car_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "j1kmMrFkM963"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": 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       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "id": "3OUpk_s6A7A3",
    "outputId": "8f85dc6c-fea4-40e4-89af-e393a9b0b31c"
   },
   "outputs": [],
   "source": [
    "#from google.colab import files\n",
    "#uploaded=files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "nBwcho7JBCOh"
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv('car data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "OqOWnoW9BJI5",
    "outputId": "37f76f8f-9669-4c0e-bfe3-c1390191b0ec"
   },
   "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>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ritz</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
       "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
       "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
       "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
       "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
       "4    swift  2014           4.60           6.87       42450    Diesel   \n",
       "\n",
       "  Seller_Type Transmission  Owner  \n",
       "0      Dealer       Manual      0  \n",
       "1      Dealer       Manual      0  \n",
       "2      Dealer       Manual      0  \n",
       "3      Dealer       Manual      0  \n",
       "4      Dealer       Manual      0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "z2gsweqGBMZt",
    "outputId": "c069dfa1-da2a-4925-d6c3-4b0055b3eb1c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-lSlBTHgBWKp",
    "outputId": "566f6c48-b660-4319-b70e-ab0d51e4aeee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Dealer' 'Individual']\n",
      "['Petrol' 'Diesel' 'CNG']\n",
      "['Manual' 'Automatic']\n",
      "[0 1 3]\n"
     ]
    }
   ],
   "source": [
    "print(df['Seller_Type'].unique())\n",
    "print(df['Fuel_Type'].unique())\n",
    "print(df['Transmission'].unique())\n",
    "print(df['Owner'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sik_ZhBPBpam",
    "outputId": "06c6c2db-7ada-402c-b85c-9107b02b0f25"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Name         0\n",
       "Year             0\n",
       "Selling_Price    0\n",
       "Present_Price    0\n",
       "Kms_Driven       0\n",
       "Fuel_Type        0\n",
       "Seller_Type      0\n",
       "Transmission     0\n",
       "Owner            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##check missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "rp5maBNnCIuH",
    "outputId": "d3300c73-04b5-4ad8-e876-2bd81d08922d"
   },
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2013.627907</td>\n",
       "      <td>4.661296</td>\n",
       "      <td>7.628472</td>\n",
       "      <td>36947.205980</td>\n",
       "      <td>0.043189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.891554</td>\n",
       "      <td>5.082812</td>\n",
       "      <td>8.644115</td>\n",
       "      <td>38886.883882</td>\n",
       "      <td>0.247915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2003.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>48767.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>92.600000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Selling_Price  Present_Price     Kms_Driven       Owner\n",
       "count   301.000000     301.000000     301.000000     301.000000  301.000000\n",
       "mean   2013.627907       4.661296       7.628472   36947.205980    0.043189\n",
       "std       2.891554       5.082812       8.644115   38886.883882    0.247915\n",
       "min    2003.000000       0.100000       0.320000     500.000000    0.000000\n",
       "25%    2012.000000       0.900000       1.200000   15000.000000    0.000000\n",
       "50%    2014.000000       3.600000       6.400000   32000.000000    0.000000\n",
       "75%    2016.000000       6.000000       9.900000   48767.000000    0.000000\n",
       "max    2018.000000      35.000000      92.600000  500000.000000    3.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "VV33_D5NCSKh"
   },
   "outputs": [],
   "source": [
    "final_dataset=df[['Year','Selling_Price','Present_Price','Kms_Driven','Fuel_Type','Seller_Type','Transmission','Owner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "NSWUUQFeCX5h",
    "outputId": "166e6446-f283-42a9-ee91-9745936aa96c"
   },
   "outputs": [
    {
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       "      <td>Diesel</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  \n",
       "0       Manual      0  \n",
       "1       Manual      0  \n",
       "2       Manual      0  \n",
       "3       Manual      0  \n",
       "4       Manual      0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "P6zt_xsmCauu",
    "outputId": "d4625893-f3fb-4951-f37c-2f8f933354a5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 8)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "UheDC5nBCgPS"
   },
   "outputs": [],
   "source": [
    "final_dataset['Age']=2022-final_dataset['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "iPxZF3ClDA-I",
    "outputId": "51409982-db36-4730-d62f-732c33b9472c"
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    {
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      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  Age  \n",
       "0       Manual      0    8  \n",
       "1       Manual      0    9  \n",
       "2       Manual      0    5  \n",
       "3       Manual      0   11  \n",
       "4       Manual      0    8  "
      ]
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "final_dataset.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "2p6_cNyIDFd0"
   },
   "outputs": [],
   "source": [
    "final_dataset.drop(['Year'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
