German_house_Kaggle.ipynb

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  "metadata": {
    "colab": {
      "name": "German house Kaggle.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPKzNSEOEWpNsOvyGdg+KUU",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SamuelLawrence876/Analysing-Berling-AirBnB-Rental/blob/master/German_house_Kaggle.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l7GUSKXDwKj1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "6c0eb93a-4e11-4af4-f6d9-e78a0bb081b5"
      },
      "source": [
        "import os \n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import datetime \n",
        "#WordCounting\n",
        "from wordcloud import WordCloud, ImageColorGenerator, STOPWORDS\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "#warnings\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "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": "k88wya-y2MzA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir ~/.kaggle\n",
        "#!cd ~/.kaggle\n",
        "!touch ~/.kaggle/kaggle.json\n",
        "\n",
        "api_token = {\"username\":\"xxx\",\"key\":\"xxx\"}\n",
        "\n",
        "import json\n",
        "\n",
        "with open('/root/.kaggle/kaggle.json', 'w') as file:\n",
        "    json.dump(api_token, file)\n",
        "\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l43ub-Ej2M1l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "24a7b501-e55c-4f65-f809-222cfe08a49a"
      },
      "source": [
        "!kaggle datasets download -d brittabettendorf/berlin-airbnb-data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading berlin-airbnb-data.zip to /content\n",
            " 99% 87.0M/87.7M [00:01<00:00, 39.0MB/s]\n",
            "100% 87.7M/87.7M [00:01<00:00, 58.6MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ZlFb7MR4WpJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import zipfile\n",
        "\n",
        "zf = zipfile.ZipFile('berlin-airbnb-data.zip') \n",
        "df_calender_summary = pd.read_csv(zf.open('calendar_summary.csv'))\n",
        "df_listings = pd.read_csv(zf.open('listings.csv'))\n",
        "df_listings_summary = pd.read_csv(zf.open('listings_summary.csv'))\n",
        "df_neighbourhoods = pd.read_csv(zf.open('neighbourhoods.csv'))\n",
        "df_reviews1 = pd.read_csv(zf.open('reviews.csv'))\n",
        "df_reviews_summary = pd.read_csv(zf.open('reviews_summary.csv'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eZoGLUlk8796",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "outputId": "72a2be31-f88a-475b-8228-1c4f3c12e2ab"
      },
      "source": [
        "df_listings.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_name</th>\n",
              "      <th>neighbourhood_group</th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>room_type</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>last_review</th>\n",
              "      <th>reviews_per_month</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015</td>\n",
              "      <td>Berlin-Mitte Value! Quiet courtyard/very central</td>\n",
              "      <td>2217</td>\n",
              "      <td>Ian</td>\n",
              "      <td>Mitte</td>\n",
              "      <td>Brunnenstr. Süd</td>\n",
              "      <td>52.534537</td>\n",
              "      <td>13.402557</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>60</td>\n",
              "      <td>4</td>\n",
              "      <td>118</td>\n",
              "      <td>2018-10-28</td>\n",
              "      <td>3.76</td>\n",
              "      <td>4</td>\n",
              "      <td>141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2695</td>\n",
              "      <td>Prenzlauer Berg close to Mauerpark</td>\n",
              "      <td>2986</td>\n",
              "      <td>Michael</td>\n",
              "      <td>Pankow</td>\n",
              "      <td>Prenzlauer Berg Nordwest</td>\n",
              "      <td>52.548513</td>\n",
              "      <td>13.404553</td>\n",
              "      <td>Private room</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2018-10-01</td>\n",
              "      <td>1.42</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3176</td>\n",
              "      <td>Fabulous Flat in great Location</td>\n",
              "      <td>3718</td>\n",
              "      <td>Britta</td>\n",
              "      <td>Pankow</td>\n",
              "      <td>Prenzlauer Berg Südwest</td>\n",
              "      <td>52.534996</td>\n",
              "      <td>13.417579</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>90</td>\n",
              "      <td>62</td>\n",
              "      <td>143</td>\n",
              "      <td>2017-03-20</td>\n",
              "      <td>1.25</td>\n",
              "      <td>1</td>\n",
              "      <td>220</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     id  ... availability_365\n",
              "0  2015  ...              141\n",
              "1  2695  ...                0\n",
              "2  3176  ...              220\n",
              "\n",
              "[3 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQrzxqee88BF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "outputId": "cce242aa-fd9a-4c63-def4-0aab69e195d8"
      },
      "source": [
        "df_listings_summary.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>listing_url</th>\n",
              "      <th>scrape_id</th>\n",
              "      <th>last_scraped</th>\n",
              "      <th>name</th>\n",
              "      <th>summary</th>\n",
              "      <th>space</th>\n",
              "      <th>description</th>\n",
              "      <th>experiences_offered</th>\n",
              "      <th>neighborhood_overview</th>\n",
              "      <th>notes</th>\n",
              "      <th>transit</th>\n",
              "      <th>access</th>\n",
              "      <th>interaction</th>\n",
              "      <th>house_rules</th>\n",
              "      <th>thumbnail_url</th>\n",
              "      <th>medium_url</th>\n",
              "      <th>picture_url</th>\n",
              "      <th>xl_picture_url</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_url</th>\n",
              "      <th>host_name</th>\n",
              "      <th>host_since</th>\n",
              "      <th>host_location</th>\n",
              "      <th>host_about</th>\n",
              "      <th>host_response_time</th>\n",
              "      <th>host_response_rate</th>\n",
              "      <th>host_acceptance_rate</th>\n",
              "      <th>host_is_superhost</th>\n",
              "      <th>host_thumbnail_url</th>\n",
              "      <th>host_picture_url</th>\n",
              "      <th>host_neighbourhood</th>\n",
              "      <th>host_listings_count</th>\n",
              "      <th>host_total_listings_count</th>\n",
              "      <th>host_verifications</th>\n",
              "      <th>host_has_profile_pic</th>\n",
              "      <th>host_identity_verified</th>\n",
              "      <th>street</th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>neighbourhood_cleansed</th>\n",
              "      <th>...</th>\n",
              "      <th>beds</th>\n",
              "      <th>bed_type</th>\n",
              "      <th>amenities</th>\n",
              "      <th>square_feet</th>\n",
              "      <th>price</th>\n",
              "      <th>weekly_price</th>\n",
              "      <th>monthly_price</th>\n",
              "      <th>security_deposit</th>\n",
              "      <th>cleaning_fee</th>\n",
              "      <th>guests_included</th>\n",
              "      <th>extra_people</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>maximum_nights</th>\n",
              "      <th>calendar_updated</th>\n",
              "      <th>has_availability</th>\n",
              "      <th>availability_30</th>\n",
              "      <th>availability_60</th>\n",
              "      <th>availability_90</th>\n",
              "      <th>availability_365</th>\n",
              "      <th>calendar_last_scraped</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>first_review</th>\n",
              "      <th>last_review</th>\n",
              "      <th>review_scores_rating</th>\n",
              "      <th>review_scores_accuracy</th>\n",
              "      <th>review_scores_cleanliness</th>\n",
              "      <th>review_scores_checkin</th>\n",
              "      <th>review_scores_communication</th>\n",
              "      <th>review_scores_location</th>\n",
              "      <th>review_scores_value</th>\n",
              "      <th>requires_license</th>\n",
              "      <th>license</th>\n",
              "      <th>jurisdiction_names</th>\n",
              "      <th>instant_bookable</th>\n",
              "      <th>is_business_travel_ready</th>\n",
              "      <th>cancellation_policy</th>\n",
              "      <th>require_guest_profile_picture</th>\n",
              "      <th>require_guest_phone_verification</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>reviews_per_month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015</td>\n",
              "      <td>https://www.airbnb.com/rooms/2015</td>\n",
              "      <td>20181107122246</td>\n",
              "      <td>2018-11-07</td>\n",
              "      <td>Berlin-Mitte Value! Quiet courtyard/very central</td>\n",
              "      <td>Great location!  30 of 75 sq meters. This wood...</td>\n",
              "      <td>A+++ location! This „Einliegerwohnung“ is an e...</td>\n",
              "      <td>Great location!  30 of 75 sq meters. This wood...</td>\n",
              "      <td>none</td>\n",
              "      <td>It is located in the former East Berlin area o...</td>\n",
              "      <td>This is my home, not a hotel. I rent out occas...</td>\n",
              "      <td>Close to U-Bahn U8 and U2 (metro), Trams M12, ...</td>\n",
              "      <td>Simple kitchen/cooking, refrigerator, microwav...</td>\n",
              "      <td>Always available</td>\n",
              "      <td>No parties No events No pets No smoking, not e...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/260fd609-7...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2217</td>\n",
              "      <td>https://www.airbnb.com/users/show/2217</td>\n",
              "      <td>Ian</td>\n",
              "      <td>2008-08-18</td>\n",
              "      <td>Key Biscayne, Florida, United States</td>\n",
              "      <td>Believe in sharing economy.</td>\n",
              "      <td>within an hour</td>\n",
              "      <td>96%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>t</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/21428a22-4...</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/21428a22-4...</td>\n",
              "      <td>Mitte</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>['email', 'phone', 'reviews', 'jumio', 'offlin...</td>\n",
              "      <td>t</td>\n",
              "      <td>t</td>\n",
              "      <td>Berlin, Berlin, Germany</td>\n",
              "      <td>Mitte</td>\n",
              "      <td>Brunnenstr. Süd</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Real Bed</td>\n",
              "      <td>{TV,\"Cable TV\",Wifi,Kitchen,Gym,Heating,\"Famil...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>$60.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>$200.00</td>\n",
              "      <td>$30.00</td>\n",
              "      <td>1</td>\n",
              "      <td>$28.00</td>\n",
              "      <td>4</td>\n",
              "      <td>1125</td>\n",
              "      <td>3 months ago</td>\n",
              "      <td>t</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>51</td>\n",
              "      <td>141</td>\n",
              "      <td>2018-11-07</td>\n",
              "      <td>118</td>\n",
              "      <td>2016-04-11</td>\n",
              "      <td>2018-10-28</td>\n",
              "      <td>93.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>t</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>strict_14_with_grace_period</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>4</td>\n",
              "      <td>3.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2695</td>\n",
