Research and Development/final-feature-engineering-pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this final notebook we would add scaling and modelling (Lasso Regression) to our scikit-learn pipeline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import joblib\n",
    "\n",
    "# scikit-learn\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler, Binarizer\n",
    "\n",
    "# feature-engine\n",
    "from feature_engine.imputation import(\n",
    "    AddMissingIndicator,\n",
    "    MeanMedianImputer,\n",
    "    CategoricalImputer\n",
    ")\n",
    "\n",
    "from feature_engine.encoding import (\n",
    "    RareLabelEncoder,\n",
    "    OrdinalEncoder\n",
    ")\n",
    "\n",
    "from feature_engine.transformation import (\n",
    "    LogTransformer,\n",
    "    YeoJohnsonTransformer\n",
    ")\n",
    "\n",
    "\n",
    "from feature_engine.selection import DropFeatures\n",
    "from feature_engine.wrappers import SklearnTransformerWrapper\n",
    "\n",
    "\n",
    "import preprocessors as pp\n",
    "\n",
    "\n",
    "pd.pandas.set_option('display.max_columns', None)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 81)\n"
     ]
    },
    {
     "data": {
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       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
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       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
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       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</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",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
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       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
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       "      <td>VinylSd</td>\n",
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       "      <td>Y</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
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       "      <td>CBlock</td>\n",
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       "      <td>6</td>\n",
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       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
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       "      <td>Lvl</td>\n",
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       "      <td>Gtl</td>\n",
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       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
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       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>162.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
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       "      <td>GLQ</td>\n",
       "      <td>486</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>434</td>\n",
       "      <td>920</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>1786</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>608</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
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       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
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       "      <td>Gtl</td>\n",
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       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
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       "      <td>7</td>\n",
       "      <td>5</td>\n",
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       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
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       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
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       "      <td>ALQ</td>\n",
       "      <td>216</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>540</td>\n",
       "      <td>756</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "      <td>1717</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>3</td>\n",
       "      <td>642</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</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>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>655</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>490</td>\n",
       "      <td>1145</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3</td>\n",
       "      <td>836</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities LotConfig LandSlope Neighborhood Condition1  \\\n",
