building_a_content_based_recommendation_system.ipynb

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  "metadata": {
    "colab": {
      "name": "building_a_content_based_recommendation_system.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Lawrence-Krukrubo/Building-a-Content-Based-Movie-Recommender-System/blob/master/building_a_content_based_recommendation_system.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u1Xr-Ev-Ldpx",
        "colab_type": "text"
      },
      "source": [
        "<h1 align=\"center\"><font size=\"5\">CONTENT-BASED FILTERING</font></h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bK2qUtpLj5M",
        "colab_type": "text"
      },
      "source": [
        "Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous, and can be commonly seen in online stores, movies databases and job finders. In this notebook, we will explore Content-based recommendation systems and implement a simple version of one using Python and the Pandas library."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YXrPt-PknWLR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# For creating and manipulating structured tabular data\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HXGYyI7jhB19",
        "colab_type": "text"
      },
      "source": [
        "Let's set maximum rows to be displayed at any time to not more than 20"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HhA5Hq8Z01TL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pd.set_option('max_rows', 20)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IhX7MtnLmlth",
        "colab_type": "text"
      },
      "source": [
        "### Saving the raw files from github\n",
        "\n",
        "Both files have been saved in raw .csv format in  the code cell below, but if you want to download directly from the website, click this [link](https://grouplens.org/datasets/movielens/) and <br>\n",
        "Select the file name 'ml-latest-small.zip (size: 1 MB)'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFZuXp8hf08p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "movies_data = 'https://raw.githubusercontent.com/Blackman9t/EDA/master/movies.csv'\n",
        "ratings_data = 'https://raw.githubusercontent.com/Blackman9t/EDA/master/ratings.csv'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FIc8dBC6msuC",
        "colab_type": "text"
      },
      "source": [
        "### Defining additional NaN values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F61xcupkkVVE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "missing_values = ['na','--','?','-','None','none','non']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yj6767Aum9zE",
        "colab_type": "text"
      },
      "source": [
        "### Reading the data to the data frame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1EtKRS7Lm80I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "movies_df = pd.read_csv(movies_data, na_values=missing_values)\n",
        "ratings_df = pd.read_csv(ratings_data, na_values=missing_values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vMxlGcBing-K",
        "colab_type": "code",
        "outputId": "516c64ee-33fd-43ef-81cc-b2f9ad13f079",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        }
      },
      "source": [
        "print('Movies_df Shape:',movies_df.shape)\n",
        "movies_df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Movies_df Shape: (9742, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\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>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale (1995)</td>\n",
              "      <td>Comedy|Drama|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Father of the Bride Part II (1995)</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Heat (1995)</td>\n",
              "      <td>Action|Crime|Thriller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>Sabrina (1995)</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>Tom and Huck (1995)</td>\n",
              "      <td>Adventure|Children</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>Sudden Death (1995)</td>\n",
              "      <td>Action</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>GoldenEye (1995)</td>\n",
              "      <td>Action|Adventure|Thriller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9732</th>\n",
              "      <td>193565</td>\n",
              "      <td>Gintama: The Movie (2010)</td>\n",
              "      <td>Action|Animation|Comedy|Sci-Fi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9733</th>\n",
              "      <td>193567</td>\n",
              "      <td>anohana: The Flower We Saw That Day - The Movi...</td>\n",
              "      <td>Animation|Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9734</th>\n",
              "      <td>193571</td>\n",
              "      <td>Silver Spoon (2014)</td>\n",
              "      <td>Comedy|Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9735</th>\n",
              "      <td>193573</td>\n",
              "      <td>Love Live! The School Idol Movie (2015)</td>\n",
              "      <td>Animation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9736</th>\n",
              "      <td>193579</td>\n",
              "      <td>Jon Stewart Has Left the Building (2015)</td>\n",
              "      <td>Documentary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9737</th>\n",
              "      <td>193581</td>\n",
              "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
              "      <td>Action|Animation|Comedy|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9738</th>\n",
              "      <td>193583</td>\n",
              "      <td>No Game No Life: Zero (2017)</td>\n",
              "      <td>Animation|Comedy|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9739</th>\n",
              "      <td>193585</td>\n",
              "      <td>Flint (2017)</td>\n",
              "      <td>Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9740</th>\n",
              "      <td>193587</td>\n",
              "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
              "      <td>Action|Animation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9741</th>\n",
              "      <td>193609</td>\n",
              "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9742 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      movieId  ...                                       genres\n",
              "0           1  ...  Adventure|Animation|Children|Comedy|Fantasy\n",
              "1           2  ...                   Adventure|Children|Fantasy\n",
              "2           3  ...                               Comedy|Romance\n",
              "3           4  ...                         Comedy|Drama|Romance\n",
              "4           5  ...                                       Comedy\n",
              "5           6  ...                        Action|Crime|Thriller\n",
              "6           7  ...                               Comedy|Romance\n",
              "7           8  ...                           Adventure|Children\n",
              "8           9  ...                                       Action\n",
              "9          10  ...                    Action|Adventure|Thriller\n",
              "...       ...  ...                                          ...\n",
              "9732   193565  ...               Action|Animation|Comedy|Sci-Fi\n",
              "9733   193567  ...                              Animation|Drama\n",
              "9734   193571  ...                                 Comedy|Drama\n",
              "9735   193573  ...                                    Animation\n",
              "9736   193579  ...                                  Documentary\n",
              "9737   193581  ...              Action|Animation|Comedy|Fantasy\n",
              "9738   193583  ...                     Animation|Comedy|Fantasy\n",
              "9739   193585  ...                                        Drama\n",
              "9740   193587  ...                             Action|Animation\n",