              "      <td>https://www.airbnb.com/rooms/2695</td>\n",
              "      <td>20181107122246</td>\n",
              "      <td>2018-11-07</td>\n",
              "      <td>Prenzlauer Berg close to Mauerpark</td>\n",
              "      <td>NaN</td>\n",
              "      <td>In the summertime we are spending most of our ...</td>\n",
              "      <td>In the summertime we are spending most of our ...</td>\n",
              "      <td>none</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Within walking distance you'll find the S-Bahn...</td>\n",
              "      <td>Außer deinem Zimmer kannst du noch die Küche u...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/97d4f6e8-1...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2986</td>\n",
              "      <td>https://www.airbnb.com/users/show/2986</td>\n",
              "      <td>Michael</td>\n",
              "      <td>2008-09-16</td>\n",
              "      <td>Berlin, Berlin, Germany</td>\n",
              "      <td>Living with my wife in Berlin.  Like travellin...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>f</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/50434955-1...</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/50434955-1...</td>\n",
              "      <td>Prenzlauer Berg</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>['email', 'phone', 'reviews', 'jumio', 'govern...</td>\n",
              "      <td>t</td>\n",
              "      <td>t</td>\n",
              "      <td>Berlin, Berlin, Germany</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Prenzlauer Berg Nordwest</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Real Bed</td>\n",
              "      <td>{Wifi,Kitchen,Elevator,Heating,Washer,Essentia...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>$17.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>$0.00</td>\n",
              "      <td>$0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>$0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>40</td>\n",
              "      <td>7 weeks ago</td>\n",
              "      <td>t</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2018-11-07</td>\n",
              "      <td>6</td>\n",
              "      <td>2018-07-04</td>\n",
              "      <td>2018-10-01</td>\n",
              "      <td>100.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>t</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>flexible</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>1</td>\n",
              "      <td>1.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3176</td>\n",
              "      <td>https://www.airbnb.com/rooms/3176</td>\n",
              "      <td>20181107122246</td>\n",
              "      <td>2018-11-07</td>\n",
              "      <td>Fabulous Flat in great Location</td>\n",
              "      <td>This beautiful first floor apartment  is situa...</td>\n",
              "      <td>1st floor (68m2) apartment on Kollwitzplatz/ P...</td>\n",
              "      <td>This beautiful first floor apartment  is situa...</td>\n",
              "      <td>none</td>\n",
              "      <td>The neighbourhood is famous for its variety of...</td>\n",
              "      <td>We welcome FAMILIES and cater especially for y...</td>\n",
              "      <td>We are 5 min walk  away from the tram M2, whic...</td>\n",
              "      <td>The apartment will be entirely yours. We are c...</td>\n",
              "      <td>Feel free to ask any questions prior to bookin...</td>\n",
              "      <td>It’s a non smoking flat, which likes to be tre...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/243355/84a...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3718</td>\n",
              "      <td>https://www.airbnb.com/users/show/3718</td>\n",
              "      <td>Britta</td>\n",
              "      <td>2008-10-19</td>\n",
              "      <td>Coledale, New South Wales, Australia</td>\n",
              "      <td>We love to travel ourselves a lot and prefer t...</td>\n",
              "      <td>within a day</td>\n",
              "      <td>100%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>f</td>\n",
              "      <td>https://a0.muscache.com/im/users/3718/profile_...</td>\n",
              "      <td>https://a0.muscache.com/im/users/3718/profile_...</td>\n",
              "      <td>Prenzlauer Berg</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>['email', 'phone', 'facebook', 'reviews', 'man...</td>\n",
              "      <td>t</td>\n",
              "      <td>t</td>\n",
              "      <td>Berlin, Berlin, Germany</td>\n",
              "      <td>Prenzlauer Berg</td>\n",
              "      <td>Prenzlauer Berg Südwest</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Real Bed</td>\n",
              "      <td>{Internet,Wifi,Kitchen,\"Buzzer/wireless interc...</td>\n",
              "      <td>720.0</td>\n",
              "      <td>$90.00</td>\n",
              "      <td>$520.00</td>\n",
              "      <td>$1,900.00</td>\n",
              "      <td>$200.00</td>\n",
              "      <td>$50.00</td>\n",
              "      <td>2</td>\n",
              "      <td>$20.00</td>\n",
              "      <td>62</td>\n",
              "      <td>1125</td>\n",
              "      <td>a week ago</td>\n",
              "      <td>t</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>220</td>\n",
              "      <td>2018-11-07</td>\n",
              "      <td>143</td>\n",
              "      <td>2009-06-20</td>\n",
              "      <td>2017-03-20</td>\n",
              "      <td>92.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>t</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>t</td>\n",
              "      <td>f</td>\n",
              "      <td>strict_14_with_grace_period</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>1</td>\n",
              "      <td>1.25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 96 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     id  ... reviews_per_month\n",
              "0  2015  ...              3.76\n",
              "1  2695  ...              1.42\n",
              "2  3176  ...              1.25\n",
              "\n",
              "[3 rows x 96 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j6Hlu-xu1gjO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "outputId": "ca43c91c-b90e-4f23-b909-9e9659bcfa8f"
      },
      "source": [
        "df_listings_summary.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'listing_url', 'scrape_id', 'last_scraped', 'name', 'summary',\n",
              "       'space', 'description', 'experiences_offered', 'neighborhood_overview',\n",
              "       'notes', 'transit', 'access', 'interaction', 'house_rules',\n",
              "       'thumbnail_url', 'medium_url', 'picture_url', 'xl_picture_url',\n",
              "       'host_id', 'host_url', 'host_name', 'host_since', 'host_location',\n",
              "       'host_about', 'host_response_time', 'host_response_rate',\n",
              "       'host_acceptance_rate', 'host_is_superhost', 'host_thumbnail_url',\n",
              "       'host_picture_url', 'host_neighbourhood', 'host_listings_count',\n",
              "       'host_total_listings_count', 'host_verifications',\n",
              "       'host_has_profile_pic', 'host_identity_verified', 'street',\n",
              "       'neighbourhood', 'neighbourhood_cleansed',\n",
              "       'neighbourhood_group_cleansed', 'city', 'state', 'zipcode', 'market',\n",
              "       'smart_location', 'country_code', 'country', 'latitude', 'longitude',\n",
              "       'is_location_exact', 'property_type', 'room_type', 'accommodates',\n",
              "       'bathrooms', 'bedrooms', 'beds', 'bed_type', 'amenities', 'square_feet',\n",
              "       'price', 'weekly_price', 'monthly_price', 'security_deposit',\n",
              "       'cleaning_fee', 'guests_included', 'extra_people', 'minimum_nights',\n",
              "       'maximum_nights', 'calendar_updated', 'has_availability',\n",
              "       'availability_30', 'availability_60', 'availability_90',\n",
              "       'availability_365', 'calendar_last_scraped', 'number_of_reviews',\n",
              "       'first_review', 'last_review', 'review_scores_rating',\n",
              "       'review_scores_accuracy', 'review_scores_cleanliness',\n",
              "       'review_scores_checkin', 'review_scores_communication',\n",
              "       'review_scores_location', 'review_scores_value', 'requires_license',\n",
              "       'license', 'jurisdiction_names', 'instant_bookable',\n",
              "       'is_business_travel_ready', 'cancellation_policy',\n",
              "       'require_guest_profile_picture', 'require_guest_phone_verification',\n",
              "       'calculated_host_listings_count', 'reviews_per_month'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7NK1AZcfFcu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e63ab445-15ba-4a38-bd46-78b72bc370fd"
      },
      "source": [
        "#what columns have the most unique values?\n",
        "for i in df_listings_summary.columns:\n",
        "    print(\"Column name {} and unique values are {}\".format(i,len(df_listings_summary[i].unique())))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Column name id and unique values are 22552\n",
            "Column name listing_url and unique values are 22552\n",
            "Column name scrape_id and unique values are 1\n",
            "Column name last_scraped and unique values are 2\n",
            "Column name name and unique values are 21874\n",
            "Column name summary and unique values are 21042\n",
            "Column name space and unique values are 13599\n",
            "Column name description and unique values are 21998\n",
            "Column name experiences_offered and unique values are 1\n",
            "Column name neighborhood_overview and unique values are 10782\n",
            "Column name notes and unique values are 6688\n",
            "Column name transit and unique values are 12309\n",
            "Column name access and unique values are 9947\n",
            "Column name interaction and unique values are 9585\n",
            "Column name house_rules and unique values are 10351\n",
            "Column name thumbnail_url and unique values are 1\n",
            "Column name medium_url and unique values are 1\n",
            "Column name picture_url and unique values are 22465\n",
            "Column name xl_picture_url and unique values are 1\n",
            "Column name host_id and unique values are 19180\n",
            "Column name host_url and unique values are 19180\n",
            "Column name host_name and unique values are 5998\n",
            "Column name host_since and unique values are 2915\n",
            "Column name host_location and unique values are 1037\n",
            "Column name host_about and unique values are 8880\n",
            "Column name host_response_time and unique values are 5\n",
            "Column name host_response_rate and unique values are 65\n",
            "Column name host_acceptance_rate and unique values are 1\n",
            "Column name host_is_superhost and unique values are 3\n",
            "Column name host_thumbnail_url and unique values are 19101\n",
            "Column name host_picture_url and unique values are 19101\n",
            "Column name host_neighbourhood and unique values are 182\n",
            "Column name host_listings_count and unique values are 32\n",
            "Column name host_total_listings_count and unique values are 32\n",
            "Column name host_verifications and unique values are 301\n",
            "Column name host_has_profile_pic and unique values are 3\n",
            "Column name host_identity_verified and unique values are 3\n",
            "Column name street and unique values are 86\n",
            "Column name neighbourhood and unique values are 92\n",
            "Column name neighbourhood_cleansed and unique values are 136\n",
            "Column name neighbourhood_group_cleansed and unique values are 12\n",
            "Column name city and unique values are 61\n",
