Boat_price_Predictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9589e8af",
   "metadata": {},
   "source": [
    "# BOAT PRICE PREDICTION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8481611f",
   "metadata": {},
   "source": [
    "This project focuses on predicting boat prices using various machine learning algorithms. The dataset was sourced from Kaggle and involves multiple steps, including data extraction, transformation, loading (ETL), cleaning, exploratory data analysis (EDA), and model building. The primary objective is to develop and evaluate regression models to accurately predict boat prices based on various features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "666b02e8",
   "metadata": {},
   "source": [
    "## 1.0 IMPORTING THE LIBRARIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3928bf7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import cufflinks as cf\n",
    "from plotly import __version__\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode,plot, iplot\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b269a52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.9.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For Notebooks connections\n",
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0d0b8ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For offline use\n",
    "#cf.go_offline()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0352f2de",
   "metadata": {},
   "source": [
    "##  2.0 DATA EXTRACTION,TRANSFORM AND LOAD (ETL) AND EXPLORATORY"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50fe7451",
   "metadata": {},
   "source": [
    "### 2.1 LOAD, TRANSFORM THE DATASET\n",
    "   + Data extracted manually form Kaggle \n",
    "   + Transform and Load by Pandas(row and columns) \n",
    "   + The url (https://www.kaggle.com/datasets/mexwell/boat-price-prediction/data?select=Boats_No_Price_dataset.csv )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "650198bb",
   "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>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>type</th>\n",
       "      <th>boatClass</th>\n",
       "      <th>make</th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>condition</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>beam_ft</th>\n",
       "      <th>...</th>\n",
       "      <th>minEngineYear</th>\n",
       "      <th>engineCategory</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip</th>\n",
       "      <th>created_date</th>\n",
       "      <th>created_month</th>\n",
       "      <th>created_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>7252689</td>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>Aquasport</td>\n",
       "      <td>210 CC</td>\n",
       "      <td>1992</td>\n",
       "      <td>used</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.50</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>outboard-4s</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>217053</td>\n",
       "      <td>Englewood</td>\n",
       "      <td>FL</td>\n",
       "      <td>34224</td>\n",
       "      <td>2019-10-16</td>\n",
       "      <td>10</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>7228300</td>\n",
       "      <td>power</td>\n",
       "      <td>power-sportcruiser</td>\n",
       "      <td>Formula</td>\n",
       "      <td>400 Super Sport</td>\n",
       "      <td>2018</td>\n",
       "      <td>used</td>\n",
       "      <td>40.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>...</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>inboard-outboard</td>\n",
       "      <td>539000.0</td>\n",
       "      <td>44260</td>\n",
       "      <td>Harsens Island</td>\n",
       "      <td>MI</td>\n",
       "      <td>48028</td>\n",
       "      <td>2019-09-24</td>\n",
       "      <td>9</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>7271336</td>\n",
       "      <td>power</td>\n",
       "      <td>power-deck</td>\n",
       "      <td>Bayliner</td>\n",
       "      <td>Element 180</td>\n",
       "      <td>2020</td>\n",
       "      <td>new</td>\n",
       "      <td>18.0</td>\n",
       "      <td>7.42</td>\n",
       "      <td>...</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>outboard-4s</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>220570</td>\n",
       "      <td>Marietta</td>\n",
       "      <td>OH</td>\n",
       "      <td>45750</td>\n",
       "      <td>2019-11-02</td>\n",
       "      <td>11</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>7222952</td>\n",
       "      <td>power</td>\n",
       "      <td>power-expresscruiser</td>\n",
       "      <td>Regal</td>\n",
       "      <td>32 Express</td>\n",
       "      <td>2015</td>\n",
       "      <td>used</td>\n",
       "      <td>32.0</td>\n",
       "      <td>10.33</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>multiple</td>\n",
       "      <td>169995.0</td>\n",
       "      <td>34834</td>\n",
       "      <td>North Charleston</td>\n",
       "      <td>SC</td>\n",
       "      <td>29405</td>\n",
       "      <td>2019-09-19</td>\n",
       "      <td>9</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>6824832</td>\n",
       "      <td>power</td>\n",
       "      <td>power-aft</td>\n",
       "      <td>Carver</td>\n",
       "      <td>440 Aft Cabin Motor Yacht</td>\n",
       "      <td>1994</td>\n",
       "      <td>used</td>\n",
       "      <td>44.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>inboard</td>\n",
       "      <td>109900.0</td>\n",
       "      <td>17942</td>\n",
       "      <td>Middle River</td>\n",
       "      <td>MD</td>\n",
       "      <td>21220</td>\n",
       "      <td>2018-08-29</td>\n",
       "      <td>8</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0       id   type             boatClass       make  \\\n",
       "0           1  7252689  power          power-center  Aquasport   \n",
       "1           3  7228300  power    power-sportcruiser    Formula   \n",
       "2           5  7271336  power            power-deck   Bayliner   \n",
       "3           6  7222952  power  power-expresscruiser      Regal   \n",
       "4           8  6824832  power             power-aft     Carver   \n",
       "\n",
       "                       model  year condition  length_ft  beam_ft  ...  \\\n",
       "0                     210 CC  1992      used       21.0     8.50  ...   \n",
       "1            400 Super Sport  2018      used       40.0    11.00  ...   \n",
       "2                Element 180  2020       new       18.0     7.42  ...   \n",
       "3                 32 Express  2015      used       32.0    10.33  ...   \n",
       "4  440 Aft Cabin Motor Yacht  1994      used       44.0    15.00  ...   \n",
       "\n",
       "   minEngineYear    engineCategory     price  sellerId              city  \\\n",
       "0            NaN       outboard-4s   16500.0    217053         Englewood   \n",
       "1         2018.0  inboard-outboard  539000.0     44260    Harsens Island   \n",
       "2         2019.0       outboard-4s   26995.0    220570          Marietta   \n",
       "3            NaN          multiple  169995.0     34834  North Charleston   \n",
       "4         1994.0           inboard  109900.0     17942      Middle River   \n",
       "\n",
       "   state    zip created_date  created_month  created_year  \n",
       "0     FL  34224   2019-10-16             10          2019  \n",
       "1     MI  48028   2019-09-24              9          2019  \n",
       "2     OH  45750   2019-11-02             11          2019  \n",
       "3     SC  29405   2019-09-19              9          2019  \n",
       "4     MD  21220   2018-08-29              8          2018  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Boats_Cleaned_dataset.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78fbcab9",
   "metadata": {},
   "source": [
    "### 2.2 DATA EXPLORATORY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "75e3581d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18903 entries, 0 to 18902\n",
      "Data columns (total 26 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Unnamed: 0      18903 non-null  int64  \n",
      " 1   id              18903 non-null  int64  \n",
      " 2   type            18903 non-null  object \n",
      " 3   boatClass       18903 non-null  object \n",
      " 4   make            18903 non-null  object \n",
      " 5   model           18868 non-null  object \n",
      " 6   year            18903 non-null  int64  \n",
      " 7   condition       18903 non-null  object \n",
      " 8   length_ft       18903 non-null  float64\n",
      " 9   beam_ft         12399 non-null  float64\n",
      " 10  dryWeight_lb    7094 non-null   float64\n",
      " 11  hullMaterial    18903 non-null  object \n",
      " 12  fuelType        15951 non-null  object \n",
      " 13  numEngines      18903 non-null  int64  \n",
      " 14  totalHP         18055 non-null  float64\n",
      " 15  maxEngineYear   2205 non-null   float64\n",
      " 16  minEngineYear   2174 non-null   float64\n",
      " 17  engineCategory  8410 non-null   object \n",
      " 18  price           18903 non-null  float64\n",
      " 19  sellerId        18903 non-null  int64  \n",
      " 20  city            18847 non-null  object \n",
      " 21  state           18903 non-null  object \n",
      " 22  zip             10215 non-null  object \n",
      " 23  created_date    18903 non-null  object \n",
      " 24  created_month   18903 non-null  int64  \n",
      " 25  created_year    18903 non-null  int64  \n",
      "dtypes: float64(7), int64(7), object(12)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a138ece",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "sns.heatmap(df.isnull(), yticklabels=False, cbar =True, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a66da640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'id', 'type', 'boatClass', 'make', 'model', 'year',\n",
       "       'condition', 'length_ft', 'beam_ft', 'dryWeight_lb', 'hullMaterial',\n",
       "       'fuelType', 'numEngines', 'totalHP', 'maxEngineYear', 'minEngineYear',\n",
       "       'engineCategory', 'price', 'sellerId', 'city', 'state', 'zip',\n",
       "       'created_date', 'created_month', 'created_year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CHECK ALL COLUMN \n",
    "df.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18220527",
   "metadata": {},
   "source": [
    "## 3.0 DATA CLEANING AND PROCESSING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd4c2fb3",
   "metadata": {},
   "source": [
    "### >> 3.1 DATA CLEANING\n",
    "+ DROP `'Unnamed: 0', 'id', 'created_date', 'created_month' 'sellerId', 'year'` AS IS OF NO USE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4f6e0294",
   "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>type</th>\n",
       "      <th>boatClass</th>\n",
       "      <th>make</th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>condition</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>beam_ft</th>\n",
       "      <th>dryWeight_lb</th>\n",
       "      <th>hullMaterial</th>\n",
       "      <th>...</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>maxEngineYear</th>\n",
       "      <th>minEngineYear</th>\n",
       "      <th>engineCategory</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip</th>\n",
       "      <th>created_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>Aquasport</td>\n",
       "      <td>210 CC</td>\n",
       "      <td>1992</td>\n",
       "      <td>used</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.50</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>...</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>outboard-4s</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>217053</td>\n",
       "      <td>Englewood</td>\n",
       "      <td>FL</td>\n",
       "      <td>34224</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>power</td>\n",
       "      <td>power-sportcruiser</td>\n",
       "      <td>Formula</td>\n",
       "      <td>400 Super Sport</td>\n",
       "      <td>2018</td>\n",
       "      <td>used</td>\n",
       "      <td>40.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>16100.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>...</td>\n",
       "      <td>800.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>inboard-outboard</td>\n",
       "      <td>539000.0</td>\n",
       "      <td>44260</td>\n",
       "      <td>Harsens Island</td>\n",
       "      <td>MI</td>\n",
       "      <td>48028</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>power</td>\n",
       "      <td>power-deck</td>\n",
       "      <td>Bayliner</td>\n",
       "      <td>Element 180</td>\n",
       "      <td>2020</td>\n",
       "      <td>new</td>\n",
       "      <td>18.0</td>\n",
       "      <td>7.42</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>...</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>outboard-4s</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>220570</td>\n",
       "      <td>Marietta</td>\n",
       "      <td>OH</td>\n",
       "      <td>45750</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>power</td>\n",
       "      <td>power-expresscruiser</td>\n",
       "      <td>Regal</td>\n",
       "      <td>32 Express</td>\n",
       "      <td>2015</td>\n",
       "      <td>used</td>\n",
       "      <td>32.0</td>\n",
       "      <td>10.33</td>\n",
       "      <td>12650.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>...</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>multiple</td>\n",
       "      <td>169995.0</td>\n",
       "      <td>34834</td>\n",
       "      <td>North Charleston</td>\n",
       "      <td>SC</td>\n",
       "      <td>29405</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>power</td>\n",
       "      <td>power-aft</td>\n",
       "      <td>Carver</td>\n",
       "      <td>440 Aft Cabin Motor Yacht</td>\n",
       "      <td>1994</td>\n",
       "      <td>used</td>\n",
       "      <td>44.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>...</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>inboard</td>\n",
       "      <td>109900.0</td>\n",
       "      <td>17942</td>\n",
       "      <td>Middle River</td>\n",
       "      <td>MD</td>\n",
       "      <td>21220</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    type             boatClass       make                      model  year  \\\n",
       "0  power          power-center  Aquasport                     210 CC  1992   \n",
       "1  power    power-sportcruiser    Formula            400 Super Sport  2018   \n",
       "2  power            power-deck   Bayliner                Element 180  2020   \n",
       "3  power  power-expresscruiser      Regal                 32 Express  2015   \n",
       "4  power             power-aft     Carver  440 Aft Cabin Motor Yacht  1994   \n",
       "\n",
       "  condition  length_ft  beam_ft  dryWeight_lb hullMaterial  ... totalHP  \\\n",
       "0      used       21.0     8.50        3000.0   fiberglass  ...   150.0   \n",
       "1      used       40.0    11.00       16100.0   fiberglass  ...   800.0   \n",
       "2       new       18.0     7.42        2000.0   fiberglass  ...    75.0   \n",
       "3      used       32.0    10.33       12650.0   fiberglass  ...   600.0   \n",
       "4      used       44.0    15.00       32000.0   fiberglass  ...   700.0   \n",
       "\n",
       "   maxEngineYear  minEngineYear    engineCategory     price sellerId  \\\n",
       "0            NaN            NaN       outboard-4s   16500.0   217053   \n",
       "1         2018.0         2018.0  inboard-outboard  539000.0    44260   \n",
       "2         2019.0         2019.0       outboard-4s   26995.0   220570   \n",
       "3            NaN            NaN          multiple  169995.0    34834   \n",
       "4         1994.0         1994.0           inboard  109900.0    17942   \n",
       "\n",
       "               city  state    zip created_year  \n",
       "0         Englewood     FL  34224         2019  \n",
       "1    Harsens Island     MI  48028         2019  \n",
       "2          Marietta     OH  45750         2019  \n",
       "3  North Charleston     SC  29405         2019  \n",
       "4      Middle River     MD  21220         2018  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Unnamed: 0', 'id', 'created_date','created_month'], axis=1, inplace=True)\n",
    "#df.drop(['Unnamed: 0', 'id', 'created_date','created_month', 'year', 'sellerId'], axis=1, inplace=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b2ef8bc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18903 entries, 0 to 18902\n",
      "Data columns (total 22 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   type            18903 non-null  object \n",
      " 1   boatClass       18903 non-null  object \n",
      " 2   make            18903 non-null  object \n",
      " 3   model           18868 non-null  object \n",
      " 4   year            18903 non-null  int64  \n",
      " 5   condition       18903 non-null  object \n",
      " 6   length_ft       18903 non-null  float64\n",
      " 7   beam_ft         12399 non-null  float64\n",
      " 8   dryWeight_lb    7094 non-null   float64\n",
      " 9   hullMaterial    18903 non-null  object \n",
      " 10  fuelType        15951 non-null  object \n",
      " 11  numEngines      18903 non-null  int64  \n",
      " 12  totalHP         18055 non-null  float64\n",
      " 13  maxEngineYear   2205 non-null   float64\n",
      " 14  minEngineYear   2174 non-null   float64\n",
      " 15  engineCategory  8410 non-null   object \n",
      " 16  price           18903 non-null  float64\n",
      " 17  sellerId        18903 non-null  int64  \n",
      " 18  city            18847 non-null  object \n",
      " 19  state           18903 non-null  object \n",
      " 20  zip             10215 non-null  object \n",
      " 21  created_year    18903 non-null  int64  \n",
      "dtypes: float64(7), int64(4), object(11)\n",
      "memory usage: 3.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "937673b4",
   "metadata": {},
   "source": [
    "##### Display all Column with `null` value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "593dd212",
   "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>beam_ft</th>\n",
       "      <th>dryWeight_lb</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>maxEngineYear</th>\n",
       "      <th>minEngineYear</th>\n",
       "      <th>engineCategory</th>\n",
       "      <th>city</th>\n",
       "      <th>zip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>210 CC</td>\n",
       "      <td>8.50</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>outboard-4s</td>\n",
       "      <td>Englewood</td>\n",
       "      <td>34224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>400 Super Sport</td>\n",
       "      <td>11.00</td>\n",
       "      <td>16100.0</td>\n",
       "      <td>diesel</td>\n",
       "      <td>800.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>inboard-outboard</td>\n",
       "      <td>Harsens Island</td>\n",
       "      <td>48028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Element 180</td>\n",
       "      <td>7.42</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>outboard-4s</td>\n",
       "      <td>Marietta</td>\n",
       "      <td>45750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32 Express</td>\n",
       "      <td>10.33</td>\n",
       "      <td>12650.0</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>multiple</td>\n",
       "      <td>North Charleston</td>\n",
       "      <td>29405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>440 Aft Cabin Motor Yacht</td>\n",
       "      <td>15.00</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>diesel</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>inboard</td>\n",
       "      <td>Middle River</td>\n",
       "      <td>21220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18898</th>\n",
       "      <td>250 Play</td>\n",
       "      <td>8.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Acworth</td>\n",
       "      <td>30102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18899</th>\n",
       "      <td>Captiva 200 MTX</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2795.0</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New Baltimore</td>\n",
       "      <td>48047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18900</th>\n",
       "      <td>2200</td>\n",
       "      <td>7.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Seabrook</td>\n",
       "      <td>77586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18901</th>\n",
       "      <td>250</td>\n",
       "      <td>8.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sunrise Beach</td>\n",
       "      <td>65079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18902</th>\n",
       "      <td>2655 Ciera</td>\n",
       "      <td>9.42</td>\n",
       "      <td>6350.0</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>220.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>inboard-outboard</td>\n",
       "      <td>Gainesville</td>\n",
       "      <td>30501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18903 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           model  beam_ft  dryWeight_lb  fuelType  totalHP  \\\n",
       "0                         210 CC     8.50        3000.0  gasoline    150.0   \n",
       "1                400 Super Sport    11.00       16100.0    diesel    800.0   \n",
       "2                    Element 180     7.42        2000.0  gasoline     75.0   \n",
       "3                     32 Express    10.33       12650.0  gasoline    600.0   \n",
       "4      440 Aft Cabin Motor Yacht    15.00       32000.0    diesel    700.0   \n",
       "...                          ...      ...           ...       ...      ...   \n",
       "18898                   250 Play     8.50           NaN  gasoline      NaN   \n",
       "18899            Captiva 200 MTX     8.00        2795.0  gasoline      0.0   \n",
       "18900                       2200     7.67           NaN  gasoline      NaN   \n",
       "18901                        250     8.50           NaN  gasoline      NaN   \n",
       "18902                 2655 Ciera     9.42        6350.0  gasoline    220.0   \n",
       "\n",
       "       maxEngineYear  minEngineYear    engineCategory              city    zip  \n",
       "0                NaN            NaN       outboard-4s         Englewood  34224  \n",
       "1             2018.0         2018.0  inboard-outboard    Harsens Island  48028  \n",
       "2             2019.0         2019.0       outboard-4s          Marietta  45750  \n",
       "3                NaN            NaN          multiple  North Charleston  29405  \n",
       "4             1994.0         1994.0           inboard      Middle River  21220  \n",
       "...              ...            ...               ...               ...    ...  \n",
       "18898            NaN            NaN               NaN           Acworth  30102  \n",
       "18899            NaN            NaN               NaN     New Baltimore  48047  \n",
       "18900            NaN            NaN               NaN          Seabrook  77586  \n",
       "18901            NaN            NaN               NaN     Sunrise Beach  65079  \n",
       "18902         2002.0         2002.0  inboard-outboard       Gainesville  30501  \n",
       "\n",
       "[18903 rows x 10 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_with_null = []\n",
    "for col in df.keys():\n",
    "    if df[col].isnull().any():\n",
    "        cols_with_null.append(col)\n",
    "        \n",
    "df[cols_with_null]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "868bb4de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "sns.heatmap(df[cols_with_null].isnull(), yticklabels=False, cbar =True, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b21cf5b5",
   "metadata": {},
   "source": [
    "### >> 3.2 ADVANCE DATA CLEANING \n",
    "+ `FOR OBJECT DATA TYPE `\n",
    "    + DROP COLUMN WITH ATMOST 100 Unique Value\n",
    "    + IF MISSING VALUE IS LESS THAN 3000, REMOVE THE MISSING VALUE ELSE DROP THE COLUMN\n",
    "+ `FOR NUMERIC DATA TYPE`\n",
    "    + IF MISSING VALUE IS UP TO 1000 IN A NUMERIC TYPE COLUMN, DROP THE COLUMN\n",
    "    + REMOVE ROW WITH MISSING VALUE FOR NUMERIC TYPE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b831ee",
   "metadata": {},
   "source": [
    "#### >>>>3.2.1 FOR OBJECT DATA TYPE\n",
