Used vehicles.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting used car prices using Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction and Objetives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Approximately 40 million used vehicles are sold each year. Effective pricing strategies can help any company to efficiently sell its products in a competitive market and making profit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### There are two main goals I want to achieve with this Data Science Project. First, to estimate the price of used cars by taking into account a set of features, based on historical data. Second, to get a better understanding on the most relevant features that help determine the price of a used vehicle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # Import Pandas for data manipulation using dataframes\n",
    "import numpy as np # Import Numpy for data statistical analysis \n",
    "import matplotlib.pyplot as plt # Import matplotlib for data visualisation\n",
    "import seaborn as sns # Statistical data visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "database = pd.read_csv('vehicles.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(423857, 25)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "database.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'url', 'region', 'region_url', 'price', 'year', 'manufacturer',\n",
       "       'model', 'condition', 'cylinders', 'fuel', 'odometer', 'title_status',\n",
       "       'transmission', 'vin', 'drive', 'size', 'type', 'paint_color',\n",
       "       'image_url', 'description', 'county', 'state', 'lat', 'long'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "database.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 423857 entries, 0 to 423856\n",
      "Data columns (total 25 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            423857 non-null  int64  \n",
      " 1   url           423857 non-null  object \n",
      " 2   region        423857 non-null  object \n",
      " 3   region_url    423857 non-null  object \n",
      " 4   price         423857 non-null  int64  \n",
      " 5   year          328743 non-null  float64\n",
      " 6   manufacturer  313242 non-null  object \n",
      " 7   model         325384 non-null  object \n",
      " 8   condition     176719 non-null  object \n",
      " 9   cylinders     197679 non-null  object \n",
      " 10  fuel          327214 non-null  object \n",
      " 11  odometer      270585 non-null  float64\n",
      " 12  title_status  327759 non-null  object \n",
      " 13  transmission  328065 non-null  object \n",
      " 14  vin           184420 non-null  object \n",
      " 15  drive         231119 non-null  object \n",
      " 16  size          102627 non-null  object \n",
      " 17  type          241157 non-null  object \n",
      " 18  paint_color   222203 non-null  object \n",
      " 19  image_url     329661 non-null  object \n",
      " 20  description   329654 non-null  object \n",
      " 21  county        0 non-null       float64\n",
      " 22  state         423857 non-null  object \n",
      " 23  lat           324404 non-null  float64\n",
      " 24  long          324404 non-null  float64\n",
      "dtypes: float64(5), int64(2), object(18)\n",
      "memory usage: 80.8+ MB\n"
     ]
    }
   ],
   "source": [
    "database.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>odometer</th>\n",
       "      <th>county</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.238570e+05</td>\n",
       "      <td>4.238570e+05</td>\n",
       "      <td>328743.000000</td>\n",
       "      <td>2.705850e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>324404.000000</td>\n",
       "      <td>324404.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.190602e+09</td>\n",
       "      <td>5.779437e+04</td>\n",
       "      <td>2009.827358</td>\n",
       "      <td>9.942578e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.731429</td>\n",
       "      <td>-93.949367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.159269e+06</td>\n",
       "      <td>1.002581e+07</td>\n",
       "      <td>10.135809</td>\n",
       "      <td>1.204833e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.902977</td>\n",
       "      <td>17.930566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.178302e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-80.386400</td>\n",
       "      <td>-161.394000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.186688e+09</td>\n",
       "      <td>4.499000e+03</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>4.432400e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.054500</td>\n",
       "      <td>-106.678000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.192068e+09</td>\n",
       "      <td>9.995000e+03</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>9.174900e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.366900</td>\n",
       "      <td>-88.009900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.194909e+09</td>\n",
       "      <td>1.899500e+04</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>1.372370e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.517075</td>\n",
       "      <td>-80.832500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.197006e+09</td>\n",
       "      <td>3.808256e+09</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.383400</td>\n",
       "      <td>115.524000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id         price           year      odometer  county  \\\n",
       "count  4.238570e+05  4.238570e+05  328743.000000  2.705850e+05     0.0   \n",
       "mean   7.190602e+09  5.779437e+04    2009.827358  9.942578e+04     NaN   \n",
       "std    5.159269e+06  1.002581e+07      10.135809  1.204833e+05     NaN   \n",
       "min    7.178302e+09  0.000000e+00    1900.000000  0.000000e+00     NaN   \n",
       "25%    7.186688e+09  4.499000e+03    2007.000000  4.432400e+04     NaN   \n",
       "50%    7.192068e+09  9.995000e+03    2012.000000  9.174900e+04     NaN   \n",
       "75%    7.194909e+09  1.899500e+04    2016.000000  1.372370e+05     NaN   \n",
       "max    7.197006e+09  3.808256e+09    2021.000000  1.000000e+07     NaN   \n",
       "\n",
       "                 lat           long  \n",
       "count  324404.000000  324404.000000  \n",
       "mean       38.731429     -93.949367  \n",
       "std         5.902977      17.930566  \n",
       "min       -80.386400    -161.394000  \n",
       "25%        35.054500    -106.678000  \n",
       "50%        39.366900     -88.009900  \n",
       "75%        42.517075     -80.832500  \n",
       "max        80.383400     115.524000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can see there is a big difference between the max and the 75% of each price/odometer. \n",
    "#Likewise, there is a big difference between the min and the 25% in year/price/odometer\n",
    "#Thus, we will handle the outliers later on the kernel.\n",
    "database.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>url</th>\n",
       "      <th>region</th>\n",
       "      <th>region_url</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>condition</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>...</th>\n",
       "      <th>drive</th>\n",
       "      <th>size</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>image_url</th>\n",
       "      <th>description</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7184791621</td>\n",
       "      <td>https://duluth.craigslist.org/ctd/d/duluth-200...</td>\n",
       "      <td>duluth / superior</td>\n",
       "      <td>https://duluth.craigslist.org</td>\n",
       "      <td>6995</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>gmc</td>\n",
       "      <td>new sierra 1500</td>\n",
       "      <td>excellent</td>\n",
       "      <td>8 cylinders</td>\n",
       "      <td>...</td>\n",
       "      <td>4wd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>red</td>\n",
       "      <td>https://images.craigslist.org/00n0n_f06ykBMcdh...</td>\n",
       "      <td>2000 *** GMC New Sierra 1500 Ext Cab 157.5  WB...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mn</td>\n",
       "      <td>46.8433</td>\n",
       "      <td>-92.2550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7184773187</td>\n",
       "      <td>https://duluth.craigslist.org/cto/d/saginaw-20...</td>\n",
       "      <td>duluth / superior</td>\n",
       "      <td>https://duluth.craigslist.org</td>\n",
       "      <td>8750</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>sonata</td>\n",
       "      <td>excellent</td>\n",
       "      <td>4 cylinders</td>\n",
       "      <td>...</td>\n",
       "      <td>fwd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>grey</td>\n",
       "      <td>https://images.craigslist.org/00d0d_kgZ6xoeRw2...</td>\n",
       "      <td>For Sale:  2013 Hyundai Sonata GLS - $8750.  O...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mn</td>\n",
       "      <td>46.9074</td>\n",
       "      <td>-92.4638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7193375964</td>\n",
       "      <td>https://newhaven.craigslist.org/cto/d/stratfor...</td>\n",
       "      <td>new haven</td>\n",
       "      <td>https://newhaven.craigslist.org</td>\n",
       "      <td>10900</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>prius</td>\n",
       "      <td>good</td>\n",
       "      <td>4 cylinders</td>\n",
       "      <td>...</td>\n",
       "      <td>fwd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>blue</td>\n",
       "      <td>https://images.craigslist.org/00d0d_3sHGxPbY2O...</td>\n",
       "      <td>2013 Prius V Model Two. One owner—must sell my...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ct</td>\n",
       "      <td>41.1770</td>\n",
       "      <td>-73.1336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7195108810</td>\n",
       "      <td>https://albuquerque.craigslist.org/cto/d/albuq...</td>\n",
       "      <td>albuquerque</td>\n",
       "      <td>https://albuquerque.craigslist.org</td>\n",
       "      <td>12500</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>mitsubishi</td>\n",
       "      <td>lancer</td>\n",
       "      <td>good</td>\n",
       "      <td>4 cylinders</td>\n",
       "      <td>...</td>\n",
       "      <td>4wd</td>\n",
       "      <td>mid-size</td>\n",
       "      <td>sedan</td>\n",
       "      <td>grey</td>\n",
       "      <td>https://images.craigslist.org/00m0m_4a8Pb6JbMG...</td>\n",
       "      <td>2003 Mitsubishi Lancer Evolution, silver.  Abo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nm</td>\n",
       "      <td>35.1868</td>\n",
       "      <td>-106.6650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7184712241</td>\n",
       "      <td>https://duluth.craigslist.org/ctd/d/rush-city-...</td>\n",
       "      <td>duluth / superior</td>\n",
       "      <td>https://duluth.craigslist.org</td>\n",
       "      <td>16995</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>gmc</td>\n",
       "      <td>sierra classic 2500hd</td>\n",
       "      <td>good</td>\n",
       "      <td>8 cylinders</td>\n",
       "      <td>...</td>\n",
       "      <td>4wd</td>\n",
       "      <td>full-size</td>\n",
       "      <td>truck</td>\n",
       "      <td>white</td>\n",
       "      <td>https://images.craigslist.org/01414_g093aPtSMW...</td>\n",
       "      <td>**Bad Credit, No Credit... No Problem!**2007 G...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mn</td>\n",
       "      <td>45.6836</td>\n",
       "      <td>-92.9648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                                                url  \\\n",
       "0  7184791621  https://duluth.craigslist.org/ctd/d/duluth-200...   \n",
       "1  7184773187  https://duluth.craigslist.org/cto/d/saginaw-20...   \n",
       "2  7193375964  https://newhaven.craigslist.org/cto/d/stratfor...   \n",
       "3  7195108810  https://albuquerque.craigslist.org/cto/d/albuq...   \n",
       "4  7184712241  https://duluth.craigslist.org/ctd/d/rush-city-...   \n",
       "\n",
       "              region                          region_url  price    year  \\\n",
       "0  duluth / superior       https://duluth.craigslist.org   6995  2000.0   \n",
       "1  duluth / superior       https://duluth.craigslist.org   8750  2013.0   \n",
       "2          new haven     https://newhaven.craigslist.org  10900  2013.0   \n",
       "3        albuquerque  https://albuquerque.craigslist.org  12500  2003.0   \n",
       "4  duluth / superior       https://duluth.craigslist.org  16995  2007.0   \n",
       "\n",
       "  manufacturer                  model  condition    cylinders  ... drive  \\\n",
       "0          gmc        new sierra 1500  excellent  8 cylinders  ...   4wd   \n",