            "Column name state and unique values are 20\n",
            "Column name zipcode and unique values are 216\n",
            "Column name market and unique values are 7\n",
            "Column name smart_location and unique values are 61\n",
            "Column name country_code and unique values are 1\n",
            "Column name country and unique values are 1\n",
            "Column name latitude and unique values are 22552\n",
            "Column name longitude and unique values are 22552\n",
            "Column name is_location_exact and unique values are 2\n",
            "Column name property_type and unique values are 33\n",
            "Column name room_type and unique values are 3\n",
            "Column name accommodates and unique values are 16\n",
            "Column name bathrooms and unique values are 17\n",
            "Column name bedrooms and unique values are 13\n",
            "Column name beds and unique values are 19\n",
            "Column name bed_type and unique values are 5\n",
            "Column name amenities and unique values are 19985\n",
            "Column name square_feet and unique values are 112\n",
            "Column name price and unique values are 295\n",
            "Column name weekly_price and unique values are 388\n",
            "Column name monthly_price and unique values are 427\n",
            "Column name security_deposit and unique values are 143\n",
            "Column name cleaning_fee and unique values are 113\n",
            "Column name guests_included and unique values are 15\n",
            "Column name extra_people and unique values are 65\n",
            "Column name minimum_nights and unique values are 102\n",
            "Column name maximum_nights and unique values are 222\n",
            "Column name calendar_updated and unique values are 75\n",
            "Column name has_availability and unique values are 1\n",
            "Column name availability_30 and unique values are 31\n",
            "Column name availability_60 and unique values are 61\n",
            "Column name availability_90 and unique values are 91\n",
            "Column name availability_365 and unique values are 366\n",
            "Column name calendar_last_scraped and unique values are 2\n",
            "Column name number_of_reviews and unique values are 306\n",
            "Column name first_review and unique values are 2229\n",
            "Column name last_review and unique values are 1313\n",
            "Column name review_scores_rating and unique values are 47\n",
            "Column name review_scores_accuracy and unique values are 10\n",
            "Column name review_scores_cleanliness and unique values are 10\n",
            "Column name review_scores_checkin and unique values are 9\n",
            "Column name review_scores_communication and unique values are 10\n",
            "Column name review_scores_location and unique values are 9\n",
            "Column name review_scores_value and unique values are 10\n",
            "Column name requires_license and unique values are 2\n",
            "Column name license and unique values are 1388\n",
            "Column name jurisdiction_names and unique values are 1\n",
            "Column name instant_bookable and unique values are 2\n",
            "Column name is_business_travel_ready and unique values are 1\n",
            "Column name cancellation_policy and unique values are 5\n",
            "Column name require_guest_profile_picture and unique values are 2\n",
            "Column name require_guest_phone_verification and unique values are 2\n",
            "Column name calculated_host_listings_count and unique values are 23\n",
            "Column name reviews_per_month and unique values are 769\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nuqFQlAjeK2C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#List of all catagories we're using to predict \n",
        "df2 = df_listings_summary[['minimum_nights','street','maximum_nights','cleaning_fee','property_type','extra_people','zipcode',\n",
        "                          'room_type','bed_type','review_scores_value', 'instant_bookable','host_has_profile_pic', 'requires_license','price']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TzpeKQ9zgKZW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#catagorical features for model\n",
        "catagorical_features = df_listings_summary[['room_type','bed_type','review_scores_value', 'instant_bookable','host_has_profile_pic', 'requires_license','price']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YNgsIKsOlU3z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#continueous features\n",
        "df = df_listings_summary[['minimum_nights','street','maximum_nights','cleaning_fee','property_type','extra_people','zipcode']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5dOMkRFHY0qb",
        "colab_type": "text"
      },
      "source": [
        "What do the graphs have to say about the catagories?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VI-QywChjp6j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cc3ff113-dc75-4514-f923-0150715d410a"
      },
      "source": [
        "for Categorical_Values in catagorical_features.columns:\n",
        "    cat_num = catagorical_features[Categorical_Values].value_counts()\n",
        "    print('graph for %s: total = %d' % (Categorical_Values, len(cat_num)))\n",
        "    chart = sns.barplot(x=cat_num.index, y=cat_num)\n",
        "    chart.set_xticklabels(chart.get_xticklabels(), rotation=90)\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "graph for room_type: total = 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "graph for bed_type: total = 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "graph for review_scores_value: total = 9\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "graph for instant_bookable: total = 2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "graph for host_has_profile_pic: total = 2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "graph for requires_license: total = 2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "graph for price: total = 295\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SYJ+PPFT1T1R1nqouAN4AfEdVfxe4FXi9OzsfuN5/3+D/8fvfUVXdhyInSZIkDcbTPo8/Bt4rIouxNY0r/fqVwJF+/b3AxV2SL0mSJHG6MW31JKr6XeC7/vth4IVt3OwAfnOfCpYkSZIMy3gaeSRJkiT7Cak8kiRJko5J5ZEkSZJ0TCqPJEmSpGNSeeyGVZd8qNsiJEmSjDtSeSRJkiQdk8pjBKz81Ae6LUKSJMm4IpVHkiRJ0jGpPJIkSZKOSeWRJEmSdEwqjxGy8lN/0m0RkiRJxg2pPJIkSZKOSeWRJEmSdEwqjyRJkqRjunok+/7Gik/9T9B+lD7mvvOT3RYnSZKka+TII0mSJOmYHHnsJUs/8RaUHaju4Cnv/kq3xUmSJNkn5MgjSZIk6ZhUHqPII584r9siJEmS7BNSeSRJkiQdk8pjlPnFJ87ttghJkiRjzj5XHiIyX0RuFZH7ROReEfkffv0IEblFRB7y78P9uojIx0VksYjcJSKn7muZkyRJkla6MfLoB96nqicDpwHvFJGTgYuBb6vqScC3/T/AWcBJ/rkQuGTfi5wkSZLU7HPloaorVfUO//0EcD8wFzgXuNqdXQ3E6vO5wOfUuA2YJSLH7WOxO+Kh/5tTV0mSTGy6uuYhIguA5wE/Ao5R1ZV+axVwjP+eCyytHlvm15p+XSgii0Rk0dq1a8dM5pHywCdNgdz/yVQkSZJMPLqmPETkEOBLwB+p6ub6nqoqoJ34p6qXq+pCVV04e/bsUZR077n3U+d0W4QkSZJRpSs7zEVkCqY4/lVVr/PLq0XkOFVd6dNSa/z6cmB+9fg8v7ZfcfenzkEFBlEGBU59x1e7LVKSJMke0w1rKwGuBO5X1X+sbt0AnO+/zweur66/2a2uTgM2VdNb+y23X/qabouQJEmyx3Rj5PES4E3A3SJyp1/7U+BvgS+KyAXAEuC3/N5NwKuAxcA24K37VtwkSZKkyT5XHqr6n4AMcfv0Nu4VeOeYCtVlfnTZ2bzo7Td2W4wkSZIRkzvMkyRJko5J5ZEkSZJ0TCqPccJtl5/dbRGSJElGTCqPccQPLj+b/0olkiTJfkC+SXAc8p+fPptBFAV+9W1f67Y4SZIku5AjjyRJkqRjcuSxH3DrFa9GgUFAUV7x+zd1W6QkSQ5wcuSRJEmSdEwqj/2Qb175Kr5x5au6LUaSJAcwqTz2Y76eCiRJki6RyiNJkiTpmFQeE4QbrzqLr151VrfFSJLkACGVxwTjhqvO4iupRJIkGWNSeUxQvnzVWVz3mTO7LUaSJBOUVB5JkiRJx+QmwQOEf//MmWWjoShvfMvN3RYpSZL9mFQeByif/+wZvmMd3vSWm7n6s69Exf4r8Hvnf7O7AiZJMq5J5ZG05YrPnWGKRODCN9ko5ZJ/OePJ0cu7/nuOXJLkQCaVR7JHfOJffeQipkze+zs383+uOYP3vTGVSpIcCKTySEaVf7jmDAZdodTfH/wtUyp//u9nMgD8zW9+o5tiJkmyl6TySLrCxdeaEhkQ6Ac+/jpTJm+/7kwue20qliQZ7+w3ykNEzgQ+BkwCrlDVv+2ySMkYccGXz6RPoBfYKbBdQJjO18/5Mmdd/wZgKjAFK76TQSfx9fM+3lWZk+RAY79QHiIyCfgk8ApgGfATEblBVe/rrmTJeOGsr7yHr5/3UQBe9ZU/xrYwTQLt8d/2uem/fYBXffmvn/wviP1WAYSvvfa9ALz6uo8BUn16/P5F+zZiSTJO2S+UB/BCYLGqPgwgIv8GnAuk8ki6wqu/dBmtykVcEQk3vu6tAJz9pc/aPW11d+Prf9fuX/v5XfwI9zf+5ut5zbXXtgnDlNgNr38NAOdc+7WWsGtZvvL6V3Detd+ya9LTcNPDl1/3UgBe+6Uf0KJIPZxrX3cqAK//0s883OJGmATSwxdeeyJvuO4RJtPDv7z2KaOf0Mm4RVS12zLsFhF5PXCmqv6+/38T8CJVfVfl5kLgQv/7DODxhjfrgKOGCWZ39yeSHynn6Pqxv8g5Gn6knKPrx3iT80hVPXQ3fgH7z8hjt6jq5cDl8V9EFjXuL2xe6+T+RPIj5Tww5RwNP1LOCS/nuuH8qdlfzrZaDsyv/s/za0mSJEkX2F+Ux0+Ak0TkBBGZCrwBuKHLMiVJkhyw7BfTVqraLyLvAm7GTHWvUtV7d/PY5SO81sn9ieRHyjm6fuwvco6GHynn6Pqxv8jZwn6xYJ4kSZKML/aXaaskSZJkHJHKI0mSJOmYVB5JkiRJx+wXC+adICJHAKjq+nb/d+d+NMLc22vDhDEL2Lg3so41w8VnNNJ6tGVKJiZ1nh/I+T+WcZ8QC+Yicjzw98DpwEbgIOBY7ByGzdjJ4NOBlcB3gG3Ay4ATgR3YSXszgA3Az4AHgaXY2XyzgDnYKO0wD3Ijpnh/1e/1+/VJwIB/P+HXDnE5Jrt/4vd73c9Nfu8Ql3UJsNPdrAaeDZzgbgf9eQXWAIuAh4FjgO3ANOAIoM9lGgAeAA4HnulxmeFhrwAec38UONXdzPTnBtyPLR7v6ZXsO4BVHo4AW13GI/25ydjJhf0u12RP423+TOTFMmxX61R31wsc6vGZ4fEddD8jPcX9VXe/0f3Z6nIe73LMAA52OdSfw5/ZVuXTIHEmh8VLq2vxfNwfdDc7KWVqRnVfPX7bG+kwicJglW5apU3MAqjHr88/Pe5HnB0S8d7h16f583HuSPjRR8lD3E2kZ8goHoeplQzxMslBf3awSoNJVRi4P73+PQmrd5MrNwNV2kfZCfnqfF3vYc2s0jzSO/I65JtSpeNkysFlEbeQM/JXqrAm0VoWwp+tWN3v9zjMrGSIfNqO1dWdHu5MT7se9zvKxKCHM52Sl3E96vVU/24+O82fi/TW6tmQZYDWenEQu5afAUqbUxMyPOHuZniYIcd24CHgSuBKVe1jGCaK8vgh8E9YxD8LPItSeWXoJ/eaKIiRiEOF1c+uGdksxMMRhaFuIMaCaDjqgtiMX1NubVxvFqiovHszRdp8vpO0C/qpTzjc1Z92fjavtWuABmn1A1rTL2iGU/sR+Vt3EOrnmuEN5b9Wn6acu/s9UPkbeVY3nuGmfq5Wis18r58PIowIp1kvhsuDiHdTrjq+YEr1oOqZuiGNxjzCrOPdVCh1eWvnppatXZ62K7MM476dm1rxahv39fWeIdxEJyM6r1Ma959wWQ9yt5uwTuz9qvrbDMNEUR4PqepJInInRZtuxRLyadiW+yOxnvbxlB4nlJ7sWkwr16e7RWFVf3YB1mM+hvaN1wqsFxZ+NDN9CdazOrLxfI/L8DBwEtarP6byox5xrMNGAws8Hv2UhgJs5/0crCd1hF+rC9egxwFsp35dyCOcFf57biVnyBHxnOdyHkXp3ddpug5L02e2kQFstDAXO4PsKEqPLeQZcD8GgaNpbQwjznHKwLzq+ajog1h6nwAsxspB3UCGnI/6tfm0r6RL/d6jWJrXz2rlZsDDajb+VM+GX+141J8b6nTBJf7dLFtBU876fsRnGZZ2C4bwY4n7H997Iudj/n38EPcfwdIpwmgqp0kelz53R+OeNvxYQPs0X0LJ16Hk1EY8mnk637/n0Vp2h4trrTxWYGV8JWUmpFaE0a405ah51OMY3+14BBt9zB3i/i+wEc2cxnXB6sbJ2GxLjarq04fwD5g4C+a3i8insMZyMnAHluHHU4b4PVgj1IsNz+ohePREjsYUCLQ2lrX2j6mnvsqP+ESB66/cD1CmcCZThqiTK3fxfw5lWgQPQynD2MivaNQjHnWh7nd3Mc3TbLRDUU2mtTcX96jiWCuO8EeqMLe6P9sq+cJNVP61lCF63ZM6zNNmm4cxyZ+ZXIW1EusRTalkivSIKY26YakVxwCWn3hYMUVUxzcanXqapE6HiLtgHZL6WnM0eDBFgTV7kDMoI4vm6CQ4hNYptmavXbAGoBl++LnFf8+ideqi9mfQ5WzX8xVap7+a94LpVVo0Rz6DlJet1CPY2s2syl3dWEcdiBmDaZUfA9U98bTqwzodkfdQyk8/Vv6PoExjRV2a7L/ruNadRGhNt7re1fFQyjRdfT3iUo+SttKarnU5CDmi3jbTfJbfO7gRTjyDp8MOWpVSuOsBZlPaipAvOnuzsFPLo+5M9XhtYDdMlJHHVOAC4GJMw0fjEfP0UCpcPeyd1PAq5hInV//bZXjdW2/2eLZS5kKD6CmHW8Uye3r1v23UKBWiDieej7nTuFZX2FrxtZMzFOHURnhNN+2ux726ssCulWwoP+q54WZDNdxwf6jwm+nbzs2esLvn99b/ZFfGQ551KsNolIPh6tpwDDUlPNQU4O7CGMDWhd+uqo8MF/CEUB41InIW8C5s3eNgrAcTDUt8D2CNcr1wGho3ejrNnnZNzJ1uxXrWfVgvNxba+rC5w63u/pDK30mUnu4WSq9iOqVnTCVjn4exCpvumu3+RU8seuRTKn9rZdfvn17/X7+GT6owerGFy4MaabYdG1ov87CPx3p00fsM/2MRfRtlUfMg92cLNj11CDYaiUXy6CFHTzF6hlEh4vl+93cjNio7GDMCiMXiSNsd2HxtP2UhcBM2bF9GMS44zp9fgI0K1NNgDWVK7xgPZ4PL1leFEx2MY1y+tf7MEx4e7namu5lGGf1Odffr3P0hnq49HsZWbMplEtYrnFX50ePpsIHS+ZhKa1mIUeUyl/MIT+/IszBE2Ob5db9fP9rDPpjSY428X+3hPJPSserDplJmYVMuh7rMWz3saR7GXR5mLcMUD+cw9y9Gpgd7msVIIerIYpfhIM+zMESY7nF40NNsFjbte5B/HvfPGuA/sKnL51PK1iEud4xW1P8fipWjHf4ZoNSb6S53xC8MQp7w31MpC9/TPU5RZrZgZTCm0Rdg5TCMGdZhBjurXYZ5WNmI+tGLresuBp4KPJcyZbvCZV3u8Z3v/q33PJkDLPR4b3W3YeywBTOsuXUExz49yYRQHiIyGRt5nEeZ96t74JOwRuFa4DqskLy2cj8Ja5huA74ALFHV1W3M/Wa5nxuxwvVXwBlYpRjAKtMqbPRzrF+LhuUIrKAMYJVrDVZA5lTyLQJup1gj3Qm8Gfh9rJDEkHu5y3kTVrB2YoWC6vdMYKWqPiAihwGvwxrN7S7/7cBSj9sx2DtQTscaAsUK3SqP5y9jlml9WAX6BXAPVhHwNDwNeA5W2GNqZCn2wq7Z7uZQrMBGXtwCvMpl3Y4V4kPdr5kUy6BNlGH0dKzyxKjtLk+PnwNnejgneVqd6J9o2LZ42v8Iy8vJlHWoaNRCieBpfizWyNTrRY9RFP7TsHyN6bQVnj69HoejqjyJxjWmTqOxPIxSXqd4mq/xfJrmfmyhWPFswhqTAZdvrj8finA71jisxRq4HVhj0uvxn+TxnunPzsQas5heDQuhdR7GLEojV0+D7cDyeCmlsTuMMq0UHYKYBTiMYvGz3dPiYc+PQzyNnkOxVMLTINJ/DqWhD6u9mIoOf2dS8js6bdFB2krrulc0oA8Dt1b5eIL7O9nDiHz/qaenYmVsnudhv6fBE+7+EEr5Xe5uNvon4jCHsi63zOP+dM+P6dVnnadVdC7Diu5x/z7c8y7WN6Nj0O/yxZRpWLb9FKu7U7A6P49iCbkKbwNV9efshomiPK7BMuZa7MTdN7OrVcFY0c6qBDobyo7E7VBTM/uKTi2mxmpKZ0+nHdpZvI02ozV90a10Gw9+dhpe81pzamY0LPXGgrGYPms+U09lDzf9G98xA7EBuERV/3a4gCaK8nhQVZ8uIjdT9hs8gimQZ1LM95ZgQ8V6WFz3BjdjPbRIlJgO6MN64se6m3oaqh7hPIH1FE70a7FoFj3P1f5sLAbGwuIkTPM/jPXyN1HWTULWmL7ZifUQnuJ+9lIWmWPEcDilsQwZYjpoB2U6bTZlNBGFJxb4dng6ht+xQCdYj/Y4lzOmZ2IdA3e/ExuCn1alU12IY+i+w9Mjppoir6KHF/8PpSjQJyhTSoMej94qPWIEsYxWa6udWC+rNr1c6eEcTavVWFSy1di00WNY2agXcGPhcY3L+1RaF3AjrsuwHl74JQ13g+7/FKzn2a5SrvL4h7FEyIH/XouNUH7hckR5qQ0VHsfK76+zE4wAACAASURBVFNpXYyOqc6VWI84Rs9QFsLDn6V+bS6t+RmsdPdz2HXxfdDlOwnrkR9DMX7oobWebMHyLqYyt2K96H5shH8iVl9OpOQ5lHK/lDK1GGkUdSTSU2i1nJTKzRq/t5oyKqsNDcDybBJltqNex4v9FIdTpiejDoS8g5Uckd5UfvV4PI73uJ7QuB9l8AGsDhzeSO8oZ/djZTdkqON7OzZiv51W02ZR1ZMYholibbVeRH4TUwyKzQvOxRJ0ACsAYaURc8Mx5z+INdTrKUPUOuHBMjoas3WUhrTuyfZgGRwL9lAyo55njjd1HUSxlojNPtGYP04xWx2kVIzJ/nxMuYRSqXsVm12WVZT8rS2MprmbbbSax0YlnUSZU6/9DiUXhX/Q0+xgitKJTw9W+Z5Gq215s6GJShprNv2U+d9pWEMU0zu1ZVpYnmzEGpnaUqc2wz60EcfmaDSUXFMZ1G52VL/r9aQoJz0UW/pIo7qRovq9s/pfu5NK/qF6iDsb16NxiUZxs98LI5G6IYtntlRyhh9xL6zwouFrxreWq1YK9TUom1ObbsKfevNcpHnIEg1qrA+FXANYeYg4hbtDqudieizKz0ZKfoclXpTxCH9nQ06twox8j02BQZ2v0Q4EdWdgCqY0erD6FI12lNW6Q7aTXctd+DWF1jagvh/pPNPDqvNBKnezsDYlwq2trY6mtFMRXoQ5LBNl5LEA+Dvgv/mlrViCRmZB2Rkdve5ZtO4dUKyh20oxdwxLp0js8KOHsnM4CnkUvuhJ1z2+upLGtWiwoDQEMd8chTwqcm2mF8RoKUwfoXU38QZ/bhKl8awbHjBFdDS7jlKa1mR1b6ZuDPoplWQGu1aykD/myut70RuLnfZTqjSJMGJU1Pwd/kb+bfF0CBPMdmaqtfzxPdQmtboxafoR4dfp2WzQa3f1KEcrN01lOtQ0C238CFnqUe9QctblLZRdjOBg1/QJdmf11m5KpZZjuCmXZlmq41Nv8mtuymwXr91NWw0VdtN9OzdDpSe0pk8zT+sw6rRvN8KJa9HWtEvz+plmHtXWofX9duWoXVzrchkybQDOV9VvtHFfHpwIyiMQkcOB/w38FrY4V2dYO9pV3k6IBiimoNplbLOnVhegyLAehi/sYS0VljXNwj8S2UdSqdqt30TvPBRFfRRC7SZ6frU/Ea+QP+z3hzPprWnKUffM250eUPce60q0E1O2qykLvzMo1nXxbCzExt6caLgiL5v5XXce4kiX6JxMahPfupMSe2Nq67uQN3b9TqHV+i3u91a/hWJuXfcg+ygWWeFPrTAiT7a7TNOqe6EAY+E9FmzrDkIcqyGU/Q7R0YrRxzZPjx73P47SqBvJerQao4Q6X6NnHovR9fPRwYujRWLkFcqn3+9tohgczK1kqUdmdT2MdBqovqPzOKm6D6W8RZxjNFi7C/9jTSHqcm3VGPe3UPI39v6EDLHovd3lP4JitRjPb6aM9ne4fwdRjiaqy/RA9dx67I2t/wp8R1Uj7kMyoZRHO0TkWFVdNdT/3bkfjTD39tpQYQDsraxjzXDxGY20Hm2ZkolJnecHcv6PZtwPBOXxNVV9tYic6pf+CrgEM6kEW9hboap3uJu4H/cWAfj9sxvPBSsqNx8CXtAmjItcjvDjnModlQxfazx7jsuwApijqje6Hxf5/T+POAALVfVyj8fCkN1/r2jIebb7fWkVl/r+hZWcK7B3xs/xMD4kIvHKykubMlZuIj1/UqXVoup+M64hxznApZUci4ArgL+o5PxLVT3b4xFxXUj1bnt//nK/FvfegfU+l3uaf8jzbEXj+YWe3he6DLV8kd4RzofcTR1Gnd4RRk2Et8LluLxK00UexpP5Mowfc9rIGXkW/iwEFjXkrNMr/Igw6vi1i2v9utLwJ+7XcsT1kDPicSOlbNZlL8r4h6p8jXS61NN1UZVW4e8NVZxXAO9Q1VPdTaTBC4Dr3a+5XhfDj0izGxoyXFjFfw7t06KWsy4fN3hdjfSMMlin2YpGXOLek/54GHdQ6ukcGuXD07yO6yLgL93PGzyMG12GczFjl6ivi1zOWoYoD2czDBPuSPYmqvpq/3lR/BeRD9NaaOcAb6M08B+u7kUj/TasALY0tM6cys3tnuEtYVRyhB83VO7Cj7e1ke+G6pkVwI3AC8I/Efl0FYeokBf575B9UUP2iMtl7qbZeL8NG0qHmzmVuwjjsiqspoxPyuHxeU2VVi+gKOR2eRF+X1TJcZE3Bi1p1fAvviPOEY/LKMrtIkwp3S4iz6/Cvd1/h9tIs6/6b6nc4dfbnRMUz7+N1sa/fjaI8Gp3kaYL3f1XK7dD+VG7Czkj/PDnMkrDJdX/kHdF5Xf4F2G2i+tl1e+Fjf+1HHF9TsOft1Xy1HGM+EDJz0ini7AGNJ6LtPuqx+HJtFTVUys3Cyl18u3AZap6e8OPUASvacgglLLRTJeglrMuH+dgdfX26v+iNs+0yOH3oh7c7rKd6rKHX83y0RJXrH04VUSOo5TntwHnVO1GlP8XuJy1DF+ltX1ry4QZeYiIYGe0hNlcHJpX//+xeoSHcP/k/RGGeTywWVU3+qL9QszKY1Hj2gPAvao66Eep/DLwaOO9Hn+gqp9qE0YPQOPZ31DVj4xUzn3BEGnxc1W9p43btnFtuDkcGFDVzf6/4/cSDJF2j3bixwjDGRfvixCRo1R13XiRJxjTd0rYBte6Dq9hiHotIkdUm2KfvK+qq0dbrn1JFZ96Y/CzKesjC7HDD3+sqt8bwo/O69dEUB4i8krgU5iJ7nLMLvpXsZ7Dcv8chWnWR7AFuGdji0uPYSOwYzF77iewArgUW3wL01nB7OO3A/cCL8L2kKi7P9LdHeN+fAN4JbZYdyJl4RnK4tsSynEaz8Wsn2ZSrJ4e9DAGgO9jvYSw0w8z1Tixc6O7O5KyELza/X4E+Aq23+CtlKMo7gaeB/yKP78NG9LG87GDeL7LVZuTxvENB1EWMrcA3wZejS3aHVJl0yC2eLkEa8Qfwxb8pnu4kTZHUhaZw1w4zB37/H68JyQW++J9Hj/0+DzL0yQWEx+nLIbGLuc4gDLOB9uGlZP5FGu7yKse9/8WzPz3JS5jLKyHqXIf1qOc5vkZi687KO+KiP0XUyiLyfFOi/r9IHFkTuwaDwu52uQ0LNTiWIxBDyNMWuuF1F6sN/kUytE60ygL7rEPagutJ09PBm52OVYBHwT+ETsZIBb6Y2PZ7CrdwhrwKApxYkDIF3kS6bvV3Yef6/x7vqfPE/67fsfKVix/Y59EHD6Kfz8OLKB1kTtOEZDquZ3Y/pHvYm3HCZWMX8L2QkAp0wOengdTFqIjD8PsfKvLvBX4L6yenexhLcV2lIe14jbKCRYKfBmrt39MKcdbKFNz2/zz65RTuttZxFHdizK61P/PoRh+DGLlaCfWFrxcVR9lGCaK8rgfOAt4P7a9/gqsMB9OqYjtrJJqC5sZexh809/7sAa/vhYVJip2T5vnooFonqMVz0fDvZqyAau2coopyNgY1qwstXVSHVZzk2BYiYRFSNMiLUyRg3qz23rKUS3R+NeHP4aFWU/jWvyOMKIAH+PXo6HfTjnaGsoR+00TRaG8kCksWmK/SG2RFftKNlKOtAj5wrwyGtAtFJPpMAluZ0K6J/RX32FjPwlTZrEZD1r3P4SFVSfWdiOVr5ei7Poo1ktDWSeuoTT6sGuahH/xIrIwp45OVO1fpPtmrDGN8lVvrBvOOjE2GkY5iPITijL8hnIGVqenJzRNvCNO9fW6vNfxbDbo8UxtUt200mrGNc6DCxP4voYfTfcbKJZb4X+Yyq/HytlMTNFuwTrWD6nqacMlwkTZJBgHwZ0G/BmmWWdgm/ZCo0LRuNFzjWOap2MFbEl1LUwOo8f3GMXMElrfZgfFnPNkSqGJggMlrfuBj1NMCcM0NHpg67GRzVaKOV39VrO5lIoXjXR9fz12KmawhV3Xtuoj4+vCHDJsobVxjF32YdJ5b/VsXVBrxfEoVjHrN6zVlTQ2TMZBgrW5IlgvFsqBjrOwUVGkLZTTAEKGOEARLP+jh1pvLOyt4jIVq0S1yW34Hw16VOR4I2RtGhtptwXrsdYb3rbTOjpST5NozGIkGsopzHrDdLeHsqMd7DyiepNclOnomMSmtjiErx9rJGPEBqVHHmkeSrkeEccBkCFL8yj6ZuMUG83iel/DLe5PTA3VdSZ2lYcsIf/dWGNWN7rRUNabQMO/yPPeyp97sJ3VPbS+7RH3eyblPLjIj3q0uYFS9uKzEztvrrkXI8pDfWhkPNND2RgM1o5E21Gf8t2D5VetBKjc7Kj8nVTJrsA3sTyNDdCRB1GGd2JtW4zkB7HOElidjTPOngO82O8dyW6YKMrjKsyi5xgsIe7EGounUhJMsEI+gGVmbWMdhfJYrPGF0mhGJq6ndaNaFLjouSh29EIUwHpjUA/lsLIBlw9KQYjKEA3CMyg7he+q/IyKFD3jDf69pXJzFHbAYd27rhVZ7AwP2SI+UQljuF0/E41r+FlXqn5KYxSyCWX6bV3lfkflRxx4F4foDVbPN23XobVhxuO9nGKLH+lzCGWzZWyG62m4iU8d53qjZYQ9iOVp7MavOwOhgMGOBvkSpexE41vviYAyRRLlrrbx30nZ/xH7aqLHrpgi2URreYXSs1/m/6MzEtMotRyRDmsou7Hr/QiDWCMfPfPgMUoexcGe4T7KbEzNraN1GjLiPt+/p7q8W/x/NIJR3zZj07wxyoWydyHSIhpLKHUKymuMB7GG8CRKfsWeh+i1b6KUxxmUetQs9/XIuLkHZRArg5F3vRQFv5hymOcT1TPzqvgq1qiHjHEAZO0+0inqVaRtrXheRikTkaehuPqxdnEWJa0HK793YIYIq7HOzb3u/0/ZHao6IT5Yj7+XctqoDvGpC0fzE5uimm4HG9d0iDCiYEYPeGAId/2eWbGRaii51mKLf49X1x6mVJi6dxS9zaHiG0do78DWT+5ohFePLnQIv5r+1s/GRrxmmvVWbqKxjsayVhDRa2ymZ7zbvL8Rbmykq+VcSmnQarc7scai7u1p43uAXeWqP3E0flPGQWwOOhqQFRRl3uy1tsvjndhU52JsjvunWGPZLp3blbvI27rsDZdXccLsPbQqdMVMoZv5MgD8msf9Cy5j0/9edpVzI/D1xvXYcNgufYcrb6Hc67hHed1IOb23WWba5Wfkfy1XL9ZJu4sy6m+OOtqlaW9Dpk3V73Vt8mOg8d2sQ8Plc51366pnH2oja30qcl8jnHi1Qbs2cDM2gv4CcNDu2twJseYRiMhT/OdMbNFrDqWBeT62mPlMbPpjEtaD2oQ1ps/DFp8EWxhc7u56sQL4dGwN5Taskr0EW6yej/Vy1qnqX1WyzAI+4v4tdllOwQ4LfFBVPyEin8XOf/oP/5wH/IaH+S3gPVimHgq8gmKXfxRwPvBjbIG6D+t9HO/h/QbWi/mKy/t8j+eXVfUhEXmmx+dobFH/BVgD9ufYsHULZq43CzvyRYBrsEbkOuDD7mYjptwexKYIrvVnnko5lHIDtlD4LazB+v9c1hke96nAu7GR43HAH3i83utuHvT4/QnWIz0eeJ2qvlZEnuthHIst4F6BHe89yfPkHlV9qMqTp7l8m7GF8+dhPfNpmDK9FZsue6Gn+Q7MzPqHlR+HebptwjosC4FP+u33ebp/DBv5vtzz8nMe7ka/dgxWHj6ju24SPdifXetuZmHlMQ5NPNzTdC3lKPX5mEXfGR7+NR7/hz1uG7E6sNDjdgW2cL4WayynAC9W1evc6uZlnkZ3qOpGGojIKR6Pf8LKxlGeL08H7lLVuyq3R3g6/zQseUTkKKyMnIodpT8DG2mfiPWO78Tq1s9Utc/dvwwzQjkeK3unAC9R1T91eV6MlfXjsIb3LlVdJSIxxdMDnKKq/+UyPAsr909gdWgzME1Vt7klZhiJnIQ10M/E2pKTsM7c3ViZOQUrx9OwsvNTj88zPA4PUQ4ffDF2jNIzKMY1YXQTazE7sbqwEasDx3q8tgA/asRpB8WyLAx2fuLyPF1VLxWR6cBUVd0kIi+J+HsavAQrL4tU9fFmPu+OCaE8ROSXgI9imftu//3r2JDwL7Ae9w3Aa7ACOwP4n1hP9Q+Bz2NzfzOAL2LD4puxAv4IlcmohzcTs5/+Q0y7vxtreF+LjSiuBr6H9WBuwpSHAh/ArFXitY93Ar/nz56LFbTzVfV+D+dU4E2UTXaCWbr0+DM/xgrZOykLbz/GzPW+LyJ/A/wtZvkzV1WvacThLEpP7CGsEXg+ZdH/TKyQ3oxVss+5Ke5M4H9gjdwUrICf7XG/AGucZmENws+wRmW53/sXyvtGPk05buIRTFG839PzLX7vRViDOc/9WOFpegfWiN+pqveKyIlYxf5PzELlJR6XZZ7Or6rC+jGmHBf4tR9iFV9cxkWYwpqGKZlJ2FTPsVhDjt9/ObbJcRXwuy7nUqwRvMGfnYf1aOf7/2M9T9Z73DZhVlybsb0MS1X1MyLyq1gZjqM41no+3uzXn4ktbP6rx+e3sbLdgym+bZhi2Obp/FxMmURP/TBMmSz2vJuOjRTitb//7nn8qKfz4x7np2H14mCP+7dVdaXYS9g+SJlqvRfrwB2LNfjTKe+9+BHlxOZjMcW3kvJ+kymYtdNMioVRvMxLsHL5c8yq7mSs3Kqn0dc8nLMor0kOq8lez9Mwuuh3/zd4Htzrsr7ew16HdcDixVF92KhmlcuwBCsXd3uaRMfiYKzMf8ndnoPVhyOx9cgYoW52WeZj7c/LMUX2RaytOp7yOu1lmBL6KNY53IGV3x5MCd6PtSv9WNnopXVKcJAyHXqXf/8Ea/OehSnBsLj7FPBpVY0p4/Z0e7pplKasvu+J/T7KEO4uWhe/B9l1OiGGvn2Ut+HFd3OIurVy124Y+3ibZ+J3jGSaw+Xa7HULrQvRD7aRtTk8rYe/MbUT00IxdN1ZhRnyb2FXOQYo86/xqadaYoi+s/K7lmEb7Yfa9dSAAj/wtLiPMr0RYQ01ZTScv7v7tJtybHe/uY7TnC6o1452YIqyTudIg+b0Srtwm/9jTj+eizWGgTbPtPOrr3F/qGmRQcox9qvZNS2acW6G067sD2AjznbPR5jtyla7T11Wh/rsrizU9+sp3bqebGjz3HDhbh2B+y1trte/m9OunZbt5jrkSJ+rp2nr9jDux6h0pX/fiSmiL+y23e12wz9KyuOnlRLpxbRzXYFq64bhEnpD9UxdYKKCKq2KZUvDTV34o6DGMwPu56oqQ9sVqEGKZVctbx9DN67tPu3kiGtbKPPq8arN5jNNmWJNo3YXjWp876zuN9eO6oJbV4QfV+7i+brR2Vn9XlulWSj5kGVTm+fC322Vu+Z6SB3fyP+QPRZwm+l6RyPNahmHamAGKe8OUfd3e/XMcGUzlEO9hrOM1oahdjtUWajDqv2ry0dTScTbJOP/A428bXa0trKrTAPYyHZl5X+9HjCIdb7CSKCP8ta/cN+cu1dshFjLGmuBdVwGsRFLM+6PVdfqjkPUly2Vn4qVvZ0Nv2ONY3Pltl5zjWdjfS7yZ2MjvLrsNfMprKyaMtafOl71c4uxMraC9mu5dTsWr6u9kzK1Pmy7O1GsrcJa5FBglaq+h2KqKJTd5lASqzmXu55icgfldNQwK4xXk9Zmmj0U81+whab6XQiP+L2Yc51BeWFLWGGFW6rftbljWJLEprAIO+SPxbOHq3vRmOHfYb0RvfuYVw9/I061XXwcTxBxCzv2+A82OgprnZ6G3GEpE5VscnUvXo0p2FRLUFsFCdbgDPrveEVtbX4YDTaVn9GAxd4VpXXPS/37AYrRAdi0A7Q21mHqu66K+/OqcMNibSfFIi38q63QBijWWEoxz9Xq2qbqf11mowGhkmstreawA9X1sMaJZ+qGrKa2+Kn9eLRyewTFPBxsCiVk20lrmQwLuCXVtbBKO5GSlxG/uv0JE9hQxmspZZjK7U6KNdg0WpVlpE0o5QhvDq0WXkqxfKsVRv3/wcpvsCmnsGKK0Xek76TqO95ZDiXNNzeuLaM1L5pu6nTpw/L/F5TyXrcRtbVZrXT6XJYwh15Z+Vkbp/Rj02Fb/NOD1YMN7I5ujxpGaeTxdmxO8i6skf84pZf/Q0rhCu29CWsc615WbanVX92LSt+cNoh7N2M92173t9nLrwt29LaicK+lvEMkTCD7KEP8AWx+uNkbHMAWli+vrtXxUGw+s11vtLaMaTfVET2lWu4ftIlLLUso6u20pt+g58G6Kq4P+PWNWKG+p0qXb9I6Uvopu46K4ndYPrXrscfU3VC9+fAr3iW/seE2RjR1z7yenmr6MYiVs3gn9tbKnzrNI17bsTn7aOC20zo6Gkre+hMK7u4qzvXIpM6fFZgi3klRgu3CaNejrePRTNMH2XW6JqYvv87Q01zx3S49m2EPNyJrZ9UWPfvIr/uHCL8Oc2A34ShlzSee66PV/L5dXJ8Y4nozzSPPop43R3PNz6PsOgvwROPZkK9dORpw9/FM0yLyVuCE3bW7E2LBHEBEjsYWDSMT/ggrXG8Bfh94F2XxcQVmyfB6zPLhqZSh5SqsgTsXW2jbgJ1Q+WzMwqnedDcFU1g/pVibLPB7q7ETZd/vfn4EuNj96ccarauwHtDtHt4TFBv3wyhvG5yKFZinYKOZo7EF+U9jC/9nURav1/lzcVhgbGqbjB2A9lSsF3UE5Q14D/vvb2NGADs9nH6sURSKldJ5lN3HsXEuNiF9F/hn4EJssfokl3Odf78JeB1mfTQPs4xZ5vc/gXUCXoSNMGI/wFZMKc+jLFqup/T0D6EsisbRH9tdnr/FFsBf5Pm4xt19G7PeicVvwRbMf+F5+D3Kjtw4rmUVVrHAFmg3Yvm+2OU7BevhRq/vVJdjNTYN8EyKBdo3MOu6Psza7T89D17m4Ybxwn9gvf4TsIXhs7D1hXl+bY27jQ2xL3YZrvPvn2HldSlmOLIO+F+YBd1fY4vCnwC2q+r/EZFfBn7J43qxp8lfYwvrg+7nBmzxvc/T9zasbv2qx+9QbBH9L0VkCnYczrM9PTZiSnM2dkTNIVi5u8/z+ZVYHdhA2bi7Diuz87G6fL7L+G2sA3G8p/1kl/nlni5x/Mjj2EgxjhW5FVtYfsJlfyNWZ3/g7s+gvAr6Rsxy7Vys7t3paRxv65yG1btb3e9XY+XlYKxT9EUP61lYve4B/q9/z8Ia9+9hVpXPwsp7lI0HMeOY+VhZv9WvfwNrM57naf4CT/Mfu9vfxhbo53qY8Q6WH2OGJi9zOW8DrgR+08M60uVZp6q3MAImkvL4Dla4dmCN/XlYRTuHcnbR41jB+wXWsD2CVbBfwgrXE1jj8RWscjwXK+zbsMJfvyd50J97FGtEBMvEX/PwHsIq+QbK2TfLsIL1EJbxk7HGZ6pfO8LD/AWmUPqA/+5+fwOzdJmPVe457ucDWO/zs5gFSLzs6NkUo4Hn+bX7PI4xpfNsykuL+rEC9jnsZVo9WMNyPmbd8RD2oq0nXM4L3M8+4KVYo3idp/3VWGG8Eas8glWmHoqSC9PlxZ5+j2MKc5v7+0XPw9lY5X6Gp/1jmKnwS7AGNqYPQg7x9PhHzKLqW54Hv1bl1Zmq+uve4diqqltF5MUez0OBj6lqPczfhcaz07HOyIie7QS3bDsJeFhVN/i1U1X1jjZufwlr6Ob6peXA9ar68zZuj1LVdc3royDvk7J1KM+pakfoH4U1oiOKc/X8S7GytgnL5zswI5oVWB0OK79NWFmbiinVBzFry6c1/W9zOOewMribIdN1uOer+J8KPBZ+eHoMhBxNPzyNfwtTFDHzAWYGfBymJB7HFPDxWP26SM1q8nhgjarucPPkt2Dt3H2YtVVMEbeP60RQHiJyPtZo/TnW+3galkgxBwml16i0vk84roVpW72LHMq89wCtxw/0UN6cNkA5AC/eVx7z0Vspu8XD3XbKWVoh12asJxO7q7dRjlUINyHzDspoYrC6HsP4mZSF6di1GvPbMW0S738epBzStwar6DFXv5Ny2F3E5XAPJ84p0ioua12mWdjoYCOmEMKUspYjvuu8CNliDabX026gyoN6t3s/Za1FKYcsTqLMx0/29DoEayxirnuLxyVkiOmjtZiCjt3EP8IU6N/7cyf69wmUacavY6PbGLFtwHqwsft7u4c1gFXMBZ5mYYTQ688dSTlja7On2xENGR93v3qxBnI71ms9xp+LzWkzPB3rd2APYr3np2P1QrDOxzswRT/g17didSHSfxJWJn7F0+2HLsN2WufGl2GN/nasl/t2WvMYygGch3h49VrC9kaeTQX+ATNnj/QL093nUMrAo1idh9byBaXMQOv0TV2X12PlPMrSEs+j6FSFH81zq6JebsIU1EJaTxWI87gmU9qMyMdoT1Z6GoeZ9/Hu70bKGw9jp3h9GGS9f6VJyBtnboXM8QbC6PxG3QIrN3diZelg4Cuq+ntt/H6SiaI8noJV4GuwaZwZFDt9aTjX3VyrC9ueMlzhbRbuoeRq56a+B6XRbXfQ4O78aLoJRdpT/Q4FWLtpnusT7qA08tA6b10r46HkGkkYkZbt4tnOjTauhdva/zgZOPyLBd74H+sVUfHrxdFwW8sc4cVieJwVFsem1HLElOQg1vicROtCP7TmbXwifuuxRgfKYY07PexJDT8Ua+iPoNUIIRq3HkrnJ14720yPMIKIs7fqY02aSHWv7gD10XoYYZ03zboX+Rd7mOqwokGd1MYNlINRa7+aaVqHEeHLMPeDun6EDBGXZkczOmjN8+XqMtOsw3UZDQOcKQ33NP6Hwo/OZ8jwOJbnscAe57lNr+7/B7bXZzmlY7JCVU9hGCaEtZWqLsGmWN6NFf512OJrZHp99lP0lushWW1VsYNWIjMHGtejUDTdNQtbhFe7G2xcq58f2PvFwQAAD9dJREFU7nfTDygLpU2azw3lX12hpPpd/5eG27qxrv1qnhVW99aasgUhe5yRtLVxL8wUodWCq07HWo5atohDnCgc1+rw64P/oDSKkQbTsDIVo8L6LKhopKdU13soB1VG/Yr59jp94gyvCPNkWi3Semitn7EIXp9pdWTlrj4cs+lHpNMRVXgxzRpnX4U1YDNNIo5xPw6crBv5njafuhzUshxEa+PY7Kj1Y/P7oeCU1h5/M10ivjEKD9bTakEVdb7eq9XXeCY6BFDqu7JrPY8yFCOAOi5R3uoytrnxfK8/01tdrxVS+BcdguYIo6eNn9FBC2UTMhxJUU7h5lBKms7E1nh6sFF5lPXdMiGUB4CqXoJNJbwPG3a9BmtYd1CG6NB6zHh/dS0sbGLaqdn7bqbVILapbzGlxxLuN1NMXWNoeLXLExUKbE2mNs+Ls7UivLjXR9nEE25jr0b04iLs2lS47s3UpovNeNRmf49V/sVzYSkV1hjbq+d7aLXq2IGtyaygGAaso9i2xzNhfRRhTXI30bhEgZ9K6VFPr9zG+UXS8DMIC64BbDE0rE/6sPWS2tY+5Iqpq/r5Jf5/oLq2itbpmqV+LcIIC7JonBa7u7Dlr00ra7/DCi2oOy5C2ScClid91fPn+e9oHGvif21pFf73V25+4c/fR0m72sQ01pZWNb7Dv4Hq+lmNOIQCi/SrzZajUyBYr3gmxfS87hgMYGtW9UnMVPdiFLgZW+COUVzUhSmUk55jyi4MZSKOd/j3W/xaU1kplt8D2LpkO5PWsJzcTjF9rZ8Pa8YBymGXoZCi7enFysISiql2nVczKKcJ152k+vj8SJ9J2Hpkc7QeU2srKebtA+7He9vEq4UJMW3VREQOwRqaeNlOzCVvp3WYOYgtsi3Dpg0OovS0dmKL10uq5+IY41Oxwh0FPF7YsxlbBL4Ta/BehlXYe9WOcDgas5SK91LciS1q/Yr7dQfWSz3a/X0c6xX8gqJUXoxVvjswy6il2KLYKR7nb7p8H8DMeZ/m8t1G61rBZMzy6QfY3PpczHjgHk+TOVjhfTrW+D3b761yd7/v95dh5059GuvZbnG5o/Dfi/WqwYbEL/Tnd7i7Tdji+zJsvvV/Y4YIP/ewP4ydB/RCrAKdiB1BcZOn1aEej2VYgX+Pp+kjWKV9Efbu6pdh6wJrsIX0o4Hf8fQ4G5vj/g5l3WsOZjn2OGaRczhWXv7d47Ta/Z7lsu7AGqynUOajt/v9u7AjRU5yv2d7WM/ztD0ZO9Lld7BG9T6P05GUF/88jFnm/Kn7+euetxd7Xv4etjbzFH8+jAfmYKbr78MMIrZhlnBh0XaLx/3LWPl4lqdd9MLXuR9nYOVwOVa+LsManeOwfH8Otjg+APyuqn5NRP7R43osVheX+jO3YmuU1/v3oOdl1MltmIHIKo/PORTjko9RjpJ5MdbZebE/ezl2xMt52ALxZRTz12gYn/DnD8bKw7uw8nuQx/sD2LErW6rnDvbwZ2Dl51JMOX7Bn59FUUz4c6/F6u67KeX0IEzh3IYZgByMHQVyBdbbf8Kv9WJl7FDP463A/+9+HY2VQcUMcq7ALKyirToBMxj5MsUa6w6sfpzjfsWJBms8zM9jVqG92LE696lqHIk0JBNSeXTKnljOjPSZ0XbXxu3xWMMxDRvJnO2/L8Mao0MxS5NH2vkNT+58PxhrIN+IFfbpWIPwAGbJ9XKs0oX11r2U3tBHMEXx51hF+gzW2MUZUz2UHqOEPJQNVe/AGqhQ0rXsT8Y/4t147nDKhqh/ohy7/T7KxsGomDHPewpmZbMeewdMKIx7sQoU97d7XGsru+iJ9mINgWKN8c+xRfW3UCzu7t2NH4Nt3HRyvxdTFCFzWNc9F1MM93u8Y9ruuSMMY2/kPJWy0FzP9d+NNXSvxRRb0w/Fysx6rNzFgn3E76We3k0ZmmHUeaD+zFZ//gFMWfVj5eNFVZq90OW4F+sIRFpFD31v8myk6dmMy5VYJ+JXKC+Da/qxHit/MygWk1fT2g68DauXH3E378EU7oPV87H2tgj4m5FYDKbygCfNfFX1MRH5e6yR/Dlu0rk3z4y2uzZul2AWQbdjvfQb/Pc7MVv4n2O901Pa+Y1VjrdgCuLp2EjkaKyy3Yf1fCZhvcRXUBqlsCH/R6wB/glmVtyLNeifx/Z1bKO8snUH1sBfjDV2n8Uq9DSst9jr/tWyPxn/yhz7fnf7c2yE+AyKtdgOyj6dJVhjFkewTMWmRMLqpJeycz9ey9pT3W8uftaWeFspo9EdlMXHwzycaBCH82OwjZtO7jdl7qe8HS6stGoDg/4RhjFackKx8oqF91BkQ4XRSZ60CyPWrGJ6tR/L8wgjwoz82UmZOainS0czLUbqR7v0itHPUOHE5uboWK3BRh8DlNcvh1VhrI3UBjI7/PnDKTv7D8falE+q6o0MQbvFzAMKN/M9Efg1t3X+baz3sgV4ioi8GTu59a5On8F6FaPmTlXvaoT9Umy6aTvlBNzjsKHqcVjBeQHWoPyZiDzW8PtZ7uYf3d0MbATwfKwCHk2xWPt1ytztzOr36ZiCWUhZMDwIm4qqj+A4BGtUX481uFdjvbvnYEpgJqZoznQZevzZiP98j/cHPL7i7uM00EOw6RHFepRK6T1Ooey1EWwE0lP9h2IWHb/rtZSgPr4m/Kj9O656rmYoP5qLzlAq90ju1zKHOXi9kB5I9exIwhhtOSex68L4UH4MlScHV88NF0Y9139Q9dxgdT/CGKCsozXXNkczLfbEj3BTPzdUekUdPQirJ0J5z8dzaH1JVRDxnU5RyoPYVF4fZdPpkMpjwiyY7wXfxTT2Xdi84GpsTvy72HzvdymLpp0+M9rummHfimX0NdiCmGLzvl/HehGXYMPWfmwO9O6G3yuwXuo12LRTH1ZYVld+hWXKV/z3TmzaKV5+dKXL+BjWa4kD7uIQw+Xu3w5szvujHs5nsB7PKmzuuM/lua6S/foq/tdjSuNuTGmsxKZBlnnY6/xaHD2zkbJoH3P3EZdYsKwtb+pTkZsvFwpiwVIpb5aM3/2UY0xqfxnGj/r6wB7crw/T21zJs6oKv7/6PdIwRkvOkKeWofm76Ud9v86TzUO4bYbRV33HWyx7KQYGUS5jTa6XXeUa7bQYqR97ml5r/drK6vfl2Drkdmw08jhWD+u4Rx1YjdW5WJNahq3FxN6m9nT7XKrx8MEW2VZ7wp3t144Hvr63z4y2uzZuP+m/V2MKJn4vqvy4Zyi/K782Uc7YCiuPqHBhdRPWSqsx5XUP5Yyps92vaHjD9nyTF8y7PLxank1YA7gS29m+syH7dcC3/PcZlGNJYp1mJXb8xdexheSdlZ+b/NNHOUk3zl2q96DUR8kPtLlf/67dRMVbQquy2VqFNxI/2rnp5H5T5v4q3nsTxmjI2Yc1RP0d+tFJngx6fOsw+qs8iA5PbyOcQVrTrBn30U6LPU2vwRGE8wRWb4bK8zXseqx87Wc/tv4RZXcHcPru2s1c83DcQmtQVbf5/4OBKdrmTWqdPjPa7ppu/fc0Si9lGtCvdgRBHAfSP5Tf4Rc2Ep1OKVBTsemgqHxKMXWcSjlKZFJjUftQrKcYO9zjDKNQPvUCYQyXBVvU21nJfg/wUv/9A2wRfR02J3sTNj0l7l+fP7+h8vMQbKg+4DJtoljUhfXdJn9uGuX90PX92soOd9NTuY/TmMOKbiWtFnO786Odm07uh8yxQBqmpzGNBbYWdlqHYeytnF90mXZgPeCfYCPWX8EWcC9q48ex2Jl0RwL/5e7j7XpXYYvcEcY12NEzP8IWlU/BXr60AbMcuhGbsjoHKyuvxcrCN7BjaP6S8uK4J7BzumJtcSzybKR+xEbNaX5/s/9vF87pLv8vsDI+HxthzKMohnuxNZCpnp6nY+thd2Frn+92/z4PvBmzzJoHXKqq9YnXu5DKIxm3iMi92OtRN4vIfwIvU9XBuKeqz+quhOMHEenD3irYrkK/XlUPbXN9LOW5IxofEblTVZ9b3fupqj6vzTO3YArgNmzN7PnAa1T18eYzbfz875hp9XWYpdXhWKM6H5uOWUAx912ANaTnYAYm0IU02ls8z/8TO1fuLmzd8XpMCcRi/D9jZssvw9L21yln6VHl0VJVnV/53ZK+7TjgF8yTcc2HgVtF5JNYT/TfReQGrAJ8o6uSjT/uAj6iqvc0b4jIy7sgzzwR+Tg2+pktIlO0vNa0eVxNMFtVL/Xff+gK4fsicg67KsUpIjJNVXcAqOq/iMhfYIYfvZhp+PnYHpcvYHuFpmLK5U3YOtpJqvpW6Foa7S13YXu9HsVO8I49Zr+Lpdd2VX2rG+GsxwxIeikzBmtE5O2YscoGEXkPNmJ8OeXdJ0OSyiMZt6jqF0XkDmya4+lYeX0R8HlV/WZXhRt//BGtR1bU/Ld9KYjzv6rfi7CpvA0iciylt9+knUJYhb0z5+CG2yuwsvC96toF7u592LTUZkyBfACb4vpnzHR7G3YS7duqZ7uRRnvLHwGbVXUL8B4R+SZmxTgJs2C83qekp2Kben+BHTT5SWx09n+wtBnEjsN/I9Ype4zWtGlLTlsl4xpfQ9mmqlt8g+P7sIZoVI8+nwjsq2PiO5Snzrth5fGe7x2q+r3G9ecC/6Cqr2jjfzO+xwD/pKoPVxtoj8CsDs/DzMDjuI+2m1H3JzwNZlDevPhhbGPu/ZhF43mUNcHTsA7YkxsBReQYLA07rl9pqpuMd/6NcqDfh7G9HhuwBb6klX/DFpvB0uppdDetmnk3rDyq+lHgfjfmQESmi8ifAW/A5vHb+d+M76uw9Qyw02JP9mvf9Pu/g21m3YCNWrqdRnvLv2FrXUdiafAHWOP/O9gROG/GRmgXYcfjPAtLj4jvNexh/UrlkYxbGhsiz8c2OC7CzzwSkTeLyHO6KeN4Ybyl1V7IMyIFOIT/kzDLuaeJyK3YBto52GjkGKxHPhPrqbfdjLo/lSeP93OwExY+gCmI6f6JU6CPxKbqZmBpMwmzsjpFRK7FzpHbozKTyiMZz3yXzjdwHqh8l/GVVh3L06HCaef/JZTXDV+HTdVcTnnD3j+z+82o+1N5+i5lT9Miyl6PSyh7RTZhU1o7sbWMpf5Zjh3wuJk9LTPd3qCXn/wM92EPNnAeqJ/xlladykM5Efi52H6EH7v7p1S/D9uN/7FZtN5AG7vvh9qMut+WJ0+DzZhZ7nqPc725tz7i/a3YqRS3Rnz3pszkgnky7tmTDZwHKuMtrTqVR0QuAj6ENXhvU9UbfeH7MlU9q437dv7PAjZo6wbag7BeervNqPt1eZLqFRQe56Np3dwbr4zeTDmTblvEd0/LTCqPJEnGFeNNASbtSeWRJEmSdEwumCdJkiQdk8ojSZIk6ZhUHkmSJEnHpPJIkiRJOub/AZKl5fLH/tpLAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ccK0pKckfok",
        "colab_type": "text"
      },
      "source": [
        "## Based on our analysis we can conclude that:\n",
        "\n",
        "\n",
        "*   Apparments are the predominant property type \n",
        "*   Supper hosts make up the minority of property renters\n",
        "*   Mosts review range from 8 stars to 10\n",
        "*   Most propertys are not instant bookable\n",
        "*   Most people have just listen 1 house while some own 2 and 3 \n",
        "*   Most listed propertys are not instant bookable\n",
        "*   1 bedroom houses were among the most common \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CGQeFFn9ZemK",
        "colab_type": "text"
      },
      "source": [
        "What's the maxiumum/minimum cost per night?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cB7Zkg6hZUy_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b233a4b8-6aa3-40ef-f04e-a8c125db3e1e"
      },
      "source": [
        "catagorical_features['price'].max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'$999.00'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ERdBu4ukZw4X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c4a6c95-ba94-4ecf-f9c0-b9cb6bdf3506"
      },
      "source": [
        "catagorical_features['price'].min()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'$0.00'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "52KqHQydZ2ZE",
        "colab_type": "text"
      },
      "source": [
        "correlation between features:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7j4vxBRYrCJY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36017f45-baa2-4307-c30f-7c44a65e1a59"
      },
      "source": [
        "catagorical_features.head(1)"
      ],
      "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>room_type</th>\n",
              "      <th>bed_type</th>\n",
              "      <th>review_scores_value</th>\n",
              "      <th>instant_bookable</th>\n",
              "      <th>host_has_profile_pic</th>\n",
              "      <th>requires_license</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>Real Bed</td>\n",
              "      <td>9.0</td>\n",
              "      <td>f</td>\n",
              "      <td>t</td>\n",
              "      <td>t</td>\n",
              "      <td>$60.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         room_type  bed_type  ...  requires_license   price\n",
              "0  Entire home/apt  Real Bed  ...                 t  $60.00\n",
              "\n",
              "[1 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MFtWQO8XaSvk",
        "colab_type": "text"
      },
      "source": [
        "# Cleaning Data: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fXSWthpVwj3w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "catagorical_features['price'] = catagorical_features['price'].apply(lambda x: float(x.replace(',','').replace('$',''))) #Removing symbolls"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a0wmYto9afMO",
        "colab_type": "text"
      },
      "source": [
        "Numberical representation of data types"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EWuuPmZxXcam",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "catagorical_features['room_type'] = catagorical_features['room_type'].apply(lambda x: (x.replace('Entire home/apt','1').replace('Private room','2').replace('Shared room','3')))\n",
        "catagorical_features['room_type'] = catagorical_features['room_type'].astype(int) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7usJmrCYCQb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "catagorical_features['bed_type'] = catagorical_features['bed_type'].apply(lambda x: (x.replace('Real Bed','1').replace('Pull-out Sofa','2').replace('Futon','3')).replace('Airbed','4'))\n",
        "catagorical_features['bed_type'] = catagorical_features['room_type'].astype(int)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fKTJ82K3nVn3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "catagorical_features['instant_bookable'] = catagorical_features.host_has_profile_pic.map(dict(t=1, f=0))\n",
        "catagorical_features['host_has_profile_pic'] = catagorical_features.host_has_profile_pic.map(dict(t=1, f=0))\n",
        "catagorical_features['requires_license'] = catagorical_features.requires_license.map(dict(t=1, f=0))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P_7yEQ9bcR-n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16de8e0c-3741-4c76-ad65-9494d27c847c"
      },
      "source": [
        "catagorical_features.head(1)"
      ],
      "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>room_type</th>\n",
              "      <th>bed_type</th>\n",
              "      <th>review_scores_value</th>\n",
              "      <th>instant_bookable</th>\n",
              "      <th>host_has_profile_pic</th>\n",
              "      <th>requires_license</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>60.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   room_type  bed_type  ...  requires_license  price\n",
              "0          1         1  ...                 1   60.0\n",
              "\n",
              "[1 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D9onpWogasq7",
        "colab_type": "text"
      },
      "source": [
        "# Prepocessing our data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AAArj8H6btLo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hqA6_4o2ySRS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cat_features=['room_type','bed_type','review_scores_value', 'instant_bookable','host_has_profile_pic', 'requires_license','price']\n",
        "out_feature=\"price\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nFeUo45Y2gMb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "lbl_encoders={}\n",
        "for feature in cat_features:\n",
        "    lbl_encoders[feature]=LabelEncoder()\n",
        "    catagorical_features[feature]=lbl_encoders[feature].fit_transform(catagorical_features[feature])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JG9JnFXRmTaD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12249cf0-0aa3-421a-ff9e-66c0860e360f"
      },
      "source": [
        "z, ax = plt.subplots(figsize=(20, 11))\n",
        "sns.heatmap(catagorical_features.corr(), center=0,\n",
        "            square=True, cmap=\"twilight\", linewidths=1, cbar_kws={\"shrink\": 1})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f575180e160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x792 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8GZ5Mp5DbAm-",
        "colab_type": "text"
      },
      "source": [
        "Creating numpy array from our catagorical features:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8wa-b4xEsIKD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cat_features = np.stack([catagorical_features['room_type'], catagorical_features['bed_type'],catagorical_features['review_scores_value'],\n",
        "                          catagorical_features['instant_bookable'],catagorical_features['host_has_profile_pic'],catagorical_features['requires_license']],1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5mYsSbKjmKI5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "790155d2-7c65-4864-a4f2-a9c14384968b"
      },
      "source": [
        "cat_features"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[   0,    0,    7,    1,    1,    1],\n",
              "       [   1,    1,    8,    1,    1,    1],\n",
              "       [   0,    0,    7,    1,    1,    1],\n",
              "       ...,\n",
              "       [   0,    0,  134,    1,    1,    1],\n",
              "       [   1,    1, 1649,    1,    1,    1],\n",
              "       [   1,    1, 4443,    1,    1,    1]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2s8LkMGlbHBI",
        "colab_type": "text"
      },
      "source": [
        "Converting Catagory features into tensors: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LHW-kWDLsIRi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84b83464-1178-4845-d0d1-15813b43bc8d"
      },
      "source": [
        "import torch\n",
        "cat_features=torch.tensor(cat_features,dtype=torch.int64)\n",
        "cat_features"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[   0,    0,    7,    1,    1,    1],\n",
              "        [   1,    1,    8,    1,    1,    1],\n",
