ML_Model Deployment.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",  
      "metadata": {
        "id": "3M14iVObri7v"
      },
      "source": [
        "\n",
        "## Exploring the Data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "o3b4GDl8ri7w"
      },
      "outputs": [],
      "source": [
        "import polars as pl\n",
        "import polars.selectors as cs\n",
        "import sklearn\n",
        "import catboost\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "ju6Faljjri7x",
        "outputId": "aba7e6c5-4471-472c-eea3-bde1aadb6c1a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (21_613, 23)\n",
              "┌────────────┬──────────┬──────────┬───────────┬───┬────────────┬───────────┬───────────┬──────────┐\n",
              "│ id         ┆ price    ┆ bedrooms ┆ bathrooms ┆ … ┆ sqft_lot15 ┆ date_year ┆ date_mont ┆ date_day │\n",
              "│ ---        ┆ ---      ┆ ---      ┆ ---       ┆   ┆ ---        ┆ ---       ┆ h         ┆ ---      │\n",
              "│ i64        ┆ f64      ┆ i64      ┆ f64       ┆   ┆ i64        ┆ i64       ┆ ---       ┆ i64      │\n",
              "│            ┆          ┆          ┆           ┆   ┆            ┆           ┆ i64       ┆          │\n",
              "╞════════════╪══════════╪══════════╪═══════════╪═══╪════════════╪═══════════╪═══════════╪══════════╡\n",
              "│ 7129300520 ┆ 221900.0 ┆ 3        ┆ 1.0       ┆ … ┆ 5650       ┆ 2014      ┆ 10        ┆ 13       │\n",
              "│ 6414100192 ┆ 538000.0 ┆ 3        ┆ 2.25      ┆ … ┆ 7639       ┆ 2014      ┆ 12        ┆ 9        │\n",
              "│ 5631500400 ┆ 180000.0 ┆ 2        ┆ 1.0       ┆ … ┆ 8062       ┆ 2015      ┆ 2         ┆ 25       │\n",
              "│ 2487200875 ┆ 604000.0 ┆ 4        ┆ 3.0       ┆ … ┆ 5000       ┆ 2014      ┆ 12        ┆ 9        │\n",
              "│ 1954400510 ┆ 510000.0 ┆ 3        ┆ 2.0       ┆ … ┆ 7503       ┆ 2015      ┆ 2         ┆ 18       │\n",
              "│ …          ┆ …        ┆ …        ┆ …         ┆ … ┆ …          ┆ …         ┆ …         ┆ …        │\n",
              "│ 263000018  ┆ 360000.0 ┆ 3        ┆ 2.5       ┆ … ┆ 1509       ┆ 2014      ┆ 5         ┆ 21       │\n",
              "│ 6600060120 ┆ 400000.0 ┆ 4        ┆ 2.5       ┆ … ┆ 7200       ┆ 2015      ┆ 2         ┆ 23       │\n",
              "│ 1523300141 ┆ 402101.0 ┆ 2        ┆ 0.75      ┆ … ┆ 2007       ┆ 2014      ┆ 6         ┆ 23       │\n",
              "│ 291310100  ┆ 400000.0 ┆ 3        ┆ 2.5       ┆ … ┆ 1287       ┆ 2015      ┆ 1         ┆ 16       │\n",
              "│ 1523300157 ┆ 325000.0 ┆ 2        ┆ 0.75      ┆ … ┆ 1357       ┆ 2014      ┆ 10        ┆ 15       │\n",
              "└────────────┴──────────┴──────────┴───────────┴───┴────────────┴───────────┴───────────┴──────────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (21_613, 23)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>id</th><th>price</th><th>bedrooms</th><th>bathrooms</th><th>sqft_living</th><th>sqft_lot</th><th>floors</th><th>waterfront</th><th>view</th><th>condition</th><th>grade</th><th>sqft_above</th><th>sqft_basement</th><th>yr_built</th><th>yr_renovated</th><th>zipcode</th><th>lat</th><th>long</th><th>sqft_living15</th><th>sqft_lot15</th><th>date_year</th><th>date_month</th><th>date_day</th></tr><tr><td>i64</td><td>f64</td><td>i64</td><td>f64</td><td>i64</td><td>i64</td><td>f64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td></tr></thead><tbody><tr><td>7129300520</td><td>221900.0</td><td>3</td><td>1.0</td><td>1180</td><td>5650</td><td>1.0</td><td>0</td><td>0</td><td>3</td><td>7</td><td>1180</td><td>0</td><td>1955</td><td>0</td><td>98178</td><td>47.5112</td><td>-122.257</td><td>1340</td><td>5650</td><td>2014</td><td>10</td><td>13</td></tr><tr><td>6414100192</td><td>538000.0</td><td>3</td><td>2.25</td><td>2570</td><td>7242</td><td>2.0</td><td>0</td><td>0</td><td>3</td><td>7</td><td>2170</td><td>400</td><td>1951</td><td>1991</td><td>98125</td><td>47.721</td><td>-122.319</td><td>1690</td><td>7639</td><td>2014</td><td>12</td><td>9</td></tr><tr><td>5631500400</td><td>180000.0</td><td>2</td><td>1.0</td><td>770</td><td>10000</td><td>1.0</td><td>0</td><td>0</td><td>3</td><td>6</td><td>770</td><td>0</td><td>1933</td><td>0</td><td>98028</td><td>47.7379</td><td>-122.233</td><td>2720</td><td>8062</td><td>2015</td><td>2</td><td>25</td></tr><tr><td>2487200875</td><td>604000.0</td><td>4</td><td>3.0</td><td>1960</td><td>5000</td><td>1.0</td><td>0</td><td>0</td><td>5</td><td>7</td><td>1050</td><td>910</td><td>1965</td><td>0</td><td>98136</td><td>47.5208</td><td>-122.393</td><td>1360</td><td>5000</td><td>2014</td><td>12</td><td>9</td></tr><tr><td>1954400510</td><td>510000.0</td><td>3</td><td>2.0</td><td>1680</td><td>8080</td><td>1.0</td><td>0</td><td>0</td><td>3</td><td>8</td><td>1680</td><td>0</td><td>1987</td><td>0</td><td>98074</td><td>47.6168</td><td>-122.045</td><td>1800</td><td>7503</td><td>2015</td><td>2</td><td>18</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>263000018</td><td>360000.0</td><td>3</td><td>2.5</td><td>1530</td><td>1131</td><td>3.0</td><td>0</td><td>0</td><td>3</td><td>8</td><td>1530</td><td>0</td><td>2009</td><td>0</td><td>98103</td><td>47.6993</td><td>-122.346</td><td>1530</td><td>1509</td><td>2014</td><td>5</td><td>21</td></tr><tr><td>6600060120</td><td>400000.0</td><td>4</td><td>2.5</td><td>2310</td><td>5813</td><td>2.0</td><td>0</td><td>0</td><td>3</td><td>8</td><td>2310</td><td>0</td><td>2014</td><td>0</td><td>98146</td><td>47.5107</td><td>-122.362</td><td>1830</td><td>7200</td><td>2015</td><td>2</td><td>23</td></tr><tr><td>1523300141</td><td>402101.0</td><td>2</td><td>0.75</td><td>1020</td><td>1350</td><td>2.0</td><td>0</td><td>0</td><td>3</td><td>7</td><td>1020</td><td>0</td><td>2009</td><td>0</td><td>98144</td><td>47.5944</td><td>-122.299</td><td>1020</td><td>2007</td><td>2014</td><td>6</td><td>23</td></tr><tr><td>291310100</td><td>400000.0</td><td>3</td><td>2.5</td><td>1600</td><td>2388</td><td>2.0</td><td>0</td><td>0</td><td>3</td><td>8</td><td>1600</td><td>0</td><td>2004</td><td>0</td><td>98027</td><td>47.5345</td><td>-122.069</td><td>1410</td><td>1287</td><td>2015</td><td>1</td><td>16</td></tr><tr><td>1523300157</td><td>325000.0</td><td>2</td><td>0.75</td><td>1020</td><td>1076</td><td>2.0</td><td>0</td><td>0</td><td>3</td><td>7</td><td>1020</td><td>0</td><td>2008</td><td>0</td><td>98144</td><td>47.5941</td><td>-122.299</td><td>1020</td><td>1357</td><td>2014</td><td>10</td><td>15</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# King County House Sales dataset from OpenML (includes Seattle)\n",
        "# this is an ARFF file, which is a text file with a specific format\n",
        "url = 'https://www.openml.org/data/download/22044765/dataset'\n",
        "cols = ['id', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view',\n",
        "        'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated',\n",
        "        'zipcode', 'lat', 'long', 'sqft_living15', 'sqft_lot15', 'date_year', 'date_month', 'date_day']\n",
        "\n",
        "raw = pl.read_csv(url, new_columns=cols, skip_rows=31, has_header=False)\n",
        "raw"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "_XWlMcUcri7y",
        "outputId": "df39d045-05ab-4cde-b381-c1998a9011ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (9, 24)\n",
              "┌───────────┬───────────┬───────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
              "│ statistic ┆ id        ┆ price     ┆ bedrooms ┆ … ┆ sqft_lot1 ┆ date_year ┆ date_mont ┆ date_day  │\n",
              "│ ---       ┆ ---       ┆ ---       ┆ ---      ┆   ┆ 5         ┆ ---       ┆ h         ┆ ---       │\n",
              "│ str       ┆ f64       ┆ f64       ┆ f64      ┆   ┆ ---       ┆ f64       ┆ ---       ┆ f64       │\n",
              "│           ┆           ┆           ┆          ┆   ┆ f64       ┆           ┆ f64       ┆           │\n",
              "╞═══════════╪═══════════╪═══════════╪══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
              "│ count     ┆ 21613.0   ┆ 21613.0   ┆ 21613.0  ┆ … ┆ 21613.0   ┆ 21613.0   ┆ 21613.0   ┆ 21613.0   │\n",
              "│ null_coun ┆ 0.0       ┆ 0.0       ┆ 0.0      ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       │\n",
              "│ t         ┆           ┆           ┆          ┆   ┆           ┆           ┆           ┆           │\n",
              "│ mean      ┆ 4.5803e9  ┆ 540088.14 ┆ 3.370842 ┆ … ┆ 12768.455 ┆ 2014.3229 ┆ 6.574423  ┆ 15.688197 │\n",
              "│           ┆           ┆ 1767      ┆          ┆   ┆ 652       ┆ 54        ┆           ┆           │\n",
              "│ std       ┆ 2.8766e9  ┆ 367127.19 ┆ 0.930062 ┆ … ┆ 27304.179 ┆ 0.467616  ┆ 3.115308  ┆ 8.635063  │\n",
              "│           ┆           ┆ 6483      ┆          ┆   ┆ 631       ┆           ┆           ┆           │\n",
              "│ min       ┆ 1.000102e ┆ 75000.0   ┆ 0.0      ┆ … ┆ 651.0     ┆ 2014.0    ┆ 1.0       ┆ 1.0       │\n",
              "│           ┆ 6         ┆           ┆          ┆   ┆           ┆           ┆           ┆           │\n",
              "│ 25%       ┆ 2.1230e9  ┆ 321950.0  ┆ 3.0      ┆ … ┆ 5100.0    ┆ 2014.0    ┆ 4.0       ┆ 8.0       │\n",
              "│ 50%       ┆ 3.9049e9  ┆ 450000.0  ┆ 3.0      ┆ … ┆ 7620.0    ┆ 2014.0    ┆ 6.0       ┆ 16.0      │\n",
              "│ 75%       ┆ 7.3089e9  ┆ 645000.0  ┆ 4.0      ┆ … ┆ 10083.0   ┆ 2015.0    ┆ 9.0       ┆ 23.0      │\n",
              "│ max       ┆ 9.9000e9  ┆ 7.7e6     ┆ 33.0     ┆ … ┆ 871200.0  ┆ 2015.0    ┆ 12.0      ┆ 31.0      │\n",
              "└───────────┴───────────┴───────────┴──────────┴───┴───────────┴───────────┴───────────┴───────────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (9, 24)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>statistic</th><th>id</th><th>price</th><th>bedrooms</th><th>bathrooms</th><th>sqft_living</th><th>sqft_lot</th><th>floors</th><th>waterfront</th><th>view</th><th>condition</th><th>grade</th><th>sqft_above</th><th>sqft_basement</th><th>yr_built</th><th>yr_renovated</th><th>zipcode</th><th>lat</th><th>long</th><th>sqft_living15</th><th>sqft_lot15</th><th>date_year</th><th>date_month</th><th>date_day</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;count&quot;</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td><td>21613.0</td></tr><tr><td>&quot;null_count&quot;</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>&quot;mean&quot;</td><td>4.5803e9</td><td>540088.141767</td><td>3.370842</td><td>2.114757</td><td>2079.899736</td><td>15106.967566</td><td>1.494309</td><td>0.007542</td><td>0.234303</td><td>3.40943</td><td>7.656873</td><td>1788.390691</td><td>291.509045</td><td>1971.005136</td><td>84.402258</td><td>98077.939805</td><td>47.560053</td><td>-122.213896</td><td>1986.552492</td><td>12768.455652</td><td>2014.322954</td><td>6.574423</td><td>15.688197</td></tr><tr><td>&quot;std&quot;</td><td>2.8766e9</td><td>367127.196483</td><td>0.930062</td><td>0.770163</td><td>918.440897</td><td>41420.511515</td><td>0.539989</td><td>0.086517</td><td>0.766318</td><td>0.650743</td><td>1.175459</td><td>828.090978</td><td>442.575043</td><td>29.373411</td><td>401.67924</td><td>53.505026</td><td>0.138564</td><td>0.140828</td><td>685.391304</td><td>27304.179631</td><td>0.467616</td><td>3.115308</td><td>8.635063</td></tr><tr><td>&quot;min&quot;</td><td>1.000102e6</td><td>75000.0</td><td>0.0</td><td>0.0</td><td>290.0</td><td>520.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>1.0</td><td>290.0</td><td>0.0</td><td>1900.0</td><td>0.0</td><td>98001.0</td><td>47.1559</td><td>-122.519</td><td>399.0</td><td>651.0</td><td>2014.0</td><td>1.0</td><td>1.0</td></tr><tr><td>&quot;25%&quot;</td><td>2.1230e9</td><td>321950.0</td><td>3.0</td><td>1.75</td><td>1427.0</td><td>5040.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>3.0</td><td>7.0</td><td>1190.0</td><td>0.0</td><td>1951.0</td><td>0.0</td><td>98033.0</td><td>47.471</td><td>-122.328</td><td>1490.0</td><td>5100.0</td><td>2014.0</td><td>4.0</td><td>8.0</td></tr><tr><td>&quot;50%&quot;</td><td>3.9049e9</td><td>450000.0</td><td>3.0</td><td>2.25</td><td>1910.0</td><td>7618.0</td><td>1.5</td><td>0.0</td><td>0.0</td><td>3.0</td><td>7.0</td><td>1560.0</td><td>0.0</td><td>1975.0</td><td>0.0</td><td>98065.0</td><td>47.5718</td><td>-122.23</td><td>1840.0</td><td>7620.0</td><td>2014.0</td><td>6.0</td><td>16.0</td></tr><tr><td>&quot;75%&quot;</td><td>7.3089e9</td><td>645000.0</td><td>4.0</td><td>2.5</td><td>2550.0</td><td>10688.0</td><td>2.0</td><td>0.0</td><td>0.0</td><td>4.0</td><td>8.0</td><td>2210.0</td><td>560.0</td><td>1997.0</td><td>0.0</td><td>98118.0</td><td>47.678</td><td>-122.125</td><td>2360.0</td><td>10083.0</td><td>2015.0</td><td>9.0</td><td>23.0</td></tr><tr><td>&quot;max&quot;</td><td>9.9000e9</td><td>7.7e6</td><td>33.0</td><td>8.0</td><td>13540.0</td><td>1.651359e6</td><td>3.5</td><td>1.0</td><td>4.0</td><td>5.0</td><td>13.0</td><td>9410.0</td><td>4820.0</td><td>2015.0</td><td>2015.0</td><td>98199.0</td><td>47.7776</td><td>-121.315</td><td>6210.0</td><td>871200.0</td><td>2015.0</td><td>12.0</td><td>31.0</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "raw.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "c8N1UQ18ri7z",
        "outputId": "9505bddd-3b21-4f94-b893-cf74e9a321e8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (23, 23)\n",
              "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
              "│ id        ┆ price     ┆ bedrooms  ┆ bathrooms ┆ … ┆ sqft_lot1 ┆ date_year ┆ date_mont ┆ date_day │\n",
              "│ ---       ┆ ---       ┆ ---       ┆ ---       ┆   ┆ 5         ┆ ---       ┆ h         ┆ ---      │\n",
              "│ f64       ┆ f64       ┆ f64       ┆ f64       ┆   ┆ ---       ┆ f64       ┆ ---       ┆ f64      │\n",
              "│           ┆           ┆           ┆           ┆   ┆ f64       ┆           ┆ f64       ┆          │\n",
              "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
              "│ 1.0       ┆ -0.016762 ┆ 0.001286  ┆ 0.00516   ┆ … ┆ -0.138798 ┆ 0.010128  ┆ -0.011708 ┆ 0.001939 │\n",
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              "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 1        │\n",
              "│ 0.00516   ┆ 0.525138  ┆ 0.515884  ┆ 1.0       ┆ … ┆ 0.087175  ┆ -0.026596 ┆ 0.007392  ┆ -0.00530 │\n",
              "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 4        │\n",
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              "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 6        │\n",
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              "│ -0.002901 ┆ 0.585379  ┆ 0.391638  ┆ 0.568634  ┆ … ┆ 0.183192  ┆ -0.021734 ┆ 0.002449  ┆ -0.00853 │\n",
              "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 9        │\n",
              "│ -0.138798 ┆ 0.082447  ┆ 0.029244  ┆ 0.087175  ┆ … ┆ 1.0       ┆ -0.000085 ┆ 0.003546  ┆ -0.00287 │\n",
              "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 1        │\n",
              "│ 0.010128  ┆ 0.003576  ┆ -0.009838 ┆ -0.026596 ┆ … ┆ -0.000085 ┆ 1.0       ┆ -0.782389 ┆ -0.00610 │\n",
              "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 3        │\n",
              "│ -0.011708 ┆ -0.010081 ┆ -0.001533 ┆ 0.007392  ┆ … ┆ 0.003546  ┆ -0.782389 ┆ 1.0       ┆ -0.06066 │\n",
              "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 4        │\n",
              "│ 0.001939  ┆ -0.01467  ┆ -0.008031 ┆ -0.005304 ┆ … ┆ -0.002871 ┆ -0.006103 ┆ -0.060664 ┆ 1.0      │\n",
              "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
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            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "raw.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 791
        },
        "id": "ilQ8iTzXri7z",
        "outputId": "8cac985d-ea4f-45b7-f73c-fa8921f1e580"
      },
      "outputs": [
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              "#T_63033_row1_col9, #T_63033_row2_col5, #T_63033_row5_col2, #T_63033_row9_col1 {\n",
              "  background-color: #f0f4f6;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row1_col10, #T_63033_row3_col10, #T_63033_row10_col1, #T_63033_row10_col3 {\n",
              "  background-color: #3783bb;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_63033_row1_col11, #T_63033_row11_col1 {\n",
              "  background-color: #4291c2;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_63033_row1_col12, #T_63033_row12_col1, #T_63033_row13_col18, #T_63033_row18_col13 {\n",
              "  background-color: #a9d1e5;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row1_col13, #T_63033_row3_col14, #T_63033_row4_col16, #T_63033_row5_col13, #T_63033_row6_col16, #T_63033_row13_col1, #T_63033_row13_col5, #T_63033_row14_col3, #T_63033_row16_col4, #T_63033_row16_col6, #T_63033_row16_col18, #T_63033_row18_col16 {\n",
              "  background-color: #edf2f5;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row1_col14, #T_63033_row2_col17, #T_63033_row6_col17, #T_63033_row14_col1, #T_63033_row17_col2, #T_63033_row17_col6 {\n",
              "  background-color: #deebf2;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row1_col15, #T_63033_row8_col13, #T_63033_row11_col12, #T_63033_row12_col11, #T_63033_row13_col8, #T_63033_row15_col1 {\n",
              "  background-color: #f9f0eb;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row1_col18, #T_63033_row18_col1 {\n",
              "  background-color: #4997c5;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_63033_row1_col19, #T_63033_row2_col8, #T_63033_row7_col10, #T_63033_row7_col12, #T_63033_row8_col2, #T_63033_row8_col15, #T_63033_row10_col7, #T_63033_row12_col7, #T_63033_row15_col8, #T_63033_row19_col1 {\n",
              "  background-color: #e7f0f4;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row2_col3, #T_63033_row3_col2 {\n",
              "  background-color: #62a7ce;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_63033_row2_col4, #T_63033_row4_col2 {\n",
              "  background-color: #4c99c6;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_63033_row2_col6, #T_63033_row4_col5, #T_63033_row5_col4, #T_63033_row6_col2, #T_63033_row9_col12, #T_63033_row12_col9 {\n",
              "  background-color: #d5e7f1;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row2_col9, #T_63033_row2_col19, #T_63033_row3_col16, #T_63033_row6_col7, #T_63033_row6_col8, #T_63033_row7_col6, #T_63033_row7_col15, #T_63033_row7_col19, #T_63033_row8_col6, #T_63033_row9_col2, #T_63033_row14_col16, #T_63033_row15_col7, #T_63033_row16_col3, #T_63033_row16_col14, #T_63033_row19_col2, #T_63033_row19_col7 {\n",
              "  background-color: #f2f5f6;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row2_col10, #T_63033_row4_col6, #T_63033_row6_col4, #T_63033_row10_col2 {\n",
              "  background-color: #a0cce2;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row2_col11, #T_63033_row11_col2 {\n",
              "  background-color: #71b0d3;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_63033_row2_col12, #T_63033_row12_col2 {\n",
              "  background-color: #b1d5e7;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row2_col13, #T_63033_row13_col2 {\n",
              "  background-color: #dae9f2;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row2_col15, #T_63033_row15_col2 {\n",
              "  background-color: #fce2d2;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row3_col4, #T_63033_row4_col3, #T_63033_row4_col18, #T_63033_row10_col11, #T_63033_row11_col10, #T_63033_row18_col4 {\n",
              "  background-color: #2870b1;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_63033_row3_col6, #T_63033_row3_col13, #T_63033_row6_col3, #T_63033_row13_col3 {\n",
              "  background-color: #68abd0;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_63033_row3_col7, #T_63033_row7_col3, #T_63033_row14_col15, #T_63033_row15_col14 {\n",
              "  background-color: #eaf1f5;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row3_col8, #T_63033_row8_col3, #T_63033_row11_col19, #T_63033_row19_col11 {\n",
              "  background-color: #d2e6f0;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row3_col9, #T_63033_row9_col3 {\n",
              "  background-color: #fbe6da;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row3_col11, #T_63033_row11_col3 {\n",
              "  background-color: #3480b9;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_63033_row3_col12, #T_63033_row4_col8, #T_63033_row8_col4, #T_63033_row12_col3 {\n",
              "  background-color: #b6d7e8;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row3_col15, #T_63033_row15_col3 {\n",
