lasso_reg_ames_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.linear_model import Lasso, LassoCV, LinearRegression\n",
    "from yellowbrick.regressor import AlphaSelection\n",
    "from yellowbrick.model_selection import FeatureImportances\n",
    "from yellowbrick.features import RadViz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"AMES_Final_DF.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2925, 274)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Overall Qual</th>\n",
       "      <th>Overall Cond</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>Year Remod/Add</th>\n",
       "      <th>Mas Vnr Area</th>\n",
       "      <th>BsmtFin SF 1</th>\n",
       "      <th>BsmtFin SF 2</th>\n",
       "      <th>Bsmt Unf SF</th>\n",
       "      <th>...</th>\n",
       "      <th>Sale Type_ConLw</th>\n",
       "      <th>Sale Type_New</th>\n",
       "      <th>Sale Type_Oth</th>\n",
       "      <th>Sale Type_VWD</th>\n",
       "      <th>Sale Type_WD</th>\n",
       "      <th>Sale Condition_AdjLand</th>\n",
       "      <th>Sale Condition_Alloca</th>\n",
       "      <th>Sale Condition_Family</th>\n",
       "      <th>Sale Condition_Normal</th>\n",
       "      <th>Sale Condition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>141.0</td>\n",
       "      <td>31770</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1960</td>\n",
       "      <td>1960</td>\n",
       "      <td>112.0</td>\n",
       "      <td>639.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1961</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1958</td>\n",
       "      <td>1958</td>\n",
       "      <td>108.0</td>\n",
       "      <td>923.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93.0</td>\n",
       "      <td>11160</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1968</td>\n",
       "      <td>1968</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1997</td>\n",
       "      <td>1998</td>\n",
       "      <td>0.0</td>\n",
       "      <td>791.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 274 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lot Frontage  Lot Area  Overall Qual  Overall Cond  Year Built  \\\n",
       "0         141.0     31770             6             5        1960   \n",
       "1          80.0     11622             5             6        1961   \n",
       "2          81.0     14267             6             6        1958   \n",
       "3          93.0     11160             7             5        1968   \n",
       "4          74.0     13830             5             5        1997   \n",
       "\n",
       "   Year Remod/Add  Mas Vnr Area  BsmtFin SF 1  BsmtFin SF 2  Bsmt Unf SF  ...  \\\n",
       "0            1960         112.0         639.0           0.0        441.0  ...   \n",
       "1            1961           0.0         468.0         144.0        270.0  ...   \n",
       "2            1958         108.0         923.0           0.0        406.0  ...   \n",
       "3            1968           0.0        1065.0           0.0       1045.0  ...   \n",
       "4            1998           0.0         791.0           0.0        137.0  ...   \n",
       "\n",
       "   Sale Type_ConLw  Sale Type_New  Sale Type_Oth  Sale Type_VWD  \\\n",
       "0                0              0              0              0   \n",
       "1                0              0              0              0   \n",
       "2                0              0              0              0   \n",
       "3                0              0              0              0   \n",
       "4                0              0              0              0   \n",
       "\n",
       "   Sale Type_WD   Sale Condition_AdjLand  Sale Condition_Alloca  \\\n",
       "0              1                       0                      0   \n",
