project_3_level_1(1).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **MEHWISH ZAMAN (DATA ANALYST)**\n",
        "***AN INTERN AT OASIS INFOBYTE***\n"
      ],
      "metadata": {
        "id": "IMWslYy2SO8t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Idea: Cleaning Data**\n",
        "\n",
        "# DESCRIPTION:\n",
        "Data cleaning is the process of fixing or removing incorrect, corrupted, duplicate, or incomplete\n",
        "data within a dataset. Messy data leads to unreliable outcomes. Cleaning data is an essential\n",
        "part of data analysis, and demonstrating your data cleaning skills is key to landing a job. Here\n",
        "are some projects to test out your data cleaning skills:\n",
        "\n",
        "\n",
        "# Key Concepts and Challenges:\n",
        "  \n",
        "\n",
        "1. Data Integrity: Ensuring the accuracy, consistency, and reliability of data throughout the cleaning process.\n",
        "2. Missing Data Handling: Dealing with missing values by either imputing them or making informed decisions on how to handle gaps in the dataset.\n",
        "3. Duplicate Removal: Identifying and eliminating duplicate records to maintain data uniqueness.\n",
        "4. Standardization: Consistent formatting and units across the dataset for accurate analysis.\n",
        "5. Outlier Detection: Identifying and addressing outliers that may skew analysis or model performance."
      ],
      "metadata": {
        "id": "WFsEh4AHS8k5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "HJw5YC04-aO8"
      },
      "outputs": [],
      "source": [
        "# Import Libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime as dt\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/archive_3.zip"
      ],
      "metadata": {
        "id": "Wo5A0VjF_2HY"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load Dataset\n",
        "# Dataset First Look\n",
        "df = pd.read_csv('/content/AB_NYC_2019.csv')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "id": "3lqXGf7TAJ_j",
        "outputId": "e8c62753-6f2f-403f-86ec-0c92580311c3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             id                                               name   host_id  \\\n",
              "0          2539                 Clean & quiet apt home by the park      2787   \n",
              "1          2595                              Skylit Midtown Castle      2845   \n",
              "2          3647                THE VILLAGE OF HARLEM....NEW YORK !      4632   \n",
              "3          3831                    Cozy Entire Floor of Brownstone      4869   \n",
              "4          5022   Entire Apt: Spacious Studio/Loft by central park      7192   \n",
              "...         ...                                                ...       ...   \n",
              "48890  36484665    Charming one bedroom - newly renovated rowhouse   8232441   \n",
              "48891  36485057      Affordable room in Bushwick/East Williamsburg   6570630   \n",
              "48892  36485431            Sunny Studio at Historical Neighborhood  23492952   \n",
              "48893  36485609               43rd St. Time Square-cozy single bed  30985759   \n",
              "48894  36487245  Trendy duplex in the very heart of Hell's Kitchen  68119814   \n",
              "\n",
              "           host_name neighbourhood_group       neighbourhood  latitude  \\\n",
              "0               John            Brooklyn          Kensington  40.64749   \n",
              "1           Jennifer           Manhattan             Midtown  40.75362   \n",
              "2          Elisabeth           Manhattan              Harlem  40.80902   \n",
              "3        LisaRoxanne            Brooklyn        Clinton Hill  40.68514   \n",
              "4              Laura           Manhattan         East Harlem  40.79851   \n",
              "...              ...                 ...                 ...       ...   \n",
              "48890        Sabrina            Brooklyn  Bedford-Stuyvesant  40.67853   \n",
              "48891        Marisol            Brooklyn            Bushwick  40.70184   \n",
              "48892  Ilgar & Aysel           Manhattan              Harlem  40.81475   \n",
              "48893            Taz           Manhattan      Hell's Kitchen  40.75751   \n",
              "48894     Christophe           Manhattan      Hell's Kitchen  40.76404   \n",
              "\n",
              "       longitude        room_type  price  minimum_nights  number_of_reviews  \\\n",
              "0      -73.97237     Private room    149               1                  9   \n",
              "1      -73.98377  Entire home/apt    225               1                 45   \n",
              "2      -73.94190     Private room    150               3                  0   \n",
              "3      -73.95976  Entire home/apt     89               1                270   \n",
              "4      -73.94399  Entire home/apt     80              10                  9   \n",
              "...          ...              ...    ...             ...                ...   \n",