              "  background-color: #fdd9c4;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row3_col17, #T_63033_row17_col3 {\n",
              "  background-color: #cae1ee;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row3_col18, #T_63033_row18_col3 {\n",
              "  background-color: #4f9bc7;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_63033_row4_col7, #T_63033_row7_col4, #T_63033_row8_col14, #T_63033_row14_col8 {\n",
              "  background-color: #e3edf3;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row4_col9, #T_63033_row6_col15, #T_63033_row9_col4, #T_63033_row9_col14, #T_63033_row14_col9, #T_63033_row15_col6, #T_63033_row21_col22, #T_63033_row22_col21 {\n",
              "  background-color: #f9efe9;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row4_col10, #T_63033_row10_col4 {\n",
              "  background-color: #276eb0;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_63033_row4_col11, #T_63033_row11_col4 {\n",
              "  background-color: #15508d;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_63033_row4_col12, #T_63033_row12_col4 {\n",
              "  background-color: #84bcd9;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row4_col13, #T_63033_row13_col4 {\n",
              "  background-color: #acd2e5;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row4_col14, #T_63033_row14_col4 {\n",
              "  background-color: #ecf2f5;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row4_col15, #T_63033_row15_col4 {\n",
              "  background-color: #fddbc7;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row4_col17, #T_63033_row17_col4 {\n",
              "  background-color: #c5dfec;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row4_col19, #T_63033_row5_col11, #T_63033_row11_col5, #T_63033_row18_col19, #T_63033_row19_col4, #T_63033_row19_col18 {\n",
              "  background-color: #d4e6f1;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row5_col8, #T_63033_row7_col11, #T_63033_row8_col5, #T_63033_row8_col19, #T_63033_row11_col7, #T_63033_row12_col14, #T_63033_row12_col15, #T_63033_row13_col19, #T_63033_row14_col12, #T_63033_row15_col12, #T_63033_row19_col8, #T_63033_row19_col13 {\n",
              "  background-color: #e9f0f4;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row5_col10, #T_63033_row10_col5, #T_63033_row10_col16, #T_63033_row12_col16, #T_63033_row16_col10, #T_63033_row16_col12 {\n",
              "  background-color: #e1edf3;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row5_col16, #T_63033_row8_col17, #T_63033_row16_col5, #T_63033_row17_col8 {\n",
              "  background-color: #f9ebe3;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row5_col17, #T_63033_row17_col5 {\n",
              "  background-color: #c7e0ed;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row5_col18, #T_63033_row18_col5 {\n",
              "  background-color: #dbeaf2;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row5_col19, #T_63033_row10_col18, #T_63033_row18_col10, #T_63033_row19_col5 {\n",
              "  background-color: #2f79b5;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_63033_row6_col9, #T_63033_row9_col6, #T_63033_row11_col15, #T_63033_row15_col11 {\n",
              "  background-color: #facab1;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row6_col10, #T_63033_row10_col6 {\n",
              "  background-color: #7bb6d6;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row6_col12, #T_63033_row12_col6 {\n",
              "  background-color: #fbceb7;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row6_col13, #T_63033_row13_col6 {\n",
              "  background-color: #6eaed2;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_63033_row6_col18, #T_63033_row8_col12, #T_63033_row8_col18, #T_63033_row12_col8, #T_63033_row18_col6, #T_63033_row18_col8 {\n",
              "  background-color: #b8d8e9;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row7_col8, #T_63033_row8_col7 {\n",
              "  background-color: #90c4dd;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row7_col17, #T_63033_row9_col20, #T_63033_row17_col7, #T_63033_row20_col9 {\n",
              "  background-color: #f8f1ed;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row8_col9, #T_63033_row9_col8 {\n",
              "  background-color: #eff3f5;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row8_col11, #T_63033_row10_col12, #T_63033_row11_col8, #T_63033_row12_col10 {\n",
              "  background-color: #d7e8f1;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row9_col10, #T_63033_row10_col9, #T_63033_row12_col17, #T_63033_row13_col16, #T_63033_row15_col19, #T_63033_row16_col13, #T_63033_row17_col12, #T_63033_row19_col15 {\n",
              "  background-color: #fbe3d4;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row9_col11, #T_63033_row11_col9 {\n",
              "  background-color: #fce0d0;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row9_col13, #T_63033_row13_col9 {\n",
              "  background-color: #f6af8e;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row9_col17, #T_63033_row17_col9 {\n",
              "  background-color: #fae8de;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row9_col18, #T_63033_row16_col19, #T_63033_row18_col9, #T_63033_row19_col16 {\n",
              "  background-color: #faeae1;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row10_col13, #T_63033_row13_col10 {\n",
              "  background-color: #7eb8d7;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row10_col15, #T_63033_row15_col10 {\n",
              "  background-color: #fdddcb;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row10_col17, #T_63033_row12_col18, #T_63033_row17_col10, #T_63033_row18_col12 {\n",
              "  background-color: #d1e5f0;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row10_col19, #T_63033_row19_col10 {\n",
              "  background-color: #e0ecf3;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row11_col13, #T_63033_row13_col11 {\n",
              "  background-color: #87beda;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row11_col17, #T_63033_row17_col11 {\n",
              "  background-color: #a2cde3;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row11_col18, #T_63033_row18_col11 {\n",
              "  background-color: #2c75b4;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_63033_row13_col14, #T_63033_row14_col13 {\n",
              "  background-color: #fcd5bf;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row13_col15, #T_63033_row15_col13 {\n",
              "  background-color: #f6b394;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row13_col17, #T_63033_row17_col13 {\n",
              "  background-color: #8dc2dc;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row14_col17, #T_63033_row17_col14 {\n",
              "  background-color: #f9eee7;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row15_col17, #T_63033_row17_col15 {\n",
              "  background-color: #db6b55;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_63033_row15_col18, #T_63033_row18_col15 {\n",
              "  background-color: #f9c6ac;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row17_col18, #T_63033_row18_col17 {\n",
              "  background-color: #a7d0e4;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_63033_row20_col21, #T_63033_row21_col20 {\n",
              "  background-color: #b41c2d;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_63033\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_63033_level0_col0\" class=\"col_heading level0 col0\" >id</th>\n",
              "      <th id=\"T_63033_level0_col1\" class=\"col_heading level0 col1\" >price</th>\n",
              "      <th id=\"T_63033_level0_col2\" class=\"col_heading level0 col2\" >bedrooms</th>\n",
              "      <th id=\"T_63033_level0_col3\" class=\"col_heading level0 col3\" >bathrooms</th>\n",
              "      <th id=\"T_63033_level0_col4\" class=\"col_heading level0 col4\" >sqft_living</th>\n",
              "      <th id=\"T_63033_level0_col5\" class=\"col_heading level0 col5\" >sqft_lot</th>\n",
              "      <th id=\"T_63033_level0_col6\" class=\"col_heading level0 col6\" >floors</th>\n",
              "      <th id=\"T_63033_level0_col7\" class=\"col_heading level0 col7\" >waterfront</th>\n",
              "      <th id=\"T_63033_level0_col8\" class=\"col_heading level0 col8\" >view</th>\n",
              "      <th id=\"T_63033_level0_col9\" class=\"col_heading level0 col9\" >condition</th>\n",
              "      <th id=\"T_63033_level0_col10\" class=\"col_heading level0 col10\" >grade</th>\n",
              "      <th id=\"T_63033_level0_col11\" class=\"col_heading level0 col11\" >sqft_above</th>\n",
              "      <th id=\"T_63033_level0_col12\" class=\"col_heading level0 col12\" >sqft_basement</th>\n",
              "      <th id=\"T_63033_level0_col13\" class=\"col_heading level0 col13\" >yr_built</th>\n",
              "      <th id=\"T_63033_level0_col14\" class=\"col_heading level0 col14\" >yr_renovated</th>\n",
              "      <th id=\"T_63033_level0_col15\" class=\"col_heading level0 col15\" >zipcode</th>\n",
              "      <th id=\"T_63033_level0_col16\" class=\"col_heading level0 col16\" >lat</th>\n",
              "      <th id=\"T_63033_level0_col17\" class=\"col_heading level0 col17\" >long</th>\n",
              "      <th id=\"T_63033_level0_col18\" class=\"col_heading level0 col18\" >sqft_living15</th>\n",
              "      <th id=\"T_63033_level0_col19\" class=\"col_heading level0 col19\" >sqft_lot15</th>\n",
              "      <th id=\"T_63033_level0_col20\" class=\"col_heading level0 col20\" >date_year</th>\n",
              "      <th id=\"T_63033_level0_col21\" class=\"col_heading level0 col21\" >date_month</th>\n",
              "      <th id=\"T_63033_level0_col22\" class=\"col_heading level0 col22\" >date_day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row0\" class=\"row_heading level0 row0\" >id</th>\n",
              "      <td id=\"T_63033_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
              "      <td id=\"T_63033_row0_col1\" class=\"data row0 col1\" >-0.016762</td>\n",
              "      <td id=\"T_63033_row0_col2\" class=\"data row0 col2\" >0.001286</td>\n",
              "      <td id=\"T_63033_row0_col3\" class=\"data row0 col3\" >0.005160</td>\n",
              "      <td id=\"T_63033_row0_col4\" class=\"data row0 col4\" >-0.012258</td>\n",
              "      <td id=\"T_63033_row0_col5\" class=\"data row0 col5\" >-0.132109</td>\n",
              "      <td id=\"T_63033_row0_col6\" class=\"data row0 col6\" >0.018525</td>\n",
              "      <td id=\"T_63033_row0_col7\" class=\"data row0 col7\" >-0.002721</td>\n",
              "      <td id=\"T_63033_row0_col8\" class=\"data row0 col8\" >0.011592</td>\n",
              "      <td id=\"T_63033_row0_col9\" class=\"data row0 col9\" >-0.023783</td>\n",
              "      <td id=\"T_63033_row0_col10\" class=\"data row0 col10\" >0.008130</td>\n",
              "      <td id=\"T_63033_row0_col11\" class=\"data row0 col11\" >-0.010842</td>\n",
              "      <td id=\"T_63033_row0_col12\" class=\"data row0 col12\" >-0.005151</td>\n",
              "      <td id=\"T_63033_row0_col13\" class=\"data row0 col13\" >0.021380</td>\n",
              "      <td id=\"T_63033_row0_col14\" class=\"data row0 col14\" >-0.016907</td>\n",
              "      <td id=\"T_63033_row0_col15\" class=\"data row0 col15\" >-0.008224</td>\n",
              "      <td id=\"T_63033_row0_col16\" class=\"data row0 col16\" >-0.001891</td>\n",
              "      <td id=\"T_63033_row0_col17\" class=\"data row0 col17\" >0.020799</td>\n",
              "      <td id=\"T_63033_row0_col18\" class=\"data row0 col18\" >-0.002901</td>\n",
              "      <td id=\"T_63033_row0_col19\" class=\"data row0 col19\" >-0.138798</td>\n",
              "      <td id=\"T_63033_row0_col20\" class=\"data row0 col20\" >0.010128</td>\n",
              "      <td id=\"T_63033_row0_col21\" class=\"data row0 col21\" >-0.011708</td>\n",
              "      <td id=\"T_63033_row0_col22\" class=\"data row0 col22\" >0.001939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row1\" class=\"row_heading level0 row1\" >price</th>\n",
              "      <td id=\"T_63033_row1_col0\" class=\"data row1 col0\" >-0.016762</td>\n",
              "      <td id=\"T_63033_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
              "      <td id=\"T_63033_row1_col2\" class=\"data row1 col2\" >0.308350</td>\n",
              "      <td id=\"T_63033_row1_col3\" class=\"data row1 col3\" >0.525138</td>\n",
              "      <td id=\"T_63033_row1_col4\" class=\"data row1 col4\" >0.702035</td>\n",
              "      <td id=\"T_63033_row1_col5\" class=\"data row1 col5\" >0.089661</td>\n",
              "      <td id=\"T_63033_row1_col6\" class=\"data row1 col6\" >0.256794</td>\n",
              "      <td id=\"T_63033_row1_col7\" class=\"data row1 col7\" >0.266369</td>\n",
              "      <td id=\"T_63033_row1_col8\" class=\"data row1 col8\" >0.397293</td>\n",
              "      <td id=\"T_63033_row1_col9\" class=\"data row1 col9\" >0.036362</td>\n",
              "      <td id=\"T_63033_row1_col10\" class=\"data row1 col10\" >0.667434</td>\n",
              "      <td id=\"T_63033_row1_col11\" class=\"data row1 col11\" >0.605567</td>\n",
              "      <td id=\"T_63033_row1_col12\" class=\"data row1 col12\" >0.323816</td>\n",
              "      <td id=\"T_63033_row1_col13\" class=\"data row1 col13\" >0.054012</td>\n",
              "      <td id=\"T_63033_row1_col14\" class=\"data row1 col14\" >0.126434</td>\n",
              "      <td id=\"T_63033_row1_col15\" class=\"data row1 col15\" >-0.053203</td>\n",
              "      <td id=\"T_63033_row1_col16\" class=\"data row1 col16\" >0.307003</td>\n",
              "      <td id=\"T_63033_row1_col17\" class=\"data row1 col17\" >0.021626</td>\n",
              "      <td id=\"T_63033_row1_col18\" class=\"data row1 col18\" >0.585379</td>\n",
              "      <td id=\"T_63033_row1_col19\" class=\"data row1 col19\" >0.082447</td>\n",
              "      <td id=\"T_63033_row1_col20\" class=\"data row1 col20\" >0.003576</td>\n",
              "      <td id=\"T_63033_row1_col21\" class=\"data row1 col21\" >-0.010081</td>\n",
              "      <td id=\"T_63033_row1_col22\" class=\"data row1 col22\" >-0.014670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row2\" class=\"row_heading level0 row2\" >bedrooms</th>\n",
              "      <td id=\"T_63033_row2_col0\" class=\"data row2 col0\" >0.001286</td>\n",
              "      <td id=\"T_63033_row2_col1\" class=\"data row2 col1\" >0.308350</td>\n",
              "      <td id=\"T_63033_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
              "      <td id=\"T_63033_row2_col3\" class=\"data row2 col3\" >0.515884</td>\n",
              "      <td id=\"T_63033_row2_col4\" class=\"data row2 col4\" >0.576671</td>\n",
              "      <td id=\"T_63033_row2_col5\" class=\"data row2 col5\" >0.031703</td>\n",
              "      <td id=\"T_63033_row2_col6\" class=\"data row2 col6\" >0.175429</td>\n",
              "      <td id=\"T_63033_row2_col7\" class=\"data row2 col7\" >-0.006582</td>\n",
              "      <td id=\"T_63033_row2_col8\" class=\"data row2 col8\" >0.079532</td>\n",
              "      <td id=\"T_63033_row2_col9\" class=\"data row2 col9\" >0.028472</td>\n",
              "      <td id=\"T_63033_row2_col10\" class=\"data row2 col10\" >0.356967</td>\n",
              "      <td id=\"T_63033_row2_col11\" class=\"data row2 col11\" >0.477600</td>\n",
              "      <td id=\"T_63033_row2_col12\" class=\"data row2 col12\" >0.303093</td>\n",
              "      <td id=\"T_63033_row2_col13\" class=\"data row2 col13\" >0.154178</td>\n",
              "      <td id=\"T_63033_row2_col14\" class=\"data row2 col14\" >0.018841</td>\n",
              "      <td id=\"T_63033_row2_col15\" class=\"data row2 col15\" >-0.152668</td>\n",
              "      <td id=\"T_63033_row2_col16\" class=\"data row2 col16\" >-0.008931</td>\n",
              "      <td id=\"T_63033_row2_col17\" class=\"data row2 col17\" >0.129473</td>\n",
              "      <td id=\"T_63033_row2_col18\" class=\"data row2 col18\" >0.391638</td>\n",
              "      <td id=\"T_63033_row2_col19\" class=\"data row2 col19\" >0.029244</td>\n",
              "      <td id=\"T_63033_row2_col20\" class=\"data row2 col20\" >-0.009838</td>\n",
              "      <td id=\"T_63033_row2_col21\" class=\"data row2 col21\" >-0.001533</td>\n",
              "      <td id=\"T_63033_row2_col22\" class=\"data row2 col22\" >-0.008031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row3\" class=\"row_heading level0 row3\" >bathrooms</th>\n",
              "      <td id=\"T_63033_row3_col0\" class=\"data row3 col0\" >0.005160</td>\n",
              "      <td id=\"T_63033_row3_col1\" class=\"data row3 col1\" >0.525138</td>\n",
              "      <td id=\"T_63033_row3_col2\" class=\"data row3 col2\" >0.515884</td>\n",
              "      <td id=\"T_63033_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
              "      <td id=\"T_63033_row3_col4\" class=\"data row3 col4\" >0.754665</td>\n",
              "      <td id=\"T_63033_row3_col5\" class=\"data row3 col5\" >0.087740</td>\n",
              "      <td id=\"T_63033_row3_col6\" class=\"data row3 col6\" >0.500653</td>\n",
              "      <td id=\"T_63033_row3_col7\" class=\"data row3 col7\" >0.063744</td>\n",
              "      <td id=\"T_63033_row3_col8\" class=\"data row3 col8\" >0.187737</td>\n",
              "      <td id=\"T_63033_row3_col9\" class=\"data row3 col9\" >-0.124982</td>\n",
              "      <td id=\"T_63033_row3_col10\" class=\"data row3 col10\" >0.664983</td>\n",
              "      <td id=\"T_63033_row3_col11\" class=\"data row3 col11\" >0.685342</td>\n",
              "      <td id=\"T_63033_row3_col12\" class=\"data row3 col12\" >0.283770</td>\n",
              "      <td id=\"T_63033_row3_col13\" class=\"data row3 col13\" >0.506019</td>\n",
              "      <td id=\"T_63033_row3_col14\" class=\"data row3 col14\" >0.050739</td>\n",
              "      <td id=\"T_63033_row3_col15\" class=\"data row3 col15\" >-0.203866</td>\n",
              "      <td id=\"T_63033_row3_col16\" class=\"data row3 col16\" >0.024573</td>\n",
              "      <td id=\"T_63033_row3_col17\" class=\"data row3 col17\" >0.223042</td>\n",
              "      <td id=\"T_63033_row3_col18\" class=\"data row3 col18\" >0.568634</td>\n",
              "      <td id=\"T_63033_row3_col19\" class=\"data row3 col19\" >0.087175</td>\n",
              "      <td id=\"T_63033_row3_col20\" class=\"data row3 col20\" >-0.026596</td>\n",
              "      <td id=\"T_63033_row3_col21\" class=\"data row3 col21\" >0.007392</td>\n",
              "      <td id=\"T_63033_row3_col22\" class=\"data row3 col22\" >-0.005304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row4\" class=\"row_heading level0 row4\" >sqft_living</th>\n",
              "      <td id=\"T_63033_row4_col0\" class=\"data row4 col0\" >-0.012258</td>\n",
              "      <td id=\"T_63033_row4_col1\" class=\"data row4 col1\" >0.702035</td>\n",
              "      <td id=\"T_63033_row4_col2\" class=\"data row4 col2\" >0.576671</td>\n",
              "      <td id=\"T_63033_row4_col3\" class=\"data row4 col3\" >0.754665</td>\n",
              "      <td id=\"T_63033_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
              "      <td id=\"T_63033_row4_col5\" class=\"data row4 col5\" >0.172826</td>\n",
              "      <td id=\"T_63033_row4_col6\" class=\"data row4 col6\" >0.353949</td>\n",
              "      <td id=\"T_63033_row4_col7\" class=\"data row4 col7\" >0.103818</td>\n",
              "      <td id=\"T_63033_row4_col8\" class=\"data row4 col8\" >0.284611</td>\n",
              "      <td id=\"T_63033_row4_col9\" class=\"data row4 col9\" >-0.058753</td>\n",
              "      <td id=\"T_63033_row4_col10\" class=\"data row4 col10\" >0.762704</td>\n",
              "      <td id=\"T_63033_row4_col11\" class=\"data row4 col11\" >0.876597</td>\n",
              "      <td id=\"T_63033_row4_col12\" class=\"data row4 col12\" >0.435043</td>\n",
              "      <td id=\"T_63033_row4_col13\" class=\"data row4 col13\" >0.318049</td>\n",
              "      <td id=\"T_63033_row4_col14\" class=\"data row4 col14\" >0.055363</td>\n",
              "      <td id=\"T_63033_row4_col15\" class=\"data row4 col15\" >-0.199430</td>\n",
              "      <td id=\"T_63033_row4_col16\" class=\"data row4 col16\" >0.052529</td>\n",
