Regression_NYC_A2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "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": "3.7.3"
    },
    "colab": {
      "name": "Regression_NYC_A2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kvinne-anc/Regression-Models/blob/main/Regression_NYC_A2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oEXs-AwLDJNF"
      },
      "source": [
        "Lambda School Data Science\n",
        "\n",
        "*Unit 2, Sprint 1, Module 2*\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7IXUfiQ2UKj6"
      },
      "source": [
        "# Regression 2\n",
        "\n",
        "## Assignment\n",
        "\n",
        "You'll continue to **predict how much it costs to rent an apartment in NYC,** using the dataset from renthop.com.\n",
        "\n",
        "- [ ] Do train/test split. Use data from April & May 2016 to train. Use data from June 2016 to test.\n",
        "- [ ] Engineer at least two new features. (See below for explanation & ideas.)\n",
        "- [ ] Fit a linear regression model with at least two features.\n",
        "- [ ] Get the model's coefficients and intercept.\n",
        "- [ ] Get regression metrics RMSE, MAE, and $R^2$, for both the train and test data.\n",
        "- [ ] What's the best test MAE you can get? Share your score and features used with your cohort on Slack!\n",
        "- [ ] As always, commit your notebook to your fork of the GitHub repo.\n",
        "\n",
        "\n",
        "#### [Feature Engineering](https://en.wikipedia.org/wiki/Feature_engineering)\n",
        "\n",
        "> \"Some machine learning projects succeed and some fail. What makes the difference? Easily the most important factor is the features used.\" — Pedro Domingos, [\"A Few Useful Things to Know about Machine Learning\"](https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf)\n",
        "\n",
        "> \"Coming up with features is difficult, time-consuming, requires expert knowledge. 'Applied machine learning' is basically feature engineering.\" — Andrew Ng, [Machine Learning and AI via Brain simulations](https://forum.stanford.edu/events/2011/2011slides/plenary/2011plenaryNg.pdf) \n",
        "\n",
        "> Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work. \n",
        "\n",
        "#### Feature Ideas\n",
        "- Does the apartment have a description?\n",
        "- How long is the description?\n",
        "- How many total perks does each apartment have?\n",
        "- Are cats _or_ dogs allowed?\n",
        "- Are cats _and_ dogs allowed?\n",
        "- Total number of rooms (beds + baths)\n",
        "- Ratio of beds to baths\n",
        "- What's the neighborhood, based on address or latitude & longitude?\n",
        "\n",
        "## Stretch Goals\n",
        "- [ ] If you want more math, skim [_An Introduction to Statistical Learning_](http://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf),  Chapter 3.1, Simple Linear Regression, & Chapter 3.2, Multiple Linear Regression\n",
        "- [ ] If you want more introduction, watch [Brandon Foltz, Statistics 101: Simple Linear Regression](https://www.youtube.com/watch?v=ZkjP5RJLQF4)\n",
        "(20 minutes, over 1 million views)\n",
        "- [ ] Add your own stretch goal(s) !"
      ]
    },
    {
      "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": "cvrw-T3bZOuW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "362f385a-e5f2-4bbd-cdf0-716d63a47e60"
      },
      "source": [
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Read New York City apartment rental listing data\n",
        "df = pd.read_csv(DATA_PATH+'apartments/renthop-nyc.csv')\n",
        "assert df.shape == (49352, 34)\n",
        "\n",
        "# Remove the most extreme 1% prices,\n",
        "# the most extreme .1% latitudes, &\n",
        "# the most extreme .1% longitudes\n",
        "df = df[(df['price'] >= np.percentile(df['price'], 0.5)) & \n",
        "        (df['price'] <= np.percentile(df['price'], 99.5)) & \n",
        "        (df['latitude'] >= np.percentile(df['latitude'], 0.05)) & \n",
        "        (df['latitude'] < np.percentile(df['latitude'], 99.95)) &\n",
        "        (df['longitude'] >= np.percentile(df['longitude'], 0.05)) & \n",
        "        (df['longitude'] <= np.percentile(df['longitude'], 99.95))]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YHiiLzYrYhg3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "outputId": "11f613df-ac2a-4633-8003-f8aa0f518500"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "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>bathrooms</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>created</th>\n",
              "      <th>description</th>\n",
              "      <th>display_address</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>price</th>\n",
              "      <th>street_address</th>\n",
              "      <th>interest_level</th>\n",
              "      <th>elevator</th>\n",
              "      <th>cats_allowed</th>\n",
              "      <th>hardwood_floors</th>\n",
              "      <th>dogs_allowed</th>\n",
              "      <th>doorman</th>\n",
              "      <th>dishwasher</th>\n",
              "      <th>no_fee</th>\n",
              "      <th>laundry_in_building</th>\n",
              "      <th>fitness_center</th>\n",
              "      <th>pre-war</th>\n",
              "      <th>laundry_in_unit</th>\n",
              "      <th>roof_deck</th>\n",
              "      <th>outdoor_space</th>\n",
              "      <th>dining_room</th>\n",
              "      <th>high_speed_internet</th>\n",
              "      <th>balcony</th>\n",
              "      <th>swimming_pool</th>\n",
              "      <th>new_construction</th>\n",
              "      <th>terrace</th>\n",
              "      <th>exclusive</th>\n",
              "      <th>loft</th>\n",
              "      <th>garden_patio</th>\n",
              "      <th>wheelchair_access</th>\n",
              "      <th>common_outdoor_space</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.5</td>\n",
              "      <td>3</td>\n",
              "      <td>2016-06-24 07:54:24</td>\n",
              "      <td>A Brand New 3 Bedroom 1.5 bath ApartmentEnjoy ...</td>\n",
              "      <td>Metropolitan Avenue</td>\n",
              "      <td>40.7145</td>\n",
              "      <td>-73.9425</td>\n",
              "      <td>3000</td>\n",
              "      <td>792 Metropolitan Avenue</td>\n",
              "      <td>medium</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-06-12 12:19:27</td>\n",
              "      <td></td>\n",
              "      <td>Columbus Avenue</td>\n",
              "      <td>40.7947</td>\n",
              "      <td>-73.9667</td>\n",
              "      <td>5465</td>\n",
              "      <td>808 Columbus Avenue</td>\n",