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    "id": "8etAm5teDgUu",
    "outputId": "6ed4eff8-c80b-4297-93aa-3b6c9d604844"
   },
   "outputs": [
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      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0           3.35           5.59       27000    Petrol      Dealer   \n",
       "1           4.75           9.54       43000    Diesel      Dealer   \n",
       "2           7.25           9.85        6900    Petrol      Dealer   \n",
       "3           2.85           4.15        5200    Petrol      Dealer   \n",
       "4           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  Age  \n",
       "0       Manual      0    8  \n",
       "1       Manual      0    9  \n",
       "2       Manual      0    5  \n",
       "3       Manual      0   11  \n",
       "4       Manual      0    8  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "dJZ2cO3sDinV"
   },
   "outputs": [],
   "source": [
    "final_dataset=pd.get_dummies(final_dataset, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
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    "id": "pKm0SidtD8i4",
    "outputId": "6866b820-32f3-4809-f205-333c57028fb2"
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       "      <th>2</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven  Owner  Age  Fuel_Type_Diesel  \\\n",
       "0           3.35           5.59       27000      0    8                 0   \n",
       "1           4.75           9.54       43000      0    9                 1   \n",
       "2           7.25           9.85        6900      0    5                 0   \n",
       "3           2.85           4.15        5200      0   11                 0   \n",
       "4           4.60           6.87       42450      0    8                 1   \n",
       "\n",
       "   Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
       "0                 1                       0                    1  \n",
       "1                 0                       0                    1  \n",
       "2                 1                       0                    1  \n",
       "3                 1                       0                    1  \n",
       "4                 0                       0                    1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bdKYTqBAD_sQ",
    "outputId": "9da96b3e-bba3-4b72-fcb6-79d37cb5c5a9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 332
    },
    "id": "G36eS345EGcV",
    "outputId": "52a2653f-0817-4b3b-8b47-0772970b6ad1"
   },
   "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>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Selling_Price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.878983</td>\n",
       "      <td>0.029187</td>\n",
       "      <td>-0.088344</td>\n",
       "      <td>-0.236141</td>\n",
       "      <td>0.552339</td>\n",
       "      <td>-0.540571</td>\n",
       "      <td>-0.550724</td>\n",
       "      <td>-0.367128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Price</th>\n",
       "      <td>0.878983</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.203647</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>0.047584</td>\n",
       "      <td>0.473306</td>\n",
       "      <td>-0.465244</td>\n",
       "      <td>-0.512030</td>\n",
       "      <td>-0.348715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kms_Driven</th>\n",
       "      <td>0.029187</td>\n",
       "      <td>0.203647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089216</td>\n",
       "      <td>0.524342</td>\n",
       "      <td>0.172515</td>\n",
       "      <td>-0.172874</td>\n",
       "      <td>-0.101419</td>\n",
       "      <td>-0.162510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Owner</th>\n",
       "      <td>-0.088344</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>0.089216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182104</td>\n",
       "      <td>-0.053469</td>\n",
       "      <td>0.055687</td>\n",
       "      <td>0.124269</td>\n",
       "      <td>-0.050316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.236141</td>\n",
       "      <td>0.047584</td>\n",
       "      <td>0.524342</td>\n",
       "      <td>0.182104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.064315</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>0.039896</td>\n",
       "      <td>-0.000394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <td>0.552339</td>\n",
       "      <td>0.473306</td>\n",
       "      <td>0.172515</td>\n",
       "      <td>-0.053469</td>\n",
       "      <td>-0.064315</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.979648</td>\n",
       "      <td>-0.350467</td>\n",
       "      <td>-0.098643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <td>-0.540571</td>\n",
       "      <td>-0.465244</td>\n",
       "      <td>-0.172874</td>\n",
       "      <td>0.055687</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>-0.979648</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.358321</td>\n",
       "      <td>0.091013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <td>-0.550724</td>\n",
       "      <td>-0.512030</td>\n",
       "      <td>-0.101419</td>\n",
       "      <td>0.124269</td>\n",
       "      <td>0.039896</td>\n",
       "      <td>-0.350467</td>\n",
       "      <td>0.358321</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.063240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transmission_Manual</th>\n",
       "      <td>-0.367128</td>\n",
       "      <td>-0.348715</td>\n",
       "      <td>-0.162510</td>\n",
       "      <td>-0.050316</td>\n",
       "      <td>-0.000394</td>\n",
       "      <td>-0.098643</td>\n",
       "      <td>0.091013</td>\n",
