Putting it all together.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dadda7e4",
   "metadata": {},
   "source": [
    "# 7. Putting it all together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cbd23d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6574adf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>35431.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>192714.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>84714.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>154365.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14043.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Black</td>\n",
       "      <td>35820.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32042.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "      <td>155144.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5716.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>66604.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>215883.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>248360.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12732.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Make Colour  Odometer (KM)  Doors    Price\n",
       "0     Honda  White        35431.0    4.0  15323.0\n",
       "1       BMW   Blue       192714.0    5.0  19943.0\n",
       "2     Honda  White        84714.0    4.0  28343.0\n",
       "3    Toyota  White       154365.0    4.0  13434.0\n",
       "4    Nissan   Blue       181577.0    3.0  14043.0\n",
       "..      ...    ...            ...    ...      ...\n",
       "995  Toyota  Black        35820.0    4.0  32042.0\n",
       "996     NaN  White       155144.0    3.0   5716.0\n",
       "997  Nissan   Blue        66604.0    4.0  31570.0\n",
       "998   Honda  White       215883.0    4.0   4001.0\n",
       "999  Toyota   Blue       248360.0    4.0  12732.0\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"car-sales-extended-missing-data.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eec7ada4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make              object\n",
       "Colour            object\n",
       "Odometer (KM)    float64\n",
       "Doors            float64\n",
       "Price            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e02334a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make             49\n",
       "Colour           50\n",
       "Odometer (KM)    50\n",
       "Doors            50\n",
       "Price            50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2a201108",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use a pipeline this time\n",
    "# pipeline = list which contains a tuple, which has a step name and action to take\n",
    "# Fill the missing data, convert the data to numbers, build the model\n",
    "# We need to change make and colour into numbers\n",
    "# Fill te missing values into odometer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "87a21212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22188417408787875"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting data ready\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "\n",
    "# Model\n",
    "import numpy as np\n",
    "np.random.seed(42)\n",
    "\n",
    "# Import data and drop rows with missing labels\n",
    "data = pd.read_csv(\"car-sales-extended-missing-data.csv\")\n",
    "data.dropna(subset=[\"Price\"], inplace=True)\n",
    "# data.isna().sum()\n",
    "\n",
    "# Define different features and transformers of pipeline\n",
    "categorical_features = [\"Make\", \"Colour\"]\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=\"missing\")),\n",
    "    (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\"))])\n",
    "  \n",
    "\n",
    "door_feature = [\"Doors\"]\n",
    "door_transformer = Pipeline(steps=[\n",
    "    (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=4))])\n",
    "\n",
    "numeric_features = [\"Odometer (KM)\"]\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    (\"imputer\", SimpleImputer(strategy=\"mean\"))\n",
    "])\n",
    "\n",
    "# Setup preprocessing steps (fill missing values, then convert to numbers)\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        (\"cat\", categorical_transformer, categorical_features),\n",
    "        (\"door\", door_transformer, door_feature),\n",
    "        (\"num\", numeric_transformer, numeric_features)])\n",
    "\n",
    "# Create a preprocessing and modelling pipeline\n",
    "model = Pipeline(steps=[(\"preprocessor\", preprocessor),\n",
    "                        (\"model\", RandomForestRegressor())])\n",
    "\n",
    "# Split data\n",
    "X = data.drop(\"Price\", axis=1)\n",
    "y = data[\"Price\"]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# Fit and score the model\n",
    "model.fit(X_train, y_train)\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3c3358a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>35431.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blue</td>\n",
       "      <td>192714.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>84714.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>154365.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>181577.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14043.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Black</td>\n",
       "      <td>35820.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32042.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "      <td>155144.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5716.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blue</td>\n",
       "      <td>66604.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Honda</td>\n",
       "      <td>White</td>\n",
       "      <td>215883.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>248360.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12732.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>950 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Make Colour  Odometer (KM)  Doors    Price\n",
       "0     Honda  White        35431.0    4.0  15323.0\n",
       "1       BMW   Blue       192714.0    5.0  19943.0\n",
       "2     Honda  White        84714.0    4.0  28343.0\n",
       "3    Toyota  White       154365.0    4.0  13434.0\n",
       "4    Nissan   Blue       181577.0    3.0  14043.0\n",
       "..      ...    ...            ...    ...      ...\n",
       "995  Toyota  Black        35820.0    4.0  32042.0\n",
       "996     NaN  White       155144.0    3.0   5716.0\n",
       "997  Nissan   Blue        66604.0    4.0  31570.0\n",
       "998   Honda  White       215883.0    4.0   4001.0\n",
       "999  Toyota   Blue       248360.0    4.0  12732.0\n",
       "\n",
       "[950 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3a90c8f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.2s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.2s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.2s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.2s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.2s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.3s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.3s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   3.8s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   3.2s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   3.2s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   3.1s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   3.2s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   3.2s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   3.5s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   4.0s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   3.7s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   4.1s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.2s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.2s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.3s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.3s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.3s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.3s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.3s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   3.1s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   3.2s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   3.5s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   3.0s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   3.3s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.8s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.9s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.6s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.9s\n",