              "9741   193609  ...                                       Comedy\n",
              "\n",
              "[9742 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "joHbVy7fn1FK",
        "colab_type": "code",
        "outputId": "d282cd3b-48b4-4acb-82a7-8e7d4941d01a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        }
      },
      "source": [
        "print('Ratings_df Shape:',ratings_df.shape)\n",
        "ratings_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ratings_df Shape: (100836, 4)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964982703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964981247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964982224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>47</td>\n",
              "      <td>5.0</td>\n",
              "      <td>964983815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>5.0</td>\n",
              "      <td>964982931</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   userId  movieId  rating  timestamp\n",
              "0       1        1     4.0  964982703\n",
              "1       1        3     4.0  964981247\n",
              "2       1        6     4.0  964982224\n",
              "3       1       47     5.0  964983815\n",
              "4       1       50     5.0  964982931"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wRuO6Dhn1JHR",
        "colab_type": "text"
      },
      "source": [
        "### Let's first explore and prepare the movies_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2zqWh_JTNkSt",
        "colab_type": "text"
      },
      "source": [
        "Let's remove the year from the title column and place it in its own column, using the handy [extract](http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.extract.html) function of pandas, alongside python regex."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wwrk5sCzNk7e",
        "colab_type": "code",
        "outputId": "b23e3ede-5e73-449c-8bd2-44935343dafc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        }
      },
      "source": [
        "#Using regular expressions to find a year stored between parentheses\n",
        "#We specify the parantheses so we don't conflict with movies that have years in their titles\n",
        "movies_df['year'] = movies_df.title.str.extract('(\\(\\d\\d\\d\\d\\))',expand=False)\n",
        "movies_df.head(3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "        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>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>(1995)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "      <td>(1995)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "      <td>(1995)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId  ...    year\n",
              "0        1  ...  (1995)\n",
              "1        2  ...  (1995)\n",
              "2        3  ...  (1995)\n",
              "\n",
              "[3 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3jDelXSOIYz",
        "colab_type": "code",
        "outputId": "8a1d5487-9637-44ce-bb56-cb6be9cc8312",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        }
      },
      "source": [
        "#Removing the parentheses\n",
        "movies_df['year'] = movies_df.year.str.extract('(\\d\\d\\d\\d)',expand=False)\n",
        "movies_df.head(3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId  ...  year\n",
              "0        1  ...  1995\n",
              "1        2  ...  1995\n",
              "2        3  ...  1995\n",
              "\n",
              "[3 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "owtmO8PTOSIx",
        "colab_type": "code",
        "outputId": "7d86ae85-4505-436f-ba29-ef1883cdb79f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        }
      },
      "source": [
        "#Removing the years from the 'title' column\n",
        "movies_df['title'] = movies_df.title.str.replace('(\\(\\d\\d\\d\\d\\))', '')\n",
        "movies_df.head(3)"
      ],
      "execution_count": 0,
      "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",
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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>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId  ...  year\n",
              "0        1  ...  1995\n",
              "1        2  ...  1995\n",
              "2        3  ...  1995\n",
              "\n",
              "[3 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F5wqEVnbOW5O",
        "colab_type": "code",
        "outputId": "9b32ba00-81ed-41db-cea6-ca5044261c83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "source": [
        "#Applying the strip function to get rid of any ending whitespace characters that may have appeared\n",
        "movies_df['title'] = movies_df['title'].apply(lambda x: x.strip())\n",
        "movies_df.head()"
      ],
      "execution_count": 0,
      "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>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>Comedy|Drama|Romance</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId  ...  year\n",
              "0        1  ...  1995\n",
              "1        2  ...  1995\n",
              "2        3  ...  1995\n",
              "3        4  ...  1995\n",
              "4        5  ...  1995\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SHWZ0jDgOg3p",
        "colab_type": "text"
      },
      "source": [
        "With that, let's also split the values in the Genres column into a list of Genres to simplify future use. This can be achieved by applying Python's split string function on the correct column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QdYoeUXiObZd",
        "colab_type": "code",
        "outputId": "91359577-0fff-4a09-f84d-a07ee6b13b9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "source": [
        "#Every genre is separated by a | so we simply have to call the split function on |\n",
        "movies_df['genres'] = movies_df.genres.str.split('|')\n",
        "movies_df.head()"
      ],
      "execution_count": 0,
      "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>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>[Adventure, Children, Fantasy]</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>[Comedy, Romance]</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>[Comedy, Drama, Romance]</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>[Comedy]</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId  ...  year\n",
              "0        1  ...  1995\n",
              "1        2  ...  1995\n",
              "2        3  ...  1995\n",
              "3        4  ...  1995\n",
              "4        5  ...  1995\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hz2a8sp_I7Ax",
        "colab_type": "text"
      },
      "source": [
        "Let's view summary of the data, the memory consumption and if the titles are arranged logically"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7sGJ7AnduFcY",
        "colab_type": "code",
        "outputId": "bb5d9033-1ef6-4f79-80e4-c00ee1b25cb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177
        }
      },
      "source": [
        "movies_df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9742 entries, 0 to 9741\n",
            "Data columns (total 4 columns):\n",
            "movieId    9742 non-null int64\n",
            "title      9742 non-null object\n",
            "genres     9742 non-null object\n",
            "year       9729 non-null object\n",
            "dtypes: int64(1), object(3)\n",
            "memory usage: 304.5+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q6v2hkTQJMR-",
        "colab_type": "code",
        "outputId": "613ee3e9-ec6b-4d24-aa0a-a5fb8b158020",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "movies_df_original_mem = movies_df.memory_usage()\n",
        "movies_df_original_mem"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index         80\n",
              "movieId    77936\n",
              "title      77936\n",
              "genres     77936\n",
              "year       77936\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eAs-WrUkJXc5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Let's convert movieId column from int64 to int8 to save memory space\n",
        "movies_df.movieId = movies_df.movieId.astype('int32')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "05SlRtepN54u",
        "colab_type": "text"
      },
      "source": [
        "Let's check for missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mS8EDgC-MswT",
        "colab_type": "code",
        "outputId": "2e95367c-8155-4206-96a7-f5a55e77f945",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "movies_df.isna().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movieId     0\n",
              "title       0\n",
              "genres      0\n",
              "year       13\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fihTauQqPNUR",
        "colab_type": "text"
      },
      "source": [
        "let's fill movies_df missing year  values with 0 to indicate the year is not readily available. we have only 13 rows "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c25GDLqIPdyT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "movies_df.year.fillna(0, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b7L8yZzkQV4T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Let's now convert year column from int6a to int8, since it holds a max of just 4 digits of numbers. Thereby saving space.\n",
        "movies_df.year = movies_df.year.astype('int16')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WsiCgyOYQyO7",
        "colab_type": "code",
        "outputId": "8de6d333-ffb1-4951-8fb8-710f9cbb0ccc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "source": [
        "movies_df_new_mem = movies_df.memory_usage()\n",
        "\n",
        "print(movies_df_original_mem)\n",
        "print()\n",
        "print(movies_df_new_mem)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index         80\n",
            "movieId    77936\n",
            "title      77936\n",
            "genres     77936\n",
            "year       77936\n",
            "dtype: int64\n",
            "\n",
            "Index         80\n",
            "movieId    38968\n",
            "title      77936\n",
            "genres     77936\n",
            "year       19484\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H8C75yXySYv5",
        "colab_type": "text"
      },
      "source": [
        "Let's see a summary of the data types again"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KwJ6i7YFSdOX",
        "colab_type": "code",
        "outputId": "2d6d0fa9-1016-4db3-b367-e73341e57207",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "movies_df.dtypes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movieId     int32\n",
              "title      object\n",
              "genres     object\n",
              "year        int16\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cFC1mJyMPtJQ",
        "colab_type": "code",
        "outputId": "8230fe51-73fa-4f85-b61f-8f4091d6ea7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        }
      },
      "source": [
        "movies_df.head(3)"
      ],
      "execution_count": 0,
      "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>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>[Adventure, Children, Fantasy]</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>[Comedy, Romance]</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId  ...  year\n",
              "0        1  ...  1995\n",
              "1        2  ...  1995\n",
              "2        3  ...  1995\n",
              "\n",
              "[3 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fa8eT7g1g6H6",
        "colab_type": "text"
      },
      "source": [
        "Now, let's  One-Hot-Encode the list of genres. This encoding is needed for feeding categorical data. In this case, we store every different genre in columns that contain either 1 or 0. 1 shows that a movie has that genre and 0 shows that it doesn't. Let's also store this dataframe in another variable, just incase we need the one without genres at some point.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K5h-FZSnQjUu",
        "colab_type": "code",
        "outputId": "3335d56e-4786-48a8-c5ac-bdcf1724888f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        }
      },
      "source": [
        "# First let's make a copy of the movies_df\n",
        "movies_with_genres = movies_df.copy(deep=True)\n",
        "\n",
        "# Let's iterate through movies_df, then append the movie genres as columns of 1s or 0s.\n",
        "# 1 if that column contains movies in the genre at the present index and 0 if not.\n",
        "\n",
        "x = []\n",
        "for index, row in movies_df.iterrows():\n",
        "    x.append(index)\n",
        "    for genre in row['genres']:\n",
        "        movies_with_genres.at[index, genre] = 1\n",
        "\n",
        "# Confirm that every row has been iterated and acted upon\n",
        "print(len(x) == len(movies_df))\n",
        "\n",
        "movies_with_genres.head(3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\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>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>year</th>\n",
              "      <th>Adventure</th>\n",
              "      <th>Animation</th>\n",
              "      <th>Children</th>\n",
              "      <th>Comedy</th>\n",
              "      <th>Fantasy</th>\n",
              "      <th>Romance</th>\n",
              "      <th>Drama</th>\n",
              "      <th>Action</th>\n",
              "      <th>Crime</th>\n",
              "      <th>Thriller</th>\n",
              "      <th>Horror</th>\n",
              "      <th>Mystery</th>\n",
              "      <th>Sci-Fi</th>\n",
              "      <th>War</th>\n",
              "      <th>Musical</th>\n",
              "      <th>Documentary</th>\n",
              "      <th>IMAX</th>\n",
              "      <th>Western</th>\n",
              "      <th>Film-Noir</th>\n",
              "      <th>(no genres listed)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
              "      <td>1995</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>[Adventure, Children, Fantasy]</td>\n",
              "      <td>1995</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>[Comedy, Romance]</td>\n",
              "      <td>1995</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId             title  ... Film-Noir  (no genres listed)\n",
              "0        1         Toy Story  ...       NaN                 NaN\n",
              "1        2           Jumanji  ...       NaN                 NaN\n",
              "2        3  Grumpier Old Men  ...       NaN                 NaN\n",
              "\n",
              "[3 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CpHMQ-_DnbmB",
        "colab_type": "code",
        "outputId": "8fae8842-00d7-42c6-b5c0-41683c8283f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        }
      },
      "source": [
        "#Filling in the NaN values with 0 to show that a movie doesn't have that column's genre\n",
        "movies_with_genres = movies_with_genres.fillna(0)\n",
        "movies_with_genres.head(3)"
      ],
      "execution_count": 0,
      "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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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>year</th>\n",
              "      <th>Adventure</th>\n",
              "      <th>Animation</th>\n",
              "      <th>Children</th>\n",
              "      <th>Comedy</th>\n",
              "      <th>Fantasy</th>\n",
              "      <th>Romance</th>\n",
              "      <th>Drama</th>\n",
              "      <th>Action</th>\n",
              "      <th>Crime</th>\n",
              "      <th>Thriller</th>\n",
              "      <th>Horror</th>\n",
              "      <th>Mystery</th>\n",
              "      <th>Sci-Fi</th>\n",
              "      <th>War</th>\n",
              "      <th>Musical</th>\n",
              "      <th>Documentary</th>\n",
              "      <th>IMAX</th>\n",
              "      <th>Western</th>\n",
              "      <th>Film-Noir</th>\n",
              "      <th>(no genres listed)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
              "      <td>1995</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>[Adventure, Children, Fantasy]</td>\n",
              "      <td>1995</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>[Comedy, Romance]</td>\n",
              "      <td>1995</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId             title  ... Film-Noir  (no genres listed)\n",
              "0        1         Toy Story  ...       0.0                 0.0\n",
              "1        2           Jumanji  ...       0.0                 0.0\n",
              "2        3  Grumpier Old Men  ...       0.0                 0.0\n",
              "\n",
              "[3 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DWJTw17Z9xlp",
        "colab_type": "text"
      },
      "source": [
        "Let's look at the ratings data set now"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "if9KJFyG9y1J",
        "colab_type": "code",
        "outputId": "40f76441-7aab-414e-e7db-d04b51fedac0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        }
      },
      "source": [
        "# print out the shape and first five rows of ratings data.\n",
        "print('Ratings_df shape:',ratings_df.shape)          \n",
        "ratings_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ratings_df shape: (100836, 4)\n"
          ],
          "name": "stdout"
        },
        {
          "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>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964982703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964981247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964982224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>47</td>\n",
              "      <td>5.0</td>\n",
              "      <td>964983815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>5.0</td>\n",
              "      <td>964982931</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   userId  movieId  rating  timestamp\n",
              "0       1        1     4.0  964982703\n",
              "1       1        3     4.0  964981247\n",
              "2       1        6     4.0  964982224\n",
              "3       1       47     5.0  964983815\n",
              "4       1       50     5.0  964982931"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KkwbWRco9yTp",
        "colab_type": "code",
        "outputId": "2534be37-76ed-48e0-fde9-72d00b5fc677",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        }
      },
      "source": [
        "# Dropping the timestamp column\n",
        "ratings_df.drop('timestamp', axis=1, inplace=True)\n",
        "\n",
        "# Confirming the drop\n",
        "ratings_df.head(3)"
      ],
      "execution_count": 0,
      "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>userId</th>\n",
              "      <th>movieId</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>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   userId  movieId  rating\n",
              "0       1        1     4.0\n",
              "1       1        3     4.0\n",
              "2       1        6     4.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bOHF2sAlCIR0",
        "colab_type": "code",
        "outputId": "9f4aeb64-eaea-40db-82ca-74c68e2a6d39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "# Let's confirm the right data types exist per column in ratings data_set\n",
        "\n",
        "ratings_df.dtypes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "userId       int64\n",
              "movieId      int64\n",
              "rating     float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1bs57oIBKxNR",
        "colab_type": "code",
        "outputId": "d0b67426-1fa8-4928-d0d8-c088e124ee4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "# Let's check for missing values\n",
        "\n",
        "ratings_df.isna().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "userId     0\n",
              "movieId    0\n",
              "rating     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zA8jd6AQ7QSV",
        "colab_type": "text"
      },
      "source": [
        "## Content Based recommender System"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ug1HsAhl7WTI",
        "colab_type": "text"
      },
      "source": [
        "Now, let's implement a Content-Based or Item-Item recommendation systems. This technique attempts to figure out what a user's favourite aspects of an item is, and then recommends items that present those aspects. \n",
        "\n",
        "Let's begin by creating an input user to recommend movies to. The user's name will be Lawrence and we would assume Lawrence has rated the following movies with the following ratings:-\n",
        "\n",
        "Notice: To add more movies, simply increase the amount of elements in the userInput. Feel free to add more in! Just be sure to write it in with capital letters and if a movie starts with a \"The\", like \"The Matrix\" then write it in like this: 'Matrix, The' ."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wz4pTfhi-S1i",
        "colab_type": "text"
      },
      "source": [
        "Step 1: Creating Lawrence's Profile"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0SE9XiNE7Tqf",
        "colab_type": "code",
        "outputId": "a9cedd17-0ac0-4f56-bc2a-d8fe777f5ae7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "# so on a scale of 0 to 5, with 0 min and 5 max, see Lawrence's movie ratings below\n",
        "Lawrence_movie_ratings = [\n",
        "            {'title':'Predator', 'rating':4.9},\n",
        "            {'title':'Final Destination', 'rating':4.9},\n",
        "            {'title':'Mission Impossible', 'rating':4},\n",
        "            {'title':\"Beverly Hills Cop\", 'rating':3},\n",
        "            {'title':'Exorcist, The', 'rating':4.8},\n",
        "            {'title':'Waiting to Exhale', 'rating':3.9},\n",
        "            {'title':'Avengers, The', 'rating':4.5},\n",
        "            {'title':'Omen, The', 'rating':5.0}\n",
        "         ] \n",
        "Lawrence_movie_ratings = pd.DataFrame(Lawrence_movie_ratings)\n",
        "Lawrence_movie_ratings"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<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>title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4.9</td>\n",
              "      <td>Predator</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>Final Destination</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.0</td>\n",
              "      <td>Mission Impossible</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>Beverly Hills Cop</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.8</td>\n",
              "      <td>Exorcist, The</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.9</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4.5</td>\n",
              "      <td>Avengers, The</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.0</td>\n",
              "      <td>Omen, The</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   rating               title\n",
              "0     4.9            Predator\n",
              "1     4.9   Final Destination\n",
              "2     4.0  Mission Impossible\n",
              "3     3.0   Beverly Hills Cop\n",
              "4     4.8       Exorcist, The\n",
              "5     3.9   Waiting to Exhale\n",
              "6     4.5       Avengers, The\n",
              "7     5.0           Omen, The"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hcz5vMBpBW1W",
        "colab_type": "text"
      },
      "source": [
        "Add movieId to input user\n",
        "With the input complete, let's extract the input movie's ID's from the movies dataframe and add them into it.\n",
        "\n",
        "We can achieve this by first filtering out the rows that contain the input movie's title and then merging this subset with the input dataframe. We also drop unnecessary columns for the input to save memory space."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uOzFPOaA-T7b",
        "colab_type": "code",