              "        [   0,    0,    7,    1,    1,    1],\n",
              "        ...,\n",
              "        [   0,    0,  134,    1,    1,    1],\n",
              "        [   1,    1, 1649,    1,    1,    1],\n",
              "        [   1,    1, 4443,    1,    1,    1]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tzk0anXXbqJa",
        "colab_type": "text"
      },
      "source": [
        "Create continuous variable:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GLwjz_YwsINw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cont_features=[]\n",
        "for i in df2.columns:\n",
        "    if i in ['minimum_nights','street','maximum_nights','cleaning_fee','property_type','extra_people','zipcode']:\n",
        "        pass\n",
        "    else:\n",
        "        cont_features.append(i)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hy9EBaCDbG2q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e68f955-0b91-42cc-d477-bb366ee6ca62"
      },
      "source": [
        "cont_features"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['room_type',\n",
              " 'bed_type',\n",
              " 'review_scores_value',\n",
              " 'instant_bookable',\n",
              " 'host_has_profile_pic',\n",
              " 'requires_license',\n",
              " 'price']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FqZKYh1zgS5j",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "Stacking continuous variable to a tensor:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YweXvc_csIH8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9890fd98-c8a7-44fc-8543-49aac9848282"
      },
      "source": [
        "cont_values=np.stack([catagorical_features[i].values for i in cont_features],axis=1)\n",
        "cont_values=torch.tensor(cont_values,dtype=torch.float)\n",
        "cont_values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.0000e+00, 0.0000e+00, 7.0000e+00,  ..., 1.0000e+00, 1.0000e+00,\n",
              "         5.4000e+01],\n",
              "        [1.0000e+00, 1.0000e+00, 8.0000e+00,  ..., 1.0000e+00, 1.0000e+00,\n",
              "         1.1000e+01],\n",
              "        [0.0000e+00, 0.0000e+00, 7.0000e+00,  ..., 1.0000e+00, 1.0000e+00,\n",
              "         8.4000e+01],\n",
              "        ...,\n",
              "        [0.0000e+00, 0.0000e+00, 1.3400e+02,  ..., 1.0000e+00, 1.0000e+00,\n",
              "         7.9000e+01],\n",
              "        [1.0000e+00, 1.0000e+00, 1.6490e+03,  ..., 1.0000e+00, 1.0000e+00,\n",
              "         9.3000e+01],\n",
              "        [1.0000e+00, 1.0000e+00, 4.4430e+03,  ..., 1.0000e+00, 1.0000e+00,\n",
              "         3.9000e+01]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T0m55S2dgaqF",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "Dependent Feature:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cS-Z2UqrsIFb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bedc15e0-3cb6-46a7-f640-5270694db82c"
      },
      "source": [
        "y=torch.tensor(catagorical_features['price'].values,dtype=torch.float).reshape(-1,1)\n",
        "y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[54.],\n",
              "        [11.],\n",
              "        [84.],\n",
              "        ...,\n",
              "        [79.],\n",
              "        [93.],\n",
              "        [39.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WJM2-VUFQhfH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5a6b5ec-541c-43af-f851-3cf467f0688b"
      },
      "source": [
        "catagorical_features.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22552, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iqTZqCE3sIDE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c05cb6de-b4ad-4369-a05c-9ba9cfbfe6c9"
      },
      "source": [
        "cat_features.shape,cont_values.shape,y.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([22552, 6]), torch.Size([22552, 7]), torch.Size([22552, 1]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BH1XQs4ygjzt",
        "colab_type": "text"
      },
      "source": [
        "Embedding Size For Categorical columns:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lr67IirnsIA1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cat_dims = [len(catagorical_features[col].unique()) for col in ['room_type','bed_type','review_scores_value', 'instant_bookable','host_has_profile_pic', 'requires_license']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EVmlKbCssH-m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc09fd94-a31c-484b-8493-d693dfed7f46"
      },
      "source": [
        "cat_dims"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3, 3, 4444, 28, 28, 2]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KasIuj7fgtYy",
        "colab_type": "text"
      },
      "source": [
        "embedding dimentions:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M54kR3M4sH73",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "embedding_dim = [(x, min(50, (x + 1) // 2)) for x in cat_dims]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wQmoNKKlsH5L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac4ba7f9-055d-4224-fe45-a045b72632d0"
      },
      "source": [
        "embedding_dim"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(3, 2), (3, 2), (4444, 50), (28, 14), (28, 14), (2, 1)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5cmTywC_hI0q",
        "colab_type": "text"
      },
      "source": [
        "# Model Building: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CvqHmMO5sH2-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EO2uuVPzhTUN",
        "colab_type": "text"
      },
      "source": [
        "embed representation:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nJesYvom1qg5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "943bdc9e-52aa-4bcc-ca55-41ff44896ae1"
      },
      "source": [
        "embed_representation=nn.ModuleList([nn.Embedding(inp,out) for inp,out in embedding_dim])\n",
        "embed_representation"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ModuleList(\n",
              "  (0): Embedding(3, 2)\n",
              "  (1): Embedding(3, 2)\n",
              "  (2): Embedding(4444, 50)\n",
              "  (3): Embedding(28, 14)\n",
              "  (4): Embedding(28, 14)\n",
              "  (5): Embedding(2, 1)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CMmunxLO5fPK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e81f2a2-6424-4a24-bc9b-a77943aedbbf"
      },
      "source": [
        "cat_featuresz=cat_features[:6]\n",
        "cat_featuresz"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0, 0, 7, 1, 1, 1],\n",
              "        [1, 1, 8, 1, 1, 1],\n",
              "        [0, 0, 7, 1, 1, 1],\n",
              "        [1, 1, 7, 1, 1, 1],\n",
              "        [1, 1, 7, 1, 1, 1],\n",
              "        [0, 0, 8, 1, 1, 1]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K2rvuruPuice",
        "colab_type": "text"
      },
      "source": [
        "Creating embeded values:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X361sFyO1qu_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pd.set_option('display.max_rows', 500)\n",
        "embedding_val=[]\n",
        "for i,e in enumerate(embed_representation):\n",
        "    embedding_val.append(e(cat_features[:,i]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "akSAYLq11qpi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f8da430-315d-4c32-cf92-96754ec75803"
      },
      "source": [
        "embedding_val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[tensor([[-0.6985, -0.5557],\n",
              "         [ 0.8808,  0.3307],\n",
              "         [-0.6985, -0.5557],\n",
              "         ...,\n",
              "         [-0.6985, -0.5557],\n",
              "         [ 0.8808,  0.3307],\n",
              "         [ 0.8808,  0.3307]], grad_fn=<EmbeddingBackward>),\n",
              " tensor([[-1.8274,  0.7150],\n",
              "         [ 0.2041,  0.3016],\n",
              "         [-1.8274,  0.7150],\n",
              "         ...,\n",
              "         [-1.8274,  0.7150],\n",
              "         [ 0.2041,  0.3016],\n",
              "         [ 0.2041,  0.3016]], grad_fn=<EmbeddingBackward>),\n",
              " tensor([[ 1.6168,  1.5095,  1.8879,  ..., -0.3814, -0.9409,  0.2399],\n",
              "         [-0.1924,  0.3498,  0.5688,  ...,  0.5731,  0.3540, -1.3223],\n",
              "         [ 1.6168,  1.5095,  1.8879,  ..., -0.3814, -0.9409,  0.2399],\n",
              "         ...,\n",
              "         [ 0.8984, -0.0084, -1.1449,  ...,  0.5796,  1.0455,  1.4294],\n",
              "         [-0.5849,  0.4228,  0.2763,  ..., -1.4110, -0.7522, -1.2970],\n",
              "         [ 0.0162,  0.3744,  0.1670,  ...,  0.5395, -1.1781, -0.8542]],\n",
              "        grad_fn=<EmbeddingBackward>),\n",
              " tensor([[ 0.0929,  0.6269, -0.5760,  ..., -1.2819, -0.2743,  1.2044],\n",
              "         [ 0.0929,  0.6269, -0.5760,  ..., -1.2819, -0.2743,  1.2044],\n",
              "         [ 0.0929,  0.6269, -0.5760,  ..., -1.2819, -0.2743,  1.2044],\n",
              "         ...,\n",
              "         [ 0.0929,  0.6269, -0.5760,  ..., -1.2819, -0.2743,  1.2044],\n",
              "         [ 0.0929,  0.6269, -0.5760,  ..., -1.2819, -0.2743,  1.2044],\n",
              "         [ 0.0929,  0.6269, -0.5760,  ..., -1.2819, -0.2743,  1.2044]],\n",
              "        grad_fn=<EmbeddingBackward>),\n",
              " tensor([[-1.3952, -1.5203, -0.3734,  ..., -0.0833,  0.3227,  0.2668],\n",
              "         [-1.3952, -1.5203, -0.3734,  ..., -0.0833,  0.3227,  0.2668],\n",
              "         [-1.3952, -1.5203, -0.3734,  ..., -0.0833,  0.3227,  0.2668],\n",
              "         ...,\n",
              "         [-1.3952, -1.5203, -0.3734,  ..., -0.0833,  0.3227,  0.2668],\n",
              "         [-1.3952, -1.5203, -0.3734,  ..., -0.0833,  0.3227,  0.2668],\n",
              "         [-1.3952, -1.5203, -0.3734,  ..., -0.0833,  0.3227,  0.2668]],\n",
              "        grad_fn=<EmbeddingBackward>),\n",
              " tensor([[-0.1429],\n",
              "         [-0.1429],\n",
              "         [-0.1429],\n",
              "         ...,\n",
              "         [-0.1429],\n",
              "         [-0.1429],\n",
              "         [-0.1429]], grad_fn=<EmbeddingBackward>)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cZ-jEt_Z1qdD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b5edf92-bff8-4a9a-b107-314eb0313c5a"
      },
      "source": [
        "z = torch.cat(embedding_val, 1)\n",
        "z"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.6985, -0.5557, -1.8274,  ...,  0.3227,  0.2668, -0.1429],\n",
              "        [ 0.8808,  0.3307,  0.2041,  ...,  0.3227,  0.2668, -0.1429],\n",
              "        [-0.6985, -0.5557, -1.8274,  ...,  0.3227,  0.2668, -0.1429],\n",
              "        ...,\n",
              "        [-0.6985, -0.5557, -1.8274,  ...,  0.3227,  0.2668, -0.1429],\n",
              "        [ 0.8808,  0.3307,  0.2041,  ...,  0.3227,  0.2668, -0.1429],\n",
              "        [ 0.8808,  0.3307,  0.2041,  ...,  0.3227,  0.2668, -0.1429]],\n",
              "       grad_fn=<CatBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U2VIReilhp_X",
        "colab_type": "text"
      },
      "source": [
        "Implement dropupout:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_kqi9m51qYv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "droput=nn.Dropout(.4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aEgv674C1qUI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27dd966e-f423-4c5a-ee28-2b2861c2467f"
      },
      "source": [
        "final_embed=droput(z)\n",
        "final_embed"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.0000, -0.0000, -0.0000,  ...,  0.0000,  0.4446, -0.2382],\n",
              "        [ 1.4680,  0.5512,  0.0000,  ...,  0.5378,  0.4446, -0.2382],\n",
              "        [-1.1641, -0.0000, -0.0000,  ...,  0.5378,  0.0000, -0.2382],\n",
              "        ...,\n",
              "        [-1.1641, -0.9261, -3.0456,  ...,  0.5378,  0.4446, -0.2382],\n",
              "        [ 1.4680,  0.5512,  0.0000,  ...,  0.5378,  0.4446, -0.2382],\n",
