30-TensorflowArabaAnalizi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sbn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataFrame = pd.read_excel(\"merc.xlsx\")"
   ]
  },
  {
   "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>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005</td>\n",
       "      <td>5200</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>63000</td>\n",
       "      <td>325</td>\n",
       "      <td>32.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>34948</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>27000</td>\n",
       "      <td>20</td>\n",
       "      <td>61.4</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>49948</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>6200</td>\n",
       "      <td>555</td>\n",
       "      <td>28.0</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>61948</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>16000</td>\n",
       "      <td>325</td>\n",
       "      <td>30.4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>73948</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>4000</td>\n",
       "      <td>325</td>\n",
       "      <td>30.1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  price transmission  mileage  tax   mpg  engineSize\n",
       "0  2005   5200    Automatic    63000  325  32.1         1.8\n",
       "1  2017  34948    Automatic    27000   20  61.4         2.1\n",
       "2  2016  49948    Automatic     6200  555  28.0         5.5\n",
       "3  2016  61948    Automatic    16000  325  30.4         4.0\n",
       "4  2016  73948    Automatic     4000  325  30.1         4.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>price</th>\n",
       "      <th>mileage</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13119.000000</td>\n",
       "      <td>13119.000000</td>\n",
       "      <td>13119.000000</td>\n",
       "      <td>13119.000000</td>\n",
       "      <td>13119.000000</td>\n",
       "      <td>13119.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2017.296288</td>\n",
       "      <td>24698.596920</td>\n",
       "      <td>21949.559037</td>\n",
       "      <td>129.972178</td>\n",
       "      <td>55.155843</td>\n",
       "      <td>2.071530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.224709</td>\n",
       "      <td>11842.675542</td>\n",
       "      <td>21176.512267</td>\n",
       "      <td>65.260286</td>\n",
       "      <td>15.220082</td>\n",
       "      <td>0.572426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1970.000000</td>\n",
       "      <td>650.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>17450.000000</td>\n",
       "      <td>6097.500000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>45.600000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>22480.000000</td>\n",
       "      <td>15189.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>56.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>28980.000000</td>\n",
       "      <td>31779.500000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>64.200000</td>\n",
       "      <td>2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>159999.000000</td>\n",
       "      <td>259000.000000</td>\n",
       "      <td>580.000000</td>\n",
       "      <td>217.300000</td>\n",
       "      <td>6.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               year          price        mileage           tax           mpg  \\\n",
       "count  13119.000000   13119.000000   13119.000000  13119.000000  13119.000000   \n",
       "mean    2017.296288   24698.596920   21949.559037    129.972178     55.155843   \n",
       "std        2.224709   11842.675542   21176.512267     65.260286     15.220082   \n",
       "min     1970.000000     650.000000       1.000000      0.000000      1.100000   \n",
       "25%     2016.000000   17450.000000    6097.500000    125.000000     45.600000   \n",
       "50%     2018.000000   22480.000000   15189.000000    145.000000     56.500000   \n",
       "75%     2019.000000   28980.000000   31779.500000    145.000000     64.200000   \n",
       "max     2020.000000  159999.000000  259000.000000    580.000000    217.300000   \n",
       "\n",
       "         engineSize  \n",
       "count  13119.000000  \n",
       "mean       2.071530  \n",
       "std        0.572426  \n",
       "min        0.000000  \n",
       "25%        1.800000  \n",
       "50%        2.000000  \n",
       "75%        2.100000  \n",
       "max        6.200000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year            0\n",
       "price           0\n",
       "transmission    0\n",
       "mileage         0\n",
       "tax             0\n",
       "mpg             0\n",
       "engineSize      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8e904df7d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "sbn.distplot(dataFrame[\"price\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea1f54890>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbn.countplot(dataFrame[\"year\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>price</th>\n",
       "      <th>mileage</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.520712</td>\n",
       "      <td>-0.738027</td>\n",
       "      <td>0.012480</td>\n",
       "      <td>-0.094626</td>\n",
       "      <td>-0.142147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0.520712</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.537214</td>\n",
       "      <td>0.268717</td>\n",
       "      <td>-0.438445</td>\n",
       "      <td>0.516126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mileage</th>\n",
       "      <td>-0.738027</td>\n",
       "      <td>-0.537214</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.160223</td>\n",
       "      <td>0.202850</td>\n",
       "      <td>0.063652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tax</th>\n",
       "      <td>0.012480</td>\n",
       "      <td>0.268717</td>\n",
       "      <td>-0.160223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.513742</td>\n",
       "      <td>0.338341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mpg</th>\n",
       "      <td>-0.094626</td>\n",
       "      <td>-0.438445</td>\n",
       "      <td>0.202850</td>\n",
       "      <td>-0.513742</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.339862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engineSize</th>\n",
       "      <td>-0.142147</td>\n",
       "      <td>0.516126</td>\n",
       "      <td>0.063652</td>\n",
       "      <td>0.338341</td>\n",
       "      <td>-0.339862</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                year     price   mileage       tax       mpg  engineSize\n",
       "year        1.000000  0.520712 -0.738027  0.012480 -0.094626   -0.142147\n",
       "price       0.520712  1.000000 -0.537214  0.268717 -0.438445    0.516126\n",
       "mileage    -0.738027 -0.537214  1.000000 -0.160223  0.202850    0.063652\n",
       "tax         0.012480  0.268717 -0.160223  1.000000 -0.513742    0.338341\n",
       "mpg        -0.094626 -0.438445  0.202850 -0.513742  1.000000   -0.339862\n",
       "engineSize -0.142147  0.516126  0.063652  0.338341 -0.339862    1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mileage      -0.537214\n",
       "mpg          -0.438445\n",
       "tax           0.268717\n",
       "engineSize    0.516126\n",
       "year          0.520712\n",
       "price         1.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.corr()[\"price\"].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8e704cb450>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbn.scatterplot(x=\"mileage\",y=\"price\",data=dataFrame)"
