ML Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder \n",
    "from sklearn.metrics import * \n",
    "from sklearn.ensemble import RandomForestRegressor,BaggingRegressor,AdaBoostRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.linear_model import LinearRegression,Ridge,Lasso\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</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>km_driven</th>\n",
       "      <th>fuel</th>\n",
       "      <th>seller_type</th>\n",
       "      <th>transmission</th>\n",
       "      <th>owner</th>\n",
       "      <th>brands</th>\n",
       "      <th>models</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>Maruti</td>\n",
       "      <td>Maruti 800 AC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>Maruti</td>\n",
       "      <td>Maruti Wagon R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>Hyundai</td>\n",
       "      <td>Hyundai Verna 1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>Datsun</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>141000.0</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second Owner</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  selling_price  km_driven    fuel seller_type transmission  \\\n",
       "0  2007        60000.0    70000.0  Petrol  Individual       Manual   \n",
       "1  2007       135000.0    50000.0  Petrol  Individual       Manual   \n",
       "2  2012       600000.0   100000.0  Diesel  Individual       Manual   \n",
       "3  2017       250000.0    46000.0  Petrol  Individual       Manual   \n",
       "4  2014       450000.0   141000.0  Diesel  Individual       Manual   \n",
       "\n",
       "          owner   brands             models  \n",
       "0   First Owner   Maruti      Maruti 800 AC  \n",
       "1   First Owner   Maruti     Maruti Wagon R  \n",
       "2   First Owner  Hyundai  Hyundai Verna 1.6  \n",
       "3   First Owner   Datsun              Other  \n",
       "4  Second Owner    Honda              Other  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"D:\\PYTHON1\\Capstone Project\\Car Details 2.csv\")\n",
    "df.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Removing Irrelevant columns :-**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "models\n",
       "Other                    2565\n",
       "Maruti Wagon R            139\n",
       "Maruti Swift Dzire        120\n",
       "Hyundai Grand i10          75\n",
       "Maruti Alto 800            75\n",
       "Toyota Innova 2.5          72\n",
       "Hyundai Santro Xing        57\n",
       "Maruti Alto LXi            57\n",
       "Hyundai Verna 1.6          56\n",
       "Tata Indica Vista          56\n",
       "Maruti Swift VDI           55\n",
       "Maruti Alto K10            48\n",
       "Maruti Alto LX             38\n",
       "Ford Figo Diesel           36\n",
       "Hyundai EON Era            34\n",
       "Chevrolet Beat Diesel      32\n",
       "Maruti 800 AC              32\n",
       "Ford EcoSport 1.5          30\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['models'].value_counts() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It seems like there is a large number of entries labeled as \"Other,\" which could make this column less useful for certain types of analysis. \n",
    "- High frequency of \"Other\" category: With 2565 entries labeled as \"Other,\" this category dominates the data, making it less useful for distinguishing between specific models.\n",
    "- Long tail of model names: There are many models with very few entries, which might not provide statistically significant insights for each individual model.\n",
    "- In summary, whether the models column is irrelevant depends on your specific analysis goals. If model-level granularity is not crucial, you might consider it less relevant, especially given the high frequency of the \"Other\" category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>km_driven</th>\n",
       "      <th>fuel</th>\n",
       "      <th>seller_type</th>\n",
       "      <th>transmission</th>\n",
       "      <th>owner</th>\n",
       "      <th>brands</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>Maruti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>Maruti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>Hyundai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>Datsun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>141000.0</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second Owner</td>\n",
       "      <td>Honda</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  selling_price  km_driven    fuel seller_type transmission  \\\n",
       "0  2007        60000.0    70000.0  Petrol  Individual       Manual   \n",
       "1  2007       135000.0    50000.0  Petrol  Individual       Manual   \n",
       "2  2012       600000.0   100000.0  Diesel  Individual       Manual   \n",
       "3  2017       250000.0    46000.0  Petrol  Individual       Manual   \n",
       "4  2014       450000.0   141000.0  Diesel  Individual       Manual   \n",
       "\n",
       "          owner   brands  \n",
       "0   First Owner   Maruti  \n",
       "1   First Owner   Maruti  \n",
       "2   First Owner  Hyundai  \n",
       "3   First Owner   Datsun  \n",