       "1      hyundai                 sonata  excellent  4 cylinders  ...   fwd   \n",
       "2       toyota                  prius       good  4 cylinders  ...   fwd   \n",
       "3   mitsubishi                 lancer       good  4 cylinders  ...   4wd   \n",
       "4          gmc  sierra classic 2500hd       good  8 cylinders  ...   4wd   \n",
       "\n",
       "        size   type paint_color  \\\n",
       "0        NaN    NaN         red   \n",
       "1        NaN    NaN        grey   \n",
       "2        NaN    NaN        blue   \n",
       "3   mid-size  sedan        grey   \n",
       "4  full-size  truck       white   \n",
       "\n",
       "                                           image_url  \\\n",
       "0  https://images.craigslist.org/00n0n_f06ykBMcdh...   \n",
       "1  https://images.craigslist.org/00d0d_kgZ6xoeRw2...   \n",
       "2  https://images.craigslist.org/00d0d_3sHGxPbY2O...   \n",
       "3  https://images.craigslist.org/00m0m_4a8Pb6JbMG...   \n",
       "4  https://images.craigslist.org/01414_g093aPtSMW...   \n",
       "\n",
       "                                         description county state      lat  \\\n",
       "0  2000 *** GMC New Sierra 1500 Ext Cab 157.5  WB...    NaN    mn  46.8433   \n",
       "1  For Sale:  2013 Hyundai Sonata GLS - $8750.  O...    NaN    mn  46.9074   \n",
       "2  2013 Prius V Model Two. One owner—must sell my...    NaN    ct  41.1770   \n",
       "3  2003 Mitsubishi Lancer Evolution, silver.  Abo...    NaN    nm  35.1868   \n",
       "4  **Bad Credit, No Credit... No Problem!**2007 G...    NaN    mn  45.6836   \n",
       "\n",
       "       long  \n",
       "0  -92.2550  \n",
       "1  -92.4638  \n",
       "2  -73.1336  \n",
       "3 -106.6650  \n",
       "4  -92.9648  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "database.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Null</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>county</th>\n",
       "      <td>423857</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <td>321230</td>\n",
       "      <td>75.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <td>247138</td>\n",
       "      <td>58.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vin</th>\n",
       "      <td>239437</td>\n",
       "      <td>56.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylinders</th>\n",
       "      <td>226178</td>\n",
       "      <td>53.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paint_color</th>\n",
       "      <td>201654</td>\n",
       "      <td>47.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drive</th>\n",
       "      <td>192738</td>\n",
       "      <td>45.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <td>182700</td>\n",
       "      <td>43.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odometer</th>\n",
       "      <td>153272</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manufacturer</th>\n",
       "      <td>110615</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long</th>\n",
       "      <td>99453</td>\n",
       "      <td>23.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>99453</td>\n",
       "      <td>23.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>98473</td>\n",
       "      <td>23.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel</th>\n",
       "      <td>96643</td>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title_status</th>\n",
       "      <td>96098</td>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transmission</th>\n",
       "      <td>95792</td>\n",
       "      <td>22.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>95114</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>description</th>\n",
       "      <td>94203</td>\n",
       "      <td>22.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>image_url</th>\n",
       "      <td>94196</td>\n",
       "      <td>22.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>url</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_url</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Null  Percentage\n",
       "county        423857       100.0\n",
       "size          321230        75.8\n",
       "condition     247138        58.3\n",
       "vin           239437        56.5\n",
       "cylinders     226178        53.4\n",
       "paint_color   201654        47.6\n",
       "drive         192738        45.5\n",
       "type          182700        43.1\n",
       "odometer      153272        36.2\n",
       "manufacturer  110615        26.1\n",
       "long           99453        23.5\n",
       "lat            99453        23.5\n",
       "model          98473        23.2\n",
       "fuel           96643        22.8\n",
       "title_status   96098        22.7\n",
       "transmission   95792        22.6\n",
       "year           95114        22.4\n",
       "description    94203        22.2\n",
       "image_url      94196        22.2\n",
       "url                0         0.0\n",
       "price              0         0.0\n",
       "region_url         0         0.0\n",
       "state              0         0.0\n",
       "region             0         0.0\n",
       "id                 0         0.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#After understanding the database structure better, we check null values\n",
    "missing_values_count = pd.DataFrame({'Null': database.isnull().sum()})\n",
    "total = len(database)\n",
    "percentage_null = round((missing_values_count['Null']/total)*100,1)\n",
    "missing_values_count['Percentage'] = percentage_null\n",
    "missing_values_count.sort_values(by= 'Null' , ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We will be droping some columns that we won't be using\n",
    "database.drop(['lat', 'long', 'region_url', 'url', 'vin', 'county', 'id', 'image_url'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(423857, 17)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "database.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69813"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finally, we will check in case there are some duplicated rows.\n",
    "database.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We drop the duplicates and keep one of each \n",
    "database = database.drop_duplicates(keep='first')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fea9a555eb0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fea9a3f73a0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fea9a5b4820>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fea9a5e1cd0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We check the distribution of some features in our database\n",
    "database.hist(bins= 100, figsize = (20,20), color = 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fea9c51ae20>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,10])\n",
    "plt.subplot(311)\n",
    "sns.countplot(x = 'transmission', hue = 'fuel', data = database)\n",
    "plt.subplot(212)\n",
    "sns.countplot(x = 'type', hue = 'drive', data = database)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regular Expressions to fill missing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We create a variable with only the features we want\n",
    "test = database[['manufacturer', 'condition',\n",
    "       'cylinders', 'fuel', 'title_status', 'transmission',\n",
    "       'drive', 'size', 'type', 'paint_color']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-17-bfd040b68609>:11: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.\n",
      "  pd.set_option('display.max_colwidth', -1)\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>columns</th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>manufacturer</td>\n",
       "      <td>[gmc, hyundai, toyota, mitsubishi, ford, chevrolet, ram, nan, buick, jeep, dodge, subaru, nissan, audi, rover, lexus, honda, chrysler, mini, pontiac, mercedes-benz, cadillac, bmw, kia, volvo, volkswagen, jaguar, acura, saturn, mazda, mercury, lincoln, infiniti, ferrari, fiat, tesla, land rover, harley-davidson, datsun, alfa-romeo, morgan, aston-martin, porche, hennessey]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>condition</td>\n",
       "      <td>[excellent, good, fair, nan, like new, salvage, new]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cylinders</td>\n",
       "      <td>[8 cylinders, 4 cylinders, 6 cylinders, nan, 10 cylinders, 12 cylinders, 5 cylinders, 3 cylinders, other]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fuel</td>\n",
       "      <td>[gas, hybrid, diesel, other, electric, nan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>title_status</td>\n",
       "      <td>[clean, lien, rebuilt, salvage, missing, parts only, nan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>transmission</td>\n",
       "      <td>[automatic, manual, other, nan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>drive</td>\n",
       "      <td>[4wd, fwd, rwd, nan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>size</td>\n",
       "      <td>[nan, mid-size, full-size, compact, sub-compact]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>type</td>\n",
       "      <td>[nan, sedan, truck, SUV, other, mini-van, wagon, hatchback, coupe, pickup, convertible, van, bus, offroad]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>paint_color</td>\n",
       "      <td>[red, grey, blue, white, custom, silver, brown, black, nan, purple, green, orange, yellow]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        columns  \\\n",
       "0  manufacturer   \n",
       "1  condition      \n",
       "2  cylinders      \n",
       "3  fuel           \n",
       "4  title_status   \n",
       "5  transmission   \n",
       "6  drive          \n",
       "7  size           \n",
       "8  type           \n",
       "9  paint_color    \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                  values  \n",
       "0  [gmc, hyundai, toyota, mitsubishi, ford, chevrolet, ram, nan, buick, jeep, dodge, subaru, nissan, audi, rover, lexus, honda, chrysler, mini, pontiac, mercedes-benz, cadillac, bmw, kia, volvo, volkswagen, jaguar, acura, saturn, mazda, mercury, lincoln, infiniti, ferrari, fiat, tesla, land rover, harley-davidson, datsun, alfa-romeo, morgan, aston-martin, porche, hennessey]  \n",
       "1  [excellent, good, fair, nan, like new, salvage, new]                                                                                                                                                                                                                                                                                                                                   \n",
       "2  [8 cylinders, 4 cylinders, 6 cylinders, nan, 10 cylinders, 12 cylinders, 5 cylinders, 3 cylinders, other]                                                                                                                                                                                                                                                                              \n",
       "3  [gas, hybrid, diesel, other, electric, nan]                                                                                                                                                                                                                                                                                                                                            \n",
       "4  [clean, lien, rebuilt, salvage, missing, parts only, nan]                                                                                                                                                                                                                                                                                                                              \n",
       "5  [automatic, manual, other, nan]                                                                                                                                                                                                                                                                                                                                                        \n",
       "6  [4wd, fwd, rwd, nan]                                                                                                                                                                                                                                                                                                                                                                   \n",
       "7  [nan, mid-size, full-size, compact, sub-compact]                                                                                                                                                                                                                                                                                                                                       \n",
