Things_to_Do_Recommendation_System_Geeks.ipynb

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    "colab": {
      "name": "Things to Do Recommendation System - Geeks.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YVe3OrtNiUeI"
      },
      "source": [
        "See:- https://www.geeksforgeeks.org/python-implementation-of-movie-recommender-system/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        "id": "vDIT84inikKK",
        "outputId": "ba778893-c70c-449b-aa2f-79ef1db3a05e"
      },
      "source": [
        "# import pandas library\n",
        "import pandas as pd\n",
        " \n",
        "# Get the data\n",
        " \n",
        "path = 'https://raw.githubusercontent.com/TracyRenee61/AV-Recommendation-Engine/master/Things_to_do_weekend%20-%20Sheet1.csv'\n",
        " \n",
        "df = pd.read_csv(path)\n",
        " \n",
        "# Check the head of the data\n",
        "df"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>activity_id</th>\n",
              "      <th>thing_to_do</th>\n",
              "      <th>genre</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>make beds</td>\n",
              "      <td>housework</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>mop floors</td>\n",
              "      <td>housework</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>vacuum</td>\n",
              "      <td>housework</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>clean toilet</td>\n",
              "      <td>housework</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>dust furniture</td>\n",
              "      <td>housework</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>3</td>\n",
              "      <td>97</td>\n",
              "      <td>read a book</td>\n",
              "      <td>outing</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>3</td>\n",
              "      <td>98</td>\n",
              "      <td>write a book</td>\n",
              "      <td>work</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>3</td>\n",
              "      <td>99</td>\n",
              "      <td>write a blog post</td>\n",
              "      <td>work</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>3</td>\n",
              "      <td>100</td>\n",
              "      <td>work a part time job</td>\n",
              "      <td>work</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>3</td>\n",
              "      <td>101</td>\n",
              "      <td>sleep</td>\n",
              "      <td>self care</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>303 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     user_id  activity_id           thing_to_do      genre  rating\n",
              "0          1            1             make beds  housework       3\n",
              "1          1            2            mop floors  housework       3\n",
              "2          1            3                vacuum  housework       4\n",
              "3          1            4          clean toilet  housework       1\n",
              "4          1            5        dust furniture  housework       4\n",
              "..       ...          ...                   ...        ...     ...\n",
              "298        3           97           read a book     outing       7\n",
              "299        3           98          write a book       work       3\n",
              "300        3           99     write a blog post       work       3\n",
              "301        3          100  work a part time job       work       3\n",
              "302        3          101                sleep   self care       9\n",
              "\n",
              "[303 rows x 5 columns]"
            ]
          },
          "metadata": {
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          "execution_count": 87
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "PfcB2K_qlZ4P"
      },
      "source": [
        "#remove white space\n",
        "df.columns = df.columns.str.replace(' ', '')"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ktqm-u9GlmFk",
        "outputId": "a35dc5ce-42dc-41da-eb7f-1fc6859addc9"
      },
      "source": [
        "#add column count ratings\n",
        "\n",
        "df['rate_count'] = df['user_id'].groupby(df['activity_id']).transform('count')\n",
        "df['rate_count'] = df['rate_count'].astype(float)\n",
        "df"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>activity_id</th>\n",
              "      <th>thing_to_do</th>\n",
              "      <th>genre</th>\n",
              "      <th>rating</th>\n",
              "      <th>rate_count</th>\n",
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              "      <td>3.0</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>3</td>\n",
              "      <td>97</td>\n",
              "      <td>read a book</td>\n",
              "      <td>outing</td>\n",
              "      <td>7</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>3</td>\n",
              "      <td>98</td>\n",
              "      <td>write a book</td>\n",
              "      <td>work</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>3</td>\n",
              "      <td>99</td>\n",
              "      <td>write a blog post</td>\n",
              "      <td>work</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>3</td>\n",
              "      <td>100</td>\n",
              "      <td>work a part time job</td>\n",
              "      <td>work</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>3</td>\n",
              "      <td>101</td>\n",
              "      <td>sleep</td>\n",
              "      <td>self care</td>\n",
              "      <td>9</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>303 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     user_id  activity_id           thing_to_do      genre  rating  rate_count\n",
              "0          1            1             make beds  housework       3         3.0\n",
              "1          1            2            mop floors  housework       3         3.0\n",