       "0         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "1         Lvl    AllPub       FR2       Gtl      Veenker      Feedr   \n",
       "2         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "3         Lvl    AllPub    Corner       Gtl      Crawfor       Norm   \n",
       "4         Lvl    AllPub       FR2       Gtl      NoRidge       Norm   \n",
       "\n",
       "  Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n",
       "0       Norm     1Fam     2Story            7            5       2003   \n",
       "1       Norm     1Fam     1Story            6            8       1976   \n",
       "2       Norm     1Fam     2Story            7            5       2001   \n",
       "3       Norm     1Fam     2Story            7            5       1915   \n",
       "4       Norm     1Fam     2Story            8            5       2000   \n",
       "\n",
       "   YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  \\\n",
       "0          2003     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "1          1976     Gable  CompShg     MetalSd     MetalSd       None   \n",
       "2          2002     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "3          1970     Gable  CompShg     Wd Sdng     Wd Shng       None   \n",
       "4          2000     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "\n",
       "   MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure  \\\n",
       "0       196.0        Gd        TA      PConc       Gd       TA           No   \n",
       "1         0.0        TA        TA     CBlock       Gd       TA           Gd   \n",
       "2       162.0        Gd        TA      PConc       Gd       TA           Mn   \n",
       "3         0.0        TA        TA     BrkTil       TA       Gd           No   \n",
       "4       350.0        Gd        TA      PConc       Gd       TA           Av   \n",
       "\n",
       "  BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  \\\n",
       "0          GLQ         706          Unf           0        150          856   \n",
       "1          ALQ         978          Unf           0        284         1262   \n",
       "2          GLQ         486          Unf           0        434          920   \n",
       "3          ALQ         216          Unf           0        540          756   \n",
       "4          GLQ         655          Unf           0        490         1145   \n",
       "\n",
       "  Heating HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  \\\n",
       "0    GasA        Ex          Y      SBrkr       856       854             0   \n",
       "1    GasA        Ex          Y      SBrkr      1262         0             0   \n",
       "2    GasA        Ex          Y      SBrkr       920       866             0   \n",
       "3    GasA        Gd          Y      SBrkr       961       756             0   \n",
       "4    GasA        Ex          Y      SBrkr      1145      1053             0   \n",
       "\n",
       "   GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  \\\n",
       "0       1710             1             0         2         1             3   \n",
       "1       1262             0             1         2         0             3   \n",
       "2       1786             1             0         2         1             3   \n",
       "3       1717             1             0         1         0             3   \n",
       "4       2198             1             0         2         1             4   \n",
       "\n",
       "   KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu  \\\n",
       "0             1          Gd             8        Typ           0         NaN   \n",
       "1             1          TA             6        Typ           1          TA   \n",
       "2             1          Gd             6        Typ           1          TA   \n",
       "3             1          Gd             7        Typ           1          Gd   \n",
       "4             1          Gd             9        Typ           1          TA   \n",
       "\n",
       "  GarageType  GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual  \\\n",
       "0     Attchd       2003.0          RFn           2         548         TA   \n",
       "1     Attchd       1976.0          RFn           2         460         TA   \n",
       "2     Attchd       2001.0          RFn           2         608         TA   \n",
       "3     Detchd       1998.0          Unf           3         642         TA   \n",
       "4     Attchd       2000.0          RFn           3         836         TA   \n",
       "\n",
       "  GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  \\\n",
