HousePrice.ipynb

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      "name": "Copy of SalePriceFinal.ipynb",
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "7D1GF4l0WDBp"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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        "outputId": "77db7c34-2a82-4c40-d09c-ba0c41f3f731"
      },
      "source": [
        "df=pd.read_csv('https://raw.githubusercontent.com/ogut77/DataScience/main/data/nyc-rolling-sales.csv')\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>BOROUGH</th>\n",
              "      <th>NEIGHBORHOOD</th>\n",
              "      <th>BUILDING CLASS CATEGORY</th>\n",
              "      <th>TAX CLASS AT PRESENT</th>\n",
              "      <th>BLOCK</th>\n",
              "      <th>LOT</th>\n",
              "      <th>EASE-MENT</th>\n",
              "      <th>BUILDING CLASS AT PRESENT</th>\n",
              "      <th>ADDRESS</th>\n",
              "      <th>APARTMENT NUMBER</th>\n",
              "      <th>ZIP CODE</th>\n",
              "      <th>RESIDENTIAL UNITS</th>\n",
              "      <th>COMMERCIAL UNITS</th>\n",
              "      <th>TOTAL UNITS</th>\n",
              "      <th>LAND SQUARE FEET</th>\n",
              "      <th>GROSS SQUARE FEET</th>\n",
              "      <th>YEAR BUILT</th>\n",
              "      <th>TAX CLASS AT TIME OF SALE</th>\n",
              "      <th>BUILDING CLASS AT TIME OF SALE</th>\n",
              "      <th>SALE PRICE</th>\n",
              "      <th>SALE DATE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2A</td>\n",
              "      <td>392</td>\n",
              "      <td>6</td>\n",
              "      <td></td>\n",
              "      <td>C2</td>\n",
              "      <td>153 AVENUE B</td>\n",
              "      <td></td>\n",
              "      <td>10009</td>\n",
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              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1633</td>\n",
              "      <td>6440</td>\n",
              "      <td>1900</td>\n",
              "      <td>2</td>\n",
              "      <td>C2</td>\n",
              "      <td>6625000</td>\n",
              "      <td>2017-07-19 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2</td>\n",
              "      <td>399</td>\n",
              "      <td>26</td>\n",
              "      <td></td>\n",
              "      <td>C7</td>\n",
              "      <td>234 EAST 4TH   STREET</td>\n",
              "      <td></td>\n",
              "      <td>10009</td>\n",
              "      <td>28</td>\n",
              "      <td>3</td>\n",
              "      <td>31</td>\n",
              "      <td>4616</td>\n",
              "      <td>18690</td>\n",
              "      <td>1900</td>\n",
              "      <td>2</td>\n",
              "      <td>C7</td>\n",
              "      <td>-</td>\n",
              "      <td>2016-12-14 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2</td>\n",
              "      <td>399</td>\n",
              "      <td>39</td>\n",
              "      <td></td>\n",
              "      <td>C7</td>\n",
              "      <td>197 EAST 3RD   STREET</td>\n",
              "      <td></td>\n",
              "      <td>10009</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>2212</td>\n",
              "      <td>7803</td>\n",
              "      <td>1900</td>\n",
              "      <td>2</td>\n",
              "      <td>C7</td>\n",
              "      <td>-</td>\n",
              "      <td>2016-12-09 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2B</td>\n",
              "      <td>402</td>\n",
              "      <td>21</td>\n",
              "      <td></td>\n",
              "      <td>C4</td>\n",
              "      <td>154 EAST 7TH STREET</td>\n",
              "      <td></td>\n",
              "      <td>10009</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>2272</td>\n",
              "      <td>6794</td>\n",
              "      <td>1913</td>\n",
              "      <td>2</td>\n",
              "      <td>C4</td>\n",
              "      <td>3936272</td>\n",
              "      <td>2016-09-23 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2A</td>\n",
              "      <td>404</td>\n",
              "      <td>55</td>\n",
              "      <td></td>\n",
              "      <td>C2</td>\n",
              "      <td>301 EAST 10TH   STREET</td>\n",
              "      <td></td>\n",
              "      <td>10009</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2369</td>\n",
              "      <td>4615</td>\n",
              "      <td>1900</td>\n",
              "      <td>2</td>\n",
              "      <td>C2</td>\n",
              "      <td>8000000</td>\n",
              "      <td>2016-11-17 00:00:00</td>\n",
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            "text/plain": [
              "   Unnamed: 0  BOROUGH  ... SALE PRICE            SALE DATE\n",
              "0           4        1  ...    6625000  2017-07-19 00:00:00\n",
              "1           5        1  ...        -    2016-12-14 00:00:00\n",
              "2           6        1  ...        -    2016-12-09 00:00:00\n",
              "3           7        1  ...    3936272  2016-09-23 00:00:00\n",
              "4           8        1  ...    8000000  2016-11-17 00:00:00\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7_at3fKgGzyb"
      },
      "source": [
        "1. Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OsRFlwSGF_EI",
        "outputId": "fa6ad7d9-bfd1-41ba-b57c-a01f09cde60c"
      },
      "source": [
        "df.isnull().sum()\n",
        "#it seems there is no null value however when we check column values,we have null values."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0                        0\n",
              "BOROUGH                           0\n",
              "NEIGHBORHOOD                      0\n",
              "BUILDING CLASS CATEGORY           0\n",
              "TAX CLASS AT PRESENT              0\n",
              "BLOCK                             0\n",
              "LOT                               0\n",
              "EASE-MENT                         0\n",
              "BUILDING CLASS AT PRESENT         0\n",
              "ADDRESS                           0\n",
              "APARTMENT NUMBER                  0\n",
              "ZIP CODE                          0\n",
              "RESIDENTIAL UNITS                 0\n",
              "COMMERCIAL UNITS                  0\n",
              "TOTAL UNITS                       0\n",
              "LAND SQUARE FEET                  0\n",
              "GROSS SQUARE FEET                 0\n",
              "YEAR BUILT                        0\n",
              "TAX CLASS AT TIME OF SALE         0\n",
              "BUILDING CLASS AT TIME OF SALE    0\n",
              "SALE PRICE                        0\n",
              "SALE DATE                         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nuu1nZOE2GAK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53ed168e-ed61-4f02-cd60-77b6d52f5535"
      },
      "source": [
        "#check column values\n",
        "for ex in df.columns:\n",
        " print(df[ex].value_counts())\n",
        "  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4        5\n",
            "4699     5\n",
            "4710     5\n",
            "4709     5\n",
            "4708     5\n",
            "        ..\n",
            "24948    1\n",
            "24949    1\n",
            "24950    1\n",
            "24951    1\n",
            "26739    1\n",
            "Name: Unnamed: 0, Length: 26736, dtype: int64\n",
            "4    26736\n",
            "3    24047\n",
            "1    18306\n",
            "5     8410\n",
            "2     7049\n",
            "Name: BOROUGH, dtype: int64\n",
            "FLUSHING-NORTH              3068\n",
            "UPPER EAST SIDE (59-79)     1736\n",
            "UPPER EAST SIDE (79-96)     1590\n",
            "UPPER WEST SIDE (59-79)     1439\n",
            "BEDFORD STUYVESANT          1436\n",
            "                            ... \n",
            "FRESH KILLS                    2\n",
            "BRONX PARK                     1\n",
            "PELHAM BAY                     1\n",
            "EAST RIVER                     1\n",
            "CITY ISLAND-PELHAM STRIP       1\n",
            "Name: NEIGHBORHOOD, Length: 254, dtype: int64\n",
            "01 ONE FAMILY DWELLINGS                         18235\n",
            "02 TWO FAMILY DWELLINGS                         15828\n",
            "13 CONDOS - ELEVATOR APARTMENTS                 12989\n",
            "10 COOPS - ELEVATOR APARTMENTS                  12902\n",
            "03 THREE FAMILY DWELLINGS                        4384\n",
            "07 RENTALS - WALKUP APARTMENTS                   3466\n",
            "09 COOPS - WALKUP APARTMENTS                     2767\n",
            "04 TAX CLASS 1 CONDOS                            1656\n",
            "44 CONDO PARKING                                 1441\n",
            "15 CONDOS - 2-10 UNIT RESIDENTIAL                1281\n",
            "05 TAX CLASS 1 VACANT LAND                       1248\n",
            "17 CONDO COOPS                                   1201\n",
            "22 STORE BUILDINGS                                935\n",
            "12 CONDOS - WALKUP APARTMENTS                     926\n",
            "14 RENTALS - 4-10 UNIT                            671\n",
            "29 COMMERCIAL GARAGES                             587\n",
            "43 CONDO OFFICE BUILDINGS                         475\n",
            "31 COMMERCIAL VACANT LAND                         463\n",
            "08 RENTALS - ELEVATOR APARTMENTS                  382\n",
            "47 CONDO NON-BUSINESS STORAGE                     377\n",
            "21 OFFICE BUILDINGS                               350\n",
            "30 WAREHOUSES                                     326\n",
            "45 CONDO HOTELS                                   211\n",
            "27 FACTORIES                                      201\n",
            "06 TAX CLASS 1 - OTHER                            182\n",
            "41 TAX CLASS 4 - OTHER                            158\n",
            "46 CONDO STORE BUILDINGS                          154\n",
            "26 OTHER HOTELS                                   114\n",
            "37 RELIGIOUS FACILITIES                           100\n",
            "16 CONDOS - 2-10 UNIT WITH COMMERCIAL UNIT         96\n",
            "33 EDUCATIONAL FACILITIES                          69\n",
            "32 HOSPITAL AND HEALTH FACILITIES                  59\n",
            "48 CONDO TERRACES/GARDENS/CABANAS                  47\n",
            "23 LOFT BUILDINGS                                  46\n",
            "11A CONDO-RENTALS                                  43\n",
            "35 INDOOR PUBLIC AND CULTURAL FACILITIES           32\n",
            "49 CONDO WAREHOUSES/FACTORY/INDUS                  30\n",
            "28 COMMERCIAL CONDOS                               30\n",
            "38 ASYLUMS AND HOMES                               25\n",
            "36 OUTDOOR RECREATIONAL FACILITIES                 14\n",
            "42 CONDO CULTURAL/MEDICAL/EDUCATIONAL/ETC          13\n",
            "34 THEATRES                                        12\n",
            "25 LUXURY HOTELS                                   12\n",
            "18 TAX CLASS 3 - UNTILITY PROPERTIES                4\n",
            "40 SELECTED GOVERNMENTAL FACILITIES                 2\n",
            "11 SPECIAL CONDO BILLING LOTS                       2\n",
            "39 TRANSPORTATION FACILITIES                        2\n",
            "Name: BUILDING CLASS CATEGORY, dtype: int64\n",
            "1     38633\n",
            "2     30919\n",
            "4      6140\n",
            "2A     2521\n",
            "2C     1915\n",
            "1A     1444\n",
            "1B     1234\n",
            "2B      814\n",
            "        738\n",
            "1C      186\n",
            "3         4\n",
            "Name: TAX CLASS AT PRESENT, dtype: int64\n",
            "5066     404\n",
            "16       255\n",
            "2135     211\n",
            "4978     187\n",
            "1171     181\n",
            "        ... \n",
            "9410       1\n",
            "9273       1\n",
            "9528       1\n",
            "9556       1\n",
            "13959      1\n",
            "Name: BLOCK, Length: 11566, dtype: int64\n",
            "1       4125\n",
            "20       983\n",
            "12       972\n",
            "40       935\n",
            "23       911\n",
            "        ... \n",
            "2878       1\n",
            "2923       1\n",
            "3191       1\n",
            "3292       1\n",
            "679        1\n",
            "Name: LOT, Length: 2627, dtype: int64\n",
            "     84548\n",
            "Name: EASE-MENT, dtype: int64\n",
            "D4    12663\n",
            "R4    12482\n",
            "A1     6753\n",
            "A5     5683\n",
            "B2     4923\n",
            "      ...  \n",
            "R7        1\n",
            "P7        1\n",
            "HH        1\n",
            "M2        1\n",
            "Z7        1\n",
            "Name: BUILDING CLASS AT PRESENT, Length: 167, dtype: int64\n",
            "131-05 40TH   ROAD        210\n",
            "429 KENT AVENUE           158\n",
            "169 WEST 95TH   STREET    153\n",
            "131-03 40TH   ROAD        147\n",
            "265 STATE STREET          127\n",
            "                         ... \n",
            "9405 SEAVIEW AVENUE         1\n",
            "9410 AVENUE N               1\n",
            "9911 SEAVIEW AVENUE         1\n",
            "1471 EAST 99TH STREET       1\n",
            "155 CLAY PIT ROAD           1\n",
            "Name: ADDRESS, Length: 67563, dtype: int64\n",
            "        65496\n",
            "4         298\n",
            "3A        295\n",
            "2         275\n",
            "3B        275\n",
            "        ...  \n",
            "ST50        1\n",
            "ST54        1\n",
            "SU13        1\n",
            "SU21        1\n",
            "363         1\n",
            "Name: APARTMENT NUMBER, Length: 3989, dtype: int64\n",
            "10314    1687\n",
            "11354    1384\n",
            "11201    1324\n",
            "11235    1312\n",
            "11234    1165\n",
            "         ... \n",
            "10105       5\n",
            "11430       1\n",
            "10281       1\n",
            "10803       1\n",
            "10167       1\n",
            "Name: ZIP CODE, Length: 186, dtype: int64\n",
            "1      34722\n",
            "0      24783\n",
            "2      16049\n",
            "3       4608\n",
            "4       1346\n",
            "       ...  \n",
            "179        1\n",
            "75         1\n",
            "193        1\n",
            "153        1\n",
            "128        1\n",
            "Name: RESIDENTIAL UNITS, Length: 176, dtype: int64\n",
            "0       79429\n",
            "1        3558\n",
            "2         817\n",
            "3         259\n",
            "4         137\n",
            "5          74\n",
            "6          70\n",
            "7          31\n",
            "8          26\n",
            "9          20\n",
            "10         17\n",
            "12         12\n",
            "15         11\n",
            "11         10\n",
            "17          6\n",
            "14          6\n",
            "254         4\n",
            "20          4\n",
            "35          4\n",
            "13          4\n",
            "19          3\n",
            "42          3\n",
            "18          3\n",
            "22          3\n",
            "126         2\n",
            "25          2\n",
            "26          2\n",
            "436         2\n",
            "16          2\n",
            "422         2\n",
            "34          1\n",
            "172         1\n",
            "2261        1\n",
            "51          1\n",
            "184         1\n",
            "147         1\n",
            "28          1\n",
            "21          1\n",
            "32          1\n",
            "55          1\n",
            "56          1\n",
            "73          1\n",
            "31          1\n",
            "38          1\n",
            "59          1\n",
            "91          1\n",
            "30          1\n",
            "49          1\n",
            "24          1\n",
            "67          1\n",
            "318         1\n",
            "52          1\n",
            "23          1\n",
            "27          1\n",
            "62          1\n",
            "Name: COMMERCIAL UNITS, dtype: int64\n",
            "1      38356\n",
            "0      19762\n",
            "2      15914\n",
            "3       5412\n",
            "4       1498\n",
            "       ...  \n",
            "81         1\n",
            "179        1\n",
            "257        1\n",
            "82         1\n",
            "128        1\n",
            "Name: TOTAL UNITS, Length: 192, dtype: int64\n",
            " -       26252\n",
            "0        10326\n",
            "2000      3919\n",
            "2500      3470\n",
            "4000      3044\n",
            "         ...  \n",
            "5886         1\n",
            "76712        1\n",
            "6777         1\n",
            "10833        1\n",
            "10796        1\n",
            "Name: LAND SQUARE FEET, Length: 6062, dtype: int64\n",
            " -       27612\n",
            "0        11417\n",
            "2400       386\n",
            "1800       361\n",
            "2000       359\n",
            "         ...  \n",
            "13740        1\n",
            "12256        1\n",
            "3823         1\n",
            "7768         1\n",
            "64117        1\n",
            "Name: GROSS SQUARE FEET, Length: 5691, dtype: int64\n",
            "0       6970\n",
            "1920    6045\n",
            "1930    5043\n",
            "1925    4312\n",
            "1910    3585\n",
            "        ... \n",
            "1888       1\n",
            "1849       1\n",
            "1855       1\n",
            "1111       1\n",
            "1680       1\n",
            "Name: YEAR BUILT, Length: 158, dtype: int64\n",
            "1    41533\n",
            "2    36726\n",
            "4     6285\n",
            "3        4\n",
            "Name: TAX CLASS AT TIME OF SALE, dtype: int64\n",
            "R4    12989\n",
            "D4    12666\n",
            "A1     6751\n",
            "A5     5671\n",
            "B2     4918\n",
            "      ...  \n",
            "I3        1\n",
            "R7        1\n",
            "Q8        1\n",
            "Y1        1\n",
            "Z7        1\n",
            "Name: BUILDING CLASS AT TIME OF SALE, Length: 166, dtype: int64\n",
            " -         14561\n",
            "0          10228\n",
            "10           766\n",
            "450000       427\n",
            "550000       416\n",
            "           ...  \n",
            "191100         1\n",
            "1542499        1\n",
            "782990         1\n",
            "2784765        1\n",
            "69300          1\n",
            "Name: SALE PRICE, Length: 10008, dtype: int64\n",
            "2017-06-29 00:00:00    544\n",
            "2017-06-15 00:00:00    530\n",
            "2016-12-22 00:00:00    527\n",
            "2017-05-25 00:00:00    511\n",
            "2016-10-06 00:00:00    508\n",
            "                      ... \n",
            "2017-01-08 00:00:00      2\n",
            "2017-07-09 00:00:00      2\n",
            "2016-11-27 00:00:00      2\n",
            "2017-08-13 00:00:00      1\n",
            "2017-06-04 00:00:00      1\n",
            "Name: SALE DATE, Length: 364, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 939
        },
        "id": "UMgsgv6YFgkD",
        "outputId": "9507b032-9e7f-4399-ea75-40ea320edd64"
      },
      "source": [
        "#delete column with no values\n",
        "df.drop(['Unnamed: 0','APARTMENT NUMBER','EASE-MENT'],axis=1, inplace=True)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-df008e6a-868e-4081-8bba-32b06a31e639\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .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>BOROUGH</th>\n",
