Copy_of_LS_DS_213_assignment.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
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        "<a href=\"https://colab.research.google.com/github/thecodinguru/DS-Unit-2-Linear-Models/blob/master/Copy_of_LS_DS_213_assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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      "source": [
        "Lambda School Data Science\n",
        "\n",
        "*Unit 2, Sprint 1, Module 3*\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "7IXUfiQ2UKj6"
      },
      "source": [
        "# Ridge Regression\n",
        "\n",
        "## Assignment\n",
        "\n",
        "We're going back to our other **New York City** real estate dataset. Instead of predicting apartment rents, you'll predict property sales prices.\n",
        "\n",
        "But not just for condos in Tribeca...\n",
        "\n",
        "- [ ] Use a subset of the data where `BUILDING_CLASS_CATEGORY` == `'01 ONE FAMILY DWELLINGS'` and the sale price was more than 100 thousand and less than 2 million.\n",
        "- [ ] Do train/test split. Use data from January — March 2019 to train. Use data from April 2019 to test.\n",
        "- [ ] Do one-hot encoding of categorical features.\n",
        "- [ ] Do feature selection with `SelectKBest`.\n",
        "- [ ] Fit a ridge regression model with multiple features. Use the `normalize=True` parameter (or do [feature scaling](https://scikit-learn.org/stable/modules/preprocessing.html) beforehand — use the scaler's `fit_transform` method with the train set, and the scaler's `transform` method with the test set)\n",
        "- [ ] Get mean absolute error for the test set.\n",
        "- [ ] As always, commit your notebook to your fork of the GitHub repo.\n",
        "\n",
        "The [NYC Department of Finance](https://www1.nyc.gov/site/finance/taxes/property-rolling-sales-data.page) has a glossary of property sales terms and NYC Building Class Code Descriptions. The data comes from the [NYC OpenData](https://data.cityofnewyork.us/browse?q=NYC%20calendar%20sales) portal.\n",
        "\n",
        "\n",
        "## Stretch Goals\n",
        "\n",
        "Don't worry, you aren't expected to do all these stretch goals! These are just ideas to consider and choose from.\n",
        "\n",
        "- [ ] Add your own stretch goal(s) !\n",
        "- [ ] Instead of `Ridge`, try `LinearRegression`. Depending on how many features you select, your errors will probably blow up! 💥\n",
        "- [ ] Instead of `Ridge`, try [`RidgeCV`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeCV.html).\n",
        "- [ ] Learn more about feature selection:\n",
        "    - [\"Permutation importance\"](https://www.kaggle.com/dansbecker/permutation-importance)\n",
        "    - [scikit-learn's User Guide for Feature Selection](https://scikit-learn.org/stable/modules/feature_selection.html)\n",
        "    - [mlxtend](http://rasbt.github.io/mlxtend/) library\n",
        "    - scikit-learn-contrib libraries: [boruta_py](https://github.com/scikit-learn-contrib/boruta_py) & [stability-selection](https://github.com/scikit-learn-contrib/stability-selection)\n",
        "    - [_Feature Engineering and Selection_](http://www.feat.engineering/) by Kuhn & Johnson.\n",
        "- [ ] Try [statsmodels](https://www.statsmodels.org/stable/index.html) if you’re interested in more inferential statistical approach to linear regression and feature selection, looking at p values and 95% confidence intervals for the coefficients.\n",
        "- [ ] Read [_An Introduction to Statistical Learning_](http://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf), Chapters 1-3, for more math & theory, but in an accessible, readable way.\n",
        "- [ ] Try [scikit-learn pipelines](https://scikit-learn.org/stable/modules/compose.html)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "o9eSnDYhUGD7",
        "colab": {}
      },
      "source": [
        "%%capture\n",
        "import sys\n",
        "\n",
        "# If you're on Colab:\n",
        "if 'google.colab' in sys.modules:\n",
        "    DATA_PATH = 'https://raw.githubusercontent.com/LambdaSchool/DS-Unit-2-Applied-Modeling/master/data/'\n",
        "    !pip install category_encoders==2.*\n",
        "\n",
        "# If you're working locally:\n",
        "else:\n",
        "    DATA_PATH = '../data/'\n",
        "    \n",
        "# Ignore this Numpy warning when using Plotly Express:\n",
        "# FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