       "0         TA          Y           0           61              0          0   \n",
       "1         TA          Y         298            0              0          0   \n",
       "2         TA          Y           0           42              0          0   \n",
       "3         TA          Y           0           35            272          0   \n",
       "4         TA          Y         192           84              0          0   \n",
       "\n",
       "   ScreenPorch  PoolArea PoolQC Fence MiscFeature  MiscVal  MoSold  YrSold  \\\n",
       "0            0         0    NaN   NaN         NaN        0       2    2008   \n",
       "1            0         0    NaN   NaN         NaN        0       5    2007   \n",
       "2            0         0    NaN   NaN         NaN        0       9    2008   \n",
       "3            0         0    NaN   NaN         NaN        0       2    2006   \n",
       "4            0         0    NaN   NaN         NaN        0      12    2008   \n",
       "\n",
       "  SaleType SaleCondition  SalePrice  \n",
       "0       WD        Normal     208500  \n",
       "1       WD        Normal     181500  \n",
       "2       WD        Normal     223500  \n",
       "3       WD       Abnorml     140000  \n",
       "4       WD        Normal     250000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('train.csv')\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['MSSubClass'] = data['MSSubClass'].astype('O')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1314, 79), (146, 79))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop(['Id', 'SalePrice'], axis=1),\n",
    "    data['SalePrice'],\n",
    "    test_size=0.1,\n",
    "    random_state=0\n",
    ")\n",
    "\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = np.log(y_train)\n",
    "y_test = np.log(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this final pipeline will be based on feature selected for modelling\n",
    "\n",
    "CATEGORICAL_VARS_WITH_NA_FREQUENT = ['BsmtQual', 'BsmtExposure',\n",
    "                                      'BsmtFinType1', 'GarageFinish']\n",
    "\n",
    "\n",
    "CATEGORICAL_VARS_WITH_NA_MISSING = ['FireplaceQu']\n",
    "\n",
    "\n",
    "\n",
    "NUMERICAL_VARS_WITH_NA = ['LotFrontage']\n",
    "\n",
    "\n",
    "TEMPORAL_VARS = ['YearRemodAdd']\n",
    "\n",
    "REF_VAR = \"YrSold\"\n",
    "\n",
    "\n",
    "DROP_FEATURES = [\"YrSold\"]\n",
    "\n",
    "NUMERICALS_LOG_VARS = [\"LotFrontage\", \"1stFlrSF\", \"GrLivArea\"]\n",
    "\n",
    "BINARIZE_VARS = ['ScreenPorch']\n",
    "\n",
    "\n",
    "QUAL_VARS = ['ExterQual', 'BsmtQual', \n",
    "             'HeatingQC', 'KitchenQual', 'FireplaceQu']\n",
    "\n",
    "EXPOSURE_VARS = ['BsmtExposure']\n",
    "\n",
    "FINISH_VARS = ['BsmtFinType1']\n",
    "\n",
    "GARAGE_VARS = ['GarageFinish']\n",
    "\n",
    "FENCE_VARS = ['Fence']\n",
    "\n",
    "\n",
    "CATEGORICAL_VARS = ['MSSubClass',  'MSZoning',  'LotShape',  'LandContour',\n",
    "                    'LotConfig', 'Neighborhood', 'RoofStyle', 'Exterior1st',\n",
    "                    'Foundation', 'CentralAir', 'Functional', 'PavedDrive',\n",
    "                    'SaleCondition']\n",
    "\n",
    "\n",
    "QUAL_MAPPINGS = {'Po': 1, 'Fa': 2, 'TA': 3,\n",
    "                 'Gd': 4, 'Ex': 5, 'Missing': 0, 'NA': 0}\n",
    "\n",
    "EXPOSURE_MAPPINGS = {'No': 1, 'Mn': 2, 'Av': 3, 'Gd': 4}\n",
    "\n",
    "FINISH_MAPPINGS = {'Missing': 0, 'NA': 0, 'Unf': 1,\n",
    "                   'LwQ': 2, 'Rec': 3, 'BLQ': 4, 'ALQ': 5, 'GLQ': 6}\n",
    "\n",
    "GARAGE_MAPPINGS = {'Missing': 0, 'NA': 0, 'Unf': 1, 'RFn': 2, 'Fin': 3}\n",
    "\n",
    "\n",
    "FEATURES = [\n",
    "    'MSSubClass',\n",
    "    'MSZoning',\n",
    "    'LotFrontage',\n",
    "    'LotShape',\n",
    "    'LandContour',\n",
    "    'LotConfig',\n",
    "    'Neighborhood',\n",
    "    'OverallQual',\n",
    "    'OverallCond',\n",
    "    'YearRemodAdd',\n",
    "    'RoofStyle',\n",
    "    'Exterior1st',\n",
    "    'ExterQual',\n",
    "    'Foundation',\n",
    "    'BsmtQual',\n",
    "    'BsmtExposure',\n",
    "    'BsmtFinType1',\n",
    "    'HeatingQC',\n",
    "    'CentralAir',\n",
    "    '1stFlrSF',\n",
    "    '2ndFlrSF',\n",
    "    'GrLivArea',\n",
    "    'BsmtFullBath',\n",
    "    'HalfBath',\n",
    "    'KitchenQual',\n",
    "    'TotRmsAbvGrd',\n",
    "    'Functional',\n",
    "    'Fireplaces',\n",
    "    'FireplaceQu',\n",
    "    'GarageFinish',\n",
    "    'GarageCars',\n",
    "    'GarageArea',\n",