              "      <th>NEIGHBORHOOD</th>\n",
              "      <th>BUILDING CLASS CATEGORY</th>\n",
              "      <th>TAX CLASS AT PRESENT</th>\n",
              "      <th>BLOCK</th>\n",
              "      <th>LOT</th>\n",
              "      <th>BUILDING CLASS AT PRESENT</th>\n",
              "      <th>ADDRESS</th>\n",
              "      <th>ZIP CODE</th>\n",
              "      <th>RESIDENTIAL UNITS</th>\n",
              "      <th>COMMERCIAL UNITS</th>\n",
              "      <th>TOTAL UNITS</th>\n",
              "      <th>LAND SQUARE FEET</th>\n",
              "      <th>GROSS SQUARE FEET</th>\n",
              "      <th>YEAR BUILT</th>\n",
              "      <th>TAX CLASS AT TIME OF SALE</th>\n",
              "      <th>BUILDING CLASS AT TIME OF SALE</th>\n",
              "      <th>SALE PRICE</th>\n",
              "      <th>SALE DATE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2A</td>\n",
              "      <td>392</td>\n",
              "      <td>6</td>\n",
              "      <td>C2</td>\n",
              "      <td>153 AVENUE B</td>\n",
              "      <td>10009</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1633</td>\n",
              "      <td>6440</td>\n",
              "      <td>1900</td>\n",
              "      <td>2</td>\n",
              "      <td>C2</td>\n",
              "      <td>6625000</td>\n",
              "      <td>2017-07-19 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2</td>\n",
              "      <td>399</td>\n",
              "      <td>26</td>\n",
              "      <td>C7</td>\n",
              "      <td>234 EAST 4TH   STREET</td>\n",
              "      <td>10009</td>\n",
              "      <td>28</td>\n",
              "      <td>3</td>\n",
              "      <td>31</td>\n",
              "      <td>4616</td>\n",
              "      <td>18690</td>\n",
              "      <td>1900</td>\n",
              "      <td>2</td>\n",
              "      <td>C7</td>\n",
              "      <td>-</td>\n",
              "      <td>2016-12-14 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2</td>\n",
              "      <td>399</td>\n",
              "      <td>39</td>\n",
              "      <td>C7</td>\n",
              "      <td>197 EAST 3RD   STREET</td>\n",
              "      <td>10009</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>2212</td>\n",
              "      <td>7803</td>\n",
              "      <td>1900</td>\n",
              "      <td>2</td>\n",
              "      <td>C7</td>\n",
              "      <td>-</td>\n",
              "      <td>2016-12-09 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2B</td>\n",
              "      <td>402</td>\n",
              "      <td>21</td>\n",
              "      <td>C4</td>\n",
              "      <td>154 EAST 7TH STREET</td>\n",
              "      <td>10009</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>2272</td>\n",
              "      <td>6794</td>\n",
              "      <td>1913</td>\n",
              "      <td>2</td>\n",
              "      <td>C4</td>\n",
              "      <td>3936272</td>\n",
              "      <td>2016-09-23 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2A</td>\n",
              "      <td>404</td>\n",
              "      <td>55</td>\n",
              "      <td>C2</td>\n",
              "      <td>301 EAST 10TH   STREET</td>\n",
              "      <td>10009</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2369</td>\n",
              "      <td>4615</td>\n",
              "      <td>1900</td>\n",
              "      <td>2</td>\n",
              "      <td>C2</td>\n",
              "      <td>8000000</td>\n",
              "      <td>2016-11-17 00:00:00</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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84543</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>02 TWO FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>7349</td>\n",
              "      <td>34</td>\n",
              "      <td>B9</td>\n",
              "      <td>37 QUAIL LANE</td>\n",
              "      <td>10309</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2400</td>\n",
              "      <td>2575</td>\n",
              "      <td>1998</td>\n",
              "      <td>1</td>\n",
              "      <td>B9</td>\n",
              "      <td>450000</td>\n",
              "      <td>2016-11-28 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84544</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>02 TWO FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>7349</td>\n",
              "      <td>78</td>\n",
              "      <td>B9</td>\n",
              "      <td>32 PHEASANT LANE</td>\n",
              "      <td>10309</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2498</td>\n",
              "      <td>2377</td>\n",
              "      <td>1998</td>\n",
              "      <td>1</td>\n",
              "      <td>B9</td>\n",
              "      <td>550000</td>\n",
              "      <td>2017-04-21 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84545</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>02 TWO FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>7351</td>\n",
              "      <td>60</td>\n",
              "      <td>B2</td>\n",
              "      <td>49 PITNEY AVENUE</td>\n",
              "      <td>10309</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4000</td>\n",
              "      <td>1496</td>\n",
              "      <td>1925</td>\n",
              "      <td>1</td>\n",
              "      <td>B2</td>\n",
              "      <td>460000</td>\n",
              "      <td>2017-07-05 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84546</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>22 STORE BUILDINGS</td>\n",
              "      <td>4</td>\n",
              "      <td>7100</td>\n",
              "      <td>28</td>\n",
              "      <td>K6</td>\n",
              "      <td>2730 ARTHUR KILL ROAD</td>\n",
              "      <td>10309</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>208033</td>\n",
              "      <td>64117</td>\n",
              "      <td>2001</td>\n",
              "      <td>4</td>\n",
              "      <td>K6</td>\n",
              "      <td>11693337</td>\n",
              "      <td>2016-12-21 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84547</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>35 INDOOR PUBLIC AND CULTURAL FACILITIES</td>\n",
              "      <td>4</td>\n",
              "      <td>7105</td>\n",
              "      <td>679</td>\n",
              "      <td>P9</td>\n",
              "      <td>155 CLAY PIT ROAD</td>\n",
              "      <td>10309</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10796</td>\n",
              "      <td>2400</td>\n",
              "      <td>2006</td>\n",
              "      <td>4</td>\n",
              "      <td>P9</td>\n",
              "      <td>69300</td>\n",
              "      <td>2016-10-27 00:00:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>84548 rows × 19 columns</p>\n",
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            "text/plain": [
              "       BOROUGH   NEIGHBORHOOD  ... SALE PRICE            SALE DATE\n",
              "0            1  ALPHABET CITY  ...    6625000  2017-07-19 00:00:00\n",
              "1            1  ALPHABET CITY  ...        -    2016-12-14 00:00:00\n",
              "2            1  ALPHABET CITY  ...        -    2016-12-09 00:00:00\n",
              "3            1  ALPHABET CITY  ...    3936272  2016-09-23 00:00:00\n",
              "4            1  ALPHABET CITY  ...    8000000  2016-11-17 00:00:00\n",
              "...        ...            ...  ...        ...                  ...\n",
              "84543        5        WOODROW  ...     450000  2016-11-28 00:00:00\n",
              "84544        5        WOODROW  ...     550000  2017-04-21 00:00:00\n",
              "84545        5        WOODROW  ...     460000  2017-07-05 00:00:00\n",
              "84546        5        WOODROW  ...   11693337  2016-12-21 00:00:00\n",
              "84547        5        WOODROW  ...      69300  2016-10-27 00:00:00\n",
              "\n",
              "[84548 rows x 19 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qjgoOn4_JN4L"
      },
      "source": [
        "#Delete Sale Price 0 and - values\n",
        "df=df[df['SALE PRICE'] !='0']\n",
        "df=df[df['SALE PRICE'] != ' -  ']\n",
        "df['SALE PRICE']=df['SALE PRICE'].astype(int)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "VvPnEo42DVoY",
        "outputId": "60324598-df82-48fe-d7a6-27006feb424d"
      },
      "source": [
        "#replace - for 0 first and 0  for mean values for 'LAND SQUARE FEET','GROSS SQUARE FEET'. We prefer not to delete rows with 0 and '-' to keep more data\n",
        "ex1=['LAND SQUARE FEET','GROSS SQUARE FEET']\n",
        "for x in ex1:\n",
        "  df[x]=df[x].replace(' -  ',0)\n",
        "  df[x]=df[x].astype(int)\n",
        "  vl=int (df[x].mean())\n",
        "  df[x]=df[x].replace(0,vl)\n",
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-98161f9b-da45-42bf-bf6d-8a16d6dd5bc6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>BOROUGH</th>\n",
              "      <th>BLOCK</th>\n",
              "      <th>LOT</th>\n",
              "      <th>ZIP CODE</th>\n",
              "      <th>RESIDENTIAL UNITS</th>\n",
              "      <th>COMMERCIAL UNITS</th>\n",
              "      <th>TOTAL UNITS</th>\n",
              "      <th>LAND SQUARE FEET</th>\n",
              "      <th>GROSS SQUARE FEET</th>\n",
              "      <th>YEAR BUILT</th>\n",
              "      <th>TAX CLASS AT TIME OF SALE</th>\n",
              "      <th>SALE PRICE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>59759.000000</td>\n",
              "      <td>59759.000000</td>\n",
              "      <td>59759.000000</td>\n",
              "      <td>59759.000000</td>\n",
              "      <td>59759.000000</td>\n",
              "      <td>59759.000000</td>\n",
              "      <td>59759.000000</td>\n",
              "      <td>5.975900e+04</td>\n",
              "      <td>5.975900e+04</td>\n",
              "      <td>59759.000000</td>\n",
              "      <td>59759.000000</td>\n",
              "      <td>5.975900e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.939875</td>\n",
              "      <td>4151.617982</td>\n",
              "      <td>390.768621</td>\n",
              "      <td>10705.739269</td>\n",
              "      <td>1.728208</td>\n",
              "      <td>0.163172</td>\n",
              "      <td>1.903663</td>\n",
              "      <td>3.635740e+03</td>\n",
              "      <td>3.431923e+03</td>\n",
              "      <td>1810.107599</td>\n",
              "      <td>1.629629</td>\n",
              "      <td>1.494927e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.326832</td>\n",
              "      <td>3568.943382</td>\n",
              "      <td>670.844273</td>\n",
              "      <td>1264.098174</td>\n",
              "      <td>14.159398</td>\n",
              "      <td>9.821318</td>\n",
              "      <td>17.337769</td>\n",
              "      <td>3.615489e+04</td>\n",
              "      <td>2.397148e+04</td>\n",
              "      <td>505.745865</td>\n",
              "      <td>0.706166</td>\n",
              "      <td>1.232953e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.300000e+01</td>\n",
              "      <td>1.200000e+02</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1277.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>10301.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.437000e+03</td>\n",
              "      <td>1.900000e+03</td>\n",
              "      <td>1920.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.700000e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>3167.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>11207.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.437000e+03</td>\n",
              "      <td>2.274000e+03</td>\n",
              "      <td>1940.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.255000e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>6107.000000</td>\n",
              "      <td>1002.000000</td>\n",
              "      <td>11356.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.500000e+03</td>\n",
              "      <td>2.274000e+03</td>\n",
              "      <td>1966.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.070000e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>16319.000000</td>\n",
              "      <td>9106.000000</td>\n",
              "      <td>11694.000000</td>\n",
              "      <td>1844.000000</td>\n",
              "      <td>2261.000000</td>\n",
              "      <td>2261.000000</td>\n",
              "      <td>4.252327e+06</td>\n",
              "      <td>3.750565e+06</td>\n",
              "      <td>2017.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.210000e+09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-98161f9b-da45-42bf-bf6d-8a16d6dd5bc6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-98161f9b-da45-42bf-bf6d-8a16d6dd5bc6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-98161f9b-da45-42bf-bf6d-8a16d6dd5bc6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "            BOROUGH         BLOCK  ...  TAX CLASS AT TIME OF SALE    SALE PRICE\n",
              "count  59759.000000  59759.000000  ...               59759.000000  5.975900e+04\n",
              "mean       2.939875   4151.617982  ...                   1.629629  1.494927e+06\n",
              "std        1.326832   3568.943382  ...                   0.706166  1.232953e+07\n",
              "min        1.000000      1.000000  ...                   1.000000  1.000000e+00\n",
              "25%        2.000000   1277.000000  ...                   1.000000  3.700000e+05\n",
              "50%        3.000000   3167.000000  ...                   2.000000  6.255000e+05\n",
              "75%        4.000000   6107.000000  ...                   2.000000  1.070000e+06\n",
              "max        5.000000  16319.000000  ...                   4.000000  2.210000e+09\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 939
        },
        "id": "bzWVlx6V8brW",
        "outputId": "d2dabcc5-572f-42e2-9208-4da08c86d51d"
      },
      "source": [
        "#Create Age column\n",
        "df['Age']=pd.DatetimeIndex(df['SALE DATE']).year-df['YEAR BUILT']\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ca997727-401b-414e-b599-37260276f024\">\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BOROUGH</th>\n",
              "      <th>NEIGHBORHOOD</th>\n",
              "      <th>BUILDING CLASS CATEGORY</th>\n",
              "      <th>TAX CLASS AT PRESENT</th>\n",
              "      <th>BLOCK</th>\n",
              "      <th>LOT</th>\n",
              "      <th>BUILDING CLASS AT PRESENT</th>\n",
              "      <th>ADDRESS</th>\n",
              "      <th>ZIP CODE</th>\n",
              "      <th>RESIDENTIAL UNITS</th>\n",
              "      <th>COMMERCIAL UNITS</th>\n",
              "      <th>TOTAL UNITS</th>\n",
              "      <th>LAND SQUARE FEET</th>\n",
              "      <th>GROSS SQUARE FEET</th>\n",
              "      <th>YEAR BUILT</th>\n",
              "      <th>TAX CLASS AT TIME OF SALE</th>\n",
              "      <th>BUILDING CLASS AT TIME OF SALE</th>\n",
              "      <th>SALE PRICE</th>\n",
              "      <th>SALE DATE</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2A</td>\n",
              "      <td>392</td>\n",
              "      <td>6</td>\n",
              "      <td>C2</td>\n",
              "      <td>153 AVENUE B</td>\n",
              "      <td>10009</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1633</td>\n",
              "      <td>6440</td>\n",
              "      <td>1900</td>\n",
              "      <td>2</td>\n",
              "      <td>C2</td>\n",
              "      <td>6625000</td>\n",
              "      <td>2017-07-19 00:00:00</td>\n",
              "      <td>117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2B</td>\n",
              "      <td>402</td>\n",
              "      <td>21</td>\n",
              "      <td>C4</td>\n",
              "      <td>154 EAST 7TH STREET</td>\n",
              "      <td>10009</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>2272</td>\n",
              "      <td>6794</td>\n",
              "      <td>1913</td>\n",
              "      <td>2</td>\n",
              "      <td>C4</td>\n",
              "      <td>3936272</td>\n",
              "      <td>2016-09-23 00:00:00</td>\n",
              "      <td>103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2A</td>\n",
              "      <td>404</td>\n",
              "      <td>55</td>\n",
              "      <td>C2</td>\n",
              "      <td>301 EAST 10TH   STREET</td>\n",
              "      <td>10009</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2369</td>\n",
              "      <td>4615</td>\n",
              "      <td>1900</td>\n",
              "      <td>2</td>\n",
              "      <td>C2</td>\n",
              "      <td>8000000</td>\n",
              "      <td>2016-11-17 00:00:00</td>\n",
              "      <td>116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2B</td>\n",
              "      <td>406</td>\n",
              "      <td>32</td>\n",
              "      <td>C4</td>\n",
              "      <td>210 AVENUE B</td>\n",
              "      <td>10009</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1750</td>\n",
              "      <td>4226</td>\n",
              "      <td>1920</td>\n",
              "      <td>2</td>\n",
              "      <td>C4</td>\n",
              "      <td>3192840</td>\n",
              "      <td>2016-09-23 00:00:00</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>08 RENTALS - ELEVATOR APARTMENTS</td>\n",
              "      <td>2</td>\n",
              "      <td>387</td>\n",
              "      <td>153</td>\n",
              "      <td>D9</td>\n",
              "      <td>629 EAST 5TH STREET</td>\n",
              "      <td>10009</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>4489</td>\n",
              "      <td>18523</td>\n",
              "      <td>1920</td>\n",
              "      <td>2</td>\n",
              "      <td>D9</td>\n",
              "      <td>16232000</td>\n",
              "      <td>2016-11-07 00:00:00</td>\n",
              "      <td>96</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84543</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>02 TWO FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>7349</td>\n",
              "      <td>34</td>\n",
              "      <td>B9</td>\n",
              "      <td>37 QUAIL LANE</td>\n",
              "      <td>10309</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2400</td>\n",
              "      <td>2575</td>\n",
              "      <td>1998</td>\n",
              "      <td>1</td>\n",
              "      <td>B9</td>\n",
              "      <td>450000</td>\n",
              "      <td>2016-11-28 00:00:00</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84544</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>02 TWO FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>7349</td>\n",
              "      <td>78</td>\n",
              "      <td>B9</td>\n",
              "      <td>32 PHEASANT LANE</td>\n",
              "      <td>10309</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2498</td>\n",
              "      <td>2377</td>\n",
              "      <td>1998</td>\n",
              "      <td>1</td>\n",
              "      <td>B9</td>\n",
              "      <td>550000</td>\n",
              "      <td>2017-04-21 00:00:00</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84545</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>02 TWO FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>7351</td>\n",
              "      <td>60</td>\n",
              "      <td>B2</td>\n",
              "      <td>49 PITNEY AVENUE</td>\n",
              "      <td>10309</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4000</td>\n",
              "      <td>1496</td>\n",
              "      <td>1925</td>\n",
              "      <td>1</td>\n",
              "      <td>B2</td>\n",
              "      <td>460000</td>\n",