       "8  [nan, sedan, truck, SUV, other, mini-van, wagon, hatchback, coupe, pickup, convertible, van, bus, offroad]                                                                                                                                                                                                                                                                             \n",
       "9  [red, grey, blue, white, custom, silver, brown, black, nan, purple, green, orange, yellow]                                                                                                                                                                                                                                                                                             "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finally we create a chart to understand better each feature's unique values\n",
    "chart = pd.DataFrame(columns = ['columns', 'values'])\n",
    "\n",
    "for i, j in test.items():\n",
    "    chart = chart.append({'columns': i, 'values': j.unique()}, \n",
    "                 ignore_index=True)\n",
    "#We use these options to show all values in the DataFrame\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.width', None)\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.reset_option('display.max_rows', None)\n",
    "pd.reset_option('display.max_columns', None)\n",
    "pd.reset_option('display.width', None)\n",
    "pd.reset_option('display.max_colwidth', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For starters, we will be using the column Description to fill the some missing data on other columns.\n",
    "#After using Description, we have no more need for this column so we drop it.\n",
    "import re\n",
    "\n",
    "manufacturer = '(gmc | hyundai | toyota | mitsubishi | ford | chevrolet | ram | buick | jeep | dodge | subaru | nissan | audi | rover  | lexus \\\n",
    "| honda | chrysler | mini | pontiac | mercedes-benz | cadillac | bmw | kia | volvo | volkswagen | jaguar | acura | saturn | mazda | \\\n",
    "mercury | lincoln | infiniti | ferrari | fiat | tesla | land rover | harley-davidson | datsun | alfa-romeo | morgan | aston-martin | porche \\\n",
    "| hennessey)'\n",
    "condition = '(excellent | good | fair | like new | salvage | new)'\n",
    "fuel = '(gas | hybrid | diesel |electric)'\n",
    "title_status = '(clean | lien | rebuilt | salvage | missing | parts only)'\n",
    "transmission = '(automatic | manual)'\n",
    "drive = '(4x4 | awd | fwd | rwd | 4wd)'\n",
    "size = '(mid-size | full-size | compact | sub-compact)'\n",
    "type_ = '(sedan | truck | SUV | mini-van | wagon | hatchback | coupe | pickup | convertible | van | bus | offroad)'\n",
    "paint_color = '(red | grey | blue | white | custom | silver | brown | black | purple | green | orange | yellow)'\n",
    "cylinders = '(\\s[1-9] cylinders? |\\s1[0-6]? cylinders?)'\n",
    "\n",
    "keys =    ['manufacturer', 'condition', 'fuel', 'title_status', 'transmission', 'drive','size', 'type', 'paint_color' , 'cylinders']\n",
    "columns = [ manufacturer,   condition,   fuel,  title_status, transmission ,drive, size, type_, paint_color,   cylinders]\n",
    "\n",
    "for i,column in zip(keys,columns):\n",
    "    database[i] = database[i].fillna(\n",
    "      database['description'].str.extract(column, flags=re.IGNORECASE, expand=False)).str.lower()\n",
    "\n",
    "database.drop('description', axis=1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-20-d256a4e9844a>:14: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.\n",
      "  pd.set_option('display.max_colwidth', -1)\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>columns</th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>manufacturer</td>\n",
       "      <td>[gmc, hyundai, toyota, mitsubishi, ford, chevrolet, ram, nan, buick, jeep, dodge, subaru, nissan, audi, rover, lexus, honda,  ford , chrysler, mini, pontiac, mercedes-benz, cadillac, bmw,  ram , kia, volvo, volkswagen, jaguar, acura, saturn, mazda, mercury,  dodge , lincoln, infiniti, ferrari,  bmw ,  buick , fiat,  jeep ,  jaguar ,  hyundai ,  mazda ,  mercedes-benz , tesla,  chevrolet ,  honda ,  nissan ,  mini ,  land rover ,  acura ,  toyota ,  chrysler ,  infiniti ,  lexus ,  volkswagen ,  volvo ,  subaru ,  audi ,  lincoln , gmc ,  ferrari ,  morgan ,  cadillac , land rover,  kia , harley-davidson, datsun, alfa-romeo, morgan, aston-martin,  pontiac ,  mitsubishi ,  fiat ,  mercury ,  porche , porche,  saturn ,  tesla , hennessey]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>condition</td>\n",
       "      <td>[excellent, good, fair,  new, nan,  good ,  salvage , like new, excellent , salvage,  like new ,  fair , new]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cylinders</td>\n",
       "      <td>[8 cylinders, 4 cylinders, 6 cylinders, nan,  4 cylinder ,  6 cylinder , 10 cylinders, 12 cylinders, 5 cylinders,  6 cylinders , 3 cylinders,  8 cylinder ,  3 cylinder , other,  4 cylinders ,  8 cylinders ,  5 cylinder ,  10 cylinder,  5 cylinders ,  7 cylinder ,  10 cylinders,  4 cylinders ,  2 cylinder ,  12 cylinder,  3 cylinders ,  4 cylinder ,  12 cylinders,  8 cylinder , \\t8 cylinder , \\t4 cylinder ]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fuel</td>\n",
       "      <td>[gas, hybrid, diesel, other, electric, nan, gas ,  hybrid ,  diesel ]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>title_status</td>\n",
       "      <td>[clean, lien, rebuilt, salvage, missing, parts only, nan, clean ,  rebuilt ,  salvage ,  missing ]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>transmission</td>\n",
       "      <td>[automatic, manual, other, nan,  manual, automatic ]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>drive</td>\n",
       "      <td>[4wd, fwd, rwd,  4wd, nan, 4x4 ,  awd ,  fwd ,  rwd ]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>size</td>\n",
       "      <td>[nan, mid-size, full-size,  compact , compact, sub-compact,  full-size , mid-size ,  sub-compact]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>type</td>\n",
       "      <td>[nan, sedan, truck, suv, other,  truck ,  suv ,  van , mini-van, wagon, hatchback, coupe, pickup, sedan , convertible,  hatchback , van,  coupe ,  wagon ,  convertible ,  pickup ,  offroad, bus,  bus , offroad,  mini-van ]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>paint_color</td>\n",
       "      <td>[red, grey, blue, white, custom, silver, brown, black,  silver , nan, purple,  custom ,  black ,  blue ,  grey , red ,  brown , green, orange,  white , yellow,  green ,  yellow,  orange ,  purple ]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        columns  \\\n",
       "0  manufacturer   \n",
       "1  condition      \n",
       "2  cylinders      \n",
       "3  fuel           \n",
       "4  title_status   \n",
       "5  transmission   \n",
       "6  drive          \n",
       "7  size           \n",
       "8  type           \n",
       "9  paint_color    \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      values  \n",
       "0  [gmc, hyundai, toyota, mitsubishi, ford, chevrolet, ram, nan, buick, jeep, dodge, subaru, nissan, audi, rover, lexus, honda,  ford , chrysler, mini, pontiac, mercedes-benz, cadillac, bmw,  ram , kia, volvo, volkswagen, jaguar, acura, saturn, mazda, mercury,  dodge , lincoln, infiniti, ferrari,  bmw ,  buick , fiat,  jeep ,  jaguar ,  hyundai ,  mazda ,  mercedes-benz , tesla,  chevrolet ,  honda ,  nissan ,  mini ,  land rover ,  acura ,  toyota ,  chrysler ,  infiniti ,  lexus ,  volkswagen ,  volvo ,  subaru ,  audi ,  lincoln , gmc ,  ferrari ,  morgan ,  cadillac , land rover,  kia , harley-davidson, datsun, alfa-romeo, morgan, aston-martin,  pontiac ,  mitsubishi ,  fiat ,  mercury ,  porche , porche,  saturn ,  tesla , hennessey]  \n",
       "1  [excellent, good, fair,  new, nan,  good ,  salvage , like new, excellent , salvage,  like new ,  fair , new]                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              \n",
       "2  [8 cylinders, 4 cylinders, 6 cylinders, nan,  4 cylinder ,  6 cylinder , 10 cylinders, 12 cylinders, 5 cylinders,  6 cylinders , 3 cylinders,  8 cylinder ,  3 cylinder , other,  4 cylinders ,  8 cylinders ,  5 cylinder ,  10 cylinder,  5 cylinders ,  7 cylinder ,  10 cylinders,  4 cylinders ,  2 cylinder ,  12 cylinder,  3 cylinders ,  4 cylinder ,  12 cylinders,  8 cylinder , \\t8 cylinder , \\t4 cylinder ]                                                                                                                                                                                                                                                                                                                                                  \n",
       "3  [gas, hybrid, diesel, other, electric, nan, gas ,  hybrid ,  diesel ]                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      \n",
       "4  [clean, lien, rebuilt, salvage, missing, parts only, nan, clean ,  rebuilt ,  salvage ,  missing ]                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         \n",
       "5  [automatic, manual, other, nan,  manual, automatic ]                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       \n",
       "6  [4wd, fwd, rwd,  4wd, nan, 4x4 ,  awd ,  fwd ,  rwd ]                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      \n",
       "7  [nan, mid-size, full-size,  compact , compact, sub-compact,  full-size , mid-size ,  sub-compact]                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          \n",
       "8  [nan, sedan, truck, suv, other,  truck ,  suv ,  van , mini-van, wagon, hatchback, coupe, pickup, sedan , convertible,  hatchback , van,  coupe ,  wagon ,  convertible ,  pickup ,  offroad, bus,  bus , offroad,  mini-van ]                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             \n",
       "9  [red, grey, blue, white, custom, silver, brown, black,  silver , nan, purple,  custom ,  black ,  blue ,  grey , red ,  brown , green, orange,  white , yellow,  green ,  yellow,  orange ,  purple ]                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#And, we check again each unique values for the columns we modified.\n",
    "test = database[['manufacturer', 'condition',\n",
    "       'cylinders', 'fuel', 'title_status', 'transmission',\n",
    "       'drive', 'size', 'type', 'paint_color']]\n",
    "chart = pd.DataFrame(columns = ['columns', 'values'])\n",
    "\n",
    "for i, j in test.items():\n",
    "    chart = chart.append({'columns': i, 'values': j.unique()}, \n",
    "                 ignore_index=True)\n",
    "\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.width', None)\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.reset_option('display.max_rows', None)\n",
    "pd.reset_option('display.max_columns', None)\n",
    "pd.reset_option('display.width', None)\n",
    "pd.reset_option('display.max_colwidth', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "##We need to take care of some things now: \n",
    "#Inconsistent data entry. \n",
    "columns = ['manufacturer', 'condition',\n",
    "       'cylinders', 'fuel', 'title_status', 'transmission',\n",
    "       'drive', 'size', 'type', 'paint_color']\n",
    "for i in columns: \n",
    "    database[i] = database[i].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['8 cylinders', '4 cylinders', '6 cylinders', nan, '4 cylinder',\n",
       "       '6 cylinder', '10 cylinders', '12 cylinders', '5 cylinders',\n",
       "       '3 cylinders', '8 cylinder', '3 cylinder', 'other', '5 cylinder',\n",
       "       '10 cylinder', '7 cylinder', '2 cylinder', '12 cylinder'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "database['cylinders'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['8 cylinders', '4 cylinders', '6 cylinders', nan, '10 cylinders',\n",
       "       '12 cylinders', '5 cylinders', '3 cylinders', 'other',\n",
       "       '7 cylinders', '2 cylinders'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = {\"4 cylinder\" : \"4 cylinders\",\n",
    "         \"6 cylinder\" : \"6 cylinders\",\n",
    "         \"8 cylinder\" : \"8 cylinders\",\n",
    "         \"3 cylinder\" : \"3 cylinders\",\n",
    "         \"5 cylinder\" : \"5 cylinders\",\n",
    "         \"10 cylinder\": \"10 cylinders\",\n",
    "         \"7 cylinder\" : \"7 cylinders\",\n",
    "         \"2 cylinder\": '2 cylinders',\n",
    "         \"12 cylinder\": \"12 cylinders\"}\n",
    "for i,j in values.items():\n",
    "    database.replace(to_replace=i, value=j, inplace=True)\n",
    "database['cylinders'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Null</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <td>241943</td>\n",
