ML_Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ML Project.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gc42vElwdZao"
      },
      "source": [
        "from sklearn import preprocessing, metrics\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.set(style=\"ticks\", color_codes=True)\n",
        "import statsmodels.api as sm\n",
        "from scipy import stats"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bjLDp3dl6eWG"
      },
      "source": [
        "# Load the dataset\n",
        "####(we have uploaded the dataset on our github repository and used it here)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s0lLxkaNuZ-u",
        "outputId": "b053d390-7cd4-4053-c486-7287ea592a2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        }
      },
      "source": [
        "airbnb = pd.read_csv(\"https://raw.githubusercontent.com/kratos2597/Airbnb/main/AB_NYC_2019.csv?token=AMID5XNLN3B2ORJ6EI24J527TUGXS\")\n",
        "airbnb.head(5)"
      ],
      "execution_count": 8,
      "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>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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     id  ... availability_365\n",
              "0  2539  ...              365\n",
              "1  2595  ...              355\n",
              "2  3647  ...              365\n",
              "3  3831  ...              194\n",
              "4  5022  ...                0\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXBmEC0Mwj6S",
        "outputId": "cc6aa0d2-515e-40f1-beea-040bf64c3b94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "### Check datatypes\n",
        "\n",
        "airbnb.dtypes"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                  int64\n",
              "name                               object\n",
              "host_id                             int64\n",
              "host_name                          object\n",
              "neighbourhood_group                object\n",
              "neighbourhood                      object\n",
              "latitude                          float64\n",
              "longitude                         float64\n",
              "room_type                          object\n",
              "price                               int64\n",
              "minimum_nights                      int64\n",
              "number_of_reviews                   int64\n",
              "last_review                        object\n",
              "reviews_per_month                 float64\n",
              "calculated_host_listings_count      int64\n",
              "availability_365                    int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eQ89YQy0xH1y",
        "outputId": "774fba51-6447-4d4a-ece6-a66b2ca2acab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "### Checking Null Values\n",
        "airbnb.isnull().sum()"
      ],
      "execution_count": 10,
      "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": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AbrBv-nU7vAB"
      },
      "source": [
        "Let's replace reviews per month Nan by zero and null name and host name by NoName. Also, replace last review with \"Not Reviewed\"."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPiqMt9c0Xif"
      },
      "source": [
        "airbnb.fillna({'reviews_per_month':0},inplace=True)\n",
        "airbnb.fillna({'name':\"NoName\"},inplace=True)\n",
        "airbnb.fillna({'host_name':\"NoName\"},inplace=True)\n",
        "airbnb.fillna({'last_review':\"NotReviewed\"},inplace=True)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VVL-TADx9FJt",
        "outputId": "43e02093-3bad-43ca-f7bb-619f2d7fd810",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "airbnb.isnull().sum()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                0\n",
              "name                              0\n",
              "host_id                           0\n",
              "host_name                         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",
              "last_review                       0\n",
              "reviews_per_month                 0\n",
              "calculated_host_listings_count    0\n",
              "availability_365                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5CyrEB69VIm",
        "outputId": "44daf1ca-5966-42e7-e78e-6a2cd313a90f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "### Price Column\n",
        "airbnb[\"price\"].describe()"
      ],
      "execution_count": 13,
      "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": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3uxi4nCW-9Kq"
      },
      "source": [
        "We noticed that the average price is 152 and the price varies from 0 - 10K"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PcDrQ7Tu_IhH",
        "outputId": "2ae82099-b0b1-4539-ae3a-2284dbde3563",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "source": [
        "hist_price=airbnb[\"price\"].hist()\n",
        "### We observe that most listings have price less than $1000"
      ],
      "execution_count": 14,
      "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": "code",
      "metadata": {
        "id": "08gTys1pA5Aa",
        "outputId": "89a79301-7c75-4fcc-8968-9a38f6c7cb3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "source": [
        "### Let's plot histogram for prices less tha $2000\n",
        "hist_price1=airbnb[\"price\"][airbnb[\"price\"]<1000].hist()"
      ],
      "execution_count": 15,
      "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": "code",
      "metadata": {
        "id": "s91C8UEtFvow",
        "outputId": "6ca05f60-31bc-45b2-e367-38d91f058bc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 723
        }
      },
      "source": [
        "### Listings having price more than 1k\n",
        "airbnb[airbnb[\"price\"]>1000]"
      ],
      "execution_count": 16,
      "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>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>496</th>\n",
              "      <td>174966</td>\n",
              "      <td>Luxury 2Bed/2.5Bath Central Park View</td>\n",
              "      <td>836168</td>\n",
              "      <td>Henry</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Upper West Side</td>\n",
              "      <td>40.77350</td>\n",
              "      <td>-73.98697</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>2000</td>\n",
              "      <td>30</td>\n",
              "      <td>30</td>\n",
              "      <td>2018-05-05</td>\n",
              "      <td>0.33</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>762</th>\n",
              "      <td>273190</td>\n",
              "      <td>6 Bedroom Landmark West Village Townhouse</td>\n",
              "      <td>605463</td>\n",
              "      <td>West Village</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>West Village</td>\n",
              "      <td>40.73301</td>\n",
              "      <td>-74.00268</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>1300</td>\n",
              "      <td>5</td>\n",
              "      <td>28</td>\n",
              "      <td>2018-09-25</td>\n",
              "      <td>0.31</td>\n",
              "      <td>4</td>\n",
              "      <td>297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>946</th>\n",
              "      <td>363673</td>\n",
              "      <td>Beautiful 3 bedroom in Manhattan</td>\n",
              "      <td>256239</td>\n",
              "      <td>Tracey</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Upper West Side</td>\n",
              "      <td>40.80142</td>\n",
              "      <td>-73.96931</td>\n",
              "      <td>Private room</td>\n",
              "      <td>3000</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>NotReviewed</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1105</th>\n",
              "      <td>468613</td>\n",
              "      <td>$ (Phone number hidden by Airbnb) weeks - room f</td>\n",
              "      <td>2325861</td>\n",
              "      <td>Cynthia</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Lower East Side</td>\n",
              "      <td>40.72152</td>\n",
              "      <td>-73.99279</td>\n",
              "      <td>Private room</td>\n",
              "      <td>1300</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NotReviewed</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1480</th>\n",
              "      <td>664047</td>\n",
              "      <td>Lux 2Bed/2.5Bath Central Park Views</td>\n",
              "      <td>836168</td>\n",
              "      <td>Henry</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Upper West Side</td>\n",
              "      <td>40.77516</td>\n",
              "      <td>-73.98573</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>2000</td>\n",
              "      <td>30</td>\n",
              "      <td>59</td>\n",
              "      <td>2016-01-28</td>\n",
              "      <td>0.71</td>\n",
              "      <td>11</td>\n",
              "      <td>364</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>48080</th>\n",
              "      <td>36074198</td>\n",
              "      <td>Luxury apartment 2 min to times square</td>\n",
              "      <td>203565865</td>\n",
              "      <td>Vinícius</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>SoHo</td>\n",
              "      <td>40.72060</td>\n",
              "      <td>-74.00023</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>1308</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>NotReviewed</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48304</th>\n",
              "      <td>36189195</td>\n",
              "      <td>Next to Times Square/Javits/MSG! Amazing 1BR!</td>\n",
              "      <td>270214015</td>\n",
              "      <td>Rogelio</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Hell's Kitchen</td>\n",
              "      <td>40.75533</td>\n",
