Untitled69.ipynb

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  "metadata": {
    "colab": {
      "name": "Untitled69.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZzFekEhPAaQ1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "'''\n",
        "A recommendation engine, also known as a recommender system, \n",
        "is software that analyzes available data to make suggestions for something that a user might be interested in.\n",
        "\n",
        "There are basically four or three types of recommendation engines depending on who you ask:\n",
        "1.Content based recommendation engine\n",
        "2.Collaborative filtering based recommendation engine\n",
        "3.Popularity based recommendation engine\n",
        "4.Hybrid recommendation engine\n",
        "\n",
        "The engine we will use is called a content based recommendation engine and it is \n",
        "a recommendation system that takes in a movie that a user likes \n",
        "and then analyzes it to get the movies content (e.g. genre, cast, director, keywords, etc.), \n",
        "it then ranks the recommended movies based on how similar the recommended movies \n",
        "are to the liked movie using something called similarity scores\n",
        "'''\n",
        "\n",
        "#Description: Build a movie recommendation engine (more specifically a content based recommendation engine)\n",
        "\n",
        "#Resources: https://medium.com/code-heroku/building-a-movie-recommendation-engine-in-python-using-scikit-learn-c7489d7cb145"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_JFDPy3te5Wf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Import Libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IUCvzg_CiM_K",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "a5705198-68be-4d9b-81bb-f48049cca725"
      },
      "source": [
        "#Load the data\n",
        "from google.colab import files # Use to load data on Google Colab\n",
        "uploaded = files.upload() # Use to load data on Google Colab\n",
        "df = pd.read_csv(\"movie_dataset.csv\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-2fffa098-3e69-4ee9-b687-1caf7c29afe5\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-2fffa098-3e69-4ee9-b687-1caf7c29afe5\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving movie_dataset.csv to movie_dataset.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fnrmF_djd9b0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "febaec52-85a7-4933-9831-393b3db8a4db"
      },
      "source": [
        "#Print the first 3 rows of the data set\n",
        "df.head(3)"
      ],
      "execution_count": 45,
      "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>index</th>\n",
              "      <th>budget</th>\n",
              "      <th>genres</th>\n",
              "      <th>homepage</th>\n",
              "      <th>id</th>\n",
              "      <th>keywords</th>\n",
              "      <th>original_language</th>\n",
              "      <th>original_title</th>\n",
              "      <th>overview</th>\n",
              "      <th>popularity</th>\n",
              "      <th>production_companies</th>\n",
              "      <th>production_countries</th>\n",
              "      <th>release_date</th>\n",
              "      <th>revenue</th>\n",
              "      <th>runtime</th>\n",
              "      <th>spoken_languages</th>\n",
              "      <th>status</th>\n",
              "      <th>tagline</th>\n",
              "      <th>title</th>\n",
              "      <th>vote_average</th>\n",
              "      <th>vote_count</th>\n",
              "      <th>cast</th>\n",
              "      <th>crew</th>\n",
              "      <th>director</th>\n",
              "      <th>combined_features</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>237000000</td>\n",
              "      <td>Action Adventure Fantasy Science Fiction</td>\n",
              "      <td>http://www.avatarmovie.com/</td>\n",
              "      <td>19995</td>\n",
              "      <td>culture clash future space war space colony so...</td>\n",
              "      <td>en</td>\n",
              "      <td>Avatar</td>\n",
              "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
              "      <td>150.437577</td>\n",
              "      <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289...</td>\n",
              "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
              "      <td>2009-12-10</td>\n",
              "      <td>2787965087</td>\n",
              "      <td>162.0</td>\n",
              "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...</td>\n",
              "      <td>Released</td>\n",
              "      <td>Enter the World of Pandora.</td>\n",
              "      <td>Avatar</td>\n",
              "      <td>7.2</td>\n",
              "      <td>11800</td>\n",
              "      <td>Sam Worthington Zoe Saldana Sigourney Weaver S...</td>\n",
              "      <td>[{'name': 'Stephen E. Rivkin', 'gender': 0, 'd...</td>\n",
              "      <td>James Cameron</td>\n",
              "      <td>culture clash future space war space colony so...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>300000000</td>\n",
              "      <td>Adventure Fantasy Action</td>\n",
              "      <td>http://disney.go.com/disneypictures/pirates/</td>\n",
              "      <td>285</td>\n",
              "      <td>ocean drug abuse exotic island east india trad...</td>\n",
              "      <td>en</td>\n",
              "      <td>Pirates of the Caribbean: At World's End</td>\n",
              "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
