Seattle Airbnb (Project 1).ipynb

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      "name": "Project-1.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AiXVSC3Mn3PL"
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      "source": [
        "## **Seattle Airbnb Data Analysis Project**\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "36fmq03Mo8R-"
      },
      "source": [
        "We have been given the Airbnb dataset of seattle, we have the data of host, and the guests. We can use this data to increase the number of guests by improving the details that the users want. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SKbk2gIoqQz_"
      },
      "source": [
        "We have been given three datasets:\n",
        "*   Listings, including full descriptions and average review score.\n",
        "*   Reviews, including unique id for each reviewer and detailed comments.\n",
        "*   Calendar, including listing id and the price and availability      for that day.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_zfrMZuWwwY"
      },
      "source": [
        "Questions I will try to answer in this notebook:\n",
        "1.   What are the features that influence positively on the number of accommodates?\n",
        "2.   What features are the guests looking for, so that it can be improved in other houses as well.\n",
        "3.   What type of houses that people prefer(Entire home/private room/shared room)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ciyW7r6-Zb0D"
      },
      "source": [
        "We will try and follow the CRISP-DM process while ecexuting the code.\n",
        "CRISP-DM is short for Cross Industry Process for Data Mining.\n",
        "It provides a structured approach and process for a project.\n",
        "CRISP-DM process includes:\n",
        "   1. Business Understanding.\n",
        "   2. Data Understanding.\n",
        "   3. Prepare Data.\n",
        "   4. Model Data.\n",
        "   5. Results.\n",
        "   6. Deploy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cT5zdFEmn-Uj"
      },
      "source": [
        "#First, we will import all the necessary libraries:\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sDGB-RUfaqOP"
      },
      "source": [
        "#Since we have over 90 columns in the dataset, we have to set the display to be over 90:\n",
        "pd.set_option('display.max_columns', 100)\n",
        "pd.set_option('display.max_rows', 100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sP4B_MQKbQ26"
      },
      "source": [
        "Now, we will read all the datasets we have"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ly2m6TVLa6Pn"
      },
      "source": [
        "df_listings = pd.read_csv('/content/drive/MyDrive/list.csv')\n",
        "df_reviews = pd.read_csv('/content/drive/My Drive/rev.csv')\n",
        "df_cal = pd.read_csv('/content/drive/My Drive/cal.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 787
        },
        "id": "k9F33A6Abe1p",
        "outputId": "1e3bacb6-08d1-4cea-e024-e1a500dea705"
      },
      "source": [
        "df_listings.head()"
      ],
      "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>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>neighbourhood_group_cleansed</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>zipcode</th>\n",
              "      <th>market</th>\n",
              "      <th>smart_location</th>\n",
              "      <th>country_code</th>\n",
              "      <th>country</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>is_location_exact</th>\n",
              "      <th>property_type</th>\n",
              "      <th>room_type</th>\n",
              "      <th>accommodates</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>bedrooms</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>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>241032</td>\n",
              "      <td>https://www.airbnb.com/rooms/241032</td>\n",
              "      <td>20160104002432</td>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>Stylish Queen Anne Apartment</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Make your self at home in this charming one-be...</td>\n",
              "      <td>Make your self at home in this charming one-be...</td>\n",
              "      <td>none</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://a1.muscache.com/ac/pictures/67560560/c...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>956883</td>\n",
              "      <td>https://www.airbnb.com/users/show/956883</td>\n",
              "      <td>Maija</td>\n",
              "      <td>2011-08-11</td>\n",
              "      <td>Seattle, Washington, United States</td>\n",
              "      <td>I am an artist, interior designer, and run a s...</td>\n",
              "      <td>within a few hours</td>\n",
              "      <td>96%</td>\n",
              "      <td>100%</td>\n",
              "      <td>f</td>\n",
              "      <td>https://a0.muscache.com/ac/users/956883/profil...</td>\n",
              "      <td>https://a0.muscache.com/ac/users/956883/profil...</td>\n",
              "      <td>Queen Anne</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>['email', 'phone', 'reviews', 'kba']</td>\n",
              "      <td>t</td>\n",
              "      <td>t</td>\n",
              "      <td>Gilman Dr W, Seattle, WA 98119, United States</td>\n",
              "      <td>Queen Anne</td>\n",
              "      <td>West Queen Anne</td>\n",
              "      <td>Queen Anne</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>WA</td>\n",
              "      <td>98119</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>Seattle, WA</td>\n",
              "      <td>US</td>\n",
              "      <td>United States</td>\n",
              "      <td>47.636289</td>\n",
              "      <td>-122.371025</td>\n",
              "      <td>t</td>\n",
              "      <td>Apartment</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Real Bed</td>\n",
              "      <td>{TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>$85.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>$5.00</td>\n",
              "      <td>1</td>\n",
              "      <td>365</td>\n",
              "      <td>4 weeks ago</td>\n",
              "      <td>t</td>\n",
              "      <td>14</td>\n",
              "      <td>41</td>\n",
              "      <td>71</td>\n",
              "      <td>346</td>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>207</td>\n",
              "      <td>2011-11-01</td>\n",
              "      <td>2016-01-02</td>\n",
              "      <td>95.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>f</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>f</td>\n",
              "      <td>moderate</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>2</td>\n",
              "      <td>4.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>953595</td>\n",
              "      <td>https://www.airbnb.com/rooms/953595</td>\n",
              "      <td>20160104002432</td>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>Bright &amp; Airy Queen Anne Apartment</td>\n",
              "      <td>Chemically sensitive? We've removed the irrita...</td>\n",
              "      <td>Beautiful, hypoallergenic apartment in an extr...</td>\n",
              "      <td>Chemically sensitive? We've removed the irrita...</td>\n",
              "      <td>none</td>\n",
              "      <td>Queen Anne is a wonderful, truly functional vi...</td>\n",
              "      <td>What's up with the free pillows?  Our home was...</td>\n",
              "      <td>Convenient bus stops are just down the block, ...</td>\n",
              "      <td>https://a0.muscache.com/ac/pictures/14409893/f...</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/14409893/f...</td>\n",
              "      <td>https://a0.muscache.com/ac/pictures/14409893/f...</td>\n",
              "      <td>https://a0.muscache.com/ac/pictures/14409893/f...</td>\n",
              "      <td>5177328</td>\n",
              "      <td>https://www.airbnb.com/users/show/5177328</td>\n",
              "      <td>Andrea</td>\n",
              "      <td>2013-02-21</td>\n",
              "      <td>Seattle, Washington, United States</td>\n",
              "      <td>Living east coast/left coast/overseas.  Time i...</td>\n",
              "      <td>within an hour</td>\n",
              "      <td>98%</td>\n",
              "      <td>100%</td>\n",
              "      <td>t</td>\n",
              "      <td>https://a0.muscache.com/ac/users/5177328/profi...</td>\n",
              "      <td>https://a0.muscache.com/ac/users/5177328/profi...</td>\n",
              "      <td>Queen Anne</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>['email', 'phone', 'facebook', 'linkedin', 're...</td>\n",
              "      <td>t</td>\n",
              "      <td>t</td>\n",
              "      <td>7th Avenue West, Seattle, WA 98119, United States</td>\n",
              "      <td>Queen Anne</td>\n",
              "      <td>West Queen Anne</td>\n",
              "      <td>Queen Anne</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>WA</td>\n",
              "      <td>98119</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>Seattle, WA</td>\n",
              "      <td>US</td>\n",
              "      <td>United States</td>\n",
              "      <td>47.639123</td>\n",
              "      <td>-122.365666</td>\n",
              "      <td>t</td>\n",
              "      <td>Apartment</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Real Bed</td>\n",
              "      <td>{TV,Internet,\"Wireless Internet\",Kitchen,\"Free...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>$150.00</td>\n",
              "      <td>$1,000.00</td>\n",
              "      <td>$3,000.00</td>\n",
              "      <td>$100.00</td>\n",
              "      <td>$40.00</td>\n",
              "      <td>1</td>\n",
              "      <td>$0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>90</td>\n",
              "      <td>today</td>\n",
              "      <td>t</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "      <td>291</td>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>43</td>\n",
              "      <td>2013-08-19</td>\n",
              "      <td>2015-12-29</td>\n",
              "      <td>96.0</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>f</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>f</td>\n",
              "      <td>strict</td>\n",
              "      <td>t</td>\n",
              "      <td>t</td>\n",
              "      <td>6</td>\n",
              "      <td>1.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3308979</td>\n",
              "      <td>https://www.airbnb.com/rooms/3308979</td>\n",
              "      <td>20160104002432</td>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>New Modern House-Amazing water view</td>\n",
              "      <td>New modern house built in 2013.  Spectacular s...</td>\n",
              "      <td>Our house is modern, light and fresh with a wa...</td>\n",
              "      <td>New modern house built in 2013.  Spectacular s...</td>\n",
              "      <td>none</td>\n",
              "      <td>Upper Queen Anne is a charming neighborhood fu...</td>\n",
              "      <td>Our house is located just 5 short blocks to To...</td>\n",
              "      <td>A bus stop is just 2 blocks away.   Easy bus a...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://a2.muscache.com/ac/pictures/b4324e0f-a...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16708587</td>\n",
              "      <td>https://www.airbnb.com/users/show/16708587</td>\n",
              "      <td>Jill</td>\n",
              "      <td>2014-06-12</td>\n",
              "      <td>Seattle, Washington, United States</td>\n",
              "      <td>i love living in Seattle.  i grew up in the mi...</td>\n",
              "      <td>within a few hours</td>\n",
              "      <td>67%</td>\n",
              "      <td>100%</td>\n",
              "      <td>f</td>\n",
              "      <td>https://a1.muscache.com/ac/users/16708587/prof...</td>\n",
              "      <td>https://a1.muscache.com/ac/users/16708587/prof...</td>\n",
              "      <td>Queen Anne</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>['email', 'phone', 'google', 'reviews', 'jumio']</td>\n",
              "      <td>t</td>\n",
              "      <td>t</td>\n",
              "      <td>West Lee Street, Seattle, WA 98119, United States</td>\n",
              "      <td>Queen Anne</td>\n",
              "      <td>West Queen Anne</td>\n",
              "      <td>Queen Anne</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>WA</td>\n",
              "      <td>98119</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>Seattle, WA</td>\n",
              "      <td>US</td>\n",
              "      <td>United States</td>\n",
              "      <td>47.629724</td>\n",
              "      <td>-122.369483</td>\n",
              "      <td>t</td>\n",
              "      <td>House</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>11</td>\n",
              "      <td>4.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>Real Bed</td>\n",
              "      <td>{TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>$975.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>$1,000.00</td>\n",
              "      <td>$300.00</td>\n",
              "      <td>10</td>\n",
              "      <td>$25.00</td>\n",
              "      <td>4</td>\n",
              "      <td>30</td>\n",
              "      <td>5 weeks ago</td>\n",
              "      <td>t</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>17</td>\n",
              "      <td>220</td>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>20</td>\n",
              "      <td>2014-07-30</td>\n",
              "      <td>2015-09-03</td>\n",
              "      <td>97.0</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>f</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>f</td>\n",
              "      <td>strict</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>2</td>\n",
              "      <td>1.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7421966</td>\n",
              "      <td>https://www.airbnb.com/rooms/7421966</td>\n",
              "      <td>20160104002432</td>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>Queen Anne Chateau</td>\n",
              "      <td>A charming apartment that sits atop Queen Anne...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A charming apartment that sits atop Queen Anne...</td>\n",
              "      <td>none</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://a0.muscache.com/ac/pictures/94146944/6...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9851441</td>\n",
              "      <td>https://www.airbnb.com/users/show/9851441</td>\n",
              "      <td>Emily</td>\n",
              "      <td>2013-11-06</td>\n",
              "      <td>Seattle, Washington, United States</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>f</td>\n",
              "      <td>https://a2.muscache.com/ac/users/9851441/profi...</td>\n",
              "      <td>https://a2.muscache.com/ac/users/9851441/profi...</td>\n",
              "      <td>Queen Anne</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>['email', 'phone', 'facebook', 'reviews', 'jum...</td>\n",
              "      <td>t</td>\n",
              "      <td>t</td>\n",
              "      <td>8th Avenue West, Seattle, WA 98119, United States</td>\n",
              "      <td>Queen Anne</td>\n",
              "      <td>West Queen Anne</td>\n",
              "      <td>Queen Anne</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>WA</td>\n",
              "      <td>98119</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>Seattle, WA</td>\n",
              "      <td>US</td>\n",
              "      <td>United States</td>\n",
              "      <td>47.638473</td>\n",
              "      <td>-122.369279</td>\n",
              "      <td>t</td>\n",
              "      <td>Apartment</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Real Bed</td>\n",
              "      <td>{Internet,\"Wireless Internet\",Kitchen,\"Indoor ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>$100.00</td>\n",
              "      <td>$650.00</td>\n",
              "      <td>$2,300.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>$0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1125</td>\n",
              "      <td>6 months ago</td>\n",
              "      <td>t</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>143</td>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>f</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>f</td>\n",
              "      <td>flexible</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>278830</td>\n",
              "      <td>https://www.airbnb.com/rooms/278830</td>\n",
              "      <td>20160104002432</td>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>Charming craftsman 3 bdm house</td>\n",
              "      <td>Cozy family craftman house in beautiful neighb...</td>\n",
              "      <td>Cozy family craftman house in beautiful neighb...</td>\n",
              "      <td>Cozy family craftman house in beautiful neighb...</td>\n",
              "      <td>none</td>\n",
              "      <td>We are in the beautiful neighborhood of Queen ...</td>\n",
              "      <td>Belltown</td>\n",