              "        [ 0.0000,  0.0000,  0.0000,  ...,  0.5378,  0.4446, -0.2382]],\n",
              "       grad_fn=<MulBackward0>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ivm7u-e0hvat",
        "colab_type": "text"
      },
      "source": [
        "# Create a Feed Forward Neural Network:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8MocZSGvsH0g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "class FeedForwardNN(nn.Module):\n",
        "\n",
        "    def __init__(self, embedding_dim, n_cont, out_sz, layers, p=0.5):\n",
        "        super().__init__()\n",
        "        self.embeds = nn.ModuleList([nn.Embedding(inp,out) for inp,out in embedding_dim])\n",
        "        self.emb_drop = nn.Dropout(p)\n",
        "        self.bn_cont = nn.BatchNorm1d(n_cont)\n",
        "        \n",
        "        layerlist = []\n",
        "        n_emb = sum((out for inp,out in embedding_dim))\n",
        "        n_in = n_emb + n_cont\n",
        "        \n",
        "        for i in layers:\n",
        "            layerlist.append(nn.Linear(n_in,i)) \n",
        "            layerlist.append(nn.ReLU(inplace=True))\n",
        "            layerlist.append(nn.BatchNorm1d(i))\n",
        "            layerlist.append(nn.Dropout(p))\n",
        "            n_in = i\n",
        "        layerlist.append(nn.Linear(layers[-1],out_sz))\n",
        "            \n",
        "        self.layers = nn.Sequential(*layerlist)\n",
        "    \n",
        "    def forward(self, x_cat, x_cont):\n",
        "        embeddings = []\n",
        "        for i,e in enumerate(self.embeds):\n",
        "            embeddings.append(e(x_cat[:,i]))\n",
        "        x = torch.cat(embeddings, 1)\n",
        "        x = self.emb_drop(x)\n",
        "        \n",
        "        x_cont = self.bn_cont(x_cont)\n",
        "        x = torch.cat([x, x_cont], 1)\n",
        "        x = self.layers(x)\n",
        "        return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rfMU5ZIwofdZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "torch.manual_seed(100)\n",
        "model=FeedForwardNN(embedding_dim,len(cont_features),1,[100,50],p=0.4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T_zJ2ONpofYd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loss_function=nn.MSELoss()\n",
        "optimizer=torch.optim.Adam(model.parameters(),lr=0.001)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gzTDSJ_Hj2EP",
        "colab_type": "text"
      },
      "source": [
        "Creating test and train set:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LjsdgeBQofTT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size=22550\n",
        "test_size=int(batch_size*0.30)\n",
        "train_categorical=cat_features[:batch_size-test_size]\n",
        "test_categorical=cat_features[batch_size-test_size:batch_size]\n",
        "train_cont=cont_values[:batch_size-test_size]\n",
        "test_cont=cont_values[batch_size-test_size:batch_size]\n",
        "y_train=y[:batch_size-test_size]\n",
        "y_test=y[batch_size-test_size:batch_size]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FtupGTjLgOvl",
        "colab_type": "text"
      },
      "source": [
        "Size of our train / test data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d5KkVgEDofOZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "caa2cae6-3cc5-4cc7-feab-2f5eac236910"
      },
      "source": [
        "len(train_categorical),len(test_categorical),len(train_cont),len(test_cont),len(y_train),len(y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15785, 6765, 15785, 6765, 15785, 6765)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2A4zDzt-otS8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1e268a57-57d9-4ebb-a6db-e13ed645b40a"
      },
      "source": [
        "epochs=950\n",
        "final_losses=[]\n",
        "for i in range(epochs):\n",
        "    i=i+1\n",
        "    y_pred=model(train_categorical,train_cont)\n",
        "    loss=torch.sqrt(loss_function(y_pred,y_train)) ### RMSE\n",
        "    final_losses.append(loss)\n",
        "    if i%10==1:\n",
        "        print(\"Epoch number: {} and the loss : {}\".format(i,loss.item()))\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch number: 1 and the loss : 63.51482391357422\n",
            "Epoch number: 11 and the loss : 63.02560806274414\n",
            "Epoch number: 21 and the loss : 62.43962860107422\n",
            "Epoch number: 31 and the loss : 61.869998931884766\n",
            "Epoch number: 41 and the loss : 61.29853057861328\n",
            "Epoch number: 51 and the loss : 60.719024658203125\n",
            "Epoch number: 61 and the loss : 60.103485107421875\n",
            "Epoch number: 71 and the loss : 59.51629638671875\n",
            "Epoch number: 81 and the loss : 58.90555953979492\n",
            "Epoch number: 91 and the loss : 58.26404571533203\n",
            "Epoch number: 101 and the loss : 57.7086296081543\n",
            "Epoch number: 111 and the loss : 57.0576057434082\n",
            "Epoch number: 121 and the loss : 56.458744049072266\n",
            "Epoch number: 131 and the loss : 55.79574203491211\n",
            "Epoch number: 141 and the loss : 55.04395294189453\n",
            "Epoch number: 151 and the loss : 54.28525924682617\n",
            "Epoch number: 161 and the loss : 53.57961654663086\n",
            "Epoch number: 171 and the loss : 52.755374908447266\n",
            "Epoch number: 181 and the loss : 52.033287048339844\n",
            "Epoch number: 191 and the loss : 51.249664306640625\n",
            "Epoch number: 201 and the loss : 50.55517578125\n",
            "Epoch number: 211 and the loss : 49.695823669433594\n",
            "Epoch number: 221 and the loss : 48.95170211791992\n",
            "Epoch number: 231 and the loss : 48.096858978271484\n",
            "Epoch number: 241 and the loss : 47.35850524902344\n",
            "Epoch number: 251 and the loss : 46.483699798583984\n",
            "Epoch number: 261 and the loss : 45.6871452331543\n",
            "Epoch number: 271 and the loss : 44.95264434814453\n",
            "Epoch number: 281 and the loss : 44.03299331665039\n",
            "Epoch number: 291 and the loss : 43.36334991455078\n",
            "Epoch number: 301 and the loss : 42.57518768310547\n",
            "Epoch number: 311 and the loss : 41.749305725097656\n",
            "Epoch number: 321 and the loss : 41.08096694946289\n",
            "Epoch number: 331 and the loss : 40.23377990722656\n",
            "Epoch number: 341 and the loss : 39.49805450439453\n",
            "Epoch number: 351 and the loss : 38.79248046875\n",
            "Epoch number: 361 and the loss : 38.086570739746094\n",
            "Epoch number: 371 and the loss : 37.30778503417969\n",
            "Epoch number: 381 and the loss : 36.67060852050781\n",
            "Epoch number: 391 and the loss : 36.09622573852539\n",
            "Epoch number: 401 and the loss : 35.348934173583984\n",
            "Epoch number: 411 and the loss : 34.65066909790039\n",
            "Epoch number: 421 and the loss : 33.86892318725586\n",
            "Epoch number: 431 and the loss : 33.17255783081055\n",
            "Epoch number: 441 and the loss : 32.43650817871094\n",
            "Epoch number: 451 and the loss : 31.70431137084961\n",
            "Epoch number: 461 and the loss : 30.958723068237305\n",
            "Epoch number: 471 and the loss : 30.219682693481445\n",
            "Epoch number: 481 and the loss : 29.39004898071289\n",
            "Epoch number: 491 and the loss : 28.61215591430664\n",
            "Epoch number: 501 and the loss : 27.796672821044922\n",
            "Epoch number: 511 and the loss : 26.913204193115234\n",
            "Epoch number: 521 and the loss : 26.07082748413086\n",
            "Epoch number: 531 and the loss : 25.213712692260742\n",
            "Epoch number: 541 and the loss : 24.39508056640625\n",
            "Epoch number: 551 and the loss : 23.38850212097168\n",
            "Epoch number: 561 and the loss : 22.5440731048584\n",
            "Epoch number: 571 and the loss : 21.755062103271484\n",
            "Epoch number: 581 and the loss : 20.755258560180664\n",
            "Epoch number: 591 and the loss : 19.982275009155273\n",
            "Epoch number: 601 and the loss : 19.125154495239258\n",
            "Epoch number: 611 and the loss : 18.172473907470703\n",
            "Epoch number: 621 and the loss : 17.319538116455078\n",
            "Epoch number: 631 and the loss : 16.14900016784668\n",
            "Epoch number: 641 and the loss : 15.462437629699707\n",
            "Epoch number: 651 and the loss : 14.801617622375488\n",
            "Epoch number: 661 and the loss : 13.996829986572266\n",
            "Epoch number: 671 and the loss : 13.131864547729492\n",
            "Epoch number: 681 and the loss : 12.683679580688477\n",
            "Epoch number: 691 and the loss : 12.015053749084473\n",
            "Epoch number: 701 and the loss : 11.36623477935791\n",
            "Epoch number: 711 and the loss : 10.882253646850586\n",
            "Epoch number: 721 and the loss : 10.338863372802734\n",
            "Epoch number: 731 and the loss : 10.261799812316895\n",
            "Epoch number: 741 and the loss : 9.971480369567871\n",
            "Epoch number: 751 and the loss : 10.00473690032959\n",
            "Epoch number: 761 and the loss : 9.586465835571289\n",
            "Epoch number: 771 and the loss : 9.4132661819458\n",
            "Epoch number: 781 and the loss : 9.498666763305664\n",
            "Epoch number: 791 and the loss : 9.301011085510254\n",
            "Epoch number: 801 and the loss : 9.346996307373047\n",
            "Epoch number: 811 and the loss : 9.271285057067871\n",
            "Epoch number: 821 and the loss : 9.033731460571289\n",
            "Epoch number: 831 and the loss : 9.205818176269531\n",
            "Epoch number: 841 and the loss : 9.002635955810547\n",
            "Epoch number: 851 and the loss : 8.821794509887695\n",
            "Epoch number: 861 and the loss : 8.916200637817383\n",
            "Epoch number: 871 and the loss : 8.904333114624023\n",
            "Epoch number: 881 and the loss : 8.905925750732422\n",
            "Epoch number: 891 and the loss : 9.004844665527344\n",
            "Epoch number: 901 and the loss : 9.037858009338379\n",
            "Epoch number: 911 and the loss : 8.813447952270508\n",
            "Epoch number: 921 and the loss : 8.6366548538208\n",
            "Epoch number: 931 and the loss : 8.866171836853027\n",
            "Epoch number: 941 and the loss : 8.468140602111816\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MTmn80wOotNR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "356049d5-3ed8-4aa8-e6ed-905fd7ee2070"
      },
      "source": [
        "plt.plot(range(epochs), final_losses)\n",
        "plt.ylabel('RMSE Loss')\n",