              "2          1            3                vacuum  housework       4         3.0\n",
              "3          1            4          clean toilet  housework       1         3.0\n",
              "4          1            5        dust furniture  housework       4         3.0\n",
              "..       ...          ...                   ...        ...     ...         ...\n",
              "298        3           97           read a book     outing       7         3.0\n",
              "299        3           98          write a book       work       3         3.0\n",
              "300        3           99     write a blog post       work       3         3.0\n",
              "301        3          100  work a part time job       work       3         3.0\n",
              "302        3          101                sleep   self care       9         3.0\n",
              "\n",
              "[303 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "F2RyBnAOl2Yt",
        "outputId": "9d6218d3-887d-4ea6-d28c-f48c59a57420"
      },
      "source": [
        "#average ratings\n",
        "\n",
        "df['average_rating'] = df['rating'].groupby(df['thing_to_do']).transform('sum')/df['rate_count']\n",
        "df"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>activity_id</th>\n",
              "      <th>thing_to_do</th>\n",
              "      <th>genre</th>\n",
              "      <th>rating</th>\n",
              "      <th>rate_count</th>\n",
              "      <th>average_rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>make beds</td>\n",
              "      <td>housework</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.333333</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>mop floors</td>\n",
              "      <td>housework</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>vacuum</td>\n",
              "      <td>housework</td>\n",
              "      <td>4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>clean toilet</td>\n",
              "      <td>housework</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>dust furniture</td>\n",
              "      <td>housework</td>\n",
              "      <td>4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.333333</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",
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              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>3</td>\n",
              "      <td>97</td>\n",
              "      <td>read a book</td>\n",
              "      <td>outing</td>\n",
              "      <td>7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>3</td>\n",
              "      <td>98</td>\n",
              "      <td>write a book</td>\n",
              "      <td>work</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>3</td>\n",
              "      <td>99</td>\n",
              "      <td>write a blog post</td>\n",
              "      <td>work</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>3</td>\n",
              "      <td>100</td>\n",
              "      <td>work a part time job</td>\n",
              "      <td>work</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>3</td>\n",
              "      <td>101</td>\n",
              "      <td>sleep</td>\n",
              "      <td>self care</td>\n",
              "      <td>9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.333333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>303 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     user_id  activity_id  ... rate_count average_rating\n",
              "0          1            1  ...        3.0       3.333333\n",
              "1          1            2  ...        3.0       2.666667\n",
              "2          1            3  ...        3.0       3.333333\n",
              "3          1            4  ...        3.0       1.666667\n",
              "4          1            5  ...        3.0       3.333333\n",
              "..       ...          ...  ...        ...            ...\n",
              "298        3           97  ...        3.0       7.666667\n",
              "299        3           98  ...        3.0       5.000000\n",
              "300        3           99  ...        3.0       4.666667\n",
              "301        3          100  ...        3.0       3.333333\n",
              "302        3          101  ...        3.0       7.333333\n",
              "\n",
              "[303 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CgYF_iZtmEes",
        "outputId": "8f4afacb-dbae-47e3-a070-c82ebf8af44b"
      },
      "source": [
        "#calculate average\n",
        "# Calculate mean of rating average column\n",
        "C = df['average_rating'].mean()\n",
        "print(C)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6.026402640264028\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RGnBLnynmIdK",
        "outputId": "c10e7d69-d1e3-4199-9322-310359c7eaf0"
      },
      "source": [
        "# Calculate the minimum number of ratings required to be in the chart, m\n",
        "m = df['rate_count'].quantile(0.90)\n",
        "print(m)"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "--bA-g2zmSEr",
        "outputId": "b13d3ff3-1ae4-4de6-b08c-27a7098a6e29"
      },
      "source": [
        "#calculate weighted average\n",
        "v = df['rate_count']\n",
        "R = df['average_rating']\n",
        "df['score'] = (v/(v+m) * R) + (m/(m+v) * C)\n",
        "df"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\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>user_id</th>\n",
              "      <th>activity_id</th>\n",
              "      <th>thing_to_do</th>\n",
              "      <th>genre</th>\n",
              "      <th>rating</th>\n",
              "      <th>rate_count</th>\n",
              "      <th>average_rating</th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>make beds</td>\n",
              "      <td>housework</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.333333</td>\n",
              "      <td>4.679868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>mop floors</td>\n",
