Movie_Recommendation_System_.ipynb

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  "metadata": {
    "colab": {
      "name": "Movie_Recommendation_System.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZqKrvf0efOSh",
        "colab_type": "text"
      },
      "source": [
        "# **MOVIE RECOMMENDATION SYSTEM USING KNN** "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QoDi4Ns5fhHK",
        "colab_type": "text"
      },
      "source": [
        "##DATASET USED : MOVIELENS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p9wH5NVhfrNC",
        "colab_type": "text"
      },
      "source": [
        "##PROJECT DONE BY:\n",
        "\n",
        "\n",
        "1.   MAALOLAN K   -   18BLC1077\n",
        "2.   AKSHARAA G   -   18BLC1059\n",
        "3.   NIKITA RATH  -   18BLC1131\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sr_3kcKQNtBO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Importing Dependencies\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iUPjKCDyNurO",
        "colab_type": "code",
        "outputId": "c63dd80a-7429-43c2-d08c-e7f4912a7deb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ratings = pd.read_csv('ratings.csv')\n",
        "len(ratings)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "281056"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dOS5zj2LTBZB",
        "colab_type": "code",
        "outputId": "0a3fb440-c8b7-4c74-f36a-387978e69cdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ratings = ratings[['userId', 'movieId','rating']]\n",
        "len(ratings['userId'].unique())\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1931"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fzD6zqnDTDeK",
        "colab_type": "code",
        "outputId": "4c9793b8-dc12-4220-97e7-275c12d6e4a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "count_ratings = ratings.groupby('rating').count()\n",
        "count_ratings['perc_total']=round(count_ratings['userId']*100/count_ratings['userId'].sum(),1)\n",
        "count_ratings\n",
        "count_ratings['perc_total'].plot.bar()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f00064c4ba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8f7wZixATN18",
        "colab_type": "code",
        "outputId": "dee8c3e3-d68b-4bca-8dad-abbf8d7feae3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "movie_list = pd.read_csv('movies.csv')\n",
        "len(movie_list)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27278"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u0JRsBLlgsMQ",
        "colab_type": "code",
        "outputId": "7dcfec26-ab92-4f54-c8d8-208aa8647ef8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "movie_list.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale (1995)</td>\n",
              "      <td>Comedy|Drama|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Father of the Bride Part II (1995)</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId  ...                                       genres\n",
              "0        1  ...  Adventure|Animation|Children|Comedy|Fantasy\n",
              "1        2  ...                   Adventure|Children|Fantasy\n",
              "2        3  ...                               Comedy|Romance\n",
              "3        4  ...                         Comedy|Drama|Romance\n",
              "4        5  ...                                       Comedy\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WBEx8mDmTSNi",
        "colab_type": "code",
        "outputId": "3366b2c3-11ca-45f9-f906-f0a1f48b92b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "genres = movie_list['genres']\n",
        "genres.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Adventure|Animation|Children|Comedy|Fantasy\n",
              "1                     Adventure|Children|Fantasy\n",
              "2                                 Comedy|Romance\n",
              "3                           Comedy|Drama|Romance\n",
              "4                                         Comedy\n",
              "Name: genres, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlSzKp-bTWLD",
        "colab_type": "code",
        "outputId": "75c07507-3eba-4180-a637-d7aaa90d8013",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        }
      },
      "source": [
        "genre_list = \"\"\n",
        "for index,row in movie_list.iterrows():\n",
        "        genre_list += row.genres + \"|\"\n",
        "#split the string into a list of values\n",
        "genre_list_split = genre_list.split('|')\n",
        "#de-duplicate values\n",
        "new_list = list(set(genre_list_split))\n",
        "#remove the value that is blank\n",
        "new_list.remove('')\n",
        "#inspect list of genres\n",
        "new_list"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Thriller',\n",
              " 'Animation',\n",
              " 'Mystery',\n",
              " 'Fantasy',\n",
              " 'Film-Noir',\n",
              " 'Crime',\n",
              " 'Drama',\n",
              " 'Western',\n",
              " 'IMAX',\n",
              " 'Sci-Fi',\n",
              " 'Adventure',\n",
              " 'War',\n",
              " 'Children',\n",
              " 'Musical',\n",
              " 'Comedy',\n",
              " '(no genres listed)',\n",
              " 'Action',\n",
              " 'Romance',\n",
              " 'Horror',\n",
              " 'Documentary']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K2udgocfYYfX",
        "colab_type": "code",
        "outputId": "87384dcb-d66e-4499-f8ab-067ce1575bdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        }
      },
      "source": [
        "#Enriching the movies dataset by adding the various genres columns.\n",
        "movies_with_genres = movie_list.copy()\n",
        "\n",
        "for genre in new_list :\n",
        "    movies_with_genres[genre] = movies_with_genres.apply(lambda _:int(genre in _.genres), axis = 1)\n",
        "movies_with_genres.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>Thriller</th>\n",
              "      <th>Animation</th>\n",
              "      <th>Mystery</th>\n",
              "      <th>Fantasy</th>\n",
              "      <th>Film-Noir</th>\n",
              "      <th>Crime</th>\n",
              "      <th>Drama</th>\n",
              "      <th>Western</th>\n",
              "      <th>IMAX</th>\n",
              "      <th>Sci-Fi</th>\n",
              "      <th>Adventure</th>\n",
              "      <th>War</th>\n",
              "      <th>Children</th>\n",
              "      <th>Musical</th>\n",
              "      <th>Comedy</th>\n",
              "      <th>(no genres listed)</th>\n",
              "      <th>Action</th>\n",
              "      <th>Romance</th>\n",
              "      <th>Horror</th>\n",
              "      <th>Documentary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale (1995)</td>\n",
              "      <td>Comedy|Drama|Romance</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Father of the Bride Part II (1995)</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId                               title  ... Horror  Documentary\n",
              "0        1                    Toy Story (1995)  ...      0            0\n",
