Project 2 - Linear-Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Required Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "filePath = 'data/AMES_Final_DF.csv'\n",
    "data = pd.read_csv(filePath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Pre Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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",
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       "      <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2925 entries, 0 to 2924\n",
      "Columns: 274 entries, Lot Frontage to Sale Condition_Partial\n",
      "dtypes: float64(11), int64(263)\n",
      "memory usage: 6.1 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2925, 274)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lot Frontage              0\n",
       "Lot Area                  0\n",
       "Overall Qual              0\n",
       "Overall Cond              0\n",
       "Year Built                0\n",
       "                         ..\n",
       "Sale Condition_AdjLand    0\n",
       "Sale Condition_Alloca     0\n",
       "Sale Condition_Family     0\n",
       "Sale Condition_Normal     0\n",
       "Sale Condition_Partial    0\n",
       "Length: 274, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Lot Frontage</th>\n",
       "      <td>2925.0</td>\n",
       "      <td>69.683099</td>\n",
       "      <td>21.344161</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lot Area</th>\n",
       "      <td>2925.0</td>\n",
       "      <td>10109.861538</td>\n",
       "      <td>7785.509739</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>7438.0</td>\n",
       "      <td>9430.0</td>\n",
       "      <td>11526.0</td>\n",
       "      <td>215245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overall Qual</th>\n",
       "      <td>2925.0</td>\n",
       "      <td>6.091624</td>\n",
       "      <td>1.406386</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overall Cond</th>\n",
       "      <td>2925.0</td>\n",
       "      <td>5.563761</td>\n",
       "      <td>1.112262</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year Built</th>\n",
       "      <td>2925.0</td>\n",
       "      <td>1971.323761</td>\n",
       "      <td>30.228826</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sale Condition_AdjLand</th>\n",
       "      <td>2925.0</td>\n",
       "      <td>0.004103</td>\n",
       "      <td>0.063931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sale Condition_Alloca</th>\n",
       "      <td>2925.0</td>\n",
       "      <td>0.007863</td>\n",
       "      <td>0.088341</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sale Condition_Family</th>\n",
       "      <td>2925.0</td>\n",
       "      <td>0.015726</td>\n",
       "      <td>0.124437</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sale Condition_Normal</th>\n",
       "      <td>2925.0</td>\n",
       "      <td>0.824615</td>\n",
       "      <td>0.380361</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sale Condition_Partial</th>\n",
       "      <td>2925.0</td>\n",
       "      <td>0.082735</td>\n",
       "      <td>0.275528</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>274 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count          mean          std     min     25%  \\\n",
       "Lot Frontage            2925.0     69.683099    21.344161     0.0    60.0   \n",
       "Lot Area                2925.0  10109.861538  7785.509739  1300.0  7438.0   \n",
       "Overall Qual            2925.0      6.091624     1.406386     1.0     5.0   \n",
       "Overall Cond            2925.0      5.563761     1.112262     1.0     5.0   \n",
       "Year Built              2925.0   1971.323761    30.228826  1872.0  1954.0   \n",
       "...                        ...           ...          ...     ...     ...   \n",
       "Sale Condition_AdjLand  2925.0      0.004103     0.063931     0.0     0.0   \n",
       "Sale Condition_Alloca   2925.0      0.007863     0.088341     0.0     0.0   \n",
       "Sale Condition_Family   2925.0      0.015726     0.124437     0.0     0.0   \n",
       "Sale Condition_Normal   2925.0      0.824615     0.380361     0.0     1.0   \n",
       "Sale Condition_Partial  2925.0      0.082735     0.275528     0.0     0.0   \n",
       "\n",
       "                           50%      75%       max  \n",
       "Lot Frontage              70.0     80.0     313.0  \n",
       "Lot Area                9430.0  11526.0  215245.0  \n",
       "Overall Qual               6.0      7.0      10.0  \n",
       "Overall Cond               5.0      6.0       9.0  \n",
       "Year Built              1973.0   2000.0    2010.0  \n",
       "...                        ...      ...       ...  \n",
       "Sale Condition_AdjLand     0.0      0.0       1.0  \n",
       "Sale Condition_Alloca      0.0      0.0       1.0  \n",
       "Sale Condition_Family      0.0      0.0       1.0  \n",
       "Sale Condition_Normal      1.0      1.0       1.0  \n",
       "Sale Condition_Partial     0.0      0.0       1.0  \n",
       "\n",
       "[274 rows x 8 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into X and y\n",
    "X = data.drop(['SalePrice'], axis=1)\n",
    "y = data['SalePrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scale and Transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "scalar = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "scalar_X_train = scalar.fit_transform(X_train)\n",
    "scalar_X_test = scalar.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use Elastic Net model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import ElasticNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "elastic = ElasticNet()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alpha and the L1 ratio for parameter grid of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "elastic_grid = {\n",
    "    'alpha': [0.1,1,5,50,100],\n",
    "    'l1_ratio': [.1,.5,.7,.9,.95, .99,1]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GridSearchCV object for the best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_model = GridSearchCV(estimator=elastic,\n",
    "                         param_grid=elastic_grid,\n",
    "                         scoring='neg_mean_squared_error',\n",
    "                         cv=5,\n",
    "                         verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 35 candidates, totalling 175 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 139422008252.86682, tolerance: 1355206692.5276785\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 165405536738.3521, tolerance: 1307913805.6588454\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 132401459345.16559, tolerance: 1415056940.006106\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 198623915720.8425, tolerance: 1438198040.088288\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 155332691866.21832, tolerance: 1345680018.2551236\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 323508201435.63495, tolerance: 1355206692.5276785\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 336948879951.1479, tolerance: 1307913805.6588454\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 357509968449.7157, tolerance: 1415056940.006106\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 388683651672.2898, tolerance: 1438198040.088288\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 292809907400.3429, tolerance: 1345680018.2551236\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 354244712120.81134, tolerance: 1355206692.5276785\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 363490863623.8176, tolerance: 1307913805.6588454\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 408539308800.7903, tolerance: 1415056940.006106\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 407083927692.87335, tolerance: 1438198040.088288\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 355296428563.47754, tolerance: 1345680018.2551236\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 349811975084.8048, tolerance: 1355206692.5276785\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 359107413789.82996, tolerance: 1307913805.6588454\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 406611694447.3524, tolerance: 1415056940.006106\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 402834204598.1302, tolerance: 1438198040.088288\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 368658319394.37335, tolerance: 1345680018.2551236\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 302531521066.7604, tolerance: 1355206692.5276785\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 312733820929.5268, tolerance: 1307913805.6588454\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 347531367248.8479, tolerance: 1415056940.006106\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 352595740834.97144, tolerance: 1438198040.088288\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 255889875128.09814, tolerance: 1345680018.2551236\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 170731423283.77548, tolerance: 1355206692.5276785\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 186853289260.37842, tolerance: 1307913805.6588454\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 196463479537.05804, tolerance: 1415056940.006106\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 212820336042.7754, tolerance: 1438198040.088288\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4848819954.143921, tolerance: 1345680018.2551236\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=ElasticNet(),\n",
       "             param_grid={'alpha': [0.1, 1, 5, 50, 100],\n",
       "                         'l1_ratio': [0.1, 0.5, 0.7, 0.9, 0.95, 0.99, 1]},\n",
       "             scoring='neg_mean_squared_error', verbose=1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_model.fit(scalar_X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 100, 'l1_ratio': 1}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_model.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = grid_model.predict(scalar_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAE = mean_absolute_error(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "MSE = mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "RMSE = np.sqrt(MSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15711.031312161256"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21305.768675822907"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RMSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "THE END"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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