        "import warnings\n",
        "warnings.filterwarnings(action='ignore', category=FutureWarning, module='numpy')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QJBD4ruICm1m",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import pandas_profiling\n",
        "\n",
        "# Read New York City property sales data\n",
        "df = pd.read_csv(DATA_PATH+'condos/NYC_Citywide_Rolling_Calendar_Sales.csv')\n",
        "\n",
        "# Change column names: replace spaces with underscores\n",
        "df.columns = [col.replace(' ', '_') for col in df]\n",
        "\n",
        "# SALE_PRICE was read as strings.\n",
        "# Remove symbols, convert to integer\n",
        "df['SALE_PRICE'] = (\n",
        "    df['SALE_PRICE']\n",
        "    .str.replace('$','')\n",
        "    .str.replace('-','')\n",
        "    .str.replace(',','')\n",
        "    .astype(int)\n",
        ")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ql4LVuHyOZMw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# BOROUGH is a numeric column, but arguably should be a categorical feature,\n",
        "# so convert it from a number to a string\n",
        "df['BOROUGH'] = df['BOROUGH'].astype(str)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qe-OEnsIOZMy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Reduce cardinality for NEIGHBORHOOD feature\n",
        "\n",
        "# Get a list of the top 10 neighborhoods\n",
        "top10 = df['NEIGHBORHOOD'].value_counts()[:10].index\n",
        "\n",
        "# At locations where the neighborhood is NOT in the top 10, \n",
        "# replace the neighborhood with 'OTHER'\n",
        "df.loc[~df['NEIGHBORHOOD'].isin(top10), 'NEIGHBORHOOD'] = 'OTHER'"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vS3zKUHCOZM2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "bcb7c8f0-3c53-438b-a739-1f308bae28fa"
      },
      "source": [
        "#Let's explore our data\n",
        "print(df.shape)\n",
        "df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(23040, 21)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BOROUGH</th>\n",
              "      <th>NEIGHBORHOOD</th>\n",
              "      <th>BUILDING_CLASS_CATEGORY</th>\n",
              "      <th>TAX_CLASS_AT_PRESENT</th>\n",
              "      <th>BLOCK</th>\n",
              "      <th>LOT</th>\n",
              "      <th>EASE-MENT</th>\n",
              "      <th>BUILDING_CLASS_AT_PRESENT</th>\n",
              "      <th>ADDRESS</th>\n",
              "      <th>APARTMENT_NUMBER</th>\n",
              "      <th>ZIP_CODE</th>\n",
              "      <th>RESIDENTIAL_UNITS</th>\n",
              "      <th>COMMERCIAL_UNITS</th>\n",
              "      <th>TOTAL_UNITS</th>\n",
              "      <th>LAND_SQUARE_FEET</th>\n",
              "      <th>GROSS_SQUARE_FEET</th>\n",
              "      <th>YEAR_BUILT</th>\n",
              "      <th>TAX_CLASS_AT_TIME_OF_SALE</th>\n",
              "      <th>BUILDING_CLASS_AT_TIME_OF_SALE</th>\n",
              "      <th>SALE_PRICE</th>\n",
              "      <th>SALE_DATE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>13 CONDOS - ELEVATOR APARTMENTS</td>\n",
              "      <td>2</td>\n",
              "      <td>716</td>\n",
              "      <td>1246</td>\n",
              "      <td>NaN</td>\n",
              "      <td>R4</td>\n",
              "      <td>447 WEST 18TH STREET, PH12A</td>\n",
              "      <td>PH12A</td>\n",
              "      <td>10011.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10,733</td>\n",
              "      <td>1979.0</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>2</td>\n",
              "      <td>R4</td>\n",
              "      <td>0</td>\n",
              "      <td>01/01/2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>21 OFFICE BUILDINGS</td>\n",
              "      <td>4</td>\n",
              "      <td>812</td>\n",
              "      <td>68</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O5</td>\n",
              "      <td>144 WEST 37TH STREET</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10018.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2,962</td>\n",
              "      <td>15435.0</td>\n",
              "      <td>1920.0</td>\n",
              "      <td>4</td>\n",
              "      <td>O5</td>\n",
              "      <td>0</td>\n",
              "      <td>01/01/2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>21 OFFICE BUILDINGS</td>\n",
              "      <td>4</td>\n",
              "      <td>839</td>\n",
              "      <td>69</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O5</td>\n",
              "      <td>40 WEST 38TH STREET</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10018.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2,074</td>\n",
              "      <td>11332.0</td>\n",
              "      <td>1930.0</td>\n",
              "      <td>4</td>\n",
              "      <td>O5</td>\n",
              "      <td>0</td>\n",
              "      <td>01/01/2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>13 CONDOS - ELEVATOR APARTMENTS</td>\n",
              "      <td>2</td>\n",
              "      <td>592</td>\n",
              "      <td>1041</td>\n",
              "      <td>NaN</td>\n",
              "      <td>R4</td>\n",
              "      <td>1 SHERIDAN SQUARE, 8C</td>\n",
              "      <td>8C</td>\n",
              "      <td>10014.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>R4</td>\n",
              "      <td>0</td>\n",
              "      <td>01/01/2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>UPPER EAST SIDE (59-79)</td>\n",
              "      <td>15 CONDOS - 2-10 UNIT RESIDENTIAL</td>\n",
              "      <td>2C</td>\n",
              "      <td>1379</td>\n",
              "      <td>1402</td>\n",
              "      <td>NaN</td>\n",
              "      <td>R1</td>\n",
              "      <td>20 EAST 65TH STREET, B</td>\n",
              "      <td>B</td>\n",
              "      <td>10065.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>6406.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>R1</td>\n",
              "      <td>0</td>\n",
              "      <td>01/01/2019</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  BOROUGH             NEIGHBORHOOD  ... SALE_PRICE   SALE_DATE\n",
              "0       1                    OTHER  ...          0  01/01/2019\n",
              "1       1                    OTHER  ...          0  01/01/2019\n",
              "2       1                    OTHER  ...          0  01/01/2019\n",
              "3       1                    OTHER  ...          0  01/01/2019\n",
              "4       1  UPPER EAST SIDE (59-79)  ...          0  01/01/2019\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kGosgiU1PA7m",
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        "colab": {
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          "height": 820
        },
        "outputId": "6a8d464d-0e7c-48dc-e492-31274fee62db"
      },
      "source": [
        "# Use a subset of the data where BUILDING_CLASS_CATEGORY == '01 ONE FAMILY DWELLINGS' \n",
        "# and the sale price was more than 100 thousand and less than 2 million.\n",
        "\n",
        "df_single = df[(df['BUILDING_CLASS_CATEGORY']== '01 ONE FAMILY DWELLINGS') & \n",
        "               (df['SALE_PRICE'] < 2000000) & (df['SALE_PRICE'] > 100000)]\n",
        "\n",
        "print(df_single.info())\n",
        "df_single.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 3151 entries, 44 to 23035\n",
            "Data columns (total 21 columns):\n",
            " #   Column                          Non-Null Count  Dtype  \n",
            "---  ------                          --------------  -----  \n",
            " 0   BOROUGH                         3151 non-null   object \n",
            " 1   NEIGHBORHOOD                    3151 non-null   object \n",
            " 2   BUILDING_CLASS_CATEGORY         3151 non-null   object \n",
            " 3   TAX_CLASS_AT_PRESENT            3151 non-null   object \n",
            " 4   BLOCK                           3151 non-null   int64  \n",
            " 5   LOT                             3151 non-null   int64  \n",
            " 6   EASE-MENT                       0 non-null      float64\n",
            " 7   BUILDING_CLASS_AT_PRESENT       3151 non-null   object \n",
            " 8   ADDRESS                         3151 non-null   object \n",
            " 9   APARTMENT_NUMBER                1 non-null      object \n",
            " 10  ZIP_CODE                        3151 non-null   float64\n",
            " 11  RESIDENTIAL_UNITS               3151 non-null   float64\n",
            " 12  COMMERCIAL_UNITS                3151 non-null   float64\n",
            " 13  TOTAL_UNITS                     3151 non-null   float64\n",
            " 14  LAND_SQUARE_FEET                3151 non-null   object \n",
            " 15  GROSS_SQUARE_FEET               3151 non-null   float64\n",
