Ridge_Regression_NYC.ipynb

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        "<a href=\"https://colab.research.google.com/github/kvinne-anc/Regression-Models/blob/main/Ridge_Regression_NYC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "Lambda School Data Science\n",
        "\n",
        "*Unit 2, Sprint 1, Module 3*\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7IXUfiQ2UKj6"
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      "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": {
        "id": "o9eSnDYhUGD7"
      },
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QJBD4ruICm1m"
      },
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Ov_9xRTDhEo"
      },
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zptHkIJ0DhEs"
      },
      "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": null,
      "outputs": []
    },
    {
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      "source": [
        "df.tail()"
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              "  <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",
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              "      <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",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>23035</th>\n",
              "      <td>4</td>\n",
              "      <td>OTHER</td>\n",
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              "      <td>1</td>\n",
              "      <td>10965</td>\n",
              "      <td>276</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A5</td>\n",
              "      <td>111-17 FRANCIS LEWIS BLVD</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11429.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1,800</td>\n",
              "      <td>1224.0</td>\n",
              "      <td>1945.0</td>\n",
              "      <td>1</td>\n",
              "      <td>A5</td>\n",
              "      <td>510000</td>\n",
              "      <td>04/30/2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23036</th>\n",
              "      <td>4</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>09 COOPS - WALKUP APARTMENTS</td>\n",
              "      <td>2</td>\n",
              "      <td>169</td>\n",
              "      <td>29</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C6</td>\n",
              "      <td>45-14 43RD STREET, 3C</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11104.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1929.0</td>\n",
              "      <td>2</td>\n",
              "      <td>C6</td>\n",
              "      <td>355000</td>\n",
              "      <td>04/30/2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23037</th>\n",
              "      <td>4</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>10 COOPS - ELEVATOR APARTMENTS</td>\n",
              "      <td>2</td>\n",
              "      <td>131</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>D4</td>\n",
              "      <td>50-05 43RD AVENUE, 3M</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11377.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1932.0</td>\n",
              "      <td>2</td>\n",
              "      <td>D4</td>\n",
              "      <td>375000</td>\n",
              "      <td>04/30/2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23038</th>\n",
              "      <td>4</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>02 TWO FAMILY DWELLINGS</td>\n",
              "      <td>1</td>\n",
              "      <td>8932</td>\n",
              "      <td>18</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S2</td>\n",
              "      <td>91-10 JAMAICA AVE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11421.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2,078</td>\n",
              "      <td>2200.0</td>\n",
              "      <td>1931.0</td>\n",
              "      <td>1</td>\n",
              "      <td>S2</td>\n",
              "      <td>1100000</td>\n",
              "      <td>04/30/2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23039</th>\n",
              "      <td>4</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>12 CONDOS - WALKUP APARTMENTS</td>\n",
              "      <td>2</td>\n",
              "      <td>1216</td>\n",
              "      <td>1161</td>\n",
              "      <td>NaN</td>\n",
              "      <td>R2</td>\n",
              "      <td>61-05 39TH AVENUE, F5</td>\n",
              "      <td>F5</td>\n",
              "      <td>11377.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>15,151</td>\n",
              "      <td>854.0</td>\n",
              "      <td>1927.0</td>\n",
              "      <td>2</td>\n",
              "      <td>R2</td>\n",
              "      <td>569202</td>\n",
              "      <td>04/30/2019</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      BOROUGH NEIGHBORHOOD  ... SALE_PRICE   SALE_DATE\n",
              "23035       4        OTHER  ...     510000  04/30/2019\n",
              "23036       4        OTHER  ...     355000  04/30/2019\n",
              "23037       4        OTHER  ...     375000  04/30/2019\n",
              "23038       4        OTHER  ...    1100000  04/30/2019\n",
              "23039       4        OTHER  ...     569202  04/30/2019\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CU4_CKytDhEv"
      },
      "source": [
        "#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."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XiE1BOuAUPcE"
      },
      "source": [
        "data = df[(df['SALE_PRICE'] > 100000) & \n",
        "        (df['SALE_PRICE'] < 2000000) & \n",
        "        (df['BUILDING_CLASS_CATEGORY'] == 1)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QlAtYnCZWmf2",
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          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "115919ea-9ccb-40ed-ebda-3dd80e4841e3"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3151, 21)"
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          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KhhZR_NGTsKy"
      },
      "source": [
        "#Do train/test split. Use data from January — March 2019 to train. Use data from April 2019 to test."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GYf5MFsgXRq5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "37f262ff-2b4c-478a-bbfa-59f8b5300daf"
      },
      "source": [
        "data['SALE_DATE'] = pd.to_datetime(data['SALE_DATE'])\n",
        "data = data.set_index(data['SALE_DATE'])\n",
        "data = data.sort_index()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yAHITVrpX__n"
      },
      "source": [
        "split_date ='2016-06-01'\n",
        "train_data = rent.loc[rent['created'] <= split_date]\n",
        "test_data = rent.loc[rent['created'] > split_date]\n",
        "train_data.shape, test_data.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VR9aUwfGTsTF"
      },
      "source": [
        "#Do one-hot encoding of categorical features."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SYaJeXSgTsXw"
      },
      "source": [
        "#Do feature selection with SelectKBest."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nunl6K_lTsfH"
      },
      "source": [
        "#Fit a ridge regression model with multiple features. Use the normalize=True parameter \n",
        "#(or do feature scaling beforehand — use the scaler's fit_transform method with the train set, and the scaler's transform method with the test set)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fw63Yq4iTsos"
      },
      "source": [
        "#Get mean absolute error for the test set."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "noQSfY7bUDOB"
      },
      "source": [
        "#"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZsAiNykNUDRe"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
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}