Content_Based_Recommender_Systems.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#**Content Based Recommender Systems**"
      ],
      "metadata": {
        "id": "f1r5gmkukA17"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Caculating Cosine Similarity in Python"
      ],
      "metadata": {
        "id": "JwVnuGX1kOQK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from math import*\n",
        " \n",
        "def square_rooted(x):\n",
        "    return round(sqrt(sum([a*a for a in x])),3)\n",
        " \n",
        "def cosine_similarity(x,y):\n",
        "   numerator = sum(a*b for a,b in zip(x,y))\n",
        "   denominator = square_rooted(x)*square_rooted(y)\n",
        "   return round(numerator/float(denominator),3)\n",
        " \n"
      ],
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(cosine_similarity([3, 45, 7, 2], [2, 54, 13, 15]))"
      ],
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          "base_uri": "https://localhost:8080/"
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.972\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We want to base on our past movie data to recommend similar movies based on similar plot, genre, director, etc. For example if we search for Titanic movie, similar movies should be recommended to us, which can be done based on the plots, directors, cast, etc."
      ],
      "metadata": {
        "id": "1ep2S-f_JZuH"
      }
    },
    {
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              "   Unnamed: 0                     Title  Year     Rated     Released  Runtime  \\\n",
              "0           1  The Shawshank Redemption  1994         R  14 Oct 1994  142 min   \n",
              "1           2             The Godfather  1972         R  24 Mar 1972  175 min   \n",
              "2           3    The Godfather: Part II  1974         R  20 Dec 1974  202 min   \n",
              "3           4           The Dark Knight  2008     PG-13  18 Jul 2008  152 min   \n",
              "4           5              12 Angry Men  1957  APPROVED  01 Apr 1957   96 min   \n",
              "\n",
              "                  Genre              Director  \\\n",
              "0          Crime, Drama        Frank Darabont   \n",
              "1          Crime, Drama  Francis Ford Coppola   \n",
              "2          Crime, Drama  Francis Ford Coppola   \n",
              "3  Action, Crime, Drama     Christopher Nolan   \n",
              "4          Crime, Drama          Sidney Lumet   \n",
              "\n",
              "                                              Writer  \\\n",
              "0  Stephen King (short story \"Rita Hayworth and S...   \n",
              "1  Mario Puzo (screenplay), Francis Ford Coppola ...   \n",
              "2  Francis Ford Coppola (screenplay), Mario Puzo ...   \n",
              "3  Jonathan Nolan (screenplay), Christopher Nolan...   \n",
              "4  Reginald Rose (story), Reginald Rose (screenplay)   \n",
              "\n",
              "                                              Actors  \\\n",
              "0  Tim Robbins, Morgan Freeman, Bob Gunton, Willi...   \n",
              "1  Marlon Brando, Al Pacino, James Caan, Richard ...   \n",
              "2  Al Pacino, Robert Duvall, Diane Keaton, Robert...   \n",
              "3  Christian Bale, Heath Ledger, Aaron Eckhart, M...   \n",
              "4  Martin Balsam, John Fiedler, Lee J. Cobb, E.G....   \n",
              "\n",
              "                                                Plot  \\\n",
              "0  Two imprisoned men bond over a number of years...   \n",
              "1  The aging patriarch of an organized crime dyna...   \n",
              "2  The early life and career of Vito Corleone in ...   \n",
              "3  When the menace known as the Joker emerges fro...   \n",
              "4  A jury holdout attempts to prevent a miscarria...   \n",
              "\n",
              "                                     Language  Country  \\\n",
              "0                                     English      USA   \n",
              "1                     English, Italian, Latin      USA   \n",
              "2  English, Italian, Spanish, Latin, Sicilian      USA   \n",
              "3                           English, Mandarin  USA, UK   \n",
              "4                                     English      USA   \n",
              "\n",
              "                                              Awards  \\\n",
              "0  Nominated for 7 Oscars. Another 19 wins & 30 n...   \n",
              "1    Won 3 Oscars. Another 23 wins & 27 nominations.   \n",
              "2    Won 6 Oscars. Another 10 wins & 20 nominations.   \n",
              "3  Won 2 Oscars. Another 151 wins & 153 nominations.   \n",
              "4  Nominated for 3 Oscars. Another 16 wins & 8 no...   \n",
              "\n",
              "                                              Poster           Ratings.Source  \\\n",
              "0  https://images-na.ssl-images-amazon.com/images...  Internet Movie Database   \n",
              "1  https://images-na.ssl-images-amazon.com/images...  Internet Movie Database   \n",
              "2  https://images-na.ssl-images-amazon.com/images...  Internet Movie Database   \n",
              "3  https://images-na.ssl-images-amazon.com/images...  Internet Movie Database   \n",
              "4  https://images-na.ssl-images-amazon.com/images...  Internet Movie Database   \n",
              "\n",
              "  Ratings.Value  Metascore  imdbRating  imdbVotes     imdbID   Type  \\\n",