   ]
  },
  {
   "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>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6199</th>\n",
       "      <td>2020</td>\n",
       "      <td>159999</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>1350</td>\n",
       "      <td>145</td>\n",
       "      <td>21.4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10044</th>\n",
       "      <td>2020</td>\n",
       "      <td>154998</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>3000</td>\n",
       "      <td>150</td>\n",
       "      <td>21.4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011</td>\n",
       "      <td>149948</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>3000</td>\n",
       "      <td>570</td>\n",
       "      <td>21.4</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8737</th>\n",
       "      <td>2019</td>\n",
       "      <td>140319</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>785</td>\n",
       "      <td>150</td>\n",
       "      <td>22.1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6386</th>\n",
       "      <td>2018</td>\n",
       "      <td>139995</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>13046</td>\n",
       "      <td>145</td>\n",
       "      <td>21.4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019</td>\n",
       "      <td>139948</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>12000</td>\n",
       "      <td>145</td>\n",
       "      <td>21.4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9133</th>\n",
       "      <td>2019</td>\n",
       "      <td>139559</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>1000</td>\n",
       "      <td>145</td>\n",
       "      <td>22.1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8821</th>\n",
       "      <td>2020</td>\n",
       "      <td>138439</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>1000</td>\n",
       "      <td>145</td>\n",
       "      <td>22.1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5902</th>\n",
       "      <td>2018</td>\n",
       "      <td>135771</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>19000</td>\n",
       "      <td>145</td>\n",
       "      <td>21.4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7864</th>\n",
       "      <td>2018</td>\n",
       "      <td>135124</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>18234</td>\n",
       "      <td>150</td>\n",
       "      <td>21.4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8673</th>\n",
       "      <td>2019</td>\n",
       "      <td>134219</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>1000</td>\n",
       "      <td>145</td>\n",
       "      <td>24.8</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6210</th>\n",
       "      <td>2019</td>\n",
       "      <td>129990</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1000</td>\n",
       "      <td>145</td>\n",
       "      <td>24.8</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4759</th>\n",
       "      <td>2019</td>\n",
       "      <td>126000</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>250</td>\n",
       "      <td>145</td>\n",
       "      <td>24.6</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2647</th>\n",
       "      <td>2019</td>\n",
       "      <td>125796</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>637</td>\n",
       "      <td>145</td>\n",
       "      <td>24.8</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6223</th>\n",
       "      <td>2019</td>\n",
       "      <td>124999</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1500</td>\n",
       "      <td>145</td>\n",
       "      <td>31.7</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4094</th>\n",
       "      <td>2019</td>\n",
       "      <td>124366</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>880</td>\n",
       "      <td>145</td>\n",
       "      <td>24.8</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2629</th>\n",
       "      <td>2019</td>\n",
       "      <td>123846</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>2951</td>\n",
       "      <td>145</td>\n",
       "      <td>22.1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7134</th>\n",
       "      <td>2019</td>\n",
       "      <td>115359</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>1000</td>\n",
       "      <td>145</td>\n",
       "      <td>30.1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9159</th>\n",
       "      <td>2019</td>\n",
       "      <td>114199</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>891</td>\n",
       "      <td>145</td>\n",
       "      <td>22.6</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>2019</td>\n",
       "      <td>109995</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>4688</td>\n",
       "      <td>150</td>\n",
       "      <td>31.7</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   price transmission  mileage  tax   mpg  engineSize\n",
       "6199   2020  159999    Semi-Auto     1350  145  21.4         4.0\n",
       "10044  2020  154998    Automatic     3000  150  21.4         4.0\n",
       "5      2011  149948    Automatic     3000  570  21.4         6.2\n",
       "8737   2019  140319    Semi-Auto      785  150  22.1         4.0\n",
       "6386   2018  139995    Semi-Auto    13046  145  21.4         4.0\n",
       "8      2019  139948    Automatic    12000  145  21.4         4.0\n",
       "9133   2019  139559    Semi-Auto     1000  145  22.1         4.0\n",
       "8821   2020  138439    Semi-Auto     1000  145  22.1         4.0\n",
       "5902   2018  135771    Semi-Auto    19000  145  21.4         4.0\n",
       "7864   2018  135124    Semi-Auto    18234  150  21.4         4.0\n",
       "8673   2019  134219    Semi-Auto     1000  145  24.8         4.0\n",
       "6210   2019  129990    Automatic     1000  145  24.8         4.0\n",
       "4759   2019  126000    Automatic      250  145  24.6         4.0\n",
       "2647   2019  125796    Automatic      637  145  24.8         4.0\n",
       "6223   2019  124999    Automatic     1500  145  31.7         4.0\n",
       "4094   2019  124366    Semi-Auto      880  145  24.8         4.0\n",
       "2629   2019  123846    Semi-Auto     2951  145  22.1         4.0\n",
       "7134   2019  115359    Semi-Auto     1000  145  30.1         4.0\n",
       "9159   2019  114199    Semi-Auto      891  145  22.6         4.0\n",
       "1980   2019  109995    Semi-Auto     4688  150  31.7         4.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.sort_values(\"price\",ascending = False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11816</th>\n",
       "      <td>2003</td>\n",
       "      <td>650</td>\n",
       "      <td>Manual</td>\n",
       "      <td>109090</td>\n",
       "      <td>235</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12008</th>\n",
       "      <td>2010</td>\n",
       "      <td>1350</td>\n",
       "      <td>Manual</td>\n",
       "      <td>116126</td>\n",
       "      <td>145</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11765</th>\n",
       "      <td>2000</td>\n",
       "      <td>1490</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>87000</td>\n",
       "      <td>265</td>\n",
       "      <td>27.2</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11549</th>\n",
       "      <td>2002</td>\n",
       "      <td>1495</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>13800</td>\n",
       "      <td>305</td>\n",
       "      <td>39.8</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12594</th>\n",