              "      <td>2017-07-05 00:00:00</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84546</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>22 STORE BUILDINGS</td>\n",
              "      <td>4</td>\n",
              "      <td>7100</td>\n",
              "      <td>28</td>\n",
              "      <td>K6</td>\n",
              "      <td>2730 ARTHUR KILL ROAD</td>\n",
              "      <td>10309</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>208033</td>\n",
              "      <td>64117</td>\n",
              "      <td>2001</td>\n",
              "      <td>4</td>\n",
              "      <td>K6</td>\n",
              "      <td>11693337</td>\n",
              "      <td>2016-12-21 00:00:00</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84547</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>35 INDOOR PUBLIC AND CULTURAL FACILITIES</td>\n",
              "      <td>4</td>\n",
              "      <td>7105</td>\n",
              "      <td>679</td>\n",
              "      <td>P9</td>\n",
              "      <td>155 CLAY PIT ROAD</td>\n",
              "      <td>10309</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10796</td>\n",
              "      <td>2400</td>\n",
              "      <td>2006</td>\n",
              "      <td>4</td>\n",
              "      <td>P9</td>\n",
              "      <td>69300</td>\n",
              "      <td>2016-10-27 00:00:00</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>59759 rows × 20 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca997727-401b-414e-b599-37260276f024')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          document.querySelector('#df-ca997727-401b-414e-b599-37260276f024 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ca997727-401b-414e-b599-37260276f024');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       BOROUGH   NEIGHBORHOOD  ...            SALE DATE  Age\n",
              "0            1  ALPHABET CITY  ...  2017-07-19 00:00:00  117\n",
              "3            1  ALPHABET CITY  ...  2016-09-23 00:00:00  103\n",
              "4            1  ALPHABET CITY  ...  2016-11-17 00:00:00  116\n",
              "6            1  ALPHABET CITY  ...  2016-09-23 00:00:00   96\n",
              "9            1  ALPHABET CITY  ...  2016-11-07 00:00:00   96\n",
              "...        ...            ...  ...                  ...  ...\n",
              "84543        5        WOODROW  ...  2016-11-28 00:00:00   18\n",
              "84544        5        WOODROW  ...  2017-04-21 00:00:00   19\n",
              "84545        5        WOODROW  ...  2017-07-05 00:00:00   92\n",
              "84546        5        WOODROW  ...  2016-12-21 00:00:00   15\n",
              "84547        5        WOODROW  ...  2016-10-27 00:00:00   10\n",
              "\n",
              "[59759 rows x 20 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5ahKJX699tEm",
        "outputId": "6890ab64-65f2-497b-8478-af2d8bbddbf7"
      },
      "source": [
        "#delete outlier if row values greater than absolute value of 3 for non categorical columns\n",
        "from scipy import stats\n",
        "ex2=['LAND SQUARE FEET','GROSS SQUARE FEET','SALE PRICE','Age','RESIDENTIAL UNITS','COMMERCIAL UNITS']\n",
        "for x in ex2: \n",
        "  x2='z'+x\n",
        "  df[x2]=stats.zscore(df[x])\n",
        "  df = df[abs(df[x2])<3]\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-278ed6e2-ddbd-4484-a47d-e8732b61d320\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>BOROUGH</th>\n",
              "      <th>NEIGHBORHOOD</th>\n",
              "      <th>BUILDING CLASS CATEGORY</th>\n",
              "      <th>TAX CLASS AT PRESENT</th>\n",
              "      <th>BLOCK</th>\n",
              "      <th>LOT</th>\n",
              "      <th>BUILDING CLASS AT PRESENT</th>\n",
              "      <th>ADDRESS</th>\n",
              "      <th>ZIP CODE</th>\n",
              "      <th>RESIDENTIAL UNITS</th>\n",
              "      <th>COMMERCIAL UNITS</th>\n",
              "      <th>TOTAL UNITS</th>\n",
              "      <th>LAND SQUARE FEET</th>\n",
              "      <th>GROSS SQUARE FEET</th>\n",
              "      <th>YEAR BUILT</th>\n",
              "      <th>TAX CLASS AT TIME OF SALE</th>\n",
              "      <th>BUILDING CLASS AT TIME OF SALE</th>\n",
              "      <th>SALE PRICE</th>\n",
              "      <th>SALE DATE</th>\n",
              "      <th>Age</th>\n",
              "      <th>zLAND SQUARE FEET</th>\n",
              "      <th>zGROSS SQUARE FEET</th>\n",
              "      <th>zSALE PRICE</th>\n",
              "      <th>zAge</th>\n",
              "      <th>zRESIDENTIAL UNITS</th>\n",
              "      <th>zCOMMERCIAL UNITS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2A</td>\n",
              "      <td>392</td>\n",
              "      <td>6</td>\n",
              "      <td>C2</td>\n",
              "      <td>153 AVENUE B</td>\n",
              "      <td>10009</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1633</td>\n",
              "      <td>6440</td>\n",
              "      <td>1900</td>\n",
              "      <td>2</td>\n",
              "      <td>C2</td>\n",
              "      <td>6625000</td>\n",
              "      <td>2017-07-19 00:00:00</td>\n",
              "      <td>117</td>\n",
              "      <td>-0.055394</td>\n",
              "      <td>0.207994</td>\n",
              "      <td>1.521282</td>\n",
              "      <td>-0.177311</td>\n",
              "      <td>1.482422</td>\n",
              "      <td>-0.010702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2A</td>\n",
              "      <td>404</td>\n",
              "      <td>55</td>\n",
              "      <td>C2</td>\n",
              "      <td>301 EAST 10TH   STREET</td>\n",
              "      <td>10009</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2369</td>\n",
              "      <td>4615</td>\n",
              "      <td>1900</td>\n",
              "      <td>2</td>\n",
              "      <td>C2</td>\n",
              "      <td>8000000</td>\n",
              "      <td>2016-11-17 00:00:00</td>\n",
              "      <td>116</td>\n",
              "      <td>-0.035037</td>\n",
              "      <td>0.091673</td>\n",
              "      <td>1.909288</td>\n",
              "      <td>-0.179288</td>\n",
              "      <td>1.881394</td>\n",
              "      <td>-0.010702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2B</td>\n",
              "      <td>406</td>\n",
              "      <td>32</td>\n",
              "      <td>C4</td>\n",
              "      <td>210 AVENUE B</td>\n",
              "      <td>10009</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1750</td>\n",
              "      <td>4226</td>\n",
              "      <td>1920</td>\n",
              "      <td>2</td>\n",
              "      <td>C4</td>\n",
              "      <td>3192840</td>\n",
              "      <td>2016-09-23 00:00:00</td>\n",
              "      <td>96</td>\n",
              "      <td>-0.052158</td>\n",
              "      <td>0.066879</td>\n",
              "      <td>0.552775</td>\n",
              "      <td>-0.218823</td>\n",
              "      <td>2.679338</td>\n",
              "      <td>-0.010702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>09 COOPS - WALKUP APARTMENTS</td>\n",
              "      <td>2</td>\n",
              "      <td>373</td>\n",
              "      <td>40</td>\n",
              "      <td>C6</td>\n",
              "      <td>327 EAST 3 STREET, 1C</td>\n",
              "      <td>10009</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2437</td>\n",
              "      <td>2274</td>\n",
              "      <td>1920</td>\n",
              "      <td>2</td>\n",
              "      <td>C6</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-09-06 00:00:00</td>\n",
              "      <td>96</td>\n",
              "      <td>-0.033156</td>\n",
              "      <td>-0.057537</td>\n",
              "      <td>-0.348199</td>\n",
              "      <td>-0.218823</td>\n",
              "      <td>-0.512438</td>\n",
              "      <td>-0.010702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>09 COOPS - WALKUP APARTMENTS</td>\n",
              "      <td>2</td>\n",
              "      <td>373</td>\n",
              "      <td>40</td>\n",
              "      <td>C6</td>\n",
              "      <td>327 EAST 3 STREET, 1C</td>\n",
              "      <td>10009</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2437</td>\n",
              "      <td>2274</td>\n",
              "      <td>1920</td>\n",
              "      <td>2</td>\n",
              "      <td>C6</td>\n",
              "      <td>499000</td>\n",
              "      <td>2017-03-10 00:00:00</td>\n",
              "      <td>97</td>\n",
              "      <td>-0.033156</td>\n",
              "      <td>-0.057537</td>\n",
              "      <td>-0.207388</td>\n",
              "      <td>-0.216846</td>\n",
              "      <td>-0.512438</td>\n",
              "      <td>-0.010702</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",
              "      <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>84541</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>02 TWO FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>7317</td>\n",
              "      <td>126</td>\n",
              "      <td>B2</td>\n",
              "      <td>112 ROBIN COURT</td>\n",
              "      <td>10309</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>11088</td>\n",
              "      <td>2160</td>\n",
              "      <td>1994</td>\n",
              "      <td>1</td>\n",
              "      <td>B2</td>\n",
              "      <td>648000</td>\n",
              "      <td>2016-12-07 00:00:00</td>\n",
              "      <td>22</td>\n",
              "      <td>0.206122</td>\n",
              "      <td>-0.064803</td>\n",
              "      <td>-0.165343</td>\n",
              "      <td>-0.365100</td>\n",
              "      <td>0.285506</td>\n",
              "      <td>-0.010702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84543</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>02 TWO FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>7349</td>\n",
              "      <td>34</td>\n",
              "      <td>B9</td>\n",
              "      <td>37 QUAIL LANE</td>\n",
              "      <td>10309</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2400</td>\n",
              "      <td>2575</td>\n",
              "      <td>1998</td>\n",
              "      <td>1</td>\n",
              "      <td>B9</td>\n",
              "      <td>450000</td>\n",
              "      <td>2016-11-28 00:00:00</td>\n",
              "      <td>18</td>\n",
              "      <td>-0.034179</td>\n",
              "      <td>-0.038352</td>\n",
              "      <td>-0.221215</td>\n",
              "      <td>-0.373007</td>\n",
              "      <td>0.285506</td>\n",
              "      <td>-0.010702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84544</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>02 TWO FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>7349</td>\n",
              "      <td>78</td>\n",
              "      <td>B9</td>\n",
              "      <td>32 PHEASANT LANE</td>\n",
              "      <td>10309</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2498</td>\n",
              "      <td>2377</td>\n",
              "      <td>1998</td>\n",
              "      <td>1</td>\n",
              "      <td>B9</td>\n",
              "      <td>550000</td>\n",
              "      <td>2017-04-21 00:00:00</td>\n",
              "      <td>19</td>\n",
              "      <td>-0.031469</td>\n",
              "      <td>-0.050972</td>\n",
              "      <td>-0.192997</td>\n",
              "      <td>-0.371030</td>\n",
              "      <td>0.285506</td>\n",
              "      <td>-0.010702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84545</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>02 TWO FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>7351</td>\n",
              "      <td>60</td>\n",
              "      <td>B2</td>\n",
              "      <td>49 PITNEY AVENUE</td>\n",
              "      <td>10309</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4000</td>\n",
              "      <td>1496</td>\n",
              "      <td>1925</td>\n",
              "      <td>1</td>\n",
              "      <td>B2</td>\n",
              "      <td>460000</td>\n",
              "      <td>2017-07-05 00:00:00</td>\n",
              "      <td>92</td>\n",
              "      <td>0.010075</td>\n",
              "      <td>-0.107124</td>\n",
              "      <td>-0.218394</td>\n",
              "      <td>-0.226730</td>\n",
              "      <td>0.285506</td>\n",
              "      <td>-0.010702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84547</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>35 INDOOR PUBLIC AND CULTURAL FACILITIES</td>\n",
              "      <td>4</td>\n",
              "      <td>7105</td>\n",
              "      <td>679</td>\n",
              "      <td>P9</td>\n",
              "      <td>155 CLAY PIT ROAD</td>\n",
              "      <td>10309</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10796</td>\n",
              "      <td>2400</td>\n",
              "      <td>2006</td>\n",
              "      <td>4</td>\n",
              "      <td>P9</td>\n",
              "      <td>69300</td>\n",
              "      <td>2016-10-27 00:00:00</td>\n",
              "      <td>10</td>\n",
              "      <td>0.198046</td>\n",
              "      <td>-0.049506</td>\n",
              "      <td>-0.328644</td>\n",
              "      <td>-0.388821</td>\n",
              "      <td>-0.512438</td>\n",
              "      <td>0.092010</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>54025 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-278ed6e2-ddbd-4484-a47d-e8732b61d320')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-278ed6e2-ddbd-4484-a47d-e8732b61d320 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-278ed6e2-ddbd-4484-a47d-e8732b61d320');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       BOROUGH   NEIGHBORHOOD  ... zRESIDENTIAL UNITS zCOMMERCIAL UNITS\n",
              "0            1  ALPHABET CITY  ...           1.482422         -0.010702\n",
              "4            1  ALPHABET CITY  ...           1.881394         -0.010702\n",
              "6            1  ALPHABET CITY  ...           2.679338         -0.010702\n",
              "12           1  ALPHABET CITY  ...          -0.512438         -0.010702\n",
              "13           1  ALPHABET CITY  ...          -0.512438         -0.010702\n",
              "...        ...            ...  ...                ...               ...\n",
              "84541        5        WOODROW  ...           0.285506         -0.010702\n",
              "84543        5        WOODROW  ...           0.285506         -0.010702\n",
              "84544        5        WOODROW  ...           0.285506         -0.010702\n",
              "84545        5        WOODROW  ...           0.285506         -0.010702\n",
              "84547        5        WOODROW  ...          -0.512438          0.092010\n",
              "\n",
              "[54025 rows x 26 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R3mSABQE4-IT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 939
        },
        "outputId": "518c8432-d511-4e18-94dc-edb000e854b6"
      },
      "source": [
        "#delete z scores. I deleted  SALE DATE','YEAR BUILT' as it is used in Age . \n",
        "ex3=['zAge','zLAND SQUARE FEET',  'zGROSS SQUARE FEET', 'zSALE PRICE', 'zRESIDENTIAL UNITS', 'zCOMMERCIAL UNITS','SALE DATE','YEAR BUILT']\n",
        "df.drop(ex3,inplace=True,axis=1)\n",
        "df\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f52cc7be-68d0-47ad-ba3d-5b4793933341\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>BOROUGH</th>\n",
              "      <th>NEIGHBORHOOD</th>\n",
              "      <th>BUILDING CLASS CATEGORY</th>\n",
              "      <th>TAX CLASS AT PRESENT</th>\n",
              "      <th>BLOCK</th>\n",
              "      <th>LOT</th>\n",
              "      <th>BUILDING CLASS AT PRESENT</th>\n",
              "      <th>ADDRESS</th>\n",
              "      <th>ZIP CODE</th>\n",
              "      <th>RESIDENTIAL UNITS</th>\n",
              "      <th>COMMERCIAL UNITS</th>\n",
              "      <th>TOTAL UNITS</th>\n",
              "      <th>LAND SQUARE FEET</th>\n",
              "      <th>GROSS SQUARE FEET</th>\n",
              "      <th>TAX CLASS AT TIME OF SALE</th>\n",
              "      <th>BUILDING CLASS AT TIME OF SALE</th>\n",
              "      <th>SALE PRICE</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2A</td>\n",
              "      <td>392</td>\n",
              "      <td>6</td>\n",
              "      <td>C2</td>\n",
              "      <td>153 AVENUE B</td>\n",
              "      <td>10009</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1633</td>\n",
              "      <td>6440</td>\n",
              "      <td>2</td>\n",
              "      <td>C2</td>\n",
              "      <td>6625000</td>\n",
              "      <td>117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2A</td>\n",
              "      <td>404</td>\n",
              "      <td>55</td>\n",
              "      <td>C2</td>\n",
              "      <td>301 EAST 10TH   STREET</td>\n",
              "      <td>10009</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2369</td>\n",
              "      <td>4615</td>\n",
              "      <td>2</td>\n",
              "      <td>C2</td>\n",
              "      <td>8000000</td>\n",
              "      <td>116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>07 RENTALS - WALKUP APARTMENTS</td>\n",
              "      <td>2B</td>\n",
              "      <td>406</td>\n",
              "      <td>32</td>\n",
              "      <td>C4</td>\n",
              "      <td>210 AVENUE B</td>\n",
              "      <td>10009</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1750</td>\n",
              "      <td>4226</td>\n",
              "      <td>2</td>\n",
              "      <td>C4</td>\n",
              "      <td>3192840</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>09 COOPS - WALKUP APARTMENTS</td>\n",
              "      <td>2</td>\n",
              "      <td>373</td>\n",
              "      <td>40</td>\n",
              "      <td>C6</td>\n",
              "      <td>327 EAST 3 STREET, 1C</td>\n",
              "      <td>10009</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2437</td>\n",
              "      <td>2274</td>\n",
              "      <td>2</td>\n",
              "      <td>C6</td>\n",
              "      <td>1</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1</td>\n",
              "      <td>ALPHABET CITY</td>\n",
              "      <td>09 COOPS - WALKUP APARTMENTS</td>\n",
              "      <td>2</td>\n",
              "      <td>373</td>\n",
              "      <td>40</td>\n",
              "      <td>C6</td>\n",
              "      <td>327 EAST 3 STREET, 1C</td>\n",
              "      <td>10009</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2437</td>\n",
              "      <td>2274</td>\n",
              "      <td>2</td>\n",
              "      <td>C6</td>\n",
              "      <td>499000</td>\n",
              "      <td>97</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>84541</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>02 TWO FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>7317</td>\n",
              "      <td>126</td>\n",
              "      <td>B2</td>\n",
              "      <td>112 ROBIN COURT</td>\n",
              "      <td>10309</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>11088</td>\n",
              "      <td>2160</td>\n",
              "      <td>1</td>\n",
              "      <td>B2</td>\n",
              "      <td>648000</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84543</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>02 TWO FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>7349</td>\n",
              "      <td>34</td>\n",
              "      <td>B9</td>\n",
              "      <td>37 QUAIL LANE</td>\n",
              "      <td>10309</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2400</td>\n",
