Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3351cc6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9ef035c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_name</th>\n",
       "      <th>price</th>\n",
       "      <th>city</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>owner</th>\n",
       "      <th>age</th>\n",
       "      <th>power</th>\n",
       "      <th>brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TVS Star City Plus Dual Tone 110cc</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>17654.0</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>TVS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Royal Enfield Classic 350cc</td>\n",
       "      <td>119900.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Royal Enfield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Triumph Daytona 675R</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>110.0</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>8.0</td>\n",
       "      <td>675.0</td>\n",
       "      <td>Triumph</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TVS Apache RTR 180cc</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>16329.0</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>TVS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yamaha FZ S V 2.0 150cc-Ltd. Edition</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>Yamaha</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              bike_name     price       city  kms_driven  \\\n",
       "0    TVS Star City Plus Dual Tone 110cc   35000.0  Ahmedabad     17654.0   \n",
       "1           Royal Enfield Classic 350cc  119900.0      Delhi     11000.0   \n",
       "2                  Triumph Daytona 675R  600000.0      Delhi       110.0   \n",
       "3                  TVS Apache RTR 180cc   65000.0  Bangalore     16329.0   \n",
       "4  Yamaha FZ S V 2.0 150cc-Ltd. Edition   80000.0  Bangalore     10000.0   \n",
       "\n",
       "         owner  age  power          brand  \n",
       "0  First Owner  3.0  110.0            TVS  \n",
       "1  First Owner  4.0  350.0  Royal Enfield  \n",
       "2  First Owner  8.0  675.0        Triumph  \n",
       "3  First Owner  4.0  180.0            TVS  \n",
       "4  First Owner  3.0  150.0         Yamaha  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(r'Used_Bikes.csv')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "af1c6f70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32648, 8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ecb544d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_name</th>\n",
       "      <th>price</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>owner</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TVS Star City Plus Dual Tone 110cc</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>17654.0</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Royal Enfield Classic 350cc</td>\n",
       "      <td>119900.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Triumph Daytona 675R</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TVS Apache RTR 180cc</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>16329.0</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yamaha FZ S V 2.0 150cc-Ltd. Edition</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              bike_name     price  kms_driven        owner  \\\n",
       "0    TVS Star City Plus Dual Tone 110cc   35000.0     17654.0  First Owner   \n",
       "1           Royal Enfield Classic 350cc  119900.0     11000.0  First Owner   \n",
       "2                  Triumph Daytona 675R  600000.0       110.0  First Owner   \n",
       "3                  TVS Apache RTR 180cc   65000.0     16329.0  First Owner   \n",
       "4  Yamaha FZ S V 2.0 150cc-Ltd. Edition   80000.0     10000.0  First Owner   \n",
       "\n",
       "   age  \n",
       "0  3.0  \n",
       "1  4.0  \n",
       "2  8.0  \n",
       "3  4.0  \n",
       "4  3.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df1.drop(['power','brand','city'],axis=1)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea15659a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32648, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f8ed2d0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bike_name     0\n",
       "price         0\n",
       "kms_driven    0\n",
       "owner         0\n",
       "age           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f1782b0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_name</th>\n",
       "      <th>price</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>owner</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TVS Star City Plus Dual Tone 110cc</td>\n",
       "      <td>35000</td>\n",
       "      <td>17654</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Royal Enfield Classic 350cc</td>\n",
       "      <td>119900</td>\n",
       "      <td>11000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Triumph Daytona 675R</td>\n",
       "      <td>600000</td>\n",
       "      <td>110</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TVS Apache RTR 180cc</td>\n",
       "      <td>65000</td>\n",
       "      <td>16329</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yamaha FZ S V 2.0 150cc-Ltd. Edition</td>\n",
       "      <td>80000</td>\n",
       "      <td>10000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              bike_name   price  kms_driven        owner  age\n",
       "0    TVS Star City Plus Dual Tone 110cc   35000       17654  First Owner    3\n",
       "1           Royal Enfield Classic 350cc  119900       11000  First Owner    4\n",
       "2                  Triumph Daytona 675R  600000         110  First Owner    8\n",
       "3                  TVS Apache RTR 180cc   65000       16329  First Owner    4\n",
       "4  Yamaha FZ S V 2.0 150cc-Ltd. Edition   80000       10000  First Owner    3"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['price']=df2['price'].astype('int32')\n",
    "df2['kms_driven']=df2['kms_driven'].astype('int32')\n",
    "df2['age']=df2['age'].astype('int32')\n",
    "df3=df2.copy()\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "43408901",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(df3['age'],df3['price'])\n",
    "plt.xlabel('AGE')\n",
    "plt.ylabel('PRICE')\n",
    "plt.title('Price vs Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "35aa8dc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df3['kms_driven'],df3['price'],marker='+',color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "11bad7d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_name</th>\n",
       "      <th>price</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>owner</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TVS Star City Plus Dual Tone 110cc</td>\n",
       "      <td>35000</td>\n",
       "      <td>17654</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Royal Enfield Classic 350cc</td>\n",
       "      <td>119900</td>\n",
       "      <td>11000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Triumph Daytona 675R</td>\n",
       "      <td>600000</td>\n",
       "      <td>110</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TVS Apache RTR 180cc</td>\n",
       "      <td>65000</td>\n",
       "      <td>16329</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yamaha FZ S V 2.0 150cc-Ltd. Edition</td>\n",
       "      <td>80000</td>\n",
       "      <td>10000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              bike_name   price  kms_driven        owner  age\n",
       "0    TVS Star City Plus Dual Tone 110cc   35000       17654  First Owner    3\n",
       "1           Royal Enfield Classic 350cc  119900       11000  First Owner    4\n",
       "2                  Triumph Daytona 675R  600000         110  First Owner    8\n",
       "3                  TVS Apache RTR 180cc   65000       16329  First Owner    4\n",
       "4  Yamaha FZ S V 2.0 150cc-Ltd. Edition   80000       10000  First Owner    3"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4=df3.copy()\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8101d81c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  4,  8,  6,  7,  5,  2, 16, 13,  9, 10, 14, 12, 11,  1, 15, 39,\n",
       "       17, 18, 30, 19, 23, 41, 27, 37, 24, 20, 43, 38, 63, 34, 31, 22, 26,\n",
       "       25])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "185fcb6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    32648.000000\n",
       "mean         8.048211\n",
       "std          4.031700\n",
       "min          1.000000\n",
       "25%          5.000000\n",
       "50%          7.000000\n",
       "75%         10.000000\n",
       "max         63.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3344b53d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_4032\\3686898277.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df4['age'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age', ylabel='Density'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df4['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "da33d27e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4AOAqx3FUUVHR4XoXu8gyAAOIDwCuamxsVH19vdra2pLW29raVF9fr8bGRkuTAbCF+ADgqkAgoMmTJys9PT1pPT09XVOmTFEgELA0GQBbiA8ArvJ4PCopKelw3ePxWJgKgE3EBwDXjRw5UkVFRYnQ8Hg8Kioq0k033WR5MgA2EB8AjCguLlZ2drYkaciQISoqKrI8EQBbiA8ARni9XpWWlsrv92vFihXyer22RwJgSYbtAQD0HnfddZfuuusu22MAsIwjHwAAwCjiAwAAGEV8AAAAo4gPAABgFPEBwJgDBw5owYIFOnDggO1RAFhEfAAwIhqNav369QqFQlq/fr2i0ajtkQBYQnwAMGLHjh06d+6cJOncuXOqrKy0PBEAW4gPAK47efKkKisr5TiOJMlxHFVWVurkyZOWJwNgA/EBwFWO46iioqLD9UtBAqD3ID4AuKqxsVH19fVqa2tLWm9ra1N9fb0aGxstTQbAlpTiY/PmzRo/fryysrKUlZWl/Px8VVVVJfZHo1EtWbJE2dnZGjBggObPn69QKNTpQwPoPgKBgCZPnqy0tOR/btLT0zVlyhQFAgFLkwGwJaX4GDlypJ5//nk1NDTo8OHDmj59uu677z79/e9/lyStWLFCe/bs0a5du7R//341Nzdr3rx5rgwOoHvweDwqKSm54u0Vx3FUUlIij8djaTIAtqT0xXL33ntv0u1f/vKX2rx5sw4ePKiRI0dq69atqqys1PTp0yVJ27Zt05gxY3Tw4EFNnTq186YG0O05jsP5HkAvdc3nfLS1tenNN9/U+fPnlZ+fr4aGBl28eFGFhYWJ+4wePVqBQEC1tbUdPk4sFlMkEknaAPQcl04svfwIh8fj4YRToJdKOT6OHj2qAQMGKDMzU4899ph2796tsWPHKhgMqm/fvho0aFDS/f1+v4LBYIePV15eLp/Pl9hyc3NT/o8A0HVdOuE0Ho8nrcfjcU44BXqplOPj1ltv1QcffKC6ujotXrxYixYt0ocffnjNA5SVlSkcDie2pqama34sAF3PpRNO09PTk9Y54RTovVKOj759++ob3/iGJk6cqPLyck2YMEEVFRXKycnRhQsX1NLSknT/UCiknJycDh8vMzMz8emZSxuAnuPSCacdrXPCKdD7XPd1PuLxuGKxmCZOnKg+ffqopqYmse/48eNqbGxUfn7+9T4NgG5s5MiRKioqSoSGx+NRUVGRbrrpJsuTAbAhpU+7lJWVafbs2QoEAmptbVVlZaX27dund999Vz6fTw8//LBKS0s1ePBgZWVladmyZcrPz+eTLgBUXFysqqoqnT17VkOGDFFRUZHtkQBYklJ8nD59Wg8++KBOnToln8+n8ePH691339X3vvc9SdKGDRuUlpam+fPnKxaLaebMmXrllVdcGRxA9+L1elVaWqqKigqVlJTI6/XaHgmAJR6ni33OLRKJyOfzKRwOc/4HAADdRCqv33y3CwAAMIr4AAAARhEfAADAKOIDAAAYRXwAAACjiA8Axjz99NMqKCjQ008/bXsUABYRHwCMCIVC2rdvnyRp3759CoVCdgcCYA3xAcCIpUuXJt1etmyZpUkA2EZ8AHBddXW1zpw5k7R2+vRpVVdXW5oIgE3EBwBXtbW1ad26de3uW7dundra2gxPBMA24gOAq/bu3dthYLS1tWnv3r2GJwJgG/EBwFVz5sxRenp6u/syMjI0Z84cwxMBsI34AOCq9PR0Pfnkk+3uW7lyZYdhAqDnIj4AuG7cuHHtro8dO9bwJAC6AuIDgKscx1FFRYXS0pL/uUlLS1NFRYUcx7E0GQBbiA8ArmpsbFR9fb3i8XjSejweV319vRobGy1NBsAW4gOAqwKBgCZPntzuvilTpigQCBieCIBtxAcAV3k8HpWUlLS7r6SkRB6Px/BEAGwjPgC47tixY+2uHz161PAkALoC4gOAq7jCKYDLER8AXMUVTgFcjvgA4CqucArgcsQHAFdxhVMAlyM+ALhu1qxZ7a7PmDHD8CQAugLiA4DrVq5cmdI6gJ6N+ADgqmg0qkOHDrW779ChQ4pGo4YnAmAb8QHAVR2d7/FV9wPoeYgPAK7q6BofX3U/gJ6H+ADgKq/XqylTprS7b+rUqfJ6vYYnAmAb8QHAddOnT293vaCgwOwgALoE4gOAq7i8OoDLER8AXMXl1QFcjvgA4Courw7gcsQHAFdxeXUAlyM+ALhu1qxZ6t+/f9Ja//79ubw60EsRHwBcd/LkySuuZBqNRnXy5ElLEwGwifgA4CrHcVRRUSGPx5O07vF4VFFRIcdxLE0GwBbiA4CrGhsbVV9ff8UnXtra2lRfX6/GxkZLkwGwhfgA4KpAIKDJkydfcWJpenq6pkyZokAgYGkyALYQHwBc5fF4VFJS0uH65W/HAOj5iA8Arhs5cqSKiooSoeHxeFRUVKSbbrrJ8mQAbCA+ABhRXFys7OxsSdKQIUNUVFRkeSIAthAfAIzwer0qLS2V3+/XihUr+DZboBdLKT7Ky8s1efJkDRw4UMOGDdP999+v48ePJ90nGo1qyZIlys7O1oABAzR//nyFQqFOHRpA91ReXq5QKKTy8nLbowCwKKX42L9/v5YsWaKDBw/qj3/8oy5evKgZM2bo/PnzifusWLFCe/bs0a5du7R//341Nzdr3rx5nT44gO7lyJEjam1tlSS1trbqyJEjlicCYIvHuY4r/Jw5c0bDhg3T/v37dc899ygcDmvo0KGqrKzUD37wA0nSP/7xD40ZM0a1tbWaOnXq/3zMSCQin8+ncDisrKysax0NQBdTUFBwxdq+ffuMzwHAHam8fl/XOR/hcFiSNHjwYElSQ0ODLl68qMLCwsR9Ro8erUAgoNra2ut5KgDd2NNPP53SOoCeLeNafzAej2v58uWaNm2axo0bJ0kKBoPq27evBg0alHRfv9+vYDDY7uPEYjHFYrHE7Ugkcq0jAeiCYrFYh0c49u3bp1gspszMTLNDAbDqmo98LFmyRMeOHdObb755XQOUl5fL5/Mlttzc3Ot6PABdy6pVq65rP4Ce55riY+nSpdq7d6/ee+89jRw5MrGek5OjCxcuqKWlJen+oVBIOTk57T5WWVmZwuFwYmtqarqWkQB0Uc8888x17QfQ86QUH47jaOnSpdq9e7f+9Kc/adSoUUn7J06cqD59+qimpiaxdvz4cTU2Nio/P7/dx8zMzFRWVlbSBqDnyMzMbPdkU0maPn06b7kAvVBKn3b52c9+psrKSr3zzju69dZbE+s+n0/9+vWTJC1evFh/+MMftH37dmVlZWnZsmWSpAMHDnyl5+DTLkDP01F8SHziBegpUnn9Tik+OvoCqG3btuknP/mJpC8uMvb4449r586disVimjlzpl555ZUO33a5nuEBdH1XC49LCBCg+0vl9TulT7t8lU7xer3atGmTNm3alMpDAwCAXoLvdgEAAEYRHwBc9b/eUuEtF6D3IT4AuK6j88U6WgfQsxEfAFx16tSpDs8XcxxHp06dMjwRANuIDwCuWrhw4XXtB9DzEB8AXLVz587r2g+g5yE+ALhq+PDhVz3nY/jw4YYnAmAb8QHAde+9915K6wB6tpQuMgZ0N47jKBqN2h4DkqZNm6b3338/6fZnn31mcSJc4vV6+eQRjErp8uomcHl1dKbPPvtMs2fPtj0G0KVVVVUlvp8LuFapvH7ztgsAADCKt13Qo3m9XlVVVdkeA/riSyfnzp0rSdq9e7e8Xq/liXAJfxcwjfhAj+bxeDic3AV5vV7+XoBejLddAACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYFTK8fHnP/9Z9957r0aMGCGPx6O33347ab/jOFq9erWGDx+ufv36qbCwUB999FFnzQsAALq5lOPj/PnzmjBhgjZt2tTu/hdeeEEvvviitmzZorq6Ot1www2aOXOmotHodQ8LAAC6v4xUf2D27NmaPXt2u/scx9HGjRv1i1/8Qvfdd58k6Y033pDf79fbb7+tBx544Pqm7SYcxyG2gMt8+XeC3w+gfV6vVx6Px/YYrks5Pq7mxIkTCgaDKiwsTKz5fD7l5eWptra23fiIxWKKxWKJ25FIpDNHsiIajXYYaACkuXPn2h4B6JKqqqrUr18/22O4rlNPOA0Gg5Ikv9+ftO73+xP7LldeXi6fz5fYcnNzO3MkAADQxXTqkY9rUVZWptLS0sTtSCTSowLkk9sXykmz/n8zYJ/jSPHPv/hzWobUCw4tA1+FJ/65Bnyw0/YYRnXqq2JOTo4kKRQKafjw4Yn1UCik22+/vd2fyczMVGZmZmeO0aU4aRlSeh/bYwBdRF/bAwBdjmN7AAs69W2XUaNGKScnRzU1NYm1SCSiuro65efnd+ZTAQCAbirlIx+ffPKJPv7448TtEydO6IMPPtDgwYMVCAS0fPlyPfvss7rllls0atQorVq1SiNGjND999/fmXMDAIBuKuX4OHz4sL7zne8kbl86X2PRokXavn27Vq5cqfPnz+vRRx9VS0uL7r77blVXV8vr9Xbe1AAAoNtKOT4KCgrkOB2/Q+XxeLR27VqtXbv2ugYDAAA9E9/tAgAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKP4xjMXJF0Hpe2ivUEAAF3fl14nrnYdrZ6E+HBBLBZL/Hng/3vT4iQAgO4kFoupf//+tsdwHW+7AAAAozjy4YLMzMzEn1snPCCl97E4DQCgS2u7mDhK/uXXj56M+HCBx+P5vxvpfYgPAMBXkvT60YPxtgsAADCK+AAAAEYRHwAAwCjiAwAAGEV8AAAAo4gPAABgFB+1dZkn/rl6x8Vygf/BcaT451/8OS1D6iUfKQT+F8+l34tehPhw2YAPdtoeAQCALoW3XQAAgFEc+XCB1+tVVVWV7TGALiUajWru3LmSpN27d8vr9VqeCOh6esvvBfHhAo/Ho379+tkeA+iyvF4vvyNAL8bbLgAAwCjiAwAAGEV8AAAAo4gPAABgFPEBAACMIj4AAIBRxAcAADCK+AAAAEYRHwAAwCjiAwAAGEV8AAAAo4gPAABgFPEBAACMIj4AAIBRxAcAADCK+AAAAEZl2B4AcJPjOIpGo7bHgJT098DfSdfi9Xrl8Xhsj4FehPhAjxaNRjV79mzbY+Ayc+fOtT0CvqSqqkr9+vWzPQZ6Ed52AQAARnHkAz2a1+tVVVWV7TGgL94Ci8VikqTMzEwO83chXq/X9gjoZVyLj02bNmndunUKBoOaMGGCXnrpJU2ZMsWtpwPa5fF4OJzchfTv39/2CAC6AFfednnrrbdUWlqqNWvW6MiRI5owYYJmzpyp06dPu/F0AACgG3ElPtavX69HHnlEDz30kMaOHastW7aof//++vWvf+3G0wEAgG6k0+PjwoULamhoUGFh4f89SVqaCgsLVVtbe8X9Y7GYIpFI0gYAAHquTo+Ps2fPqq2tTX6/P2nd7/crGAxecf/y8nL5fL7Elpub29kjAQCALsT6R23LysoUDocTW1NTk+2RAACAizr90y5DhgxRenq6QqFQ0nooFFJOTs4V98/MzFRmZmZnjwEAALqoTj/y0bdvX02cOFE1NTWJtXg8rpqaGuXn53f20wEAgG7Glet8lJaWatGiRZo0aZKmTJmijRs36vz583rooYfceDoAANCNuBIfCxYs0JkzZ7R69WoFg0Hdfvvtqq6uvuIkVAAA0Pt4HMdxbA/xZZFIRD6fT+FwWFlZWbbHAQAAX0Eqr9/WP+0CAAB6F+IDAAAYRXwAAACjXPtW22t16RQULrMOAED3cel1+6ucStrl4qO1tVWSuMw6AADdUGtrq3w+31Xv0+U+7RKPx9Xc3KyBAwfK4/HYHgdAJ4pEIsrNzVVTUxOfZgN6GMdx1NraqhEjRigt7epndXS5+ADQc/FRegASJ5wCAADDiA8AAGAU8QHAmMzMTK1Zs4ZvsgZ6Oc75AAAARnHkAwAAGEV8AAAAo4gPAABgFPEBAACMIj4AGLNp0yZ97Wtfk9frVV5eng4dOmR7JAAWEB8AjHjrrbdUWlqqNWvW6MiRI5owYYJmzpyp06dP2x4NgGF81BaAEXl5eZo8ebJefvllSV98j1Nubq6WLVump556yvJ0AEziyAcA1124cEENDQ0qLCxMrKWlpamwsFC1tbUWJwNgA/EBwHVnz55VW1ub/H5/0rrf71cwGLQ0FQBbiA8AAGAU8QHAdUOGDFF6erpCoVDSeigUUk5OjqWpANhCfABwXd++fTVx4kTV1NQk1uLxuGpqapSfn29xMgA2ZNgeAEDvUFpaqkWLFmnSpEmaMmWKNm7cqPPnz+uhhx6yPRoAw4gPAEYsWLBAZ86c0erVqxUMBnX77berurr6ipNQAfR8XOcDAAAYxTkfAADAKOIDAAAYRXwAAACjiA8AAGAU8QEAAIwiPgAAgFHEBwAAMIr4AAAARhEfAADAKOIDAAAYRXwAAACjiA8AAGDU/wffwme4KZOSlgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df4['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8a467af9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "upper_limit: 17.321121482157235\n"
     ]
    }
   ],
   "source": [
    "upper_limit=df4['age'].mean()+2.3*df4['age'].std() \n",
    "print('upper_limit:',upper_limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4831ec17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_name</th>\n",
       "      <th>price</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>owner</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>Royal Enfield Bullet 350 cc</td>\n",
       "      <td>125000</td>\n",
       "      <td>16500</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>Bajaj Boxer CT100</td>\n",
       "      <td>11000</td>\n",
       "      <td>21311</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1219</th>\n",
       "      <td>Hero CD 100SS</td>\n",
       "      <td>25500</td>\n",
       "      <td>10000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>Royal Enfield Bullet 350cc</td>\n",
       "      <td>130000</td>\n",
       "      <td>6600</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>Hero Splendor 100cc</td>\n",
       "      <td>21000</td>\n",
       "      <td>24000</td>\n",
       "      <td>Second Owner</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32547</th>\n",
       "      <td>Hero Passion 100cc</td>\n",
       "      <td>15000</td>\n",
       "      <td>35000</td>\n",
       "      <td>Second Owner</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32566</th>\n",
       "      <td>Hero Passion 100cc</td>\n",
       "      <td>15000</td>\n",
       "      <td>35000</td>\n",
       "      <td>Second Owner</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32585</th>\n",
       "      <td>Hero Passion 100cc</td>\n",
       "      <td>15000</td>\n",
       "      <td>35000</td>\n",
       "      <td>Second Owner</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32604</th>\n",
       "      <td>Hero Passion 100cc</td>\n",
       "      <td>15000</td>\n",
       "      <td>35000</td>\n",
       "      <td>Second Owner</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32642</th>\n",
       "      <td>Hero Passion 100cc</td>\n",
       "      <td>15000</td>\n",
       "      <td>35000</td>\n",
       "      <td>Second Owner</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>685 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         bike_name   price  kms_driven         owner  age\n",
       "334    Royal Enfield Bullet 350 cc  125000       16500   First Owner   39\n",
       "1033             Bajaj Boxer CT100   11000       21311   First Owner   18\n",
       "1219                 Hero CD 100SS   25500       10000   First Owner   30\n",
       "1278    Royal Enfield Bullet 350cc  130000        6600   First Owner   18\n",
       "1298           Hero Splendor 100cc   21000       24000  Second Owner   19\n",
       "...                            ...     ...         ...           ...  ...\n",
       "32547           Hero Passion 100cc   15000       35000  Second Owner   19\n",
       "32566           Hero Passion 100cc   15000       35000  Second Owner   19\n",
       "32585           Hero Passion 100cc   15000       35000  Second Owner   19\n",
       "32604           Hero Passion 100cc   15000       35000  Second Owner   19\n",
       "32642           Hero Passion 100cc   15000       35000  Second Owner   19\n",
       "\n",
       "[685 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.loc[(df4['age']>upper_limit)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bb6289c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old_Data: 32648\n",
      "New_Data: 31963\n",
      "No.of.outliers: 685\n"
     ]
    }
   ],
   "source": [
    "df5=df4.loc[(df4['age']<upper_limit)]\n",
    "print('Old_Data:',len(df4))\n",
    "print('New_Data:',len(df5))\n",
    "print('No.of.outliers:',len(df4)-len(df5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3d6f2aa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_name</th>\n",
       "      <th>price</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>owner</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TVS Star City Plus Dual Tone 110cc</td>\n",
       "      <td>35000</td>\n",
       "      <td>17654</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Royal Enfield Classic 350cc</td>\n",
       "      <td>119900</td>\n",
       "      <td>11000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Triumph Daytona 675R</td>\n",
       "      <td>600000</td>\n",
       "      <td>110</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TVS Apache RTR 180cc</td>\n",
       "      <td>65000</td>\n",
       "      <td>16329</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yamaha FZ S V 2.0 150cc-Ltd. Edition</td>\n",
       "      <td>80000</td>\n",
       "      <td>10000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Yamaha FZs 150cc</td>\n",
       "      <td>53499</td>\n",
       "      <td>25000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda CB Hornet 160R  ABS DLX</td>\n",
       "      <td>85000</td>\n",
       "      <td>8200</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hero Splendor Plus Self Alloy 100cc</td>\n",
       "      <td>45000</td>\n",
       "      <td>12645</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Royal Enfield Thunderbird X 350cc</td>\n",