              "      <td id=\"T_63033_row4_col17\" class=\"data row4 col17\" >0.240223</td>\n",
              "      <td id=\"T_63033_row4_col18\" class=\"data row4 col18\" >0.756420</td>\n",
              "      <td id=\"T_63033_row4_col19\" class=\"data row4 col19\" >0.183286</td>\n",
              "      <td id=\"T_63033_row4_col20\" class=\"data row4 col20\" >-0.029038</td>\n",
              "      <td id=\"T_63033_row4_col21\" class=\"data row4 col21\" >0.011810</td>\n",
              "      <td id=\"T_63033_row4_col22\" class=\"data row4 col22\" >-0.007246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row5\" class=\"row_heading level0 row5\" >sqft_lot</th>\n",
              "      <td id=\"T_63033_row5_col0\" class=\"data row5 col0\" >-0.132109</td>\n",
              "      <td id=\"T_63033_row5_col1\" class=\"data row5 col1\" >0.089661</td>\n",
              "      <td id=\"T_63033_row5_col2\" class=\"data row5 col2\" >0.031703</td>\n",
              "      <td id=\"T_63033_row5_col3\" class=\"data row5 col3\" >0.087740</td>\n",
              "      <td id=\"T_63033_row5_col4\" class=\"data row5 col4\" >0.172826</td>\n",
              "      <td id=\"T_63033_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
              "      <td id=\"T_63033_row5_col6\" class=\"data row5 col6\" >-0.005201</td>\n",
              "      <td id=\"T_63033_row5_col7\" class=\"data row5 col7\" >0.021604</td>\n",
              "      <td id=\"T_63033_row5_col8\" class=\"data row5 col8\" >0.074710</td>\n",
              "      <td id=\"T_63033_row5_col9\" class=\"data row5 col9\" >-0.008958</td>\n",
              "      <td id=\"T_63033_row5_col10\" class=\"data row5 col10\" >0.113621</td>\n",
              "      <td id=\"T_63033_row5_col11\" class=\"data row5 col11\" >0.183512</td>\n",
              "      <td id=\"T_63033_row5_col12\" class=\"data row5 col12\" >0.015286</td>\n",
              "      <td id=\"T_63033_row5_col13\" class=\"data row5 col13\" >0.053080</td>\n",
              "      <td id=\"T_63033_row5_col14\" class=\"data row5 col14\" >0.007644</td>\n",
              "      <td id=\"T_63033_row5_col15\" class=\"data row5 col15\" >-0.129574</td>\n",
              "      <td id=\"T_63033_row5_col16\" class=\"data row5 col16\" >-0.085683</td>\n",
              "      <td id=\"T_63033_row5_col17\" class=\"data row5 col17\" >0.229521</td>\n",
              "      <td id=\"T_63033_row5_col18\" class=\"data row5 col18\" >0.144608</td>\n",
              "      <td id=\"T_63033_row5_col19\" class=\"data row5 col19\" >0.718557</td>\n",
              "      <td id=\"T_63033_row5_col20\" class=\"data row5 col20\" >0.005468</td>\n",
              "      <td id=\"T_63033_row5_col21\" class=\"data row5 col21\" >-0.002369</td>\n",
              "      <td id=\"T_63033_row5_col22\" class=\"data row5 col22\" >0.000634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row6\" class=\"row_heading level0 row6\" >floors</th>\n",
              "      <td id=\"T_63033_row6_col0\" class=\"data row6 col0\" >0.018525</td>\n",
              "      <td id=\"T_63033_row6_col1\" class=\"data row6 col1\" >0.256794</td>\n",
              "      <td id=\"T_63033_row6_col2\" class=\"data row6 col2\" >0.175429</td>\n",
              "      <td id=\"T_63033_row6_col3\" class=\"data row6 col3\" >0.500653</td>\n",
              "      <td id=\"T_63033_row6_col4\" class=\"data row6 col4\" >0.353949</td>\n",
              "      <td id=\"T_63033_row6_col5\" class=\"data row6 col5\" >-0.005201</td>\n",
              "      <td id=\"T_63033_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
              "      <td id=\"T_63033_row6_col7\" class=\"data row6 col7\" >0.023698</td>\n",
              "      <td id=\"T_63033_row6_col8\" class=\"data row6 col8\" >0.029444</td>\n",
              "      <td id=\"T_63033_row6_col9\" class=\"data row6 col9\" >-0.263768</td>\n",
              "      <td id=\"T_63033_row6_col10\" class=\"data row6 col10\" >0.458183</td>\n",
              "      <td id=\"T_63033_row6_col11\" class=\"data row6 col11\" >0.523885</td>\n",
              "      <td id=\"T_63033_row6_col12\" class=\"data row6 col12\" >-0.245705</td>\n",
              "      <td id=\"T_63033_row6_col13\" class=\"data row6 col13\" >0.489319</td>\n",
              "      <td id=\"T_63033_row6_col14\" class=\"data row6 col14\" >0.006338</td>\n",
              "      <td id=\"T_63033_row6_col15\" class=\"data row6 col15\" >-0.059121</td>\n",
              "      <td id=\"T_63033_row6_col16\" class=\"data row6 col16\" >0.049614</td>\n",
              "      <td id=\"T_63033_row6_col17\" class=\"data row6 col17\" >0.125419</td>\n",
              "      <td id=\"T_63033_row6_col18\" class=\"data row6 col18\" >0.279885</td>\n",
              "      <td id=\"T_63033_row6_col19\" class=\"data row6 col19\" >-0.011269</td>\n",
              "      <td id=\"T_63033_row6_col20\" class=\"data row6 col20\" >-0.022315</td>\n",
              "      <td id=\"T_63033_row6_col21\" class=\"data row6 col21\" >0.014005</td>\n",
              "      <td id=\"T_63033_row6_col22\" class=\"data row6 col22\" >-0.006662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row7\" class=\"row_heading level0 row7\" >waterfront</th>\n",
              "      <td id=\"T_63033_row7_col0\" class=\"data row7 col0\" >-0.002721</td>\n",
              "      <td id=\"T_63033_row7_col1\" class=\"data row7 col1\" >0.266369</td>\n",
              "      <td id=\"T_63033_row7_col2\" class=\"data row7 col2\" >-0.006582</td>\n",
              "      <td id=\"T_63033_row7_col3\" class=\"data row7 col3\" >0.063744</td>\n",
              "      <td id=\"T_63033_row7_col4\" class=\"data row7 col4\" >0.103818</td>\n",
              "      <td id=\"T_63033_row7_col5\" class=\"data row7 col5\" >0.021604</td>\n",
              "      <td id=\"T_63033_row7_col6\" class=\"data row7 col6\" >0.023698</td>\n",
              "      <td id=\"T_63033_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
              "      <td id=\"T_63033_row7_col8\" class=\"data row7 col8\" >0.401857</td>\n",
              "      <td id=\"T_63033_row7_col9\" class=\"data row7 col9\" >0.016653</td>\n",
              "      <td id=\"T_63033_row7_col10\" class=\"data row7 col10\" >0.082775</td>\n",
              "      <td id=\"T_63033_row7_col11\" class=\"data row7 col11\" >0.072075</td>\n",
              "      <td id=\"T_63033_row7_col12\" class=\"data row7 col12\" >0.080588</td>\n",
              "      <td id=\"T_63033_row7_col13\" class=\"data row7 col13\" >-0.026161</td>\n",
              "      <td id=\"T_63033_row7_col14\" class=\"data row7 col14\" >0.092885</td>\n",
              "      <td id=\"T_63033_row7_col15\" class=\"data row7 col15\" >0.030285</td>\n",
              "      <td id=\"T_63033_row7_col16\" class=\"data row7 col16\" >-0.014274</td>\n",
              "      <td id=\"T_63033_row7_col17\" class=\"data row7 col17\" >-0.041910</td>\n",
              "      <td id=\"T_63033_row7_col18\" class=\"data row7 col18\" >0.086463</td>\n",
              "      <td id=\"T_63033_row7_col19\" class=\"data row7 col19\" >0.030703</td>\n",
              "      <td id=\"T_63033_row7_col20\" class=\"data row7 col20\" >-0.004165</td>\n",
              "      <td id=\"T_63033_row7_col21\" class=\"data row7 col21\" >0.008132</td>\n",
              "      <td id=\"T_63033_row7_col22\" class=\"data row7 col22\" >0.010518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row8\" class=\"row_heading level0 row8\" >view</th>\n",
              "      <td id=\"T_63033_row8_col0\" class=\"data row8 col0\" >0.011592</td>\n",
              "      <td id=\"T_63033_row8_col1\" class=\"data row8 col1\" >0.397293</td>\n",
              "      <td id=\"T_63033_row8_col2\" class=\"data row8 col2\" >0.079532</td>\n",
              "      <td id=\"T_63033_row8_col3\" class=\"data row8 col3\" >0.187737</td>\n",
              "      <td id=\"T_63033_row8_col4\" class=\"data row8 col4\" >0.284611</td>\n",
              "      <td id=\"T_63033_row8_col5\" class=\"data row8 col5\" >0.074710</td>\n",
              "      <td id=\"T_63033_row8_col6\" class=\"data row8 col6\" >0.029444</td>\n",
              "      <td id=\"T_63033_row8_col7\" class=\"data row8 col7\" >0.401857</td>\n",
              "      <td id=\"T_63033_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
              "      <td id=\"T_63033_row8_col9\" class=\"data row8 col9\" >0.045990</td>\n",
              "      <td id=\"T_63033_row8_col10\" class=\"data row8 col10\" >0.251321</td>\n",
              "      <td id=\"T_63033_row8_col11\" class=\"data row8 col11\" >0.167649</td>\n",
              "      <td id=\"T_63033_row8_col12\" class=\"data row8 col12\" >0.276947</td>\n",
              "      <td id=\"T_63033_row8_col13\" class=\"data row8 col13\" >-0.053440</td>\n",
              "      <td id=\"T_63033_row8_col14\" class=\"data row8 col14\" >0.103917</td>\n",
              "      <td id=\"T_63033_row8_col15\" class=\"data row8 col15\" >0.084827</td>\n",
              "      <td id=\"T_63033_row8_col16\" class=\"data row8 col16\" >0.006157</td>\n",
              "      <td id=\"T_63033_row8_col17\" class=\"data row8 col17\" >-0.078400</td>\n",
              "      <td id=\"T_63033_row8_col18\" class=\"data row8 col18\" >0.280439</td>\n",
              "      <td id=\"T_63033_row8_col19\" class=\"data row8 col19\" >0.072575</td>\n",
              "      <td id=\"T_63033_row8_col20\" class=\"data row8 col20\" >0.001364</td>\n",
              "      <td id=\"T_63033_row8_col21\" class=\"data row8 col21\" >-0.005638</td>\n",
              "      <td id=\"T_63033_row8_col22\" class=\"data row8 col22\" >0.011174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row9\" class=\"row_heading level0 row9\" >condition</th>\n",
              "      <td id=\"T_63033_row9_col0\" class=\"data row9 col0\" >-0.023783</td>\n",
              "      <td id=\"T_63033_row9_col1\" class=\"data row9 col1\" >0.036362</td>\n",
              "      <td id=\"T_63033_row9_col2\" class=\"data row9 col2\" >0.028472</td>\n",
              "      <td id=\"T_63033_row9_col3\" class=\"data row9 col3\" >-0.124982</td>\n",
              "      <td id=\"T_63033_row9_col4\" class=\"data row9 col4\" >-0.058753</td>\n",
              "      <td id=\"T_63033_row9_col5\" class=\"data row9 col5\" >-0.008958</td>\n",
              "      <td id=\"T_63033_row9_col6\" class=\"data row9 col6\" >-0.263768</td>\n",
              "      <td id=\"T_63033_row9_col7\" class=\"data row9 col7\" >0.016653</td>\n",
              "      <td id=\"T_63033_row9_col8\" class=\"data row9 col8\" >0.045990</td>\n",
              "      <td id=\"T_63033_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
              "      <td id=\"T_63033_row9_col10\" class=\"data row9 col10\" >-0.144674</td>\n",
              "      <td id=\"T_63033_row9_col11\" class=\"data row9 col11\" >-0.158214</td>\n",
              "      <td id=\"T_63033_row9_col12\" class=\"data row9 col12\" >0.174105</td>\n",
              "      <td id=\"T_63033_row9_col13\" class=\"data row9 col13\" >-0.361417</td>\n",
              "      <td id=\"T_63033_row9_col14\" class=\"data row9 col14\" >-0.060618</td>\n",
              "      <td id=\"T_63033_row9_col15\" class=\"data row9 col15\" >0.003026</td>\n",
              "      <td id=\"T_63033_row9_col16\" class=\"data row9 col16\" >-0.014941</td>\n",
              "      <td id=\"T_63033_row9_col17\" class=\"data row9 col17\" >-0.106500</td>\n",
              "      <td id=\"T_63033_row9_col18\" class=\"data row9 col18\" >-0.092824</td>\n",
              "      <td id=\"T_63033_row9_col19\" class=\"data row9 col19\" >-0.003406</td>\n",
              "      <td id=\"T_63033_row9_col20\" class=\"data row9 col20\" >-0.045589</td>\n",
              "      <td id=\"T_63033_row9_col21\" class=\"data row9 col21\" >0.021978</td>\n",
              "      <td id=\"T_63033_row9_col22\" class=\"data row9 col22\" >-0.004643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row10\" class=\"row_heading level0 row10\" >grade</th>\n",
              "      <td id=\"T_63033_row10_col0\" class=\"data row10 col0\" >0.008130</td>\n",
              "      <td id=\"T_63033_row10_col1\" class=\"data row10 col1\" >0.667434</td>\n",
              "      <td id=\"T_63033_row10_col2\" class=\"data row10 col2\" >0.356967</td>\n",
              "      <td id=\"T_63033_row10_col3\" class=\"data row10 col3\" >0.664983</td>\n",
              "      <td id=\"T_63033_row10_col4\" class=\"data row10 col4\" >0.762704</td>\n",
              "      <td id=\"T_63033_row10_col5\" class=\"data row10 col5\" >0.113621</td>\n",
              "      <td id=\"T_63033_row10_col6\" class=\"data row10 col6\" >0.458183</td>\n",
              "      <td id=\"T_63033_row10_col7\" class=\"data row10 col7\" >0.082775</td>\n",
              "      <td id=\"T_63033_row10_col8\" class=\"data row10 col8\" >0.251321</td>\n",
              "      <td id=\"T_63033_row10_col9\" class=\"data row10 col9\" >-0.144674</td>\n",
              "      <td id=\"T_63033_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
              "      <td id=\"T_63033_row10_col11\" class=\"data row10 col11\" >0.755923</td>\n",
              "      <td id=\"T_63033_row10_col12\" class=\"data row10 col12\" >0.168392</td>\n",
              "      <td id=\"T_63033_row10_col13\" class=\"data row10 col13\" >0.446963</td>\n",
              "      <td id=\"T_63033_row10_col14\" class=\"data row10 col14\" >0.014414</td>\n",
              "      <td id=\"T_63033_row10_col15\" class=\"data row10 col15\" >-0.184862</td>\n",
              "      <td id=\"T_63033_row10_col16\" class=\"data row10 col16\" >0.114084</td>\n",
              "      <td id=\"T_63033_row10_col17\" class=\"data row10 col17\" >0.198372</td>\n",
              "      <td id=\"T_63033_row10_col18\" class=\"data row10 col18\" >0.713202</td>\n",
              "      <td id=\"T_63033_row10_col19\" class=\"data row10 col19\" >0.119248</td>\n",
              "      <td id=\"T_63033_row10_col20\" class=\"data row10 col20\" >-0.030387</td>\n",
              "      <td id=\"T_63033_row10_col21\" class=\"data row10 col21\" >0.008376</td>\n",
              "      <td id=\"T_63033_row10_col22\" class=\"data row10 col22\" >-0.012483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row11\" class=\"row_heading level0 row11\" >sqft_above</th>\n",
              "      <td id=\"T_63033_row11_col0\" class=\"data row11 col0\" >-0.010842</td>\n",
              "      <td id=\"T_63033_row11_col1\" class=\"data row11 col1\" >0.605567</td>\n",
              "      <td id=\"T_63033_row11_col2\" class=\"data row11 col2\" >0.477600</td>\n",
              "      <td id=\"T_63033_row11_col3\" class=\"data row11 col3\" >0.685342</td>\n",
              "      <td id=\"T_63033_row11_col4\" class=\"data row11 col4\" >0.876597</td>\n",
              "      <td id=\"T_63033_row11_col5\" class=\"data row11 col5\" >0.183512</td>\n",
              "      <td id=\"T_63033_row11_col6\" class=\"data row11 col6\" >0.523885</td>\n",
              "      <td id=\"T_63033_row11_col7\" class=\"data row11 col7\" >0.072075</td>\n",
              "      <td id=\"T_63033_row11_col8\" class=\"data row11 col8\" >0.167649</td>\n",
              "      <td id=\"T_63033_row11_col9\" class=\"data row11 col9\" >-0.158214</td>\n",
              "      <td id=\"T_63033_row11_col10\" class=\"data row11 col10\" >0.755923</td>\n",
              "      <td id=\"T_63033_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
              "      <td id=\"T_63033_row11_col12\" class=\"data row11 col12\" >-0.051943</td>\n",
              "      <td id=\"T_63033_row11_col13\" class=\"data row11 col13\" >0.423898</td>\n",
              "      <td id=\"T_63033_row11_col14\" class=\"data row11 col14\" >0.023285</td>\n",
              "      <td id=\"T_63033_row11_col15\" class=\"data row11 col15\" >-0.261190</td>\n",
              "      <td id=\"T_63033_row11_col16\" class=\"data row11 col16\" >-0.000816</td>\n",
              "      <td id=\"T_63033_row11_col17\" class=\"data row11 col17\" >0.343803</td>\n",
              "      <td id=\"T_63033_row11_col18\" class=\"data row11 col18\" >0.731870</td>\n",
              "      <td id=\"T_63033_row11_col19\" class=\"data row11 col19\" >0.194050</td>\n",
              "      <td id=\"T_63033_row11_col20\" class=\"data row11 col20\" >-0.023823</td>\n",
              "      <td id=\"T_63033_row11_col21\" class=\"data row11 col21\" >0.009872</td>\n",
              "      <td id=\"T_63033_row11_col22\" class=\"data row11 col22\" >-0.002475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row12\" class=\"row_heading level0 row12\" >sqft_basement</th>\n",
              "      <td id=\"T_63033_row12_col0\" class=\"data row12 col0\" >-0.005151</td>\n",
              "      <td id=\"T_63033_row12_col1\" class=\"data row12 col1\" >0.323816</td>\n",
              "      <td id=\"T_63033_row12_col2\" class=\"data row12 col2\" >0.303093</td>\n",
              "      <td id=\"T_63033_row12_col3\" class=\"data row12 col3\" >0.283770</td>\n",
              "      <td id=\"T_63033_row12_col4\" class=\"data row12 col4\" >0.435043</td>\n",
              "      <td id=\"T_63033_row12_col5\" class=\"data row12 col5\" >0.015286</td>\n",
              "      <td id=\"T_63033_row12_col6\" class=\"data row12 col6\" >-0.245705</td>\n",
              "      <td id=\"T_63033_row12_col7\" class=\"data row12 col7\" >0.080588</td>\n",
              "      <td id=\"T_63033_row12_col8\" class=\"data row12 col8\" >0.276947</td>\n",
              "      <td id=\"T_63033_row12_col9\" class=\"data row12 col9\" >0.174105</td>\n",
              "      <td id=\"T_63033_row12_col10\" class=\"data row12 col10\" >0.168392</td>\n",
              "      <td id=\"T_63033_row12_col11\" class=\"data row12 col11\" >-0.051943</td>\n",
              "      <td id=\"T_63033_row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
              "      <td id=\"T_63033_row12_col13\" class=\"data row12 col13\" >-0.133124</td>\n",
              "      <td id=\"T_63033_row12_col14\" class=\"data row12 col14\" >0.071323</td>\n",
              "      <td id=\"T_63033_row12_col15\" class=\"data row12 col15\" >0.074845</td>\n",
              "      <td id=\"T_63033_row12_col16\" class=\"data row12 col16\" >0.110538</td>\n",
              "      <td id=\"T_63033_row12_col17\" class=\"data row12 col17\" >-0.144765</td>\n",
              "      <td id=\"T_63033_row12_col18\" class=\"data row12 col18\" >0.200355</td>\n",
              "      <td id=\"T_63033_row12_col19\" class=\"data row12 col19\" >0.017276</td>\n",
              "      <td id=\"T_63033_row12_col20\" class=\"data row12 col20\" >-0.015687</td>\n",
              "      <td id=\"T_63033_row12_col21\" class=\"data row12 col21\" >0.006035</td>\n",
              "      <td id=\"T_63033_row12_col22\" class=\"data row12 col22\" >-0.010405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row13\" class=\"row_heading level0 row13\" >yr_built</th>\n",
              "      <td id=\"T_63033_row13_col0\" class=\"data row13 col0\" >0.021380</td>\n",
              "      <td id=\"T_63033_row13_col1\" class=\"data row13 col1\" >0.054012</td>\n",
              "      <td id=\"T_63033_row13_col2\" class=\"data row13 col2\" >0.154178</td>\n",
              "      <td id=\"T_63033_row13_col3\" class=\"data row13 col3\" >0.506019</td>\n",
              "      <td id=\"T_63033_row13_col4\" class=\"data row13 col4\" >0.318049</td>\n",
              "      <td id=\"T_63033_row13_col5\" class=\"data row13 col5\" >0.053080</td>\n",
              "      <td id=\"T_63033_row13_col6\" class=\"data row13 col6\" >0.489319</td>\n",
              "      <td id=\"T_63033_row13_col7\" class=\"data row13 col7\" >-0.026161</td>\n",
              "      <td id=\"T_63033_row13_col8\" class=\"data row13 col8\" >-0.053440</td>\n",
              "      <td id=\"T_63033_row13_col9\" class=\"data row13 col9\" >-0.361417</td>\n",
              "      <td id=\"T_63033_row13_col10\" class=\"data row13 col10\" >0.446963</td>\n",
              "      <td id=\"T_63033_row13_col11\" class=\"data row13 col11\" >0.423898</td>\n",
              "      <td id=\"T_63033_row13_col12\" class=\"data row13 col12\" >-0.133124</td>\n",
              "      <td id=\"T_63033_row13_col13\" class=\"data row13 col13\" >1.000000</td>\n",
              "      <td id=\"T_63033_row13_col14\" class=\"data row13 col14\" >-0.224874</td>\n",
              "      <td id=\"T_63033_row13_col15\" class=\"data row13 col15\" >-0.346869</td>\n",
              "      <td id=\"T_63033_row13_col16\" class=\"data row13 col16\" >-0.148122</td>\n",