              "      <td>-73.99866</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>2999</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>NotReviewed</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48305</th>\n",
              "      <td>36189257</td>\n",
              "      <td>2BR Near Museum Mile! Upper East Side!</td>\n",
              "      <td>272166348</td>\n",
              "      <td>Mary Rotsen</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Upper East Side</td>\n",
              "      <td>40.78132</td>\n",
              "      <td>-73.95262</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>1999</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>NotReviewed</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48523</th>\n",
              "      <td>36308562</td>\n",
              "      <td>Tasteful &amp; Trendy Brooklyn Brownstone, near Train</td>\n",
              "      <td>217732163</td>\n",
              "      <td>Sandy</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>40.68767</td>\n",
              "      <td>-73.95805</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>1369</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NotReviewed</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48535</th>\n",
              "      <td>36311055</td>\n",
              "      <td>Stunning &amp; Stylish Brooklyn Luxury, near Train</td>\n",
              "      <td>245712163</td>\n",
              "      <td>Urvashi</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>40.68245</td>\n",
              "      <td>-73.93417</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>1749</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NotReviewed</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>303</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>239 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id  ... availability_365\n",
              "496      174966  ...                0\n",
              "762      273190  ...              297\n",
              "946      363673  ...              365\n",
              "1105     468613  ...                0\n",
              "1480     664047  ...              364\n",
              "...         ...  ...              ...\n",
              "48080  36074198  ...              179\n",
              "48304  36189195  ...              222\n",
              "48305  36189257  ...              270\n",
              "48523  36308562  ...              349\n",
              "48535  36311055  ...              303\n",
              "\n",
              "[239 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mj5w1teGPCd"
      },
      "source": [
        "239 listings have price per day > 1000. These are either super lavish listings or there was an error during input. Nonetheless, since this records are skewing our data a lot, we will treat them as outliers and drop them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lR7QY5kEHsil"
      },
      "source": [
        "airbnb = airbnb[airbnb['price']<1000]"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nmyP9Sk0GkaS",
        "outputId": "7b079857-83fa-4491-cb03-d2f4b940da25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "source": [
        "### We see a more Gaussian distribution here\n",
        "hist_price2 = airbnb[\"price\"][airbnb[\"price\"]<250].hist()"
      ],
      "execution_count": 18,
      "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": "code",
      "metadata": {
        "id": "7MykX7leHWDg"
      },
      "source": [
        "### We use 250 as threshold price \n",
        "airbnb = airbnb[airbnb[\"price\"]<250]"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3P-YYAToG36r",
        "outputId": "11c6ca60-e944-4bfa-8372-bd2295978c69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "### Looking at the price column again\n",
        "airbnb[\"price\"].describe()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    42669.000000\n",
              "mean       107.897748\n",
              "std         53.803457\n",
              "min          0.000000\n",
              "25%         65.000000\n",
              "50%         99.000000\n",
              "75%        150.000000\n",
              "max        249.000000\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VF8lmriwITtT"
      },
      "source": [
        "We see that the average price is 107. Price varies between 0 to 249"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DbYnjWXTH6zj",
        "outputId": "3053d526-24c6-43fa-ac85-7b410ba5f89b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "###There are 221 unique neighbourhoods in NYC as per this data set. Most listings are in Williamsburg\n",
        "airbnb['neighbourhood'].value_counts()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Bedford-Stuyvesant    3560\n",
              "Williamsburg          3451\n",
              "Harlem                2487\n",
              "Bushwick              2404\n",
              "Upper West Side       1570\n",
              "                      ... \n",
              "Rossville                1\n",
              "Willowbrook              1\n",
              "Neponsit                 1\n",
              "Richmondtown             1\n",
              "New Dorp                 1\n",
              "Name: neighbourhood, Length: 219, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PazikjEVJsdO",
        "outputId": "8aed9439-b62a-4df4-df9e-89f2e20af034",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 824
        }
      },
      "source": [
        "### Count how many neighbourhoods appear more than 200\n",
        "dfnh = airbnb.groupby(\"neighbourhood\").filter(lambda x: x[\"neighbourhood\"].count()>200)\n",
        "dfnh"
      ],
      "execution_count": 22,
      "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>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>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>NotReviewed</td>\n",
              "      <td>0.00</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>5</th>\n",
              "      <td>5099</td>\n",
              "      <td>Large Cozy 1 BR Apartment In Midtown East</td>\n",
              "      <td>7322</td>\n",
              "      <td>Chris</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Murray Hill</td>\n",
              "      <td>40.74767</td>\n",
              "      <td>-73.97500</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>200</td>\n",
              "      <td>3</td>\n",
              "      <td>74</td>\n",
              "      <td>2019-06-22</td>\n",
              "      <td>0.59</td>\n",
              "      <td>1</td>\n",
              "      <td>129</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>NotReviewed</td>\n",
              "      <td>0.00</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>NotReviewed</td>\n",
              "      <td>0.00</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>NotReviewed</td>\n",
              "      <td>0.00</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>NotReviewed</td>\n",
              "      <td>0.00</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>NotReviewed</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>36106 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id  ... availability_365\n",
              "1          2595  ...              355\n",
              "2          3647  ...              365\n",
              "3          3831  ...              194\n",
              "4          5022  ...                0\n",
              "5          5099  ...              129\n",
              "...         ...  ...              ...\n",
              "48890  36484665  ...                9\n",
              "48891  36485057  ...               36\n",
              "48892  36485431  ...               27\n",
              "48893  36485609  ...                2\n",
              "48894  36487245  ...               23\n",
              "\n",
              "[36106 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQQAtHXaLCkM",
        "outputId": "d64cc1f3-c264-4cfe-d115-3c1f3fe8f7dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "### Most data is covered \n",
        "len(dfnh[\"neighbourhood\"])"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "36106"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQtU5cCMLNGv",
        "outputId": "63ae5c6e-2654-4094-abe7-18ae887c9b21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "### Count how many neighbourhoods appear only once\n",
        "dfnh = airbnb.groupby(\"neighbourhood\").filter(lambda x: x['neighbourhood'].count() == 1)\n",
        "len(dfnh[\"neighbourhood\"])"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yNDOv8CDLahS",
        "outputId": "1c43676a-f16a-4e42-e242-dd3ce124feb1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        }
      },
      "source": [
        "###Lets look at neighbourhood groups\n",
        "airbnb['neighbourhood_group'].value_counts()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Brooklyn         18581\n",
              "Manhattan        17270\n",
              "Queens            5413\n",
              "Bronx             1052\n",
              "Staten Island      353\n",
              "Name: neighbourhood_group, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8I-BQMrkMIrC"
      },
      "source": [
        "There are five major neighbourhood groups in NYC with Manhattan and Brooklyn accounting for 85% of the listings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ry7sBtWzMNIq"
      },
      "source": [
        "### Lets see the average listing price by neighbourhood group\n",
        "ng_price = airbnb.groupby(\"neighbourhood_group\")[\"price\"].mean()"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AiKQx6KNTZiC",
        "outputId": "486caef9-723e-423e-c093-9b5a5875e70a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        }
      },
      "source": [
        "### Manhattan is most expensive and Bronx is the least expensive place to live\n",
        "ng_price"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neighbourhood_group\n",
              "Bronx             73.796578\n",
              "Brooklyn          98.068134\n",
              "Manhattan        128.670990\n",
              "Queens            83.639941\n",
