Research and Development/ml-pipeline-scoring-new-data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML Pipeline Scoring new Data\n",
    "\n",
    "We we'll be doing here is to repeat every process from feature engineering to prediction on the test data for this project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "import scipy.stats as stats\n",
    "\n",
    "import joblib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1459, 80)\n"
     ]
    },
    {
     "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>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0  1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
       "1  1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
       "2  1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
       "3  1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... ScreenPorch PoolArea PoolQC  Fence MiscFeature  \\\n",
       "0         Lvl    AllPub  ...         120        0    NaN  MnPrv         NaN   \n",
       "1         Lvl    AllPub  ...           0        0    NaN    NaN        Gar2   \n",
       "2         Lvl    AllPub  ...           0        0    NaN  MnPrv         NaN   \n",
       "3         Lvl    AllPub  ...           0        0    NaN    NaN         NaN   \n",
       "\n",
       "  MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0       0      6    2010        WD         Normal  \n",
       "1   12500      6    2010        WD         Normal  \n",
       "2       0      3    2010        WD         Normal  \n",
       "3       0      6    2010        WD         Normal  \n",
       "\n",
       "[4 rows x 80 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('test.csv')\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "data.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 79)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop id column\n",
    "\n",
    "data.drop('Id', axis = 1, inplace=True)\n",
    "\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering\n",
    "\n",
    "1. Missing values\n",
    "3. Temporal variables\n",
    "3. Non-Gaussian distributed variables\n",
    "4. Categorical variables: remove rare labels\n",
    "5. Categorical variables: convert string to numbers\n",
    "6. Put the variables in a similar scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# during featurinf MSSubClass was cast as a categorical variable\n",
    "# the same thing will be done \n",
    "\n",
    "data['MSSubClass'] = data['MSSubClass'].astype('O')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we fill variuous category of categorical variables\n",
    "\n",
    "with_string_missing = ['Alley', 'FireplaceQu',\n",
    "                       'PoolQC', 'Fence', 'MiscFeature']\n",
    "\n",
    "with_mode_missing = {\n",
    "    'MasVnrType': 'None',\n",
    "    'BsmtQual': 'TA',\n",
    "    'BsmtCond': 'TA',\n",
    "    'BsmtExposure': 'No',\n",
    "    'BsmtFinType1': 'Unf',\n",
    "    'BsmtFinType2': 'Unf',\n",
    "    'Electrical': 'SBrkr',\n",
    "    'GarageType': 'Attchd',\n",
    "    'GarageFinish': 'Unf',\n",
    "    'GarageQual': 'TA',\n",
    "    'GarageCond': 'TA',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'TA'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with_mode_missing['GarageCond']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace missing values with new label: Missing\n",
    "\n",