              "48890  -73.94995     Private room     70               2                  0   \n",
              "48891  -73.93317     Private room     40               4                  0   \n",
              "48892  -73.94867  Entire home/apt    115              10                  0   \n",
              "48893  -73.99112      Shared room     55               1                  0   \n",
              "48894  -73.98933     Private room     90               7                  0   \n",
              "\n",
              "      last_review  reviews_per_month  calculated_host_listings_count  \\\n",
              "0      2018-10-19               0.21                               6   \n",
              "1      2019-05-21               0.38                               2   \n",
              "2             NaN                NaN                               1   \n",
              "3      2019-07-05               4.64                               1   \n",
              "4      2018-11-19               0.10                               1   \n",
              "...           ...                ...                             ...   \n",
              "48890         NaN                NaN                               2   \n",
              "48891         NaN                NaN                               2   \n",
              "48892         NaN                NaN                               1   \n",
              "48893         NaN                NaN                               6   \n",
              "48894         NaN                NaN                               1   \n",
              "\n",
              "       availability_365  \n",
              "0                   365  \n",
              "1                   355  \n",
              "2                   365  \n",
              "3                   194  \n",
              "4                     0  \n",
              "...                 ...  \n",
              "48890                 9  \n",
              "48891                36  \n",
              "48892                27  \n",
              "48893                 2  \n",
              "48894                23  \n",
              "\n",
              "[48895 rows x 16 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ca8aa6cd-0320-4152-b768-95621e78b25c\" class=\"colab-df-container\">\n",
              "    <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>name</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_name</th>\n",
              "      <th>neighbourhood_group</th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>room_type</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>last_review</th>\n",
              "      <th>reviews_per_month</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2539</td>\n",
              "      <td>Clean &amp; quiet apt home by the park</td>\n",
              "      <td>2787</td>\n",
              "      <td>John</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Kensington</td>\n",
              "      <td>40.64749</td>\n",
              "      <td>-73.97237</td>\n",
              "      <td>Private room</td>\n",
              "      <td>149</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>2018-10-19</td>\n",
              "      <td>0.21</td>\n",
              "      <td>6</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2595</td>\n",
              "      <td>Skylit Midtown Castle</td>\n",
              "      <td>2845</td>\n",
              "      <td>Jennifer</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Midtown</td>\n",
              "      <td>40.75362</td>\n",
              "      <td>-73.98377</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>225</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>2019-05-21</td>\n",
              "      <td>0.38</td>\n",
              "      <td>2</td>\n",
              "      <td>355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3647</td>\n",
              "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
              "      <td>4632</td>\n",
              "      <td>Elisabeth</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Harlem</td>\n",
              "      <td>40.80902</td>\n",
              "      <td>-73.94190</td>\n",
              "      <td>Private room</td>\n",
              "      <td>150</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3831</td>\n",
              "      <td>Cozy Entire Floor of Brownstone</td>\n",
              "      <td>4869</td>\n",
              "      <td>LisaRoxanne</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Clinton Hill</td>\n",
              "      <td>40.68514</td>\n",
              "      <td>-73.95976</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>89</td>\n",
              "      <td>1</td>\n",
              "      <td>270</td>\n",
              "      <td>2019-07-05</td>\n",
              "      <td>4.64</td>\n",
              "      <td>1</td>\n",
              "      <td>194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5022</td>\n",
              "      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n",
              "      <td>7192</td>\n",