              "Staten Island     82.606232\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q-LERD6uUMTb",
        "outputId": "5dbe98c2-1c15-4605-d5ca-e90e9cc815a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        }
      },
      "source": [
        "### Lets see the distribution of price and neighbourhood group\n",
        "plott=sns.catplot(x=\"neighbourhood_group\",y=\"price\",hue=\"room_type\", kind=\"strip\", data=airbnb)\n",
        "plott"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f3e639da3c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 489.1x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_D2FU9LEXmYj"
      },
      "source": [
        "Here we can note that Brooklyn and Manhattan tend to have more listings with price>150. We also note that most listings above price>100 are entire home type followed by private room and shared room which is the cheapest."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CXdeUuhVUnQS",
        "outputId": "8ed0e48d-d750-4c5c-d1de-451c348f9db0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        }
      },
      "source": [
        "### Checking if there are duplicate host_ids and what is the maximum number of listings per host_id\n",
        "df = airbnb.groupby([\"host_id\"])\n",
        "df.size()                                                                "
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "host_id\n",
              "2438         1\n",
              "2571         1\n",
              "2787         6\n",
              "2845         2\n",
              "2868         1\n",
              "            ..\n",
              "274273284    1\n",
              "274298453    1\n",
              "274307600    1\n",
              "274311461    1\n",
              "274321313    1\n",
              "Length: 32978, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N_NugUHCuKXv",
        "outputId": "c85f824c-1406-4289-c17e-9863e2cfa100",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "max(df.size())"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "238"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qPbVMvEGYY_r",
        "outputId": "17ccdb35-dde5-40fa-c237-899755d343bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        }
      },
      "source": [
        "### Here we can see that 32K host_ids are unique appearing only once whereas some host_ids appear as much as 238 times\n",
        "df.size().value_counts().head()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    28391\n",
              "2     2993\n",
              "3      844\n",
              "4      302\n",
              "5      151\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9saKqDv_Yvdl",
        "outputId": "0759a2c3-0c11-4c45-9d36-27cbb6dec352",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        }
      },
      "source": [
        "df.size().value_counts().tail()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "45     1\n",
              "30     1\n",
              "238    1\n",
              "16     1\n",
              "87     1\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZCY8RRZMbWO9",
        "outputId": "75d8be27-9837-4df6-eb2c-640a99492bff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "### Finding the host_id with maximum listings\n",
        "host_id_counts = airbnb[\"host_id\"].value_counts()\n",
        "max_host = host_id_counts.idxmax()\n",
        "max_host"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "219517861"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yhb3i21Absns",
        "outputId": "11000848-08a9-423a-c4af-064280200d0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 892
        }
      },
      "source": [
        "### We see that Sonder(NYC) has the max number of listings\n",
        "airbnb[airbnb[\"host_id\"]==219517861]"
      ],
      "execution_count": 34,
      "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>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>38294</th>\n",
              "      <td>30181945</td>\n",
              "      <td>Sonder | 180 Water | Premier 1BR + Rooftop</td>\n",
              "      <td>219517861</td>\n",
              "      <td>Sonder (NYC)</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Financial District</td>\n",
              "      <td>40.70771</td>\n",
              "      <td>-74.00641</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>229</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-05-29</td>\n",
              "      <td>0.73</td>\n",
              "      <td>327</td>\n",
              "      <td>219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38588</th>\n",
              "      <td>30347708</td>\n",
              "      <td>Sonder | 180 Water | Charming 1BR + Rooftop</td>\n",
              "      <td>219517861</td>\n",
              "      <td>Sonder (NYC)</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Financial District</td>\n",
              "      <td>40.70743</td>\n",
              "      <td>-74.00443</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>232</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-05-21</td>\n",
              "      <td>0.60</td>\n",
              "      <td>327</td>\n",
              "      <td>159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39771</th>\n",
              "      <td>30937594</td>\n",
              "      <td>Sonder | The Nash | Brilliant Studio + Rooftop</td>\n",
              "      <td>219517861</td>\n",
              "      <td>Sonder (NYC)</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Murray Hill</td>\n",
              "      <td>40.74845</td>\n",
              "      <td>-73.97446</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>245</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2019-06-08</td>\n",
              "      <td>0.94</td>\n",
              "      <td>327</td>\n",
              "      <td>137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39772</th>\n",
              "      <td>30937595</td>\n",
              "      <td>Sonder | 11th Ave | Bright 1BR + Gym</td>\n",
              "      <td>219517861</td>\n",
              "      <td>Sonder (NYC)</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Hell's Kitchen</td>\n",
              "      <td>40.76188</td>\n",
              "      <td>-73.99616</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>185</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-06-20</td>\n",
              "      <td>1.00</td>\n",
              "      <td>327</td>\n",
              "      <td>281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39773</th>\n",
              "      <td>30937596</td>\n",
              "      <td>Sonder | 11th Ave | Contemporary 1BR + Gym</td>\n",
              "      <td>219517861</td>\n",
              "      <td>Sonder (NYC)</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Hell's Kitchen</td>\n",
              "      <td>40.76037</td>\n",
              "      <td>-73.99744</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>185</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-06-10</td>\n",
              "      <td>1.00</td>\n",
              "      <td>327</td>\n",
              "      <td>332</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>47686</th>\n",
              "      <td>35869234</td>\n",
              "      <td>Sonder | 116 John | Classic 1BR + Gym</td>\n",
              "      <td>219517861</td>\n",
              "      <td>Sonder (NYC)</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Financial District</td>\n",
              "      <td>40.70852</td>\n",
              "      <td>-74.00510</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>198</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>NotReviewed</td>\n",
              "      <td>0.00</td>\n",
              "      <td>327</td>\n",
              "      <td>345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47687</th>\n",
              "      <td>35869466</td>\n",
              "      <td>Sonder | 116 John | Classic Studio + Gym</td>\n",
              "      <td>219517861</td>\n",
              "      <td>Sonder (NYC)</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Financial District</td>\n",
              "      <td>40.70802</td>\n",
              "      <td>-74.00642</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>180</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>NotReviewed</td>\n",
              "      <td>0.00</td>\n",
              "      <td>327</td>\n",
              "      <td>338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47691</th>\n",
              "      <td>35871510</td>\n",
              "      <td>Sonder | 116 John | Vibrant Studio + Fitness Room</td>\n",
              "      <td>219517861</td>\n",
              "      <td>Sonder (NYC)</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Financial District</td>\n",
              "      <td>40.70818</td>\n",
              "      <td>-74.00631</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>135</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>NotReviewed</td>\n",
              "      <td>0.00</td>\n",
              "      <td>327</td>\n",
              "      <td>339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47692</th>\n",
              "      <td>35871511</td>\n",
              "      <td>Sonder | 116 John | Vibrant 1BR + Fitness Room</td>\n",
              "      <td>219517861</td>\n",
              "      <td>Sonder (NYC)</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Financial District</td>\n",
              "      <td>40.70691</td>\n",
              "      <td>-74.00682</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>165</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>NotReviewed</td>\n",
              "      <td>0.00</td>\n",
              "      <td>327</td>\n",
              "      <td>342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47693</th>\n",
              "      <td>35871515</td>\n",
              "      <td>Sonder | 116 John | Stunning 1BR + Rooftop</td>\n",
              "      <td>219517861</td>\n",