              "0        9.3/10       80.0         9.3  1,825,626  tt0111161  movie   \n",
              "1        9.2/10      100.0         9.2  1,243,444  tt0068646  movie   \n",
              "2        9.0/10       85.0         9.0    856,870  tt0071562  movie   \n",
              "3        9.0/10       82.0         9.0  1,802,351  tt0468569  movie   \n",
              "4        8.9/10       96.0         8.9    494,215  tt0050083  movie   \n",
              "\n",
              "   tomatoMeter  tomatoImage  tomatoRating  tomatoReviews  tomatoFresh  \\\n",
              "0          NaN          NaN           NaN            NaN          NaN   \n",
              "1          NaN          NaN           NaN            NaN          NaN   \n",
              "2          NaN          NaN           NaN            NaN          NaN   \n",
              "3          NaN          NaN           NaN            NaN          NaN   \n",
              "4          NaN          NaN           NaN            NaN          NaN   \n",
              "\n",
              "   tomatoRotten  tomatoConsensus  tomatoUserMeter  tomatoUserRating  \\\n",
              "0           NaN              NaN              NaN               NaN   \n",
              "1           NaN              NaN              NaN               NaN   \n",
              "2           NaN              NaN              NaN               NaN   \n",
              "3           NaN              NaN              NaN               NaN   \n",
              "4           NaN              NaN              NaN               NaN   \n",
              "\n",
              "   tomatoUserReviews                                          tomatoURL  \\\n",
              "0                NaN  http://www.rottentomatoes.com/m/shawshank_rede...   \n",
              "1                NaN         http://www.rottentomatoes.com/m/godfather/   \n",
              "2                NaN  http://www.rottentomatoes.com/m/godfather_part...   \n",
              "3                NaN   http://www.rottentomatoes.com/m/the_dark_knight/   \n",
              "4                NaN  http://www.rottentomatoes.com/m/1000013-12_ang...   \n",
              "\n",
              "           DVD     BoxOffice                       Production  \\\n",
              "0  27 Jan 1998           NaN                Columbia Pictures   \n",
              "1  09 Oct 2001           NaN               Paramount Pictures   \n",
              "2  24 May 2005           NaN               Paramount Pictures   \n",
              "3  09 Dec 2008  $533,316,061  Warner Bros. Pictures/Legendary   \n",
              "4  06 Mar 2001           NaN             Criterion Collection   \n",
              "\n",
              "                                             Website  Response  \n",
              "0                                                NaN      True  \n",
              "1                        http://www.thegodfather.com      True  \n",
              "2                       http://www.thegodfather.com/      True  \n",
              "3               http://thedarkknight.warnerbros.com/      True  \n",
              "4  http://www.criterion.com/films/27871-12-angry-men      True  "
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              "      <td>1</td>\n",
              "      <td>The Shawshank Redemption</td>\n",
              "      <td>1994</td>\n",
              "      <td>R</td>\n",
              "      <td>14 Oct 1994</td>\n",
              "      <td>142 min</td>\n",
              "      <td>Crime, Drama</td>\n",
              "      <td>Frank Darabont</td>\n",
              "      <td>Stephen King (short story \"Rita Hayworth and S...</td>\n",
              "      <td>Tim Robbins, Morgan Freeman, Bob Gunton, Willi...</td>\n",
              "      <td>Two imprisoned men bond over a number of years...</td>\n",
              "      <td>English</td>\n",
              "      <td>USA</td>\n",
              "      <td>Nominated for 7 Oscars. Another 19 wins &amp; 30 n...</td>\n",
              "      <td>https://images-na.ssl-images-amazon.com/images...</td>\n",
              "      <td>Internet Movie Database</td>\n",
              "      <td>9.3/10</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9.3</td>\n",
              "      <td>1,825,626</td>\n",
              "      <td>tt0111161</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>http://www.rottentomatoes.com/m/shawshank_rede...</td>\n",
              "      <td>27 Jan 1998</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Columbia Pictures</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>The Godfather</td>\n",
              "      <td>1972</td>\n",
              "      <td>R</td>\n",
              "      <td>24 Mar 1972</td>\n",
              "      <td>175 min</td>\n",
              "      <td>Crime, Drama</td>\n",
              "      <td>Francis Ford Coppola</td>\n",
              "      <td>Mario Puzo (screenplay), Francis Ford Coppola ...</td>\n",
              "      <td>Marlon Brando, Al Pacino, James Caan, Richard ...</td>\n",
              "      <td>The aging patriarch of an organized crime dyna...</td>\n",
              "      <td>English, Italian, Latin</td>\n",
              "      <td>USA</td>\n",
              "      <td>Won 3 Oscars. Another 23 wins &amp; 27 nominations.</td>\n",
              "      <td>https://images-na.ssl-images-amazon.com/images...</td>\n",
              "      <td>Internet Movie Database</td>\n",
              "      <td>9.2/10</td>\n",
              "      <td>100.0</td>\n",