       "4  Second Owner    Honda  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('models', axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Data Preprocessing :-**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Checking Null Values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Checking Duplicates**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop_duplicates() \n",
    "df.duplicated().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3550, 8)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Checking the Data types**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year               int64\n",
       "selling_price    float64\n",
       "km_driven        float64\n",
       "fuel              object\n",
       "seller_type       object\n",
       "transmission      object\n",
       "owner             object\n",
       "brands            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fuel\n",
       "Diesel      1789\n",
       "Petrol      1701\n",
       "CNG           37\n",
       "LPG           22\n",
       "Electric       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fuel'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seller_type\n",
       "Individual          2805\n",
       "Dealer               712\n",
       "Trustmark Dealer      33\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['seller_type'].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transmission\n",
       "Manual       3238\n",
       "Automatic     312\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['transmission'].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "owner\n",
       "First Owner             2199\n",
       "Second Owner             970\n",
       "Third Owner              289\n",
       "Fourth & Above Owner      75\n",
       "Test Drive Car            17\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['owner'].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "brands\n",
       "Maruti           1057\n",
       "Hyundai           631\n",
       "Mahindra          324\n",
       "Tata              308\n",
       "Ford              220\n",
       "Honda             216\n",
       "Toyota            170\n",
       "Chevrolet         151\n",
       "Renault           108\n",
       "Volkswagen         93\n",
       "Nissan             52\n",
       "Skoda              49\n",
       "Others             33\n",
       "Fiat               32\n",
       "Audi               31\n",
       "Datsun             29\n",
       "BMW                25\n",
       "Mercedes-Benz      21\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['brands'].value_counts() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4. Separating Categorical and Numerical Features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['fuel', 'seller_type', 'transmission', 'owner', 'brands'], dtype='object')\n",
      "Index(['year', 'selling_price', 'km_driven'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "cat_cols = df.dtypes[df.dtypes == 'object'].index\n",
    "num_cols = df.dtypes[df.dtypes != 'object'].index\n",
    "print(cat_cols)\n",
    "print(num_cols) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. Encoding Object Data Type Columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "lb = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in cat_cols:\n",
    "    df[i] = lb.fit_transform(df[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year               int64\n",
       "selling_price    float64\n",
       "km_driven        float64\n",
       "fuel               int32\n",
       "seller_type        int32\n",
       "transmission       int32\n",
       "owner              int32\n",
       "brands             int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>km_driven</th>\n",
       "      <th>fuel</th>\n",
       "      <th>seller_type</th>\n",
       "      <th>transmission</th>\n",
       "      <th>owner</th>\n",
       "      <th>brands</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>141000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  selling_price  km_driven  fuel  seller_type  transmission  owner  \\\n",
       "0  2007        60000.0    70000.0     4            1             1      0   \n",
       "1  2007       135000.0    50000.0     4            1             1      0   \n",
       "2  2012       600000.0   100000.0     1            1             1      0   \n",
       "3  2017       250000.0    46000.0     4            1             1      0   \n",
       "4  2014       450000.0   141000.0     1            1             1      2   \n",
       "\n",
       "   brands  \n",
       "0       9  \n",
       "1       9  \n",
       "2       7  \n",
       "3       3  \n",
       "4       6  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fuel\n",
       "1    1789\n",
       "4    1701\n",
       "0      37\n",
       "3      22\n",
       "2       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fuel'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seller_type\n",
       "1    2805\n",
       "0     712\n",
       "2      33\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['seller_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transmission\n",
       "1    3238\n",
       "0     312\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['transmission'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "owner\n",
       "0    2199\n",
       "2     970\n",
       "4     289\n",
       "1      75\n",