              "      <td id=\"T_63033_row13_col17\" class=\"data row13 col17\" >0.409356</td>\n",
              "      <td id=\"T_63033_row13_col18\" class=\"data row13 col18\" >0.326229</td>\n",
              "      <td id=\"T_63033_row13_col19\" class=\"data row13 col19\" >0.070958</td>\n",
              "      <td id=\"T_63033_row13_col20\" class=\"data row13 col20\" >0.003507</td>\n",
              "      <td id=\"T_63033_row13_col21\" class=\"data row13 col21\" >-0.006226</td>\n",
              "      <td id=\"T_63033_row13_col22\" class=\"data row13 col22\" >-0.005766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row14\" class=\"row_heading level0 row14\" >yr_renovated</th>\n",
              "      <td id=\"T_63033_row14_col0\" class=\"data row14 col0\" >-0.016907</td>\n",
              "      <td id=\"T_63033_row14_col1\" class=\"data row14 col1\" >0.126434</td>\n",
              "      <td id=\"T_63033_row14_col2\" class=\"data row14 col2\" >0.018841</td>\n",
              "      <td id=\"T_63033_row14_col3\" class=\"data row14 col3\" >0.050739</td>\n",
              "      <td id=\"T_63033_row14_col4\" class=\"data row14 col4\" >0.055363</td>\n",
              "      <td id=\"T_63033_row14_col5\" class=\"data row14 col5\" >0.007644</td>\n",
              "      <td id=\"T_63033_row14_col6\" class=\"data row14 col6\" >0.006338</td>\n",
              "      <td id=\"T_63033_row14_col7\" class=\"data row14 col7\" >0.092885</td>\n",
              "      <td id=\"T_63033_row14_col8\" class=\"data row14 col8\" >0.103917</td>\n",
              "      <td id=\"T_63033_row14_col9\" class=\"data row14 col9\" >-0.060618</td>\n",
              "      <td id=\"T_63033_row14_col10\" class=\"data row14 col10\" >0.014414</td>\n",
              "      <td id=\"T_63033_row14_col11\" class=\"data row14 col11\" >0.023285</td>\n",
              "      <td id=\"T_63033_row14_col12\" class=\"data row14 col12\" >0.071323</td>\n",
              "      <td id=\"T_63033_row14_col13\" class=\"data row14 col13\" >-0.224874</td>\n",
              "      <td id=\"T_63033_row14_col14\" class=\"data row14 col14\" >1.000000</td>\n",
              "      <td id=\"T_63033_row14_col15\" class=\"data row14 col15\" >0.064357</td>\n",
              "      <td id=\"T_63033_row14_col16\" class=\"data row14 col16\" >0.029398</td>\n",
              "      <td id=\"T_63033_row14_col17\" class=\"data row14 col17\" >-0.068372</td>\n",
              "      <td id=\"T_63033_row14_col18\" class=\"data row14 col18\" >-0.002673</td>\n",
              "      <td id=\"T_63033_row14_col19\" class=\"data row14 col19\" >0.007854</td>\n",
              "      <td id=\"T_63033_row14_col20\" class=\"data row14 col20\" >-0.023707</td>\n",
              "      <td id=\"T_63033_row14_col21\" class=\"data row14 col21\" >0.012827</td>\n",
              "      <td id=\"T_63033_row14_col22\" class=\"data row14 col22\" >0.008127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row15\" class=\"row_heading level0 row15\" >zipcode</th>\n",
              "      <td id=\"T_63033_row15_col0\" class=\"data row15 col0\" >-0.008224</td>\n",
              "      <td id=\"T_63033_row15_col1\" class=\"data row15 col1\" >-0.053203</td>\n",
              "      <td id=\"T_63033_row15_col2\" class=\"data row15 col2\" >-0.152668</td>\n",
              "      <td id=\"T_63033_row15_col3\" class=\"data row15 col3\" >-0.203866</td>\n",
              "      <td id=\"T_63033_row15_col4\" class=\"data row15 col4\" >-0.199430</td>\n",
              "      <td id=\"T_63033_row15_col5\" class=\"data row15 col5\" >-0.129574</td>\n",
              "      <td id=\"T_63033_row15_col6\" class=\"data row15 col6\" >-0.059121</td>\n",
              "      <td id=\"T_63033_row15_col7\" class=\"data row15 col7\" >0.030285</td>\n",
              "      <td id=\"T_63033_row15_col8\" class=\"data row15 col8\" >0.084827</td>\n",
              "      <td id=\"T_63033_row15_col9\" class=\"data row15 col9\" >0.003026</td>\n",
              "      <td id=\"T_63033_row15_col10\" class=\"data row15 col10\" >-0.184862</td>\n",
              "      <td id=\"T_63033_row15_col11\" class=\"data row15 col11\" >-0.261190</td>\n",
              "      <td id=\"T_63033_row15_col12\" class=\"data row15 col12\" >0.074845</td>\n",
              "      <td id=\"T_63033_row15_col13\" class=\"data row15 col13\" >-0.346869</td>\n",
              "      <td id=\"T_63033_row15_col14\" class=\"data row15 col14\" >0.064357</td>\n",
              "      <td id=\"T_63033_row15_col15\" class=\"data row15 col15\" >1.000000</td>\n",
              "      <td id=\"T_63033_row15_col16\" class=\"data row15 col16\" >0.267048</td>\n",
              "      <td id=\"T_63033_row15_col17\" class=\"data row15 col17\" >-0.564072</td>\n",
              "      <td id=\"T_63033_row15_col18\" class=\"data row15 col18\" >-0.279033</td>\n",
              "      <td id=\"T_63033_row15_col19\" class=\"data row15 col19\" >-0.147221</td>\n",
              "      <td id=\"T_63033_row15_col20\" class=\"data row15 col20\" >0.001189</td>\n",
              "      <td id=\"T_63033_row15_col21\" class=\"data row15 col21\" >-0.000171</td>\n",
              "      <td id=\"T_63033_row15_col22\" class=\"data row15 col22\" >-0.003270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row16\" class=\"row_heading level0 row16\" >lat</th>\n",
              "      <td id=\"T_63033_row16_col0\" class=\"data row16 col0\" >-0.001891</td>\n",
              "      <td id=\"T_63033_row16_col1\" class=\"data row16 col1\" >0.307003</td>\n",
              "      <td id=\"T_63033_row16_col2\" class=\"data row16 col2\" >-0.008931</td>\n",
              "      <td id=\"T_63033_row16_col3\" class=\"data row16 col3\" >0.024573</td>\n",
              "      <td id=\"T_63033_row16_col4\" class=\"data row16 col4\" >0.052529</td>\n",
              "      <td id=\"T_63033_row16_col5\" class=\"data row16 col5\" >-0.085683</td>\n",
              "      <td id=\"T_63033_row16_col6\" class=\"data row16 col6\" >0.049614</td>\n",
              "      <td id=\"T_63033_row16_col7\" class=\"data row16 col7\" >-0.014274</td>\n",
              "      <td id=\"T_63033_row16_col8\" class=\"data row16 col8\" >0.006157</td>\n",
              "      <td id=\"T_63033_row16_col9\" class=\"data row16 col9\" >-0.014941</td>\n",
              "      <td id=\"T_63033_row16_col10\" class=\"data row16 col10\" >0.114084</td>\n",
              "      <td id=\"T_63033_row16_col11\" class=\"data row16 col11\" >-0.000816</td>\n",
              "      <td id=\"T_63033_row16_col12\" class=\"data row16 col12\" >0.110538</td>\n",
              "      <td id=\"T_63033_row16_col13\" class=\"data row16 col13\" >-0.148122</td>\n",
              "      <td id=\"T_63033_row16_col14\" class=\"data row16 col14\" >0.029398</td>\n",
              "      <td id=\"T_63033_row16_col15\" class=\"data row16 col15\" >0.267048</td>\n",
              "      <td id=\"T_63033_row16_col16\" class=\"data row16 col16\" >1.000000</td>\n",
              "      <td id=\"T_63033_row16_col17\" class=\"data row16 col17\" >-0.135512</td>\n",
              "      <td id=\"T_63033_row16_col18\" class=\"data row16 col18\" >0.048858</td>\n",
              "      <td id=\"T_63033_row16_col19\" class=\"data row16 col19\" >-0.086419</td>\n",
              "      <td id=\"T_63033_row16_col20\" class=\"data row16 col20\" >-0.029212</td>\n",
              "      <td id=\"T_63033_row16_col21\" class=\"data row16 col21\" >0.014961</td>\n",
              "      <td id=\"T_63033_row16_col22\" class=\"data row16 col22\" >-0.015655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row17\" class=\"row_heading level0 row17\" >long</th>\n",
              "      <td id=\"T_63033_row17_col0\" class=\"data row17 col0\" >0.020799</td>\n",
              "      <td id=\"T_63033_row17_col1\" class=\"data row17 col1\" >0.021626</td>\n",
              "      <td id=\"T_63033_row17_col2\" class=\"data row17 col2\" >0.129473</td>\n",
              "      <td id=\"T_63033_row17_col3\" class=\"data row17 col3\" >0.223042</td>\n",
              "      <td id=\"T_63033_row17_col4\" class=\"data row17 col4\" >0.240223</td>\n",
              "      <td id=\"T_63033_row17_col5\" class=\"data row17 col5\" >0.229521</td>\n",
              "      <td id=\"T_63033_row17_col6\" class=\"data row17 col6\" >0.125419</td>\n",
              "      <td id=\"T_63033_row17_col7\" class=\"data row17 col7\" >-0.041910</td>\n",
              "      <td id=\"T_63033_row17_col8\" class=\"data row17 col8\" >-0.078400</td>\n",
              "      <td id=\"T_63033_row17_col9\" class=\"data row17 col9\" >-0.106500</td>\n",
              "      <td id=\"T_63033_row17_col10\" class=\"data row17 col10\" >0.198372</td>\n",
              "      <td id=\"T_63033_row17_col11\" class=\"data row17 col11\" >0.343803</td>\n",
              "      <td id=\"T_63033_row17_col12\" class=\"data row17 col12\" >-0.144765</td>\n",
              "      <td id=\"T_63033_row17_col13\" class=\"data row17 col13\" >0.409356</td>\n",
              "      <td id=\"T_63033_row17_col14\" class=\"data row17 col14\" >-0.068372</td>\n",
              "      <td id=\"T_63033_row17_col15\" class=\"data row17 col15\" >-0.564072</td>\n",
              "      <td id=\"T_63033_row17_col16\" class=\"data row17 col16\" >-0.135512</td>\n",
              "      <td id=\"T_63033_row17_col17\" class=\"data row17 col17\" >1.000000</td>\n",
              "      <td id=\"T_63033_row17_col18\" class=\"data row17 col18\" >0.334605</td>\n",
              "      <td id=\"T_63033_row17_col19\" class=\"data row17 col19\" >0.254451</td>\n",
              "      <td id=\"T_63033_row17_col20\" class=\"data row17 col20\" >0.000270</td>\n",
              "      <td id=\"T_63033_row17_col21\" class=\"data row17 col21\" >-0.008134</td>\n",
              "      <td id=\"T_63033_row17_col22\" class=\"data row17 col22\" >-0.007331</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row18\" class=\"row_heading level0 row18\" >sqft_living15</th>\n",
              "      <td id=\"T_63033_row18_col0\" class=\"data row18 col0\" >-0.002901</td>\n",
              "      <td id=\"T_63033_row18_col1\" class=\"data row18 col1\" >0.585379</td>\n",
              "      <td id=\"T_63033_row18_col2\" class=\"data row18 col2\" >0.391638</td>\n",
              "      <td id=\"T_63033_row18_col3\" class=\"data row18 col3\" >0.568634</td>\n",
              "      <td id=\"T_63033_row18_col4\" class=\"data row18 col4\" >0.756420</td>\n",
              "      <td id=\"T_63033_row18_col5\" class=\"data row18 col5\" >0.144608</td>\n",
              "      <td id=\"T_63033_row18_col6\" class=\"data row18 col6\" >0.279885</td>\n",
              "      <td id=\"T_63033_row18_col7\" class=\"data row18 col7\" >0.086463</td>\n",
              "      <td id=\"T_63033_row18_col8\" class=\"data row18 col8\" >0.280439</td>\n",
              "      <td id=\"T_63033_row18_col9\" class=\"data row18 col9\" >-0.092824</td>\n",
              "      <td id=\"T_63033_row18_col10\" class=\"data row18 col10\" >0.713202</td>\n",
              "      <td id=\"T_63033_row18_col11\" class=\"data row18 col11\" >0.731870</td>\n",
              "      <td id=\"T_63033_row18_col12\" class=\"data row18 col12\" >0.200355</td>\n",
              "      <td id=\"T_63033_row18_col13\" class=\"data row18 col13\" >0.326229</td>\n",
              "      <td id=\"T_63033_row18_col14\" class=\"data row18 col14\" >-0.002673</td>\n",
              "      <td id=\"T_63033_row18_col15\" class=\"data row18 col15\" >-0.279033</td>\n",
              "      <td id=\"T_63033_row18_col16\" class=\"data row18 col16\" >0.048858</td>\n",
              "      <td id=\"T_63033_row18_col17\" class=\"data row18 col17\" >0.334605</td>\n",
              "      <td id=\"T_63033_row18_col18\" class=\"data row18 col18\" >1.000000</td>\n",
              "      <td id=\"T_63033_row18_col19\" class=\"data row18 col19\" >0.183192</td>\n",
              "      <td id=\"T_63033_row18_col20\" class=\"data row18 col20\" >-0.021734</td>\n",
              "      <td id=\"T_63033_row18_col21\" class=\"data row18 col21\" >0.002449</td>\n",
              "      <td id=\"T_63033_row18_col22\" class=\"data row18 col22\" >-0.008539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row19\" class=\"row_heading level0 row19\" >sqft_lot15</th>\n",
              "      <td id=\"T_63033_row19_col0\" class=\"data row19 col0\" >-0.138798</td>\n",
              "      <td id=\"T_63033_row19_col1\" class=\"data row19 col1\" >0.082447</td>\n",
              "      <td id=\"T_63033_row19_col2\" class=\"data row19 col2\" >0.029244</td>\n",
              "      <td id=\"T_63033_row19_col3\" class=\"data row19 col3\" >0.087175</td>\n",
              "      <td id=\"T_63033_row19_col4\" class=\"data row19 col4\" >0.183286</td>\n",
              "      <td id=\"T_63033_row19_col5\" class=\"data row19 col5\" >0.718557</td>\n",
              "      <td id=\"T_63033_row19_col6\" class=\"data row19 col6\" >-0.011269</td>\n",
              "      <td id=\"T_63033_row19_col7\" class=\"data row19 col7\" >0.030703</td>\n",
              "      <td id=\"T_63033_row19_col8\" class=\"data row19 col8\" >0.072575</td>\n",
              "      <td id=\"T_63033_row19_col9\" class=\"data row19 col9\" >-0.003406</td>\n",
              "      <td id=\"T_63033_row19_col10\" class=\"data row19 col10\" >0.119248</td>\n",
              "      <td id=\"T_63033_row19_col11\" class=\"data row19 col11\" >0.194050</td>\n",
              "      <td id=\"T_63033_row19_col12\" class=\"data row19 col12\" >0.017276</td>\n",
              "      <td id=\"T_63033_row19_col13\" class=\"data row19 col13\" >0.070958</td>\n",
              "      <td id=\"T_63033_row19_col14\" class=\"data row19 col14\" >0.007854</td>\n",
              "      <td id=\"T_63033_row19_col15\" class=\"data row19 col15\" >-0.147221</td>\n",
              "      <td id=\"T_63033_row19_col16\" class=\"data row19 col16\" >-0.086419</td>\n",
              "      <td id=\"T_63033_row19_col17\" class=\"data row19 col17\" >0.254451</td>\n",
              "      <td id=\"T_63033_row19_col18\" class=\"data row19 col18\" >0.183192</td>\n",
              "      <td id=\"T_63033_row19_col19\" class=\"data row19 col19\" >1.000000</td>\n",
              "      <td id=\"T_63033_row19_col20\" class=\"data row19 col20\" >-0.000085</td>\n",
              "      <td id=\"T_63033_row19_col21\" class=\"data row19 col21\" >0.003546</td>\n",
              "      <td id=\"T_63033_row19_col22\" class=\"data row19 col22\" >-0.002871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row20\" class=\"row_heading level0 row20\" >date_year</th>\n",
              "      <td id=\"T_63033_row20_col0\" class=\"data row20 col0\" >0.010128</td>\n",
              "      <td id=\"T_63033_row20_col1\" class=\"data row20 col1\" >0.003576</td>\n",
              "      <td id=\"T_63033_row20_col2\" class=\"data row20 col2\" >-0.009838</td>\n",
              "      <td id=\"T_63033_row20_col3\" class=\"data row20 col3\" >-0.026596</td>\n",
              "      <td id=\"T_63033_row20_col4\" class=\"data row20 col4\" >-0.029038</td>\n",
              "      <td id=\"T_63033_row20_col5\" class=\"data row20 col5\" >0.005468</td>\n",
              "      <td id=\"T_63033_row20_col6\" class=\"data row20 col6\" >-0.022315</td>\n",
              "      <td id=\"T_63033_row20_col7\" class=\"data row20 col7\" >-0.004165</td>\n",
              "      <td id=\"T_63033_row20_col8\" class=\"data row20 col8\" >0.001364</td>\n",
              "      <td id=\"T_63033_row20_col9\" class=\"data row20 col9\" >-0.045589</td>\n",
              "      <td id=\"T_63033_row20_col10\" class=\"data row20 col10\" >-0.030387</td>\n",
              "      <td id=\"T_63033_row20_col11\" class=\"data row20 col11\" >-0.023823</td>\n",
              "      <td id=\"T_63033_row20_col12\" class=\"data row20 col12\" >-0.015687</td>\n",
              "      <td id=\"T_63033_row20_col13\" class=\"data row20 col13\" >0.003507</td>\n",
              "      <td id=\"T_63033_row20_col14\" class=\"data row20 col14\" >-0.023707</td>\n",
              "      <td id=\"T_63033_row20_col15\" class=\"data row20 col15\" >0.001189</td>\n",
              "      <td id=\"T_63033_row20_col16\" class=\"data row20 col16\" >-0.029212</td>\n",
              "      <td id=\"T_63033_row20_col17\" class=\"data row20 col17\" >0.000270</td>\n",
              "      <td id=\"T_63033_row20_col18\" class=\"data row20 col18\" >-0.021734</td>\n",
              "      <td id=\"T_63033_row20_col19\" class=\"data row20 col19\" >-0.000085</td>\n",
              "      <td id=\"T_63033_row20_col20\" class=\"data row20 col20\" >1.000000</td>\n",
              "      <td id=\"T_63033_row20_col21\" class=\"data row20 col21\" >-0.782389</td>\n",
              "      <td id=\"T_63033_row20_col22\" class=\"data row20 col22\" >-0.006103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row21\" class=\"row_heading level0 row21\" >date_month</th>\n",
              "      <td id=\"T_63033_row21_col0\" class=\"data row21 col0\" >-0.011708</td>\n",
              "      <td id=\"T_63033_row21_col1\" class=\"data row21 col1\" >-0.010081</td>\n",
              "      <td id=\"T_63033_row21_col2\" class=\"data row21 col2\" >-0.001533</td>\n",
              "      <td id=\"T_63033_row21_col3\" class=\"data row21 col3\" >0.007392</td>\n",
              "      <td id=\"T_63033_row21_col4\" class=\"data row21 col4\" >0.011810</td>\n",
              "      <td id=\"T_63033_row21_col5\" class=\"data row21 col5\" >-0.002369</td>\n",
              "      <td id=\"T_63033_row21_col6\" class=\"data row21 col6\" >0.014005</td>\n",
              "      <td id=\"T_63033_row21_col7\" class=\"data row21 col7\" >0.008132</td>\n",
              "      <td id=\"T_63033_row21_col8\" class=\"data row21 col8\" >-0.005638</td>\n",
              "      <td id=\"T_63033_row21_col9\" class=\"data row21 col9\" >0.021978</td>\n",
              "      <td id=\"T_63033_row21_col10\" class=\"data row21 col10\" >0.008376</td>\n",
              "      <td id=\"T_63033_row21_col11\" class=\"data row21 col11\" >0.009872</td>\n",
              "      <td id=\"T_63033_row21_col12\" class=\"data row21 col12\" >0.006035</td>\n",
              "      <td id=\"T_63033_row21_col13\" class=\"data row21 col13\" >-0.006226</td>\n",
              "      <td id=\"T_63033_row21_col14\" class=\"data row21 col14\" >0.012827</td>\n",
              "      <td id=\"T_63033_row21_col15\" class=\"data row21 col15\" >-0.000171</td>\n",
              "      <td id=\"T_63033_row21_col16\" class=\"data row21 col16\" >0.014961</td>\n",
              "      <td id=\"T_63033_row21_col17\" class=\"data row21 col17\" >-0.008134</td>\n",
              "      <td id=\"T_63033_row21_col18\" class=\"data row21 col18\" >0.002449</td>\n",
              "      <td id=\"T_63033_row21_col19\" class=\"data row21 col19\" >0.003546</td>\n",
              "      <td id=\"T_63033_row21_col20\" class=\"data row21 col20\" >-0.782389</td>\n",
              "      <td id=\"T_63033_row21_col21\" class=\"data row21 col21\" >1.000000</td>\n",
              "      <td id=\"T_63033_row21_col22\" class=\"data row21 col22\" >-0.060664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_63033_level0_row22\" class=\"row_heading level0 row22\" >date_day</th>\n",
              "      <td id=\"T_63033_row22_col0\" class=\"data row22 col0\" >0.001939</td>\n",
              "      <td id=\"T_63033_row22_col1\" class=\"data row22 col1\" >-0.014670</td>\n",
              "      <td id=\"T_63033_row22_col2\" class=\"data row22 col2\" >-0.008031</td>\n",
              "      <td id=\"T_63033_row22_col3\" class=\"data row22 col3\" >-0.005304</td>\n",
              "      <td id=\"T_63033_row22_col4\" class=\"data row22 col4\" >-0.007246</td>\n",
              "      <td id=\"T_63033_row22_col5\" class=\"data row22 col5\" >0.000634</td>\n",
              "      <td id=\"T_63033_row22_col6\" class=\"data row22 col6\" >-0.006662</td>\n",
              "      <td id=\"T_63033_row22_col7\" class=\"data row22 col7\" >0.010518</td>\n",
              "      <td id=\"T_63033_row22_col8\" class=\"data row22 col8\" >0.011174</td>\n",
              "      <td id=\"T_63033_row22_col9\" class=\"data row22 col9\" >-0.004643</td>\n",
              "      <td id=\"T_63033_row22_col10\" class=\"data row22 col10\" >-0.012483</td>\n",
              "      <td id=\"T_63033_row22_col11\" class=\"data row22 col11\" >-0.002475</td>\n",
              "      <td id=\"T_63033_row22_col12\" class=\"data row22 col12\" >-0.010405</td>\n",
              "      <td id=\"T_63033_row22_col13\" class=\"data row22 col13\" >-0.005766</td>\n",
              "      <td id=\"T_63033_row22_col14\" class=\"data row22 col14\" >0.008127</td>\n",