        "outputId": "d1007b80-d1f0-4dad-99e6-3383dda27f6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "source": [
        "# Extracting movie Ids from movies_df and updating lawrence_movie_ratings with movie Ids.\n",
        "\n",
        "Lawrence_movie_Id = movies_df[movies_df['title'].isin(Lawrence_movie_ratings['title'])]\n",
        "\n",
        "# Merging Lawrence movie Id and ratings into the lawrence_movie_ratings data frame. \n",
        "# This action implicitly merges both data frames by the title column.\n",
        "\n",
        "Lawrence_movie_ratings = pd.merge(Lawrence_movie_Id, Lawrence_movie_ratings)\n",
        "\n",
        "# Display the merged and updated data frame.\n",
        "\n",
        "Lawrence_movie_ratings"
      ],
      "execution_count": 0,
      "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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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>year</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>[Comedy, Drama, Romance]</td>\n",
              "      <td>1995</td>\n",
              "      <td>3.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1350</td>\n",
              "      <td>Omen, The</td>\n",
              "      <td>[Horror, Mystery, Thriller]</td>\n",
              "      <td>1976</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>45662</td>\n",
              "      <td>Omen, The</td>\n",
              "      <td>[Horror, Thriller]</td>\n",
              "      <td>2006</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1997</td>\n",
              "      <td>Exorcist, The</td>\n",
              "      <td>[Horror, Mystery]</td>\n",
              "      <td>1973</td>\n",
              "      <td>4.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2153</td>\n",
              "      <td>Avengers, The</td>\n",
              "      <td>[Action, Adventure]</td>\n",
              "      <td>1998</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>89745</td>\n",
              "      <td>Avengers, The</td>\n",
              "      <td>[Action, Adventure, Sci-Fi, IMAX]</td>\n",
              "      <td>2012</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3409</td>\n",
              "      <td>Final Destination</td>\n",
              "      <td>[Drama, Thriller]</td>\n",
              "      <td>2000</td>\n",
              "      <td>4.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3527</td>\n",
              "      <td>Predator</td>\n",
              "      <td>[Action, Sci-Fi, Thriller]</td>\n",
              "      <td>1987</td>\n",
              "      <td>4.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4085</td>\n",
              "      <td>Beverly Hills Cop</td>\n",
              "      <td>[Action, Comedy, Crime, Drama]</td>\n",
              "      <td>1984</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId              title                             genres  year  rating\n",
              "0        4  Waiting to Exhale           [Comedy, Drama, Romance]  1995     3.9\n",
              "1     1350          Omen, The        [Horror, Mystery, Thriller]  1976     5.0\n",
              "2    45662          Omen, The                 [Horror, Thriller]  2006     5.0\n",
              "3     1997      Exorcist, The                  [Horror, Mystery]  1973     4.8\n",
              "4     2153      Avengers, The                [Action, Adventure]  1998     4.5\n",
              "5    89745      Avengers, The  [Action, Adventure, Sci-Fi, IMAX]  2012     4.5\n",
              "6     3409  Final Destination                  [Drama, Thriller]  2000     4.9\n",
              "7     3527           Predator         [Action, Sci-Fi, Thriller]  1987     4.9\n",
              "8     4085  Beverly Hills Cop     [Action, Comedy, Crime, Drama]  1984     3.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dg3_Bf37H6_v",
        "colab_type": "text"
      },
      "source": [
        "Lets drop some columns that we do not need such as genres and year"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SQbG0o3cICsO",
        "colab_type": "code",
        "outputId": "62b2ea74-faaa-42a5-f55e-3a67e458b118",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "source": [
        "#Dropping information we don't need such as year and genres\n",
        "Lawrence_movie_ratings = Lawrence_movie_ratings.drop(['genres','year'], 1)\n",
        "\n",
        "\n",
        "#Final input dataframe\n",
        "#If a movie you added in above isn't here, then it might not be in the original \n",
        "#dataframe or it might spelled differently, please check capitalisation.\n",
        "Lawrence_movie_ratings"
      ],
      "execution_count": 0,
      "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",
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              "        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>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>3.9</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>1350</td>\n",
              "      <td>Omen, The</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>45662</td>\n",
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              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1997</td>\n",
              "      <td>Exorcist, The</td>\n",
              "      <td>4.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2153</td>\n",
              "      <td>Avengers, The</td>\n",
              "      <td>4.5</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>89745</td>\n",
              "      <td>Avengers, The</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3409</td>\n",
              "      <td>Final Destination</td>\n",
              "      <td>4.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3527</td>\n",
              "      <td>Predator</td>\n",
              "      <td>4.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4085</td>\n",
              "      <td>Beverly Hills Cop</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId              title  rating\n",
              "0        4  Waiting to Exhale     3.9\n",
              "1     1350          Omen, The     5.0\n",
              "2    45662          Omen, The     5.0\n",
              "3     1997      Exorcist, The     4.8\n",
              "4     2153      Avengers, The     4.5\n",
              "5    89745      Avengers, The     4.5\n",
              "6     3409  Final Destination     4.9\n",
              "7     3527           Predator     4.9\n",
              "8     4085  Beverly Hills Cop     3.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ab9NETn_-c-x",
        "colab_type": "text"
      },
      "source": [
        "Step 2: Learning Lawrence's Profile"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C8e1FWhiUElQ",
        "colab_type": "text"
      },
      "source": [
        "We're going to start by learning the input's preferences, so let's get the subset of movies that the input has watched from the Dataframe containing genres defined with binary values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BAc5yPnuUJud",
        "colab_type": "code",
        "outputId": "c6a7db40-f2c9-4d4a-989a-9f6062755391",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        }
      },
      "source": [
        "# filter the selection by outputing movies that exist in both lawrence_movie_ratings and movies_with_genres\n",
        "Lawrence_genres_df = movies_with_genres[movies_with_genres.movieId.isin(Lawrence_movie_ratings.movieId)]\n",
        "Lawrence_genres_df"
      ],
      "execution_count": 0,
      "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",
              "    .dataframe tbody tr th {\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>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>year</th>\n",
              "      <th>Adventure</th>\n",
              "      <th>Animation</th>\n",
              "      <th>Children</th>\n",
              "      <th>Comedy</th>\n",
              "      <th>Fantasy</th>\n",
              "      <th>Romance</th>\n",
              "      <th>Drama</th>\n",
              "      <th>Action</th>\n",