              "      <td>2575</td>\n",
              "      <td>1</td>\n",
              "      <td>B9</td>\n",
              "      <td>450000</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84544</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>02 TWO FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>7349</td>\n",
              "      <td>78</td>\n",
              "      <td>B9</td>\n",
              "      <td>32 PHEASANT LANE</td>\n",
              "      <td>10309</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2498</td>\n",
              "      <td>2377</td>\n",
              "      <td>1</td>\n",
              "      <td>B9</td>\n",
              "      <td>550000</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84545</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>02 TWO FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>7351</td>\n",
              "      <td>60</td>\n",
              "      <td>B2</td>\n",
              "      <td>49 PITNEY AVENUE</td>\n",
              "      <td>10309</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4000</td>\n",
              "      <td>1496</td>\n",
              "      <td>1</td>\n",
              "      <td>B2</td>\n",
              "      <td>460000</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84547</th>\n",
              "      <td>5</td>\n",
              "      <td>WOODROW</td>\n",
              "      <td>35 INDOOR PUBLIC AND CULTURAL FACILITIES</td>\n",
              "      <td>4</td>\n",
              "      <td>7105</td>\n",
              "      <td>679</td>\n",
              "      <td>P9</td>\n",
              "      <td>155 CLAY PIT ROAD</td>\n",
              "      <td>10309</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10796</td>\n",
              "      <td>2400</td>\n",
              "      <td>4</td>\n",
              "      <td>P9</td>\n",
              "      <td>69300</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>54025 rows × 18 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f52cc7be-68d0-47ad-ba3d-5b4793933341')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f52cc7be-68d0-47ad-ba3d-5b4793933341 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f52cc7be-68d0-47ad-ba3d-5b4793933341');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       BOROUGH   NEIGHBORHOOD  ... SALE PRICE  Age\n",
              "0            1  ALPHABET CITY  ...    6625000  117\n",
              "4            1  ALPHABET CITY  ...    8000000  116\n",
              "6            1  ALPHABET CITY  ...    3192840   96\n",
              "12           1  ALPHABET CITY  ...          1   96\n",
              "13           1  ALPHABET CITY  ...     499000   97\n",
              "...        ...            ...  ...        ...  ...\n",
              "84541        5        WOODROW  ...     648000   22\n",
              "84543        5        WOODROW  ...     450000   18\n",
              "84544        5        WOODROW  ...     550000   19\n",
              "84545        5        WOODROW  ...     460000   92\n",
              "84547        5        WOODROW  ...      69300   10\n",
              "\n",
              "[54025 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "id": "Olwi0MfoRtnQ",
        "outputId": "fda24447-1a8b-4ddc-e7f5-8e7534325a78"
      },
      "source": [
        "#correlation\n",
        "df.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-cfe86f26-cd09-4494-b0f8-e32e41180de3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>BOROUGH</th>\n",
              "      <th>BLOCK</th>\n",
              "      <th>LOT</th>\n",
              "      <th>ZIP CODE</th>\n",
              "      <th>RESIDENTIAL UNITS</th>\n",
              "      <th>COMMERCIAL UNITS</th>\n",
              "      <th>TOTAL UNITS</th>\n",
              "      <th>LAND SQUARE FEET</th>\n",
              "      <th>GROSS SQUARE FEET</th>\n",
              "      <th>TAX CLASS AT TIME OF SALE</th>\n",
              "      <th>SALE PRICE</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>BOROUGH</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.426600</td>\n",
              "      <td>-0.247850</td>\n",
              "      <td>0.539959</td>\n",
              "      <td>0.182798</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.173028</td>\n",
              "      <td>0.165840</td>\n",
              "      <td>-0.113153</td>\n",
              "      <td>-0.369466</td>\n",
              "      <td>-0.332683</td>\n",
              "      <td>-0.072028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BLOCK</th>\n",
              "      <td>0.426600</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.242302</td>\n",
              "      <td>0.501224</td>\n",
              "      <td>0.114163</td>\n",
              "      <td>0.003381</td>\n",
              "      <td>0.104233</td>\n",
              "      <td>0.103947</td>\n",
              "      <td>-0.121519</td>\n",
              "      <td>-0.326227</td>\n",
              "      <td>-0.277502</td>\n",
              "      <td>0.060663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LOT</th>\n",
              "      <td>-0.247850</td>\n",
              "      <td>-0.242302</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.210154</td>\n",
              "      <td>-0.093181</td>\n",
              "      <td>-0.068494</td>\n",
              "      <td>-0.098103</td>\n",
              "      <td>-0.077474</td>\n",
              "      <td>0.004005</td>\n",
              "      <td>0.295831</td>\n",
              "      <td>0.201315</td>\n",
              "      <td>-0.442204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZIP CODE</th>\n",
              "      <td>0.539959</td>\n",
              "      <td>0.501224</td>\n",
              "      <td>-0.210154</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.180439</td>\n",
              "      <td>0.030947</td>\n",
              "      <td>0.180421</td>\n",
              "      <td>-0.004062</td>\n",
              "      <td>-0.024652</td>\n",
              "      <td>-0.180956</td>\n",
              "      <td>-0.220718</td>\n",
              "      <td>0.112809</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RESIDENTIAL UNITS</th>\n",
              "      <td>0.182798</td>\n",
              "      <td>0.114163</td>\n",
              "      <td>-0.093181</td>\n",
              "      <td>0.180439</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.015487</td>\n",
              "      <td>0.929836</td>\n",
              "      <td>-0.014523</td>\n",
              "      <td>0.138325</td>\n",
              "      <td>-0.406973</td>\n",
              "      <td>0.069844</td>\n",
              "      <td>0.128614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COMMERCIAL UNITS</th>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.003381</td>\n",
              "      <td>-0.068494</td>\n",
              "      <td>0.030947</td>\n",
              "      <td>0.015487</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.370655</td>\n",
              "      <td>0.199901</td>\n",
              "      <td>0.418394</td>\n",
              "      <td>0.329580</td>\n",
              "      <td>0.194122</td>\n",
              "      <td>0.063515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TOTAL UNITS</th>\n",
              "      <td>0.173028</td>\n",
              "      <td>0.104233</td>\n",
              "      <td>-0.098103</td>\n",
              "      <td>0.180421</td>\n",
              "      <td>0.929836</td>\n",
              "      <td>0.370655</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.056387</td>\n",
              "      <td>0.279148</td>\n",
              "      <td>-0.230119</td>\n",
              "      <td>0.135421</td>\n",
              "      <td>0.132432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LAND SQUARE FEET</th>\n",
              "      <td>0.165840</td>\n",
              "      <td>0.103947</td>\n",
              "      <td>-0.077474</td>\n",
              "      <td>-0.004062</td>\n",
              "      <td>-0.014523</td>\n",
              "      <td>0.199901</td>\n",
              "      <td>0.056387</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420935</td>\n",
              "      <td>0.075193</td>\n",
              "      <td>0.124004</td>\n",
              "      <td>-0.001717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GROSS SQUARE FEET</th>\n",
              "      <td>-0.113153</td>\n",
              "      <td>-0.121519</td>\n",
              "      <td>0.004005</td>\n",
              "      <td>-0.024652</td>\n",
              "      <td>0.138325</td>\n",
              "      <td>0.418394</td>\n",
              "      <td>0.279148</td>\n",
              "      <td>0.420935</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.360020</td>\n",
              "      <td>0.322497</td>\n",
              "      <td>-0.002892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TAX CLASS AT TIME OF SALE</th>\n",
              "      <td>-0.369466</td>\n",
              "      <td>-0.326227</td>\n",
              "      <td>0.295831</td>\n",
              "      <td>-0.180956</td>\n",
              "      <td>-0.406973</td>\n",
              "      <td>0.329580</td>\n",
              "      <td>-0.230119</td>\n",
              "      <td>0.075193</td>\n",
              "      <td>0.360020</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.239671</td>\n",
              "      <td>-0.177543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SALE PRICE</th>\n",
              "      <td>-0.332683</td>\n",
              "      <td>-0.277502</td>\n",
              "      <td>0.201315</td>\n",
              "      <td>-0.220718</td>\n",
              "      <td>0.069844</td>\n",
              "      <td>0.194122</td>\n",
              "      <td>0.135421</td>\n",
              "      <td>0.124004</td>\n",
              "      <td>0.322497</td>\n",
              "      <td>0.239671</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.014405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>-0.072028</td>\n",
              "      <td>0.060663</td>\n",
              "      <td>-0.442204</td>\n",
              "      <td>0.112809</td>\n",
              "      <td>0.128614</td>\n",
              "      <td>0.063515</td>\n",
              "      <td>0.132432</td>\n",
              "      <td>-0.001717</td>\n",
              "      <td>-0.002892</td>\n",
              "      <td>-0.177543</td>\n",
              "      <td>-0.014405</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cfe86f26-cd09-4494-b0f8-e32e41180de3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cfe86f26-cd09-4494-b0f8-e32e41180de3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cfe86f26-cd09-4494-b0f8-e32e41180de3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                            BOROUGH     BLOCK  ...  SALE PRICE       Age\n",
              "BOROUGH                    1.000000  0.426600  ...   -0.332683 -0.072028\n",
              "BLOCK                      0.426600  1.000000  ...   -0.277502  0.060663\n",
              "LOT                       -0.247850 -0.242302  ...    0.201315 -0.442204\n",
              "ZIP CODE                   0.539959  0.501224  ...   -0.220718  0.112809\n",
              "RESIDENTIAL UNITS          0.182798  0.114163  ...    0.069844  0.128614\n",
              "COMMERCIAL UNITS           0.011490  0.003381  ...    0.194122  0.063515\n",
              "TOTAL UNITS                0.173028  0.104233  ...    0.135421  0.132432\n",
              "LAND SQUARE FEET           0.165840  0.103947  ...    0.124004 -0.001717\n",
              "GROSS SQUARE FEET         -0.113153 -0.121519  ...    0.322497 -0.002892\n",
              "TAX CLASS AT TIME OF SALE -0.369466 -0.326227  ...    0.239671 -0.177543\n",
              "SALE PRICE                -0.332683 -0.277502  ...    1.000000 -0.014405\n",
              "Age                       -0.072028  0.060663  ...   -0.014405  1.000000\n",
              "\n",
              "[12 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RHYo48S5JkaQ"
      },
      "source": [
        "2. Model and Analysis- "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GWMY62LaUTf9",
        "outputId": "ab9bfaeb-91e9-4066-ade1-68cb3aa558a6"
      },
      "source": [
        " #I deleted Total units since there is high correlation between total and residential units.\n",
        "df.drop('TOTAL UNITS',inplace=True,axis=1)\n",
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['BOROUGH', 'NEIGHBORHOOD', 'BUILDING CLASS CATEGORY',\n",
              "       'TAX CLASS AT PRESENT', 'BLOCK', 'LOT', 'BUILDING CLASS AT PRESENT',\n",
              "       'ADDRESS', 'ZIP CODE', 'RESIDENTIAL UNITS', 'COMMERCIAL UNITS',\n",
              "       'LAND SQUARE FEET', 'GROSS SQUARE FEET', 'TAX CLASS AT TIME OF SALE',\n",
              "       'BUILDING CLASS AT TIME OF SALE', 'SALE PRICE', 'Age'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xqi7n30u-gIf",
        "outputId": "61af0796-06fb-4806-a298-91919fa29b49"
      },
      "source": [
        "#change categorical variable to category type as they have categorical values.\n",
        "ex4=['BOROUGH','ZIP CODE','TAX CLASS AT TIME OF SALE']\n",
        "for x in ex4:\n",
        " df[x]=df[x].astype('category')\n",
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BOROUGH                           category\n",
              "NEIGHBORHOOD                        object\n",
              "BUILDING CLASS CATEGORY             object\n",
              "TAX CLASS AT PRESENT                object\n",
              "BLOCK                                int64\n",
              "LOT                                  int64\n",
              "BUILDING CLASS AT PRESENT           object\n",
              "ADDRESS                             object\n",
              "ZIP CODE                          category\n",
              "RESIDENTIAL UNITS                    int64\n",
              "COMMERCIAL UNITS                     int64\n",
              "LAND SQUARE FEET                     int64\n",
              "GROSS SQUARE FEET                    int64\n",
              "TAX CLASS AT TIME OF SALE         category\n",
              "BUILDING CLASS AT TIME OF SALE      object\n",
              "SALE PRICE                           int64\n",
              "Age                                  int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D8xzi-2810rG",
        "outputId": "90463a49-d25e-4ef5-c5f2-103d4e6683dd"
      },
      "source": [
        "#change object type to category type\n",
        "for c in df.columns:\n",
        "    col_type = df[c].dtype\n",
        "    if col_type == 'object' or col_type.name == 'category':\n",
        "        df[c] = df[c].astype('category')\n",
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BOROUGH                           category\n",
              "NEIGHBORHOOD                      category\n",
              "BUILDING CLASS CATEGORY           category\n",
              "TAX CLASS AT PRESENT              category\n",
              "BLOCK                                int64\n",
              "LOT                                  int64\n",
              "BUILDING CLASS AT PRESENT         category\n",
              "ADDRESS                           category\n",
              "ZIP CODE                          category\n",
              "RESIDENTIAL UNITS                    int64\n",
              "COMMERCIAL UNITS                     int64\n",
              "LAND SQUARE FEET                     int64\n",
              "GROSS SQUARE FEET                    int64\n",
              "TAX CLASS AT TIME OF SALE         category\n",
              "BUILDING CLASS AT TIME OF SALE    category\n",
              "SALE PRICE                           int64\n",
              "Age                                  int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1_LgKz2A4yqN"
      },
      "source": [
        "#Train test split\n",
        "df3=df.copy()\n",
        "y=df3['SALE PRICE']\n",
        "df3=df3.drop('SALE PRICE',axis=1)\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(df3, y, test_size=0.2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nhwBJIT9P3T6",
        "outputId": "b55137f9-5bd7-4250-fdf1-bc36c288782c"
      },
      "source": [
        "df3.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BOROUGH                           category\n",
              "NEIGHBORHOOD                      category\n",
              "BUILDING CLASS CATEGORY           category\n",
              "TAX CLASS AT PRESENT              category\n",
              "BLOCK                                int64\n",
              "LOT                                  int64\n",
              "BUILDING CLASS AT PRESENT         category\n",
              "ADDRESS                           category\n",
              "ZIP CODE                          category\n",
              "RESIDENTIAL UNITS                    int64\n",
              "COMMERCIAL UNITS                     int64\n",
              "LAND SQUARE FEET                     int64\n",
              "GROSS SQUARE FEET                    int64\n",
              "TAX CLASS AT TIME OF SALE         category\n",
              "BUILDING CLASS AT TIME OF SALE    category\n",
              "Age                                  int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4OetKfckMXPD",
        "outputId": "673e2173-2880-40bc-cc14-d4a5a6e5b2ab"
      },
      "source": [
        "#Identify categorical variables\n",
        "categorical_features_indices = np.where(df3.dtypes == 'category')[0]\n",
        "\n",
        "categorical_features_indices"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  6,  7,  8, 13, 14])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# I will use catboost since it can handle categorical variables without changing categorical variables into dummy variables\n",
        "#With hyper parameter tuning, it is possible to increase performance of classifier.\n",
        "!pip install catboost\n",
        "import catboost as cb\n",
        "\n",
        "train_dataset = cb.Pool(X_train, y_train,cat_features=categorical_features_indices) \n",
        "test_dataset = cb.Pool(X_test, y_test,cat_features=categorical_features_indices)\n",
        "model = cb.CatBoostRegressor(loss_function='RMSE')"
      ],
      "metadata": {
        "id": "0qoPY9AoG4_-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid = {'iterations': [100,  200],\n",
        "        'learning_rate': [0.01, 0.1],\n",
        "        'depth': [2,  8],\n",
        "        'l2_leaf_reg': [0.2, 2]}\n",
        "model.grid_search(grid, train_dataset)"
      ],
      "metadata": {
        "id": "uoI4NM1ebKfJ",
        "outputId": "27b28585-7ea5-4503-b5de-498fca626b66",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "bestIteration = 99\n",
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            "\n",
            "bestTest = 896231.2884\n",
            "bestIteration = 99\n",
            "\n",
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            "\n",
            "bestTest = 855932.6889\n",
            "bestIteration = 199\n",
            "\n",
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            "197:\tlearn: 863399.7089163\ttest: 858443.9844929\tbest: 858443.9844929 (197)\ttotal: 4.35s\tremaining: 44ms\n",
            "198:\tlearn: 863343.2604837\ttest: 858393.2877827\tbest: 858393.2877827 (198)\ttotal: 4.36s\tremaining: 21.9ms\n",
            "199:\tlearn: 862972.6815284\ttest: 858188.4427467\tbest: 858188.4427467 (199)\ttotal: 4.38s\tremaining: 0us\n",
            "\n",
            "bestTest = 858188.4427\n",
            "bestIteration = 199\n",
            "\n",
            "7:\tloss: 858188.4427467\tbest: 855932.6889190 (5)\ttotal: 18.4s\tremaining: 18.4s\n",
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            "96:\tlearn: 1021602.5720721\ttest: 1032645.1247280\tbest: 1032645.1247280 (96)\ttotal: 3.37s\tremaining: 104ms\n",
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            "99:\tlearn: 1013880.6855522\ttest: 1025370.7111255\tbest: 1025370.7111255 (99)\ttotal: 3.47s\tremaining: 0us\n",
            "\n",
            "bestTest = 1025370.711\n",
            "bestIteration = 99\n",
            "\n",
            "8:\tloss: 1025370.7111255\tbest: 855932.6889190 (5)\ttotal: 21.9s\tremaining: 17s\n",
            "0:\tlearn: 1458882.7838373\ttest: 1458639.9556591\tbest: 1458639.9556591 (0)\ttotal: 45.4ms\tremaining: 4.49s\n",