    "    'PavedDrive',\n",
    "    'WoodDeckSF',\n",
    "    'ScreenPorch',\n",
    "    'SaleCondition',\n",
    "    # this one is only to calculate temporal variable:\n",
    "    \"YrSold\",\n",
    "]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1314, 37), (146, 37))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = X_train[FEATURES]\n",
    "\n",
    "X_test = X_test[FEATURES]\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_pipe = Pipeline([\n",
    "\n",
    "    # impute categorical variable with string 'missing'\n",
    "    ('missing_imputation', CategoricalImputer(\n",
    "        imputation_method='missing', variables=CATEGORICAL_VARS_WITH_NA_MISSING\n",
    "    )),\n",
    "\n",
    "    ('frequent_imputation', CategoricalImputer(\n",
    "        imputation_method='frequent', variables=CATEGORICAL_VARS_WITH_NA_FREQUENT\n",
    "    )),\n",
    "\n",
    "    # add missing indicator\n",
    "    ('missing_indicator', AddMissingIndicator(variables=NUMERICAL_VARS_WITH_NA)),\n",
    "\n",
    "    # Impute numerical variables with mean\n",
    "    ('mean_imputation', MeanMedianImputer(\n",
    "        imputation_method='mean', variables=NUMERICAL_VARS_WITH_NA\n",
    "    )),\n",
    "\n",
    "    \n",
    "    # ----- TEMPORAL VARIABLES ------\n",
    "\n",
    "    ('elapsed_time', pp.TemporalVariableTransformer(\n",
    "        variables=TEMPORAL_VARS, reference_variable=REF_VAR\n",
    "    )),\n",
    "\n",
    "    ('drop_features', DropFeatures(features_to_drop=[REF_VAR])), \n",
    "\n",
    "\n",
    "    # ==== VARIABLE TRANSFORMATION =====\n",
    "    ('log', LogTransformer(variables=NUMERICALS_LOG_VARS)),\n",
    "\n",
    "    # ('yeojohnson', YeoJohnsonTransformer(variables=NUMERICALS_YEO_VARS)),\n",
    "\n",
    "    ('binarizer', SklearnTransformerWrapper(\n",
    "        transformer=Binarizer(threshold=0), variables=BINARIZE_VARS\n",
    "    )),\n",
    "\n",
    "    # ---- MAPPER -----\n",
    "    ('mapper_qual', pp.Mapper(\n",
    "        variables=QUAL_VARS, mappings=QUAL_MAPPINGS\n",
    "    )),\n",
    "\n",
    "    ('mapper_exposure', pp.Mapper(\n",
    "        variables=EXPOSURE_VARS, mappings=EXPOSURE_MAPPINGS\n",
    "    )),\n",
    "\n",
    "    ('mapper_finish', pp.Mapper(\n",
    "        variables=FINISH_VARS, mappings=FINISH_MAPPINGS\n",
    "    )),\n",
    "\n",
    "    ('mapping_garage', pp.Mapper(\n",
    "        variables=GARAGE_VARS, mappings=GARAGE_MAPPINGS\n",
    "    )),\n",
    "\n",
    "    # ('mapper_fence', pp.Mapper(\n",
    "    #     variables=FENCE_VARS, mappings=FENCE_MAPPINGS\n",
    "    # )),\n",
    "\n",
    "    # ------- CATEGORICAL ENCODING  -------\n",
    "    ('rare_label_encoder', RareLabelEncoder(\n",
    "        tol=0.01, n_categories=1, variables=CATEGORICAL_VARS\n",
    "    )),\n",
    "\n",
    "    ('categorical_encoder', OrdinalEncoder(\n",
    "        encoding_method='ordered', variables=CATEGORICAL_VARS\n",
    "    )),\n",
    "\n",
    "    ('scaler', MinMaxScaler()),\n",
    "\n",
    "    ('Lasso', Lasso(alpha=0.001, random_state=0))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('missing_imputation',\n",
       "                 CategoricalImputer(variables=['FireplaceQu'])),\n",
       "                ('frequent_imputation',\n",
       "                 CategoricalImputer(imputation_method='frequent',\n",
       "                                    variables=['BsmtQual', 'BsmtExposure',\n",
       "                                               'BsmtFinType1',\n",
       "                                               'GarageFinish'])),\n",
       "                ('missing_indicator',\n",
       "                 AddMissingIndicator(variables=['LotFrontage'])),\n",
       "                ('mean_imputation',\n",
       "                 MeanMedianImputer(imputation_method=...\n",
       "                                             'Foundation', 'CentralAir',\n",
       "                                             'Functional', 'PavedDrive',\n",
       "                                             'SaleCondition'])),\n",
       "                ('categorical_encoder',\n",
       "                 OrdinalEncoder(variables=['MSSubClass', 'MSZoning', 'LotShape',\n",
       "                                           'LandContour', 'LotConfig',\n",
       "                                           'Neighborhood', 'RoofStyle',\n",
       "                                           'Exterior1st', 'Foundation',\n",
       "                                           'CentralAir', 'Functional',\n",