    "data[with_string_missing] = data[with_string_missing].fillna('Missing')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['MasVnrType', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Electrical', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond'])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with_mode_missing.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace missing values with frequent category\n",
    "\n",
    "for var in with_mode_missing.keys():\n",
    "    data[var].fillna(with_mode_missing[var], inplace = True)\n",
    "    #data[var].fillna(with_mode_missing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical variables\n",
    "\n",
    "To engineer missing values in numerical variables, we will\n",
    "\n",
    "1. add a binary missing value indicator column\n",
    "2. replace the missing values in the original variable with the mean "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from the feature engineering notebook we already obtained the mean \n",
    "# of the numerical variables with null values\n",
    "\n",
    "\n",
    "vars_with_na = {\n",
    "    'LotFrontage' : 69.87974098057354,\n",
    "    'MasVnrArea' : 103.7974006116208,\n",
    "    'GarageYrBlt' : 1978.2959677419356\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LotFrontage    0\n",
       "MasVnrArea     0\n",
       "GarageYrBlt    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add binary indicator to the numerical variabl;e with missing va;ues\n",
    "\n",
    "for var in vars_with_na:\n",
    "\n",
    "    data[var + '_na'] = np.where(data[var].isnull(), 1, 0)\n",
    "\n",
    "    data[var].fillna(vars_with_na[var], inplace = True)\n",
    "\n",
    "\n",
    "data[vars_with_na].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LotFrontage_na</th>\n",
       "      <th>MasVnrArea_na</th>\n",
       "      <th>GarageYrBlt_na</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      LotFrontage_na  MasVnrArea_na  GarageYrBlt_na\n",
       "1454               0              0               1\n",
       "1455               0              0               0\n",
       "1456               0              0               0\n",
       "1457               0              0               1\n",
       "1458               0              0               0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let check the binary indicator column\n",
    "\n",
    "data[['LotFrontage_na', 'MasVnrArea_na', 'GarageYrBlt_na']].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temporal Variable\n",
    "\n",
    "As in the feature engineering notebook we will calculate the elapse time between when a house a house was sold and each of the temporal variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def elapsed_time(df, var):\n",
    "\n",
    "    df[var] = df['YrSold'] - df[var]\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for var in ['YearBuilt', 'YearRemodAdd', 'GarageYrBlt']:\n",
    "    data = elapsed_time(data, var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('YrSold', axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>LotFrontage_na</th>\n",
       "      <th>MasVnrArea_na</th>\n",
       "      <th>GarageYrBlt_na</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>Missing</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Missing</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Missing</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>...</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  MSSubClass MSZoning  LotFrontage  LotArea Street    Alley LotShape  \\\n",
