backend/notebooks/car_value_predict_note.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import mysql.connector as connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    db = connection.connect(host=\"127.0.0.1\", database='car', user=\"root\", passwd=\"\", use_pure=True)\n",
    "    query = \"select cs.year, cs.listing_mileage, cs.listing_price_in_cents from car_solds cs inner join car_models mo on cs.car_model_id = mo.id inner join car_makes ma on mo.car_make_id=ma.id where ma.name like '%Kia%';\"\n",
    "    df = pd.read_sql(query, db)\n",
    "    db.close()\n",
    "except Exception as e:\n",
    "    print(str(e))\n",
    "finally:\n",
    "    db.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>year</th>\n",
       "      <th>listing_mileage</th>\n",
       "      <th>listing_price_in_cents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>53960.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>86967.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021</td>\n",
       "      <td>32261.0</td>\n",
       "      <td>2297800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2389300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  listing_mileage  listing_price_in_cents\n",
       "0  2015          53960.0                       0\n",
       "1  2015          86967.0                       0\n",
       "2  2015              NaN                       0\n",
       "3  2021          32261.0                 2297800\n",
       "4  2023             15.0                 2389300"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32241 entries, 0 to 32240\n",
      "Data columns (total 3 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   year                    32241 non-null  object \n",
      " 1   listing_mileage         28782 non-null  float64\n",
      " 2   listing_price_in_cents  32241 non-null  int64  \n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 755.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>listing_mileage</th>\n",
       "      <th>listing_price_in_cents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>28782.000000</td>\n",
       "      <td>3.224100e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>48567.133486</td>\n",
       "      <td>2.360502e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43320.342441</td>\n",
       "      <td>1.317359e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13997.000000</td>\n",
       "      <td>1.612200e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40253.000000</td>\n",
       "      <td>2.310900e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>73069.750000</td>\n",
       "      <td>3.053000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>557784.000000</td>\n",
       "      <td>9.999900e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       listing_mileage  listing_price_in_cents\n",
       "count     28782.000000            3.224100e+04\n",
       "mean      48567.133486            2.360502e+06\n",
       "std       43320.342441            1.317359e+06\n",
       "min           1.000000            0.000000e+00\n",
       "25%       13997.000000            1.612200e+06\n",
       "50%       40253.000000            2.310900e+06\n",
       "75%       73069.750000            3.053000e+06\n",
       "max      557784.000000            9.999900e+06"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3558"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year                         0\n",
       "listing_mileage           3459\n",
       "listing_price_in_cents       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>year</th>\n",
       "      <th>listing_mileage</th>\n",
       "      <th>listing_price_in_cents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021</td>\n",
       "      <td>32261.0</td>\n",
       "      <td>2297800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2389300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020</td>\n",
       "      <td>29583.0</td>\n",
       "      <td>2359000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020</td>\n",
       "      <td>83762.0</td>\n",
       "      <td>1987800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2022</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2229000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  listing_mileage  listing_price_in_cents\n",