       "1              1                       0                      0   \n",
       "2              1                       0                      0   \n",
       "3              1                       0                      0   \n",
       "4              1                       0                      0   \n",
       "\n",
       "   Sale Condition_Family  Sale Condition_Normal  Sale Condition_Partial  \n",
       "0                      0                      1                       0  \n",
       "1                      0                      1                       0  \n",
       "2                      0                      1                       0  \n",
       "3                      0                      1                       0  \n",
       "4                      0                      1                       0  \n",
       "\n",
       "[5 rows x 274 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Lot Frontage',\n",
       " 'Lot Area',\n",
       " 'Overall Qual',\n",
       " 'Overall Cond',\n",
       " 'Year Built',\n",
       " 'Year Remod/Add',\n",
       " 'Mas Vnr Area',\n",
       " 'BsmtFin SF 1',\n",
       " 'BsmtFin SF 2',\n",
       " 'Bsmt Unf SF',\n",
       " 'Total Bsmt SF',\n",
       " '1st Flr SF',\n",
       " '2nd Flr SF',\n",
       " 'Low Qual Fin SF',\n",
       " 'Gr Liv Area',\n",
       " 'Bsmt Full Bath',\n",
       " 'Bsmt Half Bath',\n",
       " 'Full Bath',\n",
       " 'Half Bath',\n",
       " 'Bedroom AbvGr',\n",
       " 'Kitchen AbvGr',\n",
       " 'TotRms AbvGrd',\n",
       " 'Fireplaces',\n",
       " 'Garage Yr Blt',\n",
       " 'Garage Cars',\n",
       " 'Garage Area',\n",
       " 'Wood Deck SF',\n",
       " 'Open Porch SF',\n",
       " 'Enclosed Porch',\n",
       " '3Ssn Porch',\n",
       " 'Screen Porch',\n",
       " 'Pool Area',\n",
       " 'Misc Val',\n",
       " 'Mo Sold',\n",
       " 'Yr Sold',\n",
       " 'SalePrice',\n",
       " 'MS SubClass_150',\n",
       " 'MS SubClass_160',\n",
       " 'MS SubClass_180',\n",
       " 'MS SubClass_190',\n",
       " 'MS SubClass_20',\n",
       " 'MS SubClass_30',\n",
       " 'MS SubClass_40',\n",
       " 'MS SubClass_45',\n",
       " 'MS SubClass_50',\n",
       " 'MS SubClass_60',\n",
       " 'MS SubClass_70',\n",
       " 'MS SubClass_75',\n",
       " 'MS SubClass_80',\n",
       " 'MS SubClass_85',\n",
       " 'MS SubClass_90',\n",
       " 'MS Zoning_C (all)',\n",
       " 'MS Zoning_FV',\n",
       " 'MS Zoning_I (all)',\n",
       " 'MS Zoning_RH',\n",
       " 'MS Zoning_RL',\n",
       " 'MS Zoning_RM',\n",
       " 'Street_Pave',\n",
       " 'Lot Shape_IR2',\n",
       " 'Lot Shape_IR3',\n",
       " 'Lot Shape_Reg',\n",
       " 'Land Contour_HLS',\n",
       " 'Land Contour_Low',\n",
       " 'Land Contour_Lvl',\n",
       " 'Utilities_NoSeWa',\n",
       " 'Utilities_NoSewr',\n",
       " 'Lot Config_CulDSac',\n",
       " 'Lot Config_FR2',\n",
       " 'Lot Config_FR3',\n",
       " 'Lot Config_Inside',\n",
       " 'Land Slope_Mod',\n",
       " 'Land Slope_Sev',\n",
       " 'Neighborhood_Blueste',\n",
       " 'Neighborhood_BrDale',\n",
       " 'Neighborhood_BrkSide',\n",
       " 'Neighborhood_ClearCr',\n",
       " 'Neighborhood_CollgCr',\n",
       " 'Neighborhood_Crawfor',\n",
       " 'Neighborhood_Edwards',\n",
       " 'Neighborhood_Gilbert',\n",
       " 'Neighborhood_Greens',\n",
       " 'Neighborhood_GrnHill',\n",
       " 'Neighborhood_IDOTRR',\n",
       " 'Neighborhood_Landmrk',\n",
       " 'Neighborhood_MeadowV',\n",
       " 'Neighborhood_Mitchel',\n",
       " 'Neighborhood_NAmes',\n",
       " 'Neighborhood_NPkVill',\n",
       " 'Neighborhood_NWAmes',\n",
       " 'Neighborhood_NoRidge',\n",
       " 'Neighborhood_NridgHt',\n",
       " 'Neighborhood_OldTown',\n",
       " 'Neighborhood_SWISU',\n",
