val_acc: 0.8315\n",
      "Epoch 19/100\n",
      "711/711 [==============================] - 0s 196us/step - loss: 0.4840 - acc: 0.7918 - val_loss: 0.4220 - val_acc: 0.8202\n",
      "Epoch 20/100\n",
      "711/711 [==============================] - 0s 188us/step - loss: 0.4868 - acc: 0.7918 - val_loss: 0.4196 - val_acc: 0.8258\n",
      "Epoch 21/100\n",
      "711/711 [==============================] - 0s 207us/step - loss: 0.4809 - acc: 0.7932 - val_loss: 0.4171 - val_acc: 0.8315\n",
      "Epoch 22/100\n",
      "711/711 [==============================] - 0s 192us/step - loss: 0.4725 - acc: 0.7961 - val_loss: 0.4138 - val_acc: 0.8427\n",
      "Epoch 23/100\n",
      "711/711 [==============================] - 0s 186us/step - loss: 0.4843 - acc: 0.7961 - val_loss: 0.4139 - val_acc: 0.8427\n",
      "Epoch 24/100\n",
      "711/711 [==============================] - 0s 188us/step - loss: 0.4731 - acc: 0.7932 - val_loss: 0.4138 - val_acc: 0.8315\n",
      "Epoch 25/100\n",
      "711/711 [==============================] - 0s 187us/step - loss: 0.4682 - acc: 0.7947 - val_loss: 0.4107 - val_acc: 0.8427\n",
      "Epoch 26/100\n",
      "711/711 [==============================] - 0s 193us/step - loss: 0.4764 - acc: 0.7961 - val_loss: 0.4110 - val_acc: 0.8427\n",
      "Epoch 27/100\n",
      "711/711 [==============================] - 0s 187us/step - loss: 0.4598 - acc: 0.7989 - val_loss: 0.4082 - val_acc: 0.8315\n",
      "Epoch 28/100\n",
      "711/711 [==============================] - 0s 207us/step - loss: 0.4678 - acc: 0.8087 - val_loss: 0.4101 - val_acc: 0.8315\n",
      "Epoch 29/100\n",
      "711/711 [==============================] - 0s 191us/step - loss: 0.4802 - acc: 0.7989 - val_loss: 0.4070 - val_acc: 0.8427\n",
      "Epoch 30/100\n",
      "711/711 [==============================] - 0s 186us/step - loss: 0.4748 - acc: 0.8003 - val_loss: 0.4086 - val_acc: 0.8427\n",
      "Epoch 31/100\n",
      "711/711 [==============================] - 0s 189us/step - loss: 0.4706 - acc: 0.7947 - val_loss: 0.4104 - val_acc: 0.8315\n",
      "Epoch 32/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.4735 - acc: 0.7848 - val_loss: 0.4040 - val_acc: 0.8371\n",
      "Epoch 33/100\n",
      "711/711 [==============================] - 0s 189us/step - loss: 0.4711 - acc: 0.8031 - val_loss: 0.4066 - val_acc: 0.8427\n",
      "Epoch 34/100\n",
      "711/711 [==============================] - 0s 184us/step - loss: 0.4695 - acc: 0.7989 - val_loss: 0.4072 - val_acc: 0.8427\n",
      "Epoch 35/100\n",
      "711/711 [==============================] - 0s 186us/step - loss: 0.4718 - acc: 0.8101 - val_loss: 0.4061 - val_acc: 0.8483\n",
      "Epoch 36/100\n",
      "711/711 [==============================] - 0s 212us/step - loss: 0.4709 - acc: 0.7961 - val_loss: 0.4073 - val_acc: 0.8427\n",
      "Epoch 37/100\n",
      "711/711 [==============================] - 0s 201us/step - loss: 0.4682 - acc: 0.8031 - val_loss: 0.4052 - val_acc: 0.8371\n",
      "Epoch 38/100\n",
      "711/711 [==============================] - 0s 193us/step - loss: 0.4614 - acc: 0.7932 - val_loss: 0.4048 - val_acc: 0.8483\n",
      "Epoch 39/100\n",
      "711/711 [==============================] - 0s 186us/step - loss: 0.4668 - acc: 0.7975 - val_loss: 0.4007 - val_acc: 0.8315\n",
      "Epoch 40/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.4655 - acc: 0.8031 - val_loss: 0.4045 - val_acc: 0.8483\n",
      "Epoch 41/100\n",
      "711/711 [==============================] - 0s 186us/step - loss: 0.4740 - acc: 0.7834 - val_loss: 0.4004 - val_acc: 0.8315\n",
      "Epoch 42/100\n",
      "711/711 [==============================] - 0s 182us/step - loss: 0.4718 - acc: 0.7947 - val_loss: 0.4041 - val_acc: 0.8483\n",
      "Epoch 43/100\n",
      "711/711 [==============================] - 0s 200us/step - loss: 0.4570 - acc: 0.8003 - val_loss: 0.3989 - val_acc: 0.8315\n",
      "Epoch 44/100\n",
      "711/711 [==============================] - 0s 207us/step - loss: 0.4626 - acc: 0.8017 - val_loss: 0.4000 - val_acc: 0.8427\n",
      "Epoch 45/100\n",
      "711/711 [==============================] - 0s 185us/step - loss: 0.4662 - acc: 0.8031 - val_loss: 0.4027 - val_acc: 0.8483\n",
      "Epoch 46/100\n",
      "711/711 [==============================] - 0s 188us/step - loss: 0.4546 - acc: 0.8045 - val_loss: 0.3977 - val_acc: 0.8315\n",
      "Epoch 47/100\n",
      "711/711 [==============================] - 0s 186us/step - loss: 0.4635 - acc: 0.8101 - val_loss: 0.3955 - val_acc: 0.8371\n",
      "Epoch 48/100\n",
      "711/711 [==============================] - 0s 186us/step - loss: 0.4534 - acc: 0.8045 - val_loss: 0.3980 - val_acc: 0.8483\n",
      "Epoch 49/100\n",
      "711/711 [==============================] - 0s 193us/step - loss: 0.4507 - acc: 0.8115 - val_loss: 0.3981 - val_acc: 0.8483\n",
      "Epoch 50/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.4600 - acc: 0.7947 - val_loss: 0.3990 - val_acc: 0.8483\n",
      "Epoch 51/100\n",
      "711/711 [==============================] - 0s 214us/step - loss: 0.4502 - acc: 0.7989 - val_loss: 0.3950 - val_acc: 0.8371\n",
      "Epoch 52/100\n",
      "711/711 [==============================] - 0s 189us/step - loss: 0.4482 - acc: 0.8031 - val_loss: 0.3940 - val_acc: 0.8371\n",
      "Epoch 53/100\n",
      "711/711 [==============================] - 0s 186us/step - loss: 0.4520 - acc: 0.8101 - val_loss: 0.3933 - val_acc: 0.8483\n",
      "Epoch 54/100\n",
      "711/711 [==============================] - 0s 189us/step - loss: 0.4707 - acc: 0.8003 - val_loss: 0.3915 - val_acc: 0.8371\n",
      "Epoch 55/100\n",
      "711/711 [==============================] - 0s 185us/step - loss: 0.4773 - acc: 0.7932 - val_loss: 0.3933 - val_acc: 0.8371\n",
      "Epoch 56/100\n",
      "711/711 [==============================] - 0s 179us/step - loss: 0.4635 - acc: 0.7989 - val_loss: 0.3969 - val_acc: 0.8371\n",
      "Epoch 57/100\n",
      "711/711 [==============================] - 0s 180us/step - loss: 0.4650 - acc: 0.7989 - val_loss: 0.3918 - val_acc: 0.8371\n",
      "Epoch 58/100\n",
      "711/711 [==============================] - 0s 202us/step - loss: 0.4550 - acc: 0.8045 - val_loss: 0.3938 - val_acc: 0.8371\n",
      "Epoch 59/100\n",
      "711/711 [==============================] - 0s 184us/step - loss: 0.4499 - acc: 0.7918 - val_loss: 0.3948 - val_acc: 0.8483\n",
      "Epoch 60/100\n",
      "711/711 [==============================] - 0s 188us/step - loss: 0.4521 - acc: 0.8087 - val_loss: 0.3919 - val_acc: 0.8483\n",
      "Epoch 61/100\n",
      "711/711 [==============================] - 0s 196us/step - loss: 0.4487 - acc: 0.7947 - val_loss: 0.3891 - val_acc: 0.8427\n",
      "Epoch 62/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.4449 - acc: 0.8186 - val_loss: 0.3874 - val_acc: 0.8427\n",
      "Epoch 63/100\n",
      "711/711 [==============================] - 0s 194us/step - loss: 0.4465 - acc: 0.8073 - val_loss: 0.3884 - val_acc: 0.8371\n",
      "Epoch 64/100\n",
      "711/711 [==============================] - 0s 191us/step - loss: 0.4491 - acc: 0.8115 - val_loss: 0.3918 - val_acc: 0.8483\n",
      "Epoch 65/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.4708 - acc: 0.8045 - val_loss: 0.3891 - val_acc: 0.8427\n",
      "Epoch 66/100\n",
      "711/711 [==============================] - 0s 218us/step - loss: 0.4647 - acc: 0.8073 - val_loss: 0.3914 - val_acc: 0.8427\n",
      "Epoch 67/100\n",
      "711/711 [==============================] - 0s 194us/step - loss: 0.4443 - acc: 0.8129 - val_loss: 0.3879 - val_acc: 0.8483\n",
      "Epoch 68/100\n",
      "711/711 [==============================] - 0s 196us/step - loss: 0.4608 - acc: 0.8115 - val_loss: 0.3892 - val_acc: 0.8427\n",
      "Epoch 69/100\n",
      "711/711 [==============================] - 0s 175us/step - loss: 0.4605 - acc: 0.7961 - val_loss: 0.3878 - val_acc: 0.8483\n",
      "Epoch 70/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.4546 - acc: 0.8031 - val_loss: 0.3892 - val_acc: 0.8427\n",
      "Epoch 71/100\n",
      "711/711 [==============================] - 0s 194us/step - loss: 0.4528 - acc: 0.8045 - val_loss: 0.3917 - val_acc: 0.8427\n",
      "Epoch 72/100\n",
      "711/711 [==============================] - 0s 201us/step - loss: 0.4732 - acc: 0.7918 - val_loss: 0.3930 - val_acc: 0.8371\n",
      "Epoch 73/100\n",
      "711/711 [==============================] - 0s 211us/step - loss: 0.4507 - acc: 0.8158 - val_loss: 0.3888 - val_acc: 0.8427\n",
      "Epoch 74/100\n",
      "711/711 [==============================] - 0s 199us/step - loss: 0.4433 - acc: 0.8059 - val_loss: 0.3880 - val_acc: 0.8427\n",
      "Epoch 75/100\n",
      "711/711 [==============================] - 0s 189us/step - loss: 0.4485 - acc: 0.8101 - val_loss: 0.3870 - val_acc: 0.8427\n",
      "Epoch 76/100\n",
      "711/711 [==============================] - 0s 172us/step - loss: 0.4563 - acc: 0.8017 - val_loss: 0.3878 - val_acc: 0.8483\n",
      "Epoch 77/100\n",
      "711/711 [==============================] - 0s 198us/step - loss: 0.4652 - acc: 0.8073 - val_loss: 0.3880 - val_acc: 0.8483\n",
      "Epoch 78/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.4551 - acc: 0.8017 - val_loss: 0.3862 - val_acc: 0.8371\n",
      "Epoch 79/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.4500 - acc: 0.8031 - val_loss: 0.3866 - val_acc: 0.8371\n",
      "Epoch 80/100\n",
      "711/711 [==============================] - 0s 214us/step - loss: 0.4613 - acc: 0.7975 - val_loss: 0.3862 - val_acc: 0.8371\n",
      "Epoch 81/100\n",
      "711/711 [==============================] - 0s 193us/step - loss: 0.4561 - acc: 0.8031 - val_loss: 0.3852 - val_acc: 0.8427\n",
      "Epoch 82/100\n",
      "711/711 [==============================] - 0s 208us/step - loss: 0.4493 - acc: 0.8101 - val_loss: 0.3895 - val_acc: 0.8483\n",
      "Epoch 83/100\n",
      "711/711 [==============================] - 0s 198us/step - loss: 0.4469 - acc: 0.7961 - val_loss: 0.3864 - val_acc: 0.8483\n",
      "Epoch 84/100\n",
      "711/711 [==============================] - 0s 196us/step - loss: 0.4441 - acc: 0.8073 - val_loss: 0.3832 - val_acc: 0.8371\n",
      "Epoch 85/100\n",
      "711/711 [==============================] - 0s 194us/step - loss: 0.4475 - acc: 0.8045 - val_loss: 0.3855 - val_acc: 0.8371\n",
      "Epoch 86/100\n",
      "711/711 [==============================] - 0s 197us/step - loss: 0.4423 - acc: 0.7989 - val_loss: 0.3832 - val_acc: 0.8371\n",
      "Epoch 87/100\n",
      "711/711 [==============================] - 0s 197us/step - loss: 0.4491 - acc: 0.8115 - val_loss: 0.3852 - val_acc: 0.8483\n",
      "Epoch 88/100\n",
      "711/711 [==============================] - 0s 210us/step - loss: 0.4379 - acc: 0.8129 - val_loss: 0.3829 - val_acc: 0.8483\n",
      "Epoch 89/100\n",
      "711/711 [==============================] - 0s 194us/step - loss: 0.4389 - acc: 0.8172 - val_loss: 0.3849 - val_acc: 0.8483\n",
      "Epoch 90/100\n",
      "711/711 [==============================] - 0s 199us/step - loss: 0.4284 - acc: 0.8256 - val_loss: 0.3834 - val_acc: 0.8483\n",
      "Epoch 91/100\n",
      "711/711 [==============================] - 0s 214us/step - loss: 0.4415 - acc: 0.8115 - val_loss: 0.3842 - val_acc: 0.8483\n",
      "Epoch 92/100\n",
      "711/711 [==============================] - 0s 194us/step - loss: 0.4543 - acc: 0.7947 - val_loss: 0.3829 - val_acc: 0.8427\n",
      "Epoch 93/100\n",
      "711/711 [==============================] - 0s 186us/step - loss: 0.4622 - acc: 0.7947 - val_loss: 0.3867 - val_acc: 0.8483\n",
      "Epoch 94/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.4498 - acc: 0.8031 - val_loss: 0.3836 - val_acc: 0.8371\n",
      "Epoch 95/100\n",
      "711/711 [==============================] - 0s 222us/step - loss: 0.4439 - acc: 0.8045 - val_loss: 0.3843 - val_acc: 0.8371\n",
      "Epoch 96/100\n",
      "711/711 [==============================] - 0s 191us/step - loss: 0.4585 - acc: 0.8017 - val_loss: 0.3840 - val_acc: 0.8427\n",
      "Epoch 97/100\n",
      "711/711 [==============================] - 0s 189us/step - loss: 0.4494 - acc: 0.8045 - val_loss: 0.3824 - val_acc: 0.8371\n",
      "Epoch 98/100\n",
      "711/711 [==============================] - 0s 194us/step - loss: 0.4436 - acc: 0.8143 - val_loss: 0.3843 - val_acc: 0.8427\n",
      "Epoch 99/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.4435 - acc: 0.8101 - val_loss: 0.3849 - val_acc: 0.8427\n",
      "Epoch 100/100\n",
      "711/711 [==============================] - 0s 195us/step - loss: 0.4416 - acc: 0.8087 - val_loss: 0.3814 - val_acc: 0.8371\n"
     ]
    }
   ],
   "source": [
    "# Fitting the train set to the model\n",
    "history = model.fit(X, y, epochs=100, batch_size=32, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 707
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3736,
     "status": "ok",
     "timestamp": 1554219283468,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "eMYj_P_Qrlp4",