       "0         20       RH         80.0    11622   Pave  Missing      Reg   \n",
       "1         20       RL         81.0    14267   Pave  Missing      IR1   \n",
       "\n",
       "  LandContour Utilities LotConfig  ...   PoolQC    Fence MiscFeature MiscVal  \\\n",
       "0         Lvl    AllPub    Inside  ...  Missing    MnPrv     Missing       0   \n",
       "1         Lvl    AllPub    Corner  ...  Missing  Missing        Gar2   12500   \n",
       "\n",
       "  MoSold SaleType  SaleCondition  LotFrontage_na  MasVnrArea_na  \\\n",
       "0      6       WD         Normal               0              0   \n",
       "1      6       WD         Normal               0              0   \n",
       "\n",
       "   GarageYrBlt_na  \n",
       "0               0  \n",
       "1               0  \n",
       "\n",
       "[2 rows x 81 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical variable transformation\n",
    "\n",
    "### Logarithmic transformation\n",
    "\n",
    "We transform the positive numerical variable in order to a Gaussian-like distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "for var in [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]:\n",
    "    data[var] = np.log(data[var])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Yeo-Johnson transformation\n",
    "\n",
    "We will apply the Yeo-Johnson transformation to LotArea\n",
    "We use the lmbda value from feature engineering notebook \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['LotArea'] = stats.yeojohnson(data['LotArea'], lmbda=-12.55283001172003)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binarized skewed Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "skewed = [\n",
    "    'BsmtFinSF2', 'LowQualFinSF', 'EnclosedPorch',\n",
    "    '3SsnPorch', 'ScreenPorch', 'MiscVal'\n",
    "]\n",
    "\n",
    "for var in skewed:\n",
    "\n",
    "    data[var] = np.where(data[var] == 0, 0, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical variables\n",
    "\n",
    "### Apply mappings\n",
    "\n",
    "\n",
    "We remap variables with specific meaning into a numerical scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "qual_mappings = {'Po': 1, 'Fa': 2, 'TA': 3, 'Gd': 4, 'Ex': 5, 'Missing': 0, 'NA': 0}\n",
    "\n",
    "qual_vars = ['ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond',\n",
    "             'HeatingQC', 'KitchenQual', 'FireplaceQu',\n",
    "             'GarageQual', 'GarageCond',\n",
    "            ]\n",
    "\n",
    "\n",
    "for var in qual_vars:\n",
    "    data[var] = data[var].map(qual_mappings)           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "exposure_mappings = {'No': 1, 'Mn': 2, 'Av': 3, 'Gd': 4}\n",
    "\n",
    "var = 'BsmtExposure'\n",
    "\n",
    "data[var] = data[var].map(exposure_mappings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "finish_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'LwQ': 2, 'Rec': 3, 'BLQ': 4, 'ALQ': 5, 'GLQ': 6}\n",
    "\n",
    "finish_vars = ['BsmtFinType1', 'BsmtFinType2']\n",
    "\n",
    "\n",
    "for var in finish_vars:\n",
    "\n",
    "    data[var] = data[var].map(finish_mappings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "garage_mappings = {'Missing': 0, 'NA': 0, 'Unf': 1, 'RFn': 2, 'Fin': 3}\n",