       "      <td>2004</td>\n",
       "      <td>1495</td>\n",
       "      <td>Manual</td>\n",
       "      <td>119000</td>\n",
       "      <td>300</td>\n",
       "      <td>34.5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11174</th>\n",
       "      <td>2001</td>\n",
       "      <td>1695</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>108800</td>\n",
       "      <td>325</td>\n",
       "      <td>31.7</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12710</th>\n",
       "      <td>2006</td>\n",
       "      <td>1695</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>153000</td>\n",
       "      <td>300</td>\n",
       "      <td>33.6</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12766</th>\n",
       "      <td>2004</td>\n",
       "      <td>1780</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>118000</td>\n",
       "      <td>265</td>\n",
       "      <td>41.5</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12009</th>\n",
       "      <td>2007</td>\n",
       "      <td>1800</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>84000</td>\n",
       "      <td>200</td>\n",
       "      <td>42.8</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11764</th>\n",
       "      <td>1998</td>\n",
       "      <td>1990</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>99300</td>\n",
       "      <td>265</td>\n",
       "      <td>32.1</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11808</th>\n",
       "      <td>1998</td>\n",
       "      <td>1990</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>113557</td>\n",
       "      <td>265</td>\n",
       "      <td>32.1</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11383</th>\n",
       "      <td>2005</td>\n",
       "      <td>1995</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>105000</td>\n",
       "      <td>260</td>\n",
       "      <td>43.5</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11378</th>\n",
       "      <td>2004</td>\n",
       "      <td>1995</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>165000</td>\n",
       "      <td>330</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11857</th>\n",
       "      <td>2002</td>\n",
       "      <td>2140</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>52700</td>\n",
       "      <td>325</td>\n",
       "      <td>31.4</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11906</th>\n",
       "      <td>2007</td>\n",
       "      <td>2478</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>81000</td>\n",
       "      <td>160</td>\n",
       "      <td>49.6</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11795</th>\n",
       "      <td>2005</td>\n",
       "      <td>2490</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>101980</td>\n",
       "      <td>200</td>\n",
       "      <td>47.9</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12765</th>\n",
       "      <td>2004</td>\n",
       "      <td>2495</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>104000</td>\n",
       "      <td>325</td>\n",
       "      <td>31.7</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11943</th>\n",
       "      <td>2005</td>\n",
       "      <td>2690</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>109000</td>\n",
       "      <td>325</td>\n",
       "      <td>32.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11263</th>\n",
       "      <td>2007</td>\n",
       "      <td>2795</td>\n",
       "      <td>Manual</td>\n",
       "      <td>79485</td>\n",
       "      <td>200</td>\n",
       "      <td>45.6</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2006</td>\n",
       "      <td>2880</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>66000</td>\n",
       "      <td>160</td>\n",
       "      <td>52.3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  price transmission  mileage  tax   mpg  engineSize\n",
       "11816  2003    650       Manual   109090  235  40.0         1.4\n",
       "12008  2010   1350       Manual   116126  145  54.3         2.0\n",
       "11765  2000   1490    Automatic    87000  265  27.2         3.2\n",
       "11549  2002   1495    Automatic    13800  305  39.8         2.7\n",
       "12594  2004   1495       Manual   119000  300  34.5         1.8\n",
       "11174  2001   1695    Automatic   108800  325  31.7         3.2\n",
       "12710  2006   1695    Automatic   153000  300  33.6         1.8\n",
       "12766  2004   1780    Automatic   118000  265  41.5         2.2\n",
       "12009  2007   1800    Automatic    84000  200  42.8         1.5\n",
       "11764  1998   1990    Automatic    99300  265  32.1         2.3\n",
       "11808  1998   1990    Automatic   113557  265  32.1         2.3\n",
       "11383  2005   1995    Automatic   105000  260  43.5         2.1\n",
       "11378  2004   1995    Semi-Auto   165000  330  20.0         3.7\n",
       "11857  2002   2140    Automatic    52700  325  31.4         2.0\n",
       "11906  2007   2478    Automatic    81000  160  49.6         2.0\n",
       "11795  2005   2490    Automatic   101980  200  47.9         2.0\n",
       "12765  2004   2495    Automatic   104000  325  31.7         1.8\n",
       "11943  2005   2690    Automatic   109000  325  32.1         1.8\n",
       "11263  2007   2795       Manual    79485  200  45.6         1.5\n",
       "49     2006   2880    Automatic    66000  160  52.3         2.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.sort_values(\"price\",ascending = True).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13119"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataFrame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "131.19"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataFrame) * 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "yuzdeDoksanDokuzDf = dataFrame.sort_values(\"price\",ascending = False).iloc[131:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>mileage</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12988.000000</td>\n",
       "      <td>12988.000000</td>\n",
       "      <td>12988.000000</td>\n",
       "      <td>12988.000000</td>\n",
       "      <td>12988.000000</td>\n",
       "      <td>12988.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2017.281876</td>\n",
       "      <td>24074.926933</td>\n",
       "      <td>22132.741146</td>\n",
       "      <td>129.689714</td>\n",
       "      <td>55.437142</td>\n",
       "      <td>2.050901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.228515</td>\n",
       "      <td>9866.224575</td>\n",
       "      <td>21196.776401</td>\n",
       "      <td>65.183076</td>\n",
       "      <td>15.025999</td>\n",
       "      <td>0.532596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1970.000000</td>\n",
       "      <td>650.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>17357.500000</td>\n",
       "      <td>6322.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>45.600000</td>\n",
       "      <td>1.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>22299.000000</td>\n",
       "      <td>15369.500000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>56.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>28706.000000</td>\n",
       "      <td>31982.250000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>64.200000</td>\n",
       "      <td>2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>65990.000000</td>\n",
       "      <td>259000.000000</td>\n",
       "      <td>580.000000</td>\n",
       "      <td>217.300000</td>\n",