       "      <td>68.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylinders</th>\n",
       "      <td>160239</td>\n",
       "      <td>45.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drive</th>\n",
       "      <td>113414</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <td>112516</td>\n",
       "      <td>31.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odometer</th>\n",
       "      <td>100417</td>\n",
       "      <td>28.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <td>98736</td>\n",
       "      <td>27.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paint_color</th>\n",
       "      <td>93080</td>\n",
       "      <td>26.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manufacturer</th>\n",
       "      <td>58969</td>\n",
       "      <td>16.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>52469</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel</th>\n",
       "      <td>50079</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title_status</th>\n",
       "      <td>49787</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transmission</th>\n",
       "      <td>49669</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>49388</td>\n",
       "      <td>13.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Null  Percentage\n",
       "size          241943        68.3\n",
       "cylinders     160239        45.3\n",
       "drive         113414        32.0\n",
       "condition     112516        31.8\n",
       "odometer      100417        28.4\n",
       "type           98736        27.9\n",
       "paint_color    93080        26.3\n",
       "manufacturer   58969        16.7\n",
       "model          52469        14.8\n",
       "fuel           50079        14.1\n",
       "title_status   49787        14.1\n",
       "transmission   49669        14.0\n",
       "year           49388        13.9\n",
       "region             0         0.0\n",
       "price              0         0.0\n",
       "state              0         0.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We check again NaN values \n",
    "missing_values_count = pd.DataFrame({'Null': database.isnull().sum()})\n",
    "total = len(database)\n",
    "percentage_null = round((missing_values_count['Null']/total)*100,1)\n",
    "missing_values_count['Percentage'] = percentage_null\n",
    "missing_values_count.sort_values(by= 'Null' , ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fea9fa374c0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We are going to handle those outliers of odometer and price we saw before in the description\n",
    "#First we will visualize them\n",
    "plt.figure(figsize=[10,5])\n",
    "plt.subplot(121)\n",
    "sns.distplot(database['price'], bins = 5)\n",
    "plt.subplot(122)\n",
    "sns.distplot(database['odometer'], bins = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fea88466040>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Another type of visualization to check on the outliers\n",
    "plt.figure(figsize=[5,5])\n",
    "plt.subplot(211)\n",
    "sns.boxplot(x=database['price'])\n",
    "plt.subplot(212)\n",
    "sns.boxplot(x=database['odometer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "800.0 32254.20000000007\n"
     ]
    }
   ],
   "source": [
    "#First we will drop the 10% of each side on price\n",
    "rr=sorted(database[\"price\"])\n",
    "quantile1, quantile2= np.percentile(rr,[10,90])\n",
    "print(quantile1,quantile2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(283728, 16)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "database=database[(database.price <= 32254.20000000007) \n",
    "                  & (database.price >= 800.0)]\n",
    "database.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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": [
    "#Now we handle odometer.\n",
    "#Since there are null values we cannot do what we did with price.\n",
    "#We use scatterplot to visually check the outliers.\n",
    "plt.figure(figsize=[7,5])\n",
    "ax = sns.scatterplot(x = database['odometer'], y= database['price'])\n",
    "ax.get_xaxis().get_major_formatter().set_scientific(False)\n",
    "ax.get_yaxis().get_major_formatter().set_scientific(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We will take values up until 3,000,000.\n",
    "database=database[(database.odometer < 3000000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Also we will be deleting the values equal to 0\n",
    "database.drop(database[database[\"odometer\"]==0.0].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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",
    "ax = sns.scatterplot(x = database['odometer'], y= database['price'])\n",
    "ax.get_xaxis().get_major_formatter().set_scientific(False)\n",
    "ax.get_yaxis().get_major_formatter().set_scientific(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fea83fc7a30>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASgAAAE9CAYAAAC4HP8xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAX/UlEQVR4nO3df3Dc9Z3f8efLkrFlG18O2WAjY4RP1sW+g+GILyUTmkIKrW0CNDNwQ+Z6aDqZJLR3jn+RmYAExh4yU5rUqVF7DaSXIWXSuzS90OLEds6+mMv1RwCZ2mCCuShggk0uBnHg+GwgUj79Y7/arlYraVe7q30Lvx4zO9r9fr/7/b72492Xv/vdX0opYWYW0YxGBzAzG4sLyszCckGZWVguKDMLywVlZmG5oMwsrOZKFl6wYEFqb2+vUxQzO1sdOHDg9ZTSwuLpFRVUe3s7fX19tUtlZgZIernUdD/FM7OwXFBmFpYLyszCckGZWVguKDMLywVlZmG5oMwsLBeUmYXlgjKzsCp6J/nZoLe3l/7+/kbHGNfx48cBaGtra3CS2uno6GDdunWNjmHBuKCK9Pf3c/Dw8wzNOa/RUcbUdPotAP72nffGP1/T6TcaHcGCem/cw2tsaM55nHn/2kbHGFPLkV0AoTNWYvj2mBXzMSgzC8sFZWZhuaDMLCwXlJmF5YIys7BcUGYWlgvKzMJyQZlZWC4oMwvLBWVmYbmgzCwsF5SZheWCMrOwXFBmFpYLyszCckGZWVguKDMLywVlZmG5oMwsLBeUmYXlgjKzsFxQZhZW3Qqqt7eX3t7eeq3ezAKq9eO+br+LF/3Xec2s9mr9uPdTPDMLywVlZmG5oMwsLBeUmYXlgjKzsFxQZhaWC8rMwnJBmVlYLigzC8sFZWZhuaDMLCwXlJmF5YIys7BcUGYWlgvKzMJyQZlZWC4oMwvLBWVmYbmgzCwsF5SZheWCMrOwXFBmFpYLyszCqtvv4h0/fpwzZ86wfv36em2iLvr7+5nxbmp0jLPKjLdP0t//i2l3X7HR+vv7aWlpqdn6JtyDkvRpSX2S+l577bWabdjMbCIT7kGllB4CHgJYtWpV2bsWbW1tAOzYsWOy2Rpi/fr1HHjx542OcVb51ez5dCy7YNrdV2y0Wu8F+xiUmYXlgjKzsFxQZhaWC8rMwnJBmVlYLigzC8sFZWZhuaDMLCwXlJmF5YIys7BcUGYWlgvKzMJyQZlZWC4oMwvLBWVmYbmgzCwsF5SZheWCMrOwXFBmFpYLyszCckGZWVguKDMLq24/3NnR0VGvVZtZULV+3NetoNatW1evVZtZULV+3PspnpmF5YIys7BcUGYWlgvKzMJyQZlZWC4oMwvLBWVmYbmgzCwsF5SZheWCMrOwXFBmFpYLyszCckGZWVguKDMLywVlZmG5oMwsLBeUmYXlgjKzsFxQZhaWC8rMwnJBmVlYLigzC6tuPzs1nTWdfoOWI7saHWNMTacHAEJnrETT6TeACxodwwJyQRWZDj84evz4IABtbe+VB/UF02Lcbeq5oIr4B0fN4vAxKDMLywVlZmG5oMwsLBeUmYXlgjKzsFxQZhaWC8rMwnJBmVlYLigzC8sFZWZhuaDMLCyllMpfWHoNeLnErAXA67UKVQfR80H8jM5XvegZG5nv4pTSwuKJFRXUWCT1pZRWVb2iOomeD+JndL7qRc8YMZ+f4plZWC4oMwurVgX1UI3WUy/R80H8jM5XvegZw+WryTEoM7N68FM8MwurqoKStFrSC5L6JX2+VqHK3PZRSc9KOiipL5t2nqS9kn6c/f31guXvzHK+IOmfFkz/QLaefkkPSFIVmb4m6YSkwwXTapZJ0ixJ38ymPyGpvQb57pV0PBvHg5LWNjDfRZL2S3pe0nOS1kcaw3HyRRrD2ZKelHQoy7g10hhWLKU0qRPQBPwEWAacAxwCVk52fZPY/lFgQdG0fwN8Pjv/eeD+7PzKLN8s4JIsd1M270ngQ4CA3cCaKjJ9BLgCOFyPTMC/Ar6Snb8V+GYN8t0L3FFi2UbkWwxckZ0/F/ibLEeIMRwnX6QxFDAvOz8TeAK4MsoYVvyYquLB+CHgewWX7wTurFfQEts/yuiCegFYXHBneqFUNuB7Wf7FwJGC6Z8AHqwyVzsjC6BmmYaXyc43k3tTnarMN9aDqyH5ijL8D+C6aGNYIl/IMQTmAE8D/yDqGE50quYpXhvwSsHlY9m0qZKAv5B0QNKns2kXpJR+BpD9PT+bPlbWtux88fRaqmWm/HVSSoPAW0BrDTL+kaRnsqeAw7v+Dc2XPW34HXJ7AOHGsCgfBBpDSU2SDgIngL0ppZBjWI5qCqrUsZqpfEnwwymlK4A1wB9K+sg4y46VtZG3YTKZ6pH3PwK/AVwO/Az4t43OJ2ke8OfAhpTSyfEWHWN7dc1YIl+oMUwpDaWULgeWAB+U9NvjLB7lflhSNQV1DLio4PIS4NXq4pQvpfRq9vcE8CjwQeDnkhYDZH9PTJD1WHa+eHot1TJT/jqSmoFfA96oJlxK6efZHfpXwFfJjWPD8kmaSe7B/42U0rezyWHGsFS+aGM4LKX0JvA4sJpAY1iJagrqKWC5pEsknUPuYNljtYk1PklzJZ07fB74J8DhbPtd2WJd5I4RkE2/NXv14RJgOfBktqv7C0lXZq9Q3FZwnVqpZabCdd0MfD9lBwIma/hOm/k4uXFsSL5sfX8CPJ9S2l4wK8QYjpUv2BgulPS+7HwLcC1whCBjWLFqDmABa8m9kvEToLseB8nG2O4ycq88HAKeG942uefBfwn8OPt7XsF1urOcL1DwSh2witwd6ifAv6e6g7p/Sm4X/5fk/pf5ZC0zAbOBbwH95F5hWVaDfI8AzwLPkLvjLW5gvqvIPVV4BjiYndZGGcNx8kUaw8uA/5tlOQzcU+vHRrUZKzn5neRmFpbfSW5mYbmgzCwsF5SZheWCMrOwXFBmFpYLympO0jZJ1zY6h01/fpuB1ZSkppTSUKNz2HuD96CsbJLaJR2R9PXsg7H/TdIc5b6b6x5J/xO4RdLDkm7OrvO7kv539v1ET0o6N/sw6xclPZWt5zMNvmkWVEV7UAsWLEjt7e31S2NmZ6UDBw68nkr8Ll5zJStpb2+nr6+vdqnMzABJpX4Q2E/xzCwuF5SZheWCMrOwXFBmFpYLyszCckGZWVguKDMLywVlZmG5oMwsLBeUmYVV0UddqtXb20t/f3/+8vHjxwFoa/v/P+bb0dHBunXrpjKWmQU1pQXV39/PwcPPMzTnPACaTr8FwN++05xdrstv/5nZNDWlBQUwNOc8zrx/LQAtR3YBjLpsZgY+BmVmgbmgzCwsF5SZheWCMrOwXFBmFpYLyszCckGZWVguKDMLywVlZmG5oMwsLBeUmYXlgjKzsFxQZhaWC8rMwnJBmVlYLigzC8sFZWZhuaDMLCwXlJmF5YIys7BcUGYWVt0Kqre3l97e3nqtvu7rN7PGq9vPThX+QOd0XL+ZNZ6f4plZWC4oMwvLBWVmYbmgzCwsF5SZheWCMrOwXFBmFpYLyszCckGZWVguKDMLywVlZmG5oMwsLBeUmYXlgjKzsFxQZhaWC8rMwnJBmVlYLigzC8sFZWZhuaDMLKy6/WhCvR06dAiAq6++urFB6qS1tZVTp07xzjvvlJwviYsuuoi5c+dy33330draSl9fH3fccUd+mUWLFvGZz3yGrVu30tzczODgIIsWLeLNN98EYPHixcycOROApqYmbrnlFrZt2wbA7bffziOPPMLmzZv50pe+xKJFi5g1axZXXXUVX/3qVwFYuHAhJ0+eZGhoiMHBQebPn8/Jkyfz29+yZQuPPPIIL730Evfccw+PPvooXV1d9PT0sHDhQk6cOAHAF77wBR588EF++tOfcv755/P666/zuc99ji9+8Yv57W7evJnt27eTUuK+++4D4O677yalxObNm3nggQfYsmXLiOmrV6/my1/+MhdeeCFz585FEqdPn+bVV19lw4YN7Nixg6GhIQA2b97MDTfckM/+/e9/n23btrFlyxYuu+wy7r77bt59911mzJjB22+/zSuvvJK/TVu2bKG1tTV/3YGBAbZu3Tpi+sDAAD09PUhi06ZNPPDAA3z2s5/N/73//vs5duwYvb29dHR0jPr3LsxzzTXXMDAwUPL2F+aYSHHOUrkrVYt1FFJKqeyFV61alfr6+spadv369QDs2LFjxLQDL/6cM+9fC0DLkV0AIy5/YNkFI64zlvdqMU3GTTfdxMaNG/nYxz7GqVOnRswbLqZylFq2kuuPt77m5maGhoaYO3fuqIzz5s2bMHd7eztHjx4Fcrc3pcRjjz2Wn/fyyy9z4403jpguibHu38XzJLF///785WuvvZbBwUGam5tZu3Ztfp3FGYeGhrjxxhvZuHFjfvr27dvZuXPniOnbt28flffiiy/O/x2+be3t7Tz88MOjtlWYZ9++fSXXV5xjIsU5S+Wu1GTXIelASmlV8fRp+RTP5TTS7t272b9//6gHOVBRuZRadrLlVHzdwcFBUkolM5aTe/gBDLBr1y527949Yl5Kid27d4+YPt5/vsXzUkrs3LkTyO2tDG9/cHCQ7373uyXXMXyb9uzZw8DAAJDbg9izZ8+I6QMDAyXzFv4tnFf8k2rFeXbu3MmePXtGra8wx0SKc/b394/KXalSt71adduDuvnmmzlz5syI3dX+/n5+8W7i7y+/FRi9BzX34J9x7jkquYtbaPjpneVIoqmpqaoyeS8Yb4+p3Ovv378/v7dSrubmZq6//vr8XsiuXbvyezvXX399vvzKzVa8F1WcRxIwumQLc0ykOOeSJUs4duzYiNyV7kWVuu3lrmPSe1CSPi2pT1Lfa6+9VlFgmxoppbO+nGD8PaZKrl/pWA4ODrJ3714A9u3bN2JvZ+/evezbt6+ibIV7VKXypJRKrq8wx0SKcx49enRU7kqVuu3VmvAgeUrpIeAhyO1BlbvitrY2oPQxqLH8avZ8Oso4BuWneCN5DyqnFntQUPlxt+bmZq677jogt7dTuBdx3XXXTWoPqnj95e5BDeeYSHHO4j2octcz3jons45i0/IYlI00c+ZMuru7Gx1jysycOTP/6mM508u1adMmAO66664R05uamsa9XlNTE7fddhsAXV1dzJgxY8T0rq4umpvLf8G8p6dnxOXiPJs2bSp5OwtzTKQ4Z09Pz6jclSp126s1LQvq8ccfb3SEUNasWcM111zDvHnzRs2r5IFRatlKrj/edZubm5FUMmM5uQv3KtauXcuaNWtGzJPEmjVrRkwf3tMopXiepPzbDD760Y/mtz98LKWU4du0evXq/Evqra2trF69esT01tbWknkL/xbOKz4GW5znhhtuYPXq1aPWV5hjIsU5Ozo6RuWuVKnbXq1pWVBng9bWVmbNmjXmfEksXbqUFStW5P+nuvfee0css2jRovye1fAdfNGiRcyePZvZs2dzySWX0NnZSWdnJytWrBjxP/Xtt9/O3Llzueuuu5gzZw7Lli1jxYoVfOpTn8ovs3DhQmbNmpVf9/z580dsv7u7m2XLliGJ7u5uLr30UrZu3UpLSwtLly7N59i6dSudnZ3Mnj2bpUuXMmfOHLq7u0dst6enh5UrV+Zvb1dXV/5yT08Pl1566ajpGzZsAODCCy9k+fLldHZ2smTJEmbMmMHGjRtH7BkN7z0NGx6L7u7u/Do7Ojro7Oxk6dKlI25T8Z5CV1fXqOldXV2sWLGClStX5vMW/l2+fDktLS2j9p5K5RleX6nbX4ninKVyV6oW6yg0bd8HVWr9ZjY9vafeB2VmZwcXlJmF5YIys7BcUGYWlgvKzMJyQZlZWC4oMwvLBWVmYbmgzCwsF5SZheWCMrOwXFBmFpYLyszCckGZWVguKDMLywVlZmG5oMwsLBeUmYXlgjKzsCb/kx0TmOjXgaOv38war24FtW7dunqtekrWb2aN56d4ZhaWC8rMwnJBmVlYLigzC8sFZWZhuaDMLCwXlJmF5YIys7BcUGYWlgvKzMJyQZlZWC4oMwvLBWVmYbmgzCwsF5SZheWCMrOwXFBmFpYLyszCckGZWVguKDMLywVlZmHV7VddxtJ0+g1ajuzKzg8AFFx+A7hgqiOZWVBTWlDFv2V3/PggAG1tw6V0gX/vzszyprSg/Ft2ZlYJH4Mys7BcUGYWlgvKzMJyQZlZWC4oMwvLBWVmYbmgzCwsF5SZheWCMrOwXFBmFpYLyszCUkqp/IWl14CXK1j/AuD1SkM1yHTJ6py1NV1ywvTJOpmcF6eUFhZPrKigKiWpL6W0qm4bqKHpktU5a2u65ITpk7WWOf0Uz8zCckGZWVj1LqiH6rz+WpouWZ2ztqZLTpg+WWuWs67HoMzMquGneGYWVk0KStJqSS9I6pf0+RLzJemBbP4zkq6oxXbrkPNqSW9JOpid7mlQzq9JOiHp8Bjzo4znRDmjjOdFkvZLel7Sc5LWl1im4WNaZs4oYzpb0pOSDmVZt5ZYpvoxTSlVdQKagJ8Ay4BzgEPAyqJl1gK7AQFXAk9Uu9065bwa+M5UZyuR9SPAFcDhMeY3fDzLzBllPBcDV2TnzwX+Juh9tJycUcZUwLzs/EzgCeDKWo9pLfagPgj0p5ReTCm9C/wZcFPRMjcB/znl/BB4n6TFNdh2rXOGkFL6AfDGOItEGM9ycoaQUvpZSunp7PwvgOeBtqLFGj6mZeYMIRunU9nFmdmp+IB21WNai4JqA14puHyM0YNazjL1Vm6GD2W7rbsl/dbURKtYhPEsV6jxlNQO/A65//ELhRrTcXJCkDGV1CTpIHAC2JtSqvmY1uJnp1RiWnGTlrNMvZWT4Wlyb7k/JWkt8N+B5XVPVrkI41mOUOMpaR7w58CGlNLJ4tklrtKQMZ0gZ5gxTSkNAZdLeh/wqKTfTikVHo+sekxrsQd1DLio4PIS4NVJLFNvE2ZIKZ0c3m1NKe0CZkpaMHURyxZhPCcUaTwlzST3oP9GSunbJRYJMaYT5Yw0pgWZ3gQeB1YXzap6TGtRUE8ByyVdIukc4FbgsaJlHgNuy47qXwm8lVL6WQ22XdOckhZJUnb+g+TGZ2CKc5YjwnhOKMp4Zhn+BHg+pbR9jMUaPqbl5Aw0pguzPScktQDXAkeKFqt6TKt+ipdSGpT0R8D3yL1S9rWU0nOSbs/mfwXYRe6Ifj9wGvgX1W63TjlvBv6lpEHgDHBryl6OmEqS/pTcqzULJB0DtpA7CBlmPMvMGWI8gQ8DfwA8mx0zAbgLWFqQNcKYlpMzypguBr4uqYlcSf7XlNJ3av249zvJzSwsv5PczMJyQZlZWC4oMwvLBWVmYbmgzGzSNMEHxkss/3uSfpR9wPi/TLS8C8rGJeleSXfUeRt31XP9VlcPM/oNmiVJWg7cCXw4pfRbwIaJruOCsggqLqjs/TfWYKU+MC7pNyTtkXRA0l9Len8261PAf0gp/V123RMTrd8FdZaTtEnS4ey0IZvWrdz3Zu0DfrNg2csl/TD7bp9HJf16Nv1xSV+W9APlvsvodyV9W9KPJd1XcP1/rtx3CB2U9GD2YdN/DbRk074x1nLZ9FOStkl6AvjQFA6TVeYhYF1K6QPAHcAfZ9M7gU5J/yu7H02851Wv74vxKf4J+ADwLDAXmAc8VzBtDjCf3LuA78iWfwb4R9n5bcC/y84/DtyfnV9P7vNWi4FZ5D6P1QqsAHYCM7Pl/hi4LTt/qiDTeMsl4PcaPW4+jboftZN9J1h2PzoDHCw4PZ/N+w7wKLlPG1yS3TfeN966a/FtBjZ9XQU8mlL6ewBJ3wauz6adzqY9lv39NXJ3pr/Krvt14FsF6xr+XOOzwHMp+8yVpBfJfWD0KnLl91T2UbIWcl/TUewfj7PcELkP0lpcM4A3U0qXl5h3DPhhSumXwEuSXiD3TQxPjbUyF9TZrdTXYcDkvmbknezvrwrOD19uzrb19ZTSnWVkGmu5t1PuKz4sqJTSSUkvSbolpfSt7IPNl6WUDpH7aphPAA8r9w0MncCL463Px6DObj8A/pmkOZLmAh8Hvgt8XFKLpHOBGwBSSm8BfyfpH2bX/QPgr0qtdAx/Cdws6XwASedJujib90vlvmZkouUsmOwD4/8H+E1JxyR9Evh94JOSDpE7bDD8zbXfAwYk/QjYD3wupTTuNzF4D+osllJ6WtLDwJPZpP+UUjog6Zvkjh28DPx1wVW6gK9ImkPuf76yP52eUvqRpB7gLyTNAH4J/GG2jYeAZyQ9nVL6/XGWs2BSSp8YY9aoA+ApdyBqU3Yqi7/NwMzC8lM8MwvLBWVmYbmgzCwsF5SZheWCMrOwXFBmFpYLyszCckGZWVj/D1ouSNu7b4IpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#After handling the outliers\n",