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            "\n",
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            "bestIteration = 99\n",
            "\n",
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            "\n",
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            "bestIteration = 99\n",
            "\n",
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            "\n",
            "bestTest = 796270.254\n",
            "bestIteration = 99\n",
            "\n",
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            "199:\tlearn: 867695.5704326\ttest: 889890.7778188\tbest: 889890.7778188 (199)\ttotal: 23.6s\tremaining: 0us\n",
            "\n",
            "bestTest = 889890.7778\n",
            "bestIteration = 199\n",
            "\n",
            "12:\tloss: 889890.7778188\tbest: 793298.0877266 (9)\ttotal: 56.2s\tremaining: 13s\n",
            "0:\tlearn: 1456789.2168883\ttest: 1456555.8728379\tbest: 1456555.8728379 (0)\ttotal: 120ms\tremaining: 23.9s\n",
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            "\n",
            "bestTest = 767720.5874\n",
            "bestIteration = 199\n",
            "\n",
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            "180:\tlearn: 893815.7474609\ttest: 909396.6855633\tbest: 909396.6855633 (180)\ttotal: 21.3s\tremaining: 2.23s\n",
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            "184:\tlearn: 889822.0843673\ttest: 905706.3640925\tbest: 905706.3640925 (184)\ttotal: 21.7s\tremaining: 1.76s\n",
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            "190:\tlearn: 884877.9968183\ttest: 900961.8462574\tbest: 900961.8462574 (190)\ttotal: 22.5s\tremaining: 1.06s\n",
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            "192:\tlearn: 883145.0063912\ttest: 899413.9627495\tbest: 899413.9627495 (192)\ttotal: 22.7s\tremaining: 824ms\n",
            "193:\tlearn: 882212.4795342\ttest: 898427.7664420\tbest: 898427.7664420 (193)\ttotal: 22.8s\tremaining: 706ms\n",
            "194:\tlearn: 881484.3890638\ttest: 897763.9401656\tbest: 897763.9401656 (194)\ttotal: 23s\tremaining: 589ms\n",
            "195:\tlearn: 880611.0813539\ttest: 897096.6974089\tbest: 897096.6974089 (195)\ttotal: 23.1s\tremaining: 471ms\n",
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            "197:\tlearn: 878858.6049242\ttest: 895518.7497515\tbest: 895518.7497515 (197)\ttotal: 23.3s\tremaining: 236ms\n",
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            "199:\tlearn: 877375.7998128\ttest: 894125.7286851\tbest: 894125.7286851 (199)\ttotal: 23.6s\tremaining: 0us\n",
            "\n",
            "bestTest = 894125.7287\n",
            "bestIteration = 199\n",
            "\n",
            "14:\tloss: 894125.7286851\tbest: 767720.5874058 (13)\ttotal: 1m 45s\tremaining: 7.04s\n",
            "0:\tlearn: 1460411.9622590\ttest: 1458141.3034031\tbest: 1458141.3034031 (0)\ttotal: 123ms\tremaining: 24.4s\n",
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            "198:\tlearn: 667784.7840531\ttest: 769452.1228691\tbest: 769452.1228691 (198)\ttotal: 24.9s\tremaining: 125ms\n",
            "199:\tlearn: 667378.0220089\ttest: 769190.1944505\tbest: 769190.1944505 (199)\ttotal: 25s\tremaining: 0us\n",
            "\n",
            "bestTest = 769190.1945\n",
            "bestIteration = 199\n",
            "\n",
            "15:\tloss: 769190.1944505\tbest: 767720.5874058 (13)\ttotal: 2m 10s\tremaining: 0us\n",
            "Estimating final quality...\n",
            "Training on fold [0/3]\n",
            "0:\tlearn: 1454155.2416657\ttest: 1461410.0895147\tbest: 1461410.0895147 (0)\ttotal: 142ms\tremaining: 28.3s\n",
            "1:\tlearn: 1369883.6583756\ttest: 1378797.8848496\tbest: 1378797.8848496 (1)\ttotal: 255ms\tremaining: 25.2s\n",
            "2:\tlearn: 1299423.5318991\ttest: 1309199.0203452\tbest: 1309199.0203452 (2)\ttotal: 382ms\tremaining: 25.1s\n",
            "3:\tlearn: 1236247.3175731\ttest: 1248766.8696451\tbest: 1248766.8696451 (3)\ttotal: 485ms\tremaining: 23.8s\n",
            "4:\tlearn: 1183555.0729693\ttest: 1197287.8098937\tbest: 1197287.8098937 (4)\ttotal: 608ms\tremaining: 23.7s\n",
            "5:\tlearn: 1136767.9084326\ttest: 1152449.2895138\tbest: 1152449.2895138 (5)\ttotal: 720ms\tremaining: 23.3s\n",
            "6:\tlearn: 1097401.1132988\ttest: 1114827.8056379\tbest: 1114827.8056379 (6)\ttotal: 804ms\tremaining: 22.2s\n",
            "7:\tlearn: 1062027.4122837\ttest: 1080506.7287833\tbest: 1080506.7287833 (7)\ttotal: 930ms\tremaining: 22.3s\n",
            "8:\tlearn: 1029943.8981625\ttest: 1049246.1224644\tbest: 1049246.1224644 (8)\ttotal: 1.03s\tremaining: 21.9s\n",
            "9:\tlearn: 1003337.1662362\ttest: 1025084.3379528\tbest: 1025084.3379528 (9)\ttotal: 1.16s\tremaining: 22s\n",
            "10:\tlearn: 982720.5311759\ttest: 1005935.7453746\tbest: 1005935.7453746 (10)\ttotal: 1.27s\tremaining: 21.9s\n",
            "11:\tlearn: 959710.4321428\ttest: 985044.5534846\tbest: 985044.5534846 (11)\ttotal: 1.4s\tremaining: 22s\n",
            "12:\tlearn: 938819.2872407\ttest: 967127.0459159\tbest: 967127.0459159 (12)\ttotal: 1.51s\tremaining: 21.7s\n",
            "13:\tlearn: 920121.5785267\ttest: 950685.4922715\tbest: 950685.4922715 (13)\ttotal: 1.61s\tremaining: 21.4s\n",
            "14:\tlearn: 903737.2107486\ttest: 934983.4869327\tbest: 934983.4869327 (14)\ttotal: 1.73s\tremaining: 21.3s\n",
            "15:\tlearn: 892432.7800601\ttest: 924800.8923456\tbest: 924800.8923456 (15)\ttotal: 1.81s\tremaining: 20.9s\n",
            "16:\tlearn: 880092.1413429\ttest: 914079.1696267\tbest: 914079.1696267 (16)\ttotal: 1.92s\tremaining: 20.7s\n",
            "17:\tlearn: 871591.8582239\ttest: 907834.4489623\tbest: 907834.4489623 (17)\ttotal: 2.04s\tremaining: 20.6s\n",
            "18:\tlearn: 860993.0242546\ttest: 899934.0742216\tbest: 899934.0742216 (18)\ttotal: 2.17s\tremaining: 20.7s\n",
            "19:\tlearn: 852902.7577557\ttest: 893183.5200949\tbest: 893183.5200949 (19)\ttotal: 2.29s\tremaining: 20.6s\n",
            "20:\tlearn: 848486.8418901\ttest: 889137.0270649\tbest: 889137.0270649 (20)\ttotal: 2.37s\tremaining: 20.2s\n",
            "21:\tlearn: 842903.0151312\ttest: 884580.8889475\tbest: 884580.8889475 (21)\ttotal: 2.5s\tremaining: 20.2s\n",
            "22:\tlearn: 835975.1816158\ttest: 878480.5549098\tbest: 878480.5549098 (22)\ttotal: 2.62s\tremaining: 20.2s\n",
            "23:\tlearn: 831182.3867473\ttest: 875581.5056230\tbest: 875581.5056230 (23)\ttotal: 2.73s\tremaining: 20.1s\n",
            "24:\tlearn: 825177.2578735\ttest: 871466.5075676\tbest: 871466.5075676 (24)\ttotal: 2.85s\tremaining: 19.9s\n",
            "25:\tlearn: 817720.1182593\ttest: 866185.5530205\tbest: 866185.5530205 (25)\ttotal: 2.98s\tremaining: 20s\n",
            "26:\tlearn: 814200.3044889\ttest: 862986.4442964\tbest: 862986.4442964 (26)\ttotal: 3.11s\tremaining: 19.9s\n",
            "27:\tlearn: 810726.7785372\ttest: 861169.1768252\tbest: 861169.1768252 (27)\ttotal: 3.23s\tremaining: 19.9s\n",
            "28:\tlearn: 807986.3144603\ttest: 860373.7150170\tbest: 860373.7150170 (28)\ttotal: 3.34s\tremaining: 19.7s\n",
            "29:\tlearn: 805616.8835545\ttest: 858974.7803244\tbest: 858974.7803244 (29)\ttotal: 3.46s\tremaining: 19.6s\n",
            "30:\tlearn: 802714.5312869\ttest: 857741.7056505\tbest: 857741.7056505 (30)\ttotal: 3.57s\tremaining: 19.5s\n",
            "31:\tlearn: 797365.8821819\ttest: 853789.5988529\tbest: 853789.5988529 (31)\ttotal: 3.69s\tremaining: 19.4s\n",
            "32:\tlearn: 792770.6349817\ttest: 850089.6917541\tbest: 850089.6917541 (32)\ttotal: 3.8s\tremaining: 19.3s\n",
            "33:\tlearn: 790378.4322054\ttest: 848287.3688570\tbest: 848287.3688570 (33)\ttotal: 3.92s\tremaining: 19.2s\n",
            "34:\tlearn: 787375.2578261\ttest: 846259.6868717\tbest: 846259.6868717 (34)\ttotal: 4.02s\tremaining: 19s\n",
            "35:\tlearn: 784909.4459728\ttest: 844054.4439104\tbest: 844054.4439104 (35)\ttotal: 4.15s\tremaining: 18.9s\n",
            "36:\tlearn: 782807.7483007\ttest: 842678.7626401\tbest: 842678.7626401 (36)\ttotal: 4.26s\tremaining: 18.8s\n",
            "37:\tlearn: 780156.1547691\ttest: 841203.1507733\tbest: 841203.1507733 (37)\ttotal: 4.38s\tremaining: 18.7s\n",
            "38:\tlearn: 776824.3142793\ttest: 838957.2765330\tbest: 838957.2765330 (38)\ttotal: 4.5s\tremaining: 18.6s\n",
            "39:\tlearn: 774538.4770283\ttest: 837718.1067977\tbest: 837718.1067977 (39)\ttotal: 4.62s\tremaining: 18.5s\n",
            "40:\tlearn: 771173.4962070\ttest: 835707.0284628\tbest: 835707.0284628 (40)\ttotal: 4.75s\tremaining: 18.4s\n",
            "41:\tlearn: 767914.6677433\ttest: 833730.1438887\tbest: 833730.1438887 (41)\ttotal: 4.87s\tremaining: 18.3s\n",
            "42:\tlearn: 766749.9838944\ttest: 832875.3929631\tbest: 832875.3929631 (42)\ttotal: 4.96s\tremaining: 18.1s\n",
            "43:\tlearn: 765769.1011678\ttest: 831952.2031294\tbest: 831952.2031294 (43)\ttotal: 5.08s\tremaining: 18s\n",
            "44:\tlearn: 763930.7555406\ttest: 830866.5154054\tbest: 830866.5154054 (44)\ttotal: 5.22s\tremaining: 18s\n",
            "45:\tlearn: 762508.8520328\ttest: 829456.3607874\tbest: 829456.3607874 (45)\ttotal: 5.34s\tremaining: 17.9s\n",
            "46:\tlearn: 760811.6158021\ttest: 828903.7192484\tbest: 828903.7192484 (46)\ttotal: 5.43s\tremaining: 17.7s\n",
            "47:\tlearn: 758953.9101363\ttest: 828122.1057374\tbest: 828122.1057374 (47)\ttotal: 5.53s\tremaining: 17.5s\n",
            "48:\tlearn: 757906.3796202\ttest: 827666.1571730\tbest: 827666.1571730 (48)\ttotal: 5.66s\tremaining: 17.5s\n",
            "49:\tlearn: 757354.3557963\ttest: 827694.9230262\tbest: 827666.1571730 (48)\ttotal: 5.79s\tremaining: 17.4s\n",
            "50:\tlearn: 755016.6773815\ttest: 826402.0777451\tbest: 826402.0777451 (50)\ttotal: 5.9s\tremaining: 17.3s\n",
            "51:\tlearn: 752832.7524005\ttest: 825372.8315684\tbest: 825372.8315684 (51)\ttotal: 6.03s\tremaining: 17.2s\n",
            "52:\tlearn: 751091.3249721\ttest: 824516.9636992\tbest: 824516.9636992 (52)\ttotal: 6.17s\tremaining: 17.1s\n",
            "53:\tlearn: 749334.7504033\ttest: 823777.3180007\tbest: 823777.3180007 (53)\ttotal: 6.29s\tremaining: 17s\n",
            "54:\tlearn: 746721.3045653\ttest: 822346.2309940\tbest: 822346.2309940 (54)\ttotal: 6.4s\tremaining: 16.9s\n",
            "55:\tlearn: 745725.8022034\ttest: 822215.7683004\tbest: 822215.7683004 (55)\ttotal: 6.51s\tremaining: 16.7s\n",
            "56:\tlearn: 744975.6862910\ttest: 821748.5552336\tbest: 821748.5552336 (56)\ttotal: 6.66s\tremaining: 16.7s\n",
            "57:\tlearn: 743068.4002768\ttest: 820955.1438724\tbest: 820955.1438724 (57)\ttotal: 6.77s\tremaining: 16.6s\n",
            "58:\tlearn: 742519.8838482\ttest: 820652.9398846\tbest: 820652.9398846 (58)\ttotal: 6.89s\tremaining: 16.5s\n",
            "59:\tlearn: 741179.2733215\ttest: 820361.2648266\tbest: 820361.2648266 (59)\ttotal: 7.01s\tremaining: 16.3s\n",
            "60:\tlearn: 740576.5665580\ttest: 820259.7264332\tbest: 820259.7264332 (60)\ttotal: 7.12s\tremaining: 16.2s\n",
            "61:\tlearn: 739323.4134663\ttest: 819635.5129623\tbest: 819635.5129623 (61)\ttotal: 7.26s\tremaining: 16.2s\n",
            "62:\tlearn: 738331.7323798\ttest: 819397.6923918\tbest: 819397.6923918 (62)\ttotal: 7.39s\tremaining: 16.1s\n",
            "63:\tlearn: 737882.3250123\ttest: 819232.9343141\tbest: 819232.9343141 (63)\ttotal: 7.47s\tremaining: 15.9s\n",
            "64:\tlearn: 735031.6976579\ttest: 817328.0329053\tbest: 817328.0329053 (64)\ttotal: 7.61s\tremaining: 15.8s\n",
            "65:\tlearn: 734570.2635224\ttest: 817229.7741891\tbest: 817229.7741891 (65)\ttotal: 7.74s\tremaining: 15.7s\n",
            "66:\tlearn: 733833.0992161\ttest: 816821.8945359\tbest: 816821.8945359 (66)\ttotal: 7.88s\tremaining: 15.6s\n",
            "67:\tlearn: 731144.6830864\ttest: 815428.4029143\tbest: 815428.4029143 (67)\ttotal: 8.01s\tremaining: 15.6s\n",
            "68:\tlearn: 730535.1454337\ttest: 815311.1004035\tbest: 815311.1004035 (68)\ttotal: 8.15s\tremaining: 15.5s\n",
            "69:\tlearn: 729454.6465481\ttest: 814799.2195186\tbest: 814799.2195186 (69)\ttotal: 8.27s\tremaining: 15.4s\n",
            "70:\tlearn: 727506.8025455\ttest: 813689.6452750\tbest: 813689.6452750 (70)\ttotal: 8.4s\tremaining: 15.3s\n",
            "71:\tlearn: 726375.6704132\ttest: 813216.7269200\tbest: 813216.7269200 (71)\ttotal: 8.51s\tremaining: 15.1s\n",
            "72:\tlearn: 725126.5234945\ttest: 812845.7764920\tbest: 812845.7764920 (72)\ttotal: 8.63s\tremaining: 15s\n",
            "73:\tlearn: 725046.5808988\ttest: 812741.4966377\tbest: 812741.4966377 (73)\ttotal: 8.66s\tremaining: 14.7s\n",
            "74:\tlearn: 724126.7272675\ttest: 812278.6279977\tbest: 812278.6279977 (74)\ttotal: 8.76s\tremaining: 14.6s\n",
            "75:\tlearn: 723476.6275764\ttest: 811941.6126138\tbest: 811941.6126138 (75)\ttotal: 8.89s\tremaining: 14.5s\n",
            "76:\tlearn: 721602.2539401\ttest: 811232.4137584\tbest: 811232.4137584 (76)\ttotal: 9.02s\tremaining: 14.4s\n",
            "77:\tlearn: 720452.4078227\ttest: 810404.2158698\tbest: 810404.2158698 (77)\ttotal: 9.15s\tremaining: 14.3s\n",
            "78:\tlearn: 720077.9825565\ttest: 810554.9015734\tbest: 810404.2158698 (77)\ttotal: 9.27s\tremaining: 14.2s\n",
            "79:\tlearn: 717889.1999252\ttest: 809159.1943054\tbest: 809159.1943054 (79)\ttotal: 9.39s\tremaining: 14.1s\n",
            "80:\tlearn: 716649.2261067\ttest: 808604.3963114\tbest: 808604.3963114 (80)\ttotal: 9.51s\tremaining: 14s\n",
            "81:\tlearn: 715386.1535429\ttest: 808241.0480959\tbest: 808241.0480959 (81)\ttotal: 9.63s\tremaining: 13.9s\n",
            "82:\tlearn: 714449.4889061\ttest: 807536.2885163\tbest: 807536.2885163 (82)\ttotal: 9.76s\tremaining: 13.8s\n",
            "83:\tlearn: 712572.3554025\ttest: 806967.0728448\tbest: 806967.0728448 (83)\ttotal: 9.86s\tremaining: 13.6s\n",
            "84:\tlearn: 710844.8698448\ttest: 805824.3151348\tbest: 805824.3151348 (84)\ttotal: 9.98s\tremaining: 13.5s\n",
            "85:\tlearn: 709633.4248664\ttest: 805584.2439086\tbest: 805584.2439086 (85)\ttotal: 10.1s\tremaining: 13.4s\n",
            "86:\tlearn: 708825.4057836\ttest: 805270.0560042\tbest: 805270.0560042 (86)\ttotal: 10.2s\tremaining: 13.3s\n",
            "87:\tlearn: 708206.2243268\ttest: 805144.7888336\tbest: 805144.7888336 (87)\ttotal: 10.3s\tremaining: 13.2s\n",
            "88:\tlearn: 707884.1894064\ttest: 804985.4096684\tbest: 804985.4096684 (88)\ttotal: 10.5s\tremaining: 13s\n",
            "89:\tlearn: 707442.0574786\ttest: 804846.8427876\tbest: 804846.8427876 (89)\ttotal: 10.6s\tremaining: 12.9s\n",
            "90:\tlearn: 706818.6539466\ttest: 804537.2730872\tbest: 804537.2730872 (90)\ttotal: 10.7s\tremaining: 12.8s\n",
            "91:\tlearn: 705883.4298065\ttest: 804153.7553158\tbest: 804153.7553158 (91)\ttotal: 10.8s\tremaining: 12.6s\n",
            "92:\tlearn: 704710.2669073\ttest: 804199.9629386\tbest: 804153.7553158 (91)\ttotal: 10.9s\tremaining: 12.5s\n",
            "93:\tlearn: 703431.5535005\ttest: 803321.7554760\tbest: 803321.7554760 (93)\ttotal: 11s\tremaining: 12.4s\n",
            "94:\tlearn: 702474.9434464\ttest: 803401.1338412\tbest: 803321.7554760 (93)\ttotal: 11.1s\tremaining: 12.3s\n",
            "95:\tlearn: 700874.6672684\ttest: 802724.8017408\tbest: 802724.8017408 (95)\ttotal: 11.2s\tremaining: 12.2s\n",
            "96:\tlearn: 699712.3609568\ttest: 802001.9558545\tbest: 802001.9558545 (96)\ttotal: 11.4s\tremaining: 12.1s\n",
            "97:\tlearn: 698978.5900799\ttest: 801814.9161091\tbest: 801814.9161091 (97)\ttotal: 11.5s\tremaining: 12s\n",
            "98:\tlearn: 698425.2509957\ttest: 801689.7549231\tbest: 801689.7549231 (98)\ttotal: 11.6s\tremaining: 11.8s\n",
            "99:\tlearn: 696553.4146814\ttest: 800628.9502849\tbest: 800628.9502849 (99)\ttotal: 11.7s\tremaining: 11.7s\n",
            "100:\tlearn: 695768.2203801\ttest: 800129.1973899\tbest: 800129.1973899 (100)\ttotal: 11.8s\tremaining: 11.6s\n",
            "101:\tlearn: 694188.0459269\ttest: 799397.6130644\tbest: 799397.6130644 (101)\ttotal: 11.9s\tremaining: 11.4s\n",
            "102:\tlearn: 693361.6720127\ttest: 799267.7998305\tbest: 799267.7998305 (102)\ttotal: 12s\tremaining: 11.3s\n",
            "103:\tlearn: 691839.8168205\ttest: 798274.7563393\tbest: 798274.7563393 (103)\ttotal: 12.1s\tremaining: 11.2s\n",
            "104:\tlearn: 690668.2614275\ttest: 798100.1427013\tbest: 798100.1427013 (104)\ttotal: 12.2s\tremaining: 11.1s\n",
            "105:\tlearn: 690082.2966240\ttest: 797933.0030255\tbest: 797933.0030255 (105)\ttotal: 12.4s\tremaining: 11s\n",
            "106:\tlearn: 688909.3428010\ttest: 797672.0810775\tbest: 797672.0810775 (106)\ttotal: 12.5s\tremaining: 10.9s\n",
            "107:\tlearn: 688078.2041832\ttest: 797043.9444775\tbest: 797043.9444775 (107)\ttotal: 12.6s\tremaining: 10.7s\n",
            "108:\tlearn: 687244.1791102\ttest: 796794.9335866\tbest: 796794.9335866 (108)\ttotal: 12.7s\tremaining: 10.6s\n",
            "109:\tlearn: 686646.8182271\ttest: 796811.5245926\tbest: 796794.9335866 (108)\ttotal: 12.8s\tremaining: 10.5s\n",
            "110:\tlearn: 685846.4851145\ttest: 796610.6414525\tbest: 796610.6414525 (110)\ttotal: 12.9s\tremaining: 10.4s\n",
            "111:\tlearn: 684497.2532449\ttest: 796024.5200734\tbest: 796024.5200734 (111)\ttotal: 13.1s\tremaining: 10.3s\n",
            "112:\tlearn: 684194.3060935\ttest: 795954.7413442\tbest: 795954.7413442 (112)\ttotal: 13.2s\tremaining: 10.2s\n",
            "113:\tlearn: 682123.5947303\ttest: 795016.3063663\tbest: 795016.3063663 (113)\ttotal: 13.4s\tremaining: 10.1s\n",
            "114:\tlearn: 681173.2558902\ttest: 794371.4509233\tbest: 794371.4509233 (114)\ttotal: 13.5s\tremaining: 9.95s\n",
            "115:\tlearn: 680263.6176505\ttest: 793570.2876186\tbest: 793570.2876186 (115)\ttotal: 13.6s\tremaining: 9.84s\n",
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            "117:\tlearn: 679539.5071654\ttest: 793316.9915795\tbest: 793276.2284225 (116)\ttotal: 13.8s\tremaining: 9.6s\n",
            "118:\tlearn: 678992.4501550\ttest: 793022.1188616\tbest: 793022.1188616 (118)\ttotal: 14s\tremaining: 9.5s\n",
            "119:\tlearn: 677690.7213653\ttest: 792661.7731036\tbest: 792661.7731036 (119)\ttotal: 14.1s\tremaining: 9.39s\n",
            "120:\tlearn: 677374.0932674\ttest: 792480.4177134\tbest: 792480.4177134 (120)\ttotal: 14.2s\tremaining: 9.27s\n",
            "121:\tlearn: 677058.8284335\ttest: 792467.1295560\tbest: 792467.1295560 (121)\ttotal: 14.3s\tremaining: 9.15s\n",
            "122:\tlearn: 676627.0699307\ttest: 792138.0256910\tbest: 792138.0256910 (122)\ttotal: 14.4s\tremaining: 9.04s\n",
            "123:\tlearn: 676038.0375126\ttest: 792038.7896703\tbest: 792038.7896703 (123)\ttotal: 14.6s\tremaining: 8.93s\n",
            "124:\tlearn: 675537.2495446\ttest: 791842.4751534\tbest: 791842.4751534 (124)\ttotal: 14.7s\tremaining: 8.8s\n",
            "125:\tlearn: 675059.0788810\ttest: 791845.7141518\tbest: 791842.4751534 (124)\ttotal: 14.8s\tremaining: 8.69s\n",
            "126:\tlearn: 674108.5720599\ttest: 791142.9764503\tbest: 791142.9764503 (126)\ttotal: 14.9s\tremaining: 8.57s\n",
            "127:\tlearn: 673789.0284462\ttest: 791129.8808551\tbest: 791129.8808551 (127)\ttotal: 15.1s\tremaining: 8.46s\n",
            "128:\tlearn: 672345.4793221\ttest: 790767.4816638\tbest: 790767.4816638 (128)\ttotal: 15.2s\tremaining: 8.36s\n",
            "129:\tlearn: 671545.4721566\ttest: 790008.5854998\tbest: 790008.5854998 (129)\ttotal: 15.3s\tremaining: 8.24s\n",
            "130:\tlearn: 670822.1371543\ttest: 789963.3498689\tbest: 789963.3498689 (130)\ttotal: 15.4s\tremaining: 8.13s\n",
            "131:\tlearn: 669802.7088789\ttest: 789826.4476344\tbest: 789826.4476344 (131)\ttotal: 15.5s\tremaining: 8.01s\n",
            "132:\tlearn: 669479.4927653\ttest: 789751.5928523\tbest: 789751.5928523 (132)\ttotal: 15.7s\tremaining: 7.89s\n",
            "133:\tlearn: 668587.4001684\ttest: 789365.2367791\tbest: 789365.2367791 (133)\ttotal: 15.8s\tremaining: 7.78s\n",
            "134:\tlearn: 667835.3356837\ttest: 788815.1998368\tbest: 788815.1998368 (134)\ttotal: 15.9s\tremaining: 7.66s\n",
            "135:\tlearn: 667488.2115128\ttest: 788705.3443177\tbest: 788705.3443177 (135)\ttotal: 16s\tremaining: 7.54s\n",
            "136:\tlearn: 665646.5273431\ttest: 788220.0305270\tbest: 788220.0305270 (136)\ttotal: 16.1s\tremaining: 7.42s\n",