              "      <td>9.2</td>\n",
              "      <td>1,243,444</td>\n",
              "      <td>tt0068646</td>\n",
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              "      <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",
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              "      <td>09 Oct 2001</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Paramount Pictures</td>\n",
              "      <td>http://www.thegodfather.com</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>The Godfather: Part II</td>\n",
              "      <td>1974</td>\n",
              "      <td>R</td>\n",
              "      <td>20 Dec 1974</td>\n",
              "      <td>202 min</td>\n",
              "      <td>Crime, Drama</td>\n",
              "      <td>Francis Ford Coppola</td>\n",
              "      <td>Francis Ford Coppola (screenplay), Mario Puzo ...</td>\n",
              "      <td>Al Pacino, Robert Duvall, Diane Keaton, Robert...</td>\n",
              "      <td>The early life and career of Vito Corleone in ...</td>\n",
              "      <td>English, Italian, Spanish, Latin, Sicilian</td>\n",
              "      <td>USA</td>\n",
              "      <td>Won 6 Oscars. Another 10 wins &amp; 20 nominations.</td>\n",
              "      <td>https://images-na.ssl-images-amazon.com/images...</td>\n",
              "      <td>Internet Movie Database</td>\n",
              "      <td>9.0/10</td>\n",
              "      <td>85.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>856,870</td>\n",
              "      <td>tt0071562</td>\n",
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              "      <td>24 May 2005</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Paramount Pictures</td>\n",
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              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>The Dark Knight</td>\n",
              "      <td>2008</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>18 Jul 2008</td>\n",
              "      <td>152 min</td>\n",
              "      <td>Action, Crime, Drama</td>\n",
              "      <td>Christopher Nolan</td>\n",
              "      <td>Jonathan Nolan (screenplay), Christopher Nolan...</td>\n",
              "      <td>Christian Bale, Heath Ledger, Aaron Eckhart, M...</td>\n",
              "      <td>When the menace known as the Joker emerges fro...</td>\n",
              "      <td>English, Mandarin</td>\n",
              "      <td>USA, UK</td>\n",
              "      <td>Won 2 Oscars. Another 151 wins &amp; 153 nominations.</td>\n",
              "      <td>https://images-na.ssl-images-amazon.com/images...</td>\n",
              "      <td>Internet Movie Database</td>\n",
              "      <td>9.0/10</td>\n",
              "      <td>82.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1,802,351</td>\n",
              "      <td>tt0468569</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>http://www.rottentomatoes.com/m/the_dark_knight/</td>\n",
              "      <td>09 Dec 2008</td>\n",
              "      <td>$533,316,061</td>\n",
              "      <td>Warner Bros. Pictures/Legendary</td>\n",
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              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>12 Angry Men</td>\n",
              "      <td>1957</td>\n",
              "      <td>APPROVED</td>\n",
              "      <td>01 Apr 1957</td>\n",
              "      <td>96 min</td>\n",
              "      <td>Crime, Drama</td>\n",
              "      <td>Sidney Lumet</td>\n",
              "      <td>Reginald Rose (story), Reginald Rose (screenplay)</td>\n",
              "      <td>Martin Balsam, John Fiedler, Lee J. Cobb, E.G....</td>\n",
              "      <td>A jury holdout attempts to prevent a miscarria...</td>\n",
              "      <td>English</td>\n",
              "      <td>USA</td>\n",
              "      <td>Nominated for 3 Oscars. Another 16 wins &amp; 8 no...</td>\n",
              "      <td>https://images-na.ssl-images-amazon.com/images...</td>\n",
              "      <td>Internet Movie Database</td>\n",
              "      <td>8.9/10</td>\n",
              "      <td>96.0</td>\n",
              "      <td>8.9</td>\n",
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              "      <td>06 Mar 2001</td>\n",
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              "      <td>Criterion Collection</td>\n",
              "      <td>http://www.criterion.com/films/27871-12-angry-men</td>\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-aff85965-e88d-41bf-83f3-ae2224d26ed8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-aff85965-e88d-41bf-83f3-ae2224d26ed8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ],
      "source": [
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.metrics.pairwise import cosine_similarity # performs same work as the cosine similarity we created above\n",
        "from sklearn.feature_extraction.text import CountVectorizer # performs same work as the Document Term Frequency we did earlier\n",
        "\n",
        "pd.set_option('display.max_columns', 100)\n",
        "df = pd.read_csv('https://query.data.world/s/uikepcpffyo2nhig52xxeevdialfl7')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "flG0JH7lj70B",
        "outputId": "8814844d-1282-482e-8788-92710da557cb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(250, 38)"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will base on 'Title','Genre','Director','Actors','Plot' to make the recommendation."
