Car_price_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from plotly.subplots import make_subplots\n",
    "import datetime\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuel type</th>\n",
       "      <th>engine size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Focus</td>\n",
       "      <td>2016</td>\n",
       "      <td>8000</td>\n",
       "      <td>Manual</td>\n",
       "      <td>38852</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Focus</td>\n",
       "      <td>2019</td>\n",
       "      <td>13400</td>\n",
       "      <td>Manual</td>\n",
       "      <td>11952</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Focus</td>\n",
       "      <td>2019</td>\n",
       "      <td>14600</td>\n",
       "      <td>Manual</td>\n",
       "      <td>22142</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Focus</td>\n",
       "      <td>2016</td>\n",
       "      <td>9450</td>\n",
       "      <td>Manual</td>\n",
       "      <td>14549</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Focus</td>\n",
       "      <td>2015</td>\n",
       "      <td>9999</td>\n",
       "      <td>Manual</td>\n",
       "      <td>7010</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <th>5449</th>\n",
       "      <td>5449</td>\n",
       "      <td>Focus</td>\n",
       "      <td>2019</td>\n",
       "      <td>18745</td>\n",
       "      <td>Manual</td>\n",
       "      <td>7855</td>\n",
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       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>5450</th>\n",
       "      <td>5450</td>\n",
       "      <td>Focus</td>\n",
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       "      <td>16350</td>\n",
       "      <td>Manual</td>\n",
       "      <td>13891</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5451</th>\n",
       "      <td>5451</td>\n",
       "      <td>Focus</td>\n",
       "      <td>2019</td>\n",
       "      <td>16850</td>\n",
       "      <td>Manual</td>\n",
       "      <td>13452</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5452</th>\n",
       "      <td>5452</td>\n",
       "      <td>Focus</td>\n",
       "      <td>2019</td>\n",
       "      <td>17310</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>13376</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5453</th>\n",
       "      <td>5453</td>\n",
       "      <td>Focus</td>\n",
       "      <td>2019</td>\n",
       "      <td>17610</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7886</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5454 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0   model  year  price transmission  mileage fuel type  \\\n",
       "0              0   Focus  2016   8000       Manual    38852    Petrol   \n",
       "1              1   Focus  2019  13400       Manual    11952    Petrol   \n",
       "2              2   Focus  2019  14600       Manual    22142    Petrol   \n",
       "3              3   Focus  2016   9450       Manual    14549    Diesel   \n",
       "4              4   Focus  2015   9999       Manual     7010    Diesel   \n",
       "...          ...     ...   ...    ...          ...      ...       ...   \n",
       "5449        5449   Focus  2019  18745       Manual     7855    Diesel   \n",
       "5450        5450   Focus  2019  16350       Manual    13891    Petrol   \n",
       "5451        5451   Focus  2019  16850       Manual    13452    Petrol   \n",
       "5452        5452   Focus  2019  17310    Automatic    13376    Petrol   \n",
       "5453        5453   Focus  2019  17610    Automatic     7886    Petrol   \n",
       "\n",
       "      engine size  \n",
       "0             1.0  \n",
       "1             1.0  \n",
       "2             1.5  \n",
       "3             1.6  \n",
       "4             1.6  \n",
       "...           ...  \n",
       "5449          2.0  \n",
       "5450          1.0  \n",
       "5451          1.0  \n",
       "5452          1.0  \n",
       "5453          1.0  \n",
       "\n",
       "[5454 rows x 8 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read the dataset\n",
    "df=pd.read_csv('E:\\Lam Nhut Thinh\\\\focus_cleaned.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>transmission</th>\n",
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       "      <th>1</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2019</td>\n",
       "      <td>13400</td>\n",