              "      <th>Crime</th>\n",
              "      <th>Thriller</th>\n",
              "      <th>Horror</th>\n",
              "      <th>Mystery</th>\n",
              "      <th>Sci-Fi</th>\n",
              "      <th>War</th>\n",
              "      <th>Musical</th>\n",
              "      <th>Documentary</th>\n",
              "      <th>IMAX</th>\n",
              "      <th>Western</th>\n",
              "      <th>Film-Noir</th>\n",
              "      <th>(no genres listed)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1038</th>\n",
              "      <td>1350</td>\n",
              "      <td>Omen, The</td>\n",
              "      <td>[Horror, Mystery, Thriller]</td>\n",
              "      <td>1976</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1472</th>\n",
              "      <td>1997</td>\n",
              "      <td>Exorcist, The</td>\n",
              "      <td>[Horror, Mystery]</td>\n",
              "      <td>1973</td>\n",
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              "    <tr>\n",
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              "      <td>2153</td>\n",
              "      <td>Avengers, The</td>\n",
              "      <td>[Action, Adventure]</td>\n",
              "      <td>1998</td>\n",
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              "    <tr>\n",
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              "      <td>3409</td>\n",
              "      <td>Final Destination</td>\n",
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              "      <td>2000</td>\n",
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              "    <tr>\n",
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              "      <td>3527</td>\n",
              "      <td>Predator</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3051</th>\n",
              "      <td>4085</td>\n",
              "      <td>Beverly Hills Cop</td>\n",
              "      <td>[Action, Comedy, Crime, Drama]</td>\n",
              "      <td>1984</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6216</th>\n",
              "      <td>45662</td>\n",
              "      <td>Omen, The</td>\n",
              "      <td>[Horror, Thriller]</td>\n",
              "      <td>2006</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>7693</th>\n",
              "      <td>89745</td>\n",
              "      <td>Avengers, The</td>\n",
              "      <td>[Action, Adventure, Sci-Fi, IMAX]</td>\n",
              "      <td>2012</td>\n",
              "      <td>1.0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      movieId              title  ... Film-Noir  (no genres listed)\n",
              "3           4  Waiting to Exhale  ...       0.0                 0.0\n",
              "1038     1350          Omen, The  ...       0.0                 0.0\n",
              "1472     1997      Exorcist, The  ...       0.0                 0.0\n",
              "1611     2153      Avengers, The  ...       0.0                 0.0\n",
              "2547     3409  Final Destination  ...       0.0                 0.0\n",
              "2636     3527           Predator  ...       0.0                 0.0\n",
              "3051     4085  Beverly Hills Cop  ...       0.0                 0.0\n",
              "6216    45662          Omen, The  ...       0.0                 0.0\n",
              "7693    89745      Avengers, The  ...       0.0                 0.0\n",
              "\n",
              "[9 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YMDQjIPdXg6e",
        "colab_type": "text"
      },
      "source": [
        "We'll only need the actual genre table, so let's clean this up a bit by resetting the index and dropping the movieId, title, genres and year columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3l1se7z-Xhcj",
        "colab_type": "code",
        "outputId": "65dc25b0-3aa2-4553-a488-2a3ec3267936",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        }
      },
      "source": [
        "# First, let's reset index to default and drop the existing index.\n",
        "Lawrence_genres_df.reset_index(drop=True, inplace=True)\n",
        "\n",
        "# Next, let's drop redundant columns\n",
        "Lawrence_genres_df.drop(['movieId','title','genres','year'], axis=1, inplace=True)\n",
        "\n",
        "# Let's view chamges\n",
        "\n",
        "Lawrence_genres_df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:3940: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  errors=errors)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adventure</th>\n",
              "      <th>Animation</th>\n",
              "      <th>Children</th>\n",
              "      <th>Comedy</th>\n",
              "      <th>Fantasy</th>\n",
              "      <th>Romance</th>\n",
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              "      <th>Action</th>\n",
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            ],
            "text/plain": [
              "   Adventure  Animation  Children  ...  Western  Film-Noir  (no genres listed)\n",
              "0        0.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "1        0.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "2        0.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "3        1.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "4        0.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "5        0.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "6        0.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "7        0.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "8        1.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "\n",
              "[9 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gbj6DHV3dE5_",
        "colab_type": "text"
      },
      "source": [
        "Step 3: Building Lawrence's Profile<br>\n",
        "To do this, we're going to turn each genre into weights, by multiplying Lawrence's movie ratings by lawrence_genres_df table. And then summing up the resulting table by column. This operation is actually a dot product between a matrix and a vector.\n",
        "First let's confirm the shapes of the data frames we have recently defined"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jAN1Eey1dHy1",
        "colab_type": "code",
        "outputId": "8a61a0af-740f-4941-a38c-b727a382fa11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "# let's confirm the shapes of our data frames to guide us as we do matrix multiplication\n",
        "\n",
        "print('Shape of Lawrence_movie_ratings is:',Lawrence_movie_ratings.shape)\n",
        "print('Shape of Lawrence_genres_df is:',Lawrence_genres_df.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of Lawrence_movie_ratings is: (9, 3)\n",
            "Shape of Lawrence_genres_df is: (9, 20)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xo3iXEXSfAlg",
        "colab_type": "code",
        "outputId": "e6100905-feb6-459b-9976-b7474054eb47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "# Let's find the dot product of transpose of Lawrence_genres_df by lawrence rating column\n",
        "Lawrence_profile = Lawrence_genres_df.T.dot(Lawrence_movie_ratings.rating)\n",
        "\n",
        "# Let's see the result\n",
        "Lawrence_profile"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Adventure              7.8\n",
              "Animation              0.0\n",
              "Children               0.0\n",
              "Comedy                 8.8\n",
              "Fantasy                0.0\n",
              "Romance                3.9\n",
              "Drama                 13.3\n",
              "Action                17.2\n",
              "Crime                  4.9\n",
              "Thriller              18.9\n",
              "Horror                14.9\n",
              "Mystery               10.0\n",
              "Sci-Fi                 7.5\n",
              "War                    0.0\n",
              "Musical                0.0\n",
              "Documentary            0.0\n",
              "IMAX                   3.0\n",