      ],
      "metadata": {
        "id": "OZVKYB9JKW21"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ugeAYCwPj70C",
        "outputId": "773fe271-26e9-48e9-8404-7921de781166",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Title                 Genre              Director  \\\n",
              "0  The Shawshank Redemption          Crime, Drama        Frank Darabont   \n",
              "1             The Godfather          Crime, Drama  Francis Ford Coppola   \n",
              "2    The Godfather: Part II          Crime, Drama  Francis Ford Coppola   \n",
              "3           The Dark Knight  Action, Crime, Drama     Christopher Nolan   \n",
              "4              12 Angry Men          Crime, Drama          Sidney Lumet   \n",
              "\n",
              "                                              Actors  \\\n",
              "0  Tim Robbins, Morgan Freeman, Bob Gunton, Willi...   \n",
              "1  Marlon Brando, Al Pacino, James Caan, Richard ...   \n",
              "2  Al Pacino, Robert Duvall, Diane Keaton, Robert...   \n",
              "3  Christian Bale, Heath Ledger, Aaron Eckhart, M...   \n",
              "4  Martin Balsam, John Fiedler, Lee J. Cobb, E.G....   \n",
              "\n",
              "                                                Plot  \n",
              "0  Two imprisoned men bond over a number of years...  \n",
              "1  The aging patriarch of an organized crime dyna...  \n",
              "2  The early life and career of Vito Corleone in ...  \n",
              "3  When the menace known as the Joker emerges fro...  \n",
              "4  A jury holdout attempts to prevent a miscarria...  "
            ],
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              "      <td>The Godfather</td>\n",
              "      <td>Crime, Drama</td>\n",
              "      <td>Francis Ford Coppola</td>\n",
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              "      <td>The aging patriarch of an organized crime dyna...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The Godfather: Part II</td>\n",
              "      <td>Crime, Drama</td>\n",
              "      <td>Francis Ford Coppola</td>\n",
              "      <td>Al Pacino, Robert Duvall, Diane Keaton, Robert...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>The Dark Knight</td>\n",
              "      <td>Action, Crime, Drama</td>\n",
              "      <td>Christopher Nolan</td>\n",
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              "      <td>When the menace known as the Joker emerges fro...</td>\n",
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              "      <td>12 Angry Men</td>\n",
              "      <td>Crime, Drama</td>\n",
              "      <td>Sidney Lumet</td>\n",
              "      <td>Martin Balsam, John Fiedler, Lee J. Cobb, E.G....</td>\n",
              "      <td>A jury holdout attempts to prevent a miscarria...</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ],
      "source": [
        "df = df[['Title','Genre','Director','Actors','Plot']]\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p5H4qPTNj70D"
      },
      "outputs": [],
      "source": [
        "# discarding the commas between the actors' full names and getting only the first three names\n",
        "df['Actors'] = df['Actors'].map(lambda x: x.split(',')[:3])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v7FB2Z3zj70E",
        "outputId": "04998e17-893c-4a62-c95b-6384a9f1acca",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0            [Tim Robbins,  Morgan Freeman,  Bob Gunton]\n",
              "1               [Marlon Brando,  Al Pacino,  James Caan]\n",
              "2             [Al Pacino,  Robert Duvall,  Diane Keaton]\n",
              "3        [Christian Bale,  Heath Ledger,  Aaron Eckhart]\n",
              "4           [Martin Balsam,  John Fiedler,  Lee J. Cobb]\n",
              "                             ...                        \n",
              "245           [Ray Milland,  Jane Wyman,  Phillip Terry]\n",
              "246    [Brie Larson,  John Gallagher Jr.,  Stephanie ...\n",
              "247      [Cary Grant,  Rosalind Russell,  Ralph Bellamy]\n",
              "248    [Sissy Spacek,  Jane Galloway Heitz,  Joseph A...\n",
              "249           [Dev Patel,  Saurabh Shukla,  Anil Kapoor]\n",
              "Name: Actors, Length: 250, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ],
      "source": [
        "df['Actors']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "69C9FjiNj70E"
      },
      "outputs": [],
      "source": [
        "# putting the genres in a list of words\n",
        "df['Genre'] = df['Genre'].map(lambda x: x.lower().split(','))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4Suz0lZ-j70F",
        "outputId": "ae33571a-bd84-45a1-8346-82ddb65bee2c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                 [crime,  drama]\n",
              "1                 [crime,  drama]\n",
              "2                 [crime,  drama]\n",
              "3        [action,  crime,  drama]\n",
              "4                 [crime,  drama]\n",
              "                  ...            \n",
              "245           [drama,  film-noir]\n",
              "246                       [drama]\n",
              "247    [comedy,  drama,  romance]\n",
              "248           [biography,  drama]\n",
              "249                       [drama]\n",
              "Name: Genre, Length: 250, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ],
      "source": [
        "df['Genre']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AIHW9f9uj70G"
      },
      "outputs": [],
      "source": [
        "df['Director'] = df['Director'].map(lambda x: x.split(' '))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "de500EUwj70H",
        "outputId": "fe77c8d8-2496-4552-a5e8-f25840170743",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                      [Frank, Darabont]\n",
              "1               [Francis, Ford, Coppola]\n",