              "      <td>low</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-04-17 03:26:41</td>\n",
              "      <td>Top Top West Village location, beautiful Pre-w...</td>\n",
              "      <td>W 13 Street</td>\n",
              "      <td>40.7388</td>\n",
              "      <td>-74.0018</td>\n",
              "      <td>2850</td>\n",
              "      <td>241 W 13 Street</td>\n",
              "      <td>high</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-04-18 02:22:02</td>\n",
              "      <td>Building Amenities - Garage - Garden - fitness...</td>\n",
              "      <td>East 49th Street</td>\n",
              "      <td>40.7539</td>\n",
              "      <td>-73.9677</td>\n",
              "      <td>3275</td>\n",
              "      <td>333 East 49th Street</td>\n",
              "      <td>low</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2016-04-28 01:32:41</td>\n",
              "      <td>Beautifully renovated 3 bedroom flex 4 bedroom...</td>\n",
              "      <td>West 143rd Street</td>\n",
              "      <td>40.8241</td>\n",
              "      <td>-73.9493</td>\n",
              "      <td>3350</td>\n",
              "      <td>500 West 143rd Street</td>\n",
              "      <td>low</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   bathrooms  bedrooms  ... wheelchair_access common_outdoor_space\n",
              "0        1.5         3  ...                 0                    0\n",
              "1        1.0         2  ...                 0                    0\n",
              "2        1.0         1  ...                 0                    0\n",
              "3        1.0         1  ...                 0                    0\n",
              "4        1.0         4  ...                 0                    0\n",
              "\n",
              "[5 rows x 34 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CUgEM97dYsz5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 926
        },
        "outputId": "992326d7-693e-4073-87a3-9d99b1966d71"
      },
      "source": [
        "corr = df.corr()\n",
        "corr"
      ],
      "execution_count": null,
      "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>bathrooms</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>price</th>\n",
              "      <th>elevator</th>\n",
              "      <th>cats_allowed</th>\n",
              "      <th>hardwood_floors</th>\n",
              "      <th>dogs_allowed</th>\n",
              "      <th>doorman</th>\n",
              "      <th>dishwasher</th>\n",
              "      <th>no_fee</th>\n",
              "      <th>laundry_in_building</th>\n",
              "      <th>fitness_center</th>\n",
              "      <th>pre-war</th>\n",
              "      <th>laundry_in_unit</th>\n",
              "      <th>roof_deck</th>\n",
              "      <th>outdoor_space</th>\n",
              "      <th>dining_room</th>\n",
              "      <th>high_speed_internet</th>\n",
              "      <th>balcony</th>\n",
              "      <th>swimming_pool</th>\n",
              "      <th>new_construction</th>\n",
              "      <th>terrace</th>\n",
              "      <th>exclusive</th>\n",
              "      <th>loft</th>\n",
              "      <th>garden_patio</th>\n",
              "      <th>wheelchair_access</th>\n",
              "      <th>common_outdoor_space</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bathrooms</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.524082</td>\n",
              "      <td>0.013407</td>\n",
              "      <td>-0.020099</td>\n",
              "      <td>0.687296</td>\n",
              "      <td>0.132882</td>\n",
              "      <td>0.021475</td>\n",
              "      <td>0.096922</td>\n",
              "      <td>0.024539</td>\n",
              "      <td>0.157173</td>\n",
              "      <td>0.172230</td>\n",
              "      <td>0.129547</td>\n",
              "      <td>-0.013202</td>\n",
              "      <td>0.148334</td>\n",
              "      <td>-0.016214</td>\n",
              "      <td>0.209366</td>\n",
              "      <td>0.102113</td>\n",
              "      <td>0.144450</td>\n",
              "      <td>0.233038</td>\n",
              "      <td>0.089073</td>\n",
              "      <td>0.136990</td>\n",
              "      <td>0.113315</td>\n",
              "      <td>0.066826</td>\n",
              "      <td>0.140767</td>\n",
              "      <td>-0.001944</td>\n",
              "      <td>0.014260</td>\n",
              "      <td>0.096283</td>\n",
              "      <td>0.066940</td>\n",
              "      <td>-0.003403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bedrooms</th>\n",
              "      <td>0.524082</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.003490</td>\n",
              "      <td>0.055117</td>\n",
              "      <td>0.535503</td>\n",
              "      <td>-0.024821</td>\n",
              "      <td>-0.011173</td>\n",
              "      <td>0.096420</td>\n",
              "      <td>-0.009750</td>\n",
              "      <td>-0.047562</td>\n",
              "      <td>0.152102</td>\n",
              "      <td>0.163000</td>\n",
              "      <td>0.008558</td>\n",
              "      <td>0.013150</td>\n",
              "      <td>0.001420</td>\n",
              "      <td>0.151790</td>\n",
              "      <td>0.045451</td>\n",
              "      <td>0.124912</td>\n",
              "      <td>0.189415</td>\n",
              "      <td>0.061006</td>\n",
              "      <td>0.097772</td>\n",
              "      <td>0.033441</td>\n",
              "      <td>0.000907</td>\n",
              "      <td>0.099149</td>\n",
              "      <td>-0.014880</td>\n",
              "      <td>-0.107341</td>\n",
              "      <td>0.070760</td>\n",
              "      <td>0.012306</td>\n",
              "      <td>0.003234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>latitude</th>\n",
              "      <td>0.013407</td>\n",
              "      <td>0.003490</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.329185</td>\n",
              "      <td>-0.036286</td>\n",
              "      <td>-0.010523</td>\n",
              "      <td>-0.029808</td>\n",
              "      <td>0.018653</td>\n",
              "      <td>-0.030954</td>\n",
              "      <td>-0.043393</td>\n",
              "      <td>-0.020612</td>\n",
              "      <td>-0.026788</td>\n",
              "      <td>-0.041635</td>\n",
              "      <td>-0.108455</td>\n",
              "      <td>0.026802</td>\n",
              "      <td>-0.044339</td>\n",
              "      <td>-0.063198</td>\n",
              "      <td>-0.079919</td>\n",
              "      <td>0.017146</td>\n",
              "      <td>-0.030969</td>\n",
              "      <td>0.016877</td>\n",