            " 16  YEAR_BUILT                      3151 non-null   float64\n",
            " 17  TAX_CLASS_AT_TIME_OF_SALE       3151 non-null   int64  \n",
            " 18  BUILDING_CLASS_AT_TIME_OF_SALE  3151 non-null   object \n",
            " 19  SALE_PRICE                      3151 non-null   int64  \n",
            " 20  SALE_DATE                       3151 non-null   object \n",
            "dtypes: float64(7), int64(4), object(10)\n",
            "memory usage: 541.6+ KB\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BOROUGH</th>\n",
              "      <th>NEIGHBORHOOD</th>\n",
              "      <th>BUILDING_CLASS_CATEGORY</th>\n",
              "      <th>TAX_CLASS_AT_PRESENT</th>\n",
              "      <th>BLOCK</th>\n",
              "      <th>LOT</th>\n",
              "      <th>EASE-MENT</th>\n",
              "      <th>BUILDING_CLASS_AT_PRESENT</th>\n",
              "      <th>ADDRESS</th>\n",
              "      <th>APARTMENT_NUMBER</th>\n",
              "      <th>ZIP_CODE</th>\n",
              "      <th>RESIDENTIAL_UNITS</th>\n",
              "      <th>COMMERCIAL_UNITS</th>\n",
              "      <th>TOTAL_UNITS</th>\n",
              "      <th>LAND_SQUARE_FEET</th>\n",
              "      <th>GROSS_SQUARE_FEET</th>\n",
              "      <th>YEAR_BUILT</th>\n",
              "      <th>TAX_CLASS_AT_TIME_OF_SALE</th>\n",
              "      <th>BUILDING_CLASS_AT_TIME_OF_SALE</th>\n",
              "      <th>SALE_PRICE</th>\n",
              "      <th>SALE_DATE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>3</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>01 ONE FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>5495</td>\n",
              "      <td>801</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A9</td>\n",
              "      <td>4832 BAY PARKWAY</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11230.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6,800</td>\n",
              "      <td>1325.0</td>\n",
              "      <td>1930.0</td>\n",
              "      <td>1</td>\n",
              "      <td>A9</td>\n",
              "      <td>550000</td>\n",
              "      <td>01/01/2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>4</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>01 ONE FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>7918</td>\n",
              "      <td>72</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A1</td>\n",
              "      <td>80-23 232ND STREET</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11427.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4,000</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>1940.0</td>\n",
              "      <td>1</td>\n",
              "      <td>A1</td>\n",
              "      <td>200000</td>\n",
              "      <td>01/01/2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>2</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>01 ONE FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>4210</td>\n",
              "      <td>19</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A1</td>\n",
              "      <td>1260 RHINELANDER AVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10461.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3,500</td>\n",
              "      <td>2043.0</td>\n",
              "      <td>1925.0</td>\n",
              "      <td>1</td>\n",
              "      <td>A1</td>\n",
              "      <td>810000</td>\n",
              "      <td>01/02/2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>3</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>01 ONE FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>5212</td>\n",
              "      <td>69</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A1</td>\n",
              "      <td>469 E 25TH ST</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11226.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4,000</td>\n",
              "      <td>2680.0</td>\n",
              "      <td>1899.0</td>\n",
              "      <td>1</td>\n",
              "      <td>A1</td>\n",
              "      <td>125000</td>\n",
              "      <td>01/02/2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>3</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>01 ONE FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>7930</td>\n",
              "      <td>121</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A5</td>\n",