              "2               [Francis, Ford, Coppola]\n",
              "3                   [Christopher, Nolan]\n",
              "4                        [Sidney, Lumet]\n",
              "                     ...                \n",
              "245                      [Billy, Wilder]\n",
              "246            [Destin, Daniel, Cretton]\n",
              "247                      [Howard, Hawks]\n",
              "248                       [David, Lynch]\n",
              "249    [Danny, Boyle,, Loveleen, Tandan]\n",
              "Name: Director, Length: 250, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ],
      "source": [
        "df['Director']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b4fzV-6pj70H"
      },
      "outputs": [],
      "source": [
        "#convert the actors names to lower case do avoid duplicates. Example, so that names like 'ROBBIN' and 'robbin' will not be repeated.\n",
        "\n",
        "for index, row in df.iterrows():\n",
        "    row['Actors'] = [x.lower().replace(' ','') for x in row['Actors']]\n",
        "    row['Director'] = ''.join(row['Director']).lower()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eXCRstF5j70I",
        "outputId": "21a5335f-fa49-4c84-9395-8e4781b5b9ac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                 [timrobbins, morganfreeman, bobgunton]\n",
              "1                    [marlonbrando, alpacino, jamescaan]\n",
              "2                  [alpacino, robertduvall, dianekeaton]\n",
              "3             [christianbale, heathledger, aaroneckhart]\n",
              "4                 [martinbalsam, johnfiedler, leej.cobb]\n",
              "                             ...                        \n",
              "245                [raymilland, janewyman, phillipterry]\n",
              "246     [brielarson, johngallagherjr., stephaniebeatriz]\n",
              "247           [carygrant, rosalindrussell, ralphbellamy]\n",
              "248    [sissyspacek, janegallowayheitz, josepha.carpe...\n",
              "249                [devpatel, saurabhshukla, anilkapoor]\n",
              "Name: Actors, Length: 250, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ],
      "source": [
        "df['Actors']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "slWbw88vj70J",
        "outputId": "a86c67a7-7596-421f-ce86-4395c930c668",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                  frankdarabont\n",
              "1             francisfordcoppola\n",
              "2             francisfordcoppola\n",
              "3               christophernolan\n",
              "4                    sidneylumet\n",
              "                 ...            \n",
              "245                  billywilder\n",
              "246          destindanielcretton\n",
              "247                  howardhawks\n",
              "248                   davidlynch\n",
              "249    dannyboyle,loveleentandan\n",
              "Name: Director, Length: 250, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ],
      "source": [
        "df['Director']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rake_nltk"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W1EVcxzvOm-D",
        "outputId": "931de9fa-8915-4215-a62a-5ca0671c0b46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: rake_nltk in /usr/local/lib/python3.7/dist-packages (1.0.6)\n",
            "Requirement already satisfied: nltk<4.0.0,>=3.6.2 in /usr/local/lib/python3.7/dist-packages (from rake_nltk) (3.7)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nltk<4.0.0,>=3.6.2->rake_nltk) (4.63.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk<4.0.0,>=3.6.2->rake_nltk) (7.1.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.7/dist-packages (from nltk<4.0.0,>=3.6.2->rake_nltk) (2022.3.15)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk<4.0.0,>=3.6.2->rake_nltk) (1.1.0)\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EuDdDiWHj70J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f929e80-ac12-476a-b236-f5f7be115b3e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ],
      "source": [
        "import rake_nltk\n",
        "from rake_nltk import Rake #for extracting significant keywords in the entire text available.\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yj0DIfzcj70K"
      },
      "outputs": [],
      "source": [
        "# initializing the new column\n",
        "df['Key_words'] = \"\"\n",
        "\n",
        "for index, row in df.iterrows():\n",
        "    plot = row['Plot']\n",
        "    \n",
        "    # instantiating Rake, by default is uses english stopwords from NLTK\n",
        "    # and discard all puntuation characters\n",
        "    r = Rake()\n",
        "\n",
        "    # extracting the words by passing the text\n",
        "    r.extract_keywords_from_text(plot)\n",
        "\n",
        "    # getting the dictionary whith key words and their scores\n",
        "    key_words_dict_scores = r.get_word_degrees()\n",
        "    \n",
        "    # assigning the key words to the new column\n",
        "    row['Key_words'] = list(key_words_dict_scores.keys())\n",
        "\n",
        "# dropping the Plot column\n",
        "df.drop(columns = ['Plot'], inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tXr1GpXPj70L",
        "outputId": "a2b964b9-8f68-4f9d-b003-15bf42edf884",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "defaultdict(<function rake_nltk.rake.Rake._build_word_co_occurance_graph.<locals>.<lambda>>,\n",
              "            {'?\"': 2,\n",
              "             'accused': 1,\n",
              "             'cheating': 1,\n",
              "             'indian': 2,\n",
              "             'millionaire': 2,\n",
              "             'mumbai': 3,\n",
              "             'reflects': 3,\n",
              "             'slums': 1,\n",
              "             'teen': 3,\n",
              "             'upbringing': 1,\n",
              "             'version': 2,\n",