    "##### >>>>>> 3.2.1.1 `DROP COLUMN WITH ATMOST 100 Unique Value`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "324f862f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "+ DROP COLUMN WITH 99 || > unique Value: i.e\n",
      "make --> consists `960` unique value\n",
      "model --> consists `7900` unique value\n",
      "city --> consists `1150` unique value\n",
      "zip --> consists `1047` unique value\n"
     ]
    }
   ],
   "source": [
    "# CHECK FOR COLUMN OBJECT DT \n",
    "df_to_drop_col_objType = []\n",
    "print()\n",
    "print('+ DROP COLUMN WITH 99 || > unique Value: i.e')\n",
    "for col in df:\n",
    "    if df[col].dtypes == 'object' and len(df[col].unique()) >99:\n",
    "        df_to_drop_col_objType.append(col)\n",
    "\n",
    "for col in df_to_drop_col_objType:\n",
    "    print(col, end=' --> consists ')\n",
    "    print(f'`{len(df[col].unique())}` unique value')     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c9d96faa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "make      960\n",
      "model    7899\n",
      "city     1149\n",
      "zip      1046\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "\n",
    "print(df[df_to_drop_col_objType].nunique())\n",
    "sns.barplot(x=df_to_drop_col_objType, y=df[df_to_drop_col_objType].nunique().values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "17adc6e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DROP  ['make', 'model', 'city', 'zip']\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>type</th>\n",
       "      <th>boatClass</th>\n",
       "      <th>year</th>\n",
       "      <th>condition</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>beam_ft</th>\n",
       "      <th>dryWeight_lb</th>\n",
       "      <th>hullMaterial</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>numEngines</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>maxEngineYear</th>\n",
       "      <th>minEngineYear</th>\n",
       "      <th>engineCategory</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>state</th>\n",
       "      <th>created_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>1992</td>\n",
       "      <td>used</td>\n",
       "      <td>21.00</td>\n",
       "      <td>8.50</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>outboard-4s</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>217053</td>\n",
       "      <td>FL</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>power</td>\n",
       "      <td>power-sportcruiser</td>\n",
       "      <td>2018</td>\n",
       "      <td>used</td>\n",
       "      <td>40.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>16100.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>800.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>inboard-outboard</td>\n",
       "      <td>539000.0</td>\n",
       "      <td>44260</td>\n",
       "      <td>MI</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>power</td>\n",
       "      <td>power-deck</td>\n",
       "      <td>2020</td>\n",
       "      <td>new</td>\n",
       "      <td>18.00</td>\n",
       "      <td>7.42</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>outboard-4s</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>220570</td>\n",
       "      <td>OH</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>power</td>\n",
       "      <td>power-expresscruiser</td>\n",
       "      <td>2015</td>\n",
       "      <td>used</td>\n",
       "      <td>32.00</td>\n",
       "      <td>10.33</td>\n",
       "      <td>12650.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>multiple</td>\n",
       "      <td>169995.0</td>\n",
       "      <td>34834</td>\n",
       "      <td>SC</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>power</td>\n",
       "      <td>power-aft</td>\n",
       "      <td>1994</td>\n",
       "      <td>used</td>\n",
       "      <td>44.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>inboard</td>\n",
       "      <td>109900.0</td>\n",
       "      <td>17942</td>\n",
       "      <td>MD</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18898</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2013</td>\n",
       "      <td>used</td>\n",
       "      <td>25.00</td>\n",
       "      <td>8.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31973.0</td>\n",
       "      <td>34647</td>\n",
       "      <td>GA</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18899</th>\n",
       "      <td>power</td>\n",
       "      <td>power-runabout</td>\n",
       "      <td>2013</td>\n",
       "      <td>used</td>\n",
       "      <td>19.33</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2795.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>6335</td>\n",
       "      <td>MI</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18900</th>\n",
       "      <td>power</td>\n",
       "      <td>power-bay</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>22.00</td>\n",
       "      <td>7.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39995.0</td>\n",
       "      <td>65602</td>\n",
       "      <td>TX</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18901</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2004</td>\n",
       "      <td>used</td>\n",
       "      <td>25.00</td>\n",
       "      <td>8.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16900.0</td>\n",
       "      <td>3180</td>\n",
       "      <td>MO</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18902</th>\n",
       "      <td>power</td>\n",
       "      <td>power-cruiser</td>\n",
       "      <td>2002</td>\n",
       "      <td>new</td>\n",
       "      <td>26.58</td>\n",
       "      <td>9.42</td>\n",
       "      <td>6350.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>inboard-outboard</td>\n",
       "      <td>17900.0</td>\n",
       "      <td>152266</td>\n",
       "      <td>GA</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18903 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        type             boatClass  year condition  length_ft  beam_ft  \\\n",
       "0      power          power-center  1992      used      21.00     8.50   \n",
       "1      power    power-sportcruiser  2018      used      40.00    11.00   \n",
       "2      power            power-deck  2020       new      18.00     7.42   \n",
       "3      power  power-expresscruiser  2015      used      32.00    10.33   \n",
       "4      power             power-aft  1994      used      44.00    15.00   \n",
       "...      ...                   ...   ...       ...        ...      ...   \n",
       "18898  power         power-pontoon  2013      used      25.00     8.50   \n",
       "18899  power        power-runabout  2013      used      19.33     8.00   \n",
       "18900  power             power-bay  2019       new      22.00     7.67   \n",
       "18901  power         power-pontoon  2004      used      25.00     8.50   \n",
       "18902  power         power-cruiser  2002       new      26.58     9.42   \n",
       "\n",
       "       dryWeight_lb hullMaterial  fuelType  numEngines  totalHP  \\\n",
       "0            3000.0   fiberglass  gasoline           1    150.0   \n",
       "1           16100.0   fiberglass    diesel           2    800.0   \n",
       "2            2000.0   fiberglass  gasoline           1     75.0   \n",
       "3           12650.0   fiberglass  gasoline           2    600.0   \n",
       "4           32000.0   fiberglass    diesel           2    700.0   \n",
       "...             ...          ...       ...         ...      ...   \n",
       "18898           NaN     aluminum  gasoline           0      NaN   \n",
       "18899        2795.0   fiberglass  gasoline           1      0.0   \n",
       "18900           NaN   fiberglass  gasoline           0      NaN   \n",
       "18901           NaN     aluminum  gasoline           0      NaN   \n",
       "18902        6350.0   fiberglass  gasoline           1    220.0   \n",
       "\n",
       "       maxEngineYear  minEngineYear    engineCategory     price  sellerId  \\\n",
       "0                NaN            NaN       outboard-4s   16500.0    217053   \n",
       "1             2018.0         2018.0  inboard-outboard  539000.0     44260   \n",
       "2             2019.0         2019.0       outboard-4s   26995.0    220570   \n",
       "3                NaN            NaN          multiple  169995.0     34834   \n",
       "4             1994.0         1994.0           inboard  109900.0     17942   \n",
       "...              ...            ...               ...       ...       ...   \n",
       "18898            NaN            NaN               NaN   31973.0     34647   \n",
       "18899            NaN            NaN               NaN   26995.0      6335   \n",
       "18900            NaN            NaN               NaN   39995.0     65602   \n",
       "18901            NaN            NaN               NaN   16900.0      3180   \n",
       "18902         2002.0         2002.0  inboard-outboard   17900.0    152266   \n",
       "\n",
       "      state  created_year  \n",
       "0        FL          2019  \n",
       "1        MI          2019  \n",
       "2        OH          2019  \n",
       "3        SC          2019  \n",
       "4        MD          2018  \n",
       "...     ...           ...  \n",
       "18898    GA          2019  \n",
       "18899    MI          2019  \n",
       "18900    TX          2018  \n",
       "18901    MO          2019  \n",
       "18902    GA          2019  \n",
       "\n",
       "[18903 rows x 18 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('DROP ', df_to_drop_col_objType)\n",
    "df_clean = df.drop(df_to_drop_col_objType, axis=1)\n",
    "df_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29cae672",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9aa7ac5d",
   "metadata": {},
   "source": [
    "##### >>>>>> 3.2.1.2  ` IF MISSING VALUE IS LESS THAN 3000, (FILL WITH MOST FREQUENT VALUE) ELSE DROP THE COLUMN` FOR OBJECT DT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "03a29220",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FUELTYPE\n",
      "False    15951\n",
      "True      2952\n",
      "Name: fuelType, dtype: int64\n",
      "\n",
      "ENGINECATEGORY\n",
      "True     10493\n",
      "False     8410\n",
      "Name: engineCategory, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check object data-type column(s) with missing value\n",
    "df_nan_obj_col = []\n",
    "for col in df_clean:\n",
    "    if df_clean[col].dtypes == 'object' and df_clean[col].isnull().any():\n",
    "        df_nan_obj_col.append(col)\n",
    "        print(col.upper())\n",
    "        print(df_clean[col].isnull().value_counts())\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aee5d3d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.heatmap(df_clean[df_nan_obj_col].isnull(), yticklabels=False, cbar =True, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1ce2004b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Value in  fuelType 2952\n",
      "['gasoline' 'diesel' nan 'other' 'electric']\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>type</th>\n",
       "      <th>boatClass</th>\n",
       "      <th>year</th>\n",
       "      <th>condition</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>beam_ft</th>\n",
       "      <th>dryWeight_lb</th>\n",
       "      <th>hullMaterial</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>numEngines</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>maxEngineYear</th>\n",
       "      <th>minEngineYear</th>\n",
       "      <th>engineCategory</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>state</th>\n",
       "      <th>created_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12828</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>26.17</td>\n",
       "      <td>8.50</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>115.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>outboard</td>\n",
       "      <td>35240.0</td>\n",
       "      <td>1250</td>\n",
       "      <td>KS</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12468</th>\n",
       "      <td>power</td>\n",
       "      <td>power-jon</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>19.17</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>outboard</td>\n",
       "      <td>20465.0</td>\n",
       "      <td>1078</td>\n",
       "      <td>MO</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13901</th>\n",
       "      <td>power</td>\n",
       "      <td>power-aluminum</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>17.58</td>\n",
       "      <td>8.08</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>115.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>outboard</td>\n",
       "      <td>29015.0</td>\n",
       "      <td>55964</td>\n",
       "      <td>MN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17038</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>24.17</td>\n",
       "      <td>8.50</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>outboard</td>\n",
       "      <td>28855.0</td>\n",
       "      <td>55964</td>\n",
       "      <td>MN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12554</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>21.92</td>\n",
       "      <td>8.50</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>outboard</td>\n",
       "      <td>26535.0</td>\n",
       "      <td>155353</td>\n",
       "      <td>TX</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13030</th>\n",
       "      <td>power</td>\n",
       "      <td>power-aluminum</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>16.00</td>\n",
       "      <td>6.33</td>\n",
       "      <td>875.0</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>outboard</td>\n",
       "      <td>17390.0</td>\n",
       "      <td>1125</td>\n",
       "      <td>OK</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11990</th>\n",
       "      <td>power</td>\n",
       "      <td>power-jon</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>14.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>562.0</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>outboard</td>\n",
       "      <td>4834.0</td>\n",
       "      <td>60577</td>\n",
       "      <td>CA</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18443</th>\n",
       "      <td>power</td>\n",
       "      <td>power-skiff</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>19.33</td>\n",
       "      <td>7.75</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>115.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>outboard</td>\n",
       "      <td>31050.0</td>\n",
       "      <td>1237</td>\n",
       "      <td>TX</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11255</th>\n",
       "      <td>power</td>\n",
       "      <td>power-jon</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>11.92</td>\n",
       "      <td>4.33</td>\n",
       "      <td>126.0</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>outboard</td>\n",
       "      <td>849.0</td>\n",
       "      <td>1003</td>\n",
       "      <td>NY</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11857</th>\n",
       "      <td>power</td>\n",
       "      <td>power-jon</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>14.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>562.0</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>outboard</td>\n",
       "      <td>4494.0</td>\n",
       "      <td>1213</td>\n",
       "      <td>IN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        type       boatClass  year condition  length_ft  beam_ft  \\\n",
       "12828  power   power-pontoon  2019       new      26.17     8.50   \n",
       "12468  power       power-jon  2019       new      19.17     7.00   \n",
       "13901  power  power-aluminum  2019       new      17.58     8.08   \n",
       "17038  power   power-pontoon  2019       new      24.17     8.50   \n",
       "12554  power   power-pontoon  2019       new      21.92     8.50   \n",
       "13030  power  power-aluminum  2019       new      16.00     6.33   \n",
       "11990  power       power-jon  2019       new      14.00     6.00   \n",
       "18443  power     power-skiff  2019       new      19.33     7.75   \n",
       "11255  power       power-jon  2019       new      11.92     4.33   \n",
       "11857  power       power-jon  2019       new      14.00     6.00   \n",
       "\n",
       "       dryWeight_lb hullMaterial fuelType  numEngines  totalHP  maxEngineYear  \\\n",
       "12828        2200.0     aluminum      NaN           1    115.0            NaN   \n",
       "12468        1020.0     aluminum      NaN           1     90.0            NaN   \n",
       "13901        1525.0     aluminum      NaN           1    115.0            NaN   \n",
       "17038        1920.0     aluminum      NaN           1     90.0            NaN   \n",
       "12554        1700.0     aluminum      NaN           1      0.0            NaN   \n",
       "13030         875.0     aluminum      NaN           1     50.0            NaN   \n",
       "11990         562.0     aluminum      NaN           1      0.0            NaN   \n",
       "18443        1900.0   fiberglass      NaN           1    115.0            NaN   \n",
       "11255         126.0     aluminum      NaN           1      0.0            NaN   \n",
       "11857         562.0     aluminum      NaN           1      0.0            NaN   \n",
       "\n",
       "       minEngineYear engineCategory    price  sellerId state  created_year  \n",
       "12828            NaN       outboard  35240.0      1250    KS          2019  \n",
       "12468            NaN       outboard  20465.0      1078    MO          2019  \n",
       "13901            NaN       outboard  29015.0     55964    MN          2019  \n",
       "17038            NaN       outboard  28855.0     55964    MN          2019  \n",
       "12554            NaN       outboard  26535.0    155353    TX          2018  \n",
       "13030            NaN       outboard  17390.0      1125    OK          2018  \n",
       "11990            NaN       outboard   4834.0     60577    CA          2018  \n",
       "18443            NaN       outboard  31050.0      1237    TX          2019  \n",
       "11255            NaN       outboard    849.0      1003    NY          2018  \n",
       "11857            NaN       outboard   4494.0      1213    IN          2018  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CHECK MISSING Value for 1st OBJECT(FuelType)\n",
    "\n",
    "print('Missing Value in ',df_nan_obj_col[0], len(df_clean[df_clean[df_nan_obj_col[0]].isnull()]))\n",
    "print(df_clean[df_nan_obj_col[0]].unique())\n",
    "df_clean[df_clean[df_nan_obj_col[0]].isnull()].sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d0422499",
   "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>type</th>\n",
       "      <th>boatClass</th>\n",
       "      <th>year</th>\n",
       "      <th>condition</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>beam_ft</th>\n",
       "      <th>dryWeight_lb</th>\n",
       "      <th>hullMaterial</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>numEngines</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>maxEngineYear</th>\n",
       "      <th>minEngineYear</th>\n",
       "      <th>engineCategory</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>state</th>\n",
       "      <th>created_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>1992</td>\n",
       "      <td>used</td>\n",
       "      <td>21.00</td>\n",
       "      <td>8.50</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>outboard-4s</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>217053</td>\n",
       "      <td>FL</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>power</td>\n",
       "      <td>power-sportcruiser</td>\n",
       "      <td>2018</td>\n",
       "      <td>used</td>\n",
       "      <td>40.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>16100.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>800.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>inboard-outboard</td>\n",
       "      <td>539000.0</td>\n",
       "      <td>44260</td>\n",
       "      <td>MI</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>power</td>\n",
       "      <td>power-deck</td>\n",
       "      <td>2020</td>\n",
       "      <td>new</td>\n",
       "      <td>18.00</td>\n",
       "      <td>7.42</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>outboard-4s</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>220570</td>\n",
       "      <td>OH</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>power</td>\n",
       "      <td>power-expresscruiser</td>\n",
       "      <td>2015</td>\n",
       "      <td>used</td>\n",
       "      <td>32.00</td>\n",
       "      <td>10.33</td>\n",
       "      <td>12650.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>multiple</td>\n",
       "      <td>169995.0</td>\n",
       "      <td>34834</td>\n",
       "      <td>SC</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>power</td>\n",
       "      <td>power-aft</td>\n",
       "      <td>1994</td>\n",
       "      <td>used</td>\n",
       "      <td>44.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>inboard</td>\n",
       "      <td>109900.0</td>\n",
       "      <td>17942</td>\n",
       "      <td>MD</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18898</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2013</td>\n",
       "      <td>used</td>\n",
       "      <td>25.00</td>\n",
       "      <td>8.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31973.0</td>\n",
       "      <td>34647</td>\n",
       "      <td>GA</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18899</th>\n",
       "      <td>power</td>\n",
       "      <td>power-runabout</td>\n",
       "      <td>2013</td>\n",
       "      <td>used</td>\n",
       "      <td>19.33</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2795.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>6335</td>\n",
       "      <td>MI</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18900</th>\n",
       "      <td>power</td>\n",
       "      <td>power-bay</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>22.00</td>\n",
       "      <td>7.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39995.0</td>\n",
       "      <td>65602</td>\n",
       "      <td>TX</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18901</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2004</td>\n",
       "      <td>used</td>\n",
       "      <td>25.00</td>\n",
       "      <td>8.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16900.0</td>\n",
       "      <td>3180</td>\n",
       "      <td>MO</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18902</th>\n",
       "      <td>power</td>\n",
       "      <td>power-cruiser</td>\n",
       "      <td>2002</td>\n",
       "      <td>new</td>\n",
       "      <td>26.58</td>\n",
       "      <td>9.42</td>\n",
       "      <td>6350.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>inboard-outboard</td>\n",
       "      <td>17900.0</td>\n",
       "      <td>152266</td>\n",
       "      <td>GA</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18903 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        type             boatClass  year condition  length_ft  beam_ft  \\\n",
       "0      power          power-center  1992      used      21.00     8.50   \n",
       "1      power    power-sportcruiser  2018      used      40.00    11.00   \n",
       "2      power            power-deck  2020       new      18.00     7.42   \n",
       "3      power  power-expresscruiser  2015      used      32.00    10.33   \n",
       "4      power             power-aft  1994      used      44.00    15.00   \n",
       "...      ...                   ...   ...       ...        ...      ...   \n",
       "18898  power         power-pontoon  2013      used      25.00     8.50   \n",
       "18899  power        power-runabout  2013      used      19.33     8.00   \n",
       "18900  power             power-bay  2019       new      22.00     7.67   \n",
       "18901  power         power-pontoon  2004      used      25.00     8.50   \n",
       "18902  power         power-cruiser  2002       new      26.58     9.42   \n",
       "\n",
       "       dryWeight_lb hullMaterial  fuelType  numEngines  totalHP  \\\n",
       "0            3000.0   fiberglass  gasoline           1    150.0   \n",
       "1           16100.0   fiberglass    diesel           2    800.0   \n",
       "2            2000.0   fiberglass  gasoline           1     75.0   \n",