       "      <td>Manual</td>\n",
       "      <td>11952</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>Focus</td>\n",
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       "      <td>Manual</td>\n",
       "      <td>22142</td>\n",
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       "      <td>1.5</td>\n",
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       "      <td>Focus</td>\n",
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       "      <td>Petrol</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>16850</td>\n",
       "      <td>Manual</td>\n",
       "      <td>13452</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5452</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2019</td>\n",
       "      <td>17310</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>13376</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5453</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2019</td>\n",
       "      <td>17610</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7886</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5454 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  year  price transmission  mileage fuel type  engine size\n",
       "0      Focus  2016   8000       Manual    38852    Petrol          1.0\n",
       "1      Focus  2019  13400       Manual    11952    Petrol          1.0\n",
       "2      Focus  2019  14600       Manual    22142    Petrol          1.5\n",
       "3      Focus  2016   9450       Manual    14549    Diesel          1.6\n",
       "4      Focus  2015   9999       Manual     7010    Diesel          1.6\n",
       "...      ...   ...    ...          ...      ...       ...          ...\n",
       "5449   Focus  2019  18745       Manual     7855    Diesel          2.0\n",
       "5450   Focus  2019  16350       Manual    13891    Petrol          1.0\n",
       "5451   Focus  2019  16850       Manual    13452    Petrol          1.0\n",
       "5452   Focus  2019  17310    Automatic    13376    Petrol          1.0\n",
       "5453   Focus  2019  17610    Automatic     7886    Petrol          1.0\n",
       "\n",
       "[5454 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop unnecessary columns\n",
    "df=df.drop('Unnamed: 0',axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th>model</th>\n",
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       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuel type</th>\n",
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       "      <td>Focus</td>\n",
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       "      <td>Manual</td>\n",
       "      <td>7010</td>\n",
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       "      <td>7</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>5450</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2019</td>\n",
       "      <td>16350</td>\n",
       "      <td>Manual</td>\n",
       "      <td>13891</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5451</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2019</td>\n",
       "      <td>16850</td>\n",
       "      <td>Manual</td>\n",
       "      <td>13452</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
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       "      <th>5452</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2019</td>\n",
       "      <td>17310</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>13376</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5453</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2019</td>\n",
       "      <td>17610</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7886</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
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      ],
      "text/plain": [
       "       model  year  price transmission  mileage fuel type  engine size  age\n",
       "0      Focus  2016   8000       Manual    38852    Petrol          1.0    6\n",
       "1      Focus  2019  13400       Manual    11952    Petrol          1.0    3\n",
       "2      Focus  2019  14600       Manual    22142    Petrol          1.5    3\n",
       "3      Focus  2016   9450       Manual    14549    Diesel          1.6    6\n",
       "4      Focus  2015   9999       Manual     7010    Diesel          1.6    7\n",
       "...      ...   ...    ...          ...      ...       ...          ...  ...\n",