              "      <td>5521 WHITTY LANE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11203.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1,710</td>\n",
              "      <td>1872.0</td>\n",
              "      <td>1940.0</td>\n",
              "      <td>1</td>\n",
              "      <td>A5</td>\n",
              "      <td>620000</td>\n",
              "      <td>01/02/2019</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    BOROUGH NEIGHBORHOOD  ... SALE_PRICE   SALE_DATE\n",
              "44        3        OTHER  ...     550000  01/01/2019\n",
              "61        4        OTHER  ...     200000  01/01/2019\n",
              "78        2        OTHER  ...     810000  01/02/2019\n",
              "108       3        OTHER  ...     125000  01/02/2019\n",
              "111       3        OTHER  ...     620000  01/02/2019\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7-Ed5MLPDVN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0ce3f460-dfc6-4841-be70-dee3bebf8d23"
      },
      "source": [
        "# I'll assign Sales Price as our target\n",
        "\n",
        "y = df_single['SALE_PRICE']\n",
        "\n",
        "y.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3151,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OnjF1FaoPFUu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "da55a124-f2f1-4560-a700-907a53aeb484"
      },
      "source": [
        "# I'll put the rest of the features into a dataframe.\n",
        "# Also, I'll need to drop our target from the dataframe\n",
        "\n",
        "X = df_single.drop('SALE_PRICE', axis = 1)\n",
        "X.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3151, 20)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "22wjcu94PG0m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "0f79afe6-0d4a-4d1b-ce05-ca4ca0b4aed9"
      },
      "source": [
        "#Setting training data to January — March 2019 to train. Setting testing data from April 2019\n",
        "\n",
        "X_train = X[(X['SALE_DATE'] >= '01/01/2019') & (X['SALE_DATE'] < '04/01/2019')]\n",
        "y_train = y[y.index.isin(X_train.index)]\n",
        "\n",
        "X_test = X[X['SALE_DATE'] >= '04/01/2019']\n",
        "y_test = y[y.index.isin(X_test.index)]\n",
        "\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2507, 20)\n",
            "(644, 20)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XHGCp8dRPLzZ",
        "colab_type": "text"
      },
      "source": [
        "#Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7rSg2hFiPN05",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        },
        "outputId": "99b9fc8f-3ea4-48fd-cf4a-d891d14af573"
      },
      "source": [
        "#Explore our categorical data\n",
        "\n",
        "print(df_single.info())\n",
        "df_single.describe(exclude='number')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 3151 entries, 44 to 23035\n",
            "Data columns (total 21 columns):\n",
            " #   Column                          Non-Null Count  Dtype  \n",
            "---  ------                          --------------  -----  \n",
            " 0   BOROUGH                         3151 non-null   object \n",
            " 1   NEIGHBORHOOD                    3151 non-null   object \n",
            " 2   BUILDING_CLASS_CATEGORY         3151 non-null   object \n",
            " 3   TAX_CLASS_AT_PRESENT            3151 non-null   object \n",
            " 4   BLOCK                           3151 non-null   int64  \n",
            " 5   LOT                             3151 non-null   int64  \n",
            " 6   EASE-MENT                       0 non-null      float64\n",
            " 7   BUILDING_CLASS_AT_PRESENT       3151 non-null   object \n",
            " 8   ADDRESS                         3151 non-null   object \n",
            " 9   APARTMENT_NUMBER                1 non-null      object \n",
            " 10  ZIP_CODE                        3151 non-null   float64\n",
            " 11  RESIDENTIAL_UNITS               3151 non-null   float64\n",
            " 12  COMMERCIAL_UNITS                3151 non-null   float64\n",
            " 13  TOTAL_UNITS                     3151 non-null   float64\n",
            " 14  LAND_SQUARE_FEET                3151 non-null   object \n",
            " 15  GROSS_SQUARE_FEET               3151 non-null   float64\n",
            " 16  YEAR_BUILT                      3151 non-null   float64\n",
            " 17  TAX_CLASS_AT_TIME_OF_SALE       3151 non-null   int64  \n",
            " 18  BUILDING_CLASS_AT_TIME_OF_SALE  3151 non-null   object \n",