              "      <td>0.027020</td>\n",
              "      <td>-0.056093</td>\n",
              "      <td>0.006466</td>\n",
              "      <td>-0.060054</td>\n",
              "      <td>-0.015650</td>\n",
              "      <td>-0.000589</td>\n",
              "      <td>-0.068330</td>\n",
              "      <td>-0.117199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>longitude</th>\n",
              "      <td>-0.020099</td>\n",
              "      <td>0.055117</td>\n",
              "      <td>0.329185</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.251004</td>\n",
              "      <td>-0.189836</td>\n",
              "      <td>-0.058475</td>\n",
              "      <td>-0.108493</td>\n",
              "      <td>-0.070329</td>\n",
              "      <td>-0.275734</td>\n",
              "      <td>-0.169220</td>\n",
              "      <td>-0.088033</td>\n",
              "      <td>-0.044562</td>\n",
              "      <td>-0.254960</td>\n",
              "      <td>0.000196</td>\n",
              "      <td>-0.130139</td>\n",
              "      <td>-0.161466</td>\n",
              "      <td>-0.098595</td>\n",
              "      <td>-0.024793</td>\n",
              "      <td>-0.125635</td>\n",
              "      <td>-0.035474</td>\n",
              "      <td>-0.075046</td>\n",
              "      <td>-0.108001</td>\n",
              "      <td>-0.049016</td>\n",
              "      <td>0.046755</td>\n",
              "      <td>-0.060018</td>\n",
              "      <td>-0.029846</td>\n",
              "      <td>-0.063635</td>\n",
              "      <td>-0.102955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>0.687296</td>\n",
              "      <td>0.535503</td>\n",
              "      <td>-0.036286</td>\n",
              "      <td>-0.251004</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.207169</td>\n",
              "      <td>0.051453</td>\n",
              "      <td>0.101503</td>\n",
              "      <td>0.060401</td>\n",
              "      <td>0.276215</td>\n",
              "      <td>0.223899</td>\n",
              "      <td>0.132240</td>\n",
              "      <td>-0.019417</td>\n",
              "      <td>0.228775</td>\n",
              "      <td>-0.029122</td>\n",
              "      <td>0.271195</td>\n",
              "      <td>0.122929</td>\n",
              "      <td>0.142146</td>\n",
              "      <td>0.242911</td>\n",
              "      <td>0.090269</td>\n",
              "      <td>0.139140</td>\n",
              "      <td>0.134513</td>\n",
              "      <td>0.071431</td>\n",
              "      <td>0.145973</td>\n",
              "      <td>-0.013251</td>\n",
              "      <td>0.007100</td>\n",
              "      <td>0.103672</td>\n",
              "      <td>0.072517</td>\n",
              "      <td>0.011517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>elevator</th>\n",
              "      <td>0.132882</td>\n",
              "      <td>-0.024821</td>\n",
              "      <td>-0.010523</td>\n",
              "      <td>-0.189836</td>\n",
              "      <td>0.207169</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.033347</td>\n",
              "      <td>0.270831</td>\n",
              "      <td>0.034833</td>\n",
              "      <td>0.614558</td>\n",
              "      <td>0.349832</td>\n",
              "      <td>0.227895</td>\n",
              "      <td>0.141097</td>\n",
              "      <td>0.431070</td>\n",
              "      <td>-0.097015</td>\n",
              "      <td>0.134158</td>\n",
              "      <td>0.332028</td>\n",
              "      <td>0.204343</td>\n",
              "      <td>0.200591</td>\n",
              "      <td>0.277666</td>\n",
              "      <td>0.168081</td>\n",
              "      <td>0.183664</td>\n",
              "      <td>0.184178</td>\n",
              "      <td>0.135329</td>\n",
              "      <td>0.025895</td>\n",
              "      <td>0.054918</td>\n",
              "      <td>0.084056</td>\n",
              "      <td>0.155396</td>\n",
              "      <td>0.114882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cats_allowed</th>\n",
              "      <td>0.021475</td>\n",
              "      <td>-0.011173</td>\n",
              "      <td>-0.029808</td>\n",
              "      <td>-0.058475</td>\n",
              "      <td>0.051453</td>\n",
              "      <td>0.033347</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.177633</td>\n",
              "      <td>0.937245</td>\n",
              "      <td>0.088480</td>\n",
              "      <td>-0.047880</td>\n",
              "      <td>-0.024052</td>\n",
              "      <td>0.105644</td>\n",
              "      <td>0.126886</td>\n",
              "      <td>0.047500</td>\n",
              "      <td>-0.008827</td>\n",
              "      <td>0.027590</td>\n",
              "      <td>0.066713</td>\n",
              "      <td>-0.022404</td>\n",
              "      <td>0.077759</td>\n",
              "      <td>0.019979</td>\n",
              "      <td>0.009704</td>\n",
              "      <td>0.042710</td>\n",
              "      <td>0.006152</td>\n",
              "      <td>0.031414</td>\n",
              "      <td>-0.037966</td>\n",
              "      <td>0.006690</td>\n",
              "      <td>0.039656</td>\n",
              "      <td>0.104496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hardwood_floors</th>\n",
              "      <td>0.096922</td>\n",
              "      <td>0.096420</td>\n",
              "      <td>0.018653</td>\n",
              "      <td>-0.108493</td>\n",
              "      <td>0.101503</td>\n",
              "      <td>0.270831</td>\n",
              "      <td>-0.177633</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.185663</td>\n",
              "      <td>0.205119</td>\n",
              "      <td>0.634983</td>\n",
              "      <td>0.342971</td>\n",
              "      <td>-0.144728</td>\n",
              "      <td>0.167510</td>\n",
              "      <td>0.013435</td>\n",
              "      <td>0.360716</td>\n",
              "      <td>0.278727</td>\n",
              "      <td>0.188913</td>\n",
              "      <td>0.317281</td>\n",
              "      <td>0.237935</td>\n",
              "      <td>0.178089</td>\n",
              "      <td>0.170589</td>\n",
              "      <td>0.187635</td>\n",
              "      <td>0.181005</td>\n",
              "      <td>-0.194436</td>\n",
              "      <td>0.116572</td>\n",
              "      <td>0.161751</td>\n",
              "      <td>0.124829</td>\n",
              "      <td>-0.120489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dogs_allowed</th>\n",
              "      <td>0.024539</td>\n",
              "      <td>-0.009750</td>\n",
              "      <td>-0.030954</td>\n",