              "1        2                      Jumanji (1995)  ...      0            0\n",
              "2        3             Grumpier Old Men (1995)  ...      0            0\n",
              "3        4            Waiting to Exhale (1995)  ...      0            0\n",
              "4        5  Father of the Bride Part II (1995)  ...      0            0\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sHOMpKBGYxUo",
        "colab_type": "text"
      },
      "source": [
        "#Calculating the sparsity"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1WOqTkFIYygy",
        "colab_type": "code",
        "outputId": "ef09e37f-b010-4af4-fe37-d59bdf141851",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "no_of_users = len(ratings['userId'].unique())\n",
        "no_of_movies = len(ratings['movieId'].unique())\n",
        "\n",
        "sparsity = round(1.0 - len(ratings)/(1.0*(no_of_movies*no_of_users)),3)\n",
        "print(sparsity)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.987\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lRXCLnQVY1Si",
        "colab_type": "text"
      },
      "source": [
        "# Counting the number of unique movies in the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H3aPOwRZY_OC",
        "colab_type": "code",
        "outputId": "a594ef98-8575-42a4-a426-d94e8e2b93e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(ratings['movieId'].unique())"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10897"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGgoNODsZFWq",
        "colab_type": "text"
      },
      "source": [
        "# Finding the average rating for movie and the number of ratings for each movie\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0lF3sQSAZHBb",
        "colab_type": "code",
        "outputId": "350230a9-d326-4a48-ef7a-2a91b1d74eeb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "avg_movie_rating = pd.DataFrame(ratings.groupby('movieId')['rating'].agg(['mean','count']))\n",
        "#avg_movie_rating['movieId']= avg_movie_rating.index\n",
        "# inspecting the average movie rating data frame\n",
        "avg_movie_rating.head()"
      ],
      "execution_count": 18,
      "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>mean</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.980548</td>\n",
              "      <td>694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.257282</td>\n",
              "      <td>309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.151685</td>\n",
              "      <td>178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.096774</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.136598</td>\n",
              "      <td>194</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             mean  count\n",
              "movieId                 \n",
              "1        3.980548    694\n",
              "2        3.257282    309\n",
              "3        3.151685    178\n",
              "4        3.096774     31\n",
              "5        3.136598    194"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0AdQx7BtSAD0",
        "colab_type": "code",
        "outputId": "7e22e1d6-b242-43a4-8a87-aaa2ff40bf6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(avg_movie_rating)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10897"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ePbFfwA8SAzY",
        "colab_type": "code",
        "outputId": "958ba888-0b0b-4c9f-883a-0bb1a62df676",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#calculate the percentile count. It gives the no of ratings at least 70% of the movies have\n",
        "np.percentile(avg_movie_rating['count'],70)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7cTJO1NHSKwg",
        "colab_type": "code",
        "outputId": "920a7dc0-b56d-4c9e-f628-f93f404a560f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "#Get the average movie rating across all movies \n",
        "avg_rating_all=ratings['rating'].mean()\n",
        "avg_rating_all\n",
        "#set a minimum threshold for number of reviews that the movie has to have\n",
        "min_reviews=30\n",
        "min_reviews\n",
        "movie_score = avg_movie_rating.loc[avg_movie_rating['count']>min_reviews]\n",
        "movie_score.head()"
      ],
      "execution_count": 21,
      "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>mean</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.980548</td>\n",
              "      <td>694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.257282</td>\n",
              "      <td>309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.151685</td>\n",
              "      <td>178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.096774</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.136598</td>\n",
              "      <td>194</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             mean  count\n",
              "movieId                 \n",
              "1        3.980548    694\n",
              "2        3.257282    309\n",
              "3        3.151685    178\n",
              "4        3.096774     31\n",
              "5        3.136598    194"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aDOjZyMTSOot",
        "colab_type": "code",
        "outputId": "76626528-dfbc-4674-9372-67cf8e9bdd6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(movie_score)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1949"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mn8OafJkbZZK",
        "colab_type": "text"
      },
      "source": [
        "###Join movie details to movie ratings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jiFTgjAZSPXo",
        "colab_type": "code",
        "outputId": "3c7ec812-e410-45c3-a39b-6225223573d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        }
      },
      "source": [
        "movies_with_genres.index.name = None\n",
        "movies_with_genres = movies_with_genres.rename_axis(None) \n",
        "movie_score = pd.merge(movie_score,movies_with_genres,on='movieId')\n",
        "movie_score.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>mean</th>\n",
              "      <th>count</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>Thriller</th>\n",
              "      <th>Animation</th>\n",
              "      <th>Mystery</th>\n",
              "      <th>Fantasy</th>\n",
              "      <th>Film-Noir</th>\n",
              "      <th>Crime</th>\n",
              "      <th>Drama</th>\n",
              "      <th>Western</th>\n",
              "      <th>IMAX</th>\n",
              "      <th>Sci-Fi</th>\n",
              "      <th>Adventure</th>\n",
              "      <th>War</th>\n",
              "      <th>Children</th>\n",
              "      <th>Musical</th>\n",
              "      <th>Comedy</th>\n",
              "      <th>(no genres listed)</th>\n",
              "      <th>Action</th>\n",
              "      <th>Romance</th>\n",
              "      <th>Horror</th>\n",
              "      <th>Documentary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>3.980548</td>\n",
              "      <td>694</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>3.257282</td>\n",