       "      <td>6.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               year         price        mileage           tax           mpg  \\\n",
       "count  12988.000000  12988.000000   12988.000000  12988.000000  12988.000000   \n",
       "mean    2017.281876  24074.926933   22132.741146    129.689714     55.437142   \n",
       "std        2.228515   9866.224575   21196.776401     65.183076     15.025999   \n",
       "min     1970.000000    650.000000       1.000000      0.000000      1.100000   \n",
       "25%     2016.000000  17357.500000    6322.000000    125.000000     45.600000   \n",
       "50%     2018.000000  22299.000000   15369.500000    145.000000     56.500000   \n",
       "75%     2019.000000  28706.000000   31982.250000    145.000000     64.200000   \n",
       "max     2020.000000  65990.000000  259000.000000    580.000000    217.300000   \n",
       "\n",
       "         engineSize  \n",
       "count  12988.000000  \n",
       "mean       2.050901  \n",
       "std        0.532596  \n",
       "min        0.000000  \n",
       "25%        1.675000  \n",
       "50%        2.000000  \n",
       "75%        2.100000  \n",
       "max        6.200000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yuzdeDoksanDokuzDf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea2814fd0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "sbn.distplot(yuzdeDoksanDokuzDf[\"price\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>price</th>\n",
       "      <th>mileage</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13119.000000</td>\n",
       "      <td>13119.000000</td>\n",
       "      <td>13119.000000</td>\n",
       "      <td>13119.000000</td>\n",
       "      <td>13119.000000</td>\n",
       "      <td>13119.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2017.296288</td>\n",
       "      <td>24698.596920</td>\n",
       "      <td>21949.559037</td>\n",
       "      <td>129.972178</td>\n",
       "      <td>55.155843</td>\n",
       "      <td>2.071530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.224709</td>\n",
       "      <td>11842.675542</td>\n",
       "      <td>21176.512267</td>\n",
       "      <td>65.260286</td>\n",
       "      <td>15.220082</td>\n",
       "      <td>0.572426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1970.000000</td>\n",
       "      <td>650.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>17450.000000</td>\n",
       "      <td>6097.500000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>45.600000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>22480.000000</td>\n",
       "      <td>15189.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>56.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>28980.000000</td>\n",
       "      <td>31779.500000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>64.200000</td>\n",
       "      <td>2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>159999.000000</td>\n",
       "      <td>259000.000000</td>\n",
       "      <td>580.000000</td>\n",
       "      <td>217.300000</td>\n",
       "      <td>6.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               year          price        mileage           tax           mpg  \\\n",
       "count  13119.000000   13119.000000   13119.000000  13119.000000  13119.000000   \n",
       "mean    2017.296288   24698.596920   21949.559037    129.972178     55.155843   \n",
       "std        2.224709   11842.675542   21176.512267     65.260286     15.220082   \n",
       "min     1970.000000     650.000000       1.000000      0.000000      1.100000   \n",
       "25%     2016.000000   17450.000000    6097.500000    125.000000     45.600000   \n",
       "50%     2018.000000   22480.000000   15189.000000    145.000000     56.500000   \n",
       "75%     2019.000000   28980.000000   31779.500000    145.000000     64.200000   \n",
       "max     2020.000000  159999.000000  259000.000000    580.000000    217.300000   \n",
       "\n",
       "         engineSize  \n",
       "count  13119.000000  \n",
       "mean       2.071530  \n",
       "std        0.572426  \n",
       "min        0.000000  \n",
       "25%        1.800000  \n",
       "50%        2.000000  \n",
       "75%        2.100000  \n",
       "max        6.200000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1970    24999.000000\n",
       "1997     9995.000000\n",
       "1998     8605.000000\n",
       "1999     5995.000000\n",
       "2000     5743.333333\n",
       "2001     4957.900000\n",
       "2002     5820.444444\n",
       "2003     4878.000000\n",
       "2004     4727.615385\n",
       "2005     4426.111111\n",
       "2006     4036.875000\n",
       "2007     5136.045455\n",
       "2008     6967.437500\n",
       "2009     6166.764706\n",
       "2010     8308.473684\n",
       "2011    12624.894737\n",
       "2012    10845.140351\n",
       "2013    11939.842466\n",
       "2014    14042.936864\n",
       "2015    16731.780020\n",
       "2016    19307.892948\n",
       "2017    21514.307854\n",
       "2018    25720.162918\n",
       "2019    31290.020865\n",
       "2020    35433.282337\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.groupby(\"year\").mean()[\"price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1970    24999.000000\n",
       "1997     9995.000000\n",
       "1998     8605.000000\n",
       "1999     5995.000000\n",
       "2000     5743.333333\n",
       "2001     4957.900000\n",
       "2002     5820.444444\n",
       "2003     4878.000000\n",
       "2004     4727.615385\n",
       "2005     4426.111111\n",
       "2006     4036.875000\n",
       "2007     5136.045455\n",
       "2008     6967.437500\n",
       "2009     6166.764706\n",
       "2010     8308.473684\n",
       "2011     8913.459459\n",
       "2012    10845.140351\n",
       "2013    11939.842466\n",
       "2014    14042.936864\n",
       "2015    16647.822222\n",
       "2016    19223.558943\n",
       "2017    21356.280421\n",
       "2018    24800.844506\n",
       "2019    30289.524832\n",
       "2020    34234.794872\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yuzdeDoksanDokuzDf.groupby(\"year\").mean()[\"price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1997     9995.000000\n",
       "1998     8605.000000\n",
       "1999     5995.000000\n",
       "2000     5743.333333\n",
       "2001     4957.900000\n",
       "2002     5820.444444\n",
       "2003     4878.000000\n",
       "2004     4727.615385\n",
       "2005     4426.111111\n",
       "2006     4036.875000\n",
       "2007     5136.045455\n",
       "2008     6967.437500\n",
       "2009     6166.764706\n",
       "2010     8308.473684\n",
       "2011    12624.894737\n",
       "2012    10845.140351\n",
       "2013    11939.842466\n",
       "2014    14042.936864\n",
       "2015    16731.780020\n",
       "2016    19307.892948\n",
       "2017    21514.307854\n",
       "2018    25720.162918\n",
       "2019    31290.020865\n",
       "2020    35433.282337\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame[dataFrame.year != 1970].groupby(\"year\").mean()[\"price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataFrame = yuzdeDoksanDokuzDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>price</th>\n",
       "      <th>mileage</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12988.000000</td>\n",
       "      <td>12988.000000</td>\n",
       "      <td>12988.000000</td>\n",
       "      <td>12988.000000</td>\n",
       "      <td>12988.000000</td>\n",
       "      <td>12988.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2017.281876</td>\n",
       "      <td>24074.926933</td>\n",
       "      <td>22132.741146</td>\n",