       "      <td>0.063240</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Selling_Price  Present_Price  Kms_Driven     Owner  \\\n",
       "Selling_Price                1.000000       0.878983    0.029187 -0.088344   \n",
       "Present_Price                0.878983       1.000000    0.203647  0.008057   \n",
       "Kms_Driven                   0.029187       0.203647    1.000000  0.089216   \n",
       "Owner                       -0.088344       0.008057    0.089216  1.000000   \n",
       "Age                         -0.236141       0.047584    0.524342  0.182104   \n",
       "Fuel_Type_Diesel             0.552339       0.473306    0.172515 -0.053469   \n",
       "Fuel_Type_Petrol            -0.540571      -0.465244   -0.172874  0.055687   \n",
       "Seller_Type_Individual      -0.550724      -0.512030   -0.101419  0.124269   \n",
       "Transmission_Manual         -0.367128      -0.348715   -0.162510 -0.050316   \n",
       "\n",
       "                             Age  Fuel_Type_Diesel  Fuel_Type_Petrol  \\\n",
       "Selling_Price          -0.236141          0.552339         -0.540571   \n",
       "Present_Price           0.047584          0.473306         -0.465244   \n",
       "Kms_Driven              0.524342          0.172515         -0.172874   \n",
       "Owner                   0.182104         -0.053469          0.055687   \n",
       "Age                     1.000000         -0.064315          0.059959   \n",
       "Fuel_Type_Diesel       -0.064315          1.000000         -0.979648   \n",
       "Fuel_Type_Petrol        0.059959         -0.979648          1.000000   \n",
       "Seller_Type_Individual  0.039896         -0.350467          0.358321   \n",
       "Transmission_Manual    -0.000394         -0.098643          0.091013   \n",
       "\n",
       "                        Seller_Type_Individual  Transmission_Manual  \n",
       "Selling_Price                        -0.550724            -0.367128  \n",
       "Present_Price                        -0.512030            -0.348715  \n",
       "Kms_Driven                           -0.101419            -0.162510  \n",
       "Owner                                 0.124269            -0.050316  \n",
       "Age                                   0.039896            -0.000394  \n",
       "Fuel_Type_Diesel                     -0.350467            -0.098643  \n",
       "Fuel_Type_Petrol                      0.358321             0.091013  \n",
       "Seller_Type_Individual                1.000000             0.063240  \n",
       "Transmission_Manual                   0.063240             1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "QW33ajy3E4Kr"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "Ld-0K4RUE_BD",
    "outputId": "a4710a76-eafb-4c89-ff2f-da256de405c8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f159fcaa7c0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1620x1620 with 90 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(final_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "Hj3aRJjpGWbK",
    "outputId": "eac62be8-7a0d-40e2-eb50-e849479770ac"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#get correlations of each features in dataset\n",
    "corrmat = final_dataset.corr()\n",
    "top_corr_features = corrmat.index\n",
    "plt.figure(figsize=(20,20))\n",
    "#plot heat map\n",
    "g=sns.heatmap(final_dataset[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "4GWqL3jJGevr"
   },
   "outputs": [],
   "source": [
    "X=final_dataset.iloc[:,1:]\n",
    "y=final_dataset.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ry0Lw8o8GoU5",
    "outputId": "597e938a-8260-483c-fa2f-5dd4959e4303"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Present_Price  Kms_Driven  Owner  Age  Fuel_Type_Diesel  \\\n",
      "0             5.59       27000      0    8                 0   \n",
      "1             9.54       43000      0    9                 1   \n",
      "2             9.85        6900      0    5                 0   \n",
      "3             4.15        5200      0   11                 0   \n",
      "4             6.87       42450      0    8                 1   \n",
      "..             ...         ...    ...  ...               ...   \n",
      "296          11.60       33988      0    6                 1   \n",
      "297           5.90       60000      0    7                 0   \n",
      "298          11.00       87934      0   13                 0   \n",
      "299          12.50        9000      0    5                 1   \n",
      "300           5.90        5464      0    6                 0   \n",
      "\n",
      "     Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
      "0                   1                       0                    1  \n",
      "1                   0                       0                    1  \n",
      "2                   1                       0                    1  \n",
      "3                   1                       0                    1  \n",
      "4                   0                       0                    1  \n",
      "..                ...                     ...                  ...  \n",
      "296                 0                       0                    1  \n",
      "297                 1                       0                    1  \n",
      "298                 1                       0                    1  \n",
      "299                 0                       0                    1  \n",
      "300                 1                       0                    1  \n",
      "\n",
      "[301 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JLZ8nrRnHAwY",
    "outputId": "e8ff12ae-4f51-4584-8f82-479a5628d497"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       3.35\n",
      "1       4.75\n",
      "2       7.25\n",
      "3       2.85\n",
      "4       4.60\n",
      "       ...  \n",