    "outputId": "3ac5f36d-75eb-45bc-cb6b-1fec91aa230e"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFZCAYAAACizedRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsnWd4FUUXgN+9PZ2EJgIqIL33LhJC\nb5KAREFBED+KAopUQQQREGmCgIqoiEgRQhMIhNBESuhSBUOVnoSEJDe37n4/NvcmN7lpEIq47/Pw\nkLs7O3N2dnfOzJkzZwRJkiQUFBQUFBQUnhhUj1sABQUFBQUFBVcU5aygoKCgoPCEoShnBQUFBQWF\nJwxFOSsoKCgoKDxhKMpZQUFBQUHhCUNRzgoKCgoKCk8YinJW+Ncwfvx42rRpQ5s2bahcuTLNmzd3\n/k5KSspTXm3atCEmJibbNDNmzGDZsmUPInK+07t3b8LCwvIlr/Lly3Pz5k0iIiIYPXr0A5W3cuVK\n59+5qVsFBYXs0TxuARQUcsuECROcfwcGBjJt2jTq1KlzX3mFh4fnmGbYsGH3lfe/jZYtW9KyZcv7\nvv7OnTt89913vPrqq0Du6lZBQSF7lJGzwlPDG2+8waxZs2jbti1HjhwhJiaGvn370qZNGwIDA/nh\nhx+caR2jxgMHDtC9e3dmzJhB27ZtCQwMJCoqCoBRo0Yxf/58QO4MLF++nK5du9KkSROmTp3qzOvr\nr7+mYcOGhISEsHTpUgIDA93K9+uvv9K2bVtatWpFjx49uHbtGgBhYWEMHjyYMWPG0Lp1a9q1a8f5\n8+cBuHr1Kt26dSMoKIhhw4Zht9sz5btr1y46duzocqxz587s3r072zpwEBYWRu/evXMsLzIyko4d\nO9K6dWuCg4M5c+YMAKGhoVy/fp02bdpgsVicdQvw008/0a5dO9q0acOAAQOIi4tz1u2cOXN46623\naN68OW+99RYpKSmZZEtJSWHo0KG0bt2awMBAPv/8c+e5q1ev0qNHD1q2bElISAinTp3K9nhgYCCH\nDh1yXu/4/c8//9CkSRMmT55Mz549s71XgG+//ZYWLVrQunVrpkyZgt1up3Hjxpw4ccKZ5ueff2bg\nwIGZ7kdBIbcoylnhqeLkyZNs3LiRWrVqsWDBAkqUKEF4eDiLFy9mxowZ3LhxI9M1p0+fpnr16mze\nvJnXX3+dBQsWuM374MGDrFixgtWrV/Pzzz9z8+ZNzp8/z3fffce6dev45Zdfshw1xsbGMnHiRH74\n4Qe2bt3Kc88951T8ALt37+b1119ny5Yt1K9fn8WLFwMwffp0GjZsyLZt2+jVqxdHjhzJlHfDhg25\nefMmV69eBWTldPPmTRo1apTrOnCQVXk2m41Ro0bx6aefsmXLFhdFOXnyZIoVK0Z4eDg6nc6Z17Fj\nx1i0aBFLliwhPDycZ599lhkzZjjPh4eHM2vWLCIiIoiLiyMiIiKTPMuWLSM5OZnw8HDWrFlDWFiY\nU8GOGzeO9u3bExERwYABAxgxYkS2x7MjPj6eihUr8vPPP2d7r4cOHWLVqlWsW7eODRs2cPjwYbZu\n3Urbtm357bffnPlFRETQvn37HMtVUMgKRTkrPFU0a9YMlUp+rceOHcu4ceMAKFmyJIULF+aff/7J\ndI2XlxdBQUEAVK5cmevXr7vNu2PHjqjVaooWLUrBggW5ceMGBw8epF69ehQpUgS9Xk9ISIjbawsW\nLMjhw4d55plnAKhTp45TmQKUKVOGKlWqAFCpUiWnAj106BDt2rUDoFq1apQuXTpT3jqdjubNm7N9\n+3YAtm3bRlBQEBqNJtd14CCr8jQaDXv37qVGjRpu5XfHzp07ad26NQULFgSgW7du/PHHH87zzZo1\no0CBAmg0GsqVK+e209CnTx/mz5+PIAj4+flRtmxZ/vnnH8xmMwcOHKBDhw4AtGjRgpUrV2Z5PCes\nVqvTtJ/dve7evZtmzZrh7e2NTqdjyZIltGrVivbt27Np0yZEUSQ+Pp6TJ0/SvHnzHMtVUMgKZc5Z\n4anCz8/P+feJEyecI0WVSsWdO3cQRTHTNT4+Ps6/VSqV2zQA3t7ezr/VajV2u5179+65lFm0aFG3\n19rtdubMmcP27dux2+0kJydTqlQptzI48gZISEhwKdfX19dt/q1bt+ann36iV69ebNu2zWlSzW0d\nOMiuvCVLlrBmzRosFgsWiwVBELLMByAuLo4iRYq45BUbG5vjPafn0qVLTJ06lQsXLqBSqbh58ybB\nwcHEx8cjiqIzD0EQ8PLy4tatW26P54RarXa576zu9e7duy735OHhAUDNmjXRarVERUVx8+ZNmjRp\ngqenZ47lKihkhTJyVnhqGT58OK1bt2bLli2Eh4fj7++f72V4e3tjNBqdv2/fvu023aZNm9i+fTs/\n//wzW7ZsYfDgwbnK39fX18UT3TFnm5GmTZty9uxZLl26xKVLl2jQoAGQ9zrIqrwjR46wcOFCFixY\nwJYtW5g0aVKOshcqVIj4+Hjn7/j4eAoVKpTjdemZOHEiZcuWZfPmzYSHh1OhQgUA/P39EQSBu3fv\nAiBJEpcvX87yuCRJmTpeCQkJbsvM7l79/f2deYOsrB2/27dvT3h4OOHh4U7rg4LC/aIoZ4WnltjY\nWKpUqYIgCKxZs4aUlBQXRZofVKtWjQMHDhAXF4fFYmHt2rVZylK8eHECAgK4e/cumzdvJjk5Ocf8\na9So4ZyLPXLkCFeuXHGbTqfT0aRJE7744gtatGiBWq12lpuXOsiqvLi4OAoWLMizzz5LSkoKa9as\nwWg0IkkSGo0Go9GIzWZzyevll18mIiLCqbyWL19Os2bNcrzn9MTGxlKxYkXUajV//PEHly9fxmg0\notPpaNy4MWvWrAHg999/55133snyuCAIFC5cmLNnzwJyZ8lsNrstM7t7DQwMZPv27SQkJGCz2Rg0\naBB79uwBoEOHDmzbto2jR4/m+T4VFDKiKGeFp5YhQ4YwaNAgOnbsiNFopHv37owbNy5LBXc/VKtW\njS5dutClSxfefPPNLOcZO3ToQHx8PC1btmTYsGEMHTqUmzdvunh9u2P48OHs2LGDoKAgli5dSqNG\njbJM27p1a7Zt20bbtm2dx/JaB1mV17RpU4oUKUJQUBB9+vShV69e+Pj4MHjwYMqXL4+fnx+NGzd2\nma+vVq0a77zzDj169KBNmzYkJiby/vvvZ3u/GRkwYACff/45HTp0ICoqinfffZe5c+dy+PBhPvvs\nM3bs2EGLFi2YPXs206dPB8jy+MCBA/nxxx/p0KED0dHRvPjii27LzO5ea9SoQd++fXnllVdo3749\nlSpVcs5vly9fngIFCtCkSRMMBkOe7lNBISOCsp+zgsKDIUmSc05y586dzJ49O8sRtMLTTb9+/ejZ\ns6cyclZ4YJSRs4LCAxAXF0eDBg24du0akiSxefNmp5evwn+Lw4cPc+3aNZo2bfq4RVF4ClC8tRUU\nHoCAgACGDh1K7969EQSB0qVL52pdrcLTxejRozly5AhffPGFcymfgsKDoJi1FRQUFBQUnjCULp6C\ngoKCgsIThqKcFRQUFBQUnjCemDnnO3cSH+h6f39P7t7N3zWs/0WUeswflHrMH5R6zB+Ueswf8rse\nCxf2yfLcUzNy1mjUj1uEpwKlHvMHpR7zB6Ue8welHvOHR1mPT41yVlBQUFBQeFpQlLOCgoKCgsIT\nhqKcFRQUFBQUnjAU5aygoKCgoPCEoShnBQUFBQWFJwxFOSsoKCgoKDxh5Gqd8+TJkzl+/DiCIDBm\nzBiqVavmPLd06VLWr1+PSqWiSpUqfPTRR4SFhfHll1/y3HPPAdCoUSMGDBjwcO5AQUFBQUHhKSNH\n5RwVFcXly5dZsWIF0dHRjBkzhhUrVgCQlJTEokWL2Lp1KxqNhj59+nDs2DEA2rVrx8iRIx+u9AoK\nCgoKCk8hOZq19+3bR1BQEABlypQhISGBpKQkALRaLVqtFqPRiM1mIyUlBT8/v4crsYKCgoKCwlNO\njso5JiYGf39/5++AgADu3LkDgF6vZ9CgQQQFBdG8eXOqV69OqVKlAHnE3bdvX3r16sXp06cfkvgK\nCgoKCgpPH3mOrZ1+h8mkpCS++eYbwsPD8fb2plevXpw9e5bq1asTEBDAyy+/zNGjRxk5ciQbNmzI\nNl9/f88HDo2WXZxShdzzX6pHoxE2bIBOncDDI3fXbNwIlSvDCy+4Px8VBVevQq1a7uvxwgWIjoaW\nLXNXXlISrF4NoaGg1+fumpUr4eLF7NO89BI0bJi7/K5fhwMHoEuX3KW/HzZuhJMnXY+1agU1a97f\n+xgTA+Hhcr1pctHS2e2wdi00awaFCrlPs20bPPcclCt3XyLlGbMZliyB2Ni0YxoN9OwJRYvmLa+s\nvuvz52HNGki/efBzz8Frr92HwIAowvffu8p8PzRtCo0auT936RIcOQLBwQ9Wxv3wqNrHHF/ZIkWK\nEBMT4/x9+/ZtChcuDEB0dDQlS5YkICAAgDp16nDy5Em6du1KmTJlAKhZsyZxcXHY7XbU6qyV74MG\nEy9c2OeBN89Q+G/VoyTB//5nYO1aLaGhVubMMeV4zenTKjp08KJ1axtLlqRkOi+K0LatFwaDiqNH\nExGEzHm89ZYHv/+u5tixZIoVy347dUmCfv0MrF+vJTbWxBtvWHOU8eRJFd27e+WYrmBBkT//TEar\nzTEpffsaCA/XsmGDkfr17TlfkEdu3BDo1MkLUXStsIkTITIyiTJl8r7t/LBhepYs0XH8uJkRIyw5\npp89W8fkyXpq17azfr0xU71s26bm9dc9KVRIZOdOI0WK5F2mvDJihJ4ff9RlOn70qIVZs8y5zier\n7/rOHYGXX/bkzp3MRtSAgGRq1RLzJjCwcaOGfv1y2dPNBo1GYvNmI9Wru8qQlAQtWnhx8aKKiIjk\nTOcfJvndPman6HNUzo0bN2bu3LmEhoZy6tQpihQpgre3NwDFixcnOjoak8mEwWDg5MmTNGvWjIUL\nF1KsWDE6dOjAuXPnCAgIyFYxKyg8Dn79VcPatXILvHy5lhYtbHTubMv2mlWr5E9m3z41ogiqDG3a\n2bMq4uLkgxcvCpQu7dqAW61w8KAaSRI4cEDNK69kX96KFRrWr9c6y8yNcl61Sk4/ZoyZqlXdK9IV\nK7SsXatl5041LVtmr2zv3oXISE1q3pqHopzDwjSIosDAgRaaNpXr5OxZFRMmGOjf34ONG43oMuuo\nLDGbcdbbzJk6Xn7ZRr16WTfiR46omDZNLuDwYTUzZugYNSpNod+5IzB4sAGAmBgVQ4caWLo0xW3n\nK7/YskXNjz/qqFjRzrhxZmdZw4bJnbUpU8wYDPefvyTB++8buHNHxbvvmmncWH6up0+r+fRTPatX\na6lVK/cdAAeOb2T27BSKFr2/DszVqypGjJCf/bZtyXil62uOG6fn4kVVallaqlfPu4z/BgQpvZ06\nC6ZPn86hQ4cQBIHx48dz+vRpfHx8aNmyJcuXLycsLAy1Wk3NmjUZMWIEN2/eZPjw4UiShM1my7T8\nyh0P2hv5L434Hib/lXq8dEkgMFD+4r/7LoU+fTzQamHnzmSKF3f/SYgi1K7txbVrcsOwY0cylSu7\nNvjff69l1Ci5xfzyyxRee81V+R4+rKJtW7ncPn0sTJ2adcNy8aIso0oFggB+fhKHDydne192O9Sq\n5UVyssDJk0lZNt4OOYKDrXz9dfYWg8WLtQwfLmfk7y9x4kRSnhRlbggM9OTsWRUnTiRTsGBa/Q8f\n7sPixTBkiJmPPsp59Otg0yYNvXt70KSJjb171ZQoIbFjRzI+bgYqjpHYpUsCP/xgYtw4PdeuCaxd\nm0KDBnYkCXr29CAiQsP48SZ27tSwa5eGKVNM9O2bc2fpfrh1Sx7RJiUJbNlipFKltPds4kQdX32l\nZ9GiFDp2zL5z58Ddd/3DD1pGjjTw0ks2Vq5McXY0rVaoWlV+7/78MzlXUwIOEhKgcmVvSpUS2b3b\n+ECdl48/1vP11zreeMPCjBnyd7Jhg4a+fT2oUsXOtWsqdDqJY8eSeVRjv0c5cs5VEJIPP/yQ5cuX\ns2zZMipUqEBwcDAtUyfMQkNDWblyJcuWLWPEiBEAPPPMMyxZsoSff/6Z5cuX56iYFRQeJTYbDBzo\nQVKSwNSpJgID7UyaZCYhQWDQIAP2LAaG+/eruXZNhb+/rDwOHMjcIqQ/5u78/v3Zn3dgtcoyJicL\nfP65iUaNbFy9quLatexbu3371Ny4oaJjR2u2o6patUReeEEkPFxD6uKLLFm9WoMgSHToYOXuXYGd\nO/O3JfzrLxUnT6oJDLS7KGaAOXPg+edF5szRsXdv7stdvVrWKJ98YmbIEAtXrqicnaaMOEZiAwda\nadfOxrx5cmdl0CAD9+7Bjz9qiYjQ8NJLNgYMsDJ3romAAJEJE/T89Vf+x3GSJBgyxEBsrIqPPza7\nKGaAkBCbyz3eD+fOqRg/Xo+/v8RXX5lcLEBaLXTqZCMmRsXvv+ftWW/cqMFiEQgJsT2wVeGjj8xU\nqmRnyRIdmzZpuH5dYNgwAx4eEl9/baJjRyu3bqnYs+fptMoqEcIU/nPMnq3j0CE1r7xipVs3uaHr\n0cNK27ZW9u7VMG+e+2GhozEcPVruxWdUrpIkK99ChUT8/GD//syNp+Oa0qVFTp9WkZDgXsaZM3Uc\nPqwmONhK1642pyk5O4WeXkZHA54VggAhIVaMRoHw8Kwb+atXBfbv19CwoZ333rOklpGLSeo8EBbm\nkDnzKNTXF+bPl0d1gwYZiI/POb/ERNi6VUPZsnaqVhX58EMLNWva+fVXLWvWuN7rhg0ali7VUbWq\nnVGj5OfaoIGdoUMtXL2q4u23PTIpsWeekZg504zJJNC/vwFzPltVFy3Ssn27hsBAG2+/nblOKlcW\nqVjRzrZtmlzVR0bMZujf34DJJDBzpolnnslsKUrrAOTtWTvSBwc/uEVBr4evvzZhMEh88IGed94x\nEB8vMGGCmXLlRLp2vT8Z/y3kyqz9KFDM2k8G2dVjZKSaxYu1SFJal7hIEZHJk81uvYivXRP45BM9\nJlNaeq1WYuRIC+XL586JY/p0HcePuyqk5s1t9OmTu48/KkrFggU6bDZZBkmS7+OZZ2QzZ4ECaWlj\nY2VTYmyswKZNRmrUSJPRYoEqVbzR62UzWtWqXmi1cOxYsnOEcOWKQJ063rRvb0UUtWzeDCdOJDnn\n3UQRKlXywssLunWzMnOmnmXLjLRo4TpUP3xYRfv2nhQvLrF9ezJ+fnDokIp27bzo3dvCtGnutYHZ\nLJsUvbwkjh5NzjQfnpHoaIGGDb1p0cLGsmWZndsA5szRMWmSnhkzTPTsaaVhQy9u3BA4dSqJVNeT\nB0KSoG5dL2Jj5Tw9PV3PO97H6dN1TJump2JFO889l9ZkVa5sZ/hwi4tZc/lyDYMHezBqlJkPPpA7\nFBcuyFMEajU0apRW3/v2qbFaISLCSLlyac/baoWOHT05ckTO+IcfUmjf3rXD43A4q1nT7uIc1qiR\nPMJ2x969ar79VovdnvWwcudONd7eEjt3GrOcs3U8l5kz5eeSE+m/608+0TN/vo6ePS3MnOn+Xcrp\nubjjxg2BGjW8qFvXzm+/uX+f7odFi7SMHi1bPVq3tvHTT/JcvyhCnTpexMfLMma32uLGDYG5c3X0\n62ehVKnMdZqSApMn67l0Ke2jEQSJnj2ttGqV9r48UQ5hCgogf6xjxxqIjs7c4jdtanfr2PTNNzrW\nrcvcqzUYYP78nD2jz59XMW1aZq2/Y4c8okyvWN1x65bAm296OB20HPj5Scyfb8p0fcGCEnPnmnj1\nVU8GDHB1RImM1BAfL9C/vxW1GurXt7Nxo5arVwWnsnCYrOvXt6NWy8o5KkrtnBc8f152Fmve3Eq9\nenbnNRmV8/z5OkRRYPbsFBwxfapVE/HwkLIdOW/bpuHePYGePa05KmaAMmUkatSws3Onmjt3BAoX\nztxorV6tQaeT6NjR6hxtf/GFnk2bNLz6au7mO7Pj4EEVV66o6NbNmq0CGDrUwuHDaiIjNZw5k3Z8\nyxYNnp4weHDafLTDIS796K10aYkvvjDxwQcGtmxJa/Z0Ovl4esUMsml3/vwUgoM96dTJlkkxA0yc\naObkSTVHj7o+ky1bNBQuLDlHdg7++UegVy8PEhKyt/d6ekrMmWPK1pmqSxcrkybpWb1akyvl7GDX\nLjXz5+soXVpk4sSsh/yCINff7Nl6tm7V5Oi4CLBmjQZJEnK02uSVPn2sHDyo5vhxNTNnmpydYZVK\nlnHOHD0RERo6dXJfrt0ur8rYv1/Dvn1qwsONmQYTkybpWbgws8Vsxw4NERFGKlR4dB7hDhSztkKu\nOH5cRXS0PJcZHZ1IdHQiERGyc5I7s5LdLn+sfn4SZ8/K6f/+O5HnnxfZtElDcvZ+Tan5yo3ol1+m\nOMscMcKMxSLw22/Zm7JEEd57z0BcnIpPPzU5r4+OTuTkySQaNnQ/sfzyy3b697cQHa3i44/TvmCH\nLF27yg1hgwZpytWBQ3E2aGCnaVPXYxnP161rR6WSXK4HuHcvzSTbpEmajDod1K5t5+xZVZamzDST\ndu4b65AQK3a7wPr1mfvpp06pOHNGTYsWNmdHxpF3fpkSHfnkJLNGA7/8kuLyHI8eTaJoUZGpU3Uc\nPy43ZbduCezZo6ZOHTsvvOCq3Lp1s3HuXJJLHufPJ2Vy2nNQurTEkSPJWSoxLy/YvNnokt/u3cl4\neUmMHGng8uU0JWy3w7vvGkhIEJg2zfV9zPjv7NmkHD3oS5aUaNBAdna7fj13k7txcfI3odFILFiQ\nkqPlI6+m7dWrtWg0UpZK8n4RBNm8/ccfyZk6kA4ZHR7i7pg7V8f+/XJbdOqUmsmTXTVzZKSahQt1\nlCtn5/TptPdj0aIUzGYhdQogX28pVyjKWSFXOD7Qbt2s+PiAjw9Ury5SpYqdyEg1cXGu6f/4Q82t\nWyo6dbISECCn9/VNm+dMP3pxhyTJZXp6yh+7o8zu3eVG3DFPmRULF2rZuVNDixY23nknTWYfn5wD\neWR0RMk4hwm4nQM+cECNp6dElSoidevKo7L05x2KuEEDOz4+8tzh0aNqlw9/0yYNZrN7h5p69exI\nksDBg5lHz/fuQUSEhvLl7VSpkvte/iuv2FCpJOdoMz1pHZK0xrZ0aYlatezs2qXm9u0H8/ixWmH9\neg2FCom89FLOy7MEAZfnWLy4bOmw2QT69/cgOVnuEIqikKWy9/BwzSOnwDM5WSBUKtf8KlQQmTLF\nRGKi7FxoS626efN07N2roV07K716ub6PGf/ldnlUcLANSRJy/BZA/p6GDTNw86aKkSMt1KyZ8ztS\nvrxI5cruv++MnDun4sQJ9059+YW7Z1GxokilSnYiIzXcvZv5/NGj8hK5YsVEdu5MpkwZkQULdOza\nJX9DjiVyWq3EggUmChWSnM+hY0cbb75p4fRpNZ99lsvoP/mIopwVcsQxCvb3lwgMdG1EQ0Ks2GyC\nc02pgzQnH9dedHBw7nrjhw+ruHxZRdu2Npc1jiVLStSvb+OPP9TcuOFeOZw6peLTT/UUKiQye7Yp\nz16jGR1Rvv9eh8nkqjCrVBHx8kpTvrGxAufOySM2jUZuYGvUsHPihMrpDX3ggBp/f4myZeWGsUED\nOxaLwLFjacrWnUnWgbvRuoPffstaqWdH0aISTZvaOXxYzcWLaReKIqxZo8XHR6JlS9dnGBJiRRQF\n1q17sFmxnTvVxMaq6NLFlqflOunJaOlYvVqLWi3luF79YdK9u43Ona1ERWmYM0ce1U+dquOZZ0QX\ns+yD0qmTFY1GytXI9ocfYONGLQ0b2nj33dwvScvq+87I/Vht8ouQEBtWq8CGDa4yJiXBgAEe2GwC\nc+eaKF5cthhoNBLvvWcgNlbggw/kdd4ffWR2drzTM2GCmRdftPPNNzp27Hi0XuGKclbIkT171Ny+\nLZu0M65v7dLFhiBILss6TCbYsEFL8eKiU6E4KFdOpGpVOzt2qImNzbqVys7cGRKS9YghJQUGDjRg\nsQjMnp39vF12VKggMn68mbg4lbPXnF5hajRQp46dc+fk+4iKShsVO2jQwI4oChw6pObaNYGrV1XU\nr29zjgAcaR3XZmeSBbk82RSe+b4fxEvWUcdhYWmNm2PZWIcOtkwjuc6dbajVuVMK2ZFbk3ZOpLd0\nHD+upnlzO4UKPT4/V0GAL74w8eyzIl98oaN37zQFkRpMMV8ICIAWLeycOqXm7Nmsm/ILFwQGDwZf\nX4l580x5WhMcHJz5+86Iw8rl5SXRuvWj7xQFB1vdyvjxx3ouXFAxcKDFaZmpUUNk1CgLN2+qaNPG\nky1bNDRtaqN/f/fvoJeX3FHXamWFni5Y5kNHcQhTyBFHI5rRwQXg2WclGjWy88cfGq5eFShZUiIi\nQkNiokCvXha3pqiQECuffGJg3TqNW69rqxXWrdNQsKBIs2aZzZ2dOlkZM0YeJQ0a5Hr9pEl6zpxR\n89ZbFhcvy/uhTx8rkZEatm3TuFWY9evb2bVLQ1SU2sVknf48yIouLk5wOQa4OIUNHpyzSdbbG6pW\nFTl2TEVKSppJ9sIFWanXq2dz8WTOLe3b2xgxQmLmTB3ffiv3vhymdneyFCki8dJLdnbs0FC+/P27\nbCckQKlSYq5MrNnhsHS0auWZauF49KO3jBQoAPPmmQgO9uDaNRX9+1vcvssPSkiIlS1bNLRp45nl\ndE1Kivw8v/nGRIkSeXs/0n/fWT1rUYSEBCFHp76HRfHiEg0b2tm7N01GSYL4eIEqVezOpY8OBg2y\nEBmpZt8+jdt13hmpVk1W6J9+qmfkSJg69WHeTRqKclbIlpQU2WRaooToVCYZCQmx8ccfGsLCtAwZ\nYslxrW2XLjYmTJBHXu6U8+7damJiVPTpY3Eb99kxYtiyRcNff6mcy7K2b09z7Bg//sEXnwoCzJ5t\nYuhQA337ZjYFpjczHzigRqOR52Md1K1rRxBk07c75Vy0qESpUiJRUWrsdnJlkm3QwM7x42qOHVPT\nsKEdmw0GDfJAkgT69bs/peTjA8OGWTJZIl58UXSGdMzI4MEWYmIE55zq/VCsGLz3niVfzLwVKshT\nGGvWaGnb9vGZtNPTuLGdqVMF6Nz+AAAgAElEQVTNHD6s5qOPHk6IyTZtbAQF2XIMThMaqqZLl/ur\nl6FDLdy7l/2zLlUK+vfPvbk8v3HImD6AUOXKsid+xk6LWi2vFhk9Wk+fPtYc49uDrNAvXxaoVCmf\nQ+Nlg7LOWcGFjPW4fr2Gt9/2YPBgM2PHuv/4HCH7SpcWWb/eSJUq3pQpI7JrV9abmYSEePD77xoO\nHkzi+eddX8EBAwysXq1l48Zk6tZ1P6pau1bDO+94MHSomTFjZEXRrJkn8fEC4eFGt/NH+Y3RCC++\n6E3ZsiLnz6uoXl1k82b5nh312KyZJ5cuqShRQuSff1ScP+8a+nLwYAPLl2v57rsU3n7bg6AgG7/8\nkvUaUUf4wjFjzAwdamHaNB3Tp+sJDrayYEH+zWc+KSjfdf6g1GP+8MSF71T47+JYopDd2kU/PwgK\nsnH2rJpp0/TO8H3Z4W6eEyA5GTZv1vD88yJ16mStYFu1suHlJREWpkUU0wL4Z+XY8TDw9JQ91s+c\nUWOzCW43hGjQwE5KisD582pq17ZnmrNv0ECup/Hj5e59TibZ9KbyqCgVM2fqKFFC5PPPnz7FrKDw\nX0ZRzgpZ4tiNqFIlOxUrZq/wHMr4u+9k7dOlS/ZKpkMHG3q97MSR3nYTHq7BaJTnDbNTNp6e8lzp\nlSvyDkE5OXY8LNKb+t0pZ3dzzO7OX7umwtNTok2b7Ds1RYpIlC4tm8IHDpQnnefPNzmDlSgoKDwd\nKMr5P0RSEnmaI9ywQYvVmruIPy1b2vD1lbVsgwY2SpbMfrbE11e+5tw5NRERas6fV3H+vIrlyx0e\nvDmX6RhlLl+uzZVjx8PA1QEss8wZvbczUrq0RKFCcscn47KxrMu0kZQkcOWKisGDLW7zVVBQ+Hej\nKOf/CHFxULu2NyNH5m4xvcUib38IuVueYzBAhw5yutyG73Ok69nTk8aNvWjc2ItduzRUq2Z3rgXO\njqZN7RQuLKebPt2UK8eO/MahkMuXt7tdJvPssxLPPSeiVkvUqZNZiQpCmtLOrZexI32NGnJcaQUF\nhacPxVv7P8K6dVru3hVYtUrLhAnmHEP3ff65jtOn1bz+uiXL/Y0zMnKkhZIlJWcUr5xo3drGhx+a\nXSJNCQKEhubueo1GXq5y7Zoq1/va5jcBATBvXkq2loJZs0zExgpZ1vmoURbq1rVnCvCSFZ0727hy\nxUyPHla33uwKCgr/fhRv7f8IHTt6cOCA3Bf76quULDctKFzYh7VrjQQHe/D88/KuSPmx+9B/DeV9\nzB+UeswflHrMHxRvbYV85epVgQMHNJQpI5uAs4vsdPeuvG+uSkWuguMrKCgoKOQ/inL+D+BYrvTu\nu/Km81ltWiBJ8L//wfXrKoYPt1C79qPfJk1BQUFBQVHObrFa5WhT1scfBfCBkePeynvyduhgzXbT\nghUrNPz6K9SrZ2PIEMXRSEFB4RFhMvFUNLj5iKKc3RAeriE01JMxYx79NmH5zalTKs6eVdOypQ0/\nP9mZSKXKvGnBxYsCo0cb8PEhz8HxFRQUFO4biwX/wMb49Xz1cUvyRKEoZzc4Ni9fvFhHePi/W0ul\n7fwjO4AVLSpvWnDkiJoLF+T7tFph4EAPkpMFFiwgUzhNBQUFhYeF4dflaP4+j3bndoTY2MctzhOD\nopzdcPdu2nzs++8buHXr3xkXUd6TV4Ovr0RQUJp3tmM9rUNxz5yp4/BhNcHBVnr0eCyiKigo/Bex\n2/GYOwsAQZLQ7Yx8vPI8QSjK2Q3x8bIyfuMNC7GxKgYPNiD+C32j9u9Xc/26ig4drC578rZvb8PD\nQzZtHzigZtYsHSVLyvGZFRQUFB4Vuo3r0VyIxlqnnvx7+7bHLNGTg6Kc3eAYOX/4oYXAQBs7dmhY\ntOjfF+0hq60bvb3lACAXLqh48005PvO8eUp8ZgUFhUeIJOH55UwklYrEuQuwF30G3Y5t/CtHQg8B\nJUKYGxzKuUABiS+/NPHyy55MnKjn5Ek1gvDvmY/dsEHLM8+INGqUOfJUSIiVtWvlqGEffGBW4jMr\nKCg8UrQ7ItGeOI6pczD2MmWxBAbhsexnNCeOY6te83GL99hRlLMb4uMFPDwkPDzAw0NizhwTb73l\nwbJl/77Rc79+Free182b2ylZUqRYMZFhw5RlUwoKCo8WzzkzAUgZ/D4A1lTlrIuMUJQzinJ2y927\nAgUKpI2QW7a08+efSSQk/Lscw9Rqsoz5rNPBnj3JqNUo8ZkV/pV4LFyAdud27i1eJgdaV/jXoDl4\nAN3ePVgCg7BVrQ6ApVlzJJUKXWQExg9GPGYJHz/KG+2G+HiB4sVd5z0CAiAg4N9j0s4NHh6PWwIF\nhfvHsHQJmtMn0e7egTWw5eMWRyEPOEbNxiHDnMekAv7YatdFc/ggQvxdpAL+j0u8JwLFISwDNhsk\nJAj4+z9dilhB4anCZkP99zkADKtWPmZhFPKCx/y56LdsxlqvAdYGjVzOWVq0RBBFdLt2PCbpnhwU\n5ZwBh+k6vVlbQUHhyUJ9+SKCRfaV0G/6DZKTH7NEucNj4QIC6lVHiIl53KI8Fgw//YD3Jx9hL/Ys\n9+Z9K+8Rmw5LC9kColWWVCnKOSPx8fL/yshZQeHJRX32LABiQACCMRn9lk2PWaLcodu8EfWlixiW\n/fy4RXnk6FevxHv4UMSCBUlYtR7x+RcypbFVrY5YqLC83vlR7mYsihgWf4/61MlHV2YOKMo5A+mX\nUSkoKDyZaP46A4DxvQ8AueHPD4R7CQ91na3mL7lT4bHkh//Uel7d5o34vPs/JB9fElauxV62nPuE\nKhWW5i1Q37qJ+uSJRydf5FZ8hg+lQOe2aE4cf2TlZoeinDPgiA5WoMBjFkRBQSFL1KnK2dyhE9Zq\nNdDtiHzguMzaP36nYMXSBNSqjNeEcahP/JmvozchLhbVndsAqC9dRLt7Z77l/SQi3I3DsORH/F5p\nh2/v10FvIGHZKqd3dlZYAoMA5IAkjwjD4u8BEBLv4ffqK6jP/fXIys4KRTlnIC5OVs5Pm2e2wn8Y\nScJz1hfoNm983JLkG5q//kLy9EQs+RzmkFcRbDb068Jc0xw5hNfHYxAS7+WYnxB/F59B74AoIiQm\n4jnvSwJaNMH/pfpoDh7IJ5nlUbOlcVMAPFIVwlNHSgo+/ftQsEpZfIYNRrd3D9b6DYlfHoatbv0c\nL7e83AJJENBFRjwCYUH1z1V027ZirVWbpOlfooqNxa9rJ1SXLz2S8rOU67GW/gSSNnJWlPN/AklC\nfeHvxy3FA6O6fi1LM6kufBNeUz7Fa+K4+y/AbH7sjZWTVE9tW9nyoFJhfiUYSRAwhP3qTKI5fhS/\nrp3x/PorPKd/nn1+koT38PdRX7+G8cNRxJ76m4Tvf8bcoTOav87iNXlivoitPiuP9k2hPbBWqYYu\nfCOqmzcyyaK69k++lJdrkpJQ3bieb9np14VhCFuFvVRpksZNJPbIKRLWh2Nr0DBX10sFC2KrURPt\noShISck3ubLC8PNiBFHE9GYfTG/0JmnCZNQ3b1AgpBOqf64+9PKzQlHOGXDMOSsOYf8N9KtWENCg\nFrqNGx63KPeNLnwTATUr4TPw7cxmWIsFr08+AkAT/TfCnTv3VYbPkAEENKz1RJj71JdkT217+QoA\niMWexdrkJbRR+1FduYz6r7P4de+CkJyEWLAgHt99jepCdJb56Vcuw7AuDGu9BvK6W4MBS4dO3Pt+\nCdaq1dEePJAvSkJzTh452ytUxNSrD4LdjmHpT2kJRBGfIQMpWLNSJivAQ0MUKdC9C/5N60NSUr5k\nqd8kf0v3Fv9CyntDEUuUzHMe1vqNEKxWtEcP54tMWRdkxbD0J0RfP0ydgwFIGfAuyR+OQn3lEgH1\nquP7elf0q1bkW/3kFkU5Z0AZOT9GrFZ83v0f+jWrHlmRjsZR/9u6R1ZmfqK6dROf9wchSBKGsFXo\nf13uct5j0bdoLl5ADAgAQHtgX57L0OzfhyFsFYLNhmHxonyROzd4fjEFpk/PdFydah62la/oPGYO\neVW+5ssZskkyLo6kWV+R+PlMBKsV74kfuy1DdfEC3qM+RPT2kZf2ZIg0Zm3yEoLFIivoB8Qp94vl\nMId0Q/TyxrDkRzm4giThNXYkhuVL5fuY8fkjcRjTh/2K9uABVPcS0O3Z/eAZJiej27kdW/kK2MuU\nve9sHOuftfv3PrhM2aDbshn1rZuYXg0FLy/ncePw0STOnIutSlX027biO7AfhSqXgVmzHqo86VGU\ncwaUkfPjQ7dlM4aVy/Aa/5HcYD1kVNf+Qbd3j1z2zsh/n/esKOIzeACq2FiMg4YgevvgPepDVJcu\nAiDExuI543PEAgVInPkVcB/KWRTx/niU/KePL4aVy8FozNfbcIeQlCgrqHHjMo1aHZ7a9goVnMfM\nHToh6fV4LPkR9a2bJH06BdPrb2Dp+ArW+g3Rb9qA9o/fXQuxWvEd2A9VchJJn89wu7TH2vQlALT5\noLg0Z89gf+4F8PJC8vbBHPIq6uvX0EVG4DnlUzy/+wZbxUqY23ZAc/YMuq3hmTORJDSHD4IpH7Z3\nNRrxmvSJ82eWc7wmk6wkc/F96HZEIphMmNt1eCDRrPUaAA9fOXukdjZNb/ZxPSEImHr2In7rLuL2\nHSZ5+Gjsz78A1649VHnSoyjnDChLqR4fjg9FffOG+4Ypn9GHySN00d8fVWwsmmNHHnqZ+YnHom/Q\n7YjEEhhE8scTSZo6HVVSIr4D+4HNhtcXk1HdS8A4bCSWwCAknQ7tgbw1dvpVK9AeO4opuCsp/f6H\nKiEe/fo1D+mO0tAcOoggirJi2PeHyzmHp7atXJpylnz9MLduB0DyyI9I+d8g+YQgkDRxMgBe40aD\nXd59TX3hbwp0bIX28EFMwV0xd+3uVg5rg0ZIajW633c90P0IsbGoYu5gS9ehMPV6CwCfD97Da/Z0\nbKVKE79yHcmjZd8Azy+nZ5qmMPy4CP+2LfDt0/OBPck9F8yV59kHDkb0KyB7R7vJ0+uzTyjQqQ1+\nr3bJcW7aYdK2tH0w5SwVKoStbDk0B6MeWkdddfECul07sNZviL1CxSzT2cuUxTh8NHd3H3BryXlY\nKMo5A/HxAgaDhKfn45bkv4XqQjS6XTuwv1AKSFPUDxPD6pVIWi3JYycA/66N3tWnT+E18WPEggW5\n9+UCEATM3UIxdQlBeygKn6GDMCz+HluZF0l5qx8YDNiq10Rz4s/cz50lJ+M16RMkg4HksRMw9eyN\npFI9kmeTfsSk2+46otOcPev01E5P0pTpxC8Py7Rpgq1mbUyvvob25J8YVvyCYcmP+Ac2QXvkMKau\n3UmcPidTpCoHkrcPtpq10Rw7kiuv76xwzjenM8XbqlbHWrsOqju3sRcvQcKq9UhFi2KvUBFzm/Zo\nDx9yGe2rz5/DO9V/QL9tK4bvF963PKqbN/CcOwuxUGGMw0ZgadYc9ZXLqP8+75rQapW/E0FAt3sH\n/s0aoNuw1n2mViu6iC3Yi5fIl12lrPUbokpOQnP64QQG8VjyIwApvfpkn/AxoSjnDGTckUrh0eD4\nUJJHjcVarwG6HZFO8+zDQH3mNJrTJ7G0aIW50yvy6OgRLd14UFQXovEd8DaC2Uzi7PlIRYvKJwSB\npGmzsBcvgWHlMgS7neRPPpO3IEMeBQp2O9rDB3NVjue8L1HfvIFxwLuIJUoiliiJJagV2sOHHjxQ\ngySh+209qiuX3Z7WRu1HEgTw9HR9LjYb6ujz2MrJntouWRYujDUwyK2iTf5oPJKnJ94fDsFn2GAk\nrY5733xP4vyF4O2draiWpi/J9ZaTidVkQrfpNzT7M08dODy1beXKu8r18aeYg1qRsGqdS2fDOEQO\nruL55YxUISz49O+LkJJC4pQvEAMC8J4w1plvXvGaPBHBaCR59DgkH1+53sjcEdLt3oEqJgbTW2+T\nOG0WgtmMX9838Rk8IJNpXbt3D6qEeMxt22fZ2ckL1vqyd3eep2IkCfWJP9H/uhwhKdF9GqMRw7Il\niAEBmDt0fkBJHw6Kcs5AfLyy6cUjx2yWP5SCBTG37+TsyToU9gPjxlTnWHZjDumG5FcAW516aI4c\nQoh7sEAWWZX3oAi3buHx7XwKtGlOwQY10Zw5RUqvvlhat3Ut2q8AifO+RVKpsLwciKVVG+c5a/3c\nz+Oprl/Dc96X2IsUdUbhAjClPhvD4h8e6H70K37Br09PvEcNy3zSYkF7+CD2CpUgKAhN9N/Ojlqa\np3bWZkh3iMWexTj4AwSbDUuTl7i7cy/mLl1zda21STMAtL+7mXe229Hu2oH3kIEUrPwifr1fp0Bo\nl0yxvtPmyV3ltjZszL1fVmVynrLVroulyUvodu1Ac/woXlMnoT1xnJTX38DU938kzvwKwWTCt39f\nMJtzWw2yLH8eQ7/iF2yVqmB6/Q0gXeCPDB1UfeqmIqau3TH17svdyD1Yq9fEsHwp3uPHuKZ1mLTb\ndcyTPFnhVM5uOjvuUF28gOfMafg3rUdAiyb4DnoH/+aNM61TV588gX+b5qhiYzH16AUGQ77Im9/k\nSjlPnjyZ7t27Exoayp9//ulybunSpXTv3p3XXnuNzz77DACr1cqwYcN47bXX6NmzJ1evPr61YnnB\nboeEBGW++VGj/20dqrg4TKE9Qa/H3PEVRH9/DMuWQOrmBveL4fuFBFQth+bIobSDoog+7FdEbx/M\nrWTlZmnREkGSMu2Go7oQTUDVchh++C53BZrN+HVuC6+++kBygxxKUr98KX5dO1Owenm8x45Cc/wY\nluYtuDf3a5KmfOH2OmujJsQdOEbCkhUuIxink03U/uwLttnwHj4UISWF5I/Gu4wsLYEtsZcoiX71\nyqxHJTmgungB79HDAdDt3pnJXKz58xhCSgrWBg2hjdy5cEw5pI1AK5BXjO8PJy5yjxzXuXiJXF9n\nrVMPSa/P7BRms1GgQysKdOuMx7KfkXx9sdaug2A0otu53SWp+q+zSIIgr83OrbyD5U6Rz5BBeMz7\nElup0iRNktdsW9p1IOWN3mhOn8Trswkgimj37sF72BAKViwlB1Rx10m02/EePRxBkkj6dIq86Tsg\nPlMMW+Wq8vy+w+EvORn95o3YXyiFrXZd+fIXyxK/YQu2ipXw+OE7dFs3y2lFEV34JkR//0w7Td0v\n4vMvYH+mGJoD+3Ls8BqW/EjB+jXwmjoJ9eVLmDu+Qkrfd1BduUyBjq3x/PwzsFjwmDcH/zbN0Zw9\nQ0rfd0gePjpfZH0Y5Kico6KiuHz5MitWrOCzzz5zKmCApKQkFi1axNKlS1m2bBnR0dEcO3aM3377\nDV9fX5YtW0b//v2ZMWPGQ72J/CIhASRJMWs/agw/yaOwlDd6px4wYOreA1VMjLM3fr/o169BffsW\nfqHBqE+fAkATdQD11StY2nd0bmrt2A0n48jB+5OxqG/dlPefTXUmyg6vzyag278X1q69P49akwnd\nxg349nmDgpVfxHfwAHS7d2CrWZvEKV8Qe/wvElaswdz99UzLftIjPv8C6PUux6QC/tgqVpLN2lZr\nFhfKHuD6iC1YXmqO+dXXXM+r1Zh69kKVnIR+9a/u88gOmw3fQe+gSk7CWqs2gsWCbttWlyTaA3Ln\nwdqgEbSVO08Oc2vaWuG8K2cEAXvVapnM4Tni4YG1bn20J/90sawYfvoB7eGDWJo1J359OHGHT5I0\nRXYYyvjeav46i/jc8+TFmcXarDnW6jXlOVeVKpMJPmniFGxlXsTz668IqFGRAq+0w2PJDwjx8Rh+\nXe52cw3POTPRHjyAqXMw1qbNXM5ZAoMQzGZ0e+V5bn34RgRjMqbgbq5maoOBewsWIen1+AwdhHD7\nNppjR1DfuI6lVdts38s8IQhY6zdEffsWqosXsk6XkoLX1EmIPr7cm7OA2NPR3Fv0E0lTppOwdhPi\ns8XxmvE5BauWxXvCWCS/AiQsWyU/qyd01Ay5UM779u0jKEg2eZQpU4aEhASSUh1KtFotWq0Wo9GI\nzWYjJSUFPz8/9u3bR8uWcmPXqFEjjhz5d3jBOtY4K2btR4f6r7Po9v2BpVlzxNJlnMdNb8qerIaM\nIQ6NxtwrPZsN7bEjiD6+qOLjKdCtM+oLf2MISzXVhaSNbm2VqyIWLiKP0FKXjGh/34U+fCOSIKC+\n9k+m+biMaHftwPNreckSViuaM6dyJ6coot29E++hgyhYpSx+b/VA/9s67C+UInnMx8RGHSd+cySm\nvv9DKlIkd3lmgbV+QwSj0f2csSThPXIYhlUrsNauS8KPS50jq/SYeryJpFbj8eOiPJvwPWd9gfZQ\nFKbgrs7lXbrNv7mkcXiUW+s3hBdewFa2nLwG12xO89TOo1n7QbE2SV1S9Ye89E6Iv4vXtM9S10cv\nlDsSKhW26jWxP1tcXm2Q2gFyemqXz2OHQhAwfigvY0seNdY5enXi5UXigu+Q9HoEo5GUHm8Sv3oD\ncfuPIvr64T1mhEvwFc2RQ3hOm4z92eIkTZuZqbiMHVTHZiLmkMxWIHulyiSP/QRVTAw+QwagTw3i\nY84nk7YDa2pUseysPYblS1HduY3prbcxh/ZA8vFNu75hY+7u3Iupa3dUd+9ibtOOuF37sbRola9y\nPgxyVM4xMTH4+/s7fwcEBHAnNcqQXq9n0KBBBAUF0bx5c6pXr06pUqWIiYkhIDXogUqlQhAELA9o\nnnwUpK1xfsyC/Icw/CQr35RefV2O218si6VpM3R//I765Al5V5t+vSlU4QX8W76UK49j9ZnTCEYj\n5k6vkDhlOqo7t/EL6YR+XRhi4SLOBheQd8MJDEIVc0dWXHY73uNGIwkCSTPnyrJmEwtZiIvF573+\nSBoNKX36AaA5djRXdeD94RAKdO2Exy9LkHx8ML47lLjIPdzdfQDj0A8RUz3Y84O04A4Z5vEkCa+J\nH+OxeBG2ylVJWLYqS0cpsegzWNp2QHPqBNrUdeK5QRN1AM8Zn2MvUZKkz2dir1gJ+wul0G2LSJs3\nFUW0UfuxP/c84rPFgdQRndGIdv/eVE9tr/uKOvUgWFLnnXV75CVVnjO/QBUXh3Hoh64dJkHA0rY9\nqoR45xIw53zzfXQoLK3bEnPiPClD3MzNA7YatYg9eobYU3+TNOsrrE2bIT7/AknTZiIYk/Ed+Lbc\nSUhKkuenRZHEr75B8g/IlJe1bn1Ebx+027chxMSg2xGJtVqNLHeQSuk3AMvLgegjI/D4Zh6ShweW\nZs3zfI/ZYa2fQzASmw3PeXOQDAaM7wx0m0Ty9SNx/kJiT5zj3uJlSIUK5auMDw0pB8aOHStFREQ4\nf4eGhkoXLlyQJEmSEhMTpXbt2kmxsbGS2WyWQkNDpTNnzkhvvfWWdObMGec1TZs2lcxmc7blWK22\nnER56GzaJEkgSVOmPG5J/iNcuSJJfn6SVKyYJFksmc+vXCk/EJVK/h8kqWBB+f9+/XLOf8ECOe3C\nhfLvKVPS8hkyJHP65cvlc5MmydeAJPXqJZ+rV0+SBEGSLl3KfJ0oSlKXLmkvz4kT8t99+uQso+Me\nq1eXpF27JMluz/maB+HKFbm8V15xPT5pkny8fHlJunUr53wOHJDT167tXuabNyXp7bclqXPntH/F\nisl1uGtXWrphw+R8Nm6Uf586Jf/u2TMtzZYt8rHBgyVJp5OkOnXyft8PisUiSV5eklShgiSdOydJ\nWq0klSolSSkpmdNGRsryDhok/543T/7900+PVuaePeVyx42T30WQpBEjsr/G8R6//778/4wZ2ae/\nfj3tm+zSJf9kd2CzSZKvrySVLev+/M8/y2UPHJj/ZT9mcpwcKFKkCDExMc7ft2/fpnDhwgBER0dT\nsmRJ5yi5Tp06nDx5kiJFinDnzh0qVKiA1WpFkiR0qcs5suLu3QeLOlS4sA937tyfg4qDS5c0gAda\nrYk7d7KYk3vKyY96zA36tavxHv4+qoQEkt77gJR4E5DBXN0oEP+y5RASEzG/EoK566vYylfEv00g\nmoULSWjcHEs2kYh8dv6OAYgrVxX7nUToOwjPO3fxXDCXu8GvycfSIdRsQEGVCtuKX1HfvIHg6Unc\nsDGIdxLR9+iNb1QUyXPmYRzluoGEYelP+KxZg6VhYxJ69wdJorCnJ7b9UdzNpi5V1/7Bv987CJ6e\n3F3wPfYXy0Jscpbp8wVDAQJKPofw++/E3r4HgoDHwgV4jx2LveRzxC9fgyh4QE7vQKmK+IS8imH1\nSu7N+xZzaI+0c6KIX+jrZHSKkgQB4+hxGCvWdOavebk1/jNmkLJsJUl1m2LYvA0fILFGXUx3EuX3\nsWJNCnl4IP24GJXFgqlMORIfwTuaEd8GjdBHRmB9oxdaq5WEsROwJFohMUNbUbEmBf39kcLWEDfu\nM7wPH8MDuPvsC9geodzCJ1Pw37Ub1aRJCJKEtWp14gePyPbZGpo0x2fNGqQvvwRBIK5lB8TsZNZ4\no5s9H993enMvJBTLQ7g/37r10UdGEHMq2tVKIYr4fzYZtVpNXJ8B2cuZT+R3+1i4sE+W53I0azdu\n3JgtW7YAcOrUKYoUKYJ3qrmrePHiREdHY0qdAzx58iQvvPACjRs3JjxcjvC0Y8cO6tfPeZuwJwFl\nzvnhI9xLwGfQO/i+8xaC1ULijDmkvDfUfWKdjrt7DhJ3/CzJEydjq1YD9Hrufb0IyWDA54N3M+/q\nkw7NkUOI3j7Y060tNY4aS8yF69grVsqUXvIPwFarDtoTx1HduY3xvfcRnykGgLlzMKKvH4aff3Jx\nptLu34v3mOGIvn4kzvtWnqPVaKBmTXl+NKsNE+x2fN6VI24lTZwiK+ZHhLVeA1SxsajPn8PwyxK8\nPxqJvegzxOfRizl57CdIHh6yt3C6aQaP775Gt3M75hYtiTl3mZjzV+R/0dcwDv3QJQ9bnbqIhYug\nD98kL0van26+2YHBgKVxU1T3EuRrHvF8swPnkqoD+7A0aIQlq/WxGg2WVm1R37iO5vjRNE/tF92b\nhx8Wkq8f9+YtlJ25PIF5BlUAACAASURBVDxIXPCdc817VjiWVAmiiLXJS873P9trWrcl5uIN2Rns\nIWDLYr2zLmILmjOnMXfpKjvbPWXkqJxr1apF5cqVCQ0NZdKkSYwfP56wsDAiIiIoVKgQffv25c03\n3+S1116jYsWK1KlTh3bt2iGKIq+99hpLly5l2DD38yVPGo69nJ9Kb+2UFHze6+/0jM4zViueM6dR\noFUzCrRM+5feCzo7VFev4DFnFv4vN8Lw63KsNWtxN/J3TG/0zj5ggSBkOm8vX4Gk8ZNQxcXh815/\ntzF/hfi7aM6fw1azdmanJjdOTg4cTjH2Z4tjHPBe2glPT0zdX0N9+xa68E0AaI4dwff1bmC1kvj1\nd67zoLVrI9jtaE6dcFuOx/y56P74HXOb9nIdPEIc885ek8bj/cF7iP7+JPy6DrFU6TzlIxYvgXHA\ne7I3+1ezgdTIZZ+ORyxUiMQvFyAV8EfyK4DkV8D9HLZajblNO3mu/2AU2gP7EAMCXDpUkPZcAOzl\nc78cKT9xxNkGSJ44Odv31pwavlK/6Tc0f53Js6d2fmFr0JCEFWtgx45MdeoOsXgJbKlrsd05gmVJ\nXj3g84DTTyIqnXKWJGeAFseSs6eNXPm8f/iha2+3QrplDKGhoYSGhrqcV6vVTJkyJR/Ee7Q8zSNn\nr08/lkMXrvgFRBFT7745X5SK6kI0voP6oT18CEmrBW1a71s4noz/nt0kj/2ElHcGunykQmws+g1r\nMaxe6ez1SjodyR8MxzhsFGi1930/pj790EVuRb9tKx4LF6TFUk5Fc0Teas5ap07e8g3uhn7lMpIn\nTsnUmJre7IPnwq/x+Ol77GXL4RcajGBM5t63P2AJau2aUWq5muNHsdWp5yrbn8fwmvop9iJFSZz1\nVb5EU8oLjlGpPnwTorcPCSvWZBtbODuM7w7F8MsSPOfPwdytO74D+iKYzdxb9FOuPcst7TrgseRH\nPBZ9g/rqFcxt2mWqE0tgmnJ+XCNnW+WqWJq8hK16TWw1amWb1vJyIJKHB/oVv6CKicGc0dP6EWJt\n1hwK++Q8VZFKSt//YfjlJ8wdn4zIWdYatZB0OnSREc56V924gfZQFOY27e773X3SyacFaU8HT+uO\nVLrIrfKONy+WRRUfj/fID5C8vDB3C83+QknCsPQnvMeOktc7du1O0tTpSL5+aXlv3YzP0EF4fzwG\n3bYIkj6fjubYUfRhv8o71NhsSIKApclLmIO7ybsHFcgHd3hBIHH2fLQvN8Rr8kRM3V93ydcRojLT\n8pMcEEuV5u6BY27P2ctXwNKgEbpdOygQ3AFVXByJs+dh6dQlc+JU5aw9djTjTDpeE8YhWK0kzv0a\nqWDBPMmXH9jLlUcsVBghOYl7v/yao6LJFm9vksd8jO/gARRo2wJVfDwpb72dJxOnpUkzRG8fDKl7\nGDs8dNMjliqNrVx5VHduP3JPbSdqNQlhv+WcDsDTE0vzIOd65/vx1H5cmHr1cUaCeyIwGLDWqYdu\n7x58B7ztcuppHTWDopxdeBr3chbu3MFn8EAknY573/4IQIEu7fEZPADJy9utQ5VwNw79hnUYVi5D\nG7Vfnk/95nu34Q4trdoSt3M/Pu//v707D4yqPPQ+/j0zk4VskIQEDWUzsggCSrGIuFAgoLi0tdWi\nRepFi4pWb8VKzIsNuAAuUK32VqtQFb0lgmjVqlgXXDCCLM0VlNWCrCEhC9kmmeW8f0wySUhCFobM\nJOf3+SfMmXMmTx4jP579diL+tZqE0T/0v+cado4vkH/2c7ynpwT8ZzOTkymfeScxD9xP5IrlVPzm\nNv97NeHsGhHYFovz19MJ//ILbPl5lD600L/9YQMDBuCNjsGRU385le3QQcI+/xTXqNG4fjw+oGVr\nMZuNolffAJsNz+AhJ/1xlddeh2vJXwnL2Yx7wEBKMx9q3QdERFCVNpHI118DarcZPV7xsiyM8vJT\n2oUaSJWTr/CH8/F7akvrlDz5Pw127/P84AcNeqU6E4VzHUVFBuHhnehEKtMk9ne3Y8s7Qum8+XjO\nHgpA8d9X0u0XPyFuxo2U//Z3mFHVh4ybXvi/TSS++y5G9aSnyvFplD72xAlbK2ZyMsdefpXIF5cS\n8ebruEaNpvLqa5pcHxlIzim/Inrhg0S+uJSKm2/1dYd6vTg2bcDTp2/A1zRWXvETnP96D9d5o3De\ndEvTN9rtuIcN93Xnl5b6x1sjVq3EMM16G6AEQ83vQkDYbJQsforohQ9S9v/mtmlsteqyK4h8/TXM\nLl18E/8a0dox8WCrSpuEabdjeDydtuu1vXj79PVvTGQVCuc6Cgp8W3e28xDgKRP5whIi3n+Pqot/\nTMUttQv03SN/RPGy5XS9/hdEL3qkwXPus4fVtnhbOnvXMHDeeFOrxrIDwezencorriJy1UrCvvwC\n1+gx2L/bja2oCGedccqAiYig5JmmNyOpyz38XMKz1+LY8jXu6p2OIl57FdPhoPKqnwa+bEHkGTqM\nY6+0YTvPalUTJuKNjfO1mpuZUdxRmPEJVI2bQNiX2e0+U1s6PoVzHUVFBsnJDWf+BkPkyy8StfhR\nCt/7uHVbNlZVEb7mQyJWrSDi7TfxxsdT8tRfGnQFui66hIK1G/x7Fdfoes4Qirq3fDlNKHD++iYi\nV60k8sWluEaPwbFhPQCukcGbhAPgPsd3pm1Yzibc54/Gvn0bYVv+j8qJl2ImtP9YcygzY2IpXPMF\nZmzT6z47opL/eQ6juDgoM7WlY1M4V/N6oagIBg4MjfHmyOWvYN+/j4j3/tmy7pyKCqIfyiRyZRa2\nwkIA3yk2j/6xyfFeb+8+VB2/PrAVszpDhev8C3APGEjE2/+gNP8Rwjb6TqBq7WSwQKsJ55ptPCNW\nNb1XsVDvPOPOwr+MTKSVOsbMinZw7FgInUhVWuo/4vD4U5Ka0uX5Z4l67hnMsHDKb5lJ4eqPKfxy\ns28ZRWdnGDin/RdGVRWRy1/BsWkDZkQE7iEBHFdtA0/fM/DGdfVNCjNNIl9biTc6hspJk4NaLhEJ\nfWo5VwulQy/C1mdjuN2+P3/2ie9M4xONw1VUEPXM03hjYilc+5Ul/6XuvPY6oh+aS5e/PYft4AHc\nI0YGf+zSZsM9/BzCPv+UsI8/xP79HpzXTFEXp4g0Sy3najXhHKiWc5e//g/hb7/ZpmfDP/Md6u4e\nOAhbaQlhX6074f11j0yzYjCD76ziyp/+HPu+7zE8HlxB7tKu4R5+LoZpEvPAHwCCPktbRDoGhXO1\nQO4OZjuwn5g56cRkZrTp+bDPP8UMC6PsXt/zJ+zarjkyLSKiySPTrKKizth8sCeD1XAP9y0Lcnyz\nBW/3JFwXjw1ugUSkQ1A4Vwtky7nm8Hj7vu8xcnNb9axRVIjj//6Na+SPqBo/ETMigvCPPmjy/og3\nXvN1l143FbNHj5Mqd0fn/uF5/nHmYE8Gq+Eafq7/z86fXu07FENEpBkK52o1LeeEhJMP54h3arf4\nC6ue2NVSYV+s9R3vduHFEBXlWxr0zRZshw42vNnrJeqpP2La7ZTfftfJFrvjMwyOPbuU4qUvt+p0\npVPJ26cv3m6+oQbN0haRllI4VwtUy9koOEpY9lrM6kk/NdtItlTY558AvnXIUHsaT2OtZ/+RaT/9\nOd4+fU+i1J2HZ8BAqq64KtjFqGUYOH/1ayonXuqbpCYi0gIK52qBOvQi/P33MDweKn5zG6Zh4Ghl\nOId//ilmly64qv8irxo/0Xf9+HC2wJFpnUVZ5oMce/nVdj99SkQ6LoVztUC1nGu6tJ2/vB7PgIGE\nbd4E1cui6op6dD5x1/8CnLVnFhlHjuDY9q3vSL/qZUCe1DPx9O5D2Ccf1/uc8Hf/6TsybdJleM4a\nfFJlFhGR0KJwrhaQ2dplZYSv+RD3gIF4zuyP64fnYZSXYd/2bf37nE66/OVpIj54n+iH5/kvh6/1\nLaGquvCS2nsNg6pxE7AdKyaselvKsC8+J+7W6ZiRkZTf27YZ4SIiEroUztUKCw0cDpPo6LZ/Rvia\njzCcTionXwnUzhg+ftw5/NOPsZWVAhD17J8J+/hD332f+8LZdfEl9e6vOWg+7KMPcGzeSNzUX4LH\nQ/ELr+AeOrztBRYRkZCkcK5WVGQQH39yJ1LVnN1addnlAP6NMBzHzdgOf/efAJQseAwzLIzY396K\ncfQo4Z99gjeua4PArbrwYszwcCJXraTrlKsxyss49swSXKfi1CUREQk6hXO1wsKT7NJ2uQh//z08\nKT1xnzMCAM/AQXijY+q3nD0eIla/gzcpGed//Yay9PuxH8ml643XY9/zH1wXXAh2e/3PjonBNeoC\n7N/vwVZYSMkTf6bqys515KCIiNRSOFNzItXJHXoRlr0WW3GRr9Vc0/y223GP+CGOHdsxiot8963/\nElt+PpWXXg42GxUzf0vVmIsIW5cNgOuiixv9/MorfwL4WtuVU37V5nKKiEjoUzgDJSXg9RondehF\nTZd2zXhzjdqu7Y0AhFfP5q68/ArfDXY7JU8/i7d6T+yqMY2Hs/PX08nfuhvnTbe0vZAiItIhaC9B\nArCMyu0m/N1/4u3WDdf5F9R/q3q9ctjGr3CNHUfEu2/jjY3DVWdGtrfnDzi2bDmODV81vSzKMDCT\nktpWPhER6VAUztQuo2prOEf9aTH2QwepmDYdwsLqvVezmYhj41fYt3yN/fu9OH/28wbHGbrOv6BB\nsIuIiDWpW5valnNb9tV2bPyKqMcW4EnpSdn/+0OD983kZDy9+xK28ava2dzHdX2LiIjUpXCm7d3a\nRmkJcbfdDF4vJU8/ixmf0Oh9rpEjsRUV0eXFJZjh4f79skVERBqjcKbt+2pHz0nHvuc/VNx+l+8U\nqSbUbEZiy8+n6uKxmDGxbS+siIh0egpnoKTEF85xcS0P5/C3/kGX/12Ga+hwytLnnPBeV52zhdWl\nLSIizdGEMKCiwve1S5dmbjRN7N9sJXLVCiJfWILZpQslf3m+weSu47nPHoYZEQEuF5WTJgem0CIi\n0mkpnIGKCl/LuUuXJlrObjddnvkzka/+L47qQyy8sXGULH4Kz4CBzX+D8HDKb78Lw+3WcigREWmW\nwpnmW85hX3xOzAP3Y4aHU3n5VTivvoaqCRNb0NSuVd5M17eIiEgNhTPgdPpazpGRjbecbQcPAFC6\ncBHOqb9ut3KJiIg1aUIYzbecbbmHAfCedlo7lUhERKxM4UzzY87+cO6hcBYRkVNP4Qw4nb6vkZGN\nv28/7AtnT7LCWURETj2FM1BebuBwmMdvi+1nyz2MabNhdu/evgUTERFLUjjjazmfaOK1LTcXb3IP\nsNvbr1AiImJZCmd8Y85NrnE2TWxHDmu8WURE2o3CGV/LuanxZuNYMYbTibdHj/YtlIiIWJbCGV/L\nOSqqqZnauYBmaouISPtROONb59xUy9m/jCpZLWcREWkflg9nr9e3Q5jWOIuISKiwfDg3t8ZZ3doi\nItLeFM7V4dx8y1nd2iIi0j5adPDF/PnzycnJwTAMMjIyGDZsGAC5ubncc889/vv27dvHrFmzcLlc\nPPnkk/Tu3RuACy64gNtuu+0UFP/k1W7d2fj7tiPq1hYRkfbVbDivX7+evXv3kpWVxe7du8nIyCAr\nKwuAHj16sGzZMgDcbjc33HAD48aNY/Xq1UyePJnZs2ef2tIHQPMt5+pu7aTk9iqSiIhYXLPd2tnZ\n2UyYMAGA1NRUiouLKS0tbXDf66+/zqRJk4iOjg58KU+h8vJmWs65h/EmJkJ4eDuWSkRErKzZlnN+\nfj5Dhgzxv05ISCAvL4+YmJh6961YsYKlS5f6X69fv56bbroJt9vN7NmzGTx48Am/T3x8FA7HyW2P\nmZQU2+pnaiaCJSSEk5TUSAAfyYU+fdr02R2VlX7WU0n1GBiqx8BQPQZGe9Vji8ac6zLNht2/mzdv\n5owzzvAH9vDhw0lISGDs2LFs3ryZ2bNn89Zbb53wcwsLy1tblHqSkmLJyytp9XOHDtmBKEyzkry8\nqvpvlpeTdOwYVYlJFLfhszuittaj1Kd6DAzVY2CoHgMj0PV4oqBvNpyTk5PJz8/3vz5y5AhJSUn1\n7lmzZg2jR4/2v05NTSU1NRWAc889l4KCAjweD/YQPDiiosL3tbExZ61xFhGRYGh2zHnMmDGsXr0a\ngK1bt5KcnNygS/vrr79m0KBB/tfPPfccb7/9NgA7duwgISEhJIMZfBuQQOPrnLXGWUREgqHZlvOI\nESMYMmQIU6ZMwTAMMjMzWbVqFbGxsaSlpQGQl5dHYmKi/5krr7yS3//+9yxfvhy3283DDz986n6C\nk1TTcm5sb2177iEAPKcpnEVEpP20aMy57lpmoF4rGWgwnnzaaaf5l1iFupp1zo23nNWtLSIi7c/y\nO4SdeMy5uls7WeEsIiLtR+Hcopaztu4UEZH2Y/lwrt0hrOF76tYWEZFgsHw417ScG5sQZjuSizeu\na9Pbh4mIiJwCCufqMeemurXVpS0iIu1N4ew/leq4lnNVFbaCAnVpi4hIu7N8ONeMOR/fcrYdqZmp\nrZaziIi0L8uHc1MtZ00GExGRYFE4+9c517+urTtFRCRYLB/OTqdBRISJ7bia0BpnEREJFsuHc0WF\n1jiLiEhoUThXGERGNr7GGRTOIiLS/hTOzbac1a0tIiLtS+HcVMs5NxczKgozNi4IpRIRESuzfDg7\nnRAV1fC6Lfewb42zYbR/oURExNIsHc4eD1RVGQ13B/N4sOUd0XiziIgEhaXDual9tW35eRheLx6F\ns4iIBIHFw7m53cE0GUxERNqfpcO5yX21tcZZRESCyNLh3GTLOS8PAG9ScruXSURExOLh7Pt6/Dpn\no6AAADMhsZ1LJCIiYvlwbqLlXOgLZ298QruXSURExOLh7PvaoOVcHc5mosJZRETan6XD2en0tZyP\n3yHMdvQooJaziIgEh6XD+UQtZ9MwMLt2a/9CiYiI5Vk6nGuXUjUccza7dQO7PQilEhERq7N0ONdM\nCDt+b21bQYG6tEVEJGgsHc7l5Y2MOZumr1tb4SwiIkFi6XCu6dauO+ZslJZguN14ExTOIiISHJYO\n59oJYbUtZ/8GJGo5i4hIkFg6nGuXUtVe0wYkIiISbJYO55qWc1RU3Zazb42zmaitO0VEJDgsHs6N\ntJwL1HIWEZHgsng4+77WHXP2d2trQpiIiASJxcO55uCL2muaECYiIsFm6XB2OsEwTCIiaq9pQpiI\niASbpcO5osKgSxcwjNpr/hOp1K0tIiJBYulwdjob2VdbE8JERCTILB3ONS3nuozCQsyoqPpTuEVE\nRNqRxcO5YQbbCnXohYiIBJfFw9mot4wKwHb0qMJZRESCyuLhXH8ZFZWVGOVlmAnaHUxERILH0ZKb\n5s+fT05ODoZhkJGRwbBhwwDIzc3lnnvu8d+3b98+Zs2axaWXXkp6ejoHDx7EbrezYMECevXqdWp+\ngjZyucDjMepNCKvdgCQ+WMUSERFpPpzXr1/P3r17ycrKYvfu3WRkZJCVlQVAjx49WLZsGQBut5sb\nbriBcePG8fbbbxMXF8eiRYv4/PPPWbRoEU888cSp/UlaqXZf7dpr2oBERERCQbPd2tnZ2UyYMAGA\n1NRUiouLKS0tbXDf66+/zqRJk4iOjiY7O5u0tDQALrjgAjZt2hTgYp+82n21G2k5K5xFRCSImg3n\n/Px84uNru3kTEhLIy8trcN+KFSv4xS9+4X8moXoTD5vNhmEYVFVVBarMAVG7r3btNX/LWRuQiIhI\nELVozLku0zQbXNu8eTNnnHEGMTExLX7mePHxUTgc9tYWp56kpNgW33vkSM33DSMpKcz3wl0OQEyf\nnsS04rM6m9bUozRN9RgYqsfAUD0GRnvVY7PhnJycTH5+vv/1kSNHSEpKqnfPmjVrGD16dL1n8vLy\nGDRoEC6XC9M0CQ8PP+H3KSwsb23Z60lKiiUvr6TF9x84YAOigSry8ioB6PL9QWKAYkcXqlrxWZ1J\na+tRGqd6DAzVY2CoHgMj0PV4oqBvtlt7zJgxrF69GoCtW7eSnJzcoIX89ddfM2jQoHrPvPfeewB8\n/PHHjBo1qk0FP5VqT6SqM+Z89CigMWcREQmuZlvOI0aMYMiQIUyZMgXDMMjMzGTVqlXExsb6J33l\n5eWRmFi7Nnjy5Ml88cUXXHfddYSHh7Nw4cJT9xO0kdPp+1p3zFkTwkREJBS0aMy57lpmoF4rGeCt\nt96q97pmbXMoKy9v2HLWiVQiIhIKLLtDWE3Lue7e2raCAky7HTOua3AKJSIigoXDubExZ6OwwLcB\nSd0DnkVERNqZZcO50ZZzYQFedWmLiEiQWTaca1rOUVHVLWevF6OoSFt3iohI0Fk2nMurl1XXzNY2\nioswvF7N1BYRkaCzbDg7nfX31q49kUrhLCIiwWXZcD5+b22jegMSdWuLiEiwWTacm2w5K5xFRCTI\nLBvOx5/nrBOpREQkVFg4nOuvc7YVFgJqOYuISPBZOJx9X2vWOfu37qyzR7iIiEgwWDicDex2k7Dq\no5xtBRpzFhGR0GDZcHY6fa3mmp06NSFMRERChWXDuaKiiROp4uODVSQRERHAwuHsdBr1z3I+ehRv\nbBz+fm4REZEgsWw4N9Zy1gYkIiISCiwcznVazqZZfSKVurRFRCT4LBnOpulrOdfsDkZ5OUZlpVrO\nIiISEiwZzpWVYJq1LWfN1BYRkVBiyXB2On1fdSKViIiEIkuGc83WnQ331dbuYCIiEnwWDWffV51I\nJSIiocii4Vxz6IXvtU6kEhGRUGLRcPZ9rVnnbD90EABvco9gFUlERMTPkuHsdPpazjUnUtl37gDA\nfeaAYBVJRETEz5LhXNty9n2179qBN64rZnJy8AolIiJSzZLhXNNy7tLFBJcL+3e78fTvX3tElYiI\nSBBZMpzLy31fu3QB+949GG43nv4Dg1soERGRapYM57otZ/uO7YDGm0VEJHRYMpxr1zn7xpsBPAPU\nchYRkdBg0XCubTk7qmdqe/r3D2aRRERE/CwZzjV7a3fpAvad2zHDwvD06RfcQomIiFSzZDiXlVWv\nc47wYt+5E88ZqeBwBLlUIiIiPpYM5y1bbBiGSWpMLraSY3g0GUxEREKI5cK5qgo2bbJz1lleEg5/\nC4C7v8JZRERCh+XCOSfHhtNpcP75Hv+2nR6Fs4iIhBDLhfOXX/rGls8/34Njp2+Ns8JZRERCieXC\nef16OwCjRnmw79wJgOdMLaMSEZHQYalw9nph3To7vXt7Of10E/uuHXhSemLGxAa7aCIiIn6WCucd\nO2wUFRmMGuXBKC3BfvCAZmqLiEjIsVQ4f/mlr0v7/PM92HdVd2lrZzAREQkx1g3n6pnabp1GJSIi\nIaZF22LNnz+fnJwcDMMgIyODYcOG+d87dOgQd999Ny6Xi8GDB/PAAw+wbt067rrrLvpXt0oHDBjA\n/ffff2p+glZYv95OYqKXM8/0Yl+hZVQiIhKamg3n9evXs3fvXrKysti9ezcZGRlkZWX531+4cCHT\np08nLS2NefPmcfDgQQB+9KMf8ac//enUlbyV9u832L/fxmWXuTAMag+80GlUIiISYprt1s7OzmbC\nhAkApKamUlxcTGlpKQBer5eNGzcybtw4ADIzM0lJSTmFxW27detql1CB76hIb2wc3uQewSyWiIhI\nA82Gc35+PvHx8f7XCQkJ5OXlAVBQUEB0dDQLFizguuuuY9GiRf77du3axa233sp1113H2rVrT0HR\nW6fueDNuN/bvdvsmgxlGkEsmIiJSX6uPYjJNs96fc3NzmTZtGj179mTGjBmsWbOGs846izvuuIPL\nLruMffv2MW3aNN5//33Cw8Ob/Nz4+CgcDnvbfopqSUlNr1feuBGiomDcuGjC/nMAXC7Chp59wmes\nSnUSGKrHwFA9BobqMTDaqx6bDefk5GTy8/P9r48cOUJSUhIA8fHxpKSk0Lt3bwBGjx7Nzp07GTt2\nLJMnTwagd+/edO/endzcXHr16tXk9yksLD+pHyQpKZa8vJImPhu2bInloovcFBVVEP7lJroCpb36\nUdHEM1Z1onqUllM9BobqMTBUj4ER6Ho8UdA32609ZswYVq9eDcDWrVtJTk4mJiYGAIfDQa9evdiz\nZ4///X79+vHmm2+yZMkSAPLy8jh69Cg9egRvbLfulp1A7YEX2oBERERCULMt5xEjRjBkyBCmTJmC\nYRhkZmayatUqYmNjSUtLIyMjg/T0dEzTZMCAAYwbN47y8nLuuecePvzwQ1wuF3Pnzj1hl3agvf66\ng5dfDvO/3r/f928Qfzjv3QOA54zUdiuTiIhIS7VozPmee+6p93rQoEH+P/fp04e///3v9d6PiYnh\nmWeeCUDx2iYnx85nn9X/0Xr39jJypC+cbbmHAPCefnq7l01ERKQ5rZ4Q1hHMnVvJ/fdX1rtms9VO\nzLYdPozZpQtmXNcglE5EROTEOmU4A9hPMPHbdvgQ3h6naRmViIiEJEvtrQ2A240t7wie09SlLSIi\nocly4WzLO4JhmnhPOy3YRREREWmU9cL5cPVksB5qOYuISGiyYDgfBsCrbm0REQlRFgzn6pazurVF\nRCREWS+ca9Y4q+UsIiIhynrh7O/WVstZRERCk/XCObc6nHsonEVEJDRZLpzthw/jjYnFjNHxaSIi\nEposF8623EPq0hYRkZBmrXCuqsKWn6/JYCIiEtIsFc62I7mAxptFRCS0WSucD2sZlYiIhD6LhXPN\nTO0eQS6JiIhI06wVztqAREREOgBLhbNd+2qLiEgHYKlwrhlz9mhCmIiIhDBLhrNma4uISCizVjjn\nHsbbtRtERQW7KCIiIk2yVjgf1u5gIiIS+qwTzhUV2IqK8PbQZDAREQltlgln/2lUajmLiEiIs044\naxmViIh0ENYJ5yO+cPao5SwiIiHOMuFs9y+jUstZRERCm2XCubZbWy1nEREJbRYKZ+2rLSIiHYN1\nwrlmtnayTqQSi+pZ9QAAEN1JREFUEZHQZp1wPnwIb2IiREQEuygiIiInZKFwPqzJYCIi0iFYI5xL\nS7GVHNNkMBER6RAsEc52/xpntZxFRCT0WSKctYxKREQ6EouEszYgERGRjsMi4ax9tUVEpOOwSDjX\nbECibm0REQl91gjnQwcB8PZQOIuISOizRDg7du7AjIpSt7aIiHQInT+c3W7su3bgHjAQbJ3/xxUR\nkY6v06eVfc9/MKqq8Aw8K9hFERERaZHOH87btwHgHjAoyCURERFpGUdLbpo/fz45OTkYhkFGRgbD\nhg3zv3fo0CHuvvtuXC4XgwcP5oEHHmj2mfbk2P4tAJ5BCmcRkfb21FN/ZPv2bykoOIrT6SQlpSdx\ncV2ZP/+xZp995523iI6O4ZJLftzo+08+uYhrrplCSkrPQBc76JoN5/Xr17N3716ysrLYvXs3GRkZ\nZGVl+d9fuHAh06dPJy0tjXnz5nHw4EH2799/wmfak706nN3q1hYRaXe//e3vAF/Qfvfdbu64479b\n/OzkyVee8P277pp1UmULZc2Gc3Z2NhMmTAAgNTWV4uJiSktLiYmJwev1snHjRhYvXgxAZmYmACtW\nrGjymfbm2L4dMyoa7w96tfv3FhGRxm3atIHly1+mvLycO+74HZs3b2TNmg/xer2MHj2G6dNnsGTJ\ns3Tr1o1+/VJZtepVDMPG3r3/YezY8UyfPoM77pjB3Xffy8cff0hZWSnff7+XAwf2c+edsxg9egwv\nv/wCH3zwPikpPXG73UyZ8itGjBjpL8NXX63j+eefISwsjNjYWB54YCFhYWE88cTjfPPNFux2O7//\n/X2cccaZPPHE4+zc+S1eL/5rp1Kz4Zyfn8+QIUP8rxMSEsjLyyMmJoaCggKio6NZsGABW7duZeTI\nkcyaNeuEzzQlPj4Kh8N+Uj9MUlJs/QtuN+zaAcOGkdSj60l9tpU0qEdpE9VjYKgeAyMpKRZ+/3tY\nsSKwH3zNNfBY813UsbGRREWF+/97dusWxZ4937F69WrCw8PZuXMLK1ZkYbPZGD9+PLfffgvR0RHE\nxETSrVsU27d/y7vvvovX62XcuHHMnj2L8HAH8fHRREdHcPDg97z44t/49NNPWb58ORdffD5vvLGS\n1atXU1paysSJE7n11t/U+30yDBdPPvlHevXqxb333su2bf8mMjKS4uKjvP76a3z11VesXfsJHk8F\nxcVHefXVV/3XRo06N7D1eJwWjTnXZZpmvT/n5uYybdo0evbsyYwZM1izZs0Jn2lKYWF5a4tST1JS\nLHl5JfWu2XftJKGqCmfqAEqOe08a11g9SuupHgND9RgYNfUYXV5FhLf5v49bo7K8irIW/DcqKXFS\nXl7l/+9ZVFROv36pFBdXApW43Qa//OV12O12CgoK+e67A5SVVRIW5qSoqJwzzxxAaakb8GVKXl4J\nVVVuCgvLKCurZODAIeTllRAREUtBQRE5Od/St+8ZlJS4gAgGDRpCUVF5vd8nmy2S2bPvw+PxcPDg\nAYYMOYfCwgIGDPB9Vt++g+jbdxCvvPIiAwb4Gpw11wLxe3mif3g2G87Jycnk5+f7Xx85coSkpCQA\n4uPjSUlJoXfv3gCMHj2anTt3nvCZ9mTfpvFmEZEaZXMfomzuQ8Euhl9YWBgAhw8fIivrFZYufYWo\nqChuuOHaBvfa7SfuWa37vmmamCbY6uxtYRgNn1mw4EEee+wJ+vbtx+LFjwBgs9kxTW+9+xq7dqo1\nu5RqzJgxrF69GoCtW7eSnJzs7552OBz06tWLPXv2+N/v16/fCZ9pT44dvmVUnoED2/17i4hIyxQV\nFREfH09UVBTbt2/j8OHDuFyuk/rM008/ne++243b7aawsJBt1Y21usrKSunR4zRKSkrYtGkjLpeL\ns84azKZNGwDYsWMbixY90ui1U63ZlvOIESMYMmQIU6ZMwTAMMjMzWbVqFbGxsaSlpZGRkUF6ejqm\naTJgwADGjRuHzWZr8EwwaKa2iEjo699/AF26RHHbbdMZOvQcfvKTq1m06BGGDRve5s9MSEgkLe1S\nfvObafTp04/Bg4c0aH1fffU13HbbTfTq1Ztf/WoaS5f+lb/8ZSl9+vRj5sybAZg1K53U1DP57LNP\nuP7663G5PMyalX5SP29LGGZLBoTbwcn23zc2NhV/yWjse/eQ/90Bbd3ZQhrjCwzVY2CoHgPDqvX4\nzjtvkZZ2KXa7nWnTprB48VMkJ/do8+cFuh5Pasy5w3K7se/eiXvI2QpmERELOnr0KDNm/JqwsHAm\nTrz0pIK5vXXacPbvqa1tO0VELOmGG27khhtuDHYx2qTTNik1U1tERDqqThvO2lNbREQ6qk4bzvYd\nOo1KREQ6pk4bzo5t27SntoiIdEidM5xrZmoPHKiZ2iIiQXTLLf/VYAOQZ555mr///eVG79+0aQNz\n5twLQHr63Q3ef+21LJYsebbJ77dr106+/34vAJmZ91FZ6Wxr0YOqUyaXZmqLiISGtLRJfPTRv+pd\nW7PmIyZMmNjsswsXLm719/vkk4/Yt+97AObNW0BERGSrPyMUdMqlVJqpLSISGsaPn8htt93EzJl3\nArBt27ckJSWRlJTc6JGNdV1++Xj++c8P2bBhPX/60yISEhJJTOzuPwLy4Yfnkpd3hIqKCqZPn8Fp\np53OP/6xik8++Yj4+Hj+8If7eOmlLEpLS1iw4AFcLhc2m4309PsxDIOHH55LSkpPdu3ayYABA0lP\nv7/e93///XdZuTILu91G376pPP74QtxuNw89lElu7iHCwyOYM2ce8fEJDa4lJSWfVL11ynDWTG0R\nkYbmzo3grbcC+9f+lVe6mTu3ssn34+MTSEnpyTffbGHw4LP56KN/kZZ2KQAlJSVkZj5ESkpPHnzw\nD6xbl01UVFSDz3j22ae5//4H6d9/APfccycpKT0pKTnGj350PpdddgUHDuzn/vvTWbr0ZUaNGs3Y\nseMZPPhs//PPP/8MV1zxE8aPn8jHH3/A0qV/5aabbmH79m+ZN28+8fEJ/OxnkykpKSE2tnbXroqK\nChYteorY2Fhuv/03bN++nbVr15OYmMjcuQ/zwQer+fzzT3E4HA2u/exnvzipeu2U4aw9tUVEQkda\n2qV8+OG/GDz4bNau/ZS//GUpAN26deORRx7yH9n4wx+e12g4Hzp0iP79BwBwzjkjqKysJDY2jm+/\n3cqbb67CMGwcO1bc5Pffvv1bbr31DgBGjBjJCy88D0DPnr1ITOwOQPfuSZSVldYL57i4OO67bxYA\ne/f+h6KiIrZv38bIkecBMGHCJAAef3xhg2snq1OGsxnXDXfqmXh7/iDYRRERCRlz51aesJV7qlxy\nyY956aWlpKVNolev3sTFxQGNH9nYmLpHP9YcB/Gvf73HsWPH+POfn+fYsWPcfPMNJyiB4X/O5XJj\nGL7PO/4gjLpHTbhcLhYvfpQXXvhfEhO7c++9/139jA3vcWdiN3btZHXKCWGljy6m8POvNFNbRCQE\nREVFk5ran5de+pu/SxsaP7KxMd27J/H993swTZPNmzcCvmMmTz89BZvNxieffOR/1jAMPB5Pvefr\nHvn4739vZNCg5ntVy8vLsNvtJCZ2Jzf3MNu2fYvL5WLQoMFs2vQVAGvXfsZLLy1t9NrJ6pQtZ4Wy\niEhoSUu7lIceyiQz80H/tcaObJwxY2aDZ2fMmMmcObM57bTT/YdXjB07jvT0u/nmmy1cfvlVJCcn\n87e/Pcfw4efyxBOP1esev/nmW1mw4EHeeusNHI4w7rvvftxu9wnL27VrN847bxQ33zyNM8/sz/XX\n38CCBQv4619fYsOG9dxxxwzsdgdz5sylW7f4BtdOVqc+MlJaT/UYGKrHwFA9BobqMTDa88hINTFF\nRERCjMJZREQkxCicRUREQozCWUREJMQonEVEREKMwllERCTEKJxFRERCjMJZREQkxCicRUREQozC\nWUREJMSEzPadIiIi4qOWs4iISIhROIuIiIQYhbOIiEiIUTiLiIiEGIWziIhIiFE4i4iIhBhHsAsQ\nCPPnzycnJwfDMMjIyGDYsGHBLlKH8eijj7Jx40bcbje33HILQ4cO5d5778Xj8ZCUlMRjjz1GeHh4\nsIvZITidTq644gpmzpzJ6NGjVY9t8Oabb/L888/jcDi48847GThwoOqxlcrKypg9ezbFxcW4XC5u\nv/12kpKSmDt3LgADBw5k3rx5wS1kCNuxYwczZ87kxhtvZOrUqRw6dKjR38E333yTF198EZvNxrXX\nXss111wT2IKYHdy6devMGTNmmKZpmrt27TKvvfbaIJeo48jOzjZvvvlm0zRNs6CgwLzkkkvM9PR0\n85133jFN0zQXLVpkvvLKK8EsYoeyePFi8+qrrzZfe+011WMbFBQUmBMnTjRLSkrM3Nxcc86cOarH\nNli2bJn5+OOPm6ZpmocPHzYnTZpkTp061czJyTFN0zTvvvtuc82aNcEsYsgqKyszp06das6ZM8dc\ntmyZaZpmo7+DZWVl5sSJE81jx46ZFRUV5uWXX24WFhYGtCwdvls7OzubCRMmAJCamkpxcTGlpaVB\nLlXHcN555/Hkk08CEBcXR0VFBevWrWP8+PEA/PjHPyY7OzuYRewwdu/eza5duxg7diyA6rENsrOz\nGT16NDExMSQnJ/Pggw+qHtsgPj6eoqIiAI4dO0a3bt04cOCAv0dR9di08PBwnnvuOZKTk/3XGvsd\nzMnJYejQocTGxhIZGcmIESPYtGlTQMvS4cM5Pz+f+Ph4/+uEhATy8vKCWKKOw263ExUVBcDKlSu5\n+OKLqaio8HcbJiYmqi5b6JFHHiE9Pd3/WvXYevv378fpdHLrrbdy/fXXk52drXpsg8svv5yDBw+S\nlpbG1KlTuffee4mLi/O/r3psmsPhIDIyst61xn4H8/PzSUhI8N9zKnKnU4w512VqN9JW++CDD1i5\nciVLly5l4sSJ/uuqy5Z54403OOecc+jVq1ej76seW66oqIinn36agwcPMm3atHp1p3psmX/84x+k\npKSwZMkStm3bxu23305sbKz/fdVj2zVVd6eiTjt8OCcnJ5Ofn+9/feTIEZKSkoJYoo7ls88+45ln\nnuH5558nNjaWqKgonE4nkZGR5Obm1uvekcatWbOGffv2sWbNGg4fPkx4eLjqsQ0SExM599xzcTgc\n9O7dm+joaOx2u+qxlTZt2sSFF14IwKBBg6isrMTtdvvfVz22TmP/LzeWO+ecc05Av2+H79YeM2YM\nq1evBmDr1q0kJycTExMT5FJ1DCUlJTz66KM8++yzdOvWDYALLrjAX5/vv/8+F110UTCL2CE88cQT\nvPbaa7z66qtcc801zJw5U/XYBhdeeCFffvklXq+XwsJCysvLVY9t0KdPH3JycgA4cOAA0dHRpKam\nsmHDBkD12FqN/Q4OHz6cr7/+mmPHjlFWVsamTZsYOXJkQL9vpziV6vHHH2fDhg0YhkFmZiaDBg0K\ndpE6hKysLJ566in69evnv7Zw4ULmzJlDZWUlKSkpLFiwgLCwsCCWsmN56qmn6NmzJxdeeCGzZ89W\nPbbS8uXLWblyJQC33XYbQ4cOVT22UllZGRkZGRw9ehS3281dd91FUlISf/jDH/B6vQwfPpz77rsv\n2MUMSVu2bOGRRx7hwIEDOBwOevToweOPP056enqD38H33nuPJUuWYBgGU6dO5aqrrgpoWTpFOIuI\niHQmHb5bW0REpLNROIuIiIQYhbOIiEiIUTiLiIiEGIWziIhIiFE4i4iIhBiFs4iISIhROIuIiISY\n/w8a3FLXriMfvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFZCAYAAACizedRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3WdgFNXawPH/bEuy6YEECE1AagAR\nkCJFCQQCiGCDgIICyhXhgoJ6kVcEpNhAFMWGwBVQmiLqpSOCNOm9SJPeEkivW+b9sMmSkJ5skk3y\n/D5lZ+bMPHu8l2dPmXMUVVVVhBBCCOE0NCUdgBBCCCEykuQshBBCOBlJzkIIIYSTkeQshBBCOBlJ\nzkIIIYSTkeQshBBCOBlJzqJMmzhxIqGhoYSGhhIUFESnTp3sn+Pi4vJ1r9DQUCIiInK8ZubMmSxZ\nsqQwITvcCy+8wMqVKx1yr/r163Pjxg02btzIW2+9VajnLV++3P53Xuo2r8aNG8cXX3zhkHsJUVJ0\nJR2AEEVp8uTJ9r+Dg4P58MMPadmyZYHutW7dulyvGTt2bIHuXdqEhIQQEhJS4PLh4eF8++239O3b\nF8hb3QpRnkjLWZRrAwcOZNasWXTv3p0DBw4QERHB0KFDCQ0NJTg4mAULFtivTWs17t69m379+jFz\n5ky6d+9OcHAwe/bsATK22oKDg1m6dClPP/007du35/3337ff66uvvqJt27Y89dRTfP/99wQHB2cZ\n34oVK+jevTtdu3bl2Wef5erVqwCsXLmSUaNGMX78eLp160aPHj04c+YMAJcvX+aZZ56hS5cujB07\nFovFkum+W7dupVevXhmO9e7dmz///DPHOkizcuVKXnjhhVyf9/vvv9OrVy+6devGk08+ycmTJwEI\nCwvj2rVrhIaGkpKSYq9bgIULF9KjRw9CQ0MZPnw4d+7csdft7NmzGTx4MJ06dWLw4MEkJiZm958W\ngFOnThEWFkZoaCi9e/dm27ZtAMTHxzNixAi6d+9O586defvttzGZTNkeF6K4SXIW5d6xY8dYvXo1\nzZs358svv6RatWqsW7eO7777jpkzZ3L9+vVMZU6cOMEDDzzA2rVrGTBgAF9++WWW9967dy/Lli3j\np59+YvHixdy4cYMzZ87w7bff8ssvv/DDDz9k22q8ffs27777LgsWLGDDhg3UqFEjQ3ftn3/+yYAB\nA1i/fj2tW7fmu+++A2DGjBm0bduWTZs28fzzz3PgwIFM927bti03btzg8uXLgC3B3rhxg4cffjjP\ndZAmu+eZzWbGjRvHlClTWL9+PcHBwXzwwQcATJ8+nSpVqrBu3ToMBoP9XocOHWLevHksWrSIdevW\nERgYyMyZM+3n161bx6xZs9i4cSN37txh48aN2cZltVoZM2YMzz33HOvWrWPq1KmMHTuWuLg4Vq1a\nhZeXF2vXrmX9+vVotVrOnj2b7XEhipskZ1HuPfLII2g0tv8rvP3220yYMAGA6tWr4+/vz5UrVzKV\ncXd3p0uXLgAEBQVx7dq1LO/dq1cvtFotlSpVokKFCly/fp29e/fSqlUrAgICcHFx4amnnsqybIUK\nFdi/fz+VK1cGoGXLlvZkClCnTh0aN24MQKNGjewJdN++ffTo0QOApk2bUrt27Uz3NhgMdOrUic2b\nNwOwadMmunTpgk6ny3MdpMnueTqdjp07d9KsWbMs48/Kli1b6NatGxUqVADgmWeeYceOHfbzjzzy\nCD4+Puh0OurVq5fjj4YrV64QERFBz549AWjSpAmBgYEcPXoUPz8/Dh48yPbt27FarUyePJmGDRtm\ne1yI4iZjzqLc8/b2tv999OhRe0tRo9EQHh6O1WrNVMbT09P+t0ajyfIaAA8PD/vfWq0Wi8VCTExM\nhmdWqlQpy7IWi4XZs2ezefNmLBYL8fHx1KpVK8sY0u4NEB0dneG5Xl5eWd6/W7duLFy4kOeff55N\nmzbxyiuv5KsO0uT0vEWLFvHzzz+TkpJCSkoKiqJkex+AO3fuEBAQkOFet2/fzvU7Z3cvT0/PDM/0\n8vLizp079OzZk+joaD799FPOnz/P448/zltvvUX37t2zPJ6+dS9EcZCWsxDpvPHGG3Tr1o3169ez\nbt06fH19Hf4MDw8PEhIS7J9v3bqV5XVr1qxh8+bNLF68mPXr1zNq1Kg83d/LyyvDTPS0Mdt7dejQ\ngVOnTnHhwgUuXLhAmzZtgPzXQXbPO3DgAHPnzuXLL79k/fr1TJ06NdfYK1asSFRUlP1zVFQUFStW\nzLVcVipUqEB0dDTp9/aJioqyt8rDwsJYsWIFa9as4fjx46xatSrH40IUJ0nOQqRz+/ZtGjdujKIo\n/PzzzyQmJmZIpI7QtGlTdu/ezZ07d0hJScn2H//bt29TtWpV/Pz8iIyMZO3atcTHx+d6/2bNmtnH\nYg8cOMClS5eyvM5gMNC+fXs++ugjOnfujFartT83P3WQ3fPu3LlDhQoVCAwMJDExkZ9//pmEhARU\nVUWn05GQkIDZbM5wr0cffZSNGzcSGRkJwNKlS3nkkUdy/c5ZqVatGpUrV2bNmjX22CIiImjatClz\n5szhxx9/BGw9F9WqVUNRlGyPC1HcJDkLkc7o0aMZMWIEvXr1IiEhgX79+jFhwoRsE1xBNG3alCee\neIInnniCQYMG0alTpyyve+yxx4iKiiIkJISxY8fy6quvcuPGjQyzvrPyxhtv8Mcff9ClSxe+//57\nHn744Wyv7datG5s2baJ79+72Y/mtg+ye16FDBwICAujSpQtDhgzh+eefx9PTk1GjRlG/fn28vb1p\n165dhvH6pk2bMmzYMJ599llCQ0OJjY3ltddey/H7ZkdRFD7++GMWL15M9+7dmTp1Kp9++ilGo5He\nvXvzyy+/0K1bN0JDQ9Hr9fTu3Tvb40IUN0X2cxai+Kmqam+RbdmyhU8++US6T4UQdtJyFqKY3blz\nhzZt2nD16lVUVWXt2rX2Gc1CCAHSchaiRCxZsoT58+ejKAq1a9dm2rRp9olKQgghyVkIIYRwMtKt\nLYQQQjgZSc5CCCGEk3GaFcLCw2MLVd7X10hkpGPfRy2PpB4dQ+rRMaQeHUPq0TEcXY/+/p7Znisz\nLWedTlvSIZQJUo+OIfXoGFKPjiH16BjFWY9lJjkLIYQQZYUkZyGEEMLJSHIWQgghnIwkZyGEEMLJ\nSHIWQgghnIwkZyGEEMLJSHIWQgghnIzTLEIihBCi7Pjss1n8/fdJ7ty5TVJSEoGBVfHy8mb69I9y\nLbtmzW+4u3vwyCNZ73X+6aczeeaZMAIDqxYotpEjhzFmzJvUrn1/gcoXB0nOQgghHO7f/34NsCXa\n8+fPMXLkq3ku26NHrxzPjx49tlCxlQaSnIUQQhSbAwf2sXTpYhISEhg58jUOHtzPli2/Y7Vaadu2\nHUOGDGPevK/x8fGhVq06rFy5HEXRcPHiPzz6aGeGDBlmb/n+8cfvxMfHcenSRa5evcKoUWNp27Yd\nixf/l02bNhAYWBWz2UxY2LM0b94yUyxxcXFMmzaJuLhYzGYzr776BvXrN+CTTz7i1KmTWCwWnnji\naXr06MUnn3zEuXOnSUpKsR8rSmUyOWtPnkATfgtTx0dLOhQhhCh5b7yB37LlDr1lcq8+xE+aWqCy\n586dZcmSlRgMBg4e3M8XX3yLRqOhb9/e9Os3IMO1J04c54cffsJqtfLMM70YMmRYhvO3bt1kxozZ\n/PXXTn755SeCghqzcuUKliz5ifj4eMLCniQs7Nks41ixYglBQY157rkXOHXqBJ999jHTp3/Ezp3b\nWb78F8xmM2vW/EZMTDQ7d27njz82c/16JGvW/Fag750fZTI5u0+bhGHLZiLOXgFX15IORwghRDr3\n318Xg8EAgKurKyNHDkOr1RIVFUVMTEyGa+vXb4BrDv+ON23aDICAgADi4uK4cuUytWvXwcXFFRcX\nVxo2DMq27KlTJxg0aCgADRo04sqVy3h5eVO9ek3GjRtDp05dCA3ticFgoHr1mgwfPpx27R4lNLRn\nYasgV2UyOVsrVUZJSUF7/hyWRtn/hxFCiHLho4+48+Y7JR2FnV6vB+DGjessW/Y98+d/j9FoZODA\nvpmu1Wpz3mwi/XlVVVFV0GjuvoikKNmXVRQFVVXtn61WKwAzZ87m779PsXHjOtatW82sWXOYOXM2\nt25dYvnylfZjRalMvkplqVsPAN2Zv0s4EiGEENmJiorC19cXo9HI33+f4saNG5hMpkLds0qVKpw/\nfw6z2UxkZCSnTp3M9toGDRpx8OA+AI4dO0qtWnW4fv0aK1YspX79Bowc+SrR0dH2Y0FBQfZjRa1M\ntpzTkrP2zOkSjkQIIUR26tath5ubkeHDh9CkSTN6936SmTM/oGnTBwp8Tz+/CoSEhPLSS4OoWbMW\njRoFZdv67tu3P9OnT2bUqJexWq2MGfMfKlb059ixw/z++wb0ej09ez5uPxYWFgZo6Nnz8QLHl1eK\nmr5NX4LCw2MLVd7f39N+D82li1Ro2YSkJ54i9usFjgiv3Ehfj6LgpB4dQ+rRMcpbPa5Z8xshIaFo\ntVoGDQrj448/IyCgUqHv6+h69Pf3zPZcmWw5W6tVRzUa0Z2WlrMQQpQ3t2/fZtiw59HrDXTtGuqQ\nxFzcymRyRqPBXKeubczZagVNmRxaF0IIkYWBA19g4MAXSjqMQimzWctSty5KUhKay5dKOhQhhBAi\nX8pwcq4PyIxtIYQQpU+eurWnT5/O4cOHURSF8ePH07RpU/u569evM2bMGEwmE40aNeLdd99l9+7d\njB49mrp16wJQr149JkyYUDTfIBvmerbkrD19Grp0K9ZnCyGEEIWRa3Les2cPFy9eZNmyZZw7d47x\n48ezbNky+/n333+fIUOGEBISwuTJk7l27RoArVq1Yvbs2UUXeS7SWs7aszIpTAghROmSa7f2rl27\n6NKlCwB16tQhOjqauLg4wLaayv79+wkODgZg4sSJBAYGFmG4eWepVRtVo0F3Wrq1hRCiuP3rX4Mz\nLQDy1Vefs2TJ4iyvP3BgH2+//SYA48aNyXT+p5+WMW/e19k+7+zZM1y6dBGAiRPfIjk5qaCh8/TT\nvUhISChweUfIteUcERFBUNDdJTD9/PwIDw/Hw8ODO3fu4O7uznvvvcfx48dp2bIlY8fatvI6e/Ys\nL7/8MtHR0YwcOZJ27drl+BxfXyM6Xc7LtOUm4ztjnlCnDvqzp/Gv6JHzGm4ig5zevRN5J/XoGFKP\njlHc9fjEE73566+tdOjQyn5s+/YtLFy4MMtYfHyMuLjo8ff3ZN68uZnOe3i4YjK5ZPs9li7dQePG\njfH3b8wXX3xeqNi1Wg0VK3rg7u6e6Vxx1WO+X6VKv2aJqqrcvHmTQYMGUbVqVYYNG8aWLVto2LAh\nI0eOpHv37ly+fJlBgwaxYcMG+0LnWYmMLNyvlKxeDveqUxeXM2eIOPkPqr9/oe5fXpS3xQqKitSj\nY0g9OkZJ1GPr1h0ZPnwoL7zwMgCnTp3E17cCGo2RNWs28e23X6HX6/H09OTdd98nKiqB5GQT4eGx\n9OzZmdWrf2ffvj3Mnj0TP78KVKhQkcDAqly/Hsm0aZMID79FYmIiQ4YMo3LlKvzwwxJ8fHzQaFx5\n5523WLhwGXFxsbz33ruYTCY0Gg3jxk1AURSmTZtEYGBVzp49Q7169Rk3LuOcKIvFSkREHBcuXM9Q\n/sMP30en8+Dddydw+3YEKSkpDB36L1q2bJXpWJs2D+daR4VahCQgIICIiAj751u3buGfmuh8fX0J\nDAykRo0aALRt25YzZ87w6KOP0qNHDwBq1KhBxYoVuXnzJtWrV881WEey1K0P69agO3sakyRnIUQ5\n9cYbsGxZ5lZgYfTqZWbSpORsz/v6+hEYWJUTJ47RqFFjNm/eSEhIKACxsbFMnDiVwMCqTJnyDrt3\n78JoNGa6x9dff86ECVOoW7cer78+isDAqsTGxtCqVRu6d3+Mq1evMGHCOObPX0zr1m159NHONGrU\n2F7+22+/4rHHetO5c1f++GMT8+d/w9Ch/+Lvv08yefJ0fH39eOKJHsTGxuLpmTlR3lv+888/p1ev\np4mOjmLOnLnExsaya9cOzp07m+lYYeU65tyuXTvWr18PwPHjxwkICMDDwwMAnU5H9erVuXDhgv18\nrVq1+PXXX5k3bx4A4eHh3L59m0qVin+FFnPaGtsy7iyEEMUuJCSU33/fCMCOHX/y6KOdAfDx8eGD\nD6YycuQwDh7cT0xM1htJXL9+nbqp/443a9YcAE9PL06ePM7w4UOYNm1StmUB/v77JA8+2AKA5s1b\ncib11dqqVatToUJFNBoNFSv6Ex8fl6fyJ06coGbN+0hIiGfKlAkcOLCXLl26ZnmssHJtOTdv3pyg\noCDCwsJQFIWJEyeycuVKPD09CQkJYfz48YwbNw5VValXrx7BwcEkJCTw+uuv8/vvv2MymZg0aVKO\nXdpF5e4GGJKchRDl10cfwZtvxhf7cx95pBMLF84nJKQb1avXwMvLC4D33pvCRx99wn331eLjjz/I\ntnz6rR/ThlQ3blxHTEwMc+Z8S0xMDC++ODCHCO5uCWkymVEU2/3u3Qgj+y0mMpbXaDS4urry9df/\n5ejRI6xd+xs7dmxj/PiJWR4rjDyNOb/++usZPjdo0MD+d82aNVmyZEmG8x4eHnz11VeFCswR7FtH\nSstZCCGKndHoTp06dVm4cIG9SxsgPj6OSpUqExsby4ED+6lTp26W5StW9OfSpQtUr16Tgwf3ExTU\nhKioKKpUCUSj0bB162b7FpOKomCxWDKUb9iwEQcO7CMkJJRDh/bToEHDfMV/b/nGjRvz99+nuHDh\nPN269SAoqDGvvPJilscKq2yurZ1K9fLGUrkK2rNnSjoUIYQol0JCQpk6dSITJ06xH3vyyWcYPnwo\n1avX4NlnBzF//jcMG/ZKprLDhr3C22//h8qVq9g3r3j00WDGjRvDiRPH6NnzcQICAliwYC4PPPAg\nn3zyUYax6xdffJn33pvCb7+tQqfT89ZbEzCbzXmO/d7yM2Z8QFycma+/nsMvv6xEo9EwYMBAqlQJ\nzHSssMrklpHpeT/1OIZtWwg/fw1Sx8pF9mR2rGNIPTqG1KNjSD06RnFuGVlm19ZOY0ldQlR3TlrP\nQgghSocyn5zNact4npFlPIUQQpQOZT45W9I2wJAZ20IIIUqJcpOcdael5SyEEKJ0KPPJ2RpQCaun\nl7SchRBClBplPjmjKFjq1UP7z3lIfR9OCCGEcGZlPzkDlvvroZhMaC9dKOlQhBBCiFyVj+RcqzYA\n2gv/lHAkQgghRO7KR3KuXQcAzT/nSzgSIYQQInflIzmntZzPnyvhSIQQQojclY/kfF8tANukMCGE\nEMLJlYvkrHr7YK1QQZKzEEKIUqFcJGcAy3210V66CPnYkUQIIYQoCeUnOdeug2I2o7l8qaRDEUII\nIXJUfpJz2qQw6doWQgjh5CQ5CyGEEE6m/CXnC5KchRBCOLfyk5xTFyKRlrMQQghnV26Ss+rji9XX\nVxYiEUII4fTKTXIGW9e29uIFsFhKOhQhhBAiW+UrOd9XG8VkQnP1SkmHIoQQQmSrfCVnmbEthBCi\nFChfyTltUpiMOwshhHBi5Ss5S8tZCCFEKVDOknNqy1nedRZCCOHEylVyVv38sHp5S8tZCCGEUytX\nyRlFwVK7NtoL/4DVWtLRCCGEEFkqX8kZ27izkpyM5trVkg5FCCGEyFK5TM4gk8KEEEI4r/KXnO+T\n5CyEEMK5lcnk/P33et5+2yXLc/YZ25KchRBCOKkymZxXr9bxzTcGYmMzn5OFSIQQQji7Mpmcq1Wz\nzcS+eDHz11MrVsTq4SnvOgshhHBaZTI516xpS86XLmXx9RTFtjuVvE4lhBDCSZXJ5FyjhgrApUtK\nlucttWqjJCaiuXmjOMMSQggh8qRMJuccW86A5f77AdCe/rvYYhJCCCHyqkwm5xo1cknODRoBoDt5\nvNhiEkIIIfJKl5eLpk+fzuHDh1EUhfHjx9O0aVP7uevXrzNmzBhMJhONGjXi3XffzbVMUfPxAS8v\nNdtubXPDIAC0J08UW0xCCCFEXuXact6zZw8XL15k2bJlTJs2jWnTpmU4//777zNkyBB+/PFHtFot\n165dy7VMcahRw8qlSxpUNfM5S+06qAaDtJyFEEI4pVyT865du+jSpQsAderUITo6mri4OACsViv7\n9+8nODgYgIkTJxIYGJhjmeJSs6aVhASF8PAsWs96PZa69dGdOgkWS7HGJYQQQuQm1+QcERGBr6+v\n/bOfnx/h4eEA3LlzB3d3d9577z369+/PzJkzcy1TXHKbsW1u2AglKUnedxZCCOF08jTmnJ6arp9Y\nVVVu3rzJoEGDqFq1KsOGDWPLli05lsmOr68RnU6b33Ay8Pf3tP8dZBtWJirKHX//LC5+qDn8uAy/\nq/9Am+aFem5Zk74eRcFJPTqG1KNjSD06RnHVY67JOSAggIiICPvnW7du4Z+a7Xx9fQkMDKRGjRoA\ntG3bljNnzuRYJjuRkQkF+gJp/P09CQ+/u16nn58WMHLsWDLh4SmZrjfUqIM3EP/XPhI6di3Us8uS\ne+tRFIzUo2NIPTqG1KNjOLoec0r0uXZrt2vXjvXr1wNw/PhxAgIC8PDwAECn01G9enUuXLhgP1+r\nVq0cyxSXXLu1GzUGQCcztoUQQjiZXFvOzZs3JygoiLCwMBRFYeLEiaxcuRJPT09CQkIYP34848aN\nQ1VV6tWrR3BwMBqNJlOZ4la9evbrawNYK1fB6uODVmZsCyGEcDKKmpcB4WJQ2K6CrLobmjRxx9UV\n9u6Nz7KMd+/u6P/aScQ/18FoLNTzywrp/nIMqUfHkHp0DKlHx3Cqbu3SrEYNlStXFMzmrM9bGjZC\nUVV0p08Vb2BCCCFEDsp4crZisShcuyYrhQkhhCg9ynRyzm0DjLTkrDsh485CCCGcR5lOznc3wMhm\n68iGDQGZsS2EEMK5lPHknPY6VdZfU/X0wlK9hqyxLYQQwqmU8eSc8+tUYFvGUxN+CyXdoilCCCFE\nSSrTyTkwUEWnU7NtOQNY0sadpfUshBDCSZTp5KzTQdWqKhcvZj3mDLaWM0hyFkII4TzKdHIGW9f2\nrVsaEhOzPi+vUwkhhHA2ZT45p71Odfly1l/Vcn9dVL1eWs5CCCGcRplPzrltgIFej+X+euhOnQKr\ntRgjE0IIIbJWDpJz3mZsKwnxaGUxEiGEEE6gzCfn3FYJAzA3exAA366P4DV0EPqtf0grWgghRIkp\n88k5125tIHHIMGI/nIWlfkNcfluFzzO98W3/EJqrV4orTCGEEMKuzCfnihVVjEY1x25tDAaSXhhK\n5ObtRK7ZRHLPx9GdPYPriqXFF6gQQgiRqswnZ0WxjTvn1K2d/mJzy1bETX0fAO2xo0UcnRBCCJFZ\nmU/OYOvajolRiIzM2/XWwKpYfX3RHTtStIEJIYQQWSgXyblePQsAp05p81ZAUTA3bor2n/MocbFF\nGJkQQgiRWblIzo0a2WZenziR969rDmqCoqpoT8jKYUIIIYqXJOdsmBs3AZCubSGEEMWuXCTnunWt\n6PUqJ07ksVsbMDduCoDuuEwKE0IIUbzKRXLW66FePSsnT2qwWPJWxlK3HqrBIC1nIYQQxa5cJGew\ndW0nJCg5bh+ZgV6PuUEjdCdPgNlctMEJIYQQ6ZSj5GxrMh8/np+u7SYoSUloz50tqrCEEEKITMpN\ncg4Ksk0KO35cJoUJIYRwbuUmORdkxrYlbVKYrBQmhBCiGJWb5BwQoFKxojV/M7YbBQGgOyotZyGE\nEMWn3CRnsLWeL13SEJvHRb9UL28sNe9Dd/wIqGrRBieEEEKkKlfJOW3cOb/vO2tu30Zz43pRhSWE\nEEJkUK6Sc9qMbVkpTAghhDMrZ8m5IMt4yqQwIYQQxatcJed69azodPldxjOt5SzJWQghRPEoV8nZ\nxcW2zvaJExqs1ryVSdvbWSvd2kIIIYpJuUrOAA0bWomPV7h0KY/LeKbu7ayTvZ2FEEIUk3KXnO+O\nO+ejazvI1rWtPX68SGISQggh0it3yTkoKG2N7QLM2D56qEhiEkIIIdIrd8m5IDO2Ta3aAGDY+keR\nxCSEEEKkV+6Sc+XKKn5++VvG03pfLcwNGtqSc0JCEUYnhBBClMPkrCi21vOFCwpxcXkvl9KtB0pS\nEoY/txRZbEIIIQSALi8XTZ8+ncOHD6MoCuPHj6dp06b2c8HBwVSuXBmt1tYSnTFjBhcuXGD06NHU\nrVsXgHr16jFhwoQiCL9gGjWysn27jlOnNLRsmbd3qpK7dcf46UwM69eQEtqjiCMUQghRnuWanPfs\n2cPFixdZtmwZ586dY/z48SxbtizDNXPnzsXd3d3++cKFC7Rq1YrZs2c7PmIHaNjQlpBPntTmOTmb\nm7fE6h+Ay/q1xFksoM17t7gQQgiRH7l2a+/atYsuXboAUKdOHaKjo4nLT3+wE2rY0DZj++TJfPTq\nazQkd+uOJiIc3YF9RRSZEEIIkYfkHBERga+vr/2zn58f4eHhGa6ZOHEi/fv3Z8aMGaipWyuePXuW\nl19+mf79+7Njxw4Hh1049etbURQ1f8kZ27gzgMv6tUURlhBCCAHkccw5PfWefY1HjRpFhw4d8Pb2\nZsSIEaxfv54HH3yQkSNH0r17dy5fvsygQYPYsGEDBoMh2/v6+hrR6QrXVezv75nH66BOHTh5UkfF\nip4oeVwsjCcfg2FuGDetw/jpzIIH6uTyWo8iZ1KPjiH16BhSj45RXPWYa3IOCAggIiLC/vnWrVv4\n+/vbP/fp08f+d8eOHTl9+jShoaH06GFrZdaoUYOKFSty8+ZNqlevnu1zIiML94qSv78n4eF5X16z\nXj1X1qzRc+xYHJUrq7kXSOX1SCdc1q3h9u5DWGvXKUioTi2/9SiyJvXoGFKPjiH16BiOrsecEn2u\n/brt2rVj/fr1ABw/fpyAgAA8PDwAiI2NZejQoaSkpACwd+9e6taty6+//sq8efMACA8P5/bt21Sq\nVKnQX8SR0iaF5WcxEkjXtb1BuraFEEIUjVxbzs2bNycoKIiwsDAURWHixImsXLkST09PQkJC6Nix\nI/369cPFxYVGjRoRGhpKfHx4UThoAAAgAElEQVQ8r7/+Or///jsmk4lJkybl2KVdEtJWCjt5UkNw\nsCXP5ZJDQvFQFAzr15L48siiCk8IIUQ5pqj3DiKXkMJ2FeS3u+HcOYW2bT145hkTc+Yk5etZPt07\nozt0gNsnzqH6+uU3VKcm3V+OIfXoGFKPjiH16BhO1a1dVt13n4qbW/5nbAMkh/ZAsVgw/L6xCCIT\nQghR3pXb5KzVQr16Vs6c0WA2569sStfuABg2byqCyIQQQpR35TY5g23cOTlZ4fz5/FWDpUFDrH5+\n6PfsLqLIhBBClGflOjkXaKUwAEXB9FBrtJcuoLl+rQgiE0IIUZ6V8+RcsNepAEyt2gKg3/OXQ2MS\nQgghJDlTgJYzYGptS846Sc5CCCEcrFwn54AAlYoVrZw4kf9lQ80PNEN1cUG/W5KzEEIIxyrXyRls\nredLlzTke6MtFxfMzZqjO3YEJU7eHxRCCOE45T45p18pLL9MrduiWK3o9ssWkkIIIRyn3CfnuzO2\n89+1bWrVGgD97l0OjUkIIUT5Vu6Tc6Fazg+lJWcZdxZCCOE45T4516tnRVEKtoyn6uuHuX4D9Pv3\nku9lxoQQQohslPvkbDRCrVoqJ05oKcgWIKZWbVES4tEdP+r44IQQQpRL5T45g23cOSpK4cYNJd9l\nTa3bADLuLIQQwnEkOXN33PnYsYKsFJaWnGXcWQghhGNIcgZatbLN2N65U5fvstaa92GpVNm2Uphz\nbI0thBCilJPkDDz0kAW9XmXHjvy/ToWiYGrdFu3NG2guXgBVRb/9T7xeeBbjjPcdHqsQQoiyL/9N\nxTLIaITmzS3s3aslJga8vPJX3tyqNfz6M8bZH6M7fhT9wQMAuKz5DUvVaiT3f64IohZCCFFWScs5\nVbt2FqxWhV27CrAYSeomGG6Lv0N36CDJPR8nesH3WL198HzzNXQH9zs6XCGEEGWYJOdU7dvbxp13\n7Mh/Z4K5cVOSnu5H4sAXiNyxj5gFi0np2YuYr+dBSgpeg59DCQ93dMhCCCHKKEnOqVq0sGAwFHDc\nWasl9ou5xM2cjeX+uvbDpuAQ4se/g/baVbxeeh5MJgdGLIQQoqyS5JzKzQ1atrRw7JiGyEjH3Tdx\n1BiSez6OYed23KdOctyNhRBClFmSnNNp186Cqirs2uXAeXKKQuxnX2KpeR9u336FEnnHcfcWQghR\nJklyTufuuHMBurZzoHp4kvjCiygmEy6rVjr03kIIIcoeSc7pNG9uwdW1gOPOuUh+6hlUjQbX5Usc\nfm8hhBBliyTndFxcbAuSnDih5fbt/K+znRNr5SqYHumEfv9etOfOOPTeQgghyhZJzvdo1y5tKU/H\nt56T+vYHwGXFUoffWwghRNkhyfkeacm5SLq2uz+G1d0D1xXLwGp1+P2FEEKUDZKc7/HggxaMRrVI\nWs4YjSQ/3gft5Uvo/9rp+PsLIYQoEyQ538NgsI07nzqlJTzcsePOAMnPhAHgIhPDhBBCZEOScxbS\nXqkqitaz6eH2WKpWw+XXVZCQ4PD7CyGEKP0kOWehdWtbct67twi6tjUakp4JQxMXi8u61Y6/vxBC\niFJPknMWHnjAgk6nsm9fESRn7nZtuy77oUjuL4QQonST5JwFNzdo3NjK0aMakpIcf39L3XqYWrbC\n8Mfv8lqVEEKITCQ5Z6NlSwsmk8KRI0VTRbGffoHVyxvP10ai+2tXkTxDCCFE6STJORstW9rGnYuq\na9tStx4x8xaCxYL34AFoLvxTJM8RQghR+khyzkZRJ2cA0yOdiPvgYzS3b+P97DMo0VFF9iwhhBCl\nhyTnbFSvrhIQYGXfPi2qWnTPSRo0mISXR6I7cxqvIQMhPj77i83mogtECCGE05DknA1FsbWeb9zQ\ncO2a4xcjSS9+4hSSQ3ti2LYVnz490Ny4njGWmGg8h79IxfurycpiQghRDkhyzkGLFrb1r4uyaxsA\nrZaYb78jccBA9IcP4hMajPbYUQB0f+3Ct1M7XH9ajpKQgMe416UFLYQQZZwuLxdNnz6dw4cPoygK\n48ePp2nTpvZzwcHBVK5cGa3WlsBmzJhBpUqVcixTWjz00N1x5969izghGgzEzfocS+378Zg6Ed/H\nupL0xFO4LlkMQPyYN9FevoTriqW4LvovSYNfLNp4hBBClJhck/OePXu4ePEiy5Yt49y5c4wfP55l\ny5ZluGbu3Lm4u7vnq0xpUNSLkWSiKCSOeg1Lrdp4jRyG2/cLsdSoScycuZhbt0G5eRPD2tW4vz+F\n5D5Povr6FU9cQgghilWu3dq7du2iS5cuANSpU4fo6Gji4uIcXsYZpS1GcuRI0SxGkp2UXr2J+t8G\n4se/Q+Tm7ZhbtwFArVSJhDFvoomMxP3D6cUXkBBCiGKVa8s5IiKCoKAg+2c/Pz/Cw8Px8PCwH5s4\ncSJXr16lRYsWjB07Nk9l7uXra0SnK1wL1d/fs1Dls9KhAxw6BJcve/Lwww6/ffaC20Nwe9zvPf5/\nb8KShbj9dx5ur/4bGjd2+KOLoh7LI6lHx5B6dAypR8cornrM05hzeuo97xWNGjWKDh064O3tzYgR\nI1i/fn2uZbISGVm4HZr8/T0JD48t1D2yEhSkA9zYtCmJunVNDr9/QRgmTcP72b6kvDKS6B9/tU0t\nd5CiqsfyRurRMaQeHUPq0TEcXY85Jfpcu7UDAgKIiIiwf7516xb+/v72z3369KFChQrodDo6duzI\n6dOncy1TmrRoUfSLkeRXSkgoyZ1DMGzbimHjupIORwghhIPlmpzbtWtnbw0fP36cgIAAe/d0bGws\nQ4cOJSUlBYC9e/dSt27dHMuUNjVqqPj7W50qOQPEvzMFALev5pRwJEIIIRwt127t5s2bExQURFhY\nGIqiMHHiRFauXImnpychISF07NiRfv364eLiQqNGjQgNDUVRlExlSqu0xUjWrtVz9apC1apFuFxY\nPlgaNiLlkU4Ytv6B9thRLI2blHRIQgghHERR8zIgXAwK249flGMqn31mYMoUF+bOTSz6953zwbBx\nHd7P9iWx/3PEffqFQ+4pY1OOIfXoGFKPjiH16BhONeYsoFUr27jztm3O1bWd0rkr5tp1bKuHhYeX\ndDhCCCEcRJJzHrRoYcHHR2XTJl2RboKRbxoNiS8NR0lJwe27eRnPmc24fr8QzZXLJRObEEKIApPk\nnAc6HXTubObaNQ3HjjlXlSX1G4DVyxu3Bd9CcnLqwSS8hg7C87WRuE9/t2QDFEIIkW/OlWmcWLdu\ntrHm9evz/Wp40fLwIOm559GE38Jl1U8QF4f3s31xWfs/APTb/8S5mvtCCCFyI8k5j4KDzeh0Khs2\nOFlyBhKHDkPVaDB+8Rk+z/TGsG0LyaE9Se7+GNob19GePVPSIQohhMgHSc555OUFbdtaOHRIy40b\nRbu/c35Zq9cgpefj6E4eR79/L0lP9yNm3kJSunQFQL9tawlHKIQQIj8kOedD1662ru2NG52v9Zzw\n71exeniS8NLLxH7+Nej1pLTvCIBh+595uEECxhnvw+zZRRypEEKI3DhflnFiXbuamTABNmzQMXCg\nc6yzncbcrDm3T1+0zV5LZb2vFpZq1dHv+BOsVtBk/VtMv3M7Hq+NRPfPeQAMXhVJ6dmrWOIWQgiR\nmbSc86FWLZV69Sz8+aeWxMSSjiYLunt+aykKKR0eQRMZifb4sUyXK7ExeLzxGj59eqC9eIHEgYPB\n1RXPMSPRXLua+/Pi4tAdOuCg4IUQQqSR5JxPXbuaSUxUnG5BkuyYsuvaTk7Gp3tn3L6bh7lBQ6LW\nbCJu5qfw8cdoIiPxHDEMLJYc7+0xcTy+XR9Fd3B/UYUvhBDlkiTnfOra1ZawnO6VqmykJWf9ti0Z\njrsu+wHd6b9JeqovkRv/xNy8pe3Eyy+T3P0xDDu2YfxsVvY3TkzE5eefbPf6flFRhC6EEOWWJOd8\neughC76+Khs3OtlqYdmwVgnEfH9d9Lt2gil1nNxkwjj7Y1QXF+InTQUXl7sFFIXYWZ9hqRKI8YNp\n6PbvzfK+LhvWoomzrTHrsuonnLOfXwghSidJzvmk1dpWC7txQ8PRo6Wj+kztO6KJvzs+7PLTcrSX\nLpL07CCslSpnul71q0DsnG/AasVr+IuQuiVoei4/LQcguVt3NDHR9kVPHE174njexr+FEKIMKR3Z\nxcmkrRa2Zk3p6NpO6fAIkDrubDZj/GQGql5Pwr9fy7aMqX1HEocOQ3vhH1yXfp/hnHLnNobfN2IO\namJreUOmaxxBiY3Bt0cXfEKDUW7dcvj9hRDCWUlyLoDOnc24u6ssXarH7Dw7SGbL1K4DYFvK02XV\nT+jOnyMp7DmsVavlWC5x9FhUV1eMn8zI0Hp2+XUVislE0lN9sdSpi+mh1ui3/oHm6hWHxm1Yuxol\nIR7tjet4/WswpaKyhRDCASQ5F4CHB/Tta+LaNU2pmBim+lXA1Lgp+j1/Yfz4Q1StloRR2bea01gr\nVSbx+SFor1zGdcli+3HXn5ajKgrJTz4NQFLYsyiqiuuKpQ6N22WVbcKZqXVbDDu2ySYeTkyJvIPb\nnNmQkFDSoQhRJkhyLqDBg22Tq+bP15dwJHljat8RJTkZ3dkzJPXtj7XmfXkqlzDytQytZ83lS+h3\n78LUrgPWwKoAJPd+AtXNDZcli3PcZEOJjcF98gQ0ly7m+lwl8g6GLZsxNXmA6O+XY65zP8bPP8Hw\nv1/zFLcoXp6v/RuPyW/jtmhBSYciRJkgybmAGjSw8vDDZrZt03HmjPNXo6mD7ZUqVaMhYfTYPJdT\nK1Ui8fmhaK9ewfWHRbisXAFA8lN9717j5U1yj17o/jmPbs/ubO9lnDUD45xPcft6Tq7PdVnzPxSz\nmeTeT6J6eROz4HtUoxHPUcNlIw8nY/jfr7is+c329/q1JRyNEGWD82cVJzZkiK31/N//On/r2dS2\nHVb/AJIGDsZau06+yiaMfBXVzQ3jpzNxXb4E1WAg+bHHM1yT1P85AFyXLs7qFig3b+I272sADNu3\n5frMtC7t5D5PAmBp0JDYjz9DExeL18B+aG5cz9d3KEmaa1fRHT1c0mEUCSU6Co+3Xkd1ccFyXy30\nu3agREWWdFhClHqSnAuhe3czlSpZWbpUT1xcSUeTM9XDk9uHTxH3wcz8l03XetadOU1K1+6o3j4Z\nrjG174ilWnVcVq2E+PhM9zDOnomSmIjq5obu5HGUiIhsn6eEh6PfthVTi5ZYa9S0H09+8hniX30d\n3bmzePfpkfkVq8RE3CdPwPuZ3jm24IuVyYRPnx74du6Ad98+ZW41Nfd3J6K9eYOEsf8hacBAFIsF\nw6YNJR2W09AePYLht1UlHYYohSQ5F4JeDwMHmoiNVVi50vlbz+h02W5+kZu01jNAUroubTuNhqSw\nZ9HEx+E5bmyGsWfN1Su4fTcfS437SBj5KgD6ndm3nl3+9wuK1Upy7yczx/HWBOJfex3d+XP49O6O\n5vIl21fbvxffzu0xzvkUw9Y/8H0sBM+Xh6C5cjnnL6aqtlnmWbzL7QguPy5De+EfLAGVMGzZjG+3\nTngN6o/25IkieV5x0u/cjtuiBZgbBpEwYjTJ3XoA0rWdnue4sXi9+HyOP0aFyIok50IaNMiETqcy\nf76+VKwYVlBqQADx498hpWMn+z7R90oY+SqmB5vjuuwHjB9/aD9u/PhDlJQU4l//DymdOgNg2Jb9\nNpYuv6wEIPnxJzKfVBQSxk0g/o230F68gE+fHrhPeAufniHozp4h4V+vELXyf5iaPYjryh/xa9cS\n43vvZvmal+6vXfj06kaFBxtRsVYVfDp3wOPVEbjOn4sSHZWf6sma2Yz7rI9QDQaiNmwh6ufVmFq1\nwWXdanwe61q6u3+TkvAY82/U1BXl0OuxNGiIpcZ9GH7fWGQ/dgrFakV75nTxPc9kQnf0MIqqojt+\ntPieK8oESc6FVLmySo8eZk6c0LJ7d+nYDKOgEv81gugff8m43Gd6RiPRi5ZjqVET9w+m4bJiKZrz\n53D9YRHm++uS/HQ/zM2aY/XwRL99a5a30Ny4jn7XDlLaPGyfDZ6JopDwxlvEj3sb7eVLGL+eg7Va\nDaJWrSF+yvuY2nckat0fxHz2FVYvb9xnzcCveRDeT/XCZen36A7sw+u5vvg+3g39nr9I6fAI5sZN\n0J0+hdsPi/AcNxbf9q0K3QJMazUnDRiINbAqpnYdiPptPQkjRqOJjcGw9Y9C3b8kuX03D935cyQO\nG353XXZFIbl7DzRxseh3bi/ZALPg8tNy/Nq1xO2rz4vlebpTJ1CSkmx/H5PkLPJHkrMDpL1WVRom\nhhU1NSCA6O9XYPXyxvPVEXiN/BeKxULCf/7P1q2u02Fq0xbdubNorl/LVN7l159RVDXLLu17JYx5\nk9iPPyNh9Fgit+zA9HD7uyc1GpL7DeDOrgPEzpyNuVUbDNu24jVqOL6hwbhsWEdK23ZErt5I9E+/\nEbV+CxH/XOfO1r+If+MtNJF38B7YD8+Xh6Lcvp3/ijCbMc76yLYS26gxd48rCsm9bT0Cht835v++\nTkK/zfbjKjF1mCJNSmrXtsu61cUeU270f+0EwH3aZLSnThb583QH726nqjt2pMifJ8oWSc4O8PDD\nFu6/38Lq1TqiHNAbWtpZ6jcgZoHtnWf9vj2YGzUmuVcf+3lTe9tyovp7t7EEXFatRNVoMlyfk6Tn\nnif+/yaienhmfYGHB0kDXyDqt/Xc3nOY+DfHk/xYb6KX/Ej0qjWYH2p991qdDkvDRiS88RaRm7Zh\nat4C15Ur8OvwELq/duW9AgCWLEH3z3mS+g/EWq16hlPmps2wVvRHv3kTWK35u68zUFX0e3djqXFf\nprXZTa3bYvX2sfU6ONk4j+7IYVSNBiU5Gc9XXiryrvf0e51Lt7bIL0nODqAoEBZmJjlZ4eefpfUM\nYOrwCLGffoEloBJx707PMBHN1D51OdEdGSeFaY8fQ79vD6Z2HVADAhwek/W+WiS8Po6Y+YtI6dzV\n9h8uG5YGDYlavYm4SdNQoqLwGvFSlrPQwbY5h/bokbvJyGKBqVNtrebRYzIX0GhI6dQZ7a2bpfIf\nbe25s2giIzE91CrzSb2elM4haK9eQetMXbkpKehOHsfc9AESnx2E/tgRjDPeL9JH6g/sR3Vzw9Si\npW2sW3ZuE/kgydlB+vUzodWqLFkiyTlN8jNh3Dl6GlPHRzMcNwc1werjY9uIIx336ZMBSBgxqrhC\nzJlWS+Ir/yZxxGi0ly/hnsU/5ro9u/EN6Yhf5/b4NQ/C4z9jcH9/Kpw+bVu/vHqNLG+d0jkEyFvX\ntm7vbjxHDHOad7t1e22vqZnS9zqkk9K9JwAu69cUW0y50f59CiUlBXOTZsS/Ox1LjZoYZ39cdK/c\nJSSg/fsk5iYPYG7WHMViQXeq9M/QF8VHkrODVKqk0rmzhUOHtJw4IdVql1XrVKvF1LY92ksX0Vy8\nANjGA102rifl4faYOnUp3hhzEf/aG1hq3IfbV5+jPX7Mfly5eROvoQPBYiG55+MocXG4LfgW46cz\nQafLutWcKuXRYFSNJtfk7LJyBT5PPobriqW4ffGZw75TYehzS86dOqPq9RjWpSZnqxXdX7swvvcu\n2vNniyvMDPSpi8CYmz6A6ulF7Odfg6riNXIYRbFIge7oERSLBdODzTE3bmo75oCeBCUiAu++fdCX\n4smEIm8kizhQWJhtYpi0nnOXkrqcqGHHNlBV3KdMBCD+7Uk5djeXCKOR2A9nolgseL4+2jZObDLh\n9dLzaG/eIP7tycQsWMztE+eI+nk1Ca+Mgm++ybCAyr1UvwqYH2yBbt+erF/bUlWMH72H18tDUQ0u\nWD29cP1pebY7cylRkcU2fq3fuxvV6I6lYaMsz6te3pgebo/+yCHcJ7yFX6sH8H28G+6zZuAx/s1i\nifFeunTJGcDU5mESXxmF9sI/uC1Z5PDn6Q/ZFpsxN2uOuXETWwwOmBRm/GI2hi2bcZ86yenG9IVj\nSXJ2oK5dzVSoYOXHH3VO+ZqnMzG1syVn/fY/MWxYh37vbpK7P4a5ZRbjmE7AFBxCUp8n0e/fi+vC\nBbi/OwHDXztJ7tWHxLRueL0eU7sOtj2uBw/O9Z4pXbqiWCyZW0GpE5bcP3oPS42aRK3ZRPLTfdGE\n38KwdXOm+2iPHaVC47oYZ37giK+aIyUqEt3fpzC1aGmbfZ+N5FDbrG3j13NQbt8mqW9/zEFNMGze\nVCKtZ92Rw6haLeaGQfZjiYNs/410+/Y4/nmpK8GZH2yOuX5DVK220C1nJSoS1wXfAqA/fBDdgX2F\njlM4L0nODmQwwNNPm7l9W8OGDc6/lWRJsjRoiLViRfTb/8R9+mRUjYb48e+UdFg5ip/yPlZPLzze\neQvj119grlef2E/nFLiln+W4s6ri+dpIXH9ajqnFQ0Su3YylfgOS+vYHwGX5kkz3cZ/1EUpKim3t\n8tT3aouKfv9egKwng6WTFPYcCaPGEPPNAm4fP0vs51/bu/ld588t0hgzsVjQHT+KpX5DcHW1H7be\nVwurry/6dK88OYru4AGs3j5YatUBV1cs9RqgO37MNlmwgNzmfYMmPo7kkG62z3mtR6u1UM91Rq7z\nvsanR5dsJ2mWBZKcHax/f1vX9tKl0rWdI0UhpV1HtDeuozt5gqR+A7DUb1DSUeXIWqky8W9PQklK\nwurhadspK7tXuPIg7ZUqw+ZN9i5K18Xf4frjMkzNWxC18n+o/v62a5u3xFznflzWrkaJibbfQ3v6\nbwz/+wVVUdDcuWPfMKSo6PbaWpnmbMab7dzdiX97Esl9ngKjEYDkno9jqVQZ1yXfF8k4b3a0586i\nJCTYu7TtFAXzAw+ivfAPSuSdLMu6Lvovxo8/xHXeN7isXGF7/S2XPauVqEh0/5zH3OxB+w83c+Mm\nKAnxaC+cL9iXiI/Hbe6XWH18iP1qHub76+Lyy0qU8PCcy1mt+HR9FDp2LDOzxZWbN/GYMhH9vj1O\nNenQ0SQ5O1ijRlaaNbOwaZOWGzecbOzUyZjap25j6eJCwhtvlXA0eZP0/BDi3plC9JKfsNStV7ib\npb1SdfMG2mNH0R09jMf4N7D6+BAz9ztIXcscsC1e0m8ASlISLr/9Yj9snP0xiqoS/+50VI0GtwV5\na00ZP5iG2+xZ+Q5Zn5qcTS0eyndZ9HqSnh+CJjYG1xVL81++gHRHDgFgujc5A6YHm9uuOXQw0znt\n8WN4jh2F+/tT8XzrdbxeHopP2JP49gzJsYci7V6mB1vYjxV2Upjb4v+iuXOHxKH/QvX0InHISygp\nKbj+sDDHcvrdu9AfOQQ7d+I5dlSZGKd2n/UhSuoPpKL+MVqSJDkXgf79TVitCsuXS+s5JymdQ1Dd\n3EgYMSrTQh1OS6MhceRozK3bOOR2aV3brqt+wmvoIJTkZGLnfJPlK1hJT/cD7nZtay5ewOWn5Zgb\nNCTxpeGkdA1Ff/BArmOR2uPHcJ/5Ae7TJ9s3DskTsxndgX2Y6zdA9fHNe7l0EgcORtXrcZv/TbEl\nCt2R1MlgTZplOmduZkug+kOZu7YN27YAEP/q68R8s4DYD2eRHNoT3fGjeEx+O9vn6Q/enQxmf459\nUlgBknNyMm5ffIZqdCfxpZdth/r2RzW64/bd/GwnCQK4LP3e9kf16rj+uAy3b77I//OdiObCP7gu\nXIC5Vm3MDRpi2Lwpb+vgq6ptHkApWvRHknMRePJJE66uKt9+qycmpqSjcV7WatWJOPkPCf/J/h+6\nsi7tlSrjZ7PQXviHhFFjSAkJzfJaa7XqpLTviGHXDjQXL2D8/FPb0qijxth+NAx+Cch9LNKYura0\nYrXmfdwS0J48gSY+LttXqPJCrVSJ5F590P19KssV4oqC7uhhVEXBHNQ40zlzWss5i3HntPiSXhhK\ncp+nSHphKDFfzcNcvwFu877Jdu31tHul3RvuJmdtAWZsu/64DO31ayQOGozqVwGwzYhP6huG9spl\nDBvWZV0wPh6XX1dhqV4Ddu7EElAJ90lvo/9zS75jcBbu709FMZtJeGsCyU88jZKSgmFt7kvFGv73\nC77dOjnN64h5Icm5CHh7w6hRKdy4oWHKlGw2iRA2RqPzvTpVjNJeqQJIaduO+HE5/1BJmxhm/PxT\nXJcswlLzPtu4LmB6pJNtXPqXldmuB665fg2XlSsw318Xa0V/XBd/l+dJNbm935xXiS/+C7BNcCpy\nViu6I4ex1LkfPDwyn65cBUvlKhmW2gTAbEa/ayfm2nUybsBiNBLz9QJUFxc8Rw/Pcn143aEDWCpX\nwVol0H5M9fXDUq06uqP5TM4WC26zP0Y1GEgcPjLDqcQhw4Dsf4y5rP4VTXyc7X8z1aoRM38xaDR4\nvfS8fX2B0kR79AiuK1dgavIAyY8/QVLq+vtpu9jlxGX1bwAYv/ysyCdNOook5yIyalQKDRpY+O47\nA3/9VbZ3qxKFkzhsOKbWbYn9en6OrycBpDz2OKrRiNt381BSUkj492t3y2g0JA1+ESU5Gdfvsx6L\ndJv7FYrJROKI0SQ+PwRNdFSex3/TknNhX3czt3gI0wMPYli3On/d6gWguXgBTWxM5slg6eNp1hzt\njesZVmDTHTmEJjbGvg58epZGQcRNmobmzh08RwzLMBNac/0a2hvXM3Rp25/TuAnaWzdRbt7Mc/wu\nP/9oW6O934AMyR5sbzykPNwew59/ZLkVpuuyH4C7P+jMrVoT9/5MNJGReA1+DkymPMfhDNJWEIz/\nv4mg0WCtXQdT02YYtv6BcieHzWksFgx/bAJAE34L1yzeeHBGkpyLiMEAM2cmoSgqY8e6lJYfa6IE\nJD/xNFG/rcdauUqu16oeniT36AWApUogSf0GZDif1G+APXnf+/qMEhuD63fzsfoHkPRUXxJfeNE2\n/vvtV3ka/9Xv3YPV19fWCi0MRSFx6DAUqxXjZ/mflJYf9sVHshhvTpNV13Zal3baOvD3ShryEsmh\nPTBs/xP3iePtM+iz6sVnc5QAACAASURBVNK2Pycoddw5r+upJyfj/t4UVIOBhNFjs7wkcait9Wz8\naHqG/4aay5cwbNtq23q1Vu27cQ98gaSwZ9EfO4LbV3PyFocT0O/cjsvvG0lp3xFT6p7wAMm9n0Qx\nm3FZ879sy+r270MTGUly11BUgwG3OZ+WilfLJDkXoYcesjJ0qIkzZ7R88omhpMMRZUTi80NRNRoS\nxv4n097aqrcPSU/1Q3v5UqaxSNfFC9HExti6lV1dbeO/vZ9Ed/pv9FsyL26SnnLzJtpLFzC1bJVh\nE5OCSu7zFOb76+L233l3Jy0VAf2RjCuDZcXULG3G9n77MUPqlpgpD2ednFEUYj+Zg6VqNYzffEmF\npg3wGDcWlzW/Zbhnevmdse02fy7ay5dIHDIs29XmUro/hqlFS1xXrcRtzmz78bTWYXLYs5nKxE2e\nhrViRdxnvIfm0sU8xVLSjLM+AlJbzemGwdK2X3VZlX3XtuH39QAkDRhEUr8B6P45j2H1r0UYrWPk\n6f9l06dPp1+/foSFhXHkSNZjJjNnzmTgwIEA7N69mzZt2jBw4EAGDhzIlClTHBdxKTN+fDJVq1r5\n7DMDJ0/KbyFReObWbbh9+iJJg7JehSxxiG1imNfwF22zcy0WMJlw++YLVKORxOeH3L02dfav29wv\nc3ymfl8e32/OK1dXYhYvw+rjg+fro9Ht/ivzNUlJhd7WMe01KnOTptleY272IGDbRQqAlBT0e/7C\n3LBRjrujqX4ViNy8nbi3J2P19cVt/lx7Uky7Z4bnpM3YPp77uLMSHYXxk4+wenmT8GrWrWbbzXTE\n/PcHLFUCcZ/yDoaN60BVcV32A6qbG8m9emeO29ePuMnTURIT8Rg31ulfr1LCw9Fv24qpRUvM97zC\nZ61RE1OLh9Bv35rtO9+GTRtR9XpSOj5K4iv/RlUUjJ994vTfO9dssWfPHi5evMiyZcuYNm0a06