              "      <td>309</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3.151685</td>\n",
              "      <td>178</td>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>3.096774</td>\n",
              "      <td>31</td>\n",
              "      <td>Waiting to Exhale (1995)</td>\n",
              "      <td>Comedy|Drama|Romance</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>3.136598</td>\n",
              "      <td>194</td>\n",
              "      <td>Father of the Bride Part II (1995)</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId      mean  count  ... Romance Horror  Documentary\n",
              "0        1  3.980548    694  ...       0      0            0\n",
              "1        2  3.257282    309  ...       0      0            0\n",
              "2        3  3.151685    178  ...       1      0            0\n",
              "3        4  3.096774     31  ...       1      0            0\n",
              "4        5  3.136598    194  ...       0      0            0\n",
              "\n",
              "[5 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AFgO-iU3dDJz",
        "colab_type": "text"
      },
      "source": [
        "###list top scored movies over the whole range of movies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MusynqCUABZs",
        "colab_type": "code",
        "outputId": "a520e41f-de52-4bd8-d953-8fb473063ce2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "pd.DataFrame(movie_score.sort_values(['mean'],ascending=False)[['title','count','mean','genres']][:10])"
      ],
      "execution_count": 24,
      "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",
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              "\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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>Shawshank Redemption, The (1994)</td>\n",
              "      <td>870</td>\n",
              "      <td>4.470690</td>\n",
              "      <td>Crime|Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Usual Suspects, The (1995)</td>\n",
              "      <td>666</td>\n",
              "      <td>4.385886</td>\n",
              "      <td>Crime|Mystery|Thriller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>375</th>\n",
              "      <td>Godfather, The (1972)</td>\n",
              "      <td>565</td>\n",
              "      <td>4.375221</td>\n",
              "      <td>Crime|Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1606</th>\n",
              "      <td>City of God (Cidade de Deus) (2002)</td>\n",
              "      <td>182</td>\n",
              "      <td>4.370879</td>\n",
              "      <td>Action|Adventure|Crime|Drama|Thriller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>792</th>\n",
              "      <td>On the Waterfront (1954)</td>\n",
              "      <td>68</td>\n",
              "      <td>4.360294</td>\n",
              "      <td>Crime|Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>560</th>\n",
              "      <td>Treasure of the Sierra Madre, The (1948)</td>\n",
              "      <td>68</td>\n",
              "      <td>4.345588</td>\n",
              "      <td>Action|Adventure|Drama|Western</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>500</th>\n",
              "      <td>Paths of Glory (1957)</td>\n",
              "      <td>48</td>\n",
              "      <td>4.343750</td>\n",
              "      <td>Drama|War</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1939</th>\n",
              "      <td>Intouchables (2011)</td>\n",
              "      <td>37</td>\n",
              "      <td>4.324324</td>\n",
              "      <td>Comedy|Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>390</th>\n",
              "      <td>Charade (1963)</td>\n",
              "      <td>57</td>\n",
              "      <td>4.307018</td>\n",
              "      <td>Comedy|Crime|Mystery|Romance|Thriller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>531</th>\n",
              "      <td>Godfather: Part II, The (1974)</td>\n",
              "      <td>388</td>\n",
              "      <td>4.304124</td>\n",
              "      <td>Crime|Drama</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         title  ...                                 genres\n",
              "174           Shawshank Redemption, The (1994)  ...                            Crime|Drama\n",
              "39                  Usual Suspects, The (1995)  ...                 Crime|Mystery|Thriller\n",
              "375                      Godfather, The (1972)  ...                            Crime|Drama\n",
              "1606       City of God (Cidade de Deus) (2002)  ...  Action|Adventure|Crime|Drama|Thriller\n",
              "792                   On the Waterfront (1954)  ...                            Crime|Drama\n",
              "560   Treasure of the Sierra Madre, The (1948)  ...         Action|Adventure|Drama|Western\n",
              "500                      Paths of Glory (1957)  ...                              Drama|War\n",
              "1939                       Intouchables (2011)  ...                           Comedy|Drama\n",
              "390                             Charade (1963)  ...  Comedy|Crime|Mystery|Romance|Thriller\n",
              "531             Godfather: Part II, The (1974)  ...                            Crime|Drama\n",
              "\n",
              "[10 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZH8jJY6-AHNa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Gives the best movies according to genre based on mean rating\n",
        "def best_movies_by_genre(genre,top_n):\n",
        "    return pd.DataFrame(movie_score.loc[(movie_score[genre]==1)].sort_values(['mean'],ascending=False)[['title','count','mean']][:top_n])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZRmeL-vIdO7f",
        "colab_type": "text"
      },
      "source": [
        "###Top 10 Musical Movies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZva0vPPAbLJ",
        "colab_type": "code",
        "outputId": "4d87e41b-bb26-4891-c89c-1308dbb072d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "best_movies_by_genre('Musical',10)  "
      ],
      "execution_count": 26,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1893</th>\n",
              "      <td>Dr. Horrible's Sing-Along Blog (2008)</td>\n",
              "      <td>56</td>\n",
              "      <td>4.232143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>561</th>\n",
              "      <td>Duck Soup (1933)</td>\n",
              "      <td>78</td>\n",
              "      <td>4.153846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1502</th>\n",
              "      <td>Fiddler on the Roof (1971)</td>\n",
              "      <td>46</td>\n",
              "      <td>4.108696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381</th>\n",
              "      <td>Singin' in the Rain (1952)</td>\n",
              "      <td>143</td>\n",
              "      <td>4.094406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>940</th>\n",
              "      <td>Nashville (1975)</td>\n",
              "      <td>37</td>\n",