       "      <td>129.689714</td>\n",
       "      <td>55.437142</td>\n",
       "      <td>2.050901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.228515</td>\n",
       "      <td>9866.224575</td>\n",
       "      <td>21196.776401</td>\n",
       "      <td>65.183076</td>\n",
       "      <td>15.025999</td>\n",
       "      <td>0.532596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1970.000000</td>\n",
       "      <td>650.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>17357.500000</td>\n",
       "      <td>6322.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>45.600000</td>\n",
       "      <td>1.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>22299.000000</td>\n",
       "      <td>15369.500000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>56.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>28706.000000</td>\n",
       "      <td>31982.250000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>64.200000</td>\n",
       "      <td>2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>65990.000000</td>\n",
       "      <td>259000.000000</td>\n",
       "      <td>580.000000</td>\n",
       "      <td>217.300000</td>\n",
       "      <td>6.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               year         price        mileage           tax           mpg  \\\n",
       "count  12988.000000  12988.000000   12988.000000  12988.000000  12988.000000   \n",
       "mean    2017.281876  24074.926933   22132.741146    129.689714     55.437142   \n",
       "std        2.228515   9866.224575   21196.776401     65.183076     15.025999   \n",
       "min     1970.000000    650.000000       1.000000      0.000000      1.100000   \n",
       "25%     2016.000000  17357.500000    6322.000000    125.000000     45.600000   \n",
       "50%     2018.000000  22299.000000   15369.500000    145.000000     56.500000   \n",
       "75%     2019.000000  28706.000000   31982.250000    145.000000     64.200000   \n",
       "max     2020.000000  65990.000000  259000.000000    580.000000    217.300000   \n",
       "\n",
       "         engineSize  \n",
       "count  12988.000000  \n",
       "mean       2.050901  \n",
       "std        0.532596  \n",
       "min        0.000000  \n",
       "25%        1.675000  \n",
       "50%        2.000000  \n",
       "75%        2.100000  \n",
       "max        6.200000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataFrame = dataFrame[dataFrame.year != 1970]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1997     9995.000000\n",
       "1998     8605.000000\n",
       "1999     5995.000000\n",
       "2000     5743.333333\n",
       "2001     4957.900000\n",
       "2002     5820.444444\n",
       "2003     4878.000000\n",
       "2004     4727.615385\n",
       "2005     4426.111111\n",
       "2006     4036.875000\n",
       "2007     5136.045455\n",
       "2008     6967.437500\n",
       "2009     6166.764706\n",
       "2010     8308.473684\n",
       "2011     8913.459459\n",
       "2012    10845.140351\n",
       "2013    11939.842466\n",
       "2014    14042.936864\n",
       "2015    16647.822222\n",
       "2016    19223.558943\n",
       "2017    21356.280421\n",
       "2018    24800.844506\n",
       "2019    30289.524832\n",
       "2020    34234.794872\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.groupby(\"year\").mean()[\"price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6177</th>\n",
       "      <td>2019</td>\n",
       "      <td>65990</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>5076</td>\n",
       "      <td>150</td>\n",
       "      <td>30.4</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5779</th>\n",
       "      <td>2020</td>\n",
       "      <td>65990</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>999</td>\n",
       "      <td>145</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3191</th>\n",
       "      <td>2020</td>\n",
       "      <td>65980</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>3999</td>\n",
       "      <td>145</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4727</th>\n",
       "      <td>2019</td>\n",
       "      <td>65000</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>3398</td>\n",
       "      <td>145</td>\n",
       "      <td>27.2</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8814</th>\n",
       "      <td>2019</td>\n",
       "      <td>64999</td>\n",
       "      <td>Semi-Auto</td>\n",
       "      <td>119</td>\n",
       "      <td>145</td>\n",
       "      <td>40.9</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year  price transmission  mileage  tax   mpg  engineSize\n",
       "6177  2019  65990    Semi-Auto     5076  150  30.4         3.0\n",
       "5779  2020  65990    Semi-Auto      999  145  28.0         4.0\n",
       "3191  2020  65980    Semi-Auto     3999  145  28.0         4.0\n",
       "4727  2019  65000    Semi-Auto     3398  145  27.2         4.0\n",
       "8814  2019  64999    Semi-Auto      119  145  40.9         3.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataFrame = dataFrame.drop(\"transmission\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dataFrame[\"price\"].values\n",
    "x = dataFrame.drop(\"price\",axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([65990, 65990, 65980, ...,  1490,  1350,   650])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.01900e+03, 5.07600e+03, 1.50000e+02, 3.04000e+01, 3.00000e+00],\n",
       "       [2.02000e+03, 9.99000e+02, 1.45000e+02, 2.80000e+01, 4.00000e+00],\n",
       "       [2.02000e+03, 3.99900e+03, 1.45000e+02, 2.80000e+01, 4.00000e+00],\n",
       "       ...,\n",
       "       [2.00000e+03, 8.70000e+04, 2.65000e+02, 2.72000e+01, 3.20000e+00],\n",
       "       [2.01000e+03, 1.16126e+05, 1.45000e+02, 5.43000e+01, 2.00000e+00],\n",
       "       [2.00300e+03, 1.09090e+05, 2.35000e+02, 4.00000e+01, 1.40000e+00]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.3,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9090"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3897"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = scaler.fit_transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9090, 5)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(12,activation=\"relu\"))\n",
    "model.add(Dense(12,activation=\"relu\"))\n",
    "model.add(Dense(12,activation=\"relu\"))\n",
    "model.add(Dense(12,activation=\"relu\"))\n",
    "\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(optimizer=\"adam\",loss=\"mse\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 9090 samples, validate on 3897 samples\n",
      "Epoch 1/300\n",
      "9090/9090 [==============================] - 1s 111us/sample - loss: 672143917.0605 - val_loss: 688100674.4142\n",
      "Epoch 2/300\n",
      "9090/9090 [==============================] - 0s 16us/sample - loss: 672119985.9186 - val_loss: 688059536.6692\n",
      "Epoch 3/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 672035997.8526 - val_loss: 687897484.3500\n",
      "Epoch 4/300\n",
      "9090/9090 [==============================] - 0s 16us/sample - loss: 671723828.3828 - val_loss: 687332857.3980\n",
      "Epoch 5/300\n",
      "9090/9090 [==============================] - 0s 16us/sample - loss: 670714750.9439 - val_loss: 685617307.8696\n",
      "Epoch 6/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 667950765.3421 - val_loss: 681316964.9679\n",
      "Epoch 7/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 661634442.3498 - val_loss: 672107938.8986\n",
      "Epoch 8/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 648988113.2497 - val_loss: 654688013.2368\n",