       " 'Neighborhood_Sawyer',\n",
       " 'Neighborhood_SawyerW',\n",
       " 'Neighborhood_Somerst',\n",
       " 'Neighborhood_StoneBr',\n",
       " 'Neighborhood_Timber',\n",
       " 'Neighborhood_Veenker',\n",
       " 'Condition 1_Feedr',\n",
       " 'Condition 1_Norm',\n",
       " 'Condition 1_PosA',\n",
       " 'Condition 1_PosN',\n",
       " 'Condition 1_RRAe',\n",
       " 'Condition 1_RRAn',\n",
       " 'Condition 1_RRNe',\n",
       " 'Condition 1_RRNn',\n",
       " 'Condition 2_Feedr',\n",
       " 'Condition 2_Norm',\n",
       " 'Condition 2_PosA',\n",
       " 'Condition 2_PosN',\n",
       " 'Condition 2_RRAe',\n",
       " 'Condition 2_RRAn',\n",
       " 'Condition 2_RRNn',\n",
       " 'Bldg Type_2fmCon',\n",
       " 'Bldg Type_Duplex',\n",
       " 'Bldg Type_Twnhs',\n",
       " 'Bldg Type_TwnhsE',\n",
       " 'House Style_1.5Unf',\n",
       " 'House Style_1Story',\n",
       " 'House Style_2.5Fin',\n",
       " 'House Style_2.5Unf',\n",
       " 'House Style_2Story',\n",
       " 'House Style_SFoyer',\n",
       " 'House Style_SLvl',\n",
       " 'Roof Style_Gable',\n",
       " 'Roof Style_Gambrel',\n",
       " 'Roof Style_Hip',\n",
       " 'Roof Style_Mansard',\n",
       " 'Roof Style_Shed',\n",
       " 'Roof Matl_Membran',\n",
       " 'Roof Matl_Metal',\n",
       " 'Roof Matl_Roll',\n",
       " 'Roof Matl_Tar&Grv',\n",
       " 'Roof Matl_WdShake',\n",
       " 'Roof Matl_WdShngl',\n",
       " 'Exterior 1st_AsphShn',\n",
       " 'Exterior 1st_BrkComm',\n",
       " 'Exterior 1st_BrkFace',\n",
       " 'Exterior 1st_CBlock',\n",
       " 'Exterior 1st_CemntBd',\n",
       " 'Exterior 1st_HdBoard',\n",
       " 'Exterior 1st_ImStucc',\n",
       " 'Exterior 1st_MetalSd',\n",
       " 'Exterior 1st_Plywood',\n",
       " 'Exterior 1st_PreCast',\n",
       " 'Exterior 1st_Stone',\n",
       " 'Exterior 1st_Stucco',\n",
       " 'Exterior 1st_VinylSd',\n",
       " 'Exterior 1st_Wd Sdng',\n",
       " 'Exterior 1st_WdShing',\n",
       " 'Exterior 2nd_AsphShn',\n",
       " 'Exterior 2nd_Brk Cmn',\n",
       " 'Exterior 2nd_BrkFace',\n",
       " 'Exterior 2nd_CBlock',\n",
       " 'Exterior 2nd_CmentBd',\n",
       " 'Exterior 2nd_HdBoard',\n",
       " 'Exterior 2nd_ImStucc',\n",
       " 'Exterior 2nd_MetalSd',\n",
       " 'Exterior 2nd_Other',\n",
       " 'Exterior 2nd_Plywood',\n",
       " 'Exterior 2nd_PreCast',\n",
       " 'Exterior 2nd_Stone',\n",
       " 'Exterior 2nd_Stucco',\n",
       " 'Exterior 2nd_VinylSd',\n",
       " 'Exterior 2nd_Wd Sdng',\n",
       " 'Exterior 2nd_Wd Shng',\n",
       " 'Mas Vnr Type_BrkFace',\n",
       " 'Mas Vnr Type_CBlock',\n",
       " 'Mas Vnr Type_None',\n",
       " 'Mas Vnr Type_Stone',\n",
       " 'Exter Qual_Fa',\n",
       " 'Exter Qual_Gd',\n",
       " 'Exter Qual_TA',\n",
       " 'Exter Cond_Fa',\n",
       " 'Exter Cond_Gd',\n",
       " 'Exter Cond_Po',\n",
       " 'Exter Cond_TA',\n",
       " 'Foundation_CBlock',\n",
       " 'Foundation_PConc',\n",
       " 'Foundation_Slab',\n",
       " 'Foundation_Stone',\n",
       " 'Foundation_Wood',\n",
       " 'Bsmt Qual_Fa',\n",
       " 'Bsmt Qual_Gd',\n",
       " 'Bsmt Qual_None',\n",
       " 'Bsmt Qual_Po',\n",
       " 'Bsmt Qual_TA',\n",
       " 'Bsmt Cond_Fa',\n",
       " 'Bsmt Cond_Gd',\n",
       " 'Bsmt Cond_None',\n",
       " 'Bsmt Cond_Po',\n",
       " 'Bsmt Cond_TA',\n",
       " 'Bsmt Exposure_Gd',\n",
       " 'Bsmt Exposure_Mn',\n",
       " 'Bsmt Exposure_No',\n",
       " 'Bsmt Exposure_None',\n",
       " 'BsmtFin Type 1_BLQ',\n",
       " 'BsmtFin Type 1_GLQ',\n",
       " 'BsmtFin Type 1_LwQ',\n",
       " 'BsmtFin Type 1_None',\n",
       " 'BsmtFin Type 1_Rec',\n",
       " 'BsmtFin Type 1_Unf',\n",
       " 'BsmtFin Type 2_BLQ',\n",
       " 'BsmtFin Type 2_GLQ',\n",
       " 'BsmtFin Type 2_LwQ',\n",