       "3           12650.0   fiberglass  gasoline           2    600.0   \n",
       "4           32000.0   fiberglass    diesel           2    700.0   \n",
       "...             ...          ...       ...         ...      ...   \n",
       "18898           NaN     aluminum  gasoline           0      NaN   \n",
       "18899        2795.0   fiberglass  gasoline           1      0.0   \n",
       "18900           NaN   fiberglass  gasoline           0      NaN   \n",
       "18901           NaN     aluminum  gasoline           0      NaN   \n",
       "18902        6350.0   fiberglass  gasoline           1    220.0   \n",
       "\n",
       "       maxEngineYear  minEngineYear    engineCategory     price  sellerId  \\\n",
       "0                NaN            NaN       outboard-4s   16500.0    217053   \n",
       "1             2018.0         2018.0  inboard-outboard  539000.0     44260   \n",
       "2             2019.0         2019.0       outboard-4s   26995.0    220570   \n",
       "3                NaN            NaN          multiple  169995.0     34834   \n",
       "4             1994.0         1994.0           inboard  109900.0     17942   \n",
       "...              ...            ...               ...       ...       ...   \n",
       "18898            NaN            NaN               NaN   31973.0     34647   \n",
       "18899            NaN            NaN               NaN   26995.0      6335   \n",
       "18900            NaN            NaN               NaN   39995.0     65602   \n",
       "18901            NaN            NaN               NaN   16900.0      3180   \n",
       "18902         2002.0         2002.0  inboard-outboard   17900.0    152266   \n",
       "\n",
       "      state  created_year  \n",
       "0        FL          2019  \n",
       "1        MI          2019  \n",
       "2        OH          2019  \n",
       "3        SC          2019  \n",
       "4        MD          2018  \n",
       "...     ...           ...  \n",
       "18898    GA          2019  \n",
       "18899    MI          2019  \n",
       "18900    TX          2018  \n",
       "18901    MO          2019  \n",
       "18902    GA          2019  \n",
       "\n",
       "[18903 rows x 18 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill the missing values with the mode (most frequent value) of the column\n",
    "\n",
    "mode_fuelType = df_clean['fuelType'].mode()[0]  # Calculate the mode of fuelType\n",
    "df_clean['fuelType'].fillna(mode_fuelType, inplace=True)  # Fill missing values with the mode\n",
    "df_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "44fdcb1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.heatmap(df_clean[df_nan_obj_col].isnull(), yticklabels=False, cbar =True, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2966ac34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NaN Value in  engineCategory 10493\n",
      "['outboard-4s' 'inboard-outboard' 'multiple' 'inboard' nan 'outboard'\n",
      " 'outboard-2s' 'other' 'v-drive' 'electric']\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>type</th>\n",
       "      <th>boatClass</th>\n",
       "      <th>year</th>\n",
       "      <th>condition</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>beam_ft</th>\n",
       "      <th>dryWeight_lb</th>\n",
       "      <th>hullMaterial</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>numEngines</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>maxEngineYear</th>\n",
       "      <th>minEngineYear</th>\n",
       "      <th>engineCategory</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>state</th>\n",
       "      <th>created_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3035</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>27.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99244.0</td>\n",
       "      <td>27369</td>\n",
       "      <td>MN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>power</td>\n",
       "      <td>power-aluminum</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>18.75</td>\n",
       "      <td>7.92</td>\n",
       "      <td>1720.0</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28393.0</td>\n",
       "      <td>12667</td>\n",
       "      <td>WI</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8732</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>2019</td>\n",
       "      <td>used</td>\n",
       "      <td>17.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39995.0</td>\n",
       "      <td>5107</td>\n",
       "      <td>NV</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8728</th>\n",
       "      <td>power</td>\n",
       "      <td>power-skiwake</td>\n",
       "      <td>2007</td>\n",
       "      <td>used</td>\n",
       "      <td>22.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19999.0</td>\n",
       "      <td>6055</td>\n",
       "      <td>FL</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2472</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2006</td>\n",
       "      <td>used</td>\n",
       "      <td>24.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24999.0</td>\n",
       "      <td>46516</td>\n",
       "      <td>MN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6972</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2018</td>\n",
       "      <td>new</td>\n",
       "      <td>25.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>136500.0</td>\n",
       "      <td>34588</td>\n",
       "      <td>TN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3011</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pwc</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>132.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10499.0</td>\n",
       "      <td>46516</td>\n",
       "      <td>MN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18367</th>\n",
       "      <td>power</td>\n",
       "      <td>power-inflatable</td>\n",
       "      <td>2018</td>\n",
       "      <td>new</td>\n",
       "      <td>10.80</td>\n",
       "      <td>6.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pvc</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1295.0</td>\n",
       "      <td>27179</td>\n",
       "      <td>WI</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17541</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>2003</td>\n",
       "      <td>used</td>\n",
       "      <td>19.00</td>\n",
       "      <td>7.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>32307</td>\n",
       "      <td>GA</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6613</th>\n",
       "      <td>power</td>\n",
       "      <td>power-cruiser</td>\n",
       "      <td>2018</td>\n",
       "      <td>used</td>\n",
       "      <td>27.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94997.0</td>\n",
       "      <td>257210</td>\n",
       "      <td>VA</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        type         boatClass  year condition  length_ft  beam_ft  \\\n",
       "3035   power     power-pontoon  2019       new      27.00      NaN   \n",
       "2050   power    power-aluminum  2019       new      18.75     7.92   \n",
       "8732   power      power-center  2019      used      17.00     7.00   \n",
       "8728   power     power-skiwake  2007      used      22.00      NaN   \n",
       "2472   power     power-pontoon  2006      used      24.00      NaN   \n",
       "6972   power     power-pontoon  2018       new      25.00      NaN   \n",
       "3011   power         power-pwc  2019       new     132.00      NaN   \n",
       "18367  power  power-inflatable  2018       new      10.80     6.33   \n",
       "17541  power      power-center  2003      used      19.00     7.67   \n",
       "6613   power     power-cruiser  2018      used      27.00      NaN   \n",
       "\n",
       "       dryWeight_lb hullMaterial  fuelType  numEngines  totalHP  \\\n",
       "3035            NaN        other     other           1      0.0   \n",
       "2050         1720.0     aluminum     other           1      0.0   \n",
       "8732            NaN   fiberglass     other           1      0.0   \n",
       "8728            NaN   fiberglass     other           1      0.0   \n",
       "2472            NaN        other     other           1      0.0   \n",
       "6972            NaN        other     other           1      0.0   \n",
       "3011            NaN        other     other           1      0.0   \n",
       "18367           NaN          pvc     other           0      NaN   \n",
       "17541           NaN   fiberglass  gasoline           0      NaN   \n",
       "6613            NaN        other     other           1      0.0   \n",
       "\n",
       "       maxEngineYear  minEngineYear engineCategory     price  sellerId state  \\\n",
       "3035             NaN            NaN            NaN   99244.0     27369    MN   \n",
       "2050             NaN            NaN            NaN   28393.0     12667    WI   \n",
       "8732             NaN            NaN            NaN   39995.0      5107    NV   \n",
       "8728             NaN            NaN            NaN   19999.0      6055    FL   \n",
       "2472             NaN            NaN            NaN   24999.0     46516    MN   \n",
       "6972             NaN            NaN            NaN  136500.0     34588    TN   \n",
       "3011             NaN            NaN            NaN   10499.0     46516    MN   \n",
       "18367            NaN            NaN            NaN    1295.0     27179    WI   \n",
       "17541            NaN            NaN            NaN    9999.0     32307    GA   \n",
       "6613             NaN            NaN            NaN   94997.0    257210    VA   \n",
       "\n",
       "       created_year  \n",
       "3035           2019  \n",
       "2050           2019  \n",
       "8732           2019  \n",
       "8728           2019  \n",
       "2472           2019  \n",
       "6972           2018  \n",
       "3011           2019  \n",
       "18367          2019  \n",
       "17541          2019  \n",
       "6613           2019  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CHECK NaN Value FOR 2ND OBJECT data-type COLUMN with MISSING Value\n",
    "\n",
    "print('NaN Value in ',df_nan_obj_col[1], len(df_clean[df_clean[df_nan_obj_col[1]].isnull()]))\n",
    "print(df_clean[df_nan_obj_col[1]].unique())\n",
    "df_clean[df_clean[df_nan_obj_col[1]].isnull()].sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "179b251b",
   "metadata": {},
   "source": [
    "##### The missing Data is greater than the half of the dataset \n",
    "    + Drop to avoid data misleading "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e31a5413",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "engineCategory\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>type</th>\n",
       "      <th>boatClass</th>\n",
       "      <th>year</th>\n",
       "      <th>condition</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>beam_ft</th>\n",
       "      <th>dryWeight_lb</th>\n",
       "      <th>hullMaterial</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>numEngines</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>maxEngineYear</th>\n",
       "      <th>minEngineYear</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>state</th>\n",
       "      <th>created_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>1992</td>\n",
       "      <td>used</td>\n",
       "      <td>21.00</td>\n",
       "      <td>8.50</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>150.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>217053</td>\n",
       "      <td>FL</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>power</td>\n",
       "      <td>power-sportcruiser</td>\n",
       "      <td>2018</td>\n",
       "      <td>used</td>\n",
       "      <td>40.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>16100.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>800.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>539000.0</td>\n",
       "      <td>44260</td>\n",
       "      <td>MI</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>power</td>\n",
       "      <td>power-deck</td>\n",
       "      <td>2020</td>\n",
       "      <td>new</td>\n",
       "      <td>18.00</td>\n",
       "      <td>7.42</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>220570</td>\n",
       "      <td>OH</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>power</td>\n",
       "      <td>power-expresscruiser</td>\n",
       "      <td>2015</td>\n",
       "      <td>used</td>\n",
       "      <td>32.00</td>\n",
       "      <td>10.33</td>\n",
       "      <td>12650.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>169995.0</td>\n",
       "      <td>34834</td>\n",
       "      <td>SC</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>power</td>\n",
       "      <td>power-aft</td>\n",
       "      <td>1994</td>\n",
       "      <td>used</td>\n",
       "      <td>44.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>109900.0</td>\n",
       "      <td>17942</td>\n",
       "      <td>MD</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18898</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2013</td>\n",
       "      <td>used</td>\n",
       "      <td>25.00</td>\n",
       "      <td>8.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31973.0</td>\n",
       "      <td>34647</td>\n",
       "      <td>GA</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18899</th>\n",
       "      <td>power</td>\n",
       "      <td>power-runabout</td>\n",
       "      <td>2013</td>\n",
       "      <td>used</td>\n",
       "      <td>19.33</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2795.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>6335</td>\n",
       "      <td>MI</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18900</th>\n",
       "      <td>power</td>\n",
       "      <td>power-bay</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>22.00</td>\n",
       "      <td>7.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39995.0</td>\n",
       "      <td>65602</td>\n",
       "      <td>TX</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18901</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2004</td>\n",
       "      <td>used</td>\n",
       "      <td>25.00</td>\n",
       "      <td>8.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16900.0</td>\n",
       "      <td>3180</td>\n",
       "      <td>MO</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18902</th>\n",
       "      <td>power</td>\n",
       "      <td>power-cruiser</td>\n",
       "      <td>2002</td>\n",
       "      <td>new</td>\n",
       "      <td>26.58</td>\n",
       "      <td>9.42</td>\n",
       "      <td>6350.0</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>17900.0</td>\n",
       "      <td>152266</td>\n",
       "      <td>GA</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18903 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        type             boatClass  year condition  length_ft  beam_ft  \\\n",
       "0      power          power-center  1992      used      21.00     8.50   \n",
       "1      power    power-sportcruiser  2018      used      40.00    11.00   \n",
       "2      power            power-deck  2020       new      18.00     7.42   \n",
       "3      power  power-expresscruiser  2015      used      32.00    10.33   \n",
       "4      power             power-aft  1994      used      44.00    15.00   \n",
       "...      ...                   ...   ...       ...        ...      ...   \n",
       "18898  power         power-pontoon  2013      used      25.00     8.50   \n",
       "18899  power        power-runabout  2013      used      19.33     8.00   \n",
       "18900  power             power-bay  2019       new      22.00     7.67   \n",
       "18901  power         power-pontoon  2004      used      25.00     8.50   \n",
       "18902  power         power-cruiser  2002       new      26.58     9.42   \n",
       "\n",
       "       dryWeight_lb hullMaterial  fuelType  numEngines  totalHP  \\\n",
       "0            3000.0   fiberglass  gasoline           1    150.0   \n",
       "1           16100.0   fiberglass    diesel           2    800.0   \n",
       "2            2000.0   fiberglass  gasoline           1     75.0   \n",
       "3           12650.0   fiberglass  gasoline           2    600.0   \n",
       "4           32000.0   fiberglass    diesel           2    700.0   \n",
       "...             ...          ...       ...         ...      ...   \n",
       "18898           NaN     aluminum  gasoline           0      NaN   \n",
       "18899        2795.0   fiberglass  gasoline           1      0.0   \n",
       "18900           NaN   fiberglass  gasoline           0      NaN   \n",
       "18901           NaN     aluminum  gasoline           0      NaN   \n",
       "18902        6350.0   fiberglass  gasoline           1    220.0   \n",
       "\n",
       "       maxEngineYear  minEngineYear     price  sellerId state  created_year  \n",
       "0                NaN            NaN   16500.0    217053    FL          2019  \n",
       "1             2018.0         2018.0  539000.0     44260    MI          2019  \n",
       "2             2019.0         2019.0   26995.0    220570    OH          2019  \n",
       "3                NaN            NaN  169995.0     34834    SC          2019  \n",
       "4             1994.0         1994.0  109900.0     17942    MD          2018  \n",
       "...              ...            ...       ...       ...   ...           ...  \n",
       "18898            NaN            NaN   31973.0     34647    GA          2019  \n",
       "18899            NaN            NaN   26995.0      6335    MI          2019  \n",
       "18900            NaN            NaN   39995.0     65602    TX          2018  \n",
       "18901            NaN            NaN   16900.0      3180    MO          2019  \n",
       "18902         2002.0         2002.0   17900.0    152266    GA          2019  \n",
       "\n",
       "[18903 rows x 17 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DROP COLUMN\n",
    "print(df_nan_obj_col[1])\n",
    "df_clean = df_clean.drop([df_nan_obj_col[1]], axis=1)\n",
    "df_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bca48bcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preview the missing Column\n",
    "plt.figure(figsize=(12,5))\n",
    "sns.heatmap(df_clean.isnull(), yticklabels=False, cbar =True, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78d41b2f",
   "metadata": {},
   "source": [
    "#### 3.2.2 FOR NUMERIC DATA TYPE\n",
    "##### >>>>>> 3.2.2.1  `IF MISSING VALUE IS UP TO 1000 IN A NUMERIC TYPE COLUMN, DROP THE COLUMN`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a3ab2291",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18903 entries, 0 to 18902\n",
      "Data columns (total 17 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   type           18903 non-null  object \n",
      " 1   boatClass      18903 non-null  object \n",
      " 2   year           18903 non-null  int64  \n",
      " 3   condition      18903 non-null  object \n",
      " 4   length_ft      18903 non-null  float64\n",
      " 5   beam_ft        12399 non-null  float64\n",
      " 6   dryWeight_lb   7094 non-null   float64\n",
      " 7   hullMaterial   18903 non-null  object \n",
      " 8   fuelType       18903 non-null  object \n",
      " 9   numEngines     18903 non-null  int64  \n",
      " 10  totalHP        18055 non-null  float64\n",
      " 11  maxEngineYear  2205 non-null   float64\n",
      " 12  minEngineYear  2174 non-null   float64\n",
      " 13  price          18903 non-null  float64\n",
      " 14  sellerId       18903 non-null  int64  \n",
      " 15  state          18903 non-null  object \n",
      " 16  created_year   18903 non-null  int64  \n",
      "dtypes: float64(7), int64(4), object(6)\n",
      "memory usage: 2.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "94df0655",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEAM_FT\n",
      "False    12399\n",
      "True      6504\n",
      "Name: beam_ft, dtype: int64\n",
      "\n",
      "DRYWEIGHT_LB\n",
      "True     11809\n",
      "False     7094\n",
      "Name: dryWeight_lb, dtype: int64\n",
      "\n",
      "TOTALHP\n",
      "False    18055\n",
      "True       848\n",
      "Name: totalHP, dtype: int64\n",
      "\n",
      "MAXENGINEYEAR\n",
      "True     16698\n",
      "False     2205\n",
      "Name: maxEngineYear, dtype: int64\n",
      "\n",
      "MINENGINEYEAR\n",
      "True     16729\n",
      "False     2174\n",
      "Name: minEngineYear, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check `int` or `Float` data-type column(s) with missing value\n",
    "df_nan_num_col = []\n",
    "for col in df_clean:\n",
    "    if (df_clean[col].dtypes == 'float64' or df_clean[col].dtypes == 'int64') and df_clean[col].isnull().any():\n",
    "        df_nan_num_col.append(col)\n",
    "        print(col.upper())\n",
    "        print(df_clean[col].isnull().value_counts())\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "643a402a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1677aa1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# VISUAL COLUMN WITH MISSING VALUE (in NUMERKIC TYPE)\n",
    "plt.figure(figsize=(9,5))\n",
    "sns.heatmap(df[df_nan_num_col].isnull(), yticklabels=False, cbar =True, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7beb62f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['beam_ft', 'dryWeight_lb', 'maxEngineYear', 'minEngineYear']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EXCLUDE COLUMN THAT DOES NOT HAVE UPTO 1,000 MISSING VALUE\n",
    "df_nan_to_drop_num_col = [col for col in df_nan_num_col if col != 'totalHP']\n",
    "df_nan_to_drop_num_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "afcb322c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DROP  ['beam_ft', 'dryWeight_lb', 'maxEngineYear', 'minEngineYear']\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>type</th>\n",
       "      <th>boatClass</th>\n",
       "      <th>year</th>\n",
       "      <th>condition</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>hullMaterial</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>numEngines</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>state</th>\n",
       "      <th>created_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>1992</td>\n",
       "      <td>used</td>\n",
       "      <td>21.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>150.0</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>217053</td>\n",
       "      <td>FL</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>power</td>\n",
       "      <td>power-sportcruiser</td>\n",
       "      <td>2018</td>\n",
       "      <td>used</td>\n",
       "      <td>40.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>800.0</td>\n",
       "      <td>539000.0</td>\n",
       "      <td>44260</td>\n",
       "      <td>MI</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>power</td>\n",
       "      <td>power-deck</td>\n",
       "      <td>2020</td>\n",
       "      <td>new</td>\n",
       "      <td>18.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>75.0</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>220570</td>\n",
       "      <td>OH</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>power</td>\n",
       "      <td>power-expresscruiser</td>\n",
       "      <td>2015</td>\n",
       "      <td>used</td>\n",
       "      <td>32.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>600.0</td>\n",
       "      <td>169995.0</td>\n",
       "      <td>34834</td>\n",
       "      <td>SC</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>power</td>\n",
       "      <td>power-aft</td>\n",
       "      <td>1994</td>\n",
       "      <td>used</td>\n",
       "      <td>44.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>700.0</td>\n",
       "      <td>109900.0</td>\n",
       "      <td>17942</td>\n",
       "      <td>MD</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18898</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2013</td>\n",
       "      <td>used</td>\n",
       "      <td>25.00</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31973.0</td>\n",
       "      <td>34647</td>\n",
       "      <td>GA</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18899</th>\n",
       "      <td>power</td>\n",
       "      <td>power-runabout</td>\n",
       "      <td>2013</td>\n",
       "      <td>used</td>\n",
       "      <td>19.33</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>6335</td>\n",
