Movie_Recommendation_System.ipynb

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      "name": "Movie_Recommendation_System",
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#MOVIE RECOMMENDATION SYSTEM:"
      ],
      "metadata": {
        "id": "cDIVMc5uBAyU"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mbyk_Pktn0BW"
      },
      "source": [
        "#The first dataset contains the following features:-\n",
        "\n",
        "#movie_id - A unique identifier for each movie.\n",
        "#cast - The name of lead and supporting actors.\n",
        "#crew - The name of Director, Editor, Composer, Writer etc.\n",
        "\n",
        "#The second dataset has the following features:-\n",
        "\n",
        "#budget - The budget in which the movie was made.\n",
        "#genre - The genre of the movie, Action, Comedy ,Thriller etc.\n",
        "#homepage - A link to the homepage of the movie.\n",
        "#id - This is infact the movie_id as in the first dataset.\n",
        "#keywords - The keywords or tags related to the movie.\n",
        "#original_language - The language in which the movie was made.\n",
        "#original_title - The title of the movie before translation or adaptation.\n",
        "#overview - A brief description of the movie.\n",
        "#popularity - A numeric quantity specifying the movie popularity.\n",
        "#production_companies - The production house of the movie.\n",
        "#production_countries - The country in which it was produced.\n",
        "#release_date - The date on which it was released.\n",
        "#revenue - The worldwide revenue generated by the movie.\n",
        "#runtime - The running time of the movie in minutes.\n",
        "#status - \"Released\" or \"Rumored\".\n",
        "#tagline - Movie's tagline.\n",
        "#title - Title of the movie.\n",
        "#vote_average - average ratings the movie recieved.\n",
        "#vote_count - the count of votes recieved.\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7r794FvbNmGg"
      },
      "source": [
        "import pandas as pd \n",
        "import numpy as np \n",
        "df1=pd.read_csv('/content/tmdb_5000_credits.csv.zip')\n",
        "df2=pd.read_csv('/content/tmdb_5000_movies.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "65hS4JWjkm4z",
        "outputId": "98db8980-c1a4-44bc-a005-66032afa2ed4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        }
      },
      "source": [
        "df1.columns = ['id','tittle','cast','crew']\n",
        "df2= df2.merge(df1,on='id')\n",
        "df2.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe thead th {\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>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>tittle</th>\n",
              "      <th>cast</th>\n",
              "      <th>crew</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>237000000</td>\n",
              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
              "      <td>http://www.avatarmovie.com/</td>\n",
              "      <td>19995</td>\n",
              "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</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>Avatar</td>\n",
              "      <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
              "      <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>300000000</td>\n",
              "      <td>[{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...</td>\n",
              "      <td>http://disney.go.com/disneypictures/pirates/</td>\n",
              "      <td>285</td>\n",
              "      <td>[{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...</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>Pirates of the Caribbean: At World's End</td>\n",
              "      <td>[{\"cast_id\": 4, \"character\": \"Captain Jack Spa...</td>\n",
              "      <td>[{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>245000000</td>\n",
              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
              "      <td>http://www.sonypictures.com/movies/spectre/</td>\n",
              "      <td>206647</td>\n",
              "      <td>[{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...</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>Spectre</td>\n",
              "      <td>[{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...</td>\n",
              "      <td>[{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>250000000</td>\n",
              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n",
              "      <td>http://www.thedarkknightrises.com/</td>\n",
              "      <td>49026</td>\n",
              "      <td>[{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...</td>\n",
              "      <td>en</td>\n",
              "      <td>The Dark Knight Rises</td>\n",
              "      <td>Following the death of District Attorney Harve...</td>\n",
              "      <td>112.312950</td>\n",
              "      <td>[{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...</td>\n",
              "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
              "      <td>2012-07-16</td>\n",
              "      <td>1084939099</td>\n",
              "      <td>165.0</td>\n",