       "5449   Focus  2019  18745       Manual     7855    Diesel          2.0    3\n",
       "5450   Focus  2019  16350       Manual    13891    Petrol          1.0    3\n",
       "5451   Focus  2019  16850       Manual    13452    Petrol          1.0    3\n",
       "5452   Focus  2019  17310    Automatic    13376    Petrol          1.0    3\n",
       "5453   Focus  2019  17610    Automatic     7886    Petrol          1.0    3\n",
       "\n",
       "[5454 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate age of the cars\n",
    "df['age'] = datetime.datetime.now().year-df['year']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>engine size</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5454.000000</td>\n",
       "      <td>5454.000000</td>\n",
       "      <td>5454.000000</td>\n",
       "      <td>5454.000000</td>\n",
       "      <td>5454.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2017.113495</td>\n",
       "      <td>13581.958379</td>\n",
       "      <td>22876.582692</td>\n",
       "      <td>1.362926</td>\n",
       "      <td>4.886505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.170301</td>\n",
       "      <td>4737.282779</td>\n",
       "      <td>20596.754387</td>\n",
       "      <td>0.400450</td>\n",
       "      <td>2.170301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2002.000000</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>10489.250000</td>\n",
       "      <td>9137.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>13299.500000</td>\n",
       "      <td>15944.500000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>16987.250000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>54995.000000</td>\n",
       "      <td>177644.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              year         price        mileage  engine size          age\n",
       "count  5454.000000   5454.000000    5454.000000  5454.000000  5454.000000\n",
       "mean   2017.113495  13581.958379   22876.582692     1.362926     4.886505\n",
       "std       2.170301   4737.282779   20596.754387     0.400450     2.170301\n",
       "min    2002.000000    495.000000       1.000000     0.000000     2.000000\n",
       "25%    2016.000000  10489.250000    9137.000000     1.000000     3.000000\n",
       "50%    2017.000000  13299.500000   15944.500000     1.500000     5.000000\n",
       "75%    2019.000000  16987.250000   30000.000000     1.600000     6.000000\n",
       "max    2020.000000  54995.000000  177644.000000     2.500000    20.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#some basic stats \n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ai3inDQCDEG0AGIRoA8AgRBsABuFENGBJL/ST5Zwox3rknTYADEK0AWAQog0Ag1jxaFfVtVW1r6r2V9XulX5+ABjVika7qs5J8p4k353ksiRvrarLVnIOADCqlX6nfWWS/d3919395SR3JLluhecAAENa6WhvTvL4zO2D0xgAcBIr/ee0a4mxPm6nql1Jdk03/6Gq9p3Gc16Y5MnTOH49sVbzsU7zO2trVb94Nh511fhnan7rYa2+frk7VjraB5NcMnN7S5JDi3fq7tuS3HYmnrCq9nb39jPxWGudtZqPdZqftZqPdZrfel+rlf54/M+TbKuqS6vqxUl2JLl7hecAAENa0Xfa3f1MVb0zye8nOSfJ+7v7kZWcAwCMasV/e7y7P57k4yv4lGfkY/Z1wlrNxzrNz1rNxzrNb12vVXUfdx4YAPAC5GdMAWAQazra6/EnU6vq/VV1pKoenhm7oKruq6rHpuvzZ+67cVqffVV1zcz4a6vqoem+W6qqpvFzq+o3p/FPVtXWlXx9Z0pVXVJVf1RVj1bVI1X17mncWs2oqpdU1f1V9elpnf7zNG6dllFV51TVp6rqd6fb1mqRqjowvb4Hq2rvNGad5tHda/KShRPdPpfkVUlenOTTSS5b7XmtwOv+d0lek+ThmbFfSrJ72t6d5Ben7cumdTk3yaXTep0z3Xd/ktdl4c/W35vku6fxH03yv6btHUl+c7Vf8ymu06Ykr5m2X5bkr6b1sFbPX6dKct60/aIkn0xylXU64Zr9xyS/keR3p9vW6vg1OpDkwkVj1mmetVvtCZzFfyhel+T3Z27fmOTG1Z7XCr32rXl+tPcl2TRtb0qyb6k1ycJZ/a+b9vnszPhbk/za7D7T9oYs/MhBrfZrPgNrdleS77JWJ1yjr07yF0n+jXVado22JNmT5DvzXLSt1fHrdCDHR9s6zXFZyx+P+8nU51zc3YeTZLq+aBpfbo02T9uLx593THc/k+TvkrzirM18BUwfnX17Ft5FWqtFpo97H0xyJMl93W2dlvc/kvxUkn+ZGbNWx+skf1BVD9TCL2Am1mkuK/5HvlbQXD+Zus4tt0YnWrs1ta5VdV6S307y493999NXYkvuusTYulir7n42yRVV9XVJPlpVl59g93W7TlX1PUmOdPcDVXX1PIcsMbYu1irJ67v7UFVdlOS+qvrsCfZdz+t0nLX8Tnuun0xdJ56oqk1JMl0fmcaXW6OD0/bi8ecdU1Ubknxtki+etZmfRVX1oiwE+0Pd/ZFp2Foto7ufSvLHSa6NdVrK65N8X1UdyMLfYPidVfXrsVbH6e5D0/WRJB/Nwt8AaZ3msJaj7SdTn3N3kp3T9s4sfH97bHzHdKblpUm2Jbl/+mjq6aq6ajob822Ljjn2WD+Y5A97+uJoJNPrel+SR7v7V2buslYzqmrj9A47VfXSJP8hyWdjnY7T3Td295bu3pqF/978YXf/UKzV81TV11TVy45tJ3ljkodjneaz2l+qn81Lkjdl4azgzyX5mdWezwq95g8nOZzk/2Xh/zbfnoXvcvYkeWy6vmBm/5+Z1mdfpjMvp/HtWfgX6XNJfjXP/RDPS5L8VpL9WThz81Wr/ZpPcZ3+bRY+LvvLJA9OlzdZq+PW6VuTfGpap4eT/Nw0bp1OvG5X57kT0azV89fmVVk4G/zTSR459t9m6zTfxS+iAcAg1vLH4wCwpog2AAxCtAFgEKINAIMQbQAYhGgDwCBEGwAGIdoAMIj/D59WX7lxdSipAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize numeric columns\n",