              "      <td>Sonder (NYC)</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Financial District</td>\n",
              "      <td>40.70772</td>\n",
              "      <td>-74.00673</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>165</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>NotReviewed</td>\n",
              "      <td>0.00</td>\n",
              "      <td>327</td>\n",
              "      <td>347</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>238 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id  ... availability_365\n",
              "38294  30181945  ...              219\n",
              "38588  30347708  ...              159\n",
              "39771  30937594  ...              137\n",
              "39772  30937595  ...              281\n",
              "39773  30937596  ...              332\n",
              "...         ...  ...              ...\n",
              "47686  35869234  ...              345\n",
              "47687  35869466  ...              338\n",
              "47691  35871510  ...              339\n",
              "47692  35871511  ...              342\n",
              "47693  35871515  ...              347\n",
              "\n",
              "[238 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a2l7eSTNcVx_"
      },
      "source": [
        "Listing id and Host name are not useful for our analysis so I will drop them"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PVH6AEAGcLgV"
      },
      "source": [
        "airbnb = airbnb.drop(columns = [\"id\",\"host_name\"])"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dryZNDxVcbdf"
      },
      "source": [
        "### Let's Analyse the listing name column\n",
        "airbnb[\"name_length\"]=airbnb['name'].map(str).apply(len)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gvhqaB80dW6L",
        "outputId": "12da2f4e-9997-45b4-abe6-c21ec65a9b54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "###Max and Min name length\n",
        "print(airbnb[\"name_length\"].max())\n",
        "print(airbnb[\"name_length\"].min())\n",
        "print(airbnb[\"name_length\"].idxmax())\n",
        "print(airbnb[\"name_length\"].idxmin())"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "179\n",
            "1\n",
            "25832\n",
            "4033\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YSXGe6QDdsb7",
        "outputId": "a385626d-b1dc-4bf5-c869-6a03710c3a8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "### Max name\n",
        "airbnb.at[25832,'name']"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'The printing studio bedroom with garden in Bedstuy  !!Brooklyn. Historic neighborhood close to everything ! Full kitchen bathroom BBQ and porch at your disposal. Learn to print !!'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rGq6YvnKd4kx",
        "outputId": "d3d137b5-ded4-4f3b-c486-3d5a0f8f9951",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "airbnb.at[4022,'name']"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Gorgeous Apartment in Battery Park!'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y_324fpMeYWz",
        "outputId": "1920fe83-be0c-490d-8d89-cbdac07a69a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "airbnb.at[4033,'name']"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsqMRyhpefSq",
        "outputId": "4d73367f-4fe7-4727-e632-140147d202d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "### Let's figure if name length has an impact on how much it is noticed.\n",
        "###We can assume higher number of reviews mean more people lived there \n",
        "###and hence more people \"noticed\" the listing\n",
        "airbnb.plot.scatter(x='name_length', y=\"number_of_reviews\")"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3e60e48550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ixm1K3gG5MZe",
        "outputId": "823e1c8e-d75e-4800-a40b-d52b4b6fd8fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "airbnb[\"name_length\"].corr(airbnb[\"number_of_reviews\"]) "
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.004684119349132562"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87oF0ElR5SsJ",
        "outputId": "29497dc1-50e3-4cea-d454-80d4d534e9cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "###There is hardly any relationship there. Lets try between price and name length \n",
        "airbnb[airbnb[\"name_length\"]<50].plot.scatter(x=\"name_length\", y =\"price\")"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3e60e40dd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pA2Rqtpr5q6i",
        "outputId": "29b601dd-aab4-4099-84b3-1d33d7b85179",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "airbnb[\"name_length\"].corr(airbnb[\"price\"])"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.056295731411209415"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-3Nq7MJW7SOi",
        "outputId": "3c1aff21-8ea7-4e8f-9a59-8aad3f768e42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "airbnb.name_length.hist()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3e610bc940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7UZePEtK7Y1v",
        "outputId": "82f20a3e-24b6-4b57-961d-cfc55a70c04d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "### Lets look at room_type variable\n",
        "airbnb['room_type'].value_counts()\n",
        "### Most listings are either Entire home or Private room"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Private room       21753\n",
              "Entire home/apt    19790\n",
              "Shared room         1126\n",
              "Name: room_type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7U7uv5X7dpJ",
        "outputId": "74ec902c-8967-453e-88e9-62df268b361d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        }
      },
      "source": [
        "### Average price per room_type\n",
        "rt_price = airbnb.groupby(\"room_type\")[\"price\"].mean()\n",
        "rt_price"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "room_type\n",
              "Entire home/apt    144.977211\n",
              "Private room        76.802832\n",
              "Shared room         56.925400\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JuRI_FRx7szA",
        "outputId": "5437af1e-0bf0-467b-ac32-37f0ef2344f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "### Analysing minimum nights\n",
        "airbnb[\"minimum_nights\"].describe()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    42669.000000\n",
              "mean         6.856359\n",
              "std         19.981924\n",
              "min          1.000000\n",
              "25%          1.000000\n",
              "50%          2.000000\n",
              "75%          5.000000\n",
              "max       1250.000000\n",
              "Name: minimum_nights, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I5N43GII8ykG"
      },
      "source": [
        "Again, range is between 1 night to 1250 nights. Quite odd."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NC5CQ18c8m2J",
        "outputId": "2da28cf3-d8e5-49b3-fd34-3a7694f07db5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "### Analysing minimum nights\n",
        "### We see most values are between 1 to 100\n",
        "hist_mn=airbnb[\"minimum_nights\"].hist()\n",
        "hist_mn"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3e60fd4e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "676csdpN874Y",
        "outputId": "b534930a-f041-42eb-90cf-24b194479700",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        }
      },
      "source": [
        "### Closer look\n",
        "hist_mn1=airbnb[\"minimum_nights\"][airbnb[\"minimum_nights\"]<10].hist()\n",
        "hist_mn1"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3e61084eb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "28bzBhn39DsM",
        "outputId": "17b9ddca-b77e-4f09-80ea-0509289af683",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "airbnb[\"minimum_nights\"][airbnb['minimum_nights']>30]"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6         45\n",
              "14        90\n",
              "36        60\n",
              "81        31\n",
              "106       90\n",
              "        ... \n",
              "48312     90\n",
              "48325     60\n",
              "48368     60\n",
              "48446    365\n",
              "48752     32\n",
              "Name: minimum_nights, Length: 628, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tLZZ_001-bHS"
      },
      "source": [
        "### We replace all records with min nights > 30 by 30\n",
        "airbnb.loc[(airbnb.minimum_nights >30),\"minimum_nights\"]=30"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mq9lJa_O--sQ",
        "outputId": "298ed79c-0164-4051-9593-15dc1b083ddf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "hist_mn2=airbnb[\"minimum_nights\"][airbnb[\"minimum_nights\"]<30].hist()\n",
        "hist_mn2"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3e60eb7780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EcUmoK85_UYJ",
        "outputId": "1a16c7b8-8ab7-4b90-c058-2178939bbfcc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "### Does minimum_nights have impact on price?\n",
        "airbnb[\"minimum_nights\"].corr(airbnb[\"price\"])"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.059554361719124246"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e1yyvTMc_sQq",
        "outputId": "77b854a6-81b4-4de1-860c-8c951c21ff50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "###now lets analyse availability_365 column\n",
        "airbnb[\"availability_365\"].describe()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    42669.000000\n",
              "mean       107.622747\n",
              "std        129.731981\n",
              "min          0.000000\n",
              "25%          0.000000\n",
              "50%         37.000000\n",
              "75%        211.000000\n",
              "max        365.000000\n",
              "Name: availability_365, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZSv88R3D_0Nk",
        "outputId": "41f4ec83-fedd-4b9e-b085-c14d7deccb7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        }