    "\n",
    "var = 'GarageFinish'\n",
    "\n",
    "data[var] = data[var].map(garage_mappings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "fence_mappings = {'Missing': 0, 'NA': 0, 'MnWw': 1, 'GdWo': 2, 'MnPrv': 3, 'GdPrv': 4}\n",
    "\n",
    "var = 'Fence'\n",
    "\n",
    "data[var] = data[var].map(fence_mappings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MSZoning',\n",
       " 'Utilities',\n",
       " 'Exterior1st',\n",
       " 'Exterior2nd',\n",
       " 'BsmtFinSF1',\n",
       " 'BsmtUnfSF',\n",
       " 'TotalBsmtSF',\n",
       " 'BsmtFullBath',\n",
       " 'BsmtHalfBath',\n",
       " 'KitchenQual',\n",
       " 'Functional',\n",
       " 'GarageCars',\n",
       " 'GarageArea',\n",
       " 'SaleType']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check .for null in the data set\n",
    "\n",
    "with_null = [\n",
    "    var for var in data.columns if data[var].isnull().sum() > 0\n",
    "]\n",
    "\n",
    "with_null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let check that these columns were also in the train set\n",
    "\n",
    "[ \n",
    "    var for var in with_null if var in list(\n",
    "        with_mode_missing.keys()\n",
    " \n",
    "    ) + with_string_missing + list(vars_with_na.keys())\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### important \n",
    "\n",
    "This tells there was a good number of variables with missing value in the test data that were not in the original data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing Rare Labels\n",
    "\n",
    "For the remaining categorical variables we will label with less that are present in <1% in the train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we are going to create a rare label dictionary from feature engineering notebook\n",
    "\n",
    "frequent_ls = {\n",
    "    'MSZoning': ['FV', 'RH', 'RL', 'RM'],\n",
    "    'Street': ['Pave'],\n",
    "    'Alley': ['Grvl', 'Missing', 'Pave'],\n",
    "    'LotShape': ['IR1', 'IR2', 'Reg'],\n",
    "    'LandContour': ['Bnk', 'HLS', 'Low', 'Lvl'],\n",
    "    'Utilities': ['AllPub'],\n",
    "    'LotConfig': ['Corner', 'CulDSac', 'FR2', 'Inside'],\n",
    "    'LandSlope': ['Gtl', 'Mod'],\n",
    "    'Neighborhood': ['Blmngtn', 'BrDale', 'BrkSide', 'ClearCr', 'CollgCr', 'Crawfor',\n",
    "                     'Edwards', 'Gilbert', 'IDOTRR', 'MeadowV', 'Mitchel', 'NAmes', 'NWAmes',\n",
    "                     'NoRidge', 'NridgHt', 'OldTown', 'SWISU', 'Sawyer', 'SawyerW',\n",
    "                     'Somerst', 'StoneBr', 'Timber'],\n",
    "\n",
    "    'Condition1': ['Artery', 'Feedr', 'Norm', 'PosN', 'RRAn'],\n",
    "    'Condition2': ['Norm'],\n",
    "    'BldgType': ['1Fam', '2fmCon', 'Duplex', 'Twnhs', 'TwnhsE'],\n",
    "    'HouseStyle': ['1.5Fin', '1Story', '2Story', 'SFoyer', 'SLvl'],\n",
    "    'RoofStyle': ['Gable', 'Hip'],\n",
    "    'RoofMatl': ['CompShg'],\n",
    "    'Exterior1st': ['AsbShng', 'BrkFace', 'CemntBd', 'HdBoard', 'MetalSd', 'Plywood',\n",
    "                    'Stucco', 'VinylSd', 'Wd Sdng', 'WdShing'],\n",
    "\n",
    "    'Exterior2nd': ['AsbShng', 'BrkFace', 'CmentBd', 'HdBoard', 'MetalSd', 'Plywood',\n",
    "                    'Stucco', 'VinylSd', 'Wd Sdng', 'Wd Shng'],\n",
    "\n",
    "    'MasVnrType': ['BrkFace', 'None', 'Stone'],\n",
    "    'Foundation': ['BrkTil', 'CBlock', 'PConc', 'Slab'],\n",