      "[CV] END model__max_depth=None, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   3.4s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.3s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.4s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.3s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.3s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.1s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.7s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.4s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.1s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.4s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.1s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.1s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.4s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.1s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.1s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.1s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.1s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.1s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.2s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.3s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time=   0.1s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.8s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.5s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.4s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.4s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time=   2.0s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.1s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   1.9s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.0s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.1s\n",
      "[CV] END model__max_depth=5, model__max_features=auto, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time=   2.0s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('preprocessor',\n",
       "                                        ColumnTransformer(transformers=[('cat',\n",
       "                                                                         Pipeline(steps=[('imputer',\n",
       "                                                                                          SimpleImputer(fill_value='missing',\n",
       "                                                                                                        strategy='constant')),\n",
       "                                                                                         ('onehot',\n",
       "                                                                                          OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                                                         ['Make',\n",
       "                                                                          'Colour']),\n",
       "                                                                        ('door',\n",
       "                                                                         Pipeline(steps=[('imputer',\n",
       "                                                                                          SimpleImputer(fill_value=4,\n",
       "                                                                                                        strategy='constant'))]),\n",
       "                                                                         ['Doors']),\n",
       "                                                                        ('num',\n",
       "                                                                         Pipeline(steps=[('imputer',\n",
       "                                                                                          SimpleImputer())]),\n",
       "                                                                         ['Odometer '\n",
       "                                                                          '(KM)'])])),\n",
       "                                       ('model', RandomForestRegressor())]),\n",
       "             param_grid={'model__max_depth': [None, 5],\n",
       "                         'model__max_features': ['auto'],\n",
       "                         'model__min_samples_split': [2, 4],\n",
       "                         'model__n_estimators': [100, 1000],\n",
       "                         'preprocessor__num__imputer__strategy': ['mean',\n",
       "                                                                  'median']},\n",
       "             verbose=2)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use GSCV using pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "pipe_grid = {\n",
    "    \"preprocessor__num__imputer__strategy\": [\"mean\", \"median\"],\n",
    "    \"model__n_estimators\": [100, 1000],\n",
    "    \"model__max_depth\": [None, 5],\n",
    "    \"model__max_features\": [\"auto\"],\n",
    "    \"model__min_samples_split\": [2, 4]\n",
    "}\n",
    "\n",
    "gs_model = GridSearchCV(model, pipe_grid, cv=5, verbose=2)\n",
    "gs_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e95d0e4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method BaseEstimator.get_params of GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('preprocessor',\n",
       "                                        ColumnTransformer(transformers=[('cat',\n",
       "                                                                         Pipeline(steps=[('imputer',\n",
       "                                                                                          SimpleImputer(fill_value='missing',\n",
       "                                                                                                        strategy='constant')),\n",
       "                                                                                         ('onehot',\n",
       "                                                                                          OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                                                         ['Make',\n",
       "                                                                          'Colour']),\n",
       "                                                                        ('door',\n",
       "                                                                         Pipeline(steps=[('imputer',\n",
       "                                                                                          SimpleImputer(fill_value=4,\n",
       "                                                                                                        strategy='constant'))]),\n",
       "                                                                         ['Doors']),\n",
       "                                                                        ('num',\n",
       "                                                                         Pipeline(steps=[('imputer',\n",
       "                                                                                          SimpleImputer())]),\n",
       "                                                                         ['Odometer '\n",
       "                                                                          '(KM)'])])),\n",
       "                                       ('model', RandomForestRegressor())]),\n",
       "             param_grid={'model__max_depth': [None, 5],\n",
       "                         'model__max_features': ['auto'],\n",
       "                         'model__min_samples_split': [2, 4],\n",
       "                         'model__n_estimators': [100, 1000],\n",
       "                         'preprocessor__num__imputer__strategy': ['mean',\n",
       "                                                                  'median']},\n",
       "             verbose=2)>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_model.get_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b6449b55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3350919462971861"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1315890",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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