              "      <td>Laura</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>East Harlem</td>\n",
              "      <td>40.79851</td>\n",
              "      <td>-73.94399</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>80</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>2018-11-19</td>\n",
              "      <td>0.10</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48890</th>\n",
              "      <td>36484665</td>\n",
              "      <td>Charming one bedroom - newly renovated rowhouse</td>\n",
              "      <td>8232441</td>\n",
              "      <td>Sabrina</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>40.67853</td>\n",
              "      <td>-73.94995</td>\n",
              "      <td>Private room</td>\n",
              "      <td>70</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48891</th>\n",
              "      <td>36485057</td>\n",
              "      <td>Affordable room in Bushwick/East Williamsburg</td>\n",
              "      <td>6570630</td>\n",
              "      <td>Marisol</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bushwick</td>\n",
              "      <td>40.70184</td>\n",
              "      <td>-73.93317</td>\n",
              "      <td>Private room</td>\n",
              "      <td>40</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48892</th>\n",
              "      <td>36485431</td>\n",
              "      <td>Sunny Studio at Historical Neighborhood</td>\n",
              "      <td>23492952</td>\n",
              "      <td>Ilgar &amp; Aysel</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Harlem</td>\n",
              "      <td>40.81475</td>\n",
              "      <td>-73.94867</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>115</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48893</th>\n",
              "      <td>36485609</td>\n",
              "      <td>43rd St. Time Square-cozy single bed</td>\n",
              "      <td>30985759</td>\n",
              "      <td>Taz</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Hell's Kitchen</td>\n",
              "      <td>40.75751</td>\n",
              "      <td>-73.99112</td>\n",
              "      <td>Shared room</td>\n",
              "      <td>55</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48894</th>\n",
              "      <td>36487245</td>\n",
              "      <td>Trendy duplex in the very heart of Hell's Kitchen</td>\n",
              "      <td>68119814</td>\n",
              "      <td>Christophe</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Hell's Kitchen</td>\n",
              "      <td>40.76404</td>\n",
              "      <td>-73.98933</td>\n",
              "      <td>Private room</td>\n",
              "      <td>90</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>48895 rows × 16 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca8aa6cd-0320-4152-b768-95621e78b25c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ca8aa6cd-0320-4152-b768-95621e78b25c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ca8aa6cd-0320-4152-b768-95621e78b25c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0b660293-a753-416a-ab2b-c89f84dbab5f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0b660293-a753-416a-ab2b-c89f84dbab5f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0b660293-a753-416a-ab2b-c89f84dbab5f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_f0422868-1a74-404d-8e11-83e0c3cb8451\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_f0422868-1a74-404d-8e11-83e0c3cb8451 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Rows & Columns count.\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n0DNo8HaAazO",
        "outputId": "c181ada4-9f8c-4535-90ca-d4204c91955a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(48895, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Columns.\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qJJCn_q-BR0g",
        "outputId": "db3d8eb2-2bce-4b38-abee-26f42257eb22"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'name', 'host_id', 'host_name', 'neighbourhood_group',\n",
              "       'neighbourhood', 'latitude', 'longitude', 'room_type', 'price',\n",
              "       'minimum_nights', 'number_of_reviews', 'last_review',\n",
              "       'reviews_per_month', 'calculated_host_listings_count',\n",
              "       'availability_365'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Describe.\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "SQNo9IamAwiG",
        "outputId": "859344ce-0b94-4b92-dcb6-5e858219447c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 id       host_id      latitude     longitude         price  \\\n",
              "count  4.889500e+04  4.889500e+04  48895.000000  48895.000000  48895.000000   \n",
              "mean   1.901714e+07  6.762001e+07     40.728949    -73.952170    152.720687   \n",
              "std    1.098311e+07  7.861097e+07      0.054530      0.046157    240.154170   \n",
              "min    2.539000e+03  2.438000e+03     40.499790    -74.244420      0.000000   \n",
              "25%    9.471945e+06  7.822033e+06     40.690100    -73.983070     69.000000   \n",