       "3      17\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['owner'].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "brands\n",
       "9     1057\n",
       "7      631\n",
       "8      324\n",
       "15     308\n",
       "5      220\n",
       "6      216\n",
       "16     170\n",
       "2      151\n",
       "13     108\n",
       "17      93\n",
       "11      52\n",
       "14      49\n",
       "12      33\n",
       "4       32\n",
       "0       31\n",
       "3       29\n",
       "1       25\n",
       "10      21\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['brands'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6. Separating Data into x(independent) and y(dependent) features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3550, 7)\n",
      "(3550,)\n"
     ]
    }
   ],
   "source": [
    "x = df.drop('selling_price', axis=1)\n",
    "y = df['selling_price']\n",
    "\n",
    "print(x.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**7. Train-Test Split on Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3017, 7)\n",
      "(533, 7)\n",
      "(3017,)\n",
      "(533,)\n"
     ]
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.15, random_state=81)\n",
    "\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8. Model Evaluation Function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_model(model, x_train,y_train,x_test,y_test,mname):\n",
    "    model.fit(x_train,y_train)\n",
    "    ypred = model.predict(x_test)\n",
    "    train_r2 = model.score(x_train,y_train)\n",
    "    test_r2 = model.score(x_test,y_test)\n",
    "    test_mae = mean_absolute_error(y_test,ypred)\n",
    "    test_mse = mean_squared_error(y_test,ypred)\n",
    "    test_rmse = np.sqrt(test_mse)\n",
    "    data = {'Train_R2':train_r2,'Test_R2':test_r2,'Test_MAE':test_mae,\n",
    "            'Test_MSE':test_mse, 'Test_RMSE':test_rmse}\n",
    "    res = pd.DataFrame(data,index=[mname])\n",
    "    return res "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**9. Comprehensive Evaluation and Comparison of Regression Models**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1) LinReg**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Train_R2</th>\n",
       "      <th>Test_R2</th>\n",
       "      <th>Test_MAE</th>\n",
       "      <th>Test_MSE</th>\n",
       "      <th>Test_RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Regression</th>\n",
       "      <td>0.503125</td>\n",
       "      <td>0.568589</td>\n",
       "      <td>190590.64827</td>\n",
       "      <td>8.423765e+10</td>\n",
       "      <td>290237.226303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Train_R2   Test_R2      Test_MAE      Test_MSE  \\\n",
       "Linear Regression  0.503125  0.568589  190590.64827  8.423765e+10   \n",
       "\n",
       "                       Test_RMSE  \n",
       "Linear Regression  290237.226303  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr1 = LinearRegression()\n",
    "lr1_res = eval_model(lr1, x_train,y_train,x_test,y_test,'Linear Regression')\n",
    "lr1_res "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2) DT Reg**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Train_R2</th>\n",
       "      <th>Test_R2</th>\n",
       "      <th>Test_MAE</th>\n",
       "      <th>Test_MSE</th>\n",
       "      <th>Test_RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision Tree Regression</th>\n",
       "      <td>0.762127</td>\n",
       "      <td>0.746431</td>\n",
       "      <td>140764.483802</td>\n",
       "      <td>4.951212e+10</td>\n",
       "      <td>222513.185062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Train_R2   Test_R2       Test_MAE      Test_MSE  \\\n",
       "Decision Tree Regression  0.762127  0.746431  140764.483802  4.951212e+10   \n",
       "\n",
       "                              Test_RMSE  \n",
       "Decision Tree Regression  222513.185062  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt1 = DecisionTreeRegressor(criterion= 'squared_error', max_depth= 8, max_features= None, min_samples_leaf= 7, min_samples_split= 2, splitter= 'best')\n",
    "dt1_res = eval_model(dt1, x_train,y_train,x_test,y_test,'Decision Tree Regression')\n",
    "dt1_res "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3) RF Reg**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Train_R2</th>\n",
       "      <th>Test_R2</th>\n",
       "      <th>Test_MAE</th>\n",
       "      <th>Test_MSE</th>\n",
       "      <th>Test_RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Random Forest Regression</th>\n",
       "      <td>0.863412</td>\n",
       "      <td>0.815202</td>\n",
       "      <td>124742.234526</td>\n",
       "      <td>3.608387e+10</td>\n",
       "      <td>189957.552089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Train_R2   Test_R2       Test_MAE      Test_MSE  \\\n",
       "Random Forest Regression  0.863412  0.815202  124742.234526  3.608387e+10   \n",
       "\n",
       "                              Test_RMSE  \n",
       "Random Forest Regression  189957.552089  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf1 = RandomForestRegressor(bootstrap=True,max_depth=9,min_samples_leaf=1,min_samples_split=5,n_estimators=300) \n",
    "# rf1 = RandomForestRegressor(bootstrap=True,max_depth=10,min_samples_leaf=1,min_samples_split=5,n_estimators=500) \n",