       "                                           'PavedDrive', 'SaleCondition'])),\n",
       "                ('scaler', MinMaxScaler()),\n",
       "                ('Lasso', Lasso(alpha=0.001, random_state=0))])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train mse: 781396630\n",
      "train rmse: 27953\n",
      "train r2: 0.8748530315439078\n",
      "====================================\n",
      "test mse: 1060769014\n",
      "test rmse: 32569\n",
      "test r2: 0.8456415571208442\n",
      "Average house price  163000\n"
     ]
    }
   ],
   "source": [
    "pred = price_pipe.predict(X_train)\n",
    "\n",
    "\n",
    "# np.exp -> convert log transform to original value\n",
    "print(f'train mse: {int(mean_squared_error(np.exp(y_train), np.exp(pred)))}') \n",
    "\n",
    "print(f'train rmse: {int(mean_squared_error(np.exp(y_train), np.exp(pred), squared=False))}')\n",
    "\n",
    "print(f'train r2: {r2_score(np.exp(y_train), np.exp(pred))}')\n",
    "\n",
    "print('====================================')\n",
    "\n",
    "\n",
    "pred = price_pipe.predict(X_test)\n",
    "\n",
    "\n",
    "print(f'test mse: {int(mean_squared_error(np.exp(y_test), np.exp(pred)))}')\n",
    "\n",
    "print(f'test rmse: {int(mean_squared_error(np.exp(y_test), np.exp(pred), squared=False))}')\n",
    "\n",
    "print(f'test r2: {r2_score(np.exp(y_test), np.exp(pred))}')\n",
    "\n",
    "\n",
    "print('Average house price ', int(np.exp(y_train).median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Evaluation of Lasso Regression')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WS1JWoKoqIr4k6c3AU8As4NMRcX3Lc2Y2TjTS/bWTq4PaNZDQWq9m4Eh7UP26FCwkTZY0MyIebHXmzMaL4XZ/bce8UkV5/qmxq0hV1U+ATWXbG9M0M2uzTq4Omjunm88fsxfdUycjoHvqZC/MNEYU6VU1KSKeL21ExPPp6nxm1mZTp3Sxbv3AkPROqQ7yQMKxqUiJY62kt5c2JB1FsnyrmbXRwuW9PPPXDUPSuybK1UHWUkVKHKcBl0m6EBDwMPD+lubKzGqav/geBjYNXX156y0mDftbfqdNfWKdqUivqvuAAyRtk24/0/JcmVlNee0YT/YPrboqopN7aFlnyQ0ckt4bET+U9JGKdAAi4sstzpuZVdHs7q6d3EPLOku1No7SCPFtcx5m1kbNXjejk3toWWfJLXFExEWSJgJPRcQF9Z5Y0iXAkcCaiNgzTZsPvA14HrgP+EBE9GUc+yDwNEnX3w0R0ZOmbw8sAGYCDwLvioh19ebNbCxo9roZHrBnRSmZhqrKDtLvImK/uk8svR54Bvh+WeA4FLghIjZI+gJARHwi49gHgZ6IeLwi/YvAExFxvqQzge2yjq/U09MTS5curfcjmI0rlW0ckJRgPPZi/JK0rPTFvVyRXlW3pD2qFgDPlhIj4g/VDoqImyXNrEhbUrZ5K/COAu9f7ijgjenz7wE3kUz5bjbmZPVwgtatzOeV/6yoIiWOGzOSIyIOqXnyJHBcWypxVLx2DbAgIoas7SHpAWAdyYy8F0XExWl6X0RMTZ8LWFfazjjHqcCpADNmzNj3oYceqpVds46R9e2/a4JAMLDxhf/Z0VwicNffztdIieOdlVVGTcjM2cAG4LKcXV4XEb2SXgJcL2lVRNxcvkNEhKTcqJcGm4shqapqUtbNMjVyE8w6NquHU9aYjWb3ehqpm7m7/o5uub2qJL1N0lrgDkmPSDqwGW8o6SSSRvMTIqe4ExG96c81wFVAqY3lMUk7p+fZGVjTjDyZNaKRpV/zjs1qpM7TrF5PI7mErdfqGN2qdcf9HPB3ETEdOBb4fKNvJulw4OPA2yNifc4+W5dWGEwXjToUuCt9+WrgxPT5icDPGs2TWaMauQnmHTsxHS9VRKnXU6Prcozkzdxdf0e3aoFjQ0SsAoiI26hz7Iaky4HfArPSEsvJwIXpea6XtELSN9N9p0u6Lj10J+A3km4HfgcsiohfpK+dD7xZ0p+B/5lum7VVIzfBvH02RgwZo9E1QXRNHBxQSuM2mlFaGMmb+Vhd9XC8qNbG8ZKKUeODtmuNHI+I4zOSv5Oz72rgiPT5/cDeOfv9BXhTtfc1q6XZ9fiNjH/IO7a7rK2jSK+qg86/oeFR3yM5jsNrdYxu1QLHtxhcyqjcNht1WtEo28hNsNqxeVOSZ6U1o7Qwkjdzd/0d3aqNHD9vJDNiNhJaMR9TIzfBZt1Am1FaGOmbudfqGL1qjuMYCzxy3Ep2O3MRWX/xAh44/60jnZ2m8ahva4W8cRxFFnIyGzPGaqOsl2m1kVRkAKDZmDGWG2Vd9WMjpWaJQ9JOkr4j6efp9h5p11qzUcffzM0aV6TEcSnwXeDsdPtPJBMeZnatNet0/mZu1pgigWPHiPixpLMA0inRN9Y6yMwa40kArVMVaRx/VtIOJDPVIukA4MmW5spsnMsaCX7GghXMPm9JS+aOMqtHkRLHR0jmiHq5pFuAadS/joaZUbwUkTXeBKCvf8CzyFrb1QwcEfEHSW8AZpF0d78nIgZanjOzUahaYKhn1Hq1Ed/NnkrdrF41A4ekdwK/iIiVkj4F7CPps7VWADQb6yqDxMGvmsYVy3pzA0M9o9bzRoKXeBZZa6cibRz/EhFPS3odyQSD3wH+vbXZMmutRqcgz2qDuOzW/6o6LXk980nNO2zWkNlxy02fOrnhz2A2XEXaOEr/CW8FvhURiyR9toV5MmupalVGUGyupqzSQ97kPaXAUM98UqX3PO+alaxbP7hmeHLXRA5+1TSvoGdtU6TE0SvpIuA44DpJWxY8zqwj5VUZnXv1ysJrWtRTVVQKDFmliGqj1ufO6Wb5pw/lK8fNHjJg8cZVa72CnrVNkRLHu4DDgS9FRF+6ZOu81mbLrHXybvp9/UP7fJTfjMtLIlOndA0pCWTpmqDNgWG4s89mDVj88IIVmfu67cNGQpGSw47AUuA5STOALmBVS3NlVqbZdfn1TmhYvg54qSTyzF83DFmNL8s2W00adNOfO6ebW848hAuOmw0kAWA4n2msTtZoo0OREscikupbAVsBuwH3AK9pYb7MgOYuvFTqBdXb148Y3CYxuWsiIlg/sCnz2MpqoYFNydETBJuqrEzQl1EqacZnGsuTNVrnq1niiIi9IuK16c9XAvuRrCVelaRLJK2RdFdZ2nxJqyTdIekqSVMzjttF0o2S/ihppaR/LnvtXEm96XrlKyQdUfiT2qhUrQtrPcp7QcEL34TghXaDLav0YsqzKaBropg6uSvz9awSQDM+kydrtHaqe1r1dEDg/gV2vRS4EPh+Wdr1wFnpfFdfAM4CPlFx3Abgo+n7bAssk3R9RPwxff2CiPhSvfm20alWF9ZGRmIHyQ33ljMPAfLbDWoZ2BhIyTf+IiWAZizzCp6s0dqnyLTqHyl7fEzSfwCrax0XETcDT1SkLYmIDenmrcDLMo57tDS4MCKeBu4G/N8xTlWry88aS1FvL6jy9GrtA7VaM/rWDxQuAbh9wka7Io3j25Y9tiRp8ziqCe/9QeDn1XaQNBOYA9xWlnx6WtV1iaTtqhx7qqSlkpauXbu2Cdm1dqjWhbWeKp8iN+t5h83KbPDumiBOOGAGE5UfPqZPnczcOd3MO2wW06dOZnVfP/MX35MZxOrtlmvWaYq0cZwXEecB/wZ8NSIui4i/NvKmks4mqZK6rMo+2wBXAGdExFNp8r8DLwdmA4+mecrL98UR0RMRPdOmTWsku9ZG1eryGx2JXXmznjunm/nv2JvtprzQXjF1chfz37k3n527Fwfsnv09Ren5i5aA3