       "3  2021          32261.0                 2297800\n",
       "4  2023             15.0                 2389300\n",
       "5  2020          29583.0                 2359000\n",
       "6  2020          83762.0                 1987800\n",
       "9  2022             10.0                 2229000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(subset=['listing_mileage', 'listing_price_in_cents'], inplace=True)\n",
    "df = df[df['listing_price_in_cents'] != 0]\n",
    "final_data = df[['year', 'listing_mileage', 'listing_price_in_cents']]\n",
    "final_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year                      0\n",
       "listing_mileage           0\n",
       "listing_price_in_cents    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 26244 entries, 3 to 32240\n",
      "Data columns (total 3 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   year                    26244 non-null  object \n",
      " 1   listing_mileage         26244 non-null  float64\n",
      " 2   listing_price_in_cents  26244 non-null  int64  \n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 820.1+ KB\n"
     ]
    }
   ],
   "source": [
    "final_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26244, 3)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>year</th>\n",
       "      <th>listing_mileage</th>\n",
       "      <th>listing_price_in_cents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021</td>\n",
       "      <td>32261.0</td>\n",
       "      <td>2297800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2389300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020</td>\n",
       "      <td>29583.0</td>\n",
       "      <td>2359000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020</td>\n",
       "      <td>83762.0</td>\n",
       "      <td>1987800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2022</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2229000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  listing_mileage  listing_price_in_cents\n",
       "3  2021          32261.0                 2297800\n",
       "4  2023             15.0                 2389300\n",
       "5  2020          29583.0                 2359000\n",
       "6  2020          83762.0                 1987800\n",
       "9  2022             10.0                 2229000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 26244 entries, 3 to 32240\n",
      "Data columns (total 3 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   year                    26244 non-null  int64  \n",
      " 1   listing_mileage         26244 non-null  float64\n",
      " 2   listing_price_in_cents  26244 non-null  int64  \n",
      "dtypes: float64(1), int64(2)\n",
      "memory usage: 820.1 KB\n"
     ]
    }
   ],
   "source": [
    "final_data['year'] = pd.to_numeric(df['year'], errors='ignore')\n",
    "final_data.info()\n",
    "final_data['age'] = 2022 - final_data['year']\n",
    "final_data.drop(['year'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>listing_mileage</th>\n",
       "      <th>listing_price_in_cents</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32261.0</td>\n",
       "      <td>2297800</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.0</td>\n",
       "      <td>2389300</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>29583.0</td>\n",
       "      <td>2359000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>83762.0</td>\n",
       "      <td>1987800</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2229000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_mileage  listing_price_in_cents  age\n",
       "3          32261.0                 2297800    1\n",
       "4             15.0                 2389300   -1\n",
       "5          29583.0                 2359000    2\n",
       "6          83762.0                 1987800    2\n",
       "9             10.0                 2229000    0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26244, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "def detect_outlier(data):\n",
    "    outlier = []\n",
    "    threshold = 3\n",
    "    mean = np.mean(data)\n",
    "    std = np.std(data)\n",
    "    for i in data:\n",