              "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>The Legend Ends</td>\n",
              "      <td>The Dark Knight Rises</td>\n",
              "      <td>7.6</td>\n",
              "      <td>9106</td>\n",
              "      <td>The Dark Knight Rises</td>\n",
              "      <td>[{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...</td>\n",
              "      <td>[{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>260000000</td>\n",
              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
              "      <td>http://movies.disney.com/john-carter</td>\n",
              "      <td>49529</td>\n",
              "      <td>[{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...</td>\n",
              "      <td>en</td>\n",
              "      <td>John Carter</td>\n",
              "      <td>John Carter is a war-weary, former military ca...</td>\n",
              "      <td>43.926995</td>\n",
              "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}]</td>\n",
              "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
              "      <td>2012-03-07</td>\n",
              "      <td>284139100</td>\n",
              "      <td>132.0</td>\n",
              "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Lost in our world, found in another.</td>\n",
              "      <td>John Carter</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2124</td>\n",
              "      <td>John Carter</td>\n",
              "      <td>[{\"cast_id\": 5, \"character\": \"John Carter\", \"c...</td>\n",
              "      <td>[{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      budget  ...                                               crew\n",
              "0  237000000  ...  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...\n",
              "1  300000000  ...  [{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...\n",
              "2  245000000  ...  [{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...\n",
              "3  250000000  ...  [{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...\n",
              "4  260000000  ...  [{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#DEMOGRAPHIC FILTERING"
      ],
      "metadata": {
        "id": "ruCu9KWgBzCx"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mhDxevd-cEtN",
        "outputId": "07a0bc71-e90f-40f7-91d5-affe0ca5d71e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "C= df2['vote_average'].mean()\n",
        "print(C)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6.092171559442011\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AOukAXYccUsy",
        "outputId": "43270f64-9008-443b-c568-5ae63124f100",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m= df2['vote_count'].quantile(0.9)\n",
        "print(m)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1838.4000000000015\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rL4Q8Reqcaow",
        "outputId": "8190333e-3ef5-49bd-fe31-fb61d0db2853",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "q_movies = df2.copy().loc[df2['vote_count'] >= m]\n",
        "q_movies.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(481, 23)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RFqRQxXLceVI"
      },
      "source": [
        "def weighted_rating(x, m=m, C=C):\n",
        "    v = x['vote_count']\n",
        "    R = x['vote_average']\n",
        "    # Calculation based on the IMDB formula\n",
        "    return (v/(v+m) * R) + (m/(m+v) * C)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RUtbKizpcgcX"
      },
      "source": [
        "# Define a new feature 'score' and calculate its value with `weighted_rating()`\n",
        "q_movies['score'] = q_movies.apply(weighted_rating, axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJq6F8CgcjNw",
        "outputId": "a23c081a-8073-4dd6-bb44-981f012cc1ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "#Sort movies based on score calculated above\n",
        "q_movies = q_movies.sort_values('score', ascending=False)\n",
        "\n",
        "#Print the top 10 movies\n",
        "q_movies[['title', 'vote_count', 'vote_average', 'score']].head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .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>vote_count</th>\n",
              "      <th>vote_average</th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1881</th>\n",
              "      <td>The Shawshank Redemption</td>\n",
              "      <td>8205</td>\n",
              "      <td>8.5</td>\n",
              "      <td>8.059258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>662</th>\n",
              "      <td>Fight Club</td>\n",
              "      <td>9413</td>\n",
              "      <td>8.3</td>\n",
              "      <td>7.939256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>The Dark Knight</td>\n",
              "      <td>12002</td>\n",
              "      <td>8.2</td>\n",
              "      <td>7.920020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3232</th>\n",
              "      <td>Pulp Fiction</td>\n",
              "      <td>8428</td>\n",
              "      <td>8.3</td>\n",
              "      <td>7.904645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>Inception</td>\n",
              "      <td>13752</td>\n",