              "      <td>The nearest public transit bus (D Line) is 2 b...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://a1.muscache.com/ac/pictures/6120468/b0...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1452570</td>\n",
              "      <td>https://www.airbnb.com/users/show/1452570</td>\n",
              "      <td>Emily</td>\n",
              "      <td>2011-11-29</td>\n",
              "      <td>Seattle, Washington, United States</td>\n",
              "      <td>Hi, I live in Seattle, Washington but I'm orig...</td>\n",
              "      <td>within an hour</td>\n",
              "      <td>100%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>f</td>\n",
              "      <td>https://a0.muscache.com/ac/users/1452570/profi...</td>\n",
              "      <td>https://a0.muscache.com/ac/users/1452570/profi...</td>\n",
              "      <td>Queen Anne</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>['email', 'phone', 'facebook', 'reviews', 'kba']</td>\n",
              "      <td>t</td>\n",
              "      <td>t</td>\n",
              "      <td>14th Ave W, Seattle, WA 98119, United States</td>\n",
              "      <td>Queen Anne</td>\n",
              "      <td>West Queen Anne</td>\n",
              "      <td>Queen Anne</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>WA</td>\n",
              "      <td>98119</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>Seattle, WA</td>\n",
              "      <td>US</td>\n",
              "      <td>United States</td>\n",
              "      <td>47.632918</td>\n",
              "      <td>-122.372471</td>\n",
              "      <td>t</td>\n",
              "      <td>House</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Real Bed</td>\n",
              "      <td>{TV,\"Cable TV\",Internet,\"Wireless Internet\",Ki...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>$450.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>$700.00</td>\n",
              "      <td>$125.00</td>\n",
              "      <td>6</td>\n",
              "      <td>$15.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1125</td>\n",
              "      <td>7 weeks ago</td>\n",
              "      <td>t</td>\n",
              "      <td>30</td>\n",
              "      <td>60</td>\n",
              "      <td>90</td>\n",
              "      <td>365</td>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>38</td>\n",
              "      <td>2012-07-10</td>\n",
              "      <td>2015-10-24</td>\n",
              "      <td>92.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>f</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>f</td>\n",
              "      <td>strict</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>1</td>\n",
              "      <td>0.89</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id                           listing_url       scrape_id last_scraped  \\\n",
              "0   241032   https://www.airbnb.com/rooms/241032  20160104002432   2016-01-04   \n",
              "1   953595   https://www.airbnb.com/rooms/953595  20160104002432   2016-01-04   \n",
              "2  3308979  https://www.airbnb.com/rooms/3308979  20160104002432   2016-01-04   \n",
              "3  7421966  https://www.airbnb.com/rooms/7421966  20160104002432   2016-01-04   \n",
              "4   278830   https://www.airbnb.com/rooms/278830  20160104002432   2016-01-04   \n",
              "\n",
              "                                  name  \\\n",
              "0         Stylish Queen Anne Apartment   \n",
              "1   Bright & Airy Queen Anne Apartment   \n",
              "2  New Modern House-Amazing water view   \n",
              "3                   Queen Anne Chateau   \n",
              "4       Charming craftsman 3 bdm house   \n",
              "\n",
              "                                             summary  \\\n",
              "0                                                NaN   \n",
              "1  Chemically sensitive? We've removed the irrita...   \n",
              "2  New modern house built in 2013.  Spectacular s...   \n",
              "3  A charming apartment that sits atop Queen Anne...   \n",
              "4  Cozy family craftman house in beautiful neighb...   \n",
              "\n",
              "                                               space  \\\n",
              "0  Make your self at home in this charming one-be...   \n",
              "1  Beautiful, hypoallergenic apartment in an extr...   \n",
              "2  Our house is modern, light and fresh with a wa...   \n",
              "3                                                NaN   \n",
              "4  Cozy family craftman house in beautiful neighb...   \n",
              "\n",
              "                                         description experiences_offered  \\\n",
              "0  Make your self at home in this charming one-be...                none   \n",
              "1  Chemically sensitive? We've removed the irrita...                none   \n",
              "2  New modern house built in 2013.  Spectacular s...                none   \n",
              "3  A charming apartment that sits atop Queen Anne...                none   \n",
              "4  Cozy family craftman house in beautiful neighb...                none   \n",
              "\n",
              "                               neighborhood_overview  \\\n",
              "0                                                NaN   \n",
              "1  Queen Anne is a wonderful, truly functional vi...   \n",
              "2  Upper Queen Anne is a charming neighborhood fu...   \n",
              "3                                                NaN   \n",
              "4  We are in the beautiful neighborhood of Queen ...   \n",
              "\n",
              "                                               notes  \\\n",
              "0                                                NaN   \n",
              "1  What's up with the free pillows?  Our home was...   \n",
              "2  Our house is located just 5 short blocks to To...   \n",
              "3                                                NaN   \n",
              "4                                          Belltown    \n",
              "\n",
              "                                             transit  \\\n",
              "0                                                NaN   \n",
              "1  Convenient bus stops are just down the block, ...   \n",
              "2  A bus stop is just 2 blocks away.   Easy bus a...   \n",
              "3                                                NaN   \n",
              "4  The nearest public transit bus (D Line) is 2 b...   \n",
              "\n",
              "                                       thumbnail_url  \\\n",
              "0                                                NaN   \n",
              "1  https://a0.muscache.com/ac/pictures/14409893/f...   \n",
              "2                                                NaN   \n",
              "3                                                NaN   \n",
              "4                                                NaN   \n",
              "\n",
              "                                          medium_url  \\\n",
              "0                                                NaN   \n",
              "1  https://a0.muscache.com/im/pictures/14409893/f...   \n",
              "2                                                NaN   \n",
              "3                                                NaN   \n",
              "4                                                NaN   \n",
              "\n",
              "                                         picture_url  \\\n",
              "0  https://a1.muscache.com/ac/pictures/67560560/c...   \n",
              "1  https://a0.muscache.com/ac/pictures/14409893/f...   \n",
              "2  https://a2.muscache.com/ac/pictures/b4324e0f-a...   \n",
              "3  https://a0.muscache.com/ac/pictures/94146944/6...   \n",
              "4  https://a1.muscache.com/ac/pictures/6120468/b0...   \n",
              "\n",
              "                                      xl_picture_url   host_id  \\\n",
              "0                                                NaN    956883   \n",
              "1  https://a0.muscache.com/ac/pictures/14409893/f...   5177328   \n",
              "2                                                NaN  16708587   \n",
              "3                                                NaN   9851441   \n",
              "4                                                NaN   1452570   \n",
              "\n",
              "                                     host_url host_name  host_since  \\\n",
              "0    https://www.airbnb.com/users/show/956883     Maija  2011-08-11   \n",
              "1   https://www.airbnb.com/users/show/5177328    Andrea  2013-02-21   \n",
              "2  https://www.airbnb.com/users/show/16708587      Jill  2014-06-12   \n",
              "3   https://www.airbnb.com/users/show/9851441     Emily  2013-11-06   \n",
              "4   https://www.airbnb.com/users/show/1452570     Emily  2011-11-29   \n",
              "\n",
              "                        host_location  \\\n",
              "0  Seattle, Washington, United States   \n",
              "1  Seattle, Washington, United States   \n",
              "2  Seattle, Washington, United States   \n",
              "3  Seattle, Washington, United States   \n",
              "4  Seattle, Washington, United States   \n",
              "\n",
              "                                          host_about  host_response_time  \\\n",
              "0  I am an artist, interior designer, and run a s...  within a few hours   \n",
              "1  Living east coast/left coast/overseas.  Time i...      within an hour   \n",
              "2  i love living in Seattle.  i grew up in the mi...  within a few hours   \n",
              "3                                                NaN                 NaN   \n",
              "4  Hi, I live in Seattle, Washington but I'm orig...      within an hour   \n",
              "\n",
              "  host_response_rate host_acceptance_rate host_is_superhost  \\\n",
              "0                96%                 100%                 f   \n",
              "1                98%                 100%                 t   \n",
              "2                67%                 100%                 f   \n",
              "3                NaN                  NaN                 f   \n",
              "4               100%                  NaN                 f   \n",
              "\n",
              "                                  host_thumbnail_url  \\\n",
              "0  https://a0.muscache.com/ac/users/956883/profil...   \n",
              "1  https://a0.muscache.com/ac/users/5177328/profi...   \n",
              "2  https://a1.muscache.com/ac/users/16708587/prof...   \n",
              "3  https://a2.muscache.com/ac/users/9851441/profi...   \n",
              "4  https://a0.muscache.com/ac/users/1452570/profi...   \n",
              "\n",
              "                                    host_picture_url host_neighbourhood  \\\n",
              "0  https://a0.muscache.com/ac/users/956883/profil...         Queen Anne   \n",
              "1  https://a0.muscache.com/ac/users/5177328/profi...         Queen Anne   \n",
              "2  https://a1.muscache.com/ac/users/16708587/prof...         Queen Anne   \n",
              "3  https://a2.muscache.com/ac/users/9851441/profi...         Queen Anne   \n",
              "4  https://a0.muscache.com/ac/users/1452570/profi...         Queen Anne   \n",
              "\n",
              "   host_listings_count  host_total_listings_count  \\\n",
              "0                  3.0                        3.0   \n",
              "1                  6.0                        6.0   \n",
              "2                  2.0                        2.0   \n",
              "3                  1.0                        1.0   \n",
              "4                  2.0                        2.0   \n",
              "\n",
              "                                  host_verifications host_has_profile_pic  \\\n",
              "0               ['email', 'phone', 'reviews', 'kba']                    t   \n",
              "1  ['email', 'phone', 'facebook', 'linkedin', 're...                    t   \n",
              "2   ['email', 'phone', 'google', 'reviews', 'jumio']                    t   \n",
              "3  ['email', 'phone', 'facebook', 'reviews', 'jum...                    t   \n",
              "4   ['email', 'phone', 'facebook', 'reviews', 'kba']                    t   \n",
              "\n",
              "  host_identity_verified                                             street  \\\n",
              "0                      t      Gilman Dr W, Seattle, WA 98119, United States   \n",
              "1                      t  7th Avenue West, Seattle, WA 98119, United States   \n",
              "2                      t  West Lee Street, Seattle, WA 98119, United States   \n",
              "3                      t  8th Avenue West, Seattle, WA 98119, United States   \n",
              "4                      t       14th Ave W, Seattle, WA 98119, United States   \n",
              "\n",
              "  neighbourhood neighbourhood_cleansed neighbourhood_group_cleansed     city  \\\n",
              "0    Queen Anne        West Queen Anne                   Queen Anne  Seattle   \n",
              "1    Queen Anne        West Queen Anne                   Queen Anne  Seattle   \n",
              "2    Queen Anne        West Queen Anne                   Queen Anne  Seattle   \n",
              "3    Queen Anne        West Queen Anne                   Queen Anne  Seattle   \n",
              "4    Queen Anne        West Queen Anne                   Queen Anne  Seattle   \n",
              "\n",
              "  state zipcode   market smart_location country_code        country  \\\n",
              "0    WA   98119  Seattle    Seattle, WA           US  United States   \n",
              "1    WA   98119  Seattle    Seattle, WA           US  United States   \n",
              "2    WA   98119  Seattle    Seattle, WA           US  United States   \n",
              "3    WA   98119  Seattle    Seattle, WA           US  United States   \n",
              "4    WA   98119  Seattle    Seattle, WA           US  United States   \n",
              "\n",
              "    latitude   longitude is_location_exact property_type        room_type  \\\n",
              "0  47.636289 -122.371025                 t     Apartment  Entire home/apt   \n",
              "1  47.639123 -122.365666                 t     Apartment  Entire home/apt   \n",
              "2  47.629724 -122.369483                 t         House  Entire home/apt   \n",
              "3  47.638473 -122.369279                 t     Apartment  Entire home/apt   \n",
              "4  47.632918 -122.372471                 t         House  Entire home/apt   \n",
              "\n",
              "   accommodates  bathrooms  bedrooms  beds  bed_type  \\\n",
              "0             4        1.0       1.0   1.0  Real Bed   \n",
              "1             4        1.0       1.0   1.0  Real Bed   \n",
              "2            11        4.5       5.0   7.0  Real Bed   \n",
              "3             3        1.0       0.0   2.0  Real Bed   \n",
              "4             6        2.0       3.0   3.0  Real Bed   \n",
              "\n",
              "                                           amenities  square_feet    price  \\\n",
              "0  {TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...          NaN   $85.00   \n",
              "1  {TV,Internet,\"Wireless Internet\",Kitchen,\"Free...          NaN  $150.00   \n",
              "2  {TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...          NaN  $975.00   \n",
              "3  {Internet,\"Wireless Internet\",Kitchen,\"Indoor ...          NaN  $100.00   \n",
              "4  {TV,\"Cable TV\",Internet,\"Wireless Internet\",Ki...          NaN  $450.00   \n",
              "\n",
              "  weekly_price monthly_price security_deposit cleaning_fee  guests_included  \\\n",
              "0          NaN           NaN              NaN          NaN                2   \n",
              "1    $1,000.00     $3,000.00          $100.00       $40.00                1   \n",
              "2          NaN           NaN        $1,000.00      $300.00               10   \n",
              "3      $650.00     $2,300.00              NaN          NaN                1   \n",
              "4          NaN           NaN          $700.00      $125.00                6   \n",
              "\n",
              "  extra_people  minimum_nights  maximum_nights calendar_updated  \\\n",
              "0        $5.00               1             365      4 weeks ago   \n",
              "1        $0.00               2              90            today   \n",
              "2       $25.00               4              30      5 weeks ago   \n",
              "3        $0.00               1            1125     6 months ago   \n",
              "4       $15.00               1            1125      7 weeks ago   \n",
              "\n",
              "  has_availability  availability_30  availability_60  availability_90  \\\n",
              "0                t               14               41               71   \n",
              "1                t               13               13               16   \n",
              "2                t                1                6               17   \n",