              "      <td>-0.070329</td>\n",
              "      <td>0.060401</td>\n",
              "      <td>0.034833</td>\n",
              "      <td>0.937245</td>\n",
              "      <td>-0.185663</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.095434</td>\n",
              "      <td>-0.043839</td>\n",
              "      <td>-0.011414</td>\n",
              "      <td>0.093035</td>\n",
              "      <td>0.131521</td>\n",
              "      <td>0.051610</td>\n",
              "      <td>0.002510</td>\n",
              "      <td>0.034104</td>\n",
              "      <td>0.067859</td>\n",
              "      <td>-0.016154</td>\n",
              "      <td>0.089425</td>\n",
              "      <td>0.024472</td>\n",
              "      <td>0.009675</td>\n",
              "      <td>0.055655</td>\n",
              "      <td>0.005219</td>\n",
              "      <td>0.032729</td>\n",
              "      <td>-0.041014</td>\n",
              "      <td>0.009229</td>\n",
              "      <td>0.048123</td>\n",
              "      <td>0.106071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>doorman</th>\n",
              "      <td>0.157173</td>\n",
              "      <td>-0.047562</td>\n",
              "      <td>-0.043393</td>\n",
              "      <td>-0.275734</td>\n",
              "      <td>0.276215</td>\n",
              "      <td>0.614558</td>\n",
              "      <td>0.088480</td>\n",
              "      <td>0.205119</td>\n",
              "      <td>0.095434</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.312710</td>\n",
              "      <td>0.257031</td>\n",
              "      <td>0.077216</td>\n",
              "      <td>0.604863</td>\n",
              "      <td>-0.054614</td>\n",
              "      <td>0.166397</td>\n",
              "      <td>0.388921</td>\n",
              "      <td>0.205656</td>\n",
              "      <td>0.195814</td>\n",
              "      <td>0.312036</td>\n",
              "      <td>0.160789</td>\n",
              "      <td>0.263833</td>\n",
              "      <td>0.219943</td>\n",
              "      <td>0.126936</td>\n",
              "      <td>-0.074747</td>\n",
              "      <td>0.010080</td>\n",
              "      <td>0.075956</td>\n",
              "      <td>0.168161</td>\n",
              "      <td>0.126708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dishwasher</th>\n",
              "      <td>0.172230</td>\n",
              "      <td>0.152102</td>\n",
              "      <td>-0.020612</td>\n",
              "      <td>-0.169220</td>\n",
              "      <td>0.223899</td>\n",
              "      <td>0.349832</td>\n",
              "      <td>-0.047880</td>\n",
              "      <td>0.634983</td>\n",
              "      <td>-0.043839</td>\n",
              "      <td>0.312710</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.366541</td>\n",
              "      <td>-0.102877</td>\n",
              "      <td>0.275843</td>\n",
              "      <td>0.008460</td>\n",
              "      <td>0.441321</td>\n",
              "      <td>0.307143</td>\n",
              "      <td>0.226304</td>\n",
              "      <td>0.351799</td>\n",
              "      <td>0.256543</td>\n",
              "      <td>0.228604</td>\n",
              "      <td>0.193021</td>\n",
              "      <td>0.207113</td>\n",
              "      <td>0.194097</td>\n",
              "      <td>-0.105287</td>\n",
              "      <td>0.056860</td>\n",
              "      <td>0.175388</td>\n",
              "      <td>0.137787</td>\n",
              "      <td>-0.086532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_fee</th>\n",
              "      <td>0.129547</td>\n",
              "      <td>0.163000</td>\n",
              "      <td>-0.026788</td>\n",
              "      <td>-0.088033</td>\n",
              "      <td>0.132240</td>\n",
              "      <td>0.227895</td>\n",
              "      <td>-0.024052</td>\n",
              "      <td>0.342971</td>\n",
              "      <td>-0.011414</td>\n",
              "      <td>0.257031</td>\n",
              "      <td>0.366541</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.033720</td>\n",
              "      <td>0.300008</td>\n",
              "      <td>0.004821</td>\n",
              "      <td>0.276804</td>\n",
              "      <td>0.269647</td>\n",
              "      <td>0.212788</td>\n",
              "      <td>0.227117</td>\n",
              "      <td>0.248743</td>\n",
              "      <td>0.125943</td>\n",
              "      <td>0.192543</td>\n",
              "      <td>0.186170</td>\n",
              "      <td>0.134888</td>\n",
              "      <td>-0.019004</td>\n",
              "      <td>-0.009957</td>\n",
              "      <td>0.101490</td>\n",
              "      <td>0.122778</td>\n",
              "      <td>0.030246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>laundry_in_building</th>\n",
              "      <td>-0.013202</td>\n",
              "      <td>0.008558</td>\n",
              "      <td>-0.041635</td>\n",
              "      <td>-0.044562</td>\n",
              "      <td>-0.019417</td>\n",
              "      <td>0.141097</td>\n",
              "      <td>0.105644</td>\n",
              "      <td>-0.144728</td>\n",
              "      <td>0.093035</td>\n",
              "      <td>0.077216</td>\n",
              "      <td>-0.102877</td>\n",
              "      <td>0.033720</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.113193</td>\n",
              "      <td>-0.098556</td>\n",
              "      <td>-0.088349</td>\n",
              "      <td>-0.040502</td>\n",
              "      <td>0.242385</td>\n",
              "      <td>-0.051854</td>\n",
              "      <td>-0.009624</td>\n",
              "      <td>-0.029896</td>\n",
              "      <td>-0.015731</td>\n",
              "      <td>-0.033337</td>\n",
              "      <td>-0.025751</td>\n",
              "      <td>0.046117</td>\n",
              "      <td>-0.029917</td>\n",
              "      <td>-0.032673</td>\n",
              "      <td>-0.028224</td>\n",
              "      <td>0.595203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fitness_center</th>\n",
              "      <td>0.148334</td>\n",
              "      <td>0.013150</td>\n",
              "      <td>-0.108455</td>\n",
              "      <td>-0.254960</td>\n",
              "      <td>0.228775</td>\n",
              "      <td>0.431070</td>\n",
              "      <td>0.126886</td>\n",
              "      <td>0.167510</td>\n",
              "      <td>0.131521</td>\n",
              "      <td>0.604863</td>\n",
              "      <td>0.275843</td>\n",
              "      <td>0.300008</td>\n",
              "      <td>0.113193</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.060509</td>\n",
