Numerical Methods.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment V2</b>\n",
    "\n",
    "Thanks for taking the time to improve the project! Now it's perfect! The project is accepted. Keep up the good work on the next sprint! :)\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Review**\n",
    "\n",
    "Hi, my name is Dmitry and I will be reviewing your project.\n",
    "  \n",
    "You can find my comments in colored markdown cells:\n",
    "  \n",
    "<div class=\"alert alert-success\">\n",
    "  If everything is done successfully.\n",
    "</div>\n",
    "  \n",
    "<div class=\"alert alert-warning\">\n",
    "  If I have some (optional) suggestions, or questions to think about, or general comments.\n",
    "</div>\n",
    "  \n",
    "<div class=\"alert alert-danger\">\n",
    "  If a section requires some corrections. Work can't be accepted with red comments.\n",
    "</div>\n",
    "  \n",
    "Please don't remove my comments, as it will make further review iterations much harder for me.\n",
    "  \n",
    "Feel free to reply to my comments or ask questions using the following template:\n",
    "  \n",
    "<div class=\"alert alert-info\">\n",
    "  For your comments and questions.\n",
    "</div>\n",
    "  \n",
    "First of all, thank you for turning in the project! You did a great job overall, but there are a couple of problems in the modeling section which need to be fixed before the project is accepted. Let me know if you have any questions!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rusty Bargain used car sales service is developing an app to attract new customers. In that app, you can quickly find out the market value of your car. You have access to historical data: technical specifications, trim versions, and prices. You need to build the model to determine the value. \n",
    "\n",
    "Rusty Bargain is interested in:\n",
    "\n",
    "- the quality of the prediction;\n",
    "- the speed of the prediction;\n",
    "- the time required for training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -qq catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.api.types import is_string_dtype\n",
    "from pandas.api.types import is_numeric_dtype\n",
    "import random\n",
    "\n",
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV,cross_validate\n",
    " \n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "pd.options.mode.chained_assignment = None\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor \n",
    "from catboost import CatBoostRegressor, Pool \n",
    "from lightgbm import LGBMRegressor \n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "from IPython.display import display\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/datasets/car_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def percent_na(df, num):\n",
    "    count = 0\n",
    "    df = df.copy()\n",
    "    s = (df.isna().sum() / df.shape[0])\n",
    "    for column, percent in zip(s.index, s.values):\n",
    "        num_of_nulls = df[column].isna().sum()\n",
    "        if num_of_nulls == 0:\n",
    "            continue\n",
    "        else:\n",
    "            count += 1\n",
    "        print('Column {} has {:.{}%} percent of Nulls, and {} of nulls'.format(column, percent, num, num_of_nulls))\n",
    "    if count != 0:\n",
    "        print(\"\\033[1m\" + 'There are {} columns with NA.'.format(count) + \"\\033[0m\")\n",
    "    else:\n",
    "        print()\n",
    "        print(\"\\033[1m\" + 'There are no columns with NA.' + \"\\033[0m\")\n",
    "        \n",
    "def get_info(df):\n",
    "    \"\"\"\n",
    "    This function uses the head(), info(), describe(), shape() and duplicated() \n",
    "    methods to display the general information about the dataset.\n",
    "    \"\"\"\n",
    "    print(\"\\033[1m\" + '-'*100 + \"\\033[0m\")\n",
    "    print('Head:')\n",
    "    print()\n",
    "    display(df.head())\n",
    "    print('-'*100)\n",
    "    print('Info:')\n",
    "    print()\n",
    "    display(df.info())\n",
    "    print('-'*100)\n",
    "    print('Describe:')\n",
    "    print()\n",
    "    display(df.describe())\n",
    "    print('-'*100)\n",
    "    display(df.describe(include='object'))\n",
    "    print()\n",
    "    print('Columns with nulls:')\n",
    "    display(percent_na(df, 4)) \n",
    "    print('-'*100)\n",
    "    print('Shape:')\n",
    "    print(df.shape)\n",
    "    print('-'*100)\n",
    "    print('Duplicated:')\n",
    "    print(\"\\033[1m\" + 'We have {} duplicated rows.\\n'.format(df.duplicated().sum()) + \"\\033[0m\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overview of the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m----------------------------------------------------------------------------------------------------\u001b[0m\n",
      "Head:\n",
      "\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateCrawled</th>\n",
       "      <th>Price</th>\n",
       "      <th>VehicleType</th>\n",
       "      <th>RegistrationYear</th>\n",
       "      <th>Gearbox</th>\n",
       "      <th>Power</th>\n",
       "      <th>Model</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>RegistrationMonth</th>\n",
       "      <th>FuelType</th>\n",
       "      <th>Brand</th>\n",
       "      <th>NotRepaired</th>\n",
       "      <th>DateCreated</th>\n",
       "      <th>NumberOfPictures</th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>LastSeen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24/03/2016 11:52</td>\n",
       "      <td>480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993</td>\n",
       "      <td>manual</td>\n",
       "      <td>0</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>70435</td>\n",
       "      <td>07/04/2016 03:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24/03/2016 10:58</td>\n",
       "      <td>18300</td>\n",
       "      <td>coupe</td>\n",
       "      <td>2011</td>\n",
       "      <td>manual</td>\n",
       "      <td>190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125000</td>\n",
       "      <td>5</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>audi</td>\n",
       "      <td>yes</td>\n",
       "      <td>24/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>66954</td>\n",
       "      <td>07/04/2016 01:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14/03/2016 12:52</td>\n",
       "      <td>9800</td>\n",
       "      <td>suv</td>\n",
       "      <td>2004</td>\n",
       "      <td>auto</td>\n",
       "      <td>163</td>\n",
       "      <td>grand</td>\n",
       "      <td>125000</td>\n",
       "      <td>8</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>jeep</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>90480</td>\n",
       "      <td>05/04/2016 12:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17/03/2016 16:54</td>\n",
       "      <td>1500</td>\n",
       "      <td>small</td>\n",
       "      <td>2001</td>\n",
       "      <td>manual</td>\n",
       "      <td>75</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no</td>\n",
       "      <td>17/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>91074</td>\n",
       "      <td>17/03/2016 17:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31/03/2016 17:25</td>\n",
       "      <td>3600</td>\n",
       "      <td>small</td>\n",
       "      <td>2008</td>\n",
       "      <td>manual</td>\n",
       "      <td>69</td>\n",
       "      <td>fabia</td>\n",
       "      <td>90000</td>\n",
       "      <td>7</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>skoda</td>\n",
       "      <td>no</td>\n",
       "      <td>31/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>60437</td>\n",
       "      <td>06/04/2016 10:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "        DateCrawled  Price VehicleType  RegistrationYear Gearbox  Power  \\\n",
       "0  24/03/2016 11:52    480         NaN              1993  manual      0   \n",
       "1  24/03/2016 10:58  18300       coupe              2011  manual    190   \n",
