linear_reg_car_prices.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 106,
   "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",
    "import pickle\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.linear_model import LinearRegression, Ridge, RidgeCV, Lasso, LassoCV\n",
    "from yellowbrick.regressor import PredictionError, ResidualsPlot, ManualAlphaSelection, AlphaSelection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_pickle(\"car_data_dummy.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15915, 144)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>price</th>\n",
       "      <th>km</th>\n",
       "      <th>previous_owners</th>\n",
       "      <th>inspection_new</th>\n",
       "      <th>nr_of_doors</th>\n",
       "      <th>nr_of_seats</th>\n",
       "      <th>gears</th>\n",
       "      <th>cc_Air conditioning</th>\n",
       "      <th>cc_Air suspension</th>\n",
       "      <th>cc_Armrest</th>\n",
       "      <th>...</th>\n",
       "      <th>body_color_Violet</th>\n",
       "      <th>body_color_White</th>\n",
       "      <th>body_color_Yellow</th>\n",
       "      <th>upholstery_Part/Full Leather</th>\n",
       "      <th>gearing_type_Manual</th>\n",
       "      <th>gearing_type_Semi-automatic</th>\n",
       "      <th>drive_chain_front</th>\n",
       "      <th>drive_chain_rear</th>\n",
       "      <th>fuel_new_Diesel</th>\n",
       "      <th>fuel_new_LPG/CNG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15770</td>\n",
       "      <td>56013.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14500</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14640</td>\n",
       "      <td>83450.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14500</td>\n",
       "      <td>73000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16790</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   price       km  previous_owners  inspection_new  nr_of_doors  nr_of_seats  \\\n",
       "0  15770  56013.0              2.0               1            5            5   \n",
       "1  14500  80000.0              1.0               0            3            4   \n",
       "2  14640  83450.0              1.0               0            4            4   \n",
       "3  14500  73000.0              1.0               0            3            4   \n",
       "4  16790  16200.0              1.0               1            5            5   \n",
       "\n",
       "   gears  cc_Air conditioning  cc_Air suspension  cc_Armrest  ...  \\\n",
       "0    6.0                    1                  0           1  ...   \n",
       "1    7.0                    1                  0           0  ...   \n",
       "2    6.0                    1                  0           0  ...   \n",
       "3    6.0                    0                  1           1  ...   \n",
       "4    6.0                    1                  0           1  ...   \n",
       "\n",
       "   body_color_Violet  body_color_White  body_color_Yellow  \\\n",
       "0                  0                 0                  0   \n",
       "1                  0                 0                  0   \n",
       "2                  0                 0                  0   \n",
       "3                  0                 0                  0   \n",
       "4                  0                 0                  0   \n",
       "\n",
       "   upholstery_Part/Full Leather  gearing_type_Manual  \\\n",
       "0                             0                    0   \n",
       "1                             0                    0   \n",
       "2                             0                    0   \n",
       "3                             0                    0   \n",
       "4                             0                    0   \n",
       "\n",
       "   gearing_type_Semi-automatic  drive_chain_front  drive_chain_rear  \\\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",
       "   fuel_new_Diesel  fuel_new_LPG/CNG  \n",
       "0                1                 0  \n",
       "1                0                 0  \n",
       "2                1                 0  \n",
       "3                1                 0  \n",
       "4                1                 0  \n",
       "\n",
       "[5 rows x 144 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.corr()[\"price\"].sort_values(ascending=False).head(10).plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fd0057144c0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/home/hawking/miniconda3/envs/regression/lib/python3.8/site-packages/seaborn/_core.py:1319: UserWarning: Vertical orientation ignored with only `x` specified.\n",
      "  warnings.warn(single_var_warning.format(\"Vertical\", \"x\"))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='price'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df.price, orient=\"v\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "X = df.drop([\"price\"], axis=1)\n",
    "y = df[\"price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15915, 143)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12732, 143)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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": 69,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lm = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>price</th>\n",
       "      <th>km</th>\n",
       "      <th>previous_owners</th>\n",
       "      <th>inspection_new</th>\n",
       "      <th>nr_of_doors</th>\n",
       "      <th>nr_of_seats</th>\n",
       "      <th>gears</th>\n",
       "      <th>cc_Air conditioning</th>\n",
       "      <th>cc_Air suspension</th>\n",
       "      <th>cc_Armrest</th>\n",
       "      <th>...</th>\n",
       "      <th>body_color_Violet</th>\n",
       "      <th>body_color_White</th>\n",
       "      <th>body_color_Yellow</th>\n",
       "      <th>upholstery_Part/Full Leather</th>\n",
       "      <th>gearing_type_Manual</th>\n",
       "      <th>gearing_type_Semi-automatic</th>\n",
       "      <th>drive_chain_front</th>\n",
       "      <th>drive_chain_rear</th>\n",
       "      <th>fuel_new_Diesel</th>\n",
       "      <th>fuel_new_LPG/CNG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15770</td>\n",
       "      <td>56013.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14500</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14640</td>\n",
       "      <td>83450.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14500</td>\n",
       "      <td>73000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16790</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   price       km  previous_owners  inspection_new  nr_of_doors  nr_of_seats  \\\n",
       "0  15770  56013.0              2.0               1            5            5   \n",
       "1  14500  80000.0              1.0               0            3            4   \n",
       "2  14640  83450.0              1.0               0            4            4   \n",
       "3  14500  73000.0              1.0               0            3            4   \n",
       "4  16790  16200.0              1.0               1            5            5   \n",
       "\n",