      "Epoch 9/300\n",
      "9090/9090 [==============================] - 0s 16us/sample - loss: 626344560.0176 - val_loss: 624982774.6554\n",
      "Epoch 10/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 589493100.8493 - val_loss: 578724199.4970\n",
      "Epoch 11/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 534742335.7536 - val_loss: 512808547.2599\n",
      "Epoch 12/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 460892421.8790 - val_loss: 428280885.0706\n",
      "Epoch 13/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 371371843.9780 - val_loss: 331566588.1406\n",
      "Epoch 14/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 275900347.3003 - val_loss: 237154811.4385\n",
      "Epoch 15/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 190786390.0022 - val_loss: 161329799.6736\n",
      "Epoch 16/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 131488739.1507 - val_loss: 116114956.2392\n",
      "Epoch 17/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 101778882.5083 - val_loss: 97302824.8786\n",
      "Epoch 18/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 91649996.9285 - val_loss: 91824548.6313\n",
      "Epoch 19/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 89087072.5633 - val_loss: 90297342.7005\n",
      "Epoch 20/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 88158746.4026 - val_loss: 89499565.5571\n",
      "Epoch 21/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 87541603.5336 - val_loss: 88898494.9613\n",
      "Epoch 22/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 86984457.3465 - val_loss: 88359819.1552\n",
      "Epoch 23/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 86457049.6942 - val_loss: 87819234.5045\n",
      "Epoch 24/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 85945065.8394 - val_loss: 87326589.8732\n",
      "Epoch 25/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 85446741.7998 - val_loss: 86806046.1935\n",
      "Epoch 26/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 84960949.1485 - val_loss: 86348336.0575\n",
      "Epoch 27/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 84481712.2288 - val_loss: 85873915.8142\n",
      "Epoch 28/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 84017959.7888 - val_loss: 85417872.3962\n",
      "Epoch 29/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 83555241.2321 - val_loss: 84972502.5733\n",
      "Epoch 30/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 83112070.1474 - val_loss: 84489027.6007\n",
      "Epoch 31/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 82645310.7283 - val_loss: 84057054.4973\n",
      "Epoch 32/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 82202079.0935 - val_loss: 83610854.3700\n",
      "Epoch 33/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 81758503.4587 - val_loss: 83171606.6492\n",
      "Epoch 34/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 81310851.4631 - val_loss: 82746061.2040\n",
      "Epoch 35/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 80876495.0495 - val_loss: 82294892.7134\n",
      "Epoch 36/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 80439833.3157 - val_loss: 81881302.5671\n",
      "Epoch 37/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 80020099.4279 - val_loss: 81436183.0167\n",
      "Epoch 38/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 79580562.0374 - val_loss: 80962065.8619\n",
      "Epoch 39/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 79138098.1166 - val_loss: 80561671.2261\n",
      "Epoch 40/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 78700499.8812 - val_loss: 80160272.1458\n",
      "Epoch 41/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 78270175.0979 - val_loss: 79687626.0734\n",
      "Epoch 42/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 77838366.3366 - val_loss: 79291709.4154\n",
      "Epoch 43/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 77411094.8295 - val_loss: 78861812.5861\n",
      "Epoch 44/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 76971610.2574 - val_loss: 78419561.4432\n",
      "Epoch 45/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 76537224.8053 - val_loss: 78001096.7945\n",
      "Epoch 46/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 76103780.0088 - val_loss: 77573918.8278\n",
      "Epoch 47/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 75672362.6007 - val_loss: 77153017.4760\n",
      "Epoch 48/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 75239372.1232 - val_loss: 76704398.3372\n",
      "Epoch 49/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 74805678.1166 - val_loss: 76285893.1588\n",
      "Epoch 50/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 74359561.9318 - val_loss: 75839394.8227\n",
      "Epoch 51/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 73925381.0385 - val_loss: 75415033.5992\n",
      "Epoch 52/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 73487006.0550 - val_loss: 74984678.6513\n",
      "Epoch 53/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 73049705.4433 - val_loss: 74532156.4137\n",
      "Epoch 54/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 72596813.7954 - val_loss: 74108102.5507\n",
      "Epoch 55/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 72155802.4158 - val_loss: 73648702.9120\n",
      "Epoch 56/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 71705140.1672 - val_loss: 73207427.8933\n",
      "Epoch 57/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 71267397.8834 - val_loss: 72774227.9364\n",
      "Epoch 58/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 70791299.0979 - val_loss: 72293287.8635\n",
      "Epoch 59/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 70353886.4466 - val_loss: 71834525.7696\n",
      "Epoch 60/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 69888693.2013 - val_loss: 71404313.1352\n",
      "Epoch 61/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 69430630.7019 - val_loss: 70929764.0513\n",
      "Epoch 62/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 68947183.4763 - val_loss: 70476504.5420\n",
      "Epoch 63/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 68482379.4587 - val_loss: 70002314.8165\n",
      "Epoch 64/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 68002543.6700 - val_loss: 69557591.7218\n",
      "Epoch 65/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 67531289.7998 - val_loss: 69065566.3505\n",
      "Epoch 66/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 67036219.3839 - val_loss: 68573009.4401\n",
      "Epoch 67/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 66551352.7745 - val_loss: 68075350.6605\n",
      "Epoch 68/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9090/9090 [==============================] - 0s 17us/sample - loss: 66058819.4807 - val_loss: 67601075.7157\n",
      "Epoch 69/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 65557272.0528 - val_loss: 67100845.9759\n",
      "Epoch 70/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 65053862.4686 - val_loss: 66593774.3146\n",
      "Epoch 71/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 64546217.0913 - val_loss: 66106521.9379\n",
      "Epoch 72/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 64048033.6986 - val_loss: 65620450.8032\n",
      "Epoch 73/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 63532640.5325 - val_loss: 65043557.7460\n",
      "Epoch 74/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 62982351.7228 - val_loss: 64529581.2820\n",
      "Epoch 75/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 62451174.2354 - val_loss: 63982109.1034\n",