    "plt.figure(figsize=[5,5])\n",
    "plt.subplot(211)\n",
    "sns.boxplot(x=database['price'])\n",
    "plt.subplot(212)\n",
    "sns.boxplot(x=database['odometer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Finally we deal with the outliers in year. Let's visualize them.\n",
    "plt.figure(figsize=[7,5])\n",
    "ax = sns.scatterplot(x = database['year'], y= database['price'])\n",
    "ax.get_xaxis().get_major_formatter().set_scientific(False)\n",
    "ax.get_yaxis().get_major_formatter().set_scientific(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fea83e5eee0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TAhc1JiO1GKtALyJ14WCY46awh75gXbgwWzxPf2xwbi6WKmhrTERqe6UCvYjUhYmtlcvOD/Qr2xpJxu38Ef3g3OyhL2hrilZOegV6EakLRwfGWNSQoL05ed7xeMxYs6S5RKCv/Px8gaZuRETmQP9wmo7WVMmytR3NExdNjWVynB7JaOpGRKTWnB5Js6R5ikC/pGki0J8obK2cg4ulCjR1IyIyB04NpVnaUjrQr+to5vRIhsGxDMfCi6XmdkSf1AVTIiKVdnokzZIpAn1hJ87Tr50+F+jncES/qDFBOpdnLBONnPQK9CJS89w9mKOfItC/fWMnS1tSfPapw5wopD9YNLdTNxCdfDcK9CJS80YzOcaz+SkDfSoR42duWsu/vHicZ4+coTEZm0gnPBfaIpbvRoFeRGreqaE0AB1TLMYC3HnTOhzY9fxRVrY1EqZPnxOFVMVRyXejQC8iNe/0SBDop5qjh+AK2Xds7MR9bpKZFSv8tRCVfDcK9CJS8/qHwxF9S/Ki9X725nXA3C7EQvRuPqJALyI1rzCi72i5+NWu73rjcq5d3caNly+Z0/ZE7b6xc7caISIyT8qZowdIxGP846+8fc7bE7X7xpY1ojez28zsgJl1m9n9JcrNzB4Iy/ea2eZJ5XEze9bM/rFSDRcRKTg9kiYes4mbflRbUzJOIma1M3VjZnHgQWArsAm408w2Taq2FdgYPrYDD00q/wjw4qxbKyJSQv9whiXNKWKxudtJMxNmFqk0COWM6LcA3e5+yN3TwGPAtkl1tgGPeuBJYLGZrQIwszXATwKfqWC7RUQm9A+PT7sQO98WRSixWTmBfjVwpOh1T3is3Dp/BvwWkOcizGy7me0xsz19fX1lNEtEJHA6HNFHSVuEUhWXE+hL/S3k5dQxs38HnHD3p6d7E3d/2N273L2rs7OzjGaJiAT6R6ZOf1AtbU2JmtpH3wOsLXq9Bugts85bgfeZ2asEUz7vMrO/ueTWioiUcPoieW6qZVFDsqaujN0NbDSzDWaWAu4Adk6qsxO4K9x9cwsw4O5H3f1j7r7G3deH533D3T9UyQ6IyMKWzzunIzqiH4jI1M20e5HcPWtm9wGPA3HgEXffb2b3hOU7gF3A7UA3MALcPXdNFhE5Z2A0Q96J3Bz94uZU7QR6AHffRRDMi4/tKHruwL3TfI9vAd+acQtFRC6iP7wqdukUtxGsliXNKcYyeUbSWZpT1d3frxQIIlLTTod5bqI2oi9s9yzk4akmBXoRqWmnJhKaRS3QB3l3Tg9Xf/pGgV5EatrEiD5ygT4Y0Z8aHq9ySxToRaTGFebop0toNt8mRvQjmroREZmV08NpmpJxmlLxajflPIUPnkJmzWpSoBeRmnYqghdLQZDrJh4zjehFRGYrilfFAsRixpLmFP1ajBURmZ3+kUzkFmILOlqS9GsxVkRkdvqHx+lojlaK4oIlzSltrxQRmY1MLs/xwXFWtM3tzb4v1dLWlLZXiojMxqG+YdLZPFevaqt2U0pa0pzi9IhG9CIil2x/7wAA11wWzUC/tCXF6ZE0ufzkW3jMLwV6EalZL/QO0pCIsWFZS7WbUtKSlhTuVD2LpQK9iNSs/b2DvHFVG4l4NENZYdtntRObRfOnIyIyDXfnhaODbIro/Dwo0IuIzMrrZ0YZGM1Edn4ezqVOVqAXEbkEL/QOArApwoG+cDOUmgj0ZnabmR0ws24zu79EuZnZA2H5XjPbHB5vNLPvm9kPzGy/mX280h0QkYXphaODmMEbVy6qdlOmVBjRVzvfzbSB3sziwIPAVmATcKeZbZpUbSuwMXxsBx4Kj48D73L364DrgdvCm4eLiMzK/t5BrljWUvXb9F1MYzJOcypeEyP6LUC3ux9y9zTwGLBtUp1twKMeeBJYbGarwtdDYZ1k+KjuhlIRqQsv9A6y6bL2ajdjWh0tqZoI9KuBI0Wve8JjZdUxs7iZPQecAL7u7k9denNFRODMSJrXz4xGeiG2oFYCvZU4NnlUPmUdd8+5+/XAGmCLmV1b8k3MtpvZHjPb09fXV0azRGShmliIjfDWyoIgVXH0A30PsLbo9Rqgd6Z13P0M8C3gtlJv4u4Pu3uXu3d1dnaW0SwRWaie6zkDwJtWR3/qZmmNjOh3AxvNbIOZpYA7gJ2T6uwE7gp339wCDLj7UTPrNLPFAGbWBNwKvFTB9ovIAvTMa2e4YllLZPPQF1sS5ruppmmXq909a2b3AY8DceARd99vZveE5TuAXcDtQDcwAtwdnr4K+Otw504M+Ly7/2PluyEiC4W78+zh0/zYG5ZXuyll6WhJMZLOMZbJ0Ziszn1ty9qX5O67CIJ58bEdRc8duLfEeXuBG2bZRhGRCYf7Rzg1nGbz5Yur3ZSyFKdBuGxxU1XaoCtjRaSmPHs4mJ+/Ye2SKrekPFFIg6BALyI15ZnDp2lJxXlDhK+ILRaFNAgK9CJSU545fJrr1i4mHiu1qzt62puC+9meqWJOegV6EakZI+ksLx49y+Z1tTFtA9DSECyFjoxnq9YGBXoRqRl7ewbI5b1mFmIBWlLBTpvhdK5qbVCgF5Ga8czh00DtLMQCE0nXhjWiFxGZXi1dKFWQSsRIxWMMpxXoRUQuyt15+rV+bry8dkbzBS0NcY3oRUSmc7BvmNMjGbrW116gb04lGBnXHL2IyEU9/Vo/AF3rO6rckplrbUgwpBG9iMjF7X71NB0tKa5Y1lLtpsxYc0OcEe26ERG5uD2v9tN1+RLMauNCqWIa0YuITOPE2TFePTVSk/PzAM2pOCPadSMiMrWnXw32z9fi/DwEV8cOazFWRGRqe147TUMixrU1cDPwUlpSCe2jFxG5mD2v9nPd2sWkErUZsloatL1SRGRKw+NZ9vUOclONzs9DkO8mncuTzuar8v4K9CISaV95/ii5vNfMrQNLmchgWaXpm7ICvZndZmYHzKzbzO4vUW5m9kBYvtfMNofH15rZN83sRTPbb2YfqXQHRKS+Pfb9w/zI8la6ajD1QUFLQ5DBslpbLKcN9OGNvR8EtgKbgDvNbNOkaluBjeFjO/BQeDwL/Lq7Xw3cAtxb4lwRkZIOHDvLM4fPcMdNa2ty/3zBuRF9debpyxnRbwG63f2Qu6eBx4Btk+psAx71wJPAYjNb5e5H3f0ZAHc/C7wIrK5g+0Wkjn3u+4dJxWP81OY11W7KrBQCfWRH9ASB+UjR6x4uDNbT1jGz9cANwFMzbaSILDxjmRxfevZ1fuLalXTUUFriUlpShbtMRXdEX+rvJZ9JHTNrBf4e+Ki7D5Z8E7PtZrbHzPb09fWV0SwRqWeP7z/GwGiGO29aW+2mzFrk5+gJRufFP+k1QG+5dcwsSRDk/9bdvzjVm7j7w+7e5e5dnZ2d5bRdROrYd7tP0tGS4pYrlla7KbM2MaKP8K6b3cBGM9tgZingDmDnpDo7gbvC3Te3AAPuftSC1ZO/BF509z+taMtFpK7te32Qa1e3E4vV7iJsQWGOvlo3H5k20Lt7FrgPeJxgMfXz7r7fzO4xs3vCaruAQ0A38Gngl8PjbwX+E/AuM3sufNxe6U6ISH0Zz+Z4+fhZrr2srdpNqYjC1E21bhCeKKeSu+8iCObFx3YUPXfg3hLnfYfS8/ciIlN6+dgQ2bxz7erazG0zWVMyjlmER/QiIvNtf+8AANfUyYjezILEZhHedSMiMq/29Q6wqDHBuo7majelYqp5g3AFehGJnH2vD3LNZW01fTXsZNVMVaxALyKRks3lefHoYM3mnp9KcPMRBXoREQ72DTOezdfNQmxBcypetV03CvQiEin7Xq+vhdiCVo3oRUQC+3sHaUzGuKKztdpNqajmhkSks1eKiMybfb0DbFrVRrwOrogt1toQj3SuGxGReZHJ5dn3+gBvqrP5eYDmVIIRBXoRWej29pxhJJ2ri0Rmk7U0JBhO58jnJyf/nXsK9CISGU8cPAXAzfUY6FNBvpvRzPzP0yvQi0hkfO/gKa5e1VbzNxoppZoZLBXoRSQSxjI59rx2mh+9sv5G81DdDJYK9CISCc8ePkM6m+ctdThtA+duPqIRvYgsWE8cPEnMYMsVHdVuypzQ1I2ILHhPHDrFm1a309aYrHZT5sREoK9CYjMFehGpupF0lmcPn+EtVy6rdlPmTGHXTTVy0ivQi0jVfbf7FNm885Y6XYiFGpi6MbPbzOyAmXWb2f0lys3MHgjL95rZ5qKyR8zshJntq2TDRaR+PPKdV1jZ1li3C7FQtBgbxV03ZhYHHgS2ApuAO81s06RqW4GN4WM78FBR2V8Bt1WisSJSf57vGeCJQ6e4+63rSSXqd5KhubC9MqIj+i1At7sfcvc08BiwbVKdbcCjHngSWGxmqwDc/dtAfyUbLSL14+F/O0RrQ4I7b15X7abMqWQ8RioRi+xi7GrgSNHrnvDYTOuIiJznSP8Iu54/ygdvXle3u22KVSsnfaKMOqVyhU7OylNOnYu/idl2gmkf1q2r7Cf7Z586XNHvJyKV8eW9vbg7S5pTC+L/U3dn/+uDfPapw3xwHv+CKWdE3wOsLXq9Bui9hDoX5e4Pu3uXu3d1dnbO5FQRqUEj6Sx7Xu3nujWLaW+q/9E8QEMizng2P+/vW06g3w1sNLMNZpYC7gB2TqqzE7gr3H1zCzDg7kcr3FYRqSPff6WfTM55+8aFM7BrTMYYieIcvbtngfuAx4EXgc+7+34zu8fM7gmr7QIOAd3Ap4FfLpxvZp8DngDeYGY9ZvYLFe6DiNSYTC7P9w6eYuPyVla2N1a7OfOmrSnJ4Fg05+hx910Ewbz42I6i5w7cO8W5d86mgSJSf547coah8eyCGs0DLG5Ksr93kLzP781H6nfTqohEUt6d7/zwJJe1N3JlZ0u1mzOv2pqS5PI+7zcJV6AXkXn1fM8AfUPjvOOqTszq6wbg01kcLjoPjGTm9X0V6EVk3uTyztdfPM7KtkaurcMbgE+nrRDoRxXoRaRO7Xmtn/7hNO+9ZgWxBTaaBya2kQ6Mpuf1fRXoRWRepLN5vvHSCS7vaOYNKxZVuzlV0dKQIG7GwOj87rwpa9eNiMilOjU0zrNHzrC/d4CzY1nuvGndgpubL4iZ0daUmPcRvQK9iMyZsUyO//Wtg4xlcqzraOanbljN+mULa6fNZO1NSY3oRaR+fO/gSUYzOX7pnVeytqO52s2JhPamJIf7R+b1PTVHLyJzYiyT4zvdJ7l65SIF+SLt4dWx+fz8XTSlQC8ic+K7B08ylsnz7qtXVLspkdIeXjR1anj+5ukV6EWk4s6MpPlu90muXtXGZYubqt2cSClssTw2MDZv76k5ehGpmJF0lm+/fJLvHTwJwK1XL69yi6KnvSkFQO/AKG9aMz8XjSnQi8isHeobYver/ezvHSSXd65bu5j3XL2CJS2pajctctqagrCrEb2I1IQTZ8fY9fxRXj4+RGMyRtf6JWxZv3RBpR6eqZaGBPGY0TswOm/vqUAvIjOWzeX5xoETfPvlPpLxGLdfu5Kbr1hKMq5lv+nEzGhrTHD0jEb0IhJB45kcPWdG+creoxwbHGPzusXcdu0qWhsUSmaivSmlqRsRmRuDYxnG0jk6FzVMmYbA3cl7kGkyl3eODoyyr3eQl4+fpT/cErioIcFdt1zOG1e1zWfz60Z7U0JTNyJSGe7OcDrHsYGxcLF0gLzDirYGrrmsnWwuz5nRDAMjGQZGMwyNZ8nlncmX8iRixsYVi7jx8iWsWNTAFZ2tNCbjVelTPWhvSvHC0UHyeScWm/u8P2UFejO7DfhzIA58xt3/aFK5heW3AyPAz7v7M+WcK7KQuTunhtIc7h9hOJ0lk3MakzHWL21hZXtj2al8xzM5egfGODU0zqnhNKeG0/SHz8ezeSC4MfWPXrmMJS0pfnDkDN946QSJmNHWlKS9KcmGZS20NiZIxGLEYzbxWNyUZOOKVhoSCuyV0t6cJJNz9vUO8OY1i+f8/aYN9GYWBx4E3gP0ALvNbKe7v1BUbSuwMXzcDDwE3FzmuSJzJu9OJpcnnQ0fuTyZbJ50zhlOZxkaC0awrQ2Jid0QZgQPjJiBwXnTHHl3xjI5hsZzHO4f5pWTw+TyzrLWBpa2NtDZmmJZawOtjQmakp50hJ0AAAkTSURBVHHiMSOdzTOSznFyaJwTZ8fpH04zMJrh5ND4lLeVKwT8DctaaG9Kks05mXyebM7J5p2hsWAU3jc0zonB8YlReMygoyXF0pYGLl/WwtLw+fplzRPB+i1XLGU8myMVjy3YTJLV9KbV7Xz/lVPc+9ln+PJ9b2Nx89xuQy1nRL8F6Hb3QwBm9hiwDSgO1tuAR8ObhD9pZovNbBWwvoxza07efWL+Mpd3YhaMfPLujGfzZHN5zIKAESv6Grvg9bnnU3EP/ox2n/QcJ/wvrBd+LTruk74PhWPnnVfGcYJc4kPjWYbHs2GgyYMZiaKRX2Li67kRYcxgNJNjNJ0jETOaUwlSidhE8B3P5snk8uQ9+DnGYsHPJW5GLMbEzzYRfl8s/DkU+lg0nzwwmuHU0Dgnh9OcGgqCaWE0O1cKwTiViHFqKM1rh0+TnuY9YwZLmlO0NyW5elUb6zqaWdfRTHtTkmQ8xtB4lldODvHKyeBD5KVjZ0t+n1Q8RltTkqUtKa69rJ3VS5pYvqiR9qYk8TKmAzRCr57WhgQPfehG7viLJ/mVzz3LIz9/E4mYzdmHbjmBfjVwpOh1D8Gofbo6q8s8t2Ju/P2vM5LO4XhR4AuCAIXgGJp8E/bin69hhSfFX4AgyM9FLqKYnRs1FgcymZlCEF3W2sD6ZS00JeOk4jFSiRipeIxk+DWViNGcitPakCARs4kPsVz4exJ8sBY/P/evYWY0JuM0JeMsbU2d90Ht7pwdz3JqKM3QeJaxdI6sO42JGI3JeDC6bm24aCBub0py/dolXL92CQBnxzKMZnIkYjEScSMZfpAm43MXGGTubV63hN/bdg33f/F5Nv72PwHQuaiB3b99a8Xfq5xAX+o3aXIMmqpOOecG38BsO7A9fDlkZgfKaNtMLQNOzsH3jZoF3c9XqtCQObag/z3r0DLg5M+WKHgNsN+55O97+VQF5QT6HmBt0es1QG+ZdVJlnAuAuz8MPFxGey6Zme1x9665fI8oUD/ri/pZX6rRz3IuY9sNbDSzDWaWAu4Adk6qsxO4ywK3AAPufrTMc0VEZA5NO6J396yZ3Qc8TrBF8hF3329m94TlO4BdBFsruwm2V959sXPnpCciIlJSWfvo3X0XQTAvPraj6LkD95Z7bhXN6dRQhKif9UX9rC/z3k/zydtPRESkrijVnIhInav5QG9mj5jZCTPbV3TsOjN7wsyeN7Mvm1lbePw9ZvZ0ePxpM3tX0Tk3hse7zewBi9gG5Zn0s6h8nZkNmdlvFB2LbD9n2kcze3NYtj8sbwyPR7aPMOPf2aSZ/XV4/EUz+1jROVHv51oz+2bY7v1m9pHweIeZfd3Mfhh+XVJ0zsfC/hwws58oOh7Zvs60n1WJQ8HFObX7AN4BbAb2FR3bDbwzfP5h4PfD5zcAl4XPrwVeLzrn+8BbCPb+/xOwtdp9u9R+FpX/PfB3wG/UQj9n+G+ZAPYC14WvlwLxqPfxEvr5QeCx8Hkz8Cqwvkb6uQrYHD5fBLwMbAL+GLg/PH4/8Inw+SbgB0ADsAE4WAv/ppfQz3mPQ1X/IVXoB71+0v80g5xbf1gLvFDiHANOhb9Uq4CXisruBP6i2v2aTT+B/wB8EvhdwkBfC/0st48Eu7z+psT5ke/jDPt5J/Blgg+2pWEQ6aiVfk7q8/8jyHt1AFhV9O91IHz+MeBjRfUfD4NeTfV1un5Oqjsvcajmp26msA94X/j8A5x/0VbBTwPPuvs4QaqGnqKyQgqHqCvZTzNrAf4z8PFJ9Wuxn1P9W14FuJk9bmbPmNlvhcdrsY8wdT+/AAwDR4HDwJ+4ez811k8zW08wkn0KWOHBdTaEXwt3EL9YKpWa6GuZ/Sw2L3GoXgP9h4F7zexpgj+l0sWFZnYN8AngFwuHSnyPWtiONFU/Pw78T3cfmlS/Fvs5VR8TwNuAnw2/vt/M3k1t9hGm7ucWIAdcRjCd8etmdgU11E8zayWYRvyouw9erGqJYzNKpVJNM+hnof68xaG6vPGIu78EvBfAzK4CfrJQZmZrgC8Bd7n7wfBwD0F6hoIpUzVEyUX6eTPwH83sj4HFQN7Mxgh+CWuqnxfpYw/wr+5+MizbRTDv/TfUWB/hov38IPBVd88AJ8zsu0AX8G/UQD/NLEnwe/e37v7F8PBxM1vl7kctyHJ7Ijw+VSqVyP//OcN+znscqssRvZktD7/GgN8BdoSvFwNfIZgH/G6hfvhn1VkzuyVc5b6LYJ4t0qbqp7u/3d3Xu/t64M+AP3T3T9ViP6fqI8H87ZvNrNnMEsA7Cea1a66PcNF+HgbeZYEW4BaCedzI9zNs118CL7r7nxYV7QR+Lnz+c5xr907gDjNrMLMNBPe3+H7U+zrTflYlDlV74aICCx+fI5i/zBB8Iv4C8BGCRauXgT/i3CLX7xDMdz5X9FgelnURzJMeBD5VOCcqj5n0c9J5v8v5u24i28+Z9hH4ELA/7M8f10IfL+F3tpVg59R+gvs4/GYN9fNtBFMPe4v+f7udYFH5X4Afhl87is757bA/ByjacRLlvs60n9WIQ7oyVkSkztXl1I2IiJyjQC8iUucU6EVE6pwCvYhInVOgFxGpcwr0IiJ1ToFeZA6YWbzabRApUKCXBc/Mfr+QQzx8/Qdm9qtm9ptmttvM9prZx4vK/yHMI77fzLYXHR8ys98zs6cIsi6KRIICvUhw+frPwUQKgjuA4wSX4G8BrgduNLN3hPU/7O43ElzF+KtmtjQ83kKQevhmd//OfHZA5GLqMqmZyEy4+6tmdsrMbgBWAM8CNxEkGXs2rNZKEPi/TRDc3x8eXxseP0WQZfLv57PtIuVQoBcJfAb4eWAl8AjwbuB/uPtfFFcysx8DbgXe4u4jZvYtoDEsHnP33Hw1WKRcmroRCXwJuI1gJP94+PhwmGMcM1sdZphsB06HQf6NBNkkRSJNI3oRwN3TZvZN4Ew4Kv+amV0NPBHen3mIIFvmV4F7zGwvQYbFJ6vVZpFyKXulCBOLsM8AH3D3H1a7PSKVpKkbWfDMbBPQDfyLgrzUI43oRUTqnEb0IiJ1ToFeRKTOKdCLiNQ5BXoRkTqnQC8iUucU6EVE6tz/B1ilXOFZ8kEVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Another visualization\n",
    "sns.distplot(database['year'], bins = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region          0\n",
       "price           0\n",
       "year            0\n",
       "manufacturer    0\n",
       "model           0\n",
       "condition       0\n",
       "cylinders       0\n",
       "fuel            0\n",
       "odometer        0\n",
       "title_status    0\n",
       "transmission    0\n",
       "drive           0\n",
       "size            0\n",
       "type            0\n",
       "paint_color     0\n",
       "state           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We will be eliminating rows with the year 2021 and those with year <1946\n",
    "database[database['year'] == 1945].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "database=database[(database.year > 1945 )]\n",
    "database.drop(database[database[\"year\"]==2021].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "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>Null</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <td>120239</td>\n",
       "      <td>59.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylinders</th>\n",
       "      <td>59977</td>\n",
       "      <td>29.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <td>36960</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drive</th>\n",
       "      <td>32772</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <td>22911</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paint_color</th>\n",
       "      <td>18997</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manufacturer</th>\n",
       "      <td>4542</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>1599</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel</th>\n",
       "      <td>973</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title_status</th>\n",
       "      <td>832</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transmission</th>\n",
       "      <td>729</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odometer</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Null  Percentage\n",
       "size          120239        59.8\n",
       "cylinders      59977        29.8\n",
       "condition      36960        18.4\n",
       "drive          32772        16.3\n",
       "type           22911        11.4\n",
       "paint_color    18997         9.5\n",
       "manufacturer    4542         2.3\n",
       "model           1599         0.8\n",
       "fuel             973         0.5\n",
       "title_status     832         0.4\n",
       "transmission     729         0.4\n",
       "region             0         0.0\n",
       "price              0         0.0\n",