ZN\ny3TN2bNn2bt3b4ZjrVq1YtGiRSxatIgJEyY4LuJSxsMDPvggCZNJ4dVXXUvbMI9wUqqXd7bnLEGN\niflqHqqLAY+3x+HTswvGjz9Ee/UKiQMG2mf8ApgfbIGpZStcNm3IfllNk8k+flnYyWAZ4qx9PzHz\nFoHFgvfgAfZWnPL/7d15nM31/sDx1/fsszGDGcUty1RIltBiK8tYbiSElIlKJErkFsmNhBKV0I2E\nJEKWy42GUaT8Jtnu5KKy3AplGWafs38/vz++jObOmK3DnNH7+Xh41Dnn+z3nc97zPed9PntWJqFv\nTaNyg5ugTp3cpukSUwrLvmT819csdOqXiqqEr1ZtLMl7jb2q9+xCczrxnJ+HX+hLRFXCOXwk53Z+\nR/r8D/G0ugtXnwdRUZXyHatfXwO9QsViDQoLnfkWptRUcoY/m+fvVRC96jVkLFoKdjsRTwzEsfgD\nzD/9F3eXbqiICgWe4+71AJ7WbbBv3oTt07UFHhMs7Bv+habruO/tUeDj7u490XQdewHvw3TyN6z7\nkvE2bwXh4fhjb8TTpRvW5L1Yz7eOBKsik3NSUhJxccYuQbGxsaSnp5P1P6v7vPbaa4wcOfLylPAq\n0LGjn169vOzda2byZBm9LS4/d8/enNu+G9f9fbDu2U3YG1NRJhPOwUPzHesc/CQAIe8VUHvOzqbC\ngAexJ6zH26RpQJMzGPt+Z02ZhiklhYoPP0DI7LepdFtDwl59xajZ/PQTkV06YC/FoiWmX09gOnu2\n0CbtC3y3NsGUlobpv0dzv7QvrP9eLBYLnnu7k776U2PHq4JomjEo7MhhtKzMS5f7xHFC5r2Lv1r1\n3JaNIsvfuAmZM9/FlJVpbM4CuApo0v59WbKmvYmy2wkf+3yeVeeCjX3dPwEKbAUAcHfrgdK0Akdt\nX1ga1xPXIfe+nKdHAFz28Q5/VJHJOSUlhaioi786K1WqxJnfNR+sXr2a22+/nerVq+c57/DhwwwZ\nMoQHH3yQ7du3B7DI5dPrr7uIjdX5xz9sbNwoo7fF5aeqVCHz3fdJ/3glvjp1cT42CL1mrXzHubt0\nw1+tOiEL5lGx931Yk4zPq3b2LJH3d8W+eROetu1JW/kvY6RjgLkefRznwMFYDh4gfOLfweMl+/mx\nnNu7H9atQ1ltVBg2mLBxo0s0wvhCDbVYyfl8H7H133uwfr0NpWl4W7Qs3RsqhLdlazSlCJl76cVA\nwqZORnO5jGl1v18lrgju7veTPfJvAPir/yV3Bb5L8de+gZyRz2E+dZKwl18KaDOv+cD+P9wlAcYW\nmdbt2/A2aXrJvdH1a6vhvaM51qTtmH76b57HbJs3AeA5vx45GK1FntZ3Y/tyS263R1BSRRg3bpxK\nTEzMvd23b1919OhRpZRSqampql+/fsrj8ahjx46p+Ph4pZRSJ0+eVOvXr1e6rquff/5Z3X333crt\ndhf6Ol6vr6iilHvJyUo5HEpFRSn1889lXRohfmfvXqXatVPK+IpW6q67lLrpJuP/+/dXyuO5vK/v\n9So1YoRSf/+7UmfP5n3shx+UqlfPKEubNkqlphb9fNu2KVW3rnFOQkLRx3/1lXHskCFK2WxKNW1a\nuvdRlPR0papWVSo0VKnjx/M/vnevUpqmVIMGSvlK8Z3o9ys1aZJSn31WvONdrouxvfNOpZKSSv6a\n/2vlSuP5Xnjhjz/X3LnGc02bVvhxS5cax7VqdTFubrdSERFKxcYqpet5j9+0yTj+ttuM44KQplTh\nP5dmzZpFdHQ0ffv2BaB9+/asXbuW8PBwEhISmDlzJuHh4Xg8Hn755Rd69erF2LFj8zxHr169eOut\nt7juuksv0XjmzKWbeYojOjriDz/HlbB4sZVRoxw0bepn3bocrEG2wmd5iWOwK69xtHy7g9AZ07Cf\nr3HkDH823wjZK+lCHLWsTCKGPYH9s0/x1buZ9GWr8837BaO2Hzbx74R8/BFK03A9Noisya8XPcI8\nO5sqsdXB4UDLySFn6HBj68/LwLHkQyJGPoWr1wNk/uPiAiJaWiqRndpi+e9R0patxtsuLmCvWdj1\naDpxnLDxL+JYtwYAV89eZL844ZI1Vcu3O3B8vJicUaPzLburZWYQ1fI2zCd/Q4+O4Wzy90XO3S9M\nxV73Ydu2hbO79hW6PzpKETHoERzr1pA9+kVyRo3G+vU2Int2JefxJ8ieMi3fKRHDBuP4ZBk5T40g\n+6WJxSpPoD/X0dGX3jinyGbtli1bsnGjMRR9//79xMTEEH5+pZ3OnTuzYcMGVqxYwezZs6lfvz5j\nx45l3bp1zJ8/H4AzZ85w9uxZqlatGoj3Uu7Fx3vp2dPL7t1mJk2S/mcRXHy330HG0pWkfv4VactW\nkT1uQlCs4KbCI8hYsBjnY4OwHDxA5D1xmH/8Ifdx04njhM6YTqWWTQn5+CN89RuQtmEzWa9OL97U\nr7Aw/HXq5W6o4G1dgv7mEnL17Ye3QSMcK5djOb8FJ34/FZ54DMt/j5Iz/NmAJuai6NX/Qub7i0hb\nl4C30a04Vq+kUstmxtSs/1m327FoAZE97iFkyYdUGPBQvh26QqdONhJzlWhj//Hzi3+Uhnb2rNGk\nfWuTwhMzGH3o02cYU9umv4Zl546LTdpxHQs8JWvqG/hq1SZ09gysWz4vdTkvlyKv2iZNmlC/fn36\n9u3LpEmTGD9+PKtXryYxMfGS57Rr146dO3fy0EMPMXToUCZMmIDtMvRVlUeaBtOnG/3P775r48MP\ng6zqLATga9AIb7sORR94JZnNZL06nawXx2M+cZzIezsS8u5sKt7fjUpN6hM2ZSKay03Wy1NITfwy\n37SbolzYoUpZLHjvaH453oHBbCZ78lQAwseNAaUImzQB25bPccd1JPuFspnd4r2zBWkbt5Axaw4q\nPJzwl8cR2SXO6D92uwkfNZyI50agIiJwd+yMdV8yESOH5fZVW777NyHvz8VXO5b0hcbcdceypaUu\nj33Dv9D8ftzdehbreBUZZbRE6DoVnnwc22efokJC8F5irroKjyDzvYUoq5UKTz2Bdvp0qct6ORTZ\nrH2l/FmatS84ckSja9dQUlM1Fi508de/XnpnmSupvMUxWEkcA+NScbQvW0LEyKfQzq/05L39TlwP\nPIS7W3dUxchSvZbjg/lEPD8Sb7PbSdtQ+hpfcUU8PgDHujW4evbCsXolvtgbSNu4pdBpcqVV0utR\nO3uW8HGjcaxagbJa8desheXQj3gbNCLjgyXoMVWJ7NkV684dZP19Is6hTxN5T3use/eQ9slavHe1\nIapNc8xHDnP2ux+KnA5WkIq978P2ZTGatP9H6KsTCXtrOgDujp3J+GhFoceH/GMW4RNexNMujvSl\nKwttaQmqZm1xecTGKpYudeJwwBNPONixQ0ZwC1Fc7r79SP9kLVkvjufsjn+T9ukmXA8/UurEDEZT\ntrJac5dHvdyyX5qIsttxrF6JHlGBjA+XXZbEXBqqcmVjpP+SFejRMVgO/Yjr/j7GMrPXXQ92OxkL\nFuO/5lrCJo0n4qknsO7dg+v+PnjvbguahuuBfmgeD/Y1Jd9zWTt7FuvXxWzS/h85f3sBb9NmAHja\nF9yk/XvOIcPwtIvD9sVmKt3WkKiWzYi6+04i27UifPSzl57/f5lJzbmMffGFmfj4EMLC4F//yqFu\n3bLdb7S8xjHYSBwD40rHUUtJQUVFgfnK/FgOmfkmYW9MJeP9RZfcKjQQ/kgctaxMzAcO4Lvt9nzj\nDyx7dhF531/R3G70ChU593+7c1dV006donLjuvgaNCRtU8kW/HB8tIiIZ58m66VXcD71TInLbPrt\nVxyLPyBn2DMQFlbk8drp01QY/Ajmo0fQfD7wedHcbjSnE6VpeP7alZyhw4nqEnfFas6SnIPA8uUW\nnn46hGrVdNasyaFWrbL7k5TnOAYTiWNg/Cni6HSWaD5zaVzOONpXfEzEcyPInPpmvrW8K8T3wb4p\ngXNffoO/3s3Ffs6Kfbpj2/oFZ3d+h16jZmALXFw+H/b16wj5x0ysFzZFGTGCM2OLN7K7OKRZO8g9\n8ICP8eNd/PqrifvuC+Xw4bIfHSuEuEIuc2K+3Nx9HiTl0LECN9lwPWDcd2H51+LQUs8ZTdqNbi27\nxAxgseC+rydpCVtIW/sZru49oUbJmtj/CEnOQWLYMC8vv+zi5EkT3buH8sMP8qcRQpQTl5iN4+nY\nGT0qytgz/Py0LC31HNYvEvPsn53nqTYloPl8uLt2u2zFLRFNw9u8JZnvfQAjRlyxl5UMEESefNLL\nq6+6OH3aRI8eIRw4IH8eIUQ5Zrfj7tkb05nTVBj0CFFtWlC5bi0i+95PhcceLviU89s5eroESXIu\nI/LtH2QGDvQybZqLlBQTPXqEkpQko7iFEOWX68F4wEi65qOH8bZoha9OXay7vsX8v3tbZ2Vh2/I5\nvrr18N9wYxmUNnhIcg5CAwZ4mTnTSWYm3H9/CIsXy0IlQojyydewMWkr15G2LoGUQ8dIX7Oe7LHj\nAQhZvDDPsfbPN6G53VdsOlswk+QcpPr29fHJJ04qVFCMGuVgzBi77AUthCiXvHe1wXtnC7AbSxZ7\nOnTCf2017CtXQHZ27nG2803a7q4Fbw/5ZyLJOYi1bOln48Yc6tXzs2CBjQceCOH0aRnJLYQo5ywW\nXA89jCkzA8c/zy9S4nJhS9yEv0ZN/PVvKdvyBQFJzkGuRg3F+vU53HOPl6+/ttC2bShffCH90EKI\n8s3Vrz/KZMLx4QIAbF9uwZSdZdSag2CzlbImybkcCA+HBQtcTJzoIi1No2/fUF56yY7bXdYlE0KI\n0tH/ch2e9h2w7t2DZV9y7ihtdxfpbwZJzuWGyQRDhnj57LMcYmN15syx8de/hvLWWzbWrrWwb5/p\n9103QggR9Fz9HwPAsWAetoT1+K+thq9JszIuVXAo/S7Yokw0bKiTmJjNuHF2li618Z//XGzitlgU\nkya5eewxGTkmhAh+nvYd8FerTsiSDwFw3t+nePtv/wlIFMqh8HCYMcPNzp1ZLF2aw6RJLh591ENk\npGLsWLv0SQshygeLBVe//rk3ZZT2RZKcy7EaNRRxcX4GD/Yydaqbjz5yYrXC4MEhsj63EKJcuDAw\nTK9cGe8dzcu6OEFDkvNVpEkTnbfecpGRoREfH0paWlmXSAghCqdXq07mu++T8c48sEhP6wWSnK8y\nvXr5ePppN0ePmhg0KOTCWvNCCBG03D164W0XV9bFCCqSnK9CY8d66NTJx5dfWnjmGYesLCaEEOWM\nJOerkNkM777rpEkTP598YmXAgBCZZiWEEOWIJOerVHg4rFyZQ9u2PjZvttCrVyjnzuU9RqmyKZsQ\nQojCSXK+ioWHw0cfOenVy8vu3Wa6dQtl5kwbw4Y5aN8+lBo1womLC2X7dpl6JYQQwUSGxl3lrFaY\nPdtFlSqKOXNsTJpkJGKHQ1Gzps5335np0SOULl28jB/vJjq6jAsshBBCkvOfgckEEye6ad/eR3a2\nRt26fmrUUJjNsHeviXHjHKxfbyUx0cKoUTB4MISFlXWphRDiz0uatf9E7r7bzz33+Khd20jMALfe\nqvPppznMneskOlrx6qvQqlUY//qXRfqkhRCijEhyFmga9OjhY/v2bF58Ec6c0Rg4MITevWWlMSGE\nKAuSnEWu0FCYNAm2bcumXTsf27ZZaN8+jE8+kd4PIYS4kiQ5i3xq11Z8/LGT+fOdWCwwbFgIY8bY\n8XjKumRCCPHnIFUiUSBNg3vv9VG/fjaPPhrCggU2kpPNvPeek8hIhdut4fGA3w82G9hsCpsNHA5y\n+7OFEEKUjiRnUajatRUbNuQwapSD1autNG0aXujxYWGKceOMPaU16a4WQohSkeQsihQWBu++66JF\nCz/r11uwWi/WlC0W8HrB5QKPR2PvXhMvvODgiy8szJjhIjpahnwLIURJSXIWxaJp0L+/l/79C99F\n49QpjaefdpCYaKFNm1DeesvFjTfqZGRoZGRoeL1w551+QkOvUMGFEKIckuQsAqpqVcWyZU7mzrUy\naZKd+Pj8WbhGDWPf6Vat/Hnuz8mBhAQLDRro3HijXuRrHTmi8cEHNmw2xbhxHmlGF0JcNSQ5i4Az\nmeDJJ720auVn7lwbZjNUqKCIiFCcOaOxeLGVnj1DefhhD+PHu8nO1liwwMqHH9pITdWw2xUTJhTc\nb63r8PnnZt5/38aWLRcv39q1Ff36yd6YQoirg6ZUcKwDdeZM5h86Pzo64g8/h7gycdy718SIEQ4O\nHjRTubJOerqGz6dRubJO9+4+1qyxcO6ciQ4dfLn91ocPa3zyiZWVK60cO2bMALzjDh8PPOBj/Hg7\nYMzPrl49KC5nuR4DROIYGBLHwAh0HKOjIy75mNScxRV36606iYk5zJpl4+23bdx4o87gwV569vQS\nEgLPPOPhqacu9ltfd51izx5jflZYmKJfPw+PPealQQOj6VvTYORIB6NGOfj4Y2ee2rZScOyYxuHD\nJg4fNnHokIlz5zSaNfPTpo2funV1NM2okScnm9i82UJyspk2bXz062eURwghrjSpOYs8rnQcL1x9\nBTVfz5ljZfJkO34/tGnjp08fL507+/INJlMKHngghK1bLbz9tpMHH/QB8MMPJsaMsbN9+6V/g15z\njU7Dhjp79phIScm7Jk9MjM7QoR769/cSXvgMsnzkegwMiWNgSBwD40rWnCU5izyCLY4pKRpKUeSU\nrOPHNe66KwyTCTZuzGbpUivvvmvD59O46y4fd97p58YbdWJjdSpUUPzf/5nZutXCl1+aOXvWREyM\nTlycj/bt/dxyi5+lS63Mn28jK0ujUiWdZs10qlbViYlRXHONonNnH1WrFlwmo7wRpKQETxzLq2C7\nHssriWNgSHIuBbn4AqM8x3HxYiujRjkwmRS6rnH99TqTJ7vo1Ml/yXN0HU6e1Lj2WpWv9p6aCvPm\n2ViwwMq5c3lr1dWq6axenUPt2nk/PsePawwYEEJoqJmFC7OoUiX/x+vTTy3s329i1CgPFulYKlR5\nvh6DicQxMK5kcpa1tcVVIz7eS1ycD7MZRoxws21bdqGJGYyR5dWq5U/MAFFR8PzzHg4ezObQoUy+\n/jqbVatyGDHCza+/mrjvvlB+/PHiR2j/fhP33BPKvn1mduyAnj1DOHXq4hMrBW+9ZeOxx0J44w07\nEyfai/W+vF747juTbOEpxJ9IsX63T5kyheTkZDRNY+zYsTRs2DDfMW+88Qb//ve/Wbx4cbHPESKQ\nNA0WLXKSnQ2RkYF93ooVoWJFnZtugtat/URHK1580UH37iGsWuXk7FmjxpyZqTFhgou0NAczZpjp\n3j2U1atziI5WjB5tZ/FiG9ddp2O3K+bMsXHzzX769vVd8rV37zYxapSDAwfMPPGEh1decQfujQkh\nglaRyfnbb7/l559/Zvny5Rw5coSxY8eyfPnyPMccPnyYnTt3YrVai32OEJeD1RrYxHwpgwZ5sVrh\n+ecd3HdfKDk5Rs147lwnPXr4qFLFgd/vZtYsO926hRIbq/PFFxYaNvSzZInxA6JTpzD+9jcHN9yQ\nQ7NmeRddyciAKVPsLFxoRSmj33vuXBs33KAzYIDM5xbialdks3ZSUhJxcXEAxMbGkp6eTlZWVp5j\nXnvtNUaOHFmic4Qo7x55xMuMGU7S08Fuh2XLjMQMRm173DgPf/ubm59/NvHFFxbatfPxz3/mULWq\nonZtxXvvOfH54JFHQvjtNw2nE7ZtMzNlio2WLcNYsMBIxmvX5vDZZzlUrqwzZoydrVtl2y8hrnZF\n1pxTUlKoX79+7u1KlSpx5swZws/PLVm9ejW333471atXL/Y5BYmKCsVi+WNfOoV1rovikzgW3zPP\nQIsWEB0NNWvmneMVExPBtGlw001w4gS8+KIFq/VibPv0gV9/hZEjNTp0CCctjdw9sx0OeOUVeO45\nM3a78bxr10K7djBoUChJSVCvXsnK6nRCcjLs3g179sCtt8KwYfmnsQUbuR4DQ+IYGFcqjiUeK/r7\nwd1paWmsXr2ahQsXcurUqWKdcympqTklLUoeMhoxMCSOJVezpvHfM2cu3vf7OHbvbtyXlpb/3Ice\ngj177Hz0kZVbbtFp1cpP69bG1K/wcKN5+4KbboIZMywMHRpC587GaPHrr8//2fr+exPTptk4ccKE\n231h1zCNEyc0/P68mXj3bg+vvebGFKRDQ+V6DAyJY2AE1QphMTExpKSk5N4+ffo00dHRAHzzzTec\nO3eOfv364fF4+OWXX5gyZUqh5wghLtI0mD7dzSuvuIu1U1evXj6OHHHzxht2WrQIY8AAL8884yEm\nRpGZCdOm2Zk3z4rfb6xRbrOB3a6wWo2V2Ro39tOokZ/YWJ3nnnPwwQc2MjM1Zs50cX7ISEAcO6bx\n3ns2HnnEQ2ysDDMXoqSKTM4tW7Zk1qxZ9O3bl/379xMTE5PbPN25c2c6d+4MwPHjx3nhhRcYO3Ys\ne/bsueQ5Qoi8NI0SbaH5/PMeatXSef11O/Pm2ViyxErv3l4++8zC6dMmatTQmTLFSYcOhU8jW7Mm\nh4ceCmXVKivZ2fDeey4cjj/4ZjBGmPfvH8KZMyYSEiwkJORQubIkaCFKosjk3KRJE+rXr0/fvn3R\nNI3x48ezevVqIiIi6NChQ7HPEUIEhqZBnz4+unf3sWSJlTfftLFokQ2HQzF6tJthwzzFSrKRkbBi\nRQ6PPBJCQoKVTp1MxMX5uOMOP7fd5icqquRlW7vWwtNPO/B4oG1bH1u2WBg40MGKFU5stvzHKwW/\n/KKRlGRm1y4zt9yi07+/N2ib2YW4UmSFMJGHxDEwrmQcc3JgwwYLt9/uL7APuigul7FxyJo1FnT9\nYp90bKxOnTp+brrJ2F+7QQOdunUL3mdbKZgxw8arr9oJDzdGordr5+fxxx18+qmVhx/2MH26O3fw\n2cGDJubNs7J1q4Xjx/Nm4pYtfcyc6eK665RcjwEicQwMWb6zFOTiCwyJY2CUxzhmZcGuXWZ27DDz\n7bdmkpPNZGTkHUDWrJmfQYM8dO3qw2o1BqwtX25l4UIrhw+b+ctfdD76yMnNNxtJPDsbunUzVk2b\nPNlFkyZ+3n7bRkKC0cFdqZLOnXf6adHCT6NGOu+8YyUhwUp4uGLKFBdPPRVS7tYoV8rYdCUx0UJi\nohldh/nzXZdci/1KKI/XYzCS5FwKcvEFhsQxMK6GOCoFp09rHDpk4scfje00N282esKuuUaneXM/\nGzdayMnRsNkU3bv7+Pvf3fmS0IkTGh07hnLmzMUactOmfp55xk3Hjv48TdhKwfLlFsaOdZCVpdGt\nG0yenFWqxLZmjYXly60MH+6hRYv8/e/Z2bBli4U2bXwl3nWsIDk5MHu2jRUrrPzyS97WgNhYnTVr\ncrjmmrL5ur0arsdgIMm5FOTiCwyJY2BcrXE8elRjwQIbS5daycrS+MtfjBXL+vXzFrjJxwW7dpno\n1y+UBg38jBjhoWVLf6Hzq48d0xg+3MH27RYiIxWTJrno3dtXrDnZfj9Mnmxj9uyLa5c//LCHl15y\nU7GiMbVs6VIr06bZOH3aRN26fhYvdlKjRum+CpWChAQL48bZOXbMRIUKirZtfXTo4KNdOz//+IeV\n2bPt1K5tJOhrr73yX7lX6/V4pUlyLgW5+AJD4hgYV3scMzPh8GETDRvqmIu5dpBSJVvwRNdh1aoI\nnntOkZOj0bGjj2nTXIUmt/R0GDIkhM8/txAbqzNmjJs337Rx8KCZqlV1Bg3y8vHHVo4cMREaqmje\n3M/nn1uoXFln4UIXd955sYZ9YbBa5crqkjXrI0c0XnrJQWKiBYtF8eSTHp591kNYWN73PWWKjbff\ntlOrlpGgq1W7sl+7hV2PyclGy8j99/tkIF4RJDmXwtX+ZXilSBwDQ+IYGNHREezalcWzzzr46isL\ndruiWzcfAwZ4uO02PTfZp6XBN9+YmTjRzuHDZtq18zF3rpOKFY1V1955x8abb9pwuzXMZsXDD3sZ\nNcpD1aqKDz6wTcoSRwAADAZJREFU8sILdsxmmDbNRfXqik2bLGzcaOHnn03Y7UZN+J57fHTs6OPs\nWRPr11v49FML331n/DJp3drHq6+6uemmSw+YmzrVxptv2omJ0WnTxk/Dhn4aNtS55RZ/sZvVjQVl\nIDy8ZD90LnU9bt9u5qGHQnA6jX3P33mnbPvGg50k51KQL8PAkDgGhsQxMC7EUSmjKfqdd4yBZwA3\n3+ynaVM/u3aZ+f57E0oZ2WrYMA/jxrnz1egPH9ZYtcpKr17efAujbNtmZuDAENLTL2a88HBFq1Y+\nfvrJxPffG0+maSr3daxWRevWfvr189K1a9FN7krBzJk2ZsywkZ198WCTSVGvnk6zZn6aNfNTt67O\nTz+ZOHjQxIEDJg4dMpOeDpmZGm63cV7jxn4GDvTQvbsPezF2Hi3oevzmGzN9+4bg9RoD/ZKSjBaE\nGTMK3wP9z0yScynIl2FgSBwDQ+IYGP8bR6Xg66/NLFpkZcMGCz6fRkiIomlTP3fc4adtWx+3315w\n7bUoR45oTJ1qp0oVRceOPlq08OfOzT5yRGP9eiubN5uJjFR07eqjUycfFSuW/HX8fjh61ERysonk\nZDN795r47jszLlfB2b1yZZ1KlRQRERAWptB1SEoyo+saVaroxMd7qVZNceqUxunTGmfOaFitUKWK\nIjpaUaWKolkzBzfckJk7/33nThN9+oTidsOCBU46dfIzf76Vl1+243ZrDBzoYcIEd7ESf1lYuNDK\n1q1mxo93U7v2lUthkpxLQb4MA0PiGBgSx8AoLI6nTmmcPKlRr55e4AIn5YnHA/v3m9i928yhQyZq\n1tS5+WadevV0YmLyf0X/8ovGBx9YWbLERmpq8dq37XZFs2ZGa8PChTZycoxV4e699+J+4vv3mxgy\nxMEPP5hp3NjP++8788ydVwoSE818/rmF7t19NG+ev4btdsPOnWbq1dMvuTKcfv73U0n7uHUdxo+3\nM3eu8QcPC1NMneqiT59L74leWl4v+Za0leRcCvJlGBgSx8CQOAaGxLFwOTmQmGjB54OqVRUxMYro\naB2vVyMlxfh35ozGoUMhJCb6+c9/jOZ/k0kxZ46L7t3zJ7WcHBgzxsGyZVYiIxXvvGMsBbtvn4kJ\nE+x89dXFhSU7dvTx4otu6tXTyciARYtsvPeelVOnTEREKJ5+2sPgwZ7c5WlPndKYM8fGokXGXPb7\n7vPRs6eXxo2N8QO6Dj//rHHggBmbTdGypT/3XKcThg0zFrWpU8fPo496mTTJTlaWRq9eXqZOdaFp\nxhzzgwfNHDumUaHCxdaDa65R1K2rF/iDIDUVVq60cuCAif/+1/j3228mWrf2MXeuK3cmgiTnUpAP\ncWBIHAND4hgYEsfAuBDH1FT45hsLUVEqz8j0gixdamHMGAcul0arVj62bzejlEb79j769fMyf76V\n7dstmEyKdu38fPONmawsjbAwo9k/MdHMuXMmqlbVGTHCw8GDJpYts+LxaLk/INLSjFp/zZo6Vaoo\nDh405emPdziMBB0X52PNGgvffmuhRQsfH3zgJDISfvpJY8iQEPbsMRMaaozqL8x11+n07u2lTx8v\ntWsrjh41NmhZtsyae66mKapXV0REKA4eNHP99TqLFzupV0+X5Fwa8iEODIljYEgcA0PiGBiljeO+\nfSYGDgzhp59M3HyznwkT3LRpYyR1peDzz8288oqdgwfNxMToDB7sZcAADxUrGqvHvfOOjTlzbDid\nF5PwU0956NPHWD99yxYza9ZYSUiw4HbDTTcZTfn16+tkZsKmTRYOHLg4sq9HDy8zZ7ry9IV7vTBt\nmo3Vq63UqGGcX6+eTs2aOhkZF1sPjhwxsWGDJTf516nj58cfjZaE6tV1Bg3yEBfn5/rrdRwOoxY/\nfbqN6dPthIUp5s510q9fqCTnkpIPcWBIHAND4hgYEsfA+CNxzMyEvXvNtGzpL3BOu99v7CF+ww16\ngQPITp7UWLrUSq1aOvfe68NSwHZLbrfx34LOP3ZMIzHRglLw6KN/bFOU7GxjHfrly6189ZWZxo11\nnnzSWI62oHKBsZnL8OEOXC6YN0+jWzdJziUiH+LAkDgGhsQxMCSOgSFxzM/jMQZ8FWe+eHKyiUcf\nDeGuu0zMmHFlknORW0YKIYQQV5uSjPBv1Ehn585sYmIiOHv28pXp92SxNiGEEKIIZnPJp379EZKc\nhRBCiCAjyVkIIYQIMpKchRBCiCAjyVkIIYQIMpKchRBCiCAjyVkIIYQIMpKchRBCiCAjyVkIIYQI\nMpKchRBCiCAjyVkIIYQIMpKchRBCiCATNLtSCSGEEMIgNWchhBAiyEhyFkIIIYKMJGchhBAiyEhy\nFkIIIYKMJGchhBAiyEhyFkIIIYKMpawLEAhTpkwhOTkZTdMYO3YsDRs2LOsilRuvv/46u3fvxufz\n8cQTT9CgQQOef/55/H4/0dHRTJs2DZvNVtbFLBdcLhddu3Zl6NChNG/eXOJYCuvWreP999/HYrEw\nfPhw6tSpI3EsoezsbEaPHk16ejper5dhw4YRHR3NhAkTAKhTpw4vv/xy2RYyiP34448MHTqURx55\nhPj4eH777bcCr8F169axaNEiTCYTffr0oXfv3oEtiCrnduzYoQYPHqyUUurw4cOqT58+ZVyi8iMp\nKUk9/vjjSimlzp07p+6++241ZswYtWHDBqWUUm+88YZasmRJWRaxXHnzzTdVz5491apVqySOpXDu\n3DnVsWNHlZmZqU6dOqXGjRsncSyFxYsXq+nTpyullDp58qTq1KmTio+PV8nJyUoppZ599lm1devW\nsixi0MrOzlbx8fFq3LhxavHixUopVeA1mJ2drTp27KgyMjKU0+lUXbp0UampqQEtS7lv1k5KSiIu\nLg6A2NhY0tPTycrKKuNSlQ+33XYbb7/9NgAVKlTA6XSyY8cO2rdvD0Dbtm1JSkoqyyKWG0eOHOHw\n4cO0adMGQOJYCklJSTRv3pzw8HBiYmJ45ZVXJI6lEBUVRVpaGgAZGRlERkZy4sSJ3BZFieOl2Ww2\n5s2bR0xMTO59BV2DycnJNGjQgIiICBwOB02aNGHPnj0BLUu5T84pKSlERUXl3q5UqRJnzpwpwxKV\nH2azmdDQUABWrlzJXXfdhdPpzG02rFy5ssSymKZOncqYMWNyb0scS+748eO4XC6GDBnCQw89RFJS\nksSxFLp06cKvv/5Khw4diI+P5/nnn6dChQq5j0scL81iseBwOPLcV9A1mJKSQqVKlXKPuRx556ro\nc/49JauRltjmzZtZuXIlCxYsoGPHjrn3SyyL55///CeNGzfmuuuuK/BxiWPxpaWlMXv2bH799Vf6\n9++fJ3YSx+JZu3Yt1apVY/78+Xz//fcMGzaMiIiI3McljqV3qdhdjpiW++QcExNDSkpK7u3Tp08T\nHR1dhiUqX7766ivmzJnD+++/T0REBKGhobhcLhwOB6dOncrTvCMKtnXrVo4dO8bWrVs5efIkNptN\n4lgKlStX5tZbb8VisXD99dcTFhaG2WyWOJbQnj17aNWqFQB169bF7Xbj8/lyH5c4lkxBn+WC8k7j\nxo0D+rrlvlm7ZcuWbNy4EYD9+/cTExNDeHh4GZeqfMjMzOT1119n7ty5REZGAtCiRYvceG7atInW\nrVuXZRHLhRkzZrBq1SpWrFhB7969GTp0qMSxFFq1asU333yDruukpqaSk5MjcSyFGjVqkJycDMCJ\nEycICwsjNjaWXbt2ARLHkiroGmzUqBH79u0jIyOD7Oxs9uzZQ7NmzQL6ulfFrlTTp09n165daJrG\n+PHjqVu3blkXqVxYvnw5s2bNolatWrn3vfbaa4wbNw632021atV49dVXsVqtZVjK8mXWrFlUr16d\nVq1aMXr0aIljCS1btoyVK1cC8OSTT9KgQQOJYwllZ2czduxYzp49i8/n45lnniE6OpqXXnoJXddp\n1KgRL7zwQlkXMyj95z//YerUqZw4cQKLxULVqlWZPn06Y8aMyXcNJiQkMH/+fDRNIz4+nm7dugW0\nLFdFchZCCCGuJuW+WVsIIYS42khyFkIIIYKMJGchhBAiyEhyFkIIIYKMJGchhBAiyEhyFkIIIYKM\nJGchhBAiyEhyFkIIIYLM/wOZHy0GVurBqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting training and validation accuracy and loss against epochs.\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs = range(1, len(acc) + 1)\n",
    "plt.plot(epochs, acc, 'r', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, 'r', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "icRNtLaato8T"
   },
   "outputs": [],
   "source": [
    "# Adding another layer to previous model.\n",
    "model = Sequential()\n",
    "model.add(Dense(64, activation='relu', input_shape=(8,)))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 403
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3241,
     "status": "ok",
     "timestamp": 1554220797405,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "NQSvqrCfto8v",