      "296     9.50\n",
      "297     4.00\n",
      "298     3.35\n",
      "299    11.50\n",
      "300     5.30\n",
      "Name: Selling_Price, Length: 301, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J3kJBa67IR-E",
    "outputId": "e5494388-00dc-465d-b4d3-91cd5f4fd9d7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.35\n",
       "1    4.75\n",
       "2    7.25\n",
       "3    2.85\n",
       "4    4.60\n",
       "Name: Selling_Price, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J832HA_GInlm",
    "outputId": "7d36271d-cff9-41b0-c507-9a300d0fc931"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Feature importance\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "model=ExtraTreesRegressor()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "87GRvp73JD3Z",
    "outputId": "e61eb34c-08f1-4467-c604-d560e924d132"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.37135528 0.04680392 0.0004834  0.07225597 0.22401699 0.01101053\n",
      " 0.12764706 0.14642684]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "UsdMOYJGJP51",
    "outputId": "4712cf69-e0ea-4ea9-fda6-b9d50b13bd56"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "feature_imp=pd.Series(model.feature_importances_, index=X.columns)\n",
    "feature_imp.nsmallest(8).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "4gcAZupssJxg"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(210, 8)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91, 8)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(210,)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "GhO_i5fgQrzy"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regressor=RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "9_OKrQuWQyqL"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cOgi4CfeQ8hy",
    "outputId": "530eea7a-e74f-4ad6-d58c-31ab14c26b84"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200]\n"
     ]
    }
   ],
   "source": [
    "##Hyper parameters\n",
    "import numpy as np\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "print(n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "3w4mBRPrRAkB"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "22Ji98XNRNvG"
   },
   "outputs": [],
   "source": [
    " #Randomized Search CV\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "# max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QyUNe_gFRiwT",
    "outputId": "4cb78cf7-aedf-4c7f-8c57-3d3ebeb94116"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200], 'max_features': ['auto', 'sqrt'], 'max_depth': [5, 10, 15, 20, 25, 30], 'min_samples_split': [2, 5, 10, 15, 100], 'min_samples_leaf': [1, 2, 5, 10]}\n"
     ]
    }
   ],
   "source": [
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}\n",
    "\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "5rmm90i_RrLF"
   },
   "outputs": [],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "# First create the base model to tune\n",
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "bBYTrv6hRvDk"
   },
   "outputs": [],
   "source": [
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A8ZDTuJbR3ci",
    "outputId": "30f95563-e182-4259-d817-b567deb31eda"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.2s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.5s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.6s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.6s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.6s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.6s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.6s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.0s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.5s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.4s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.5s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.4s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.6s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.0s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.0s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lLJFFVgoR7K6",
    "outputId": "9f34c7c0-258c-44d7-c9a7-6d6a82f7d0e6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 1000,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 25}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nFFMJ4xqSNUj",
    "outputId": "36433039-ca37-47fd-96f4-18a1f3e7677a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.090905462700716"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "_6yPJ9InSP4D"
   },
   "outputs": [],
   "source": [
    "predictions=rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 352
    },
    "id": "stu75vzjSSlA",
    "outputId": "85b18342-1970-434e-9864-65438098f7a2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/josin/anaconda3/envs/carprediction/lib/python3.8/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Selling_Price', ylabel='Density'>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "YY9fmTpzSVPl",
    "outputId": "5a0e5193-2aeb-4d7f-df43-fdbec76e9eb8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f1591614880>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "oazluVjZSYma"
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LGlqwZOpSbnD",
    "outputId": "94db70f8-2d31-4434-900f-36b0ae3b0266"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.8834829670329691\n",
      "MSE: 4.026878295181323\n",
      "RMSE: 2.006708323394639\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "Jsfq2CSsS1nU"
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "# open a file, where you ant to store the data\n",
    "file = open('random_forest_regression_model.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(rf_random, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RziWNDQ8TEOy"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "car_prediction.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}