       "2  14/03/2016 12:52   9800         suv              2004    auto    163   \n",
       "3  17/03/2016 16:54   1500       small              2001  manual     75   \n",
       "4  31/03/2016 17:25   3600       small              2008  manual     69   \n",
       "\n",
       "   Model  Mileage  RegistrationMonth  FuelType       Brand NotRepaired  \\\n",
       "0   golf   150000                  0    petrol  volkswagen         NaN   \n",
       "1    NaN   125000                  5  gasoline        audi         yes   \n",
       "2  grand   125000                  8  gasoline        jeep         NaN   \n",
       "3   golf   150000                  6    petrol  volkswagen          no   \n",
       "4  fabia    90000                  7  gasoline       skoda          no   \n",
       "\n",
       "        DateCreated  NumberOfPictures  PostalCode          LastSeen  \n",
       "0  24/03/2016 00:00                 0       70435  07/04/2016 03:16  \n",
       "1  24/03/2016 00:00                 0       66954  07/04/2016 01:46  \n",
       "2  14/03/2016 00:00                 0       90480  05/04/2016 12:47  \n",
       "3  17/03/2016 00:00                 0       91074  17/03/2016 17:40  \n",
       "4  31/03/2016 00:00                 0       60437  06/04/2016 10:17  "
      ]
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     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Info:\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 354369 entries, 0 to 354368\n",
      "Data columns (total 16 columns):\n",
      " #   Column             Non-Null Count   Dtype \n",
      "---  ------             --------------   ----- \n",
      " 0   DateCrawled        354369 non-null  object\n",
      " 1   Price              354369 non-null  int64 \n",
      " 2   VehicleType        316879 non-null  object\n",
      " 3   RegistrationYear   354369 non-null  int64 \n",
      " 4   Gearbox            334536 non-null  object\n",
      " 5   Power              354369 non-null  int64 \n",
      " 6   Model              334664 non-null  object\n",
      " 7   Mileage            354369 non-null  int64 \n",
      " 8   RegistrationMonth  354369 non-null  int64 \n",
      " 9   FuelType           321474 non-null  object\n",
      " 10  Brand              354369 non-null  object\n",
      " 11  NotRepaired        283215 non-null  object\n",
      " 12  DateCreated        354369 non-null  object\n",
      " 13  NumberOfPictures   354369 non-null  int64 \n",
      " 14  PostalCode         354369 non-null  int64 \n",
      " 15  LastSeen           354369 non-null  object\n",
      "dtypes: int64(7), object(9)\n",
      "memory usage: 43.3+ MB\n"
     ]
    },
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       "None"
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     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Describe:\n",
      "\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>RegistrationYear</th>\n",
       "      <th>Power</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>RegistrationMonth</th>\n",
       "      <th>NumberOfPictures</th>\n",
       "      <th>PostalCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>354369.000000</td>\n",
       "      <td>354369.000000</td>\n",
       "      <td>354369.000000</td>\n",
       "      <td>354369.000000</td>\n",
       "      <td>354369.000000</td>\n",
       "      <td>354369.0</td>\n",
       "      <td>354369.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4416.656776</td>\n",
       "      <td>2004.234448</td>\n",
       "      <td>110.094337</td>\n",
       "      <td>128211.172535</td>\n",
       "      <td>5.714645</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50508.689087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4514.158514</td>\n",
       "      <td>90.227958</td>\n",
       "      <td>189.850405</td>\n",
       "      <td>37905.341530</td>\n",
       "      <td>3.726421</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25783.096248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1067.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1050.000000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>125000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30165.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49413.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6400.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71083.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20000.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99998.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Price  RegistrationYear          Power        Mileage  \\\n",
       "count  354369.000000     354369.000000  354369.000000  354369.000000   \n",
       "mean     4416.656776       2004.234448     110.094337  128211.172535   \n",
       "std      4514.158514         90.227958     189.850405   37905.341530   \n",
       "min         0.000000       1000.000000       0.000000    5000.000000   \n",
       "25%      1050.000000       1999.000000      69.000000  125000.000000   \n",
       "50%      2700.000000       2003.000000     105.000000  150000.000000   \n",
       "75%      6400.000000       2008.000000     143.000000  150000.000000   \n",
       "max     20000.000000       9999.000000   20000.000000  150000.000000   \n",
       "\n",
       "       RegistrationMonth  NumberOfPictures     PostalCode  \n",
       "count      354369.000000          354369.0  354369.000000  \n",
       "mean            5.714645               0.0   50508.689087  \n",
       "std             3.726421               0.0   25783.096248  \n",
       "min             0.000000               0.0    1067.000000  \n",
       "25%             3.000000               0.0   30165.000000  \n",
       "50%             6.000000               0.0   49413.000000  \n",
       "75%             9.000000               0.0   71083.000000  \n",
       "max            12.000000               0.0   99998.000000  "
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      "----------------------------------------------------------------------------------------------------\n"
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       "      <th></th>\n",
       "      <th>DateCrawled</th>\n",
       "      <th>VehicleType</th>\n",
       "      <th>Gearbox</th>\n",
       "      <th>Model</th>\n",
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       "      <td>334664</td>\n",
       "      <td>321474</td>\n",
       "      <td>354369</td>\n",
       "      <td>283215</td>\n",
       "      <td>354369</td>\n",
       "      <td>354369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>15470</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>250</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "      <td>18592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>05/03/2016 14:25</td>\n",
       "      <td>sedan</td>\n",
       "      <td>manual</td>\n",
       "      <td>golf</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no</td>\n",
       "      <td>03/04/2016 00:00</td>\n",
       "      <td>07/04/2016 07:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>66</td>\n",
       "      <td>91457</td>\n",
       "      <td>268251</td>\n",
       "      <td>29232</td>\n",
       "      <td>216352</td>\n",
       "      <td>77013</td>\n",
       "      <td>247161</td>\n",
       "      <td>13719</td>\n",
       "      <td>654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             DateCrawled VehicleType Gearbox   Model FuelType       Brand  \\\n",
       "count             354369      316879  334536  334664   321474      354369   \n",
       "unique             15470           8       2     250        7          40   \n",
       "top     05/03/2016 14:25       sedan  manual    golf   petrol  volkswagen   \n",
       "freq                  66       91457  268251   29232   216352       77013   \n",
       "\n",
       "       NotRepaired       DateCreated          LastSeen  \n",
       "count       283215            354369            354369  \n",
       "unique           2               109             18592  \n",
