airbnb_varsha.ipynb

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    "colab": {
      "name": "airbnb_varsha.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SA61DE-q9AU8",
        "colab_type": "text"
      },
      "source": [
        "### PREDICTION OF AIRBNB LISTING PRICES"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JHEloc8_9Q1j",
        "colab_type": "text"
      },
      "source": [
        "Importing packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T5ZNlkerukxG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wVEyEwMK9isM",
        "colab_type": "text"
      },
      "source": [
        "Importing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ltcp00aiuuz9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "outputId": "e5201150-bfcf-4f90-c9a8-b03a992b2157"
      },
      "source": [
        "airbnb = pd.read_csv('/content/AirbnbListing_NYC.csv')\n",
        "airbnb.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    }\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>19-10-2018</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>21-05-2019</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>05-07-2019</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>19-11-2018</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": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9NxfwFXn9mwN",
        "colab_type": "text"
      },
      "source": [
        "Understanding the information in the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6sxFQS5ivuuc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "d0fff999-e524-4dd2-eec2-18c5784243c2"
      },
      "source": [
        "airbnb.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 48895 entries, 0 to 48894\n",
            "Data columns (total 16 columns):\n",
            " #   Column                          Non-Null Count  Dtype  \n",
            "---  ------                          --------------  -----  \n",
            " 0   id                              48895 non-null  int64  \n",
            " 1   name                            48879 non-null  object \n",
            " 2   host_id                         48895 non-null  int64  \n",
            " 3   host_name                       48874 non-null  object \n",
            " 4   neighbourhood_group             48895 non-null  object \n",
            " 5   neighbourhood                   48895 non-null  object \n",
            " 6   latitude                        48895 non-null  float64\n",
            " 7   longitude                       48895 non-null  float64\n",
            " 8   room_type                       48895 non-null  object \n",
            " 9   price                           48895 non-null  int64  \n",
            " 10  minimum_nights                  48895 non-null  int64  \n",
            " 11  number_of_reviews               48895 non-null  int64  \n",
            " 12  last_review                     38843 non-null  object \n",
            " 13  reviews_per_month               38843 non-null  float64\n",
            " 14  calculated_host_listings_count  48895 non-null  int64  \n",
            " 15  availability_365                48895 non-null  int64  \n",
            "dtypes: float64(3), int64(7), object(6)\n",
            "memory usage: 6.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J6NLp9tV9xLq",
        "colab_type": "text"
      },
      "source": [
        "Creating a new dataframe to analyse price and neighbourgood group together"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I2z5yVZPwFx0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "9b876a3e-8bc7-4beb-db54-c286fa32f189"
      },
      "source": [
        "price_ng_df = airbnb[['neighbourhood_group','price']].dropna()\n",
        "print(price_ng_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      neighbourhood_group  price\n",
            "0                Brooklyn    149\n",
            "1               Manhattan    225\n",
            "2               Manhattan    150\n",
            "3                Brooklyn     89\n",
            "4               Manhattan     80\n",
            "...                   ...    ...\n",
            "48890            Brooklyn     70\n",
            "48891            Brooklyn     40\n",
            "48892           Manhattan    115\n",
            "48893           Manhattan     55\n",
            "48894           Manhattan     90\n",
            "\n",
            "[48895 rows x 2 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ejumWtv994Ej",
        "colab_type": "text"
      },
      "source": [
        "Scatter plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X44w8Q1J1S-m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "785a58c7-cda6-4057-d98a-3c2151337610"
      },
      "source": [
        "plt.figure(figsize = (10,5))\n",
        "plt.scatter(price_ng_df['price'], price_ng_df['neighbourhood_group'], color = 'r', marker = \"*\", s = 65)\n",
        "plt.title(\"Price vs. Neighbourhood group\")\n",
        "plt.xlabel(\"Price\")\n",
        "plt.ylabel(\"Neighbourhood group\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Neighbourhood group')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sZiA6xxf99Ik",
        "colab_type": "text"
      },
      "source": [
        "Removing the outlier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jeSyYtH317eO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "9e2b7396-fe76-4f25-b246-aecbe7faaace"
      },
      "source": [