              "      <td>4.054054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>Wizard of Oz, The (1939)</td>\n",
              "      <td>332</td>\n",
              "      <td>3.989458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1129</th>\n",
              "      <td>Hard Day's Night, A (1964)</td>\n",
              "      <td>60</td>\n",
              "      <td>3.916667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1918</th>\n",
              "      <td>Scott Pilgrim vs. the World (2010)</td>\n",
              "      <td>44</td>\n",
              "      <td>3.909091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1487</th>\n",
              "      <td>Hedwig and the Angry Inch (2000)</td>\n",
              "      <td>31</td>\n",
              "      <td>3.903226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1189</th>\n",
              "      <td>Commitments, The (1991)</td>\n",
              "      <td>74</td>\n",
              "      <td>3.891892</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                      title  count      mean\n",
              "1893  Dr. Horrible's Sing-Along Blog (2008)     56  4.232143\n",
              "561                        Duck Soup (1933)     78  4.153846\n",
              "1502             Fiddler on the Roof (1971)     46  4.108696\n",
              "381              Singin' in the Rain (1952)    143  4.094406\n",
              "940                        Nashville (1975)     37  4.054054\n",
              "396                Wizard of Oz, The (1939)    332  3.989458\n",
              "1129             Hard Day's Night, A (1964)     60  3.916667\n",
              "1918     Scott Pilgrim vs. the World (2010)     44  3.909091\n",
              "1487       Hedwig and the Angry Inch (2000)     31  3.903226\n",
              "1189                Commitments, The (1991)     74  3.891892"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8t9_TNnTdWeu",
        "colab_type": "text"
      },
      "source": [
        "##Top 10 Action Movies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CMowGkErAb2u",
        "colab_type": "code",
        "outputId": "35a89074-0b04-47ef-91c6-a676e543de3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "#run function to return top recommended movies by genre\n",
        "best_movies_by_genre('Action',10)  "
      ],
      "execution_count": 27,
      "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",
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              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1606</th>\n",
              "      <td>City of God (Cidade de Deus) (2002)</td>\n",
              "      <td>182</td>\n",
              "      <td>4.370879</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>560</th>\n",
              "      <td>Treasure of the Sierra Madre, The (1948)</td>\n",
              "      <td>68</td>\n",
              "      <td>4.345588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1705</th>\n",
              "      <td>Band of Brothers (2001)</td>\n",
              "      <td>58</td>\n",
              "      <td>4.275862</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1872</th>\n",
              "      <td>Dark Knight, The (2008)</td>\n",
              "      <td>264</td>\n",
              "      <td>4.238636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1157</th>\n",
              "      <td>Fight Club (1999)</td>\n",
              "      <td>565</td>\n",
              "      <td>4.218584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>838</th>\n",
              "      <td>Seven Samurai (Shichinin no samurai) (1954)</td>\n",
              "      <td>159</td>\n",
              "      <td>4.210692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1583</th>\n",
              "      <td>Professional, The (Le professionnel) (1981)</td>\n",
              "      <td>69</td>\n",
              "      <td>4.210145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>511</th>\n",
              "      <td>Raiders of the Lost Ark (Indiana Jones and the...</td>\n",
              "      <td>623</td>\n",
              "      <td>4.202247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>387</th>\n",
              "      <td>North by Northwest (1959)</td>\n",
              "      <td>219</td>\n",
              "      <td>4.196347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1623</th>\n",
              "      <td>Laputa: Castle in the Sky (Tenkû no shiro Rapy...</td>\n",
              "      <td>41</td>\n",
              "      <td>4.195122</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  title  count      mean\n",
              "1606                City of God (Cidade de Deus) (2002)    182  4.370879\n",
              "560            Treasure of the Sierra Madre, The (1948)     68  4.345588\n",
              "1705                            Band of Brothers (2001)     58  4.275862\n",
              "1872                            Dark Knight, The (2008)    264  4.238636\n",
              "1157                                  Fight Club (1999)    565  4.218584\n",
              "838         Seven Samurai (Shichinin no samurai) (1954)    159  4.210692\n",
              "1583        Professional, The (Le professionnel) (1981)     69  4.210145\n",
              "511   Raiders of the Lost Ark (Indiana Jones and the...    623  4.202247\n",
              "387                           North by Northwest (1959)    219  4.196347\n",
              "1623  Laputa: Castle in the Sky (Tenkû no shiro Rapy...     41  4.195122"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iTRx8ENQdc4f",
        "colab_type": "text"
      },
      "source": [
        "##Top 10 Children Movies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfRDhIQsAhIh",
        "colab_type": "code",
        "outputId": "64a587ef-7267-48b2-9394-43d3791296e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "#run function to return top recommended movies by genre\n",
        "best_movies_by_genre('Children',10)  "
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1600</th>\n",
              "      <td>My Neighbor Totoro (Tonari no Totoro) (1988)</td>\n",
              "      <td>65</td>\n",
              "      <td>4.207692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1623</th>\n",
              "      <td>Laputa: Castle in the Sky (Tenkû no shiro Rapy...</td>\n",
              "      <td>41</td>\n",
              "      <td>4.195122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>494</th>\n",
              "      <td>Wallace &amp; Gromit: The Wrong Trousers (1993)</td>\n",
              "      <td>191</td>\n",
              "      <td>4.159686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>346</th>\n",
              "      <td>Wallace &amp; Gromit: A Close Shave (1995)</td>\n",
              "      <td>155</td>\n",
              "      <td>4.112903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1915</th>\n",
              "      <td>Toy Story 3 (2010)</td>\n",
              "      <td>91</td>\n",
              "      <td>4.104396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1912</th>\n",
              "      <td>How to Train Your Dragon (2010)</td>\n",
              "      <td>68</td>\n",