       "top             no  03/04/2016 00:00  07/04/2016 07:16  \n",
       "freq        247161             13719               654  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Columns with nulls:\n",
      "Column VehicleType has 10.5794% percent of Nulls, and 37490 of nulls\n",
      "Column Gearbox has 5.5967% percent of Nulls, and 19833 of nulls\n",
      "Column Model has 5.5606% percent of Nulls, and 19705 of nulls\n",
      "Column FuelType has 9.2827% percent of Nulls, and 32895 of nulls\n",
      "Column NotRepaired has 20.0791% percent of Nulls, and 71154 of nulls\n",
      "\u001b[1mThere are 5 columns with NA.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Shape:\n",
      "(354369, 16)\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Duplicated:\n",
      "\u001b[1mWe have 262 duplicated rows.\n",
      "\u001b[0m\n",
      "\n"
     ]
    }
   ],
   "source": [
    "get_info(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary \n",
    "Looking over the data I can see that the data have 354369 rows 16 collumns. 5 of the collumns contain NaN values, because these columns will be utilized in our model building process I will find the best way to fill them without compromising the integrity of the data. There are also 262 duplicate rows, column names which are in capital letters, and data types which should be changed to more appropriate types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment</b>\n",
    "\n",
    "The data was loaded and inspected!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Column names\n",
    "I will change the column names to snake case. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={'DateCrawled': 'date_crawled', 'Price': 'price', 'VehicleType': 'vehicle_type', 'RegistrationYear': 'registration_year', 'Gearbox': 'gearbox',\n",
    "       'Power': 'power', 'Model': 'model', 'Mileage': 'mileage', 'RegistrationMonth': 'registration_month', 'FuelType': 'fuel_type', 'Brand': 'brand',\n",
    "       'NotRepaired': 'not_repaired', 'DateCreated': 'date_created', 'NumberOfPictures': 'number_of_pictures', 'PostalCode': 'postal_code', 'LastSeen': 'last_seen'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment</b>\n",
    "\n",
    "Nice!\n",
    "    \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Missing Values\n",
    "I will inspect the details of the missing values to help me assess and choose the best way to fill them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_values_table(df):\n",
    "       \n",
    "        mis_val = df.isnull().sum()\n",
    "        \n",
    "        \n",
    "        mis_val_percent = 100 * df.isnull().sum() / len(df)\n",
    "        \n",
    "    \n",
    "        mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n",
    "        \n",
    "        \n",
    "        mis_val_table_ren_columns = mis_val_table.rename(\n",
    "        columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n",
    "        \n",
    "        \n",
    "        mis_val_table_ren_columns = mis_val_table_ren_columns[\n",
    "            mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(\n",
    "        '% of Total Values', ascending=False).round(1)\n",
    "        \n",
    "        \n",
    "        print (\"Your selected dataframe has \" + str(df.shape[1]) + \" columns.\\n\"      \n",
    "            \"There are \" + str(mis_val_table_ren_columns.shape[0]) +\n",
    "              \" columns that have missing values.\")\n",
    "        \n",
    "        \n",
    "        return mis_val_table_ren_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 16 columns.\n",
      "There are 5 columns that have missing values.\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>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>not_repaired</th>\n",
       "      <td>71154</td>\n",
       "      <td>20.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicle_type</th>\n",
       "      <td>37490</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel_type</th>\n",
       "      <td>32895</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gearbox</th>\n",
       "      <td>19833</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>19705</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Missing Values  % of Total Values\n",
       "not_repaired           71154               20.1\n",
       "vehicle_type           37490               10.6\n",
       "fuel_type              32895                9.3\n",
       "gearbox                19833                5.6\n",
       "model                  19705                5.6"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_table(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The column 'not_repaired' contains 20% NaN. This is significant. I believe it is a default answer. i.e. when the entry is made if it is 'not_repaired' the entry was left blank instead of entered as 'Yes'. I could go off of this assumption but because I cannot say for sure, I will fill the NaN in this case with a new option 'unknown'. For the rest I will fill using a function based on the model. I will fill the model based off of the brand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_missing_value(unique_value_feature, col_to_replace):  \n",
    "    \"\"\"\n",
    "    This function is used to replace missing value in a column\n",
    "    using possible unique value feature for those missing values    \n",
    "    \"\"\"\n",
    "    # get unique value of feature from this dataframe\n",
    "    for value in df[unique_value_feature].unique().tolist():\n",
    "        # get specific value of features using possible value from column value to replace\n",
    "        specific_value_df = df[df[unique_value_feature] == value].dropna()[col_to_replace]    \n",
    "        value_replacement_list = specific_value_df.unique().tolist()\n",
    "        # for the missing value, assign a random choice of the replacement value for that unknown value\n",
    "        if value_replacement_list != []:\n",
    "            df.loc[(df[unique_value_feature] == value) & (df[col_to_replace] != df[col_to_replace]), col_to_replace] = random.choice(value_replacement_list)\n",
    "        else:\n",
    "            df.loc[(df[unique_value_feature] == value) & (df[col_to_replace] != df[col_to_replace]), col_to_replace] = df[col_to_replace].mode() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_missing_value('brand', 'model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_missing_value('model', 'vehicle_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_missing_value('model', 'fuel_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_missing_value('model', 'gearbox')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no         247161\n",
       "unknown     71154\n",
       "yes         36054\n",
       "Name: not_repaired, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['not_repaired'].fillna('unknown', inplace=True)\n",
    "df.not_repaired.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 16 columns.\n",
      "There are 4 columns that have missing values.\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>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>3374</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gearbox</th>\n",
       "      <td>1104</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel_type</th>\n",
       "      <td>1104</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicle_type</th>\n",
       "      <td>1042</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Missing Values  % of Total Values\n",
       "model                   3374                1.0\n",
       "gearbox                 1104                0.3\n",
       "fuel_type               1104                0.3\n",
       "vehicle_type            1042                0.3"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_table(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for the remaining NaN I have the option to drop them but I believe it will be better to replace the 'NaN' with the entry 'unknown'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(value='unknown', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 16 columns.\n",