       "      <td>MI</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18900</th>\n",
       "      <td>power</td>\n",
       "      <td>power-bay</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>22.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39995.0</td>\n",
       "      <td>65602</td>\n",
       "      <td>TX</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18901</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2004</td>\n",
       "      <td>used</td>\n",
       "      <td>25.00</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16900.0</td>\n",
       "      <td>3180</td>\n",
       "      <td>MO</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18902</th>\n",
       "      <td>power</td>\n",
       "      <td>power-cruiser</td>\n",
       "      <td>2002</td>\n",
       "      <td>new</td>\n",
       "      <td>26.58</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>17900.0</td>\n",
       "      <td>152266</td>\n",
       "      <td>GA</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18903 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        type             boatClass  year condition  length_ft hullMaterial  \\\n",
       "0      power          power-center  1992      used      21.00   fiberglass   \n",
       "1      power    power-sportcruiser  2018      used      40.00   fiberglass   \n",
       "2      power            power-deck  2020       new      18.00   fiberglass   \n",
       "3      power  power-expresscruiser  2015      used      32.00   fiberglass   \n",
       "4      power             power-aft  1994      used      44.00   fiberglass   \n",
       "...      ...                   ...   ...       ...        ...          ...   \n",
       "18898  power         power-pontoon  2013      used      25.00     aluminum   \n",
       "18899  power        power-runabout  2013      used      19.33   fiberglass   \n",
       "18900  power             power-bay  2019       new      22.00   fiberglass   \n",
       "18901  power         power-pontoon  2004      used      25.00     aluminum   \n",
       "18902  power         power-cruiser  2002       new      26.58   fiberglass   \n",
       "\n",
       "       fuelType  numEngines  totalHP     price  sellerId state  created_year  \n",
       "0      gasoline           1    150.0   16500.0    217053    FL          2019  \n",
       "1        diesel           2    800.0  539000.0     44260    MI          2019  \n",
       "2      gasoline           1     75.0   26995.0    220570    OH          2019  \n",
       "3      gasoline           2    600.0  169995.0     34834    SC          2019  \n",
       "4        diesel           2    700.0  109900.0     17942    MD          2018  \n",
       "...         ...         ...      ...       ...       ...   ...           ...  \n",
       "18898  gasoline           0      NaN   31973.0     34647    GA          2019  \n",
       "18899  gasoline           1      0.0   26995.0      6335    MI          2019  \n",
       "18900  gasoline           0      NaN   39995.0     65602    TX          2018  \n",
       "18901  gasoline           0      NaN   16900.0      3180    MO          2019  \n",
       "18902  gasoline           1    220.0   17900.0    152266    GA          2019  \n",
       "\n",
       "[18903 rows x 13 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DROP COLUMN THAT HAVE MORE THAN 1,000 MISSING VALUE\n",
    "print('DROP ', df_nan_to_drop_num_col)\n",
    "df_clean = df_clean.drop(df_nan_to_drop_num_col, axis=1)\n",
    "#df_clean.drop(df_nan_num_col, axis=1, inplace=True)\n",
    "df_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "36fa1c4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18903 entries, 0 to 18902\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   type          18903 non-null  object \n",
      " 1   boatClass     18903 non-null  object \n",
      " 2   year          18903 non-null  int64  \n",
      " 3   condition     18903 non-null  object \n",
      " 4   length_ft     18903 non-null  float64\n",
      " 5   hullMaterial  18903 non-null  object \n",
      " 6   fuelType      18903 non-null  object \n",
      " 7   numEngines    18903 non-null  int64  \n",
      " 8   totalHP       18055 non-null  float64\n",
      " 9   price         18903 non-null  float64\n",
      " 10  sellerId      18903 non-null  int64  \n",
      " 11  state         18903 non-null  object \n",
      " 12  created_year  18903 non-null  int64  \n",
      "dtypes: float64(3), int64(4), object(6)\n",
      "memory usage: 1.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36a50967",
   "metadata": {},
   "source": [
    " ##### >>>>>> 3.2.2.1  `IF MISSING VALUE IS LESS THAN 1000 IN A NUMERIC TYPE COLUMN, REMOVE NaN ROW`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2546566a",
   "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>type</th>\n",
       "      <th>boatClass</th>\n",
       "      <th>year</th>\n",
       "      <th>condition</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>hullMaterial</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>numEngines</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>state</th>\n",
       "      <th>created_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>1992</td>\n",
       "      <td>used</td>\n",
       "      <td>21.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>150.0</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>217053</td>\n",
       "      <td>FL</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>power</td>\n",
       "      <td>power-sportcruiser</td>\n",
       "      <td>2018</td>\n",
       "      <td>used</td>\n",
       "      <td>40.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>800.0</td>\n",
       "      <td>539000.0</td>\n",
       "      <td>44260</td>\n",
       "      <td>MI</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>power</td>\n",
       "      <td>power-deck</td>\n",
       "      <td>2020</td>\n",
       "      <td>new</td>\n",
       "      <td>18.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>75.0</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>220570</td>\n",
       "      <td>OH</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>power</td>\n",
       "      <td>power-expresscruiser</td>\n",
       "      <td>2015</td>\n",
       "      <td>used</td>\n",
       "      <td>32.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>600.0</td>\n",
       "      <td>169995.0</td>\n",
       "      <td>34834</td>\n",
       "      <td>SC</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>power</td>\n",
       "      <td>power-aft</td>\n",
       "      <td>1994</td>\n",
       "      <td>used</td>\n",
       "      <td>44.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>700.0</td>\n",
       "      <td>109900.0</td>\n",
       "      <td>17942</td>\n",
       "      <td>MD</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18895</th>\n",
       "      <td>power</td>\n",
       "      <td>power-house</td>\n",
       "      <td>2005</td>\n",
       "      <td>used</td>\n",
       "      <td>100.00</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>425000.0</td>\n",
       "      <td>34888</td>\n",
       "      <td>KY</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18896</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>1990</td>\n",
       "      <td>used</td>\n",
       "      <td>28.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>1</td>\n",
       "      <td>315.0</td>\n",
       "      <td>49000.0</td>\n",
       "      <td>61420</td>\n",
       "      <td>FL</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18897</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pilot</td>\n",
       "      <td>1973</td>\n",
       "      <td>used</td>\n",
       "      <td>29.00</td>\n",
       "      <td>other</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>32168</td>\n",
       "      <td>GA</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18899</th>\n",
       "      <td>power</td>\n",
       "      <td>power-runabout</td>\n",
       "      <td>2013</td>\n",
       "      <td>used</td>\n",
       "      <td>19.33</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>6335</td>\n",
       "      <td>MI</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18902</th>\n",
       "      <td>power</td>\n",
       "      <td>power-cruiser</td>\n",
       "      <td>2002</td>\n",
       "      <td>new</td>\n",
       "      <td>26.58</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>17900.0</td>\n",
       "      <td>152266</td>\n",
       "      <td>GA</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18055 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        type             boatClass  year condition  length_ft hullMaterial  \\\n",
       "0      power          power-center  1992      used      21.00   fiberglass   \n",
       "1      power    power-sportcruiser  2018      used      40.00   fiberglass   \n",
       "2      power            power-deck  2020       new      18.00   fiberglass   \n",
       "3      power  power-expresscruiser  2015      used      32.00   fiberglass   \n",
       "4      power             power-aft  1994      used      44.00   fiberglass   \n",
       "...      ...                   ...   ...       ...        ...          ...   \n",
       "18895  power           power-house  2005      used     100.00     aluminum   \n",
       "18896  power          power-center  1990      used      28.00   fiberglass   \n",
       "18897  power           power-pilot  1973      used      29.00        other   \n",
       "18899  power        power-runabout  2013      used      19.33   fiberglass   \n",
       "18902  power         power-cruiser  2002       new      26.58   fiberglass   \n",
       "\n",
       "       fuelType  numEngines  totalHP     price  sellerId state  created_year  \n",
       "0      gasoline           1    150.0   16500.0    217053    FL          2019  \n",
       "1        diesel           2    800.0  539000.0     44260    MI          2019  \n",
       "2      gasoline           1     75.0   26995.0    220570    OH          2019  \n",
       "3      gasoline           2    600.0  169995.0     34834    SC          2019  \n",
       "4        diesel           2    700.0  109900.0     17942    MD          2018  \n",
       "...         ...         ...      ...       ...       ...   ...           ...  \n",
       "18895  gasoline           2      0.0  425000.0     34888    KY          2018  \n",
       "18896    diesel           1    315.0   49000.0     61420    FL          2019  \n",
       "18897  gasoline           1      0.0   10000.0     32168    GA          2017  \n",
       "18899  gasoline           1      0.0   26995.0      6335    MI          2019  \n",
       "18902  gasoline           1    220.0   17900.0    152266    GA          2019  \n",
       "\n",
       "[18055 rows x 13 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean = df_clean[df_clean['totalHP'].notnull()]\n",
    "df_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "91c993a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "sns.heatmap(df_clean.isnull(), yticklabels=False, cbar =True, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c783f1f",
   "metadata": {},
   "source": [
    "## 4.0 EXPLORATORY DATA ANALYSIS (EDA) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b288856d",
   "metadata": {},
   "source": [
    "### >> 4.1 DATA EXPLORATORY\n",
    "+ Detect Outlier From numeric Column (Price, length_ft, etc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "616d7d90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year\n",
      "length_ft\n",
      "numEngines\n",
      "totalHP\n",
      "price\n",
      "sellerId\n",
      "created_year\n"
     ]
    }
   ],
   "source": [
    "# Check `int` or `Float` data-type column(s) \n",
    "for col in df_clean:\n",
    "    if (df_clean[col].dtypes == 'float64' or df_clean[col].dtypes == 'int64'):\n",
    "        print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4753d022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>numEngines</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>created_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.222239</td>\n",
       "      <td>-0.300089</td>\n",
       "      <td>-0.194967</td>\n",
       "      <td>-0.028056</td>\n",
       "      <td>0.065940</td>\n",
       "      <td>0.275208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length_ft</th>\n",
       "      <td>-0.222239</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.351975</td>\n",
       "      <td>0.357813</td>\n",
       "      <td>0.343944</td>\n",
       "      <td>-0.057879</td>\n",
       "      <td>-0.118081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numEngines</th>\n",
       "      <td>-0.300089</td>\n",
       "      <td>0.351975</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.625390</td>\n",
       "      <td>0.366473</td>\n",
       "      <td>-0.084159</td>\n",
       "      <td>-0.244772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totalHP</th>\n",
       "      <td>-0.194967</td>\n",
       "      <td>0.357813</td>\n",
       "      <td>0.625390</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.589721</td>\n",
       "      <td>-0.024495</td>\n",
       "      <td>-0.213557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.028056</td>\n",
       "      <td>0.343944</td>\n",
       "      <td>0.366473</td>\n",
       "      <td>0.589721</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.016534</td>\n",
       "      <td>-0.099106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sellerId</th>\n",
       "      <td>0.065940</td>\n",
       "      <td>-0.057879</td>\n",
       "      <td>-0.084159</td>\n",
       "      <td>-0.024495</td>\n",
       "      <td>-0.016534</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.060988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_year</th>\n",
       "      <td>0.275208</td>\n",
       "      <td>-0.118081</td>\n",
       "      <td>-0.244772</td>\n",
       "      <td>-0.213557</td>\n",
       "      <td>-0.099106</td>\n",
       "      <td>0.060988</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  year  length_ft  numEngines   totalHP     price  sellerId  \\\n",
       "year          1.000000  -0.222239   -0.300089 -0.194967 -0.028056  0.065940   \n",
       "length_ft    -0.222239   1.000000    0.351975  0.357813  0.343944 -0.057879   \n",
       "numEngines   -0.300089   0.351975    1.000000  0.625390  0.366473 -0.084159   \n",
       "totalHP      -0.194967   0.357813    0.625390  1.000000  0.589721 -0.024495   \n",
       "price        -0.028056   0.343944    0.366473  0.589721  1.000000 -0.016534   \n",
       "sellerId      0.065940  -0.057879   -0.084159 -0.024495 -0.016534  1.000000   \n",
       "created_year  0.275208  -0.118081   -0.244772 -0.213557 -0.099106  0.060988   \n",
       "\n",
       "              created_year  \n",
       "year              0.275208  \n",
       "length_ft        -0.118081  \n",
       "numEngines       -0.244772  \n",
       "totalHP          -0.213557  \n",
       "price            -0.099106  \n",
       "sellerId          0.060988  \n",
       "created_year      1.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sns.pairplot(df_clean)\n",
    "#df_clean.corr()\n",
    "#df_clean.drop('sellerId', axis=1, inplace=True )\n",
    "df_clean.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aebafb8f",
   "metadata": {},
   "source": [
    "#### >>>> 4.1.1 CHECK FOR PRICE OUTLIER "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7a6806b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.boxplot(x='price', y='length_ft', data=df_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e7057d4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e32a094f10>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_clean['price'], df_clean['length_ft'], color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "26cc5a2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min price 500.0\n",
      "MAX Price 14000000.0\n"
     ]
    }
   ],
   "source": [
    "# Remove record with 5,000,000 up ward\n",
    "print('min price', df_clean['price'].min())\n",
    "print('MAX Price', df_clean['price'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a0d1cbc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e32edecc70>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1224x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17,9))\n",
    "sns.displot(df_clean['price'], kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "849f7fa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e360314d60>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1224x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17,9))\n",
    "sns.displot(df_clean[(df_clean['price']<150000)]['price'], kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "938f31bb",
   "metadata": {},
   "source": [
    "#### `>>>>>> REMOVE THE OUTLIER (SET MAX PRICE TO `150,000`)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0e3beefc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e32b078e80>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove record with 5,000,000 up ward\n",
    "#df_clean_outlier =df_clean[(df_clean['price'] < 5000000)]  # 5m\n",
    "#df_clean_outlier =df_clean[(df_clean['price'] < 2000000)]  #2m\n",
    "#df_clean_outlier =df_clean[(df_clean['price'] < 1000000)]  #1m\n",
    "#df_clean_outlier =df_clean[(df_clean['price'] < 999999)]   #1m -1\n",
    "#df_clean_outlier =df_clean[(df_clean['price'] < 500000)]   #500k\n",
    "#df_clean_outlier =df_clean[(df_clean['price'] < 400000)]   #400k\n",
    "df_clean_outlier = df_clean[(df_clean['price']<300000)]   #300k\n",
    "#df_clean_outlier = df_clean[(df_clean['price']<200000)]   #200k\n",
    "#df_clean_outlier = df_clean[(df_clean['price']<150000)]   #150k\n",
    "\n",
    "\n",
    "sns.displot(df_clean_outlier['price'], kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "982e6585",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min price 500.0\n",
      "MAX Price 299999.0\n",
      "Lenght (17575, 13)\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>type</th>\n",
       "      <th>boatClass</th>\n",
       "      <th>year</th>\n",
       "      <th>condition</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>hullMaterial</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>numEngines</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>state</th>\n",
       "      <th>created_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3339</th>\n",
       "      <td>power</td>\n",
       "      <td>power-house</td>\n",
       "      <td>2004</td>\n",
       "      <td>used</td>\n",
       "      <td>84.00</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299999.0</td>\n",
       "      <td>29402</td>\n",
       "      <td>MO</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16160</th>\n",
       "      <td>power</td>\n",
       "      <td>power-cruiser</td>\n",
       "      <td>2015</td>\n",
       "      <td>used</td>\n",
       "      <td>38.50</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>430.0</td>\n",
       "      <td>299995.0</td>\n",
       "      <td>35188</td>\n",
       "      <td>OH</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16383</th>\n",
       "      <td>power</td>\n",
       "      <td>power-cuddy</td>\n",
       "      <td>2018</td>\n",
       "      <td>used</td>\n",
       "      <td>33.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>600.0</td>\n",
       "      <td>299995.0</td>\n",
       "      <td>14386</td>\n",
       "      <td>FL</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16531</th>\n",
       "      <td>power</td>\n",
       "      <td>power-cruiser</td>\n",
       "      <td>2017</td>\n",
       "      <td>new</td>\n",
       "      <td>36.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>760.0</td>\n",
       "      <td>299948.0</td>\n",
       "      <td>14353</td>\n",
       "      <td>WI</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13209</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>33.33</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299900.0</td>\n",
       "      <td>1176</td>\n",
       "      <td>VA</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13163</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>33.33</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299900.0</td>\n",
       "      <td>1102</td>\n",
       "      <td>FL</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13169</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>33.33</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299900.0</td>\n",
       "      <td>1107</td>\n",
       "      <td>NC</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13174</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>33.33</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299900.0</td>\n",
       "      <td>1110</td>\n",
       "      <td>TX</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13179</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>33.33</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299900.0</td>\n",
       "      <td>1114</td>\n",
       "      <td>TX</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17208</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>2018</td>\n",
       "      <td>used</td>\n",
       "      <td>29.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>600.0</td>\n",
       "      <td>299900.0</td>\n",
       "      <td>6654</td>\n",
       "      <td>MA</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13193</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>33.33</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299900.0</td>\n",
       "      <td>1120</td>\n",
       "      <td>MD</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13197</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>33.33</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299900.0</td>\n",
       "      <td>1166</td>\n",
       "      <td>FL</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10588</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>2017</td>\n",
       "      <td>used</td>\n",
       "      <td>34.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>3</td>\n",
       "      <td>900.0</td>\n",
       "      <td>299900.0</td>\n",
       "      <td>59565</td>\n",
       "      <td>FL</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13215</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>33.33</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299900.0</td>\n",
       "      <td>1196</td>\n",
       "      <td>SC</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13229</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>33.33</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299900.0</td>\n",
       "      <td>1237</td>\n",
       "      <td>TX</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13237</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>33.33</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299900.0</td>\n",
       "      <td>1255</td>\n",
       "      <td>FL</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11148</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>33.33</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299900.0</td>\n",
       "      <td>56985</td>\n",
       "      <td>NJ</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11153</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>33.33</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299900.0</td>\n",
       "      <td>60580</td>\n",
       "      <td>CT</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18087</th>\n",
       "      <td>power</td>\n",
       "      <td>power-downeast</td>\n",
       "      <td>2003</td>\n",
       "      <td>used</td>\n",
       "      <td>40.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>1</td>\n",
       "      <td>660.0</td>\n",
       "      <td>299900.0</td>\n",
       "      <td>62480</td>\n",
       "      <td>NY</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11226</th>\n",
       "      <td>power</td>\n",
       "      <td>power-highperf</td>\n",
       "      <td>1991</td>\n",
       "      <td>used</td>\n",
       "      <td>41.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>299900.0</td>\n",
       "      <td>56218</td>\n",
       "      <td>FL</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        type       boatClass  year condition  length_ft hullMaterial  \\\n",
       "3339   power     power-house  2004      used      84.00        other   \n",