              "Western                0.0\n",
              "Film-Noir              0.0\n",
              "(no genres listed)     0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l7ZRBsU0k3ko",
        "colab_type": "text"
      },
      "source": [
        "Just by Eye-balling his profile, it is clear that Lawrence loves 'Thriller', 'Action' and 'Horror' movies the most… apt as can be.<br>\n",
        "Now, we have the weights for all his preferences. This is known as the User Profile. We can now recommend movies that satisfy Lawrence.<br>\n",
        "Let's start by editing the original movies_with_genres data frame that contains all movies and their genres columns."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l2IKNelL-1gF",
        "colab_type": "text"
      },
      "source": [
        "Step 4: Deploying The Content-Based Recommender System."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3mV0FVBKmHZJ",
        "colab_type": "code",
        "outputId": "6055c6a2-740d-41a4-ea01-a0021acc9937",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        }
      },
      "source": [
        "# let's set the index to the movieId\n",
        "movies_with_genres = movies_with_genres.set_index(movies_with_genres.movieId)\n",
        "\n",
        "# let's view the head\n",
        "movies_with_genres.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>year</th>\n",
              "      <th>Adventure</th>\n",
              "      <th>Animation</th>\n",
              "      <th>Children</th>\n",
              "      <th>Comedy</th>\n",
              "      <th>Fantasy</th>\n",
              "      <th>Romance</th>\n",
              "      <th>Drama</th>\n",
              "      <th>Action</th>\n",
              "      <th>Crime</th>\n",
              "      <th>Thriller</th>\n",
              "      <th>Horror</th>\n",
              "      <th>Mystery</th>\n",
              "      <th>Sci-Fi</th>\n",
              "      <th>War</th>\n",
              "      <th>Musical</th>\n",
              "      <th>Documentary</th>\n",
              "      <th>IMAX</th>\n",
              "      <th>Western</th>\n",
              "      <th>Film-Noir</th>\n",
              "      <th>(no genres listed)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
              "      <td>1995</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>[Adventure, Children, Fantasy]</td>\n",
              "      <td>1995</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>[Comedy, Romance]</td>\n",
              "      <td>1995</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>[Comedy, Drama, Romance]</td>\n",
              "      <td>1995</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>[Comedy]</td>\n",
              "      <td>1995</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         movieId                        title  ... Film-Noir  (no genres listed)\n",
              "movieId                                        ...                              \n",
              "1              1                    Toy Story  ...       0.0                 0.0\n",
              "2              2                      Jumanji  ...       0.0                 0.0\n",
              "3              3             Grumpier Old Men  ...       0.0                 0.0\n",
              "4              4            Waiting to Exhale  ...       0.0                 0.0\n",
              "5              5  Father of the Bride Part II  ...       0.0                 0.0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Uq4Wn9XoM3Z",
        "colab_type": "text"
      },
      "source": [
        "Let's delete irrelevant columns from the movies_with_genres data frame that contains all 9742 movies and distinctive columns of genres."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k2EWtHxYn9gF",
        "colab_type": "code",
        "outputId": "4add5850-f4cc-4e14-8896-2f475fc5b01d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        }
      },
      "source": [
        "# Deleting four unnecessary columns.\n",
        "movies_with_genres.drop(['movieId','title','genres','year'], axis=1, inplace=True)\n",
        "\n",
        "# Viewing changes.\n",
        "movies_with_genres.head()"
      ],
      "execution_count": 0,
      "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>Adventure</th>\n",
              "      <th>Animation</th>\n",
              "      <th>Children</th>\n",
              "      <th>Comedy</th>\n",
              "      <th>Fantasy</th>\n",
              "      <th>Romance</th>\n",
              "      <th>Drama</th>\n",
              "      <th>Action</th>\n",
              "      <th>Crime</th>\n",
              "      <th>Thriller</th>\n",
              "      <th>Horror</th>\n",
              "      <th>Mystery</th>\n",
              "      <th>Sci-Fi</th>\n",
              "      <th>War</th>\n",
              "      <th>Musical</th>\n",
              "      <th>Documentary</th>\n",
              "      <th>IMAX</th>\n",
              "      <th>Western</th>\n",
              "      <th>Film-Noir</th>\n",
              "      <th>(no genres listed)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Adventure  Animation  Children  ...  Western  Film-Noir  (no genres listed)\n",
              "movieId                                  ...                                        \n",
              "1              1.0        1.0       1.0  ...      0.0        0.0                 0.0\n",
              "2              1.0        0.0       1.0  ...      0.0        0.0                 0.0\n",
              "3              0.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "4              0.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "5              0.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6MIuTp2kshx0",
        "colab_type": "text"
      },
      "source": [
        "With Lawrence's profile and the complete list of movies and their genres in hand, we're going to take the weighted average of every movie based on his profile and recommend the top twenty movies that match his preference."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LTKkuZXpuo4m",
        "colab_type": "code",
        "outputId": "00b7e7b8-f7d6-48cf-8dda-d39ca8d8c4d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "source": [
        "# Multiply the genres by the weights and then take the weighted average.\n",
        "recommendation_table_df = (movies_with_genres.dot(Lawrence_profile)) / Lawrence_profile.sum()\n",
        "\n",
        "# Let's view the recommendation table\n",
        "recommendation_table_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movieId\n",
              "1    0.150635\n",
              "2    0.070780\n",
              "3    0.115245\n",
              "4    0.235935\n",
              "5    0.079855\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JBWFhl7Zvq6G",
        "colab_type": "text"
      },
      "source": [
        "Let's sort the recommendation table in descending order"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i3ZZlRYvvvoH",
        "colab_type": "code",
        "outputId": "9ba22607-6b28-4d63-e616-fef1379c0c5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "# Let's sort values from great to small\n",
        "recommendation_table_df.sort_values(ascending=False, inplace=True)\n",
        "\n",
        "#Just a peek at the values\n",
        "recommendation_table_df.head(20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movieId\n",
              "81132    0.869328\n",
              "43932    0.742287\n",
              "7235     0.707804\n",
              "36509    0.692377\n",
              "79132    0.678766\n",
              "51545    0.663339\n",
              "198      0.651543\n",
              "6395     0.651543\n",
              "54771    0.651543\n",
              "74685    0.651543\n",
              "26701    0.651543\n",
              "4956     0.634301\n",
              "60684    0.634301\n",
              "4210     0.627949\n",
              "71999    0.622505\n",
              "31804    0.621597\n",
              "2617     0.613430\n",
              "72165    0.613430\n",