            " 19  SALE_PRICE                      3151 non-null   int64  \n",
            " 20  SALE_DATE                       3151 non-null   object \n",
            "dtypes: float64(7), int64(4), object(10)\n",
            "memory usage: 541.6+ KB\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BOROUGH</th>\n",
              "      <th>NEIGHBORHOOD</th>\n",
              "      <th>BUILDING_CLASS_CATEGORY</th>\n",
              "      <th>TAX_CLASS_AT_PRESENT</th>\n",
              "      <th>BUILDING_CLASS_AT_PRESENT</th>\n",
              "      <th>ADDRESS</th>\n",
              "      <th>APARTMENT_NUMBER</th>\n",
              "      <th>LAND_SQUARE_FEET</th>\n",
              "      <th>BUILDING_CLASS_AT_TIME_OF_SALE</th>\n",
              "      <th>SALE_DATE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3151</td>\n",
              "      <td>3151</td>\n",
              "      <td>3151</td>\n",
              "      <td>3151</td>\n",
              "      <td>3151</td>\n",
              "      <td>3151</td>\n",
              "      <td>1</td>\n",
              "      <td>3151</td>\n",
              "      <td>3151</td>\n",
              "      <td>3151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>3135</td>\n",
              "      <td>1</td>\n",
              "      <td>1035</td>\n",
              "      <td>11</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>4</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>01 ONE FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>A1</td>\n",
              "      <td>125-27 LUCAS STREET</td>\n",
              "      <td>RP.</td>\n",
              "      <td>4,000</td>\n",
              "      <td>A1</td>\n",
              "      <td>01/31/2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1580</td>\n",
              "      <td>2959</td>\n",
              "      <td>3151</td>\n",
              "      <td>3111</td>\n",
              "      <td>1185</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>289</td>\n",
              "      <td>1186</td>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       BOROUGH NEIGHBORHOOD  ... BUILDING_CLASS_AT_TIME_OF_SALE   SALE_DATE\n",
              "count     3151         3151  ...                           3151        3151\n",
              "unique       5            7  ...                             11          91\n",
              "top          4        OTHER  ...                             A1  01/31/2019\n",
              "freq      1580         2959  ...                           1186          78\n",
              "\n",
              "[4 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NKSphMlwPOK6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "4424a39b-486b-4946-d791-e07e3cd699ca"
      },
      "source": [
        "# I need to create a subset of just the categorical data\n",
        "# Also need to drop NaN values in Apartment Number\n",
        "\n",
        "df_single_train = df_single.select_dtypes(exclude='number').drop('APARTMENT_NUMBER', axis = 1)\n",
        "\n",
        "df_single_train.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BOROUGH</th>\n",
              "      <th>NEIGHBORHOOD</th>\n",
              "      <th>BUILDING_CLASS_CATEGORY</th>\n",
              "      <th>TAX_CLASS_AT_PRESENT</th>\n",
              "      <th>BUILDING_CLASS_AT_PRESENT</th>\n",
              "      <th>ADDRESS</th>\n",
              "      <th>LAND_SQUARE_FEET</th>\n",
              "      <th>BUILDING_CLASS_AT_TIME_OF_SALE</th>\n",
              "      <th>SALE_DATE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>3</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>01 ONE FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>A9</td>\n",
              "      <td>4832 BAY PARKWAY</td>\n",
              "      <td>6,800</td>\n",
              "      <td>A9</td>\n",
              "      <td>01/01/2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>4</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>01 ONE FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>A1</td>\n",
              "      <td>80-23 232ND STREET</td>\n",
              "      <td>4,000</td>\n",
              "      <td>A1</td>\n",
              "      <td>01/01/2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>2</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>01 ONE FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>A1</td>\n",
              "      <td>1260 RHINELANDER AVE</td>\n",