       "   gears  cc_Air conditioning  cc_Air suspension  cc_Armrest  ...  \\\n",
       "0    6.0                    1                  0           1  ...   \n",
       "1    7.0                    1                  0           0  ...   \n",
       "2    6.0                    1                  0           0  ...   \n",
       "3    6.0                    0                  1           1  ...   \n",
       "4    6.0                    1                  0           1  ...   \n",
       "\n",
       "   body_color_Violet  body_color_White  body_color_Yellow  \\\n",
       "0                  0                 0                  0   \n",
       "1                  0                 0                  0   \n",
       "2                  0                 0                  0   \n",
       "3                  0                 0                  0   \n",
       "4                  0                 0                  0   \n",
       "\n",
       "   upholstery_Part/Full Leather  gearing_type_Manual  \\\n",
       "0                             0                    0   \n",
       "1                             0                    0   \n",
       "2                             0                    0   \n",
       "3                             0                    0   \n",
       "4                             0                    0   \n",
       "\n",
       "   gearing_type_Semi-automatic  drive_chain_front  drive_chain_rear  \\\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",
       "   fuel_new_Diesel  fuel_new_LPG/CNG  \n",
       "0                1                 0  \n",
       "1                0                 0  \n",
       "2                1                 0  \n",
       "3                1                 0  \n",
       "4                1                 0  \n",
       "\n",
       "[5 rows x 144 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y_pred = lm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.8911819282388025 \n",
      " mae: 1724.4861527162286 \n",
      " mse: 5872648.048024018 \n",
      " rmse: 2423.3547094934365\n"
     ]
    }
   ],
   "source": [
    "eval_metrics(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fd005510760>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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':'Prediction Error for LinearRegression'}, xlabel='$y$', ylabel='$\\\\hat{y}$'>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualizer = PredictionError(lm)\n",
    "visualizer.fit(X_train, y_train)\n",
    "visualizer.score(X_test, y_test)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Prediction Error for LinearRegression'}, xlabel='$y$', ylabel='$\\\\hat{y}$'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualizer_residual = ResidualsPlot(lm)\n",
    "\n",
    "visualizer_residual.fit(X_train, y_train)\n",
    "visualizer_residual.score(X_test, y_test)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "cv_scores = cross_val_score(estimator=lm, X=X_train, y=y_train, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8833004671323662"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Let's do a Ridge regression now.\n",
    "\n",
    "ridge_model = Ridge(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(normalize=True)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y_pred_ridge = ridge_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.826594021345416 \n",
      " mae: 2128.4531254597186 \n",
      " mse: 9358301.112854883 \n",
      " rmse: 3059.1340462383932\n"
     ]
    }
   ],
   "source": [
    "eval_metrics(y_test, y_pred_ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "alpha_space = np.linspace(0.1, 20, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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':'Ridge Alpha Error'}, xlabel='alpha', ylabel='error (or score)'>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualizer = ManualAlphaSelection(\n",
    "    Ridge(normalize=True),\n",
    "    alphas=alpha_space,\n",
    "    cv=5\n",
    ")\n",
    "visualizer.fit(X_train, y_train)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "ridge_model_best_alpha = Ridge(0.01, normalize=True).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y_pred_best_alpha = ridge_model_best_alpha.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.8910528128065272 \n",
      " mae: 1722.8934676922102 \n",
      " mse: 5879616.095509599 \n",
      " rmse: 2424.791969532562\n"
     ]
    }
   ],
   "source": [
    "eval_metrics(y_test, y_pred_best_alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# As we can see here, Ridge regression was not the best fit for this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Let's try Lasso regression now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lasso_model = Lasso(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(normalize=True)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y_pred_lasso = lasso_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.8811119597668843 \n",
      " mae: 1783.8119116527605 \n",
      " mse: 6416099.88220145 \n",
      " rmse: 2533.00214808465\n"
     ]
    }
   ],
   "source": [
    "eval_metrics(y_test, y_pred_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lasso_cv_model = LassoCV(alphas=alpha_space, normalize=True, cv=5).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_cv_model.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "alpha_values = np.linspace(0.1, 20, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "model = LassoCV(alphas=alpha_values, normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualizer = AlphaSelection(model)\n",
    "visualizer.fit(X_train, y_train)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.8910341654854181 \n",
      " mae: 1721.8057428554287 \n",
      " mse: 5880622.446312729 \n",
      " rmse: 2424.999473466485\n"
     ]
    }
   ],
   "source": [
    "lasso_model = Lasso(0.1, normalize=True).fit(X_train, y_train)\n",
    "y_pred = lasso_model.predict(X_test)\n",
    "eval_metrics(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Saving our trained model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "pickle.dump(lm, open(\"saved_model\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Loading a saved model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "trained_model = pickle.load(open(\"saved_model\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Now let's test our saved trained model with new data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "new_data = {\n",
    "    \"hp\": 100,\n",
    "    \"age\": 3,\n",
    "    \"km\": 50000,\n",
    "    \"model\": \"A2\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "new_df = pd.DataFrame.from_dict([new_data])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "new_df = pd.get_dummies(new_df).reindex(columns = X.columns, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "pred = trained_model.predict(new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14860.88835509])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Learn more about streamlit to create an interface for a trained model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
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