              "      <td>8.1</td>\n",
              "      <td>7.863239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3337</th>\n",
              "      <td>The Godfather</td>\n",
              "      <td>5893</td>\n",
              "      <td>8.4</td>\n",
              "      <td>7.851236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Interstellar</td>\n",
              "      <td>10867</td>\n",
              "      <td>8.1</td>\n",
              "      <td>7.809479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>809</th>\n",
              "      <td>Forrest Gump</td>\n",
              "      <td>7927</td>\n",
              "      <td>8.2</td>\n",
              "      <td>7.803188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>329</th>\n",
              "      <td>The Lord of the Rings: The Return of the King</td>\n",
              "      <td>8064</td>\n",
              "      <td>8.1</td>\n",
              "      <td>7.727243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1990</th>\n",
              "      <td>The Empire Strikes Back</td>\n",
              "      <td>5879</td>\n",
              "      <td>8.2</td>\n",
              "      <td>7.697884</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              title  ...     score\n",
              "1881                       The Shawshank Redemption  ...  8.059258\n",
              "662                                      Fight Club  ...  7.939256\n",
              "65                                  The Dark Knight  ...  7.920020\n",
              "3232                                   Pulp Fiction  ...  7.904645\n",
              "96                                        Inception  ...  7.863239\n",
              "3337                                  The Godfather  ...  7.851236\n",
              "95                                     Interstellar  ...  7.809479\n",
              "809                                    Forrest Gump  ...  7.803188\n",
              "329   The Lord of the Rings: The Return of the King  ...  7.727243\n",
              "1990                        The Empire Strikes Back  ...  7.697884\n",
              "\n",
              "[10 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L6usZUgPc4-n",
        "outputId": "00ba6ada-ad05-41cb-fc2a-ad049c7e657e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        }
      },
      "source": [
        "#Based on popularity\n",
        "pop= df2.sort_values('popularity', ascending=False)\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(12,4))\n",
        "\n",
        "plt.barh(pop['title'].head(10),pop['popularity'].head(10), align='center',color='skyblue')\n",
        "plt.gca().invert_yaxis()\n",
        "plt.xlabel(\"Popularity\")\n",
        "plt.title(\"Popular Movies\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Popular Movies')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#CONTENT BASED FILTERING"
      ],
      "metadata": {
        "id": "orx9bbv9B2WI"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iyQub3DkdBIP",
        "outputId": "f6e3b64d-b832-4f0e-fc7d-7bedf2508750",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "#Plot description based Recommender\n",
        "df2['overview'].head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    In the 22nd century, a paraplegic Marine is di...\n",
              "1    Captain Barbossa, long believed to be dead, ha...\n",
              "2    A cryptic message from Bond’s past sends him o...\n",
              "3    Following the death of District Attorney Harve...\n",
              "4    John Carter is a war-weary, former military ca...\n",
              "Name: overview, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eLDugyOddL4H",
        "outputId": "cc65647a-eb0f-4cc9-8b8f-640466621b8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Import TfIdfVectorizer from scikit-learn\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "#Define a TF-IDF Vectorizer Object. Remove all english stop words such as 'the', 'a'\n",
        "tfidf = TfidfVectorizer(stop_words='english')\n",
        "\n",
        "#Replace NaN with an empty string\n",
        "df2['overview'] = df2['overview'].fillna('')\n",
        "\n",
        "#Construct the required TF-IDF matrix by fitting and transforming the data\n",
        "tfidf_matrix = tfidf.fit_transform(df2['overview'])\n",
        "\n",
        "#Output the shape of tfidf_matrix\n",
        "tfidf_matrix.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4803, 20978)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbRuWl88dOAQ"
      },
      "source": [
        "# Import linear_kernel\n",
        "from sklearn.metrics.pairwise import linear_kernel\n",
        "\n",
        "# Compute the cosine similarity matrix\n",
        "cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R8GzcwOldPnJ"
      },
      "source": [
        "#Construct a reverse map of indices and movie titles\n",
        "indices = pd.Series(df2.index, index=df2['title']).drop_duplicates()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rEEsirmkdSAY"
      },
      "source": [
        "# Function that takes in movie title as input and outputs most similar movies\n",
        "def get_recommendations(title, cosine_sim=cosine_sim):\n",