       "year               0         0.0\n",
       "odometer           0         0.0\n",
       "state              0         0.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's check NaN values again\n",
    "missing_values_count = pd.DataFrame({'Null': database.isnull().sum()})\n",
    "total = len(database)\n",
    "percentage_null = round((missing_values_count['Null']/total)*100,1)\n",
    "missing_values_count['Percentage'] = percentage_null\n",
    "missing_values_count.sort_values(by= 'Null' , ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Condition - Status - Odometer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200483, 16)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We will first drop the unusable rows of status and condition\n",
    "drop = database.loc[database.title_status.isnull() & database.condition.isnull()] #unusable rows\n",
    "database = database.loc[database.title_status.isnull() & database.condition.notnull() \n",
    "                        | database.title_status.notnull() & database.condition.isnull() \n",
    "                         | database.title_status.notnull() & database.condition.notnull()] #the dataframe with the usable rows\n",
    "database.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "odometer\n",
       "(0, 30000]            21852\n",
       "(30000, 60000]        30166\n",
       "(60000, 90000]        33104\n",
       "(90000, 115000]       31193\n",
       "(115000, 150000]      38307\n",
       "(150000, 10000000]    45537\n",
       "Name: title_status, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I believe these 3 columns have a relationship so I will use odometer to infer their values\n",
    "#First we want to create bins to contain the values of the odometer\n",
    "bins = [0, 30000, 60000, 90000,115000, 150000,10000000]\n",
    "groups = database.groupby(pd.cut(database.odometer, bins))\n",
    "groups.title_status.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "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>odometer</th>\n",
       "      <th>(0, 30000]</th>\n",
       "      <th>(30000, 60000]</th>\n",
       "      <th>(60000, 90000]</th>\n",
       "      <th>(90000, 115000]</th>\n",
       "      <th>(115000, 150000]</th>\n",
       "      <th>(150000, 10000000]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>clean</th>\n",
       "      <td>20487</td>\n",
       "      <td>28516</td>\n",
       "      <td>31353</td>\n",
       "      <td>30154</td>\n",
       "      <td>37249</td>\n",
       "      <td>44333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lien</th>\n",
       "      <td>182</td>\n",
       "      <td>267</td>\n",
       "      <td>304</td>\n",
       "      <td>183</td>\n",
       "      <td>186</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>missing</th>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parts only</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rebuilt</th>\n",
       "      <td>779</td>\n",
       "      <td>972</td>\n",
       "      <td>993</td>\n",
       "      <td>569</td>\n",
       "      <td>524</td>\n",
       "      <td>515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salvage</th>\n",
       "      <td>382</td>\n",
       "      <td>387</td>\n",
       "      <td>421</td>\n",
       "      <td>259</td>\n",
       "      <td>324</td>\n",
       "      <td>445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "odometer      (0, 30000]  (30000, 60000]  (60000, 90000]  (90000, 115000]  \\\n",
       "title_status                                                                \n",
       "clean              20487           28516           31353            30154   \n",
       "lien                 182             267             304              183   \n",
       "missing               18              21              26               22   \n",
       "parts only             4               3               7                6   \n",
       "rebuilt              779             972             993              569   \n",
       "salvage              382             387             421              259   \n",
       "\n",
       "odometer      (115000, 150000]  (150000, 10000000]  \n",
       "title_status                                        \n",
       "clean                    37249               44333  \n",
       "lien                       186                 175  \n",
       "missing                     22                  50  \n",
       "parts only                   2                  19  \n",
       "rebuilt                    524                 515  \n",
       "salvage                    324                 445  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\"title_status\" doesn't bring us that much of a value since most values are \"clean\".\n",
    "bins = [0, 30000, 60000, 90000,115000, 150000,10000000]\n",
    "groups = database.groupby(['title_status', pd.cut(database.odometer, bins)])\n",
    "groups.size().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Thus we will add \"clean\" to the NaN values\n",
    "database.title_status.fillna(\"clean\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>odometer</th>\n",
       "      <th>(0, 30000]</th>\n",
       "      <th>(30000, 60000]</th>\n",
       "      <th>(60000, 90000]</th>\n",
       "      <th>(90000, 115000]</th>\n",
       "      <th>(115000, 150000]</th>\n",
       "      <th>(150000, 10000000]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>excellent</th>\n",
       "      <td>4772</td>\n",
       "      <td>8277</td>\n",
       "      <td>11237</td>\n",
       "      <td>11293</td>\n",
       "      <td>13310</td>\n",
       "      <td>12473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fair</th>\n",
       "      <td>425</td>\n",
       "      <td>482</td>\n",
       "      <td>532</td>\n",
       "      <td>546</td>\n",
       "      <td>1008</td>\n",
       "      <td>3065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>6637</td>\n",
       "      <td>8444</td>\n",
       "      <td>9157</td>\n",
       "      <td>8844</td>\n",
       "      <td>11619</td>\n",
       "      <td>17517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>like new</th>\n",
       "      <td>2231</td>\n",
       "      <td>2396</td>\n",
       "      <td>2119</td>\n",
       "      <td>1600</td>\n",
       "      <td>1710</td>\n",
       "      <td>1231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new</th>\n",
       "      <td>4065</td>\n",
       "      <td>4867</td>\n",
       "      <td>3912</td>\n",
       "      <td>3103</td>\n",
       "      <td>3248</td>\n",
       "      <td>3326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salvage</th>\n",
       "      <td>83</td>\n",
       "      <td>73</td>\n",
       "      <td>87</td>\n",
       "      <td>75</td>\n",
       "      <td>91</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "odometer   (0, 30000]  (30000, 60000]  (60000, 90000]  (90000, 115000]  \\\n",
       "condition                                                                \n",
       "excellent        4772            8277           11237            11293   \n",
       "fair              425             482             532              546   \n",
       "good             6637            8444            9157             8844   \n",
       "like new         2231            2396            2119             1600   \n",
       "new              4065            4867            3912             3103   \n",
       "salvage            83              73              87               75   \n",
       "\n",
       "odometer   (115000, 150000]  (150000, 10000000]  \n",
       "condition                                        \n",
       "excellent             13310               12473  \n",
       "fair                   1008                3065  \n",
       "good                  11619               17517  \n",
       "like new               1710                1231  \n",
       "new                    3248                3326  \n",
       "salvage                  91                 176  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now we compare odometer and condition\n",
    "#Makes no sense to compare condition and title_status since most of the values are \"clean\"\n",
    "bins = [0, 30000, 60000, 90000,115000, 150000,10000000]\n",
    "groups = database.groupby(['condition', pd.cut(database.odometer, bins)])\n",
    "groups.size().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now we add values to those NaN in \"condition\" using odometer range of values\n",
    "\n",
    "m1 = (database['odometer'] > 60000) & (database['odometer'] <= 150000)\n",
    "m2 = (database['odometer'] <= 60000) | (database['odometer'] > 150000)\n",
    "\n",
    "database.loc[m1,'condition'] = database.loc[m1,'condition'].fillna('excellent')\n",
    "database.loc[m2,'condition'] = database.loc[m2,'condition'].fillna(\"good\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel</th>\n",
       "      <th>odometer</th>\n",
       "      <th>transmission</th>\n",
       "      <th>drive</th>\n",
       "      <th>size</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>state</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>duluth / superior</td>\n",
       "      <td>6995</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>gmc</td>\n",
       "      <td>new sierra 1500</td>\n",
       "      <td>8 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>167783.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>red</td>\n",
       "      <td>mn</td>\n",
       "      <td>excellent &amp; clean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>duluth / superior</td>\n",
       "      <td>8750</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>sonata</td>\n",
       "      <td>4 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>90821.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>fwd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>grey</td>\n",
       "      <td>mn</td>\n",
       "      <td>excellent &amp; clean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>new haven</td>\n",
       "      <td>10900</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>prius</td>\n",
       "      <td>4 cylinders</td>\n",
       "      <td>hybrid</td>\n",
       "      <td>92800.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>fwd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>blue</td>\n",
       "      <td>ct</td>\n",
       "      <td>good &amp; clean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>duluth / superior</td>\n",
       "      <td>16995</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>gmc</td>\n",
       "      <td>sierra classic 2500hd</td>\n",
       "      <td>8 cylinders</td>\n",
       "      <td>diesel</td>\n",
       "      <td>254217.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>full-size</td>\n",
       "      <td>truck</td>\n",
       "      <td>white</td>\n",
       "      <td>mn</td>\n",
       "      <td>good &amp; clean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>duluth / superior</td>\n",
       "      <td>13995</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>f-150</td>\n",
       "      <td>6 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>188406.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>full-size</td>\n",
       "      <td>truck</td>\n",
       "      <td>grey</td>\n",
       "      <td>mn</td>\n",
       "      <td>good &amp; clean</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              region  price    year manufacturer                  model  \\\n",
       "0  duluth / superior   6995  2000.0          gmc        new sierra 1500   \n",
       "1  duluth / superior   8750  2013.0      hyundai                 sonata   \n",
       "2          new haven  10900  2013.0       toyota                  prius   \n",
       "4  duluth / superior  16995  2007.0          gmc  sierra classic 2500hd   \n",
       "5  duluth / superior  13995  2012.0         ford                  f-150   \n",
       "\n",
       "     cylinders    fuel  odometer transmission drive       size   type  \\\n",
       "0  8 cylinders     gas  167783.0    automatic   4wd        NaN    NaN   \n",
       "1  4 cylinders     gas   90821.0    automatic   fwd        NaN    NaN   \n",
       "2  4 cylinders  hybrid   92800.0    automatic   fwd        NaN    NaN   \n",
       "4  8 cylinders  diesel  254217.0    automatic   4wd  full-size  truck   \n",
       "5  6 cylinders     gas  188406.0    automatic   4wd  full-size  truck   \n",
       "\n",
       "  paint_color state             status  \n",
       "0         red    mn  excellent & clean  \n",
       "1        grey    mn  excellent & clean  \n",
       "2        blue    ct       good & clean  \n",
       "4       white    mn       good & clean  \n",
       "5        grey    mn       good & clean  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We combine condition and title_status and then we drop them\n",