    "outputId": "4481b558-cd42-4acb-9f2c-c22ce777724d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_29 (Dense)             (None, 64)                576       \n",
      "_________________________________________________________________\n",
      "dropout_21 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_30 (Dense)             (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dropout_22 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_31 (Dense)             (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dropout_23 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_32 (Dense)             (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 8,961\n",
      "Trainable params: 8,961\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qvUFr3joto9E"
   },
   "outputs": [],
   "source": [
    "# Compiling the model\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 3729
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 19524,
     "status": "ok",
     "timestamp": 1554220822674,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "EVn83_LCto9W",
    "outputId": "396e85e7-8931-4f8e-c750-2d52195dc3ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 711 samples, validate on 178 samples\n",
      "Epoch 1/100\n",
      "711/711 [==============================] - 1s 2ms/step - loss: 0.6780 - acc: 0.5682 - val_loss: 0.6249 - val_acc: 0.6461\n",
      "Epoch 2/100\n",
      "711/711 [==============================] - 0s 190us/step - loss: 0.6366 - acc: 0.6076 - val_loss: 0.5883 - val_acc: 0.6461\n",
      "Epoch 3/100\n",
      "711/711 [==============================] - 0s 189us/step - loss: 0.6072 - acc: 0.6315 - val_loss: 0.5604 - val_acc: 0.6685\n",
      "Epoch 4/100\n",
      "711/711 [==============================] - 0s 234us/step - loss: 0.6052 - acc: 0.6723 - val_loss: 0.5438 - val_acc: 0.7697\n",
      "Epoch 5/100\n",
      "711/711 [==============================] - 0s 222us/step - loss: 0.5918 - acc: 0.7328 - val_loss: 0.5211 - val_acc: 0.7865\n",
      "Epoch 6/100\n",
      "711/711 [==============================] - 0s 213us/step - loss: 0.5803 - acc: 0.7511 - val_loss: 0.4994 - val_acc: 0.7865\n",
      "Epoch 7/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.5518 - acc: 0.7567 - val_loss: 0.4801 - val_acc: 0.7865\n",
      "Epoch 8/100\n",
      "711/711 [==============================] - 0s 219us/step - loss: 0.5513 - acc: 0.7553 - val_loss: 0.4699 - val_acc: 0.7921\n",
      "Epoch 9/100\n",
      "711/711 [==============================] - 0s 214us/step - loss: 0.5266 - acc: 0.7609 - val_loss: 0.4634 - val_acc: 0.7921\n",
      "Epoch 10/100\n",
      "711/711 [==============================] - 0s 228us/step - loss: 0.5146 - acc: 0.7834 - val_loss: 0.4589 - val_acc: 0.7921\n",
      "Epoch 11/100\n",
      "711/711 [==============================] - 0s 238us/step - loss: 0.5350 - acc: 0.7750 - val_loss: 0.4526 - val_acc: 0.7865\n",
      "Epoch 12/100\n",
      "711/711 [==============================] - 0s 214us/step - loss: 0.5100 - acc: 0.7862 - val_loss: 0.4473 - val_acc: 0.7865\n",
      "Epoch 13/100\n",
      "711/711 [==============================] - 0s 214us/step - loss: 0.5021 - acc: 0.7862 - val_loss: 0.4422 - val_acc: 0.7865\n",
      "Epoch 14/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.4974 - acc: 0.7876 - val_loss: 0.4375 - val_acc: 0.8146\n",
      "Epoch 15/100\n",
      "711/711 [==============================] - 0s 221us/step - loss: 0.4960 - acc: 0.7904 - val_loss: 0.4355 - val_acc: 0.8202\n",
      "Epoch 16/100\n",
      "711/711 [==============================] - 0s 212us/step - loss: 0.4994 - acc: 0.7820 - val_loss: 0.4363 - val_acc: 0.8315\n",
      "Epoch 17/100\n",
      "711/711 [==============================] - 0s 246us/step - loss: 0.4937 - acc: 0.7820 - val_loss: 0.4326 - val_acc: 0.8202\n",
      "Epoch 18/100\n",
      "711/711 [==============================] - 0s 212us/step - loss: 0.4929 - acc: 0.7792 - val_loss: 0.4277 - val_acc: 0.8202\n",
      "Epoch 19/100\n",
      "711/711 [==============================] - 0s 215us/step - loss: 0.4789 - acc: 0.7989 - val_loss: 0.4283 - val_acc: 0.8258\n",
      "Epoch 20/100\n",
      "711/711 [==============================] - 0s 213us/step - loss: 0.4953 - acc: 0.7834 - val_loss: 0.4307 - val_acc: 0.8315\n",
      "Epoch 21/100\n",
      "711/711 [==============================] - 0s 219us/step - loss: 0.4875 - acc: 0.7918 - val_loss: 0.4299 - val_acc: 0.8202\n",
      "Epoch 22/100\n",
      "711/711 [==============================] - 0s 219us/step - loss: 0.4874 - acc: 0.8045 - val_loss: 0.4273 - val_acc: 0.8315\n",
      "Epoch 23/100\n",
      "711/711 [==============================] - 0s 226us/step - loss: 0.4858 - acc: 0.7989 - val_loss: 0.4277 - val_acc: 0.8258\n",
      "Epoch 24/100\n",
      "711/711 [==============================] - 0s 243us/step - loss: 0.4622 - acc: 0.7890 - val_loss: 0.4259 - val_acc: 0.8202\n",
      "Epoch 25/100\n",
      "711/711 [==============================] - 0s 209us/step - loss: 0.4884 - acc: 0.7947 - val_loss: 0.4241 - val_acc: 0.8371\n",
      "Epoch 26/100\n",
      "711/711 [==============================] - 0s 207us/step - loss: 0.4597 - acc: 0.8003 - val_loss: 0.4184 - val_acc: 0.8258\n",
      "Epoch 27/100\n",
      "711/711 [==============================] - 0s 227us/step - loss: 0.4672 - acc: 0.7961 - val_loss: 0.4179 - val_acc: 0.8258\n",
      "Epoch 28/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.4773 - acc: 0.7989 - val_loss: 0.4208 - val_acc: 0.8427\n",
      "Epoch 29/100\n",
      "711/711 [==============================] - 0s 204us/step - loss: 0.4855 - acc: 0.7975 - val_loss: 0.4145 - val_acc: 0.8315\n",
      "Epoch 30/100\n",
      "711/711 [==============================] - 0s 243us/step - loss: 0.4765 - acc: 0.7961 - val_loss: 0.4252 - val_acc: 0.8427\n",
      "Epoch 31/100\n",
      "711/711 [==============================] - 0s 229us/step - loss: 0.4755 - acc: 0.7961 - val_loss: 0.4189 - val_acc: 0.8315\n",
      "Epoch 32/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.4884 - acc: 0.7989 - val_loss: 0.4199 - val_acc: 0.8315\n",
      "Epoch 33/100\n",
      "711/711 [==============================] - 0s 221us/step - loss: 0.4652 - acc: 0.8045 - val_loss: 0.4147 - val_acc: 0.8315\n",
      "Epoch 34/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.4464 - acc: 0.8045 - val_loss: 0.4094 - val_acc: 0.8315\n",
      "Epoch 35/100\n",
      "711/711 [==============================] - 0s 220us/step - loss: 0.4766 - acc: 0.8017 - val_loss: 0.4092 - val_acc: 0.8315\n",
      "Epoch 36/100\n",
      "711/711 [==============================] - 0s 222us/step - loss: 0.4746 - acc: 0.8045 - val_loss: 0.4154 - val_acc: 0.8371\n",
      "Epoch 37/100\n",
      "711/711 [==============================] - 0s 246us/step - loss: 0.4825 - acc: 0.7890 - val_loss: 0.4155 - val_acc: 0.8427\n",
      "Epoch 38/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.4712 - acc: 0.8031 - val_loss: 0.4101 - val_acc: 0.8315\n",
      "Epoch 39/100\n",
      "711/711 [==============================] - 0s 217us/step - loss: 0.4782 - acc: 0.8045 - val_loss: 0.4131 - val_acc: 0.8371\n",
      "Epoch 40/100\n",
      "711/711 [==============================] - 0s 227us/step - loss: 0.4738 - acc: 0.7947 - val_loss: 0.4116 - val_acc: 0.8315\n",
      "Epoch 41/100\n",
      "711/711 [==============================] - 0s 220us/step - loss: 0.4736 - acc: 0.7989 - val_loss: 0.4079 - val_acc: 0.8371\n",
      "Epoch 42/100\n",
      "711/711 [==============================] - 0s 224us/step - loss: 0.4750 - acc: 0.8031 - val_loss: 0.4126 - val_acc: 0.8315\n",
      "Epoch 43/100\n",
      "711/711 [==============================] - 0s 230us/step - loss: 0.4729 - acc: 0.8031 - val_loss: 0.4113 - val_acc: 0.8427\n",
      "Epoch 44/100\n",
      "711/711 [==============================] - 0s 238us/step - loss: 0.4534 - acc: 0.7989 - val_loss: 0.4081 - val_acc: 0.8315\n",
      "Epoch 45/100\n",
      "711/711 [==============================] - 0s 219us/step - loss: 0.4751 - acc: 0.8003 - val_loss: 0.4132 - val_acc: 0.8427\n",
      "Epoch 46/100\n",
      "711/711 [==============================] - 0s 211us/step - loss: 0.4668 - acc: 0.8045 - val_loss: 0.4076 - val_acc: 0.8315\n",
      "Epoch 47/100\n",
      "711/711 [==============================] - 0s 222us/step - loss: 0.4584 - acc: 0.8143 - val_loss: 0.4119 - val_acc: 0.8427\n",
      "Epoch 48/100\n",
      "711/711 [==============================] - 0s 224us/step - loss: 0.4738 - acc: 0.7932 - val_loss: 0.4127 - val_acc: 0.8315\n",
      "Epoch 49/100\n",
      "711/711 [==============================] - 0s 225us/step - loss: 0.4604 - acc: 0.8017 - val_loss: 0.4111 - val_acc: 0.8371\n",
      "Epoch 50/100\n",
      "711/711 [==============================] - 0s 250us/step - loss: 0.4754 - acc: 0.8087 - val_loss: 0.4100 - val_acc: 0.8427\n",
      "Epoch 51/100\n",
      "711/711 [==============================] - 0s 214us/step - loss: 0.4535 - acc: 0.8101 - val_loss: 0.4038 - val_acc: 0.8427\n",
      "Epoch 52/100\n",
      "711/711 [==============================] - 0s 223us/step - loss: 0.4650 - acc: 0.8003 - val_loss: 0.4074 - val_acc: 0.8427\n",
      "Epoch 53/100\n",
      "711/711 [==============================] - 0s 223us/step - loss: 0.4631 - acc: 0.8017 - val_loss: 0.4034 - val_acc: 0.8315\n",
      "Epoch 54/100\n",
      "711/711 [==============================] - 0s 209us/step - loss: 0.4596 - acc: 0.8017 - val_loss: 0.4065 - val_acc: 0.8315\n",
      "Epoch 55/100\n",
      "711/711 [==============================] - 0s 229us/step - loss: 0.4486 - acc: 0.8158 - val_loss: 0.4082 - val_acc: 0.8315\n",
      "Epoch 56/100\n",
      "711/711 [==============================] - 0s 217us/step - loss: 0.4683 - acc: 0.7947 - val_loss: 0.4045 - val_acc: 0.8483\n",
      "Epoch 57/100\n",
      "711/711 [==============================] - 0s 238us/step - loss: 0.4573 - acc: 0.7989 - val_loss: 0.4066 - val_acc: 0.8539\n",
      "Epoch 58/100\n",
      "711/711 [==============================] - 0s 223us/step - loss: 0.4542 - acc: 0.8073 - val_loss: 0.4069 - val_acc: 0.8427\n",
      "Epoch 59/100\n",
      "711/711 [==============================] - 0s 214us/step - loss: 0.4531 - acc: 0.8172 - val_loss: 0.4054 - val_acc: 0.8427\n",
      "Epoch 60/100\n",
      "711/711 [==============================] - 0s 228us/step - loss: 0.4660 - acc: 0.7975 - val_loss: 0.4061 - val_acc: 0.8427\n",
      "Epoch 61/100\n",
      "711/711 [==============================] - 0s 217us/step - loss: 0.4606 - acc: 0.8031 - val_loss: 0.4059 - val_acc: 0.8483\n",
      "Epoch 62/100\n",
      "711/711 [==============================] - 0s 215us/step - loss: 0.4618 - acc: 0.8143 - val_loss: 0.4032 - val_acc: 0.8427\n",
      "Epoch 63/100\n",
      "711/711 [==============================] - 0s 249us/step - loss: 0.4414 - acc: 0.7961 - val_loss: 0.4016 - val_acc: 0.8483\n",
      "Epoch 64/100\n",
      "711/711 [==============================] - 0s 218us/step - loss: 0.4525 - acc: 0.7989 - val_loss: 0.4009 - val_acc: 0.8483\n",
      "Epoch 65/100\n",
      "711/711 [==============================] - 0s 215us/step - loss: 0.4581 - acc: 0.7961 - val_loss: 0.4011 - val_acc: 0.8371\n",
      "Epoch 66/100\n",
      "711/711 [==============================] - 0s 230us/step - loss: 0.4600 - acc: 0.8045 - val_loss: 0.4029 - val_acc: 0.8315\n",
      "Epoch 67/100\n",
      "711/711 [==============================] - 0s 208us/step - loss: 0.4600 - acc: 0.7989 - val_loss: 0.4042 - val_acc: 0.8371\n",
      "Epoch 68/100\n",
      "711/711 [==============================] - 0s 216us/step - loss: 0.4640 - acc: 0.8073 - val_loss: 0.3988 - val_acc: 0.8427\n",
      "Epoch 69/100\n",
      "711/711 [==============================] - 0s 222us/step - loss: 0.4504 - acc: 0.8214 - val_loss: 0.4031 - val_acc: 0.8483\n",
      "Epoch 70/100\n",
      "711/711 [==============================] - 0s 247us/step - loss: 0.4442 - acc: 0.8087 - val_loss: 0.3985 - val_acc: 0.8483\n",
      "Epoch 71/100\n",
      "711/711 [==============================] - 0s 220us/step - loss: 0.4501 - acc: 0.8003 - val_loss: 0.3969 - val_acc: 0.8427\n",
      "Epoch 72/100\n",
      "711/711 [==============================] - 0s 217us/step - loss: 0.4468 - acc: 0.8017 - val_loss: 0.3988 - val_acc: 0.8371\n",
      "Epoch 73/100\n",
      "711/711 [==============================] - 0s 210us/step - loss: 0.4419 - acc: 0.8129 - val_loss: 0.3984 - val_acc: 0.8539\n",
      "Epoch 74/100\n",
      "711/711 [==============================] - 0s 221us/step - loss: 0.4608 - acc: 0.7918 - val_loss: 0.3978 - val_acc: 0.8539\n",
      "Epoch 75/100\n",
      "711/711 [==============================] - 0s 227us/step - loss: 0.4456 - acc: 0.8101 - val_loss: 0.3978 - val_acc: 0.8427\n",
      "Epoch 76/100\n",
      "711/711 [==============================] - 0s 232us/step - loss: 0.4572 - acc: 0.8101 - val_loss: 0.4020 - val_acc: 0.8483\n",
      "Epoch 77/100\n",
      "711/711 [==============================] - 0s 226us/step - loss: 0.4659 - acc: 0.8087 - val_loss: 0.3995 - val_acc: 0.8427\n",
      "Epoch 78/100\n",
      "711/711 [==============================] - 0s 232us/step - loss: 0.4425 - acc: 0.8017 - val_loss: 0.3951 - val_acc: 0.8371\n",
      "Epoch 79/100\n",
      "711/711 [==============================] - 0s 223us/step - loss: 0.4572 - acc: 0.8129 - val_loss: 0.3969 - val_acc: 0.8427\n",
      "Epoch 80/100\n",
      "711/711 [==============================] - 0s 215us/step - loss: 0.4429 - acc: 0.8017 - val_loss: 0.3994 - val_acc: 0.8315\n",
      "Epoch 81/100\n",
      "711/711 [==============================] - 0s 225us/step - loss: 0.4481 - acc: 0.8158 - val_loss: 0.3941 - val_acc: 0.8258\n",
      "Epoch 82/100\n",
      "711/711 [==============================] - 0s 217us/step - loss: 0.4712 - acc: 0.7975 - val_loss: 0.3986 - val_acc: 0.8315\n",
      "Epoch 83/100\n",
      "711/711 [==============================] - 0s 246us/step - loss: 0.4535 - acc: 0.8101 - val_loss: 0.3977 - val_acc: 0.8146\n",
      "Epoch 84/100\n",
      "711/711 [==============================] - 0s 213us/step - loss: 0.4484 - acc: 0.8045 - val_loss: 0.3960 - val_acc: 0.8315\n",
      "Epoch 85/100\n",
      "711/711 [==============================] - 0s 221us/step - loss: 0.4477 - acc: 0.8017 - val_loss: 0.3978 - val_acc: 0.8258\n",
      "Epoch 86/100\n",
      "711/711 [==============================] - 0s 225us/step - loss: 0.4599 - acc: 0.7975 - val_loss: 0.3987 - val_acc: 0.8371\n",
      "Epoch 87/100\n",
      "711/711 [==============================] - 0s 215us/step - loss: 0.4496 - acc: 0.8031 - val_loss: 0.3934 - val_acc: 0.8315\n",
      "Epoch 88/100\n",
      "711/711 [==============================] - 0s 221us/step - loss: 0.4436 - acc: 0.8129 - val_loss: 0.3908 - val_acc: 0.8371\n",
      "Epoch 89/100\n",
      "711/711 [==============================] - 0s 236us/step - loss: 0.4364 - acc: 0.8115 - val_loss: 0.3893 - val_acc: 0.8371\n",
      "Epoch 90/100\n",
      "711/711 [==============================] - 0s 227us/step - loss: 0.4434 - acc: 0.8073 - val_loss: 0.3952 - val_acc: 0.8258\n",
      "Epoch 91/100\n",