D5ho12Ruar2BH4AbJ9uPw6cGBF3VT0w/3wnAUcCb4qIzP4okrpIgsZlEXFlKT0iHivb51vAtcPJg40ueXX5wxmJXas9pFq7wa33r8vOoJLjDjr/hsJzUbl9wkazIo3jFwMfiYgbASS9MU07sN43k3Q48HHgDRGxPmcfkcyHdXdEfLnitZ0j4tF082hgWMHLxoYiXVIXLu8dNG3H1MldXHDcbObO6d48PqQ8kEB+cNmY/T2HUnKzGr3NOl2RwLF1KWgARMRNkraudZCky4E3AjtKegQ4h6QX1ZbA9Ul84NaIOE3SdODbEXEEcBDwPuBOSSvS030yIq4DvihpNkmHmAeBDxX5kDY21SpFLFzey7yf3s7Axhdu+H39A5yxYAVnVPSg6u3r5yMLVjBxojbvXzm+YqKUGTxKbR/1lIDMRrMigeN+Sf9CUl0F8F7g/loHRcTxGcmZ65RHxGrgiPT5b8jpxBIR7yuQXxtHKoNHqWG8NI15edCoZROwqWL/8qqm4/ffhR/e+l9Djjt+/10AD8qz8aNI4PggcB5Qamv4dZpmNuLqWQOj2noW9ShVNX127l4AXH7bw2yMYKLE8fvvsjl9uHNRmY02ymmfHlN6enpi6dKl7c6GNahyqg5gyNQhJZO7JvDXgU25U53Xo3yQoNl4ImlZRPRUpueWOCRdQ/4SA0TE25uUN7NC6lkDoz9nzql6dU2Uq5rMKlSrqipN6yHgW8Dftz47Zvna0jtp7BfIzeqWGzgi4lel55KeKd82a4da63C3wsCmyByHYTaeFV3Jz9+7rGWKrreRN/p76y2yZ7WtvVpGMR6HYTZYtTaO7cs2J6bzQm3+X4yIJ4YeZVafetamyOu1BGR2gz123+7NPaAa8eKcKdPNxqvcXlWSHmDwsgXlIiJ2b2XGmsm9qjrXQeffkFn9VG9PpqwR4ue+PVlrrDKo1GuCktHh7l5r401er6rcqqqI2C0idk9/Vj5GTdCwztbMaTr+WtaTqq9/gA8vWMHSh54YNKHgcGwKak7bbjaeFG3jMGuJZq1NkddV97J0pPctZx7CA+e/le4q5602bXrJcFYfNBtrHDisreYdNouuCYNv2F0T6h87kVdCCdh8o1+4vJf1z28Yss/krol85bjZ/Nu79h7S+F7Pe5mNF3UvHWvWdJVf9DO++NdaIrZaV93Vff2Zo87hhbaQ8nOV3mdCzqSGnrTQxrvcEoek7as9RjKTNnZlTUQ4sDEGVQcVWSBp3mGzctswpk+dnFmVBbD1lpMGBY25c7o3V2tllUA8aaFZ9aqqZcDS9Oda4E/An9Pny1qfNRsPijSOF10idkrGeI7SjX44jfBeqc8sW7WR47vB5pX2rkrXw0DSW4C5I5I7G/OKrGFR66afVw0FsFXXhMLvk8Ur9ZkNVaRx/IBS0ACIiJ8zjNX/zLJGiGeNBhdw8KteWCe+Vs+rvGoogHXrBzjryjs5+FXTXO1k1iRFAsdqSZ+SNDN9nA2sbnXGbGzJa6cAOHbf7kHtEwFcsax3cxtG3lQjpZt+rV5O/QMbufy2h+kf2Li5y62rncyGr0ivquNJln29iuR/+uY0zaywWu0UlX2X+gc28tEf3w7UXiCpyOSHpd5RGyM2Bx0HDbPhKbyQk6StI+LZuk4uXQIcCayJiD3TtPnA24DngfuAD0REX8axhwNfBSaSrEd+fpq+G/AjYAeSRvr3RcTz1fLhKUfab7czF2XOlFkqaeT9FU7umjioZJDVLRfqn1bEizOZ1Vb3lCNlBx4o6Y/A3en23pK+UfB9LwUOr0i7HtgzIl5L0lPrrIz3nAj8X+AtwB7A8ZL2SF/+AnBBRLwCWAecXDAv1iYLl/cyIWdU9vSpk6s2UJeXSqpVd5V6PxXlQXxmw1ekjeMC4DDgLwARcTvw+iInj4ibgScq0pZERGn47q3AyzIO3Q+4NyLuT0sTPwKOkiTgEOCn6X7fwz28OtbC5b3MPm8JZyxYkTmQrlRlVN4QnqVUDVWtuqs0/qJo8PAgPrPhKzRyPCIe1uBvjMOfanSwDwILMtK7gYfLth8B9iepnuorCzyPpPtah6nWRRaSeaFKVVC15n4qNWjnlRJ6+/o56PwbWJ2WRGpxbyqzxhQJHA9LOhAISV3AP5NWWzUi7Z21Abis0XPlnP9U4FSAGTNmtOItjPypQKp1kQXYFLG53aJWtVGptJLXCC4ovDJgt6dGN2tYkcBxGkkjdTfQCywB/qGRN5V0Ekmj+Zsiu3W+F9ilbPtladpfgKmSJqWljlL6EBFxMXAxJI3jjeTXslVbhKlWMJggsduZi5g+dTIvntxFX/9A7r6l6qd5h83KLMXU+uUKOOGAGXx27l419jSzIooEjlkRcUJ5gqSDgFuG84Zpb6mPA2+IiPU5u/0eeGXag6oXeDfwnogISTcC7yBp9zgR+Nlw8mGNq9bmUKuLbKkU0dvXT9dE0TVBDGzKbweBod1yp07p2rxwUxbBoJ5XpeosL8hk1pgijeNfL5g2hKTLgd8CsyQ9Iulk4EJgW+B6SSskfTPdd7qk6wDS0sTpwGKSarEfR8TK9LSfAD4i6V6SNo/vFMmLNV+1NoesQXuQrKZXaWBjsM1WkzaXLKoN0iufhHDKFvnfe7qnTuaB89+6ucttrUkSofja52bjXbU1x/+WZGqRaZI+UvbSi0jGVtQUEVkDBTNv9BGxGjiibPs64LqM/e4n6XVlZWpNO94K1docIOkiW5mnDy9YkXmuvvUDLP/0oXW9f7XqsPLG71q9saC+tc/NxrtqVVVbANuk+2xblv4USVWRdYiRvOmVB6gXT+7K3CeAMxasyGyInr/4nmFNNpglL3BtN6Vr0Hs2OgOvA4fZYNXWHP9VRJxHMsnheWWPL0fEn0cwj1ZD0WnHG1U5AK9agzbkr5vRrMkG8851ztteMyityPK0zVz73GysK9LG8W1JU0sbkraTtLh1WbJ6jdRNr1YX2yylOadKwaOZa1wUPVeRYNWstc/NxoMivap2LJ9LKiLWSXpJ67Jk9RruWhP1Gm4g2hgxqOosb42L4bTTFFkvo9YkiZDd1dcDBc2yFQkcmyTNiIj/ApC0K7W7ztsIGqmbXpFZaPNkzXZbrtXtNLUCTJHgYmaJIoHjbOA3kn5F0mHm70hHZFtnqPemN9weWHkD8IqqLHmU64TGaa/2Z1ZMzcAREb+QtA9wQJp0RkQ83tpsWb2K3vQa+WZfGaAmSJmTF1aTFwzySjK9ff0sXN7rG7pZB8ltHJf0qvTnPsAMklX/VgMz0jQbhRrtgVU+AK/eoFFS2VaycHkv2ZOuJ7IG65lZ+1QrcXwUOAX4t4zXgmR6cxtlmtUDq3SzH07oqGy0n7/4nqrn8XgKs86SGzgi4pT058Ejlx1rteH0wMpqE6l1s8+T1WhfJGiVpk53g7VZ+1WbcuSYagdGxJXNz461WlYDtyB3MaW8NpHhNJCXr8FRrmhvLU8DYtYZqg0AfFv6OJlkfqkT0se3SRZgslFo7pxujt23e1CbQgBXLOvNbEfIaxPJUxqMlyWvTSRvQsQsrRgRb2b1qVZV9QEASUuAPSLi0XR7Z5K1xG2UunHV2iHVTHntCPW0fQg2V2PllSDm/eR2zrtmJX3rB4Z0BS6fA0sid8p0TwNi1l5FxnHsUgoaqcdIelnZKFVPA3k9g/4C+PCCFbx4chddE8XAxqEljIFNsTkgVFY9VQatg86/YURGxJtZfYrMVfVLSYslnZSu3LcI+M/WZstaqZ55mbKqkap1nd08+WHBlvNqVU/NnBDRzJqnZuCIiNOBbwJ7p4+LI+KfWp0xa515h82iq2JFpa4JGnJDLvWm6h/YOGhxpRMOmFGzTWJgU2w+ppa8ElAzJ0Q0s+YpUlUF8Afg6Yj4T0lTJG0bEU+3MmPWYpX39Irtyt5UGyM2f9ufO6ebnl2339wmkVe42BiRuyRsuWpVT54GxKzz1CxxSDoF+ClwUZrUDSxsYZ6soOEudTp/8T1D2h8GNsagKqNaI8zLR5B359z4p07uql6vhauezEajIm0c/wgcRLLyH+kiTp5Wvc0qF1Xq7evnjAUrmPOvS6oGkIXLe3Mbu8urjOppQM9ri5DIbCCfKLnqyWwUK1JV9VxEPK+0vlrSJAo0fUq6BDgSWBMRe6Zp7wTOBV4N7BcRSzOOmwUsKEvaHfh0RHxF0rkk06CsTV/7ZLo2+biTt6jSuvUDzPvp7Zx79Uqe7B/c5XXh8l7m/fT23HNOkNjtzEVMnzqZqVO6MrvDZlUr5c3Om7e++KYIHjj/rQU/qZl1miKB41eSPglMlvRm4B+AawocdylwIfD9srS7gGN4odpriIi4B5gNIGki0AtcVbbLBRHxpQLvP6ZVG8swsDE2L+ta3uU1q4qqXGmAXm9fP10TNKRLbbVqpay2iGauL25mnaNIVdUnSL7h3wl8CLgO+FStgyLiZuCJirS708BQ1JuA+yLioTqOGRfqufmW2ibqGTg3sCnYeotJuT2airSvuDut2dhUtcSRfuNfGRGvAr41Mlka5N3A5RVpp0t6P7AU+GhErMs6UNKppAtOzZgx9sYr1ruoUqkKqZ4V/J7sH2DFOYcOSa+2pgcMrrI6dt9ubly1tuFV9Ya7+JSZNZ+ixpoKkn4G/FNp6di6Ti7NBK4ttXGUpd8EfCyrjaNsny1I1v94TUQ8lqbtBDxO0sbyGWDniKg5b1ZPT08sXZr7VqPWwuW9nHv1ys3VUtV0pzfbeT+9vWp1VeUxt5z5wuz5pZt3XvDZbkoXfx3YNGQJ20YbwCsDVbPOa2bVSVoWET2V6UWqqrYDVkr6paSrS4/mZ3GItwB/KAUNgIh4LCI2RsQmkhLQfiOQj441d043K845lK8cN3tzldJ2U7qGDO4rH38x/x17s92Urs2vTZ3cxXsPmEHXxIoBgRMHDwgs78WVZ936gYYWicrT6OJTZtZcRRrH/6Xluch2PBXVVJJ2Lps362iSxvZxr7Jhulq1TlYj9sLlvSz43cODT1pRKMnrxVVEo5MSNmvxKTNrjmrrcWwFnAa8gqRh/DsRsaHoiSVdDrwR2FHSI8A5JI3lXwemAYskrYiIwyRNB74dEUekx24NvJmkMb7cFyXNJrmtPZjxulH/aOv5i+8ZMrp7YFNw7tUra44OL6LRXlTDWXzKzFqnWonje8AA8GuSaqM9gH8ueuKIOD7npasqEyJiNXBE2fazwA4Z+72v6PuPN400Hud9c+/rHyjUflJN1hxY9crqCODeWWbtUy1w7BERewFI+g7wu5HJktWrWi+nIsGj3t5W9dhmq0kNN2DnDTB0w7hZe1QLHJu/akbEBhWc6dRGXrXG4yI313mHzWLeT26vORnhcPTlLMZUL092aNY5qvWq2lvSU+njaeC1peeSnhqpDFptjTYez53TzTZbFZsoufz7Q6nzVvfUyYN6apVzO4TZ2FNt6dhii0Bb29XTeJzXFlK0ZFA+7GfLSS+Mpcgba+F2CLOxp8g4DutwRaf2yJpR96wr72Th8l6m5pQYqqmcZt2LLpmND0UXcrIOVrTxOK8t5LxrVvLMXwv3tB6kvDrM7RBm44MDxxhR5Kad1+aRNX16ue2mdBFBZtdct2GYjT+uqhpHhnuTn7LFJM59+2s8062ZAQ4c40peW8jUydXbN1b39bsNw8w2c1XVODJ3TjdLH3qCy297mI0RTJQ4dt9uenbdvuoU7aWSitswzAxc4hiViiyilHfcFct6N6/0tzGCK5Ylx37+mL0yx2K4OsrMKrnE0WLNXoCokelFqo0wv+XMQzaPx/DUHmZWjQNHCzU6h1SWRqYXKTLC3NVRZlaLq6paqBULEDUyvUheryp3qTWzejhwtFArFiBq5OZfdIS5mVk1DhwNqNVI3Ypv+I3c/N2l1syawW0cw1Sk/aJVCxBtOWnC5nNO6ZrAll0T+PCCFcxffE/Nxmy3YZhZoxw4hqlII3WzFyDKmoF2/cAm1g9sArKDl3tJmVmztSxwSLoEOBJYExF7pmnvBM4FXg3sFxFLc459EHga2AhsiIieNH17YAEwk2TN8XdFxLpWfYZqirZfNPMbflawqlQevFrRq8vMrJVtHJcCh1ek3QUcA9xc4PiDI2J2KWikzgR+GRGvBH6ZbrdFO3ooFW1UL+3Xil5dZmYtCxwRcTPwREXa3RHRyF3rKOB76fPvAXMbOFdD2tFDqWhQKu3Xil5dZmad2qsqgCWSlkk6tSx9p4h4NH3+38BOeSeQdKqkpZKWrl27tukZbEcPpaxgleXZ5zawcHmvx22YWUt0auP46yKiV9JLgOslrUpLMJtFREiKnOOJiIuBiwF6enpy92vESPdQympsP/hV01h0x6OD1tTo6x/grCvv5Nh9u7liWa+XczWzpurIwBERvenPNZKuAvYjaRd5TNLOEfGopJ2BNe3MZztkBasbV60dshhT/8BGbly1ls8fs5d7VZlZU3Vc4JC0NTAhIp5Onx8K/Gv68tXAicD56c+ftSofw+3G2o7ur9XaMjxuw8yarWVtHJIuB34LzJL0iKSTJR0t6RHgb4FFkhan+06XdF166E7AbyTdDvwOWBQRv0hfOx94s6Q/A/8z3W66UjfW3r5+ghe6sdaavny4xzXKbRlmNpIU0ZLq/47S09MTS5dmDhnJdND5N9Cb8S2+e+pkbjnzkKYf16isgYGTuyYOaqz3QEAzq5ekZRVDIoAOrKrqBEW7sVbejLOCRrXzNUutEeoeCGhmzeTAkSEvCJRX/WTdjEXSj7jaca1SrS2jkTU8zMwqOXBkKDI5YdbNOGBI8GhH99dOKQmZ2djkwJGhyOSEeTfdIGnTaFdbQqeVhMxs7HHgyFGrG2veN/lWN4TX0sklITMbGzp1ypGO16mr6dUqCXkBJzNrlEscw9TstTaapVNLQmY2djhwNKATR2W3atVBM7MSB44xplNLQmY2djhwjEGdWBIys7HDgaMDeXoQM+tkDhzD1Kqbu6cHMbNO58AxDK28uRedHsSlEjNrF4/jGIZqN/dGFZlgsV3Tt5uZgQPHsBSdPXc4iqyt0crAZWZWiwPHMLRy4aQiI9JbGbjMzGpx4BiGVk43MndON58/Zq+q04N4xT8zayc3jhdU2Rh97L7d3LhqbUsap2uNw/DocDNrp5YFDkmXAEcCayJizzTtncC5wKuB/SJiyHquknYBvk+y9ngAF0fEV9PXzgVOAdamu38yIq6rPEezZfWiumJZb9smCvTocDNrp1aWOC4FLiQJAiV3AccAF1U5bgPw0Yj4g6RtgWWSro+IP6avXxARX2pFhvN04gp6Hh1uZu3SssARETdLmlmRdjeApGrHPQo8mj5/WtLdQDfwx9yDWsyN0WZmL+joxvE08MwBbitLPl3SHZIukbTdSOTDjdFmZi/o2MAhaRvgCuCMiHgqTf534OXAbJJSyb9VOf5USUslLV27dm3eboV06qJNZmbt0JGBQ1IXSdC4LCKuLKVHxGMRsTEiNgHfAvbLO0dEXBwRPRHRM23atIbyU6SLrJnZeNFx3XGVNIB8B7g7Ir5c8drOaRsIwNEkje0jwo3RZmaJlpU4JF0O/BaYJekRSSdLOlrSI8DfAoskLU73nS6p1K32IOB9wCGSVqSPI9LXvijpTkl3AAcDH25V/s3MLJsiot15aLmenp5YunTIkBEzM6tC0rKI6KlM78g2DjMz61wOHGZmVhcHDjMzq8u4aOOQtBZ4qN35qNOOwOPtzkQH8fUYzNdjMF+PwZp1PXaNiCHjGcZF4BiNJC3NapQar3w9BvP1GMzXY7BWXw9XVZmZWV0cOMzMrC4OHJ3r4nZnoMP4egzm6zGYr8dgLb0ebuMwM7O6uMRhZmZ1ceAwM7O6OHCMsHQBqjWS7ipLe6eklZI2ScrtQifpwXSSxxWSxsTkWznXY76kVemCXVdJmppz7OGS7pF0r6QzRyzTLdTg9Rgvfx+fSa/FCklLJE3POfZESX9OHyeOXK5bp8HrsbFs4tirG8pIRPgxgg/g9cA+wF1laa8GZgE3AT1Vjn0Q2LHdn2EErsehwKT0+ReAL2QcNxG4D9gd2AK4Hdij3Z+nXddjnP19vKjs+f8Gvplx3PbA/enP7dLn27X787TreqSvPdOsfLjEMcIi4mbgiYq0uyPinjZlqa1yrseSiNiQbt4KvCzj0P2AeyPi/oh4HvgRcFRLMzsCGrgeY1LO9XiqbHNrIKuHz2HA9RHxRESsA64HDm9ZRkdIA9ejqRw4RpcAlkhaJunUdmdmhHwQ+HlGejfwcNn2I2naWJd3PWAc/X1I+pykh4ETgE9n7DKu/j4KXA+ArdLltG+VNLeR93PgGF1eFxH7AG8B/lHS69udoVaSdDawAbis3XnpBAWux7j5+4iIsyNiF5JrcXq789NuBa/HrpFMQ/Ie4CuSXj7c93PgGEUiojf9uQa4iiprro92kk4CjgROiLSCtkIvsEvZ9svStDGpwPUYV38fZS4Djs1IH1d/H2Xyrkf538f9JO2pc4b7Jg4co4SkrSVtW3pO0mA6YmuujyRJhwMfB94eEetzdvs98EpJu0naAng30FhPkQ5V5HqMs7+PV5ZtHgWsythtMXCopO0kbUdyPRaPRP5GWpHrkV6HLdPnO5Is0f3HYb9pu3sJjLcHcDnwKDBAUu96MnB0+vw54DFgcbrvdOC69PnuJD2HbgdWAme3+7O08HrcS1I/vSJ9fLPyeqTbRwB/IuldNa6vxzj7+7iCJCjeAVwDdKf79gDfLjv2g+m1uxf4QLs/SzuvB3AgcGf693EncHIj+fCUI2ZmVhdXVZmZWV0cOMzMrC4OHGZmVhcHDjMzq4sDh5mZ1cWBw8YsSTuUzQb635J6y7a3aNJ73FQ+o7GkmeUzl44USSdJWpt+tj9KOiVnv7ePlZmErX0mtTsDZq0SEX8BZgNIOpdkdtAvlV6XNClemDxwLFgQEadLegmwUtLVEfFY6cX0817NGB0oaSPHJQ4bVyRdKumbkm4DvijpXEkfK3v9Lkkz0+fvlfS79Fv8RZIm1vleW0n6brpGxnJJB6fpJ0m6sGy/ayW9UdLENH93pcd8OH395ZJ+kU5e+GtJr6r2vpFMOXIfsGvG59383pJ2UrK+x+3p48BmfG4b+1zisPHoZcCBEbExLYkMIenVwHHAQRExIOkbJDOPfj9j98sk9afPtwA2pc//EYiI2Cu92S+R9DdV8jWbZNTvnmkepqbpFwOnRcSfJe0PfAM4JO8kknYnGUl+b8bnPals168Bv4qIo9PgsE2dn9vGKQcOG49+EhEba+zzJmBf4PeSACYDa3L2PSEilkLSxgFcm6a/Dvg6QESskvQQUC1w3A/sLunrwCKSQLMNyXQRP0nzAbBlzvHHSXodydQ1H4qIJ9Jj8j7vIcD70/xtBJ6U9L46PreNUw4cNh49W/Z8A4OrbLdKfwr4XkSc1YL3z3zPiFgnaW+SRYhOA94FnAH0RcTsAuddEBFZU2o/m5GWp5Wf28YIt3HYePcgyVKcSNoH2C1N/yXwjrShGUnbS9q1znP/mqSah7SKagZwT/qesyVNkLQL6fTn6aylEyLiCuBTwD6RrO72gKR3pvsoDS7N8Evgf6XnnSjpxTTnc9sY58Bh490VwPaSVpIsgPMngIj4I8nNe4mkO0iWHt25znN/A5gg6U5gAXBSRDwH3AI8QDKt9deAP6T7dwM3SVoB/BAofes/AThZUmnm22YtkfvPwMFp/paRrNnejM9tY5xnxzUzs7q4xGFmZnVx4DAzs7o4cJiZWV0cOMzMrC4OHGZmVhcHDjMzq4sDh5mZ1eX/A3l7OTqkolnjAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, price_pipe.predict(X_test))\n",