       "      <td>145000</td>\n",
       "      <td>9190</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Royal Enfield Classic Desert Storm 500cc</td>\n",
       "      <td>88000</td>\n",
       "      <td>19000</td>\n",
       "      <td>Second Owner</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Yamaha YZF-R15 2.0 150cc</td>\n",
       "      <td>72000</td>\n",
       "      <td>20000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Yamaha FZ25 250cc</td>\n",
       "      <td>95000</td>\n",
       "      <td>9665</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   bike_name   price  kms_driven  \\\n",
       "0         TVS Star City Plus Dual Tone 110cc   35000       17654   \n",
       "1                Royal Enfield Classic 350cc  119900       11000   \n",
       "2                       Triumph Daytona 675R  600000         110   \n",
       "3                       TVS Apache RTR 180cc   65000       16329   \n",
       "4       Yamaha FZ S V 2.0 150cc-Ltd. Edition   80000       10000   \n",
       "5                           Yamaha FZs 150cc   53499       25000   \n",
       "6              Honda CB Hornet 160R  ABS DLX   85000        8200   \n",
       "7        Hero Splendor Plus Self Alloy 100cc   45000       12645   \n",
       "8          Royal Enfield Thunderbird X 350cc  145000        9190   \n",
       "9   Royal Enfield Classic Desert Storm 500cc   88000       19000   \n",
       "10                  Yamaha YZF-R15 2.0 150cc   72000       20000   \n",
       "11                         Yamaha FZ25 250cc   95000        9665   \n",
       "\n",
       "           owner  age  \n",
       "0    First Owner    3  \n",
       "1    First Owner    4  \n",
       "2    First Owner    8  \n",
       "3    First Owner    4  \n",
       "4    First Owner    3  \n",
       "5    First Owner    6  \n",
       "6    First Owner    3  \n",
       "7    First Owner    3  \n",
       "8    First Owner    3  \n",
       "9   Second Owner    7  \n",
       "10   First Owner    7  \n",
       "11   First Owner    4  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "14441bd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L/t/fHyOZ+KiCXC73n5XRx4gPAI7IoO+PEcxlFwAgKfEBACQlPgCApMQHAJCU+AAAkhIfAEBS4gMASEp8AABJiQ8AICnxAQAkJT4AgKTEBwCQlPgAAJISHwBAUlWJj9deey0+/elPx/jx46O5uTnOOeec2Lx5czUOBQA0mKasP/Af//hHXHTRRTF79uzo7u6O9773vfHyyy/H8ccfn/WhAIAGlHl83HvvvdHe3h4//OEPK9umTp2a9WEAgAaV+WWXxx57LM4///z4xCc+ERMmTIhzzz03HnjggUPuXyqVolgsDloAgJEr8/j4y1/+EitXroxTTz01nnjiifjCF74QixcvjjVr1gy5f1dXV7S2tlaW9vb2rEcCAOpI5vExMDAQ5513Xtx9991x7rnnxvXXXx+f/exnY9WqVUPu39nZGb29vZWlp6cn65EAgDqSeXxMnjw5zjzzzEHbzjjjjHj11VeH3D+fz8fYsWMHLQDAyJV5fFx00UWxffv2QdteeumlOOmkk7I+FADQgDKPj5tuuimee+65uPvuu2PHjh3x0EMPxQ9+8INYtGhR1ocCABpQ5vExY8aMWLt2bTz88MNx9tlnx5133hnLly+Pa665JutDAQANKPN/5yMi4rLLLovLLrusGh8NADQ4z3YBAJISHwBAUuIDAEhKfAAASYkPACAp8QEAJCU+AICkxAcAkJT4AACSEh8AQFLiAwBISnwAAElV5cFy/Edu4N9RrvUQUA/K5YiBf7/x86imiFyutvNAnci9+XdxFBEfVfZ/2x6u9QgAUFdcdgEAknLmowoKhUJ0d3fXegyoK/39/TF//vyIiFi7dm0UCoUaTwT152j5uxAfVZDL5aK5ubnWY0DdKhQK/kbgKOayCwCQlPgAAJISHwBAUuIDAEhKfAAASYkPACAp8QEAJCU+AICkxAcAkJT4AACSEh8AQFLiAwBISnwAAEmJDwAgqarHxz333BO5XC5uvPHGah8KAGgAVY2PF198Mb7//e/H+9///moeBgBoIFWLj/3798c111wTDzzwQBx//PHVOgwA0GCqFh+LFi2KSy+9NObMmXPY/UqlUhSLxUELADByNVXjQx955JHYunVrvPjii/9z366urrj99turMQYAUIcyP/PR09MTX/7yl+PBBx+MQqHwP/fv7OyM3t7eytLT05P1SABAHcn8zMeWLVti7969cd5551W2HTx4MDZt2hT3339/lEqlGD16dOW1fD4f+Xw+6zEAgDqVeXx86EMfij/84Q+Dti1cuDBOP/30uOWWWwaFBwBw9Mk8PlpaWuLss88etG3MmDExfvz4t20HAI4+/oVTACCpqvzfLm+1cePGFIcBABqAMx8AQFLiAwBISnwAAEmJDwAgKfEBACQlPgCApMQHAJCU+AAAkhIfAEBS4gMASEp8AABJiQ8AICnxAQAkJT4AgKTEBwCQlPgAAJISHwBAUuIDAEhKfAAASYkPACAp8QEAJCU+AICkxAcAkJT4AACSEh8AQFLiAwBISnwAAEmJDwAgKfEBACQlPgCApMQHAJBU5vHR1dUVM2bMiJaWlpgwYUJceeWVsX379qwPAwA0qMzj4+mnn45FixbFc889F08++WQcOHAgPvzhD0dfX1/WhwIAGlBT1h+4fv36QeurV6+OCRMmxJYtW+KSSy7J+nAAQIPJPD7eqre3NyIixo0bN+TrpVIpSqVSZb1YLFZ7JACghqp6w+nAwEDceOONcdFFF8XZZ5895D5dXV3R2tpaWdrb26s5EgBQY1WNj0WLFsUf//jHeOSRRw65T2dnZ/T29laWnp6eao4EANRY1S67fOlLX4rHH388Nm3aFCeeeOIh98vn85HP56s1BgBQZzKPj3K5HDfccEOsXbs2Nm7cGFOnTs36EABAA8s8PhYtWhQPPfRQ/OxnP4uWlpbYvXt3RES0trZGc3Nz1ocDABpM5vd8rFy5Mnp7e2PWrFkxefLkyvLoo49mfSgAoAFV5bILAMCheLYLAJCU+AAAkhIfAEBS4gMASEp8AABJiQ8AICnxAQAkJT4AgKTEBwCQlPgAAJISHwBAUuIDAEgq8wfLQT0pl8vR399f6zGIGPR78DupL4VCIXK5XK3H4CgiPhjR+vv7o6Ojo9Zj8Bbz58+v9Qj8l+7u7mhubq71GBxFXHYBAJJy5oMRrVAoRHd3d63HIN64BFYqlSIiIp/PO81fRwqFQq1H4CgjPhjRcrmc08l15Ljjjqv1CEAdcNkFAEhKfAAASYkPACAp8QEAJCU+AICkxAcAkJT4AACSEh8AQFLiAwBISnwAAEmJDwAgKfEBACQlPgCApOruqbblcjkiIorFYo0nAQCO1Jvf229+jx9O3cXHvn37IiKivb29xpMAAMO1b9++aG1tPew+ufKRJEpCAwMDsWvXrmhpaYlcLlfrcYAMFYvFaG9vj56enhg7dmytxwEyVC6XY9++fdHW1hajRh3+ro66iw9g5CoWi9Ha2hq9vb3iA45ibjgFAJISHwBAUuIDSCafz8dtt90W+Xy+1qMANeSeDwAgKWc+AICkxAcAkJT4AACSEh8AQFLiA0hmxYoVcfLJJ0ehUIiZM2fGCy+8UOuRgBoQH0ASjz76aCxZsiRuu+222Lp1a0ybNi3mzp0be/furfVoQGL+V1sgiZkzZ8aMGTPi/vvvj4g3nuPU3t4eN9xwQyxdurTG0wEpOfMBVN2//vWv2LJlS8yZM6eybdSoUTFnzpx49tlnazgZUAviA6i6v//973Hw4MGYOHHioO0TJ06M3bt312gqoFbEBwCQlPgAqu6EE06I0aNHx549ewZt37NnT0yaNKlGUwG1Ij6Aqjv22GPjAx/4QGzYsKGybWBgIDZs2BAXXnhhDScDaqGp1gMAR4clS5bEggUL4vzzz48LLrggli9fHn19fbFw4cJajwYkJj6AJK666qr429/+Frfeemvs3r07pk+fHuvXr3/bTajAyOff+QAAknLPBwCQlPgAAJISHwBAUuIDAEhKfAAASYkPACAp8QEAJCU+AICkxAcAkJT4AACSEh8AQFLiAwBI6v8BEaP2fBHmi+UAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df5['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "56730e10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_name</th>\n",
       "      <th>price</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>owner</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TVS Star City Plus Dual Tone 110cc</td>\n",
       "      <td>35000</td>\n",
       "      <td>17654</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Royal Enfield Classic 350cc</td>\n",
       "      <td>119900</td>\n",
       "      <td>11000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Triumph Daytona 675R</td>\n",
       "      <td>600000</td>\n",
       "      <td>110</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TVS Apache RTR 180cc</td>\n",
       "      <td>65000</td>\n",
       "      <td>16329</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yamaha FZ S V 2.0 150cc-Ltd. Edition</td>\n",
       "      <td>80000</td>\n",
       "      <td>10000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Yamaha FZs 150cc</td>\n",
       "      <td>53499</td>\n",
       "      <td>25000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda CB Hornet 160R  ABS DLX</td>\n",
       "      <td>85000</td>\n",
       "      <td>8200</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hero Splendor Plus Self Alloy 100cc</td>\n",
       "      <td>45000</td>\n",
       "      <td>12645</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Royal Enfield Thunderbird X 350cc</td>\n",
       "      <td>145000</td>\n",
       "      <td>9190</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Royal Enfield Classic Desert Storm 500cc</td>\n",
       "      <td>88000</td>\n",
       "      <td>19000</td>\n",
       "      <td>Second Owner</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  bike_name   price  kms_driven         owner  \\\n",
       "0        TVS Star City Plus Dual Tone 110cc   35000       17654   First Owner   \n",
       "1               Royal Enfield Classic 350cc  119900       11000   First Owner   \n",
       "2                      Triumph Daytona 675R  600000         110   First Owner   \n",
       "3                      TVS Apache RTR 180cc   65000       16329   First Owner   \n",
       "4      Yamaha FZ S V 2.0 150cc-Ltd. Edition   80000       10000   First Owner   \n",
       "5                          Yamaha FZs 150cc   53499       25000   First Owner   \n",
       "6             Honda CB Hornet 160R  ABS DLX   85000        8200   First Owner   \n",
       "7       Hero Splendor Plus Self Alloy 100cc   45000       12645   First Owner   \n",
       "8         Royal Enfield Thunderbird X 350cc  145000        9190   First Owner   \n",
       "9  Royal Enfield Classic Desert Storm 500cc   88000       19000  Second Owner   \n",
       "\n",
       "   age  \n",
       "0    3  \n",
       "1    4  \n",
       "2    8  \n",
       "3    4  \n",
       "4    3  \n",
       "5    6  \n",
       "6    3  \n",
       "7    3  \n",
       "8    3  \n",
       "9    7  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "82b0561c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "750000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5['kms_driven'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f8801d10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df5['kms_driven'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f0c0c03f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "upper_limit_1: 100071.41345750711\n"
     ]
    }
   ],
   "source": [
    "upper_limit_1=df5['kms_driven'].mean()+3.4*df5['kms_driven'].std()\n",
    "print('upper_limit_1:',upper_limit_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7c5a3662",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_name</th>\n",
       "      <th>price</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>owner</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Yamaha FZ 150cc</td>\n",
       "      <td>40000</td>\n",
       "      <td>357000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Yamaha SZ 150cc</td>\n",
       "      <td>25000</td>\n",
       "      <td>172082</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>Honda CB Unicorn Dazzler 150cc</td>\n",
       "      <td>16800</td>\n",
       "      <td>134200</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>Hero Karizma R 223cc</td>\n",
       "      <td>27000</td>\n",
       "      <td>101250</td>\n",
       "      <td>Third Owner</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>Hero Karizma R 223cc</td>\n",
       "      <td>27000</td>\n",
       "      <td>101250</td>\n",
       "      <td>Third Owner</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>Bajaj CT 100 100cc</td>\n",
       "      <td>10600</td>\n",
       "      <td>107000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>Honda Dream Yuga 110cc</td>\n",
       "      <td>30000</td>\n",
       "      <td>105500</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2112</th>\n",
       "      <td>Honda Dream Yuga 110cc</td>\n",
       "      <td>30000</td>\n",
       "      <td>105500</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2658</th>\n",
       "      <td>Bajaj Discover 125cc</td>\n",
       "      <td>13000</td>\n",