    "rf1_res = eval_model(rf1, x_train,y_train,x_test,y_test,'Random Forest Regression')\n",
    "rf1_res "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4) Ridge**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Train_R2</th>\n",
       "      <th>Test_R2</th>\n",
       "      <th>Test_MAE</th>\n",
       "      <th>Test_MSE</th>\n",
       "      <th>Test_RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ridge</th>\n",
       "      <td>0.503122</td>\n",
       "      <td>0.568242</td>\n",
       "      <td>190619.744606</td>\n",
       "      <td>8.430551e+10</td>\n",
       "      <td>290354.10699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Train_R2   Test_R2       Test_MAE      Test_MSE     Test_RMSE\n",
       "Ridge  0.503122  0.568242  190619.744606  8.430551e+10  290354.10699"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rid1 = Ridge()\n",
    "rid1_res = eval_model(rid1, x_train,y_train,x_test,y_test,'Ridge')\n",
    "rid1_res "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5) Bagging Reg**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Train_R2</th>\n",
       "      <th>Test_R2</th>\n",
       "      <th>Test_MAE</th>\n",
       "      <th>Test_MSE</th>\n",
       "      <th>Test_RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bagging Regression</th>\n",
       "      <td>0.904646</td>\n",
       "      <td>0.779868</td>\n",
       "      <td>138886.236727</td>\n",
       "      <td>4.298318e+10</td>\n",
       "      <td>207323.842832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Train_R2   Test_R2       Test_MAE      Test_MSE  \\\n",
       "Bagging Regression  0.904646  0.779868  138886.236727  4.298318e+10   \n",
       "\n",
       "                        Test_RMSE  \n",
       "Bagging Regression  207323.842832  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag1 = BaggingRegressor(bootstrap=False,bootstrap_features=True,max_features=1.0,max_samples=0.7,n_estimators=500) \n",
    "bag1_res = eval_model(bag1, x_train,y_train,x_test,y_test,'Bagging Regression')\n",
    "bag1_res "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6) AdaBoost Reg**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Train_R2</th>\n",
       "      <th>Test_R2</th>\n",
       "      <th>Test_MAE</th>\n",
       "      <th>Test_MSE</th>\n",
       "      <th>Test_RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AdaBoost Regression</th>\n",
       "      <td>0.407798</td>\n",
       "      <td>0.485031</td>\n",
       "      <td>255486.262813</td>\n",
       "      <td>1.005535e+11</td>\n",
       "      <td>317101.65171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Train_R2   Test_R2       Test_MAE      Test_MSE  \\\n",
       "AdaBoost Regression  0.407798  0.485031  255486.262813  1.005535e+11   \n",
       "\n",
       "                        Test_RMSE  \n",
       "AdaBoost Regression  317101.65171  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adab1 = AdaBoostRegressor(n_estimators=150,loss='square',learning_rate=1.0)\n",
    "adab1_res = eval_model(adab1, x_train,y_train,x_test,y_test,'AdaBoost Regression')\n",
    "adab1_res "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**7) KNN Reg**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Train_R2</th>\n",
       "      <th>Test_R2</th>\n",
       "      <th>Test_MAE</th>\n",
       "      <th>Test_MSE</th>\n",
       "      <th>Test_RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KNN Regression</th>\n",
       "      <td>0.397407</td>\n",
       "      <td>0.234937</td>\n",
       "      <td>218316.490723</td>\n",
       "      <td>1.493871e+11</td>\n",
       "      <td>386506.221109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Train_R2   Test_R2       Test_MAE      Test_MSE      Test_RMSE\n",
       "KNN Regression  0.397407  0.234937  218316.490723  1.493871e+11  386506.221109"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Knn1 = KNeighborsRegressor(metric='manhattan',n_neighbors=9) \n",
    "Knn1_res = eval_model(Knn1, x_train,y_train,x_test,y_test,'KNN Regression')\n",
    "Knn1_res "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**10. Regression Model Performance Comparison**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Train_R2</th>\n",
       "      <th>Test_R2</th>\n",
       "      <th>Test_MAE</th>\n",
       "      <th>Test_MSE</th>\n",
       "      <th>Test_RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Regression</th>\n",
       "      <td>0.503125</td>\n",
       "      <td>0.568589</td>\n",
       "      <td>190590.648270</td>\n",
       "      <td>8.423765e+10</td>\n",
       "      <td>290237.226303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree Regression</th>\n",
       "      <td>0.762127</td>\n",
       "      <td>0.746431</td>\n",
       "      <td>140764.483802</td>\n",
       "      <td>4.951212e+10</td>\n",
       "      <td>222513.185062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest Regression</th>\n",
       "      <td>0.863412</td>\n",
       "      <td>0.815202</td>\n",
       "      <td>124742.234526</td>\n",
       "      <td>3.608387e+10</td>\n",
       "      <td>189957.552089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge</th>\n",
       "      <td>0.503122</td>\n",
       "      <td>0.568242</td>\n",
       "      <td>190619.744606</td>\n",
       "      <td>8.430551e+10</td>\n",