       " 'BsmtFin Type 2_None',\n",
       " 'BsmtFin Type 2_Rec',\n",
       " 'BsmtFin Type 2_Unf',\n",
       " 'Heating_GasA',\n",
       " 'Heating_GasW',\n",
       " 'Heating_Grav',\n",
       " 'Heating_OthW',\n",
       " 'Heating_Wall',\n",
       " 'Heating QC_Fa',\n",
       " 'Heating QC_Gd',\n",
       " 'Heating QC_Po',\n",
       " 'Heating QC_TA',\n",
       " 'Central Air_Y',\n",
       " 'Electrical_FuseF',\n",
       " 'Electrical_FuseP',\n",
       " 'Electrical_Mix',\n",
       " 'Electrical_SBrkr',\n",
       " 'Kitchen Qual_Fa',\n",
       " 'Kitchen Qual_Gd',\n",
       " 'Kitchen Qual_Po',\n",
       " 'Kitchen Qual_TA',\n",
       " 'Functional_Maj2',\n",
       " 'Functional_Min1',\n",
       " 'Functional_Min2',\n",
       " 'Functional_Mod',\n",
       " 'Functional_Sal',\n",
       " 'Functional_Sev',\n",
       " 'Functional_Typ',\n",
       " 'Fireplace Qu_Fa',\n",
       " 'Fireplace Qu_Gd',\n",
       " 'Fireplace Qu_None',\n",
       " 'Fireplace Qu_Po',\n",
       " 'Fireplace Qu_TA',\n",
       " 'Garage Type_Attchd',\n",
       " 'Garage Type_Basment',\n",
       " 'Garage Type_BuiltIn',\n",
       " 'Garage Type_CarPort',\n",
       " 'Garage Type_Detchd',\n",
       " 'Garage Type_None',\n",
       " 'Garage Finish_None',\n",
       " 'Garage Finish_RFn',\n",
       " 'Garage Finish_Unf',\n",
       " 'Garage Qual_Fa',\n",
       " 'Garage Qual_Gd',\n",
       " 'Garage Qual_None',\n",
       " 'Garage Qual_Po',\n",
       " 'Garage Qual_TA',\n",
       " 'Garage Cond_Fa',\n",
       " 'Garage Cond_Gd',\n",
       " 'Garage Cond_None',\n",
       " 'Garage Cond_Po',\n",
       " 'Garage Cond_TA',\n",
       " 'Paved Drive_P',\n",
       " 'Paved Drive_Y',\n",
       " 'Sale Type_CWD',\n",
       " 'Sale Type_Con',\n",
       " 'Sale Type_ConLD',\n",
       " 'Sale Type_ConLI',\n",
       " 'Sale Type_ConLw',\n",
       " 'Sale Type_New',\n",
       " 'Sale Type_Oth',\n",
       " 'Sale Type_VWD',\n",
       " 'Sale Type_WD ',\n",
       " 'Sale Condition_AdjLand',\n",
       " 'Sale Condition_Alloca',\n",
       " 'Sale Condition_Family',\n",
       " 'Sale Condition_Normal',\n",
       " 'Sale Condition_Partial']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "X = df.drop(\"SalePrice\", axis=1)\n",
    "y = df[\"SalePrice\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "scaled_X_train = scaler.fit_transform(X_train)\n",
    "scaled_X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def eval_metrics(actual, pred):\n",
    "    rmse = np.sqrt(mean_squared_error(actual, pred))\n",
    "    mae = mean_absolute_error(actual, pred)\n",
    "    mse = mean_squared_error(actual, pred)\n",
    "    score = r2_score(actual, pred)\n",
    "    return print(\"r2_score:\", score, \"\\n\", \"mae:\", mae, \"\\n\", \"mse:\", mse, \"\\n\",\n",
    "                 \"rmse:\", rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lasso_model = Lasso(max_iter=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(max_iter=100000)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_model.fit(scaled_X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y_pred = lasso_model.predict(scaled_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.9169431989298119 \n",
      " mae: 14702.24680030985 \n",
      " mse: 495364082.2476377 \n",
      " rmse: 22256.776097351514\n"
     ]
    }
   ],
   "source": [
    "eval_metrics(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "cv_scores = cross_val_score(estimator=lasso_model, X=scaled_X_train, y=y_train, cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9180258720535323"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "cv_scores_neg_mean = cross_val_score(estimator=lasso_model, X=scaled_X_train,\n",
    "                                     y=y_train, scoring=\"neg_mean_squared_error\",\n",