    "for i in ['price','mileage','engine size','age']:\n",
    "    plt.figure(figsize = (8,6))\n",
    "    plt.hist(df[i])\n",
    "    plt.title(i.upper())\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAF1CAYAAAAnXamsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAYeklEQVR4nO3df7RdZX3n8fcnibospKKDXhWx8Q9GYwG1cxfUoR1vhoZgdIF1qgW7Kp1mmQIj/lh2rYairR1lkpm2OlUEBhsWuFYJ1aVILAwkw9wjOqOUhOGnwUoZHGMYGaRirjJDk3znj3uSHjLnJpdzb3KeyXm/1jrr7P3sZ+/9PX+cfPI8e9+zU1VIkqQ2LRh2AZIkaWYGtSRJDTOoJUlqmEEtSVLDDGpJkhpmUEuS1DCDWpKkhhnU0ghI0knyd0met1/7OUnuSPKTJI91ly9Mku72a5I8nWSq53XPcD6FNJoMaukIl2QJ8MtAAWf1tH8I+DPgj4GXAmPA+cBpwHN7DvHvqurontfrDlPpkoBFwy5A0iH3buCbwB3AecAXkrwA+NfAu6vqiz19/xvwG4e/REkzMailI9+7gU8wHdTfTDIGvAF4HnDjMAuTdHBOfUtHsCS/BPwc8Pmq2gr8LfAu4Fjg8ara1dP3vyb5UZKnkvyznsP8brd97+vaw/ohpBFnUEtHtvOATVX1eHf9um7bD4Fjk+ybVauqf1pVx3S39f7b8CdVdUzP67zDVbwkp76lI1aS5wPvBBYm+Z/d5ucBxwA/Bf4PcDbwxf5HkNQCg1o6cr0N2A2cBDzd0/55pu/+/iPg8u6fYt3CdHifDBx1mOuUdADxedTSkSnJLcADVfWh/drfCXwKeAXw68D7gROBnwAPA+uBa6rq6STXMH1Nuzfo/3dVHXvoP4EkMKglSWqaN5NJktQwg1qSpIYZ1JIkNcygliSpYQa1JEkNa/LvqI899thasmTJsMuQRs5PfvITjjrKP6OWDretW7c+XlUv7retyaBesmQJW7ZsGXYZ0sjpdDpMTEwMuwxp5CT57kzbnPqWJKlhBrUkSQ0zqCVJaphBLUlSwwxqSZIaZlBLktQwg1qSpIYZ1JIkNcygliSpYQcN6iTHJ5lMsi3JA0ne321/UZLNSb7TfX/hDPufmeTbSR5Ksma+P4CkuduwYQMnnngip59+OieeeCIbNmwYdkmSumbzE6K7gA9V1V1JFgNbk2wGfgu4rarWdQN4DfB7vTsmWQh8BlgObAfuTLKxqr41nx9C0uA2bNjAJZdcwvr169m9ezcLFy5k1apVAJx77rlDrk7SQUfUVfVoVd3VXd4JbAOOA84Gru12uxZ4W5/dTwEeqqqHq+pp4PrufpIacemll7J+/XqWLVvGokWLWLZsGevXr+fSSy8ddmmSeJYP5UiyBHgDcAcwVlWPwnSYJ3lJn12OA77Xs74dOHWGY68GVgOMjY3R6XSeTWmSBrRt2zZ2795Np9NhamqKTqfD7t272bZtm99DqQGzDuokRwNfBD5QVT9OMqvd+rRVv45VdRVwFcD4+Hj5BB/p8Fi6dCkLFy5kYmJi39OzJicnWbp0qU/Skhowq7u+kzyH6ZD+i6r6Urf5B0le1t3+MuCxPrtuB47vWX8FsGPwciXNt0suuYRVq1YxOTnJrl27mJycZNWqVVxyySXDLk0SsxhRZ3rovB7YVlWf6Nm0ETgPWNd9v7HP7ncCJyR5FfB94BzgXXMtWtL82XvD2EUXXcS2bdtYunQpl156qTeSSY1IVd+Z6H/okPwS8DXgPmBPt/n3mb5O/XnglcD/AN5RVU8keTnw51W1srv/SuDfAwuBq6vqoHeojI+P15YtWwb7RJIGtnfqW9LhlWRrVY3323bQEXVVfZ3+15oBTu/Tfwewsmf9ZuDm2ZUqSZJ6+ctkkiQ1zKCWJKlhBrUkSQ0zqCVJaphBLUlSwwxqSZIaZlBLktQwg1qSpIYZ1JI4+eSTScKyZctIwsknnzzskiR1GdTSiDv55JO57777OOuss7jhhhs466yzuO+++wxrqREGtTTi9ob0jTfeyDHHHMONN964L6wlDZ9BLYn169cfcF3S8BjUkli1atUB1yUNj0EtjbiTTjqJjRs3cvbZZ/OjH/2Is88+m40bN3LSSScNuzRJzOJ51MPg86ilw2vvDWV7nXTSSdx7771DrEgaLQd6HrUjaknce++9VBWTk5NUlSEtNcSgliSpYQa1JEkNM6glSWqYQS1JUsMMakmSGmZQS5LUMINakqSGGdSSJDXMoJYkqWEGtSRJDTOoJUlqmEEtSVLDDGpJkhpmUEuS1DCDWpKkhhnUkiQ1bNHBOiS5Gngr8FhVndht+0vg1d0uxwA/qqrX99n3EWAnsBvYVVXj81S3JEkj4aBBDVwDXAZ8bm9DVf363uUkfwo8eYD9l1XV44MWKEnSKDtoUFfV7UmW9NuWJMA7gX8+v2VJkiSY+zXqXwZ+UFXfmWF7AZuSbE2yeo7nkiRp5Mxm6vtAzgU2HGD7aVW1I8lLgM1JHqyq2/t17Ab5aoCxsTE6nc4cS5P0bE1NTfndkxozcFAnWQS8HfgnM/Wpqh3d98eS3ACcAvQN6qq6CrgKYHx8vCYmJgYtTdKAOp0Ofvektsxl6vtXgAeranu/jUmOSrJ47zJwBnD/HM4nSdLIOWhQJ9kAfAN4dZLtSVZ1N53DftPeSV6e5Obu6hjw9ST3AH8N3FRVt8xf6ZIkHflmc9f3uTO0/1afth3Ayu7yw8Dr5lifJEkjba43k0k6AixcuJA9e/bsW1+wYAG7d+8eYkWS9vInRKURtzekjz76aK644gqOPvpo9uzZw8KFC4ddmiQMamnk7Q3pnTt38prXvIadO3fuC2tJw2dQS+KrX/3qAdclDY9BLYk3velNB1yXNDwGtTTiFixYwNTUFIsXL+bBBx9k8eLFTE1NsWCB/zxILfCub2nE7d69m4ULFzI1NcUFF1wAeNe31BL/yyyJ3bt3U1VMTk5SVYa01BCDWpKkhhnUkiQ1zKCWJKlhBrUkSQ0zqCVJaphBLUlSwwxqSZIaZlBLktQwg1oSK1asYMGCBSxbtowFCxawYsWKYZckqcuglkbcihUr2LRpE+effz5f+cpXOP/889m0aZNhLTXC3/qWRtzmzZu54IILuPzyy+l0Olx++eUAXHnllUOuTBI4opZGXlWxdu3aZ7StXbuWqhpSRZJ6GdTSiEvCxRdf/Iy2iy++mCRDqkhSL6e+pRG3fPlyrrjiCgBWrlzJhRdeyBVXXMEZZ5wx5MokAaTF6a3x8fHasmXLsMuQRsaKFSvYvHkzVUUSli9fzq233jrssqSRkWRrVY332+aIWtK+UO50OkxMTAy3GEnP4DVqSZIaZlBLktQwg1qSpIYZ1JIkNcygliSpYQa1JEkNM6glSWqYQS1JUsMOGtRJrk7yWJL7e9o+muT7Se7uvlbOsO+ZSb6d5KEka+azcEmSRsFsfpnsGuAy4HP7tX+yqv5kpp2SLAQ+AywHtgN3JtlYVd8asFZJh0i/B3C0+PPC0ig66Ii6qm4Hnhjg2KcAD1XVw1X1NHA9cPYAx5F0CO0N6SSsW7fuGeuShm8u16jfm+Te7tT4C/tsPw74Xs/69m6bpMYkYc+ePZx66qns2bPHkJYaMuhDOa4APgZU9/1Pgd/er0+/b/qMc2lJVgOrAcbGxuh0OgOWJunZWrt2LZ1Oh6mpKTqdDmvXrmXNmjV+D6UGzOoxl0mWAH9VVSfOdluSNwIfraoV3fWLAapq7cHO52MupcMnyb4R9d6nZy1YsICq8jq1dJgc6DGXA019J3lZz+qvAvf36XYncEKSVyV5LnAOsHGQ80k6tKqKBQsWcMcdd+wLaUltOOjUd5INwARwbJLtwB8CE0lez/RU9iPA73T7vhz486paWVW7krwXuBVYCFxdVQ8ckk8haWBVRRKqijVr1jyjXdLwzWrq+3Bz6lsajr1T35IOr3mf+pYkSYeHQS1JUsMMakmSGmZQS5LUMINakqSGGdSSJDXMoJYkqWEGtSRJDRv0oRySjiA+j1pqlyNqacT1hvSFF17Yt13S8BjUkoDpEfQ73vEOR9JSYwxqSXzqU5864Lqk4TGoJfG+973vgOuShseglgRMX5P+whe+4LVpqTEGtTTieq9JX3755X3bJQ2PQS2JqqKqmJyc3LcsqQ0GtSRJDTOoJUlqmEEtSVLDDGpJkhpmUEuS1DCDWpKkhhnUkiQ1zKCWJKlhBrUkSQ1bNOwCJA1fv9/39tfJpDY4opZGXG9Iv/3tb+/bLml4DGpJwPQI+qKLLnIkLTXGoJbEBz/4wQOuSxoeg1oSn/zkJw+4Lml4DGpJwPQ16U9/+tNem5YaY1BLI673mvSXvvSlvu2ShseglkRVUVVMTk7uW5bUhoMGdZKrkzyW5P6etj9O8mCSe5PckOSYGfZ9JMl9Se5OsmU+C5ckaRTMZkR9DXDmfm2bgROr6mTgb4CLD7D/sqp6fVWND1aiJEmj66BBXVW3A0/s17apqnZ1V78JvOIQ1CZJ0sibj58Q/W3gL2fYVsCmJAX8h6q6aqaDJFkNrAYYGxuj0+nMQ2mSno2pqSm/e1Jj5hTUSS4BdgF/MUOX06pqR5KXAJuTPNgdof8/uiF+FcD4+HhNTEzMpTRJA+h0Ovjdk9oy8F3fSc4D3gr8Rs1wi2hV7ei+PwbcAJwy6PkkSRpFAwV1kjOB3wPOqqqfztDnqCSL9y4DZwD39+srSZL6m82fZ20AvgG8Osn2JKuAy4DFTE9n353kym7flye5ubvrGPD1JPcAfw3cVFW3HJJPIUnSEeqg16ir6tw+zetn6LsDWNldfhh43Zyqk3RY+DxqqV3+Mpk04npD+sMf/nDfdknDY1BLAqZH0KeffrojaakxBrUkrr/++gOuSxoeg1oS55xzzgHXJQ2PQS0JmL4mfdttt3ltWmqMQS2NuN5r0h//+Mf7tksaHoNaks+jlhpmUEuS1DCDWpKkhhnUkiQ1zKCWJKlhBrUkSQ0zqCVJaphBLUlSwwxqSZIaZlBLktSwRcMuQNLw9ft9b3+dTGqDI2ppxPWG9Lve9a6+7ZKGx6CWBEyPoN/znvc4kpYaY1BL4iMf+cgB1yUNj0EtiY997GMHXJc0PAa1JGD6mvRnP/tZr01LjTGopRHXe036uuuu69suaXgMaklUFVXF5OTkvmVJbTCoJUlqmEEtSVLDDGpJkhpmUEuS1DCDWpKkhhnUkiQ1zKCWJKlhBrUkSQ07aFAnuTrJY0nu72l7UZLNSb7TfX/hDPuemeTbSR5KsmY+C5ckaRTMZkR9DXDmfm1rgNuq6gTgtu76MyRZCHwGeDPwWuDcJK+dU7WSJI2YgwZ1Vd0OPLFf89nAtd3la4G39dn1FOChqnq4qp4Gru/uJ0mSZmnRgPuNVdWjAFX1aJKX9OlzHPC9nvXtwKkzHTDJamA1wNjYGJ1OZ8DSpNG0bNmyYZewz+Tk5LBLkI4Ygwb1bPR7Vt6Mv/RfVVcBVwGMj4/XxMTEISpLOjLNx4M0lqy5iUfWvWUeqpE0Xwa96/sHSV4G0H1/rE+f7cDxPeuvAHYMeD5JkkbSoEG9ETivu3wecGOfPncCJyR5VZLnAud095MkSbM0mz/P2gB8A3h1ku1JVgHrgOVJvgMs766T5OVJbgaoql3Ae4FbgW3A56vqgUPzMSRJOjId9Bp1VZ07w6bT+/TdAazsWb8ZuHng6iRJGnH+MpkkSQ0zqCVJaphBLUlSwwxqSZIaZlBLktQwg1qSpIYZ1JIkNcygliSpYQa1JEkNM6glSWqYQS1JUsMMakmSGmZQS5LUMINakqSGGdSSJDXMoJYkqWEGtSRJDTOoJUlqmEEtSVLDDGpJkhpmUEuS1DCDWpKkhhnUkiQ1zKCWJKlhBrUkSQ0zqCVJaphBLUlSwwxqSZIaZlBLktQwg1qSpIYZ1JIkNcygliSpYQMHdZJXJ7m75/XjJB/Yr89Ekid7+vzB3EuWJGl0LBp0x6r6NvB6gCQLge8DN/Tp+rWqeuug55EkaZTN19T36cDfVtV35+l4kiSJOYyo93MOsGGGbW9Mcg+wA/jdqnqgX6ckq4HVAGNjY3Q6nXkqTdKz4XdPakuqam4HSJ7LdAj/fFX9YL9tPwvsqaqpJCuBP6uqEw52zPHx8dqyZcuc6pL07C1ZcxOPrHvLsMuQRk6SrVU13m/bfEx9vxm4a/+QBqiqH1fVVHf5ZuA5SY6dh3NKkjQS5iOoz2WGae8kL02S7vIp3fP9cB7OKUnSSJjTNeokPwMsB36np+18gKq6Evg14IIku4CngHNqrnPtkiSNkDkFdVX9FPhH+7Vd2bN8GXDZXM4hSdIo85fJJElqmEEtSVLDDGpJkhpmUEuS1DCDWpKkhhnUkiQ1zKCWJKlhBrUkSQ0zqCVJaphBLUlSwwxqSZIaZlBLktQwg1qSpIYZ1JIkNcygliSpYQa1JEkNM6glSWqYQS1JUsMMakmSGrZo2AVIgtf90SaefOrvh10GAEvW3DTU87/g+c/hnj88Y6g1SC0xqKUGPPnU3/PIurcMuww6nQ4TExNDrWHY/1GQWuPUtyRJDTOoJUlqmEEtSVLDDGpJkhpmUEuS1DCDWpKkhhnUkiQ1zKCWJKlhBrUkSQ0zqCVJaphBLUlSw+YU1EkeSXJfkruTbOmzPUk+leShJPcm+YW5nE+SpFEzHw/lWFZVj8+w7c3ACd3XqcAV3XdJkjQLh3rq+2zgczXtm8AxSV52iM8pSdIRY65BXcCmJFuTrO6z/Tjgez3r27ttkiRpFuY69X1aVe1I8hJgc5IHq+r2nu3ps0/1O1A36FcDjI2N0el05lia9P+PxUvXcNK1a4ZdxrRrh3v6xUuh0zlquEVIDZlTUFfVju77Y0luAE4BeoN6O3B8z/orgB0zHOsq4CqA8fHxGvbD66XDaeeadTyy7i3DLoNOp8Owv3tL1tzExHnDrUFqycBT30mOSrJ47zJwBnD/ft02Au/u3v39i8CTVfXowNVKkjRi5jKiHgNuSLL3ONdV1S1JzgeoqiuBm4GVwEPAT4F/ObdyJUkaLQMHdVU9DLyuT/uVPcsF/KtBzyFJ0qjzl8kkSWqYQS1JUsMMakmSGmZQS5LUMINakqSGGdSSJDXMoJYkqWEGtSRJDTOoJUlqmEEtSVLDDGpJkhpmUEuS1DCDWpKkhhnUkiQ1zKCWJKlhBrUkSQ0zqCVJaphBLUlSwwxqSZIaZlBLktQwg1qSpIYZ1JIkNcygliSpYQa1JEkNM6glSWqYQS1JUsMMakmSGmZQS5LUsEXDLkDStCVrbhp2CdNuGW4dL3j+c4Z6fqk1BrXUgEfWvWXYJQDT/1lopRZJ05z6liSpYQa1JEkNGziokxyfZDLJtiQPJHl/nz4TSZ5Mcnf39QdzK1eSpNEyl2vUu4APVdVdSRYDW5Nsrqpv7dfva1X11jmcR5KkkTXwiLqqHq2qu7rLO4FtwHHzVZgkSZqna9RJlgBvAO7os/mNSe5J8h+T/Px8nE+SpFEx5z/PSnI08EXgA1X14/023wX8XFVNJVkJfBk4YYbjrAZWA4yNjdHpdOZamqQB+N2T2pKqGnzn5DnAXwG3VtUnZtH/EWC8qh4/UL/x8fHasmXLwHVJGox/Ry0NR5KtVTXeb9tc7voOsB7YNlNIJ3lptx9JTume74eDnlOSpFEzl6nv04DfBO5Lcne37feBVwJU1ZXArwEXJNkFPAWcU3MZwkuSNGIGDuqq+jqQg/S5DLhs0HNIkjTq/GUySZIaZlBLktQwg1qSpIb5mEvpCNH9A4u5H+ffzv0Y3jMqzR9H1NIRoqrm/JqcnJyX40iaPwa1JEkNM6glSWqYQS1JUsMMakmSGmZQS5LUMINakqSGGdSSJDXMoJYkqWEGtSRJDTOoJUlqmEEtSVLDDGpJkhpmUEuS1LC0+KSbJP8L+O6w65BG0LHA48MuQhpBP1dVL+63ocmgljQcSbZU1fiw65D0D5z6liSpYQa1JEkNM6gl9bpq2AVIeiavUUuS1DBH1JIkNcygliSpYQa1JEkNM6ilEZLky0m2Jnkgyepu26okf5Okk+SzSS7rtr84yReT3Nl9nTbc6qXR5M1k0ghJ8qKqeiLJ84E7gRXAfwF+AdgJ/Gfgnqp6b5LrgMur6utJXgncWlVLh1a8NKIWDbsASYfV+5L8anf5eOA3ga9W1RMASb4A/OPu9l8BXptk774/m2RxVe08nAVLo86glkZEkgmmw/eNVfXTJB3g28BMo+QF3b5PHZ4KJfXjNWppdLwA+LtuSL8G+EXgZ4A3JXlhkkXAv+jpvwl4796VJK8/rNVKAgxqaZTcAixKci/wMeCbwPeBfwPcAfwn4FvAk93+7wPGk9yb5FvA+Ye/ZEneTCaNuCRHV9VUd0R9A3B1Vd0w7LokTXNELemjSe4G7gf+O/DlIdcjqYcjakmSGuaIWpKkhhnUkiQ1zKCWJKlhBrUkSQ0zqCVJaphBLUlSw/4vIR31vd9DFhQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in ['price','mileage','engine size','age']:\n",
    "    plt.figure(figsize = (8,6))\n",
    "    df.boxplot(column=i)\n",
    "    plt.title(i.upper())\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in ['transmission','fuel type']:\n",