       "      <td>135000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2759</th>\n",
       "      <td>Royal Enfield Thunderbird 350cc</td>\n",
       "      <td>110000</td>\n",
       "      <td>100670</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2939</th>\n",
       "      <td>Bajaj Discover 100cc</td>\n",
       "      <td>27000</td>\n",
       "      <td>102892</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3398</th>\n",
       "      <td>Bajaj Discover 125cc</td>\n",
       "      <td>18000</td>\n",
       "      <td>403833</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3401</th>\n",
       "      <td>Royal Enfield Thunderbird 350cc</td>\n",
       "      <td>110000</td>\n",
       "      <td>100670</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3482</th>\n",
       "      <td>Yamaha FZs 150cc</td>\n",
       "      <td>65000</td>\n",
       "      <td>216000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3587</th>\n",
       "      <td>Bajaj Discover 125cc</td>\n",
       "      <td>13000</td>\n",
       "      <td>135000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4664</th>\n",
       "      <td>Royal Enfield Bullet Electra 350cc</td>\n",
       "      <td>41000</td>\n",
       "      <td>120000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4760</th>\n",
       "      <td>Royal Enfield Bullet Electra 350cc</td>\n",
       "      <td>91804</td>\n",
       "      <td>300965</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4856</th>\n",
       "      <td>TVS Apache 150cc</td>\n",
       "      <td>23000</td>\n",
       "      <td>174638</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4986</th>\n",
       "      <td>TVS Apache 150cc</td>\n",
       "      <td>23000</td>\n",
       "      <td>174638</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5144</th>\n",
       "      <td>Bajaj Discover 125cc</td>\n",
       "      <td>10500</td>\n",
       "      <td>717794</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5200</th>\n",
       "      <td>Bajaj Discover 135cc</td>\n",
       "      <td>13500</td>\n",
       "      <td>477976</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5311</th>\n",
       "      <td>Bajaj Discover 135cc</td>\n",
       "      <td>13500</td>\n",
       "      <td>477976</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5670</th>\n",
       "      <td>Honda CBR 250R Repsol</td>\n",
       "      <td>106000</td>\n",
       "      <td>233011</td>\n",
       "      <td>Second Owner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6011</th>\n",
       "      <td>Royal Enfield Bullet Twinspark 500cc</td>\n",
       "      <td>82000</td>\n",
       "      <td>146642</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7311</th>\n",
       "      <td>TVS Apache RTR 180cc</td>\n",
       "      <td>70000</td>\n",
       "      <td>207102</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7603</th>\n",
       "      <td>Yamaha SZS 150cc</td>\n",
       "      <td>28000</td>\n",
       "      <td>566931</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7633</th>\n",
       "      <td>Hero Splendor Pro 100cc</td>\n",
       "      <td>18000</td>\n",
       "      <td>750000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7872</th>\n",
       "      <td>Bajaj Discover 125cc</td>\n",
       "      <td>29600</td>\n",
       "      <td>105000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8183</th>\n",
       "      <td>Royal Enfield Thunderbird 350cc</td>\n",
       "      <td>54000</td>\n",
       "      <td>250841</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8345</th>\n",
       "      <td>Royal Enfield Classic 350cc</td>\n",
       "      <td>113000</td>\n",
       "      <td>286055</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8365</th>\n",
       "      <td>Royal Enfield Classic 500cc</td>\n",
       "      <td>110000</td>\n",
       "      <td>194992</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8441</th>\n",
       "      <td>Hero Passion Pro 100cc</td>\n",
       "      <td>20000</td>\n",
       "      <td>150000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8689</th>\n",
       "      <td>Bajaj Pulsar 150cc</td>\n",
       "      <td>28000</td>\n",
       "      <td>109521</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8830</th>\n",
       "      <td>Royal Enfield Classic 500cc</td>\n",
       "      <td>150000</td>\n",
       "      <td>141644</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8905</th>\n",
       "      <td>Royal Enfield Thunderbird 350cc</td>\n",
       "      <td>54000</td>\n",
       "      <td>250841</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8911</th>\n",
       "      <td>Hero Passion Pro 100cc</td>\n",
       "      <td>18000</td>\n",
       "      <td>253621</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8997</th>\n",
       "      <td>TVS Apache RTR 160cc</td>\n",
       "      <td>24000</td>\n",
       "      <td>266028</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9160</th>\n",
       "      <td>Honda CB Shine 125cc</td>\n",
       "      <td>20000</td>\n",
       "      <td>654984</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9248</th>\n",
       "      <td>Bajaj Pulsar 150cc</td>\n",
       "      <td>29500</td>\n",
       "      <td>115152</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 bike_name   price  kms_driven         owner  \\\n",
       "50                         Yamaha FZ 150cc   40000      357000   First Owner   \n",
       "57                         Yamaha SZ 150cc   25000      172082   First Owner   \n",
       "589         Honda CB Unicorn Dazzler 150cc   16800      134200   First Owner   \n",
       "955                   Hero Karizma R 223cc   27000      101250   Third Owner   \n",
       "1244                  Hero Karizma R 223cc   27000      101250   Third Owner   \n",
       "1280                    Bajaj CT 100 100cc   10600      107000   First Owner   \n",
       "1872                Honda Dream Yuga 110cc   30000      105500   First Owner   \n",
       "2112                Honda Dream Yuga 110cc   30000      105500   First Owner   \n",
       "2658                  Bajaj Discover 125cc   13000      135000   First Owner   \n",
       "2759       Royal Enfield Thunderbird 350cc  110000      100670   First Owner   \n",
       "2939                  Bajaj Discover 100cc   27000      102892   First Owner   \n",
       "3398                  Bajaj Discover 125cc   18000      403833   First Owner   \n",
       "3401       Royal Enfield Thunderbird 350cc  110000      100670   First Owner   \n",
       "3482                      Yamaha FZs 150cc   65000      216000   First Owner   \n",
       "3587                  Bajaj Discover 125cc   13000      135000   First Owner   \n",
       "4664    Royal Enfield Bullet Electra 350cc   41000      120000   First Owner   \n",
       "4760    Royal Enfield Bullet Electra 350cc   91804      300965   First Owner   \n",
       "4856                      TVS Apache 150cc   23000      174638   First Owner   \n",
       "4986                      TVS Apache 150cc   23000      174638   First Owner   \n",
       "5144                  Bajaj Discover 125cc   10500      717794   First Owner   \n",
       "5200                  Bajaj Discover 135cc   13500      477976   First Owner   \n",
       "5311                  Bajaj Discover 135cc   13500      477976   First Owner   \n",
       "5670                 Honda CBR 250R Repsol  106000      233011  Second Owner   \n",
       "6011  Royal Enfield Bullet Twinspark 500cc   82000      146642   First Owner   \n",
       "7311                  TVS Apache RTR 180cc   70000      207102   First Owner   \n",
       "7603                      Yamaha SZS 150cc   28000      566931   First Owner   \n",
       "7633               Hero Splendor Pro 100cc   18000      750000   First Owner   \n",
       "7872                  Bajaj Discover 125cc   29600      105000   First Owner   \n",
       "8183       Royal Enfield Thunderbird 350cc   54000      250841   First Owner   \n",
       "8345           Royal Enfield Classic 350cc  113000      286055   First Owner   \n",
       "8365           Royal Enfield Classic 500cc  110000      194992   First Owner   \n",
       "8441                Hero Passion Pro 100cc   20000      150000   First Owner   \n",
       "8689                    Bajaj Pulsar 150cc   28000      109521   First Owner   \n",
       "8830           Royal Enfield Classic 500cc  150000      141644   First Owner   \n",
       "8905       Royal Enfield Thunderbird 350cc   54000      250841   First Owner   \n",
       "8911                Hero Passion Pro 100cc   18000      253621   First Owner   \n",
       "8997                  TVS Apache RTR 160cc   24000      266028   First Owner   \n",
       "9160                  Honda CB Shine 125cc   20000      654984   First Owner   \n",
       "9248                    Bajaj Pulsar 150cc   29500      115152   First Owner   \n",
       "\n",
       "      age  \n",
       "50      8  \n",
       "57      9  \n",
       "589    10  \n",
       "955    14  \n",
       "1244   14  \n",
       "1280   16  \n",
       "1872    8  \n",
       "2112    8  \n",
       "2658   10  \n",
       "2759    5  \n",
       "2939    8  \n",
       "3398   15  \n",
       "3401    5  \n",
       "3482    8  \n",
       "3587   10  \n",
       "4664   17  \n",
       "4760    7  \n",
       "4856   14  \n",
       "4986   14  \n",
       "5144   15  \n",
       "5200   14  \n",
       "5311   14  \n",
       "5670    4  \n",
       "6011    7  \n",
       "7311    2  \n",
       "7603    5  \n",
       "7633   10  \n",
       "7872   15  \n",
       "8183   11  \n",
       "8345    5  \n",
       "8365   11  \n",
       "8441   16  \n",
       "8689   10  \n",
       "8830   11  \n",
       "8905   11  \n",
       "8911   11  \n",
       "8997   10  \n",
       "9160   10  \n",
       "9248    8  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.loc[(df5['kms_driven']>upper_limit_1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "31f2c138",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old_Data_1: 31963\n",
      "New_Data_1: 31924\n",
      "No.of.outliers_1: 39\n"
     ]
    }
   ],
   "source": [
    "df6=df5.loc[(df5['kms_driven']<upper_limit_1)]\n",
    "print('Old_Data_1:',len(df5))\n",
    "print('New_Data_1:',len(df6))\n",
    "print('No.of.outliers_1:',len(df5)-len(df6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "26980d18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        17654\n",