      },
      "source": [
        "sns.displot(airbnb['availability_365'])"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f3e5f5ed160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FtYOsP89API4"
      },
      "source": [
        "### After analysis, We have decided to drop these columns as they will not be useful in prediction\n",
        "airbnb.drop([\"name\",'last_review',\"latitude\",'longitude'], axis=1, inplace=True)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aj55LRLLB74R"
      },
      "source": [
        "### Dropping host_id\n",
        "airbnb.drop([\"host_id\"], axis=1, inplace=True)"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zrx8OmPtCBgv",
        "outputId": "58c65871-f402-4d7b-9249-45b5d80e3562",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 722
        }
      },
      "source": [
        "### Plotting correlation matrix \n",
        "corr = airbnb.corr(method='pearson')\n",
        "plt.figure(figsize=(15,8))\n",
        "sns.heatmap(corr, annot=True)\n",
        "airbnb.columns"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['neighbourhood_group', 'neighbourhood', 'room_type', 'price',\n",
              "       'minimum_nights', 'number_of_reviews', 'reviews_per_month',\n",
              "       'calculated_host_listings_count', 'availability_365', 'name_length'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ejAPwQ5gFCcK",
        "outputId": "f3962848-f6b4-4acc-d573-f18a20c38a4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        }
      },
      "source": [
        "### Lets check out data one more time before beginning prediction.\n",
        "airbnb.dtypes"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neighbourhood_group                object\n",
              "neighbourhood                      object\n",
              "room_type                          object\n",
              "price                               int64\n",
              "minimum_nights                      int64\n",
              "number_of_reviews                   int64\n",
              "reviews_per_month                 float64\n",
              "calculated_host_listings_count      int64\n",
              "availability_365                    int64\n",
              "name_length                         int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hq8ipJa8FQ1W"
      },
      "source": [
        "## Let us start with basic Linear Regression to create a base line model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zSwV8u5EFbSZ"
      },
      "source": [
        "Making dummies for neighbourhood group and room_type"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "feAzlQ76FPTW"
      },
      "source": [
        "## without neighbourhood column\n",
        "\n",
        "dataset_onehot1 = pd.get_dummies(airbnb, columns=['neighbourhood_group',\"room_type\"], prefix = ['ng',\"rt\"],drop_first=True)\n",
        "dataset_onehot1.drop([\"neighbourhood\"], axis=1, inplace=True)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9g2aDEbuF1M9",
        "outputId": "ca8c1c21-79e9-45f4-87f5-d36188373cfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "### Checking dataframe shape\n",
        "dataset_onehot1.shape"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42669, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MC1NfDppF3sb"
      },
      "source": [
        "X1= dataset_onehot1.loc[:, dataset_onehot1.columns != 'price']"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LwLfn6TBF6Su"
      },
      "source": [
        "Y1 = dataset_onehot1[\"price\"]"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZRkWBPydF-CZ"
      },
      "source": [
        "Splitting Data into Training and testing data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-FO4MEWF8kO"
      },
      "source": [
        "x_train1, x_test1, y_train1, y_test1 = train_test_split(X1, Y1, test_size=0.20, random_state=42)"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5QxcfX-uGNZV"
      },
      "source": [
        "### Fitting Linear regression\n",
        "reg1 = LinearRegression().fit(x_train1, y_train1)"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8hdYdvMYGQHW",
        "outputId": "75dedea2-7ddf-4159-9e26-ff4e5ffc7ba5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "### R squared value\n",
        "reg1.score(x_train1, y_train1)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5033306680968379"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6iODvi46GTYy",
        "outputId": "5905401b-df20-4a11-f826-3f5373ab52e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "### Coefficients\n",
        "reg1.coef_"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-8.09520215e-01, -9.08803421e-03, -1.61641699e+00,  1.42434665e-01,\n",
              "        4.67863558e-02,  1.98696542e-01,  1.98040033e+01,  4.41704660e+01,\n",
              "        9.69056189e+00,  2.05907986e-01, -6.56442414e+01, -8.90902274e+01])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VW8CIlqZGXco"
      },
      "source": [
        "### Predicting \n",
        "y_pred1 = reg1.predict(x_test1)"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FbOFgBZ3GkGj",
        "outputId": "c9dbe717-081f-45a8-9c9a-91788dd21e6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "source": [
        "Coeff1 = pd.DataFrame(columns=[\"Variable\",\"Coefficient\"])\n",
        "Coeff1[\"Variable\"]=x_train1.columns\n",
        "Coeff1[\"Coefficient\"]=reg1.coef_\n",
        "Coeff1.sort_values(\"Coefficient\")"
      ],
      "execution_count": 70,
      "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>Variable</th>\n",
              "      <th>Coefficient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>rt_Shared room</td>\n",
              "      <td>-89.090227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>rt_Private room</td>\n",
              "      <td>-65.644241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>reviews_per_month</td>\n",
              "      <td>-1.616417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>minimum_nights</td>\n",
              "      <td>-0.809520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>number_of_reviews</td>\n",
              "      <td>-0.009088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>availability_365</td>\n",
              "      <td>0.046786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>calculated_host_listings_count</td>\n",
              "      <td>0.142435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>name_length</td>\n",
              "      <td>0.198697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ng_Staten Island</td>\n",
              "      <td>0.205908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ng_Queens</td>\n",
              "      <td>9.690562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>ng_Brooklyn</td>\n",
              "      <td>19.804003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>ng_Manhattan</td>\n",
              "      <td>44.170466</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          Variable  Coefficient\n",
              "11                  rt_Shared room   -89.090227\n",
              "10                 rt_Private room   -65.644241\n",
              "2                reviews_per_month    -1.616417\n",
              "0                   minimum_nights    -0.809520\n",
              "1                number_of_reviews    -0.009088\n",
              "4                 availability_365     0.046786\n",
              "3   calculated_host_listings_count     0.142435\n",
              "5                      name_length     0.198697\n",
              "9                 ng_Staten Island     0.205908\n",
              "8                        ng_Queens     9.690562\n",
              "6                      ng_Brooklyn    19.804003\n",
              "7                     ng_Manhattan    44.170466"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HI-uU0zhGnmH",
        "outputId": "91adfb40-31d2-4129-8cfe-10861e5f830c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "### Calculate RMSE\n",
        "rmse1 = np.sqrt(metrics.mean_squared_error(y_test1, y_pred1))\n",
        "rmse1"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37.68798547656789"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EYNo-T-UG4dL",
        "outputId": "5e2d12ac-c9e4-42ac-cc87-228edba5ee8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        }
      },
      "source": [
        "### Taking a closer look at the estimates\n",
        "X2 = sm.add_constant(x_train1)\n",
        "est = sm.OLS(y_train1, X2)\n",
        "est2 = est.fit()\n",
        "print(est2.summary())"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  price   R-squared:                       0.503\n",
            "Model:                            OLS   Adj. R-squared:                  0.503\n",
            "Method:                 Least Squares   F-statistic:                     2882.\n",
            "Date:                Sat, 24 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        07:09:23   Log-Likelihood:            -1.7259e+05\n",
            "No. Observations:               34135   AIC:                         3.452e+05\n",
            "Df Residuals:                   34122   BIC:                         3.453e+05\n",
            "Df Model:                          12                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==================================================================================================\n",
            "                                     coef    std err          t      P>|t|      [0.025      0.975]\n",
            "--------------------------------------------------------------------------------------------------\n",
            "const                            109.6210      1.526     71.839      0.000     106.630     112.612\n",
            "minimum_nights                    -0.8095      0.027    -30.109      0.000      -0.862      -0.757\n",
            "number_of_reviews                 -0.0091      0.006     -1.607      0.108      -0.020       0.002\n",
            "reviews_per_month                 -1.6164      0.163     -9.906      0.000      -1.936      -1.297\n",