    "    plt.figure(figsize = (8,6))\n",
    "    plt.scatter(data=df,x=i,y='price',color='green')\n",
    "    plt.title(i.upper());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df[['price','mileage','engine size','age']].corr(),cmap='Greens_r',vmin=-1,vmax=1,linewidths=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.get_dummies(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>mileage</th>\n",
       "      <th>engine size</th>\n",
       "      <th>age</th>\n",
       "      <th>model_ Focus</th>\n",
       "      <th>transmission_Automatic</th>\n",
       "      <th>transmission_Manual</th>\n",
       "      <th>transmission_Semi-Auto</th>\n",
       "      <th>fuel type_Diesel</th>\n",
       "      <th>fuel type_Petrol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>8000</td>\n",
       "      <td>38852</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>14600</td>\n",
       "      <td>22142</td>\n",
       "      <td>1.5</td>\n",
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       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>9450</td>\n",
       "      <td>14549</td>\n",
       "      <td>1.6</td>\n",
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       "      <td>2015</td>\n",
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       "      <td>7010</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>5449</th>\n",
       "      <td>2019</td>\n",
       "      <td>18745</td>\n",
       "      <td>7855</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5450</th>\n",
       "      <td>2019</td>\n",
       "      <td>16350</td>\n",
       "      <td>13891</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
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       "      <td>16850</td>\n",
       "      <td>13452</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5452</th>\n",
       "      <td>2019</td>\n",
       "      <td>17310</td>\n",
       "      <td>13376</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>5453</th>\n",
       "      <td>2019</td>\n",
       "      <td>17610</td>\n",
       "      <td>7886</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5454 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year  price  mileage  engine size  age  model_ Focus  \\\n",
       "0     2016   8000    38852          1.0    6             1   \n",
       "1     2019  13400    11952          1.0    3             1   \n",
       "2     2019  14600    22142          1.5    3             1   \n",
       "3     2016   9450    14549          1.6    6             1   \n",
       "4     2015   9999     7010          1.6    7             1   \n",
       "...    ...    ...      ...          ...  ...           ...   \n",
       "5449  2019  18745     7855          2.0    3             1   \n",
       "5450  2019  16350    13891          1.0    3             1   \n",
       "5451  2019  16850    13452          1.0    3             1   \n",
       "5452  2019  17310    13376          1.0    3             1   \n",
       "5453  2019  17610     7886          1.0    3             1   \n",
       "\n",
       "      transmission_Automatic  transmission_Manual  transmission_Semi-Auto  \\\n",
       "0                          0                    1                       0   \n",
       "1                          0                    1                       0   \n",
       "2                          0                    1                       0   \n",
       "3                          0                    1                       0   \n",
       "4                          0                    1                       0   \n",
       "...                      ...                  ...                     ...   \n",
       "5449                       0                    1                       0   \n",
       "5450                       0                    1                       0   \n",
       "5451                       0                    1                       0   \n",
       "5452                       1                    0                       0   \n",
       "5453                       1                    0                       0   \n",
       "\n",
       "      fuel type_Diesel  fuel type_Petrol  \n",
       "0                    0                 1  \n",
       "1                    0                 1  \n",
       "2                    0                 1  \n",