              "50%    1.967728e+07  3.079382e+07     40.723070    -73.955680    106.000000   \n",
              "75%    2.915218e+07  1.074344e+08     40.763115    -73.936275    175.000000   \n",
              "max    3.648724e+07  2.743213e+08     40.913060    -73.712990  10000.000000   \n",
              "\n",
              "       minimum_nights  number_of_reviews  reviews_per_month  \\\n",
              "count    48895.000000       48895.000000       38843.000000   \n",
              "mean         7.029962          23.274466           1.373221   \n",
              "std         20.510550          44.550582           1.680442   \n",
              "min          1.000000           0.000000           0.010000   \n",
              "25%          1.000000           1.000000           0.190000   \n",
              "50%          3.000000           5.000000           0.720000   \n",
              "75%          5.000000          24.000000           2.020000   \n",
              "max       1250.000000         629.000000          58.500000   \n",
              "\n",
              "       calculated_host_listings_count  availability_365  \n",
              "count                    48895.000000      48895.000000  \n",
              "mean                         7.143982        112.781327  \n",
              "std                         32.952519        131.622289  \n",
              "min                          1.000000          0.000000  \n",
              "25%                          1.000000          0.000000  \n",
              "50%                          1.000000         45.000000  \n",
              "75%                          2.000000        227.000000  \n",
              "max                        327.000000        365.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2af09ff9-d730-4550-88a2-d1b2318545b5\" class=\"colab-df-container\">\n",
              "    <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>host_id</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>reviews_per_month</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.889500e+04</td>\n",
              "      <td>4.889500e+04</td>\n",
              "      <td>48895.000000</td>\n",
              "      <td>48895.000000</td>\n",
              "      <td>48895.000000</td>\n",
              "      <td>48895.000000</td>\n",
              "      <td>48895.000000</td>\n",
              "      <td>38843.000000</td>\n",
              "      <td>48895.000000</td>\n",
              "      <td>48895.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.901714e+07</td>\n",
              "      <td>6.762001e+07</td>\n",
              "      <td>40.728949</td>\n",
              "      <td>-73.952170</td>\n",
              "      <td>152.720687</td>\n",
              "      <td>7.029962</td>\n",
              "      <td>23.274466</td>\n",
              "      <td>1.373221</td>\n",
              "      <td>7.143982</td>\n",
              "      <td>112.781327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.098311e+07</td>\n",
              "      <td>7.861097e+07</td>\n",
              "      <td>0.054530</td>\n",
              "      <td>0.046157</td>\n",
              "      <td>240.154170</td>\n",
              "      <td>20.510550</td>\n",
              "      <td>44.550582</td>\n",
              "      <td>1.680442</td>\n",
              "      <td>32.952519</td>\n",
              "      <td>131.622289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.539000e+03</td>\n",
              "      <td>2.438000e+03</td>\n",
              "      <td>40.499790</td>\n",
              "      <td>-74.244420</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>9.471945e+06</td>\n",
              "      <td>7.822033e+06</td>\n",
              "      <td>40.690100</td>\n",
              "      <td>-73.983070</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.190000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.967728e+07</td>\n",
              "      <td>3.079382e+07</td>\n",
              "      <td>40.723070</td>\n",
              "      <td>-73.955680</td>\n",
              "      <td>106.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.720000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>45.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.915218e+07</td>\n",
              "      <td>1.074344e+08</td>\n",
              "      <td>40.763115</td>\n",
              "      <td>-73.936275</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>2.020000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>227.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.648724e+07</td>\n",
              "      <td>2.743213e+08</td>\n",
              "      <td>40.913060</td>\n",
              "      <td>-73.712990</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>1250.000000</td>\n",
              "      <td>629.000000</td>\n",
              "      <td>58.500000</td>\n",
              "      <td>327.000000</td>\n",