    "                                     cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "531078629.98399365"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-cv_scores_neg_mean.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23045.143305781236"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(-cv_scores_neg_mean.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "alpha_values = np.linspace(0.1, 20, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lasso_cv_model = LassoCV(alphas=alpha_values, cv=5, max_iter=100000).fit(scaled_X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_cv_model.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "alpha_space = range(1, 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lasso_cv_model_new = LassoCV(alphas=alpha_space, cv=5, max_iter=100000).fit(scaled_X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "213"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_cv_model_new.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'LassoCV Alpha Error'}, xlabel='alpha', ylabel='error (or score)'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_yellowbrick = LassoCV(alphas=alpha_space, max_iter=100000)\n",
    "visualizer = AlphaSelection(model_yellowbrick)\n",
    "visualizer.fit(scaled_X_train, y_train)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Let's try our model with the best possible alpha value.\n",
    "\n",
    "lasso_model_ideal = Lasso(213, max_iter=100000).fit(scaled_X_train, y_train)\n",
    "y_pred_ideal = lasso_model_ideal.predict(scaled_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.9172336276905467 \n",
      " mae: 14563.288791576031 \n",
      " mse: 493631918.5395968 \n",
      " rmse: 22217.828843962157\n"
     ]
    }
   ],
   "source": [
    "eval_metrics(y_test, y_pred_ideal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "labels = list(map(lambda s: s.title(), X.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x7200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Feature Importances of 273 Features using Lasso'}, xlabel='coefficient value'>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viz = FeatureImportances(lasso_model_ideal, labels=labels, relative=False)\n",
    "visualizer = RadViz(size=(1080, 7200))\n",
    "\n",
    "viz.fit(scaled_X_train, y_train)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "188"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_coef = pd.DataFrame({\"features\": X.columns, \"coef\": lasso_model_ideal.coef_})\n",
    "significant_features = feature_coef.loc[feature_coef.coef != 0, :]\n",
    "len(significant_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "X_new = X[significant_features.features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2925, 188)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Linear Regression with Reduced Columns\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_new, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lm = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y_pred = lm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.9250720783054915 \n",
      " mae: 14758.67631738579 \n",
      " mse: 461117437.3953657 \n",
      " rmse: 21473.64518183547\n"
     ]
    }
   ],
   "source": [
    "eval_metrics(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Linear Regression with original columns\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lm = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y_pred = lm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.913202106116381 \n",
      " mae: 15745.198488637569 \n",
      " mse: 534166990.00557923 \n",
      " rmse: 23112.052916294113\n"
     ]
    }
   ],
   "source": [
    "eval_metrics(y_test, y_pred)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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