       "3                    1                 0  \n",
       "4                    1                 0  \n",
       "...                ...               ...  \n",
       "5449                 1                 0  \n",
       "5450                 0                 1  \n",
       "5451                 0                 1  \n",
       "5452                 0                 1  \n",
       "5453                 0                 1  \n",
       "\n",
       "[5454 rows x 11 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(['price','year'],axis=1)\n",
    "y=df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.833\n",
      "Model:                            OLS   Adj. R-squared:                  0.833\n",
      "Method:                 Least Squares   F-statistic:                     3617.\n",
      "Date:                Mon, 21 Mar 2022   Prob (F-statistic):               0.00\n",
      "Time:                        08:11:28   Log-Likelihood:                -39203.\n",
      "No. Observations:                4363   AIC:                         7.842e+04\n",
      "Df Residuals:                    4356   BIC:                         7.846e+04\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==========================================================================================\n",
      "                             coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------\n",
      "mileage                   -0.0637      0.003    -25.000      0.000      -0.069      -0.059\n",
      "engine size             6192.5260     86.555     71.544      0.000    6022.833    6362.219\n",
      "age                    -1323.9067     23.737    -55.773      0.000   -1370.444   -1277.369\n",
      "model_ Focus            6854.0533     80.734     84.897      0.000    6695.773    7012.333\n",
      "transmission_Automatic  3191.7929     83.548     38.203      0.000    3027.997    3355.589\n",
      "transmission_Manual     2358.6708     62.117     37.971      0.000    2236.890    2480.452\n",
      "transmission_Semi-Auto  1303.5895    131.438      9.918      0.000    1045.903    1561.276\n",
      "fuel type_Diesel        2224.9754     64.585     34.450      0.000    2098.355    2351.595\n",
      "fuel type_Petrol        4629.0779     42.753    108.275      0.000    4545.260    4712.896\n",
      "==============================================================================\n",
      "Omnibus:                     2896.191   Durbin-Watson:                   1.988\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           144447.946\n",
      "Skew:                           2.543   Prob(JB):                         0.00\n",
      "Kurtosis:                      30.726   Cond. No.                     2.47e+19\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 6.65e-27. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression, Lasso\n",
    "import statsmodels.api as sm \n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "lm = LinearRegression()\n",
    "X = sm.add_constant(X_train)\n",
    "model = sm.OLS(y_train,X_train).fit()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Khang Lam\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\regression\\linear_model.py:1738: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  return 1 - self.ssr/self.centered_tss\n",
      "C:\\Users\\Khang Lam\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\stats\\outliers_influence.py:195: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  vif = 1. / (1. - r_squared_i)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>transmission_Automatic</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>transmission_Manual</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>transmission_Semi-Auto</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fuel type_Diesel</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fuel type_Petrol</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mileage</td>\n",
       "      <td>3.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>age</td>\n",
       "      <td>3.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>engine size</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>model_ Focus</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Features   VIF\n",
       "4  transmission_Automatic   inf\n",
       "5     transmission_Manual   inf\n",
       "6  transmission_Semi-Auto   inf\n",
       "7        fuel type_Diesel   inf\n",