              "3                t                0                0                0   \n",
              "4                t               30               60               90   \n",
              "\n",
              "   availability_365 calendar_last_scraped  number_of_reviews first_review  \\\n",
              "0               346            2016-01-04                207   2011-11-01   \n",
              "1               291            2016-01-04                 43   2013-08-19   \n",
              "2               220            2016-01-04                 20   2014-07-30   \n",
              "3               143            2016-01-04                  0          NaN   \n",
              "4               365            2016-01-04                 38   2012-07-10   \n",
              "\n",
              "  last_review  review_scores_rating  review_scores_accuracy  \\\n",
              "0  2016-01-02                  95.0                    10.0   \n",
              "1  2015-12-29                  96.0                    10.0   \n",
              "2  2015-09-03                  97.0                    10.0   \n",
              "3         NaN                   NaN                     NaN   \n",
              "4  2015-10-24                  92.0                     9.0   \n",
              "\n",
              "   review_scores_cleanliness  review_scores_checkin  \\\n",
              "0                       10.0                   10.0   \n",
              "1                       10.0                   10.0   \n",
              "2                       10.0                   10.0   \n",
              "3                        NaN                    NaN   \n",
              "4                        9.0                   10.0   \n",
              "\n",
              "   review_scores_communication  review_scores_location  review_scores_value  \\\n",
              "0                         10.0                     9.0                 10.0   \n",
              "1                         10.0                    10.0                 10.0   \n",
              "2                         10.0                    10.0                 10.0   \n",
              "3                          NaN                     NaN                  NaN   \n",
              "4                         10.0                     9.0                  9.0   \n",
              "\n",
              "  requires_license  license jurisdiction_names instant_bookable  \\\n",
              "0                f      NaN         WASHINGTON                f   \n",
              "1                f      NaN         WASHINGTON                f   \n",
              "2                f      NaN         WASHINGTON                f   \n",
              "3                f      NaN         WASHINGTON                f   \n",
              "4                f      NaN         WASHINGTON                f   \n",
              "\n",
              "  cancellation_policy require_guest_profile_picture  \\\n",
              "0            moderate                             f   \n",
              "1              strict                             t   \n",
              "2              strict                             f   \n",
              "3            flexible                             f   \n",
              "4              strict                             f   \n",
              "\n",
              "  require_guest_phone_verification  calculated_host_listings_count  \\\n",
              "0                                f                               2   \n",
              "1                                t                               6   \n",
              "2                                f                               2   \n",
              "3                                f                               1   \n",
              "4                                f                               1   \n",
              "\n",
              "   reviews_per_month  \n",
              "0               4.07  \n",
              "1               1.48  \n",
              "2               1.15  \n",
              "3                NaN  \n",
              "4               0.89  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "0c-EyhKdblsM",
        "outputId": "583a6984-8344-46d8-b76d-217523ef5619"
      },
      "source": [
        "df_reviews.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>listing_id</th>\n",
              "      <th>id</th>\n",
              "      <th>date</th>\n",
              "      <th>reviewer_id</th>\n",
              "      <th>reviewer_name</th>\n",
              "      <th>comments</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7202016</td>\n",
              "      <td>38917982</td>\n",
              "      <td>2015-07-19</td>\n",
              "      <td>28943674</td>\n",
              "      <td>Bianca</td>\n",
              "      <td>Cute and cozy place. Perfect location to every...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7202016</td>\n",
              "      <td>39087409</td>\n",
              "      <td>2015-07-20</td>\n",
              "      <td>32440555</td>\n",
              "      <td>Frank</td>\n",
              "      <td>Kelly has a great room in a very central locat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7202016</td>\n",
              "      <td>39820030</td>\n",
              "      <td>2015-07-26</td>\n",
              "      <td>37722850</td>\n",
              "      <td>Ian</td>\n",
              "      <td>Very spacious apartment, and in a great neighb...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7202016</td>\n",
              "      <td>40813543</td>\n",
              "      <td>2015-08-02</td>\n",
              "      <td>33671805</td>\n",
              "      <td>George</td>\n",
              "      <td>Close to Seattle Center and all it has to offe...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7202016</td>\n",
              "      <td>41986501</td>\n",
              "      <td>2015-08-10</td>\n",
              "      <td>34959538</td>\n",
              "      <td>Ming</td>\n",
              "      <td>Kelly was a great host and very accommodating ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   listing_id        id        date  reviewer_id reviewer_name  \\\n",
              "0     7202016  38917982  2015-07-19     28943674        Bianca   \n",
              "1     7202016  39087409  2015-07-20     32440555         Frank   \n",
              "2     7202016  39820030  2015-07-26     37722850           Ian   \n",
              "3     7202016  40813543  2015-08-02     33671805        George   \n",
              "4     7202016  41986501  2015-08-10     34959538          Ming   \n",
              "\n",
              "                                            comments  \n",
              "0  Cute and cozy place. Perfect location to every...  \n",
              "1  Kelly has a great room in a very central locat...  \n",
              "2  Very spacious apartment, and in a great neighb...  \n",
              "3  Close to Seattle Center and all it has to offe...  \n",
              "4  Kelly was a great host and very accommodating ...  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "PlGaZKJrbpKV",
        "outputId": "0f88317c-dcc8-4a00-dc9e-518a055730f4"
      },
      "source": [
        "df_cal.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>listing_id</th>\n",
              "      <th>date</th>\n",
              "      <th>available</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>241032</td>\n",
              "      <td>2016-01-04</td>\n",
              "      <td>t</td>\n",
              "      <td>$85.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>241032</td>\n",
              "      <td>2016-01-05</td>\n",
              "      <td>t</td>\n",
              "      <td>$85.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>241032</td>\n",
              "      <td>2016-01-06</td>\n",
              "      <td>f</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>241032</td>\n",
              "      <td>2016-01-07</td>\n",
              "      <td>f</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>241032</td>\n",
              "      <td>2016-01-08</td>\n",
              "      <td>f</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   listing_id        date available   price\n",
              "0      241032  2016-01-04         t  $85.00\n",
              "1      241032  2016-01-05         t  $85.00\n",
              "2      241032  2016-01-06         f     NaN\n",
              "3      241032  2016-01-07         f     NaN\n",
              "4      241032  2016-01-08         f     NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4gQAZocBbrQG",
        "outputId": "aa6751da-944e-41be-af73-d0166b4b6932"
      },
      "source": [
        "print(\"listings number of rows and columns: \", df_listings.shape[0], df_listings.shape[1])\n",
        "print(\"reviews number of rows and columns: \", df_reviews.shape[0], df_reviews.shape[1])\n",
        "print(\"calendar number of rows and columns: \", df_cal.shape[0], df_cal.shape[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "listings number of rows and columns:  3818 92\n",
            "reviews number of rows and columns:  84849 6\n",
            "calendar number of rows and columns:  1393570 4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M1B_qjpzb9m1"
      },
      "source": [
        "We will majorly use the listings dataset, let's understand the dataset first."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8-RTCVf6b6Vv",
        "outputId": "0f9ccb14-d26c-44ff-fbe0-cb5902c655c6"
      },
      "source": [
        "df_listings.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', 'thumbnail_url', 'medium_url', 'picture_url',\n",
              "       'xl_picture_url', 'host_id', 'host_url', 'host_name', 'host_since',\n",
              "       'host_location', 'host_about', 'host_response_time',\n",
              "       'host_response_rate', 'host_acceptance_rate', 'host_is_superhost',\n",
              "       'host_thumbnail_url', 'host_picture_url', 'host_neighbourhood',\n",
              "       'host_listings_count', 'host_total_listings_count',\n",
              "       'host_verifications', 'host_has_profile_pic', 'host_identity_verified',\n",
              "       'street', '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",
              "       'cancellation_policy', 'require_guest_profile_picture',\n",
              "       'require_guest_phone_verification', 'calculated_host_listings_count',\n",
              "       'reviews_per_month'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sChe-vw-cAt7",
        "outputId": "85d79317-67b1-441b-8a56-0ae972eff792"
      },
      "source": [
        "df_listings.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3818 entries, 0 to 3817\n",
            "Data columns (total 92 columns):\n",
            " #   Column                            Non-Null Count  Dtype  \n",
            "---  ------                            --------------  -----  \n",
            " 0   id                                3818 non-null   int64  \n",
            " 1   listing_url                       3818 non-null   object \n",
            " 2   scrape_id                         3818 non-null   int64  \n",
            " 3   last_scraped                      3818 non-null   object \n",
            " 4   name                              3818 non-null   object \n",
            " 5   summary                           3641 non-null   object \n",
            " 6   space                             3249 non-null   object \n",
            " 7   description                       3818 non-null   object \n",
            " 8   experiences_offered               3818 non-null   object \n",
            " 9   neighborhood_overview             2786 non-null   object \n",
            " 10  notes                             2212 non-null   object \n",
            " 11  transit                           2884 non-null   object \n",
            " 12  thumbnail_url                     3498 non-null   object \n",
            " 13  medium_url                        3498 non-null   object \n",
            " 14  picture_url                       3818 non-null   object \n",
            " 15  xl_picture_url                    3498 non-null   object \n",
            " 16  host_id                           3818 non-null   int64  \n",
            " 17  host_url                          3818 non-null   object \n",
            " 18  host_name                         3816 non-null   object \n",
            " 19  host_since                        3816 non-null   object \n",
            " 20  host_location                     3810 non-null   object \n",
            " 21  host_about                        2959 non-null   object \n",
            " 22  host_response_time                3295 non-null   object \n",
            " 23  host_response_rate                3295 non-null   object \n",
            " 24  host_acceptance_rate              3045 non-null   object \n",
            " 25  host_is_superhost                 3816 non-null   object \n",
            " 26  host_thumbnail_url                3816 non-null   object \n",
            " 27  host_picture_url                  3816 non-null   object \n",
            " 28  host_neighbourhood                3518 non-null   object \n",
            " 29  host_listings_count               3816 non-null   float64\n",
            " 30  host_total_listings_count         3816 non-null   float64\n",
            " 31  host_verifications                3818 non-null   object \n",
            " 32  host_has_profile_pic              3816 non-null   object \n",
            " 33  host_identity_verified            3816 non-null   object \n",
            " 34  street                            3818 non-null   object \n",
            " 35  neighbourhood                     3402 non-null   object \n",
            " 36  neighbourhood_cleansed            3818 non-null   object \n",
            " 37  neighbourhood_group_cleansed      3818 non-null   object \n",
            " 38  city                              3818 non-null   object \n",
            " 39  state                             3818 non-null   object \n",
            " 40  zipcode                           3811 non-null   object \n",
            " 41  market                            3818 non-null   object \n",
            " 42  smart_location                    3818 non-null   object \n",
            " 43  country_code                      3818 non-null   object \n",
            " 44  country                           3818 non-null   object \n",
            " 45  latitude                          3818 non-null   float64\n",
            " 46  longitude                         3818 non-null   float64\n",
            " 47  is_location_exact                 3818 non-null   object \n",
            " 48  property_type                     3817 non-null   object \n",
            " 49  room_type                         3818 non-null   object \n",
            " 50  accommodates                      3818 non-null   int64  \n",
            " 51  bathrooms                         3802 non-null   float64\n",
            " 52  bedrooms                          3812 non-null   float64\n",
            " 53  beds                              3817 non-null   float64\n",
            " 54  bed_type                          3818 non-null   object \n",
            " 55  amenities                         3818 non-null   object \n",
            " 56  square_feet                       97 non-null     float64\n",
            " 57  price                             3818 non-null   object \n",
            " 58  weekly_price                      2009 non-null   object \n",
            " 59  monthly_price                     1517 non-null   object \n",
            " 60  security_deposit                  1866 non-null   object \n",
            " 61  cleaning_fee                      2788 non-null   object \n",
            " 62  guests_included                   3818 non-null   int64  \n",
            " 63  extra_people                      3818 non-null   object \n",
            " 64  minimum_nights                    3818 non-null   int64  \n",
            " 65  maximum_nights                    3818 non-null   int64  \n",
            " 66  calendar_updated                  3818 non-null   object \n",
            " 67  has_availability                  3818 non-null   object \n",
            " 68  availability_30                   3818 non-null   int64  \n",
            " 69  availability_60                   3818 non-null   int64  \n",
            " 70  availability_90                   3818 non-null   int64  \n",
            " 71  availability_365                  3818 non-null   int64  \n",
            " 72  calendar_last_scraped             3818 non-null   object \n",
            " 73  number_of_reviews                 3818 non-null   int64  \n",
            " 74  first_review                      3191 non-null   object \n",
            " 75  last_review                       3191 non-null   object \n",
            " 76  review_scores_rating              3171 non-null   float64\n",
            " 77  review_scores_accuracy            3160 non-null   float64\n",
            " 78  review_scores_cleanliness         3165 non-null   float64\n",
            " 79  review_scores_checkin             3160 non-null   float64\n",
            " 80  review_scores_communication       3167 non-null   float64\n",
            " 81  review_scores_location            3163 non-null   float64\n",
            " 82  review_scores_value               3162 non-null   float64\n",
            " 83  requires_license                  3818 non-null   object \n",