              "      <td>3,500</td>\n",
              "      <td>A1</td>\n",
              "      <td>01/02/2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>3</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>01 ONE FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>A1</td>\n",
              "      <td>469 E 25TH ST</td>\n",
              "      <td>4,000</td>\n",
              "      <td>A1</td>\n",
              "      <td>01/02/2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>3</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>01 ONE FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>A5</td>\n",
              "      <td>5521 WHITTY LANE</td>\n",
              "      <td>1,710</td>\n",
              "      <td>A5</td>\n",
              "      <td>01/02/2019</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    BOROUGH NEIGHBORHOOD  ... BUILDING_CLASS_AT_TIME_OF_SALE   SALE_DATE\n",
              "44        3        OTHER  ...                             A9  01/01/2019\n",
              "61        4        OTHER  ...                             A1  01/01/2019\n",
              "78        2        OTHER  ...                             A1  01/02/2019\n",
              "108       3        OTHER  ...                             A1  01/02/2019\n",
              "111       3        OTHER  ...                             A5  01/02/2019\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eGxkGcoQPQKJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#One Hot Encoder\n",
        "# 1. Import the class\n",
        "from sklearn.preprocessing import OneHotEncoder"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zS6WWo6vPSOg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 2. Instantiate\n",
        "ohe = OneHotEncoder(sparse=False)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zPuBQ4CRPTjo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "7ef5d1f8-87b2-4ccf-aeca-e609e4dcb310"
      },
      "source": [
        "# 3. Fit the transformer to the categorical data\n",
        "ohe.fit(df_single_train)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OneHotEncoder(categories='auto', drop=None, dtype=<class 'numpy.float64'>,\n",
              "              handle_unknown='error', sparse=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "63TRsJC0PVjj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 4. Transform the data\n",
        "df_single_trans = ohe.transform(df_single_train)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MTpWhRZePXdY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        },
        "outputId": "c2fe68dd-a591-45f5-de88-e38e8d8584e7"
      },
      "source": [
        "#Check if it worked\n",
        "print(type(df_single_trans))\n",
        "\n",
        "df_single_trans"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 1., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 1., 0., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 0., 0., 1.],\n",
              "       [0., 0., 0., ..., 0., 0., 1.],\n",
              "       [0., 0., 0., ..., 0., 0., 1.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NDqg0dhTPfg7",
        "colab_type": "text"
      },
      "source": [
        "#SIngle KBest Feature Selection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kj19Ii73Pjs0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 1. Import the class\n",
        "from sklearn.feature_selection import SelectKBest"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ttSVl-QPPlu3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 2. Instantiate\n",
        "selector = SelectKBest(k=5)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SDyYwsNLPnxA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 3. Fitting and transforming data\n",
        "# Excluding Ease-ment because of NaNs\n",
        "X_train = X_train.select_dtypes(include='number').drop('EASE-MENT', axis = 1)\n",
        "X_test = X_test.select_dtypes(include='number').drop('EASE-MENT', axis = 1)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91f62DedPqBR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "136f3ab5-c9a1-4331-f11f-a5b7745fce7b"
      },
      "source": [
        "X_train_selected = selector.fit_transform(X_train, y_train)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/feature_selection/_univariate_selection.py:114: UserWarning: Features [8] are constant.\n",
            "  UserWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/feature_selection/_univariate_selection.py:115: RuntimeWarning: invalid value encountered in true_divide\n",