        "price_ng_df = price_ng_df[price_ng_df.price < 9999]\n",
        "# To remove the 3 outliers to get a better fit of the data\n",
        "print (price_ng_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      neighbourhood_group  price\n",
            "0                Brooklyn    149\n",
            "1               Manhattan    225\n",
            "2               Manhattan    150\n",
            "3                Brooklyn     89\n",
            "4               Manhattan     80\n",
            "...                   ...    ...\n",
            "48890            Brooklyn     70\n",
            "48891            Brooklyn     40\n",
            "48892           Manhattan    115\n",
            "48893           Manhattan     55\n",
            "48894           Manhattan     90\n",
            "\n",
            "[48889 rows x 2 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nnf-IOaH4naW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "outputId": "05db55ac-0c03-4eda-d656-393e3b063b08"
      },
      "source": [
        "plt.figure(figsize = (15,6))\n",
        "plt.scatter(price_ng_df['price'], price_ng_df['neighbourhood_group'], color = 'c', s = 65)\n",
        "plt.title(\"Price vs. Neighbourhood group\")\n",
        "plt.xlabel('Price')\n",
        "plt.ylabel('Neighbourhood group')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Neighbourhood group')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oGgiFsaO-BMD",
        "colab_type": "text"
      },
      "source": [
        "Descriptive statistics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L-qFpFXY5TOG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "374aa66b-1883-4031-f96b-722d084e8646"
      },
      "source": [
        "airbnb.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>host_id</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>reviews_per_month</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.889500e+04</td>\n",
              "      <td>4.889500e+04</td>\n",
              "      <td>48895.000000</td>\n",
              "      <td>48895.000000</td>\n",
              "      <td>48895.000000</td>\n",
              "      <td>48895.000000</td>\n",
              "      <td>48895.000000</td>\n",
              "      <td>38843.000000</td>\n",
              "      <td>48895.000000</td>\n",
              "      <td>48895.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.901714e+07</td>\n",
              "      <td>6.762001e+07</td>\n",
              "      <td>40.728949</td>\n",
              "      <td>-73.952170</td>\n",
              "      <td>152.720687</td>\n",
              "      <td>7.029962</td>\n",
              "      <td>23.274466</td>\n",
              "      <td>1.373221</td>\n",
              "      <td>7.143982</td>\n",
              "      <td>112.781327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.098311e+07</td>\n",
              "      <td>7.861097e+07</td>\n",
              "      <td>0.054530</td>\n",
              "      <td>0.046157</td>\n",
              "      <td>240.154170</td>\n",
              "      <td>20.510550</td>\n",
              "      <td>44.550582</td>\n",
              "      <td>1.680442</td>\n",
              "      <td>32.952519</td>\n",
              "      <td>131.622289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.539000e+03</td>\n",
              "      <td>2.438000e+03</td>\n",
              "      <td>40.499790</td>\n",
              "      <td>-74.244420</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>9.471945e+06</td>\n",
              "      <td>7.822033e+06</td>\n",
              "      <td>40.690100</td>\n",
              "      <td>-73.983070</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.190000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.967728e+07</td>\n",
              "      <td>3.079382e+07</td>\n",
              "      <td>40.723070</td>\n",
              "      <td>-73.955680</td>\n",
              "      <td>106.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.720000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>45.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.915218e+07</td>\n",
              "      <td>1.074344e+08</td>\n",
              "      <td>40.763115</td>\n",
              "      <td>-73.936275</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>2.020000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>227.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.648724e+07</td>\n",
              "      <td>2.743213e+08</td>\n",
              "      <td>40.913060</td>\n",
              "      <td>-73.712990</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>1250.000000</td>\n",
              "      <td>629.000000</td>\n",
              "      <td>58.500000</td>\n",
              "      <td>327.000000</td>\n",
              "      <td>365.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id  ...  availability_365\n",
              "count  4.889500e+04  ...      48895.000000\n",
              "mean   1.901714e+07  ...        112.781327\n",
              "std    1.098311e+07  ...        131.622289\n",
              "min    2.539000e+03  ...          0.000000\n",
              "25%    9.471945e+06  ...          0.000000\n",
              "50%    1.967728e+07  ...         45.000000\n",
              "75%    2.915218e+07  ...        227.000000\n",
              "max    3.648724e+07  ...        365.000000\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hXBVLfU0-IQz",
        "colab_type": "text"
      },