              "      <td>housework</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.666667</td>\n",
              "      <td>4.346535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>vacuum</td>\n",
              "      <td>housework</td>\n",
              "      <td>4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.333333</td>\n",
              "      <td>4.679868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>clean toilet</td>\n",
              "      <td>housework</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.666667</td>\n",
              "      <td>3.846535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>dust furniture</td>\n",
              "      <td>housework</td>\n",
              "      <td>4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.333333</td>\n",
              "      <td>4.679868</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>3</td>\n",
              "      <td>97</td>\n",
              "      <td>read a book</td>\n",
              "      <td>outing</td>\n",
              "      <td>7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.666667</td>\n",
              "      <td>6.846535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>3</td>\n",
              "      <td>98</td>\n",
              "      <td>write a book</td>\n",
              "      <td>work</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.513201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>3</td>\n",
              "      <td>99</td>\n",
              "      <td>write a blog post</td>\n",
              "      <td>work</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>5.346535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>3</td>\n",
              "      <td>100</td>\n",
              "      <td>work a part time job</td>\n",
              "      <td>work</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.333333</td>\n",
              "      <td>4.679868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>3</td>\n",
              "      <td>101</td>\n",
              "      <td>sleep</td>\n",
              "      <td>self care</td>\n",
              "      <td>9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.333333</td>\n",
              "      <td>6.679868</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>303 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     user_id  activity_id  ... average_rating     score\n",
              "0          1            1  ...       3.333333  4.679868\n",
              "1          1            2  ...       2.666667  4.346535\n",
              "2          1            3  ...       3.333333  4.679868\n",
              "3          1            4  ...       1.666667  3.846535\n",
              "4          1            5  ...       3.333333  4.679868\n",
              "..       ...          ...  ...            ...       ...\n",
              "298        3           97  ...       7.666667  6.846535\n",
              "299        3           98  ...       5.000000  5.513201\n",
              "300        3           99  ...       4.666667  5.346535\n",
              "301        3          100  ...       3.333333  4.679868\n",
              "302        3          101  ...       7.333333  6.679868\n",
              "\n",
              "[303 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SZOjpoYTi3Af",
        "outputId": "cfd625a5-d6fa-427a-e397-3de1c89aa521"
      },
      "source": [
        "# Calculate mean rating of all movies\n",
        "df.groupby('thing_to_do')['rating'].mean().sort_values(ascending=False)\n"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "thing_to_do\n",
              "retire from work        9.000000\n",
              "go to london            8.666667\n",
              "go to isle of wight     8.666667\n",
              "go to hollywood         8.666667\n",
              "go to bournemouth       8.333333\n",
              "                          ...   \n",
              "clean floorboards       2.333333\n",
              "clean sideboards        2.000000\n",
              "clean oven              2.000000\n",
              "clean toilet            1.666667\n",
              "clean mold off walls    1.666667\n",
              "Name: rating, Length: 101, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KEJRmCvOi9yR",
        "outputId": "ec523cae-2d43-47ec-a702-31e72d2567e1"
      },
      "source": [
        "# Calculate count rating of all movies\n",
        "df.groupby('thing_to_do')['rating'].count().sort_values(ascending=False)\n"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "thing_to_do\n",
              "write a book                 3\n",
              "go to burger king            3\n",
              "go rollar skating            3\n",
              "go scuba diving              3\n",
              "go shopping                  3\n",
              "                            ..\n",
              "make fruitcake               3\n",
              "make spaghetti bolenaise     3\n",
              "make strawberry shortcake    3\n",
              "make sushi                   3\n",
              "bake a cake                  3\n",
              "Name: rating, Length: 101, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "Wf9UZTdXjDIr",
        "outputId": "3b86601c-9bd6-4263-de05-04ca1bcdf939"
      },
      "source": [
        "# creating dataframe with 'rating' count values\n",
        "ratings = pd.DataFrame(df.groupby('thing_to_do')['rating'].mean())\n",
        "ratings['num_ratings'] = pd.DataFrame(df.groupby('thing_to_do')['rating'].count())\n",
        "ratings"
      ],
      "execution_count": 96,
      "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>rating</th>\n",
              "      <th>num_ratings</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thing_to_do</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bake a cake</th>\n",
              "      <td>4.333333</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>clean floorboards</th>\n",