       "      <td>290354.106990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagging Regression</th>\n",
       "      <td>0.904646</td>\n",
       "      <td>0.779868</td>\n",
       "      <td>138886.236727</td>\n",
       "      <td>4.298318e+10</td>\n",
       "      <td>207323.842832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdaBoost Regression</th>\n",
       "      <td>0.407798</td>\n",
       "      <td>0.485031</td>\n",
       "      <td>255486.262813</td>\n",
       "      <td>1.005535e+11</td>\n",
       "      <td>317101.651710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN Regression</th>\n",
       "      <td>0.397407</td>\n",
       "      <td>0.234937</td>\n",
       "      <td>218316.490723</td>\n",
       "      <td>1.493871e+11</td>\n",
       "      <td>386506.221109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Train_R2   Test_R2       Test_MAE      Test_MSE  \\\n",
       "Linear Regression         0.503125  0.568589  190590.648270  8.423765e+10   \n",
       "Decision Tree Regression  0.762127  0.746431  140764.483802  4.951212e+10   \n",
       "Random Forest Regression  0.863412  0.815202  124742.234526  3.608387e+10   \n",
       "Ridge                     0.503122  0.568242  190619.744606  8.430551e+10   \n",
       "Bagging Regression        0.904646  0.779868  138886.236727  4.298318e+10   \n",
       "AdaBoost Regression       0.407798  0.485031  255486.262813  1.005535e+11   \n",
       "KNN Regression            0.397407  0.234937  218316.490723  1.493871e+11   \n",
       "\n",
       "                              Test_RMSE  \n",
       "Linear Regression         290237.226303  \n",
       "Decision Tree Regression  222513.185062  \n",
       "Random Forest Regression  189957.552089  \n",
       "Ridge                     290354.106990  \n",
       "Bagging Regression        207323.842832  \n",
       "AdaBoost Regression       317101.651710  \n",
       "KNN Regression            386506.221109  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_res = pd.concat([lr1_res,dt1_res,rf1_res,rid1_res,bag1_res,adab1_res,Knn1_res])\n",
    "all_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Conclusion:-**\n",
    "**Random Forest Regression has performed best on this dataset.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**11. Saving the best Model (Using pickle Library)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(rf1,open('Random_Forest_Regressor.pkl','wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**12. Loading the Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "Loaded_model = pickle.load(open('Random_Forest_Regressor.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**13. Creating the Sample Dataset of the Original Data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampled_dataset = df.sample(n=20, replace=False) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**14. Saving the Sample Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampled_dataset.to_csv('Sample Data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**15. Sample Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 8)\n"
     ]
    },
    {
     "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>year</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>km_driven</th>\n",
       "      <th>fuel</th>\n",
       "      <th>seller_type</th>\n",
       "      <th>transmission</th>\n",
       "      <th>owner</th>\n",
       "      <th>brands</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2260</th>\n",
       "      <td>2016</td>\n",
       "      <td>620000.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>2012</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1784</th>\n",
       "      <td>2018</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3123</th>\n",
       "      <td>2009</td>\n",
       "      <td>420000.0</td>\n",
       "      <td>223158.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2668</th>\n",
       "      <td>2011</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year  selling_price  km_driven  fuel  seller_type  transmission  owner  \\\n",
       "2260  2016       620000.0    70000.0     1            1             1      0   \n",
       "315   2012       350000.0   110000.0     1            1             1      2   \n",
       "1784  2018       850000.0    50000.0     1            1             1      0   \n",
       "3123  2009       420000.0   223158.4     1            0             1      0   \n",
       "2668  2011       300000.0   135000.0     1            1             1      2   \n",
       "\n",
       "      brands  \n",
       "2260       9  \n",
       "315       13  \n",
       "1784       9  \n",
       "3123      16  \n",
       "2668      16  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(sampled_dataset.shape) \n",
    "sampled_dataset.head(5) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**16. Checking how the model is performing on Sample dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([663004.42224265, 422521.73946401, 772639.86678337, 485965.33937125,\n",
       "       485906.48508019])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = Loaded_model.predict(sampled_dataset.drop(columns=['selling_price']))\n",
    "predictions[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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