              "      <td>4.102941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1117</th>\n",
              "      <td>Christmas Story, A (1983)</td>\n",
              "      <td>170</td>\n",
              "      <td>4.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1879</th>\n",
              "      <td>WALL·E (2008)</td>\n",
              "      <td>172</td>\n",
              "      <td>4.052326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>533</th>\n",
              "      <td>Grand Day Out with Wallace and Gromit, A (1989)</td>\n",
              "      <td>92</td>\n",
              "      <td>4.005435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>Wizard of Oz, The (1939)</td>\n",
              "      <td>332</td>\n",
              "      <td>3.989458</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  title  count      mean\n",
              "1600       My Neighbor Totoro (Tonari no Totoro) (1988)     65  4.207692\n",
              "1623  Laputa: Castle in the Sky (Tenkû no shiro Rapy...     41  4.195122\n",
              "494         Wallace & Gromit: The Wrong Trousers (1993)    191  4.159686\n",
              "346              Wallace & Gromit: A Close Shave (1995)    155  4.112903\n",
              "1915                                 Toy Story 3 (2010)     91  4.104396\n",
              "1912                    How to Train Your Dragon (2010)     68  4.102941\n",
              "1117                          Christmas Story, A (1983)    170  4.100000\n",
              "1879                                      WALL·E (2008)    172  4.052326\n",
              "533     Grand Day Out with Wallace and Gromit, A (1989)     92  4.005435\n",
              "396                            Wizard of Oz, The (1939)    332  3.989458"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CQJTZ_Opdg0B",
        "colab_type": "text"
      },
      "source": [
        "##Top 10 Drama Movies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMYSeAX-Ah2z",
        "colab_type": "code",
        "outputId": "12827d72-7307-49fe-b205-cb077dec9805",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "#run function to return top recommended movies by genre\n",
        "best_movies_by_genre('Drama',10) "
      ],
      "execution_count": 29,
      "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",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>Shawshank Redemption, The (1994)</td>\n",
              "      <td>870</td>\n",
              "      <td>4.470690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>375</th>\n",
              "      <td>Godfather, The (1972)</td>\n",
              "      <td>565</td>\n",
              "      <td>4.375221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1606</th>\n",
              "      <td>City of God (Cidade de Deus) (2002)</td>\n",
              "      <td>182</td>\n",
              "      <td>4.370879</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>792</th>\n",
              "      <td>On the Waterfront (1954)</td>\n",
              "      <td>68</td>\n",
              "      <td>4.360294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>560</th>\n",
              "      <td>Treasure of the Sierra Madre, The (1948)</td>\n",
              "      <td>68</td>\n",
              "      <td>4.345588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>500</th>\n",
              "      <td>Paths of Glory (1957)</td>\n",
              "      <td>48</td>\n",
              "      <td>4.343750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1939</th>\n",
              "      <td>Intouchables (2011)</td>\n",
              "      <td>37</td>\n",
              "      <td>4.324324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>531</th>\n",
              "      <td>Godfather: Part II, The (1974)</td>\n",
              "      <td>388</td>\n",
              "      <td>4.304124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)</td>\n",
              "      <td>88</td>\n",
              "      <td>4.301136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>585</th>\n",
              "      <td>Raise the Red Lantern (Da hong deng long gao g...</td>\n",
              "      <td>47</td>\n",
              "      <td>4.297872</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  title  count      mean\n",
              "174                    Shawshank Redemption, The (1994)    870  4.470690\n",
              "375                               Godfather, The (1972)    565  4.375221\n",
              "1606                City of God (Cidade de Deus) (2002)    182  4.370879\n",
              "792                            On the Waterfront (1954)     68  4.360294\n",
              "560            Treasure of the Sierra Madre, The (1948)     68  4.345588\n",
              "500                               Paths of Glory (1957)     48  4.343750\n",
              "1939                                Intouchables (2011)     37  4.324324\n",
              "531                      Godfather: Part II, The (1974)    388  4.304124\n",
              "399       Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)     88  4.301136\n",
              "585   Raise the Red Lantern (Da hong deng long gao g...     47  4.297872"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLy5E58vAlO4",
        "colab_type": "code",
        "outputId": "0d72942b-b32c-4363-aa54-d5775359f627",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "# Creating a data frame that has user ratings accross all movies in form of matrix used in matrix factorisation\n",
        "ratings_df = pd.pivot_table(ratings, index='userId', columns='movieId', aggfunc=np.max)\n",
        "ratings_df.head()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th colspan=\"81\" halign=\"left\">rating</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
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              "      <th>5</th>\n",
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              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
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              "      <th>...</th>\n",
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              "      <th>120815</th>\n",
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              "      <th>121135</th>\n",
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              "      <th>123639</th>\n",
              "      <th>124077</th>\n",
              "      <th>125916</th>\n",
              "      <th>126405</th>\n",
              "      <th>126426</th>\n",
              "      <th>127088</th>\n",
              "      <th>128488</th>\n",
              "      <th>128594</th>\n",
              "      <th>128686</th>\n",
              "      <th>128715</th>\n",
              "      <th>128832</th>\n",
              "      <th>128842</th>\n",
              "      <th>130219</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>userId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</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>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 10897 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        rating                              ...                                   \n",