    "        z_score = (i - mean)/std\n",
    "        if np.abs(z_score)>threshold:\n",
    "            outlier.append(i)\n",
    "    return outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6000000,\n",
       " 7259300,\n",
       " 6180000,\n",
       " 7259300,\n",
       " 5899800,\n",
       " 5899800,\n",
       " 5999800,\n",
       " 6050000,\n",
       " 6390000,\n",
       " 6899500,\n",
       " 6799500,\n",
       " 5998900,\n",
       " 6050000,\n",
       " 6390000,\n",
       " 6899500,\n",
       " 6799500,\n",
       " 5998900,\n",
       " 6499800,\n",
       " 6200000,\n",
       " 6493100,\n",
       " 5998700,\n",
       " 6159000,\n",
       " 6199700,\n",
       " 5968800,\n",
       " 6045000,\n",
       " 6164100,\n",
       " 6059000,\n",
       " 6918300,\n",
       " 6488800,\n",
       " 6101000,\n",
       " 6656000,\n",
       " 5959000,\n",
       " 6101000,\n",
       " 6499500,\n",
       " 6094400,\n",
       " 5999500,\n",
       " 6196400,\n",
       " 6899500,\n",
       " 6098800,\n",
       " 6199000,\n",
       " 5999800,\n",
       " 6880000,\n",
       " 5868100,\n",
       " 9999900,\n",
       " 6929500,\n",
       " 7201500,\n",
       " 6209500,\n",
       " 5966000,\n",
       " 6452400,\n",
       " 6017500,\n",
       " 6133500,\n",
       " 5988800,\n",
       " 6092000,\n",
       " 5894000,\n",
       " 6099800,\n",
       " 6299800,\n",
       " 6799900,\n",
       " 6299400,\n",
       " 5959000,\n",
       " 6199000,\n",
       " 5903000,\n",
       " 7499900,\n",
       " 6199800,\n",
       " 5890000,\n",
       " 6580000,\n",
       " 6252000,\n",
       " 5955300,\n",
       " 5992500,\n",
       " 5994700,\n",
       " 6199000,\n",
       " 6599500,\n",
       " 5898700,\n",
       " 5898800,\n",
       " 5885100,\n",
       " 5973400,\n",
       " 5980000,\n",
       " 5899500,\n",
       " 5995000,\n",
       " 5961500,\n",
       " 6499900,\n",
       " 5949900,\n",
       " 5977800,\n",
       " 6399900,\n",
       " 6499500,\n",
       " 6078000,\n",
       " 6078000,\n",
       " 6079000,\n",
       " 6458500,\n",
       " 6090000,\n",
       " 6598800,\n",
       " 6577400,\n",
       " 6186500,\n",
       " 5999900,\n",
       " 6224000,\n",
       " 5864000,\n",
       " 6798000,\n",
       " 6004000,\n",
       " 6308500,\n",
       " 6375100,\n",
       " 6000500,\n",
       " 6100000,\n",
       " 6271000,\n",
       " 6097500,\n",
       " 6102500,\n",
       " 6253200,\n",
       " 6115500,\n",
       " 6199800,\n",
       " 6498400,\n",
       " 5950000,\n",
       " 6225000,\n",
       " 5969000,\n",
       " 5936500,\n",
       " 6007000,\n",
       " 6998700,\n",
       " 6449500,\n",
       " 6749500,\n",
       " 6750000,\n",
       " 6799100,\n",
       " 6036000,\n",
       " 6199000,\n",
       " 5999700,\n",
       " 6098500,\n",
       " 6594300,\n",
       " 6499000,\n",
       " 7380900,\n",
       " 6209500,\n",
       " 6588800,\n",
       " 6099000,\n",
       " 5904000,\n",
       " 6436500,\n",
       " 5899500,\n",
       " 6078000,\n",
       " 5981500,\n",
       " 9999900,\n",
       " 6035000,\n",
       " 6101500,\n",
       " 5884500,\n",
       " 6008500,\n",
       " 6173500,\n",
       " 5971000,\n",
       " 5969100,\n",
       " 9999900,\n",
       " 6103000,\n",
       " 6031000,\n",
       " 6069000,\n",
       " 6098000,\n",
       " 6098000,\n",
       " 6480000,\n",
       " 6465800,\n",
       " 5896500,\n",
       " 6198700,\n",
       " 5900000,\n",
       " 6410000,\n",
       " 6998800,\n",
       " 5899500,\n",
       " 6102300,\n",
       " 5894500,\n",
       " 6099000,\n",
       " 5995900,\n",
       " 5897300,\n",
       " 6412600,\n",
       " 5865900,\n",
       " 6680000,\n",
       " 5990000,\n",
       " 5999900,\n",
       " 5999700,\n",
       " 6601700,\n",
       " 5987700,\n",
       " 6499900,\n",
       " 6519100,\n",
       " 6499900,\n",
       " 6456500,\n",
       " 6193400,\n",
       " 5995000,\n",
       " 5999500,\n",
       " 6600000,\n",
       " 5880000,\n",
       " 6298800,\n",
       " 5888700,\n",
       " 6069700,\n",
       " 6255700,\n",
       " 6098900,\n",
       " 6388800,\n",
       " 5999500,\n",
       " 5898800,\n",
       " 6129900,\n",
       " 5959000,\n",
       " 6000000,\n",