              "91542    0.613430\n",
              "27683    0.610708\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wJ0RlydGx8Qb",
        "colab_type": "text"
      },
      "source": [
        "Now here's the recommendation table! Complete with movie details and genres for the top 20 movies that match Lawrence's profile."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6D72OXiJ5s3X",
        "colab_type": "code",
        "outputId": "553a4dea-1c6c-4004-c9a2-ce5123ff7bb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        }
      },
      "source": [
        "# first we make a copy of the original movies_df\n",
        "copy = movies_df.copy(deep=True)\n",
        "\n",
        "# Then we set its index to movieId\n",
        "copy = copy.set_index('movieId', drop=True)\n",
        "\n",
        "# Next we enlist the top 20 recommended movieIds we defined above\n",
        "top_20_index = recommendation_table_df.index[:20].tolist()\n",
        "\n",
        "# finally we slice these indices from the copied movies df and save in a variable\n",
        "recommended_movies = copy.loc[top_20_index, :]\n",
        "\n",
        "# Now we can display the top 20 movies in descending order of preference\n",
        "recommended_movies"
      ],
      "execution_count": 0,
      "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>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>81132</th>\n",
              "      <td>Rubber</td>\n",
              "      <td>[Action, Adventure, Comedy, Crime, Drama, Film...</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43932</th>\n",
              "      <td>Pulse</td>\n",
              "      <td>[Action, Drama, Fantasy, Horror, Mystery, Sci-...</td>\n",
              "      <td>2006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7235</th>\n",
              "      <td>Ichi the Killer (Koroshiya 1)</td>\n",
              "      <td>[Action, Comedy, Crime, Drama, Horror, Thriller]</td>\n",
              "      <td>2001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36509</th>\n",
              "      <td>Cave, The</td>\n",
              "      <td>[Action, Adventure, Horror, Mystery, Sci-Fi, T...</td>\n",
              "      <td>2005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79132</th>\n",
              "      <td>Inception</td>\n",
              "      <td>[Action, Crime, Drama, Mystery, Sci-Fi, Thrill...</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51545</th>\n",
              "      <td>Pusher III: I'm the Angel of Death</td>\n",
              "      <td>[Action, Comedy, Drama, Horror, Thriller]</td>\n",
              "      <td>2005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>Strange Days</td>\n",
              "      <td>[Action, Crime, Drama, Mystery, Sci-Fi, Thriller]</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6395</th>\n",
              "      <td>Crazies, The (a.k.a. Code Name: Trixie)</td>\n",
              "      <td>[Action, Drama, Horror, Sci-Fi, Thriller]</td>\n",
              "      <td>1973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54771</th>\n",
              "      <td>Invasion, The</td>\n",
              "      <td>[Action, Drama, Horror, Sci-Fi, Thriller]</td>\n",
              "      <td>2007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74685</th>\n",
              "      <td>Crazies, The</td>\n",
              "      <td>[Action, Drama, Horror, Sci-Fi, Thriller]</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26701</th>\n",
              "      <td>Patlabor: The Movie (Kidô keisatsu patorebâ: T...</td>\n",
              "      <td>[Action, Animation, Crime, Drama, Film-Noir, M...</td>\n",
              "      <td>1989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4956</th>\n",
              "      <td>Stunt Man, The</td>\n",
              "      <td>[Action, Adventure, Comedy, Drama, Romance, Th...</td>\n",
              "      <td>1980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60684</th>\n",
              "      <td>Watchmen</td>\n",
              "      <td>[Action, Drama, Mystery, Sci-Fi, Thriller, IMAX]</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4210</th>\n",
              "      <td>Manhunter</td>\n",
              "      <td>[Action, Crime, Drama, Horror, Thriller]</td>\n",
              "      <td>1986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71999</th>\n",
              "      <td>Aelita: The Queen of Mars (Aelita)</td>\n",
              "      <td>[Action, Adventure, Drama, Fantasy, Romance, S...</td>\n",
              "      <td>1924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31804</th>\n",
              "      <td>Night Watch (Nochnoy dozor)</td>\n",
              "      <td>[Action, Fantasy, Horror, Mystery, Sci-Fi, Thr...</td>\n",
              "      <td>2004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2617</th>\n",
              "      <td>Mummy, The</td>\n",
              "      <td>[Action, Adventure, Comedy, Fantasy, Horror, T...</td>\n",
              "      <td>1999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72165</th>\n",
              "      <td>Cirque du Freak: The Vampire's Assistant</td>\n",
              "      <td>[Action, Adventure, Comedy, Fantasy, Horror, T...</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91542</th>\n",
              "      <td>Sherlock Holmes: A Game of Shadows</td>\n",
              "      <td>[Action, Adventure, Comedy, Crime, Mystery, Th...</td>\n",
              "      <td>2011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27683</th>\n",
              "      <td>Tremors 4: The Legend Begins</td>\n",
              "      <td>[Action, Comedy, Horror, Sci-Fi, Thriller, Wes...</td>\n",
              "      <td>2004</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                     title  ...  year\n",
              "movieId                                                     ...      \n",
              "81132                                               Rubber  ...  2010\n",
              "43932                                                Pulse  ...  2006\n",
              "7235                         Ichi the Killer (Koroshiya 1)  ...  2001\n",
              "36509                                            Cave, The  ...  2005\n",
              "79132                                            Inception  ...  2010\n",
              "51545                   Pusher III: I'm the Angel of Death  ...  2005\n",
              "198                                           Strange Days  ...  1995\n",
              "6395               Crazies, The (a.k.a. Code Name: Trixie)  ...  1973\n",
              "54771                                        Invasion, The  ...  2007\n",
              "74685                                         Crazies, The  ...  2010\n",
              "26701    Patlabor: The Movie (Kidô keisatsu patorebâ: T...  ...  1989\n",
              "4956                                        Stunt Man, The  ...  1980\n",
              "60684                                             Watchmen  ...  2009\n",
              "4210                                             Manhunter  ...  1986\n",
              "71999                   Aelita: The Queen of Mars (Aelita)  ...  1924\n",
              "31804                          Night Watch (Nochnoy dozor)  ...  2004\n",
              "2617                                            Mummy, The  ...  1999\n",
              "72165             Cirque du Freak: The Vampire's Assistant  ...  2009\n",
              "91542                   Sherlock Holmes: A Game of Shadows  ...  2011\n",
              "27683                         Tremors 4: The Legend Begins  ...  2004\n",
              "\n",
              "[20 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lobl-cGB-7Hh",
        "colab_type": "text"
      },
      "source": [
        "Run this cell below to kill the note book and free up space in colab"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j79kQwct-5uQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#import os, signal\n",
        "#os.kill(os.getpid(), signal.SIGKILL)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}