            "  f = msb / msw\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B1dDVqh0Pr7w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test_selected = selector.transform(X_test)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a3k3gZ4bPtzY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "8f9c2fce-cf2d-4300-eea9-56414c6e2bf3"
      },
      "source": [
        "print(X_train.shape)\n",
        "print(X_train_selected.shape)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2507, 9)\n",
            "(2507, 5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bXDOEmlLPvTm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "1805dc77-4576-4123-861f-f40df1080c75"
      },
      "source": [
        "#4. Create a mask to view selected features\n",
        "mask = selector.get_support()\n",
        "\n",
        "#Putting the values back into columns\n",
        "X_train.columns[mask]"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['RESIDENTIAL_UNITS', 'COMMERCIAL_UNITS', 'TOTAL_UNITS',\n",
              "       'GROSS_SQUARE_FEET', 'YEAR_BUILT'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBW61vHI5HXw",
        "colab_type": "text"
      },
      "source": [
        "#Ridge Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ay3fiL_e5Mgi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "92a2134f-5c0a-4a04-b933-4401f5657544"
      },
      "source": [
        "#Start with a Baseline\n",
        "\n",
        "mean_y = y_train.mean()\n",
        "\n",
        "print(f'Baseline mean (January — March 2019): ${mean_y:.2f}')"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Baseline mean (January — March 2019): $621573.74\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mL8bbb-D5NJp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cd6d4014-701e-49ae-fd99-59f92f51650f"
      },
      "source": [
        "# Lets see how much error is in our baseline train data\n",
        "# Train Error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "y_pred = [y_train.mean()] * len(y_train)\n",
        "\n",
        "mae = mean_absolute_error(y_train, y_pred)\n",
        "print(f'Train Error (January — March 2019): ${mae:.2f}')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Error (January — March 2019): $214721.53\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AqgmRs0-5PQQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "13890081-22f2-4f0a-bb78-89f96d6febc9"
      },
      "source": [
        "# Lets see how much error is in our baseline test data\n",
        "# Test Error\n",
        "\n",
        "y_pred = [y_train.mean()] * len(y_test)\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "print(f'Test Error (From April 2019): ${mae:.2f}')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Error (From April 2019): $211564.20\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9LEsQTkM5Ry9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Ridge Regression\n",
        "#1. Import model\n",
        "from sklearn.linear_model import Ridge"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lFptgeo25TWj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#2. Instantiate a class\n",
        "model = Ridge(normalize=True)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNYQwTcT5VAZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "0dfd8e84-8d63-4c8f-bc1b-b74eb5b19b11"
      },
      "source": [
        "#3. Fit the model to training data from SelectKBest\n",
        "model.fit(X_train_selected, y_train)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None, normalize=True,\n",
              "      random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dHdtgYxV5W9Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#4. Apply model to test data\n",
        "y_pred = model.predict(X_test_selected)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bER5YE9S5YL8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "bd5b7a7e-9f81-4169-b526-5b5356de1a33"
      },
      "source": [
        "#5. Print out metrics\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "\n",
        "#print(f'Test Error (From April 2019): ${mae:.2f}')\n",
        "\n",
        "print(f'MAE: {mean_absolute_error(y_test, y_pred):.2f}')\n",
        "print(f'MSE: {mean_squared_error(y_test, y_pred):.2f}')\n",
        "print(f'R2: {r2_score(y_test, y_pred):.2f}')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAE: 191852.19\n",
            "MSE: 75268378144.36\n",
            "R2: 0.15\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}