       "16160  power   power-cruiser  2015      used      38.50   fiberglass   \n",
       "16383  power     power-cuddy  2018      used      33.00   fiberglass   \n",
       "16531  power   power-cruiser  2017       new      36.00   fiberglass   \n",
       "13209  power    power-center  2019       new      33.33   fiberglass   \n",
       "13163  power    power-center  2019       new      33.33   fiberglass   \n",
       "13169  power    power-center  2019       new      33.33   fiberglass   \n",
       "13174  power    power-center  2019       new      33.33   fiberglass   \n",
       "13179  power    power-center  2019       new      33.33   fiberglass   \n",
       "17208  power    power-center  2018      used      29.00   fiberglass   \n",
       "13193  power    power-center  2019       new      33.33   fiberglass   \n",
       "13197  power    power-center  2019       new      33.33   fiberglass   \n",
       "10588  power    power-center  2017      used      34.00   fiberglass   \n",
       "13215  power    power-center  2019       new      33.33   fiberglass   \n",
       "13229  power    power-center  2019       new      33.33   fiberglass   \n",
       "13237  power    power-center  2019       new      33.33   fiberglass   \n",
       "11148  power    power-center  2019       new      33.33   fiberglass   \n",
       "11153  power    power-center  2019       new      33.33   fiberglass   \n",
       "18087  power  power-downeast  2003      used      40.00   fiberglass   \n",
       "11226  power  power-highperf  1991      used      41.00   fiberglass   \n",
       "\n",
       "       fuelType  numEngines  totalHP     price  sellerId state  created_year  \n",
       "3339      other           1      0.0  299999.0     29402    MO          2019  \n",
       "16160  gasoline           1    430.0  299995.0     35188    OH          2019  \n",
       "16383  gasoline           2    600.0  299995.0     14386    FL          2019  \n",
       "16531  gasoline           2    760.0  299948.0     14353    WI          2016  \n",
       "13209  gasoline           2      0.0  299900.0      1176    VA          2018  \n",
       "13163  gasoline           2      0.0  299900.0      1102    FL          2018  \n",
       "13169  gasoline           2      0.0  299900.0      1107    NC          2018  \n",
       "13174  gasoline           2      0.0  299900.0      1110    TX          2018  \n",
       "13179  gasoline           2      0.0  299900.0      1114    TX          2018  \n",
       "17208  gasoline           2    600.0  299900.0      6654    MA          2019  \n",
       "13193  gasoline           2      0.0  299900.0      1120    MD          2018  \n",
       "13197  gasoline           2      0.0  299900.0      1166    FL          2018  \n",
       "10588  gasoline           3    900.0  299900.0     59565    FL          2017  \n",
       "13215  gasoline           2      0.0  299900.0      1196    SC          2018  \n",
       "13229  gasoline           2      0.0  299900.0      1237    TX          2018  \n",
       "13237  gasoline           2      0.0  299900.0      1255    FL          2018  \n",
       "11148  gasoline           2      0.0  299900.0     56985    NJ          2018  \n",
       "11153  gasoline           2      0.0  299900.0     60580    CT          2018  \n",
       "18087    diesel           1    660.0  299900.0     62480    NY          2018  \n",
       "11226  gasoline           2   2000.0  299900.0     56218    FL          2017  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross Check\n",
    "print('min price', df_clean_outlier['price'].min())\n",
    "print('MAX Price', df_clean_outlier['price'].max())\n",
    "print('Lenght', df_clean_outlier.shape)\n",
    "df_clean_outlier.sort_values('price',  ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d23cfa3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 17575 entries, 0 to 18902\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   type          17575 non-null  object \n",
      " 1   boatClass     17575 non-null  object \n",
      " 2   year          17575 non-null  int64  \n",
      " 3   condition     17575 non-null  object \n",
      " 4   length_ft     17575 non-null  float64\n",
      " 5   hullMaterial  17575 non-null  object \n",
      " 6   fuelType      17575 non-null  object \n",
      " 7   numEngines    17575 non-null  int64  \n",
      " 8   totalHP       17575 non-null  float64\n",
      " 9   price         17575 non-null  float64\n",
      " 10  sellerId      17575 non-null  int64  \n",
      " 11  state         17575 non-null  object \n",
      " 12  created_year  17575 non-null  int64  \n",
      "dtypes: float64(3), int64(4), object(6)\n",
      "memory usage: 1.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df_clean_outlier.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "842e8865",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>numEngines</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>created_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.209106</td>\n",
       "      <td>-0.383953</td>\n",
       "      <td>-0.243446</td>\n",
       "      <td>-0.002013</td>\n",
       "      <td>0.067254</td>\n",
       "      <td>0.277788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length_ft</th>\n",
       "      <td>-0.209106</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.281093</td>\n",
       "      <td>0.211712</td>\n",
       "      <td>0.272732</td>\n",
       "      <td>-0.055802</td>\n",
       "      <td>-0.087105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numEngines</th>\n",
       "      <td>-0.383953</td>\n",
       "      <td>0.281093</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.604972</td>\n",
       "      <td>0.446067</td>\n",
       "      <td>-0.079950</td>\n",
       "      <td>-0.265331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totalHP</th>\n",
       "      <td>-0.243446</td>\n",
       "      <td>0.211712</td>\n",
       "      <td>0.604972</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420166</td>\n",
       "      <td>-0.028428</td>\n",
       "      <td>-0.232177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.002013</td>\n",
       "      <td>0.272732</td>\n",
       "      <td>0.446067</td>\n",
       "      <td>0.420166</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012126</td>\n",
       "      <td>-0.103600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sellerId</th>\n",
       "      <td>0.067254</td>\n",
       "      <td>-0.055802</td>\n",
       "      <td>-0.079950</td>\n",
       "      <td>-0.028428</td>\n",
       "      <td>-0.012126</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_year</th>\n",
       "      <td>0.277788</td>\n",
       "      <td>-0.087105</td>\n",
       "      <td>-0.265331</td>\n",
       "      <td>-0.232177</td>\n",
       "      <td>-0.103600</td>\n",
       "      <td>0.059163</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  year  length_ft  numEngines   totalHP     price  sellerId  \\\n",
       "year          1.000000  -0.209106   -0.383953 -0.243446 -0.002013  0.067254   \n",
       "length_ft    -0.209106   1.000000    0.281093  0.211712  0.272732 -0.055802   \n",
       "numEngines   -0.383953   0.281093    1.000000  0.604972  0.446067 -0.079950   \n",
       "totalHP      -0.243446   0.211712    0.604972  1.000000  0.420166 -0.028428   \n",
       "price        -0.002013   0.272732    0.446067  0.420166  1.000000 -0.012126   \n",
       "sellerId      0.067254  -0.055802   -0.079950 -0.028428 -0.012126  1.000000   \n",
       "created_year  0.277788  -0.087105   -0.265331 -0.232177 -0.103600  0.059163   \n",
       "\n",
       "              created_year  \n",
       "year              0.277788  \n",
       "length_ft        -0.087105  \n",
       "numEngines       -0.265331  \n",
       "totalHP          -0.232177  \n",
       "price            -0.103600  \n",
       "sellerId          0.059163  \n",
       "created_year      1.000000  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_outlier.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "07824261",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e36060ae50>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAD4CAYAAAANbUbJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcj0lEQVR4nO3dXYwc13nm8f87wyGVIe0lORobjCTOyI4ShAoWsj0QYigwdjECpAgB5FwEoDE2hI2BSUYyYAfJhRQCu8rFIIkS52OxsBfjRI68M7CixF5EF0oQiXDgDWJYGSnUBy0woi2SokVIVGSt7RCgLfLdi6reqempqj7VXd1dH88PKHT36erqc7qq3zp1zqkqc3dERKTZJsadARERGT4FexGRFlCwFxFpAQV7EZEWULAXEWmBXePOAMC1117r8/Pz486GiEitPPPMM2+6+2zIvJUI9vPz82xubo47GyIitWJmZ0PnVTOOiEgLKNiLiLSAgr2ISAso2IuItICCvYhICyjYV83GBszPw8RE9LixMe4ciUgDVGLopcQ2NmB5GS5dil6fPRu9BlhaGl++RKT2VLOvkmPHtgJ9x6VLUbqIyAAU7Kvk3Lli6SIigRTsq+Tw4WLpIiKBFOyrZHUVpqe3p01PR+kiIgNQsK+SpSVYW4O5OTCLHtfW1DkrIgPTaJyqWVpScBeR0qlmLyLSAgr2IiItoGAvItICCvYiIi2gYC8i0gIK9mXRBcxEpMI09LIMuoCZiFScavZl0AXMRKTiFOzLoAuYiUjFKdiXQRcwE5GKU7Avgy5gJiIVp2BfBl3ATEQqrmewN7MbzOxrZvaSmZ00s0/H6Q+a2XfN7EQ83ZX4zANmdtrMTpnZHcMsQCVsbESdsefORU03q6sK9CJSKSFDL98BftPdnzWzdwHPmNmT8Xt/7O5/mJzZzI4AR4GbgZ8EnjKzn3b3K2VmvDI07FJEaqBnzd7dL7j7s/HzHwAvAdflfORu4FF3v+zurwCngVvLyGwladiliNRAoTZ7M5sHPgB8M076lJk9b2YPm9mBOO064NXEx86TsnMws2Uz2zSzzYsXLxbPeVVo2KWI1EBwsDezfcBXgM+4+/eBzwPvB24BLgCf7cya8nHfkeC+5u4L7r4wOztbNN/VoWGXIlIDQcHezKaIAv2Gu38VwN1fd/cr7n4V+AJbTTXngRsSH78eeK28LFeMhl2KSA2EjMYx4M+Bl9z9jxLphxKz/TLwYvz8ceCome0xsxuBm4Cny8tyxWjYpYjUQMhonNuATwAvmNmJOO23gY+Z2S1ETTRngF8DcPeTZvYY8C2ikTz3NXYkTofuGysiFdcz2Lv7P5LeDv9EzmdWAbVjiIhUhM6gFRFpAQV7EZEWULAXEWkBBXsRkRZQsBcRaQEFexGRFlCwFxFpAQV7EZEWULAXEWkBBXsRkRZQsBcRaQEFexGRFlCwFxFpAQV7EZEWULAXEWkBBXsRkRZQsBcRaQEFexGRFlCwFxFpAQV7EZEWULAXEWkBBXsRkRZQsBcRaQEFexGRFlCwFxFpAQV7EZEWULAXEWmBnsHezG4ws6+Z2UtmdtLMPh2nHzSzJ83s5fjxQOIzD5jZaTM7ZWZ3DLMAIiLSW0jN/h3gN939Z4GfB+4zsyPA/cBxd78JOB6/Jn7vKHAzcCfwOTObHEbmRUQkTM9g7+4X3P3Z+PkPgJeA64C7gUfi2R4BPho/vxt41N0vu/srwGng1pLzLSIiBRRqszezeeADwDeB97r7BYh2CMB74tmuA15NfOx8nNa9rGUz2zSzzYsXL/aRdRERCRUc7M1sH/AV4DPu/v28WVPSfEeC+5q7L7j7wuzsbGg2RESkD0HB3symiAL9hrt/NU5+3cwOxe8fAt6I088DNyQ+fj3wWjnZFRGRfoSMxjHgz4GX3P2PEm89DtwTP78H+JtE+lEz22NmNwI3AU+Xl2URESlqV8A8twGfAF4wsxNx2m8Dvwc8ZmafBM4BvwLg7ifN7DHgW0Qjee5z9ytlZ1xERML1DPbu/o+kt8MDLGZ8ZhVYHSBfIiJSIp1BKyLSAgr2IiItoGAvItICCvYiIi2gYC8i0gIK9iIiLaBgLyLSAgr2IiItoGAvItICCvYiIi2gYC8i0gIK9iIiLaBg321jA+bnYWIietzYGHeOREQGFnKJ4/bY2IDlZbh0KXp99mz0GmBpaXz5EhEZkGr2SceObQX6jkuXonQRkRpTsE86d65YuohITSjYJx0+XCxdRKQmFOyTVldhenp72vR0lC4iUmMK9klLS7C2BnNzYBY9rq2pc1ZEak+jcbotLSm4i0jjqGYvItICCvYiIi2gYC8i0gIK9iIiLaBgLyLSAgr2IiIt0DPYm9nDZvaGmb2YSHvQzL5rZifi6a7Eew+Y2WkzO2Vmdwwr4yIiEi6kZv8XwJ0p6X/s7rfE0xMAZnYEOArcHH/mc2Y2WVZmRUSkPz2Dvbt/HXgrcHl3A4+6+2V3fwU4Ddw6QP5ERKQEg7TZf8rMno+beQ7EadcBrybmOR+niYjIGPUb7D8PvB+4BbgAfDZOt5R5PW0BZrZsZptmtnnx4sU+syEiIiH6Cvbu/rq7X3H3q8AX2GqqOQ/ckJj1euC1jGWsufuCuy/Mzs72kw0REQnUV7A3s0OJl78MdEbqPA4cNbM9ZnYjcBPw9GBZFBGRQYUMvfwy8A3gZ8zsvJl9EnjIzF4ws+eB/wz8BoC7nwQeA74F/B1wn7tfGVru09x+e3R54s50++1R+oED29PT5hERaShzT21SH6mFhQXf3NwcfEG33w7Hj+9Mn5iAq1fzP7u4CE89NXgeRERGxMyecfeFkHmbdQZtWqCH3oE+77MiIg3QrGAvIiKpFOxFRFqgWcF+cXHcORARqaRmBfunnlLAFxFJ0ZwbjmeNxBERkYbU7BXoRURyNSPYK9CLiORqRrAXEZFcCvYiIi3QjGCvETgiIrmaEezLGHJZgWsEiYgMSzOCPUQB3x327+89r/vOSUSkwZoT7CEagvn22+POhYhI5TQn2GusvYhIpuYEewV6EZFMzQn2IiKSScFeRKQFmhPsQ4dehozWERFpmOYE+5Cx9vv3w/e+N5LsiIhUSXOCPWyNtXffGfgXFxXoRaS1mhXsO9KGYR4/HqWLiLRQM4N91jBMDc8UkZZqZrAXEZFtFOxFRFqgmcE+a1SOLoUsIi3VzGCfNgxzcTFKFxFpoZ7B3sweNrM3zOzFRNpBM3vSzF6OHw8k3nvAzE6b2Skzu2NYGe8pOQzTXYFeRFotpGb/F8CdXWn3A8fd/SbgePwaMzsCHAVujj/zOTObLC23IiLSl57B3t2/DrzVlXw38Ej8/BHgo4n0R939sru/ApwGbi0nqyIi0q9+2+zf6+4XAOLH98Tp1wGvJuY7H6ftYGbLZrZpZpsXL17sMxsiIhKi7A5aS0lLveefu6+5+4K7L8zOzpacDRERSeo32L9uZocA4sc34vTzwA2J+a4HXus/eyIiUoZ+g/3jwD3x83uAv0mkHzWzPWZ2I3AT8PRgWRQRkUHt6jWDmX0Z+E/AtWZ2HvhvwO8Bj5nZJ4FzwK8AuPtJM3sM+BbwDnCfu18ZUt5FRCRQz2Dv7h/LeCv1dFR3XwVWB8mUiIiUq5ln0EJ0OWOzrUmXNxaRFmtmsNf17EVEtmlmsNf17EVEtmlmsBcRqbqNDZifh4mJ6HFjY6hf17ODVkRESraxAcvLcOlS9Prs2eg1wNLSUL6ymTV7Xc9eRKrs2LGtQN9x6VKUPiTNDPa6nr3UxYgP5aUizp0rll6C5jbjKLBL1Y3hUF4q4vDhaH2npQ9JM2v2InUwhkN5qYjVVZie3p42PR2lD0lzg70Oj6XqxnAoLxWxtARrazA3F530OTcXvR7iEV0zm3F0eCx1MIZDeamQpaWRxqNm1ux1eCx1MIZDeWmvZgZ7HR5LHYzhUF7aq5nBPuswWIfHzVDH/ph774Vdu6KgvmtX9BqiwH7mDFy9Gj2WHejr+Fu1wTjWi7uPffrQhz7kpVpfd5+edoetaXo6Spd6q+O6XVnZnt/OtLJS/netr7vPzbmbuc/MuO/eXa/fqg1K3IaBTQ+Ms2MP9D6MYO++faOfm9MG3hRzc+mBc2amuut7cjI9z5OT5X5PWhBJm+bmyv1eKSZrG+5jvRQJ9s1sxoHhHx6H0CF0+bL6Xf7t36KRLe5bo6/6/b3LXm9XMm7WlpXer7SBCWnUdzVeY+pTbG6wH7fO8M+yApBEQvtd+h19NYz1NjlZLL1focGi6X1XVa9kjatPMfQQYJjTUJpxxm2QQ7WqNUFVKT+hTRUQ5beoEg+x///vlpW/stvs875rHG32ZW836+tRc12y6a57mUXbw8exbavNvmHM+gtAVeuArFp+OnlK/kGTAWDQAN3vekvLY9ZOaXJyeJ2z3d85NRX9PqMIZsPsHF5fj8rS/Vvu3r19mUV21sPYtkN3HiXtZNoX7Ie5d+532f3WEMusWZahavlJU+RP22t9llXecf1uaeUbRe11GJ3DyXxndXJ3L7PIzrrsdTSGilG7gv0wf+BBlt3vZ8uqWfbKW+iffxT5GVTI4X1nvl7rJG0es+I18ar8bmk14qmp8gNQSBNSkfL321xXJIBnraPONtRrewr9DdK+e2Vlawc2wJFeu4L9MGtQgy67nxrVsGuERXdCWU0kMzPl5GdQRcoT+tuurOwMBEUrEFU5IhrV+ssLnP18b+jOo3uZZWwPadOuXeVV1Eo876JdwT5vIxv0kHUctbNhHwoWDUJFgsU4Orvyxt13C12fZQTqftfjoG2+3e3meQGsTKGBM3S7Cd15pC2zyG+YdhTX744qdLvJapKamAj4obdrV7DvtZENEiir1O5alqI7sND519d3dsj1WVsppTyw83fbty9sfZbZSZtcjysr+es1dAeRNd/KSnjTR9nBPqsDNWS7SStLrx3VIOul+/uT62SQ3yy0CTBv+QX/6+0K9iG1gBp1uAxd0R1Y6Px5f85h/l55f9BkHrMOndMOz4exkw9pOw/93qz58joxu6dBmnGyOoHTdva9vjfvyCx0x1Vm5atXwO9VAVtc3PmZ7phRYlnaFezde28MWXv+kBp0lcaYl6Gfccgh8w9rZ9v583UCWXcNOau23r3eJyay50v73tBOzdDto0hNtdf2W6SJI2vq94hr0Jp4d7DPmzekSSqkLT2vLGk7rZBypG0PeZ9N/gf6iVUZRhbsgTPAC8CJzpcCB4EngZfjxwO9ljNwsO+1Nx7VGNt+ldQzHywvQKU1Pezdu/UbTUzszF+vP0jaBtwrSBYZjRESWPLm65Z2FGAWlseskTuDlCO0Zt/vb1PEoN/dvS3kHY0kf8esIzPo7z+b9//P+6683zBvh5csd9FYlWPUwf7arrSHgPvj5/cDv99rOaWMs88KDIOOzBgkTyHDt4ZxRcTuQ8nFxfT8pdVqQgJs50SW5E4qb9q7d+d399rRlhHQ9uzZKmPefN07u9By5OUxefQxaK2+6M41dMraJmD7kVRoE0TI1H3BupDyz83lH5n100Hb6/+fXEbobxha7tBtMcC4g/0p4FD8/BBwqtdySjmDNmRDTSqrEy4tsGa1X6Yd+mUFy86GWnRUxpEj6ctLBvwyOsWStf2QP2xSXjttyB+i7GnfvvD27iKBqqwp7VyAkDwWGY0TWmEapBxTUzv/F2Wt617bdvf/p8j/P/Q3zJsvpD+je3kBRhnsXwGeBZ4BluO0t7vm+V7GZ5eBTWDz8OHDhQs5sDJq9mmdMRC1I+b9EZOK/OG7R3P0O/pilIEqbSMedTAf1jTKciS3m17rr6PXTiHk/9D9/YOUYc+e4f0+vcoR2jGc9v8PHX5cZjkCjTLY/2T8+B7gOeAjocE+OQ3t2ji92qazxtiGdsT2szLL7Gwr+tlOuYb1hwvZiNfXi40a0bR9/YWs89CdeZHtsLPdVnHdJYNu6FDcrKGinSPzZNzIqtR1N4/mNTWFTgWNZTQO8CDwW2NrxukWcjiXbPrp54zJfjfMMtpy6zZlrRNN410nHaE1+9DOyyJTkSbBtCm0r6f7TNus4Fxkh1Zmf0ZVT6oC9gLvSjz/J+BO4A+6Omgf6rWsoQT7vMO0tPb9rI0j7yzFcf9Z6zTlrRNN41snHUUGOZTZHDM9PViNuPtuX712Rp1ylLUtJpt98mJJ6MCHgkYV7N8XN908B5wEjsXpM8DxeOjlceBgr2UNJdjnHc71W7ucmooO3ZrS5jzKqYmBPq9vpg5Tt9BBDoNu/92XXB60HMnRSnnnXXTKU0YZOlPokMrFxXqPxilrGmnNvoptjprqM41jNM6wpm5Zw3G7BwUM2jZdpGMzJECGBtzkvKOu2SePQPLK0z28twcFe3e1D2sazpQ07ryUWZa0/0vaUMlR/46hndKh66QTnLM6XaFYhTA5Qi7ku0OOZAooEuybew/apSX48IfHnQtpmuT9TZsk7WblP/4x/OhHw/3ee+/Nf79zL+AymMFdd0Xr7/jx7PmWl6N5e9m9Gx55JDyPZ8/Cxz8enN2ymZf1Qw5gYWHBNzc3y1nYxka04Z47V95GItIxPb0zKNbV5CRcubL1OCoTE1vft2tXOd9tFt2w++zZ7HkWF+Eb3+i9/vbuhX//97DvHEaMKbBMM3vG3RdC5t3Vd4aqaGMj2is35c8oozMxAVev9p6vSdtWJ8iOMtBD9DvPz5dbIXPPD/SQX5tPCgn0ne+skXofiyYPqefn4dd/vVl/RhmdK1dgbm7cuWiPMptnJEh9a/bdtfhee3WRPGU1JzTRqJt5ZCjqW7NP61AS6ZeCWTb9No1Q32B/7ty4cyAiUhv1DfaHD487ByLSVpOT485BYfUN9qur486BiLTR9HQ0vj6rg3l9PersN4seV1aiz4QIGd/fp/p20H7xi+POgYhUzcwMvP12ej/D3BycObP1OjSwTk3Bu98Nb70VtSisrkYnbUJ2wO+833HbbVE/Y6+BJENssahvzT50zKyIFNfPGcL79oV/bmYmmrLs3h1NRUxPw5/+aVTr7q5JT08Xaw2YmdmqmX/xi/Dmm9H5AWfO7AzkIZaWos+6RzX/tLIXzWNRoddVGObU17Vxxn1dEU2amjyF3EQ++Trt/g9Z92tIXjws774T3fdx7kzXXLN1P4juK2gml9vrtp7796fnb//+4vGoHyF57IFWXAht3H8GTZrqPHUCZdZ77vl3aAoJVKH3gy0h6PWtO+CPKtCXRMFekyZN2dPExFbNufvWfFNT24Ntd8DvvhVfL+MM5C1QJNjXt4NWRLZLu77Prl3wzjtbr2dmonbtZLtz58KB3Z2PAE89NVielpb6a+OW0tW3g1akDnbvzu6InJuL6sudoXr9mpuLlvGlL20f8re+Hl2mOFk3f/PN7cG303E4SOej1IKCvdTb3r3h83YCYPdoiJmZKC0r8E5ORpfH7aR3D9nrvO6caNN5nJuDhx+OatJ5o0OSIzVWVrJP2Ol8T6ccnQDeCdIK3JIntL1nmJPa7Bs6TU5Gd/LJa/ddX49GV3R/Lu/OP/v2pXfyZY3cqEI7cT9t12rvlh4o0GZf35uXDPFMs8bYvXvrTkN798I116SfGCIitVTk5iVqxqmLa66JDvG722Tdd56e3Um/fHmrjvvDHw5+YoiI1JaC/ah0t7MWnf7sz+CJJ9JHTaitVkR60NDLsg2jWSztRi3Ly9FzBXYRCaCafVF51/QY1m3t0m7UculSlC4iEkDBvgizqN2711C6smXdqEU3cBGRQAr2RXQuP7q0BGtr2ztF19aG16SSddlT3cBFRAINLdib2Z1mdsrMTpvZ/aV/waiHjHZq7mbR9PGPR23n7vBTPxW97rw3yHT77VvfeeBAlJZ1DeyzZ7c+NzkZfTa5rMnJ/O8KsbEB8/PRqfjz89HrUeouU/L3EZFwoQPyi0zAJPBt4H3AbuA54EjW/H2dVBWdUTCaqXNCy6i+b3Ex+/KrZU95Qq9aOCx5V10UkfGfVGVmHwYedPc74tcPxDuW302bv6+TqqIFD5DLAjq/URNP5Mpb//Pz6UcV3Xf8GZa833vUR3YiFVSFk6quA15NvD4fp5XryJHSFykJ6hgWaYxhBfu0Ktm2qpiZLZvZppltXrx4sb9vOXkyfN7du/u71VqbqWNYpDGGFf3OAzckXl8PvJacwd3X3H3B3RdmZ2f7/6aVlfz3Oi29ly9HNyHuvO6+xECVLC7C/v3jzkXUIT3KIabdFheLpYtIttDG/SIT0Zm53wFuZKuD9uas+fvuoO1YWYmulAhbV1rsR0gHZlaHYZmdsx1pt0ybmEj/3MTEznxkzRvSOdsx7isvDnqnJJEGY9wdtABmdhfwJ0Qjcx5298zqYN8dtCIiLVakg3Zo18Zx9yeAJ4a1fBERCaceSxGRFlCwFxFpAQV7EZEWULAXEWmBStyD1swuAhlX++rpWuDNErMzTipL9TSlHKCyVNUgZZlz96ATlSoR7AdhZpuhQ4+qTmWpnqaUA1SWqhpVWdSMIyLSAgr2IiIt0IRgvzbuDJRIZameppQDVJaqGklZat9mLyIivTWhZi8iIj0o2IuItEBtg/3Qb2g+ADM7Y2YvmNkJM9uM0w6a2ZNm9nL8eCAx/wNxOU6Z2R2J9A/FyzltZv/dLLpPn5ntMbO/jNO/aWbzJeb9YTN7w8xeTKSNJO9mdk/8HS+b2T1DKMeDZvbdeL2ciK/MWulyxMu7wcy+ZmYvmdlJM/t0nF7H9ZJVltqtGzO7xsyeNrPn4rL8TpxezfUSei3kKk0UvKH5GPJ3Bri2K+0h4P74+f3A78fPj8T530N0/f9vA5Pxe08DHya689ffAr8Yp98L/M/4+VHgL0vM+0eADwIvjjLvwEGieyAcBA7Ezw+UXI4Hgd9Kmbey5YiXeQj4YPz8XcC/xnmu43rJKkvt1k38vfvi51PAN4Gfr+p6qWvN/lbgtLt/x91/BDwK3D3mPPVyN/BI/PwR4KOJ9Efd/bK7vwKcBm41s0PAu939Gx6t3S91faazrL8GFjs1gUG5+9eBt8aQ9zuAJ939LXf/HvAkcGfJ5chS2XLEZbng7s/Gz38AvER0T+c6rpessmSpclnc3X8Yv5yKJ6ei66WuwX40NzTvnwN/b2bPmNlynPZed78A0QYPvCdOzyrLdfHz7vRtn3H3d4D/C8wMoRwdo8j7qNbpp8zseYuaeTqH17UpR3wY/wGiWmSt10tXWaCG68bMJs3sBPAGUfCt7Hqpa7DveUPzMbvN3T8I/CJwn5l9JGferLLklbEq5S8z76Mo0+eB9wO3ABeAzw6Qp5GXw8z2AV8BPuPu38+btY+8jbQ8KWWp5bpx9yvufgvRfbZvNbOfy5l9rGWpa7DveUPzcXL31+LHN4D/TdTs9Hp8uEb8+EY8e1ZZzsfPu9O3fcbMdgH/gfAmi36MIu9DX6fu/nr857wKfIFovdSiHGY2RRQcN9z9q3FyLddLWlnqvG7i/L8N/ANRU0o110u/nRPjnCh4Q/MR520v8K7E83+KN4A/YHunzUPx85vZ3mnzHbY6bf6ZqMOn02lzV5x+H9s7bR4ruQzzbO/YHHreiTqaXiHqbDoQPz9YcjkOJZ7/BlH7aR3KYUTtuH/SlV679ZJTltqtG2AW2B8//wng/wC/VNX1MvbgOMAPfRdRT/63gWPjzk8iX++LV+hzwMlO3oja2Y4DL8ePBxOfORaX4xRxL3ycvgC8GL/3P9g64/ka4K+IOnieBt5XYv6/THQY/WOi2sMnR5V34Ffj9NPAfxlCOf4X8ALwPPA42wNMJcsRL+8XiA7RnwdOxNNdNV0vWWWp3boB/iPwL3GeXwT+6yj/60XLossliIi0QF3b7EVEpAAFexGRFlCwFxFpAQV7EZEWULAXEWkBBXsRkRZQsBcRaYH/B9gMbSGHs1qVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_clean_outlier['price'], df_clean_outlier['length_ft'], color = 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56cd3cbb",
   "metadata": {},
   "source": [
    "#### >>>> 4.1.2 CHECK FOR LENGHT_FT OUTLIER "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "65ce675f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "277.9"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df_clean_outlier['length_ft'].min())\n",
    "df_clean_outlier['length_ft'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f67151a",
   "metadata": {},
   "source": [
    "#### `>>>>>> REMOVE THE OUTLIER (SET MAX LENGHT_FT TO `100`)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9f079468",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min length_ft 1.0\n",
      "MAX length_ft 68.0\n",
      "Lenght 17395\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>type</th>\n",
       "      <th>boatClass</th>\n",
       "      <th>year</th>\n",
       "      <th>condition</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>hullMaterial</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>numEngines</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>state</th>\n",
       "      <th>created_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14698</th>\n",
       "      <td>power</td>\n",
       "      <td>power-motor</td>\n",
       "      <td>1990</td>\n",
       "      <td>used</td>\n",
       "      <td>68.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>970.0</td>\n",
       "      <td>195000.0</td>\n",
       "      <td>18223</td>\n",
       "      <td>MD</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16625</th>\n",
       "      <td>power</td>\n",
       "      <td>power-house</td>\n",
       "      <td>1991</td>\n",
       "      <td>used</td>\n",
       "      <td>67.00</td>\n",
       "      <td>steel</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>540.0</td>\n",
       "      <td>129900.0</td>\n",
       "      <td>56775</td>\n",
       "      <td>AZ</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11224</th>\n",
       "      <td>power</td>\n",
       "      <td>power-motor</td>\n",
       "      <td>1995</td>\n",
       "      <td>used</td>\n",
       "      <td>65.00</td>\n",
       "      <td>steel</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>574.0</td>\n",
       "      <td>149000.0</td>\n",
       "      <td>28309</td>\n",
       "      <td>TX</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12555</th>\n",
       "      <td>power</td>\n",
       "      <td>power-house</td>\n",
       "      <td>2014</td>\n",
       "      <td>new</td>\n",
       "      <td>65.00</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>440.0</td>\n",
       "      <td>39900.0</td>\n",
       "      <td>34439</td>\n",
       "      <td>AZ</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7563</th>\n",
       "      <td>power</td>\n",
       "      <td>power-motor</td>\n",
       "      <td>1991</td>\n",
       "      <td>used</td>\n",
       "      <td>64.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>259000.0</td>\n",
       "      <td>34373</td>\n",
       "      <td>PA</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12109</th>\n",
       "      <td>power</td>\n",
       "      <td>power-house</td>\n",
       "      <td>1978</td>\n",
       "      <td>used</td>\n",
       "      <td>63.00</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>330.0</td>\n",
       "      <td>89500.0</td>\n",
       "      <td>34888</td>\n",
       "      <td>KY</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15666</th>\n",
       "      <td>power</td>\n",
       "      <td>power-house</td>\n",
       "      <td>1999</td>\n",
       "      <td>used</td>\n",
       "      <td>63.00</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>140.0</td>\n",
       "      <td>166750.0</td>\n",
       "      <td>61430</td>\n",
       "      <td>WA</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13554</th>\n",
       "      <td>power</td>\n",
       "      <td>power-motor</td>\n",
       "      <td>1988</td>\n",
       "      <td>used</td>\n",
       "      <td>63.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>249000.0</td>\n",
       "      <td>63935</td>\n",
       "      <td>FL</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5423</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>62.50</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53837.0</td>\n",
       "      <td>35234</td>\n",
       "      <td>AL</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14017</th>\n",
       "      <td>power</td>\n",
       "      <td>power-flybridge</td>\n",
       "      <td>1999</td>\n",
       "      <td>used</td>\n",
       "      <td>62.00</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>28311</td>\n",
       "      <td>MA</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12115</th>\n",
       "      <td>power</td>\n",
       "      <td>power-motor</td>\n",
       "      <td>1987</td>\n",
       "      <td>used</td>\n",
       "      <td>62.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>970.0</td>\n",
       "      <td>269900.0</td>\n",
       "      <td>21445</td>\n",
       "      <td>NJ</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18770</th>\n",
       "      <td>power</td>\n",
       "      <td>power-antique</td>\n",
       "      <td>1960</td>\n",
       "      <td>used</td>\n",
       "      <td>62.00</td>\n",
       "      <td>wood</td>\n",
       "      <td>diesel</td>\n",
       "      <td>1</td>\n",
       "      <td>671.0</td>\n",
       "      <td>229000.0</td>\n",
       "      <td>153142</td>\n",
       "      <td>WA</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10469</th>\n",
       "      <td>power</td>\n",
       "      <td>power-motor</td>\n",
       "      <td>1981</td>\n",
       "      <td>used</td>\n",
       "      <td>61.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>52419</td>\n",
       "      <td>LA</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16504</th>\n",
       "      <td>power</td>\n",
       "      <td>power-motor</td>\n",
       "      <td>1985</td>\n",
       "      <td>used</td>\n",
       "      <td>61.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>289000.0</td>\n",
       "      <td>30052</td>\n",
       "      <td>SC</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4767</th>\n",
       "      <td>power</td>\n",
       "      <td>power-motor</td>\n",
       "      <td>1995</td>\n",
       "      <td>used</td>\n",
       "      <td>60.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>1640.0</td>\n",
       "      <td>199900.0</td>\n",
       "      <td>67460</td>\n",