       "1        11000\n",
       "2          110\n",
       "3        16329\n",
       "4        10000\n",
       "         ...  \n",
       "32643    22000\n",
       "32644     6639\n",
       "32645    20373\n",
       "32646    84186\n",
       "32647    60857\n",
       "Name: kms_driven, Length: 31924, dtype: int32"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6['kms_driven']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c20e7d39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_name</th>\n",
       "      <th>price</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>owner</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TVS Star City Plus Dual Tone 110cc</td>\n",
       "      <td>35000</td>\n",
       "      <td>17654</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Royal Enfield Classic 350cc</td>\n",
       "      <td>119900</td>\n",
       "      <td>11000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Triumph Daytona 675R</td>\n",
       "      <td>600000</td>\n",
       "      <td>110</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TVS Apache RTR 180cc</td>\n",
       "      <td>65000</td>\n",
       "      <td>16329</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yamaha FZ S V 2.0 150cc-Ltd. Edition</td>\n",
       "      <td>80000</td>\n",
       "      <td>10000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Yamaha FZs 150cc</td>\n",
       "      <td>53499</td>\n",
       "      <td>25000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda CB Hornet 160R  ABS DLX</td>\n",
       "      <td>85000</td>\n",
       "      <td>8200</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hero Splendor Plus Self Alloy 100cc</td>\n",
       "      <td>45000</td>\n",
       "      <td>12645</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Royal Enfield Thunderbird X 350cc</td>\n",
       "      <td>145000</td>\n",
       "      <td>9190</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Royal Enfield Classic Desert Storm 500cc</td>\n",
       "      <td>88000</td>\n",
       "      <td>19000</td>\n",
       "      <td>Second Owner</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  bike_name   price  kms_driven         owner  \\\n",
       "0        TVS Star City Plus Dual Tone 110cc   35000       17654   First Owner   \n",
       "1               Royal Enfield Classic 350cc  119900       11000   First Owner   \n",
       "2                      Triumph Daytona 675R  600000         110   First Owner   \n",
       "3                      TVS Apache RTR 180cc   65000       16329   First Owner   \n",
       "4      Yamaha FZ S V 2.0 150cc-Ltd. Edition   80000       10000   First Owner   \n",
       "5                          Yamaha FZs 150cc   53499       25000   First Owner   \n",
       "6             Honda CB Hornet 160R  ABS DLX   85000        8200   First Owner   \n",
       "7       Hero Splendor Plus Self Alloy 100cc   45000       12645   First Owner   \n",
       "8         Royal Enfield Thunderbird X 350cc  145000        9190   First Owner   \n",
       "9  Royal Enfield Classic Desert Storm 500cc   88000       19000  Second Owner   \n",
       "\n",
       "   age  \n",
       "0    3  \n",
       "1    4  \n",
       "2    8  \n",
       "3    4  \n",
       "4    3  \n",
       "5    6  \n",
       "6    3  \n",
       "7    3  \n",
       "8    3  \n",
       "9    7  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "77c30931",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31924, 5)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "332c8fc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_4032\\4138401387.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df6['price'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='price', ylabel='Density'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df6['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e8e47207",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df6['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "71980b62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.192400e+04\n",
       "mean     6.936156e+04\n",
       "std      9.139900e+04\n",
       "min      4.400000e+03\n",
       "25%      2.500000e+04\n",
       "50%      4.300000e+04\n",
       "75%      8.200000e+04\n",
       "max      1.900000e+06\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6['price'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "58a97923",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "upper_limit_2: 105921.15687094998\n"
     ]
    }
   ],
   "source": [
    "upper_limit_2=df6['price'].mean()+0.4*df6['price'].std()\n",
    "print('upper_limit_2:',upper_limit_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "aea1af76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_name</th>\n",
       "      <th>price</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>owner</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Royal Enfield Classic 350cc</td>\n",
       "      <td>119900</td>\n",
       "      <td>11000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Triumph Daytona 675R</td>\n",
       "      <td>600000</td>\n",
       "      <td>110</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Royal Enfield Thunderbird X 350cc</td>\n",
       "      <td>145000</td>\n",
       "      <td>9190</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Bajaj Pulsar RS200 ABS</td>\n",
       "      <td>120000</td>\n",
       "      <td>23000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Benelli 302R 300CC</td>\n",
       "      <td>240000</td>\n",
       "      <td>15025</td>\n",
       "      <td>Second Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32600</th>\n",
       "      <td>Yamaha Fazer 25 250cc</td>\n",
       "      <td>123000</td>\n",
       "      <td>14500</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32626</th>\n",
       "      <td>Harley-Davidson Street 750 ABS</td>\n",
       "      <td>450000</td>\n",
       "      <td>3430</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32627</th>\n",
       "      <td>Bajaj Dominar 400 ABS</td>\n",
       "      <td>139000</td>\n",
       "      <td>21300</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32636</th>\n",
       "      <td>KTM RC 390cc</td>\n",
       "      <td>196700</td>\n",
       "      <td>13216</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32638</th>\n",
       "      <td>Yamaha Fazer 25 250cc</td>\n",
       "      <td>123000</td>\n",
       "      <td>14500</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4512 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               bike_name   price  kms_driven         owner  \\\n",
       "1            Royal Enfield Classic 350cc  119900       11000   First Owner   \n",
       "2                   Triumph Daytona 675R  600000         110   First Owner   \n",
       "8      Royal Enfield Thunderbird X 350cc  145000        9190   First Owner   \n",
       "16                Bajaj Pulsar RS200 ABS  120000       23000   First Owner   \n",
       "18                    Benelli 302R 300CC  240000       15025  Second Owner   \n",
       "...                                  ...     ...         ...           ...   \n",
       "32600              Yamaha Fazer 25 250cc  123000       14500   First Owner   \n",
       "32626     Harley-Davidson Street 750 ABS  450000        3430   First Owner   \n",
       "32627              Bajaj Dominar 400 ABS  139000       21300   First Owner   \n",
       "32636                       KTM RC 390cc  196700       13216   First Owner   \n",
       "32638              Yamaha Fazer 25 250cc  123000       14500   First Owner   \n",
       "\n",
       "       age  \n",
       "1        4  \n",
       "2        8  \n",
       "8        3  \n",
       "16       3  \n",
       "18       3  \n",
       "...    ...  \n",
       "32600    4  \n",
       "32626    4  \n",
       "32627    4  \n",
       "32636    4  \n",
       "32638    4  \n",
       "\n",
       "[4512 rows x 5 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.loc[(df6['price']>upper_limit_2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "15b69e78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_name</th>\n",
       "      <th>price</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>owner</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TVS Star City Plus Dual Tone 110cc</td>\n",
       "      <td>35000</td>\n",
       "      <td>17654</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TVS Apache RTR 180cc</td>\n",
       "      <td>65000</td>\n",
       "      <td>16329</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yamaha FZ S V 2.0 150cc-Ltd. Edition</td>\n",
       "      <td>80000</td>\n",
       "      <td>10000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Yamaha FZs 150cc</td>\n",
       "      <td>53499</td>\n",
       "      <td>25000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda CB Hornet 160R  ABS DLX</td>\n",
       "      <td>85000</td>\n",
       "      <td>8200</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              bike_name  price  kms_driven        owner  age\n",
       "0    TVS Star City Plus Dual Tone 110cc  35000       17654  First Owner    3\n",
       "3                  TVS Apache RTR 180cc  65000       16329  First Owner    4\n",
       "4  Yamaha FZ S V 2.0 150cc-Ltd. Edition  80000       10000  First Owner    3\n",
       "5                      Yamaha FZs 150cc  53499       25000  First Owner    6\n",
       "6         Honda CB Hornet 160R  ABS DLX  85000        8200  First Owner    3"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7=df6.loc[(df6['price']<upper_limit_2)]\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "100dac18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old_Data_2: 31924\n",
      "New_Data_2: 27412\n",
      "No.of.outliers_2: 4512\n"
     ]
    }
   ],
   "source": [
    "print('Old_Data_2:',len(df6))\n",
    "print('New_Data_2:',len(df7))\n",
    "print('No.of.outliers_2:',len(df6)-len(df7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0d46d9ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105900"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7['price'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "04835ae6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4400"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7['price'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "da0eab33",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b1cac052",