    "    'Heating': ['GasA', 'GasW'],\n",
    "    'CentralAir': ['N', 'Y'],\n",
    "    'Electrical': ['FuseA', 'FuseF', 'SBrkr'],\n",
    "    'Functional': ['Min1', 'Min2', 'Mod', 'Typ'],\n",
    "    'GarageType': ['Attchd', 'Basment', 'BuiltIn', 'Detchd'],\n",
    "    'PavedDrive': ['N', 'P', 'Y'],\n",
    "    'PoolQC': ['Missing'],\n",
    "    'MiscFeature': ['Missing', 'Shed'],\n",
    "    'SaleType': ['COD', 'New', 'WD'],\n",
    "    'SaleCondition': ['Abnorml', 'Family', 'Normal', 'Partial'],\n",
    "    'MSSubClass': ['20', '30', '50', '60', '70', '75', '80', '85', '90', '120', '160', '190'],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "for var in frequent_ls.keys():\n",
    "\n",
    "    data[var] = np.where(data[var].isin(\n",
    "        frequent_ls\n",
    "    ), data[var], 'Rare')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding of Categorrical variables\n",
    "\n",
    "In this section we transform the string in categorical variables into numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "ordinal_mappings = {\n",
    "    'MSZoning': {'Rare': 0, 'RM': 1, 'RH': 2, 'RL': 3, 'FV': 4},\n",
    "    'Street': {'Rare': 0, 'Pave': 1},\n",
    "    'Alley': {'Grvl': 0, 'Pave': 1, 'Missing': 2},\n",
    "    'LotShape': {'Reg': 0, 'IR1': 1, 'Rare': 2, 'IR2': 3},\n",
    "    'LandContour': {'Bnk': 0, 'Lvl': 1, 'Low': 2, 'HLS': 3},\n",
    "    'Utilities': {'Rare': 0, 'AllPub': 1},\n",
    "    'LotConfig': {'Inside': 0, 'FR2': 1, 'Corner': 2, 'Rare': 3, 'CulDSac': 4},\n",
    "    'LandSlope': {'Gtl': 0, 'Mod': 1, 'Rare': 2},\n",
    "    'Neighborhood': {'IDOTRR': 0, 'MeadowV': 1, 'BrDale': 2, 'Edwards': 3,\n",
    "                     'BrkSide': 4, 'OldTown': 5, 'Sawyer': 6, 'SWISU': 7,\n",
    "                     'NAmes': 8, 'Mitchel': 9, 'SawyerW': 10, 'Rare': 11,\n",
    "                     'NWAmes': 12, 'Gilbert': 13, 'Blmngtn': 14, 'CollgCr': 15,\n",
    "                     'Crawfor': 16, 'ClearCr': 17, 'Somerst': 18, 'Timber': 19,\n",
    "                     'StoneBr': 20, 'NridgHt': 21, 'NoRidge': 22},\n",
    "    \n",
    "    'Condition1': {'Artery': 0, 'Feedr': 1, 'Norm': 2, 'RRAn': 3, 'Rare': 4, 'PosN': 5},\n",
    "    'Condition2': {'Rare': 0, 'Norm': 1},\n",
    "    'BldgType': {'2fmCon': 0, 'Duplex': 1, 'Twnhs': 2, '1Fam': 3, 'TwnhsE': 4},\n",
    "    'HouseStyle': {'SFoyer': 0, '1.5Fin': 1, 'Rare': 2, '1Story': 3, 'SLvl': 4, '2Story': 5},\n",
    "    'RoofStyle': {'Gable': 0, 'Rare': 1, 'Hip': 2},\n",
    "    'RoofMatl': {'CompShg': 0, 'Rare': 1},\n",
    "    'Exterior1st': {'AsbShng': 0, 'Wd Sdng': 1, 'WdShing': 2, 'MetalSd': 3,\n",
    "                    'Stucco': 4, 'Rare': 5, 'HdBoard': 6, 'Plywood': 7,\n",
    "                    'BrkFace': 8, 'CemntBd': 9, 'VinylSd': 10},\n",
    "    \n",
    "    'Exterior2nd': {'AsbShng': 0, 'Wd Sdng': 1, 'MetalSd': 2, 'Wd Shng': 3,\n",
    "                    'Stucco': 4, 'Rare': 5, 'HdBoard': 6, 'Plywood': 7,\n",
    "                    'BrkFace': 8, 'CmentBd': 9, 'VinylSd': 10},\n",
    "    \n",
    "    'MasVnrType': {'Rare': 0, 'None': 1, 'BrkFace': 2, 'Stone': 3},\n",
    "    'Foundation': {'Slab': 0, 'BrkTil': 1, 'CBlock': 2, 'Rare': 3, 'PConc': 4},\n",
    "    'Heating': {'Rare': 0, 'GasW': 1, 'GasA': 2},\n",
    "    'CentralAir': {'N': 0, 'Y': 1},\n",
    "    'Electrical': {'Rare': 0, 'FuseF': 1, 'FuseA': 2, 'SBrkr': 3},\n",