              "      <td>0.185003</td>\n",
              "      <td>0.467424</td>\n",
              "      <td>0.243543</td>\n",
              "      <td>0.181309</td>\n",
              "      <td>0.380143</td>\n",
              "      <td>0.128291</td>\n",
              "      <td>0.349652</td>\n",
              "      <td>0.322501</td>\n",
              "      <td>0.104746</td>\n",
              "      <td>-0.070290</td>\n",
              "      <td>-0.021882</td>\n",
              "      <td>0.084319</td>\n",
              "      <td>0.227019</td>\n",
              "      <td>0.196382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pre-war</th>\n",
              "      <td>-0.016214</td>\n",
              "      <td>0.001420</td>\n",
              "      <td>0.026802</td>\n",
              "      <td>0.000196</td>\n",
              "      <td>-0.029122</td>\n",
              "      <td>-0.097015</td>\n",
              "      <td>0.047500</td>\n",
              "      <td>0.013435</td>\n",
              "      <td>0.051610</td>\n",
              "      <td>-0.054614</td>\n",
              "      <td>0.008460</td>\n",
              "      <td>0.004821</td>\n",
              "      <td>-0.098556</td>\n",
              "      <td>-0.060509</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.030830</td>\n",
              "      <td>0.028846</td>\n",
              "      <td>-0.052219</td>\n",
              "      <td>0.010238</td>\n",
              "      <td>0.040087</td>\n",
              "      <td>-0.040016</td>\n",
              "      <td>-0.037909</td>\n",
              "      <td>-0.058051</td>\n",
              "      <td>-0.021496</td>\n",
              "      <td>-0.067159</td>\n",
              "      <td>-0.032330</td>\n",
              "      <td>-0.022076</td>\n",
              "      <td>0.017245</td>\n",
              "      <td>-0.066845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>laundry_in_unit</th>\n",
              "      <td>0.209366</td>\n",
              "      <td>0.151790</td>\n",
              "      <td>-0.044339</td>\n",
              "      <td>-0.130139</td>\n",
              "      <td>0.271195</td>\n",
              "      <td>0.134158</td>\n",
              "      <td>-0.008827</td>\n",
              "      <td>0.360716</td>\n",
              "      <td>0.002510</td>\n",
              "      <td>0.166397</td>\n",
              "      <td>0.441321</td>\n",
              "      <td>0.276804</td>\n",
              "      <td>-0.088349</td>\n",
              "      <td>0.185003</td>\n",
              "      <td>0.030830</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.203273</td>\n",
              "      <td>0.180365</td>\n",
              "      <td>0.249222</td>\n",
              "      <td>0.195122</td>\n",
              "      <td>0.159753</td>\n",
              "      <td>0.182092</td>\n",
              "      <td>0.246230</td>\n",
              "      <td>0.153368</td>\n",
              "      <td>-0.078204</td>\n",
              "      <td>0.044501</td>\n",
              "      <td>0.159506</td>\n",
              "      <td>0.126700</td>\n",
              "      <td>-0.061032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>roof_deck</th>\n",
              "      <td>0.102113</td>\n",
              "      <td>0.045451</td>\n",
              "      <td>-0.063198</td>\n",
              "      <td>-0.161466</td>\n",
              "      <td>0.122929</td>\n",
              "      <td>0.332028</td>\n",
              "      <td>0.027590</td>\n",
              "      <td>0.278727</td>\n",
              "      <td>0.034104</td>\n",
              "      <td>0.388921</td>\n",
              "      <td>0.307143</td>\n",
              "      <td>0.269647</td>\n",
              "      <td>-0.040502</td>\n",
              "      <td>0.467424</td>\n",
              "      <td>0.028846</td>\n",
              "      <td>0.203273</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.187427</td>\n",
              "      <td>0.182764</td>\n",
              "      <td>0.507095</td>\n",
              "      <td>0.135779</td>\n",
              "      <td>0.261222</td>\n",
              "      <td>0.359374</td>\n",
              "      <td>0.127016</td>\n",
              "      <td>-0.044092</td>\n",
              "      <td>0.027824</td>\n",
              "      <td>0.095689</td>\n",
              "      <td>0.329260</td>\n",
              "      <td>-0.016265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>outdoor_space</th>\n",
              "      <td>0.144450</td>\n",
              "      <td>0.124912</td>\n",
              "      <td>-0.079919</td>\n",
              "      <td>-0.098595</td>\n",
              "      <td>0.142146</td>\n",
              "      <td>0.204343</td>\n",
              "      <td>0.066713</td>\n",
              "      <td>0.188913</td>\n",
              "      <td>0.067859</td>\n",
              "      <td>0.205656</td>\n",
              "      <td>0.226304</td>\n",
              "      <td>0.212788</td>\n",
              "      <td>0.242385</td>\n",
              "      <td>0.243543</td>\n",
              "      <td>-0.052219</td>\n",
              "      <td>0.180365</td>\n",
              "      <td>0.187427</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.259025</td>\n",
              "      <td>0.169862</td>\n",
              "      <td>0.375174</td>\n",
              "      <td>0.123128</td>\n",
              "      <td>0.118544</td>\n",
              "      <td>0.406945</td>\n",
              "      <td>-0.020858</td>\n",
              "      <td>0.043845</td>\n",
              "      <td>0.360510</td>\n",
              "      <td>0.076578</td>\n",
              "      <td>0.409598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dining_room</th>\n",
              "      <td>0.233038</td>\n",
              "      <td>0.189415</td>\n",
              "      <td>0.017146</td>\n",
              "      <td>-0.024793</td>\n",
              "      <td>0.242911</td>\n",
              "      <td>0.200591</td>\n",
              "      <td>-0.022404</td>\n",
              "      <td>0.317281</td>\n",
              "      <td>-0.016154</td>\n",
              "      <td>0.195814</td>\n",
              "      <td>0.351799</td>\n",
              "      <td>0.227117</td>\n",
              "      <td>-0.051854</td>\n",
              "      <td>0.181309</td>\n",
              "      <td>0.010238</td>\n",
              "      <td>0.249222</td>\n",
              "      <td>0.182764</td>\n",
              "      <td>0.259025</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.181729</td>\n",
              "      <td>0.241474</td>\n",
              "      <td>0.166509</td>\n",
              "      <td>0.112610</td>\n",
              "      <td>0.234905</td>\n",
              "      <td>-0.069710</td>\n",
              "      <td>0.058566</td>\n",