            "137:\tlearn: 664360.2549173\ttest: 787789.2759001\tbest: 787789.2759001 (137)\ttotal: 16.3s\tremaining: 7.32s\n",
            "138:\tlearn: 663981.3395240\ttest: 787668.1640977\tbest: 787668.1640977 (138)\ttotal: 16.4s\tremaining: 7.2s\n",
            "139:\tlearn: 662730.3032962\ttest: 787186.4498637\tbest: 787186.4498637 (139)\ttotal: 16.5s\tremaining: 7.09s\n",
            "140:\tlearn: 661957.6067587\ttest: 786716.9566640\tbest: 786716.9566640 (140)\ttotal: 16.7s\tremaining: 6.97s\n",
            "141:\tlearn: 660955.3443308\ttest: 786478.0810614\tbest: 786478.0810614 (141)\ttotal: 16.8s\tremaining: 6.85s\n",
            "142:\tlearn: 660073.7885034\ttest: 786181.9724435\tbest: 786181.9724435 (142)\ttotal: 16.9s\tremaining: 6.73s\n",
            "143:\tlearn: 659290.6762420\ttest: 786096.0640205\tbest: 786096.0640205 (143)\ttotal: 17s\tremaining: 6.62s\n",
            "144:\tlearn: 658859.3319824\ttest: 785967.5195755\tbest: 785967.5195755 (144)\ttotal: 17.1s\tremaining: 6.5s\n",
            "145:\tlearn: 658148.6729626\ttest: 785839.3212738\tbest: 785839.3212738 (145)\ttotal: 17.3s\tremaining: 6.39s\n",
            "146:\tlearn: 657301.7056364\ttest: 785688.1681556\tbest: 785688.1681556 (146)\ttotal: 17.4s\tremaining: 6.27s\n",
            "147:\tlearn: 656755.1183845\ttest: 785635.2440614\tbest: 785635.2440614 (147)\ttotal: 17.5s\tremaining: 6.16s\n",
            "148:\tlearn: 656478.9148696\ttest: 785607.5805021\tbest: 785607.5805021 (148)\ttotal: 17.6s\tremaining: 6.04s\n",
            "149:\tlearn: 655421.2945284\ttest: 785026.8581193\tbest: 785026.8581193 (149)\ttotal: 17.8s\tremaining: 5.92s\n",
            "150:\tlearn: 654742.6855974\ttest: 784669.3564611\tbest: 784669.3564611 (150)\ttotal: 17.9s\tremaining: 5.81s\n",
            "151:\tlearn: 654382.0798454\ttest: 784263.1108896\tbest: 784263.1108896 (151)\ttotal: 18.3s\tremaining: 5.77s\n",
            "152:\tlearn: 653370.2962880\ttest: 784007.2727856\tbest: 784007.2727856 (152)\ttotal: 18.7s\tremaining: 5.74s\n",
            "153:\tlearn: 652183.9881976\ttest: 784114.9972071\tbest: 784007.2727856 (152)\ttotal: 18.8s\tremaining: 5.61s\n",
            "154:\tlearn: 650441.4973277\ttest: 783514.5689645\tbest: 783514.5689645 (154)\ttotal: 18.9s\tremaining: 5.49s\n",
            "155:\tlearn: 649599.8527131\ttest: 783135.1693873\tbest: 783135.1693873 (155)\ttotal: 19s\tremaining: 5.37s\n",
            "156:\tlearn: 648956.0058440\ttest: 783063.1651756\tbest: 783063.1651756 (156)\ttotal: 19.2s\tremaining: 5.25s\n",
            "157:\tlearn: 648559.3388259\ttest: 782943.3004877\tbest: 782943.3004877 (157)\ttotal: 19.3s\tremaining: 5.12s\n",
            "158:\tlearn: 647826.0473118\ttest: 782951.0149213\tbest: 782943.3004877 (157)\ttotal: 19.4s\tremaining: 5s\n",
            "159:\tlearn: 647053.2916160\ttest: 782871.1772331\tbest: 782871.1772331 (159)\ttotal: 19.5s\tremaining: 4.89s\n",
            "160:\tlearn: 645668.5546135\ttest: 782863.3677956\tbest: 782863.3677956 (160)\ttotal: 19.7s\tremaining: 4.77s\n",
            "161:\tlearn: 644561.0519274\ttest: 782838.3303599\tbest: 782838.3303599 (161)\ttotal: 19.8s\tremaining: 4.65s\n",
            "162:\tlearn: 644251.6861246\ttest: 782646.7047502\tbest: 782646.7047502 (162)\ttotal: 20s\tremaining: 4.53s\n",
            "163:\tlearn: 643916.1583899\ttest: 782579.2348995\tbest: 782579.2348995 (163)\ttotal: 20.1s\tremaining: 4.4s\n",
            "164:\tlearn: 643623.8930395\ttest: 782430.6976107\tbest: 782430.6976107 (164)\ttotal: 20.2s\tremaining: 4.29s\n",
            "165:\tlearn: 643129.3952182\ttest: 782295.4927866\tbest: 782295.4927866 (165)\ttotal: 20.3s\tremaining: 4.16s\n",
            "166:\tlearn: 642455.2646721\ttest: 782255.4319292\tbest: 782255.4319292 (166)\ttotal: 20.5s\tremaining: 4.04s\n",
            "167:\tlearn: 642144.6084782\ttest: 782143.6863415\tbest: 782143.6863415 (167)\ttotal: 20.6s\tremaining: 3.92s\n",
            "168:\tlearn: 641308.4525994\ttest: 781948.8775972\tbest: 781948.8775972 (168)\ttotal: 20.7s\tremaining: 3.8s\n",
            "169:\tlearn: 640608.6890037\ttest: 781775.3929912\tbest: 781775.3929912 (169)\ttotal: 20.8s\tremaining: 3.67s\n",
            "170:\tlearn: 640431.9144669\ttest: 781735.2535574\tbest: 781735.2535574 (170)\ttotal: 20.9s\tremaining: 3.55s\n",
            "171:\tlearn: 640023.7710380\ttest: 781594.6184154\tbest: 781594.6184154 (171)\ttotal: 21.1s\tremaining: 3.43s\n",
            "172:\tlearn: 639510.8590304\ttest: 781338.9070079\tbest: 781338.9070079 (172)\ttotal: 21.2s\tremaining: 3.31s\n",
            "173:\tlearn: 638781.2117315\ttest: 781229.8055457\tbest: 781229.8055457 (173)\ttotal: 21.3s\tremaining: 3.18s\n",
            "174:\tlearn: 638401.0059877\ttest: 780978.5867306\tbest: 780978.5867306 (174)\ttotal: 21.4s\tremaining: 3.06s\n",
            "175:\tlearn: 637820.9523287\ttest: 781099.7193760\tbest: 780978.5867306 (174)\ttotal: 21.5s\tremaining: 2.94s\n",
            "176:\tlearn: 637564.5302835\ttest: 781323.2031257\tbest: 780978.5867306 (174)\ttotal: 21.7s\tremaining: 2.81s\n",
            "177:\tlearn: 637372.9516193\ttest: 781303.4421567\tbest: 780978.5867306 (174)\ttotal: 21.8s\tremaining: 2.69s\n",
            "178:\tlearn: 637067.5695926\ttest: 781380.8689088\tbest: 780978.5867306 (174)\ttotal: 21.9s\tremaining: 2.57s\n",
            "179:\tlearn: 636355.9571030\ttest: 781710.5700992\tbest: 780978.5867306 (174)\ttotal: 22s\tremaining: 2.45s\n",
            "180:\tlearn: 636101.3126644\ttest: 781766.7061390\tbest: 780978.5867306 (174)\ttotal: 22.2s\tremaining: 2.33s\n",
            "181:\tlearn: 635674.5382238\ttest: 781619.8452880\tbest: 780978.5867306 (174)\ttotal: 22.3s\tremaining: 2.2s\n",
            "182:\tlearn: 635426.1393529\ttest: 781433.1468019\tbest: 780978.5867306 (174)\ttotal: 22.4s\tremaining: 2.08s\n",
            "183:\tlearn: 634842.4825534\ttest: 781092.9038429\tbest: 780978.5867306 (174)\ttotal: 22.5s\tremaining: 1.96s\n",
            "184:\tlearn: 633591.2177066\ttest: 780577.2502955\tbest: 780577.2502955 (184)\ttotal: 22.6s\tremaining: 1.84s\n",
            "185:\tlearn: 633311.6614035\ttest: 780413.8531679\tbest: 780413.8531679 (185)\ttotal: 22.8s\tremaining: 1.71s\n",
            "186:\tlearn: 631693.0511451\ttest: 780099.8415119\tbest: 780099.8415119 (186)\ttotal: 22.9s\tremaining: 1.59s\n",
            "187:\tlearn: 630921.9480754\ttest: 780011.8579437\tbest: 780011.8579437 (187)\ttotal: 23s\tremaining: 1.47s\n",
            "188:\tlearn: 630373.8885779\ttest: 779713.1416605\tbest: 779713.1416605 (188)\ttotal: 23.2s\tremaining: 1.35s\n",
            "189:\tlearn: 629690.9334131\ttest: 779745.0731318\tbest: 779713.1416605 (188)\ttotal: 23.3s\tremaining: 1.22s\n",
            "190:\tlearn: 629527.6888213\ttest: 779614.2858798\tbest: 779614.2858798 (190)\ttotal: 23.4s\tremaining: 1.1s\n",
            "191:\tlearn: 629385.3212497\ttest: 779616.9838000\tbest: 779614.2858798 (190)\ttotal: 23.5s\tremaining: 979ms\n",
            "192:\tlearn: 629096.3624148\ttest: 779634.0877731\tbest: 779614.2858798 (190)\ttotal: 23.7s\tremaining: 858ms\n",
            "193:\tlearn: 628401.6006592\ttest: 779560.8329663\tbest: 779560.8329663 (193)\ttotal: 23.8s\tremaining: 735ms\n",
            "194:\tlearn: 628030.8834535\ttest: 779570.6692414\tbest: 779560.8329663 (193)\ttotal: 23.9s\tremaining: 613ms\n",
            "195:\tlearn: 627280.7231017\ttest: 779111.8057060\tbest: 779111.8057060 (195)\ttotal: 24s\tremaining: 490ms\n",
            "196:\tlearn: 626844.5933027\ttest: 778987.2663021\tbest: 778987.2663021 (196)\ttotal: 24.1s\tremaining: 368ms\n",
            "197:\tlearn: 626566.9890769\ttest: 778947.4172126\tbest: 778947.4172126 (197)\ttotal: 24.3s\tremaining: 245ms\n",
            "198:\tlearn: 625834.1404831\ttest: 779002.7649762\tbest: 778947.4172126 (197)\ttotal: 24.4s\tremaining: 123ms\n",
            "199:\tlearn: 624833.0231279\ttest: 778886.2785638\tbest: 778886.2785638 (199)\ttotal: 24.5s\tremaining: 0us\n",
            "\n",
            "bestTest = 778886.2786\n",
            "bestIteration = 199\n",
            "\n",
            "Training on fold [1/3]\n",
            "0:\tlearn: 1474700.3245914\ttest: 1422368.1683063\tbest: 1422368.1683063 (0)\ttotal: 108ms\tremaining: 21.5s\n",
            "1:\tlearn: 1392966.2647155\ttest: 1341909.8828242\tbest: 1341909.8828242 (1)\ttotal: 224ms\tremaining: 22.2s\n",
            "2:\tlearn: 1321731.4407454\ttest: 1272738.9197835\tbest: 1272738.9197835 (2)\ttotal: 342ms\tremaining: 22.5s\n",
            "3:\tlearn: 1258376.5885980\ttest: 1210475.8312675\tbest: 1210475.8312675 (3)\ttotal: 447ms\tremaining: 21.9s\n",
            "4:\tlearn: 1203766.6450797\ttest: 1158269.8923786\tbest: 1158269.8923786 (4)\ttotal: 558ms\tremaining: 21.8s\n",
            "5:\tlearn: 1157503.3478773\ttest: 1111928.7499213\tbest: 1111928.7499213 (5)\ttotal: 658ms\tremaining: 21.3s\n",
            "6:\tlearn: 1117626.6966569\ttest: 1073780.2311726\tbest: 1073780.2311726 (6)\ttotal: 771ms\tremaining: 21.2s\n",
            "7:\tlearn: 1082665.0235587\ttest: 1039702.4102289\tbest: 1039702.4102289 (7)\ttotal: 870ms\tremaining: 20.9s\n",
            "8:\tlearn: 1052566.0278415\ttest: 1011743.3723866\tbest: 1011743.3723866 (8)\ttotal: 975ms\tremaining: 20.7s\n",
            "9:\tlearn: 1025025.5482208\ttest: 984961.3501779\tbest: 984961.3501779 (9)\ttotal: 1.08s\tremaining: 20.5s\n",
            "10:\tlearn: 1001260.4805482\ttest: 962210.4890753\tbest: 962210.4890753 (10)\ttotal: 1.2s\tremaining: 20.6s\n",
            "11:\tlearn: 979877.1538459\ttest: 943464.6359036\tbest: 943464.6359036 (11)\ttotal: 1.31s\tremaining: 20.6s\n",
            "12:\tlearn: 962923.7067546\ttest: 927637.8381915\tbest: 927637.8381915 (12)\ttotal: 1.45s\tremaining: 20.9s\n",
            "13:\tlearn: 943815.7338721\ttest: 909547.1723404\tbest: 909547.1723404 (13)\ttotal: 1.57s\tremaining: 20.9s\n",
            "14:\tlearn: 932365.5967234\ttest: 899319.9769430\tbest: 899319.9769430 (14)\ttotal: 1.69s\tremaining: 20.9s\n",
            "15:\tlearn: 919532.2716096\ttest: 888320.8235140\tbest: 888320.8235140 (15)\ttotal: 1.79s\tremaining: 20.6s\n",
            "16:\tlearn: 909956.4249026\ttest: 880727.3821105\tbest: 880727.3821105 (16)\ttotal: 1.92s\tremaining: 20.6s\n",
            "17:\tlearn: 897470.6007411\ttest: 870038.0250102\tbest: 870038.0250102 (17)\ttotal: 2.04s\tremaining: 20.7s\n",
            "18:\tlearn: 888837.1840614\ttest: 862093.3263407\tbest: 862093.3263407 (18)\ttotal: 2.18s\tremaining: 20.7s\n",
            "19:\tlearn: 880267.1419351\ttest: 855141.2322019\tbest: 855141.2322019 (19)\ttotal: 2.29s\tremaining: 20.6s\n",
            "20:\tlearn: 873746.3263640\ttest: 848681.4407372\tbest: 848681.4407372 (20)\ttotal: 2.42s\tremaining: 20.6s\n",
            "21:\tlearn: 867547.6433684\ttest: 843762.8780547\tbest: 843762.8780547 (21)\ttotal: 2.52s\tremaining: 20.4s\n",
            "22:\tlearn: 861830.4091616\ttest: 838451.4899954\tbest: 838451.4899954 (22)\ttotal: 2.62s\tremaining: 20.1s\n",
            "23:\tlearn: 855355.3718323\ttest: 834103.7653326\tbest: 834103.7653326 (23)\ttotal: 2.73s\tremaining: 20.1s\n",
            "24:\tlearn: 849886.7312825\ttest: 830299.0178249\tbest: 830299.0178249 (24)\ttotal: 2.85s\tremaining: 19.9s\n",
            "25:\tlearn: 843591.8807580\ttest: 825613.1285421\tbest: 825613.1285421 (25)\ttotal: 2.96s\tremaining: 19.8s\n",
            "26:\tlearn: 837495.4861389\ttest: 821203.0929389\tbest: 821203.0929389 (26)\ttotal: 3.09s\tremaining: 19.8s\n",
            "27:\tlearn: 833316.0627345\ttest: 819167.5040505\tbest: 819167.5040505 (27)\ttotal: 3.22s\tremaining: 19.8s\n",
            "28:\tlearn: 829000.4329194\ttest: 815890.8806110\tbest: 815890.8806110 (28)\ttotal: 3.34s\tremaining: 19.7s\n",
            "29:\tlearn: 825331.5738330\ttest: 813517.5555422\tbest: 813517.5555422 (29)\ttotal: 3.47s\tremaining: 19.7s\n",
            "30:\tlearn: 821118.7702905\ttest: 809970.8370415\tbest: 809970.8370415 (30)\ttotal: 3.6s\tremaining: 19.6s\n",
            "31:\tlearn: 817166.7914289\ttest: 806924.0884373\tbest: 806924.0884373 (31)\ttotal: 3.74s\tremaining: 19.6s\n",
            "32:\tlearn: 813654.2439226\ttest: 805300.8042882\tbest: 805300.8042882 (32)\ttotal: 3.84s\tremaining: 19.4s\n",
            "33:\tlearn: 810866.2305902\ttest: 803908.4388414\tbest: 803908.4388414 (33)\ttotal: 3.97s\tremaining: 19.4s\n",
            "34:\tlearn: 809045.9211540\ttest: 803240.4122089\tbest: 803240.4122089 (34)\ttotal: 4.09s\tremaining: 19.3s\n",
            "35:\tlearn: 806469.3735783\ttest: 801534.6761496\tbest: 801534.6761496 (35)\ttotal: 4.22s\tremaining: 19.2s\n",
            "36:\tlearn: 804121.2020987\ttest: 799927.0359754\tbest: 799927.0359754 (36)\ttotal: 4.34s\tremaining: 19.1s\n",
            "37:\tlearn: 802318.7131596\ttest: 798405.7136306\tbest: 798405.7136306 (37)\ttotal: 4.45s\tremaining: 19s\n",
            "38:\tlearn: 799444.6528129\ttest: 795717.8352859\tbest: 795717.8352859 (38)\ttotal: 4.55s\tremaining: 18.8s\n",
            "39:\tlearn: 797378.6710709\ttest: 793729.9131667\tbest: 793729.9131667 (39)\ttotal: 4.67s\tremaining: 18.7s\n",
            "40:\tlearn: 793886.5210571\ttest: 790423.3338729\tbest: 790423.3338729 (40)\ttotal: 4.76s\tremaining: 18.5s\n",
            "41:\tlearn: 792459.0164284\ttest: 789578.0197401\tbest: 789578.0197401 (41)\ttotal: 4.86s\tremaining: 18.3s\n",
            "42:\tlearn: 791034.5885293\ttest: 788567.5135609\tbest: 788567.5135609 (42)\ttotal: 4.99s\tremaining: 18.2s\n",
            "43:\tlearn: 789840.5325355\ttest: 787656.8914663\tbest: 787656.8914663 (43)\ttotal: 5.13s\tremaining: 18.2s\n",
            "44:\tlearn: 788494.8113956\ttest: 786771.9026205\tbest: 786771.9026205 (44)\ttotal: 5.25s\tremaining: 18.1s\n",
            "45:\tlearn: 786786.5710340\ttest: 785850.5891969\tbest: 785850.5891969 (45)\ttotal: 5.38s\tremaining: 18s\n",
            "46:\tlearn: 783577.1305215\ttest: 782603.2347208\tbest: 782603.2347208 (46)\ttotal: 5.51s\tremaining: 17.9s\n",
            "47:\tlearn: 781739.7740341\ttest: 782044.4758597\tbest: 782044.4758597 (47)\ttotal: 5.62s\tremaining: 17.8s\n",
            "48:\tlearn: 778879.6952180\ttest: 780139.5317034\tbest: 780139.5317034 (48)\ttotal: 5.75s\tremaining: 17.7s\n",
            "49:\tlearn: 777505.5395536\ttest: 779406.7971638\tbest: 779406.7971638 (49)\ttotal: 5.87s\tremaining: 17.6s\n",
            "50:\tlearn: 775014.7873246\ttest: 778975.9826586\tbest: 778975.9826586 (50)\ttotal: 5.99s\tremaining: 17.5s\n",
            "51:\tlearn: 773600.1182725\ttest: 778919.2570925\tbest: 778919.2570925 (51)\ttotal: 6.11s\tremaining: 17.4s\n",
            "52:\tlearn: 771845.7965514\ttest: 778239.7964362\tbest: 778239.7964362 (52)\ttotal: 6.19s\tremaining: 17.2s\n",
            "53:\tlearn: 770663.8629742\ttest: 777489.3515712\tbest: 777489.3515712 (53)\ttotal: 6.33s\tremaining: 17.1s\n",
            "54:\tlearn: 769568.0417310\ttest: 776354.6168156\tbest: 776354.6168156 (54)\ttotal: 6.46s\tremaining: 17s\n",
            "55:\tlearn: 768477.6875872\ttest: 775718.1566706\tbest: 775718.1566706 (55)\ttotal: 6.58s\tremaining: 16.9s\n",
            "56:\tlearn: 767463.0723134\ttest: 775242.2992357\tbest: 775242.2992357 (56)\ttotal: 6.68s\tremaining: 16.8s\n",
            "57:\tlearn: 765596.2213112\ttest: 774406.6440355\tbest: 774406.6440355 (57)\ttotal: 6.82s\tremaining: 16.7s\n",
            "58:\tlearn: 764275.6554995\ttest: 774019.7861231\tbest: 774019.7861231 (58)\ttotal: 6.95s\tremaining: 16.6s\n",
            "59:\tlearn: 763677.2056131\ttest: 773496.0107903\tbest: 773496.0107903 (59)\ttotal: 7.06s\tremaining: 16.5s\n",
            "60:\tlearn: 762392.5509737\ttest: 773515.3315800\tbest: 773496.0107903 (59)\ttotal: 7.2s\tremaining: 16.4s\n",
            "61:\tlearn: 760633.5703610\ttest: 772640.8502930\tbest: 772640.8502930 (61)\ttotal: 7.31s\tremaining: 16.3s\n",
            "62:\tlearn: 759501.1073616\ttest: 772120.8473621\tbest: 772120.8473621 (62)\ttotal: 7.43s\tremaining: 16.1s\n",
            "63:\tlearn: 757941.5235330\ttest: 771123.6775208\tbest: 771123.6775208 (63)\ttotal: 7.57s\tremaining: 16.1s\n",
            "64:\tlearn: 756935.5952760\ttest: 770953.6946392\tbest: 770953.6946392 (64)\ttotal: 7.67s\tremaining: 15.9s\n",
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            "\n",
            "bestTest = 738275.0446\n",
            "bestIteration = 198\n",
            "\n",
            "Training on fold [2/3]\n",
            "0:\tlearn: 1442959.5862181\ttest: 1490064.3362549\tbest: 1490064.3362549 (0)\ttotal: 118ms\tremaining: 23.4s\n",
            "1:\tlearn: 1362922.7937581\ttest: 1409241.7628114\tbest: 1409241.7628114 (1)\ttotal: 237ms\tremaining: 23.5s\n",
            "2:\tlearn: 1293304.2250725\ttest: 1338954.0968636\tbest: 1338954.0968636 (2)\ttotal: 328ms\tremaining: 21.6s\n",
            "3:\tlearn: 1232400.2025269\ttest: 1278644.1051440\tbest: 1278644.1051440 (3)\ttotal: 431ms\tremaining: 21.1s\n",
            "4:\tlearn: 1177391.2601907\ttest: 1224501.3687280\tbest: 1224501.3687280 (4)\ttotal: 559ms\tremaining: 21.8s\n",
            "5:\tlearn: 1132955.3440726\ttest: 1179459.7205445\tbest: 1179459.7205445 (5)\ttotal: 678ms\tremaining: 21.9s\n",
            "6:\tlearn: 1093887.1177633\ttest: 1141306.5103066\tbest: 1141306.5103066 (6)\ttotal: 805ms\tremaining: 22.2s\n",
            "7:\tlearn: 1056343.8230373\ttest: 1101885.6892533\tbest: 1101885.6892533 (7)\ttotal: 902ms\tremaining: 21.6s\n",
            "8:\tlearn: 1021341.0566509\ttest: 1069772.6250410\tbest: 1069772.6250410 (8)\ttotal: 1s\tremaining: 21.3s\n",
            "9:\tlearn: 992920.8436042\ttest: 1041826.4470509\tbest: 1041826.4470509 (9)\ttotal: 1.12s\tremaining: 21.3s\n",
            "10:\tlearn: 971834.5966372\ttest: 1021869.1409037\tbest: 1021869.1409037 (10)\ttotal: 1.24s\tremaining: 21.3s\n",
            "11:\tlearn: 948808.7208497\ttest: 999786.3380560\tbest: 999786.3380560 (11)\ttotal: 1.35s\tremaining: 21.1s\n",
            "12:\tlearn: 933022.5907618\ttest: 984408.0305224\tbest: 984408.0305224 (12)\ttotal: 1.45s\tremaining: 20.9s\n",
            "13:\tlearn: 918226.0912508\ttest: 970524.3503518\tbest: 970524.3503518 (13)\ttotal: 1.56s\tremaining: 20.7s\n",
            "14:\tlearn: 903008.6116175\ttest: 955142.9647268\tbest: 955142.9647268 (14)\ttotal: 1.66s\tremaining: 20.5s\n",
            "15:\tlearn: 890067.5693672\ttest: 941591.6445897\tbest: 941591.6445897 (15)\ttotal: 1.78s\tremaining: 20.5s\n",
            "16:\tlearn: 878902.2376193\ttest: 930037.7945595\tbest: 930037.7945595 (16)\ttotal: 1.89s\tremaining: 20.3s\n",
            "17:\tlearn: 869901.4713385\ttest: 919825.2302281\tbest: 919825.2302281 (17)\ttotal: 2.02s\tremaining: 20.5s\n",
            "18:\tlearn: 862158.1561744\ttest: 913385.5595431\tbest: 913385.5595431 (18)\ttotal: 2.13s\tremaining: 20.3s\n",
            "19:\tlearn: 854955.6211962\ttest: 906562.5389759\tbest: 906562.5389759 (19)\ttotal: 2.25s\tremaining: 20.3s\n",
            "20:\tlearn: 847357.5056388\ttest: 900087.3533402\tbest: 900087.3533402 (20)\ttotal: 2.38s\tremaining: 20.3s\n",
            "21:\tlearn: 842392.8301527\ttest: 895862.6199060\tbest: 895862.6199060 (21)\ttotal: 2.5s\tremaining: 20.2s\n",
            "22:\tlearn: 836665.5602493\ttest: 890189.8725066\tbest: 890189.8725066 (22)\ttotal: 2.6s\tremaining: 20s\n",
            "23:\tlearn: 832190.9229973\ttest: 886534.1725186\tbest: 886534.1725186 (23)\ttotal: 2.71s\tremaining: 19.9s\n",
            "24:\tlearn: 827151.0385314\ttest: 881603.1231011\tbest: 881603.1231011 (24)\ttotal: 2.81s\tremaining: 19.7s\n",
            "25:\tlearn: 822102.8082371\ttest: 875964.9332890\tbest: 875964.9332890 (25)\ttotal: 2.92s\tremaining: 19.5s\n",
            "26:\tlearn: 817907.5469755\ttest: 872726.0737290\tbest: 872726.0737290 (26)\ttotal: 3.05s\tremaining: 19.5s\n",
            "27:\tlearn: 814368.7610717\ttest: 869270.9407080\tbest: 869270.9407080 (27)\ttotal: 3.15s\tremaining: 19.4s\n",
            "28:\tlearn: 811563.5096961\ttest: 867636.0275725\tbest: 867636.0275725 (28)\ttotal: 3.28s\tremaining: 19.3s\n",
            "29:\tlearn: 809036.1156882\ttest: 865620.5994751\tbest: 865620.5994751 (29)\ttotal: 3.39s\tremaining: 19.2s\n",
            "30:\tlearn: 804114.0373904\ttest: 861579.9522878\tbest: 861579.9522878 (30)\ttotal: 3.53s\tremaining: 19.3s\n",
            "31:\tlearn: 801561.2460935\ttest: 860128.8018296\tbest: 860128.8018296 (31)\ttotal: 3.63s\tremaining: 19.1s\n",
            "32:\tlearn: 798208.3325042\ttest: 856227.2283003\tbest: 856227.2283003 (32)\ttotal: 3.75s\tremaining: 19s\n",
            "33:\tlearn: 795939.0353595\ttest: 855430.6610794\tbest: 855430.6610794 (33)\ttotal: 3.87s\tremaining: 18.9s\n",
            "34:\tlearn: 793119.9014837\ttest: 853407.0142041\tbest: 853407.0142041 (34)\ttotal: 3.98s\tremaining: 18.8s\n",