      "Epoch 76/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 61919014.6227 - val_loss: 63462578.7673\n",
      "Epoch 77/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 61367757.2717 - val_loss: 62887443.4817\n",
      "Epoch 78/300\n",
      "9090/9090 [==============================] - 0s 18us/sample - loss: 60798082.1298 - val_loss: 62354756.3849\n",
      "Epoch 79/300\n",
      "9090/9090 [==============================] - 0s 18us/sample - loss: 60249398.6227 - val_loss: 61807171.8419\n",
      "Epoch 80/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 59663382.6799 - val_loss: 61190221.1342\n",
      "Epoch 81/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 59090858.1078 - val_loss: 60620093.8681\n",
      "Epoch 82/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 58483564.2112 - val_loss: 59999997.9092\n",
      "Epoch 83/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 57898723.4719 - val_loss: 59398339.0105\n",
      "Epoch 84/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 57280155.2431 - val_loss: 58780948.5676\n",
      "Epoch 85/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 56661130.8691 - val_loss: 58199108.3788\n",
      "Epoch 86/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 56014594.0902 - val_loss: 57528815.9979\n",
      "Epoch 87/300\n",
      "9090/9090 [==============================] - 0s 18us/sample - loss: 55408342.9967 - val_loss: 56880264.5727\n",
      "Epoch 88/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 54740792.6469 - val_loss: 56235867.2394\n",
      "Epoch 89/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 54085484.3828 - val_loss: 55556213.2954\n",
      "Epoch 90/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 53435257.5270 - val_loss: 54859154.6995\n",
      "Epoch 91/300\n",
      "9090/9090 [==============================] - 0s 18us/sample - loss: 52739631.1199 - val_loss: 54184853.9061\n",
      "Epoch 92/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 52048474.1518 - val_loss: 53499928.9248\n",
      "Epoch 93/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 51345132.9549 - val_loss: 52772384.6703\n",
      "Epoch 94/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 50652361.8746 - val_loss: 52046142.8545\n",
      "Epoch 95/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 49918778.6095 - val_loss: 51343986.3998\n",
      "Epoch 96/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 49199314.3674 - val_loss: 50584885.0593\n",
      "Epoch 97/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 48470166.8295 - val_loss: 49817833.3467\n",
      "Epoch 98/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 47732514.3542 - val_loss: 49061655.8347\n",
      "Epoch 99/300\n",
      "9090/9090 [==============================] - 0s 18us/sample - loss: 46984351.1551 - val_loss: 48306224.4752\n",
      "Epoch 100/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 46252795.2783 - val_loss: 47537661.8219\n",
      "Epoch 101/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 45498218.3762 - val_loss: 46769137.9420\n",
      "Epoch 102/300\n",
      "9090/9090 [==============================] - 0s 18us/sample - loss: 44741323.9340 - val_loss: 46027695.4519\n",
      "Epoch 103/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 44008529.0253 - val_loss: 45226229.0654\n",
      "Epoch 104/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 43263848.3256 - val_loss: 44470797.3872\n",
      "Epoch 105/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 42523138.5765 - val_loss: 43711783.1116\n",
      "Epoch 106/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 41811451.5710 - val_loss: 42960489.8707\n",
      "Epoch 107/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 41088889.6106 - val_loss: 42224634.4516\n",
      "Epoch 108/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 40377196.1276 - val_loss: 41502043.1609\n",
      "Epoch 109/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 39695876.6601 - val_loss: 40796595.1322\n",
      "Epoch 110/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 39043017.7580 - val_loss: 40122793.1106\n",
      "Epoch 111/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 38400008.0924 - val_loss: 39418599.4057\n",
      "Epoch 112/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 37797130.9791 - val_loss: 38797798.8247\n",
      "Epoch 113/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 37217377.4455 - val_loss: 38175726.9069\n",
      "Epoch 114/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 36614936.3520 - val_loss: 37625295.0618\n",
      "Epoch 115/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 36104738.0748 - val_loss: 37015367.7331\n",
      "Epoch 116/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 35561489.5094 - val_loss: 36448513.5694\n",
      "Epoch 117/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 35055347.6700 - val_loss: 35916628.5030\n",
      "Epoch 118/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 34573429.2079 - val_loss: 35405177.4175\n",
      "Epoch 119/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 34104291.6612 - val_loss: 34900901.8768\n",
      "Epoch 120/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 33656684.2684 - val_loss: 34435780.7108\n",
      "Epoch 121/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 33235955.2189 - val_loss: 33948951.3472\n",
      "Epoch 122/300\n",
      "9090/9090 [==============================] - 0s 16us/sample - loss: 32780069.7712 - val_loss: 33497065.9877\n",
      "Epoch 123/300\n",
      "9090/9090 [==============================] - 0s 16us/sample - loss: 32353070.1518 - val_loss: 33020746.3197\n",
      "Epoch 124/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 31878782.3190 - val_loss: 32562205.7295\n",
      "Epoch 125/300\n",
      "9090/9090 [==============================] - 0s 16us/sample - loss: 31437482.1606 - val_loss: 32116200.0790\n",
      "Epoch 126/300\n",
      "9090/9090 [==============================] - 0s 16us/sample - loss: 31006415.3597 - val_loss: 31650075.1563\n",
      "Epoch 127/300\n",
      "9090/9090 [==============================] - 0s 16us/sample - loss: 30558287.7162 - val_loss: 31211367.6177\n",
      "Epoch 128/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 30146893.8834 - val_loss: 30919341.5653\n",
      "Epoch 129/300\n",
      "9090/9090 [==============================] - 0s 16us/sample - loss: 29708895.6018 - val_loss: 30371423.8306\n",
      "Epoch 130/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 29291772.7173 - val_loss: 29972850.0041\n",
      "Epoch 131/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 28893347.0099 - val_loss: 29583791.8686\n",
      "Epoch 132/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 28514235.3267 - val_loss: 29186202.7123\n",
      "Epoch 133/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 28085291.5292 - val_loss: 28794058.6148\n",
      "Epoch 134/300\n",
      "9090/9090 [==============================] - 0s 16us/sample - loss: 27674384.9703 - val_loss: 28388673.4303\n",
      "Epoch 135/300\n",
      "9090/9090 [==============================] - 0s 16us/sample - loss: 27285235.8614 - val_loss: 28031823.5355\n",
      "Epoch 136/300\n",
      "9090/9090 [==============================] - 0s 16us/sample - loss: 26914239.9692 - val_loss: 27648728.8514\n",
      "Epoch 137/300\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "9090/9090 [==============================] - 0s 17us/sample - loss: 19188739.4400 - val_loss: 20768873.2941\n",