   "metadata": {},
   "outputs": [],
   "source": [
    "le_owner=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0e547004",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_4032\\912226171.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df7['Owner_Type']=le_owner.fit_transform(df7['owner'])\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>bike_name</th>\n",
       "      <th>price</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>owner</th>\n",
       "      <th>age</th>\n",
       "      <th>Owner_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TVS Star City Plus Dual Tone 110cc</td>\n",
       "      <td>35000</td>\n",
       "      <td>17654</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TVS Apache RTR 180cc</td>\n",
       "      <td>65000</td>\n",
       "      <td>16329</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yamaha FZ S V 2.0 150cc-Ltd. Edition</td>\n",
       "      <td>80000</td>\n",
       "      <td>10000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Yamaha FZs 150cc</td>\n",
       "      <td>53499</td>\n",
       "      <td>25000</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda CB Hornet 160R  ABS DLX</td>\n",
       "      <td>85000</td>\n",
       "      <td>8200</td>\n",
       "      <td>First Owner</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              bike_name  price  kms_driven        owner  age  \\\n",
       "0    TVS Star City Plus Dual Tone 110cc  35000       17654  First Owner    3   \n",
       "3                  TVS Apache RTR 180cc  65000       16329  First Owner    4   \n",
       "4  Yamaha FZ S V 2.0 150cc-Ltd. Edition  80000       10000  First Owner    3   \n",
       "5                      Yamaha FZs 150cc  53499       25000  First Owner    6   \n",
       "6         Honda CB Hornet 160R  ABS DLX  85000        8200  First Owner    3   \n",
       "\n",
       "   Owner_Type  \n",
       "0           0  \n",
       "3           0  \n",
       "4           0  \n",
       "5           0  \n",
       "6           0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7['Owner_Type']=le_owner.fit_transform(df7['owner'])\n",
    "df7.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "106d8d86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['First Owner', 'Second Owner', 'Third Owner',\n",
       "       'Fourth Owner Or More'], dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7['owner'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6a715248",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_name</th>\n",
       "      <th>price</th>\n",
       "      <th>kms_driven</th>\n",
       "      <th>age</th>\n",
       "      <th>Owner_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TVS Star City Plus Dual Tone 110cc</td>\n",
       "      <td>35000</td>\n",
       "      <td>17654</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TVS Apache RTR 180cc</td>\n",
       "      <td>65000</td>\n",
       "      <td>16329</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yamaha FZ S V 2.0 150cc-Ltd. Edition</td>\n",
       "      <td>80000</td>\n",
       "      <td>10000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Yamaha FZs 150cc</td>\n",
       "      <td>53499</td>\n",
       "      <td>25000</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda CB Hornet 160R  ABS DLX</td>\n",
       "      <td>85000</td>\n",
       "      <td>8200</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              bike_name  price  kms_driven  age  Owner_Type\n",
       "0    TVS Star City Plus Dual Tone 110cc  35000       17654    3           0\n",
       "3                  TVS Apache RTR 180cc  65000       16329    4           0\n",
       "4  Yamaha FZ S V 2.0 150cc-Ltd. Edition  80000       10000    3           0\n",
       "5                      Yamaha FZs 150cc  53499       25000    6           0\n",
       "6         Honda CB Hornet 160R  ABS DLX  85000        8200    3           0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8=df7.drop(['owner'],axis=1)\n",
    "df8.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "29af5991",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 3, 1])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8['Owner_Type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8bfc364d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bajaj  Pulsar 180cc</th>\n",
       "      <th>Bajaj Avenger 150cc</th>\n",
       "      <th>Bajaj Avenger 180cc</th>\n",
       "      <th>Bajaj Avenger 200cc</th>\n",
       "      <th>Bajaj Avenger 220cc</th>\n",
       "      <th>Bajaj Avenger Cruise 220</th>\n",
       "      <th>Bajaj Avenger Cruise 220 ABS</th>\n",
       "      <th>Bajaj Avenger Street 150</th>\n",
       "      <th>Bajaj Avenger Street 160 ABS</th>\n",
       "      <th>Bajaj Avenger Street 180</th>\n",
       "      <th>...</th>\n",
       "      <th>Yamaha SZX 150cc</th>\n",
       "      <th>Yamaha Saluto 125cc</th>\n",
       "      <th>Yamaha Saluto 125cc Disc Special Edition</th>\n",
       "      <th>Yamaha Saluto 125cc-Special Edition</th>\n",
       "      <th>Yamaha Saluto RX 110cc</th>\n",
       "      <th>Yamaha YBR 110cc</th>\n",
       "      <th>Yamaha YZF-R15 150cc</th>\n",
       "      <th>Yamaha YZF-R15 2.0 150cc</th>\n",
       "      <th>Yamaha YZF-R15 S 150cc</th>\n",
       "      <th>Yamaha YZF-R15 V3 150cc</th>\n",
       "    </tr>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 333 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Bajaj  Pulsar 180cc  Bajaj Avenger 150cc  Bajaj Avenger 180cc  \\\n",
       "0                     0                    0                    0   \n",
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       "10                    0                    0                    0   \n",
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       "12                    0                    0                    0   \n",
       "\n",
       "    Bajaj Avenger 200cc  Bajaj Avenger 220cc  Bajaj Avenger Cruise 220  \\\n",
       "0                     0                    0                         0   \n",
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       "4                     0                    0                         0   \n",
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       "    Bajaj Avenger Cruise 220 ABS  Bajaj Avenger Street 150  \\\n",
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       "\n",
       "    Bajaj Avenger Street 160 ABS  Bajaj Avenger Street 180  ...  \\\n",
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       "10                             0                         0  ...   \n",
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       "12                             0                         0  ...   \n",
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       "    Yamaha SZX 150cc  Yamaha Saluto 125cc  \\\n",
       "0                  0                    0   \n",
       "3                  0                    0   \n",
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       "9                  0                    0   \n",
       "10                 0                    0   \n",
       "11                 0                    0   \n",
       "12                 0                    0   \n",
       "\n",
       "    Yamaha Saluto 125cc Disc Special Edition  \\\n",
       "0                                          0   \n",
       "3                                          0   \n",
       "4                                          0   \n",
       "5                                          0   \n",
       "6                                          0   \n",
       "7                                          0   \n",
       "9                                          0   \n",
       "10                                         0   \n",
       "11                                         0   \n",
       "12                                         0   \n",
       "\n",
       "    Yamaha Saluto 125cc-Special Edition  Yamaha Saluto RX 110cc  \\\n",
       "0                                     0                       0   \n",
       "3                                     0                       0   \n",
       "4                                     0                       0   \n",
       "5                                     0                       0   \n",
       "6                                     0                       0   \n",
       "7                                     0                       0   \n",
       "9                                     0                       0   \n",
       "10                                    0                       0   \n",
       "11                                    0                       0   \n",
       "12                                    0                       0   \n",
       "\n",
       "    Yamaha YBR 110cc  Yamaha YZF-R15 150cc  Yamaha YZF-R15 2.0 150cc  \\\n",
       "0                  0                     0                         0   \n",
       "3                  0                     0                         0   \n",
       "4                  0                     0                         0   \n",
       "5                  0                     0                         0   \n",
       "6                  0                     0                         0   \n",
       "7                  0                     0                         0   \n",
       "9                  0                     0                         0   \n",
       "10                 0                     0                         1   \n",
       "11                 0                     0                         0   \n",
       "12                 0                     0                         0   \n",
       "\n",
       "    Yamaha YZF-R15 S 150cc  Yamaha YZF-R15 V3 150cc  \n",
       "0                        0                        0  \n",
       "3                        0                        0  \n",
       "4                        0                        0  \n",
       "5                        0                        0  \n",
       "6                        0                        0  \n",
       "7                        0                        0  \n",
       "9                        0                        0  \n",
       "10                       0                        0  \n",
       "11                       0                        0  \n",
       "12                       0                        0  \n",
       "\n",