      "There are 0 columns that have missing values.\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>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Missing Values, % of Total Values]\n",
       "Index: []"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_table(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment</b>\n",
    "\n",
    "Missing values were dealt with reasonably!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with the Duplicates\n",
    "Next I will drop the duplicated rows from the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>registration_year</th>\n",
       "      <th>power</th>\n",
       "      <th>mileage</th>\n",
       "      <th>registration_month</th>\n",
       "      <th>number_of_pictures</th>\n",
       "      <th>postal_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>354102.000000</td>\n",
       "      <td>354102.000000</td>\n",
       "      <td>354102.000000</td>\n",
       "      <td>354102.000000</td>\n",
       "      <td>354102.000000</td>\n",
       "      <td>354102.0</td>\n",
       "      <td>354102.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4416.417202</td>\n",
       "      <td>2004.235345</td>\n",
       "      <td>110.089596</td>\n",
       "      <td>128211.984118</td>\n",
       "      <td>5.714139</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50507.118486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4514.333007</td>\n",
       "      <td>90.261803</td>\n",
       "      <td>189.916207</td>\n",
       "      <td>37906.730492</td>\n",
       "      <td>3.726660</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25784.252508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1067.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1050.000000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>125000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30165.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49406.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6400.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71083.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20000.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99998.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               price  registration_year          power        mileage  \\\n",
       "count  354102.000000      354102.000000  354102.000000  354102.000000   \n",
       "mean     4416.417202        2004.235345     110.089596  128211.984118   \n",
       "std      4514.333007          90.261803     189.916207   37906.730492   \n",
       "min         0.000000        1000.000000       0.000000    5000.000000   \n",
       "25%      1050.000000        1999.000000      69.000000  125000.000000   \n",
       "50%      2700.000000        2003.000000     105.000000  150000.000000   \n",
       "75%      6400.000000        2008.000000     143.000000  150000.000000   \n",
       "max     20000.000000        9999.000000   20000.000000  150000.000000   \n",
       "\n",
       "       registration_month  number_of_pictures    postal_code  \n",
       "count       354102.000000            354102.0  354102.000000  \n",
       "mean             5.714139                 0.0   50507.118486  \n",
       "std              3.726660                 0.0   25784.252508  \n",
       "min              0.000000                 0.0    1067.000000  \n",
       "25%              3.000000                 0.0   30165.000000  \n",
       "50%              6.000000                 0.0   49406.000000  \n",
       "75%              9.000000                 0.0   71083.000000  \n",
       "max             12.000000                 0.0   99998.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment</b>\n",
    "\n",
    "Good!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Changing the Datatypes\n",
    "I will adjust the datatypes of the date containing columns to 'datetime64[ns]' and the integer containing columns to int32 to decrease memory usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_datatype(df, cols, type_val):\n",
    "    for col in cols:\n",
    "        df[col] = df[col].astype(type_val)\n",
    "        \n",
    "change_datatype(df, ['date_crawled', 'date_created', 'last_seen'], 'datetime64[ns]')\n",
    "change_datatype(df, ['price', 'registration_year', 'power', 'registration_month', 'number_of_pictures'], 'int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 354102 entries, 0 to 354368\n",
      "Data columns (total 16 columns):\n",
      " #   Column              Non-Null Count   Dtype         \n",
      "---  ------              --------------   -----         \n",
      " 0   date_crawled        354102 non-null  datetime64[ns]\n",
      " 1   price               354102 non-null  int32         \n",
      " 2   vehicle_type        354102 non-null  object        \n",
      " 3   registration_year   354102 non-null  int32         \n",
      " 4   gearbox             354102 non-null  object        \n",
      " 5   power               354102 non-null  int32         \n",
      " 6   model               354102 non-null  object        \n",
      " 7   mileage             354102 non-null  int64         \n",
      " 8   registration_month  354102 non-null  int32         \n",
      " 9   fuel_type           354102 non-null  object        \n",
      " 10  brand               354102 non-null  object        \n",
      " 11  not_repaired        354102 non-null  object        \n",
      " 12  date_created        354102 non-null  datetime64[ns]\n",
      " 13  number_of_pictures  354102 non-null  int32         \n",
      " 14  postal_code         354102 non-null  int64         \n",
      " 15  last_seen           354102 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](3), int32(5), int64(2), object(6)\n",
      "memory usage: 39.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Engineering the Feature:Age\n",
    "I will create this feature to help me detect outliers so I can clean up my dataset of any data that may squew the modeling process downstream."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment</b>\n",
    "\n",
    "Nice feature!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>date_crawled</th>\n",
       "      <th>price</th>\n",
       "      <th>vehicle_type</th>\n",
       "      <th>registration_year</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>power</th>\n",
       "      <th>model</th>\n",
       "      <th>mileage</th>\n",
       "      <th>registration_month</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>brand</th>\n",
       "      <th>not_repaired</th>\n",
       "      <th>date_created</th>\n",
       "      <th>number_of_pictures</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>last_seen</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-03-24 11:52:00</td>\n",
       "      <td>480</td>\n",
       "      <td>wagon</td>\n",
       "      <td>1993</td>\n",
       "      <td>manual</td>\n",
       "      <td>0</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>unknown</td>\n",
       "      <td>2016-03-24</td>\n",
       "      <td>0</td>\n",
       "      <td>70435</td>\n",
       "      <td>2016-07-04 03:16:00</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-03-24 10:58:00</td>\n",
       "      <td>18300</td>\n",
       "      <td>coupe</td>\n",
       "      <td>2011</td>\n",
       "      <td>manual</td>\n",
       "      <td>190</td>\n",
       "      <td>80</td>\n",
       "      <td>125000</td>\n",
       "      <td>5</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>audi</td>\n",
       "      <td>yes</td>\n",
       "      <td>2016-03-24</td>\n",
       "      <td>0</td>\n",
       "      <td>66954</td>\n",
       "      <td>2016-07-04 01:46:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-14 12:52:00</td>\n",
       "      <td>9800</td>\n",
       "      <td>suv</td>\n",
       "      <td>2004</td>\n",
       "      <td>auto</td>\n",
       "      <td>163</td>\n",
       "      <td>grand</td>\n",
       "      <td>125000</td>\n",