       "      <td>FL</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16772</th>\n",
       "      <td>power</td>\n",
       "      <td>power-house</td>\n",
       "      <td>1990</td>\n",
       "      <td>used</td>\n",
       "      <td>60.00</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>270.0</td>\n",
       "      <td>139900.0</td>\n",
       "      <td>34888</td>\n",
       "      <td>KY</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15845</th>\n",
       "      <td>power</td>\n",
       "      <td>power-flybridge</td>\n",
       "      <td>2008</td>\n",
       "      <td>used</td>\n",
       "      <td>60.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245000.0</td>\n",
       "      <td>33396</td>\n",
       "      <td>CA</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18135</th>\n",
       "      <td>power</td>\n",
       "      <td>power-house</td>\n",
       "      <td>1986</td>\n",
       "      <td>used</td>\n",
       "      <td>60.00</td>\n",
       "      <td>steel</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>230.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>10550</td>\n",
       "      <td>UT</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6553</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>2018</td>\n",
       "      <td>new</td>\n",
       "      <td>59.28</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25995.0</td>\n",
       "      <td>45004</td>\n",
       "      <td>TX</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14622</th>\n",
       "      <td>power</td>\n",
       "      <td>power-house</td>\n",
       "      <td>2008</td>\n",
       "      <td>used</td>\n",
       "      <td>59.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>630.0</td>\n",
       "      <td>274900.0</td>\n",
       "      <td>34739</td>\n",
       "      <td>AL</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        type        boatClass  year condition  length_ft hullMaterial  \\\n",
       "14698  power      power-motor  1990      used      68.00   fiberglass   \n",
       "16625  power      power-house  1991      used      67.00        steel   \n",
       "11224  power      power-motor  1995      used      65.00        steel   \n",
       "12555  power      power-house  2014       new      65.00     aluminum   \n",
       "7563   power      power-motor  1991      used      64.00   fiberglass   \n",
       "12109  power      power-house  1978      used      63.00     aluminum   \n",
       "15666  power      power-house  1999      used      63.00     aluminum   \n",
       "13554  power      power-motor  1988      used      63.00   fiberglass   \n",
       "5423   power     power-center  2019       new      62.50   fiberglass   \n",
       "14017  power  power-flybridge  1999      used      62.00     aluminum   \n",
       "12115  power      power-motor  1987      used      62.00   fiberglass   \n",
       "18770  power    power-antique  1960      used      62.00         wood   \n",
       "10469  power      power-motor  1981      used      61.00   fiberglass   \n",
       "16504  power      power-motor  1985      used      61.00   fiberglass   \n",
       "4767   power      power-motor  1995      used      60.00   fiberglass   \n",
       "16772  power      power-house  1990      used      60.00     aluminum   \n",
       "15845  power  power-flybridge  2008      used      60.00   fiberglass   \n",
       "18135  power      power-house  1986      used      60.00        steel   \n",
       "6553   power     power-center  2018       new      59.28   fiberglass   \n",
       "14622  power      power-house  2008      used      59.00   fiberglass   \n",
       "\n",
       "       fuelType  numEngines  totalHP     price  sellerId state  created_year  \n",
       "14698    diesel           2    970.0  195000.0     18223    MD          2015  \n",
       "16625  gasoline           2    540.0  129900.0     56775    AZ          2018  \n",
       "11224    diesel           2    574.0  149000.0     28309    TX          2018  \n",
       "12555  gasoline           2    440.0   39900.0     34439    AZ          2017  \n",
       "7563      other           1      0.0  259000.0     34373    PA          2017  \n",
       "12109  gasoline           2    330.0   89500.0     34888    KY          2019  \n",
       "15666  gasoline           1    140.0  166750.0     61430    WA          2012  \n",
       "13554    diesel           2   1300.0  249000.0     63935    FL          2017  \n",
       "5423   gasoline           1      0.0   53837.0     35234    AL          2019  \n",
       "14017    diesel           2   1400.0  250000.0     28311    MA          2017  \n",
       "12115    diesel           2    970.0  269900.0     21445    NJ          2003  \n",
       "18770    diesel           1    671.0  229000.0    153142    WA          2017  \n",
       "10469    diesel           2      0.0  225000.0     52419    LA          2011  \n",
       "16504    diesel           2   1300.0  289000.0     30052    SC          2019  \n",
       "4767     diesel           2   1640.0  199900.0     67460    FL          2018  \n",
       "16772  gasoline           2    270.0  139900.0     34888    KY          2019  \n",
       "15845    diesel           2      0.0  245000.0     33396    CA          2009  \n",
       "18135  gasoline           2    230.0    3000.0     10550    UT          2017  \n",
       "6553   gasoline           1      0.0   25995.0     45004    TX          2019  \n",
       "14622    diesel           2    630.0  274900.0     34739    AL          2019  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_outlier =df_clean_outlier[(df_clean_outlier['length_ft'] < 70)]\n",
    "print('min length_ft', df_clean_outlier['length_ft'].min())\n",
    "print('MAX length_ft', df_clean_outlier['length_ft'].max())\n",
    "print('Lenght', len(df_clean_outlier))\n",
    "df_clean_outlier.sort_values('length_ft',  ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b7641de1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e36065fa00>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_clean_outlier['price'], df_clean_outlier['length_ft'], color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "39ea01a1",
   "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>type</th>\n",
       "      <th>boatClass</th>\n",
       "      <th>year</th>\n",
       "      <th>condition</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>hullMaterial</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>numEngines</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>state</th>\n",
       "      <th>created_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2286</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2020</td>\n",
       "      <td>new</td>\n",
       "      <td>1.0</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45020.0</td>\n",
       "      <td>46516</td>\n",
       "      <td>MN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2314</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>1.0</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29352.0</td>\n",
       "      <td>46516</td>\n",
       "      <td>MN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2315</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2020</td>\n",
       "      <td>new</td>\n",
       "      <td>1.0</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45020.0</td>\n",
       "      <td>46516</td>\n",
       "      <td>MN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2331</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2020</td>\n",
       "      <td>new</td>\n",
       "      <td>1.0</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67690.0</td>\n",
       "      <td>46516</td>\n",
       "      <td>MN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2340</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2018</td>\n",
       "      <td>new</td>\n",
       "      <td>1.0</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37999.0</td>\n",
       "      <td>46516</td>\n",
       "      <td>MN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10414</th>\n",
       "      <td>power</td>\n",
       "      <td>power-other</td>\n",
       "      <td>2016</td>\n",
       "      <td>used</td>\n",
       "      <td>2.0</td>\n",
       "      <td>other</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4899.0</td>\n",
       "      <td>57749</td>\n",
       "      <td>TX</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11275</th>\n",
       "      <td>power</td>\n",
       "      <td>power-other</td>\n",
       "      <td>2015</td>\n",
       "      <td>used</td>\n",
       "      <td>2.0</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>250.0</td>\n",
       "      <td>15995.0</td>\n",
       "      <td>1196</td>\n",
       "      <td>SC</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15438</th>\n",
       "      <td>power</td>\n",
       "      <td>power-other</td>\n",
       "      <td>2011</td>\n",
       "      <td>used</td>\n",
       "      <td>2.0</td>\n",
       "      <td>other</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>150.0</td>\n",
       "      <td>4985.0</td>\n",
       "      <td>28292</td>\n",
       "      <td>FL</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16170</th>\n",
       "      <td>power</td>\n",
       "      <td>power-other</td>\n",
       "      <td>2015</td>\n",
       "      <td>used</td>\n",
       "      <td>1.0</td>\n",
       "      <td>other</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>5109</td>\n",
       "      <td>IL</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17443</th>\n",
       "      <td>power</td>\n",
       "      <td>power-other</td>\n",
       "      <td>2016</td>\n",
       "      <td>used</td>\n",
       "      <td>1.0</td>\n",
       "      <td>other</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>5162</td>\n",
       "      <td>OH</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>159 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        type      boatClass  year condition  length_ft hullMaterial  fuelType  \\\n",
       "2286   power  power-pontoon  2020       new        1.0        other     other   \n",
       "2314   power  power-pontoon  2019       new        1.0        other     other   \n",
       "2315   power  power-pontoon  2020       new        1.0        other     other   \n",
       "2331   power  power-pontoon  2020       new        1.0        other     other   \n",
       "2340   power  power-pontoon  2018       new        1.0        other     other   \n",
       "...      ...            ...   ...       ...        ...          ...       ...   \n",
       "10414  power    power-other  2016      used        2.0        other  gasoline   \n",
       "11275  power    power-other  2015      used        2.0        other     other   \n",
       "15438  power    power-other  2011      used        2.0        other  gasoline   \n",
       "16170  power    power-other  2015      used        1.0        other  gasoline   \n",
       "17443  power    power-other  2016      used        1.0        other  gasoline   \n",
       "\n",
       "       numEngines  totalHP    price  sellerId state  created_year  \n",
       "2286            1      0.0  45020.0     46516    MN          2019  \n",
       "2314            1      0.0  29352.0     46516    MN          2019  \n",
       "2315            1      0.0  45020.0     46516    MN          2019  \n",
       "2331            1      0.0  67690.0     46516    MN          2019  \n",
       "2340            1      0.0  37999.0     46516    MN          2019  \n",
       "...           ...      ...      ...       ...   ...           ...  \n",
       "10414           1     25.0   4899.0     57749    TX          2016  \n",
       "11275           1    250.0  15995.0      1196    SC          2016  \n",
       "15438           1    150.0   4985.0     28292    FL          2017  \n",
       "16170           1     15.0   2750.0      5109    IL          2019  \n",
       "17443           1     20.0   3200.0      5162    OH          2018  \n",
       "\n",
       "[159 rows x 13 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_outlier[(df_clean_outlier['length_ft'] <= 2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "362ad023",
   "metadata": {},
   "source": [
    "#### >>>> 4.1.3 CHECK FOR NUMENGINES OUTLIER "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "da295543",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e36172af40>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q7s56Oo3X1QWnTs11bWwGSToaEZV62/xmtpkVt3cvLF06NrZ0aRa3tuVEYWbF9fZCf//YMbP7+z00apvzmNlm1hiPmV06vqIwM7NcThRmZpbLicLMzHI5UZiZWS4nCjMzy+VEYWZmuZwozMwslxOFmZnlcqIwM7NcThRmZpbLicLMzHI5UZiZWS4nCjMzy+VEYWZmuZwozMwslxOFmTVmYCAbEnXBgmw+MNDsGtks88BFZlbcwAD09cGlS9n66dPZOngwozbmKwozK2737mtJourSpSxubcuJwsyKO3Omsbi1BScKMytu9erG4tYWnCjMrLi9e2Hp0rGxpUuzuLWtSROFpOcknZf0Xk1suaRDkk6m+bKabY9IGpL0gaQtNfH1ko6lbU9KUoovkfRKih+W1F1TZkf6GScl7ZixVpvZ1PT2Qn8/dHWBlM37+/0gu80VuaJ4Htg6LvYw8GZErAHeTOtIWgv0AHelMk9JWpjKPA30AWvSVD3mTuBCRNwJPAE8no61HNgDfAnYAOypTUhm1iS9vXDqFFy9ms2dJNrepIkiIn4IjI4LbwP2peV9wP018Zcj4tOI+BAYAjZIWgHcFBFvR0QAL4wrUz3Wq8CmdLWxBTgUEaMRcQE4xPUJy8zMZtlUn1HcHhHnANL8thRfCXxUs99wiq1My+PjY8pExGXgE+CWnGNdR1KfpEFJgyMjI1NskpmZ1TPTD7NVJxY58amWGRuM6I+ISkRUOjo6ClXUzKbIb2aXzlQTxcfpdhJpfj7Fh4FVNft1AmdTvLNOfEwZSYuAm8ludU10LDNrluqb2adPQ8S1N7OdLNraVBPFAaDaC2kH8FpNvCf1ZLqD7KH1kXR76qKkjen5w/ZxZarHegB4Kz3HeAPYLGlZeoi9OcXMrFn8ZnYpTfqtJ0nfBu4BbpU0TNYT6Q+B/ZJ2AmeALwNExHFJ+4H3gcvAQxFxJR1qF1kPqhuAg2kCeBZ4UdIQ2ZVETzrWqKTHgHfSfo9GxPiH6mY2l/xmdikp++W9fVQqlRgcHGyskOo9Dkna7M+nZficzE/d3dntpvG6urKustayJB2NiEq9bX4z28yK85vZpeREYWbF+c3sUvJ4FGbWmN5eJ4aS8RWFmZnlcqIws8b4hbvS8a0nMyvOQ6GWkq8ozKw4v3BXSk4UZlacX7grJScKMyvOQ6GWkhOFmRXnF+5KyYnCzIrzC3el5ERhZma53D3WzIpz99hS8hWFmRXn7rGl5ERhZsW5e2wpOVGYWXHLlzcWt7bgRGFmZrmcKMysuNEJRiOeKG5twYnCzIrzm9ml5ERhZsX5zexScqIws+L8ZnYpOVGYmVkuv5ltZsX5zexS8hWFmRXnN7NLaVqJQtIpScckvStpMMWWSzok6WSaL6vZ/xFJQ5I+kLSlJr4+HWdI0pOSlOJLJL2S4ocldU+nvmY2TX4zu5Rm4oriX0TEuoiopPWHgTcjYg3wZlpH0lqgB7gL2Ao8JWlhKvM00AesSdPWFN8JXIiIO4EngMdnoL5mNlXuHltKs3HraRuwLy3vA+6vib8cEZ9GxIfAELBB0grgpoh4OyICeGFcmeqxXgU2Va82zKwJ3D22lKabKAL4n5KOSkpPtLg9Is4BpPltKb4S+Kim7HCKrUzL4+NjykTEZeAT4JbxlZDUJ2lQ0uDIyMg0m2RmE3L32FKabq+nuyPirKTbgEOSfpqzb70rgciJ55UZG4joB/oBKpXKddvNbAb19joxlMy0rigi4myanwf+AtgAfJxuJ5Hm59Puw8CqmuKdwNkU76wTH1NG0iLgZsAflTEzm0NTThSS/omkX6kuA5uB94ADwI602w7gtbR8AOhJPZnuIHtofSTdnrooaWN6/rB9XJnqsR4A3krPMczMbI5M59bT7cBfpGfLi4A/j4j/IekdYL+kncAZ4MsAEXFc0n7gfeAy8FBEXEnH2gU8D9wAHEwTwLPAi5KGyK4keqZRXzMzmwK12y/olUolBgcHGyuU15Gqzf58WobPidmcknS05jWHMfxmtpmZ5XKiMDOzXE4UZmaWy4nCzMxyOVGYmVkuJwoza8zAAHR3w4IF2XxgoNk1slnmgYvMrDgPXFRKvqIws+I8cFEpOVGYWXEeuKiUnCjMrDgPXFRKThRmVpwHLiolJwozK84DF5WSez2ZWWM8cFHp+IrCzMxyOVGYmVkuJwozM8vlRGFmZrmcKMzMLJcThZmZ5XKiMDOzXE4UZmaWy4nCzMxyOVGYmVkuJwozM8vVEolC0lZJH0gakvRws+tjZlYm8z5RSFoI/FfgN4G1wG9LWtvcWpmZlce8TxTABmAoIv46In4BvAxsa3KdzMxKoxUSxUrgo5r14RT7/yT1SRqUNDgyMjKnlbNZsmlTY3EzmzWtkChUJxZjViL6I6ISEZWOjo45qpbNqu9///qksGlTFjezOdUKAxcNA6tq1juBs02qi80lJwWzeaEVrijeAdZIukPSZ4Ae4MCM/oSIxuJmZiUy768oIuKypK8BbwALgeci4vgs/KAZP6SZWTuY94kCICJeB15vdj3MzMqoFW49mZlZEzlRmJlZLicKMzPL5URhZma5FG3W20fSCHB6Goe4FfjbGapOM7VLO8Btma/apS3t0g6YXlu6IqLuG8ttlyimS9JgRFSaXY/papd2gNsyX7VLW9qlHTB7bfGtJzMzy+VEYWZmuZwortff7ArMkHZpB7gt81W7tKVd2gGz1BY/ozAzs1y+ojAzs1xOFGZmlquUiULSc5LOS3pvgu2S9KSkIUk/kfTFua5jUQXaco+kTyS9m6b/ONd1LELSKkk/kHRC0nFJX6+zT0ucl4JtmffnRdJnJR2R9OPUjj+os0+rnJMibZn356RK0kJJ/0vS9+psm/lzEhGlm4B/DnwReG+C7fcBB8lG19sIHG52nafRlnuA7zW7ngXasQL4Ylr+FeB/A2tb8bwUbMu8Py/pz/nGtLwYOAxsbNFzUqQt8/6c1NT13wN/Xq++s3FOSnlFERE/BEZzdtkGvBCZvwI+J2nF3NSuMQXa0hIi4lxE/CgtXwROMG5sdFrkvBRsy7yX/pz/Ia0uTtP43i+tck6KtKUlSOoE/hXw3ybYZcbPSSkTRQErgY9q1odpwX/oNX4jXXIflHRXsyszGUndwBfIfuur1XLnJact0ALnJd3ieBc4DxyKiJY9JwXaAi1wToD/DPwH4OoE22f8nDhR1Kc6sZb87QP4Edk3XP4Z8F+A/97c6uSTdCPwHeAbEfGz8ZvrFJm352WStrTEeYmIKxGxjmys+g2Sfn3cLi1zTgq0Zd6fE0n/GjgfEUfzdqsTm9Y5caKobxhYVbPeCZxtUl2mJSJ+Vr3kjmykwMWSbm1yteqStJjsP9aBiPhunV1a5rxM1pZWOi8AEfH3wF8CW8dtaplzUjVRW1rknNwN/BtJp4CXgX8p6aVx+8z4OXGiqO8AsD31HtgIfBIR55pdqamQ9E8lKS1vIDvnf9fcWl0v1fFZ4EREfGuC3VrivBRpSyucF0kdkj6Xlm8A7gV+Om63Vjknk7alFc5JRDwSEZ0R0Q30AG9FxO+M223Gz0lLjJk90yR9m6yHw62ShoE9ZA+3iIhnyMbnvg8YAi4B/7Y5NZ1cgbY8AOySdBn4v0BPpK4R88zdwFeAY+k+MsA3gdXQcuelSFta4bysAPZJWkj2n+b+iPiepH8HLXdOirSlFc5JXbN9TvwJDzMzy+VbT2ZmlsuJwszMcjlRmJlZLicKMzPL5URhZma5nCjMzCyXE4WZmeX6f3KYN0+binKHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_clean_outlier['numEngines'], df_clean_outlier['price'], color = 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "734d0c1f",
   "metadata": {},
   "source": [
    "#### `>>>>>> REMOVE THE OUTLIER (SET MAX_NO OF NUMENGINES TO `2`)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9240f013",
   "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>type</th>\n",
       "      <th>boatClass</th>\n",
       "      <th>year</th>\n",
       "      <th>condition</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>hullMaterial</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>numEngines</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>state</th>\n",
       "      <th>created_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>1992</td>\n",
       "      <td>used</td>\n",
       "      <td>21.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>150.0</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>217053</td>\n",
       "      <td>FL</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>power</td>\n",
       "      <td>power-deck</td>\n",
       "      <td>2020</td>\n",
       "      <td>new</td>\n",
       "      <td>18.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>75.0</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>220570</td>\n",
       "      <td>OH</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>power</td>\n",
       "      <td>power-expresscruiser</td>\n",
       "      <td>2015</td>\n",
       "      <td>used</td>\n",
       "      <td>32.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>600.0</td>\n",
       "      <td>169995.0</td>\n",
       "      <td>34834</td>\n",
       "      <td>SC</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>power</td>\n",
       "      <td>power-aft</td>\n",
       "      <td>1994</td>\n",
       "      <td>used</td>\n",
       "      <td>44.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>700.0</td>\n",
       "      <td>109900.0</td>\n",
       "      <td>17942</td>\n",
       "      <td>MD</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>power</td>\n",
       "      <td>power-convertible</td>\n",
       "      <td>1971</td>\n",
       "      <td>used</td>\n",
       "      <td>42.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>840.0</td>\n",
       "      <td>59500.0</td>\n",
       "      <td>16876</td>\n",
       "      <td>CT</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18894</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2017</td>\n",
       "      <td>new</td>\n",
       "      <td>22.33</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>90.0</td>\n",
       "      <td>37631.0</td>\n",
       "      <td>5103</td>\n",
       "      <td>MI</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18896</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>1990</td>\n",
       "      <td>used</td>\n",
       "      <td>28.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>1</td>\n",
       "      <td>315.0</td>\n",
       "      <td>49000.0</td>\n",
       "      <td>61420</td>\n",
       "      <td>FL</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18897</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pilot</td>\n",
       "      <td>1973</td>\n",
       "      <td>used</td>\n",
       "      <td>29.00</td>\n",
       "      <td>other</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>32168</td>\n",
       "      <td>GA</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18899</th>\n",
       "      <td>power</td>\n",
       "      <td>power-runabout</td>\n",
       "      <td>2013</td>\n",
       "      <td>used</td>\n",
       "      <td>19.33</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>6335</td>\n",
       "      <td>MI</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18902</th>\n",
       "      <td>power</td>\n",
       "      <td>power-cruiser</td>\n",
       "      <td>2002</td>\n",
       "      <td>new</td>\n",
       "      <td>26.58</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>17900.0</td>\n",
       "      <td>152266</td>\n",
       "      <td>GA</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17372 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        type             boatClass  year condition  length_ft hullMaterial  \\\n",
       "0      power          power-center  1992      used      21.00   fiberglass   \n",
       "2      power            power-deck  2020       new      18.00   fiberglass   \n",
       "3      power  power-expresscruiser  2015      used      32.00   fiberglass   \n",
       "4      power             power-aft  1994      used      44.00   fiberglass   \n",
       "9      power     power-convertible  1971      used      42.00   fiberglass   \n",
       "...      ...                   ...   ...       ...        ...          ...   \n",
       "18894  power         power-pontoon  2017       new      22.33     aluminum   \n",
       "18896  power          power-center  1990      used      28.00   fiberglass   \n",
       "18897  power           power-pilot  1973      used      29.00        other   \n",
       "18899  power        power-runabout  2013      used      19.33   fiberglass   \n",
       "18902  power         power-cruiser  2002       new      26.58   fiberglass   \n",
       "\n",
       "       fuelType  numEngines  totalHP     price  sellerId state  created_year  \n",
       "0      gasoline           1    150.0   16500.0    217053    FL          2019  \n",
       "2      gasoline           1     75.0   26995.0    220570    OH          2019  \n",
       "3      gasoline           2    600.0  169995.0     34834    SC          2019  \n",
       "4        diesel           2    700.0  109900.0     17942    MD          2018  \n",
       "9        diesel           2    840.0   59500.0     16876    CT          2018  \n",
       "...         ...         ...      ...       ...       ...   ...           ...  \n",
       "18894  gasoline           1     90.0   37631.0      5103    MI          2016  \n",
       "18896    diesel           1    315.0   49000.0     61420    FL          2019  \n",
       "18897  gasoline           1      0.0   10000.0     32168    GA          2017  \n",
       "18899  gasoline           1      0.0   26995.0      6335    MI          2019  \n",
       "18902  gasoline           1    220.0   17900.0    152266    GA          2019  \n",
       "\n",
       "[17372 rows x 13 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_outlier = df_clean_outlier[df_clean_outlier['numEngines'] < 3]\n",
    "df_clean_outlier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5adf455",
   "metadata": {},
   "source": [
    "#### >>>> 4.1.3 CHECK FOR TOTALHP OUTLIER "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "76a449b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e361793b50>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_clean_outlier['totalHP'], df_clean_outlier['price'], color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "741048cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2700.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df_clean_outlier['totalHP'].min())\n",
    "df_clean_outlier['totalHP'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c5542b68",
   "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>type</th>\n",
       "      <th>boatClass</th>\n",
       "      <th>year</th>\n",
       "      <th>condition</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>hullMaterial</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>numEngines</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>state</th>\n",
       "      <th>created_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>1992</td>\n",
       "      <td>used</td>\n",
       "      <td>21.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>150.0</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>217053</td>\n",
       "      <td>FL</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>power</td>\n",
       "      <td>power-deck</td>\n",
       "      <td>2020</td>\n",
       "      <td>new</td>\n",
       "      <td>18.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>75.0</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>220570</td>\n",
       "      <td>OH</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>sail</td>\n",
       "      <td>sail-racercruiser</td>\n",