      "711/711 [==============================] - 0s 213us/step - loss: 0.4560 - acc: 0.8017 - val_loss: 0.3938 - val_acc: 0.8371\n",
      "Epoch 92/100\n",
      "711/711 [==============================] - 0s 212us/step - loss: 0.4451 - acc: 0.7989 - val_loss: 0.3935 - val_acc: 0.8427\n",
      "Epoch 93/100\n",
      "711/711 [==============================] - 0s 223us/step - loss: 0.4368 - acc: 0.8073 - val_loss: 0.3942 - val_acc: 0.8427\n",
      "Epoch 94/100\n",
      "711/711 [==============================] - 0s 235us/step - loss: 0.4512 - acc: 0.8143 - val_loss: 0.3927 - val_acc: 0.8371\n",
      "Epoch 95/100\n",
      "711/711 [==============================] - 0s 214us/step - loss: 0.4504 - acc: 0.7961 - val_loss: 0.3913 - val_acc: 0.8596\n",
      "Epoch 96/100\n",
      "711/711 [==============================] - 0s 250us/step - loss: 0.4448 - acc: 0.7947 - val_loss: 0.3907 - val_acc: 0.8427\n",
      "Epoch 97/100\n",
      "711/711 [==============================] - 0s 226us/step - loss: 0.4462 - acc: 0.8143 - val_loss: 0.3917 - val_acc: 0.8427\n",
      "Epoch 98/100\n",
      "711/711 [==============================] - 0s 210us/step - loss: 0.4486 - acc: 0.8059 - val_loss: 0.3909 - val_acc: 0.8427\n",
      "Epoch 99/100\n",
      "711/711 [==============================] - 0s 227us/step - loss: 0.4384 - acc: 0.8031 - val_loss: 0.3924 - val_acc: 0.8146\n",
      "Epoch 100/100\n",
      "711/711 [==============================] - 0s 215us/step - loss: 0.4500 - acc: 0.8003 - val_loss: 0.3913 - val_acc: 0.8202\n"
     ]
    }
   ],
   "source": [
    "# Fitting the train set to the model\n",
    "history = model.fit(X, y, epochs=100, batch_size=32, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 707
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4151,
     "status": "ok",
     "timestamp": 1554220834009,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "ttCwz-M4to9p",
    "outputId": "bf33fbd0-65ad-4ab1-9617-ae420722bb2e"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFZCAYAAACizedRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFZCAYAAACizedRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting training and validation accuracy and loss against epochs.\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs = range(1, len(acc) + 1)\n",
    "plt.plot(epochs, acc, 'r', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, 'r', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RepWBJ1cxYIh"
   },
   "outputs": [],
   "source": [
    "# Will use this model with 3 hidden layers containing 64 hidden units each on test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2uNQ-vDVyNcL"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fZmusGDCOagv"
   },
   "source": [
    "## Further Improvement\n",
    "The performance can be further improved by:\n",
    "1. Spending more time on tuning the hyperparameters of the ANN.\n",
    "2. Using powerful boosting algorithms like XGBoost and LGBM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HF3cFTYhOagv"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vhhM5Vm0OaYU"
   },
   "source": [
    "## Importing the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 241
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2815,
     "status": "ok",
     "timestamp": 1554217329200,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "LMkOreNrOaaB",
    "outputId": "deba5478-3a8b-400f-e839-2fd7df22e1c7"
   },
   "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>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 144,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('/content/drive/My Drive/Colab Notebooks/Internships/Titanic/data/test.csv')\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JdXTCabnOaaM"
   },
   "source": [
    "## Analyzing and modifying the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4252,
     "status": "ok",
     "timestamp": 1554217343131,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "fXA6SD65OaaN",
    "outputId": "0893d28d-a697-4d0e-ddde-0bbb8434a92e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 90
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2364,
     "status": "ok",
     "timestamp": 1554217348601,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "PT4IqLj1Oaag",
    "outputId": "1cbeb599-dc51-46b3-be35-ea3e07af8c44"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    218\n",
       "1    107\n",
       "2     93\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 146,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.Pclass.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 72
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2719,
     "status": "ok",
     "timestamp": 1554217353512,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "Jbys4Ef5Oaaw",
    "outputId": "8f493235-5e43-41c7-a989-0871059b4f78"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      266\n",
       "female    152\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.Sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2248,
     "status": "ok",
     "timestamp": 1554217359960,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "M4G3bGJjOabe",
    "outputId": "650d09d3-b504-473b-ce62-1e02b1987c39"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86"
      ]
     },
     "execution_count": 148,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.Age.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "J2vdDpzrOabr"
   },
   "outputs": [],
   "source": [
    "# Filling the NA values in Age with mean value in a new column.\n",
    "df_test['Age_NA_Is_Mean'] = df_test.Age.fillna(df_test.Age.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 164
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2542,
     "status": "ok",
     "timestamp": 1554217366354,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "lFfXmL0SOacC",
    "outputId": "d585707f-787a-44f4-f3a0-7bd077c4acc9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    283\n",
       "1    110\n",
       "2     14\n",
       "4      4\n",
       "3      4\n",
       "8      2\n",
       "5      1\n",
       "Name: SibSp, dtype: int64"
      ]
     },
     "execution_count": 150,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.SibSp.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 182
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2711,
     "status": "ok",
     "timestamp": 1554217370853,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "Nt36GWXMOacj",
    "outputId": "7db7217a-b4dd-4a8c-f70f-9878341b9793"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    324\n",
       "1     52\n",
       "2     33\n",
       "3      3\n",
       "9      2\n",
       "4      2\n",
       "6      1\n",
       "5      1\n",
       "Name: Parch, dtype: int64"
      ]
     },
     "execution_count": 151,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.Parch.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 98
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3035,
     "status": "ok",
     "timestamp": 1554217375001,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "2Bl0JW9zOadW",
    "outputId": "30e9e17b-c8a3-4cc2-d153-cbb470be6aae"
   },
   "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>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Age_NA_Is_Mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>1044</td>\n",
       "      <td>3</td>\n",
       "      <td>Storey, Mr. Thomas</td>\n",
       "      <td>male</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>60.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                Name   Sex   Age  SibSp  Parch Ticket  \\\n",
       "152         1044       3  Storey, Mr. Thomas  male  60.5      0      0   3701   \n",
       "\n",
       "     Fare Cabin Embarked  Age_NA_Is_Mean  \n",
       "152   NaN   NaN        S            60.5  "
      ]
     },
     "execution_count": 152,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test[df_test.Fare.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EWjdQ3inOadg"
   },
   "outputs": [],
   "source": [
    "df_test.Fare.fillna(df_test[(df_test.Embarked == 'S') & (df_test.Pclass == 3) & (df_test.SibSp == 0) \n",
    "        & (df_test.Parch == 0)].Fare.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 90
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2198,
     "status": "ok",
     "timestamp": 1554217382091,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "wnUqyqgaOadp",
    "outputId": "1cf7a109-3962-4f19-e434-5309f4014b9c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    270\n",
       "C    102\n",
       "Q     46\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 154,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.Embarked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 329
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2074,
     "status": "ok",
     "timestamp": 1554217386073,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "xz-TteUkOafk",
    "outputId": "b0360815-4d0a-4ddd-825d-baadab0499c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      "PassengerId       418 non-null int64\n",
      "Pclass            418 non-null int64\n",
      "Name              418 non-null object\n",
      "Sex               418 non-null object\n",
      "Age               332 non-null float64\n",
      "SibSp             418 non-null int64\n",
      "Parch             418 non-null int64\n",
      "Ticket            418 non-null object\n",
      "Fare              418 non-null float64\n",
      "Cabin             91 non-null object\n",
      "Embarked          418 non-null object\n",
      "Age_NA_Is_Mean    418 non-null float64\n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 39.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 127
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2026,
     "status": "ok",
     "timestamp": 1554217389203,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "g7zyFlouOaft",
    "outputId": "489ab17f-07fd-4620-983d-e89ad314f96d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    892\n",
       "1    893\n",
       "2    894\n",
       "3    895\n",
       "4    896\n",
       "Name: PassengerId, dtype: int64"
      ]
     },
     "execution_count": 156,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "passenger_id = df_test.PassengerId\n",
    "passenger_id[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lT_s4xR4Oafy"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Lc2krCT3Oafz"
   },
   "source": [
    "## Preprocessing the test set for model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1449,
     "status": "ok",
     "timestamp": 1554217397886,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "t9dnMBXgOaf0",
    "outputId": "2077a099-1991-48a6-98e7-deedc8fc52ad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 'male', 0, 0, 7.8292, 'Q', 34.5], dtype=object)"
      ]
     },
     "execution_count": 157,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = df_test.iloc[:, [1,3,5,6,8,10,11]].values\n",
    "X_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qFKzKg_4Oaf6"
   },
   "outputs": [],
   "source": [
    "# Label encoding on test set\n",
    "X_test[:, 1] = labelencoder_sex.transform(X_test[:, 1])\n",
    "X_test[:, 5] = labelencoder_embarked.transform(X_test[:, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DX61gIEXOagB"
   },
   "outputs": [],
   "source": [
    "# One-Hot encoding on test set\n",
    "X_test = ohe.transform(X_test).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yn7l2iVMOagE"
   },
   "outputs": [],
   "source": [
    "X_test = X_test[:, [1,3,4,5,6,7,8,9]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2ksTyMubOagX"
   },
   "outputs": [],
   "source": [
    "# Normalization on test set\n",
    "X_test = norm.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zgnahoCrOaga"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1j8K-5hdOagd"
   },
   "source": [
    "## Making predictions on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kDG9_RXQOage"
   },
   "outputs": [],
   "source": [
    "# Making predictions on the test set using classifier_gs_svm\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 109
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1882,
     "status": "ok",
     "timestamp": 1554222047370,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "HLDI7ZoyOagh",
    "outputId": "731f5426-dee4-4afa-d73c-45f21831e04d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False]])"
      ]
     },
     "execution_count": 224,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predictions\n",
    "y_pred[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 109
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1983,
     "status": "ok",
     "timestamp": 1554221944351,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "8T967taP0LlY",
    "outputId": "27a1534f-e8d0-4f4e-ebf6-4be04c01b8d9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False]])"
      ]
     },
     "execution_count": 221,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = (y_pred > 0.5)\n",
    "y_pred[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2756,
     "status": "ok",
     "timestamp": 1554222357807,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "-oppW6HHOagk",
    "outputId": "23250f64-34ca-4cdd-f380-381fb27a6f63"
   },
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892     False\n",
       "1          893     False\n",
       "2          894     False\n",
       "3          895     False\n",
       "4          896     False"
      ]
     },
     "execution_count": 234,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preparing the submission format for the predictions.\n",
    "pred_df = pd.concat({'PassengerId': pd.Series(passenger_id), 'Survived': pd.Series(y_pred[:,0])}, \n",
    "                    axis=1)\n",
    "pred_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UOKfNyA73Ro6"
   },
   "outputs": [],
   "source": [
    "survival_map = {False: 0, True: 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "axIGXJFC2t9Q"
   },
   "outputs": [],
   "source": [
    "pred_df.Survived = pred_df.Survived.map(survival_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2613,
     "status": "ok",
     "timestamp": 1554222532270,
     "user": {
      "displayName": "Mohit Raj",
      "photoUrl": "https://lh4.googleusercontent.com/-THuQGyH-qf0/AAAAAAAAAAI/AAAAAAAAAKE/sT7jOYuqFyI/s64/photo.jpg",
      "userId": "17943522262909727589"
     },
     "user_tz": -330
    },
    "id": "JgwJPLvU3jzv",
    "outputId": "1e123b31-7543-4036-c157-6885421f11d5"
   },
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         0"
      ]
     },
     "execution_count": 239,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7xv-9iwGOago"
   },
   "outputs": [],
   "source": [
    "# Exporting the predictions to a csv file.\n",
    "pred_df.to_csv('/content/drive/My Drive/Colab Notebooks/Internships/Titanic/data/Survival_Prediction_ANN.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xdUWkSkJOagr"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "titanic_machine_learning_from_disaster_with_ann.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}