        "plt.xlabel('epoch');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FSiWT6X9jcy5tVJ6/fvwdz4xZyFF19xQJi7wEf7pzbqBzLst7vA2c9BDcOfe4c66Sc64q0B34wjl3AzARuMZbrScw+vRKl2iTGBfkP92bctt51Rg0ZRWd+kxi6SYN7yyS3/IS/NvMrIfXLTNoZj2AbWfwOx8FHjSzZYTa/N88g21JlAkEjCcurcdrPZqzde8hOr/4FV8v1TkekfxkJ2tLNbOzgP8QGrbBAZOBe51za8JfXkhGRobLzMwsqF8nhcSXSzbz9JiFrNq2n15tqvLkpfUIBszvskQihpnNdM5l/HJ5Xnr1rHLOXeGcS3fOlXHOXUmob75IWLWrU4Yx97Tl0oblGfjtSmr88WPWbN/vd1kiEe9078B1Xb5WIXICxYvE81L3JpxdvhgAHftMYvOegz5XJRLZTjf49fe2FJi4YIDRd7fhkYvqcDgrm5Z/mcAbX6/wuyyRiBV3oie8QdmO+xQKfilgCXEB7mpXkyppyTwxcgH/99/FfLdxD3+7uiHxQd06WuRUnDD4gZmETuYeL+QPh6cckdxd1qgC7euUoesr3zJ85lo27T7IWze3UPiLnIIT/m9xzlVzzlX3fv7yUb0gixTJKSUxjk97n0ens8vy9dKt1HriEzJXbve7LJGIocMkiUhxwQD9ejTjznY1KF4knt++MY2P52/QUA8ieaDgl4gVHwzw6EV1+fCO1iQEA9w1ZBb3DJ3Nko262lckNwp+iXi1yxblq0fac03zSvx33gYu7fs189bu9LsskULrhMFvZh1yTFf7xXNXh7MokVOVlpLAP69pRP8bm5OSGMctb89g2oozGVlEJHrldsT/Qo7pEb947skw1CJyRsyMLvXLMfyO1hw8kk23AVN5bMQ8snRrR5GfyS347QTTx5sXKTRqlS3KxIfaUbdcUd6fsYaeA6ez71DWyV8oEiNyC353gunjzYsUKulFExl7b1u6ZVRmyvJttH/hSz5fuNHvskQKhdyCv7qZjTGzsTmmj81Xy+V1IoVCfDDA369pRP8bQ4MT3v7uTB4dPo/dB4/4XJmIv3K7crdrjukXfvHcL+dFCq3O9crSpmYpbnsnk2GZa1i38wADe+lqX4lduV25Oynng9A4/LuBxd68SMRITohj8K3ncHf7GnyzbCs935quUT4lZuXWnfM1M6vvTRcH5gLvALPN7PoCqk8k35gZD3epy+MX12Xqim20/MsEvlyy2e+yRApcbn/rnuecW+hN9wK+d841BJoDj4S9MpEw+f0FNRh86zkA3DxwBk+MnK8unxJTcgv+nCNwdgZGATjn1DVCIt65NUsz+bEO3NT6LIZMW80jw+eRna3OahIbcju5u9PMLgPWAW2AWwHMLA4oUgC1iYRVhRJF+HPXBpROTaTP+O/ZsvcQr9+UQVJ80O/SRMIqtyP+3wP3AAOB+3Mc6XcE/hvuwkQKyr0davLM5fX4eulWerwxjblrNM6PRDeLhGFsMzIyXGZmpt9lSJQbNmM1//r8e7buPcT9nWpzT/uaBAK6SF0il5nNdM5l/HJ5brde7JvbBp1z9+VHYSKFRbcWVTi3RmnuHDKTPuO/Z9nmvbzUvQlmCn+JLrm18d8BLAA+ANaj8XkkBlROS2bsPW3pM/57/vPFMjbuPsg7t7RUu79EldyCvzxwLdANyAKGAcOdc2oAlahmZjzQqTY79h9m8NTVdPzXJAb2akHtskX9Lk0kX+R25e4259xrzrn2hPrxlwAWmdmNBVadiE8CAeP/rmzI/13ZgHU7D3D9gKm8N22132WJ5IuTDlZiZs2A3kAP4BNgZriLEikserQ6i/EPnE/V0in8ceR8Hhg2x++SRM5YbkM2/NnMZgIPApOADOfcrc65RXnZsJklmdl0M5trZgvN7FlveTUzm2Zmy8xsmJkl5MueiIRJrbJFGfK7cygSH2Tk7HU8+MEcdu3XCJ8SuU7YndPMsoEfgP3eomMrGuCcc41y3XCoK0SKc26vmcUD3xD6y+FB4CPn3Ptm9how1znXL7dtqTunFAb7D2fx/Cff8c6UVVQonsTHvc+jRLKOW6TwOuXunJzhmPsu9I2y15uN9x4O6AD81ls+CHgGyDX4RQqD5IQ4nr2iPg0qFuePH83nprem8/Tl9Wl+Vkm/SxM5Jbmd3F11vAewBmibl42bWdDM5gCbgfHAcmCnc+7YffDWAhVP8NrbzSzTzDK3bNlyKvskEjZmxnUZlfnntY2Yt3YXv+k3WVf6SsTJrY2/mJk9bmYvm9mFFnIvsAK4Li8bd84ddc41ASoBLYG6eS3MOTfAOZfhnMtIT0/P68tECsRVTSvx/u2tALjl7RnMXLXd54pE8i63Xj3vAnWA+cDvgInANcCVzrmuubzuV7y+/xOB1kAJb6A3CH0hrDvVokUKg1bVSzHhDxeQGBfgN/2m8MrEZX6XJJInud5z1zl3s3OuP3A9UA/o4pzLU382M0s3sxLedBFCQzsv5qcvEICewOjTLV7EbzXSUxl9T1va1izNPz9bwj8+/Y7DWRrbXwq33IL/x/5qzrmjwFrn3Kncq648MNHM5gEzgPHOuXHAo8CDZrYMKAW8eeplixQe6UUTGXBTc6qnp/Dql8t56MO5GttfCrXcevU0NrPd3rQBRbz5Y905i+W2YefcPKDpcZavINTeLxI1khPiGHHHuTwyYh5j5q4nOSHI365uqAHepFDKrVdP0DlXzHsUdc7F5ZjONfRFYlHJlAQG3NicG1udxfsz1nDNa1PYvu/wyV8oUsBOOmSDiOSdmfHnrvXpdHZZZq7aQY83prH3UNbJXyhSgBT8IvnMzOh7fROubV6JxRt30+ONaew+qCEepPBQ8IuEQXJCHP+8tjGv9WjOgnW7uLbfFNZs33/yF4oUAAW/SBh1qV+ON29uwYZdB7hzyEx2qM1fCgEFv0iYXVA7nX9c05jvN+6lY59JTF2xze+SJMYp+EUKwEUNyjH4d+ew71AW3QdMpe+EpZxoZFyRcFPwixSQltXSvKGc4+kz/nv+8dkSv0uSGKXgFylANdJTmfbHjgD0+3I5n8zf4HNFEosU/CIFLDEuyPQnOlI9PYV7h85mwFfL/S5JYoyCX8QHZYom8dGd53JerdL89ePveHfKSr9Lkhii4BfxSYnkBPrfmMHZ5Yvx3LjFjJu33u+SJEYo+EV8lBAXYOht59CgYjHuHTqbMXMV/hJ+Cn4Rn5VITmDATRnUr1CM+4bO5t2pq/wuSaKcgl+kECidmkif65qQXjSRp0Yt4PoBU8k6qhu6SHgo+EUKidplizLlsQ6USI5nyoptPPjBXL9Lkiil4BcpROKCgR9v4j5m7no+yFzjc0USjRT8IoVM3XLFWPaXi2lTsxRPjlrAx7rIS/KZgl+kEIoLBujbvSmlUhK4a8gserwxTTdxl3yj4BcppEqlJjL67jZUT0/hm2Vb+evHi/0uSaKEgl+kECtTLInP7j8fgLcnr1RXT8kXCn6RQi4+GGDcvW2pWKIIT41awPOffOd3SRLhFPwiEaBBxeJ8/sD5XNmkAq9NWk6fzzWks5y+OL8LEJG8SUmM4/nfNGLPwSz6frGMbfsO81zXBgQC5ndpEmF0xC8SQZLig7zaoxnn105nyLTVvDd9td8lSQRS8ItEmMS4IIN6taBBxWI8OWoBT4ycT3a2buMoeafgF4lAZkbf7k0pmhjHkGmrGTFrrd8lSQQJW/CbWWUzm2hmi8xsoZn19panmdl4M1vq/SwZrhpEoln19FS+fLgdpVISeGTEPIbNULOP5E04j/izgD845+oBrYC7zawe8BgwwTlXC5jgzYvIaSiVmsjHvc+jZdU0Hh0xn799spgjGtVTTiJswe+c2+Ccm+VN7wEWAxWBrsAgb7VBwJXhqkEkFpQtlsQrNzQjMS5A/0kruK7/FJxTm7+cWIG08ZtZVaApMA0o65w7NurURqDsCV5zu5llmlnmli1bCqJMkYhVOjWRhc92oU3NUsxevZP7h83RCV85obAHv5mlAiOA+51zu3M+50KHJcf9dDrnBjjnMpxzGenp6eEuUyTixQUDvN2rJeWKJTF6znr+OHK+3yVJIRXW4DezeEKhP8Q595G3eJOZlfeeLw9sDmcNIrEkPhjg8wfPp2PdMrw/Yw0PfjCH/Yez/C5LCplw9uox4E1gsXOuT46nxgA9vemewOhw1SASi4olxfOPaxqRlpLAR7PW8dL/lqrNX34mnEf8bYAbgQ5mNsd7XAI8D3Q2s6VAJ29eRPJRqdREJj/WgUsblqf/Vyt4bISafeQnYRurxzn3DXCiQUQ6huv3ikhIUnyQPt0aUzIlnsFTV3PUOf55TSNCf4xLLNMgbSJRLDEuyFOX1WPz7kMMn7mWtJQE/njJ2X6XJT7TkA0iUS4xLsi/rmtMQjDAgK9W8NSoBX6XJD5T8IvEgKJJ8cx75kLqlivKu1NX8fwn35GlK3xjloJfJEYkxQcZdXcbLqidzmuTlvPUaB35xyoFv0gMSYoP8tbNLbisUXmGTl/DoMkr/S5JfKDgF4kxwYDxj2sacW6NUjw9ZiHd+k9h14EjfpclBUjBLxKDkhPi6NejOVc3rci0H7Zz2zuZusgrhij4RWJU8SLx9OnWhPigMf2H7TR69nM27jrod1lSABT8IjHuvdtaAbDnYBYPD5/rczVSEBT8IjGuRdU0lv/1Eh67uC5fL91Kn8+XqNknyin4RYRgwOjR6izOLl+Mvl8so90LX7Jz/2G/y5IwUfCLCACpiXGMuacNFzcox6pt+2ny5/Es3bTH77IkDBT8IvKj+GCAfj2a0+e6xgB0fvEr3p2y0teaJP8p+EXkV65uVolebaoC8KcxC8lcud3fgiRfKfhF5Lievrw+M5/sRPEi8Vzz2hQmLtms+/hGCQW/iJxQqdREHuhUm4RggF4DZ/DQ8LkcztLgbpFOwS8iuep5blUmPtyOxLgAH81aR8c+X7J+5wG/y5IzoOAXkZOqWKIIC57tQoe6ZViz/QCd+0xi4fpdfpclp0nBLyJ5Eh8M8NbNLXizZwb7jxzl0r7f8NGstX6XJadBwS8ip6Tj2WUZfOs5pKUk8OAHc6nz5CcMnrrK77LkFCj4ReSUtalZmndvbQnAoaxsnhy1gA271O4fKRT8InJa6lcozvxnLuTu9jUAaP23L6j62H/5Yes+nyuTk1Hwi8hpK5oUz8Nd6vLWzRk/Lmv/wpe88JkGeivMFPwicsY61C3Ld89dxP2dagHw8sRlVHv8Y96dspId+zTYW2FjkfCtnJGR4TIzM/0uQ0TyYOTstbzw2fesy9HX/6L65ejWsjLt65TxsbLYY2YznXMZv1qu4BeR/Jad7Zi7didXvTr5Z8s/vu886lUo5lNVsUfBLyIF7uCRo/zt48UMmvJTd89LGpajZHICvTvWokyxJB+ri34FHvxm9hZwGbDZOdfAW5YGDAOqAiuB65xzO062LQW/SOT7bOFGnh2zkPU57uvbuV5Z4oPGLW2qkVE1zcfqopMfwX8+sBd4J0fw/wPY7px73sweA0o65x492bYU/CLR4Wi2Y+j01Tw5asGvnuuWUZlaZVPp0eoskuKDPlQXfXxp6jGzqsC4HMG/BGjnnNtgZuWBL51zdU62HQW/SHRZtnkvs1btYOy89WzcdZClm/f++FzltCJULpnM5OXbADCDG86pwoOd65CWksDmPQf5aNY6bj+vOoGA+bULEaGwBP9O51wJb9qAHcfmj/Pa24HbAapUqdJ81SpdEi4SrY5mO7bvO0zv92czb+0u9h7KOu5693eqxeTl25j+w3ZG3Nma5melkZ3tWL19P8kJQbKyHRVKFCng6guvQhf83vwO51zJk21HR/wisWPvoSzW7TjA/sNZxAcDDJ+5lrcnrzzuuqmJcb/6kqhbrijJCUFa1yhFWkoiTauUIDkhyDdLt3JRg3JkZ0OVUskczXas23GAKqWSyc52ZGU7DmYdpVhSfAHsZcEoLMGvph4ROWXOOQ4eyea96at5btwiAKqkJbN6+/5frRswOJ0bhSXFBzh4JHSTmeqlU1ixdR8j7mxNpZLJ7D98lE27D7Jq2z461ytHckKQldv2UbtMUbKd44PMtZxXqzQVSxQhc9UOWlQtiZnx+cKNnNRDdAEAAAeASURBVFOtFAezjpIQDFAkIci+Q1mUSk08o/cjrwpL8P8T2Jbj5G6ac+6Rk21HwS8ix+w/nMXRbEdqYhxz1+6iYcXiZDvHyNnruKJxBQJmLNu8l+tfn8quA0fo3bEWw2eu5fza6Xy6YAM79h/xrfbEuACHsrJ59YZm1ClXlE8XbKR+hWLcPHAGzaqUoP+NGZROTWDwtNUsWLuLv1zVgLjg6Q+w4EevnqFAO6A0sAl4GhgFfABUAVYR6s550rs4K/hFJD8czsomPmg4FzppvHr7fiqVTOaTBRsImJFxVkkw+HjeBt6evJLiyQns2HeY5IQgVzeryKL1uxk1Z/2vtntB7XQWb9jN5j2HzrjGoolx7MnRfPVJ7/M4u/zpXfSmC7hERPLR/sNZxAUC7D54hNJe082i9bvJdo4xc9dTvngSdcsVIy5oLFi3i+SEII+OmA/AFY0rMGbu+hM2V9VIT2H5ltAop989d9Fpd289UfDHndbWRERiXHJCKD5L52ivPzYcRYOKxX+2bgvv4rSL6pcnKSGAYXRtUoEOdctgZmzZc4jE+AC79h+hTLFEEuOCfLZwI9v2Hg7LNQ0KfhGRAlI8+aceQx3PLvvjdHrR0JdHzh5FXeqXC1sdGpZZRCTGKPhFRGKMgl9EJMYo+EVEYoyCX0Qkxij4RURijIJfRCTGKPhFRGJMRAzZYGZbCI3tczpKA1vzsZxIo/3X/sfy/kNsvwdnOefSf7kwIoL/TJhZ5vHGqogV2n/tfyzvP+g9OB419YiIxBgFv4hIjImF4B/gdwE+0/7Htljff9B78CtR38YvIiI/FwtH/CIikoOCX0QkxkR18JvZRWa2xMyWeTd3jypmVtnMJprZIjNbaGa9veVpZjbezJZ6P0t6y83M+nrvxzwza+bvHuQPMwua2WwzG+fNVzOzad5+DjOzBG95oje/zHu+qp915xczK2Fmw83sOzNbbGatY+kzYGYPeJ//BWY21MySYu0zcKqiNvjNLAi8AlwM1AOuN7N6/laV77KAPzjn6gGtgLu9fXwMmOCcqwVM8OYh9F7U8h63A/0KvuSw6A0szjH/d+BF51xNYAdwq7f8VmCHt/xFb71o8BLwqXOuLtCY0HsRE58BM6sI3AdkOOcaAEGgO7H3GTg1zrmofACtgc9yzD8OPO53XWHe59FAZ2AJUN5bVh5Y4k33B67Psf6P60XqA6hEKNg6AOMAI3SVZtwvPwfAZ0BrbzrOW8/83ocz3P/iwA+/3I9Y+QwAFYE1QJr3bzoO6BJLn4HTeUTtET8/fSCOWesti0ren6xNgWlAWefcBu+pjcCxm3tG43vyb+ARINubLwXsdM5lefM59/HH/fee3+WtH8mqAVuAgV5z1xtmlkKMfAacc+uAF4DVwAZC/6Yzia3PwCmL5uCPGWaWCowA7nfO7c75nAsd2kRln10zuwzY7Jyb6XctPooDmgH9nHNNgX381KwDRP1noCTQldAXYAUgBbjI16IiQDQH/zqgco75St6yqGJm8YRCf4hz7iNv8SYzK+89Xx7Y7C2PtvekDXCFma0E3ifU3PMSUMLM4rx1cu7jj/vvPV8c2FaQBYfBWmCtc26aNz+c0BdBrHwGOgE/OOe2OOeOAB8R+lzE0mfglEVz8M8Aanln9xMInfAZ43NN+crMDHgTWOyc65PjqTFAT2+6J6G2/2PLb/J6drQCduVoDog4zrnHnXOVnHNVCf37fuGcuwGYCFzjrfbL/T/2vlzjrR/RR8LOuY3AGjOr4y3qCCwiRj4DhJp4WplZsvf/4dj+x8xn4LT4fZIhnA/gEuB7YDnwhN/1hGH/2hL6E34eMMd7XEKozXICsBT4H5DmrW+EejotB+YT6gnh+37k03vRDhjnTVcHpgPLgA+BRG95kje/zHu+ut9159O+NwEyvc/BKKBkLH0GgGeB74AFwLtAYqx9Bk71oSEbRERiTDQ39YiIyHEo+EVEYoyCX0Qkxij4RURijIJfRCTGKPhFwszM2h0bOVSkMFDwi4jEGAW/iMfMepjZdDObY2b9vXH+95rZi9547xPMLN1bt4mZTfXGtB+ZY7z7mmb2PzOba2azzKyGt/nUHGPmD/GuMhXxhYJfBDCzs4FuQBvnXBPgKHADoUG/Mp1z9YFJwNPeS94BHnXONSJ0Beyx5UOAV5xzjYFzCY0YCaGRU+8ndG+I6oTGkxHxRdzJVxGJCR2B5sAM72C8CKGBzbKBYd46g4GPzKw4UMI5N8lbPgj40MyKAhWdcyMBnHMHAbztTXfOrfXm5wBVgW/Cv1siv6bgFwkxYJBz7vGfLTR76hfrne4YJ4dyTB9F//fER2rqEQmZAFxjZmXgx/sWn0Xo/8ixUR5/C3zjnNsF7DCz87zlNwKTnHN7gLVmdqW3jUQzSy7QvRDJAx11iADOuUVm9iTwuZkFgCPA3YRubNLSe24zofMAEBra9zUv2FcAvbzlNwL9zezP3jauLcDdEMkTjc4pkgsz2+ucS/W7DpH8pKYeEZEYoyN+EZEYoyN+EZEYo+AXEYkxCn4RkRij4BcRiTEKfhGRGPP/DZDxIiiy12MAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w79cupbdh7gg",
        "colab_type": "text"
      },
      "source": [
        "Validating the Test Data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JcvlQNtgotHX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "28db91e3-6107-4f3a-9d26-de35b8d21dfd"
      },
      "source": [
        "y_pred=\"\"\n",
        "with torch.no_grad():\n",
        "    y_pred=model(test_categorical,test_cont)\n",
        "    loss=torch.sqrt(loss_function(y_pred,y_test))\n",
        "print('RMSE: {}'.format(loss))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE: 11.671492576599121\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HyuJ2lU_otBi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_verify=pd.DataFrame(y_test.tolist(),columns=[\"Test\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-klLc8PbofI_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_predicted=pd.DataFrame(y_pred.tolist(),columns=[\"Prediction\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vb2wW3edr-GD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "5ebc5730-e492-4ba9-c2b2-6997fb83e4cb"
      },
      "source": [
        "model.eval()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FeedForwardNN(\n",
              "  (embeds): ModuleList(\n",
              "    (0): Embedding(3, 2)\n",
              "    (1): Embedding(3, 2)\n",
              "    (2): Embedding(4444, 50)\n",
              "    (3): Embedding(28, 14)\n",
              "    (4): Embedding(28, 14)\n",
              "    (5): Embedding(2, 1)\n",
              "  )\n",
              "  (emb_drop): Dropout(p=0.4, inplace=False)\n",
              "  (bn_cont): BatchNorm1d(7, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (layers): Sequential(\n",
              "    (0): Linear(in_features=90, out_features=100, bias=True)\n",
              "    (1): ReLU(inplace=True)\n",
              "    (2): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (3): Dropout(p=0.4, inplace=False)\n",
              "    (4): Linear(in_features=100, out_features=50, bias=True)\n",
              "    (5): ReLU(inplace=True)\n",
              "    (6): BatchNorm1d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (7): Dropout(p=0.4, inplace=False)\n",
              "    (8): Linear(in_features=50, out_features=1, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BbQOdjYIis23",
        "colab_type": "text"
      },
      "source": [
        "Prediction VS Actual:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UyR5lnw0pIsT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "e9a3c9a7-65d5-4ce7-94f5-64ac5c443c7a"
      },
      "source": [
        "final_output=pd.concat([data_verify,data_predicted],axis=1)\n",
        "final_output['Difference']=final_output['Test']-final_output['Prediction']\n",
        "final_output.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>Test</th>\n",
              "      <th>Prediction</th>\n",
              "      <th>Difference</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>34.0</td>\n",
              "      <td>27.611403</td>\n",
              "      <td>6.388597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>34.0</td>\n",
              "      <td>34.376282</td>\n",
              "      <td>-0.376282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14.0</td>\n",
              "      <td>11.158849</td>\n",
              "      <td>2.841151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>39.0</td>\n",
              "      <td>35.164410</td>\n",
              "      <td>3.835590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>74.0</td>\n",
              "      <td>76.735863</td>\n",
              "      <td>-2.735863</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Test  Prediction  Difference\n",
              "0  34.0   27.611403    6.388597\n",
              "1  34.0   34.376282   -0.376282\n",
              "2  14.0   11.158849    2.841151\n",
              "3  39.0   35.164410    3.835590\n",
              "4  74.0   76.735863   -2.735863"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0UltEqCki6tI",
        "colab_type": "text"
      },
      "source": [
        "# On average our model was off by:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-oDemmuB47GZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ce77be3b-7389-4b82-e980-bde7ed8900ff"
      },
      "source": [
        "final_output['Difference'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.744384959293839"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yf9eNheei-oW",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "# Saving the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jbFLzwWMpIlZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "torch.save(model,'Kaggle_House_data.pt')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MWhBk5yYpIes",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "torch.save(model.state_dict(),'Kaggle_House_data.pt')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}