            "35:\tlearn: 788989.2736672\ttest: 851288.8908715\tbest: 851288.8908715 (35)\ttotal: 4.09s\tremaining: 18.6s\n",
            "36:\tlearn: 786375.6867033\ttest: 848926.6051652\tbest: 848926.6051652 (36)\ttotal: 4.21s\tremaining: 18.5s\n",
            "37:\tlearn: 783949.0818270\ttest: 847339.0655635\tbest: 847339.0655635 (37)\ttotal: 4.31s\tremaining: 18.4s\n",
            "38:\tlearn: 781823.1413639\ttest: 845351.4305166\tbest: 845351.4305166 (38)\ttotal: 4.44s\tremaining: 18.3s\n",
            "39:\tlearn: 778943.1372650\ttest: 844167.6243392\tbest: 844167.6243392 (39)\ttotal: 4.57s\tremaining: 18.3s\n",
            "40:\tlearn: 777545.9708575\ttest: 842311.5504570\tbest: 842311.5504570 (40)\ttotal: 4.69s\tremaining: 18.2s\n",
            "41:\tlearn: 775863.5836599\ttest: 841407.1810100\tbest: 841407.1810100 (41)\ttotal: 4.8s\tremaining: 18.1s\n",
            "42:\tlearn: 775109.7423177\ttest: 840537.8832413\tbest: 840537.8832413 (42)\ttotal: 4.95s\tremaining: 18.1s\n",
            "43:\tlearn: 773860.3878577\ttest: 839495.9036613\tbest: 839495.9036613 (43)\ttotal: 5.08s\tremaining: 18s\n",
            "44:\tlearn: 772287.1355255\ttest: 838214.3416761\tbest: 838214.3416761 (44)\ttotal: 5.22s\tremaining: 18s\n",
            "45:\tlearn: 770564.6731925\ttest: 837085.9684310\tbest: 837085.9684310 (45)\ttotal: 5.34s\tremaining: 17.9s\n",
            "46:\tlearn: 769006.0748350\ttest: 835535.1393292\tbest: 835535.1393292 (46)\ttotal: 5.49s\tremaining: 17.9s\n",
            "47:\tlearn: 765966.5447192\ttest: 833608.4356139\tbest: 833608.4356139 (47)\ttotal: 5.61s\tremaining: 17.8s\n",
            "48:\tlearn: 764787.1952462\ttest: 833149.5749522\tbest: 833149.5749522 (48)\ttotal: 5.71s\tremaining: 17.6s\n",
            "49:\tlearn: 763102.8179247\ttest: 831600.9600699\tbest: 831600.9600699 (49)\ttotal: 5.79s\tremaining: 17.4s\n",
            "50:\tlearn: 761783.4862663\ttest: 830815.9867934\tbest: 830815.9867934 (50)\ttotal: 5.89s\tremaining: 17.2s\n",
            "51:\tlearn: 760988.3533434\ttest: 830291.0958088\tbest: 830291.0958088 (51)\ttotal: 6.02s\tremaining: 17.1s\n",
            "52:\tlearn: 760177.6920991\ttest: 830033.5785233\tbest: 830033.5785233 (52)\ttotal: 6.13s\tremaining: 17s\n",
            "53:\tlearn: 756831.8178834\ttest: 827501.2075599\tbest: 827501.2075599 (53)\ttotal: 6.27s\tremaining: 17s\n",
            "54:\tlearn: 756189.2038313\ttest: 826986.9519816\tbest: 826986.9519816 (54)\ttotal: 6.39s\tremaining: 16.9s\n",
            "55:\tlearn: 754006.9629926\ttest: 825173.9443296\tbest: 825173.9443296 (55)\ttotal: 6.52s\tremaining: 16.8s\n",
            "56:\tlearn: 753328.6376429\ttest: 824808.1414173\tbest: 824808.1414173 (56)\ttotal: 6.65s\tremaining: 16.7s\n",
            "57:\tlearn: 752922.9091275\ttest: 824601.4036916\tbest: 824601.4036916 (57)\ttotal: 6.77s\tremaining: 16.6s\n",
            "58:\tlearn: 750276.6840188\ttest: 823220.0045293\tbest: 823220.0045293 (58)\ttotal: 6.88s\tremaining: 16.4s\n",
            "59:\tlearn: 747331.3302784\ttest: 820598.0466367\tbest: 820598.0466367 (59)\ttotal: 7.01s\tremaining: 16.3s\n",
            "60:\tlearn: 745130.2597819\ttest: 819957.1918287\tbest: 819957.1918287 (60)\ttotal: 7.15s\tremaining: 16.3s\n",
            "61:\tlearn: 742425.4367911\ttest: 817727.2057257\tbest: 817727.2057257 (61)\ttotal: 7.28s\tremaining: 16.2s\n",
            "62:\tlearn: 741687.4710417\ttest: 817708.1199056\tbest: 817708.1199056 (62)\ttotal: 7.38s\tremaining: 16s\n",
            "63:\tlearn: 741086.8375178\ttest: 817514.1733017\tbest: 817514.1733017 (63)\ttotal: 7.5s\tremaining: 15.9s\n",
            "64:\tlearn: 739332.8612717\ttest: 816763.2831326\tbest: 816763.2831326 (64)\ttotal: 7.6s\tremaining: 15.8s\n",
            "65:\tlearn: 737971.3303364\ttest: 816662.0032992\tbest: 816662.0032992 (65)\ttotal: 7.74s\tremaining: 15.7s\n",
            "66:\tlearn: 736687.1529082\ttest: 816419.1022124\tbest: 816419.1022124 (66)\ttotal: 7.86s\tremaining: 15.6s\n",
            "67:\tlearn: 735817.8565539\ttest: 815704.6393270\tbest: 815704.6393270 (67)\ttotal: 7.97s\tremaining: 15.5s\n",
            "68:\tlearn: 735082.5163663\ttest: 815574.9489794\tbest: 815574.9489794 (68)\ttotal: 8.09s\tremaining: 15.4s\n",
            "69:\tlearn: 734471.3242707\ttest: 814914.1403664\tbest: 814914.1403664 (69)\ttotal: 8.23s\tremaining: 15.3s\n",
            "70:\tlearn: 733479.8850369\ttest: 814620.3931554\tbest: 814620.3931554 (70)\ttotal: 8.35s\tremaining: 15.2s\n",
            "71:\tlearn: 731221.1100209\ttest: 814030.7838862\tbest: 814030.7838862 (71)\ttotal: 8.48s\tremaining: 15.1s\n",
            "72:\tlearn: 730719.8191065\ttest: 813957.2065073\tbest: 813957.2065073 (72)\ttotal: 8.58s\tremaining: 14.9s\n",
            "73:\tlearn: 729794.4158948\ttest: 813574.7303298\tbest: 813574.7303298 (73)\ttotal: 8.72s\tremaining: 14.8s\n",
            "74:\tlearn: 729280.3103842\ttest: 813433.1296620\tbest: 813433.1296620 (74)\ttotal: 8.81s\tremaining: 14.7s\n",
            "75:\tlearn: 728009.2883106\ttest: 812469.3481651\tbest: 812469.3481651 (75)\ttotal: 8.94s\tremaining: 14.6s\n",
            "76:\tlearn: 726359.3742472\ttest: 812008.6900581\tbest: 812008.6900581 (76)\ttotal: 9.05s\tremaining: 14.5s\n",
            "77:\tlearn: 724906.8778131\ttest: 811583.8501504\tbest: 811583.8501504 (77)\ttotal: 9.18s\tremaining: 14.4s\n",
            "78:\tlearn: 722911.7266191\ttest: 809896.4767967\tbest: 809896.4767967 (78)\ttotal: 9.28s\tremaining: 14.2s\n",
            "79:\tlearn: 721408.1161018\ttest: 808897.7362833\tbest: 808897.7362833 (79)\ttotal: 9.41s\tremaining: 14.1s\n",
            "80:\tlearn: 720152.4812824\ttest: 808657.8091336\tbest: 808657.8091336 (80)\ttotal: 9.53s\tremaining: 14s\n",
            "81:\tlearn: 719195.2459809\ttest: 808655.9372733\tbest: 808655.9372733 (81)\ttotal: 9.64s\tremaining: 13.9s\n",
            "82:\tlearn: 718422.1141311\ttest: 808470.5705314\tbest: 808470.5705314 (82)\ttotal: 9.77s\tremaining: 13.8s\n",
            "83:\tlearn: 716522.4141889\ttest: 807612.0651374\tbest: 807612.0651374 (83)\ttotal: 9.9s\tremaining: 13.7s\n",
            "84:\tlearn: 715712.7599983\ttest: 807383.2529746\tbest: 807383.2529746 (84)\ttotal: 10s\tremaining: 13.5s\n",
            "85:\tlearn: 714912.7949170\ttest: 807250.9781528\tbest: 807250.9781528 (85)\ttotal: 10.1s\tremaining: 13.4s\n",
            "86:\tlearn: 713231.5186016\ttest: 806327.3717180\tbest: 806327.3717180 (86)\ttotal: 10.2s\tremaining: 13.3s\n",
            "87:\tlearn: 711029.6087261\ttest: 805580.4341872\tbest: 805580.4341872 (87)\ttotal: 10.4s\tremaining: 13.2s\n",
            "88:\tlearn: 709235.4995062\ttest: 804666.5462660\tbest: 804666.5462660 (88)\ttotal: 10.5s\tremaining: 13.1s\n",
            "89:\tlearn: 708885.4224283\ttest: 804650.5470074\tbest: 804650.5470074 (89)\ttotal: 10.6s\tremaining: 13s\n",
            "90:\tlearn: 708728.2950971\ttest: 804576.5944252\tbest: 804576.5944252 (90)\ttotal: 10.7s\tremaining: 12.9s\n",
            "91:\tlearn: 707727.0192626\ttest: 804179.2652703\tbest: 804179.2652703 (91)\ttotal: 10.9s\tremaining: 12.8s\n",
            "92:\tlearn: 706062.1156263\ttest: 803519.5372781\tbest: 803519.5372781 (92)\ttotal: 11s\tremaining: 12.6s\n",
            "93:\tlearn: 705078.4287537\ttest: 802930.6758203\tbest: 802930.6758203 (93)\ttotal: 11.1s\tremaining: 12.5s\n",
            "94:\tlearn: 703933.0670880\ttest: 803538.3029627\tbest: 802930.6758203 (93)\ttotal: 11.2s\tremaining: 12.4s\n",
            "95:\tlearn: 703587.3622646\ttest: 803370.5834089\tbest: 802930.6758203 (93)\ttotal: 11.3s\tremaining: 12.3s\n",
            "96:\tlearn: 702653.4594420\ttest: 802756.2045975\tbest: 802756.2045975 (96)\ttotal: 11.4s\tremaining: 12.2s\n",
            "97:\tlearn: 702043.2097829\ttest: 802764.3699808\tbest: 802756.2045975 (96)\ttotal: 11.6s\tremaining: 12s\n",
            "98:\tlearn: 701103.4446935\ttest: 802427.8601405\tbest: 802427.8601405 (98)\ttotal: 11.7s\tremaining: 11.9s\n",
            "99:\tlearn: 700448.2832665\ttest: 802509.3477960\tbest: 802427.8601405 (98)\ttotal: 11.8s\tremaining: 11.8s\n",
            "100:\tlearn: 699679.3859016\ttest: 802177.6603689\tbest: 802177.6603689 (100)\ttotal: 11.9s\tremaining: 11.7s\n",
            "101:\tlearn: 698769.6621264\ttest: 802163.3874213\tbest: 802163.3874213 (101)\ttotal: 12s\tremaining: 11.6s\n",
            "102:\tlearn: 697779.1985180\ttest: 802244.7577652\tbest: 802163.3874213 (101)\ttotal: 12.2s\tremaining: 11.4s\n",
            "103:\tlearn: 696447.8035826\ttest: 800986.4399655\tbest: 800986.4399655 (103)\ttotal: 12.3s\tremaining: 11.3s\n",
            "104:\tlearn: 695902.4999041\ttest: 800513.2657430\tbest: 800513.2657430 (104)\ttotal: 12.4s\tremaining: 11.2s\n",
            "105:\tlearn: 694001.6212523\ttest: 799675.4174317\tbest: 799675.4174317 (105)\ttotal: 12.5s\tremaining: 11.1s\n",
            "106:\tlearn: 693400.9858557\ttest: 799825.7388745\tbest: 799675.4174317 (105)\ttotal: 12.7s\tremaining: 11s\n",
            "107:\tlearn: 692746.0606301\ttest: 799391.8494786\tbest: 799391.8494786 (107)\ttotal: 12.8s\tremaining: 10.9s\n",
            "108:\tlearn: 691215.6639165\ttest: 798448.2560841\tbest: 798448.2560841 (108)\ttotal: 12.9s\tremaining: 10.8s\n",
            "109:\tlearn: 690852.3428090\ttest: 798380.9796226\tbest: 798380.9796226 (109)\ttotal: 13s\tremaining: 10.7s\n",
            "110:\tlearn: 690282.3674861\ttest: 798243.7917927\tbest: 798243.7917927 (110)\ttotal: 13.1s\tremaining: 10.5s\n",
            "111:\tlearn: 688862.3383367\ttest: 797592.1920120\tbest: 797592.1920120 (111)\ttotal: 13.3s\tremaining: 10.4s\n",
            "112:\tlearn: 687934.7991579\ttest: 797379.0480254\tbest: 797379.0480254 (112)\ttotal: 13.4s\tremaining: 10.3s\n",
            "113:\tlearn: 686899.9574970\ttest: 797268.9404547\tbest: 797268.9404547 (113)\ttotal: 13.5s\tremaining: 10.2s\n",
            "114:\tlearn: 685329.8488680\ttest: 796447.7440486\tbest: 796447.7440486 (114)\ttotal: 13.6s\tremaining: 10.1s\n",
            "115:\tlearn: 684878.5872979\ttest: 796071.0593773\tbest: 796071.0593773 (115)\ttotal: 13.8s\tremaining: 9.96s\n",
            "116:\tlearn: 684421.7013874\ttest: 796080.3813514\tbest: 796071.0593773 (115)\ttotal: 13.9s\tremaining: 9.84s\n",
            "117:\tlearn: 683660.6298445\ttest: 795951.1625751\tbest: 795951.1625751 (117)\ttotal: 14s\tremaining: 9.73s\n",
            "118:\tlearn: 682754.7423790\ttest: 795805.8688235\tbest: 795805.8688235 (118)\ttotal: 14.1s\tremaining: 9.62s\n",
            "119:\tlearn: 681010.4370460\ttest: 795080.4381858\tbest: 795080.4381858 (119)\ttotal: 14.3s\tremaining: 9.5s\n",
            "120:\tlearn: 680191.6501921\ttest: 794976.7600421\tbest: 794976.7600421 (120)\ttotal: 14.4s\tremaining: 9.38s\n",
            "121:\tlearn: 679578.7316386\ttest: 794646.4713788\tbest: 794646.4713788 (121)\ttotal: 14.5s\tremaining: 9.26s\n",
            "122:\tlearn: 678991.6283195\ttest: 794468.3508074\tbest: 794468.3508074 (122)\ttotal: 14.6s\tremaining: 9.14s\n",
            "123:\tlearn: 677898.2505390\ttest: 794171.2040931\tbest: 794171.2040931 (123)\ttotal: 14.7s\tremaining: 9.03s\n",
            "124:\tlearn: 677144.2197882\ttest: 793122.9387618\tbest: 793122.9387618 (124)\ttotal: 14.9s\tremaining: 8.91s\n",
            "125:\tlearn: 676123.5666805\ttest: 792361.9614129\tbest: 792361.9614129 (125)\ttotal: 15s\tremaining: 8.79s\n",
            "126:\tlearn: 674938.8526332\ttest: 791894.9462217\tbest: 791894.9462217 (126)\ttotal: 15.1s\tremaining: 8.68s\n",
            "127:\tlearn: 674035.3575858\ttest: 791745.6433402\tbest: 791745.6433402 (127)\ttotal: 15.2s\tremaining: 8.56s\n",
            "128:\tlearn: 673515.0773232\ttest: 791592.9668143\tbest: 791592.9668143 (128)\ttotal: 15.3s\tremaining: 8.44s\n",
            "129:\tlearn: 672954.0221547\ttest: 790945.6691750\tbest: 790945.6691750 (129)\ttotal: 15.4s\tremaining: 8.32s\n",
            "130:\tlearn: 672528.3639391\ttest: 790570.3967579\tbest: 790570.3967579 (130)\ttotal: 15.6s\tremaining: 8.2s\n",
            "131:\tlearn: 671605.2392963\ttest: 789783.3068017\tbest: 789783.3068017 (131)\ttotal: 15.7s\tremaining: 8.08s\n",
            "132:\tlearn: 670173.4386328\ttest: 789647.1886221\tbest: 789647.1886221 (132)\ttotal: 15.8s\tremaining: 7.97s\n",
            "133:\tlearn: 669103.9614367\ttest: 789202.2080662\tbest: 789202.2080662 (133)\ttotal: 15.9s\tremaining: 7.85s\n",
            "134:\tlearn: 668427.2586234\ttest: 789055.7980460\tbest: 789055.7980460 (134)\ttotal: 16s\tremaining: 7.73s\n",
            "135:\tlearn: 667959.6773851\ttest: 789068.4554993\tbest: 789055.7980460 (134)\ttotal: 16.2s\tremaining: 7.61s\n",
            "136:\tlearn: 667216.4581469\ttest: 788802.4765036\tbest: 788802.4765036 (136)\ttotal: 16.3s\tremaining: 7.49s\n",
            "137:\tlearn: 666420.2009350\ttest: 788736.3828166\tbest: 788736.3828166 (137)\ttotal: 16.4s\tremaining: 7.37s\n",
            "138:\tlearn: 665285.9575672\ttest: 788343.1576740\tbest: 788343.1576740 (138)\ttotal: 16.5s\tremaining: 7.25s\n",
            "139:\tlearn: 664755.7771657\ttest: 788322.2829701\tbest: 788322.2829701 (139)\ttotal: 16.6s\tremaining: 7.13s\n",
            "140:\tlearn: 664450.4607276\ttest: 788240.7440159\tbest: 788240.7440159 (140)\ttotal: 16.8s\tremaining: 7.01s\n",
            "141:\tlearn: 663666.0283313\ttest: 787852.8094828\tbest: 787852.8094828 (141)\ttotal: 16.9s\tremaining: 6.9s\n",
            "142:\tlearn: 663092.6282475\ttest: 787707.3835908\tbest: 787707.3835908 (142)\ttotal: 17s\tremaining: 6.78s\n",
            "143:\tlearn: 661737.1042821\ttest: 787595.4520884\tbest: 787595.4520884 (143)\ttotal: 17.1s\tremaining: 6.66s\n",
            "144:\tlearn: 661150.3528492\ttest: 787562.0374268\tbest: 787562.0374268 (144)\ttotal: 17.3s\tremaining: 6.54s\n",
            "145:\tlearn: 660599.8171995\ttest: 787223.7584745\tbest: 787223.7584745 (145)\ttotal: 17.4s\tremaining: 6.43s\n",
            "146:\tlearn: 660259.4322905\ttest: 787089.3295412\tbest: 787089.3295412 (146)\ttotal: 17.5s\tremaining: 6.31s\n",
            "147:\tlearn: 659844.0430690\ttest: 787137.1532771\tbest: 787089.3295412 (146)\ttotal: 17.6s\tremaining: 6.19s\n",
            "148:\tlearn: 659481.6895841\ttest: 786820.0978395\tbest: 786820.0978395 (148)\ttotal: 17.7s\tremaining: 6.07s\n",
            "149:\tlearn: 659259.7835163\ttest: 787000.0193420\tbest: 786820.0978395 (148)\ttotal: 17.9s\tremaining: 5.96s\n",
            "150:\tlearn: 658352.7823641\ttest: 786676.9368401\tbest: 786676.9368401 (150)\ttotal: 18s\tremaining: 5.84s\n",
            "151:\tlearn: 657784.4447259\ttest: 786588.5922854\tbest: 786588.5922854 (151)\ttotal: 18.1s\tremaining: 5.72s\n",
            "152:\tlearn: 657355.1788434\ttest: 786418.1156008\tbest: 786418.1156008 (152)\ttotal: 18.2s\tremaining: 5.6s\n",
            "153:\tlearn: 655805.4677674\ttest: 786224.2895266\tbest: 786224.2895266 (153)\ttotal: 18.4s\tremaining: 5.48s\n",
            "154:\tlearn: 655002.9484798\ttest: 785958.1142242\tbest: 785958.1142242 (154)\ttotal: 18.5s\tremaining: 5.36s\n",
            "155:\tlearn: 654241.4366318\ttest: 785581.9194407\tbest: 785581.9194407 (155)\ttotal: 18.6s\tremaining: 5.24s\n",
            "156:\tlearn: 653870.9528263\ttest: 785484.7887555\tbest: 785484.7887555 (156)\ttotal: 18.7s\tremaining: 5.12s\n",
            "157:\tlearn: 653578.7386451\ttest: 785314.9787106\tbest: 785314.9787106 (157)\ttotal: 18.8s\tremaining: 5.01s\n",
            "158:\tlearn: 653325.3642133\ttest: 785128.2831891\tbest: 785128.2831891 (158)\ttotal: 19s\tremaining: 4.89s\n",
            "159:\tlearn: 652921.9418002\ttest: 785246.1775631\tbest: 785128.2831891 (158)\ttotal: 19.3s\tremaining: 4.82s\n",
            "160:\tlearn: 652725.2610702\ttest: 785077.4390390\tbest: 785077.4390390 (160)\ttotal: 19.7s\tremaining: 4.76s\n",
            "161:\tlearn: 651877.8265451\ttest: 784920.3103420\tbest: 784920.3103420 (161)\ttotal: 19.8s\tremaining: 4.66s\n",
            "162:\tlearn: 651566.4202776\ttest: 784856.0102745\tbest: 784856.0102745 (162)\ttotal: 20s\tremaining: 4.54s\n",
            "163:\tlearn: 651097.2081627\ttest: 784829.2132316\tbest: 784829.2132316 (163)\ttotal: 20.1s\tremaining: 4.41s\n",
            "164:\tlearn: 650493.7465631\ttest: 784687.3946280\tbest: 784687.3946280 (164)\ttotal: 20.2s\tremaining: 4.29s\n",
            "165:\tlearn: 649390.2884888\ttest: 784630.5071555\tbest: 784630.5071555 (165)\ttotal: 20.3s\tremaining: 4.17s\n",
            "166:\tlearn: 648830.3712484\ttest: 784570.2864822\tbest: 784570.2864822 (166)\ttotal: 20.5s\tremaining: 4.04s\n",
            "167:\tlearn: 648084.8059011\ttest: 784246.4987726\tbest: 784246.4987726 (167)\ttotal: 20.6s\tremaining: 3.92s\n",
            "168:\tlearn: 647595.1179949\ttest: 783823.1444438\tbest: 783823.1444438 (168)\ttotal: 20.7s\tremaining: 3.8s\n",
            "169:\tlearn: 647014.8387879\ttest: 783608.1073004\tbest: 783608.1073004 (169)\ttotal: 20.8s\tremaining: 3.67s\n",
            "170:\tlearn: 645774.0694346\ttest: 782787.1849341\tbest: 782787.1849341 (170)\ttotal: 21s\tremaining: 3.56s\n",
            "171:\tlearn: 645476.2185579\ttest: 782766.2113036\tbest: 782766.2113036 (171)\ttotal: 21.1s\tremaining: 3.43s\n",
            "172:\tlearn: 644169.7733493\ttest: 782712.1320933\tbest: 782712.1320933 (172)\ttotal: 21.2s\tremaining: 3.31s\n",
            "173:\tlearn: 643562.8291808\ttest: 782806.8839390\tbest: 782712.1320933 (172)\ttotal: 21.3s\tremaining: 3.19s\n",
            "174:\tlearn: 642993.8543088\ttest: 782882.7523754\tbest: 782712.1320933 (172)\ttotal: 21.5s\tremaining: 3.07s\n",
            "175:\tlearn: 642352.4532021\ttest: 782511.5572747\tbest: 782511.5572747 (175)\ttotal: 21.6s\tremaining: 2.94s\n",
            "176:\tlearn: 641796.4706832\ttest: 782412.1281613\tbest: 782412.1281613 (176)\ttotal: 21.7s\tremaining: 2.82s\n",
            "177:\tlearn: 640981.5918855\ttest: 781902.3188954\tbest: 781902.3188954 (177)\ttotal: 21.9s\tremaining: 2.7s\n",
            "178:\tlearn: 640791.2762452\ttest: 781890.3763464\tbest: 781890.3763464 (178)\ttotal: 22s\tremaining: 2.58s\n",
            "179:\tlearn: 639966.3958360\ttest: 782001.3118888\tbest: 781890.3763464 (178)\ttotal: 22.1s\tremaining: 2.45s\n",
            "180:\tlearn: 638922.5012564\ttest: 781644.6843448\tbest: 781644.6843448 (180)\ttotal: 22.2s\tremaining: 2.33s\n",
            "181:\tlearn: 638278.9787500\ttest: 781540.7922968\tbest: 781540.7922968 (181)\ttotal: 22.3s\tremaining: 2.21s\n",
            "182:\tlearn: 637550.9907982\ttest: 781172.1789580\tbest: 781172.1789580 (182)\ttotal: 22.5s\tremaining: 2.09s\n",
            "183:\tlearn: 636943.1607555\ttest: 781296.6441515\tbest: 781172.1789580 (182)\ttotal: 22.6s\tremaining: 1.96s\n",
            "184:\tlearn: 636424.3260992\ttest: 781235.7815010\tbest: 781172.1789580 (182)\ttotal: 22.7s\tremaining: 1.84s\n",
            "185:\tlearn: 636074.6656066\ttest: 781290.0925644\tbest: 781172.1789580 (182)\ttotal: 22.8s\tremaining: 1.72s\n",
            "186:\tlearn: 635778.2238339\ttest: 781207.3720355\tbest: 781172.1789580 (182)\ttotal: 22.9s\tremaining: 1.59s\n",
            "187:\tlearn: 635319.2448318\ttest: 781123.1088018\tbest: 781123.1088018 (187)\ttotal: 23.1s\tremaining: 1.47s\n",
            "188:\tlearn: 634933.7157385\ttest: 781168.3929975\tbest: 781123.1088018 (187)\ttotal: 23.2s\tremaining: 1.35s\n",
            "189:\tlearn: 634600.3534448\ttest: 781274.6683777\tbest: 781123.1088018 (187)\ttotal: 23.3s\tremaining: 1.23s\n",
            "190:\tlearn: 633982.6527307\ttest: 780666.2247563\tbest: 780666.2247563 (190)\ttotal: 23.5s\tremaining: 1.1s\n",
            "191:\tlearn: 633508.9347585\ttest: 780370.0641281\tbest: 780370.0641281 (191)\ttotal: 23.6s\tremaining: 982ms\n",
            "192:\tlearn: 632285.9065212\ttest: 780012.2512699\tbest: 780012.2512699 (192)\ttotal: 23.7s\tremaining: 859ms\n",
            "193:\tlearn: 630560.6429318\ttest: 779027.6501226\tbest: 779027.6501226 (193)\ttotal: 23.8s\tremaining: 736ms\n",
            "194:\tlearn: 629223.8696337\ttest: 778169.0883657\tbest: 778169.0883657 (194)\ttotal: 23.9s\tremaining: 613ms\n",
            "195:\tlearn: 628222.1126467\ttest: 777528.6907846\tbest: 777528.6907846 (195)\ttotal: 24s\tremaining: 489ms\n",
            "196:\tlearn: 627846.8389455\ttest: 777378.2990218\tbest: 777378.2990218 (196)\ttotal: 24.1s\tremaining: 367ms\n",
            "197:\tlearn: 627289.4777713\ttest: 777152.8011859\tbest: 777152.8011859 (197)\ttotal: 24.2s\tremaining: 245ms\n",
            "198:\tlearn: 626366.8015229\ttest: 776768.1072290\tbest: 776768.1072290 (198)\ttotal: 24.4s\tremaining: 122ms\n",
            "199:\tlearn: 625996.0395562\ttest: 776710.4790195\tbest: 776710.4790195 (199)\ttotal: 24.5s\tremaining: 0us\n",