        "    # Get the index of the movie that matches the title\n",
        "    idx = indices[title]\n",
        "\n",
        "    # Get the pairwsie similarity scores of all movies with that movie\n",
        "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
        "\n",
        "    # Sort the movies based on the similarity scores\n",
        "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "\n",
        "    # Get the scores of the 10 most similar movies\n",
        "    sim_scores = sim_scores[1:11]\n",
        "\n",
        "    # Get the movie indices\n",
        "    movie_indices = [i[0] for i in sim_scores]\n",
        "\n",
        "    # Return the top 10 most similar movies\n",
        "    return df2['title'].iloc[movie_indices]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQEyAWYhHPlJ",
        "outputId": "7fb12356-8076-4674-89b3-d46ae5a18c61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mov=input(\"Enter the movie name\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter the movie nameThe Avengers\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zCo14PLbdURv",
        "outputId": "5db1a269-2024-40c4-9c96-6feeac1d55ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "get_recommendations(mov)\n",
        "#For example:get_recommendations('The Avengers')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7               Avengers: Age of Ultron\n",
              "3144                            Plastic\n",
              "1715                            Timecop\n",
              "4124                 This Thing of Ours\n",
              "3311              Thank You for Smoking\n",
              "3033                      The Corruptor\n",
              "588     Wall Street: Money Never Sleeps\n",
              "2136         Team America: World Police\n",
              "1468                       The Fountain\n",
              "1286                        Snowpiercer\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FvDWOhjedgcZ"
      },
      "source": [
        "#Credits, Genres and Keywords Based Recommender\n",
        "# Parse the stringified features into their corresponding python objects\n",
        "from ast import literal_eval\n",
        "\n",
        "features = ['cast', 'crew', 'keywords', 'genres']\n",
        "for feature in features:\n",
        "  df2[feature] = df2[feature].apply(literal_eval)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6hCnN7pEdmPo"
      },
      "source": [
        "# Get the director's name from the crew feature. If director is not listed, return NaN\n",
        "def get_director(x):\n",
        "    for i in x:\n",
        "        if i['job'] == 'Director':\n",
        "            return i['name']\n",
        "    return np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vl8Xdw3mdoa6"
      },
      "source": [
        "# Returns the list top 3 elements or entire list; whichever is more.\n",
        "def get_list(x):\n",
        "  if isinstance(x, list):\n",
        "      names = [i['name'] for i in x]\n",
        "      #Check if more than 3 elements exist. If yes, return only first three. If no, return entire list.\n",
        "      if len(names) > 3:\n",
        "          names = names[:3]\n",
        "      return names\n",
        "\n",
        "  #Return empty list in case of missing/malformed data\n",
        "  return []"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1wceNnhdrHA"
      },
      "source": [
        "# Define new director, cast, genres and keywords features that are in a suitable form.\n",
        "df2['director'] = df2['crew'].apply(get_director)\n",
        "\n",
        "features = ['cast', 'keywords', 'genres']\n",
        "for feature in features:\n",
        "    df2[feature] = df2[feature].apply(get_list)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VF9BVmdqds3Z",
        "outputId": "4208f404-6027-4169-9130-492c5b65d53d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "# Print the new features of the first 3 films\n",
        "df2[['title', 'cast', 'director', 'keywords', 'genres']].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>cast</th>\n",
              "      <th>director</th>\n",
              "      <th>keywords</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Avatar</td>\n",
              "      <td>[Sam Worthington, Zoe Saldana, Sigourney Weaver]</td>\n",
              "      <td>James Cameron</td>\n",
              "      <td>[culture clash, future, space war]</td>\n",
              "      <td>[Action, Adventure, Fantasy]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Pirates of the Caribbean: At World's End</td>\n",
              "      <td>[Johnny Depp, Orlando Bloom, Keira Knightley]</td>\n",
              "      <td>Gore Verbinski</td>\n",
              "      <td>[ocean, drug abuse, exotic island]</td>\n",
              "      <td>[Adventure, Fantasy, Action]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Spectre</td>\n",
              "      <td>[Daniel Craig, Christoph Waltz, Léa Seydoux]</td>\n",
              "      <td>Sam Mendes</td>\n",
              "      <td>[spy, based on novel, secret agent]</td>\n",
              "      <td>[Action, Adventure, Crime]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>The Dark Knight Rises</td>\n",