              "      <td>365.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2af09ff9-d730-4550-88a2-d1b2318545b5')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2af09ff9-d730-4550-88a2-d1b2318545b5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2af09ff9-d730-4550-88a2-d1b2318545b5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-37b2cd02-e9f2-43aa-93a2-7919ab74d54d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-37b2cd02-e9f2-43aa-93a2-7919ab74d54d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-37b2cd02-e9f2-43aa-93a2-7919ab74d54d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Info.\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-CznIBHWB0oX",
        "outputId": "092fd930-b827-4277-dbd8-dcc644ca3936"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 48895 entries, 0 to 48894\n",
            "Data columns (total 16 columns):\n",
            " #   Column                          Non-Null Count  Dtype  \n",
            "---  ------                          --------------  -----  \n",
            " 0   id                              48895 non-null  int64  \n",
            " 1   name                            48879 non-null  object \n",
            " 2   host_id                         48895 non-null  int64  \n",
            " 3   host_name                       48874 non-null  object \n",
            " 4   neighbourhood_group             48895 non-null  object \n",
            " 5   neighbourhood                   48895 non-null  object \n",
            " 6   latitude                        48895 non-null  float64\n",
            " 7   longitude                       48895 non-null  float64\n",
            " 8   room_type                       48895 non-null  object \n",
            " 9   price                           48895 non-null  int64  \n",
            " 10  minimum_nights                  48895 non-null  int64  \n",
            " 11  number_of_reviews               48895 non-null  int64  \n",
            " 12  last_review                     38843 non-null  object \n",
            " 13  reviews_per_month               38843 non-null  float64\n",
            " 14  calculated_host_listings_count  48895 non-null  int64  \n",
            " 15  availability_365                48895 non-null  int64  \n",
            "dtypes: float64(3), int64(7), object(6)\n",
            "memory usage: 6.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking Unique Values for each variable.\n",
        "unique_values = df.nunique()\n",
        "unique_values\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hg__s6ZHBBIi",
        "outputId": "0cb114e1-aacd-49ab-fadf-c115755bad01"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                48895\n",
              "name                              47905\n",
              "host_id                           37457\n",
              "host_name                         11452\n",
              "neighbourhood_group                   5\n",
              "neighbourhood                       221\n",
              "latitude                          19048\n",
              "longitude                         14718\n",
              "room_type                             3\n",
              "price                               674\n",
              "minimum_nights                      109\n",
              "number_of_reviews                   394\n",
              "last_review                        1764\n",
              "reviews_per_month                   937\n",
              "calculated_host_listings_count       47\n",
              "availability_365                    366\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for duplicates\n",
        "print(f\"Number of duplicates:\\n{df.duplicated().sum()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QOFoUorSBFmR",
        "outputId": "1bf40cc1-c9ba-4e4b-f0d5-dba41cb3b00f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of duplicates:\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing Values/Null Values Count.\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gYw8BEI2B8lE",
        "outputId": "7ed63102-5f7d-4cd5-d192-b73c81425822"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                    0\n",
              "name                                 16\n",
              "host_id                               0\n",
              "host_name                            21\n",
              "neighbourhood_group                   0\n",
              "neighbourhood                         0\n",
              "latitude                              0\n",
              "longitude                             0\n",
              "room_type                             0\n",
              "price                                 0\n",
              "minimum_nights                        0\n",
              "number_of_reviews                     0\n",
              "last_review                       10052\n",
              "reviews_per_month                 10052\n",
              "calculated_host_listings_count        0\n",
              "availability_365                      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping id, host_name, last_review columns as they are unnecessary and will not impact our analysis and dataset\n",
        "df.drop(['id','host_name','last_review'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "QYu3zg2zRkGr"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Filling null entries in name column with 'Unknown'\n",