    "plt.xlabel('True House Price')\n",
    "plt.ylabel('Predicted House Price')\n",
    "plt.title('Evaluation of Lasso Regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's plot residual errors distribution\n",
    "\n",
    "y_test.reset_index(drop=True, inplace=True)\n",
    "\n",
    "\n",
    "preds = pd.Series(price_pipe.predict(X_test))\n",
    "\n",
    "errors = y_test - preds\n",
    "errors.hist(bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['price_pipe.joblib']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(price_pipe, 'price_pipe.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Score new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1459, 37)\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('test.csv')\n",
    "\n",
    "data.drop('Id', axis = 1, inplace=True)\n",
    "\n",
    "data['MSSubClass'] = data['MSSubClass'].astype('O')\n",
    "\n",
    "\n",
    "data = data[FEATURES]\n",
    "\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_var_with_na = [\n",
    "    var for var in FEATURES \n",
    "    if var not in CATEGORICAL_VARS_WITH_NA_MISSING +\n",
    "    CATEGORICAL_VARS_WITH_NA_FREQUENT + \n",
    "    NUMERICAL_VARS_WITH_NA and \n",
    "    data[var].isnull().sum() > 0\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MSZoning',\n",
       " 'Exterior1st',\n",
       " 'BsmtFullBath',\n",
       " 'KitchenQual',\n",
       " 'Functional',\n",
       " 'GarageCars',\n",
       " 'GarageArea']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_var_with_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>MSZoning</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>Functional</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RH</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>730.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RL</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1.0</td>\n",
       "      <td>312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RL</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2.0</td>\n",
       "      <td>482.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RL</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RL</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2.0</td>\n",
       "      <td>506.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  MSZoning Exterior1st  BsmtFullBath KitchenQual Functional  GarageCars  \\\n",
       "0       RH     VinylSd           0.0          TA        Typ         1.0   \n",
       "1       RL     Wd Sdng           0.0          Gd        Typ         1.0   \n",
       "2       RL     VinylSd           0.0          TA        Typ         2.0   \n",
       "3       RL     VinylSd           0.0          Gd        Typ         2.0   \n",
       "4       RL     HdBoard           0.0          Gd        Typ         2.0   \n",
       "\n",
       "   GarageArea  \n",
       "0       730.0  \n",
       "1       312.0  \n",
       "2       482.0  \n",
       "3       470.0  \n",
       "4       506.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[new_var_with_na].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSZoning        0.002742\n",
       "Exterior1st     0.000685\n",
       "BsmtFullBath    0.001371\n",
       "KitchenQual     0.000685\n",
       "Functional      0.001371\n",
       "GarageCars      0.000685\n",
       "GarageArea      0.000685\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[new_var_with_na].isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1449, 37)\n"
     ]
    }
   ],
   "source": [
    "# since the percentage of null values in this variables is small, we drop them\n",
    "# sunset defines the columns to look for null values\n",
    "data.dropna(subset=new_var_with_na, inplace = True)\n",
    "\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_pred = price_pipe.predict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(np.exp(new_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
}