              "movieId 1      2      3      4      5       ... 128686 128715 128832 128842 130219\n",
              "userId                                      ...                                   \n",
              "1          NaN    3.5    NaN    NaN    NaN  ...    NaN    NaN    NaN    NaN    NaN\n",
              "2          NaN    NaN    4.0    NaN    NaN  ...    NaN    NaN    NaN    NaN    NaN\n",
              "3          4.0    NaN    NaN    NaN    NaN  ...    NaN    NaN    NaN    NaN    NaN\n",
              "4          NaN    NaN    NaN    NaN    NaN  ...    NaN    NaN    NaN    NaN    NaN\n",
              "5          NaN    3.0    NaN    NaN    NaN  ...    NaN    NaN    NaN    NaN    NaN\n",
              "\n",
              "[5 rows x 10897 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2dcviU1edt9U",
        "colab_type": "text"
      },
      "source": [
        "###Merging ratings and movies dataframes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zyN_hS3AArP3",
        "colab_type": "code",
        "outputId": "3b154209-54cd-4de6-b409-5d9607ac4ff8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "ratings_movies = pd.merge(ratings,movie_list, on = 'movieId')\n",
        "ratings_movies"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3.5</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>29</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>34</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</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>281051</th>\n",
              "      <td>1931</td>\n",
              "      <td>890</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Baton Rouge (Bâton rouge) (1988)</td>\n",
              "      <td>Thriller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281052</th>\n",
              "      <td>1931</td>\n",
              "      <td>894</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Liebelei (1933)</td>\n",
              "      <td>Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281053</th>\n",
              "      <td>1931</td>\n",
              "      <td>895</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Venice/Venice (1992)</td>\n",
              "      <td>Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281054</th>\n",
              "      <td>1931</td>\n",
              "      <td>985</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Small Wonders (1995)</td>\n",
              "      <td>Documentary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281055</th>\n",
              "      <td>1931</td>\n",
              "      <td>989</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Brother of Sleep (Schlafes Bruder) (1995)</td>\n",
              "      <td>Drama</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>281056 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        userId  ...                      genres\n",
              "0            1  ...  Adventure|Children|Fantasy\n",
              "1            5  ...  Adventure|Children|Fantasy\n",
              "2           13  ...  Adventure|Children|Fantasy\n",
              "3           29  ...  Adventure|Children|Fantasy\n",
              "4           34  ...  Adventure|Children|Fantasy\n",
              "...        ...  ...                         ...\n",
              "281051    1931  ...                    Thriller\n",
              "281052    1931  ...                     Romance\n",
              "281053    1931  ...                       Drama\n",
              "281054    1931  ...                 Documentary\n",
              "281055    1931  ...                       Drama\n",
              "\n",
              "[281056 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RxJ8moz8A9Ke",
        "colab_type": "text"
      },
      "source": [
        "### Gets the other top 10 movies which are watched by the people who saw a particular movie"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NT9bUMfiA3Bg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_other_movies(movie_name):\n",
        "    #get all users who watched a specific movie\n",
        "    df_movie_users_series = ratings_movies.loc[ratings_movies['title']==movie_name]['userId']\n",
        "    #convert to a data frame\n",
        "    df_movie_users = pd.DataFrame(df_movie_users_series,columns=['userId'])\n",
        "    #get a list of all other movies watched by these users\n",
        "    other_movies = pd.merge(df_movie_users,ratings_movies,on='userId')\n",
        "    #get a list of the most commonly watched movies by these other user\n",
        "    other_users_watched = pd.DataFrame(other_movies.groupby('title')['userId'].count()).sort_values('userId',ascending=False)\n",
        "    other_users_watched['perc_who_watched'] = round(other_users_watched['userId']*100/other_users_watched['userId'][0],1)\n",
        "    return other_users_watched[1:11]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "04Vz7i29BO-B",
        "colab_type": "code",
        "outputId": "db5de5c6-d039-4cce-fc6e-23d754b07980",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "get_other_movies('Gone Girl (2014)')"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>perc_who_watched</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Shawshank Redemption, The (1994)</th>\n",
              "      <td>19</td>\n",
              "      <td>90.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Inception (2010)</th>\n",
              "      <td>19</td>\n",
              "      <td>90.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Matrix, The (1999)</th>\n",
              "      <td>18</td>\n",
              "      <td>85.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lord of the Rings: The Fellowship of the Ring, The (2001)</th>\n",
              "      <td>17</td>\n",
              "      <td>81.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lord of the Rings: The Two Towers, The (2002)</th>\n",
              "      <td>16</td>\n",
              "      <td>76.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fight Club (1999)</th>\n",
              "      <td>16</td>\n",
              "      <td>76.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lord of the Rings: The Return of the King, The (2003)</th>\n",
              "      <td>16</td>\n",
              "      <td>76.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Usual Suspects, The (1995)</th>\n",
              "      <td>16</td>\n",
              "      <td>76.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dark Knight, The (2008)</th>\n",
              "      <td>16</td>\n",
              "      <td>76.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pulp Fiction (1994)</th>\n",
              "      <td>15</td>\n",
              "      <td>71.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    userId  perc_who_watched\n",
              "title                                                                       \n",
              "Shawshank Redemption, The (1994)                        19              90.5\n",
              "Inception (2010)                                        19              90.5\n",
              "Matrix, The (1999)                                      18              85.7\n",