              "      <td id=\"T_63033_row22_col15\" class=\"data row22 col15\" >-0.003270</td>\n",
              "      <td id=\"T_63033_row22_col16\" class=\"data row22 col16\" >-0.015655</td>\n",
              "      <td id=\"T_63033_row22_col17\" class=\"data row22 col17\" >-0.007331</td>\n",
              "      <td id=\"T_63033_row22_col18\" class=\"data row22 col18\" >-0.008539</td>\n",
              "      <td id=\"T_63033_row22_col19\" class=\"data row22 col19\" >-0.002871</td>\n",
              "      <td id=\"T_63033_row22_col20\" class=\"data row22 col20\" >-0.006103</td>\n",
              "      <td id=\"T_63033_row22_col21\" class=\"data row22 col21\" >-0.060664</td>\n",
              "      <td id=\"T_63033_row22_col22\" class=\"data row22 col22\" >1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "(raw\n",
        " .to_pandas(use_pyarrow_extension_array=True)\n",
        " .corr()\n",
        " .style.background_gradient(cmap='RdBu', vmin=-1, vmax=1)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "z01Qni0Zri7z",
        "outputId": "b09ef442-ab59-4e1d-afd3-e598a9e4798d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "_=(raw.to_pandas()\n",
        " .plot.scatter('sqft_living', 'price', alpha=0.1)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lvkgxv6sFZlt",
        "outputId": "adf1de28-3d53-4621-bc58-7d7f0ebc1119"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['id',\n",
              " 'price',\n",
              " 'bedrooms',\n",
              " 'bathrooms',\n",
              " 'sqft_living',\n",
              " 'sqft_lot',\n",
              " 'floors',\n",
              " 'waterfront',\n",
              " 'view',\n",
              " 'condition',\n",
              " 'grade',\n",
              " 'sqft_above',\n",
              " 'sqft_basement',\n",
              " 'yr_built',\n",
              " 'yr_renovated',\n",
              " 'zipcode',\n",
              " 'lat',\n",
              " 'long',\n",
              " 'sqft_living15',\n",
              " 'sqft_lot15',\n",
              " 'date_year',\n",
              " 'date_month',\n",
              " 'date_day']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nPdZHl9pri71"
      },
      "source": [
        "\n",
        "## Data Preprocessing\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "TzNJHV6jri71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "outputId": "a2e25ed0-6683-4d1f-a770-40aef8c01d54"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (21_613, 21)\n",
              "┌────────────┬──────────┬──────────┬───────────┬───┬──────────┬────────────┬───────────┬───────────┐\n",
              "│ id         ┆ price    ┆ bedrooms ┆ bathrooms ┆ … ┆ long     ┆ sqft_livin ┆ sqft_lot1 ┆ date      │\n",
              "│ ---        ┆ ---      ┆ ---      ┆ ---       ┆   ┆ ---      ┆ g15        ┆ 5         ┆ ---       │\n",
              "│ i64        ┆ f64      ┆ i64      ┆ f64       ┆   ┆ f64      ┆ ---        ┆ ---       ┆ date      │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆ i64        ┆ i64       ┆           │\n",
              "╞════════════╪══════════╪══════════╪═══════════╪═══╪══════════╪════════════╪═══════════╪═══════════╡\n",
              "│ 7129300520 ┆ 221900.0 ┆ 3        ┆ 1.0       ┆ … ┆ -122.257 ┆ 1340       ┆ 5650      ┆ 2014-10-1 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 3         │\n",
              "│ 6414100192 ┆ 538000.0 ┆ 3        ┆ 2.25      ┆ … ┆ -122.319 ┆ 1690       ┆ 7639      ┆ 2014-12-0 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 9         │\n",
              "│ 5631500400 ┆ 180000.0 ┆ 2        ┆ 1.0       ┆ … ┆ -122.233 ┆ 2720       ┆ 8062      ┆ 2015-02-2 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 5         │\n",
              "│ 2487200875 ┆ 604000.0 ┆ 4        ┆ 3.0       ┆ … ┆ -122.393 ┆ 1360       ┆ 5000      ┆ 2014-12-0 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 9         │\n",
              "│ 1954400510 ┆ 510000.0 ┆ 3        ┆ 2.0       ┆ … ┆ -122.045 ┆ 1800       ┆ 7503      ┆ 2015-02-1 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 8         │\n",
              "│ …          ┆ …        ┆ …        ┆ …         ┆ … ┆ …        ┆ …          ┆ …         ┆ …         │\n",
              "│ 263000018  ┆ 360000.0 ┆ 3        ┆ 2.5       ┆ … ┆ -122.346 ┆ 1530       ┆ 1509      ┆ 2014-05-2 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 1         │\n",
              "│ 6600060120 ┆ 400000.0 ┆ 4        ┆ 2.5       ┆ … ┆ -122.362 ┆ 1830       ┆ 7200      ┆ 2015-02-2 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 3         │\n",
              "│ 1523300141 ┆ 402101.0 ┆ 2        ┆ 0.75      ┆ … ┆ -122.299 ┆ 1020       ┆ 2007      ┆ 2014-06-2 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 3         │\n",
              "│ 291310100  ┆ 400000.0 ┆ 3        ┆ 2.5       ┆ … ┆ -122.069 ┆ 1410       ┆ 1287      ┆ 2015-01-1 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 6         │\n",
              "│ 1523300157 ┆ 325000.0 ┆ 2        ┆ 0.75      ┆ … ┆ -122.299 ┆ 1020       ┆ 1357      ┆ 2014-10-1 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 5         │\n",
              "└────────────┴──────────┴──────────┴───────────┴───┴──────────┴────────────┴───────────┴───────────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (21_613, 21)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>id</th><th>price</th><th>bedrooms</th><th>bathrooms</th><th>sqft_living</th><th>sqft_lot</th><th>floors</th><th>waterfront</th><th>view</th><th>condition</th><th>grade</th><th>sqft_above</th><th>sqft_basement</th><th>yr_built</th><th>yr_renovated</th><th>zipcode</th><th>lat</th><th>long</th><th>sqft_living15</th><th>sqft_lot15</th><th>date</th></tr><tr><td>i64</td><td>f64</td><td>i64</td><td>f64</td><td>i64</td><td>i64</td><td>f64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>cat</td><td>f64</td><td>f64</td><td>i64</td><td>i64</td><td>date</td></tr></thead><tbody><tr><td>7129300520</td><td>221900.0</td><td>3</td><td>1.0</td><td>1180</td><td>5650</td><td>1.0</td><td>0</td><td>0</td><td>3</td><td>7</td><td>1180</td><td>0</td><td>1955</td><td>null</td><td>&quot;98178&quot;</td><td>47.5112</td><td>-122.257</td><td>1340</td><td>5650</td><td>2014-10-13</td></tr><tr><td>6414100192</td><td>538000.0</td><td>3</td><td>2.25</td><td>2570</td><td>7242</td><td>2.0</td><td>0</td><td>0</td><td>3</td><td>7</td><td>2170</td><td>400</td><td>1951</td><td>1991</td><td>&quot;98125&quot;</td><td>47.721</td><td>-122.319</td><td>1690</td><td>7639</td><td>2014-12-09</td></tr><tr><td>5631500400</td><td>180000.0</td><td>2</td><td>1.0</td><td>770</td><td>10000</td><td>1.0</td><td>0</td><td>0</td><td>3</td><td>6</td><td>770</td><td>0</td><td>1933</td><td>null</td><td>&quot;98028&quot;</td><td>47.7379</td><td>-122.233</td><td>2720</td><td>8062</td><td>2015-02-25</td></tr><tr><td>2487200875</td><td>604000.0</td><td>4</td><td>3.0</td><td>1960</td><td>5000</td><td>1.0</td><td>0</td><td>0</td><td>5</td><td>7</td><td>1050</td><td>910</td><td>1965</td><td>null</td><td>&quot;98136&quot;</td><td>47.5208</td><td>-122.393</td><td>1360</td><td>5000</td><td>2014-12-09</td></tr><tr><td>1954400510</td><td>510000.0</td><td>3</td><td>2.0</td><td>1680</td><td>8080</td><td>1.0</td><td>0</td><td>0</td><td>3</td><td>8</td><td>1680</td><td>0</td><td>1987</td><td>null</td><td>&quot;98074&quot;</td><td>47.6168</td><td>-122.045</td><td>1800</td><td>7503</td><td>2015-02-18</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>263000018</td><td>360000.0</td><td>3</td><td>2.5</td><td>1530</td><td>1131</td><td>3.0</td><td>0</td><td>0</td><td>3</td><td>8</td><td>1530</td><td>0</td><td>2009</td><td>null</td><td>&quot;98103&quot;</td><td>47.6993</td><td>-122.346</td><td>1530</td><td>1509</td><td>2014-05-21</td></tr><tr><td>6600060120</td><td>400000.0</td><td>4</td><td>2.5</td><td>2310</td><td>5813</td><td>2.0</td><td>0</td><td>0</td><td>3</td><td>8</td><td>2310</td><td>0</td><td>2014</td><td>null</td><td>&quot;98146&quot;</td><td>47.5107</td><td>-122.362</td><td>1830</td><td>7200</td><td>2015-02-23</td></tr><tr><td>1523300141</td><td>402101.0</td><td>2</td><td>0.75</td><td>1020</td><td>1350</td><td>2.0</td><td>0</td><td>0</td><td>3</td><td>7</td><td>1020</td><td>0</td><td>2009</td><td>null</td><td>&quot;98144&quot;</td><td>47.5944</td><td>-122.299</td><td>1020</td><td>2007</td><td>2014-06-23</td></tr><tr><td>291310100</td><td>400000.0</td><td>3</td><td>2.5</td><td>1600</td><td>2388</td><td>2.0</td><td>0</td><td>0</td><td>3</td><td>8</td><td>1600</td><td>0</td><td>2004</td><td>null</td><td>&quot;98027&quot;</td><td>47.5345</td><td>-122.069</td><td>1410</td><td>1287</td><td>2015-01-16</td></tr><tr><td>1523300157</td><td>325000.0</td><td>2</td><td>0.75</td><td>1020</td><td>1076</td><td>2.0</td><td>0</td><td>0</td><td>3</td><td>7</td><td>1020</td><td>0</td><td>2008</td><td>null</td><td>&quot;98144&quot;</td><td>47.5941</td><td>-122.299</td><td>1020</td><td>1357</td><td>2014-10-15</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "def tweak_housing(df):\n",
        "    return (df\n",
        "            .with_columns(zipcode=pl.col('zipcode').cast(pl.String).cast(pl.Categorical),\n",
        "                          date=pl.date(pl.col('date_year'), pl.col('date_month'), pl.col('date_day')),\n",
        "                          yr_renovated=pl.col('yr_renovated').replace(0, None),\n",
        "                          )\n",
        "            .select(['id', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors',\n",
        "                     'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement',\n",
        "                     'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long', 'sqft_living15',\n",
        "                     'sqft_lot15', 'date',  #'date_year', 'date_month', 'date_day',\n",
        "                     ])\n",
        "    )\n",
        "\n",
        "tweak_housing(raw)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yl5KmzDMri71"
      },
      "source": [
        "\n",
        "## Sklearn Pipelines\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "scrolled": true,
        "id": "q8c0v16Ori72"
      },
      "outputs": [],
      "source": [
        "# The difference between sklearn pipelines and transformers is\n",
        "# that a pipeline is a sequence of steps. A transformer transforms\n",
        "# the data, and a pipeline is a sequence of transformers.\n",
        "# A ColumnTransformer applies multiple transformers to different\n",
        "# columns of the input data.\n",
        "\n",
        "from sklearn.pipeline import Pipeline  #pipeline: seq. of transformer\n",
        "from sklearn.compose import ColumnTransformer  #apply preprocessing transformations to specific column\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder #StandardScaler: u=0, δ=1; OHE: categorical -> numerical\n",
        "from sklearn.impute import SimpleImputer #simpleimputer: fill in missing value\n",
        "from sklearn.model_selection import train_test_split #split dataset -> training & testing set\n",
        "from sklearn.preprocessing import FunctionTransformer # convert function -> transformer -> to stick to Pipeline\n",
        "from sklearn.base import BaseEstimator, TransformerMixin #custom transformer in SK-learn -> implement custom pre-processing/ feature transformations\n",
        "from sklearn import set_config\n",
        "set_config(transform_output='polars')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "FFBqnlDbri72",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e7add173-2cc1-4bea-b2a1-1c3eecb1e01d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['id', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'lat', 'long', 'sqft_living15', 'sqft_lot15']\n"
          ]
        }
      ],
      "source": [
        "print(tweak_housing(raw).select(cs.numeric()).columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true
        },
        "scrolled": true,
        "id": "o9SYlvdnri72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "79910982-6662-4edd-d5da-96e129415f5c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (21_613, 3)\n",
              "┌───────────┬───────────┬─────────────┐\n",
              "│ bedrooms  ┆ bathrooms ┆ sqft_living │\n",
              "│ ---       ┆ ---       ┆ ---         │\n",
              "│ f64       ┆ f64       ┆ f64         │\n",
              "╞═══════════╪═══════════╪═════════════╡\n",
              "│ -0.398737 ┆ -1.447464 ┆ -0.979835   │\n",
              "│ -0.398737 ┆ 0.175607  ┆ 0.533634    │\n",
              "│ -1.473959 ┆ -1.447464 ┆ -1.426254   │\n",
              "│ 0.676485  ┆ 1.149449  ┆ -0.13055    │\n",
              "│ -0.398737 ┆ -0.149007 ┆ -0.435422   │\n",
              "│ …         ┆ …         ┆ …           │\n",
              "│ -0.398737 ┆ 0.500221  ┆ -0.598746   │\n",
              "│ 0.676485  ┆ 0.500221  ┆ 0.250539    │\n",
              "│ -1.473959 ┆ -1.772078 ┆ -1.154047   │\n",
              "│ -0.398737 ┆ 0.500221  ┆ -0.522528   │\n",
              "│ -1.473959 ┆ -1.772078 ┆ -1.154047   │\n",
              "└───────────┴───────────┴─────────────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (21_613, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>bedrooms</th><th>bathrooms</th><th>sqft_living</th></tr><tr><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>-0.398737</td><td>-1.447464</td><td>-0.979835</td></tr><tr><td>-0.398737</td><td>0.175607</td><td>0.533634</td></tr><tr><td>-1.473959</td><td>-1.447464</td><td>-1.426254</td></tr><tr><td>0.676485</td><td>1.149449</td><td>-0.13055</td></tr><tr><td>-0.398737</td><td>-0.149007</td><td>-0.435422</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>-0.398737</td><td>0.500221</td><td>-0.598746</td></tr><tr><td>0.676485</td><td>0.500221</td><td>0.250539</td></tr><tr><td>-1.473959</td><td>-1.772078</td><td>-1.154047</td></tr><tr><td>-0.398737</td><td>0.500221</td><td>-0.522528</td></tr><tr><td>-1.473959</td><td>-1.772078</td><td>-1.154047</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# for equal consideration of ML-Model\n",
        "numeric_features = ['bedrooms', 'bathrooms', 'sqft_living']\n",
        "std = StandardScaler()\n",
        "std.fit_transform(tweak_housing(raw).select(numeric_features)) #fit: calc. u & δm;  transform: changes/alters the val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true
        },
        "id": "PESKp0yJri73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "e82a258c-de5b-42cd-bf3e-f4533e192308"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (21_613, 3)\n",
              "┌───────────┬───────────┬─────────────┐\n",
              "│ bedrooms  ┆ bathrooms ┆ sqft_living │\n",
              "│ ---       ┆ ---       ┆ ---         │\n",
              "│ f64       ┆ f64       ┆ f64         │\n",
              "╞═══════════╪═══════════╪═════════════╡\n",
              "│ -0.398737 ┆ -1.447464 ┆ -0.979835   │\n",
              "│ -0.398737 ┆ 0.175607  ┆ 0.533634    │\n",
              "│ -1.473959 ┆ -1.447464 ┆ -1.426254   │\n",
              "│ 0.676485  ┆ 1.149449  ┆ -0.13055    │\n",
              "│ -0.398737 ┆ -0.149007 ┆ -0.435422   │\n",
              "│ …         ┆ …         ┆ …           │\n",
              "│ -0.398737 ┆ 0.500221  ┆ -0.598746   │\n",
              "│ 0.676485  ┆ 0.500221  ┆ 0.250539    │\n",
              "│ -1.473959 ┆ -1.772078 ┆ -1.154047   │\n",
              "│ -0.398737 ┆ 0.500221  ┆ -0.522528   │\n",
              "│ -1.473959 ┆ -1.772078 ┆ -1.154047   │\n",
              "└───────────┴───────────┴─────────────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (21_613, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>bedrooms</th><th>bathrooms</th><th>sqft_living</th></tr><tr><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>-0.398737</td><td>-1.447464</td><td>-0.979835</td></tr><tr><td>-0.398737</td><td>0.175607</td><td>0.533634</td></tr><tr><td>-1.473959</td><td>-1.447464</td><td>-1.426254</td></tr><tr><td>0.676485</td><td>1.149449</td><td>-0.13055</td></tr><tr><td>-0.398737</td><td>-0.149007</td><td>-0.435422</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>-0.398737</td><td>0.500221</td><td>-0.598746</td></tr><tr><td>0.676485</td><td>0.500221</td><td>0.250539</td></tr><tr><td>-1.473959</td><td>-1.772078</td><td>-1.154047</td></tr><tr><td>-0.398737</td><td>0.500221</td><td>-0.522528</td></tr><tr><td>-1.473959</td><td>-1.772078</td><td>-1.154047</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "#Pipeline: to automate repeated task for multiple-steps ML process\n",
        "numeric_features = ['bedrooms', 'bathrooms', 'sqft_living']\n",
        "\n",
        "num_pipeline = Pipeline([\n",
        "     ('std', StandardScaler())])\n",
        "\n",
        "num_pipeline.fit_transform(\n",
        "    tweak_housing(raw)\n",
        "    .select(numeric_features)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true
        },
        "scrolled": true,
        "id": "P-D68frMri73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "2a7aab57-587d-46e2-fc86-716a60a9c18e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (21_613, 3)\n",
              "┌───────────┬───────────┬─────────────┐\n",
              "│ bedrooms  ┆ bathrooms ┆ sqft_living │\n",
              "│ ---       ┆ ---       ┆ ---         │\n",
              "│ f64       ┆ f64       ┆ f64         │\n",
              "╞═══════════╪═══════════╪═════════════╡\n",
              "│ -0.398737 ┆ -1.447464 ┆ -0.979835   │\n",
              "│ -0.398737 ┆ 0.175607  ┆ 0.533634    │\n",
              "│ -1.473959 ┆ -1.447464 ┆ -1.426254   │\n",
              "│ 0.676485  ┆ 1.149449  ┆ -0.13055    │\n",
              "│ -0.398737 ┆ -0.149007 ┆ -0.435422   │\n",
              "│ …         ┆ …         ┆ …           │\n",
              "│ -0.398737 ┆ 0.500221  ┆ -0.598746   │\n",
              "│ 0.676485  ┆ 0.500221  ┆ 0.250539    │\n",
              "│ -1.473959 ┆ -1.772078 ┆ -1.154047   │\n",
              "│ -0.398737 ┆ 0.500221  ┆ -0.522528   │\n",
              "│ -1.473959 ┆ -1.772078 ┆ -1.154047   │\n",
              "└───────────┴───────────┴─────────────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (21_613, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>bedrooms</th><th>bathrooms</th><th>sqft_living</th></tr><tr><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>-0.398737</td><td>-1.447464</td><td>-0.979835</td></tr><tr><td>-0.398737</td><td>0.175607</td><td>0.533634</td></tr><tr><td>-1.473959</td><td>-1.447464</td><td>-1.426254</td></tr><tr><td>0.676485</td><td>1.149449</td><td>-0.13055</td></tr><tr><td>-0.398737</td><td>-0.149007</td><td>-0.435422</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>-0.398737</td><td>0.500221</td><td>-0.598746</td></tr><tr><td>0.676485</td><td>0.500221</td><td>0.250539</td></tr><tr><td>-1.473959</td><td>-1.772078</td><td>-1.154047</td></tr><tr><td>-0.398737</td><td>0.500221</td><td>-0.522528</td></tr><tr><td>-1.473959</td><td>-1.772078</td><td>-1.154047</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# simple imputer: if a value is missing add 'median' - value\n",
        "numeric_features = ['bedrooms', 'bathrooms', 'sqft_living']\n",
        "\n",
        "num_pipeline = Pipeline([\n",
        "    ('imputer', SimpleImputer(strategy='median')),\n",
        "    ('std', StandardScaler())])\n",
        "\n",
        "num_pipeline.fit_transform(\n",
        "    tweak_housing(raw)\n",
        "    .select(numeric_features)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "scrolled": true,
        "id": "Hjlg4YThri73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "outputId": "bd97a2c9-7f7f-4696-b63f-4866a37d9e35"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (21_613, 70)\n",
              "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
              "│ zipcode_9 ┆ zipcode_9 ┆ zipcode_9 ┆ zipcode_9 ┆ … ┆ zipcode_9 ┆ zipcode_9 ┆ zipcode_9 ┆ zipcode_ │\n",
              "│ 8001      ┆ 8002      ┆ 8003      ┆ 8004      ┆   ┆ 8178      ┆ 8188      ┆ 8198      ┆ 98199    │\n",
              "│ ---       ┆ ---       ┆ ---       ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---      │\n",
              "│ f64       ┆ f64       ┆ f64       ┆ f64       ┆   ┆ f64       ┆ f64       ┆ f64       ┆ f64      │\n",
              "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
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              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
              "│ …         ┆ …         ┆ …         ┆ …         ┆ … ┆ …         ┆ …         ┆ …         ┆ …        │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