            "calculated_host_listings_count     0.1424      0.008     18.082      0.000       0.127       0.158\n",
            "availability_365                   0.0468      0.002     26.685      0.000       0.043       0.050\n",
            "name_length                        0.1987      0.020      9.866      0.000       0.159       0.238\n",
            "ng_Brooklyn                       19.8040      1.349     14.680      0.000      17.160      22.448\n",
            "ng_Manhattan                      44.1705      1.356     32.576      0.000      41.513      46.828\n",
            "ng_Queens                          9.6906      1.428      6.787      0.000       6.892      12.489\n",
            "ng_Staten Island                   0.2059      2.583      0.080      0.936      -4.857       5.268\n",
            "rt_Private room                  -65.6442      0.427   -153.848      0.000     -66.481     -64.808\n",
            "rt_Shared room                   -89.0902      1.315    -67.764      0.000     -91.667     -86.513\n",
            "==============================================================================\n",
            "Omnibus:                     2348.378   Durbin-Watson:                   1.999\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3132.292\n",
            "Skew:                           0.617   Prob(JB):                         0.00\n",
            "Kurtosis:                       3.825   Cond. No.                     2.69e+03\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 2.69e+03. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9dLKyzKQOCMk"
      },
      "source": [
        "Model Interpretation\n",
        "Now that we have the results of our model, lets try to interpret it in detail.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "1.   We first look at the Adjusted R square value since this is a Multiple linear regression. It tell us that our independent variables can explain 50.3% of variations in our dependent variable, which is price.\n",
        "The constant or the y intercept has a value of 109.56. This means that putting all other x variables at 0, an Entire Apt/Home in Bronx will have a predicted price of 109.56. Remember when we created dummy variables we dropped one dummy from each column which we use as reference.\n",
        "2.   Let's now look at the coefficients. The coefficient of ng_Manhattan is 44.09. We interpret as: Everything else being constant, an Entire Apt/ Home in Manhattan will cost 44.09 more that same in Bronx. We can similarly interpret coefficient of minimum night as: With everything else being constant, with every one unit increase in minimum number of nights, the predicted price decreases by 0.8075.\n",
        "The std error is nothing but sample standard deviation for each variable. the t column shows the value of t statistic which is the z score of the sample variable. Z score tells us how far a sample is from its mean. A Z score of 2 tells us that the sample is two standard deviation away from the mean.\n",
        "P values suggests how significant these estimates are. Considering \n",
        "\n",
        "1.   alpha of 0.05, we can reject the Null hypothesis for all variables except number_of_reviews, and ng_Staten Island. Alpha is the degree of error we are willing to accept. we have chosen alpha as 5% which is also most commonly used.\n",
        "4. The confidence intervals show the upper bound and lower bound for the TRUE POPULATION coefficient with 95% confidence."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GghEhM7bOdIo"
      },
      "source": [
        "## Lets try to use the neighbourhood variable. It has more than 200 distinct values.\n",
        "Hence, when we create dummies we will have large number of variables."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBFITPWq2d2C"
      },
      "source": [
        "dataset_onehot2 = pd.get_dummies(airbnb, columns=['neighbourhood_group',\"neighbourhood\",\"room_type\"], prefix = ['ng',\"nh\",\"rt\"],drop_first=True)"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eEz-GcvV2zzB",
        "outputId": "a5b76c4a-454d-41a4-9e40-25b2101bcb12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "dataset_onehot2.shape"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42669, 231)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qnmhioF73FMI"
      },
      "source": [
        "XL1 = dataset_onehot2.loc[:,dataset_onehot2.columns !='price']\n",
        "YL1 = dataset_onehot2[\"price\"]\n",
        "x_trainL11, x_testL11, y_trainL11, y_testL11 = train_test_split(XL1,YL1,test_size=0.20, random_state=42)"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bQQAeL2v36so"
      },
      "source": [
        "We will use Lasso regression because it has the ability to nullify parameters that do not improve the model. Also, the dataset isn't large enough and hence Lasso is a good choice as it would add a little bit bias but reduce variance greatly. Starting with alpha=0.1 We can use crossvalidation and check for many values for alpha to find best one, but I will save this process for the next model which I think will procduce better results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NgeQDolW3zTM",
        "outputId": "50ccda43-b4c5-4275-dfba-73dce636d5b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "regL1 = Lasso(alpha=0.01)\n",
        "regL1.fit(x_trainL11,y_trainL11)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,\n",
              "      normalize=False, positive=False, precompute=False, random_state=None,\n",
              "      selection='cyclic', tol=0.0001, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WfvB0wes4OVJ",
        "outputId": "931e8e12-92ca-4cfd-d8fa-3062bcda3921",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "### R Squared\n",
        "### This regularised model did way better than the normal regresion\n",
        "regL1.score(x_trainL11, y_trainL11)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5672687881098055"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tvp5BFw25Dyw",
        "outputId": "cc8eeb9f-4e0a-44d0-de65-6626c7c0bf78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "### RMSE\n",
        "### Smaller value than earlier\n",
        "y_predL1= regL1.predict(x_testL11)\n",
        "print(np.sqrt(metrics.mean_squared_error(y_testL11,y_predL1)))"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "35.122928878780456\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wZlrfU-c5XjS",
        "outputId": "15fc695f-21f4-4c6c-97d8-afc24c6d8ad6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "### We can see that some parameters have zero coefficients.\n",
        "regL1.coef_"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-8.74108236e-01, -1.54617057e-02, -1.21937375e+00,  8.53509662e-02,\n",
              "        5.35827161e-02,  1.33032667e-01,  1.25894104e+01,  5.02217532e+01,\n",
              "        6.30345411e+00, -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\n",
              "        0.00000000e+00,  7.63815202e+00, -0.00000000e+00,  0.00000000e+00,\n",
              "       -5.54357765e-01,  0.00000000e+00,  0.00000000e+00, -0.00000000e+00,\n",
              "        0.00000000e+00,  0.00000000e+00,  6.14970076e-01,  0.00000000e+00,\n",
              "        0.00000000e+00,  0.00000000e+00, -9.25171095e+00, -0.00000000e+00,\n",
              "        2.15210517e+01, -1.25645271e+01,  0.00000000e+00, -0.00000000e+00,\n",
              "       -0.00000000e+00, -3.46151208e+00,  2.93815492e+01, -4.40132764e+00,\n",
              "        0.00000000e+00, -1.35292724e+00,  0.00000000e+00, -7.53591116e+00,\n",
              "        2.39801650e+01, -0.00000000e+00,  0.00000000e+00,  1.25661957e+01,\n",
              "       -6.68172734e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
              "       -0.00000000e+00, -0.00000000e+00,  1.77925788e+01,  0.00000000e+00,\n",
              "        2.19739411e+01, -0.00000000e+00,  2.36120788e+00, -0.00000000e+00,\n",
              "        0.00000000e+00,  0.00000000e+00, -0.00000000e+00, -1.00691324e+01,\n",
              "        0.00000000e+00, -9.36309803e+00,  3.24240457e+01,  4.18289061e+00,\n",
              "       -0.00000000e+00, -0.00000000e+00,  2.54089178e+01, -0.00000000e+00,\n",
              "       -0.00000000e+00, -6.35982706e+00, -1.93706505e+01, -0.00000000e+00,\n",
              "       -1.06677933e+01, -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\n",
              "        0.00000000e+00, -0.00000000e+00, -0.00000000e+00,  0.00000000e+00,\n",
              "       -0.00000000e+00,  0.00000000e+00,  1.25439145e+01, -9.74644748e+00,\n",
              "        8.44196486e+00, -0.00000000e+00,  2.02917151e+00,  0.00000000e+00,\n",
              "        0.00000000e+00,  2.42860298e+01,  0.00000000e+00, -0.00000000e+00,\n",
              "       -0.00000000e+00,  2.10526598e+01,  3.26354283e+00, -0.00000000e+00,\n",
              "       -0.00000000e+00, -1.70088210e+00,  0.00000000e+00,  2.09497668e+01,\n",
              "        1.62664390e+01,  0.00000000e+00, -2.66496504e+01,  1.07339039e+01,\n",
              "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
              "       -0.00000000e+00, -0.00000000e+00, -0.00000000e+00, -4.02195262e+01,\n",
              "        0.00000000e+00, -0.00000000e+00, -0.00000000e+00,  0.00000000e+00,\n",
              "       -7.59662324e+00,  0.00000000e+00, -0.00000000e+00,  0.00000000e+00,\n",
              "       -1.07912900e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
              "       -0.00000000e+00,  1.76495915e+01,  0.00000000e+00, -2.72090076e+00,\n",
              "       -0.00000000e+00, -1.69100140e+01,  0.00000000e+00, -0.00000000e+00,\n",
              "       -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,  1.19531290e+01,\n",
              "       -8.76003400e+00, -0.00000000e+00, -2.14132561e+01,  0.00000000e+00,\n",
              "       -0.00000000e+00,  0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\n",
              "       -0.00000000e+00,  9.86841871e+00,  1.14172298e+01,  0.00000000e+00,\n",