       "      <td>1986</td>\n",
       "      <td>used</td>\n",
       "      <td>30.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25500.0</td>\n",
       "      <td>16876</td>\n",
       "      <td>CT</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>23.70</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>200.0</td>\n",
       "      <td>44507.0</td>\n",
       "      <td>34914</td>\n",
       "      <td>DE</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>power</td>\n",
       "      <td>power-motor</td>\n",
       "      <td>2000</td>\n",
       "      <td>used</td>\n",
       "      <td>55.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299000.0</td>\n",
       "      <td>6123</td>\n",
       "      <td>FL</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18894</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2017</td>\n",
       "      <td>new</td>\n",
       "      <td>22.33</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>90.0</td>\n",
       "      <td>37631.0</td>\n",
       "      <td>5103</td>\n",
       "      <td>MI</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18896</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>1990</td>\n",
       "      <td>used</td>\n",
       "      <td>28.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>1</td>\n",
       "      <td>315.0</td>\n",
       "      <td>49000.0</td>\n",
       "      <td>61420</td>\n",
       "      <td>FL</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18897</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pilot</td>\n",
       "      <td>1973</td>\n",
       "      <td>used</td>\n",
       "      <td>29.00</td>\n",
       "      <td>other</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>32168</td>\n",
       "      <td>GA</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18899</th>\n",
       "      <td>power</td>\n",
       "      <td>power-runabout</td>\n",
       "      <td>2013</td>\n",
       "      <td>used</td>\n",
       "      <td>19.33</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>6335</td>\n",
       "      <td>MI</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18902</th>\n",
       "      <td>power</td>\n",
       "      <td>power-cruiser</td>\n",
       "      <td>2002</td>\n",
       "      <td>new</td>\n",
       "      <td>26.58</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>17900.0</td>\n",
       "      <td>152266</td>\n",
       "      <td>GA</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16383 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        type          boatClass  year condition  length_ft hullMaterial  \\\n",
       "0      power       power-center  1992      used      21.00   fiberglass   \n",
       "2      power         power-deck  2020       new      18.00   fiberglass   \n",
       "11      sail  sail-racercruiser  1986      used      30.00   fiberglass   \n",
       "12     power      power-pontoon  2019       new      23.70     aluminum   \n",
       "13     power        power-motor  2000      used      55.00   fiberglass   \n",
       "...      ...                ...   ...       ...        ...          ...   \n",
       "18894  power      power-pontoon  2017       new      22.33     aluminum   \n",
       "18896  power       power-center  1990      used      28.00   fiberglass   \n",
       "18897  power        power-pilot  1973      used      29.00        other   \n",
       "18899  power     power-runabout  2013      used      19.33   fiberglass   \n",
       "18902  power      power-cruiser  2002       new      26.58   fiberglass   \n",
       "\n",
       "       fuelType  numEngines  totalHP     price  sellerId state  created_year  \n",
       "0      gasoline           1    150.0   16500.0    217053    FL          2019  \n",
       "2      gasoline           1     75.0   26995.0    220570    OH          2019  \n",
       "11       diesel           1      0.0   25500.0     16876    CT          2011  \n",
       "12     gasoline           1    200.0   44507.0     34914    DE          2019  \n",
       "13       diesel           2      0.0  299000.0      6123    FL          2013  \n",
       "...         ...         ...      ...       ...       ...   ...           ...  \n",
       "18894  gasoline           1     90.0   37631.0      5103    MI          2016  \n",
       "18896    diesel           1    315.0   49000.0     61420    FL          2019  \n",
       "18897  gasoline           1      0.0   10000.0     32168    GA          2017  \n",
       "18899  gasoline           1      0.0   26995.0      6335    MI          2019  \n",
       "18902  gasoline           1    220.0   17900.0    152266    GA          2019  \n",
       "\n",
       "[16383 rows x 13 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_outlier[(df_clean_outlier['totalHP'] < 400) & (df_clean_outlier['totalHP'] > -1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c47a776",
   "metadata": {},
   "source": [
    "#### `>>>>>> REMOVE THE OUTLIER (SET MAX_NO OF TOTALHP TO `399` and MIN_NO TO 0)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7dd136f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min totalHP 0.0\n",
      "MAX totalHP 385.0\n",
      "Lenght 16383\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>type</th>\n",
       "      <th>boatClass</th>\n",
       "      <th>year</th>\n",
       "      <th>condition</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>hullMaterial</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>numEngines</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>state</th>\n",
       "      <th>created_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17088</th>\n",
       "      <td>power</td>\n",
       "      <td>power-runabout</td>\n",
       "      <td>2005</td>\n",
       "      <td>used</td>\n",
       "      <td>28.67</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>385.0</td>\n",
       "      <td>31900.0</td>\n",
       "      <td>5913</td>\n",
       "      <td>NJ</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11181</th>\n",
       "      <td>power</td>\n",
       "      <td>power-sportcruiser</td>\n",
       "      <td>1996</td>\n",
       "      <td>used</td>\n",
       "      <td>28.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>380.0</td>\n",
       "      <td>23500.0</td>\n",
       "      <td>16863</td>\n",
       "      <td>NY</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18821</th>\n",
       "      <td>power</td>\n",
       "      <td>power-bowrider</td>\n",
       "      <td>2015</td>\n",
       "      <td>used</td>\n",
       "      <td>28.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>380.0</td>\n",
       "      <td>94800.0</td>\n",
       "      <td>67460</td>\n",
       "      <td>FL</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12782</th>\n",
       "      <td>power</td>\n",
       "      <td>power-bowrider</td>\n",
       "      <td>2013</td>\n",
       "      <td>used</td>\n",
       "      <td>27.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>380.0</td>\n",
       "      <td>84500.0</td>\n",
       "      <td>17929</td>\n",
       "      <td>NJ</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>power</td>\n",
       "      <td>power-skiwake</td>\n",
       "      <td>2015</td>\n",
       "      <td>used</td>\n",
       "      <td>24.50</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>380.0</td>\n",
       "      <td>84900.0</td>\n",
       "      <td>34745</td>\n",
       "      <td>MO</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12886</th>\n",
       "      <td>power</td>\n",
       "      <td>power-bowrider</td>\n",
       "      <td>2018</td>\n",
       "      <td>new</td>\n",
       "      <td>27.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>380.0</td>\n",
       "      <td>179999.0</td>\n",
       "      <td>42094</td>\n",
       "      <td>MD</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15556</th>\n",
       "      <td>power</td>\n",
       "      <td>power-cruiser</td>\n",
       "      <td>1998</td>\n",
       "      <td>used</td>\n",
       "      <td>29.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2</td>\n",
       "      <td>380.0</td>\n",
       "      <td>33900.0</td>\n",
       "      <td>21445</td>\n",
       "      <td>NY</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14400</th>\n",
       "      <td>power</td>\n",
       "      <td>power-skiwake</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>27.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>380.0</td>\n",
       "      <td>199685.0</td>\n",
       "      <td>223836</td>\n",
       "      <td>WI</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18818</th>\n",
       "      <td>power</td>\n",
       "      <td>power-other</td>\n",
       "      <td>2017</td>\n",
       "      <td>used</td>\n",
       "      <td>28.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>380.0</td>\n",
       "      <td>147900.0</td>\n",
       "      <td>44163</td>\n",
       "      <td>CT</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18561</th>\n",
       "      <td>power</td>\n",
       "      <td>power-highperf</td>\n",
       "      <td>1998</td>\n",
       "      <td>used</td>\n",
       "      <td>24.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>380.0</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>27207</td>\n",
       "      <td>TN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        type           boatClass  year condition  length_ft hullMaterial  \\\n",
       "17088  power      power-runabout  2005      used      28.67   fiberglass   \n",
       "11181  power  power-sportcruiser  1996      used      28.00   fiberglass   \n",
       "18821  power      power-bowrider  2015      used      28.00   fiberglass   \n",
       "12782  power      power-bowrider  2013      used      27.00   fiberglass   \n",
       "70     power       power-skiwake  2015      used      24.50   fiberglass   \n",
       "12886  power      power-bowrider  2018       new      27.00   fiberglass   \n",
       "15556  power       power-cruiser  1998      used      29.00   fiberglass   \n",
       "14400  power       power-skiwake  2019       new      27.00   fiberglass   \n",
       "18818  power         power-other  2017      used      28.00   fiberglass   \n",
       "18561  power      power-highperf  1998      used      24.00   fiberglass   \n",
       "\n",
       "       fuelType  numEngines  totalHP     price  sellerId state  created_year  \n",
       "17088  gasoline           1    385.0   31900.0      5913    NJ          2019  \n",
       "11181  gasoline           2    380.0   23500.0     16863    NY          2018  \n",
       "18821  gasoline           1    380.0   94800.0     67460    FL          2019  \n",
       "12782  gasoline           1    380.0   84500.0     17929    NJ          2019  \n",
       "70     gasoline           1    380.0   84900.0     34745    MO          2019  \n",
       "12886  gasoline           1    380.0  179999.0     42094    MD          2017  \n",
       "15556  gasoline           2    380.0   33900.0     21445    NY          2019  \n",
       "14400  gasoline           1    380.0  199685.0    223836    WI          2018  \n",
       "18818  gasoline           1    380.0  147900.0     44163    CT          2019  \n",
       "18561  gasoline           1    380.0   21000.0     27207    TN          2019  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_outlier =df_clean_outlier[(df_clean_outlier['totalHP'] < 400) & (df_clean_outlier['totalHP'] > -1)]\n",
    "print('min totalHP', df_clean_outlier['totalHP'].min())\n",
    "print('MAX totalHP', df_clean_outlier['totalHP'].max())\n",
    "print('Lenght', len(df_clean_outlier))\n",
    "df_clean_outlier.sort_values('totalHP',  ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f324470",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6b072d97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e361801790>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_clean_outlier['price'], df_clean_outlier['totalHP'], color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5640c5af",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.scatter(df_clean_outlier['year'], df_clean_outlier['price'], color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "60bbb58e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>numEngines</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>created_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.375466</td>\n",
       "      <td>-0.285977</td>\n",
       "      <td>0.024782</td>\n",
       "      <td>0.102996</td>\n",
       "      <td>0.063617</td>\n",
       "      <td>0.242935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length_ft</th>\n",
       "      <td>-0.375466</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.400038</td>\n",
       "      <td>0.075594</td>\n",
       "      <td>0.571832</td>\n",
       "      <td>-0.049062</td>\n",
       "      <td>-0.158062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numEngines</th>\n",
       "      <td>-0.285977</td>\n",
       "      <td>0.400038</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003977</td>\n",
       "      <td>0.251977</td>\n",
       "      <td>-0.081409</td>\n",
       "      <td>-0.197148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totalHP</th>\n",
       "      <td>0.024782</td>\n",
       "      <td>0.075594</td>\n",
       "      <td>0.003977</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.039556</td>\n",
       "      <td>-0.002628</td>\n",
       "      <td>-0.150612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0.102996</td>\n",
       "      <td>0.571832</td>\n",
       "      <td>0.251977</td>\n",
       "      <td>0.039556</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002543</td>\n",
       "      <td>-0.024856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sellerId</th>\n",
       "      <td>0.063617</td>\n",
       "      <td>-0.049062</td>\n",
       "      <td>-0.081409</td>\n",
       "      <td>-0.002628</td>\n",
       "      <td>-0.002543</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.051828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_year</th>\n",
       "      <td>0.242935</td>\n",
       "      <td>-0.158062</td>\n",
       "      <td>-0.197148</td>\n",
       "      <td>-0.150612</td>\n",
       "      <td>-0.024856</td>\n",
       "      <td>0.051828</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  year  length_ft  numEngines   totalHP     price  sellerId  \\\n",
       "year          1.000000  -0.375466   -0.285977  0.024782  0.102996  0.063617   \n",
       "length_ft    -0.375466   1.000000    0.400038  0.075594  0.571832 -0.049062   \n",
       "numEngines   -0.285977   0.400038    1.000000  0.003977  0.251977 -0.081409   \n",
       "totalHP       0.024782   0.075594    0.003977  1.000000  0.039556 -0.002628   \n",
       "price         0.102996   0.571832    0.251977  0.039556  1.000000 -0.002543   \n",
       "sellerId      0.063617  -0.049062   -0.081409 -0.002628 -0.002543  1.000000   \n",
       "created_year  0.242935  -0.158062   -0.197148 -0.150612 -0.024856  0.051828   \n",
       "\n",
       "              created_year  \n",
       "year              0.242935  \n",
       "length_ft        -0.158062  \n",
       "numEngines       -0.197148  \n",
       "totalHP          -0.150612  \n",
       "price            -0.024856  \n",
       "sellerId          0.051828  \n",
       "created_year      1.000000  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_outlier.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5d8be41c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.pairplot(df_clean_outlier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0f96167e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.pairplot(df_clean_outlier)\n",
    "#plt.figure(figsize=(12,5))\n",
    "#sns.heatmap(train.isnull(), yticklabels=False, cbar =True, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "47e96d1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e360618430>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1008x504 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,7))\n",
    "\n",
    "sns.displot(df_clean_outlier['price'], kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5817677f",
   "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>type</th>\n",
       "      <th>boatClass</th>\n",
       "      <th>year</th>\n",
       "      <th>condition</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>hullMaterial</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>numEngines</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>state</th>\n",
       "      <th>created_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>1992</td>\n",
       "      <td>used</td>\n",
       "      <td>21.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>150.0</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>217053</td>\n",
       "      <td>FL</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>power</td>\n",
       "      <td>power-deck</td>\n",
       "      <td>2020</td>\n",
       "      <td>new</td>\n",
       "      <td>18.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>75.0</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>220570</td>\n",
       "      <td>OH</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>sail</td>\n",
       "      <td>sail-racercruiser</td>\n",
       "      <td>1986</td>\n",
       "      <td>used</td>\n",
       "      <td>30.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25500.0</td>\n",
       "      <td>16876</td>\n",
       "      <td>CT</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2019</td>\n",
       "      <td>new</td>\n",
       "      <td>23.70</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>200.0</td>\n",
       "      <td>44507.0</td>\n",
       "      <td>34914</td>\n",
       "      <td>DE</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>power</td>\n",
       "      <td>power-motor</td>\n",
       "      <td>2000</td>\n",
       "      <td>used</td>\n",
       "      <td>55.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299000.0</td>\n",
       "      <td>6123</td>\n",
       "      <td>FL</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18894</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pontoon</td>\n",
       "      <td>2017</td>\n",
       "      <td>new</td>\n",
       "      <td>22.33</td>\n",
       "      <td>aluminum</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>90.0</td>\n",
       "      <td>37631.0</td>\n",
       "      <td>5103</td>\n",
       "      <td>MI</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18896</th>\n",
       "      <td>power</td>\n",
       "      <td>power-center</td>\n",
       "      <td>1990</td>\n",
       "      <td>used</td>\n",
       "      <td>28.00</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>diesel</td>\n",
       "      <td>1</td>\n",
       "      <td>315.0</td>\n",
       "      <td>49000.0</td>\n",
       "      <td>61420</td>\n",
       "      <td>FL</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18897</th>\n",
       "      <td>power</td>\n",
       "      <td>power-pilot</td>\n",
       "      <td>1973</td>\n",
       "      <td>used</td>\n",
       "      <td>29.00</td>\n",
       "      <td>other</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>32168</td>\n",
       "      <td>GA</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18899</th>\n",
       "      <td>power</td>\n",
       "      <td>power-runabout</td>\n",
       "      <td>2013</td>\n",
       "      <td>used</td>\n",
       "      <td>19.33</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>6335</td>\n",
       "      <td>MI</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18902</th>\n",
       "      <td>power</td>\n",
       "      <td>power-cruiser</td>\n",
       "      <td>2002</td>\n",
       "      <td>new</td>\n",
       "      <td>26.58</td>\n",
       "      <td>fiberglass</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>17900.0</td>\n",
       "      <td>152266</td>\n",
       "      <td>GA</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16383 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        type          boatClass  year condition  length_ft hullMaterial  \\\n",
       "0      power       power-center  1992      used      21.00   fiberglass   \n",
       "2      power         power-deck  2020       new      18.00   fiberglass   \n",
       "11      sail  sail-racercruiser  1986      used      30.00   fiberglass   \n",
       "12     power      power-pontoon  2019       new      23.70     aluminum   \n",
       "13     power        power-motor  2000      used      55.00   fiberglass   \n",
       "...      ...                ...   ...       ...        ...          ...   \n",
       "18894  power      power-pontoon  2017       new      22.33     aluminum   \n",
       "18896  power       power-center  1990      used      28.00   fiberglass   \n",
       "18897  power        power-pilot  1973      used      29.00        other   \n",
       "18899  power     power-runabout  2013      used      19.33   fiberglass   \n",
       "18902  power      power-cruiser  2002       new      26.58   fiberglass   \n",
       "\n",
       "       fuelType  numEngines  totalHP     price  sellerId state  created_year  \n",
       "0      gasoline           1    150.0   16500.0    217053    FL          2019  \n",
       "2      gasoline           1     75.0   26995.0    220570    OH          2019  \n",
       "11       diesel           1      0.0   25500.0     16876    CT          2011  \n",
       "12     gasoline           1    200.0   44507.0     34914    DE          2019  \n",
       "13       diesel           2      0.0  299000.0      6123    FL          2013  \n",
       "...         ...         ...      ...       ...       ...   ...           ...  \n",
       "18894  gasoline           1     90.0   37631.0      5103    MI          2016  \n",
       "18896    diesel           1    315.0   49000.0     61420    FL          2019  \n",
       "18897  gasoline           1      0.0   10000.0     32168    GA          2017  \n",
       "18899  gasoline           1      0.0   26995.0      6335    MI          2019  \n",
       "18902  gasoline           1    220.0   17900.0    152266    GA          2019  \n",
       "\n",
       "[16383 rows x 13 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3e50420d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.jointplot( x='fico', y='int.rate', data=df,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d505b9f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.figure(figsize=(11,7))\n",
    "#sns.lmplot(y='int.rate',x='fico',data=df_clean_outlier, hue='credit.policy',\n",
    " #          col='not.fully.paid', palette='Set1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "60a09d19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 11)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0,3,5,6,\n",
    "1,11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca2cd8df",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ee74297e",
   "metadata": {},
   "source": [
    "## 5.0 TRANSFORMATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6425b6f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.compose import ColumnTransformer\n",
    "#from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e3bff938",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder, [0,3,5,6])],  remainder='passthrough')\n",
    "#ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [0,3,5,6])], remainder='passthrough')\n",
    "#df_clean_outlier_transform = ct.fit_transform(df_clean_outlier)\n",
    "#X = np.array(ct.fit_transform(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce98590a",
   "metadata": {},
   "source": [
    "#### DIMENSIONAL REDUCTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c08154b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "58e48eae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type\n",
      "boatClass\n",
      "condition\n",
      "hullMaterial\n",
      "fuelType\n",
      "state\n"
     ]
    }
   ],
   "source": [
    "# to select the number of n_component\n",
    "for col in df_clean_outlier:\n",
    "    if df[col].dtypes == 'object':\n",
    "        print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "105a4442",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type_pca</th>\n",
       "      <th>boatClass_pca</th>\n",
       "      <th>condition_pca</th>\n",
       "      <th>hullMaterial_pca</th>\n",
       "      <th>fuelType_pca</th>\n",