    "database['status'] = database['condition'] + ' & ' + database['title_status']\n",
    "database.drop(['condition', 'title_status'], axis = 1, inplace=True)\n",
    "database.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other methods to deal with NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "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>Null</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <td>119731</td>\n",
       "      <td>59.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylinders</th>\n",
       "      <td>59471</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drive</th>\n",
       "      <td>32742</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <td>22838</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paint_color</th>\n",
       "      <td>18796</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manufacturer</th>\n",
       "      <td>4540</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>1599</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel</th>\n",
       "      <td>794</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transmission</th>\n",
       "      <td>589</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odometer</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Null  Percentage\n",
       "size          119731        59.7\n",
       "cylinders      59471        29.7\n",
       "drive          32742        16.3\n",
       "type           22838        11.4\n",
       "paint_color    18796         9.4\n",
       "manufacturer    4540         2.3\n",
       "model           1599         0.8\n",
       "fuel             794         0.4\n",
       "transmission     589         0.3\n",
       "region             0         0.0\n",
       "price              0         0.0\n",
       "year               0         0.0\n",
       "odometer           0         0.0\n",
       "state              0         0.0\n",
       "status             0         0.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_count = pd.DataFrame({'Null': database.isnull().sum()})\n",
    "total = len(database)\n",
    "percentage_null = round((missing_values_count['Null']/total)*100,1)\n",
    "missing_values_count['Percentage'] = percentage_null\n",
    "missing_values_count.sort_values(by= 'Null' , ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#First we delete \"size\" since it has so many NaN values\n",
    "database.drop('size', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#I will be deleting the NaN values < 5%  and \"size\" since \n",
    "database=database.dropna(subset=['transmission', 'fuel', 'model', 'manufacturer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-50-01593bc51ae6>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  database[i] = database[i].fillna(method='ffill')\n"
     ]
    }
   ],
   "source": [
    "#Now we will be using \"ffill\" for those NaN values between 10% and 30%\n",
    "columns = ['drive', 'type', 'paint_color']\n",
    "for i in columns:\n",
    "    database[i] = database[i].fillna(method='ffill')"
   ]
  },
  {
   "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>Null</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cylinders</th>\n",
       "      <td>56914</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manufacturer</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odometer</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transmission</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drive</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paint_color</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Null  Percentage\n",
       "cylinders     56914        29.5\n",
       "type              3         0.0\n",
       "region            0         0.0\n",
       "price             0         0.0\n",
       "year              0         0.0\n",
       "manufacturer      0         0.0\n",
       "model             0         0.0\n",
       "fuel              0         0.0\n",
       "odometer          0         0.0\n",
       "transmission      0         0.0\n",
       "drive             0         0.0\n",
       "paint_color       0         0.0\n",
       "state             0         0.0\n",
       "status            0         0.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We check again since there must be left some NaN values\n",
    "missing_values_count = pd.DataFrame({'Null': database.isnull().sum()})\n",
    "total = len(database)\n",
    "percentage_null = round((missing_values_count['Null']/total)*100,1)\n",
    "missing_values_count['Percentage'] = percentage_null\n",
    "missing_values_count.sort_values(by= 'Null' , ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We delete that subset of NaN values\n",
    "database=database.dropna(subset=['type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "drive  cylinders   \n",
       "4wd    10 cylinders      254\n",
       "       12 cylinders        4\n",
       "       2 cylinders         1\n",
       "       3 cylinders        48\n",
       "       4 cylinders     14303\n",
       "       5 cylinders       309\n",
       "       6 cylinders     20458\n",
       "       8 cylinders     17239\n",
       "       other              73\n",
       "4x4    10 cylinders        4\n",
       "       3 cylinders         1\n",
       "       4 cylinders       138\n",
       "       5 cylinders         3\n",
       "       6 cylinders       198\n",
       "       8 cylinders       128\n",
       "awd    10 cylinders        1\n",
       "       3 cylinders         1\n",
       "       4 cylinders       964\n",
       "       5 cylinders        14\n",
       "       6 cylinders      1012\n",
       "       8 cylinders       215\n",
       "       other               4\n",
       "fwd    10 cylinders       30\n",
       "       12 cylinders        8\n",
       "       3 cylinders       206\n",
       "       4 cylinders     35698\n",
       "       5 cylinders       746\n",
       "       6 cylinders     17217\n",
       "       8 cylinders      1872\n",
       "       other             171\n",
       "rwd    10 cylinders      259\n",
       "       12 cylinders       56\n",
       "       2 cylinders         6\n",
       "       3 cylinders        16\n",
       "       4 cylinders      3670\n",
       "       5 cylinders       153\n",
       "       6 cylinders      9213\n",
       "       8 cylinders     11365\n",
       "       other              88\n",
       "Name: cylinders, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finally instead of doing the ffill we will use drive to guess cylinders' NaN values\n",
    "database.groupby(['drive', 'cylinders']).cylinders.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We add a value to the Nan values of cylinders using drive \n",
    "values = {'4wd': '6 cylinders',\n",
    "              '4x4': '6 cylinders',\n",
    "              'awd': '6 cylinders',\n",
    "              'fwd': '4 cylinders',\n",
    "              'rwd': '8 cylinders'}\n",
    "\n",
    "database.loc[database[\"cylinders\"].isna(), 'cylinders'] = database.loc[\n",
    "    database[\"cylinders\"].isna(), 'drive'].map(lambda x: values[x])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Region - State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "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>region</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel</th>\n",
       "      <th>odometer</th>\n",
       "      <th>transmission</th>\n",
       "      <th>drive</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>duluth / superior (mn)</td>\n",
       "      <td>16995</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>gmc</td>\n",
       "      <td>sierra classic 2500hd</td>\n",
       "      <td>8 cylinders</td>\n",
       "      <td>diesel</td>\n",
       "      <td>254217.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>truck</td>\n",
       "      <td>white</td>\n",
       "      <td>good &amp; clean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>duluth / superior (mn)</td>\n",
       "      <td>13995</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>f-150</td>\n",
       "      <td>6 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>188406.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>truck</td>\n",
       "      <td>grey</td>\n",
       "      <td>good &amp; clean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>duluth / superior (mn)</td>\n",
       "      <td>7995</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>equinox</td>\n",
       "      <td>4 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>108124.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>suv</td>\n",
       "      <td>grey</td>\n",
       "      <td>good &amp; clean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>duluth / superior (mn)</td>\n",
       "      <td>8995</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>traverse</td>\n",
       "      <td>6 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>178054.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>suv</td>\n",
       "      <td>white</td>\n",
       "      <td>good &amp; clean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>duluth / superior (mn)</td>\n",
       "      <td>10995</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>explorer</td>\n",
       "      <td>6 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>170259.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>suv</td>\n",
       "      <td>white</td>\n",
       "      <td>good &amp; clean</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   region  price    year manufacturer                  model  \\\n",
       "4  duluth / superior (mn)  16995  2007.0          gmc  sierra classic 2500hd   \n",
       "5  duluth / superior (mn)  13995  2012.0         ford                  f-150   \n",
       "6  duluth / superior (mn)   7995  2010.0    chevrolet                equinox   \n",
       "7  duluth / superior (mn)   8995  2011.0    chevrolet               traverse   \n",
       "8  duluth / superior (mn)  10995  2014.0         ford               explorer   \n",
       "\n",
       "     cylinders    fuel  odometer transmission drive   type paint_color  \\\n",
       "4  8 cylinders  diesel  254217.0    automatic   4wd  truck       white   \n",
       "5  6 cylinders     gas  188406.0    automatic   4wd  truck        grey   \n",
       "6  4 cylinders     gas  108124.0    automatic   4wd    suv        grey   \n",
       "7  6 cylinders     gas  178054.0    automatic   4wd    suv       white   \n",
       "8  6 cylinders     gas  170259.0    automatic   4wd    suv       white   \n",
       "\n",
       "         status  \n",
       "4  good & clean  \n",
       "5  good & clean  \n",
       "6  good & clean  \n",
       "7  good & clean  \n",
       "8  good & clean  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Since they are deeply connected we will join these 2 columns\n",
    "#We combine condition and title_status and then we drop them\n",
    "database['region'] = database['region'] + ' (' + database['state'] + ')'\n",
    "database.drop(['state'], axis = 1, inplace=True)\n",
    "database.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Variable Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(193060, 13)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "database.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 193060 entries, 4 to 423856\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   region        193060 non-null  object \n",
      " 1   price         193060 non-null  int64  \n",
      " 2   year          193060 non-null  float64\n",
      " 3   manufacturer  193060 non-null  object \n",
      " 4   model         193060 non-null  object \n",
      " 5   cylinders     193060 non-null  object \n",
      " 6   fuel          193060 non-null  object \n",
      " 7   odometer      193060 non-null  float64\n",
      " 8   transmission  193060 non-null  object \n",
      " 9   drive         193060 non-null  object \n",
      " 10  type          193060 non-null  object \n",
      " 11  paint_color   193060 non-null  object \n",
      " 12  status        193060 non-null  object \n",
      "dtypes: float64(2), int64(1), object(10)\n",
      "memory usage: 20.6+ MB\n"
     ]
    }
   ],
   "source": [
    "database.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>region</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel</th>\n",
       "      <th>transmission</th>\n",
       "      <th>drive</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>status</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>odometer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>13022</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>16995</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>254217.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region  manufacturer  model  cylinders  fuel  transmission  drive  type  \\\n",