              "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (21_613, 70)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>zipcode_98001</th><th>zipcode_98002</th><th>zipcode_98003</th><th>zipcode_98004</th><th>zipcode_98005</th><th>zipcode_98006</th><th>zipcode_98007</th><th>zipcode_98008</th><th>zipcode_98010</th><th>zipcode_98011</th><th>zipcode_98014</th><th>zipcode_98019</th><th>zipcode_98022</th><th>zipcode_98023</th><th>zipcode_98024</th><th>zipcode_98027</th><th>zipcode_98028</th><th>zipcode_98029</th><th>zipcode_98030</th><th>zipcode_98031</th><th>zipcode_98032</th><th>zipcode_98033</th><th>zipcode_98034</th><th>zipcode_98038</th><th>zipcode_98039</th><th>zipcode_98040</th><th>zipcode_98042</th><th>zipcode_98045</th><th>zipcode_98052</th><th>zipcode_98053</th><th>zipcode_98055</th><th>zipcode_98056</th><th>zipcode_98058</th><th>zipcode_98059</th><th>zipcode_98065</th><th>zipcode_98070</th><th>zipcode_98072</th><th>zipcode_98074</th><th>zipcode_98075</th><th>zipcode_98077</th><th>zipcode_98092</th><th>zipcode_98102</th><th>zipcode_98103</th><th>zipcode_98105</th><th>zipcode_98106</th><th>zipcode_98107</th><th>zipcode_98108</th><th>zipcode_98109</th><th>zipcode_98112</th><th>zipcode_98115</th><th>zipcode_98116</th><th>zipcode_98117</th><th>zipcode_98118</th><th>zipcode_98119</th><th>zipcode_98122</th><th>zipcode_98125</th><th>zipcode_98126</th><th>zipcode_98133</th><th>zipcode_98136</th><th>zipcode_98144</th><th>zipcode_98146</th><th>zipcode_98148</th><th>zipcode_98155</th><th>zipcode_98166</th><th>zipcode_98168</th><th>zipcode_98177</th><th>zipcode_98178</th><th>zipcode_98188</th><th>zipcode_98198</th><th>zipcode_98199</th></tr><tr><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# OHE: categorical -> numerical, unknown_categories: False, to handle unknown catergory\n",
        "cat_features = ['zipcode']\n",
        "\n",
        "ohe = OneHotEncoder(handle_unknown='ignore',\n",
        "                    sparse_output=False)\n",
        "\n",
        "ohe.fit_transform(tweak_housing(raw).select(cat_features)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true
        },
        "id": "nV5BQRNUri74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "outputId": "1610250d-91ed-4257-ebee-dd84fd8d33ed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (21_613, 10)\n",
              "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
              "│ zipcode_9 ┆ zipcode_9 ┆ zipcode_9 ┆ zipcode_9 ┆ … ┆ zipcode_9 ┆ zipcode_9 ┆ zipcode_9 ┆ zipcode_ │\n",
              "│ 8023      ┆ 8034      ┆ 8038      ┆ 8042      ┆   ┆ 8115      ┆ 8117      ┆ 8118      ┆ infreque │\n",
              "│ ---       ┆ ---       ┆ ---       ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ nt_sklea │\n",
              "│ f64       ┆ f64       ┆ f64       ┆ f64       ┆   ┆ f64       ┆ f64       ┆ f64       ┆ rn       │\n",
              "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ ---      │\n",
              "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ f64      │\n",
              "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 1.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 1.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 1.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 1.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 1.0      │\n",
              "│ …         ┆ …         ┆ …         ┆ …         ┆ … ┆ …         ┆ …         ┆ …         ┆ …        │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 1.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 1.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 1.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 1.0      │\n",
              "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
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              "}\n",
              "</style>\n",
              "<small>shape: (21_613, 10)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>zipcode_98023</th><th>zipcode_98034</th><th>zipcode_98038</th><th>zipcode_98042</th><th>zipcode_98052</th><th>zipcode_98103</th><th>zipcode_98115</th><th>zipcode_98117</th><th>zipcode_98118</th><th>zipcode_infrequent_sklearn</th></tr><tr><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "#select max categories\n",
        "cat_features = ['zipcode']\n",
        "\n",
        "ohe = OneHotEncoder(handle_unknown='ignore',\n",
        "                    sparse_output=False, max_categories=10)\n",
        "\n",
        "ohe.fit_transform(\n",
        "    tweak_housing(raw)\n",
        "    .select(cat_features)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "scrolled": true,
        "id": "4FwS51RUri74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "outputId": "69dd2652-872e-449a-d566-e815cb653ad8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (21_613, 10)\n",
              "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
              "│ zipcode_9 ┆ zipcode_9 ┆ zipcode_9 ┆ zipcode_9 ┆ … ┆ zipcode_9 ┆ zipcode_9 ┆ zipcode_9 ┆ zipcode_ │\n",
              "│ 8023      ┆ 8034      ┆ 8038      ┆ 8042      ┆   ┆ 8115      ┆ 8117      ┆ 8118      ┆ infreque │\n",
              "│ ---       ┆ ---       ┆ ---       ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ nt_sklea │\n",
              "│ f64       ┆ f64       ┆ f64       ┆ f64       ┆   ┆ f64       ┆ f64       ┆ f64       ┆ rn       │\n",
              "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ ---      │\n",
              "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ f64      │\n",
              "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 1.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 1.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 1.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 1.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 1.0      │\n",
              "│ …         ┆ …         ┆ …         ┆ …         ┆ … ┆ …         ┆ …         ┆ …         ┆ …        │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 1.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 1.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 1.0      │\n",
              "│ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 1.0      │\n",
              "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (21_613, 10)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>zipcode_98023</th><th>zipcode_98034</th><th>zipcode_98038</th><th>zipcode_98042</th><th>zipcode_98052</th><th>zipcode_98103</th><th>zipcode_98115</th><th>zipcode_98117</th><th>zipcode_98118</th><th>zipcode_infrequent_sklearn</th></tr><tr><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr><tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "cat_features = ['zipcode']\n",
        "\n",
        "cat_pipeline = Pipeline(steps=[\n",
        "    ('cat', OneHotEncoder(handle_unknown='ignore', sparse_output=False))])\n",
        "\n",
        "cat_pipeline.set_params(cat__max_categories=10)\n",
        "cat_pipeline.fit_transform(\n",
        "    tweak_housing(raw)\n",
        "    .select(cat_features)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "d0Rn7GK6ri74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "outputId": "069cda3b-6f24-440b-db9c-d1339e9f2fad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (21_613, 21)\n",
              "┌────────────┬──────────┬──────────┬───────────┬───┬──────────┬────────────┬───────────┬───────────┐\n",
              "│ id         ┆ price    ┆ bedrooms ┆ bathrooms ┆ … ┆ long     ┆ sqft_livin ┆ sqft_lot1 ┆ date      │\n",
              "│ ---        ┆ ---      ┆ ---      ┆ ---       ┆   ┆ ---      ┆ g15        ┆ 5         ┆ ---       │\n",
              "│ i64        ┆ f64      ┆ i64      ┆ f64       ┆   ┆ f64      ┆ ---        ┆ ---       ┆ date      │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆ i64        ┆ i64       ┆           │\n",
              "╞════════════╪══════════╪══════════╪═══════════╪═══╪══════════╪════════════╪═══════════╪═══════════╡\n",
              "│ 7129300520 ┆ 221900.0 ┆ 3        ┆ 1.0       ┆ … ┆ -122.257 ┆ 1340       ┆ 5650      ┆ 2014-10-1 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 3         │\n",
              "│ 6414100192 ┆ 538000.0 ┆ 3        ┆ 2.25      ┆ … ┆ -122.319 ┆ 1690       ┆ 7639      ┆ 2014-12-0 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 9         │\n",
              "│ 5631500400 ┆ 180000.0 ┆ 2        ┆ 1.0       ┆ … ┆ -122.233 ┆ 2720       ┆ 8062      ┆ 2015-02-2 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 5         │\n",
              "│ 2487200875 ┆ 604000.0 ┆ 4        ┆ 3.0       ┆ … ┆ -122.393 ┆ 1360       ┆ 5000      ┆ 2014-12-0 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 9         │\n",
              "│ 1954400510 ┆ 510000.0 ┆ 3        ┆ 2.0       ┆ … ┆ -122.045 ┆ 1800       ┆ 7503      ┆ 2015-02-1 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 8         │\n",
              "│ …          ┆ …        ┆ …        ┆ …         ┆ … ┆ …        ┆ …          ┆ …         ┆ …         │\n",
              "│ 263000018  ┆ 360000.0 ┆ 3        ┆ 2.5       ┆ … ┆ -122.346 ┆ 1530       ┆ 1509      ┆ 2014-05-2 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 1         │\n",
              "│ 6600060120 ┆ 400000.0 ┆ 4        ┆ 2.5       ┆ … ┆ -122.362 ┆ 1830       ┆ 7200      ┆ 2015-02-2 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 3         │\n",
              "│ 1523300141 ┆ 402101.0 ┆ 2        ┆ 0.75      ┆ … ┆ -122.299 ┆ 1020       ┆ 2007      ┆ 2014-06-2 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 3         │\n",
              "│ 291310100  ┆ 400000.0 ┆ 3        ┆ 2.5       ┆ … ┆ -122.069 ┆ 1410       ┆ 1287      ┆ 2015-01-1 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 6         │\n",
              "│ 1523300157 ┆ 325000.0 ┆ 2        ┆ 0.75      ┆ … ┆ -122.299 ┆ 1020       ┆ 1357      ┆ 2014-10-1 │\n",
              "│            ┆          ┆          ┆           ┆   ┆          ┆            ┆           ┆ 5         │\n",
              "└────────────┴──────────┴──────────┴───────────┴───┴──────────┴────────────┴───────────┴───────────┘"
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              "}\n",
              "</style>\n",
              "<small>shape: (21_613, 21)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>id</th><th>price</th><th>bedrooms</th><th>bathrooms</th><th>sqft_living</th><th>sqft_lot</th><th>floors</th><th>waterfront</th><th>view</th><th>condition</th><th>grade</th><th>sqft_above</th><th>sqft_basement</th><th>yr_built</th><th>yr_renovated</th><th>zipcode</th><th>lat</th><th>long</th><th>sqft_living15</th><th>sqft_lot15</th><th>date</th></tr><tr><td>i64</td><td>f64</td><td>i64</td><td>f64</td><td>i64</td><td>i64</td><td>f64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>cat</td><td>f64</td><td>f64</td><td>i64</td><td>i64</td><td>date</td></tr></thead><tbody><tr><td>7129300520</td><td>221900.0</td><td>3</td><td>1.0</td><td>1180</td><td>5650</td><td>1.0</td><td>0</td><td>0</td><td>3</td><td>7</td><td>1180</td><td>0</td><td>1955</td><td>null</td><td>&quot;98178&quot;</td><td>47.5112</td><td>-122.257</td><td>1340</td><td>5650</td><td>2014-10-13</td></tr><tr><td>6414100192</td><td>538000.0</td><td>3</td><td>2.25</td><td>2570</td><td>7242</td><td>2.0</td><td>0</td><td>0</td><td>3</td><td>7</td><td>2170</td><td>400</td><td>1951</td><td>1991</td><td>&quot;98125&quot;</td><td>47.721</td><td>-122.319</td><td>1690</td><td>7639</td><td>2014-12-09</td></tr><tr><td>5631500400</td><td>180000.0</td><td>2</td><td>1.0</td><td>770</td><td>10000</td><td>1.0</td><td>0</td><td>0</td><td>3</td><td>6</td><td>770</td><td>0</td><td>1933</td><td>null</td><td>&quot;98028&quot;</td><td>47.7379</td><td>-122.233</td><td>2720</td><td>8062</td><td>2015-02-25</td></tr><tr><td>2487200875</td><td>604000.0</td><td>4</td><td>3.0</td><td>1960</td><td>5000</td><td>1.0</td><td>0</td><td>0</td><td>5</td><td>7</td><td>1050</td><td>910</td><td>1965</td><td>null</td><td>&quot;98136&quot;</td><td>47.5208</td><td>-122.393</td><td>1360</td><td>5000</td><td>2014-12-09</td></tr><tr><td>1954400510</td><td>510000.0</td><td>3</td><td>2.0</td><td>1680</td><td>8080</td><td>1.0</td><td>0</td><td>0</td><td>3</td><td>8</td><td>1680</td><td>0</td><td>1987</td><td>null</td><td>&quot;98074&quot;</td><td>47.6168</td><td>-122.045</td><td>1800</td><td>7503</td><td>2015-02-18</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>263000018</td><td>360000.0</td><td>3</td><td>2.5</td><td>1530</td><td>1131</td><td>3.0</td><td>0</td><td>0</td><td>3</td><td>8</td><td>1530</td><td>0</td><td>2009</td><td>null</td><td>&quot;98103&quot;</td><td>47.6993</td><td>-122.346</td><td>1530</td><td>1509</td><td>2014-05-21</td></tr><tr><td>6600060120</td><td>400000.0</td><td>4</td><td>2.5</td><td>2310</td><td>5813</td><td>2.0</td><td>0</td><td>0</td><td>3</td><td>8</td><td>2310</td><td>0</td><td>2014</td><td>null</td><td>&quot;98146&quot;</td><td>47.5107</td><td>-122.362</td><td>1830</td><td>7200</td><td>2015-02-23</td></tr><tr><td>1523300141</td><td>402101.0</td><td>2</td><td>0.75</td><td>1020</td><td>1350</td><td>2.0</td><td>0</td><td>0</td><td>3</td><td>7</td><td>1020</td><td>0</td><td>2009</td><td>null</td><td>&quot;98144&quot;</td><td>47.5944</td><td>-122.299</td><td>1020</td><td>2007</td><td>2014-06-23</td></tr><tr><td>291310100</td><td>400000.0</td><td>3</td><td>2.5</td><td>1600</td><td>2388</td><td>2.0</td><td>0</td><td>0</td><td>3</td><td>8</td><td>1600</td><td>0</td><td>2004</td><td>null</td><td>&quot;98027&quot;</td><td>47.5345</td><td>-122.069</td><td>1410</td><td>1287</td><td>2015-01-16</td></tr><tr><td>1523300157</td><td>325000.0</td><td>2</td><td>0.75</td><td>1020</td><td>1076</td><td>2.0</td><td>0</td><td>0</td><td>3</td><td>7</td><td>1020</td><td>0</td><td>2008</td><td>null</td><td>&quot;98144&quot;</td><td>47.5941</td><td>-122.299</td><td>1020</td><td>1357</td><td>2014-10-15</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "# transformer from a function\n",
        "tweak_transformer = FunctionTransformer(tweak_housing)\n",
        "\n",
        "tweak_transformer.fit_transform(raw)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "Uy5CEklari74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "outputId": "97d5f627-591d-4a7e-848d-68f12240307f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (21_613, 73)\n",
              "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
              "│ num__bedr ┆ num__bath ┆ num__sqft ┆ cat__zipc ┆ … ┆ cat__zipc ┆ cat__zipc ┆ cat__zipc ┆ cat__zip │\n",
              "│ ooms      ┆ rooms     ┆ _living   ┆ ode_98001 ┆   ┆ ode_98178 ┆ ode_98188 ┆ ode_98198 ┆ code_981 │\n",
              "│ ---       ┆ ---       ┆ ---       ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ 99       │\n",
              "│ f64       ┆ f64       ┆ f64       ┆ f64       ┆   ┆ f64       ┆ f64       ┆ f64       ┆ ---      │\n",
              "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ f64      │\n",
              "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
              "│ -0.398737 ┆ -1.447464 ┆ -0.979835 ┆ 0.0       ┆ … ┆ 1.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
              "│ -0.398737 ┆ 0.175607  ┆ 0.533634  ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
              "│ -1.473959 ┆ -1.447464 ┆ -1.426254 ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
              "│ 0.676485  ┆ 1.149449  ┆ -0.13055  ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
              "│ -0.398737 ┆ -0.149007 ┆ -0.435422 ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
              "│ …         ┆ …         ┆ …         ┆ …         ┆ … ┆ …         ┆ …         ┆ …         ┆ …        │\n",
              "│ -0.398737 ┆ 0.500221  ┆ -0.598746 ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
              "│ 0.676485  ┆ 0.500221  ┆ 0.250539  ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
              "│ -1.473959 ┆ -1.772078 ┆ -1.154047 ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
              "│ -0.398737 ┆ 0.500221  ┆ -0.522528 ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
              "│ -1.473959 ┆ -1.772078 ┆ -1.154047 ┆ 0.0       ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
              "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (21_613, 73)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>num__bedrooms</th><th>num__bathrooms</th><th>num__sqft_living</th><th>cat__zipcode_98001</th><th>cat__zipcode_98002</th><th>cat__zipcode_98003</th><th>cat__zipcode_98004</th><th>cat__zipcode_98005</th><th>cat__zipcode_98006</th><th>cat__zipcode_98007</th><th>cat__zipcode_98008</th><th>cat__zipcode_98010</th><th>cat__zipcode_98011</th><th>cat__zipcode_98014</th><th>cat__zipcode_98019</th><th>cat__zipcode_98022</th><th>cat__zipcode_98023</th><th>cat__zipcode_98024</th><th>cat__zipcode_98027</th><th>cat__zipcode_98028</th><th>cat__zipcode_98029</th><th>cat__zipcode_98030</th><th>cat__zipcode_98031</th><th>cat__zipcode_98032</th><th>cat__zipcode_98033</th><th>cat__zipcode_98034</th><th>cat__zipcode_98038</th><th>cat__zipcode_98039</th><th>cat__zipcode_98040</th><th>cat__zipcode_98042</th><th>cat__zipcode_98045</th><th>cat__zipcode_98052</th><th>cat__zipcode_98053</th><th>cat__zipcode_98055</th><th>cat__zipcode_98056</th><th>cat__zipcode_98058</th><th>cat__zipcode_98059</th><th>cat__zipcode_98065</th><th>cat__zipcode_98070</th><th>cat__zipcode_98072</th><th>cat__zipcode_98074</th><th>cat__zipcode_98075</th><th>cat__zipcode_98077</th><th>cat__zipcode_98092</th><th>cat__zipcode_98102</th><th>cat__zipcode_98103</th><th>cat__zipcode_98105</th><th>cat__zipcode_98106</th><th>cat__zipcode_98107</th><th>cat__zipcode_98108</th><th>cat__zipcode_98109</th><th>cat__zipcode_98112</th><th>cat__zipcode_98115</th><th>cat__zipcode_98116</th><th>cat__zipcode_98117</th><th>cat__zipcode_98118</th><th>cat__zipcode_98119</th><th>cat__zipcode_98122</th><th>cat__zipcode_98125</th><th>cat__zipcode_98126</th><th>cat__zipcode_98133</th><th>cat__zipcode_98136</th><th>cat__zipcode_98144</th><th>cat__zipcode_98146</th><th>cat__zipcode_98148</th><th>cat__zipcode_98155</th><th>cat__zipcode_98166</th><th>cat__zipcode_98168</th><th>cat__zipcode_98177</th><th>cat__zipcode_98178</th><th>cat__zipcode_98188</th><th>cat__zipcode_98198</th><th>cat__zipcode_98199</th></tr><tr><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>-0.398737</td><td>-1.447464</td><td>-0.979835</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>-0.398737</td><td>0.175607</td><td>0.533634</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>-1.473959</td><td>-1.447464</td><td>-1.426254</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>0.676485</td><td>1.149449</td><td>-0.13055</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>-0.398737</td><td>-0.149007</td><td>-0.435422</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>-0.398737</td><td>0.500221</td><td>-0.598746</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>0.676485</td><td>0.500221</td><td>0.250539</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>-1.473959</td><td>-1.772078</td><td>-1.154047</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>-0.398737</td><td>0.500221</td><td>-0.522528</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>-1.473959</td><td>-1.772078</td><td>-1.154047</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "# Transformer: Numeric/Categorical in ML-Pipeline\n",
        "categorical_features = ['zipcode']\n",
        "\n",
        "numeric_transformer = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(strategy='median')),\n",
        "    ('scaler', StandardScaler())])\n",
        "\n",
        "ct = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', numeric_transformer, numeric_features),\n",
        "        ('cat', OneHotEncoder(handle_unknown='ignore',\n",
        "                              sparse_output=False), categorical_features)])\n",
        "\n",
        "ct.fit_transform(\n",
        "    tweak_housing(raw)\n",
        "    .select([*numeric_features, *cat_features])\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "_Sz_SI-cri75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "80f90b3b-fe83-49d9-81ae-e9224dc54a41"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (21_613, 3)\n",
              "┌─────────┬──────────┬───────────────┐\n",
              "│ zipcode ┆ price    ┆ zip_mean      │\n",
              "│ ---     ┆ ---      ┆ ---           │\n",
              "│ i64     ┆ f64      ┆ f64           │\n",
              "╞═════════╪══════════╪═══════════════╡\n",
              "│ 98178   ┆ 221900.0 ┆ 310612.755725 │\n",
              "│ 98125   ┆ 538000.0 ┆ 469455.770732 │\n",
              "│ 98028   ┆ 180000.0 ┆ 462480.035336 │\n",
              "│ 98136   ┆ 604000.0 ┆ 551688.673004 │\n",