    "    'Functional': {'Rare': 0, 'Min2': 1, 'Mod': 2, 'Min1': 3, 'Typ': 4},\n",
    "    'GarageType': {'Rare': 0, 'Detchd': 1, 'Basment': 2, 'Attchd': 3, 'BuiltIn': 4},\n",
    "    'PavedDrive': {'N': 0, 'P': 1, 'Y': 2},\n",
    "    'PoolQC': {'Missing': 0, 'Rare': 1},\n",
    "    'MiscFeature': {'Rare': 0, 'Shed': 1, 'Missing': 2},\n",
    "    'SaleType': {'COD': 0, 'Rare': 1, 'WD': 2, 'New': 3},\n",
    "    'SaleCondition': {'Rare': 0, 'Abnorml': 1, 'Family': 2, 'Normal': 3, 'Partial': 4},\n",
    "    'MSSubClass': {'30': 0, 'Rare': 1, '190': 2, '90': 3, '160': 4, '50': 5, '85': 6,\n",
    "                   '70': 7, '80': 8, '20': 9, '75': 10, '120': 11, '60': 12},\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "for var in ordinal_mappings.keys():\n",
    "\n",
    "    # get dict values in ordinal_mapping variable\n",
    "    ordinal_label = ordinal_mappings[var]\n",
    "    \n",
    "    # use dictionary to replace the categorical string\n",
    "    data[var] = data[var].map(ordinal_label)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_null = [\n",
    "    var for var in data.columns if data[var].isnull().sum() > 0\n",
    "]\n",
    "\n",
    "len(check_null)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we do a qucik fix\n",
    "# replace all null values with zeros\n",
    "\n",
    "data.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling\n",
    "\n",
    "We will scale features to with minmax scaler from sklearn. Already, we saved this value already in our feature engineering notebook\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/arnold/Desktop/MlOp/env/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator MinMaxScaler from version 1.0.2 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "scaler = joblib.load('minmax_scaler.joblib')\n",
    "\n",
    "\n",
    "data = pd.DataFrame(\n",
    "    scaler.transform(data),\n",
    "    columns=data.columns\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 36)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load pre-selected features\n",
    "# from feature selection notebook\n",
    "# ==========================\n",
    "\n",
    "\n",
    "features = pd.read_csv('selected_features.csv')\n",
    "features = features['0'].to_list()\n",
    "\n",
    "\n",
    "data = data[features]\n",
    "\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/arnold/Desktop/MlOp/env/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Lasso from version 1.0.2 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAVKElEQVR4nO3dcYyc9X3n8ff3IBCSzdkmzq0s27qlLUpEcZviPUqUKNotd6kDVU0lFIFQY3KcrLYk5a6uCjTSJfcHOvdONErUuyRu4aCXiIXSVHC0OUId9qJIh1M7JdhACA5xGlvGbhpwuilq6973/phnk2GZ9e7M88zuPD+/X9Jon/k9z/yezzye/fq3v3nmmchMJEll+WerHUCS1DyLuyQVyOIuSQWyuEtSgSzuklSgc1c7AMD69etzYmKiVh8/+MEPeOMb39hMoBVk7pXTxszQztxtzAzty33gwIHvZuZbeq0bieI+MTHB/v37a/UxOzvL1NRUM4FWkLlXThszQztztzEztC93RHx7sXVOy0hSgZYs7hFxd0ScjIhDPdbtioiMiPXV/YiIT0TE4Yh4KiIuG0ZoSdKZLWfkfg+wbWFjRGwG3gP8VVfze4GLq9tO4JP1I0qS+rVkcc/MLwHf67HqY8BvAd3XL9gO/GF2PAGsjYgNjSSVJC3bQG+oRsR24Fhmfi0iuldtBL7Tdf9o1Xa8Rx876YzuGR8fZ3Z2dpAoPzQ3N1e7j9Vg7pXTxszQztxtzAztzd1TZi55AyaAQ9XyG4B9wJrq/hFgfbX8CPCursftBSaX6n/r1q1Z1+OPP167j9Vg7pXTxsyZ7czdxsyZ7csN7M9F6uogI/cfBy4C5kftm4CvRsTlwDFgc9e2m6o2SdIK6vtUyMw8mJn/IjMnMnOCztTLZZn5IvAw8P7qrJkrgFOZ+ZopGUnScC3nVMj7gP8LvDUijkbETWfY/M+AF4DDwO8Dv9ZISklSX5aclsnM65dYP9G1nMDN9WMJYOK2P+3ZfmT31SucRFLb+AlVSSqQxV2SCmRxl6QCWdwlqUAWd0kqkMVdkgpkcZekAlncJalAI/E1e2e7xT6sJEmDcuQuSQWyuEtSgSzuklQgi7skFcjiLkkFsrhLUoEs7pJUIIu7JBXI4i5JBbK4S1KBLO6SVCCvLdNCfnG2pKUsOXKPiLsj4mREHOpq+68R8fWIeCoi/iQi1natuz0iDkfEcxHx80PKLUk6g+VMy9wDbFvQ9hhwaWb+FPAN4HaAiLgEuA74yeox/z0izmksrSRpWZYs7pn5JeB7C9q+kJmnq7tPAJuq5e3ATGb+fWZ+CzgMXN5gXknSMkRmLr1RxATwSGZe2mPd/wLuz8zPRMTvAU9k5meqdXcBn8/MB3s8biewE2B8fHzrzMxMrScyNzfH2NhYrT5Ww9zcHN869U+N9LVl45pG+lmONh7vNmaGduZuY2ZoX+7p6ekDmTnZa12tN1Qj4sPAaeCz/T42M/cAewAmJydzamqqThRmZ2ep28dqmJ2d5c4v/6CRvo7cMNVIP8vRxuPdxszQztxtzAztzd3LwMU9Im4EfgG4Mn80/D8GbO7abFPVphXgWTSS5g10nntEbAN+C/jFzPy7rlUPA9dFxPkRcRFwMfCV+jElSf1YcuQeEfcBU8D6iDgKfITO2THnA49FBHTm2X8lM5+OiAeAZ+hM19ycmc1MKEuSlm3J4p6Z1/dovusM298B3FEnlCSpHi8/IEkFsrhLUoEs7pJUIIu7JBXI4i5JBbK4S1KBLO6SVCCLuyQVyOIuSQWyuEtSgSzuklQgvyB7CLz0rqTV5shdkgrkyH0F9RrR79pyGv8ZJDXNkbskFcjiLkkFsrhLUoEs7pJUIIu7JBXI4i5JBbK4S1KBlizuEXF3RJyMiENdbRdGxGMR8Xz1c13VHhHxiYg4HBFPRcRlwwwvSeptOSP3e4BtC9puA/Zm5sXA3uo+wHuBi6vbTuCTzcSUJPVjyeKemV8CvregeTtwb7V8L3BNV/sfZscTwNqI2NBQVknSMg065z6emcer5ReB8Wp5I/Cdru2OVm2SpBUUmbn0RhETwCOZeWl1/+XMXNu1/qXMXBcRjwC7M/PLVfte4NbM3N+jz510pm4YHx/fOjMzU+uJzM3NMTY2VquPphw8dmrZ245fACdeGWIYYMvGNY33OUrHe7namBnambuNmaF9uaenpw9k5mSvdYNesepERGzIzOPVtMvJqv0YsLlru01V22tk5h5gD8Dk5GROTU0NGKVjdnaWun005cZFLvnby64tp7nz4HAvHHbkhqnG+xyl471cbcwM7czdxszQ3ty9DDot8zCwo1reATzU1f7+6qyZK4BTXdM3kqQVsuSQMSLuA6aA9RFxFPgIsBt4ICJuAr4NvK/a/M+Aq4DDwN8BHxhCZknSEpYs7pl5/SKrruyxbQI31w0lSarHT6hKUoEs7pJUIIu7JBXI4i5JBfKbmc8Cvb6Ye96R3VevYBJJK8WRuyQVyOIuSQWyuEtSgSzuklQgi7skFcjiLkkFsrhLUoEs7pJUIIu7JBXI4i5JBbK4S1KBLO6SVCCLuyQVyOIuSQWyuEtSgSzuklQgi7skFahWcY+I/xART0fEoYi4LyJeHxEXRcS+iDgcEfdHxHlNhZUkLc/AX7MXERuBXwcuycxXIuIB4DrgKuBjmTkTEZ8CbgI+2UhaNW6xr+Dz6/ekdqs7LXMucEFEnAu8ATgO/BzwYLX+XuCamvuQJPUpMnPwB0fcAtwBvAJ8AbgFeCIzf6Javxn4fGZe2uOxO4GdAOPj41tnZmYGzgEwNzfH2NhYrT6acvDYqWVvO34BnHhliGEGtGXjmjOuH6XjvVxtzAztzN3GzNC+3NPT0wcyc7LXujrTMuuA7cBFwMvAHwHblvv4zNwD7AGYnJzMqampQaMAMDs7S90+mnLjIlMdvezacpo7Dw78zzA0R26YOuP6UTrey9XGzNDO3G3MDO3N3UudaZl/DXwrM/86M/8R+BzwTmBtNU0DsAk4VjOjJKlPdYr7XwFXRMQbIiKAK4FngMeBa6ttdgAP1YsoSerXwMU9M/fReeP0q8DBqq89wK3Ab0TEYeDNwF0N5JQk9aHWZG9mfgT4yILmF4DL6/QrSarHT6hKUoFG7zSNFlnsA0CStNocuUtSgSzuklQgi7skFcjiLkkFsrhLUoEs7pJUIIu7JBXI4i5JBbK4S1KBLO6SVCCLuyQVyOIuSQWyuEtSgSzuklQgi7skFcjiLkkFsrhLUoEs7pJUIIu7JBWoVnGPiLUR8WBEfD0ino2Id0TEhRHxWEQ8X/1c11RYSdLy1B25fxz435n5NuCngWeB24C9mXkxsLe6L0laQQMX94hYA7wbuAsgM/8hM18GtgP3VpvdC1xTL6IkqV+RmYM9MOLtwB7gGTqj9gPALcCxzFxbbRPAS/P3Fzx+J7ATYHx8fOvMzMxAOebNzc0xNjZWq49+HTx2qnYf4xfAiVcaCNOwLRvX9Gyff84Lcy+2/ShZjddIE9qYu42ZoX25p6enD2TmZK91dYr7JPAE8M7M3BcRHwe+D3you5hHxEuZecZ598nJydy/f/9AOebNzs4yNTVVq49+Tdz2p7X72LXlNHcePLeBNM06svvqnu3zz3lh7sW2HyWr8RppQhtztzEztC93RCxa3OvMuR8Fjmbmvur+g8BlwImI2FDteANwssY+JEkDGHjImJkvRsR3IuKtmfkccCWdKZpngB3A7urnQ40k1Uhb7K+YNozopRLVnQ/4EPDZiDgPeAH4AJ2/Bh6IiJuAbwPvq7kPSVKfahX3zHwS6DXfc2WdfiVJ9fgJVUkqkMVdkgpkcZekAlncJalAFndJKpDFXZIKZHGXpAJZ3CWpQBZ3SSrQ6F2OUCOhiSteSlo9jtwlqUAWd0kqkMVdkgpkcZekAlncJalAFndJKpDFXZIKZHGXpAJZ3CWpQBZ3SSqQlx/QUC12GYMju69e4STS2aX2yD0izomIv4yIR6r7F0XEvog4HBH3R8R59WNKkvrRxLTMLcCzXfd/B/hYZv4E8BJwUwP7kCT1oVZxj4hNwNXAH1T3A/g54MFqk3uBa+rsQ5LUv8jMwR8c8SDwn4E3Ab8J3Ag8UY3aiYjNwOcz89Iej90J7AQYHx/fOjMzM3AOgLm5OcbGxmr10a+Dx07V7mP8AjjxSgNhVljd3Fs2rmkuzDKtxmukCW3M3cbM0L7c09PTBzJzste6gd9QjYhfAE5m5oGImOr38Zm5B9gDMDk5mVNTfXfxKrOzs9Tto183NnDN811bTnPnwfa9r10395EbppoLs0yr8RppQhtztzEztDd3L3WqyjuBX4yIq4DXA/8c+DiwNiLOzczTwCbgWP2YkqR+DDznnpm3Z+amzJwArgO+mJk3AI8D11ab7QAeqp1SktSXYXyI6VbgNyLiMPBm4K4h7EOSdAaNTPZm5iwwWy2/AFzeRL+SpMF4+QFJKpDFXZIK1L5z8FbBYtdHkaRRZXHXSPFCY1IznJaRpAJZ3CWpQBZ3SSqQxV2SCmRxl6QCWdwlqUAWd0kqkMVdkgpkcZekAlncJalAFndJKpDFXZIKZHGXpAJ5VUi1mleRlHpz5C5JBbK4S1KBnJaRKk7xqCQDj9wjYnNEPB4Rz0TE0xFxS9V+YUQ8FhHPVz/XNRdXkrQcdaZlTgO7MvMS4Arg5oi4BLgN2JuZFwN7q/uSpBU08LRMZh4HjlfLfxsRzwIbge3AVLXZvcAscGutlDrr+SXlUn8aeUM1IiaAnwH2AeNV4Qd4ERhvYh+SpOWLzKzXQcQY8H+AOzLzcxHxcmau7Vr/Uma+Zt49InYCOwHGx8e3zszM1MoxNzfH2NhYrT4Wc/DYqaH0CzB+AZx4ZWjdD03d3Fs2runZ3tSx7tX/Uq+Rxfa9WNaVMszX9rC0MTO0L/f09PSBzJzsta5WcY+I1wGPAI9m5u9Wbc8BU5l5PCI2ALOZ+dYz9TM5OZn79+8fOAfA7OwsU1NTtfpYzDCnBHZtOc2dB9t30lLd3IudgTLsY/2hG7Yvun5Uz5YZ5mt7WNqYGdqXOyIWLe51zpYJ4C7g2fnCXnkY2FEt7wAeGnQfkqTB1BkyvhP4ZeBgRDxZtf02sBt4ICJuAr4NvK9WQklS3+qcLfNlIBZZfeWg/UqS6vPyA5JUIIu7JBWofadpqAh+KEkaLkfuklSgs3Lk7qhRUukcuUtSgSzuklQgi7skFeisnHPX2cv3W3S2cOQuSQVy5C4tod/R/mpfRVICR+6SVCSLuyQVqOhpGd88k3S2Krq4S6NkVL/pSWVyWkaSCmRxl6QCOS0jrbKl3hvateU0N3Zt4zSOlsORuyQVqPUj9/lRz8LRjSSdzVpf3CU160zTRE4JtYfTMpJUoKGN3CNiG/Bx4BzgDzJz97D2JY2SYX94zmvdaDmGUtwj4hzgvwH/BjgK/EVEPJyZzwxjf5L65ye4V9ZKf4htWNMylwOHM/OFzPwHYAbYPqR9SZIWiMxsvtOIa4Ftmfnvqvu/DPxsZn6wa5udwM7q7luB52rudj3w3Zp9rAZzr5w2ZoZ25m5jZmhf7n+ZmW/ptWLVzpbJzD3Anqb6i4j9mTnZVH8rxdwrp42ZoZ2525gZ2pu7l2FNyxwDNnfd31S1SZJWwLCK+18AF0fERRFxHnAd8PCQ9iVJWmAo0zKZeToiPgg8SudUyLsz8+lh7KtLY1M8K8zcK6eNmaGduduYGdqb+zWG8oaqJGl1+QlVSSqQxV2SSpSZI3cDjgAHgSeB/VXbhcBjwPPVz3VVewCfAA4DTwGXdfWzo9r+eWBHV/vWqv/D1WNjgIx3AyeBQ11tQ8+42D5q5v4onbOZnqxuV3Wtu73K8Bzw813t26q2w8BtXe0XAfuq9vuB86r286v7h6v1E31k3gw8DjwDPA3cMurH+wyZR/1Yvx74CvC1Kvd/GnRfTT2fmrnvAb7VdbzfPiqvkWHfVj3AIv9QR4D1C9r+y/wLAbgN+J1q+Srg89U/1hXAvq4D/kL1c121PP/L/5Vq26ge+94BMr4buIxXF8mhZ1xsHzVzfxT4zR7bXlL9spxf/eJ9k84b5OdUyz8GnFdtc0n1mAeA66rlTwG/Wi3/GvCpavk64P4+Mm+Y/+UD3gR8o8o2ssf7DJlH/VgHMFYtv45Osb2i3301+Xxq5r4HuLbH9qv+Ghn2bdUDLPIPdYTXFvfngA1dvzjPVcufBq5fuB1wPfDprvZPV20bgK93tb9quz5zTvDqIjn0jIvto2buj9K74NwO3N51/1HgHdXt0YXbVS/67wLnVu0/3G7+sdXyudV2ff/FVD3+ITrXLWrF8V6QuTXHGngD8FXgZ/vdV5PPp2bue+hd3EfuNdL0bVTn3BP4QkQcqC5TADCemcer5ReB8Wp5I/CdrscerdrO1H60R3sTViLjYvuo64MR8VRE3B0R6wbM/Wbg5cw83SP3Dx9TrT9Vbd+XiJgAfobOyKwVx3tBZhjxYx0R50TEk3Sm7x6jM9Lud19NPp+Bcmfm/PG+ozreH4uI8xfmXma+1fidrGVUi/u7MvMy4L3AzRHx7u6V2fkvMlcl2TKtRMYG9/FJ4MeBtwPHgTsb6LNxETEG/DHw7zPz+93rRvV498g88sc6M/8pM99O55PllwNvW91Ey7Mwd0RcSuevgrcB/4rOVMutQ84wMrVpJIt7Zh6rfp4E/oTOC+xERGwAqH6erDZf7FIHZ2rf1KO9CSuRcbF9DCwzT1S/GP8P+H06x3uQ3H8DrI2Icxe0v6qvav2aavtliYjX0SmSn83Mz1XNI328e2Vuw7Gel5kv03lT+B0D7KvJ5zNo7m2ZeTw7/h74Hwx+vFf0d7IJI1fcI+KNEfGm+WXgPcAhOpcv2FFttoPOHCZV+/uj4wrgVPUn0qPAeyJiXfWn73vozOEdB74fEVdERADv7+qrrpXIuNg+Bjb/wqz8Ep3jPb+v6yLi/Ii4CLiYzptKPS8vUY1aHgeuXeQYzOe+Fvhitf1y8gVwF/BsZv5u16qRPd6LZW7BsX5LRKytli+g8z7BswPsq8nnM2jur3cV3QCu4dXHe2R/Jxux2pP+C2903kX/Gj86penDVfubgb10Tjf6c+DCqj3ofDHIN+mcpjTZ1de/pXPa0mHgA13tk3T+kb8J/B6Dvdl0H50/q/+RzvzbTSuRcbF91Mz9P6tcT9F5oW7o2v7DVYbn6DqriM7ZBt+o1n14wb/fV6rn80fA+VX766v7h6v1P9ZH5nfR+VP3KbpOIRzl432GzKN+rH8K+Msq3yHgPw66r6aeT83cX6yO9yHgM/zojJpVf40M++blBySpQCM3LSNJqs/iLkkFsrhLUoEs7pJUIIu7JBXI4i5JBbK4S1KB/j/vV6YlOvICDwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load trained model\n",
    "\n",
    "lin_model = joblib.load('linear_regression.joblib')\n",
    "\n",
    "pred = lin_model.predict(data)\n",
    "\n",
    "pd.Series(np.exp(pred)).hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.13 ('env': venv)",
   "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": "3.8.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "8957aee21f29590461af2a48414c66c4d86b7c449264058c3c62786bbf5308d6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}