              "        0.00000000e+00, -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\n",
              "        0.00000000e+00,  6.16526418e+00,  0.00000000e+00,  0.00000000e+00,\n",
              "       -0.00000000e+00,  0.00000000e+00, -2.74320558e+00,  2.40462985e+01,\n",
              "       -0.00000000e+00,  0.00000000e+00, -4.04509726e+00,  4.31211323e+00,\n",
              "        0.00000000e+00,  0.00000000e+00,  1.57314639e+01, -2.61463507e+00,\n",
              "       -7.28727793e+00, -0.00000000e+00,  1.70573080e-01, -7.09408362e+00,\n",
              "       -0.00000000e+00, -0.00000000e+00, -5.18551549e-01,  0.00000000e+00,\n",
              "        3.66837901e+00, -1.65139034e+01,  0.00000000e+00, -2.32766344e+00,\n",
              "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00, -5.49885002e+00,\n",
              "        0.00000000e+00, -0.00000000e+00,  1.42884540e+01, -0.00000000e+00,\n",
              "        0.00000000e+00, -0.00000000e+00,  1.88999557e+01,  2.42780596e+00,\n",
              "       -0.00000000e+00, -6.12939218e+00,  0.00000000e+00, -0.00000000e+00,\n",
              "       -0.00000000e+00,  4.89688416e-01, -2.51750491e+00,  1.19149026e+01,\n",
              "       -0.00000000e+00, -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\n",
              "       -0.00000000e+00,  2.12421594e+01, -2.01889270e+00, -0.00000000e+00,\n",
              "        0.00000000e+00, -4.46933223e+00, -2.67160415e+00, -0.00000000e+00,\n",
              "        3.24722848e+01, -0.00000000e+00, -3.88611567e+01, -0.00000000e+00,\n",
              "        0.00000000e+00,  1.90030321e+01, -0.00000000e+00, -0.00000000e+00,\n",
              "       -0.00000000e+00, -0.00000000e+00,  2.27856484e+01,  0.00000000e+00,\n",
              "        3.22295500e+00, -6.18423733e+00, -0.00000000e+00,  1.95573705e+00,\n",
              "       -6.16897114e+01, -8.45855686e+01])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qkvi9SII5i2v",
        "outputId": "b1ef76d4-45e7-4dff-b167-a586e8e8ff58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "source": [
        "CoeffLS1 = pd.DataFrame(columns=[\"Variable\",\"Coefficients\"])\n",
        "CoeffLS1[\"Variable\"]=x_trainL11.columns\n",
        "CoeffLS1[\"Coefficients\"]=regL1.coef_\n",
        "CoeffLS1.sort_values(\"Coefficients\", ascending = False)"
      ],
      "execution_count": 80,
      "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>Variable</th>\n",
              "      <th>Coefficients</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>ng_Manhattan</td>\n",
              "      <td>50.221753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>nh_Vinegar Hill</td>\n",
              "      <td>32.472285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>nh_DUMBO</td>\n",
              "      <td>32.424046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>nh_Brooklyn Heights</td>\n",
              "      <td>29.381549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>nh_Downtown Brooklyn</td>\n",
              "      <td>25.408918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>nh_Harlem</td>\n",
              "      <td>-26.649650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214</th>\n",
              "      <td>nh_Washington Heights</td>\n",
              "      <td>-38.861157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>nh_Inwood</td>\n",
              "      <td>-40.219526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>228</th>\n",
              "      <td>rt_Private room</td>\n",
              "      <td>-61.689711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>rt_Shared room</td>\n",
              "      <td>-84.585569</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>230 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Variable  Coefficients\n",
              "7             ng_Manhattan     50.221753\n",
              "212        nh_Vinegar Hill     32.472285\n",
              "62                nh_DUMBO     32.424046\n",
              "34     nh_Brooklyn Heights     29.381549\n",
              "66    nh_Downtown Brooklyn     25.408918\n",
              "..                     ...           ...\n",
              "102              nh_Harlem    -26.649650\n",
              "214  nh_Washington Heights    -38.861157\n",
              "111              nh_Inwood    -40.219526\n",
              "228        rt_Private room    -61.689711\n",
              "229         rt_Shared room    -84.585569\n",
              "\n",
              "[230 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QIgXCepc5oW2"
      },
      "source": [
        "### Finally, lets try Random forest regressor which we believe will give best results\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HetxO0jK5lZ6",
        "outputId": "87ec80c1-8b62-4e73-91da-6684dfb3266c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        }
      },
      "source": [
        "### Initially, lets build a tree without any constraints.\n",
        "regrRM = RandomForestRegressor(n_estimators=300)\n",
        "regrRM.fit(x_trainL11, y_trainL11)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=300, n_jobs=None, oob_score=False,\n",
              "                      random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "irGphuHb601a",
        "outputId": "f3dc7554-ebb1-4fae-f70d-0fccb3b29fcb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "### We get R squared value as 93.6%! There is obviously a problem of overfitting.\n",
        "\n",
        "print(regrRM.score(x_trainL11, y_trainL11))\n",
        "y_predL1= regrRM.predict(x_testL11)\n",
        "print(np.sqrt(metrics.mean_squared_error(y_testL11,y_predL1)))"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9359121650375394\n",
            "34.90212188870185\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "034le2Qq6_BL",
        "outputId": "7e8ff085-d320-4b3f-89a8-c123d7089b95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "### Using feature importance, we can see which feature had most weight\n",
        "regrRM.feature_importances_"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5.21229895e-02, 5.79542097e-02, 7.71462916e-02, 3.75438186e-02,\n",
              "       7.35108942e-02, 8.47088470e-02, 3.87133894e-03, 6.09536989e-02,\n",
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              "       2.02250042e-04, 6.75016841e-05, 3.03067580e-03, 4.37163822e-04,\n",
              "       3.87236435e-05, 2.55582854e-04, 2.45778155e-04, 1.59549327e-04,\n",
              "       7.78876785e-04, 2.88886045e-04, 1.24996565e-04, 3.24568060e-04,\n",
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              "       6.78170841e-06, 2.37760165e-03, 9.01403738e-05, 4.21330564e-04,\n",
              "       1.39839365e-03, 1.68065431e-05, 6.24816209e-05, 2.58892417e-03,\n",
              "       9.56412887e-04, 5.81976485e-05, 2.30318521e-04, 1.01352472e-04,\n",
              "       9.35947159e-06, 1.39645968e-04, 1.93206544e-03, 1.18816993e-06,\n",
              "       5.00061650e-04, 5.00737343e-05, 2.52582216e-04, 1.14288005e-04,\n",
              "       1.54808717e-04, 5.94358393e-05, 4.59210196e-05, 4.30934757e-05,\n",
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              "       1.80574091e-05, 8.03723833e-06, 8.05968060e-04, 1.40531389e-04,\n",
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              "       3.33109964e-04, 4.66580130e-04, 8.91934348e-04, 1.37390418e-04,\n",
              "       4.17258259e-04, 2.17771550e-03, 2.18198695e-04, 7.09221014e-05,\n",
              "       2.61140631e-04, 1.44781543e-03, 9.23266295e-04, 8.61922984e-06,\n",
              "       9.63730096e-06, 1.50702283e-04, 2.96784651e-04, 3.09929489e-03,\n",
              "       1.06131458e-03, 8.80587019e-05, 9.82298118e-03, 3.30331187e-03,\n",
              "       1.77705233e-04, 1.13543856e-04, 1.34307176e-05, 1.36760664e-04,\n",
              "       1.68142559e-06, 1.05291076e-06, 1.74467228e-05, 3.98712470e-03,\n",
              "       4.46437189e-04, 7.89530690e-04, 6.80702797e-05, 4.84439287e-06,\n",
              "       6.75730302e-04, 2.18210278e-04, 7.49319496e-05, 8.12282971e-05,\n",
              "       1.15744834e-03, 1.34333749e-04, 5.33080683e-05, 4.21294946e-04,\n",
              "       6.32281367e-07, 1.77841070e-03, 1.40438163e-04, 1.84024518e-03,\n",
              "       8.77128970e-05, 3.08669483e-04, 1.14226070e-04, 3.69001587e-04,\n",
              "       3.39632066e-05, 7.99300910e-05, 1.30453579e-05, 2.40815555e-03,\n",
              "       6.40315706e-04, 2.69384785e-05, 2.01595139e-03, 1.55042454e-04,\n",
              "       7.46675156e-05, 1.83095907e-04, 7.54405035e-05, 2.55337959e-05,\n",
              "       3.36361346e-05, 1.65988923e-03, 2.94321703e-04, 5.92502285e-05,\n",
              "       1.99633722e-04, 9.81260737e-06, 1.69533506e-06, 2.02411837e-05,\n",
              "       2.50809442e-04, 6.79236982e-04, 3.40147186e-05, 2.86810782e-04,\n",
              "       3.03474306e-06, 3.39281993e-05, 1.71036809e-04, 2.02520915e-03,\n",
              "       1.39135842e-04, 4.58450362e-05, 2.77088511e-05, 3.56938139e-04,\n",
              "       7.36627874e-05, 5.13275506e-05, 1.28838414e-03, 1.39226792e-03,\n",
              "       2.28527329e-04, 1.65817782e-05, 2.68167476e-04, 2.52725232e-04,\n",
              "       2.78849454e-04, 6.61998177e-07, 7.09028282e-04, 3.93596596e-05,\n",
              "       2.97211377e-04, 6.19446953e-04, 6.62156747e-05, 1.29788338e-04,\n",
              "       0.00000000e+00, 5.11526082e-05, 9.31635511e-05, 5.50895512e-04,\n",
              "       3.74605217e-05, 2.43311652e-07, 1.13458136e-03, 1.97204662e-05,\n",
              "       2.41694329e-05, 1.79958874e-04, 1.04266796e-03, 3.62041508e-04,\n",
              "       2.94017434e-06, 2.25334589e-04, 1.62543188e-04, 3.05730269e-05,\n",
              "       1.05657798e-04, 6.58943676e-04, 1.01534545e-03, 9.71892475e-04,\n",
              "       1.81619527e-05, 4.72471691e-06, 1.49124307e-04, 1.22673721e-05,\n",
              "       3.39828200e-05, 5.57792400e-04, 3.02589934e-04, 4.66390566e-06,\n",