              "      <td>2.333333</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>clean mold off walls</th>\n",
              "      <td>1.666667</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>clean oven</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>clean sideboards</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>watch a youtube video</th>\n",
              "      <td>6.666667</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>watch an american football game</th>\n",
              "      <td>4.666667</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>work a part time job</th>\n",
              "      <td>3.333333</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>write a blog post</th>\n",
              "      <td>4.666667</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>write a book</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>101 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   rating  num_ratings\n",
              "thing_to_do                                           \n",
              "bake a cake                      4.333333            3\n",
              "clean floorboards                2.333333            3\n",
              "clean mold off walls             1.666667            3\n",
              "clean oven                       2.000000            3\n",
              "clean sideboards                 2.000000            3\n",
              "...                                   ...          ...\n",
              "watch a youtube video            6.666667            3\n",
              "watch an american football game  4.666667            3\n",
              "work a part time job             3.333333            3\n",
              "write a blog post                4.666667            3\n",
              "write a book                     5.000000            3\n",
              "\n",
              "[101 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mVCtFqiBjJNK"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set_style('white')\n",
        "%matplotlib inline"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "jgDtmJhBjPEp",
        "outputId": "c050f56e-9d14-44f0-c2e2-253ee37062eb"
      },
      "source": [
        "# plot graph of 'num of ratings column'\n",
        "plt.figure(figsize =(10, 4))\n",
        " \n",
        "ratings['num_ratings'].hist(bins = 70)"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc75f6de690>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "cD_YPJjAjUgA",
        "outputId": "659ba43c-183b-4cf6-e809-80dfd3f9e493"
      },
      "source": [
        "# plot graph of 'ratings' column\n",
        "plt.figure(figsize =(10, 4))\n",
        "ratings['rating'].hist(bins = 70)\n"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc75f82ce50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "s0Qu5RvbrdIE",
        "outputId": "f2d42db6-562b-46bd-c4c7-49f73ed77204"
      },
      "source": [
        "# plot graph of 'score' column\n",
        "plt.figure(figsize =(10, 4))\n",
        "df['score'].hist(bins = 70)"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc75eb56e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "FP0pFz-Fr2xf",
        "outputId": "213144ab-762a-46c8-ed7c-18f814816613"
      },
      "source": [
        "# plot graph of 'score' column\n",
        "plt.figure(figsize =(10, 4))\n",
        "df['genre'].hist(bins = 70)\n"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc75ea10ad0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R1qeSBvfjpVM",
        "outputId": "f2993ddf-84fa-466b-fc38-4a5c9311f15e"
      },
      "source": [
        "# Sorting values according to\n",
        "# the 'num of rating column'\n",
        "activity_mat = df.pivot_table(index ='user_id',columns ='thing_to_do', values ='rating')\n",
        "print(activity_mat)"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "thing_to_do  bake a cake  clean floorboards  ...  write a blog post  write a book\n",
            "user_id                                      ...                                 \n",
            "1                      6                  2  ...                  8             8\n",
            "2                      3                  1  ...                  3             4\n",
            "3                      4                  4  ...                  3             3\n",
            "\n",
            "[3 rows x 101 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "n8QasUpMoDwD",
        "outputId": "40be07a4-8faf-4305-dd64-9cab081466f8"
      },
      "source": [
        "ratings.sort_values('num_ratings', ascending = False).head(10)"
      ],
      "execution_count": 103,
      "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>rating</th>\n",
              "      <th>num_ratings</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thing_to_do</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bake a cake</th>\n",
              "      <td>4.333333</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>make beds</th>\n",
              "      <td>3.333333</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>make tiramasu</th>\n",
              "      <td>5.333333</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>make tempura</th>\n",
              "      <td>4.666667</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>make sushi</th>\n",
              "      <td>4.666667</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>make strawberry shortcake</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>make spaghetti bolenaise</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>make fruitcake</th>\n",
              "      <td>4.333333</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>make chocolate cake</th>\n",
              "      <td>5.666667</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>make carrot cake</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             rating  num_ratings\n",
              "thing_to_do                                     \n",
              "bake a cake                4.333333            3\n",