            " 84  license                           0 non-null      float64\n",
            " 85  jurisdiction_names                3818 non-null   object \n",
            " 86  instant_bookable                  3818 non-null   object \n",
            " 87  cancellation_policy               3818 non-null   object \n",
            " 88  require_guest_profile_picture     3818 non-null   object \n",
            " 89  require_guest_phone_verification  3818 non-null   object \n",
            " 90  calculated_host_listings_count    3818 non-null   int64  \n",
            " 91  reviews_per_month                 3191 non-null   float64\n",
            "dtypes: float64(17), int64(13), object(62)\n",
            "memory usage: 2.7+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qluoaOQZcMtP"
      },
      "source": [
        "Now, let's create a subset, to consider only those columns that we require."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ri9eSa4XcJlx"
      },
      "source": [
        "df_index = df_listings[['id','instant_bookable','host_response_rate', 'host_acceptance_rate','host_id','zipcode','room_type','accommodates','bathrooms','bedrooms','beds','reviews_per_month','number_of_reviews','price','property_type']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "G0AjRFMRcYJy",
        "outputId": "1b1d7942-8fee-470c-fe76-cd3518751701"
      },
      "source": [
        "df_index.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>id</th>\n",
              "      <th>instant_bookable</th>\n",
              "      <th>host_response_rate</th>\n",
              "      <th>host_acceptance_rate</th>\n",
              "      <th>host_id</th>\n",
              "      <th>zipcode</th>\n",
              "      <th>room_type</th>\n",
              "      <th>accommodates</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>beds</th>\n",
              "      <th>reviews_per_month</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>price</th>\n",
              "      <th>property_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>241032</td>\n",
              "      <td>f</td>\n",
              "      <td>96%</td>\n",
              "      <td>100%</td>\n",
              "      <td>956883</td>\n",
              "      <td>98119</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.07</td>\n",
              "      <td>207</td>\n",
              "      <td>$85.00</td>\n",
              "      <td>Apartment</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>953595</td>\n",
              "      <td>f</td>\n",
              "      <td>98%</td>\n",
              "      <td>100%</td>\n",
              "      <td>5177328</td>\n",
              "      <td>98119</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.48</td>\n",
              "      <td>43</td>\n",
              "      <td>$150.00</td>\n",
              "      <td>Apartment</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3308979</td>\n",
              "      <td>f</td>\n",
              "      <td>67%</td>\n",
              "      <td>100%</td>\n",
              "      <td>16708587</td>\n",
              "      <td>98119</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>11</td>\n",
              "      <td>4.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.15</td>\n",
              "      <td>20</td>\n",
              "      <td>$975.00</td>\n",
              "      <td>House</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7421966</td>\n",
              "      <td>f</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9851441</td>\n",
              "      <td>98119</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>$100.00</td>\n",
              "      <td>Apartment</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>278830</td>\n",
              "      <td>f</td>\n",
              "      <td>100%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1452570</td>\n",
              "      <td>98119</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.89</td>\n",
              "      <td>38</td>\n",
              "      <td>$450.00</td>\n",
              "      <td>House</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id instant_bookable host_response_rate host_acceptance_rate   host_id  \\\n",
              "0   241032                f                96%                 100%    956883   \n",
              "1   953595                f                98%                 100%   5177328   \n",
              "2  3308979                f                67%                 100%  16708587   \n",
              "3  7421966                f                NaN                  NaN   9851441   \n",
              "4   278830                f               100%                  NaN   1452570   \n",
              "\n",
              "  zipcode        room_type  accommodates  bathrooms  bedrooms  beds  \\\n",
              "0   98119  Entire home/apt             4        1.0       1.0   1.0   \n",
              "1   98119  Entire home/apt             4        1.0       1.0   1.0   \n",
              "2   98119  Entire home/apt            11        4.5       5.0   7.0   \n",
              "3   98119  Entire home/apt             3        1.0       0.0   2.0   \n",
              "4   98119  Entire home/apt             6        2.0       3.0   3.0   \n",
              "\n",
              "   reviews_per_month  number_of_reviews    price property_type  \n",
              "0               4.07                207   $85.00     Apartment  \n",
              "1               1.48                 43  $150.00     Apartment  \n",
              "2               1.15                 20  $975.00         House  \n",
              "3                NaN                  0  $100.00     Apartment  \n",
              "4               0.89                 38  $450.00         House  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OS0nR8jPciQP"
      },
      "source": [
        "From the above, we can see that our dataset is not clean. There are null values and the prices have symbols like '$' in them, that we have to get rid of. So let's do that."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LcVpDD46cgja",
        "outputId": "0b4e8499-e825-487f-9e41-3b8cdbb6d5f0"
      },
      "source": [
        "df_index.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                        0\n",
              "instant_bookable          0\n",
              "host_response_rate      523\n",
              "host_acceptance_rate    773\n",
              "host_id                   0\n",
              "zipcode                   7\n",
              "room_type                 0\n",
              "accommodates              0\n",
              "bathrooms                16\n",
              "bedrooms                  6\n",
              "beds                      1\n",
              "reviews_per_month       627\n",
              "number_of_reviews         0\n",
              "price                     0\n",
              "property_type             1\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "id": "QH2NK3HHc2S2",
        "outputId": "c782513d-f073-460a-8772-305abfa370d1"
      },
      "source": [
        "#Fill the NaN values with zero\n",
        "df_index.fillna(0, inplace=True)\n",
        "df_index.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4327: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  downcast=downcast,\n"
          ],
          "name": "stderr"
        },
        {
          "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>id</th>\n",
              "      <th>instant_bookable</th>\n",
              "      <th>host_response_rate</th>\n",
              "      <th>host_acceptance_rate</th>\n",
              "      <th>host_id</th>\n",
              "      <th>zipcode</th>\n",
              "      <th>room_type</th>\n",
              "      <th>accommodates</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>beds</th>\n",
              "      <th>reviews_per_month</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>price</th>\n",
              "      <th>property_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>241032</td>\n",
              "      <td>f</td>\n",
              "      <td>96%</td>\n",
              "      <td>100%</td>\n",
              "      <td>956883</td>\n",
              "      <td>98119</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.07</td>\n",
              "      <td>207</td>\n",
              "      <td>$85.00</td>\n",
              "      <td>Apartment</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>953595</td>\n",
              "      <td>f</td>\n",
              "      <td>98%</td>\n",
              "      <td>100%</td>\n",
              "      <td>5177328</td>\n",
              "      <td>98119</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.48</td>\n",
              "      <td>43</td>\n",
              "      <td>$150.00</td>\n",
              "      <td>Apartment</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3308979</td>\n",
              "      <td>f</td>\n",
              "      <td>67%</td>\n",
              "      <td>100%</td>\n",
              "      <td>16708587</td>\n",
              "      <td>98119</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>11</td>\n",
              "      <td>4.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.15</td>\n",
              "      <td>20</td>\n",
              "      <td>$975.00</td>\n",
              "      <td>House</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7421966</td>\n",
              "      <td>f</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9851441</td>\n",
              "      <td>98119</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>$100.00</td>\n",
              "      <td>Apartment</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>278830</td>\n",
              "      <td>f</td>\n",
              "      <td>100%</td>\n",
              "      <td>0</td>\n",
              "      <td>1452570</td>\n",
              "      <td>98119</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.89</td>\n",
              "      <td>38</td>\n",
              "      <td>$450.00</td>\n",
              "      <td>House</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id instant_bookable host_response_rate host_acceptance_rate   host_id  \\\n",
              "0   241032                f                96%                 100%    956883   \n",
              "1   953595                f                98%                 100%   5177328   \n",
              "2  3308979                f                67%                 100%  16708587   \n",
              "3  7421966                f                  0                    0   9851441   \n",
              "4   278830                f               100%                    0   1452570   \n",
              "\n",
              "  zipcode        room_type  accommodates  bathrooms  bedrooms  beds  \\\n",
              "0   98119  Entire home/apt             4        1.0       1.0   1.0   \n",
              "1   98119  Entire home/apt             4        1.0       1.0   1.0   \n",
              "2   98119  Entire home/apt            11        4.5       5.0   7.0   \n",
              "3   98119  Entire home/apt             3        1.0       0.0   2.0   \n",
              "4   98119  Entire home/apt             6        2.0       3.0   3.0   \n",
              "\n",
              "   reviews_per_month  number_of_reviews    price property_type  \n",
              "0               4.07                207   $85.00     Apartment  \n",
              "1               1.48                 43  $150.00     Apartment  \n",
              "2               1.15                 20  $975.00         House  \n",
              "3               0.00                  0  $100.00     Apartment  \n",
              "4               0.89                 38  $450.00         House  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kiYP8KqCc47m",
        "outputId": "3b90cd28-b94e-4f5b-c756-525853fedf9d"
      },
      "source": [
        "#The price column has str value of $ and commas, we have to remove them.\n",
        "df_index['price'] = df_index['price'].str.replace(\"$\",\"\")\n",
        "df_index['price'] = df_index['price'].str.replace(\",\",\"\")\n",
        "df_index['price'] = df_index['price'].str.replace(\"'\",\"\")\n",
        "df_index['price'] = df_index['price'].astype(float)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dB__mtBvc-pn",
        "outputId": "35334b67-dedc-4a50-c2f3-49a044789684"
      },
      "source": [
        "df_index['price'].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     85.0\n",
              "1    150.0\n",
              "2    975.0\n",
              "3    100.0\n",
              "4    450.0\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iRrpaxujfBvT"
      },
      "source": [
        "Now that we have a cleaner subset, let's get the correlation between all the column elements. A figure of how strongly they are related to each other.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "h4ZvNkAIdCDs",
        "outputId": "5fe5d891-78fe-43f7-bcad-fc156281a91f"
      },
      "source": [
        "#Let's get the correlation between the values of the dataset:\n",
        "corr = df_index.corr()\n",
        "cmap = sns.cm.rocket_r\n",
        "ax = sns.heatmap(corr, annot=True, center=0, cmap=cmap, linecolor='blue')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6yzD+9U+IsE0vN+44QPb2A5et16f7/fgN6o7VbMaaX0DsmHfsyo3b9+HRriX1tnxue0SjxN46q+dx9hHVXsO0BYTOHofW1QXjjhJ7M77dSNjbL1Bn3QIoMhH/SskFSb1tS9B5uqNx0uPVpS3nnnydglOx9vq37cerfRQNty1Sp9W/8mHJ/ms/4nRPNVqOn7KAiHfGoXV1JnvHgeLHEkImPIPrXXXBaqUwLon4SR9X2Be5O3/HPboltX5egiW/gKRJ7xeX1Vy1gNh+IwBInjGPoJnj0bo4k7NrP7k79wHg2b0DPgN7AZCz6VeyV20EwJJlJOPLVUSIeWC1krvzd1xSDpfbBsuZw1jqNcF12GwwFVK4/vPiMten3yB/6VTQ63FVXgKtDrRazOeOYjqkjidtSB2c+41C4+KBrn4zrA/0If/zyeXqulFjquB0HNk7D9Dgp3lgsZL2zUbCpgz967I73SZorFcxi0xS9VQwkeUCUAR8cblHFuR6ejceuZ7ejUeup3dzqIr19AJ9Iit9zknOjPlH5kJlpFfNCCE8bf9bgVHV3ByJRCKpEHlPTyKRSCS3DbfCIwv//ruSEolEIpFUEhnpSSQSiaRS6DT//jhJOj2JRCKRVAqZ3pRIJBKJ5F+EjPQkEolEUink7E1JtVIdz8z9L2HfTdcJMKN+42rRazJVTzKkurJIOp3lpuvMyna96ToB/H47Uy16NRqvatFbFdwK6U3p9CQSiURSKWSkJ5FIJJLbBk0Vzt5UFOVhYC6gAxYLIWaVKb8D+BKoYZOZIIRYf7165UQWiUQikVQKzVX8uxyKouiA+UA31OXUBiiKclcZscmAEEI0Bx4HFlSFDTLSk0gkEkmlqMIV0VsBp4QQZwAURVkJ9EZdR/QSVsDb9tkHiK8KxdLpSSQSiaTKKWfB64W29UABwoHSy3nEAa3LVDEN2KgoymjAA+hcFe2STk8ikUgkleJKacvSlF7w+hoZACwVQryvKEpbYLmiKI2FENc1xVje05NIJBJJpdBptJX+uwIXgZqlvkdQvFZ9MYMBASCE2A24AgFcJzLSuwVp2q45T04dglanZdvKTaz+xH4l8uj+HRn02lOkGdQVqTcuW8e2lZsBWHHmOy4cvwBAanwy7w2ZWSVtWrTwfXp070xScgrNmneqkjrLI+C14bhHt8Kal0/Sa+9TcOyUg4zf2KfxeqQzOh9PzkT1uar6Q6cMw7N9FNb8AuJe/pD8v087yLg2rkfEu+PQuDhj3L6fhOn2F7v+g/sSOmkwx1oMxJyehdbLnYg543EKC0Sj05Ky+HuMP2y02ydo8nN4tmtpW0R2DgVHHfW63F2f0FkvonV1xrhjH0lvfgZA2IcTcK4TDoDOyxNztpFzvUfj2qQhITPUBWjRaEiZt4LsjbuL6wt+vZTOV+eQX45O17vrEzq7RGfijM+Ky3yf6IXvoJ5gsWDcvo+kd77AKTyIuj9/RuFZdaHgvIMxnH3lMyLeGIp3xxZY8wo49+Jc8v5yfIbO7Z561J4zBo2rC1lbDxA3dZFqUw1P6sx/GeeaQRTGJnF2xDuYM3PQ+XhQ670xuNQKwVJQyPnx88iPuVBSoVaL9/sLsaQmY3xzop0u96FjcGrRGmtBATlz38Z85qR9Y5xd8Hz1DXQhYVgtFor2/UbespLj7Hx/B9wGPI3VasV89jQ5c2Y42HOJ6xlTQWMH4vt/D2FKU1d2T3xvGcbt+9HV8KLm/Im4NWlAxndbSJj2aYX6q4l9QANFUeqgOrvHgYFlZC4AnYCliqLcier0kq9XsXR6txgarZZnZjzHzEFTSTWk8tbqdzmw+XeH1ch3r/2FpVMWOexfmF/IxO7jqrxdy5YJFixYwpIlc68sfI24R7fEqVY4Fx5+BpcmjQicOpq4x8c6yOVs20PmitXU+vmLq6rfs30UzrXDONlxGG7NIgmbMYIz/V5ykAubMZKLE+eRdzCGWl9Mw7NdC4w71JWunUID8HywOYUXk4rl/Z/oQcGpC1wYOh2dnzcNNn/GqXVbocgEgEe7KJxrh3OmyxBcm0YS8sYozj/meIxC3hiJYfJc8g/FELF4Oh7RUeTs3E/8CyUzwYMmDMGcnQNAwYnznOs3FswWdIG+1Fk9n+wte8FsUXXWCud05yG4NoskZPoozvUvX2fC5LnkH4yhZimd7q2b4NWpDWcfGYm10ITOz6d4n8ILCcWrvQN4d2iDS51Qjj74PO7NG3LHzOHEPPKyg647Zj7P+Vfmk/vnCeotm4J3+3vJ2v4HISMeJfvXwyQu+I7gEY8SPOJR4t9eRsiox8j9+wxnhr6NS71war75HKcGTCmuz7Vnf8yx59G4u9vpcWrRGm1oBJnPD0LX8C48hr9I1svDHdqT/8M3mI78CXo9XtM/wOne1hT9sRdtaDiu/QeR9epIrDlGND41HPa9RFWMqZQvfiB18fd28paCQpI++AqXhrVwbVirQv1XS1VNZBFCmBRFGQVsQH0c4QshxN+KokwH9gshVgMvAYsURRmHOqnladu6o9eFdHrloChKbWCtEOKaXwOiKEp7oFAI8dtlZJ4HcoUQy6pKf/1mDTCcSyApNhGA3Wt+IapLawend7PZ9cteatWKuKE6PDq2JftHNWItOHwcrZcHugA/zClpdnIFh49fU/3enVuT8f1WQI1QdN4e6AN9MSWnF8voA33RebqRdzAGgIzvt+LdpU3xCSpk8lASZy3hjoWTi/exWkHr4QaA1t0Nc0Y2mEpWTvfs1IbM77cAkH8oRrUr0BdzKb26QF+0nu7kH1L1Zn6/Bc/ObcjZud/OBq9uD3LhSTWqseYXFG/XujirDbkk17kNmT/YdB5UdZZnq9bTnXybrZk/bMGri6rTd2APUhb+D2uh6rjNtkikPHy6tiLtu20A5P55Qu3XIF9MSaV0Bfmi83Qn988TAKR9tw2fh1qTtf0PfLq25oQyCYDUb7fSULxF/NvLcG1QE8OC7wAoOH0Rl5pB6AN8MKVk4hTij1NUG/L+txzX3opde5xaPUDhtg1qu08cRePhicbXD2t6qXFUWKA6PACTCfOZE2j9AwFw6dqLgvXfY80xqv2cmVGh7VUxpsrDmldA7v6jONcKrVDmWriae3pXwvbM3foy26aU+nwUuL/KFNqQTu/G0R4wAhU6PSFEleccfEP8SE1IKf6empBK/eYNHORadWvLna3uJuFsPMumf0GabR8nF2feWvMeZpOZ1Z+sYv/GvVXdxBuGPigAk6Ek+2FKTEEf7O/g9K65/hB/ikr1bZEhFX2Iv/0JKsSfIkOqgwyAV+fWFBlSyT9+1q7etGVruWPh60TuWYbWw43YMbPtHJBTsKNdTsEBdk5PlUlxkCmNW1RjTCkZFJ0vmfnt2iSS0LdfwCksiPhX3gOzOkdAHxxAUUIpnYYU9MEB9raW0VlkkwFwrhOGe9TdBL34lBp1zFpM/hE1RegcEUKdH+dhNuaS/MEynEP8KYwvqacwIQXnEH87p+cc4k9hQmopmVScbf2qD/ApljUlpaMPUKPKvGNnqdGtLTm/H8W9WQOcw4NwCg3AlJJJxLQh5H75KRo3+ygPQOsfgCWlJBK3pCSj9Q/EnF7+ONJ4eOLU8j7y13wLgC5MvbjzmvUxGq2WvK+XUvTn7+Xue71jCsD/yZ749utI3pFTJLy1GEtWTrm6qoIqfGSh2pBOr2J0iqIsAu5DzTn3BiKBTwF34DTwrBAiXVGUMcDzgAn1OZMJtu9mRVH+A4wWQuwqq0BRlGmAUQjxnqIoLYBL+baNZWWrkj827+O31TsxFZroNLArI+aM4U1b2mf0fUNJT0wjqGYwk7+ewYXj50m6YLiRzbkt0Li6EDhC4dxTrzuUeUbfS/6xM5wb9BrOtUKpvWwG5x45giUnr0rb4N2zHdnrttttyz8cw9kew3GuV5PQ2S9i3L4fa2HR9SvT6dD5eHGu/zhcmzQkfO5ETnd8FlNyGqfaPYU5IxvXu+sT8cnr5MbEXrm+q8F2vWCY/x013xhKo58/IO/4eXL/PgNmC96dojClZqA7fQJ942bXp0urw+OlKeSv/Q5LYoK6TadDFxZB9qSxaP0D8Xp7HlljnimO/KqS1BXrSZq3EqxWgl78D6GThnDx1Rt3C+FWQDq9imkADBBCDFUURQCPAq+gOrAdttzzVOAFVCdXRwhRoChKDSFEhqIon2JzaJXUtwQYJYTYqSjKuxUJlX72RVdOebohDf/Qkit8/1B/0g32V6jGjOziz1tXbmbgxKdK9k9UZZNiEzm65y9qN67zj3Z6PgN64f1YNwDyj5xAHxJYXKYPDsCUmFrRrpXC74ke+P7fQwDkHT6JU6m+dQrxx2Swr99kSMWp1FX4JRnnWiE4RwRTf9082/YA6q35kDN9XsS3f2eSP1WjhMLzCRTGJuI/cgAe9ze32XXSwa6ixBRKU5SYgj4koGIZnRavrvdxru+Ycu0sPB2L1tODOmvnY80vsNkayCW3qw8JwFRGp6mMTqdSMiZDCtkb1SRH/uETYLWi8/PGnJaFuTAb30E9qfF/D6H19sSaX4hzWACX4hPn0AAKy/RroSEV59CSfnUO9S+WMaVkFqdD9UG+mFLVVKrFmMf5lz4q3ufu3xZScMGAb68H8OnSCt1DK9E4O6Nx98Dr3U/R6NXToelUDNqAoOL9tAGBWFLLnz/hMXI8loQ4CmxRHoAlNRnTiWNgNmNJMmC5GIs2NALzKTWtXlVjCsCcUpI6TV+5gVqLp5bbzqpCdwtM+P/3W3DjOCuEOGj7fACoB9QQQuywbfsSiLZ9PgyssEV1pqtVpChKDVvdO22bllckK4RYKISIEkJElVd++tBJQuqEElgzCJ2Tnra9HuDAJvvUSo0g3+LPLbq05OIp9X6fh7cHemf1h+/l60XDqEZcPFnFV+FVTObXa4jtN4LYfiPI2fIbXr3V51ddmjTCkp173anNtOXrON1zDKd7jiFr025q9O0IgFuzSMzZuXZpKABTcjpmYx5uzSIBqNG3I1mb91IQc57jrf7DiejBnIgeTJEhhdO9XsCUkkFhfDKe9zUFQBdQA5e6EaQt+pZzvUdzrvdosjfvxqevOuPVtWkkFmOOXWoTwJycjsWYi2tTVa9P304Yt+wpLve4rzmFZ+LsLgKcIoJBp54C9GFBaD3cOP9/4zn7yGiMm3fj08ems1kkluyccm21GHNxtdnq06cT2ZtVndmb9+DepgkAzrXD0TjpMadlofPzBq2W9BVriRv5FpbsHNJW78Lv0Q4AuDdviDk7xy61CWra0mzMxb15QwD8Hu1A5kZ1XGdu+h3//upx8e/fkUxbSl7n7YHGSR3P/gO6YNx7FIsxj/jZy/mr1WAyhz2O8b3pFB3+g+yXnydr3BCyxg2haM8unDuoTknX8C6sOTn29/NsuA0ajMbdg9zF8+y2F+35BSdbBKnx8kEbXhNLYklKuarGFKj3+y7h/VBb8k+cd2hnVaLRaCr9909FRnoVU1Dqsxn1pacV0QPVAfYCJimKcs+NbNjlsJgtLJ2yiInLpqLV6dguNhN3Mpb+Lw7g7OFTHNi8j4ef7kGLLq0wm8wYM418Ol69Gg5rEMGQmSOwWixotFpWf7KqyibAfLV8Pu2i2xIQ4Me5M/t5Y/p7LFm6skrqvkTuzt9xj25JrZ+XYMkvIGnS+8VlNVctILbfCAD8XxqMV48OaFxdqL31K7K++5m0+V9dsX7jtv14tY+i4bZFWPILiHvlw+Kyems/4nRPNYqKn7KAiHfGoXV1JnvHAYzb91dUJQDJ81YS8e4L1P/pY0CDYfYSzOlZxeU52/fh2a4ldTd/jiWvAMPED4rLav84j3O91ZmQidMWEDprHBpXF3J27idnR4le7x7RZK3dQWncWtxNxLDHsJpMYLGS+MaCYr3G7fvwaNeSels+tz0mUaKzzup5xbMvDdMWEDp7HFpXF4w7SnRmfLuRsLdfoM66BVBkIv6VOQC4t7yHwLH/KdZpmPoxGev/xKv13dz9y6dY8go4/1KJE2n08wccf1idNRo76TNqzRmD1tWZzG1/kLVNnchhmP8ddT55Gf/HO1MYl8zZEe8A4Fo/glofjAUr5J+4wPmX7Z1TRRQd2INTVOP8VVMAACAASURBVBt8Pv2v+sjCvJLZr94fLCZr3BA0/oG4KU9ijj2P9xx1FnTB+u8p2LSOoj9/x6l5S3w+/hKr2ULe0k+wZmeVq+t6x1TIhGdwvasuWK0UxiURP+nj4v0b7vwcrac7Gic93l3agPqOy9Kv+bpqboVVFjRW63XPAL3lKDt7UlGU8YAn0Bc1BbnLdj/OB3Va7R1CiHOKojgB51EH12DAWwhRYb6hzD29w8AIIcQviqLMBnpcafbmgFp9bvrBq6719I5X03p6+flO1aJXr7/569qB3fyZm0ZeNfVx7XsrnlV5I4k/Uj3r6TU+s/a6PVZU6IOVHiH7E3b9Iz2kTG9eHU8B79ocVDNgOuqtta8URTkC/Al8JITIANYAfRVFOagoyoOVqPsZYL6iKAfhFrickkgktxy3QnpTRnr/YmSkd+ORkd6NR0Z6N4eqiPTahLWv9AjZE7/9H+n5ZKQnkUgkktsGOZHlJqAoyiTgsTKb/yeEeKs62iORSCTXwj85bVlZpNO7Cdicm3RwEonkX82tMHtTOj2JRCKRVIpbwenJe3oSiUQiuW2Qkd6/mKkuhTdd54xqmkXZ6NRf1aJXCW1VLXoP5yRUi970wuwrC1UxO8Nv7OobFZF0zKNa9I4tvPm/W4AtVVCHTt7Tk0gkEsntQlUuLVRdyPSmRCKRSG4bZKQnkUgkkkpxK0xkkU5PIpFIJJVCPqcnkUgkktsGGelJJBKJ5LbhVpjIIp2eRCKRSCpFVc58VBTlYWAu6ko1i4UQs8qRUYBpgBU4JIQYeL165exNiUQikVSKqlpaSFEUHTAf6Ia6/ugARVHuKiPTAJgI3C+EuBt4oSpskJHeLUTQ5OfwbNfSttL1HAqOnnaQcbm7PqGzXkTr6oxxxz6S3vwMgLAPJ+BcJxwAnZcn5mxj8YrcAPrQQOqu/5S0BV+RseTbSrUn4LXhuEe3wpqXT9Jr71Nw7JSDjN/Yp/F6pDM6H0/ORPW5FrMvy6KF79Oje2eSklNo1rxTldXbpF1znpw6GK1Oy7aVm1nzySq78uj+HRj42lOkGdIA2LhsPdtXbiYgPJBxC19Fo9Gid9KxYel6tqzYUGm993dow4Q3x6HTafluxWo+n7fcrvyV6WNpdX8LAFzdXPEL8OW+hl1oef+9vDq95JxRp34tXn7+dbb+tPOKOtt3eoDpb09Aq9Px9fLvmP/hYrvysIhQ5i6YibePF1qdlrff+ICtm3YBcOfdDZk9ZyqeXp5YrBZ6dPw/Cgqu7eFs/4nD8XiwFZb8fJImvU9heeNpjDqetN6enG11dePpRvx+XJs0JGSG7Xek0ZAybwX8uKtc/S3bRzHyjeFodVrWf/0zK+d/4yDTrmc0T734BFarldPHzjBzlBocbTz/E2ePnwMg6WISrz9b4drV/xRaAaeEEGcAFEVZCfTGfmX3ocB8IUQ6gBAiqSoUS6d3nZRdZf0yMvcJIf57o9rh0S4K59rhnOkyBNemkYS8MYrzj41zkAt5YySGyXPJPxRDxOLpeERHkbNzP/EvlGQWgiYMwZydY7df0MShGHfur3R73KNb4lQrnAsPP4NLk0YETh1N3ONjHeRytu0hc8Vqav38xVVYW3mWLRMsWLCEJUvmVlmdGq2WZ2YM4+1B00g1pPLm6nf4Y/PvXDwZZye3Z+2vLJ2yyG5belI6U/tOwFRowsXdlXc2zuXApt/JSEq/ol6tVsvkWeMZqozBEJ/ENxuWsG3DLs6cOFcs886UEjsHDn6MO+9pCMC+X/+gf6cnAfCu4c1Pe/7Hb9v3VkrnW+9OYkDfoSTEJ7J+6zds/GkbJ2NKHMLYl55jzQ8/s+yLb2gQWY/l4hPaNO2KTqfjo89mMfb5iRz9KwZfXx+KikxX1Fke7g+2xPmOcC50t42n10dzcWA542n7HjL/u5o71l/deLpRv5+CE+c5128smC3oAn2ps3o+2rW/YjHbr5eo1WoZ8+YoXhk4geSEFBasm8fujbs5f/JCsUx4nTAGjHqcMX3HYcw0UsO/RnFZYX4hzz00/KpsvhauZiKLoijDgGGlNi0UQiy0fQ4HYkuVxQGty1TR0FbPr6gp0GlCiJ+vts1lkU7v5lAbGAjcMKfn2akNmd+rLxrKPxSD1ssDXaAv5uSSk6ku0Betpzv5h2IAyPx+C56d25BTxpl5dXuQC09OLKm7c1uK4gxY8woq3R6Pjm3J/nEzAAWHj6vtCfDDnJJmJ1dw+PjVGXqV7PplL7VqVe1rruo3a0DiuQSSYhMB2L3mF1p0aeXg9MrDXOqk7+TshEZb+ZPIPffexYWzccSdjwfgpx820fHhaDunV5rufbsw/53FDtu79urArq17yK/E8Wze4h7OnYnlwnnVth9Xreeh7h3snB5Y8fTyBMDb25NEg3pB3q7jfRz7+wRH/1LHW3p6ZmVNdcC9Q1uyV9+48XSjfj/W/JI+1ro4V7hKb6NmkVw8F0/CBQMA237cwX1d77Nzej0Gdmf1l6sxZhoByEi9+Yvg6q7C6dkc3MIrClaMHmgAtAcigJ2KotwjhLguw6vN6SmK8gNQE3AF5gohFtpubM5E9eopQohOiqJ4AvOAKNSbmW8IIb5TFGUA8BqgAdYJIV611WsEPgG6Awk2mXeAO4AXhBCrFUV5GugDeKB26nuAM/AEUAB0F0KkKYrSDPgUcAdOA88KIdIVRWkBXLqU3FjKptrAclu9AKOEEL8Bs4A7FUU5CHwJfGTb1h5wQQ3hP1MUJRT4BvBGPTbDhRDl50LK4BQcgMmQXPzdlJiCU3CA3Y9WlUlxkCmNW1RjTCkZFNlOrBp3V/yH9ufCM5PwH/xoZZoCgD7IsT36YH+Hk9S/Ed8QP1ITSvoxLSGV+s0bOsi17NaGRq3uIuFsPMunf0FaQioAfqH+vLJkMsG1Q/nvzC8rFeUBBIUEYogvyfAkxidxz713lysbGhFC+B1h7P3FMTrv1qcLyz79ulI6Q0KDib9Y8h7QhPhEmrdoYifz/qz5/HfVIp4dOhA3Dzce7zMEgLr1aoPVyopvF+If4MuPq37ik4+uLaLXlzO+q3I83ajfD4Brk0hC334Bp7Ag4l95zyHKAwgIDSA5oUR/siGZO5s3spOJqKNevM39/gO0Oi3L5ixn33b1+Dq7OLNg3ceYzWZWzv+GXzf8di3dcDO5iHr+v0SEbVtp4oC9Qogi4KyiKCdQz9f7rkdxdU5keVYI0QLVmY1RFCUYWAQ8KoRoSsmiq68DmUKIe4QQTYCtiqKEAbOBjkAzoKWiKJcS+B7AVtuNz2zgTaAL0BeYXkp/Y6Af0BJ1rbtcIURzYDfwpE1mGfCqTe8R4FKifAkw2tbO0iQBXYQQ9wL/h+rcACYAu4QQzYQQHwCDbTa1tOkfqihKHdRocIMQohnQFDhYttMURRmmKMp+RVEqn2u8Crx7tiN73fbi7wGjB5G29Aesufk3Qt0tyx+b9zP2/ueY8PA4juw6xPA5Jam4tIRUJjw8jnHRw4l+tAPeAT5Vrr9bny5sXLsNi8X+BBsQ5E+DRvX4ddueKtPV59Ee/O+/PxDVuBNPKsP56NNZaDQadHodLdvcy6hhr9Cn2xN069GJB6LLZrBuLcr+fgDyD8dwtsdwzvV/Af/nFJxcnK6pbp1eS3idcF58bDxvjXybF98Zh4e3en09sM1/GNFjFDNHvc2Iac8TWiv0ek0pFy2aSv9dgX1AA0VR6iiK4gw8DqwuI/MDamCAoigBqOnOM9dvQ/UxRlGUQ8AeVI8/DNgphDgLIIS4dAnXGXWWD7bt6aiOYrsQIlkIYQJWANE2kULgUt73CLDDdqVwBDXNeIltQohsIUQykAmsKbVPbUVRfIAaQogdtu1fAtGKotSwbb80A6D0TAInYJGiKEeA/6HOSiqPrsCTtshvL+BPyRXMM4qiTAPuEUI4vPJeCLFQCBElhIiqMagntX+cR+0f52FKTkMfElgspw8OoCgxxW7fosQU9CEBFcvotHh1vY+sdSWTG9yaRhL08rPU27oE36d64zvscXwGPlKuUT4DelFz1QJqrlpQbntMiakVdMe/i3RDGv6hJf3oF+pPmsHeNmNGNqZCNZW5beVm6jSu61BPRlI6sScu0KhVRcPEniRDMiFhQcXfg8OCSCoVnZSmW5/O/LRqo8P2h3t3YstPOzCZzJXSaUhIJCy85AQaGhaMISHRTubx//RjzQ/qZJwD+w7h4uqMn78vCfGJ7P3tAOlpGeTn5bN10y4aN62crQDej/ci4tsFRHy7APMNGE834/dTmsLTsVhy8qkTWduhLCUhhcDQEv2BIYGkJNjbl5yQwm8bd2M2mTHEGog7E0eEbfJMim38JVwwcGj3YRo0rl+5TrhKqmr2pu28PQrYABxTN4m/FUWZrijKpRPMBiBVUZSjwDbgZSHEdZ9EqiW9qShKe1Rn1lYIkasoynbUqKbR5farJEVCiEuJcwtquhIhhEVRlNL2lr6hYSn13cK198s4IBE1StMCFYVHGtRI0WHanqIo0UAPYKmiKHOEEMsqUpaxYi0ZK9YC4NG+Jb7/6UX2uh24No3EYsyxS80AmJPTsRhzcW0aSf6hGHz6diJ9ecnFlcd9zSk8E2d3Mrkw8JXizwGjB0F+Lpn/LXtBppL59Royv1avHdyjW+Ez6BGM67fj0qQRluzcWyK1CXD60ElC6oQSWDOINEMabXs9wMdjPrCTqRHkW5y2bNGlJRdPqffE/EL8yU7PpqigEA9vDyKj7uSnxeX3Z1n++vMYd9StSfgdoSQmJNOtTxdeGT7FQa5O/Vp4+3hzcP8Rh7Jufbvy4VsLKm3rwT/+ok69O6h5RziGhCR69+vOyKEv28lcvJjAA9FtEF//QP2GdXFxcSE1JY0dW35lxJhncXVzpaiwiDb3R7HokwqHswNZK9eQtbLUeBrwCMafbOPJeP3j6Wb8fpwigilKSAazBX1YEM51IzDE2l80ABw/FEN4nXBCaoaQYkihQ+92vDXK/rG1Xzf8RsfeHdggNuLt601E3QgSzifg6eNJQV4BRYVFePt6c3fLu/nmE3FdfVMRVRklCSHWA+vLbJtS6rMVeNH2V2VU1z09HyDd5vAaAW1Q7+1FK4pSRwhxVlEUP1u0twkYie0ZDUVRfIHfgY9sIW86MAD1vl+VIYTIVBQlXVGUB2331Z5AjRozFEXJUBTlASHEL8CgMnbF2RzsU6j3JkFNs3qVktsADFcUZasQokhRlIao+ewA2/6LFEVxAe5FTbFekZzt+/Bs15K6mz/HkleAYWLJSbj2j/OKHz9InLaA0Fnj0Li6kLNzPzk7SrKk3j2iyVq7w6HuayF35++4R7ek1s9LsOQXkDTp/eKymqsWENtvBAD+Lw3Gq0cHNK4u1N76FVnf/Uza/K+qpA0AXy2fT7votgQE+HHuzH7emP4eS5auvK46LWYLS6csYsKyqWh1WraLLVw8GUv/Fwdw5vAp/ti8j4ee7kGLLi0xm8wYM418Nl4dnmH1I/jP5KexWq1oNBrWLfyB2JgLV9CoYjabmTnxPT5bORedTsv3X6/ldMxZRr4ylL8PHWf7BvX2b7c+Xfjpx00O+4fVDCUkLIj9v/1ZaVvNZjOTX3mL/363EK1OyzcrvufE8dOMnziKQwf/ZtNP25g++V3enfsGQ0c8idVqZdzISQBkZmaxcMGXrN/yDVasbN20iy0br/yIRHnk7vwd9wdbcsdPS7DkFZD8esl4ivh2AXH91fHk9+JgvLqr46nW5q/IWvUz6QuuPJ5u1O/HrcXdRAx7DKvJBBYriW8sICs9y0G/xWxh3usfM3vFTLRaLT99s4HzJ87z9PgniTl0gt2b9rBv+36iolvwxdZFmC0WFr65iKyMbO5qcRfjZo/FarGg0WpZOf8buwkwEns01gpmE91IbCf0H1DTjTFADdSn7t1QJ7JogSQhRBfbRJb5QAvAjDqRZdXlJrIIITxtn6cBRiHEe6XLbBNZooQQo2zbz9m+p5QuKzOR5QzwTJmJLFbUiSzdhRCNbQ9Tfmfb/jMw0qbPCdXR+QNLUd9C8CbQy9b+ZNSJNX2Al4EiwAg8eSndWx7HG3a/6QdPr69cWqyque0Wkc2Xi8jeaIoKdVcWugGMzL22xzauly1xG6/7HWL/V6tPpc8535z/4R/5zrJqcXqSqkE6vRuPdHo3Hun0bg5V4fQGXIXT+/of6vTka8gkEolEctsgH06XSCQSSaWQqyxIJBKJ5LZBrqcnkUgkktuGf7/Lk05PIpFIJJVEe4WHzv8NSKf3L6ao6ObPPjOZqmfuU3XNohQJv1eL3q4hZd9wd3No5hp203XqnR2fW7sZWB1fgXlTCNN5Vo/iKuBWmPkonZ5EIpFIKoWcyCKRSCSS24ZbIdK7FWyQSCQSiaRSyEhPIpFIJJXiahaR/acinZ5EIpFIKsWt8JyeTG9KJBKJ5LZBRnoSiUQiqRT//jhPOj2JRCKRVJJbIb0pnd4tROjUYXi1b4Elv4C48XPJ//u0g4xr43rUfPcFNK7OZG8/QMIbC+3KA4b0IXTSYI7eOwhzehYerRtTa+FkCuPU1Z6zNvxG8rySRVhDpwzDs30U1vwC4l7+sEKdEe+OQ+PijHH7fhKm2+v0H9yX0EmDOdZiIOb0LLRe7kTMGY9TWCAanZaUxd/DN+UvbtukXXOenDoYrU7LtpWbWfPJKrvy6P4dGPjaU6QZ1FW2Ny5bz/aVmwkID2TcwlfRaLTonXRsWLqeLSscFrK/JhYtfJ8e3TuTlJxCs+adqqTOS7Ro14Lnpj2HVqdlw8oN/G/B/xxkHuz5IIPGDcJqtXL26FneGfMOANOXTadR80Yc3X+Uac9Mq7TOJu2a88TUZ9UFc1duZs0n39vr69+BAa89Sbqtjzct+4ntKzfjX9zHGnROOjYuXc/WFRuvyl7fl0fi9kArrPkFpE59h8Ljpxxkaox8Bo8eXdB6exH7QK/i7Z6P9sRL6Q0WM5bcfNLenEPR2YoXVw14bTju0a2w5uWT9Nr7FBxz1OVyV32CZo5H4+pC7s7fSZn5CQDOkXUJmjoajbsbpouJGF6ZjTUnF31YMHesXUTRuTgA8g8dh1c+L1f/PWX6eW05/fx4mX7esXIzAC9/+Tr1mjfkxP5jzHl25uW69Lq4Fe6H3XJOT1GU2sBaIUTjSso/DWwUQsTbvp/DtqDsjWrjjcCrfQtcaodxosNzuDWLJPzN4ZzuO95BLvzNEcRN/Ji8gzHUXjINz3YtMO44AIBTaACeDzan8GKS3T45+45yfsh0ADSakuW0PNtH4Vw7jJMdh+HWLJKwGSM40+8lB51hM0ZyceI88g7GUOuLK+v0f6IHBacucGHodHR+3jTY/Bm6Vb9iLrJfh0yj1fLMjGG8PWgaqYZU3lz9Dn9s/p2LJ+Ps5Pas/ZWlUxbZbUtPSmdq3wmYCk24uLvyzsa5HNj0OxlJ6Vfs6yuxbJlgwYIlLFky97rrKo1Wq2XEmyOYNGgSKQkpfLjmQ/Zs2kPsydhimbDaYSgjFMb3G48x04iPv09x2XeffYeLmwvdB3WvtE6NVstTM4Yya9AbpBlSmb76HQ5s3kd8OX28bMpiu20ZSelMK9XHszZ+yB+b9lW6j13vb4XTHeHE934K53vuxG/iWAxPjXaQy925h+xvfiTshy/ttuf8vBXjd2sBcItui+9Lw0kaNbFcXe7RLXGqFc6Fh5/BpUkjAqeOJu7xsQ5ygVPGkDTlQwoOHyf0szdxfzCK3F37CZr+AinvLiJ//xG8+nXF99n+pM1bBkBRbAKx/UaUqsXDod5L/Ty7VD//UU4/7y2nnwHWLfwBF1cXOgzqWq59VcWt8HD6reC4r5engat695KiKP+4iwWvLm1IX7UVgLyDMei8PdAH+trJ6AN90Xq6k3cwBoD0VVvx7tqmuDz09SEYZi2BSi4s7N25NRnfX1mnztOtWGfG91vx7lKiM2TyUBLL6LRaQevhBoDW3Q1zRjYWk+PitfWbNSDxXAJJsYmYi0zsXvMLLbpU7nVl5iITpkLViTo5O6HRVt2Pedcve0lLz6iy+i7RsFlD4s/FY7hgwFRkYueanbTt2tZO5uGBD7N22VqMmUYAMlMzi8sO/XqIPGPeVems16w+iecSSLb18Z5r7mP9Vfexe/v7MK7dBEDhkWNovTzRBfg5yBUeOYY5Jc1huzUnt/izxs31suPao2Nbsn9Uo6aCw8fRenk46NIF+KH1dKfg8HEAsn/cjEen+1T7akeQv/8IAHm//Yln1weuxtTr6meAo78eIS/n6o7ttaC9ir9/Kv+4k3cVoVcUZQVwL/A38CQwHugFuAG/Ac8BjwJRwApFUfKAS2eQ0Yqi9AKcgMeEEMcVRZkG1APqAhcURZkIfAEEAMnAM0KIC7ZIs7ztS4E8oDkQBDxra1dbYK8Q4mlFUXTA57Y2WYEvhBAfVMZgp2B/ihJKgtOihFScQvwxJZdcVTuF+GMqLWNIwSnYHwCvLq0pMqSSf+ycQ93u90ZSf/1HmBLTMLz9OQUn1RSRPqSMTkMq+jI69SH+FBlSHWQAvDrbdB4/a6cvbdla7lj4OpF7lqH1cCN2zGys5ZywfEP8SC2lPy0hlfrNGzrItezWhkat7iLhbDzLp39BWoLaHr9Qf15ZMpng2qH8d+aXVRLl3Uj8Q/xJiS+xNyUhhchmkXYy4XXCAXhv1XtotVpWfLCCA7ao+lrwDfEv7i9Q+7he8wYOcq26taVRq7swnE3gqzJ9PH7JJIJrh/L1VfaxLigAc2Jy8XdTUjK6wIByHVxFeCqP4D2oPxonPYnPvVyhnD4oAJOhlK7EFPTB/na69MH+mBJT7GWCAgAoPHUej05tydmyG8+HHkQfElgs5xQeQs3v5mMx5pL60Zew0368Q+X7uWW3tkTa+nlFqX6+WVRlnKcoysPAXEAHLBZCzKpA7lHgW6ClEGL/9er9Jzvk6yESWCCEuBPIAkYAHwshWtrSnm5ATyHEt8B+YJAQopkQ4tKlUooQ4l7gE1RneYm7gM5CiAHAPOBLIUQTYAXwkU2mou0AvqhObhywGvgAuBu4R1GUZkAzIFwI0VgIcQ+wpKxhiqIMUxRlv6Io133wL6FxdSFoxGMkfrDCoSzv79PEPDCYU93HkPrlGu74bHKV6QwcoZD04VcOZZ7R95J/7AwxbZ7kdM8xhE17HjdPt2vS88fm/Yy9/zkmPDyOI7sOMXxOScoqLSGVCQ+PY1z0cKIf7YB3gM9lavp3oNPrCKsdxqvKq8wePZsxs8fg4e2YTqtK/ty8jxfuf47XHn6Rv3Yd4rk5Y4rL0hJSee3hF3kpegQPVkMfG8Vq4ns/SfpHi/EZMuiG6UmaPAefx3sR8b+P0Xi4YbWl4k3JaZzr9B9iHx1JyuzPCH5nAq7XOJb/3LyPcfc/x6Ry+vnfhu0Cfz7QDfW8OkBRlLvKkfMCxgJ7q0r3rer0YoUQv9o+fwU8AHRQFGWvoihHgI6ozqYiLs2GOADULrV9dSnH2Bb4r+3zcpuOy20HWCOEsAJHgEQhxBEhhAU1Gq0NnAHqKooyz3YV5PD6eSHEQiFElBAiyu+J7tRfN5f66+ZiSk7DKTSgWM4p1D7CAluUVVomJICixFSca4XgHBFMg/UfEblrMU4hAdRf8yH6gBpYjHlYcvMByN5+AJ2PJ/V/+ph6az/ClJRurzPEH1MZnSaDGnGWlbmks/66eTTc+TlOIQHUs+n07d+ZrA27ASg8n0BhbCJh9SLKdgXphjT8S+n3C/UnrYx+Y0Z2cYpt28rN1Glc16GejKR0Yk9coFErh9/cP4pUQyoBYSX2BoQGkJpob29KQgp7N+3FbDKTGJvIxbMXCat97SsnpBtS8QstOX5+of7FEykuYcwwVqqP405cIPIKfeypPELo158S+vWnmJPT0AWXREz6oEDMydd2qz13wzbc299vt81nQC9qrlpAzVULMCWn2UVn+uAATGX61pSYij44wF4mSW1P0dlY4oe+RtxjozCu207RhQRVqKgIS2Y2AAVHT2GKjSe0juPxuNp+3r5yM7XL6ecbjRZNpf+uQCvglBDijBCiEFgJ9C5HbgYwG8ivOhtuTcrmwqzAAqC/LYJaBLheZv8C2/9m7FPAOdfZrkv1Wkp9vvRdL4RIB5oC24HnAcc71qVIW76eUz3GcqrHWLI27sG3X0cA3JpFYs7OtUszApiS07EYc3GzpcR8+3Uke9MeCmLOc6zlE8Q8OISYB4dQZEjhVK8XMKVkoA+oUby/W9MGWPLyOdVtFKd7jiFr025q9L2yTrMxr1hnjb4dydq8l4KY8xxv9R9ORA/mRPRgigwpnLbpLIxPxvM+dWkdXUANXOpGkHTB4GD/6UMnCakTSmDNIHROetr2eoADm/bZydQIKrnH2KJLSy6eUicG+IX44+TiDICHtweRUXeScPri5bq72jlx6ARhdcIIrhmM3klPdK9o9mzaYyeze8Nu7ml7DwDevt6E1wnHUE7fVZYzh07Z9XGbXg/wxxX6OP6U2o+l+9jd24OGlehjo1hNwoDnSRjwPHnbf8WzZxcAnO+5E4sx56pSm/qa4cWf3R5sTVGs/aSQzK/XENtvBLH9RpCz5Te8encGwKVJIyzZuQ66zClpWIy5uDRpBIBX787kbFUvznR+tghWo8H3+YFkCnUCjdbXB7TqaVYfEYJTrXCSLiQ6tLUy/exTqp/vLdXPNxPdVfyVzkrZ/oaVqiociC31Pc62rRhFUe4Fagoh1lWlDbfqPb07FEVpK4TYDQwEfgHuA1IURfH8f/bOMzyqogvAb3bTK+mVLkUUpBeFoNIRAflwULCggFKkioiIdAUFEUQ6iIJYBqRL76CCBKRI7y09QEhCssmW78fdbMqmbCAExPs+zz7ZO/fMnDtzJ3vuzD0zB+iMMkcMkAR43IWOP4BXUEZz3YA9haQXihDCDmxq2AAAIABJREFUD0iXUv4qhDiNMkq1iaQdEXg8V5fKO+dhStVxbViW5+Bjv03n3AvKtF7kJ7MJMy9ZSN51kKSdBb/v8Wr7DD7d2mIyGDCl6bhqdn8HSN4Rgcezdam8Y76yTGLYNMu5iuu+5nw7ZfolctQswr4YjMbZkaRdB0neWfDMbNyMnwmbPIjHNnwD2BH9+SKSbiZZyRkNRr4bNZ/hi0crbt5yG9fPXqXzkFe5cPQch7YeoFX3F6jToh4GvYHkxGTmDp0BQMhjYbw2sjsmkwk7Ozt+m7eKq6fzd2cvCj8smUnT8Eb4+flw6UIEY8dNYdF3PxeesRCMBiOzP5nNhCUT0Gg1bP5lM1fOXOG1Ia9x9thZ9m/Zz8FdB6kdXps52+ZgNBhZ+OlCkm4pbffF8i8oXbE0zm7OLN6/mGkfTOPQ7kOF6vx+1AKGLR6FRqthl7mN/zfkFS4ePc+hrQdo2b0ttVvUw6A3kpKYlKONu458E5MJ7Oxg/bzVXCtCG6fu3Y9L4/qErF6sLFkYM9lyLvinOUS92huAUgN74db6eeycnQjd8BPJqzaQOHcxHl064NygNuj1GG8nkzDqi/xUcWf3X7iG16PsxkUY03TEfvyl5VzpFbMs3pdx42cQ8NlQNE6OpOyJ4M5uxTC5t30Or67KcomULb+TtEJZmuFStzo+/d8AvR6T0Ujs2K9JMTsZ5W7nxaMW8IG5nXeb27mTuZ3/3nqAVt3bUqtFPYx6I8mJScwztzPAyGUTCK4YirObM9P3zWfBsJkc233Y5ra+H0gp5wHzChXMAyGEBpiK4mhYrNjl5SDwb8bsSLIR5V1dHeAE8DowAngViAbOAJellGPML0k/Q3EyaQScxLxkQQhRF5gipXzW7MiSLKWcYtZTFuWdW26HlfzSv0NZSrE897KKzHPAWXPezBH4R1LKDfnV9Vj5F0v85mVfslCSfGZweCB6/2tBZH3sCpoAuT986vtggshmpJV8EGaAsSn39x1rfiy5vOKe/VA+KtfV5h+AiZd+zFefEKIRMEZK2cp8/BGAlHKi+dgLOA9kPiEEATeA9vfqzPLIGb3/EqrRu/+oRu/+oxq9kqE4jN7HRTB6nxZs9OxRBh/NgOvAAaCrlPJ4PvI7gaGq96aKioqKSolhV4RPQUgp9cB7wCaU2TUppTwuhBgnhGh/ny4feHTf6amoqKioPMRIKdcD63OljcpH9tni0qsaPRUVFRUVm7A3/fu3IVONnoqKioqKTTwK78MehTqoqKioqKjYhDrSU1FRUVGxiX//5KZq9P7V2NtbRx6439g9oF5/NCXqgeh9UEsHNkcfeSB6n/ApW+I6ndwdS1wngCHjwUx0xRrvfzSE+8WjMDWoGj0VFRUVFZtQI6erqKioqPxn+PebPNXoqaioqKjYiDq9qaKioqLyn0HzCOxa+SgYbhUVFRUVFZtQR3oqKioqKjahvtNTUVFRUfnP8CgYjEehDipmAj95F/em9TCm6oj6cCppJ85byTg/8RjBnw9B4+xI8q4DxIyfaznn/fqLeHdrB0YjyTsPEPvFt2hLeRA6YwQu1Stza8VWYsfPzlFewMhsOodPRZeHTqcnHiN4UpbO2AmKzpBpw3EsrwRL1nq4Y0hK5lKH/jjXqEzQ+P5KZjs74mcsheV5B09+5rmGDJ8wGK1Ww69L17BwxpIc54eNG0j9Z+oodXdxxsfPm6crt6DeM7X5cNwgi1z5x8ryQe9P2L5hd2HNDECdpnV4d8y7aLQaNv28iWWzllnJNGnXhG6Du2Eymbh44iJfmAPwjls8jqq1qnIi4gRj3hpjkz5bmD/vS15o25zYuHhq1mpWbOU+/VwDPhw/CI1Wy8qla/n2m5xtPHTsAOo9UxsAFxdnvP28aVKlFQCHru/h7EmlT0Rfj2Hgmx8WSbfHgP44NWyISZdG4sRJ6M+ctZJx79kDl9atsHP3ILZ1G0u6JjAQr+HD0JQqhel2ErcmfIoxLs4mvb7D++LapB7GNB1xI6eQfvKclYx3/+54tG+BxtOdSw06WNKd61THd1hvHCtXIHbYZ6RssS2OdN1n69B7TG+0Wg0bftqIzKNPhbdrwmuDXwOTiQsnLzCp/xcEhAYwav4naDR22Nvbs/q7Nfz2w/o8NKjAf8Do5Q7YWlJ5Sxq3pnVxLBvK+eY9ca5ZhaBx73Gp82AruaCx/YgaOZ20w6cpvWAcbuF1SdkdgWuDGng0a8jF9v0wpevR+ngBYNSlEzdtCU6Vy+FUqay1znKhXGjRE+enqhA09j0uv5y3zuiR00k7cpqwbDojB02yyAQM74khKQUA3ZnLXOo0EAxGtP7elF8zE+3KjRgMORfjazQaRk4aSi8xgOjIWH7ZtIgdm/Zw4cwli8wXo7IiyHft8TKPV68MwIHfD9G52RsAeJbyZMO+Zfyxc79Nba3RaOg7oS8fd/uY+Kh4pq2dxr4t+7h69qpFJqRcCKKvYGinoSQnJuPl62U59+vcX3FycaJtt7Y26bOVxYsls2YtYtGi6YUL24hGo2HExKG8KwYSExXLjxsXsnNzzjaeMvpry/dXe3Sm6pOVLce6NB1dmne/K92ODRtgHxZGfNduOFSrhueQwdzo3ddKTvfHn9xZuRK/pUtzpHv27UPqps2kbdyEY+1aeLzTi8RPPytUr0uTejiUDeXqC2/hVKMqfiMHENltgJXcnV37uP3TGkr/tihHuj4qlrhPpuD1Zmeb66rRaOg3oR8fdR1BfFQ8M9ZNZ9+W/Vw5mxVpPqRcCF36dWFIp/dz9KkbsTcY3HEIGekZOLs6M3frHP7cso8bMTds1m/zdaqOLP9NzAEQHyo8mjckcdU2ANIOn0bj4Ya9v3cOGXt/bzTurqQdPg1A4qpteLRoCIB31xeIn7cMU7oeAMONRABMqTpSD57ApEu30unerCGJK806jyg6tbl0ajN1HjHrXLkN9+YNra+/TRNur9ul6EzTgcEIgMbJEfIJdFy9djWuXLzGtcuR6DP0bFi1hedbh+fbRm1fasH6FVus0lu++Bx7tu8jLVWXb97sVK5ZmchLkURfiUafoWf32t00atkoh0zrrq1Zt3gdyYlK4OfEhETLuSO/HyE1ufh35dizdz83bt4q1jKfrFWNqxevcf2K0sYbV23l2VZN8pVv3bEFG1Zat/Hd4Nz4GVI3bQIg48QJNO7uaHx9rOQyTpzAmGD9A68tV5b0Q4cASD/0N06Nn7FJr9tzT5O0RqmD7ugppV/7WevVHT2FId5arz4yhvQzF/Ptt3lRJVef2rlmF41a5vw/adO1NWu/X2vVp/QZejLSMwBwcHRAo7l/b96KK57eg+Sh+/G+T9gLIZYCtYHjwBvA48BUwB2IB7pLKaOEEHWAb835NmcWIIToDnQyy2uFEC+Z5SoAd4B3pJRHhRA++aSPAcqb08sAg4GGQBuUyMEvSikzhBCTgPaAHtgspRxqUwUD/ciIypq60UfHYx/ohz7uZg4ZfXS85TjDLAPgWD4E17pPEDDkTYy6dGInLSDtmPVUUnYcAv3QR2fTGROPQ6Afhmw6HXLpzJTJjkvdJ9HH3yLjcqQlzblGFYInDsIhJIDIYVOsRnkAAUH+REfGWo5jImOpXvuJPK81OCyI0DIh7N9rHXi5TccWLJ7zU4F1zY5vkC/xkVl1io+Kp0rNKjlkQs3TtlNWTEGj0bD0q6Uc3HXQZh0PCwHB/kRHxliOY6PiqF67Wp6yShsH89ferHo6Ojny46aFGPQGvp3xAzs22jZ9DKDx88cQm9W/DHFxaPz88zRweaE/dx7n8HDuLP8Vp/AmaNzcsPP0xHS74Ejt2gDfHP3aEBOPNsA3TwNXXPgG+REXmaUzPiqeqrVy9qmwCkqfmrpiChqtlh+++oGInUpb+wf7Me77cYSUC2bBpwvvyygPHo1R0qNQB1uoAsySUj4O3Ab6ATOAzlLKTCP3qVl2EdBfSpnXpou1zXmaAmOBv6WUNYARwGKzTH7pABWB51GM2g/ADilldSAVeEEI4Qu8BDxhzj8h9wUIId4RQkQIIax/ve8FrRatlweXOg8m9vOFhE7/qFiLLwjPdk1J+m1njrS0o6e5+EIfLnUehO+7Akene9ufsU3HFmxetwOj0Zgj3S/Al0pVK/L7jn33VH5utPZaQsqF8KH4kM/7f86Azwfg5ulWrDoeNlp3bM7WXG3cpm4nurbqwfA+Y/hg/EDCyoaW2PUkzZqNY82n8F0wH8eaTykGNNf9/zeh1WoJLR/KB+JDJr43iUGfD7T0qbioePq07MtbTXrQonNzSvmVui/XoCnC52HlYb624uSqlPJ38/cfgFbAk8AWIcRhYCQQJoQoBZSSUmY+ji7JVc4WKWXmI1TjzPNSyu2ArxDCs4B0gA1SygzgGKAFNprTjwHlgEQgDVgohOiEMlLMgZRynpSyrpSyrne3dpRfM4Pya2agj72BQ7C/Rc4+yA99THyOvPqYeOyDskZZDtlk9NHxJG3+A4C0o2fAZELr40luSnVrR7nVMyi3egb6uBvYB2XTGehHRi6dGbl0WsloNXi0fJrbv+U9Akg/fxVjShqVqlawOhcbHUdQSIDlODAkgNjovB0V2nRszoYVm63SW3doxrYNu9Drbd+8OyE6Ab+QrDr5BfuREJOQQyY+Kp79W/Zj0BuIuRrD9YvXCSkXYrOOh4XYqDiCQgItxwHB/sRE5d3GrTs0t5rajDWP8q9fiSTij0NUrV45r6wWXF/qiO/CBfguXIAxIQFtQFb/0vr7Y4y3zREFwJiQwK2Ro0jo2Yvk+QsBMCUn5ynr+cqLhC6bTeiy2Rhy9WttoB+G2IQ88xUXCdHx+Idk6fQL9iM+2rpP7duyz9Knrl24bplRyORGzA0unb7Mk/XvjxuCncn2z8PKf8Xo5b4FScBxKWVN86e6lLKlDeWk3ON16ACklEYgQ0qZeV1GwF5KqQfqA8uBdmQZxTy5uXQdF9v352L7/iRv/ROvjorHnnPNKhiTUnJMbQLo425iTL6Ds3kqzqtjM5K2KiOcpK37cG1YAwDHcqHYOdhjuGE9DXRr6ToudejPpQ79Sdr6J14vmXU+VQVjckqOqU0AQ6bOp8w6X2pG8rasUZXb07VIv3ANfTaj4RAWCFqla9qHBOBYIYzrV62jLPzz90nKVChNaJlg7B3sadOxBTs2WXvKlX+sLJ5enhyOOGZ1rs1LLVm/0toYFsSZI2cIKR9CYOlA7B3sCX8xnH1bco4U/9z0J9UbVQfA09uT0PKhRF+JLpKeh4Hjh09SpkKYpY1bd2zOrs17reTKPVYWj1IeHIn4x5Lm4eWBg6MDAKV8vKhZrwYXzlwsUN+dlatI6NGThB490e3Zi0srxQvUoVo1jCkpNk9tAth5eVnCgrh160rq+vw9Gm//vJbrL/fh+st9SNn+Bx7tWwDgVKOq0q/v49QmwOkjZwgtl9Wnnm3f1KpP/bH5T2qY/0c9vT0JqxBK1OUo/IL8cHRWZkLcvdx5ol41rl24dl+uUx3p/XsoI4TI9DToCuwD/DPThBAOQognpJS3gFtCiMZm2W4FlLkn87wQ4lkgXkp5u4D0QhFCuANeUsr1KO/8bI5rk7zzAOlXo6m4bSHBEwYSPWaW5Vz5NTMs36PHzCL404FU3LaQ9CtRpOxSZklvLd+MY+kgyv82i9BpHxI5bKolT8Udiwj8qBelOjWn4u7FOFYsDUDKzgNkXI2mwtaFBE0YSEw2neVWZ+mMMeussHUhGdl0Ani+EG5xYMnEpc4TlF8zk3KrZxA2cyQxY2dx60YiuTEYDHz20RTm/jydtXt/ZtOabZw/fZF+w3rlcLZo07EFG1ZbO1eElA4mKCSAiD/+LryBs2E0GJn9yWwmLJnA3O1z2bNuD1fOXOG1Ia/RoEUDAA7uOkjSzSTmbJvDpF8msfDThSTdSgLgi+VfMGL2CJ565ikW719M7fDaRdKfHz8smcne3WuoUrkily5E8Fb3V+65TIPBwMQRU5n901es2vMTm9ds5/zpi/Qd1pOmLRtb5Fp3bM6mVVtz5K1QqSw/bfoWue17Fvz6DYtmLMnh9VkYun370EdF4vfTUjyHDeX21GmWc74LF1i+u/d+F//ly7BzdsJ/+TLc3+oOgGPNmvgtXYLf0iVofHxIXvKDTXpT9/xFxrUoSq//Dv8xg4mfkNWXQ5dlLdnxGdyTMluXYufsRJmtS/Hu8zoATk9UpszWpbi1CMdv1EDCVs4rVKfRYGTmJ7P57IcJzN8xj93r9nD5zBXeeP91Gpr7VMTOg9y+dZt52+byhZzEfHOfKlOpNNPXTGP2pplMXvYFy+eu4NKpSzbVtag8CkbPzlQED6N/I+ZlBxuBCKAOcAJ4HagMfA14oTj0TJNSzs/myGJCcWRpK6V80uzIUldK+Z653PwcVgpyZEmWUk4x50+WUrqbv48BkoGlwGrAGcUBaoqU8vv86nayUtsSv3kPKp5e58T7+6SdH2WcrL32SoL/Ujy9TZUfTDy9OzcfjN4+t7QPRO+mqxvu+b93YdhrNv/m9Lj2Q4H6hBCtgekor3oWSCkn5To/BOiJ4tQXB7wtpbxc5IvOxSNv9B5lVKN3/1GN3v1HNXolQ3EYvUWhthu9t67nb/SEEFrgDNACuAYcAF6VUp7IJvMcsF9KeUcI0Qd4VkrZ5a4v3sx/ZcmCioqKiso9UozPvPWBc1LKCwBCiJ+BDigzcQBIKXdkk98HvFYcilWjp6KioqJiE0V5VyeEeAd4J1vSPCll5gvOUOBqtnPXgAYFFNcD2FAE9fmiGj0VFRUVFZsoyjZkZgNXuBdPIQghXgPqAk3vtSxQjZ6KioqKio0U4/TmdaB0tuMwc1oOhBDNgY+BplJK2/YJLATV6KmoqKio2ITGasnzXXMAqCSEKI9i7F5BWU5mQQhRC5gLtJZSxloXcXc8zMspVFRUVFQeQcwbcbwHbAJOKknyuBBinBCivVlsMspex8uEEIeFEGuKQ7e6ZOFfzD8V2pX4zdNqH8zehc3j73l5zl3xnGeVwoXuA8d0D2b3luM3Sr6dzz6e9wbW95uM9AezdOCDlAczwbb6yrp7np1cFtzN5t+cl6OWPpTBFtTpTRUVFRUVm3gorVgRUac3VVRUVFT+M6gjPRUVFRUVm9A8Aq/DVKOnoqKiomITj8LUoGr0VFRUVFRs4lF4p6caPRUVFRUVmyjGdXoPDNXoqaioqKjYRFG2IXtYUY3eI0rwqHdwf7YupjQd1z6YRtrx81Yyzk9WJGzyYOycHEneGUHUOGWbvICBXfHu0gq9OXBrzJTFJO+MsMoPEPjJu7g3rYcxVUfUh1NJO5GHniceI/jzIWicHUnedYCY8XMt57xffxHvbu3AaCR55wFiv/gWh9AAKmycS/pFJfpz6uHT0PeDPPU/26wx4yYOR6PV8tOSX5k5bUGO8yFhwUyf9RmeXh5otBomjv2K7VuU6OqPP1GZz6eOxt3DHaPJyAvPd0GnSy+saQGo0bQWr49+G41Ww86ft7J29soc55t0fo5XR7zBzWglJNKWxRvY+fNWfEP9GTzvQ+zs7NA6aNn83Xq2L7U9cvvTzzXgw/GD0Gi1rFy6lm+/WZLj/NCxA6j3jBKU1sXFGW8/b5pUUaKPH7q+h7MnlfsTfT2GgW9+aLPegpg/70teaNuc2Lh4atZqVixlZuLzYV9cG9fHlKYj7pPJpJ86ZyXj/d5buL/YHI2nB5cbtbeke77+PzxeagMGA4abicSPnoI+Kv+NPfxH9MEtvB6mNB3RI75Ed8Jal1O1xwia+D52Tk6k7D5A3GdKUFmnqhUIGNMfO0dHMBiIHfcNacfOoPF0J+jTwTiUDsGkSyd65FQ4bLXbFgC1mtam15h30Gg1bPl5M7/OWp7j/POdm9H947dJiE4AYP3369jys9J33vioO3WfrweA/Ppn9q7dU1Cz3jV26khPBSyBatdJKZ8sybz54f5sXRzLhXD2+XdwqVmFkPF9udDpfSu5kPH9uP7RDFIPn6bst2Nwb1qH5F0HAYj/dhUJC1Za5cmOW9O6OJYN5XzznjjXrELQuPe41HmwlVzQ2H5EjZxO2uHTlF4wDrfwuqTsjsC1QQ08mjXkYvt+mNL1aH28LHnSr0RxsX3/AvVrNBo+nfwxr77Ui6jIGNZv/4XNG3Zw9nSW4R34/rusXbWRxd/+QqUqFVkiZ9PwqZZotVq+njuJgb0/4sQ/p/H29iIjQ1+gvkzsNBreHN+LSd3GciM6gXFrvuDg1gNEnr2WQ27fut9ZPCqnEb4Ve5MxLw1Hn67HydWZSZuncWjLAW7F3ixUr0ajYcTEobwrBhITFcuPGxeyc/OeHNHIp4z+2vL91R6dqfpkZcuxLk1Hl+bdbapjUVi8WDJr1iIWLZperOW6NK6PQ5lQrr3YHafqj+M7cgBRrw2wkruzax+3f15N2NrvcqSnnzpHZNd+mNJ0eLzcDu/BvYgb9mmeutzC6+FYNoRLrd/G+amqBIx6j6uvDLKSCxzdn5hR00k7corQueNxbVKXO3si8Bvag4SZS7mzJwK38Hr4De3JtTeH4fPOK6SdvEBk//E4lA8j8JN+0OUTq3I1Gg3vTujD6G4jSYhKYMrar/hry36unr2aQ27v2j3MGzUnR1qd5+tS8cmKDGrdHwdHBz6VEzm4I4LU5NTCmvg/yaPgjKOSC8/mDbi1cjugjJK0nm7Y+3vnkLH390br7qKMooBbK7fj2aJhkfR4NG9I4qptAKQdPo3GI289GndX0sx6Eldtw8Osx7vrC8TPW4YpXTE2BvPI0lZq1anOpQtXuXL5GhkZGaxesZ5WbZ/LJWXC3cMdAE9Pd2KilSf9ps8/zcnjZzjxj3JdN28mYjTatttMxZqPEXMpirirMRgy9Oxbu5c6LerblNeQoUdvrq+Doz12GttdA56sVY2rF69x/Uok+gw9G1dt5dlWTfKVb92xBRtWbrG5/Ltlz9793Lh5q9jLdX2uEclrtwKgO3YSjYc7Wj/roL66YycxxFsHGU47cARTms4iYx/gn68ut+cbcXu1uS8fOYXW0x2tf05dWn8fpS8fOQXA7dXbcG/2tHLSBBp3VwA07m7oY5XRmONjZUjdfxiAjIvXsA8NxMuvlJX+SjUrE30pipgrMegz9OxZu5v6LW37fyxTqQzH9x/HaDCiS9Vx6eQlaj9bx6a8RUVThM/DijrSKz7shRBLgdrAceAN4HFgKsr+cfFAdylllBCiDvCtOZ9lbksI8QSwCHBE6Tf/k1KeLfKFBPmSERVvOc6ITsA+yBd93M2cMuZpkuwymfi+0Q7vTs+TeuwcUZ8uwHg7xVpPoB8ZUXGWY310PPaBfjn1BPqhj85+LYoMgGP5EFzrPkHAkDcx6tKJnbSAtGNKdR3Dgii/egaG5DvEfbUYNlpvjxUUHEjk9SjLcVRkDLXq1Mgh8+Wkmfy4Yj5v9+qKi5sLr3TsCUCFiuXAZGLp8nn4+nmzesUGZn/9LbbgHeTLjaistrsRlUDFWpWs5Oq3aUTV+tWIvhjFD+O+teTxCfZl6KKPCSwXzE+ffW/TKA8gINif6MgYy3FsVBzVa+e9hVdwWBChZYL5a+9BS5qjkyM/blqIQW/g2xk/sGPjbpv0PijsA/zQx2RNRxpi4tEG+OVp4ArD46U2pP7+V/66An3JiM7el+OwD/DFEJelyz7Al4yYrL6sj4nDPlD5n4mbOIfQ+Z/i/0Ev7DR2XOk6BADdqQu4t3iG1IPHca5eGYeQQPyCfUmMz/mQ4BvkS3xklv6EqHgq17TeAq9R26d5osETRF6MZOHY+cRHxXPxxEVeGfwqq+atxMnFiepP17AaIRYXD7Mxs5VHoQ4PC1WAWVLKx4HbQD9gBtBZSplp5DLnVhYB/aWUT+UqozcwXUpZEyV+1LVc5xFCvCOEiBBC5P2SrRhIWLqeM8/24twLA8iIvUHwxz3vjyKtFq2XB5c6Dyb284WETv8IAH3cDc41fZOLHfoT+9l8QqcOw93D7a5UdPzfCyz7cRV1n2zGG6IPX8+ZpLxPs9dSr2Ft3ntnGB3bvE6bF5rROLygGJZF4++tBxj0zLuMaD2Ef/Yc4d2pWdNyN6ISGNF6CO+H96XJ/57D08+rgJLujtYdm7N13Y4co9c2dTvRtVUPhvcZwwfjBxJWNrTY9T6MuL3QDMdqlbn13bL7psPrlXbETZrLxedfJ3bSXAInKNP8N+dLNB7ulFkxk1KvdUB38jxGw93tX3tg61/0evptBrbqz+E9fzNwqqLj8J6/Obg9gs9XTmboNx9w+uApjEZDsdUtO3aYbP48rKgjveLjqpTyd/P3H4ARwJPAFiEEgBaIEkKUAkpJKTMfs5cAbczf/wQ+FkKEASvyGuVlD8yYfcNpn9dfwLuL4rCQevQsDsF+ljwOQb7os43qAPTRCThkG9lllzFkewq9+fMmyi4YbTn27taOUjn0+JP55sA+yA99tidhAH1MPPZB2a8lS0YfHU/S5j8ASDt6BkwmtD6eGG7cxpCepKQfP0f6lSgqVCzH0cPHc5QdHRVDSGiw5Tg4JJDoqJgcMq+81onXXn4XgIMHjuDk7IiPrzdRkTHs/+MgN28odd2+ZQ9PPlWNvbv3Uxg3oxPwCc5qO59gX4vDSibJt5It33f8vJVXPnrdqpxbsTe5duYKVepX48D6PwvVGxsVR1BIoOU4INifmGwj7ey07tCczz6akjO/ecR9/UokEX8comr1yly7nLdTxYPCo0t7PDq1BSD9+GnsAwPQodx3baAfhtj4grJb4dygFqV6diWqx/uQkZHjnFfXF/Hq3BoA3T9ncAjyJ818zj7I3zJFmYk+NgGHwKy+bB/ojz5GkfHs2Nzi1JK8cQ+B45X3gcaUO8R8PNWSp/zW74m+Yr2ZeEJ0An4hWdOvvsF+JMTk1J90K8nyfctPm3nzo7csx8vBTh6hAAAgAElEQVS+kSz7RgIw5OuhRF6ILKBV7p5HYZT0KNThYSH3o00ScFxKWdP8qS6lbFlQAVLKH4H2QCqwXgjxvK3Kbyz5jfPtBnC+3QBub/mTUi8pWV1qVsGQdCfHlCOAPu4mhuRUXMxTKKVeep7bW5Uf/Ozv5TxbNSLtTNbU4s2l67jYvj8X2/cneeufeHVUvPWca1bBmJSSpx5j8h2czXq8OjYjaes+pYG27sO1oTId6VguFDsHeww3bqP18QSN0jUdSgfhWDaEK5esBr0cPvQP5SuWoXSZUBwcHOjQqS2bN+zIIXP9ehSNw5V3I49VroCTkxMJ8TfYte13qlarhLOLM1qtlobP1M3hAFMQF46cI6h8MP6lA9A62NPwxcYc2nIgh0ypgKw2rNOiHpHnFOPiE+SLg5MjAK6eblSu+zhR520zPMcPn6RMhTBCywRj72BP647N2bV5r5VcucfK4lHKgyMR/1jSPLw8cHB0UK7Nx4ua9Wpw4cxFm/SWJEm/rCGyS28iu/QmZcfvuL/YHACn6o9jSk4p0tSmY9WK+H0yiJiBozDesH7nmPjjWq506seVTv1I3vYnnh3MffmpqhiTUnJMbQIY4m4offmpqgB4dmhGynblYUUfm4BLPaUvuzSsScZlxehoPNzAQRlbeL3cmtSIY3k6mJw9cobg8iEElA7E3sGeJi+G89eWnA9g3tn6VP0WDbh2TpnC1Gg0eJTyAKBs1XKUe7w8f+8+ZHM7FQV1pKeSnTJCiEZSyj9RgiHuA3plpgkhHIDK5phRt4QQjaWUe4FumQUIISoAF6SUXwshygA1gO1FvZDkHRF4PFuXyjvmY0zTcW3YNMu5iuu+5nw7ZaotctQswr4YjMbZkaRdBy3LEoKGv4VztQpgMpF+LZbIj7/JW8/OA7g1rUfFbQuVJQvDv7KcK79mhsX7MnrMLII/H4zG2YnkXRGk7FL03Fq+mZCJgyj/2yzI0BM5THkidq1XHf+Br2HS68FoInr0N9y6Ze3kYjAYGDnsU378dR4arYZflq7kzKnzDP3oPY4cPs6WDTsYN3Iyk6ePpVffNzCZTAzu9zEAiYm3mTfre9Zv+wUTJrZv2cO2zba94zIajHw/agHDFo9Co9WwS27j+tmr/G/IK1w8ep5DWw/Qsntbareoh0FvJCUxiblDZwAQ8lgYXUe+ickEdnawft5qrp2+YpNeg8HAxBFTmf3TV2i0Wlb9tI7zpy/Sd1hPjh8+ZTGArTs2Z9OqrTnyVqhUlk8mf4jRaESj0bBoxpIcXp/3wg9LZtI0vBF+fj5cuhDB2HFTWPTdz/dcbuqev3Bt3ICwdd8rSxZGZY1cQ36ZQ2SX3gB4D+qJe9vnsXN2ovTmH0lasYFbc5bgM/gdNK4uBExWvCX10bHEDhyVp66UXX/hFl6Pcpu+NS9ZyBqdlVkxkyud+gEQM+4b85IFR+7siSBlt/KwEzNqOgEjemOn1WLUpRMzSvFkdaxYhqCJ74MJdOcuEzPyK2vlKH1q3idzGLNkHBqthm2/bOHqmSt0HdKNc8fO8teWv2j3Vnvqt6iPQW8k+VYS099X/q+1Dlom/vo5AHeS7vDVwCl3PYVaGI/C4nQ1nl4xYF52sBGIAOoAJ4DXgcrA14AXygPGNCnl/GyOLCYUR5a2UsonhRDDzfkygGigq5Qy30dbNZ7e/UeNp3f/UePplQzFEU9vZ+DLNv/mPBuz7KHctUw1ev9iVKN3/1GN3v1HNXolQ3EYvT1BnW3+zWkSvfyhNHrq9KaKioqKik08Ck4gj0IdVFRUVFRUbEId6amoqKio2ISd3b//dZhq9FRUVFRUbEJTjEZPCNEamI6yhnmBlHJSrvNOwGIU58AEoIuU8tK96lWnN1VUVFRUbMKuCJ+CEEJogZkoG3NUA14VQuT2aOoB3JRSPgZ8BXxeHHVQjZ6KioqKik1o7Ew2fwqhPnBOSnlBSpkO/Ax0yCXTAfje/H050EwIcc8eoer05r8YZ+eMwoWKmdtJziWuE2B3aNgD0WvvePuB6HVyd3wgelMTS375QKWTJ0pcJ0DroJoPRO+CcsmFCz2kFOWdnhDiHeCdbEnzzNsoAoQC2XfFvgbk3vzWIiOl1AshEgFflM377xrV6KmoqKio2ERR3ull3yf4YUKd3lRRUVFRsQk7O9s/hXAdKJ3tOMyclqeMEMIeZWerBO4RdaSnoqKiolLSHAAqCSHKoxi3V1D2LM7OGuBNlOgznYHtUsp7dh9VR3oqKioqKjah1Rht/hSElFIPvAdsAk4qSfK4EGKcEKK9WWwh4CuEOAcMAYYXRx3UkZ6KioqKik3YMG1pM1LK9cD6XGmjsn1PA14uPo0K6khPRUVFReU/gzrSU1FRUVGxCXUbMpWHCr8RfXANr48pNY3YEV+iO3nOSsap2mMEfDYUO2cn7uz+i/jPZgPgWKUCAaP7Y+fqgv56DNHDPseUckc5V7k8AWMGYOfuhlFvImn/CTyb1sKUquPSkOmk/nPBSo9L9YqUmzoAO2cnbm8/yLXR8wHQlnKn/MwPcCwdQPrVWC72/QJDYgpaLzfKThmAU9kgjLp0Lg+dQVr24KoaDWG/zEAfm0B0v7wDgQL4ftQHtyb1MaalEfvxl6Tn0QY+A7rj0b45Gk93LtbvWKQ2zo73B/1waVwfU5qOhNFfkH7KWlepfm/h9kILNJ4eXG38oiXd/X/t8BAdwGjAeCeNGxOmknHRtmCyHgP649SwISZdGokTJ6E/c9ZKxr1nD1xat8LO3YPY1m0s6ZrAQLyGD0NTqhSm20ncmvApxri4QnX6fNgXV3Nd4z6ZnGddvd97C/cXm6Px9OByo/aWdM/X/4fHS23AYMBwM5H40VPQR8XaVNeCmD/vS15o25zYuHhq1mp2z+XlRe2mdXhnzDtotBo2/7yZ5bOWWck0bteYroO7YTKZuHjiIlMGTL5rfQ/i3haF4tyG7EGhTm8+IriG18OhbChXWr9F7Ojp+I/un6ec/6gBxI6axpXWb+FQNhTXJnUBCBg3iPip33K1Y2+St/2O99udlQxaDYGfDyN27Ayutn+HqK+X4VQ2iBNNenP5w5mU+axPnnrKfNaby8NmcqJJb5zKB+P5bG0Agvr+j6Tfj3IivA9Jvx8lsO//lPT3XubO8QucbDmQS4OmETamZ47yvF7rSPqFq1Z6crRBk3o4lgnlStu3iBszHf9P8m6DlJ37uPbKgALLKgznZ+rjUCaUyA5vkjDhK3w+Gpin3J3d+4h+4z3ra9i4naguvYh6tTe3v/8F7/fzbsfcODZsgH1YGPFdu3F78pd4Dhmcp5zujz9JeLe3Vbpn3z6kbtpMwls9SP7+ezze6VWoTpfGSl2vvdid+HHT8B2Zd9vd2bWPyG7WbZ5+6hyRXftx/eV3SdmyG+/Bheu0hcWLJS+061YsZeWFRqOhz4Q+jH5zNH2b9aFp+3BKVyqdQyakXAgv9xV80OkD+jXvy/yxd78s7UHc26JipzHZ/HlYua9GTwgRIoRYfj91/FsRQtQUQrTNdjxGCDH0bstze74RSau3AqA7egqNhxtaP58cMlo/HzTuruiOngIgafVW3Jo9DYBDuTDSIo4BkPrH37i3bAyA6zN1SD9zkfTTymjO4+nq3Fi+HYA7f59B6+mGfYB3Dj32Ad5o3V258/cZAG78ugOvVspmC14tG5Bgzp+wfDulWjUEwLlSaZL+UPTrzl/HqXQA9n5eyrUF+eIaXp+kXzcU2AauzzUiaU3BbZB5zhCfb0B6m3B99mmS120BIP3YSTQe7nnqSj92Mk9dmaNoADsXZ7AxmLNz42dI3bQJgIwTJ9C4u6PxtdabceIExgRrvdpyZUk/dEi5tkN/49T4mUJ1uj7XiOS15nYtoK66fOqaduAIpjSdRcY+wL9QnbawZ+9+bty8VSxl5UXlmpWJuhRJzJVo9Bl6dq/dTcOWDXPItOrait8WryMlUdllJTEh8a71PYh7W1SKcZ3eA6NIRk8IYSeEsDmPlDJSStm56JdV8pgXP5YkNYG2hUrZiH2AH/rorKkMfUw89oG+OWUCfdHHxOeUCfADIP3cZdyaNQLAvVUT7IOUHyaHsmGYTCZC5n1K2PJv8GjwBOmRWWWkR8XjGJRTj2OQL+lRCdlkEiwy9n5e6GNvKvpjb1oMW+rJi5Rqo+h3rVkJx9AAHIKVawsb05OEqQswFWIY7AMLb4PiQhvghyEmm67YOLT+fkUqw120J2T1YrwH9uLGFzNtyqPx88cQm6XXEBeHxs92I6I/dx7n8HAAnMKboHFzw87Ts8A89gF+6GOypiMNMfFoA4pW10w8XmpD6u9/3VXeksY3yJe4bH09Pioe31z9KaR8KKEVQvlixWSmrPqS2k3r3LW+B3Fvi8qjMNIr9IdeCFEOZS3FfpQQD1II0Q5wAlZKKUcLISYBV6WUM815xgDJKJuErpNSPmneVXsS8Kw570wp5VwhxExgk5RyjRBiJcqu2m8LId4GKgKfARJlxb4WGC+l/CWfa71klm0DpAJdpZTnhBD+wBygjFl0kJTyd/N1VgQqAFeAV/MoszvQEXADKgFTAEfgdUAHtJVS3hBC1DTrcAXOA29LKW8KIXaa2+45oBTKzuH7gXGAixCiMTDRrK6aWb4MME1K+XUe12PZz+6zvBrhLokdORX/EX3w7t2NlB1/YsrQA2Bnr8Wl9pNcFf0xpekou/1HXKtXJOXAyeJRbP7fiJ75K6XH9qLqxq9IPXWZO8cvgMGIZ7O66BNukX7iHM71ahSPzoeEZLmGZLkG19bP49WzGwmjv7jvOpNmzcZz8EBcWrcm/egR5UfWWPCaquLC7YVmOFarTNTb75eIvpJAa68lpFwIH4nh+AX7MWnZ57zXsh8pt1NK/FpK4t7+lxxZKqGsjPdEWRlfHyV6xBohRDjwCzANJVQEgABaoRipTHoAiVLKeuY4Sb8LITYDe4AmKKvvQ4Fgs3wTlJ23WwORUsoXAIQQXoVca6KUsroQ4g3zNbVDidn0lZRyrxCiDIoRf9wsXw1oLKVMLaDMJ4FagDNwDvhQSllLCPEVkKlnMdBfSrlLCDEOGA0MMue3l1LWN09njpZSNhdCjALqSinfM9drDFAVxTh6AKeFELOllDl2lc6+n13c+G9Mni8rL7LTjp2xjM7APOqJybljjz4mAftAv5wyscqTbMbFq0T2GgGAQ9lQ3MKV6Uh9dBz6hJuEfquEutJdica1ZmVLGY7BfqRH59STHp2AY7BvNhlfi4w+PhH7AG9llBfgjd48HWRMTuXy+1k2/ok/5qG7Eo33i43xalGfUs2+x87JEY2bKwGThhE7XDEQnq+8iGdnpQ10/xTeBveCu2iPx0vK4Fx3/AzawGy6AvwxxN3dPrh3Nu3A96OB+e6v5PpSR1zatQMg49QptAH+ZHYKrb8/xnjbnRWMCQncGqk4Atm5uOAc3hRTsvUGyB5d2uPRSalr+vHT2AcGoOO4ojPQD0Ns0erq3KAWpXp2JarH+5BR8hul3w0J0Qn4h2T9v/gF+5GQqz8lRMVz+u/TGPQGYq7GEHnxOiHlQjh71NoBJS8exL39r2PrVOVlKeU+oKX58zdwCOVHupKU8m8gwPwO7ymU0Vpur4OWwBtCiMMoIx1fFGO6B2hijqV0AogRQgQDjYA/gGNACyHE50KIJlLKwibNf8r2t5H5e3PgG7PuNYCnEMLdfG5NIQYPYIeUMklKGQckAmvN6ceAcmZDXEpKucuc/j0Qni3/CvPfg0C5AvT8JqXUSSnjgVggsKCLSvxpLVc79eVqp76kbPsDjw7NAXCqURVj0h2r9yuG+BsYk+/gVKMqAB4dmpOy/U8AtD7mZwk7O7x7dyVRrgPgzu8HsXOw51rXwVx9+T3sNBqcK4YC4FqrMoakFMt0ZSb62JsYku/gWksxjj7/e47EzcqUVuKWv/Dt/DwAvp2fJ3HzfkW/pxt2DsozmO+rLUjefwJjciqRny/hn/o9uNLqTWI+mEjqX0csBg/g9s9ruda5L9c69yVl+x94tM/WBsnWbXAvJMs1RL3am6hXe5O683fc27UAwLH64xiTU4qky750qOW7S5MGZFy9lq/snZWrSOjRk4QePdHt2YtLq1YAOFSrhjElJc/3O/lh5+VleeHi1q0rqevX5ymX9MsaIrv0JrJLb1J2/I77i+Z2rf44piLW1bFqRfw+GUTMwFEYb9y/d3DFzZkjZwgpH0pg6UDsHewJfzGc/Vv255D5c9M+qjeqDoCntych5UOJvhJts44HcW/vhWIMLfTAsHWklzlWtwMmSinn5iGzDGUUGIQy8suNHcpIaFPuE0KIUigjut2AD8pIMVlKmQQkCSFqo7z/miCE2CalHFfAtZry+K4BGppX+GfXm71uBaHL9t2Y7diIbW2YKW8oRD67nsJkc3Bn91+4htej7MZFGNN0xH78peVc6RWzuNqpLwBx42cQ8NlQNE6OpOyJ4M7uAwC4t30Or66KS33Klt9JWrEZAOPtZG59v4IwOQNMJm5tPYTG1Zkn9s7BmKrj8vszLHqqbvyKU60Vj7OrH8+l7NQBaJwdSdxxiNs7DgLKNGb52R/g+0pz0q/FcbGvYsCcHwuj7FcDwQRpZ65w+YOscovUBk3qUWbDIoypOuI+yWqDsOWzuNZZaQOfIT3waPscds5OlN36A7dXbOTmrB+KpCt1735cGtcnZPViZcnCmCw39eCf5hD1quJdV2pgL9xaP4+dsxOhG34iedUGEucuxqNLB5wb1Aa9HuPtZBJG2Ta1qdu3D8dGDfD7aSkmnY7EiVlxNX0XLiChh+L16t77XVyaN8fO2Qn/5ctI/e03khd9h2PNmni82wtMJtKPHOX2V9MKr+uev3Bt3ICwdd8rSxZGTbGcC/llDpFdlLp6D+qJe1ulrqU3/0jSig3cmrMEn8HvoHF1IWDyJwDoo2OJHZj/shNb+WHJTJqGN8LPz4dLFyIYO24Ki777+Z7LzcRoMDLnk9mMWzIejVbDll+2cOXMFboNeY2zx87y15b9HNp1kNrhtZi1bTZGg5FFn35L0q2ku9L3IO5tUbF7BPz9i+q8sQkYL4RYKqVMFkKEAhlSylgUQzcf8AOa5pO3jxBiu5QyQwhRGbgupUwB9qFMBT6PMgJcbv4ghAgBbkgpfxBC3AJ65lF2drqgvDvsgrJRKcBmoD8w2VxmTSnl4SLWPV+klIlCiJvmkegelPd9uwrJloQyjVlsxE+YmWegqUyDB6A7fparHd61kkn8YRWJP6zKs9zktdtJXqt4XBYUTy/T4AHcOXqOk82tXdsNt5I496r1D17KodOcaNrXKj07aQeOEn3gaIEy8Z/m7RCSafAAbkxdyI2pCwssxxZuTMrbMGcaPIBb0+dza/p8K5mbU2bdtd6kr6aT189q5o8iQPKcuSTPsX421e3ahW5XYV0zj7In5l3XTIMHcHPaAm5OW2AlE/3uh0XWZwuvvd7vvpSbnYgdEUTsiMiRtnRqzgekBeMXwHjret8ND+LeFoX/0js9AKSUm4UQjwN/mkdJycBrQKx5s1APFEMWlUf2BShTe4fM0W/jUBxEQJnibGl2OrmMMtrbYz5XHZgshDACGUBhC5q8hRBHUUZNmY4pA4CZ5nR7lBGl9UKXe+NNYI4QwhW4ALxViPwOYLh5ynViIbIqKioqD5yH2SvTVuwKcwP/N2H23qxrfif2yHOuWqsSv3kPKnK6l1da4UL3AXtHwwPR6+SufyB6UxMdSlznfy9yesl7dgIE7d55z6vnLtVsYfNvTrnDWx7K1XrqNmQqKioqKjbxKIz0/pVGz7yer3yu5A+llOXuocxWwOe5ki9KKV+62zJVVFRUVB4u/pVG734YIrNXqZVnqYqKioqKgkarjvRUVFRUVP4jPArTm4/AqgsVFRUVFRXbUEd6/2JC3gwpcZ0+f1jHzisJYk+6PRC9ppLZltIKQ8aDeR7V60te74PyotwYXWxLdYtEmk+1B6K3OPgvLk5XUVFRUfmPUlKL04UQPigbnpQDLgFCSnkzl0xNYDbKntAG4NP8ghFk5xGw2yoqKioqJYGdxvbPPTIc2CalrARsMx/n5g7whpTyCZRtLKeZt7QsENXoqaioqKjYRAnG0+uAsnE/5r8dcwtIKc9IKc+av0eibNJfaABCdXpTRUVFRcUmSvCdXmC27SyjKSTijBCiPkqc0/OFFawaPRUVFRUV2yjCO73sAa/NzDPHA808vxUlKk9uPs5+IKU0CSHyVWwORbcEeFNKWajrmWr0VFRUVFSKnewBr/M53zy/c0KIGCFEsJQyymzUYvOR8wR+Az42x3wtFPWdnoqKioqKTZSgI8salMg1mP+uzi0ghHAEVgKLpZTLbS1YHek9ojg064q2Yg3ISEe3fiGmmMtWMk6vfoidmxfoMwBIk1PgThKasMo4NuuKXUAY6WvmYDgdYZU3L1x7DcChTgNMOh0p0ydiuHA2p4CjE+4fjkUbFILJaCTjwB+kLs56EHR85jlcXu2OyWTCcPE8KVPH56srYOS7uDethzFVR9TwqehOWE/lOz3xGMGThqBxdiR51wFiJygxyEKmDcexvBK5XOvhjiEpmUsd+uNcozJB4/srme3siJ+xlLQde3OU6TeiD67h9TGlphE74kt0J89Z6632GAGfDcXO2Yk7u/8i/rPZSv2qVCBgdH/sXF3QX48hetjnmFLuYB8SSJl188m4pERPTztyioQJ0/Otu+/wvrg2qYcxTUfcyCmk53EN3v2749G+BRpPdy416GBJd65THd9hvXGsXIHYYZ+RsmWPVd5M/Ef0wS28HqY0HdEjvkR3Iu+6Bk18HzsnJ1J2HyDOXFenqhUIGNMfO0dHMBiIHfcNacfOoPF0J+jTwTiUDsGkSyd65FQ4nH/U+ExqN63DO2PeQaPVsPnnzSyftcxKpnG7xnQd3A2TycTFExeZMmByHiXdG/PnfckLbZsTGxdPzVrNir18n2F9cWlcH1OajvhRk0k/Zd3mpd57C/d2zdF4enDl6faWdM/X/of7S23AYMBwM5H4MVMwROU5QLprSvCd3iRACiF6AJdRAosjhKgL9JZS9jSnhQO+Qoju5nzdC4uVqhq9RxBNhRpofAJJmzccTUgFHFu+jm7JhDxl09fNwxh9KUea6XYCuvULcKjf2madDnUaoAkOI7F3N7SVq+HWZwi3P7AOfZi26hf0x/4Ge3s8xn2FQ+0GZBzajyY4FOfO3bj9YT9MKcnYeeXveezWtC6O5UK50KInzk9VIWjse1x+ebCVXNDYfkSPnE7akdOELRiHW3hdUnZHEDlokkUmYHhPDElKqBfdmctc6jQQDEa0/t6UXzOTS8/+AQblNYFreD0cyoZypfVbONWoiv/o/lx7ZaCVXv9RA4gdNQ3d0VMEz52Aa5O63NkTQcC4QcRPnk9axDE8OrXE++3O3JixGICMq1E5gv1qtHnX3aWJcg1XX1CuwW/kACK7WQfrvbNrH7d/WkPp3xblSNdHxRL3yRS83uycb/sCuIXXw7FsCJdav43zU1UJGPUeV18ZZCUXOLo/MaOmk3bkFKFzx1vq6je0Bwkzl3JnTwRu4fXwG9qTa28Ow+edV0g7eYHI/uNxKB9G4Cf9oMvIAq9Fo9HQZ0IfRnYbSUJUPF+t/Yr9W/Zx9exVi0xIuRBe7iv4oNMHpCQm4+XrVWCZd8vixZJZsxaxaFH+DyV3i0vj+tiXCeV6++44VX8c348HEPW69b1N3bWPpJ9XE7rmuxzp6afOEdWtH6Y0HR4vt8NnUC/iPvy0WK/RroQshpQyAbB6qpBSRmAOJC6l/AH4IbdMYTyw6U0hxE6z1S4pfZOFEMeFEMX/+KeUP04Ike8cdUmirVQL/T9/AGCMvICdkyu42f4jYLqdgCnuGhQh1qJD/cak71D26zacOYGdmzt23j45hdJ1isED0OsxXDiDxlfxMHZq+SK69SsxpSQr15B4K19d7s0akrhyGwBpR06j8XBD6++dQ0br743G3ZW0I6cBSFy5DffmDa3K8mjThNvrlGjTpjSdxcBpnByt6u/2fCOSVm8FQHf0lKLXL2cdtX4+aNxd0R09BUDS6q24NXtaaaNyYaRFHAMg9Y+/cW/ZON865ofbc0+TtGZLgdeQec4Qf8MqXR8ZQ/qZi4XeW7fnG3F7dWYbn0Lr6Y7WP1dd/X3MbazU9fbqbbib64oJNO6uAGjc3dDHJgDg+FgZUvcrD+IZF69hHxpIKb+Cl1ZVrlmZqEuRxFyJRp+hZ/fa3TRsmfNeturait8WryMlUek/iQmJBZZ5t+zZu58bN/Pvm/eC67ONSFln7l/HTqLxcM/73h47mee9TYs4ovRhQHf0JNrAQr33/5P8K9/pCSHu5nnjHaCGlPKD+1G+lHKUlHLrXVxXsaNxL4XpdtY/hSnpJhoP7zxlHdv2wLn7WOyffvHedPr6YYzPmkoxxsdZDFpe2Lm541DvaTKOHgRAGxKGJqQ0HpO+wfOLWTjUqp9vXodAP/TRcZZjfUw8DoF+ecjEFyjjUvdJ9PG3yLgcaUlzrlGF8r/NpvzaWUSP/sZiBAHsA6z12gf65ijTPtAXfUxOvfYBit70c5dxa9YIAPdWTbAPymofh9AgSv86k9DvJ+Nc58l8664N8M1xDYaYeLQBvvnK3y32gb5kZK9rdBz2ufTYB/iSkaOucZb2iJs4B/+hPSm/fQn+w3oS/5Uy4tSduoB7i2cAcK5eGYeQQHyDc96X3PgG+RIXmaUnPioe31ztHlI+lNAKoXyxYjJTVn1J7aZ17qLWDxZtgB/66Kz/IX1MPNqAgtsmP9xfakPq3r+K69IslOA7vftGoT/uQohywAZgL/A0cB1l4eAGYKiUMkII4QdESCnLmedWOwJuQCVgCsr6idf/3955h0dRbn/8k00hARJa6CggCl5EBVTwrDQAACAASURBVAEBL2DFig3l4AW7gAgqxfKzgr1fvYpYUcByhYOKCqIIKlVQQUGuKIh0CCWhJQFCks3vj3c22RRCgJ1Zsnk/zzNPdmbeme87y7Jnz/ue9xwgC7hYVQPfyNeJyGinHzer6k8iUgUYCbQCYoFHVPVz5749gKpANHBmCX2NAp4DLgLygCdUdYKIfOFct0hEni4pVY2IjAX2AW2AeSIyChiFWey4B+gHpAC/AU1V1e/09U/gOOBtYIqqfiwibYEXHc1U4EZMmpyvVLWtiJwKLAYaq+o6EfkbOBm4BBjhtN2lql0P9u9zJOyf/CZ5GTshLp5KVwwi76QzyP39BzclDb5oqtw1nH1TPsG/xVmKEx1NdINGpD84GF+t2iQ+PZLdd96U7/m5QVL3M0n/cmahY/t+W87qS24jrtkx1H92GJvm/Uje/uyQ6G196EVqP3AbNQb0IfP7+eRlm+roOdu2s+bca/HvSjdzZCMfYcOV/cjL3BMS3XBQ7ZrubHvmTTKmz6PqhV2o+8RQNt58PzveVmo/MIBjPx3F/r/WkPXH3/hzjzzBaXRMNA2aNOB+uY/k+sk8M/FZbj9/EJm7w1OlPJxUufhcKrVsTsotd4X+5kexMSsrZX2EE4BRTrqXncBVB2nfCmOg2gNPAntUtQ0wH7g+qF1lVW0NDATedY49CHynqqcDZwPPO8YF4DTgalUtZvAcegCtgVOB85xr66vqZcBeVW19kNxsjYAzVHUYJtT2DlVtC9wNvKaquzDGKqDfHZimqvnfiiISizHaVzvXvovJCbcViHdCbLsAC4EuItIY2Kqqe4DhwAWqeipQMEMdhIj0F5GFIlIouiSmzTnE3/go8Tc+Sl7mLqKSCoZFohJr4E/fUexeeRnOMM3+feQsW4Cv/nGlvDXFqXTxFSS9NJqkl0bj37EdX3Kd/HO+5Nr407aVeF2VQXfjT9lA1uSCgCt/2jb2/zQPcnPxb92Mf+N6fPUb5Z+v3qc7TT4fSZPPR5KzbXshLymmbnIhjwMge0sqMfWSD9wm2kfi+Wew+8vZJfZx/9/r8Wfuo+bt13PMp69xzKevlaibsyWt0HU5W9KIqVtYN2er0c1evZ5N/R5gQ8/byfhyJtnrHIOfnY1/VzoAWctWkrN+E7GNG+bfI+maS2k48XUaTnyd3CJ9iK6bTO7Wwn04XJKuuZRjPx3FsZ+OInfbdmKDn7Ve7fwhyvxn3ZpWyHuOqVs7//1IuuI8MqbPAyDj6znEn9wcAH/mHrY8+CLregxi8/89T3TNamwOvA8HIG1zGrUbFOgk108mrcj7npaSyo/TfyQ3J5ct67ewafVGGjTxPiH7oZLY6zIaTHiDBhPeIDd1OzH1Cv4PxdRNJndrailXFye+Qxuq9e3NlsHDITs0P9aCiQRPr6xdWx0UEbMIkwS0NL5X1XRV3QbsAiY7x5cWufYjAFWdDSQ5edPOB+4TkcXATCAeONZpPz3ISyyJzsBHqpqrqluAWRjDW1YmqmquiFTFeLUTnX68CdR32kwAejmvr3H2g2mBMfrTnWsfwhhTgB+Af2Iijp5y/nYBAiF084CxItIP480WQ1XfUtV2qlpoPjTn1+/YN3YE+8aOIGfFL8S0MnMrvgbHkZe1FzKLzHFE+SChqnntiyb6+FPJSz14FF0wWVM/Y/fQvuwe2pfsBXOIO/sCAKKbtyQvM5O8HcX/qRL63EJU5SrsGT2y0PHsBXOJbWWy7UclVsPX8Bj8WwqGHXd+OIU1l9/BmsvvIH3GfKpdaea4409tgT8jk9xthY167rYd+DP2EH9qCwCqXXkuGd8WLOOpckYb9q/aUMhoxTaqC9Hmv0RMgzrEHdeIHe9OZH2PgazvMZDMb38g8XIzbVvplBPxp+8pNreSm7odf8YeKp1yIgCJl59H5nfzzftS05lXjYqixoDe7NIpAPhqVAOfo9uoHrGNG5KzYXP+PXePn8zGnrexsedtZH73A4mXdSvoQ0ZmifM7h8Pu8ZNZ12MQ63oMIuPb+SRdHniPT8SfnknutiLPum278x6bZ026/Nz8Z83ZmkZC+1MASOjYOn8I2ZdYBWLNAFO1nheyd+FS9mbsLbVfK5asoEHThtQ9pi4xsTF0vbQrP07/sVCb+dMWcHKnk00/aiTRoGlDNq/bXNLtjirSJ3zBpl4D2NRrAHu+n0eV7s7n6+R/HPK/bVyLZtR6aAhbhwzH79K8I75D2I5Syjp3lRX0OhdIAHIoeLT4Utr7g/b9RTSLzqbnAVHAVaq6PPiEiHQA3B6rCNzfB+x0vNCifAE85WQBbwt8V+R8FPC7qnYq4drZGCPXGLPu5P8wz/wlgKoOcJ7zEsxQbFsniumQ8K/6DX+zU4jv/yzk7Gf/1Hfyz8Xf+Cj7xo6AmBji5S4TJujzkbtmGTlLTECHr15T4nrcTlSlKkQf35q8zlew753SI+yyFy0gtl1Hqr3xX7NkYWRBhGTSS6PZPbQvUbVqkyDXk7t+LUkvvg1A1tRJZE3/kuxffyK2TXuqvTqOvFw/e8e+Tl767hK1Mmf+TNUz23PcjHfw781i8/0v5Z9r8vlI1lxulh1seeQ16j8zlKj4SmTOXkjmrALnOOmSrvkBLAES2p5Eo/49ycvJAX8eWx59Df/Ogj7smf0Tlbu2p/HXY/Dvy2Lrg//OP3fMp6/lR19ue3wkdZ66G1+lODLnLGTP7J8BqHrx2VTrbeZOM6fPI/3Tb4xuu5Opecf1kJNDnt/P1kdfwb87vcRn3zvnJyp3PZ1jpo4lb18WWx96If9cw4mvs7GniZitObQvVS85m6j4Shw740PSP/maHa+/T6WTmlP35RH4EhOpfGZHagy8jg1X9i+mkznrJ6p0bU+Tae86SxZezD937KejWNdjkHmPH3vVWbIQx545C8l0nnXL8Jep88AAoqKj8WftZ8twE+0Y1+xY6j19F+RB1sq1bHnopWLaRfHn+nnj4dd57P3H8UX7mD5hOutWrKPPsGv5a+lf/DT9R36ZtYjTurbhtW9fx5/rZ8yT75K+s+T38Ej44P1RnNm1E8nJNVmzaiGPPvYCY8aOD8m99875iYTOHWg4eZxZsjCi4N+2wYQ32NRrAAA1hvSlykXnEBVfiUbT/kvGpK/Y+cb71BjaH1/lBOo8/zBgInW3Dhkekr4FiPJFhfR+4SAq7yBRXM6c3hRVbeXs342Zq2oELFLV10VkCDAkaE6vnare7rRf4+ynBp8TkZnAn84XfWfgdVU9WUSewpSKuMNJP9NGVX8tet8D9LUHcCtwMVATM4TYQVU3i0iGqlYt5dqxznN+7Oz/ALykqhOducJTVHWJc24iZv4vXVUHBl+PMYrLgOtUdb4z3NlcVX933svZwGxVvVZEpmK8wlNVdYeINFPVv537/Qz0K23NyZ5nb/K8jPG+ClZPLyYmNyy6vujwVKgORz29YZkHWJ/hMuGqp7eyZXjq6TVZPP2ILdaOnmeV+YNZY+LMo9JCHskn/AXgNhH5FTi8ECPY51z/BnCLc+xxTADLbyLyu7NfViZhAk2WYDywe1X1cMc4+gC3iMgS4HdM8E6ACcC1FB/aRFX3A1cDzzrXLsYMlaKqazCeYGAiaS7GowyMzT0vIktF5H+YodAlh9l3i8VisZTAQT09y9GL9fTcx3p67mM9PW8Ihae3s9fZZf5gVp/w/VHp6dmMLBaLxWIpG0dxgEpZKZdGT0ROxpSSCCZLVTuU4doHgZ5FDk9U1dDm67FYLJZIIwICWcql0VPVpZj1eIdz7ZOYtYMWi8ViOQQiIXqzXBo9i8VisYQBO7xpsVgslgqD9fQs4eTvNw4tRVEoiIpK9FwTYPD+/WHRbRB9wKWdrrLVX3qWEreIj/I+knJ0E/fyq5bGvprhiaI8ftmysOjmhEX16MMaPYvFYrGUCTunZ7FYLJaKQ0z5n9Qr/09gsVgsFksZsZ6exWKxWMpEVJQd3rRYLBZLRcHO6VksFoulwuCR0XNKt03A1F9dA0hQYv6ibZMwlW0+K60KTwA7p2exWCyWsuHzlX07Mu4DvlXVE4Bvnf0D8TgFlWsOijV6FovFYikbvqiyb0fG5cA45/U44IqSGolIW6Au8E1Zb2yHNyOI+iP6k3hWW/z7sthw98vs+/3vYm3iWzXjmOeHEBUfR/rMRaQ8+lah88l9r6D+g7ew7LQ+5O4wVcOrdGhF/eH9iIqJIXfHLlb/6/4CzeH9qXpWO/L2ZbHhnv8cULPR80OJqhRHxsyFpDxmNOsM7k2NXheQs30XAFteeI+MmQuJrp7IMaPuJ+GUE9j5ybekPPLGAZ+5/VntGPTobfiifUz96GvGjypW4pAzu3flhmHXkZeXx99/rOKp201V92/WfsXqP9cAsHXjVh6+eURpb28hTj6zDdeNuBlftI+Z42cw5fVJhc53ufpsrnngenZs3g7A9Pe+Ytb4GQDcM+5hmrVpzoqFf/DizU+VWROg3VltGfDIAKKjfXz10dfoaxOLtenavQvXDr0W8vJY9ccqnrnjOeo0rMPwtx/G54siJiaGz8d+wZcfTC2TZpszT6PfI/1N1fLx3/DJax8XOn/O1edy44M3k7Y5DYCp46Ywfbz5Drr+/htpd057APSV8cydPOeQnjfxzjuo1LEjeVn72PX0M+Ss+KtYm6p9byHhwguIqprI1gsvyj/uq1uXavfdi696dfJ2p7PziSfxb9tWJt2a9w4kofPppoL58OfZ/+fKYm2q334TVbufhy8pkXVnXJZ/POnaq6h65UWQm0vujl2kPvICuSlbD+m5i/L2W//mkovPY+u2VFq3OfeI7nUkHMo6PRHpD/QPOvSWqr51oPZFqKuqKc7rzRjDVvT+PuDfmNqm55W1X9boeYSIPIapmD7DjfsnntWWSk0asOLsW0lo3YKGT9zG31feXaxdwycGsuH+V9m7eDlNxjxC1TPbkjFrEQCx9ZOp2qUN+zcW/Af1JVahweO3sebGR8jetI2Y5KT8c1XPakdckwb8dU5/Elq3oMHjA1nV465img0eH8TG+0eyd/FyGr9bWDP13c9IG13YYPiz9rP1pQ+o1Lwx8c0bH/CZfT4fdz5xO/f2vo9tKam89uVI5n8zn7V/rSt43qYN+Nft13DnlUPJ2JVB9VrV88/t37efWy+47WBvbTGifD5ueLwfz/Z5lO2b03jsi+f4ZcbPbPprQ6F2P06Zx3vDRxe7/su3PqNSfCXO7nP+Ien6fD4GPTGI+3s/QGpKKiOnvMyC6T+yLuh5GzRpQK9BvRjW4y4ydmVQrVY1ALZv3c7QK4aRvT+b+MrxvDnjDeZPX8D2LdsPqnnrE7cxos9DpKWk8cLkl/hp+o+s/2t9oXZzJ8/hreGFf5y0PacdzVo1Y8iFdxAbF8uT+jSLvl/I3oyyZZuJ69iBmEaNSO3dh9iWLUkaNpTtAwYWa5f1w3z2TJpE8ocfFjqeNPA29k77hn1fTyPutDYk9u/HricP/iMjofPpxBzbkI2X3Uilk/9BrQfvJOW6O4u12ztrAenjP6fhF2MLHd//50pS+gwib18WiT27U3NIP7b935HluH/vPeW118YwZszLR3QfL3EM3AGNnIjMAOqVcOrBIvfJE5GS6vgNBKaq6gYRKXO/7PCmB4hItKoOd8vgASR268iOT78DYO/i5UQnVSGmdo1CbWJq18BXtTJ7Fy8HYMen35F0fsf88/Uf7svmZ8ZAUGHh6pefye5p88neZH4h56btyj+XdF4Hdk46uGZ01YR8zZ2TviOpW0dKI29vFnsWLiMvq/TUYye2bsHGNZtIWbeZnOwcvv98Fmecf0ahNpf0vpgvxn1Bxi6T6mpn2s5S71kWmrU+ni1rUti2fgu52TksmDyXtt1OL/P1y+YtZW/moacZa9G6OZvWbGKz87wzv5hFp/MLv5cX9b6QyeMm5z/vLuffKyc7h+z92QDExsXiK+Mv9hNaN2fzmhS2rNtCTnYOcybP5vTzS//3C3DsCcfy+4+/48/1k7U3izV/rOG0s9qW9XGJ7/xP9k6bBkD2smX4qlbFV6tmsXbZy5bhTytuvKObNGb/L78AsP+XX6nU+Z9l0q18Vicyp5j/qllL/8CXWJXo5OK6WUv/IDe1uO6+hUvI25dl2vz2B9F1a5dJtzTmzP2R7TuO/LN7xIRweFNVz1PVViVsnwNbRKQ+gPO3JFe5E3C7iKwBXgCuF5FnDqZrPb0jRESaAF8Di4DTgN+B6zHRRBOAbsBzInIhMEVVPxaR9sDLQBUgCzgX2AM8A5wFVAJGqeqbZe1HbN1aZKcU5OLMTkkjtl4tcrYVBDzF1qtFTnCbzanE1q0FQGK3DmRvTmPfH2sK3bdS0wZExcbQ9KOniK6SQNrYL/INXUy9Ipqb04gpohlTrxbZzrBXcJsAta7vTo0e57B36UpSnhyNf3dmWR+Z5PrJbEspGK7atnkb/2hzYqE2jZo2AuDlSS/hi/bx3ovv8/PMhQDEVYrjtS9fJTc3l/GjJjBv2g9l0q1RrxbbUwqeaXtKGs3anFCsXfuLOtHi9JZsXp3Ch4+9W+iaw6FWvWS2bSp43tSUVE5s06JQm0bHNQTgxU9fwBcdzQcvfcDCmcarrl0/mcfGPUaDJvUZ/eQ7B/XyjGYtUoM001JSad66RbF2nS4+g5M6nMSm1Zt459G3SU1JZfWy1Vwz9F989tYkKiVU4uQzTinmIZaGL7k2uVsLtHO3bcOXXLtEA1cSOSv/Jr5rV/Z8/AmVunbBV6UKUUlJ5O3eXep10XWSydlc8B2bsyWV6DrJJRq4g1H1yovYO/enQ77uqCXKMz/pC+AGzHfiDcDnRRuoap/AaxG5EWinqqUFvADW0wsVLYDXVPUfwG6M2w2Qpqqnqer4QEMRicMYw8GqeipmLHovcAuwS1XbA+2BfiLStKiQiPQXkYUisjBUnY+Kr0SdgT3Z8tKHxc/FRJPQqhlrbn6U1TeMoPYd1xDXtEFIdNM+nMqKs/qx8pI7yd66nfoP9g3JfYOJjvHRsGlDhvW8mycHPc2w54ZSJakKAL07XsvAS27nqdufZuAjA6jfuH7IdH+d8TND/3krD144jP/NWcKtLxYfHnOD6OhoGjZtyD3yfzx9+zMMeXZw/vNuS0nltvMHclOXW+h29XlUT65+kLuVjZ9n/ES/M25m8AV3sHjOrwx+cSgAi+f8yqLvFvLspOe5+9V7WL7oT/z+3JBoloX0114nrvWp1Br9NnGtTzUG1O/3TL/KxedSqWVzdo0rPu9abvEukOUZoJuI/IX5jnwGQETaiUjxOYNDwHp6oWG9qs5zXn8ABL7hikdVGAOZoqo/A6jqbgAROR84RUSudtpVA04AVgdfHDxOvnH463k1r7kAgL2//UVs/eT8drH1C3tY4HhZwW3qJZO9JY24xvWIa1SXE6a+kn/8+Mn/4e8rhpGdkkbOjnTy9maRuzeL3O27aDLucXJ3ZxbXrFeLnCKaOZuNx1lSm9zUguGaHeOn0Xh02QNJwHg6tesXDB3Vrleb1CLe1LaUVP749U9yc3LZvH4zG1ZtoFHThixfsoJUpx8p6zazZP5vnNDqeFLWpnAwdmxOo2b9gmeqWb9WfsBKgIydBZUDZo6fwTX3X3dIz1YSaZtTqd2g4HmT6yfnP0OA1JRU/ly8nNycXLas38KGVRtp2LQhK5asyG+zfct21ixfS6vTWzF36tyDaKaRHKRZq34yaVsKa6bvTM9/Pf2jb7jh/pvy9ye+qkx8VQEY9srdbFq1qVS9yldeQUL37gBk//kn0XVqk+2ci65dG39q2QJRAPxpaex8aDgAUQkJxHc9k7yMkis6JPa6jMQeFwOQ9ftyYurVIYvfAYipm0zu1kOraBLfoQ3V+vZm8y13QXb2wS8oL3i0Tk9V0zAjYEWPLwSK/TpW1bHA2LLc23p6oaHoJGtgv+xjdRAF3KGqrZ2tqaqWGoa7/f2prLxkMCsvGczubxZQo8c5ACS0bkFu+p5Cw4wAOdt24M/YQ4IzPFWjxzmkT19A1vK1/NH+OpZ36cvyLn3J3pzKykuHkJO6k93TF1ClXUuI9hEVXwlflcqs7fsYf3e/k93T51P9yoNr5mbszdesfuU57J7xI0Ch+b+kCzqxb8XaQ3i74M8ly2nYtCH1jqlHTGwMZ19+Jj9Mn1+ozbxpP9C606lGo0YSjY5rRMraFKpWq0psXGz+8ZPan8TaMuqvWrKSek3rU/uYOkTHxtDx0s78Mv3nQm2q1Sl4ttO6tWfTyo2H9GwlsXzJCho2aUDdY+oSExvDWZedyYLpCwq1+eGb+ZzS8RQg8LwNSVmbQnK9ZOLi4wCoWq0qJ7VvyYZVG4ppFOWvJSuo37QBdRzNLpd25afpPxZqUyPoWU/v1oENK80Qps/nI7G6KUXV+MQmNPlHU36d/UupensmfUbaLX1Ju6UvWXPmknCB+VEX27Il/szMMg9tAkRVqwZO2qwqfXqzd+qBo1XTJ3zBpl4D2NRrAHu+n0eV7iYYsNLJ/8CfkXlIQ5txLZpR66EhbB0yHP/RMA8XQqJ8vjJvRyvW0wsNx4pIJ1WdD/QG5gJtDtB2OVBfRNqr6s8ikogZ3pwG3CYi36lqtog0BzaqapkMZ/r3C0k8ux3NZ75F3t4sNtxbEOV1/Jcvs/KSwQBsevh1GjlLFjJmLSLdme85EFl/byB99iJO+Gok+PPYodPIcoxDxvcLSTyrHc2/f9ssk7j3P/nXNZvyCn93Nw7vpuGv0ei5ofji40iftYgMZ06t3n03Ed/yOMjLY/+GrWx68NX865vPfgdf1cpExcaQ1K0jjXvfVygqE8Cf62fkw6/y7IdP4fP5+GrCNNauWMuNd1/P8iUrmD99AT/PXEi7rm1597u3yfX7eeuJt9m9M52WbVsy9NnB5Pn9RPl8jB81odj9D4Q/1897w0dzz3vD8UX7mK3fsvGv9fQYdg2rf/ubX2f8zAU3Xkybbu3x5/jJ2JXOW3ePzL/+oYlPUL9ZQ+KrxPPygrcZfe8ols5eXCbdUQ+/zlMfPIEvOppvJnzD2hXruP6u61jx2woWTP+RhTMXcVrX03jr2zfx+3N5+8l3SN+Zzgldjqffw/1MkFJUFB+/+SlrnOUaB9N86+E3eOT9x/BF+/h2wnTWr1hH72F9WLn0L36a/hPdb7qM07udTm6On4yd6bx8l/kcRMdG8/QnzwKwJ30PLw1+AX9u2YcXsxYsIK5TB5I/+pC8rCx2Pf1s/rla74wm7Rbzg7/qgFtJOO88ouIrUfvjiez98ksyxowlrnVrEm81z7x/yW/sfuk/B5IqxN45P5HQuQMNJ48zSxZGvJB/rsGEN9jUawAANYb0pcpF5xAVX4lG0/5LxqSv2PnG+9QY2h9f5QTqPP8wADkpW9k6ZHiZn7skPnh/FGd27URyck3WrFrIo4+9wJix4w9+YaiJgDRkUXl5JUWCWspKUCDLQqAtJoDlOudvO1VNddqNpXAgy0ggAWPwzsMEsjwBXIrx+rYBV6jqLg7A0qaXev6PFxUVns+LLSLrDeEoIvtmuIrI7o4Ni27Yisju33jEFivz4RKXDpRIlcf1qLSQ1tMLDTmqem2RY02Cd1T1xqDXPwMlxX0/4GwWi8Vy9OFd9KZrWKNnsVgslrIR4/1IQKixRu8IUdU1QKtw98NisVhcJwLm9Mq/r2qxWCwWSxmxnp7FYrFYyoad07NYLBZLhSEChjet0bNYLBZLmTiaF52Xmby8PLtVwK1nz579rW7kaVrdyNUMp24kbRFgti2HSf+DN7G65VDT6kauZjh1IwZr9CwWi8VSYbBGz2KxWCwVBmv0Ki5vWd2I1LS6kasZTt2IwSactlgsFkuFwXp6FovFYqkwWKNnsVgslgqDNXoWi8ViqTBYo2exWCyHgYjUEJFTwt0Py6FhA1kqACLSo7TzqvqpS7pLgQN+wFTVtS8MEfknsFhVM0XkWuA04GVVXeuWpqM7GBgDpAOjgTbAfar6jcu6zwFPAHuBr4FTgKGq+oHLus2ADaqaJSJnObrvqepON3Ud7WigLkHpFFV1ncuaM4HLHM1FwFZgnqoOc1m3MXCCqs4QkQQgRlXT3dSMVKynVzG41NluAd4B+jjbaOBmF3W7O7pfO1tAd6qzucnrwB4RORW4C/gbeM9lTYCbVXU3cD5QA7gOeMYD3fMd3e7AGuB44B4PdD8BckXkeEw4/THAf90WFZE7gC3AdOBLZ5viti5QzXmfe2CMewfgPDcFRaQf8DHwpnOoEfCZm5qRjDV6FQBVvUlVbwJigZaqepWqXgWc5BxzS3et41l1U9V7VXWps92HMQpukqOqecDlwKuqOgpIdFkTIJCG/mLgfVX9PeiYmwS8nUuAiaq6ywNNAL+q5gBXAiNV9R6gvge6g4EWqnqSqp7sbF4MNcaISH1A8MbIAgwC/gnsBlDVv4A6HmlHHNboVSyOUdWUoP0twLEe6EY5w40AiMgZuP/ZSxeR+zGe1pci4sNFAx/EIhH5BmP0polIIuD3QHeKiPwJtAW+FZHawD4PdLNF5F/ADRQYAS/e5/WAV4Y9mMeAacBKVf1ZRI4D/nJZM0tV9wd2RCSGUqYNLKVjSwtVLL4VkWnAR85+L2CGB7q3AO+KSDWM17MDd4dVwTxbb8xw42YRORZ43mVNMM/aGlilqntEpBZwk9uiqnqfM6+3S1VzRSQT4+W6zU3AAOBJVV0tIk2B990SE5HA3NkqYKaIfAlkBc6r6otuaTtMVtWJQXqrgKtc1pwlIg8ACSLSDRgITHZZM2KxgSwVDCeopYuzO1tVJ3moXQ3Aq6G3IpP/lYFoLyb/nYi+JhQOsHAlWChIMxoztFlU120j4CkiMqKU03mq+pjL+isxIyRznG2u259nZ5TiFsyUQBTG0xztDN9bDhHr6VUwnC9fV7+AA4jItar6QdCv88DxQF9cMHsjcgAAE/tJREFU+0J2Jv/7AzWBZkBD4A3gXLc0Hd13MRGMv1MwrJmH++/5ZMxw5lI8GE4NV2Suqj7q6PcM9rgCx9zQLKJ/vDNq0AXzI2OUiOxU1dYuyiYA76rq25D/AycB2OOiZsRijV4FQETmqmpnEUmn8BdVFObXcZJL0lWcv14EkBRlEHA68COYyX8R8WLyv6OqtvRApyiNPArkCNDd+TvI+RsY0rwWb+ab7gcmluFYSBGRRpigki7AqZgfN3Pd1AS+xUSIZjj7CcA3wBku60Yk1uhVAFS1s/PXU+Ojqm86fx8trZ2I3K+qT4dYPktV9we8Sg8n/+eLSEtVXeaBVjBficj5bq8HDBBY7ygi3VS1TdCp/xORX4D73NAVkYswQUINReSVoFNJQI4bmkVYB/wMPKWqAzzQA4hX1YDBQ1UznOF6y2FgjZ7laKAnEGqjF67J//cwhm8zJsAi4E277YUtACY58z/ZuO/FB4gSkX+q6jzwJDJ3E7AQs0B8UdDxdGCoi7oB2gCdgd4ich8mcnOWqr7jomamiJymqr8AiEhbTBICy2FgjZ7laMCNdWz3YSb/lwK3AlMDcyIu8w5mmYQnc2tBvAh0ApZ6HOAQHJkLsBMXI3NVdQmwRET+q6rZbumUpi8if2OSHXTBDOeeifl3d4shwEQR2YT5v1IPE51sOQys0bMcDbjxJX2Hqr4M5Bs6ERnsHHOTbar6hcsaJbEe+J/XEX2qugg41evIXOB0EXkEaIz5Hgt4tse5KSoiC4FKwA+Y6M2ubqe2c9YDngi0cA4tD4fBjxSs0bMcDbjh6d0AFDVwN5ZwLNT8KiL/xQylBq8fczt6M7Bu7Ss8XLcmInWBp4AGqnqRiLQEOrk83AfGsxqKGeLMdVkrmItUdZsXQiJyjqp+V0Lu3OYi4sVnKiKxRs/iOsFzPgc4FrKIOyc7SG+gqYgEe1yJwPZQ6ZRCAsboBKdZ82LJwmpni3M2rxiLSbD9oLO/ApiAu8N9YBbhf+WyRkn4ROQdvDHyZwLfYfLXFsWLz1REYo2exQtGYqoclHhMVZ8KodYPQAqQDPw76Hg68FsIdUrEyXHqOUHr16o6+xmlXxEyklVVnZRvqGqOiHjheX0vIs9jvviDPdtfXNYdi0dGXlVHOIFJX6mqhvr+FRVr9CyuISKdMGuJahdZoJ4ERLuh6cyvrMUEdXiOs45rJGYtF5h5n8GqusFl3VaYtXI1nf1U4Hon4bWbZDqp1vIc3Y54kxOzg/O3XdCxPOAcl3U9NfKq6heRewFr9EKENXoWN4kDqmI+Z8FrBHcDV7sp7Hz5jgT+4fQjGsj0IIR/DKa0TiA7yLXOsW4u674FDFPV7wGc2nZv4/4C5mHAF8BxIjIPqI3L/7YAqnq22xoHIBxGfoaI3I3xKDMDB1XVi+H6iMMaPYtrqOoszHq5sUGLmX1AVacmmZu8ClyDmS9sB1wPNHdZE6C2qo4J2h8rIkM80K0SMHgAqjpTRKqUdkGIWAZMwqTESsfUeVvhtqgTLToC6OocmgU85kH0aMDIN/PQyPfCGNmBRY67GqkaqVijZ/GCp0VkACbK7mcgSUReVlVXqx6o6koRiVbVXGCMiPyKSVXlJmliKrUHKln8C0hzWRNglYg8TOF0YKs80H0P47kH5mV7O31wOw/mu8D/MHXtwKyNHIMp7uoaqvqLiJyJWT4QhTfLB1piDF5njPGbg8kjazkMrNGzeEFLVd0tIn2ArzALxxfhbqmfPSISByx2Su6k4E39yJsxw6ovOfvz8KC0kKP7KAURfXNwv3wTQKsiuUa/FxEvUrA1U1MIOcCjIrLYLbEwLx8Yh/lhEUi71ts5Jge8wnJArNGzeEGsiMQCV2CqmGeLiNuLqK/DzOPdjlnPdQzu1z0LBNJc5rZOCbo7gDvFFK3N8zB68xcR6aiqCwBEpAMmTZjb7BWRzqo619H9J+6m5grn8oFw/bCISKzRs3jBm8AaYAkw26lz5+qcXlCWjL0YD8gTwhi9eTJmqDE4evMGVf2fS3qB0kKxwA8iss7Zbwz86YZmEW4DxklBYeLtmOQDrqCqgTp+fZ3hci8J1w+LiMQaPYvrqOorFAzNAKwVEVei78JV5y2IcEVvvknx6M23cC96s/vBm7iHqi7GpD9LcvbdDowKsFpEvsZEUn7nUdq3thT8sAA4Flge+Kx7XFKq3GONnsV1DhRphzuh3uGu81Yhojfdzjd5MESkOiYitwkQE1SY+E6XpU/EfMYGAe+IyBRgfGCY1SUudPHeFQ5r9Cxe4FmkXbjqvAVR0aI3w8VUTDklT6tZqOoezEJxFZEamFyus3Ap2YKjGdYfGJGGNXoWL/A00s7B6zpvAYKjN/MwadG8jt4MhLV7Eb0ZLuJVddjBm4UeZ8lCL4wHthAbRVmusEbP4gVeR9pB4TpvUcAOXDYCIhKNqajtafSmo/tpGLOUhIP3RaQfMIXCuTddzVIiImuAXzHe3j2qmln6FZajjai8PE/Lb1kqICLSGrOuKFBodAcmstD1BNBe13kTkbnAOaq63wu9IN1vgR4e1rMLKyIyCHgSU7Q28CXmRT29JA+DZiwuYD09ixf8ATwHNAOqYwJYrsDFqgdFg2dExKs0VauAeU5Zo+A8ia7WtQMygKUiMr2IrtuBHeHiLuB4VU31WLeeiEwC6qpqKxE5BbhMVZ/wuB+Ww8SLOQ6L5XPMot59wEbMF7Tbw0LvYnJBirPtxgTPuM3fmCE3HybJdmBzm0+Bh4HZmGw3gS1SWYnJ9+k1b2NS2WUDOKMV14ShH5bDxHp6Fi9opKpeh12HI3gmv66d16jquHDohpFMTIq57yk8p+e2Z1tZVX8KLJFwyHFZ0xJCrNGzeMEPInKyqi71UDMcwTOISHPgbpz1Y4HjqupKnbejYDF+uPjM2bwmVUSaUVBa6GpMXldLOcEaPYtrBH0hxwA3icgqzK/yKNzPJOFpmqogJmIy4I/GVJVwm3Avxg8LB/NsReSTIp5+qBiEyXRzoohsBFYDfVzQsbiENXoWNwlbqqowpqnKUdXXPdI6GhbjH62EPIrTWRoyUFXPc7Ld+FQ1PdQ6FnexRs/iGuHMJOF1mioRqem8nCwiAzGFVT1bP0b4FuMfrYTcy1XVXBHp7Ly26/PKKdboWSIVr9NULcJ80UY5+/cEncvD/SrXwYvxwaxfi+SMLOHiV2c5ykQKLw1xs7SQJYRYo2eJVDxNU6WqTQFEJF5V9wWfE5F4D/QXYYZzPV2MfxQTdfAmh0U8JpdqcGCS2/X0LCHEGj1LpBKWNFWYXJunleFYSBGRusBTQANVvUhEWgKdVPUdN3XDgTO39p6qlhZA8n9uaKtqqXlUReR+VX3aDW1LaLBGzxKp7AeeBx4kKE0VLg0zikg9oCGQICJtKPA0koDKbmgWYSxm8f2Dzv4KTM23iDN6ztxaYxGJO1C6N1X9xut+OfQErNE7irFGzxKpeJ2m6gLMkohGQHDKsXTgAQ/0k1VVReR+AFXNERGvK3x7SbjSvR0Mt4ZVLSHCGj1LpOJpmipn3dg4EblKVT/xSjeITBGpRcGi6Y64U6T3aOFvZwukeztaiNi1kZGCNXqWSCUsaapU9RMRuQQ4CRP0EDj+mJu6wDDgC+A4EZkH1AaudlkzbATSvYlIZaew69GC9fSOciryOh5LZPMZpvTMD3iYgFlE3sAUGL0D8wXYE2jsti6wDLM28GdgCyYx8goPdMOCiHQSkWXAn87+qSLymot6zzp/ex6k6US3+mAJDdbTs0QkYUzAfIaqniIiv6nqoyLyb+ArD3Tfw1SSeMrZ741JSXawL+nyyn8w86hfAKjqEhHp6qLexSJyH6bCwgENm6o+daBzlqMDa/QsEYmIdAcex3hZMRTk+0xyWTqQ1HqPiDTArOmq77ImQCtVbRm0/73jCUUsqrq+SLUDNwN3vsYUP64qIrtxPk9497myhAhr9CyRyn+AHsBSVfUyuGCKkwLtOQqGU0d7oPuLiHRU1QUAItIBWOiBbrhY76RayxORWGAwplixK6jqPcA9IvK5ql7ulo7FfazRs0Qq64H/eWzwAF7AVHjoAswH5gCuJaAOqmQRiynhtM7Zb4wz3xWhDABexqyN3ARMo6DShGuo6uVOIoD2zqEfVXWb27qW0GGNniVSuReYKiKzKBy96fY6rnGYtXmvOPu9MfNtcsArjoywVbIIJ876S89L+jiBLC8AMzFDmyNF5B5V/djrvlgOD2v0LJHKk0AGZtlAnIe6ns6thbOSRTgRkeMwnl5HjGc7Hxiqqqtcln4IaK+qW51+1AZmANbolROs0bNEKg1UtVUYdCva3Fq4+C8wCrjS2b8G+Ajo4LKuL2DwHNKwS7/KFdboWSKVqSJyvlc5GCvw3Fq4qKyq7wftfyAi9xywdej4WkSmYQwsmDWZUz3QtYSIqLw8mzXHEnmISDpQBZN4Ots57FpouYiUugC9og5DuoWzWHwHMB7z46IXUAOTZNzVahoi0gPo7OzOUdVJbmlZQo81ehaLpdwhIqtLOZ2nqm4X7S0REZmvqp3CoW0pG3Z40xKxiMhlQCBLx0xVnRLO/lhCR6Bo74EQkW6qOt2r/gThesFgy5FhJ2AtEYmIPINZsLzM2QaLiK1zVnF4Nky6dujsKMd6epZI5WKgtar6AURkHPArJneiJfKx1Q4sJWI9PUskUz3odbWw9cISDsLlcVlje5RjPT1LpPI08KtTTy8KM7d3X3i7ZCnPiEg0MENVzy6l2XVe9cdyeFhPzxKRqOpHmGwdnwKfAJ1UdUJ4e2XxkDWhvqGq5gJ+ETngqIGq/i/UupbQYpcsWCISEbkS+E5Vdzn71YGzVPWz8PbMEgqcHJhfq2q6iDwEnAY8oaq/uKz7OdAGmA5kBo6r6p1u6lpCh/X0LJHKiIDBA1DVncCIMPbHEloedgxeZ+A84B1crGYRxKfAw8BsTOmowGYpJ9g5PUukUtIPOvt5jxwCBWMvAd5S1S9F5Am3RVV1nIgkAMeq6nK39Syhx34JWCKVhSLyIiYpMZhaa/YXeeSwUUTeBLoBz4pIJTwYuRKRSzGlheKApiLSGnhMVS9zW9sSGuzwpiVSuQOTd3MCJj/jPjwoMmrxDMEUjr3AGbquCXiRcPoR4HRgJ4CqLgbCkvLMcnhYT88SkahqJnaJQiRzP2ZebROAqqYAKR7oZqvqLpFCNYH9HuhaQoQ1epaIRESmAz0dLwARqQGMV9ULwtszS4hYBfwLeMWpqDEHmK2qn7us+7uI9AaiReQE4E7gB5c1LSHEDm9aIpXkgMEDUNUdQJ0w9scSQlR1jKreDJwNfAD0dP66zR3ASUAWpqbebmCIB7qWEGE9PUuk4heRY1V1HYCINMEmA44YRGQ00BLYgvHyrgZcXaMHoKp7gAeden55qprutqYltFijZ4lUHgTmisgsTBqyLkD/8HbJEkJqAdGYgJLtQKqq5rgtKiLtgXeBRGd/F3CzqtrI4HKCHd60RCSq+jXQDliOGYa6C9gb1k5ZQoaqXqmqHYDnMInFvxeRDR5IvwMMVNUmqtoEExE8xgNdS4iwnp4lIhGRvph6eo2AxZg8nPOBc8LZL0toEJHuGO+9K8bofYcZ5nSbXFXN11HVuSLiuodpCR3W6FkilcFAe2CBqp4tIicCT4W5T5bQcSHGyL2sqpvcFhOR05yXs5xF8R9h5oh7ATPd1reEDju8aYlU9qnqPgARqaSqfwItwtwnS4hQ1duBBZhgFkQkQUQSXZT8t7OdCjTH5HF9BPgH0NpFXUuIsZ6eJVLZ4FRW+AyYLiI7gLVh7pMlRIhIP0xgUk2gGWYY+w3gXDf0DlJDz1KOsEbPEpGo6pXOy0ecQrLVgK/D2CVLaBmESQf2I4Cq/iUirq/DdH5IXQ80Iej705YWKj9Yo2eJeFR1Vrj7YAk5Waq6P5AOTERi8GYd5lTMsOpSbPqxcok1ehaLpTwyS0QeABJEpBswEJjsgW68qg7zQMfiEtboWSyW8sh9wC0Yj+tWjAc22gPd9535xCmYVGQAqOp2D7QtIcAaPYvFUu5QVT/wtrN5yX7geUzGn8Bwah62vFC5wRo9i8VSbhARVVURkaWUMIenqqe43IW7gONVNdVlHYtLWKNnsVjKE4Odv93DpL8S2BMmbUsIsEbPYrGUG5xisQBXYeojup6NpQiZwGJnGUzwnJ5dslBOsEbPYrGURxIxSQe2AxOAiaq6xQPdz5zNUk6JysuzJcYsFkv5REROweS/vArYoKrnhblLlqMc6+lZLJbyzFZgM5AGeJGRZTUlB9DY6M1ygjV6Foul3CEiAwEBagMTgX6quswD6XZBr+OBnpj8n5ZygjV6FoulPHIMMERVF3spqqppRQ79R0QWAcO97Ifl8LFzehaLpVwiIp2BE1R1jIjUBqqq6mqXNU8L2vVhPL/bVPVUN3UtocN6ehaLpdwhIiMwBqcFMAaIBT4A/umy9L8pmNPLAdZghjgt5QRbRNZisZRHrgQuw6ybw1mv52YR2QAXAe8A3wLzgI3ANR7oWkKENXoWi6U8sl9V83C8LhGp4pHuZ8ClQDaQ4WyZHmlbQoAd3rRYLOUKEYkCpojIm0B1p+rBzXiTfLqRql7ogY7FJaynZ7FYyhWOh9cT+Bj4BDOvN1xVR3og/4OInOyBjsUlrKdnsVjKI78AO1X1Ho91OwM3OovUs4AoIM+D6g6WEGGNnsViKY90APqIyFqC5tQ8MD4XuXx/i8tYo2exWMojF4RDVFXXhkPXEjrs4nSLxWKxVBhsIIvFYrFYKgzW6FksFoulwmCNnsVisVgqDNboWSwWi6XC8P+CVjao0UGJ9wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QqgeGJb_fcrD"
      },
      "source": [
        "Conclusions we can draw from the heatmap:\n",
        "*   Number of beds have a strong correlation to the number of accomodates.\n",
        "*   Number of bedrooms have a strong correlation to the number of accomodates.\n",
        "\n",
        "Let's find out what number of beds and bedrooms are preferred by majority of the people.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "O7DZnnSrfZBH",
        "outputId": "c3c159a4-b513-4c61-eb2d-59ed9db8c84d"
      },
      "source": [
        "#Let's see what number of bedrooms are preferred by majority\n",
        "df_bedroom = df_index.groupby('bedrooms').id.count()\n",
        "df_bedroom = df_bedroom.reset_index()\n",
        "df_bedroom = df_bedroom.rename(columns={'id':'listings'})\n",
        "df_bedroom = df_bedroom.sort_values('listings', ascending=False)\n",
        "df_bedroom"
      ],
      "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>bedrooms</th>\n",