              "             'wants': 1})"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ],
      "source": [
        "key_words_dict_scores"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "DfV9mLkTjzNf",
        "outputId": "5cbbe975-abbb-4b48-c4a8-77f4486ad136"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Title                       Genre  \\\n",
              "0    The Shawshank Redemption             [crime,  drama]   \n",
              "1               The Godfather             [crime,  drama]   \n",
              "2      The Godfather: Part II             [crime,  drama]   \n",
              "3             The Dark Knight    [action,  crime,  drama]   \n",
              "4                12 Angry Men             [crime,  drama]   \n",
              "..                        ...                         ...   \n",
              "245          The Lost Weekend         [drama,  film-noir]   \n",
              "246             Short Term 12                     [drama]   \n",
              "247           His Girl Friday  [comedy,  drama,  romance]   \n",
              "248        The Straight Story         [biography,  drama]   \n",
              "249       Slumdog Millionaire                     [drama]   \n",
              "\n",
              "                      Director  \\\n",
              "0                frankdarabont   \n",
              "1           francisfordcoppola   \n",
              "2           francisfordcoppola   \n",
              "3             christophernolan   \n",
              "4                  sidneylumet   \n",
              "..                         ...   \n",
              "245                billywilder   \n",
              "246        destindanielcretton   \n",
              "247                howardhawks   \n",
              "248                 davidlynch   \n",
              "249  dannyboyle,loveleentandan   \n",
              "\n",
              "                                                Actors  \\\n",
              "0               [timrobbins, morganfreeman, bobgunton]   \n",
              "1                  [marlonbrando, alpacino, jamescaan]   \n",
              "2                [alpacino, robertduvall, dianekeaton]   \n",
              "3           [christianbale, heathledger, aaroneckhart]   \n",
              "4               [martinbalsam, johnfiedler, leej.cobb]   \n",
              "..                                                 ...   \n",
              "245              [raymilland, janewyman, phillipterry]   \n",
              "246   [brielarson, johngallagherjr., stephaniebeatriz]   \n",
              "247         [carygrant, rosalindrussell, ralphbellamy]   \n",
              "248  [sissyspacek, janegallowayheitz, josepha.carpe...   \n",
              "249              [devpatel, saurabhshukla, anilkapoor]   \n",
              "\n",
              "                                             Key_words  \n",
              "0    [two, imprisoned, men, bond, number, years, fi...  \n",
              "1    [aging, patriarch, organized, crime, dynasty, ...  \n",
              "2    [early, life, career, vito, corleone, 1920s, n...  \n",
              "3    [menace, known, joker, emerges, mysterious, pa...  \n",
              "4    [jury, holdout, attempts, prevent, miscarriage...  \n",
              "..                                                 ...  \n",
              "245  [desperate, life, chronic, alcoholic, followed...  \n",
              "246  [20, something, supervising, staff, member, re...  \n",
              "247  [newspaper, editor, uses, every, trick, book, ...  \n",
              "248  [old, man, makes, long, journey, lawn, mover, ...  \n",
              "249  [mumbai, teen, reflects, upbringing, slums, ac...  \n",
              "\n",
              "[250 rows x 5 columns]"
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              "      <th>0</th>\n",
              "      <td>The Shawshank Redemption</td>\n",
              "      <td>[crime,  drama]</td>\n",
              "      <td>frankdarabont</td>\n",
              "      <td>[timrobbins, morganfreeman, bobgunton]</td>\n",
              "      <td>[two, imprisoned, men, bond, number, years, fi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The Godfather</td>\n",
              "      <td>[crime,  drama]</td>\n",
              "      <td>francisfordcoppola</td>\n",
              "      <td>[marlonbrando, alpacino, jamescaan]</td>\n",
              "      <td>[aging, patriarch, organized, crime, dynasty, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The Godfather: Part II</td>\n",
              "      <td>[crime,  drama]</td>\n",
              "      <td>francisfordcoppola</td>\n",
              "      <td>[alpacino, robertduvall, dianekeaton]</td>\n",
              "      <td>[early, life, career, vito, corleone, 1920s, n...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>The Dark Knight</td>\n",
              "      <td>[action,  crime,  drama]</td>\n",
              "      <td>christophernolan</td>\n",
              "      <td>[christianbale, heathledger, aaroneckhart]</td>\n",
              "      <td>[menace, known, joker, emerges, mysterious, pa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12 Angry Men</td>\n",
              "      <td>[crime,  drama]</td>\n",
              "      <td>sidneylumet</td>\n",
              "      <td>[martinbalsam, johnfiedler, leej.cobb]</td>\n",
              "      <td>[jury, holdout, attempts, prevent, miscarriage...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>245</th>\n",
              "      <td>The Lost Weekend</td>\n",
              "      <td>[drama,  film-noir]</td>\n",
              "      <td>billywilder</td>\n",
              "      <td>[raymilland, janewyman, phillipterry]</td>\n",
              "      <td>[desperate, life, chronic, alcoholic, followed...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>Short Term 12</td>\n",
              "      <td>[drama]</td>\n",
              "      <td>destindanielcretton</td>\n",
              "      <td>[brielarson, johngallagherjr., stephaniebeatriz]</td>\n",