       "      <td>8</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>jeep</td>\n",
       "      <td>unknown</td>\n",
       "      <td>2016-03-14</td>\n",
       "      <td>0</td>\n",
       "      <td>90480</td>\n",
       "      <td>2016-05-04 12:47:00</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-17 16:54:00</td>\n",
       "      <td>1500</td>\n",
       "      <td>small</td>\n",
       "      <td>2001</td>\n",
       "      <td>manual</td>\n",
       "      <td>75</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no</td>\n",
       "      <td>2016-03-17</td>\n",
       "      <td>0</td>\n",
       "      <td>91074</td>\n",
       "      <td>2016-03-17 17:40:00</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-03-31 17:25:00</td>\n",
       "      <td>3600</td>\n",
       "      <td>small</td>\n",
       "      <td>2008</td>\n",
       "      <td>manual</td>\n",
       "      <td>69</td>\n",
       "      <td>fabia</td>\n",
       "      <td>90000</td>\n",
       "      <td>7</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>skoda</td>\n",
       "      <td>no</td>\n",
       "      <td>2016-03-31</td>\n",
       "      <td>0</td>\n",
       "      <td>60437</td>\n",
       "      <td>2016-06-04 10:17:00</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date_crawled  price vehicle_type  registration_year gearbox  power  \\\n",
       "0 2016-03-24 11:52:00    480        wagon               1993  manual      0   \n",
       "1 2016-03-24 10:58:00  18300        coupe               2011  manual    190   \n",
       "2 2016-03-14 12:52:00   9800          suv               2004    auto    163   \n",
       "3 2016-03-17 16:54:00   1500        small               2001  manual     75   \n",
       "4 2016-03-31 17:25:00   3600        small               2008  manual     69   \n",
       "\n",
       "   model  mileage  registration_month fuel_type       brand not_repaired  \\\n",
       "0   golf   150000                   0    petrol  volkswagen      unknown   \n",
       "1     80   125000                   5  gasoline        audi          yes   \n",
       "2  grand   125000                   8  gasoline        jeep      unknown   \n",
       "3   golf   150000                   6    petrol  volkswagen           no   \n",
       "4  fabia    90000                   7  gasoline       skoda           no   \n",
       "\n",
       "  date_created  number_of_pictures  postal_code           last_seen  age  \n",
       "0   2016-03-24                   0        70435 2016-07-04 03:16:00   23  \n",
       "1   2016-03-24                   0        66954 2016-07-04 01:46:00    5  \n",
       "2   2016-03-14                   0        90480 2016-05-04 12:47:00   12  \n",
       "3   2016-03-17                   0        91074 2016-03-17 17:40:00   15  \n",
       "4   2016-03-31                   0        60437 2016-06-04 10:17:00    8  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'] = df['date_created'].dt.year - df['registration_year']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['price', 'power', 'mileage', 'age']].hist(bins=30, figsize=(15, 10))\n",
    "plt.suptitle('Histogram of selected parameters', y=0.95);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These histograms show me that most cars are sold at the 0-7500 euro price range. Now lets remove any outliers. I will remove any vehilcle with horsepower less than 2039 because anything below that does not actually exist, same with vehicles with ages in the negative numbers. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the new dataset is: (339475, 17)\n",
      "\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>price</th>\n",
       "      <th>registration_year</th>\n",
       "      <th>power</th>\n",
       "      <th>mileage</th>\n",
       "      <th>registration_month</th>\n",
       "      <th>number_of_pictures</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>339475.000000</td>\n",
       "      <td>339475.000000</td>\n",
       "      <td>339475.000000</td>\n",
       "      <td>339475.000000</td>\n",
       "      <td>339475.000000</td>\n",
       "      <td>339475.0</td>\n",
       "      <td>339475.000000</td>\n",
       "      <td>339475.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4470.825109</td>\n",
       "      <td>2002.322619</td>\n",
       "      <td>108.436291</td>\n",
       "      <td>128066.440828</td>\n",
       "      <td>5.754798</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50604.676147</td>\n",
       "      <td>13.677302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4546.363192</td>\n",
       "      <td>13.952596</td>\n",
       "      <td>72.160321</td>\n",
       "      <td>37926.774105</td>\n",
       "      <td>3.708757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25808.139053</td>\n",
       "      <td>13.952606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1099.000000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>125000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30169.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2799.000000</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49492.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6500.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71229.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20000.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99998.000000</td>\n",
       "      <td>1016.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               price  registration_year          power        mileage  \\\n",
       "count  339475.000000      339475.000000  339475.000000  339475.000000   \n",
       "mean     4470.825109        2002.322619     108.436291  128066.440828   \n",
       "std      4546.363192          13.952596      72.160321   37926.774105   \n",
       "min         0.000000        1000.000000       0.000000    5000.000000   \n",
       "25%      1099.000000        1999.000000      69.000000  125000.000000   \n",
       "50%      2799.000000        2003.000000     105.000000  150000.000000   \n",
       "75%      6500.000000        2007.000000     143.000000  150000.000000   \n",
       "max     20000.000000        2016.000000    2018.000000  150000.000000   \n",
       "\n",
       "       registration_month  number_of_pictures    postal_code            age  \n",
       "count       339475.000000            339475.0  339475.000000  339475.000000  \n",
       "mean             5.754798                 0.0   50604.676147      13.677302  \n",
       "std              3.708757                 0.0   25808.139053      13.952606  \n",
       "min              0.000000                 0.0    1067.000000       0.000000  \n",
       "25%              3.000000                 0.0   30169.000000       9.000000  \n",
       "50%              6.000000                 0.0   49492.000000      13.000000  \n",
       "75%              9.000000                 0.0   71229.000000      17.000000  \n",
       "max             12.000000                 0.0   99998.000000    1016.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered = df[(df['power'] <= 2039) & (df['age'] >= 0)]\n",
    "print(f'The shape of the new dataset is: {df_filtered.shape}')\n",
    "print()\n",
    "df_filtered.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "the dataset is now cleaned up. NaN values were dealt with, column names were made uniform, data types were corrected, and outliers were removed. The next step is model training. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment</b>\n",
    "\n",
    "Very good!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model training\n",
    "First we will create dataset removing the unnecessary columns. Then I will make the training test and validation sets, as well as encode the categorical features using OHE and check over the data before we get started.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = ['vehicle_type', 'gearbox', 'model', 'fuel_type', 'brand', 'not_repaired']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_df = df_filtered.drop(['date_crawled', 'registration_year', 'registration_month','date_created', 'number_of_pictures', 'postal_code', 'last_seen'], axis=1)\n",