        "df['name'].fillna('Unknown', inplace=True)\n",
        "\n",
        "\n",
        "# Filling null entries of reviews_per_month column with mean of all the other values of that column\n",
        "mean_reviews_per_month = df['reviews_per_month'].mean()\n",
        "df['reviews_per_month'].fillna(mean_reviews_per_month, inplace=True)\n",
        "\n",
        "\n",
        "# Checking null entries after filling Null values\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "trivY8pEWPwa",
        "outputId": "bee6b4c7-b431-413b-8269-02551643ea7b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name                              0\n",
              "host_id                           0\n",
              "neighbourhood_group               0\n",
              "neighbourhood                     0\n",
              "latitude                          0\n",
              "longitude                         0\n",
              "room_type                         0\n",
              "price                             0\n",
              "minimum_nights                    0\n",
              "number_of_reviews                 0\n",
              "reviews_per_month                 0\n",
              "calculated_host_listings_count    0\n",
              "availability_365                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.price.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64IdPMIcGUrp",
        "outputId": "c825aa1e-def6-4b4e-ed00-8a2b1efa4dd8"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    48895.000000\n",
              "mean       152.720687\n",
              "std        240.154170\n",
              "min          0.000000\n",
              "25%         69.000000\n",
              "50%        106.000000\n",
              "75%        175.000000\n",
              "max      10000.000000\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "plt.figure(figsize=(6, 4))  \n",
        "sns.boxplot(data=data, y='MntTotal')\n",
        "plt.title('Box Plot for MntTotal')\n",
        "plt.ylabel('MntTotal')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ZnERwawtQleA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar(x=df['room_type'], height=df['price'],label=\"price\")\n",
        "plt.xlabel('room_type')\n",
        "plt.ylabel('price')\n",
        "plt.title('prices of rooms as per type')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "boV26U9KH3gZ",
        "outputId": "f09f5b39-edb9-4b46-8bf5-1aa2a2a90614"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Identify and drop non-numeric columns\n",
        "non_numeric_columns=df.select_dtypes(exclude=[np.number]).columns\n",
        "df_numeric=df.drop(columns=non_numeric_columns,axis=1)"
      ],
      "metadata": {
        "id": "04SR261GNAy9"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Correlation matrix\n",
        "correlation_matrix=df_numeric.corr()\n",
        "sns.heatmap(correlation_matrix,annot=True,cmap=\"coolwarm\",fmt='.2f')\n",
        "plt.title(\"Correlation Matrix\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 649
        },
        "id": "OGeoPCEBM11u",
        "outputId": "3fcb3e10-583d-4bcc-d79c-714834c3f947"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualising distribution of reviews_per_month for each neighberhood_group\n",
        "\n",
        "plt.figure(figsize = (8,4))\n",
        "plt.title('Density and distribution of reviews_per_month for each neighbourhood_group')\n",
        "sns.barplot(x='neighbourhood_group', y=\"reviews_per_month\", data=df)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "0Mlqz1YVNOBn",
        "outputId": "ea3a1e1f-369f-4847-b478-41b66f4f584d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# conclusion:\n",
        "\n",
        "1.  I had found that some of the hosts take good advantage of the Airbnb platform and provide the most listing.\n",
        "\n",
        "2.  I found the locations with most number of listings, Manhattan and Brooklyn are the neighbourhoods with more than 20000 listings.\n",
        "3.  I looked into the room_type column from there i found that \"Entire home/apt\" and \"Private room\" are the popular room types with more than 20000 listings on Airbnb platform.\n",
        "4. I proceed with analyzing price and neighbouhood group lising densities and what areas were more popular than another.\n",
        "5. I looked into the price of and analysed that 'Entire home/apt\" and private room room type are  expensive compared to shared room.\n",
        "# suggestion:\n",
        "While doing this data analysis i found out a lot of informative things and it would be nice if dataset will have couple of additional feaures such as (0-5 stars) reviews for each listing, addition of these feature would help me to determine the best reviewed host for NYC along with number_of_review column that is provided.\n",
        "Overall, I discovered a very good number of interesting relationships between features and explained each step of the process."
      ],
      "metadata": {
        "id": "XSkjvbfEZqiT"
      }
    }
  ]
}