              "      <td>139.082615</td>\n",
              "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...</td>\n",
              "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
              "      <td>2007-05-19</td>\n",
              "      <td>961000000</td>\n",
              "      <td>169.0</td>\n",
              "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>At the end of the world, the adventure begins.</td>\n",
              "      <td>Pirates of the Caribbean: At World's End</td>\n",
              "      <td>6.9</td>\n",
              "      <td>4500</td>\n",
              "      <td>Johnny Depp Orlando Bloom Keira Knightley Stel...</td>\n",
              "      <td>[{'name': 'Dariusz Wolski', 'gender': 2, 'depa...</td>\n",
              "      <td>Gore Verbinski</td>\n",
              "      <td>ocean drug abuse exotic island east india trad...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>245000000</td>\n",
              "      <td>Action Adventure Crime</td>\n",
              "      <td>http://www.sonypictures.com/movies/spectre/</td>\n",
              "      <td>206647</td>\n",
              "      <td>spy based on novel secret agent sequel mi6</td>\n",
              "      <td>en</td>\n",
              "      <td>Spectre</td>\n",
              "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
              "      <td>107.376788</td>\n",
              "      <td>[{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...</td>\n",
              "      <td>[{\"iso_3166_1\": \"GB\", \"name\": \"United Kingdom\"...</td>\n",
              "      <td>2015-10-26</td>\n",
              "      <td>880674609</td>\n",
              "      <td>148.0</td>\n",
              "      <td>[{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...</td>\n",
              "      <td>Released</td>\n",
              "      <td>A Plan No One Escapes</td>\n",
              "      <td>Spectre</td>\n",
              "      <td>6.3</td>\n",
              "      <td>4466</td>\n",
              "      <td>Daniel Craig Christoph Waltz L\\u00e9a Seydoux ...</td>\n",
              "      <td>[{'name': 'Thomas Newman', 'gender': 2, 'depar...</td>\n",
              "      <td>Sam Mendes</td>\n",
              "      <td>spy based on novel secret agent sequel mi6 Dan...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index  ...                                  combined_features\n",
              "0      0  ...  culture clash future space war space colony so...\n",
              "1      1  ...  ocean drug abuse exotic island east india trad...\n",
              "2      2  ...  spy based on novel secret agent sequel mi6 Dan...\n",
              "\n",
              "[3 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eOM5lGEquH7B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "65cd85e6-7c9d-4314-9908-a684ae3d0661"
      },
      "source": [
        "#Get a count of the number of rows/movies in the data set and the number of columns\n",
        "df.shape"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4803, 25)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mN4RNV5IiOzo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "outputId": "71ec779f-9e22-4ec6-bdbf-5e4818120f9a"
      },
      "source": [
        "#Create a list of important columns to keep a.k.a. the main content of the movie\n",
        "features = ['keywords','cast','genres','director']\n",
        "\n",
        "df[features].head(3)"
      ],
      "execution_count": 46,
      "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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              "        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>keywords</th>\n",
              "      <th>cast</th>\n",
              "      <th>genres</th>\n",
              "      <th>director</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>culture clash future space war space colony so...</td>\n",
              "      <td>Sam Worthington Zoe Saldana Sigourney Weaver S...</td>\n",
              "      <td>Action Adventure Fantasy Science Fiction</td>\n",
              "      <td>James Cameron</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ocean drug abuse exotic island east india trad...</td>\n",
              "      <td>Johnny Depp Orlando Bloom Keira Knightley Stel...</td>\n",
              "      <td>Adventure Fantasy Action</td>\n",
              "      <td>Gore Verbinski</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spy based on novel secret agent sequel mi6</td>\n",
              "      <td>Daniel Craig Christoph Waltz L\\u00e9a Seydoux ...</td>\n",
              "      <td>Action Adventure Crime</td>\n",
              "      <td>Sam Mendes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            keywords  ...        director\n",
              "0  culture clash future space war space colony so...  ...   James Cameron\n",
              "1  ocean drug abuse exotic island east india trad...  ...  Gore Verbinski\n",
              "2         spy based on novel secret agent sequel mi6  ...      Sam Mendes\n",
              "\n",
              "[3 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "maaKHGF_iVNJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Clean and preprocess the data\n",
        "for feature in features:\n",
        "    df[feature] = df[feature].fillna('') #Fill any missing values with the empty string\n",
        "   # print(df[feature])\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qWiLK23piS25",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#A function to combine the values of the important columns into a single string\n",
        "def combine_features(row):\n",