    "\n",
    "features = car_df.drop(['price'],axis=1)\n",
    "\n",
    "target = car_df.price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment</b>\n",
    "\n",
    "The list of dropped columns makes sense\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 339475 entries, 0 to 354368\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count   Dtype \n",
      "---  ------        --------------   ----- \n",
      " 0   vehicle_type  339475 non-null  object\n",
      " 1   gearbox       339475 non-null  object\n",
      " 2   power         339475 non-null  int32 \n",
      " 3   model         339475 non-null  object\n",
      " 4   mileage       339475 non-null  int64 \n",
      " 5   fuel_type     339475 non-null  object\n",
      " 6   brand         339475 non-null  object\n",
      " 7   not_repaired  339475 non-null  object\n",
      " 8   age           339475 non-null  int64 \n",
      "dtypes: int32(1), int64(2), object(6)\n",
      "memory usage: 24.6+ MB\n"
     ]
    }
   ],
   "source": [
    "features = features.fillna('NaN')\n",
    "\n",
    "features.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features_train_ohe size: 254606\n",
      "features_test_ohe size: 10609\n",
      "features_valid_ohe size: 74260\n",
      "target_train_ohe size: 254606\n",
      "target_test_ohe size: 10609\n",
      "target_valid_ohe size: 74260\n",
      "\n",
      "features_train size: 254606\n",
      "features_test size: 10609\n",
      "features_valid size: 74260\n",
      "target_train size: 254606\n",
      "target_test size: 10609\n",
      "target_valid size: 74260\n"
     ]
    }
   ],
   "source": [
    "ohe = pd.get_dummies(data = features, columns = categorical, drop_first=True)\n",
    "\n",
    "features_train_ohe, features_test_ohe, target_train_ohe, target_test_ohe = train_test_split(ohe, target, test_size=.25, random_state=12345)\n",
    "features_valid_ohe,features_test_ohe,target_valid_ohe, target_test_ohe=train_test_split(features_test_ohe,target_test_ohe, test_size=.125, random_state=12345)\n",
    "\n",
    "features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=.25, random_state=12345)\n",
    "features_valid, features_test, target_valid, target_test= train_test_split(features_test, target_test, test_size=.125, random_state=12345)\n",
    "\n",
    "print('features_train_ohe size:', features_train_ohe.shape[0])\n",
    "print('features_test_ohe size:', features_test_ohe.shape[0])\n",
    "print('features_valid_ohe size:', features_valid_ohe.shape[0])\n",
    "print('target_train_ohe size:', target_train_ohe.shape[0])\n",
    "print('target_test_ohe size:', target_test_ohe.shape[0])\n",
    "print('target_valid_ohe size:', target_valid_ohe.shape[0])\n",
    "\n",
    "print()\n",
    "print('features_train size:', features_train.shape[0])\n",
    "print('features_test size:', features_test.shape[0])\n",
    "print('features_valid size:', features_valid.shape[0])\n",
    "print('target_train size:', target_train.shape[0])\n",
    "print('target_test size:', target_test.shape[0])\n",
    "print('target_valid size:', target_valid.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment</b>\n",
    "\n",
    "Categorical features were encoded. The data was split into train, validation and test sets\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scale numerics\n",
    "numerics = ['power','mileage']\n",
    "#scale\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(features_train_ohe[numerics])\n",
    "\n",
    "features_train[numerics] = scaler.transform(features_train[numerics])\n",
    "features_valid[numerics] = scaler.transform(features_valid[numerics])\n",
    "features_test[numerics] = scaler.transform(features_test[numerics])\n",
    "\n",
    "features_train_ohe[numerics] = scaler.transform(features_train_ohe[numerics])\n",
    "features_valid_ohe[numerics] = scaler.transform(features_valid_ohe[numerics])\n",
    "features_test_ohe[numerics] = scaler.transform(features_test_ohe[numerics])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "<s><b>Reviewer's comment</b>\n",
    "\n",
    "Looks like your forgot to scale the validation set\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment V2</b>\n",
    "\n",
    "Fixed!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [model, fit_time, score_time, RMSE]\n",
       "Index: []"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#new dataframe to for the model analysis\n",
    "model_data = pd.DataFrame(columns=('model','fit_time','score_time','RMSE'))\n",
    "model_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression for Sanity Check\n",
    "The first model to test is linear regression as our sanity check. This is a way to indicate if our models are actually improving the outcomes or not. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmse(y_true, y_predict):\n",
    "    mse = mean_squared_error(y_true, y_predict)\n",
    "    return mse**.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>model</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>24.688118</td>\n",
       "      <td>0.1372</td>\n",
       "      <td>3073.533226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model   fit_time  score_time         RMSE\n",
       "0  Linear Regression  24.688118      0.1372  3073.533226"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create model\n",
    "model = LinearRegression()\n",
    "\n",
    "#create dataframe row\n",
    "lr_data = ['Linear Regression']\n",
    "\n",
    "#fit model and save time\n",
    "start = time.time()\n",
    "model.fit(features_train_ohe, target_train_ohe)\n",
    "end=time.time()\n",
    "\n",
    "#add time to row\n",
    "lr_data.append(end-start)\n",
    "\n",
    "#predict and save time\n",
    "start = time.time()\n",
    "prediction = model.predict(features_valid_ohe)\n",
    "end=time.time()\n",
    "\n",
    "#add time to row\n",
    "lr_data.append(end-start)\n",
    "\n",
    "#calculate RMSE\n",
    "lr_rmse = rmse(target_valid_ohe, prediction)\n",
    "\n",
    "#add to row\n",
    "lr_data.append(lr_rmse)\n",
    "\n",
    "model_data.loc[len(model_data)] = lr_data\n",
    "model_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "though the model and scoring times are quick the RMSE of 3068 gives us room for improvement "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "<s><b>Reviewer's comment</b>\n",
    "\n",
    "This seems like a result of different scales of train and validation sets\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment V2</b>\n",
    "\n",
    "Great!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Regressor\n",
    "Lets try a tree based model and see if we can get a better score. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>24.688118</td>\n",
       "      <td>0.137200</td>\n",
       "      <td>3073.533226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>52.560357</td>\n",
       "      <td>0.497972</td>\n",
       "      <td>1770.515887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model   fit_time  score_time         RMSE\n",
       "0  Linear Regression  24.688118    0.137200  3073.533226\n",
       "1      Random Forest  52.560357    0.497972  1770.515887"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create model\n",
    "rf_model = RandomForestRegressor(random_state=12345, n_estimators=10)\n",
    "\n",
    "#create dataframe row\n",
    "rf_data = ['Random Forest']\n",
    "\n",
    "#fit model and save time\n",
    "start = time.time()\n",
    "rf_model.fit(features_train_ohe, target_train_ohe)\n",
    "end=time.time()\n",
    "\n",
    "#add time to row\n",
    "rf_data.append(end-start)\n",
    "\n",
    "#predict and save time\n",
    "start = time.time()\n",
    "prediction = rf_model.predict(features_valid_ohe)\n",
    "end=time.time()\n",
    "\n",
    "#add time to row\n",
    "rf_data.append(end-start)\n",
    "\n",
    "#calculate RMSE\n",
    "rf_rmse = rmse(target_valid_ohe, prediction)\n",
    "\n",
    "#add to row\n",