              "      <td>[20, something, supervising, staff, member, re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>247</th>\n",
              "      <td>His Girl Friday</td>\n",
              "      <td>[comedy,  drama,  romance]</td>\n",
              "      <td>howardhawks</td>\n",
              "      <td>[carygrant, rosalindrussell, ralphbellamy]</td>\n",
              "      <td>[newspaper, editor, uses, every, trick, book, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>The Straight Story</td>\n",
              "      <td>[biography,  drama]</td>\n",
              "      <td>davidlynch</td>\n",
              "      <td>[sissyspacek, janegallowayheitz, josepha.carpe...</td>\n",
              "      <td>[old, man, makes, long, journey, lawn, mover, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>Slumdog Millionaire</td>\n",
              "      <td>[drama]</td>\n",
              "      <td>dannyboyle,loveleentandan</td>\n",
              "      <td>[devpatel, saurabhshukla, anilkapoor]</td>\n",
              "      <td>[mumbai, teen, reflects, upbringing, slums, ac...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>250 rows × 5 columns</p>\n",
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            "text/plain": [
              "                                             Genre            Director  \\\n",
              "Title                                                                    \n",
              "The Shawshank Redemption           [crime,  drama]       frankdarabont   \n",
              "The Godfather                      [crime,  drama]  francisfordcoppola   \n",
              "The Godfather: Part II             [crime,  drama]  francisfordcoppola   \n",
              "The Dark Knight           [action,  crime,  drama]    christophernolan   \n",
              "12 Angry Men                       [crime,  drama]         sidneylumet   \n",
              "\n",
              "                                                              Actors  \\\n",
              "Title                                                                  \n",
              "The Shawshank Redemption      [timrobbins, morganfreeman, bobgunton]   \n",
              "The Godfather                    [marlonbrando, alpacino, jamescaan]   \n",
              "The Godfather: Part II         [alpacino, robertduvall, dianekeaton]   \n",
              "The Dark Knight           [christianbale, heathledger, aaroneckhart]   \n",
              "12 Angry Men                  [martinbalsam, johnfiedler, leej.cobb]   \n",
              "\n",
              "                                                                  Key_words  \n",
              "Title                                                                        \n",
              "The Shawshank Redemption  [two, imprisoned, men, bond, number, years, fi...  \n",
              "The Godfather             [aging, patriarch, organized, crime, dynasty, ...  \n",
              "The Godfather: Part II    [early, life, career, vito, corleone, 1920s, n...  \n",
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              "      <td>frankdarabont</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-2c27ca58-903f-4c87-8654-6015a42a6008');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ],
      "source": [
        "df.set_index('Title', inplace = True)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D-X0uQUFj70M"
      },
      "outputs": [],
      "source": [
        "df['bag_of_words'] = ''\n",
        "columns = df.columns\n",
        "for index, row in df.iterrows():\n",
        "    words = ''\n",
        "    for col in columns:\n",
        "        if col != 'Director':\n",
        "            words = words + ' '.join(row[col])+ ' '\n",
        "        else:\n",
        "            words = words + row[col]+ ' '\n",
        "    row['bag_of_words'] = words\n",
        "    \n",
        "df.drop(columns = [col for col in df.columns if col!= 'bag_of_words'], inplace = True)"
      ]
    },
    {
      "cell_type": "code",
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        {
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          "data": {
            "text/plain": [
              "                                                               bag_of_words\n",
              "Title                                                                      \n",
              "The Shawshank Redemption  crime  drama frankdarabont timrobbins morganfr...\n",
              "The Godfather             crime  drama francisfordcoppola marlonbrando a...\n",
              "The Godfather: Part II    crime  drama francisfordcoppola alpacino rober...\n",
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            ],
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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        }
      ],
      "source": [
        "df.head()"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "onWjtNsOj70M"
      },
      "outputs": [],
      "source": [
        "# instantiating and generating the count matrix\n",
        "count = CountVectorizer()\n",
        "count_matrix = count.fit_transform(df['bag_of_words'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gxWlTzsBj70N",
        "outputId": "293df7c1-e19e-4abe-b619-361b601a5d86",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<250x2961 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 5342 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ],
      "source": [
        "count_matrix "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sb08nxyLj70N"
      },
      "outputs": [],
      "source": [
        "c=count_matrix.todense()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FK-UuRA3j70O",
        "outputId": "fb21ab5c-7654-49b6-886f-6854450ea423",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "matrix([[0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ],
      "source": [
        "c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D07KwbBwj70O",
        "outputId": "5a6ff9b0-c064-4dd9-dd62-0a58ced148c5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  (0, 584)\t1\n",