              "      <td>0.231961</td>\n",
              "      <td>0.099186</td>\n",
              "      <td>-0.042076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>high_speed_internet</th>\n",
              "      <td>0.089073</td>\n",
              "      <td>0.061006</td>\n",
              "      <td>-0.030969</td>\n",
              "      <td>-0.125635</td>\n",
              "      <td>0.090269</td>\n",
              "      <td>0.277666</td>\n",
              "      <td>0.077759</td>\n",
              "      <td>0.237935</td>\n",
              "      <td>0.089425</td>\n",
              "      <td>0.312036</td>\n",
              "      <td>0.256543</td>\n",
              "      <td>0.248743</td>\n",
              "      <td>-0.009624</td>\n",
              "      <td>0.380143</td>\n",
              "      <td>0.040087</td>\n",
              "      <td>0.195122</td>\n",
              "      <td>0.507095</td>\n",
              "      <td>0.169862</td>\n",
              "      <td>0.181729</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.142430</td>\n",
              "      <td>0.211411</td>\n",
              "      <td>0.421886</td>\n",
              "      <td>0.127417</td>\n",
              "      <td>-0.065403</td>\n",
              "      <td>0.008683</td>\n",
              "      <td>0.069316</td>\n",
              "      <td>0.400255</td>\n",
              "      <td>-0.016673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>balcony</th>\n",
              "      <td>0.136990</td>\n",
              "      <td>0.097772</td>\n",
              "      <td>0.016877</td>\n",
              "      <td>-0.035474</td>\n",
              "      <td>0.139140</td>\n",
              "      <td>0.168081</td>\n",
              "      <td>0.019979</td>\n",
              "      <td>0.178089</td>\n",
              "      <td>0.024472</td>\n",
              "      <td>0.160789</td>\n",
              "      <td>0.228604</td>\n",
              "      <td>0.125943</td>\n",
              "      <td>-0.029896</td>\n",
              "      <td>0.128291</td>\n",
              "      <td>-0.040016</td>\n",
              "      <td>0.159753</td>\n",
              "      <td>0.135779</td>\n",
              "      <td>0.375174</td>\n",
              "      <td>0.241474</td>\n",
              "      <td>0.142430</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.116666</td>\n",
              "      <td>0.074290</td>\n",
              "      <td>0.458008</td>\n",
              "      <td>-0.038353</td>\n",
              "      <td>0.039492</td>\n",
              "      <td>0.312097</td>\n",
              "      <td>0.036246</td>\n",
              "      <td>-0.026593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>swimming_pool</th>\n",
              "      <td>0.113315</td>\n",
              "      <td>0.033441</td>\n",
              "      <td>0.027020</td>\n",
              "      <td>-0.075046</td>\n",
              "      <td>0.134513</td>\n",
              "      <td>0.183664</td>\n",
              "      <td>0.009704</td>\n",
              "      <td>0.170589</td>\n",
              "      <td>0.009675</td>\n",
              "      <td>0.263833</td>\n",
              "      <td>0.193021</td>\n",
              "      <td>0.192543</td>\n",
              "      <td>-0.015731</td>\n",
              "      <td>0.349652</td>\n",
              "      <td>-0.037909</td>\n",
              "      <td>0.182092</td>\n",
              "      <td>0.261222</td>\n",
              "      <td>0.123128</td>\n",
              "      <td>0.166509</td>\n",
              "      <td>0.211411</td>\n",
              "      <td>0.116666</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.220397</td>\n",
              "      <td>0.103201</td>\n",
              "      <td>-0.049225</td>\n",
              "      <td>0.003059</td>\n",
              "      <td>0.074755</td>\n",
              "      <td>0.198217</td>\n",
              "      <td>-0.014992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>new_construction</th>\n",
              "      <td>0.066826</td>\n",
              "      <td>0.000907</td>\n",
              "      <td>-0.056093</td>\n",
              "      <td>-0.108001</td>\n",
              "      <td>0.071431</td>\n",
              "      <td>0.184178</td>\n",
              "      <td>0.042710</td>\n",
              "      <td>0.187635</td>\n",
              "      <td>0.055655</td>\n",
              "      <td>0.219943</td>\n",
              "      <td>0.207113</td>\n",
              "      <td>0.186170</td>\n",
              "      <td>-0.033337</td>\n",
              "      <td>0.322501</td>\n",
              "      <td>-0.058051</td>\n",
              "      <td>0.246230</td>\n",
              "      <td>0.359374</td>\n",
              "      <td>0.118544</td>\n",
              "      <td>0.112610</td>\n",
              "      <td>0.421886</td>\n",
              "      <td>0.074290</td>\n",
              "      <td>0.220397</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.079601</td>\n",
              "      <td>-0.047967</td>\n",
              "      <td>0.014052</td>\n",
              "      <td>0.061440</td>\n",
              "      <td>0.339891</td>\n",
              "      <td>-0.021081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>terrace</th>\n",
              "      <td>0.140767</td>\n",
              "      <td>0.099149</td>\n",
              "      <td>0.006466</td>\n",
              "      <td>-0.049016</td>\n",
              "      <td>0.145973</td>\n",
              "      <td>0.135329</td>\n",
              "      <td>0.006152</td>\n",
              "      <td>0.181005</td>\n",
              "      <td>0.005219</td>\n",
              "      <td>0.126936</td>\n",
              "      <td>0.194097</td>\n",
              "      <td>0.134888</td>\n",
              "      <td>-0.025751</td>\n",
              "      <td>0.104746</td>\n",
              "      <td>-0.021496</td>\n",
              "      <td>0.153368</td>\n",
              "      <td>0.127016</td>\n",
              "      <td>0.406945</td>\n",
              "      <td>0.234905</td>\n",
              "      <td>0.127417</td>\n",
              "      <td>0.458008</td>\n",
              "      <td>0.103201</td>\n",
              "      <td>0.079601</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.035793</td>\n",
              "      <td>0.077554</td>\n",
              "      <td>0.412350</td>\n",
              "      <td>0.047141</td>\n",
              "      <td>-0.026358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exclusive</th>\n",
              "      <td>-0.001944</td>\n",
              "      <td>-0.014880</td>\n",
              "      <td>-0.060054</td>\n",
              "      <td>0.046755</td>\n",
              "      <td>-0.013251</td>\n",
              "      <td>0.025895</td>\n",