       "[10 rows x 333 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies=pd.get_dummies(df8['bike_name'])\n",
    "dummies.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d3c69a84",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "       Bajaj  Pulsar 180cc  Bajaj Avenger 150cc  Bajaj Avenger 180cc  \\\n",
       "0                        0                    0                    0   \n",
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       "\n",
       "       Bajaj Avenger 200cc  Bajaj Avenger 220cc  Bajaj Avenger Cruise 220  \\\n",
       "0                        0                    0                         0   \n",
       "3                        0                    0                         0   \n",
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       "       Bajaj Avenger Cruise 220 ABS  Bajaj Avenger Street 150  \\\n",
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       "\n",
       "       Bajaj Avenger Street 160 ABS  Bajaj Avenger Street 180  ...  \\\n",
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       "\n",
       "       Yamaha SZR 150cc  Yamaha SZX 150cc  Yamaha Saluto 125cc  \\\n",
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       "\n",
       "       Yamaha YBR 110cc  Yamaha YZF-R15 150cc  Yamaha YZF-R15 2.0 150cc  \\\n",
       "0                     0                     0                         0   \n",
       "3                     0                     0                         0   \n",
       "4                     0                     0                         0   \n",
       "5                     0                     0                         0   \n",
       "6                     0                     0                         0   \n",
       "...                 ...                   ...                       ...   \n",
       "32643                 0                     0                         0   \n",
       "32644                 0                     0                         0   \n",
       "32645                 0                     0                         0   \n",
       "32646                 0                     0                         0   \n",
       "32647                 0                     0                         0   \n",
       "\n",
       "       Yamaha YZF-R15 S 150cc  \n",
       "0                           0  \n",
       "3                           0  \n",
       "4                           0  \n",
       "5                           0  \n",
       "6                           0  \n",
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       "32643                       0  \n",
       "32644                       0  \n",
       "32645                       0  \n",
       "32646                       0  \n",
       "32647                       0  \n",
       "\n",
       "[27412 rows x 332 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies1=dummies.drop(['Yamaha YZF-R15 V3 150cc'],axis=1)\n",
    "dummies1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8240b93c",
   "metadata": {},
   "outputs": [
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       "                              bike_name  price  kms_driven  age  Owner_Type  \\\n",
       "0    TVS Star City Plus Dual Tone 110cc  35000       17654    3           0   \n",
       "3                  TVS Apache RTR 180cc  65000       16329    4           0   \n",
       "4  Yamaha FZ S V 2.0 150cc-Ltd. Edition  80000       10000    3           0   \n",
       "5                      Yamaha FZs 150cc  53499       25000    6           0   \n",
       "6         Honda CB Hornet 160R  ABS DLX  85000        8200    3           0   \n",
       "\n",
       "   Bajaj  Pulsar 180cc  Bajaj Avenger 150cc  Bajaj Avenger 180cc  \\\n",
       "0                    0                    0                    0   \n",
       "3                    0                    0                    0   \n",
       "4                    0                    0                    0   \n",
       "5                    0                    0                    0   \n",
       "6                    0                    0                    0   \n",
       "\n",
       "   Bajaj Avenger 200cc  Bajaj Avenger 220cc  ...  Yamaha SZR 150cc  \\\n",
       "0                    0                    0  ...                 0   \n",
       "3                    0                    0  ...                 0   \n",
       "4                    0                    0  ...                 0   \n",
       "5                    0                    0  ...                 0   \n",
       "6                    0                    0  ...                 0   \n",
       "\n",
       "   Yamaha SZX 150cc  Yamaha Saluto 125cc  \\\n",
       "0                 0                    0   \n",
       "3                 0                    0   \n",
       "4                 0                    0   \n",
       "5                 0                    0   \n",
       "6                 0                    0   \n",
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       "   Yamaha Saluto 125cc Disc Special Edition  \\\n",
       "0                                         0   \n",
       "3                                         0   \n",
       "4                                         0   \n",
       "5                                         0   \n",
       "6                                         0   \n",
       "\n",
       "   Yamaha Saluto 125cc-Special Edition  Yamaha Saluto RX 110cc  \\\n",
       "0                                    0                       0   \n",
       "3                                    0                       0   \n",
       "4                                    0                       0   \n",
       "5                                    0                       0   \n",
       "6                                    0                       0   \n",
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       "   Yamaha YBR 110cc  Yamaha YZF-R15 150cc  Yamaha YZF-R15 2.0 150cc  \\\n",
       "0                 0                     0                         0   \n",
       "3                 0                     0                         0   \n",
       "4                 0                     0                         0   \n",
       "5                 0                     0                         0   \n",
       "6                 0                     0                         0   \n",
       "\n",
       "   Yamaha YZF-R15 S 150cc  \n",
       "0                       0  \n",
       "3                       0  \n",
       "4                       0  \n",
       "5                       0  \n",
       "6                       0  \n",
       "\n",
       "[5 rows x 337 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df9=pd.concat([df8,dummies1],axis=1)\n",
    "df9.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3f465e04",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   price  kms_driven  age  Owner_Type  Bajaj  Pulsar 180cc  \\\n",
       "0  35000       17654    3           0                    0   \n",
       "3  65000       16329    4           0                    0   \n",
       "4  80000       10000    3           0                    0   \n",
       "5  53499       25000    6           0                    0   \n",
       "6  85000        8200    3           0                    0   \n",
       "\n",
       "   Bajaj Avenger 150cc  Bajaj Avenger 180cc  Bajaj Avenger 200cc  \\\n",
       "0                    0                    0                    0   \n",
       "3                    0                    0                    0   \n",
       "4                    0                    0                    0   \n",
       "5                    0                    0                    0   \n",
       "6                    0                    0                    0   \n",
       "\n",
       "   Bajaj Avenger 220cc  Bajaj Avenger Cruise 220  ...  Yamaha SZR 150cc  \\\n",
       "0                    0                         0  ...                 0   \n",
       "3                    0                         0  ...                 0   \n",
       "4                    0                         0  ...                 0   \n",
       "5                    0                         0  ...                 0   \n",
       "6                    0                         0  ...                 0   \n",
       "\n",
       "   Yamaha SZX 150cc  Yamaha Saluto 125cc  \\\n",
       "0                 0                    0   \n",
       "3                 0                    0   \n",
       "4                 0                    0   \n",
       "5                 0                    0   \n",
       "6                 0                    0   \n",
       "\n",
       "   Yamaha Saluto 125cc Disc Special Edition  \\\n",
       "0                                         0   \n",
       "3                                         0   \n",
       "4                                         0   \n",
       "5                                         0   \n",
       "6                                         0   \n",
       "\n",
       "   Yamaha Saluto 125cc-Special Edition  Yamaha Saluto RX 110cc  \\\n",
       "0                                    0                       0   \n",
       "3                                    0                       0   \n",
       "4                                    0                       0   \n",
       "5                                    0                       0   \n",
       "6                                    0                       0   \n",
       "\n",
       "   Yamaha YBR 110cc  Yamaha YZF-R15 150cc  Yamaha YZF-R15 2.0 150cc  \\\n",
       "0                 0                     0                         0   \n",
       "3                 0                     0                         0   \n",
       "4                 0                     0                         0   \n",
       "5                 0                     0                         0   \n",
       "6                 0                     0                         0   \n",
       "\n",
       "   Yamaha YZF-R15 S 150cc  \n",
       "0                       0  \n",
       "3                       0  \n",
       "4                       0  \n",
       "5                       0  \n",
       "6                       0  \n",
       "\n",
       "[5 rows x 336 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10=df9.drop(['bike_name'],axis=1)\n",