    "rf_data.append(rf_rmse)\n",
    "\n",
    "model_data.loc[len(model_data)] = rf_data\n",
    "model_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "<s><b>Reviewer's comment</b>\n",
    "\n",
    "If you want to do cross-validation, that's great, but please make sure that:\n",
    "    \n",
    "1. The models are evaluated consistently: either use cross-validation for all models or use the validation set for all models\n",
    "2. Cross-validation should not be done on the whole dataset: we still need a separate test set to evaluate the final model for an unbiased estimate of its generalization performance. So the correct way is to do cross-validation on the train set. This applies to all instances of cross-validation in this project, not just the above.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment V2</b>\n",
    "\n",
    "Alright, both problems were fixed!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RMSE=1782 that is an improvement. Next we will try with hyperparameter tuning. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Hyperparameter Tuning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best hyperparameters are: {'max_depth': None, 'min_samples_leaf': 2, 'n_estimators': 50}\n"
     ]
    }
   ],
   "source": [
    "grid = {\n",
    "    \"n_estimators\" : [10, 25, 50],\n",
    "    \"max_depth\" : [None, 2, 4, 8],\n",
    "    \"min_samples_leaf\" : [2, 4, 6]\n",
    "}\n",
    "# define the model \n",
    "regressor = RandomForestRegressor(random_state = 12345)\n",
    "# define the grid search\n",
    "grid_search_rf = GridSearchCV(estimator = regressor, param_grid = grid, scoring=\"neg_mean_squared_error\", cv=3)\n",
    "# execute search\n",
    "grid_search_rf.fit(X=features_train_ohe, y=target_train_ohe)\n",
    "# summarize result\n",
    "print('The best hyperparameters are: {}'.format(grid_search_rf.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>RMSE</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>24.688118</td>\n",
       "      <td>0.137200</td>\n",
       "      <td>3073.533226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>52.560357</td>\n",
       "      <td>0.497972</td>\n",
       "      <td>1770.515887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest with Hyperparameters</td>\n",
       "      <td>238.175680</td>\n",
       "      <td>1.736716</td>\n",
       "      <td>1718.868541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                model    fit_time  score_time         RMSE\n",
       "0                   Linear Regression   24.688118    0.137200  3073.533226\n",
       "1                       Random Forest   52.560357    0.497972  1770.515887\n",
       "2  Random Forest with Hyperparameters  238.175680    1.736716  1718.868541"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create model\n",
    "rf_ht_model = RandomForestRegressor(**grid_search_rf.best_params_)\n",
    "\n",
    "#create dataframe row\n",
    "rf_ht_data = ['Random Forest with Hyperparameters']\n",
    "\n",
    "#fit model and save time\n",
    "start = time.time()\n",
    "rf_ht_model.fit(features_train_ohe, target_train_ohe)\n",
    "end=time.time()\n",
    "\n",
    "#add time to row\n",
    "rf_ht_data.append(end-start)\n",
    "\n",
    "#predict and save time\n",
    "start = time.time()\n",
    "prediction = rf_ht_model.predict(features_valid_ohe)\n",
    "end=time.time()\n",
    "\n",
    "#add time to row\n",
    "rf_ht_data.append(end-start)\n",
    "\n",
    "#calculate RMSE\n",
    "rf_ht_rmse = rmse(target_valid_ohe, prediction)\n",
    "\n",
    "#add to row\n",
    "rf_ht_data.append(rf_ht_rmse)\n",
    "\n",
    "model_data.loc[len(model_data)] = rf_ht_data\n",
    "model_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a small improvement but a large increase in time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "for column in categorical:\n",
    "    features_train[column] = features_train[column].astype('category')\n",
    "    features_valid[column] = features_valid[column].astype('category')\n",
    "    features_test[column] = features_test[column].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.5, 'max_depth': -1, 'num_leaves': 31} -3060408.7441985123\n",
      "The best hyperparameters are: {'learning_rate': 0.5, 'max_depth': -1, 'num_leaves': 31}\n"
     ]
    }
   ],
   "source": [
    "lgb = LGBMRegressor()\n",
    "\n",
    "#create parameters for feature tuning\n",
    "param = {\n",
    "        'num_leaves': [10, 31],\n",
    "    'learning_rate': [.01,.5],\n",
    "    'max_depth': [-1,5]\n",
    "}\n",
    "\n",
    "#pass parameters necessary for cross validation\n",
    "gs = GridSearchCV(estimator=lgb,param_grid=param,scoring='neg_mean_squared_error')\n",
    "\n",
    "#fit model\n",
    "start = time.time()\n",
    "lgb_model = gs.fit(X=features_train, y=target_train)\n",
    "end=time.time()\n",
    "\n",
    "print(lgb_model.best_params_, lgb_model.best_score_)\n",
    "print('The best hyperparameters are: {}'.format(gs.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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",
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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>model</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>24.688118</td>\n",
       "      <td>0.137200</td>\n",
       "      <td>3073.533226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>52.560357</td>\n",
       "      <td>0.497972</td>\n",
       "      <td>1770.515887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest with Hyperparameters</td>\n",
       "      <td>238.175680</td>\n",
       "      <td>1.736716</td>\n",
       "      <td>1718.868541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>484.066604</td>\n",
       "      <td>0.592504</td>\n",
       "      <td>1734.854643</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                model    fit_time  score_time         RMSE\n",
       "0                   Linear Regression   24.688118    0.137200  3073.533226\n",
       "1                       Random Forest   52.560357    0.497972  1770.515887\n",
       "2  Random Forest with Hyperparameters  238.175680    1.736716  1718.868541\n",
       "3                            LightGBM  484.066604    0.592504  1734.854643"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#create dataframe row\n",
    "lgb_data = ['LightGBM']\n",
    "\n",
    "#add time to row\n",
    "lgb_data.append(end-start)\n",
    "\n",
    "#predict and save time\n",
    "start = time.time()\n",
    "prediction = lgb_model.predict(features_valid)\n",
    "end=time.time()\n",
    "\n",
    "#add time to row\n",
    "lgb_data.append(end-start)\n",
    "\n",
    "#calculate RMSE\n",
    "lgb_rmse = rmse(target_valid, prediction)\n",
    "\n",
    "#add to row\n",
    "lgb_data.append(lgb_rmse)\n",
    "\n",
    "model_data.loc[len(model_data)] = lgb_data\n",
    "model_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CatBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cat_features = [\n",
    "    'vehicle_type', \n",
    "    'gearbox', \n",
    "    'model', \n",
    "    'fuel_type', \n",
    "    'brand', \n",
    "    'not_repaired',\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>model</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>24.688118</td>\n",
       "      <td>0.137200</td>\n",
       "      <td>3073.533226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>52.560357</td>\n",
       "      <td>0.497972</td>\n",
       "      <td>1770.515887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest with Hyperparameters</td>\n",
       "      <td>238.175680</td>\n",
       "      <td>1.736716</td>\n",
       "      <td>1718.868541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>484.066604</td>\n",
       "      <td>0.592504</td>\n",
       "      <td>1734.854643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CatBoost Regressor</td>\n",
       "      <td>554.359254</td>\n",
       "      <td>0.866123</td>\n",
       "      <td>1725.521247</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                model    fit_time  score_time         RMSE\n",