              "      <td>0.031414</td>\n",
              "      <td>-0.194436</td>\n",
              "      <td>0.032729</td>\n",
              "      <td>-0.074747</td>\n",
              "      <td>-0.105287</td>\n",
              "      <td>-0.019004</td>\n",
              "      <td>0.046117</td>\n",
              "      <td>-0.070290</td>\n",
              "      <td>-0.067159</td>\n",
              "      <td>-0.078204</td>\n",
              "      <td>-0.044092</td>\n",
              "      <td>-0.020858</td>\n",
              "      <td>-0.069710</td>\n",
              "      <td>-0.065403</td>\n",
              "      <td>-0.038353</td>\n",
              "      <td>-0.049225</td>\n",
              "      <td>-0.047967</td>\n",
              "      <td>-0.035793</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.032994</td>\n",
              "      <td>-0.030622</td>\n",
              "      <td>-0.034355</td>\n",
              "      <td>0.033071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loft</th>\n",
              "      <td>0.014260</td>\n",
              "      <td>-0.107341</td>\n",
              "      <td>-0.015650</td>\n",
              "      <td>-0.060018</td>\n",
              "      <td>0.007100</td>\n",
              "      <td>0.054918</td>\n",
              "      <td>-0.037966</td>\n",
              "      <td>0.116572</td>\n",
              "      <td>-0.041014</td>\n",
              "      <td>0.010080</td>\n",
              "      <td>0.056860</td>\n",
              "      <td>-0.009957</td>\n",
              "      <td>-0.029917</td>\n",
              "      <td>-0.021882</td>\n",
              "      <td>-0.032330</td>\n",
              "      <td>0.044501</td>\n",
              "      <td>0.027824</td>\n",
              "      <td>0.043845</td>\n",
              "      <td>0.058566</td>\n",
              "      <td>0.008683</td>\n",
              "      <td>0.039492</td>\n",
              "      <td>0.003059</td>\n",
              "      <td>0.014052</td>\n",
              "      <td>0.077554</td>\n",
              "      <td>-0.032994</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.079217</td>\n",
              "      <td>0.030029</td>\n",
              "      <td>-0.030238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>garden_patio</th>\n",
              "      <td>0.096283</td>\n",
              "      <td>0.070760</td>\n",
              "      <td>-0.000589</td>\n",
              "      <td>-0.029846</td>\n",
              "      <td>0.103672</td>\n",
              "      <td>0.084056</td>\n",
              "      <td>0.006690</td>\n",
              "      <td>0.161751</td>\n",
              "      <td>0.009229</td>\n",
              "      <td>0.075956</td>\n",
              "      <td>0.175388</td>\n",
              "      <td>0.101490</td>\n",
              "      <td>-0.032673</td>\n",
              "      <td>0.084319</td>\n",
              "      <td>-0.022076</td>\n",
              "      <td>0.159506</td>\n",
              "      <td>0.095689</td>\n",
              "      <td>0.360510</td>\n",
              "      <td>0.231961</td>\n",
              "      <td>0.069316</td>\n",
              "      <td>0.312097</td>\n",
              "      <td>0.074755</td>\n",
              "      <td>0.061440</td>\n",
              "      <td>0.412350</td>\n",
              "      <td>-0.030622</td>\n",
              "      <td>0.079217</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.017310</td>\n",
              "      <td>-0.026623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wheelchair_access</th>\n",
              "      <td>0.066940</td>\n",
              "      <td>0.012306</td>\n",
              "      <td>-0.068330</td>\n",
              "      <td>-0.063635</td>\n",
              "      <td>0.072517</td>\n",
              "      <td>0.155396</td>\n",
              "      <td>0.039656</td>\n",
              "      <td>0.124829</td>\n",
              "      <td>0.048123</td>\n",
              "      <td>0.168161</td>\n",
              "      <td>0.137787</td>\n",
              "      <td>0.122778</td>\n",
              "      <td>-0.028224</td>\n",
              "      <td>0.227019</td>\n",
              "      <td>0.017245</td>\n",
              "      <td>0.126700</td>\n",
              "      <td>0.329260</td>\n",
              "      <td>0.076578</td>\n",
              "      <td>0.099186</td>\n",
              "      <td>0.400255</td>\n",
              "      <td>0.036246</td>\n",
              "      <td>0.198217</td>\n",
              "      <td>0.339891</td>\n",
              "      <td>0.047141</td>\n",
              "      <td>-0.034355</td>\n",
              "      <td>0.030029</td>\n",
              "      <td>0.017310</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.017225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>common_outdoor_space</th>\n",
              "      <td>-0.003403</td>\n",
              "      <td>0.003234</td>\n",
              "      <td>-0.117199</td>\n",
              "      <td>-0.102955</td>\n",
              "      <td>0.011517</td>\n",
              "      <td>0.114882</td>\n",
              "      <td>0.104496</td>\n",
              "      <td>-0.120489</td>\n",
              "      <td>0.106071</td>\n",
              "      <td>0.126708</td>\n",
              "      <td>-0.086532</td>\n",
              "      <td>0.030246</td>\n",
              "      <td>0.595203</td>\n",
              "      <td>0.196382</td>\n",
              "      <td>-0.066845</td>\n",
              "      <td>-0.061032</td>\n",
              "      <td>-0.016265</td>\n",
              "      <td>0.409598</td>\n",
              "      <td>-0.042076</td>\n",
              "      <td>-0.016673</td>\n",
              "      <td>-0.026593</td>\n",
              "      <td>-0.014992</td>\n",
              "      <td>-0.021081</td>\n",
              "      <td>-0.026358</td>\n",
              "      <td>0.033071</td>\n",
              "      <td>-0.030238</td>\n",
              "      <td>-0.026623</td>\n",
              "      <td>-0.017225</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      bathrooms  ...  common_outdoor_space\n",
              "bathrooms              1.000000  ...             -0.003403\n",
              "bedrooms               0.524082  ...              0.003234\n",
              "latitude               0.013407  ...             -0.117199\n",
              "longitude             -0.020099  ...             -0.102955\n",
              "price                  0.687296  ...              0.011517\n",
              "elevator               0.132882  ...              0.114882\n",
              "cats_allowed           0.021475  ...              0.104496\n",
              "hardwood_floors        0.096922  ...             -0.120489\n",
              "dogs_allowed           0.024539  ...              0.106071\n",