              "│ 98074   ┆ 510000.0 ┆ 685605.77551  │\n",
              "│ …       ┆ …        ┆ …             │\n",
              "│ 98103   ┆ 360000.0 ┆ 584919.210963 │\n",
              "│ 98146   ┆ 400000.0 ┆ 359483.239583 │\n",
              "│ 98144   ┆ 402101.0 ┆ 594547.650146 │\n",
              "│ 98027   ┆ 400000.0 ┆ 616990.592233 │\n",
              "│ 98144   ┆ 325000.0 ┆ 594547.650146 │\n",
              "└─────────┴──────────┴───────────────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (21_613, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>zipcode</th><th>price</th><th>zip_mean</th></tr><tr><td>i64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>98178</td><td>221900.0</td><td>310612.755725</td></tr><tr><td>98125</td><td>538000.0</td><td>469455.770732</td></tr><tr><td>98028</td><td>180000.0</td><td>462480.035336</td></tr><tr><td>98136</td><td>604000.0</td><td>551688.673004</td></tr><tr><td>98074</td><td>510000.0</td><td>685605.77551</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>98103</td><td>360000.0</td><td>584919.210963</td></tr><tr><td>98146</td><td>400000.0</td><td>359483.239583</td></tr><tr><td>98144</td><td>402101.0</td><td>594547.650146</td></tr><tr><td>98027</td><td>400000.0</td><td>616990.592233</td></tr><tr><td>98144</td><td>325000.0</td><td>594547.650146</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "# Custom transformer\n",
        "class ZipAvgPriceAdder(BaseEstimator, TransformerMixin): #custom transformer from SK-learn\n",
        "    def __init__(self):\n",
        "        pass\n",
        "    def fit(self, X, y=None):\n",
        "        # assume X is a polars dataframe\n",
        "        self.zip_avg_price = (X\n",
        "                              .group_by('zipcode')\n",
        "                              .agg(zip_mean=pl.col('price').mean())\n",
        "        )\n",
        "        return self\n",
        "\n",
        "    def transform(self, X, y=None):\n",
        "        return X.join(self.zip_avg_price, on='zipcode')\n",
        "\n",
        "zip_adder = ZipAvgPriceAdder()\n",
        "zip_adder.fit_transform(raw.select(['zipcode', 'price']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "P8Y5z-xkri75"
      },
      "outputs": [],
      "source": [
        "# make the pipeline\n",
        "numeric_features = ['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view',\n",
        "                    'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated',\n",
        "                    'lat', 'long', 'sqft_living15', 'sqft_lot15', 'zip_mean']\n",
        "numeric_transformer = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(strategy='median')),\n",
        "    ('scaler', StandardScaler())])\n",
        "\n",
        "categorical_features = ['zipcode']\n",
        "\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', numeric_transformer, numeric_features),\n",
        "        ('cat', OneHotEncoder(handle_unknown='ignore',\n",
        "                              sparse_output=False), categorical_features)])\n",
        "\n",
        "tweak_transformer = FunctionTransformer(tweak_housing)\n",
        "\n",
        "class ZipAvgPriceAdder(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self):\n",
        "        pass\n",
        "    def fit(self, X, y=None):\n",
        "        # assume X is a polars dataframe\n",
        "        self.zip_avg_price = (X\n",
        "                              .group_by('zipcode')\n",
        "                              .agg(zip_mean=pl.col('price').mean())\n",
        "        )\n",
        "        return self\n",
        "\n",
        "    def transform(self, X, y=None):\n",
        "        return X.join(self.zip_avg_price, on='zipcode')\n",
        "\n",
        "# Append classifier to preprocessing pipeline.\n",
        "# Now we have a full prediction pipeline.\n",
        "pipe = Pipeline(steps=[('tweak', tweak_transformer),\n",
        "                      ('zip_avg_price', ZipAvgPriceAdder()),\n",
        "                      ('preprocessor', preprocessor),\n",
        "                      ])\n",
        "\n",
        "X = raw #.drop('price')\n",
        "y = raw.select('price') # Note sklearn wants a Polars dataframe for y\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "pipe.fit_transform(raw, raw.select('price'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QeCfm1-Bri75"
      },
      "outputs": [],
      "source": [
        "pipe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zUoAzhd4ri75"
      },
      "outputs": [],
      "source": [
        "# Note sklearn wants a Polars dataframe for y\n",
        "X = raw #.drop('price')\n",
        "y = raw.select('price')\n",
        "#y = raw['price']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2meHH-NGri75"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PVzg_7n4ri75"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "icoGAbpGri76"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t-V4Q-Alri76"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wpveaO3Gri77"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "id": "c-4zCGt2ri77"
      },
      "source": [
        "\n",
        "## Challenge\n",
        "\n",
        "Make a plot to explore the relationship between the number of bedrooms and the price of the house."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o9tGgPRJri77"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "an4PNNc6ri77"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uchFSM4eri78"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a6QkUcL4ri78"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-I3rLbgVri78"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q2EguWpQri78"
      },
      "source": [
        "\n",
        "## Solution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "__KIcv1Gri78"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KarCeUWEri79"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CaJHOIkxri79"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0iyBEGF0ri79"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e2YumXY2ri79"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "plaintext"
        },
        "id": "nr2uuELBri79"
      },
      "source": [
        "# Model Creation\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tAfGN9_uri79"
      },
      "source": [
        "\n",
        "## Dummy Model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "k2EQySOVri7-"
      },
      "outputs": [],
      "source": [
        "from sklearn.dummy import DummyRegressor\n",
        "\n",
        "dummy = DummyRegressor(strategy='mean')\n",
        "y = raw.select('price')\n",
        "X_train, X_test, y_train, y_test = train_test_split(raw, y, test_size=0.2, random_state=42)\n",
        "dummy_pipe = Pipeline(steps=[('tweak', tweak_transformer),\n",
        "                      ('zip_avg_price', ZipAvgPriceAdder()),\n",
        "                      ('preprocessor', preprocessor),\n",
        "                      ('dummy', dummy),\n",
        "                      ])\n",
        "\n",
        "dummy_pipe.fit(X_train, y_train)\n",
        "dummy_pipe.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tb6iPoihri7-"
      },
      "outputs": [],
      "source": [
        "dummy_pipe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "kPFg1ENQri7_"
      },
      "outputs": [],
      "source": [
        "dummy_pipe.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VtoS3N9uri7_"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Dkqqgqnri7_"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IyhP7r6hri7_"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d8X55fWMri8A"
      },
      "source": [
        "\n",
        "## Linear Regression\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kbRVAiU2ri8A"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "\n",
        "lr =  LinearRegression()\n",
        "y = raw.select('price')\n",
        "X_train, X_test, y_train, y_test = train_test_split(raw, y, test_size=0.2, random_state=42)\n",
        "lr_pipe = Pipeline(steps=[('tweak', tweak_transformer),\n",
        "                      ('zip_avg_price', ZipAvgPriceAdder()),\n",
        "                      ('preprocessor', preprocessor),\n",
        "                        ('lr', lr),\n",
        "                      ])\n",
        "\n",
        "lr_pipe.fit(X_train, y_train)\n",
        "lr_pipe.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XONyE9HIri8A"
      },
      "outputs": [],
      "source": [
        "lr_pipe.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wicxiysfri8A"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aAHJaLI4ri8A"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ktPBsM45ri8A"
      },
      "source": [
        "\n",
        "## Decision Trees\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HVvgTPB0ri8A"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "\n",
        "dt = DecisionTreeRegressor()\n",
        "y = raw.select('price')\n",
        "X_train, X_test, y_train, y_test = train_test_split(raw, y, test_size=0.2, random_state=42)\n",
        "dt_pipe = Pipeline(steps=[('tweak', tweak_transformer),\n",
        "                      ('zip_avg_price', ZipAvgPriceAdder()),\n",
        "                      ('preprocessor', preprocessor),\n",
        "                      ('dt', dt),\n",
        "                      ])\n",
        "\n",
        "dt_pipe.fit(X_train, y_train)\n",
        "dt_pipe.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jg4eX1gzri8B"
      },
      "outputs": [],
      "source": [
        "dt_pipe.set_params(dt__max_depth=1)\n",
        "dt_pipe.fit(X_train, y_train)\n",
        "dt_pipe.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AwHc0_YZri8B"
      },
      "outputs": [],
      "source": [
        "dt_pipe.set_params(dt__max_depth=9)\n",
        "dt_pipe.fit(X_train, y_train)\n",
        "dt_pipe.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K4PHB8KWri8B"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tYyDPzn_ri8B"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zw8FROsgri8B"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iOJ40PzMri8B"
      },
      "source": [
        "\n",
        "## CatBoost\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mYiyUcZ7ri8B"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "4q5kMENmri8B"
      },
      "outputs": [],
      "source": [
        "from catboost import CatBoostRegressor\n",
        "\n",
        "\n",
        "cat = CatBoostRegressor()\n",
        "# has issues with Polars input going to use a pandas_transformer\n",
        "def to_pandas(df):\n",
        "    return df.to_pandas()\n",
        "pandas_transformer = FunctionTransformer(to_pandas)\n",
        "\n",
        "y = raw.select('price')\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(raw, y, test_size=0.2, random_state=42)\n",
        "cat_pipe = Pipeline(steps=[('tweak', tweak_transformer),\n",
        "                      ('zip_avg_price', ZipAvgPriceAdder()),\n",
        "                      ('preprocessor', preprocessor),\n",
        "                      ('to_pandas', pandas_transformer),\n",
        "                      ('cat', cat),\n",
        "                      ])\n",
        "\n",
        "cat_pipe.fit(X_train, y_train.to_numpy()[:,0])\n",
        "cat_pipe.score(X_test, y_test.to_numpy()[:,0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UoAjhUU_ri8C"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ss5yDGrlri8C"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wRqqAkBTri8C"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oH6De3yqri8C"
      },
      "source": [
        "\n",
        "## Challenge\n",
        "\n",
        "Create a pipeline for a Random Forest model and train it on the data. (see `ensemble.RandomForestRegressor` in scikit-learn). What is the score?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ax-_mKY2ri8C"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aatsPJVfri8C"
      },
      "source": [
        "\n",
        "\n",
        "## Solution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VqHlnf_Qri8C"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zk5NCbnFri8C"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b-msLNazri8C"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CLAwrp8Ori8C"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qu_kKVAMri8D"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "plaintext"
        },
        "id": "mRQlffpari8D"
      },
      "source": [
        "# Evaluation\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "81tJ9233ri8D"
      },
      "source": [
        "\n",
        "## R2\n",
        "\n",
        "\n",
        "The Coefficient of Determination, R2, is a measure of how well the model fits the data. It is a value between 0 and 1. It tells us how much of the variance in the target variable is predictable from the features.\n",
        "\n",
        "A value of 0 means that the model explains none of the variability. A value of 1 means that the model explains all the variability.\n",
        "\n",
        "Note that it doesn't indicate whether a model is overfitting or underfitting the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "vC0Zt658ri8D"
      },
      "outputs": [],
      "source": [
        "cat_pipe.score(X_test, y_test.to_numpy()[:,0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "paJdHuMori8D"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X1tBWkKAri8D"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ghOSP2Prri8E"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K5heeIEsri8E"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t44fpNJMri8E"
      },
      "source": [
        "\n",
        "## Mean Squared/Absolute Error\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xHfo_s4gri8E"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "mean_squared_error(y_test, cat_pipe.predict(X_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hQiUxtT7ri8E"
      },
      "outputs": [],
      "source": [
        "# rmse\n",
        "mean_squared_error(y_test, cat_pipe.predict(X_test), squared=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "QSLjMOY-ri8E"
      },
      "outputs": [],
      "source": [
        "# absolute error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "mean_absolute_error(y_test, cat_pipe.predict(X_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iUjPIWwwri8E"
      },
      "outputs": [],
      "source": [
        "# compare to lr model\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "mean_absolute_error(y_test, lr_pipe.predict(X_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q9nrBN9lri8E"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "68bGsXXFri8E"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AS2CU8YCri8F"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UCtkTEv7ri8F"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "InPCvIf-ri8F"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y3DuixY6ri8F"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nYRIjo9Ori8F"
      },
      "source": [
        "\n",
        "## Residuals Plot\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L-Not3rAri8F"
      },
      "outputs": [],
      "source": [
        "# make a residual plot\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "ax = plt.scatter(cat_pipe.predict(X_test),\n",
        "    y_test.to_series().to_numpy() - cat_pipe.predict(X_test), alpha=0.1)\n",
        "# make labels not be scientific notation\n",
        "plt.ticklabel_format(style='plain', axis='y')\n",
        "plt.ticklabel_format(style='plain', axis='x')\n",
        "plt.ylim(-500_000, 500_000)\n",
        "plt.xlabel('Predicted price')\n",
        "plt.ylabel('Residual')\n",
        "plt.title('Residual plot')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qS9ciS-sri8F"
      },
      "outputs": [],
      "source": [
        "# plot with Polars\n",
        "(y_test\n",
        " .with_columns(predicted_price=cat_pipe.predict(X_test),\n",
        "   residual=y_test.to_series().to_numpy() - cat_pipe.predict(X_test))\n",
        " .plot.scatter('predicted_price', 'residual', alpha=0.1, yformatter='$%.0f',\n",
        "               xformatter='$%.0f')\n",
        " )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AVJv8I9Yri8F"
      },
      "outputs": [],
      "source": [
        "def residuals_plot(model, X_train, y_train, X_test, y_test):\n",
        "    return (y_test\n",
        "      .with_columns(prediction=model.predict(X_test),\n",
        "        residual=y_test.to_series().to_numpy() - model.predict(X_test),\n",
        "        type=pl.lit('test'))\n",
        "      .vstack(y_train\n",
        "        .with_columns(prediction=model.predict(X_train),\n",
        "          residual=y_train.to_series().to_numpy() - model.predict(X_train),\n",
        "          type=pl.lit('train'))\n",
        "              )\n",
        "      .reverse()\n",
        "      .plot.scatter('prediction', 'residual', alpha=0.1, yformatter='$%.0f',\n",
        "                    xformatter='$%.0f', by='type')\n",
        " )\n",
        "\n",
        "residuals_plot(cat_pipe, X_train, y_train, X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FZm5p_zxri8F"
      },
      "outputs": [],
      "source": [
        "residuals_plot(dt_pipe, X_train, y_train, X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rmKODLtKri8G"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jyBiK7dFri8G"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bFrVyhRtri8G"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1fyRyJ7Zri8G"
      },
      "source": [
        "## Challenge\n",
        "\n",
        "What is the mean squared error of the Random Forest model? What is the R2 score? What do these values tell us about the model?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UmB7ZPC0ri8G"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k6gTpJDRri8G"
      },
      "source": [
        "\n",
        "\n",
        "## Solution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6tGTSyYlri8G"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VxhiqcRnri8G"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ImK2jmwGri8G"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YUJXh7dIri8G"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cBYg4p2Vri8H"
      },
      "source": [
        "# Model Tuning\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7QN1bBz-ri8H"
      },
      "source": [
        "\n",
        "## Hyperparameters\n",
        "\n",
        "Hyperparameters are the levers we can pull to adjust the behavior of a model. They are set before the model is trained and remain constant during training."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Euq6xKtUri8H"
      },
      "source": [
        "\n",
        "## Tuning Linear Regression\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PAzV9g4Rri8H"
      },
      "outputs": [],
      "source": [
        "lr_pipe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lYVO_Hffri8H"
      },
      "outputs": [],
      "source": [
        "lr_pipe.named_steps['lr']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "9lBZjs2zri8H"
      },
      "outputs": [],
      "source": [
        "help(lr_pipe.named_steps['lr'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "NOoJMM8Cri8I"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "Ridge?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AGZlv2Lfri8I"
      },
      "outputs": [],
      "source": [
        "rr =  Ridge()\n",
        "y = raw.select('price')\n",
        "X_train, X_test, y_train, y_test = train_test_split(raw, y, test_size=0.2, random_state=42)\n",
        "rr_pipe = Pipeline(steps=[('tweak', tweak_transformer),\n",
        "                      ('zip_avg_price', ZipAvgPriceAdder()),\n",
        "                      ('preprocessor', preprocessor),\n",
        "                        ('rr', rr),\n",
        "                      ])\n",
        "\n",
        "rr_pipe.fit(X_train, y_train)\n",
        "rr_pipe.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_8zQ5Moqri8I"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "uxVXU89Cri8I"
      },
      "outputs": [],
      "source": [
        "lr_pipe.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true
        },
        "id": "28KkPbp3ri8I"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import validation_curve\n",
        "\n",
        "param_range = [0, .01, .05, .1, .5, 1, 2]\n",
        "scores = []\n",
        "for val in param_range:\n",
        "    rr_pipe.set_params(rr__alpha=val)\n",
        "    rr_pipe.fit(X_train, y_train)\n",