        "    return row['keywords'] +\" \"+row['cast']+\" \"+row[\"genres\"]+\" \"+row[\"director\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5sHEKpTsiYAI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Apply the function to each row in the dataset to store the combined strings into a new column called combined_features \n",
        "df[\"combined_features\"] = df.apply(combine_features,axis=1)\n",
        "#df[\"combined_features\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKwTlCvhiF3y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "30ea4fab-6a55-4624-99bd-4718a98d6d8c"
      },
      "source": [
        "#Print the data frame to show the new column 'combined_features'\n",
        "df.head(2)"
      ],
      "execution_count": 50,
      "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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              "    .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>index</th>\n",
              "      <th>budget</th>\n",
              "      <th>genres</th>\n",
              "      <th>homepage</th>\n",
              "      <th>id</th>\n",
              "      <th>keywords</th>\n",
              "      <th>original_language</th>\n",
              "      <th>original_title</th>\n",
              "      <th>overview</th>\n",
              "      <th>popularity</th>\n",
              "      <th>production_companies</th>\n",
              "      <th>production_countries</th>\n",
              "      <th>release_date</th>\n",
              "      <th>revenue</th>\n",
              "      <th>runtime</th>\n",
              "      <th>spoken_languages</th>\n",
              "      <th>status</th>\n",
              "      <th>tagline</th>\n",
              "      <th>title</th>\n",
              "      <th>vote_average</th>\n",
              "      <th>vote_count</th>\n",
              "      <th>cast</th>\n",
              "      <th>crew</th>\n",
              "      <th>director</th>\n",
              "      <th>combined_features</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>237000000</td>\n",
              "      <td>Action Adventure Fantasy Science Fiction</td>\n",
              "      <td>http://www.avatarmovie.com/</td>\n",
              "      <td>19995</td>\n",
              "      <td>culture clash future space war space colony so...</td>\n",
              "      <td>en</td>\n",
              "      <td>Avatar</td>\n",
              "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
              "      <td>150.437577</td>\n",
              "      <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289...</td>\n",
              "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
              "      <td>2009-12-10</td>\n",
              "      <td>2787965087</td>\n",
              "      <td>162.0</td>\n",
              "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...</td>\n",
              "      <td>Released</td>\n",
              "      <td>Enter the World of Pandora.</td>\n",
              "      <td>Avatar</td>\n",
              "      <td>7.2</td>\n",
              "      <td>11800</td>\n",
              "      <td>Sam Worthington Zoe Saldana Sigourney Weaver S...</td>\n",
              "      <td>[{'name': 'Stephen E. Rivkin', 'gender': 0, 'd...</td>\n",
              "      <td>James Cameron</td>\n",
              "      <td>culture clash future space war space colony so...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>300000000</td>\n",
              "      <td>Adventure Fantasy Action</td>\n",
              "      <td>http://disney.go.com/disneypictures/pirates/</td>\n",
              "      <td>285</td>\n",
              "      <td>ocean drug abuse exotic island east india trad...</td>\n",
              "      <td>en</td>\n",
              "      <td>Pirates of the Caribbean: At World's End</td>\n",
              "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
              "      <td>139.082615</td>\n",
              "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...</td>\n",
              "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
              "      <td>2007-05-19</td>\n",
              "      <td>961000000</td>\n",
              "      <td>169.0</td>\n",
              "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>At the end of the world, the adventure begins.</td>\n",
              "      <td>Pirates of the Caribbean: At World's End</td>\n",
              "      <td>6.9</td>\n",
              "      <td>4500</td>\n",
              "      <td>Johnny Depp Orlando Bloom Keira Knightley Stel...</td>\n",
              "      <td>[{'name': 'Dariusz Wolski', 'gender': 2, 'depa...</td>\n",
              "      <td>Gore Verbinski</td>\n",
              "      <td>ocean drug abuse exotic island east india trad...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index  ...                                  combined_features\n",
              "0      0  ...  culture clash future space war space colony so...\n",
              "1      1  ...  ocean drug abuse exotic island east india trad...\n",
              "\n",
              "[2 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AO68PVoqiabY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Convert a collection of text to a matrix/vector of token counts\n",
        "count_matrix = CountVectorizer().fit_transform(df[\"combined_features\"])\n",
        "\n",
        "#Print the count matrix\n",
        "#print(count_matrix.toarray())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v4-xd9KlicTg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "1aa1f295-41ca-41f4-ce3e-4d2c57689779"
      },