              "doorman                0.157173  ...              0.126708\n",
              "dishwasher             0.172230  ...             -0.086532\n",
              "no_fee                 0.129547  ...              0.030246\n",
              "laundry_in_building   -0.013202  ...              0.595203\n",
              "fitness_center         0.148334  ...              0.196382\n",
              "pre-war               -0.016214  ...             -0.066845\n",
              "laundry_in_unit        0.209366  ...             -0.061032\n",
              "roof_deck              0.102113  ...             -0.016265\n",
              "outdoor_space          0.144450  ...              0.409598\n",
              "dining_room            0.233038  ...             -0.042076\n",
              "high_speed_internet    0.089073  ...             -0.016673\n",
              "balcony                0.136990  ...             -0.026593\n",
              "swimming_pool          0.113315  ...             -0.014992\n",
              "new_construction       0.066826  ...             -0.021081\n",
              "terrace                0.140767  ...             -0.026358\n",
              "exclusive             -0.001944  ...              0.033071\n",
              "loft                   0.014260  ...             -0.030238\n",
              "garden_patio           0.096283  ...             -0.026623\n",
              "wheelchair_access      0.066940  ...             -0.017225\n",
              "common_outdoor_space  -0.003403  ...              1.000000\n",
              "\n",
              "[29 rows x 29 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "49wDk5AGa9Dw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "outputId": "d6c95a8b-f706-4dd0-d2a8-51ed337e2cde"
      },
      "source": [
        "plt.figure(figsize=(12, 8))\n",
        "sns.heatmap(corr);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OM2CqtiwNV2F"
      },
      "source": [
        "#Do train/test split. Use data from April & May 2016 to train. Use data from June 2016 to test."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57Nj1lHEX_7w"
      },
      "source": [
        "y = df['cats_allowed']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CUb7OZSvfVRt"
      },
      "source": [
        "X = df[['dogs_allowed', 'doorman', 'laundry_in_building']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F-mwZdlAp0B-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        },
        "outputId": "b5e891b4-87be-4ca0-8883-04eafe27cd2e"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "bathrooms               float64\n",
              "bedrooms                  int64\n",
              "created                  object\n",
              "description              object\n",
              "display_address          object\n",
              "latitude                float64\n",
              "longitude               float64\n",
              "price                     int64\n",
              "street_address           object\n",
              "interest_level           object\n",
              "elevator                  int64\n",
              "cats_allowed              int64\n",
              "hardwood_floors           int64\n",
              "dogs_allowed              int64\n",
              "doorman                   int64\n",
              "dishwasher                int64\n",
              "no_fee                    int64\n",
              "laundry_in_building       int64\n",
              "fitness_center            int64\n",
              "pre-war                   int64\n",
              "laundry_in_unit           int64\n",
              "roof_deck                 int64\n",
              "outdoor_space             int64\n",
              "dining_room               int64\n",
              "high_speed_internet       int64\n",
              "balcony                   int64\n",
              "swimming_pool             int64\n",
              "new_construction          int64\n",
              "terrace                   int64\n",
              "exclusive                 int64\n",
              "loft                      int64\n",
              "garden_patio              int64\n",
              "wheelchair_access         int64\n",
              "common_outdoor_space      int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1KNCAHccfjit",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "78a663b5-9d77-4461-a960-b213c35ac444"
      },
      "source": [
        "train = df[ df['created'] <= 2016-05-31 00:00:00]\n",
        "test = df[ df['created'] >= 2016-06-01 00:00:00 ]\n",
        "#change date time format"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-15-441baebd6c01>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    train = df[ df['created'] <= 2016-05-31 00:00:00]\u001b[0m\n\u001b[0m                                       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid token\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qpd6dBoSX__P"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RmMoiUzaNWHL"
      },
      "source": [
        "#Engineer at least two new features. (See below for explanation & ideas.)\n",
        "#cats allowed is highly correlated to dogs allowed"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v2R9OhqKYA78"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W1IZqU82YA_V"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wpYPG2gzNWUD"
      },
      "source": [
        "#Fit a linear regression model with at least two features."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0TvOe-pqYB6R"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6BmqXrBnYB9t"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uMOk4Qw8NWdb"
      },
      "source": [
        "#Get the model's coefficients and intercept."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zY1t-W0uYC1k"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_9H8WwzYC5A"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_b6iVW5yNWlM"
      },
      "source": [
        "#Get regression metrics RMSE, MAE, and  R2 , for both the train and test data."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "udLASVcgYEiR"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2e5dQUcqYElr"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fwylFE2KNhEL"
      },
      "source": [
        "#What's the best test MAE you can get? Share your score and features used with your cohort on Slack!"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2YnTtLqzNhHi"
      },
      "source": [
        "#"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nbphzOwLNhLJ"
      },
      "source": [
        "#"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFfIOq-0NhOr"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OlDYOwfWNhRu"
      },
      "source": [
        "#As always, commit your notebook to your fork of the GitHub repo."
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}