      "source": [
        "Filling up the missing values with 0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wvQIJ0_P6r2-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "airbnb['price'].fillna(0, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W3eTfxny-ZXc",
        "colab_type": "text"
      },
      "source": [
        "Correlation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ecWAeo-I7EkX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "7510fc71-f8f1-42e0-c795-dffa74966d0d"
      },
      "source": [
        "print(price_ng_df.corr())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       price\n",
            "price    1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yiQtm4PM-dX7",
        "colab_type": "text"
      },
      "source": [
        "Creating X and Y"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o5hhH5ND7LDH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "feature_cols = ['latitude', 'longitude', 'minimum_nights', 'availability_365'] \n",
        "X = airbnb[feature_cols]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M2COQVSc-hCJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "c3f961ea-3d77-49e3-cdce-7a7ae70b8620"
      },
      "source": [
        "print(type(X))\n",
        "print(X.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "(48895, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOlALokX6zBF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "b65cdedc-e9e7-4c36-e75a-c4178a324af4"
      },
      "source": [
        "y = airbnb['price']\n",
        "y.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    149\n",
              "1    225\n",
              "2    150\n",
              "3     89\n",
              "4     80\n",
              "Name: price, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c6xeusYj6-Hc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "07bef018-726f-4b5e-f188-9f2d84118f0b"
      },
      "source": [
        "print(type(y))\n",
        "print(y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.series.Series'>\n",
            "(48895,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WQE14knF-knl",
        "colab_type": "text"
      },
      "source": [
        "Splitting the data into training set and test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HkgIfzl57AAd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "def split(X,y):\n",
        "    return train_test_split(X, y, test_size=0.20, random_state=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "25zf2lGO-qCG",
        "colab_type": "text"
      },
      "source": [
        "Checking the shape and size of the newly created dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "itwPZhPH7DJM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "aff01d7b-f468-47b0-eeaa-fabc15f28d4e"
      },
      "source": [
        "X_train, X_test, y_train, y_test=split(X,y)\n",
        "print('Train cases as below')\n",
        "print('X_train shape: ',X_train.shape)\n",
        "print('y_train shape: ',y_train.shape)\n",
        "print('\\nTest cases as below')\n",
        "print('X_test shape: ',X_test.shape)\n",
        "print('y_test shape: ',y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train cases as below\n",
            "X_train shape:  (39116, 4)\n",
            "y_train shape:  (39116,)\n",
            "\n",
            "Test cases as below\n",
            "X_test shape:  (9779, 4)\n",
            "y_test shape:  (9779,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hfVUhbKZ-xt8",
        "colab_type": "text"
      },
      "source": [
        "Linear regression in Scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mx1rlMAi7HRF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "linreg = LinearRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "37aLDl2A-39U",
        "colab_type": "text"
      },
      "source": [
        "Fitting the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7JkhbIAu7Kf5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "da55f8b0-abd7-46ef-ae8f-3602a68ee713"
      },
      "source": [
        "linreg.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZnaH8aA7R9p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "0e3ac825-49fb-4ea8-d132-b43e70b2fb9b"
      },
      "source": [
        "print('Intercept:',linreg.intercept_)\n",
        "print('Coefficients:',linreg.coef_)  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Intercept: -71655.6720491075\n",
            "Coefficients: [ 2.20140486e+02 -8.49498345e+02  1.59563819e-01  1.69589406e-01]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X8amKkwm_7b8",
        "colab_type": "text"
      },
      "source": [
        "*y = -71655.67 + (220.14 * latitude) + (-849.49 * longitude) + (0.15956 * minimum_nights) + (0.16958 * availability_365)*\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XsFbEMvm7exM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred_train = linreg.predict(X_train)  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0vFsv74n7hf-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred_test = linreg.predict(X_test)  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V2txCm1D7jAe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "999f25d9-4340-4050-f7db-837c8b1ffe2a"
      },
      "source": [
        "print(y_pred_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 84.59138619 161.4168431  144.92356551 ... 136.07636613 118.87358377\n",
            " 137.24389997]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}