      "source": [
        "#Get the cosine similarity matrix from the count matrix (cos(theta))\n",
        "cosine_sim = cosine_similarity(count_matrix)\n",
        "\n",
        "#Print the cosine similarity matrix\n",
        "print(cosine_sim)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.         0.10540926 0.12038585 ... 0.         0.         0.        ]\n",
            " [0.10540926 1.         0.0761387  ... 0.03651484 0.         0.        ]\n",
            " [0.12038585 0.0761387  1.         ... 0.         0.11145564 0.        ]\n",
            " ...\n",
            " [0.         0.03651484 0.         ... 1.         0.         0.04264014]\n",
            " [0.         0.         0.11145564 ... 0.         1.         0.        ]\n",
            " [0.         0.         0.         ... 0.04264014 0.         1.        ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82rRh-7J_JQe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "42e86641-0e6f-47cf-c9de-0efcd9f0928f"
      },
      "source": [
        "#Get the number of rows and columns in the data set\n",
        "cosine_sim.shape"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4803, 4803)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qoowt_mCiebQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Helper function to get the title from the index\n",
        "def get_title_from_index(index):\n",
        "  return df[df.index == index][\"title\"].values[0]\n",
        "\n",
        "#Helper function to get the index from the title\n",
        "def get_index_from_title(title):\n",
        "  return df[df.title == title][\"index\"].values[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MBGtH3hFik9B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Get the title of the movie that the user likes\n",
        "movie_user_likes = \"The Amazing Spider-Man\"\n",
        "\n",
        "#Find that movies index\n",
        "movie_index = get_index_from_title(movie_user_likes) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2W81dduHinkJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Access the row, through the movies index, corresponding to this movie (the liked movie) in the similarity matrix, \n",
        "# by doing this we will get the similarity scores of all other movies from the current movie\n",
        "\n",
        "#Enumerate through all the similarity scores of that movie to make a tuple of movie index and similarity scores.\n",
        "#  This will convert a row of similarity scores like this- [5 0.6 0.3 0.9] to this- [(0, 5) (1, 0.6) (2, 0.3) (3, 0.9)] . \n",
        "#  Note this puts each item in the list in this form (movie index, similarity score)\n",
        "similar_movies =  list(enumerate(cosine_sim[movie_index]))\n",
        "\n",
        "\n",
        "\n",
        "#Sort the list of similar movies according to the similarity scores in descending order\n",
        "#Since the most similar movie is itself, we will discard the first element after sorting.\n",
        "sorted_similar_movies = sorted(similar_movies,key=lambda x:x[1],reverse=True)[1:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H7PhR4o05Voh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "b4e57cae-5a48-4448-ff38-b08dae44680a"
      },
      "source": [
        "#Print the sorted similar movies to the movie the user like\n",
        "# The tuples are in the form (movie_index, similarity value)\n",
        "print(sorted_similar_movies)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U1N2NGDRiqaZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "b99966e2-58e7-4fdf-89f2-1e9a0b05312d"
      },
      "source": [
        "#Create a loop to print the first 5 entries from the sorted similar movies list\n",
        "\n",
        "i=0\n",
        "print(\"Top 5 similar movies to \"+movie_user_likes+\" are:\")\n",
        "for element in sorted_similar_movies:\n",
        "    print(get_title_from_index(element[0]), ' similarity score = ', sorted_similar_movies[i][1] * 100)\n",
        "    i=i+1\n",
        "    if i>=5:\n",
        "        break"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Top 5 similar movies to The Amazing Spider-Man are:\n",
            "The Amazing Spider-Man 2  similarity score =  56.04485383178051\n",
            "Duma  similarity score =  25.53769592276246\n",
            "Highlander: Endgame  similarity score =  24.1522945769824\n",
            "Cold Mountain  similarity score =  24.077170617153843\n",
            "Spider-Man 2  similarity score =  24.019223070763076\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Br9DWz7s0mi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "2b3e45d6-7798-4b42-e5f8-547f318f2a0f"
      },
      "source": [
        "#Create a loop to print the first 5 entries from the sorted similar movies list\n",
        "\n",
        "i=0\n",
        "print(\"Top 5 similar movies to \"+movie_user_likes+\" are:\")\n",
        "for i in range( len(sorted_similar_movies)):\n",
        "    print(get_title_from_index(sorted_similar_movies[i][0]), ' similarity score = ', sorted_similar_movies[i][1] * 100)\n",
        "    i=i+1\n",
        "    if i>=5:\n",
        "        break"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Top 5 similar movies to The Amazing Spider-Man are:\n",
            "The Amazing Spider-Man 2  similarity score =  56.04485383178051\n",
            "Duma  similarity score =  25.53769592276246\n",
            "Highlander: Endgame  similarity score =  24.1522945769824\n",
            "Cold Mountain  similarity score =  24.077170617153843\n",
            "Spider-Man 2  similarity score =  24.019223070763076\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}