       "4      88            14  13022          7     0             0      0    10   \n",
       "\n",
       "   paint_color  status  price    year  odometer  \n",
       "4           10      12  16995  2007.0  254217.0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We have 13 columns. Out of those, 10 columns are categorical. \n",
    "# Apply the Label encoder to each categorical feature \n",
    "from sklearn.preprocessing import LabelEncoder \n",
    "cat_features = ['region', 'manufacturer', 'model', 'cylinders', 'fuel', 'transmission', \n",
    "                'drive', 'type', 'paint_color', 'status'] \n",
    "encoder = LabelEncoder() \n",
    "encoded = database[cat_features].apply(encoder.fit_transform)\n",
    "database.drop(cat_features, axis=1, inplace=True)\n",
    "database = pd.concat([encoded,database],axis = 1) \n",
    "database.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fea8f83dc10>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We check high correlated features with the Correlation Matrix\n",
    "#If there are none, we leave the database as it is\n",
    "sns.set(style=\"white\")\n",
    "corr = database.drop(columns = ['price']).corr()\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "f, ax = plt.subplots(figsize=(10, 10))\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,square=True, linewidths=.5, \n",
    "            cbar_kws={\"shrink\": .5}, annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting into Train and Test Set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['region', 'manufacturer', 'model', 'cylinders', 'fuel', 'transmission',\n",
       "       'drive', 'type', 'paint_color', 'status', 'price', 'year', 'odometer'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "database.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>region</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel</th>\n",
       "      <th>transmission</th>\n",
       "      <th>drive</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>status</th>\n",
       "      <th>year</th>\n",
       "      <th>odometer</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>13022</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>254217.0</td>\n",
       "      <td>16995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region  manufacturer  model  cylinders  fuel  transmission  drive  type  \\\n",
       "4      88            14  13022          7     0             0      0    10   \n",
       "\n",
       "   paint_color  status    year  odometer  price  \n",
       "4           10      12  2007.0  254217.0  16995  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#First, we change the order of the features and we put the depedent variable at the end\n",
    "column_names = ['region', 'manufacturer', 'model', 'cylinders', 'fuel', 'transmission',\n",
    "       'drive', 'type', 'paint_color', 'status', 'year',\n",
    "       'odometer' , 'price']\n",
    "\n",
    "database = database.reindex(columns = column_names ) \n",
    "database.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We split into Training and Test Set\n",
    "\n",
    "X = database.iloc[:,:-1]  #independent columns\n",
    "y = database.iloc[:,-1]    #target column i.e price range\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>region</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel</th>\n",
       "      <th>transmission</th>\n",
       "      <th>drive</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>status</th>\n",
       "      <th>year</th>\n",
       "      <th>odometer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>232399</th>\n",
       "      <td>-1.35073</td>\n",
       "      <td>-0.512069</td>\n",
       "      <td>-0.437021</td>\n",
       "      <td>0.535703</td>\n",
       "      <td>0.045659</td>\n",
       "      <td>-0.311257</td>\n",
       "      <td>-1.236546</td>\n",
       "      <td>0.478498</td>\n",
       "      <td>-0.44515</td>\n",
       "      <td>-1.071774</td>\n",
       "      <td>0.359612</td>\n",
       "      <td>0.986555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         region  manufacturer     model  cylinders      fuel  transmission  \\\n",
       "232399 -1.35073     -0.512069 -0.437021   0.535703  0.045659     -0.311257   \n",
       "\n",
       "           drive      type  paint_color    status      year  odometer  \n",
       "232399 -1.236546  0.478498     -0.44515 -1.071774  0.359612  0.986555  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now we do some Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc_X = StandardScaler()\n",
    "\n",
    "X_train2 = pd.DataFrame(sc_X.fit_transform(X_train))\n",
    "X_test2 = pd.DataFrame(sc_X.transform(X_test)) #we do the Feature Scaling\n",
    "\n",
    "X_train2.columns = X_train.columns.values \n",
    "X_test2.columns = X_test.columns.values #we give the new Scaled DataFrame column names\n",
    "\n",
    "X_train2.index = X_train.index.values\n",
    "X_test2.index = X_test.index.values #we give the new Scaled DataFrame each index\n",
    "\n",
    "X_train = X_train2\n",
    "X_test = X_test2\n",
    "\n",
    "X_train.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We chose Random Forest algorith for this project\n",
    "#Let's do Cross Validation to check the overal score in the Training Set\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "scores = []\n",
    "forest = RandomForestRegressor(n_estimators=20, random_state=0)\n",
    "acc = cross_val_score(forest, X_train, y_train, scoring='r2', cv=5)\n",
    "scores.append(round(acc.mean()*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>Metrics</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R2</td>\n",
       "      <td>85.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Metrics  Accuracy\n",
       "0      R2     85.48"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame({\n",
    "    'Metrics': ['R2'],\n",
    "    'Accuracy': scores})\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyper-parameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To improve the performance of the model, we optimise hyper-parameters\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {'n_estimators': [100, 200, 300]}\n",
    "forest = RandomForestRegressor(random_state=0)\n",
    "grid_search = GridSearchCV(estimator = forest, param_grid = param_grid, \n",
    "                          cv = 3, n_jobs = -1, verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 3 candidates, totalling 9 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   7 out of   9 | elapsed:  8.1min remaining:  2.3min\n",
      "[Parallel(n_jobs=-1)]: Done   9 out of   9 | elapsed: 10.4min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 300}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X_train, y_train)\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores2 = []\n",
    "best_grid = grid_search.best_estimator_\n",
    "acc2 = cross_val_score(best_grid, X_train, y_train, scoring='r2', cv=5)\n",
    "scores2.append(round(acc2.mean()*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Metrics</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Accuracy Tuned Param</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R2</td>\n",
       "      <td>85.48</td>\n",
       "      <td>86.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Metrics  Accuracy  Accuracy Tuned Param\n",
       "0      R2     85.48                 86.34"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame({\n",
    "    'Metrics': ['R2'],\n",
    "    'Accuracy': scores,\n",
    "    'Accuracy Tuned Param': scores2})\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Finally let's check the performance on the Test Set\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "best_grid = grid_search.best_estimator_\n",
    "y_pred = best_grid.predict(X_test)\n",
    "acc_test = round(r2_score(y_test,y_pred)*100,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>Metrics</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Accuracy Tuned Param</th>\n",
       "      <th>Accuracy Test Set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R2</td>\n",
       "      <td>85.48</td>\n",
       "      <td>86.34</td>\n",
       "      <td>87.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Metrics  Accuracy  Accuracy Tuned Param  Accuracy Test Set\n",
       "0      R2     85.48                 86.34              87.03"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame({\n",
    "    'Metrics': ['R2'],\n",
    "    'Accuracy': scores,\n",
    "    'Accuracy Tuned Param': scores2,\n",
    "    'Accuracy Test Set': acc_test})\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 1746.9\n",
      "Mean Squared Error: 7721380.42\n",
      "Root Mean Squared Error: 2778.74\n"
     ]
    }
   ],
   "source": [
    "#Other metrics apart from R2 \n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "\n",
    "print('Mean Absolute Error:', round(metrics.mean_absolute_error(y_test, y_pred),2))\n",
    "print('Mean Squared Error:', round(metrics.mean_squared_error(y_test, y_pred),2))\n",
    "print('Root Mean Squared Error:', round(np.sqrt(metrics.mean_squared_error(y_test, y_pred)),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#On to the second goal: feature importance when predicting prices\n",
    "name = []\n",
    "importance = []\n",
    "for nam, imp in zip(X, best_grid.feature_importances_):\n",
    "    name.append(nam)\n",
    "    importance.append(round(imp*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>Features</th>\n",
       "      <th>Importance (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>year</td>\n",
       "      <td>42.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>odometer</td>\n",
       "      <td>14.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cylinders</td>\n",
       "      <td>10.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>model</td>\n",
       "      <td>9.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fuel</td>\n",
       "      <td>4.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>manufacturer</td>\n",
       "      <td>4.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>type</td>\n",
       "      <td>3.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>drive</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>region</td>\n",
       "      <td>2.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>status</td>\n",
       "      <td>1.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>paint_color</td>\n",
       "      <td>1.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>transmission</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Features  Importance (%)\n",
       "10          year           42.49\n",
       "11      odometer           14.66\n",
       "3      cylinders           10.54\n",
       "2          model            9.17\n",
       "4           fuel            4.96\n",
       "1   manufacturer            4.72\n",
       "7           type            3.47\n",
       "6          drive            3.25\n",
       "0         region            2.54\n",
       "9         status            1.99\n",
       "8    paint_color            1.66\n",
       "5   transmission            0.55"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = pd.DataFrame({'Features': name, \"Importance (%)\": importance})\n",
    "features.sort_values(by='Importance (%)', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Finally we plot it \n",
    "def plot_feature_importance(importance,names,model_type):\n",
    "\n",
    "    #Create arrays from feature importance and feature names\n",
    "    feature_importance = np.array(importance)\n",
    "    feature_names = np.array(names)\n",
    "\n",
    "    #Create a DataFrame using a Dictionary\n",
    "    data={'feature_names':feature_names,'feature_importance':feature_importance}\n",
    "    fi_df = pd.DataFrame(data)\n",
    "\n",
    "    #Sort the DataFrame in order decreasing feature importance\n",
    "    fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)\n",
    "\n",
    "    #Define size of bar plot\n",
    "    plt.figure(figsize=(10,8))\n",
    "    #Plot Searborn bar chart\n",
    "    sns.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names'])\n",
    "    #Add chart labels\n",
    "    plt.title(model_type + ' FEATURE IMPORTANCE')\n",
    "    plt.xlabel('FEATURE IMPORTANCE')\n",
    "    plt.ylabel('FEATURE NAMES')\n",
    "\n",
    "\n",
    "plot_feature_importance(best_grid.feature_importances_,X.columns,'RANDOM FOREST')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### More information on this project on: https://medium.com/@jgarciaportillo/end-to-end-data-science-project-predicting-used-car-prices-using-regression-1b12386c69c8"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}