        "    scores.append(rr_pipe.score(X_test, y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LDjbZgs3ri8I"
      },
      "outputs": [],
      "source": [
        "# Our be score is at 0 (which is normal Linear Regression)\n",
        "alpha = pl.DataFrame({'val': param_range,\n",
        "              'scores': scores})\n",
        "alpha.plot(x='val', y='scores')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WMaVEuuTri8I"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FnzLuIzAri8I"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zsO1rO1Hri8I"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vNUY08DWri8I"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mRAGB5jlri8J"
      },
      "source": [
        "\n",
        "## Tuning Decision Trees\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "m0u26xkkri8J"
      },
      "outputs": [],
      "source": [
        "dt_pipe.named_steps['dt']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "vW0Ik8-wri8J"
      },
      "outputs": [],
      "source": [
        "help(dt_pipe.named_steps['dt'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "6IQsLjoDri8J"
      },
      "outputs": [],
      "source": [
        "# plot a validation curve tracking mse as the max_depth of the decision tree increases\n",
        "from sklearn.model_selection import validation_curve\n",
        "\n",
        "param_range = range(1, 20)\n",
        "train_scores, test_scores = validation_curve(\n",
        "    dt_pipe, X_train, y_train, param_name=\"dt__max_depth\", param_range=param_range,\n",
        "    scoring=\"neg_mean_squared_error\", n_jobs=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3i1yGF1Ari8J"
      },
      "outputs": [],
      "source": [
        "# make a validation curve from train_scores and test_scores\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "train_scores_mean = np.mean(train_scores, axis=1)\n",
        "train_scores_std = np.std(train_scores, axis=1)\n",
        "test_scores_mean = np.mean(test_scores, axis=1)\n",
        "test_scores_std = np.std(test_scores, axis=1)\n",
        "\n",
        "plt.title(\"Validation Curve with Decision Tree\")\n",
        "plt.xlabel(\"max_depth\")\n",
        "plt.ylabel(\"Score\")\n",
        "#plt.ylim(-1, 0)\n",
        "lw = 2\n",
        "plt.plot(param_range, train_scores_mean, label=\"Training score\",\n",
        "             color=\"darkorange\", lw=lw)\n",
        "plt.fill_between(param_range, train_scores_mean - train_scores_std,\n",
        "                 train_scores_mean + train_scores_std, alpha=0.2,\n",
        "                 color=\"darkorange\", lw=lw)\n",
        "plt.plot(param_range, test_scores_mean, label=\"Cross-validation score\",\n",
        "                color=\"navy\", lw=lw)\n",
        "\n",
        "plt.fill_between(param_range, test_scores_mean - test_scores_std,\n",
        "                    test_scores_mean + test_scores_std, alpha=0.2,\n",
        "                    color=\"navy\", lw=lw)\n",
        "plt.legend(loc=\"best\")\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CnOHXBQAri8J"
      },
      "outputs": [],
      "source": [
        "# train dt_pipe with max_depth=8\n",
        "dt8_pipe = Pipeline(steps=[('tweak', tweak_transformer),\n",
        "                        ('zip_avg_price', ZipAvgPriceAdder()),\n",
        "                        ('to_pandas', pandas_transformer),\n",
        "                        ('preprocessor', preprocessor),\n",
        "                        ('dt', DecisionTreeRegressor(max_depth=8)),\n",
        "                        ])\n",
        "\n",
        "dt8_pipe.fit(X_train, y_train)\n",
        "dt8_pipe.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2Z6fk2vvri8J"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "mean_squared_error(y_test, dt8_pipe.predict(X_test), squared=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qHe6NmB7ri8J"
      },
      "outputs": [],
      "source": [
        "dt_pipe.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zaI2vNslri8J"
      },
      "outputs": [],
      "source": [
        "mean_squared_error(y_test, dt_pipe.predict(X_test), squared=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pvw4vHYdri8K"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j8szhOyUri8K"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XMQLYJAUri8K"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JRdLvrEari8K"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3BDWpG0-ri8K"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qzogfmhsri8K"
      },
      "source": [
        "\n",
        "## Tuning CatBoost\n",
        "\n",
        "* Boosting - `iterations` (`num_trees`, `n_estimators`), `learning_rate` (`eta`), `early_stopping_rounds`\n",
        "\n",
        "* Tree based - `depth` (`max_depth`), `grow_policy`, `min_child_samples` (`min_data_in_leaf`), `max_leaves` (`num_leaves`)\n",
        "\n",
        "* Sampling - `subsample`, `sampling_frequency`, `rsm` (`colsample_bylevel`), `random_strength`, `bagging_temperature`\n",
        "\n",
        "* Regularization - `l2_leaf_reg` (`reg_lambda`), `model_shrink_rate`\n",
        "\n",
        "* Constraints - `monotone_constraints`, `feature_weights`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "MrYLwvXyri8K"
      },
      "outputs": [],
      "source": [
        "catboost.CatBoostRegressor?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e6SCw0Efri8K"
      },
      "outputs": [],
      "source": [
        "cr2 = catboost.CatBoostRegressor(iterations=3000, learning_rate=0.1,\n",
        "                                 early_stopping_rounds=10)\n",
        "X_train, X_test, y_train, y_test = train_test_split(raw.drop('price'), y,\n",
        "                                                    test_size=0.2, random_state=42)\n",
        "\n",
        "cr2.fit(X_train.to_pandas(), y_train.to_numpy(), cat_features=['zipcode'], verbose=100,\n",
        "        early_stopping_rounds=10, eval_set=(X_test.to_pandas(), y_test.to_numpy()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "OJrN-aifri8K"
      },
      "outputs": [],
      "source": [
        "# plot a validation curve tracking mse as the max_depth of the decision tree increases\n",
        "from sklearn.model_selection import validation_curve\n",
        "\n",
        "param_range = range(1, 10)\n",
        "train_scores, test_scores = validation_curve(\n",
        "    cr2, X_train.to_pandas(), y_train.to_numpy(), param_name=\"max_depth\",\n",
        "    param_range=param_range,\n",
        "    scoring=\"neg_mean_squared_error\", n_jobs=1,\n",
        "    fit_params=dict(early_stopping_rounds=10,\n",
        "                    eval_set=(X_test.to_pandas(), y_test.to_numpy())))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pWm1bC3Ori8K"
      },
      "outputs": [],
      "source": [
        "# make a validation curve from train_scores and test_scores\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "train_scores_mean = np.mean(train_scores, axis=1)\n",
        "train_scores_std = np.std(train_scores, axis=1)\n",
        "test_scores_mean = np.mean(test_scores, axis=1)\n",
        "test_scores_std = np.std(test_scores, axis=1)\n",
        "\n",
        "plt.title(\"Validation Curve with CatBoost\")\n",
        "plt.xlabel(\"max_depth\")\n",
        "plt.ylabel(\"Score\")\n",
        "#plt.ylim(-1, 0)\n",
        "lw = 2\n",
        "plt.plot(param_range, train_scores_mean, label=\"Training score\",\n",
        "             color=\"darkorange\", lw=lw)\n",
        "plt.fill_between(param_range, train_scores_mean - train_scores_std,\n",
        "                 train_scores_mean + train_scores_std, alpha=0.2,\n",
        "                 color=\"darkorange\", lw=lw)\n",
        "plt.plot(param_range, test_scores_mean, label=\"Cross-validation score\",\n",
        "                color=\"navy\", lw=lw)\n",
        "\n",
        "plt.fill_between(param_range, test_scores_mean - test_scores_std,\n",
        "                    test_scores_mean + test_scores_std, alpha=0.2,\n",
        "                    color=\"navy\", lw=lw)\n",
        "plt.legend(loc=\"best\")\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m0Z-hYL5ri8L"
      },
      "outputs": [],
      "source": [
        "# set max_depth to 4\n",
        "cr2_4 = catboost.CatBoostRegressor(iterations=3000, learning_rate=0.1,\n",
        "                                max_depth=4)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(raw.drop('price'), y,\n",
        "                                                    test_size=0.2, random_state=42)\n",
        "\n",
        "cr2_4.fit(X_train.to_pandas(), y_train.to_numpy(), cat_features=['zipcode'], verbose=100,\n",
        "        early_stopping_rounds=10, eval_set=(X_test.to_pandas(), y_test.to_numpy()))\n",
        "cr2_4.score(X_test.to_pandas(), y_test.to_numpy())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "id": "JCYYUiJkri8L"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uswrRzB9ri8L"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u_V3Vg6Rri8L"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WfrB8zbyri8L"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iasL-cN6ri8L"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-KZQd6eeri8L"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FRCr-OMWri8L"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HDOkrv59ri8L"
      },
      "source": [
        "\n",
        "## Grid Search\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "d7EFESy_ri8L"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "\n",
        "dt = DecisionTreeRegressor()\n",
        "y = raw.select('price')\n",
        "X_train, X_test, y_train, y_test = train_test_split(raw, y, test_size=0.2, random_state=42)\n",
        "dt_pipe = Pipeline(steps=[('tweak', tweak_transformer),\n",
        "                      ('zip_avg_price', ZipAvgPriceAdder()),\n",
        "                      ('preprocessor', preprocessor),\n",
        "                      ('dt', dt),\n",
        "                      ])\n",
        "\n",
        "dt_pipe.fit(X_train, y_train)\n",
        "dt_pipe.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true
        },
        "scrolled": true,
        "id": "RhjpjCzgri8L"
      },
      "outputs": [],
      "source": [
        "dt_pipe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "i4QuRKG9ri8L"
      },
      "outputs": [],
      "source": [
        "# use grid search on decision tree\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "param_grid = {\n",
        "    'dt__max_depth': [3, 6, 9],\n",
        "    'dt__min_samples_split': [10, 20, 100],\n",
        "    'dt__min_samples_leaf': [10, 20, 100],\n",
        "}\n",
        "\n",
        "grid_search = GridSearchCV(dt_pipe, param_grid, cv=5)#, scoring='neg_mean_squared_error')\n",
        "grid_search.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v9KMqQgiri8L"
      },
      "outputs": [],
      "source": [
        "grid_search.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "GvVTHjOfri8M"
      },
      "outputs": [],
      "source": [
        "# make a tree from the params\n",
        "dt = DecisionTreeRegressor()#max_depth=9, min_samples_leaf=20, min_samples_split=10)\n",
        "dt_pipe = Pipeline(steps=[('tweak', tweak_transformer),\n",
        "                      ('zip_avg_price', ZipAvgPriceAdder()),\n",
        "                      ('to_pandas', pandas_transformer),\n",
        "                      ('preprocessor', preprocessor),\n",
        "                        ('dt', dt),\n",
        "                      ])\n",
        "dt_pipe.set_params(**grid_search.best_params_)\n",
        "dt_pipe.fit(X_train, y_train)\n",
        "dt_pipe.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "Tf4YWMYsri8M"
      },
      "outputs": [],
      "source": [
        "# compare to default\n",
        "dt = DecisionTreeRegressor(random_state=42)\n",
        "dt_pipe = Pipeline(steps=[('tweak', tweak_transformer),\n",
        "                      ('zip_avg_price', ZipAvgPriceAdder()),\n",
        "                      ('to_pandas', pandas_transformer),\n",
        "                      ('preprocessor', preprocessor),\n",
        "                        ('dt', dt),\n",
        "                      ])\n",
        "\n",
        "dt_pipe.fit(X_train, y_train)\n",
        "dt_pipe.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sGm1ic15ri8M"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-fliz_97ri8M"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h-QjWAMRri8M"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dmflFJZXri8M"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4KdtVMElri8M"
      },
      "source": [
        "\n",
        "## Challenge\n",
        "\n",
        "Do a grid search to find the best depth for the random forest model. What is the best depth? What is the score of the model with the best depth?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DlPVPaZeri8M"
      },
      "source": [
        "\n",
        "## Solution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_k9r__BIri8M"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xkj3AFYgri8N"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qpXvgR4nri8N"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XwLEYnSari8N"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fIe_DFffri8N"
      },
      "source": [
        "# Model Deployment\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z4rahiG5ri8N"
      },
      "source": [
        "\n",
        "## End to end notebook\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g6IDxuyRri8N"
      },
      "outputs": [],
      "source": [
        "import polars as pl\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import FunctionTransformer\n",
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "from sklearn import set_config\n",
        "set_config(transform_output='polars')\n",
        "\n",
        "def tweak_housing(df):\n",
        "    return (df\n",
        "            .with_columns(zipcode=pl.col('zipcode').cast(pl.String).cast(pl.Categorical),\n",
        "                          date=pl.date(pl.col('date_year'), pl.col('date_month'), pl.col('date_day')),\n",
        "                          yr_renovated=pl.col('yr_renovated').replace(0, None),\n",
        "                          )\n",
        "            .select(['id', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors',\n",
        "                     'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement',\n",
        "                     'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long', 'sqft_living15',\n",
        "                     'sqft_lot15', 'date',  #'date_year', 'date_month', 'date_day',\n",
        "                     ])\n",
        "    )\n",
        "\n",
        "# make the pipeline\n",
        "numeric_features = ['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view',\n",
        "                    'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated',\n",
        "                    'lat', 'long', 'sqft_living15', 'sqft_lot15', 'zip_mean']\n",
        "numeric_transformer = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(strategy='median')),\n",
        "    ('scaler', StandardScaler())])\n",
        "\n",
        "categorical_features = ['zipcode']\n",
        "\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', numeric_transformer, numeric_features),\n",
        "        ('cat', OneHotEncoder(handle_unknown='ignore',\n",
        "                              sparse_output=False), categorical_features)])\n",
        "\n",
        "def to_pandas(df):\n",
        "    return df.to_pandas()\n",
        "pandas_transformer = FunctionTransformer(to_pandas)\n",
        "\n",
        "tweak_transformer = FunctionTransformer(tweak_housing)\n",
        "\n",
        "class ZipAvgPriceAdder(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self):\n",
        "        pass\n",
        "    def fit(self, X, y=None):\n",
        "        # assume X is a polars dataframe\n",
        "        self.zip_avg_price = (X\n",
        "                              .group_by('zipcode')\n",
        "                              .agg(zip_mean=pl.col('price').mean())\n",
        "        )\n",
        "        return self\n",
        "\n",
        "    def transform(self, X, y=None):\n",
        "        with pl.StringCache():\n",
        "            return X.join(self.zip_avg_price, on='zipcode')\n",
        "\n",
        "\n",
        "# King County House Sales dataset from OpenML (includes Seattle)\n",
        "# this is an ARFF file, which is a text file with a specific format\n",
        "url = 'https://www.openml.org/data/download/22044765/dataset'\n",
        "cols = ['id', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view',\n",
        "        'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated',\n",
        "        'zipcode', 'lat', 'long', 'sqft_living15', 'sqft_lot15', 'date_year', 'date_month', 'date_day']\n",
        "\n",
        "raw = pl.read_csv(url, new_columns=cols, skip_rows=31, has_header=False)\n",
        "\n",
        "lr =  LinearRegression()\n",
        "y = raw.select('price')\n",
        "X_train, X_test, y_train, y_test = train_test_split(raw, y, test_size=0.2, random_state=42)\n",
        "lr_pipe = Pipeline(steps=[('tweak', tweak_transformer),\n",
        "                      ('zip_avg_price', ZipAvgPriceAdder()),\n",
        "                      ('preprocessor', preprocessor),\n",
        "                      ('lr', lr),\n",
        "                      ])\n",
        "\n",
        "lr_pipe.fit(X_train, y_train)\n",
        "lr_pipe.score(X_test, y_test)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IGZfle-bri8N"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gFPgMf4Uri8N"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dU1OEVgMri8O"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sQ-bvxg6ri8O"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ng-bchLNri8O"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N0QqnmSDri8O"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wri1BShwri8O"
      },
      "source": [
        "## Using MLFlow\n",
        "\n",
        "Going to show how to persist and load a model, but can also:\n",
        "\n",
        "- Start a endpoint to serve predictions\n",
        "- Build a Docker image\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FPLkeX-2ri8O"
      },
      "outputs": [],
      "source": [
        "import mlflow"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h1YdDIInri8O"
      },
      "outputs": [],
      "source": [
        "mlflow.__version__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GdY6oBkOri8O"
      },
      "outputs": [],
      "source": [
        "model_info = mlflow.sklearn.log_model(lr_pipe, artifact_path='lr_pipe')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rH5K7nZRri8O"
      },
      "outputs": [],
      "source": [
        "model_info.artifact_path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y8EHnDrRri8O"
      },
      "outputs": [],
      "source": [
        "!tree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZynIQWlbri8P"
      },
      "outputs": [],
      "source": [
        "model_info.run_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nNEyjQcDri8P"
      },
      "outputs": [],
      "source": [
        "model = mlflow.pyfunc.load_model(f'mlruns/0/{model_info.run_id}/artifacts/lr_pipe')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bs2YBt5Sri8P"
      },
      "outputs": [],
      "source": [
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n4OW1xh0ri8P"
      },
      "outputs": [],
      "source": [
        "model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5c0niwnIri8P"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6p1HC-Thri8P"
      },
      "source": [
        "## Challenge\n",
        "\n",
        "Reformat your notebook so that you can load the data and create an optimized random forest model in a single cell. Then, use MLFlow to log the model and its parameters."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pkdg0rQqri8Q"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wIhvIHWmri8Q"
      },
      "source": [
        "\n",
        "## Solution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iZJFsZ3Iri8Q"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sHrq3njFri8Q"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EPD2fjP7ri8Q"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.1"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}