       "      <th>state_pca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.017634</td>\n",
       "      <td>-0.257808</td>\n",
       "      <td>0.907935</td>\n",
       "      <td>0.750177</td>\n",
       "      <td>0.979046</td>\n",
       "      <td>0.877958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.017634</td>\n",
       "      <td>-0.234319</td>\n",
       "      <td>-0.506279</td>\n",
       "      <td>0.750177</td>\n",
       "      <td>0.979046</td>\n",
       "      <td>-0.083853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.395879</td>\n",
       "      <td>-0.185880</td>\n",
       "      <td>0.907935</td>\n",
       "      <td>0.750177</td>\n",
       "      <td>0.291670</td>\n",
       "      <td>-0.080134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.017634</td>\n",
       "      <td>0.781600</td>\n",
       "      <td>-0.506279</td>\n",
       "      <td>-0.443088</td>\n",
       "      <td>0.979046</td>\n",
       "      <td>-0.074593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.017634</td>\n",
       "      <td>-0.189224</td>\n",
       "      <td>0.907935</td>\n",
       "      <td>0.750177</td>\n",
       "      <td>0.291670</td>\n",
       "      <td>0.877958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16378</th>\n",
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       "      <td>-0.443088</td>\n",
       "      <td>0.979046</td>\n",
       "      <td>-0.243734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16379</th>\n",
       "      <td>-0.017634</td>\n",
       "      <td>-0.257808</td>\n",
       "      <td>0.907935</td>\n",
       "      <td>0.750177</td>\n",
       "      <td>0.291670</td>\n",
       "      <td>0.877958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16380</th>\n",
       "      <td>-0.017634</td>\n",
       "      <td>-0.185179</td>\n",
       "      <td>0.907935</td>\n",
       "      <td>-0.503787</td>\n",
       "      <td>0.979046</td>\n",
       "      <td>-0.103041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16381</th>\n",
       "      <td>-0.017634</td>\n",
       "      <td>-0.203617</td>\n",
       "      <td>0.907935</td>\n",
       "      <td>0.750177</td>\n",
       "      <td>0.979046</td>\n",
       "      <td>-0.243734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16382</th>\n",
       "      <td>-0.017634</td>\n",
       "      <td>-0.254576</td>\n",
       "      <td>-0.506279</td>\n",
       "      <td>0.750177</td>\n",
       "      <td>0.979046</td>\n",
       "      <td>-0.103041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16383 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       type_pca  boatClass_pca  condition_pca  hullMaterial_pca  fuelType_pca  \\\n",
       "0     -0.017634      -0.257808       0.907935          0.750177      0.979046   \n",
       "1     -0.017634      -0.234319      -0.506279          0.750177      0.979046   \n",
       "2      1.395879      -0.185880       0.907935          0.750177      0.291670   \n",
       "3     -0.017634       0.781600      -0.506279         -0.443088      0.979046   \n",
       "4     -0.017634      -0.189224       0.907935          0.750177      0.291670   \n",
       "...         ...            ...            ...               ...           ...   \n",
       "16378 -0.017634       0.781600      -0.506279         -0.443088      0.979046   \n",
       "16379 -0.017634      -0.257808       0.907935          0.750177      0.291670   \n",
       "16380 -0.017634      -0.185179       0.907935         -0.503787      0.979046   \n",
       "16381 -0.017634      -0.203617       0.907935          0.750177      0.979046   \n",
       "16382 -0.017634      -0.254576      -0.506279          0.750177      0.979046   \n",
       "\n",
       "       state_pca  \n",
       "0       0.877958  \n",
       "1      -0.083853  \n",
       "2      -0.080134  \n",
       "3      -0.074593  \n",
       "4       0.877958  \n",
       "...          ...  \n",
       "16378  -0.243734  \n",
       "16379   0.877958  \n",
       "16380  -0.103041  \n",
       "16381  -0.243734  \n",
       "16382  -0.103041  \n",
       "\n",
       "[16383 rows x 6 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for Object type column, get there dummies values, reduce there demensionality to 1, \n",
    "df_transformed_pca = pd.DataFrame()\n",
    "for col in df_clean_outlier:\n",
    "    # Check for obj type\n",
    "    if df[col].dtypes == 'object': \n",
    "        dummy_val = pd.get_dummies(df_clean_outlier[col])\n",
    "        # Reduce the demension to 1\n",
    "        pca = PCA(n_components=1)\n",
    "        pca_value = pca.fit_transform(dummy_val)\n",
    "        \n",
    "        pca_value = pd.DataFrame(pca_value, columns=[col+'_pca'])    # Convert the numpy data to DataFrame series        \n",
    "        df_transformed_pca = pd.concat([df_transformed_pca, pca_value], axis=1)  # Add to existing df\n",
    "df_transformed_pca        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "67833c40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the index of df_transformed_pca to match the index of df_clean_outlier\n",
    "\n",
    "df_transformed_pca.index = df_clean_outlier.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6127d243",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>type_pca</th>\n",
       "      <th>boatClass_pca</th>\n",
       "      <th>condition_pca</th>\n",
       "      <th>hullMaterial_pca</th>\n",
       "      <th>fuelType_pca</th>\n",
       "      <th>state_pca</th>\n",
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       "      <th>18897</th>\n",
       "      <td>-0.017634</td>\n",
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       "    <tr>\n",
       "      <th>18899</th>\n",
       "      <td>-0.017634</td>\n",
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       "      <td>0.750177</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>18902</th>\n",
       "      <td>-0.017634</td>\n",
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       "      <td>0.750177</td>\n",
       "      <td>0.979046</td>\n",
       "      <td>-0.103041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       type_pca  boatClass_pca  condition_pca  hullMaterial_pca  fuelType_pca  \\\n",
       "18897 -0.017634      -0.185179       0.907935         -0.503787      0.979046   \n",
       "18899 -0.017634      -0.203617       0.907935          0.750177      0.979046   \n",
       "18902 -0.017634      -0.254576      -0.506279          0.750177      0.979046   \n",
       "\n",
       "       state_pca  \n",
       "18897  -0.103041  \n",
       "18899  -0.243734  \n",
       "18902  -0.103041  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed_pca.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a595b0d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>length_ft</th>\n",
       "      <th>numEngines</th>\n",
       "      <th>totalHP</th>\n",
       "      <th>price</th>\n",
       "      <th>sellerId</th>\n",
       "      <th>created_year</th>\n",
       "      <th>type_pca</th>\n",
       "      <th>boatClass_pca</th>\n",
       "      <th>condition_pca</th>\n",
       "      <th>hullMaterial_pca</th>\n",
       "      <th>fuelType_pca</th>\n",
       "      <th>state_pca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1992</td>\n",
       "      <td>21.00</td>\n",
       "      <td>1</td>\n",
       "      <td>150.0</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>217053</td>\n",
       "      <td>2019</td>\n",
       "      <td>-0.017634</td>\n",
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       "      <td>0.750177</td>\n",
       "      <td>0.979046</td>\n",
       "      <td>0.877958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>18.00</td>\n",
       "      <td>1</td>\n",
       "      <td>75.0</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>220570</td>\n",
       "      <td>2019</td>\n",
       "      <td>-0.017634</td>\n",
       "      <td>-0.234319</td>\n",
       "      <td>-0.506279</td>\n",
       "      <td>0.750177</td>\n",
       "      <td>0.979046</td>\n",
       "      <td>-0.083853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1986</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25500.0</td>\n",
       "      <td>16876</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.395879</td>\n",
       "      <td>-0.185880</td>\n",
       "      <td>0.907935</td>\n",
       "      <td>0.750177</td>\n",
       "      <td>0.291670</td>\n",
       "      <td>-0.080134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019</td>\n",
       "      <td>23.70</td>\n",
       "      <td>1</td>\n",
       "      <td>200.0</td>\n",
       "      <td>44507.0</td>\n",
       "      <td>34914</td>\n",
       "      <td>2019</td>\n",
       "      <td>-0.017634</td>\n",
       "      <td>0.781600</td>\n",
       "      <td>-0.506279</td>\n",
       "      <td>-0.443088</td>\n",
       "      <td>0.979046</td>\n",
       "      <td>-0.074593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2000</td>\n",
       "      <td>55.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299000.0</td>\n",
       "      <td>6123</td>\n",
       "      <td>2013</td>\n",
       "      <td>-0.017634</td>\n",
       "      <td>-0.189224</td>\n",
       "      <td>0.907935</td>\n",
       "      <td>0.750177</td>\n",
       "      <td>0.291670</td>\n",
       "      <td>0.877958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18894</th>\n",
       "      <td>2017</td>\n",
       "      <td>22.33</td>\n",
       "      <td>1</td>\n",
       "      <td>90.0</td>\n",
       "      <td>37631.0</td>\n",
       "      <td>5103</td>\n",
       "      <td>2016</td>\n",
       "      <td>-0.017634</td>\n",
       "      <td>0.781600</td>\n",
       "      <td>-0.506279</td>\n",
       "      <td>-0.443088</td>\n",
       "      <td>0.979046</td>\n",
       "      <td>-0.243734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18896</th>\n",
       "      <td>1990</td>\n",
       "      <td>28.00</td>\n",
       "      <td>1</td>\n",
       "      <td>315.0</td>\n",
       "      <td>49000.0</td>\n",
       "      <td>61420</td>\n",
       "      <td>2019</td>\n",
       "      <td>-0.017634</td>\n",
       "      <td>-0.257808</td>\n",
       "      <td>0.907935</td>\n",
       "      <td>0.750177</td>\n",
       "      <td>0.291670</td>\n",
       "      <td>0.877958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18897</th>\n",
       "      <td>1973</td>\n",
       "      <td>29.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>32168</td>\n",
       "      <td>2017</td>\n",
       "      <td>-0.017634</td>\n",
       "      <td>-0.185179</td>\n",
       "      <td>0.907935</td>\n",
       "      <td>-0.503787</td>\n",
       "      <td>0.979046</td>\n",
       "      <td>-0.103041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18899</th>\n",
       "      <td>2013</td>\n",
       "      <td>19.33</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26995.0</td>\n",
       "      <td>6335</td>\n",
       "      <td>2019</td>\n",
       "      <td>-0.017634</td>\n",
       "      <td>-0.203617</td>\n",
       "      <td>0.907935</td>\n",
       "      <td>0.750177</td>\n",
       "      <td>0.979046</td>\n",
       "      <td>-0.243734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18902</th>\n",
       "      <td>2002</td>\n",
       "      <td>26.58</td>\n",
       "      <td>1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>17900.0</td>\n",
       "      <td>152266</td>\n",
       "      <td>2019</td>\n",
       "      <td>-0.017634</td>\n",
       "      <td>-0.254576</td>\n",
       "      <td>-0.506279</td>\n",
       "      <td>0.750177</td>\n",
       "      <td>0.979046</td>\n",
       "      <td>-0.103041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16383 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  length_ft  numEngines  totalHP     price  sellerId  created_year  \\\n",
       "0      1992      21.00           1    150.0   16500.0    217053          2019   \n",
       "2      2020      18.00           1     75.0   26995.0    220570          2019   \n",
       "11     1986      30.00           1      0.0   25500.0     16876          2011   \n",
       "12     2019      23.70           1    200.0   44507.0     34914          2019   \n",
       "13     2000      55.00           2      0.0  299000.0      6123          2013   \n",
       "...     ...        ...         ...      ...       ...       ...           ...   \n",
       "18894  2017      22.33           1     90.0   37631.0      5103          2016   \n",
       "18896  1990      28.00           1    315.0   49000.0     61420          2019   \n",
       "18897  1973      29.00           1      0.0   10000.0     32168          2017   \n",
       "18899  2013      19.33           1      0.0   26995.0      6335          2019   \n",
       "18902  2002      26.58           1    220.0   17900.0    152266          2019   \n",
       "\n",
       "       type_pca  boatClass_pca  condition_pca  hullMaterial_pca  fuelType_pca  \\\n",
       "0     -0.017634      -0.257808       0.907935          0.750177      0.979046   \n",
       "2     -0.017634      -0.234319      -0.506279          0.750177      0.979046   \n",
       "11     1.395879      -0.185880       0.907935          0.750177      0.291670   \n",
       "12    -0.017634       0.781600      -0.506279         -0.443088      0.979046   \n",
       "13    -0.017634      -0.189224       0.907935          0.750177      0.291670   \n",
       "...         ...            ...            ...               ...           ...   \n",
       "18894 -0.017634       0.781600      -0.506279         -0.443088      0.979046   \n",
       "18896 -0.017634      -0.257808       0.907935          0.750177      0.291670   \n",
       "18897 -0.017634      -0.185179       0.907935         -0.503787      0.979046   \n",
       "18899 -0.017634      -0.203617       0.907935          0.750177      0.979046   \n",
       "18902 -0.017634      -0.254576      -0.506279          0.750177      0.979046   \n",
       "\n",
       "       state_pca  \n",
       "0       0.877958  \n",
       "2      -0.083853  \n",
       "11     -0.080134  \n",
       "12     -0.074593  \n",
       "13      0.877958  \n",
       "...          ...  \n",
       "18894  -0.243734  \n",
       "18896   0.877958  \n",
       "18897  -0.103041  \n",
       "18899  -0.243734  \n",
       "18902  -0.103041  \n",
       "\n",
       "[16383 rows x 13 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_outlier_transformed = pd.concat([df_clean_outlier, df_transformed_pca ], axis=1)\n",
    "df_clean_outlier_transformed.drop(['type', 'condition', 'hullMaterial', 'fuelType', 'boatClass', 'state'], axis=1, inplace=True)\n",
    "df_clean_outlier_transformed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9544782a",
   "metadata": {},
   "source": [
    "##  6.0 MODEL AND EVALUATION\n",
    "\n",
    "+ Let's start by splitting our data into a training set and test set\n",
    "\n",
    "### Splitting the dataset into the Training set and Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8ba2706a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16383, 13)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_outlier_transformed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1054f684",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ee921682",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_clean_outlier_transformed.drop('price', axis=1)\n",
    "y = df_clean_outlier_transformed['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "3eb89076",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8429b31f",
   "metadata": {},
   "source": [
    "## Train using Linear Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "279e7ea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "ln_regressor = LinearRegression()\n",
    "ln_regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d9b67843",
   "metadata": {},
   "outputs": [],
   "source": [
    "ln_predictions = ln_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f783644f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e363927b80>]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,7))\n",
    "\n",
    "plt.scatter(ln_regressor.predict(X_train), y_train, c=y_train, cmap='plasma')\n",
    "plt.plot(y_train, y_train, )\n",
    "#plt.scatter(ln_predictions, y_test, color = 'green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "0a68d0c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e363988dc0>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot((y_test - ln_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "3529342b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ccc6e19b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16750.030399701525"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_absolute_error(y_test, ln_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "4cdd5f83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4840999511126901, 0.4632075629433322)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score, adjusted_rand_score\n",
    "r2_score(y_train, ln_regressor.predict(X_train)), r2_score(y_test, ln_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4b3e752",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "88c80108",
   "metadata": {},
   "source": [
    "## Train using SVR model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a85c0e0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### NORMALIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d8fead57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.71927243],\n",
       "       [-0.71645079],\n",
       "       [-0.40903156],\n",
       "       ...,\n",
       "       [ 1.37617347],\n",
       "       [-0.65429086],\n",
       "       [ 0.31735951]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "sc_y = StandardScaler()\n",
    "sc_X_train = sc_X.fit_transform(X_train)\n",
    "\n",
    "sc_y_train = sc_y.fit_transform(y_train.values.reshape(len(y_train), -1))\n",
    "sc_y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "6323c58d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning:\n",
      "\n",
      "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Import SVM library\n",
    "from sklearn.svm import SVR\n",
    "svr_regressor = SVR(kernel='rbf')\n",
    "svr_regressor.fit(sc_X_train, sc_y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "1d6ee3b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_y_pred = sc_y.inverse_transform(svr_regressor.predict(sc_X.transform(X_test)).reshape(len(y_test), -1))\n",
    "svm_y_train =sc_y.inverse_transform(svr_regressor.predict(sc_X.transform(X_train)).reshape(len(X_train), -1))\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "#y_pred = sc_y.inverse_transform(regressor.predict(sc_X.transform(X_test)).reshape(-1,1))\n",
    "#print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "9cd654ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[39230.1 ],\n",
       "       [62686.64],\n",
       "       [47447.55],\n",
       "       ...,\n",
       "       [52907.13],\n",
       "       [14343.28],\n",
       "       [15495.39]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "44b49e8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7697508739636432, 0.7485459889347739)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_train, svm_y_train), r2_score(y_test, svm_y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e14e1d2d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3d0e4077",
   "metadata": {},
   "source": [
    "## Train using DECISION TREE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "6a47cc6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "decision_regressor = DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e8ef7cad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f3b9cc2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([49995.  , 72969.71, 38715.  , ..., 40249.  , 20579.  , 19195.  ])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decsn_tree_pred = decision_regressor.predict(X_test)\n",
    "decsn_tree_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "0cb383a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e363b9f280>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot((y_test - decsn_tree_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "70434e59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8880.63495835723"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_absolute_error(y_test, decsn_tree_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "6fe1ea30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9772532732488435, 0.7447688390219467)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_train,  decision_regressor.predict(X_train)), r2_score(y_test, decsn_tree_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81aa1f06",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c6c72704",
   "metadata": {},
   "source": [
    "## Train using RANDOM FOREST "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "a8004794",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1244eb5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8333741991339522,\n",
       " 0.8331390876238987,\n",
       " 0.8394307959232685,\n",
       " 0.8416464786812291,\n",
       " 0.8434282317046357,\n",
       " 0.8456075525347521,\n",
       " 0.8444608225859072,\n",
       " 0.846152351786961,\n",
       " 0.8444214453543895,\n",
       " 0.8463054282370807,\n",
       " 0.8479620595431258,\n",
       " 0.8484919683435462,\n",
       " 0.8491791295448785,\n",
       " 0.848318158065587,\n",
       " 0.8482117323687759,\n",
       " 0.8477871935543554,\n",
       " 0.8482394373958391,\n",
       " 0.8495887460717474,\n",
       " 0.8495217667208936,\n",
       " 0.8501932214533814,\n",
       " 0.849405248221873,\n",
       " 0.850434247044846,\n",
       " 0.8508780261229767,\n",
       " 0.8511656464303794,\n",
       " 0.8506568864463138,\n",
       " 0.8500283007524013,\n",
       " 0.8509352884679647,\n",
       " 0.8513126387852852,\n",
       " 0.8511528087897265,\n",
       " 0.851415789099875,\n",
       " 0.8517011325370057,\n",
       " 0.8513934174845255,\n",
       " 0.8519562336137362,\n",
       " 0.8521804358487273,\n",
       " 0.8525488914199715,\n",
       " 0.852232260003172,\n",
       " 0.8528257803056335,\n",
       " 0.8525030768260736,\n",
       " 0.8525566865733942,\n",
       " 0.8529478944368509,\n",
       " 0.853018743441785,\n",
       " 0.8527169637569818,\n",
       " 0.8532188738863415,\n",
       " 0.853430437329308,\n",
       " 0.853472306271121]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Iterate and test different possible n_estimators number\n",
    "rand_predicitions = []\n",
    "for i in range(4, 49):\n",
    "    rand_regressor = RandomForestRegressor(n_estimators=i, random_state=42)\n",
    "    rand_regressor.fit(X_train, y_train)\n",
    "    pred = rand_regressor.predict(X_test)\n",
    "    rand_predicitions.append(r2_score(y_test, pred))\n",
    "    \n",
    "rand_predicitions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "cb316777",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e3713d8430>]"
      ]
     },
     "execution_count": 99,
     "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": [
    "# Plot the prediction witht there respective number for better visualization\n",
    "plt.plot(range(4, 49), rand_predicitions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "bb31d854",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_estimators=44, random_state=42)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Using 26 as n_estimators\n",
    "rand_regressor = RandomForestRegressor(n_estimators=44,random_state=42)\n",
    "rand_regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "65a85d4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([42068.19, 72820.29, 39697.71, ..., 43692.4 , 25073.31, 19507.39])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_prediction = rand_regressor.predict(X_test)\n",
    "random_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "89e2d330",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9570570934269003, 0.853018743441785)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_train,  rand_regressor.predict(X_train)), r2_score(y_test, random_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "cfae2aea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7299.62887195792"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_absolute_error(y_test, random_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae8db190",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}