              "Lord of the Rings: The Fellowship of the Ring, ...      17              81.0\n",
              "Lord of the Rings: The Two Towers, The (2002)           16              76.2\n",
              "Fight Club (1999)                                       16              76.2\n",
              "Lord of the Rings: The Return of the King, The ...      16              76.2\n",
              "Usual Suspects, The (1995)                              16              76.2\n",
              "Dark Knight, The (2008)                                 16              76.2\n",
              "Pulp Fiction (1994)                                     15              71.4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2FBVPkzZd35o",
        "colab_type": "code",
        "outputId": "edae4861-54b4-45f2-bf88-bda0457f46bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "get_other_movies('Inception (2010)')"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>perc_who_watched</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Matrix, The (1999)</th>\n",
              "      <td>151</td>\n",
              "      <td>79.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dark Knight, The (2008)</th>\n",
              "      <td>143</td>\n",
              "      <td>75.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fight Club (1999)</th>\n",
              "      <td>139</td>\n",
              "      <td>73.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lord of the Rings: The Fellowship of the Ring, The (2001)</th>\n",
              "      <td>132</td>\n",
              "      <td>69.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Shawshank Redemption, The (1994)</th>\n",
              "      <td>126</td>\n",
              "      <td>66.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pulp Fiction (1994)</th>\n",
              "      <td>125</td>\n",
              "      <td>66.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lord of the Rings: The Return of the King, The (2003)</th>\n",
              "      <td>122</td>\n",
              "      <td>64.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lord of the Rings: The Two Towers, The (2002)</th>\n",
              "      <td>118</td>\n",
              "      <td>62.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Memento (2000)</th>\n",
              "      <td>116</td>\n",
              "      <td>61.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Forrest Gump (1994)</th>\n",
              "      <td>114</td>\n",
              "      <td>60.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    userId  perc_who_watched\n",
              "title                                                                       \n",
              "Matrix, The (1999)                                     151              79.9\n",
              "Dark Knight, The (2008)                                143              75.7\n",
              "Fight Club (1999)                                      139              73.5\n",
              "Lord of the Rings: The Fellowship of the Ring, ...     132              69.8\n",
              "Shawshank Redemption, The (1994)                       126              66.7\n",
              "Pulp Fiction (1994)                                    125              66.1\n",
              "Lord of the Rings: The Return of the King, The ...     122              64.6\n",
              "Lord of the Rings: The Two Towers, The (2002)          118              62.4\n",
              "Memento (2000)                                         116              61.4\n",
              "Forrest Gump (1994)                                    114              60.3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nF3QSouzBaCN",
        "colab_type": "text"
      },
      "source": [
        "# CLASSIFICATION USING KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UpzTY-BBBgZi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.neighbors import NearestNeighbors"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cs55ghinBhhz",
        "colab_type": "code",
        "outputId": "8553fd35-8b38-458d-da49-4ab8fc7b4725",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "avg_movie_rating.head()"
      ],
      "execution_count": 36,
      "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>mean</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.980548</td>\n",
              "      <td>694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.257282</td>\n",
              "      <td>309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.151685</td>\n",
              "      <td>178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.096774</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.136598</td>\n",
              "      <td>194</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             mean  count\n",
              "movieId                 \n",
              "1        3.980548    694\n",
              "2        3.257282    309\n",
              "3        3.151685    178\n",
              "4        3.096774     31\n",
              "5        3.136598    194"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f8OA-mIRBsTz",
        "colab_type": "text"
      },
      "source": [
        "###Including movies with more than 10 ratings only"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OY7Vcw0pBj-3",
        "colab_type": "code",
        "outputId": "cd85432d-76f3-40bc-e37f-e3c7e5a486c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "movie_plus_10_ratings = avg_movie_rating.loc[avg_movie_rating['count']>=10]\n",
        "print(len(movie_plus_10_ratings))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3927\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQTK0ZYAB1we",
        "colab_type": "code",
        "outputId": "a42f927a-5cfb-4aef-cfaf-94f736d74eda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        }
      },
      "source": [
        "movie_plus_10_ratings"
      ],
      "execution_count": 38,
      "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>mean</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.980548</td>\n",
              "      <td>694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.257282</td>\n",
              "      <td>309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.151685</td>\n",
              "      <td>178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.096774</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.136598</td>\n",
              "      <td>194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112556</th>\n",
              "      <td>3.785714</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112852</th>\n",
              "      <td>3.705882</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114180</th>\n",
              "      <td>2.850000</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115617</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116797</th>\n",
              "      <td>3.625000</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3927 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             mean  count\n",
              "movieId                 \n",
              "1        3.980548    694\n",