            "\n",
            "bestTest = 776710.479\n",
            "bestIteration = 199\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'cv_results': defaultdict(list,\n",
              "             {'iterations': [0,\n",
              "               1,\n",
              "               2,\n",
              "               3,\n",
              "               4,\n",
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              "               145,\n",
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              "               150,\n",
              "               151,\n",
              "               152,\n",
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              "               157,\n",
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              "               159,\n",
              "               160,\n",
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              "               163,\n",
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              "               165,\n",
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              "               170,\n",
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              "               194,\n",
              "               195,\n",
              "               196,\n",
              "               197,\n",
              "               198,\n",
              "               199],\n",
              "              'test-RMSE-mean': [1457947.5313586146,\n",
              "               1376649.8434950586,\n",
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              "               772507.4956593708,\n",
              "               772472.1793782875,\n",
              "               772327.7022755542,\n",
              "               772178.7007750616,\n",
              "               771800.9636517289,\n",
              "               771625.7611105811,\n",
              "               771500.9824220181,\n",
              "               771427.6747850335,\n",
              "               771077.2841234199,\n",
              "               770786.2792400626,\n",
              "               770497.0646684583,\n",
              "               770147.6971814494,\n",
              "               770050.7570908912,\n",
              "               770124.2786216143,\n",
              "               770038.2896986966,\n",
              "               769819.2357827508,\n",
              "               769574.891073364,\n",
              "               769505.6836822416,\n",
              "               769418.8514867272,\n",
              "               769348.5163943559,\n",
              "               769277.57538524,\n",
              "               769191.8629793148,\n",
              "               768986.5181476694,\n",
              "               768630.4067592003,\n",
              "               768186.367632634,\n",
              "               768091.8221245576,\n",
              "               768039.2851887965,\n",
              "               768040.7477583555,\n",
              "               767933.016756294,\n",
              "               767768.0765957679,\n",
              "               767803.5135007236,\n",
              "               767555.6073179137,\n",
              "               767608.6889249791,\n",
              "               767725.5111283717,\n",
              "               767621.2951254813,\n",
              "               767435.0431928468,\n",
              "               767251.7112712454,\n",
              "               767205.846173742,\n",
              "               767070.4413749495,\n",
              "               767054.468321567,\n",
              "               766960.5941107242,\n",
              "               766915.6939758019,\n",
              "               766845.420004625,\n",
              "               766868.8337744045,\n",
              "               766608.514645427,\n",
              "               766469.7159241326,\n",
              "               766215.3768780666,\n",
              "               765724.2085705408,\n",
              "               765438.5563965939,\n",
              "               765044.671391518,\n",
              "               764971.5045584644,\n",
              "               764800.5591984317,\n",
              "               764681.9722845036,\n",
              "               764654.1877896643],\n",
              "              'test-RMSE-std': [33980.65288470443,\n",
              "               33717.296405567766,\n",
              "               33164.120138418955,\n",
              "               34170.568194433654,\n",
              "               33290.59960174755,\n",
              "               33989.972802367425,\n",
              "               34024.068794722654,\n",
              "               31593.281336642158,\n",
              "               29425.57769199412,\n",
              "               29222.684132407263,\n",
              "               30889.38833300691,\n",
              "               29207.144935635813,\n",
              "               29100.070712842735,\n",
              "               31102.405841881457,\n",
              "               28268.051621398063,\n",
              "               27235.103454168955,\n",
              "               25161.267244724208,\n",
              "               25984.335994159548,\n",
              "               26594.980241998255,\n",
              "               26678.220220188974,\n",
              "               27077.45667897177,\n",
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              "               27130.224170924714,\n",
              "               27656.558744112954,\n",
              "               27171.137886779135,\n",
              "               26699.132781703207,\n",
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              "               26895.27052220479,\n",
              "               28014.948314909896,\n",
              "               28358.57713917764,\n",
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              "               29061.151296451422,\n",
              "               27800.52295055949,\n",
              "               27913.722543573633,\n",
              "               27136.783880472627,\n",
              "               26881.684458180047,\n",
              "               26669.906417198235,\n",
              "               26657.528666629867,\n",
              "               27000.087222222457,\n",
              "               27448.49772377939,\n",
              "               28244.821728258707,\n",
              "               27972.036404676714,\n",
              "               28055.959732611795,\n",
              "               28006.74096953304,\n",
              "               27822.802289766092,\n",
              "               27642.77577130575,\n",
              "               28837.178332989624,\n",
              "               28319.86857371326,\n",
              "               29151.651423511878,\n",
              "               29072.4120836195,\n",
              "               28740.514716287296,\n",
              "               28346.63330338114,\n",
              "               28444.698134822473,\n",
              "               27861.649057035465,\n",
              "               27989.27839481874,\n",
              "               27738.822450577147,\n",
              "               27775.78569465476,\n",
              "               27986.81816099863,\n",
              "               27694.470994065407,\n",
              "               27126.278410399602,\n",
              "               26900.980033998574,\n",
              "               26598.61964502239,\n",
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              "               27293.26176740218,\n",
              "               26612.70558040572,\n",
              "               26911.616827469734,\n",
              "               26932.46463900228,\n",
              "               26322.08248337895,\n",
              "               26472.662828331264,\n",
              "               26357.31629904034,\n",
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              "               26135.017724608195,\n",
              "               26515.243149358583,\n",
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              "               26788.906050619982,\n",
              "               26389.210543387315,\n",
              "               26106.74031645375,\n",
              "               26155.837149870404,\n",
              "               25612.912348934664,\n",
              "               25730.426570829713,\n",
              "               26755.9453552517,\n",
              "               26873.90986865322,\n",
              "               27039.96352405812,\n",
              "               26772.64008726276,\n",
              "               26772.746140027743,\n",
              "               26490.06850775591,\n",
              "               26307.856808463963,\n",
              "               26266.052505362666,\n",
              "               25654.526039136264,\n",
              "               25609.489118461937,\n",
              "               25446.24151733419,\n",
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              "               25230.5441584523,\n",
              "               25851.26760206281,\n",
              "               25848.414482689892,\n",
              "               25639.214543234455,\n",
              "               25558.163321615124,\n",
              "               26111.59334491334,\n",
              "               25914.25025457268,\n",
              "               25987.585649285575,\n",
              "               25898.582194097784,\n",
              "               26041.290995692718,\n",
              "               25428.841327412498,\n",
              "               25382.831028379835,\n",
              "               25333.72217656895,\n",
              "               25392.25370723402,\n",
              "               25374.57167162458,\n",
              "               25253.575639142742,\n",
              "               25339.224753055565,\n",
              "               25227.416617245635,\n",
              "               24866.42524072036,\n",
              "               25213.269614675075,\n",
              "               24911.43602545054,\n",
              "               24410.33574174157,\n",
              "               24392.050250007327,\n",
              "               24430.627096415265,\n",
              "               24411.05230084721,\n",
              "               24312.008352986722,\n",
              "               24048.948696361516,\n",
              "               24110.822226964498,\n",
              "               23935.229759592483,\n",
              "               23962.71073452056,\n",
              "               24373.57604278325,\n",
              "               24078.442331601425,\n",
              "               23890.81049473941,\n",
              "               23627.13666630474,\n",
              "               23935.9016203634,\n",
              "               24117.973348856813,\n",
              "               23641.99173044455,\n",
              "               23464.942125955586,\n",
              "               23447.99156766746,\n",
              "               23431.916646379887,\n",
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              "               22974.20230827562,\n",
              "               23016.689761309313,\n",
              "               23085.24274731936,\n",
              "               23038.134301229158,\n",
              "               23119.177589080926,\n",
              "               23070.52347402808,\n",
              "               22994.727965444872,\n",
              "               23289.839218305384,\n",
              "               23846.79433091217,\n",
              "               23967.255401187285,\n",
              "               24202.411891943546,\n",
              "               24095.138978553423,\n",
              "               24053.240604903564,\n",
              "               24111.48546463196,\n",
              "               24059.134298492245,\n",
              "               23982.770152736935,\n",
              "               24048.284309028568,\n",
              "               23930.722041832654,\n",
              "               23779.952898602016,\n",
              "               23825.141763071362,\n",
              "               23689.70834230068,\n",
              "               23539.664235233402,\n",
              "               23893.01158324422,\n",
              "               24245.585384552276,\n",
              "               24253.915548746867,\n",
              "               24164.282824599602,\n",
              "               24156.508561234357,\n",
              "               24375.019884161138,\n",
              "               24579.195670512443,\n",
              "               24618.311227100763,\n",
              "               24517.51380348573,\n",
              "               24474.86492167995,\n",
              "               24510.372986891656,\n",
              "               24277.08324425839,\n",
              "               24092.86046566344,\n",
              "               24372.194073154773,\n",
              "               24384.031325797103,\n",
              "               24409.188704847766,\n",
              "               24234.59699789569,\n",
              "               24222.746466959,\n",
              "               24263.33268848208,\n",
              "               24324.01569433849,\n",
              "               24365.909913606236,\n",
              "               24332.433439547047,\n",
              "               24296.697445349786,\n",
              "               24475.054182943044,\n",
              "               24394.972888871875,\n",
              "               24500.367909616285,\n",
              "               24337.31187671747,\n",
              "               24229.747883087057,\n",
              "               23967.045927293122,\n",
              "               23901.99455784722,\n",
              "               23723.457659714164,\n",
              "               23652.12000722163,\n",
              "               23559.071410439923,\n",
              "               23639.34400744564,\n",
              "               23443.563311546168,\n",
              "               23426.948887922597,\n",
              "               23570.14666455569,\n",
              "               23505.498414743834,\n",
              "               23274.28563961609,\n",
              "               23007.594085839737,\n",
              "               22896.74215493341,\n",
              "               22966.429573235087,\n",
              "               22896.348927247433,\n",
              "               22792.384631739027],\n",
              "              'train-RMSE-mean': [1457271.717491704,\n",
              "               1375257.572283062,\n",
              "               1304819.7325723085,\n",
              "               1242341.369565987,\n",
              "               1188237.659413267,\n",
              "               1142408.8667941648,\n",
              "               1102971.6425729648,\n",
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              "               944921.8615857075,\n",
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              "               856530.2246309764,\n",
              "               850947.829550763,\n",
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              "               839576.2271923056,\n",
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              "               827804.9357514521,\n",
              "               823201.1125343983,\n",
              "               819470.534114467,\n",
              "               816183.4190252405,\n",
              "               813328.1910252012,\n",
              "               809315.7796559461,\n",
              "               805364.6399014479,\n",
              "               801544.403802812,\n",
              "               799061.2327183728,\n",
              "               796513.6934879072,\n",
              "               793456.031072768,\n",
              "               791101.5457008827,\n",
              "               788807.9832519199,\n",
              "               786030.7028186806,\n",
              "               783620.0951213916,\n",
              "               780868.6627072046,\n",
              "               778745.7559438603,\n",
              "               777631.4382471554,\n",
              "               776490.007187015,\n",
              "               774904.2341539017,\n",
              "               773286.6987530818,\n",
              "               771131.6070528725,\n",
              "               768886.7429631813,\n",
              "               767191.0900281202,\n",
              "               765987.5710915298,\n",
              "               763938.316990804,\n",
              "               762473.7413388068,\n",
              "               761038.2712075171,\n",
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              "               757492.850042543,\n",
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              "               755255.7987490961,\n",
              "               753862.5102384848,\n",
              "               752357.407788819,\n",
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              "               689525.4273304777,\n",
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              "               629778.9117594371,\n",
              "               629094.0651807031,\n",
              "               628569.2348083941],\n",
              "              'train-RMSE-std': [16098.227675783533,\n",
              "               15726.148600517776,\n",
              "               14962.148375409337,\n",
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              "               6056.1493042844495,\n",
              "               5660.460655628739,\n",
              "               5098.529012327752,\n",
              "               5035.343102335804,\n",
              "               4946.827965523268,\n",
              "               4950.358568392912,\n",
              "               4975.4570433352455,\n",
              "               4950.716957542498,\n",
              "               5191.89274232654,\n",
              "               5494.9625735923655]}),\n",
              " 'params': {'depth': 8,\n",
              "  'iterations': 200,\n",
              "  'l2_leaf_reg': 0.2,\n",
              "  'learning_rate': 0.1}}"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Let's evaluate performance of model on test and training data using RMSE and r2.\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import r2_score\n",
        "pred = model.predict(X_test)\n",
        "rmse = (np.sqrt(mean_squared_error(y_test, pred)))\n",
        "r2 = r2_score(y_test, pred)\n",
        "print('Testing performance')\n",
        "print('RMSE: {:.2f}'.format(rmse))\n",
        "print('R2: {:.2f}'.format(r2))\n",
        "predTR = model.predict(X_train)\n",
        "rmseTR = (np.sqrt(mean_squared_error(y_train, predTR)))\n",
        "r2TR = r2_score(y_train, predTR)\n",
        "print('Train performance')\n",
        "print('RMSE: {:.2f}'.format(rmseTR))\n",
        "print('R2: {:.2f}'.format(r2TR))"
      ],
      "metadata": {
        "id": "F81KDGZ2bTA_",
        "outputId": "5c0b440e-ac95-4df7-c59e-8b2e8bc4884f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing performance\n",
            "RMSE: 728986.70\n",
            "R2: 0.63\n",
            "Train performance\n",
            "RMSE: 672549.99\n",
            "R2: 0.70\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "w8XMlVAHWmn2",
        "outputId": "e4722601-cc5d-40d6-9967-49aee3a6ffc0"
      },
      "source": [
        "#To get the most important features, I used feature get_feature_importance() of CatBoost\n",
        "feat_imp = pd.Series(model.get_feature_importance(), index=df3.columns)\n",
        "feat_imp.nlargest(30).plot(kind='barh', figsize=(8,10))\n",
        "#It seems that size, location and age is important for Sale Price"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1f5820c910>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}