       "0                   Linear Regression   24.688118    0.137200  3073.533226\n",
       "1                       Random Forest   52.560357    0.497972  1770.515887\n",
       "2  Random Forest with Hyperparameters  238.175680    1.736716  1718.868541\n",
       "3                            LightGBM  484.066604    0.592504  1734.854643\n",
       "4                  CatBoost Regressor  554.359254    0.866123  1725.521247"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create model\n",
    "cb_model = CatBoostRegressor(\n",
    "    cat_features=cat_features,\n",
    "    random_state = 12345)\n",
    "\n",
    "#create dataframe row\n",
    "cb_data = ['CatBoost Regressor']\n",
    "\n",
    "#fit model and save time\n",
    "start = time.time()\n",
    "cb_model.fit(features_train, target_train, cat_features=cat_features,\n",
    "             eval_set=(features_valid, target_valid), verbose=False, plot=False)\n",
    "end=time.time()\n",
    "\n",
    "#add time to row\n",
    "cb_data.append(end-start)\n",
    "\n",
    "#predict and save time\n",
    "start = time.time()\n",
    "prediction = cb_model.predict(features_valid)\n",
    "end=time.time()\n",
    "\n",
    "#add time to row\n",
    "cb_data.append(end-start)\n",
    "\n",
    "#calculate RMSE\n",
    "cb_rmse = rmse(target_valid, prediction)\n",
    "\n",
    "#add to row\n",
    "cb_data.append(cb_rmse)\n",
    "\n",
    "model_data.loc[len(model_data)] = cb_data\n",
    "model_data.head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "<s><b>Reviewer's comment</b>\n",
    "\n",
    "1. Please use the same split as for other models\n",
    "2. The test set should not be used as the eval_set for early stopping (you can use the validation set instead)\n",
    "3. The model should not be evaluated on the test set unless it is the final model (or we won't be able to get an unbiased estimate of the final model's generalization performance)\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment V2</b>\n",
    "\n",
    "Very good!\n",
    "    \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "             gamma=0, gpu_id=-1, importance_type=None,\n",
       "             interaction_constraints='', learning_rate=0.300000012,\n",
       "             max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints='()', n_estimators=100, n_jobs=4,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=12345,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model = XGBRegressor(random_state = 12345)\n",
    "xgb_model.fit(features_train_ohe, target_train_ohe) \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>model</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>24.688118</td>\n",
       "      <td>0.137200</td>\n",
       "      <td>3073.533226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>52.560357</td>\n",
       "      <td>0.497972</td>\n",
       "      <td>1770.515887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest with Hyperparameters</td>\n",
       "      <td>238.175680</td>\n",
       "      <td>1.736716</td>\n",
       "      <td>1718.868541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>484.066604</td>\n",
       "      <td>0.592504</td>\n",
       "      <td>1734.854643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CatBoost Regressor</td>\n",
       "      <td>554.359254</td>\n",
       "      <td>0.866123</td>\n",
       "      <td>1725.521247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>477.900700</td>\n",
       "      <td>1.182771</td>\n",
       "      <td>1767.409857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                model    fit_time  score_time         RMSE\n",
       "0                   Linear Regression   24.688118    0.137200  3073.533226\n",
       "1                       Random Forest   52.560357    0.497972  1770.515887\n",
       "2  Random Forest with Hyperparameters  238.175680    1.736716  1718.868541\n",
       "3                            LightGBM  484.066604    0.592504  1734.854643\n",
       "4                  CatBoost Regressor  554.359254    0.866123  1725.521247\n",
       "5                             XGBoost  477.900700    1.182771  1767.409857"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create model\n",
    "xgb_model = XGBRegressor(random_state = 12345)\n",
    "\n",
    "#create dataframe row\n",
    "xgb_data = ['XGBoost']\n",
    "\n",
    "#fit model and save time\n",
    "start = time.time()\n",
    "xgb_model.fit(features_train_ohe, target_train_ohe)\n",
    "end=time.time()\n",
    "\n",
    "#add time to row\n",
    "xgb_data.append(end-start)\n",
    "\n",
    "#predict and save time\n",
    "start = time.time()\n",
    "prediction = xgb_model.predict(features_valid_ohe)\n",
    "end=time.time()\n",
    "\n",
    "#add time to row\n",
    "xgb_data.append(end-start)\n",
    "\n",
    "#calculate RMSE\n",
    "xgb_rmse = rmse(target_valid_ohe, prediction)\n",
    "\n",
    "#add to row\n",
    "xgb_data.append(xgb_rmse)\n",
    "\n",
    "model_data.loc[len(model_data)] = xgb_data\n",
    "model_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_data = model_data.drop_duplicates()\n",
    "model_data.plot.bar(x='model',y='fit_time')\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_data.plot.bar(x='model',y='score_time')\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_data.plot.bar(x='model',y='RMSE')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightGBM Final Test scores:  score_time:  0.07635188102722168 RMSE:  1731.8916762864062\n"
     ]
    }
   ],
   "source": [
    "lgb_data_final = ['LightGBM Final Test']\n",
    "\n",
    "#predict and save time\n",
    "start = time.time()\n",
    "prediction = lgb_model.predict(features_test)\n",
    "end=time.time()\n",
    "\n",
    "#add time to row\n",
    "lgb_data_final.append(end-start)\n",
    "\n",
    "#calculate RMSE\n",
    "lgb_rmse_final = rmse(target_test, prediction)\n",
    "\n",
    "#add to row\n",
    "lgb_data_final.append(lgb_rmse_final)\n",
    "\n",
    "print('LightGBM Final Test scores: ', 'score_time: ', end-start, 'RMSE: ', lgb_rmse_final)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "Our client, Rusty Bargain, has tasked us with finding a model that can optimize time in training and prediction, and accuracy of results. After testing 6 different models I have some data that I think is useful. The sanity check linear regression returned an extremly high RMSE. The tree based algorithm showed a dramatic improvement in scoreing which improved minorly with hyperparameter tuning. The gradient boosting model, LightGBM, was used with hyperparameter tuning. The RMSE was not better than that of the other models but the training and prediction times were significantly reduced. I recommend Rusty Bargain move forward with LightGBM as their model for predicting the price of customer used cars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment</b>\n",
    "\n",
    "It's great that you tried different models, tuned their hyperparameters using cross-validation and took both RMSE and fit/predict times when analyzing the models' performance\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "<s><b>Reviewer's comment</b>\n",
    "\n",
    "Don't forget to evaluate the final selected model on the test set for an unbiased estimate of its generalization performance\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment V2</b>\n",
    "\n",
    "Excellent!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checklist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Type 'x' to check. Then press Shift+Enter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [x]  Jupyter Notebook is open\n",
    "- [ ]  Code is error free\n",
    "- [ ]  The cells with the code have been arranged in order of execution\n",
    "- [ ]  The data has been downloaded and prepared\n",
    "- [ ]  The models have been trained\n",
    "- [ ]  The analysis of speed and quality of the models has been performed"
   ]
  }
 ],
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