       "8        fuel type_Petrol   inf\n",
       "0                 mileage  3.15\n",
       "2                     age  3.04\n",
       "1             engine size  1.38\n",
       "3            model_ Focus  0.00"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif['Features'] = X_train.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1366.4695885204503"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "lm.fit(X_train,y_train)\n",
    "abs(np.mean(cross_val_score(lm,X_train,y_train, scoring = 'neg_mean_absolute_error', cv= 5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6864235833.275864, tolerance: 7787994.42025756\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6681816837.513198, tolerance: 7890013.556785788\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5902904083.8848915, tolerance: 7615717.808189227\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6427417552.610145, tolerance: 7868608.283258033\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6689458182.666908, tolerance: 7811213.468472416\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "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": [
    "alpha = []\n",
    "error = []\n",
    "\n",
    "for i in range(0,101):\n",
    "    alpha.append(i/100)\n",
    "    lml = Lasso(alpha=(i/100))\n",
    "    error.append(abs(np.mean(cross_val_score(lml,X_train,y_train, scoring = 'neg_mean_absolute_error', cv= 5))))\n",
    "plt.plot(alpha,error);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>alpha</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1366.469589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alpha        error\n",
       "0    0.0  1366.469589"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err = tuple(zip(alpha,error))\n",
    "df_err = pd.DataFrame(err, columns = ['alpha','error'])\n",
    "df_err[df_err.error == max(df_err.error)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1366.1279648310856"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_l = Lasso(1)\n",
    "lm_l.fit(X_train,y_train)\n",
    "abs(np.mean(cross_val_score(lm_l,X_train,y_train, scoring = 'neg_mean_absolute_error', cv = 5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "935.9961766853663"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf = RandomForestRegressor()\n",
    "rf.fit(X_train,y_train)\n",
    "abs(np.mean(cross_val_score(rf,X_train,y_train,scoring = 'neg_mean_absolute_error', cv = 5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import r2_score\n",
    "def evaluateRegressor(true,predicted,modelname):\n",
    "    MAE = mean_absolute_error(true,predicted)\n",
    "    RMSE = mean_squared_error(true,predicted,squared = False)\n",
    "    R_squared = r2_score(true,predicted)\n",
    "    print(modelname)\n",
    "    print(\"\\tMAE:\", MAE)\n",
    "    print(\"\\tRMSE:\", RMSE)\n",
    "    print(\"\\tR-squared:\", R_squared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_lm=lm.predict(X_test)\n",
    "y_pred_lm_l=lm_l.predict(X_test)\n",
    "y_pred_rf=rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Evaluation\n",
      "Linear Regression\n",
      "\tMAE: 1378.8456392924038\n",
      "\tRMSE: 1914.550296870124\n",
      "\tR-squared: 0.8396259990014057\n",
      "Lasso Regression\n",
      "\tMAE: 1378.9298748277422\n",
      "\tRMSE: 1914.667364361834\n",
      "\tR-squared: 0.8396063858775714\n",
      "Random Forest Regression\n",
      "\tMAE: 903.3882691184555\n",
      "\tRMSE: 1357.5576965989758\n",
      "\tR-squared: 0.9193662242638232\n"
     ]
    }
   ],
   "source": [
    "print('Model Evaluation')\n",
    "evaluateRegressor(y_test,y_pred_lm,'Linear Regression')\n",
    "evaluateRegressor(y_test,y_pred_lm_l,'Lasso Regression')\n",
    "evaluateRegressor(y_test,y_pred_rf,'Random Forest Regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scalar = []\n",
    "combine_error = []\n",
    "\n",
    "for i in range(0,101):\n",
    "    scalar.append(i/100)\n",
    "    combine_error.append(mean_absolute_error(y_test,(i/100)*y_pred_lm+((100-i)/100)*y_pred_rf))\n",
    "plt.plot(scalar,combine_error)\n",
    "plt.ylabel('error')\n",
    "plt.xlabel('scalar')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of scalar which minimize the error is 0.08\n"
     ]
    }
   ],
   "source": [
    "print(f'The value of scalar which minimize the error is {scalar[combine_error.index(min(combine_error))]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}