              "make beds                  3.333333            3\n",
              "make tiramasu              5.333333            3\n",
              "make tempura               4.666667            3\n",
              "make sushi                 4.666667            3\n",
              "make strawberry shortcake  6.000000            3\n",
              "make spaghetti bolenaise   5.000000            3\n",
              "make fruitcake             4.333333            3\n",
              "make chocolate cake        5.666667            3\n",
              "make carrot cake           6.000000            3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6l9s-GMjwYS",
        "outputId": "4b387df8-3250-44b8-928f-343b66d89cec"
      },
      "source": [
        "# analysing correlation with similar movies\n",
        "cake_user_ratings = activity_mat['make carrot cake']\n",
        "clean_user_ratings = activity_mat['make beds']\n",
        "cake_user_ratings\n"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "user_id\n",
              "1    8\n",
              "2    6\n",
              "3    4\n",
              "Name: make carrot cake, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "AYZ5UWh4j2oi",
        "outputId": "40ddd8a8-cc2a-4101-e596-04494c0c87dd"
      },
      "source": [
        "# analysing correlation with similar movies\n",
        "similar_to_cake = activity_mat.corrwith(cake_user_ratings)\n",
        "similar_to_clean = activity_mat.corrwith(clean_user_ratings)\n",
        " \n",
        "corr_cake = pd.DataFrame(similar_to_cake, columns =['Correlation'])\n",
        "corr_cake.dropna(inplace = True)\n",
        " \n",
        "corr_cake.head()\n"
      ],
      "execution_count": 105,
      "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>Correlation</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thing_to_do</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bake a cake</th>\n",
              "      <td>0.654654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>clean floorboards</th>\n",
              "      <td>-0.654654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>clean mold off walls</th>\n",
              "      <td>-0.866025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>clean oven</th>\n",
              "      <td>-0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>clean sideboards</th>\n",
              "      <td>-0.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Correlation\n",
              "thing_to_do                      \n",
              "bake a cake              0.654654\n",
              "clean floorboards       -0.654654\n",
              "clean mold off walls    -0.866025\n",
              "clean oven              -0.500000\n",
              "clean sideboards        -0.500000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "3cIYqO-cj_ae",
        "outputId": "ff060cdf-bd65-4929-af97-1e64e0d301fd"
      },
      "source": [
        "# Similar movies like starwars\n",
        "corr_cake.sort_values('Correlation', ascending = False).head(10)\n",
        "corr_cake = corr_cake.join(ratings['num_ratings'])\n",
        " \n",
        "corr_cake.head()\n",
        " \n",
        "corr_cake[corr_cake['num_ratings']>2].sort_values('Correlation', ascending = False).head()\n"
      ],
      "execution_count": 106,
      "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>Correlation</th>\n",
              "      <th>num_ratings</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thing_to_do</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>make carrot cake</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>make strawberry shortcake</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>go to wendys</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>go to japanese restaurant</th>\n",
              "      <td>0.981981</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>see stepchildren</th>\n",
              "      <td>0.981981</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           Correlation  num_ratings\n",
              "thing_to_do                                        \n",
              "make carrot cake              1.000000            3\n",
              "make strawberry shortcake     1.000000            3\n",
              "go to wendys                  1.000000            3\n",
              "go to japanese restaurant     0.981981            3\n",
              "see stepchildren              0.981981            3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "-fxpDyqWkJYY",
        "outputId": "d60f65f9-1d06-43fd-934f-a7afc1248db2"
      },
      "source": [
        "# Similar movies as of liarliar\n",
        "corr_clean = pd.DataFrame(similar_to_clean, columns =['Correlation'])\n",
        "corr_clean.dropna(inplace = True)\n",
        " \n",
        "corr_clean = corr_clean.join(ratings['num_ratings'])\n",
        "corr_clean[corr_clean['num_ratings']>2].sort_values('Correlation', ascending = False).head()\n"
      ],
      "execution_count": 107,
      "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>Correlation</th>\n",
              "      <th>num_ratings</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thing_to_do</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>go to the movies</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>clean windows</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>make beds</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>go to art gallery</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>go to a wildlife park</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       Correlation  num_ratings\n",
              "thing_to_do                                    \n",
              "go to the movies               1.0            3\n",
              "clean windows                  1.0            3\n",
              "make beds                      1.0            3\n",
              "go to art gallery              1.0            3\n",
              "go to a wildlife park          1.0            3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    }
  ]
}