       " 5999500,\n",
       " 6090000,\n",
       " 5902500,\n",
       " 5963000,\n",
       " 6590000,\n",
       " 6297200,\n",
       " 5999900,\n",
       " 6288800,\n",
       " 6099500,\n",
       " 6125500,\n",
       " 6036000,\n",
       " 6499100,\n",
       " 5885500,\n",
       " 5897100,\n",
       " 5994000,\n",
       " 5918000,\n",
       " 5889500,\n",
       " 6398800,\n",
       " 5929900,\n",
       " 6788800,\n",
       " 6399900,\n",
       " 5874900,\n",
       " 6299100,\n",
       " 5990000,\n",
       " 5999800,\n",
       " 6298800,\n",
       " 5899900,\n",
       " 5899500,\n",
       " 5997500,\n",
       " 5997500,\n",
       " 5898900,\n",
       " 6043500,\n",
       " 6479900,\n",
       " 6707900,\n",
       " 6249200,\n",
       " 6111300]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_prices = detect_outlier(final_data['listing_price_in_cents'].to_list())\n",
    "outlier_prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[209384.0,\n",
       " 209384.0,\n",
       " 241436.0,\n",
       " 190321.0,\n",
       " 220900.0,\n",
       " 187000.0,\n",
       " 232400.0,\n",
       " 177980.0,\n",
       " 194483.0,\n",
       " 192322.0,\n",
       " 192000.0,\n",
       " 178500.0,\n",
       " 185515.0,\n",
       " 212145.0,\n",
       " 193855.0,\n",
       " 181441.0,\n",
       " 202349.0,\n",
       " 183000.0,\n",
       " 221000.0,\n",
       " 177902.0,\n",
       " 176561.0,\n",
       " 175362.0,\n",
       " 176172.0,\n",
       " 195000.0,\n",
       " 199142.0,\n",
       " 180294.0,\n",
       " 189085.0,\n",
       " 182545.0,\n",
       " 203959.0,\n",
       " 186049.0,\n",
       " 215885.0,\n",
       " 194118.0,\n",
       " 187112.0,\n",
       " 220519.0,\n",
       " 199142.0,\n",
       " 214000.0,\n",
       " 199142.0,\n",
       " 174800.0,\n",
       " 269000.0,\n",
       " 190825.0,\n",
       " 200115.0,\n",
       " 212177.0,\n",
       " 199240.0,\n",
       " 208552.0,\n",
       " 176257.0,\n",
       " 224881.0,\n",
       " 184335.0,\n",
       " 180221.0,\n",
       " 239527.0,\n",
       " 179702.0,\n",
       " 174675.0,\n",
       " 178000.0,\n",
       " 203128.0,\n",
       " 192927.0,\n",
       " 179776.0,\n",
       " 177773.0,\n",
       " 208000.0,\n",
       " 223399.0,\n",
       " 196403.0,\n",
       " 202467.0,\n",
       " 184101.0,\n",
       " 241295.0,\n",
       " 190000.0,\n",
       " 190413.0,\n",
       " 184000.0,\n",
       " 182072.0,\n",
       " 226275.0,\n",
       " 186109.0,\n",
       " 232800.0,\n",
       " 244975.0,\n",
       " 191545.0,\n",
       " 177010.0,\n",
       " 177076.0,\n",
       " 199142.0,\n",
       " 175068.0,\n",
       " 177177.0,\n",
       " 178566.0,\n",
       " 180488.0,\n",
       " 205885.0,\n",
       " 218169.0,\n",
       " 207000.0,\n",
       " 177048.0,\n",
       " 189921.0,\n",
       " 190776.0,\n",
       " 177090.0,\n",
       " 212123.0,\n",
       " 175840.0,\n",
       " 181153.0,\n",
       " 179900.0,\n",
       " 204567.0,\n",
       " 178994.0,\n",
       " 185400.0,\n",
       " 193504.0,\n",
       " 177436.0,\n",
       " 219204.0,\n",
       " 230224.0,\n",
       " 184857.0,\n",
       " 178391.0,\n",
       " 177324.0,\n",
       " 196000.0,\n",
       " 196762.0,\n",
       " 228035.0,\n",
       " 185000.0,\n",
       " 251073.0,\n",
       " 197032.0,\n",
       " 199142.0,\n",
       " 185999.0,\n",
       " 182194.0,\n",
       " 175393.0,\n",
       " 210563.0,\n",
       " 292933.0,\n",
       " 200000.0,\n",
       " 190253.0,\n",
       " 186900.0,\n",
       " 195950.0,\n",
       " 185450.0,\n",
       " 207838.0,\n",
       " 182004.0,\n",
       " 182004.0,\n",
       " 189000.0,\n",
       " 180000.0,\n",
       " 181382.0,\n",
       " 180403.0,\n",
       " 216397.0,\n",
       " 180200.0,\n",
       " 264413.0,\n",
       " 206000.0,\n",
       " 182347.0,\n",
       " 220891.0,\n",
       " 218000.0,\n",
       " 176995.0,\n",
       " 205322.0,\n",
       " 181010.0,\n",
       " 195367.0,\n",
       " 202364.0,\n",
       " 184721.0,\n",
       " 202434.0,\n",
       " 234008.0,\n",
       " 190000.0,\n",
       " 183580.0,\n",
       " 185833.0,\n",
       " 183810.0,\n",
       " 174501.0,\n",
       " 230776.0,\n",
       " 175387.0,\n",