            "  (0, 768)\t1\n",
            "  (0, 1011)\t1\n",
            "  (0, 2678)\t1\n",
            "  (0, 1810)\t1\n",
            "  (0, 306)\t1\n",
            "  (0, 2765)\t1\n",
            "  (0, 1269)\t1\n",
            "  (0, 1733)\t1\n",
            "  (0, 311)\t1\n",
            "  (0, 1899)\t1\n",
            "  (0, 2950)\t1\n",
            "  (0, 969)\t1\n",
            "  (0, 2481)\t1\n",
            "  (0, 888)\t1\n",
            "  (0, 2174)\t1\n",
            "  (0, 59)\t1\n",
            "  (0, 519)\t1\n",
            "  (0, 655)\t1\n"
          ]
        }
      ],
      "source": [
        "print(count_matrix[0,:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DGZZxMfpj70P"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J6xQUP-Yj70P",
        "outputId": "08bce4f3-9fdb-4819-c46c-b7c9a2aef56b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 0.15789474, 0.13764944, ..., 0.05263158, 0.05263158,\n",
              "        0.05564149],\n",
              "       [0.15789474, 1.        , 0.36706517, ..., 0.05263158, 0.05263158,\n",
              "        0.05564149],\n",
              "       [0.13764944, 0.36706517, 1.        , ..., 0.04588315, 0.04588315,\n",
              "        0.04850713],\n",
              "       ...,\n",
              "       [0.05263158, 0.05263158, 0.04588315, ..., 1.        , 0.05263158,\n",
              "        0.05564149],\n",
              "       [0.05263158, 0.05263158, 0.04588315, ..., 0.05263158, 1.        ,\n",
              "        0.05564149],\n",
              "       [0.05564149, 0.05564149, 0.04850713, ..., 0.05564149, 0.05564149,\n",
              "        1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ],
      "source": [
        "# generating the cosine similarity matrix\n",
        "cosine_sim = cosine_similarity(count_matrix, count_matrix)\n",
        "cosine_sim"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# creating a Series for the movie titles so they are associated to an ordered numerical\n",
        "# list I will use later to match the indexes\n",
        "indices = pd.Series(df.index)\n",
        "indices[:20]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uU42dyftd91-",
        "outputId": "dc8aa741-9385-4462-d23b-bb4c966e190e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                              The Shawshank Redemption\n",
              "1                                         The Godfather\n",
              "2                                The Godfather: Part II\n",
              "3                                       The Dark Knight\n",
              "4                                          12 Angry Men\n",
              "5                                      Schindler's List\n",
              "6         The Lord of the Rings: The Return of the King\n",
              "7                                          Pulp Fiction\n",
              "8                                            Fight Club\n",
              "9     The Lord of the Rings: The Fellowship of the Ring\n",
              "10                                         Forrest Gump\n",
              "11       Star Wars: Episode V - The Empire Strikes Back\n",
              "12                                            Inception\n",
              "13                The Lord of the Rings: The Two Towers\n",
              "14                      One Flew Over the Cuckoo's Nest\n",
              "15                                           Goodfellas\n",
              "16                                           The Matrix\n",
              "17                   Star Wars: Episode IV - A New Hope\n",
              "18                                                Se7en\n",
              "19                                It's a Wonderful Life\n",
              "Name: Title, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HHo6ZubTj70P"
      },
      "outputs": [],
      "source": [
        "# function that takes in movie title as input and returns the top 10 recommended movies\n",
        "def recommendations(title, cosine_sim = cosine_sim):\n",
        "    \n",
        "    recommended_movies = []\n",
        "    \n",
        "    # gettin the index of the movie that matches the title\n",
        "    idx = indices[indices == title].index[0]\n",
        "\n",
        "    # creating a Series with the similarity scores in descending order\n",
        "    score_series = pd.Series(cosine_sim[idx]).sort_values(ascending = False)\n",
        "\n",
        "    # getting the indexes of the 10 most similar movies\n",
        "    top_10_indexes = list(score_series.iloc[1:11].index)\n",
        "    print(top_10_indexes)\n",
        "    \n",
        "    # populating the list with the titles of the best 10 matching movies\n",
        "    for i in top_10_indexes:\n",
        "        recommended_movies.append(list(df.index)[i])\n",
        "        \n",
        "    return recommended_movies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N98R0ABrj70Q",
        "outputId": "682d8f8a-9bc1-4dbe-ed27-627a5ebddbae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[132, 34, 226, 1, 61, 2, 100, 15, 164, 125]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['No Country for Old Men',\n",
              " 'The Departed',\n",
              " 'Rope',\n",
              " 'The Godfather',\n",
              " 'Reservoir Dogs',\n",
              " 'The Godfather: Part II',\n",
              " 'On the Waterfront',\n",
              " 'Goodfellas',\n",
              " 'Touch of Evil',\n",
              " 'The Big Lebowski']"
            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ],
      "source": [
        "recommendations('Fargo')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "indices[132]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "u1YgSN5QZbk6",
        "outputId": "71bdbc70-6354-430f-93b5-9c82976afb35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'No Country for Old Men'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 158
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