      "Epoch 286/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 19201612.6216 - val_loss: 20665393.0739\n",
      "Epoch 287/300\n",
      "9090/9090 [==============================] - 0s 18us/sample - loss: 19177187.2794 - val_loss: 20660028.4994\n",
      "Epoch 288/300\n",
      "9090/9090 [==============================] - 0s 18us/sample - loss: 19169395.7569 - val_loss: 20641421.1509\n",
      "Epoch 289/300\n",
      "9090/9090 [==============================] - 0s 19us/sample - loss: 19178060.1793 - val_loss: 20632535.2230\n",
      "Epoch 290/300\n",
      "9090/9090 [==============================] - 0s 18us/sample - loss: 19166475.9384 - val_loss: 20627524.0924\n",
      "Epoch 291/300\n",
      "9090/9090 [==============================] - 0s 18us/sample - loss: 19137751.0957 - val_loss: 20620151.4334\n",
      "Epoch 292/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 19125035.1122 - val_loss: 20614189.2954\n",
      "Epoch 293/300\n",
      "9090/9090 [==============================] - 0s 30us/sample - loss: 19128853.9087 - val_loss: 20599592.5453\n",
      "Epoch 294/300\n",
      "9090/9090 [==============================] - 0s 37us/sample - loss: 19120743.0385 - val_loss: 20748598.0421\n",
      "Epoch 295/300\n",
      "9090/9090 [==============================] - 0s 16us/sample - loss: 19116495.6854 - val_loss: 20588127.0736\n",
      "Epoch 296/300\n",
      "9090/9090 [==============================] - 0s 16us/sample - loss: 19101835.0110 - val_loss: 20685102.5604\n",
      "Epoch 297/300\n",
      "9090/9090 [==============================] - 0s 18us/sample - loss: 19112084.3916 - val_loss: 20564067.2040\n",
      "Epoch 298/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 19087501.3278 - val_loss: 20562236.3392\n",
      "Epoch 299/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 19066572.9901 - val_loss: 20567342.1319\n",
      "Epoch 300/300\n",
      "9090/9090 [==============================] - 0s 17us/sample - loss: 19076664.7789 - val_loss: 20539861.3598\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f8e8859e4d0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x=x_train, y = y_train,validation_data=(x_test,y_test),batch_size=250,epochs=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "kayipVerisi = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>loss</th>\n",
       "      <th>val_loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.721439e+08</td>\n",
       "      <td>6.881007e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.721200e+08</td>\n",
       "      <td>6.880595e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.720360e+08</td>\n",
       "      <td>6.878975e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.717238e+08</td>\n",
       "      <td>6.873329e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.707148e+08</td>\n",
       "      <td>6.856173e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           loss      val_loss\n",
       "0  6.721439e+08  6.881007e+08\n",
       "1  6.721200e+08  6.880595e+08\n",
       "2  6.720360e+08  6.878975e+08\n",
       "3  6.717238e+08  6.873329e+08\n",
       "4  6.707148e+08  6.856173e+08"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kayipVerisi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ea6a16850>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kayipVerisi.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "tahminDizisi = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22435.53 ],\n",
       "       [23648.12 ],\n",
       "       [24990.025],\n",
       "       ...,\n",
       "       [26338.1  ],\n",
       "       [12264.97 ],\n",
       "       [24816.426]], dtype=float32)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tahminDizisi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3220.5576017603435"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_test,tahminDizisi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>price</th>\n",
       "      <th>mileage</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12987.000000</td>\n",
       "      <td>12987.000000</td>\n",
       "      <td>12987.000000</td>\n",
       "      <td>12987.000000</td>\n",
       "      <td>12987.000000</td>\n",
       "      <td>12987.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2017.285516</td>\n",
       "      <td>24074.855779</td>\n",
       "      <td>22133.367367</td>\n",
       "      <td>129.676215</td>\n",
       "      <td>55.438392</td>\n",
       "      <td>2.051059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.189633</td>\n",
       "      <td>9866.601115</td>\n",
       "      <td>21197.472376</td>\n",
       "      <td>65.167429</td>\n",
       "      <td>15.025902</td>\n",
       "      <td>0.532313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1997.000000</td>\n",
       "      <td>650.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>17355.000000</td>\n",
       "      <td>6320.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>45.600000</td>\n",
       "      <td>1.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>22299.000000</td>\n",
       "      <td>15371.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>56.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>28706.000000</td>\n",
       "      <td>31986.500000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>64.200000</td>\n",
       "      <td>2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>65990.000000</td>\n",
       "      <td>259000.000000</td>\n",
       "      <td>580.000000</td>\n",
       "      <td>217.300000</td>\n",
       "      <td>6.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               year         price        mileage           tax           mpg  \\\n",
       "count  12987.000000  12987.000000   12987.000000  12987.000000  12987.000000   \n",
       "mean    2017.285516  24074.855779   22133.367367    129.676215     55.438392   \n",
       "std        2.189633   9866.601115   21197.472376     65.167429     15.025902   \n",
       "min     1997.000000    650.000000       1.000000      0.000000      1.100000   \n",
       "25%     2016.000000  17355.000000    6320.000000    125.000000     45.600000   \n",
       "50%     2018.000000  22299.000000   15371.000000    145.000000     56.500000   \n",
       "75%     2019.000000  28706.000000   31986.500000    145.000000     64.200000   \n",
       "max     2020.000000  65990.000000  259000.000000    580.000000    217.300000   \n",
       "\n",
       "         engineSize  \n",
       "count  12987.000000  \n",
       "mean       2.051059  \n",
       "std        0.532313  \n",
       "min        0.000000  \n",
       "25%        1.700000  \n",
       "50%        2.000000  \n",
       "75%        2.100000  \n",
       "max        6.200000  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8eb0470690>]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,tahminDizisi)\n",
    "plt.plot(y_test,y_test,\"g-*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year           2020.0\n",
       "price         65980.0\n",
       "mileage        3999.0\n",
       "tax             145.0\n",
       "mpg              28.0\n",
       "engineSize        4.0\n",
       "Name: 3191, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "yeniArabaSeries = dataFrame.drop(\"price\",axis=1).iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(yeniArabaSeries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "yeniArabaSeries = scaler.transform(yeniArabaSeries.values.reshape(-1,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[62615.453]], dtype=float32)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(yeniArabaSeries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}