       " 181010.0,\n",
       " 175470.0,\n",
       " 182241.0,\n",
       " 188000.0,\n",
       " 214767.0,\n",
       " 192814.0,\n",
       " 183720.0,\n",
       " 215005.0,\n",
       " 182448.0,\n",
       " 192426.0,\n",
       " 227778.0,\n",
       " 181000.0,\n",
       " 210520.0,\n",
       " 199142.0,\n",
       " 193697.0,\n",
       " 202656.0,\n",
       " 253957.0,\n",
       " 188860.0,\n",
       " 184000.0,\n",
       " 187000.0,\n",
       " 185952.0,\n",
       " 214040.0,\n",
       " 186020.0,\n",
       " 174709.0,\n",
       " 181870.0,\n",
       " 228642.0,\n",
       " 220200.0,\n",
       " 203100.0,\n",
       " 209546.0,\n",
       " 191178.0,\n",
       " 175460.0,\n",
       " 223811.0,\n",
       " 230919.0,\n",
       " 174814.0,\n",
       " 174814.0,\n",
       " 176000.0,\n",
       " 176000.0,\n",
       " 178365.0,\n",
       " 217000.0,\n",
       " 207257.0,\n",
       " 218703.0,\n",
       " 178805.0,\n",
       " 557784.0,\n",
       " 350000.0,\n",
       " 199142.0,\n",
       " 194046.0,\n",
       " 208000.0,\n",
       " 222957.0,\n",
       " 183480.0,\n",
       " 187146.0,\n",
       " 219115.0,\n",
       " 212933.0,\n",
       " 237653.0,\n",
       " 186200.0,\n",
       " 181858.0,\n",
       " 178145.0,\n",
       " 182407.0,\n",
       " 241077.0,\n",
       " 238711.0,\n",
       " 174563.0,\n",
       " 186515.0,\n",
       " 223944.0,\n",
       " 186217.0,\n",
       " 224214.0,\n",
       " 217000.0,\n",
       " 190852.0,\n",
       " 211988.0,\n",
       " 214707.0,\n",
       " 246911.0,\n",
       " 229123.0,\n",
       " 190000.0,\n",
       " 204000.0,\n",
       " 177360.0,\n",
       " 230000.0,\n",
       " 191439.0,\n",
       " 256000.0,\n",
       " 208000.0,\n",
       " 206859.0,\n",
       " 178479.0,\n",
       " 176580.0,\n",
       " 198212.0,\n",
       " 175543.0,\n",
       " 188177.0,\n",
       " 219355.0,\n",
       " 184672.0,\n",
       " 277918.0,\n",
       " 175988.0,\n",
       " 197401.0,\n",
       " 236521.0,\n",
       " 185387.0,\n",
       " 179505.0,\n",
       " 205287.0,\n",
       " 188215.0,\n",
       " 216882.0,\n",
       " 185937.0,\n",
       " 199530.0,\n",
       " 194896.0,\n",
       " 199565.0,\n",
       " 183000.0,\n",
       " 198255.0,\n",
       " 182020.0,\n",
       " 199660.0,\n",
       " 250000.0,\n",
       " 183650.0,\n",
       " 185717.0,\n",
       " 214626.0,\n",
       " 183000.0,\n",
       " 228300.0,\n",
       " 181772.0,\n",
       " 245656.0]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_mileages = detect_outlier(final_data['listing_mileage'].to_list())\n",
    "outlier_mileages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "if len(outlier_prices) > 0:\n",
    "    final_data.drop(final_data[final_data['listing_price_in_cents'] >= outlier_prices[0]].index, inplace = True)\n",
    "if len(outlier_mileages) > 0:\n",
    "    final_data.drop(final_data[final_data['listing_mileage'] >= outlier_mileages[0]].index, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26027, 3)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4444735015361134"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x = final_data.drop(['listing_price_in_cents'], axis = 1)\n",
    "y = final_data['listing_price_in_cents']\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 0)\n",
    "rf = RandomForestRegressor(n_estimators = 100,\n",
    "                              criterion = 'mse',\n",
    "                              random_state = 20,\n",
    "                              n_jobs = -1)\n",
    "# rf = RandomForestRegressor(bootstrap=False, max_depth=15, max_features='sqrt', min_samples_split=2, n_estimators=100)\n",
    "rf.fit(x_train,y_train)\n",
    "rf_train_pred = rf.predict(x_train)\n",
    "rf_test_pred = rf.predict(x_test)\n",
    "\n",
    "\n",
    "r2_score(y_test, rf_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51422.34"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mileage, age\n",
    "rf.predict([[80, 0]])[0]/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# open a file, where you ant to store the data\n",
    "file = open('../models/car_value_predict_note.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(rf, file)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}