              "      <td>[Christian Bale, Michael Caine, Gary Oldman]</td>\n",
              "      <td>Christopher Nolan</td>\n",
              "      <td>[dc comics, crime fighter, terrorist]</td>\n",
              "      <td>[Action, Crime, Drama]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>John Carter</td>\n",
              "      <td>[Taylor Kitsch, Lynn Collins, Samantha Morton]</td>\n",
              "      <td>Andrew Stanton</td>\n",
              "      <td>[based on novel, mars, medallion]</td>\n",
              "      <td>[Action, Adventure, Science Fiction]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                      title  ...                                genres\n",
              "0                                    Avatar  ...          [Action, Adventure, Fantasy]\n",
              "1  Pirates of the Caribbean: At World's End  ...          [Adventure, Fantasy, Action]\n",
              "2                                   Spectre  ...            [Action, Adventure, Crime]\n",
              "3                     The Dark Knight Rises  ...                [Action, Crime, Drama]\n",
              "4                               John Carter  ...  [Action, Adventure, Science Fiction]\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZnTDvmeFdz4E"
      },
      "source": [
        "# Function to convert all strings to lower case and strip names of spaces\n",
        "def clean_data(x):\n",
        "    if isinstance(x, list):\n",
        "        return [str.lower(i.replace(\" \", \"\")) for i in x]\n",
        "    else:\n",
        "        #Check if director exists. If not, return empty string\n",
        "        if isinstance(x, str):\n",
        "            return str.lower(x.replace(\" \", \"\"))\n",
        "        else:\n",
        "            return ''"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVsuZxotd1kp"
      },
      "source": [
        "# Apply clean_data function to your features.\n",
        "features = ['cast', 'keywords', 'director', 'genres']\n",
        "\n",
        "for feature in features:\n",
        "    df2[feature] = df2[feature].apply(clean_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gUFBjagsd3ga"
      },
      "source": [
        "def create_soup(x):\n",
        "    return ' '.join(x['keywords']) + ' ' + ' '.join(x['cast']) + ' ' + x['director'] + ' ' + ' '.join(x['genres'])\n",
        "df2['soup'] = df2.apply(create_soup, axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6cWTVVfrd5a_"
      },
      "source": [
        "# Import CountVectorizer and create the count matrix\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "count = CountVectorizer(stop_words='english')\n",
        "count_matrix = count.fit_transform(df2['soup'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jh4myfnkd8Rn"
      },
      "source": [
        "# Compute the Cosine Similarity matrix based on the count_matrix\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "cosine_sim2 = cosine_similarity(count_matrix, count_matrix)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qmdclS0qd-EA"
      },
      "source": [
        "# Reset index of our main DataFrame and construct reverse mapping as before\n",
        "df2 = df2.reset_index()\n",
        "indices = pd.Series(df2.index, index=df2['title'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fl-I0QmcIKC3",
        "outputId": "be62c967-bb6c-4033-8de5-1c3942227932",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mov=input(\"Enter the movie name\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter the movie nameThe Godfather\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kXxr8fgsd_7o",
        "outputId": "bbfce0b5-6f84-407f-d959-ff48feb08e3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "get_recommendations(mov, cosine_sim2)\n",
        "#For example:get_recommendations('The Godfather', cosine_sim2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "867      The Godfather: Part III\n",
              "2731      The Godfather: Part II\n",
              "4638    Amidst the Devil's Wings\n",
              "2649           The Son of No One\n",
              "1525              Apocalypse Now\n",
              "1018             The Cotton Club\n",
              "1170     The Talented Mr. Ripley\n",
              "1209               The Rainmaker\n",
              "1394               Donnie Brasco\n",
              "1850                    Scarface\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#COLLABORATIVE FILTERING"
      ],
      "metadata": {
        "id": "Zo990oMxB6oh"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bbOHHsO0eMrp",
        "outputId": "d2d0daa2-32ab-4151-e9a5-6eda42e9ac25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "source": [
        "#Single value decomposition\n",
        "!pip install surprise\n",
        "#!apt-get evaluate install surprise\n",
        "from surprise import Reader, Dataset, SVD\n",
        "#from surprise import evaluate\n",
        "reader = Reader()\n",
        "ratings = pd.read_csv('/content/ratings_small.csv.zip')\n",
        "ratings.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting surprise\n",
            "  Downloading https://files.pythonhosted.org/packages/61/de/e5cba8682201fcf9c3719a6fdda95693468ed061945493dea2dd37c5618b/surprise-0.1-py2.py3-none-any.whl\n",
            "Collecting scikit-surprise\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f5/da/b5700d96495fb4f092be497f02492768a3d96a3f4fa2ae7dea46d4081cfa/scikit-surprise-1.1.0.tar.gz (6.4MB)\n",
            "\u001b[K     |████████████████████████████████| 6.5MB 2.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-surprise->surprise) (0.14.1)\n",
            "Requirement already satisfied: numpy>=1.11.2 in /usr/local/lib/python3.6/dist-packages (from scikit-surprise->surprise) (1.18.2)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from scikit-surprise->surprise) (1.4.1)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from scikit-surprise->surprise) (1.12.0)\n",
            "Building wheels for collected packages: scikit-surprise\n",
            "  Building wheel for scikit-surprise (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for scikit-surprise: filename=scikit_surprise-1.1.0-cp36-cp36m-linux_x86_64.whl size=1678154 sha256=be5d829d740021f1f8e09562fb232e205775daf4a6b5b8a5818175e2e2e40d3c\n",
            "  Stored in directory: /root/.cache/pip/wheels/cc/fa/8c/16c93fccce688ae1bde7d979ff102f7bee980d9cfeb8641bcf\n",
            "Successfully built scikit-surprise\n",
            "Installing collected packages: scikit-surprise, surprise\n",
            "Successfully installed scikit-surprise-1.1.0 surprise-0.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
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            "text/plain": [
              "   userId  movieId  rating   timestamp\n",
              "0       1       31     2.5  1260759144\n",
              "1       1     1029     3.0  1260759179\n",
              "2       1     1061     3.0  1260759182\n",
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              "4       1     1172     4.0  1260759205"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CxiSWPCVeeJB"
      },
      "source": [
        "data = Dataset.load_from_df(ratings[['userId', 'movieId', 'rating']], reader)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O5GTWUu5egF5"
      },
      "source": [
        "svd = SVD()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vNpnxVB4h4T-",
        "outputId": "83cece01-0bb4-4ebc-8b55-74e5d1909ab5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "trainset = data.build_full_trainset()\n",
        "svd.fit(trainset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x7fc4f9a3fa20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9BtB236Gh68Q",
        "outputId": "4d2ac6b3-43b8-4704-96a5-24406eb9da0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        }
      },
      "source": [
        "ratings[ratings['userId'] == 1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "      <td>1260759131</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>1260759191</td>\n",
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              "      <td>1260759139</td>\n",
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              "      <td>1260759194</td>\n",
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              "      <td>1260759113</td>\n",
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              "      <td>1260759200</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>1260759117</td>\n",
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            "text/plain": [
              "    userId  movieId  rating   timestamp\n",
              "0        1       31     2.5  1260759144\n",
              "1        1     1029     3.0  1260759179\n",
              "2        1     1061     3.0  1260759182\n",
              "3        1     1129     2.0  1260759185\n",
              "4        1     1172     4.0  1260759205\n",
              "5        1     1263     2.0  1260759151\n",
              "6        1     1287     2.0  1260759187\n",
              "7        1     1293     2.0  1260759148\n",
              "8        1     1339     3.5  1260759125\n",
              "9        1     1343     2.0  1260759131\n",
              "10       1     1371     2.5  1260759135\n",
              "11       1     1405     1.0  1260759203\n",
              "12       1     1953     4.0  1260759191\n",
              "13       1     2105     4.0  1260759139\n",
              "14       1     2150     3.0  1260759194\n",
              "15       1     2193     2.0  1260759198\n",
              "16       1     2294     2.0  1260759108\n",
              "17       1     2455     2.5  1260759113\n",
              "18       1     2968     1.0  1260759200\n",
              "19       1     3671     3.0  1260759117"
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          "metadata": {
            "tags": []
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          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tMHR1UC1h9en",
        "outputId": "50538e75-9dfe-46c5-a075-ded8f4e814c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "svd.predict(1, 302, 3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prediction(uid=1, iid=302, r_ui=3, est=2.8101677246870045, details={'was_impossible': False})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For movie with ID 302, we get an estimated prediction of 2.618. "
      ],
      "metadata": {
        "id": "6Ud0daZd_zO5"
      }
    }
  ]
}