              "      <th>listings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.0</td>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.0</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   bedrooms  listings\n",
              "1       1.0      2417\n",
              "2       2.0       640\n",
              "0       0.0       378\n",
              "3       3.0       283\n",
              "4       4.0        69\n",
              "5       5.0        24\n",
              "6       6.0         6\n",
              "7       7.0         1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AY3a3WUzgp8o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f721dd58-4832-4a7f-dbeb-a5db365becf3"
      },
      "source": [
        "import statistics\n",
        "\n",
        "prices = df_index[df_index['bedrooms'] == 1]\n",
        "prices.head()\n",
        "prices['price'].mean()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "95.70624741414977"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q6TkkrHV6sTQ"
      },
      "source": [
        "We can conclude from the above that, most of the occupants prefer houses with 1 bedroom. But that does not give much information about the number of beds, as there can be more than one bed in a single bedroom.\n",
        "Let's do the same for number of beds."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "ROlPyg2b39az",
        "outputId": "22d371a3-8d9d-4fe2-85fa-b9f803239aeb"
      },
      "source": [
        "df_beds = df_index.groupby('beds').id.count()\n",
        "df_beds = df_beds.reset_index()\n",
        "df_beds = df_beds.rename(columns={'id':'listings'})\n",
        "df_beds = df_beds.sort_values('listings', ascending=False)\n",
        "df_beds.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>beds</th>\n",
              "      <th>listings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.0</td>\n",
              "      <td>152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.0</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   beds  listings\n",
              "1   1.0      2201\n",
              "2   2.0       912\n",
              "3   3.0       433\n",
              "4   4.0       152\n",
              "5   5.0        73"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hUtmhxoF4tTY",
        "outputId": "6d664500-f8f3-4fb9-887c-0e7f117fbd9c"
      },
      "source": [
        "prices = df_index[df_index['beds'] == 1]\n",
        "prices.head()\n",
        "prices['price'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "93.14947751022262"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "97NB8abE7FEd"
      },
      "source": [
        "Maximum number of occupants prefer a single bed house, for which they have to pay around 93.2$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kuxSE3Zr7V_R"
      },
      "source": [
        "Now, let's see if higher number of reviews leads to more numbe rof listings in the house. We cannot directly match that with accommodates, as there can be more than one occupant during a sing listing. So let's look at the number os listings."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "g1mT2_yW4yS5",
        "outputId": "584f9dea-6dcc-4ee1-9159-0bd6933071b7"
      },
      "source": [
        "df_nor = df_index.groupby('number_of_reviews').id.count()\n",
        "df_nor = df_nor.reset_index()\n",
        "df_nor = df_nor.rename(columns={'id':'listings'})\n",
        "df_nor = df_nor.sort_values('listings', ascending=False)\n",
        "df_nor.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>number_of_reviews</th>\n",
              "      <th>listings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>157</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   number_of_reviews  listings\n",
              "0                  0       627\n",
              "1                  1       310\n",
              "2                  2       224\n",
              "4                  4       160\n",
              "3                  3       157"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zzbzzvia71IK"
      },
      "source": [
        "We can conclude that the number of reviews is not a big influence on the listings, but other factors like number of beds and bedrooms have a higher influence.\n",
        "\n",
        "Now, let's find the area where we get the maximum number of listings."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "mkqJKX2I42Y2",
        "outputId": "a603b91d-2c7b-4638-dc0f-05ff1b4568a9"
      },
      "source": [
        "df_loc = df_index.groupby('zipcode').id.count()\n",
        "df_loc = df_loc.reset_index()\n",
        "df_loc = df_loc.rename(columns={'id':'listings'})\n",
        "df_loc = df_loc.sort_values('listings', ascending=False)\n",
        "df_loc.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>zipcode</th>\n",
              "      <th>listings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>98122</td>\n",
              "      <td>420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>98103</td>\n",
              "      <td>394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>98102</td>\n",
              "      <td>339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>98105</td>\n",
              "      <td>225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>98109</td>\n",
              "      <td>202</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   zipcode  listings\n",
              "17   98122       420\n",
              "3    98103       394\n",
              "2    98102       339\n",
              "5    98105       225\n",
              "9    98109       202"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "LmApdFOa45SJ",
        "outputId": "dd12c871-3be2-43a2-af13-d79e6b7ede27"
      },
      "source": [
        "import matplotlib\n",
        "from matplotlib import pyplot as plt\n",
        "matplotlib.style.use('ggplot')\n",
        "\n",
        "zcode = df_loc['zipcode']\n",
        "\n",
        "lis = df_loc['listings']\n",
        "\n",
        "df_loc.plot(kind='bar', x='zipcode', y='listings', color='#000080', figsize=(10,7), title='Property listings', legend=False)\n",
        "plt.ylabel('number of listings')\n",
        "plt.xlabel('zipcode')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'zipcode')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "diyp7MQP8JfD"
      },
      "source": [
        "Area with zipcode = '98122' has the highest number of listings."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QRLDDp_D8Tr_"
      },
      "source": [
        "Now that we have the information about what accommodates are looking for, let's check out the features that influence the rent of the houses."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8snKoI2j5aZi"
      },
      "source": [
        "df = df_listings[['host_response_rate', 'host_acceptance_rate','zipcode','room_type','accommodates','bathrooms','bedrooms','beds','price']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kSXPkGyr55HO",
        "outputId": "bf7f2d1e-1181-49f8-d3ab-25eaa4e5d961"
      },
      "source": [
        "df['price'].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     $85.00\n",
              "1    $150.00\n",
              "2    $975.00\n",
              "3    $100.00\n",
              "4    $450.00\n",
              "Name: price, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6dQ2yc4i57c0",
        "outputId": "a377a6f5-07f2-4edd-d1bc-a5183f358b87"
      },
      "source": [
        "df['price'] = df['price'].str.replace(\"$\",\"\")\n",
        "df['price'] = df['price'].str.replace(\",\",\"\")\n",
        "df['price'] = df['price'].str.replace(\"'\",\"\")\n",
        "df['price'] = df['price'].astype(float)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gGnQm3RS5-Jd",
        "outputId": "1d92d07c-ce52-4a08-b949-264d2ef8e58f"
      },
      "source": [
        "df['price'].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     85.0\n",
              "1    150.0\n",
              "2    975.0\n",
              "3    100.0\n",
              "4    450.0\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "gRHNxjq-6AR1",
        "outputId": "f939ecec-e7e1-461d-c410-e051abf98d8f"
      },
      "source": [
        "corr = df.corr()\n",
        "cmap = sns.cm.rocket_r\n",
        "ax = sns.heatmap(corr, annot=True, center=0, cmap=cmap, linecolor='blue')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-a4pZFh8i5h"
      },
      "source": [
        "We, can see that number of bedrooms and the number of accommodates have higher correlation (comparatively) to the price of the house."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NKp1JozT8xfY"
      },
      "source": [
        "We are given the location of the house (latitude and longitude). Let's see how the houses are scattered in the region."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xpQSB_1adj62"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "h_oCerB-6Ce8",
        "outputId": "35909431-e809-4a6b-d5da-ae7ec11f293c"
      },
      "source": [
        "df_listings.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aKB4A-TO9f0z"
      },
      "source": [
        "Now, let's try and answer the third question mentioned at the beginning.\n",
        "We have to see what kind of houses people prefer(Entire home/private room/shared room).\n",
        "We will use the subset created earlier (df_index) for this."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "stUiJxLP7U4S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "c7fd343b-51bc-4348-ad4a-ce00a6ddd9c6"
      },
      "source": [
        "#We know that room_type is categorical, hence we can list the number of listings based on the type of room\n",
        "\n",
        "df_roomtype = df_index.groupby('room_type').id.count()\n",
        "df_roomtype = df_roomtype.reset_index()\n",
        "df_roomtype = df_roomtype.rename(columns={'id':'listings'})\n",
        "df_roomtype"
      ],
      "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>listings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>2541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Private room</td>\n",
              "      <td>1160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Shared room</td>\n",
              "      <td>117</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         room_type  listings\n",
              "0  Entire home/apt      2541\n",
              "1     Private room      1160\n",
              "2      Shared room       117"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "wVpgUrMi96IR",
        "outputId": "fce57af2-ee21-432e-b0c2-42d3ece26dbe"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "lis = df_roomtype['listings']\n",
        "\n",
        "my_labels = df_roomtype['room_type']\n",
        "plt.pie(lis,labels=my_labels,autopct='%1.1f%%')\n",
        "plt.title('Room Type')\n",
        "plt.axis('equal')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XvzPr9Fp9-Eq"
      },
      "source": [
        "Maximum number of occupants prefer the entire house and a shared room is preferred by a very small number if people.\n",
        "\n",
        "Observations\n",
        "\n",
        "1.   Entire house:  66.6%\n",
        "2.   Private room: 30.4%\n",
        "3.   Shared room: 3.1%\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "r3voYTlP98Qk",
        "outputId": "9b027e4e-61a8-4d5f-ad04-56179369cfae"
      },
      "source": [
        "#Now, doing the same for the type of property\n",
        "df_proptype = df_index.groupby('property_type').id.count()\n",
        "df_proptype = df_proptype.reset_index()\n",
        "df_proptype = df_proptype.rename(columns={'id':'listings'})\n",
        "df_proptype"
      ],
      "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>property_type</th>\n",
              "      <th>listings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Apartment</td>\n",
              "      <td>1708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bed &amp; Breakfast</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Boat</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bungalow</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Cabin</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Camper/RV</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Chalet</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Condominium</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Dorm</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>House</td>\n",
              "      <td>1733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Loft</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Other</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Tent</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Townhouse</td>\n",
              "      <td>118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Treehouse</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Yurt</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      property_type  listings\n",
              "0                 0         1\n",
              "1         Apartment      1708\n",
              "2   Bed & Breakfast        37\n",
              "3              Boat         8\n",
              "4          Bungalow        13\n",
              "5             Cabin        21\n",
              "6         Camper/RV        13\n",
              "7            Chalet         2\n",
              "8       Condominium        91\n",
              "9              Dorm         2\n",
              "10            House      1733\n",
              "11             Loft        40\n",
              "12            Other        22\n",
              "13             Tent         5\n",
              "14        Townhouse       118\n",
              "15        Treehouse         3\n",
              "16             Yurt         1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "xZrgm2k8-kYR",
        "outputId": "da3e953b-7b61-42c7-ea06-3347db5529c6"
      },
      "source": [
        "import matplotlib\n",
        "from matplotlib import pyplot as plt\n",
        "matplotlib.style.use('ggplot')\n",
        "\n",
        "name = df_proptype['property_type']\n",
        "\n",
        "lis = df_proptype['listings']\n",
        "\n",
        "df_proptype.plot(kind='bar', x='property_type', y='listings', color='#ff0000', figsize=(10,7), title='Property listings', legend=False)\n",
        "plt.ylabel('number of listings')\n",
        "plt.xlabel('property type')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'property type')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oFtUXoHv-pVE"
      },
      "source": [
        "We can see that almost all of the guests prefer a hiuse/appartment."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tvv27ZUl-6vr"
      },
      "source": [
        "# Summary of our results:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sI_1mSh4Mo0i"
      },
      "source": [
        "1. Factors influencing the number of accommodations:\n",
        "  \n",
        "  - Number of bedrooms and beds play a huge role, as concluded from the heat map.\n",
        "  \n",
        "  - Location of the house is a big influence.\n",
        "\n",
        "  - Number of reviews is not considered to be extraneous by many.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NFxbrMvRM9U0"
      },
      "source": [
        "2. Price of the house:\n",
        "   \n",
        "  - From the heat map, we concluded that the number of bedrooms influence the price of the house.\n",
        "\n",
        "  - As the number of accommodates also affect the price of the house.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "712kQP1BM0hx"
      },
      "source": [
        "3. Types of houses people prefer:\n",
        " \n",
        "   - From the pie chart, we came to a conclusion that the entire house is preferred by many, and shared room is preferred the least.\n",
        "   \n",
        "   - From the bar graph, we can say that most of the people are looking for apartments and houses."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WVGrbB9-BVtq"
      },
      "source": [
        "Below is a table of our observations:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "VoleUljN_GZj",
        "outputId": "ba1a92a1-e113-4051-a3f2-a771ab4f1443"
      },
      "source": [
        "data = [['1 bedrooms', 2417], ['1 bed', 2201], ['Zipcode: 98122 ',420 ]]\n",
        "df = pd.DataFrame(data, columns = ['Feature', 'Number of listings'])\n",
        "df\n",
        "  "
      ],
      "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>Feature</th>\n",
              "      <th>Number of listings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1 bedrooms</td>\n",
              "      <td>2417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1 bed</td>\n",
              "      <td>2201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Zipcode: 98122</td>\n",
              "      <td>420</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Feature  Number of listings\n",
              "0       1 bedrooms                2417\n",
              "1            1 bed                2201\n",
              "2  Zipcode: 98122                  420"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yEewUdwIBhYX"
      },
      "source": [
        "3. What type of houses that people prefer(Entire home/private room/shared room)\n",
        "\n",
        "We can conclude from the pie chart:\n",
        "1.   Entire house:  66.6%\n",
        "2.   Private room: 30.4%\n",
        "3.   Shared room: 3.1%\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qD9nntgaFs4u"
      },
      "source": [
        "**Thank You!!**"
      ]
    }
  ]
}