    "df10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "24d7d667",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17654</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16329</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25000</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8200</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 335 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   kms_driven  age  Owner_Type  Bajaj  Pulsar 180cc  Bajaj Avenger 150cc  \\\n",
       "0       17654    3           0                    0                    0   \n",
       "3       16329    4           0                    0                    0   \n",
       "4       10000    3           0                    0                    0   \n",
       "5       25000    6           0                    0                    0   \n",
       "6        8200    3           0                    0                    0   \n",
       "\n",
       "   Bajaj Avenger 180cc  Bajaj Avenger 200cc  Bajaj Avenger 220cc  \\\n",
       "0                    0                    0                    0   \n",
       "3                    0                    0                    0   \n",
       "4                    0                    0                    0   \n",
       "5                    0                    0                    0   \n",
       "6                    0                    0                    0   \n",
       "\n",
       "   Bajaj Avenger Cruise 220  Bajaj Avenger Cruise 220 ABS  ...  \\\n",
       "0                         0                             0  ...   \n",
       "3                         0                             0  ...   \n",
       "4                         0                             0  ...   \n",
       "5                         0                             0  ...   \n",
       "6                         0                             0  ...   \n",
       "\n",
       "   Yamaha SZR 150cc  Yamaha SZX 150cc  Yamaha Saluto 125cc  \\\n",
       "0                 0                 0                    0   \n",
       "3                 0                 0                    0   \n",
       "4                 0                 0                    0   \n",
       "5                 0                 0                    0   \n",
       "6                 0                 0                    0   \n",
       "\n",
       "   Yamaha Saluto 125cc Disc Special Edition  \\\n",
       "0                                         0   \n",
       "3                                         0   \n",
       "4                                         0   \n",
       "5                                         0   \n",
       "6                                         0   \n",
       "\n",
       "   Yamaha Saluto 125cc-Special Edition  Yamaha Saluto RX 110cc  \\\n",
       "0                                    0                       0   \n",
       "3                                    0                       0   \n",
       "4                                    0                       0   \n",
       "5                                    0                       0   \n",
       "6                                    0                       0   \n",
       "\n",
       "   Yamaha YBR 110cc  Yamaha YZF-R15 150cc  Yamaha YZF-R15 2.0 150cc  \\\n",
       "0                 0                     0                         0   \n",
       "3                 0                     0                         0   \n",
       "4                 0                     0                         0   \n",
       "5                 0                     0                         0   \n",
       "6                 0                     0                         0   \n",
       "\n",
       "   Yamaha YZF-R15 S 150cc  \n",
       "0                       0  \n",
       "3                       0  \n",
       "4                       0  \n",
       "5                       0  \n",
       "6                       0  \n",
       "\n",
       "[5 rows x 335 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=df10.drop(['price'],axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a91d955c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    35000\n",
       "3    65000\n",
       "4    80000\n",
       "5    53499\n",
       "6    85000\n",
       "Name: price, dtype: int32"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df10['price']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4eadd2e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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=0.2,random_state=10)\n",
    "from sklearn import linear_model\n",
    "model=linear_model.LinearRegression()\n",
    "model.fit(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6e3655bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9552987830246209"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "306699c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0          TVS Star City Plus Dual Tone 110cc\n",
      "3                        TVS Apache RTR 180cc\n",
      "4        Yamaha FZ S V 2.0 150cc-Ltd. Edition\n",
      "5                            Yamaha FZs 150cc\n",
      "6               Honda CB Hornet 160R  ABS DLX\n",
      "                         ...                 \n",
      "32643                  Hero Passion Pro 100cc\n",
      "32644                    TVS Apache RTR 180cc\n",
      "32645                Bajaj Avenger Street 220\n",
      "32646               Hero Super Splendor 125cc\n",
      "32647                      Bajaj Pulsar 150cc\n",
      "Name: bike_name, Length: 27412, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df9['bike_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "68df4d79",
   "metadata": {},
   "outputs": [],
   "source": [
    "### First owner  =0\n",
    "### Second owner =2\n",
    "### Third owner  =3\n",
    "### Fourth owner =1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6a41dd0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location='Bajaj Avenger 150cc'\n",
    "np.where(X.columns==location)[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a5845c9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_price(location,kms_driven,age,Owner_Type):    \n",
    "    loc_index = np.where(X.columns==location)[0][0]\n",
    "\n",
    "    x = np.zeros(len(X.columns)) \n",
    "    x[0] = kms_driven\n",
    "    x[1] = age\n",
    "    x[2] = Owner_Type\n",
    "    if loc_index >= 0:\n",
    "        x[loc_index] = 1\n",
    "\n",
    "    return model.predict([x])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a7480323",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          TVS Star City Plus Dual Tone 110cc\n",
       "3                        TVS Apache RTR 180cc\n",
       "4        Yamaha FZ S V 2.0 150cc-Ltd. Edition\n",
       "5                            Yamaha FZs 150cc\n",
       "6               Honda CB Hornet 160R  ABS DLX\n",
       "                         ...                 \n",
       "32643                  Hero Passion Pro 100cc\n",
       "32644                    TVS Apache RTR 180cc\n",
       "32645                Bajaj Avenger Street 220\n",
       "32646               Hero Super Splendor 125cc\n",
       "32647                      Bajaj Pulsar 150cc\n",
       "Name: bike_name, Length: 27412, dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9['bike_name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a3adba3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "35345.646518917216"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('TVS Star City Plus Dual Tone 110cc',15000,3,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e93d61d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "113194.85137021846"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('Royal Enfield Classic 350cc',0,0,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1199ddb5",
   "metadata": {},
   "source": [
    "## Evaluating the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "eb8d00f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "bike_predictions=model.predict(x_test)\n",
    "mse = mean_squared_error(y_test,bike_predictions)\n",
    "rmse=np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d28b5261",
   "metadata": {},
   "source": [
    "## Joblib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1bffc6c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Snd.joblib']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "dump(predict_price, 'Snd.joblib') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab03964e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2bb50c8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b279fe9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "35345.646518917216"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "import numpy as np\n",
    "predict_price=load('Snd.joblib')\n",
    "predict_price('TVS Star City Plus Dual Tone 110cc',15000,3,0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fe82ff9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "77509ab8",
   "metadata": {},
   "source": [
    "## Pickling the model file for deployment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9d47e2d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle \n",
    "pickle.dump(model,open('Regression.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "eb005c29",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle \n",
    "#pickle.dump(predict_price,open('Regression_func.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "965c33b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickled_model = pickle.load(open('Regression_func.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b120c429",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'function' object has no attribute 'predict_price'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[63], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#testing \u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mpickled_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_price\u001b[49m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTVS Star City Plus Dual Tone 110cc\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;241m15000\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m0\u001b[39m)\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'function' object has no attribute 'predict_price'"
     ]
    }
   ],
   "source": [
    "#testing \n",
    "pickled_model.predict_price('TVS Star City Plus Dual Tone 110cc',15000,3,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "763b22df",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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