              "       2.05392931e-05, 2.68313194e-03, 2.52863336e-03, 1.98434355e-05,\n",
              "       6.67290205e-04, 1.68781274e-04, 9.94052273e-03, 2.00418389e-05,\n",
              "       0.00000000e+00, 1.49379584e-03, 1.43420782e-05, 3.67626847e-07,\n",
              "       3.26939529e-05, 5.76084269e-05, 1.04748034e-02, 1.20486101e-04,\n",
              "       5.80593311e-04, 1.57898626e-04, 2.72700301e-05, 3.90949179e-04,\n",
              "       3.48516201e-01, 6.66991811e-02])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cXf-EKa8-RDX",
        "outputId": "ba6474c2-fbaf-4606-9bbe-446773b92db2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "source": [
        "CoeffRM1 = pd.DataFrame(columns=[\"Variable\",\"FeatureImportance\"])\n",
        "CoeffRM1[\"Variable\"]=x_trainL11.columns\n",
        "CoeffRM1[\"FeatureImportance\"]=regrRM.feature_importances_\n",
        "CoeffRM1.sort_values(\"FeatureImportance\", ascending = False)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Variable</th>\n",
              "      <th>FeatureImportance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>228</th>\n",
              "      <td>rt_Private room</td>\n",
              "      <td>3.485162e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>name_length</td>\n",
              "      <td>8.470885e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>reviews_per_month</td>\n",
              "      <td>7.714629e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>availability_365</td>\n",
              "      <td>7.351089e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>rt_Shared room</td>\n",
              "      <td>6.669918e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219</th>\n",
              "      <td>nh_Westerleigh</td>\n",
              "      <td>3.676268e-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>nh_Eltingville</td>\n",
              "      <td>3.540459e-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>nh_Silver Lake</td>\n",
              "      <td>2.433117e-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>216</th>\n",
              "      <td>nh_West Farms</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>nh_Rossville</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>230 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              Variable  FeatureImportance\n",
              "228    rt_Private room       3.485162e-01\n",
              "5          name_length       8.470885e-02\n",
              "2    reviews_per_month       7.714629e-02\n",
              "4     availability_365       7.351089e-02\n",
              "229     rt_Shared room       6.669918e-02\n",
              "..                 ...                ...\n",
              "219     nh_Westerleigh       3.676268e-07\n",
              "78      nh_Eltingville       3.540459e-07\n",
              "185     nh_Silver Lake       2.433117e-07\n",
              "216      nh_West Farms       0.000000e+00\n",
              "180       nh_Rossville       0.000000e+00\n",
              "\n",
              "[230 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nO6x8T9Q-XBR",
        "outputId": "9f240ddd-9ee4-4adb-9e22-d529134c52ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        }
      },
      "source": [
        "regrRM.get_params()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bootstrap': True,\n",
              " 'ccp_alpha': 0.0,\n",
              " 'criterion': 'mse',\n",
              " 'max_depth': None,\n",
              " 'max_features': 'auto',\n",
              " 'max_leaf_nodes': None,\n",
              " 'max_samples': None,\n",
              " 'min_impurity_decrease': 0.0,\n",
              " 'min_impurity_split': None,\n",
              " 'min_samples_leaf': 1,\n",
              " 'min_samples_split': 2,\n",
              " 'min_weight_fraction_leaf': 0.0,\n",
              " 'n_estimators': 300,\n",
              " 'n_jobs': None,\n",
              " 'oob_score': False,\n",
              " 'random_state': None,\n",
              " 'verbose': 0,\n",
              " 'warm_start': False}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "imj4BSUU-daM"
      },
      "source": [
        "Lets see what we can do to prevent overfitting\n",
        "\n",
        "1. We will set max depth to 50. This ensures that branching stops after \n",
        "50th branching, otherwise each sample may have its branch and overfit.\n",
        "2. We will use min_samples_split as 5. The default value is 2. This means that each internal node will split as long as it has a minimum of two sample. We dont want that!\n",
        "3. We will use min_samples_leaf as 4. The default is 1. This means that a node is considered leaf node if it has just one sample. This can cause severe overfitting!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EEGSEgZC-zTh",
        "outputId": "95cf0c9d-564a-4164-8d0b-f4e0cd68e6e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        }
      },
      "source": [
        "regrRM2 = RandomForestRegressor(n_estimators=200, max_depth = 50, min_samples_split = 5,min_samples_leaf =4)\n",
        "regrRM2.fit(x_trainL11, y_trainL11)"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=50, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=4,\n",
              "                      min_samples_split=5, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=200, n_jobs=None, oob_score=False,\n",
              "                      random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3eH-qGFP-74O",
        "outputId": "3ec41355-2f79-4959-de34-5115e2f23b76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "### We get a smaller value for R squared\n",
        "print(regrRM2.score(x_trainL11, y_trainL11))\n",
        "y_predL1= regrRM2.predict(x_testL11)\n",
        "print(np.sqrt(metrics.mean_squared_error(y_testL11,y_predL1)))"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7976269601486193\n",
            "34.346604598649385\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YOsFjMrUA1S2",
        "outputId": "58e80782-e14a-4dbe-9c30-71d1eb69aa5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "source": [
        "CoeffRM2 = pd.DataFrame(columns=[\"Variable\",\"FeatureImportance\"])\n",
        "CoeffRM2[\"Variable\"]=x_trainL11.columns\n",
        "CoeffRM2[\"FeatureImportance\"]=regrRM2.feature_importances_\n",
        "CoeffRM2.sort_values(\"FeatureImportance\", ascending = False)"
      ],
      "execution_count": 88,
      "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>Variable</th>\n",
              "      <th>FeatureImportance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>228</th>\n",
              "      <td>rt_Private room</td>\n",
              "      <td>0.424248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>rt_Shared room</td>\n",
              "      <td>0.081244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>ng_Manhattan</td>\n",
              "      <td>0.073846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>availability_365</td>\n",
              "      <td>0.065320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>name_length</td>\n",
              "      <td>0.062693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>nh_Richmondtown</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>nh_Huguenot</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>nh_Jamaica Estates</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>nh_Laurelton</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>nh_Jamaica Hills</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>230 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               Variable  FeatureImportance\n",
              "228     rt_Private room           0.424248\n",
              "229      rt_Shared room           0.081244\n",
              "7          ng_Manhattan           0.073846\n",
              "4      availability_365           0.065320\n",
              "5           name_length           0.062693\n",
              "..                  ...                ...\n",
              "173     nh_Richmondtown           0.000000\n",
              "109         nh_Huguenot           0.000000\n",
              "114  nh_Jamaica Estates           0.000000\n",
              "121        nh_Laurelton           0.000000\n",
              "115    nh_Jamaica Hills           0.000000\n",
              "\n",
              "[230 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n6xGnEiTe1tr",
        "outputId": "e395f21c-bb1a-4b79-f7d3-d468f3c90167",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "### We finally have the least RMSE among all model!\n",
        "y_predL1= regrRM2.predict(x_testL11)\n",
        "print(np.sqrt(metrics.mean_squared_error(y_testL11,y_predL1)))"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "34.346604598649385\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "inuOTKrHflvZ"
      },
      "source": [
        "# Comparing models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oRIq0zKofjao"
      },
      "source": [
        "rmsedt = {\"Model\":[\"LR\",\"L1\",\"RFR1\",\"RFR2\"],\"RMSE\":[37.68798547656789, 35.122928878780456, 34.90212188870185, 34.346604598649385]}\n",
        "rmsedf = pd.DataFrame(rmsedt)\n",
        "rsqdt = {\"Model\":[\"LR\",\"L1\",\"RFR1\",\"RFR2\"],\"RSquared\":[0.5033306680968379,0.5672687881098055,0.9359121650375394,0.7976269601486193]}\n",
        "rsqdt = pd.DataFrame(rsqdt)"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UKJhdZiCiR30",
        "outputId": "8f3708f5-c7f8-46c4-a41c-c6644153b647",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        }
      },
      "source": [
        "sns.catplot(x=\"Model\", y=\"RMSE\", linestyles=[\"-\"],\n",
        "            kind=\"point\", data=rmsedf);"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDO7J7eXin0b",
        "outputId": "e522ec01-cf19-479f-c915-29680929a0ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        }
      },
      "source": [
        "sns.catplot(x=\"Model\", y=\"RSquared\", linestyles=[\"--\"], color =\"green\", kind=\"point\", data=rsqdt);"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}