              "2        3.257282    309\n",
              "3        3.151685    178\n",
              "4        3.096774     31\n",
              "5        3.136598    194\n",
              "...           ...    ...\n",
              "112556   3.785714     21\n",
              "112852   3.705882     34\n",
              "114180   2.850000     10\n",
              "115617   4.000000     10\n",
              "116797   3.625000     12\n",
              "\n",
              "[3927 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHdjoVlRB4vl",
        "colab_type": "code",
        "outputId": "22a8d6f6-faed-46cc-a329-68cbd04279bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "filtered_ratings = pd.merge(movie_plus_10_ratings, ratings, on=\"movieId\")\n",
        "len(filtered_ratings)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "259541"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqmVxe6XCGKJ",
        "colab_type": "code",
        "outputId": "cd4e7b72-0d3b-44ed-9e25-043828e34412",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "filtered_ratings.head()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>mean</th>\n",
              "      <th>count</th>\n",
              "      <th>userId</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>3.980548</td>\n",
              "      <td>694</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3.980548</td>\n",
              "      <td>694</td>\n",
              "      <td>6</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3.980548</td>\n",
              "      <td>694</td>\n",
              "      <td>8</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>3.980548</td>\n",
              "      <td>694</td>\n",
              "      <td>10</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>3.980548</td>\n",
              "      <td>694</td>\n",
              "      <td>11</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId      mean  count  userId  rating\n",
              "0        1  3.980548    694       3     4.0\n",
              "1        1  3.980548    694       6     5.0\n",
              "2        1  3.980548    694       8     4.0\n",
              "3        1  3.980548    694      10     4.0\n",
              "4        1  3.980548    694      11     4.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1TGBlymCkFU",
        "colab_type": "code",
        "outputId": "0e5bebb7-0a2a-4d43-b5c4-1ba28bfc71d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        }
      },
      "source": [
        "#create a matrix table with movieIds on the rows and userIds in the columns.\n",
        "movie_wide = filtered_ratings.pivot(index = 'movieId', columns = 'userId', values = 'rating').fillna(0) #replace NAN values with 0\n",
        "movie_wide.head()"
      ],
      "execution_count": 41,
      "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>userId</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>...</th>\n",
              "      <th>1892</th>\n",
              "      <th>1893</th>\n",
              "      <th>1894</th>\n",
              "      <th>1895</th>\n",
              "      <th>1896</th>\n",
              "      <th>1897</th>\n",
              "      <th>1898</th>\n",
              "      <th>1899</th>\n",
              "      <th>1900</th>\n",
              "      <th>1901</th>\n",
              "      <th>1902</th>\n",
              "      <th>1903</th>\n",
              "      <th>1904</th>\n",
              "      <th>1905</th>\n",
              "      <th>1906</th>\n",
              "      <th>1907</th>\n",
              "      <th>1908</th>\n",
              "      <th>1909</th>\n",
              "      <th>1910</th>\n",
              "      <th>1911</th>\n",
              "      <th>1912</th>\n",
              "      <th>1913</th>\n",
              "      <th>1914</th>\n",
              "      <th>1915</th>\n",
              "      <th>1916</th>\n",
              "      <th>1917</th>\n",
              "      <th>1918</th>\n",
              "      <th>1919</th>\n",
              "      <th>1920</th>\n",
              "      <th>1921</th>\n",
              "      <th>1922</th>\n",
              "      <th>1923</th>\n",
              "      <th>1924</th>\n",
              "      <th>1925</th>\n",
              "      <th>1926</th>\n",
              "      <th>1927</th>\n",
              "      <th>1928</th>\n",
              "      <th>1929</th>\n",
              "      <th>1930</th>\n",
              "      <th>1931</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 1931 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "userId   1     2     3     4     5     6     ...  1926  1927  1928  1929  1930  1931\n",
              "movieId                                      ...                                    \n",
              "1         0.0   0.0   4.0   0.0   0.0   5.0  ...   0.0   0.0   0.0   0.0   4.0   5.0\n",
              "2         3.5   0.0   0.0   0.0   3.0   0.0  ...   0.0   3.0   0.0   0.0   4.5   4.0\n",
              "3         0.0   4.0   0.0   0.0   0.0   3.0  ...   0.0   0.0   0.0   0.0   0.0   4.0\n",
              "4         0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   0.0   0.0   0.0   0.0   5.0\n",
              "5         0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   0.0   0.0   0.0   0.0   4.0\n",
              "\n",
              "[5 rows x 1931 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2U39U_X5XEVY",
        "colab_type": "text"
      },
      "source": [
        "###Specify model parameters and fit model to the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wU8u-0PRW9nb",
        "colab_type": "code",
        "outputId": "f14b11a4-73cb-4b8f-cf29-a055f8af5e3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "model_knn = NearestNeighbors(metric='cosine',algorithm='brute')\n",
        "model_knn.fit(movie_wide)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NearestNeighbors(algorithm='brute', leaf_size=30, metric='cosine',\n",
              "                 metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
              "                 radius=1.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TYs0uQTuXPMT",
        "colab_type": "text"
      },
      "source": [
        "##To get the top 10 nearest neighbours for the movie"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jE7aq1I7XJn6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def print_similar_movies(movie_name) :\n",
        "    #get the list of user ratings for a specific userId\n",
        "    query_index = movie_list.loc[movie_list['title']==movie_name]['movieId'].dropna().values[0]\n",
        "    query_index_movie_ratings = movie_wide.loc[query_index,:].values.reshape(1,-1)\n",
        "    #get the closest 10 movies and their distances from the movie specified\n",
        "    distances,indices = model_knn.kneighbors(query_index_movie_ratings,n_neighbors = 11) \n",
        "    #write a loop that prints the similar movies for a specified movie.\n",
        "    for i in range(0,len(distances.flatten())):\n",
        "        #get the title of the random movie that was chosen\n",
        "        get_movie = movie_list.loc[movie_list['movieId']==query_index]['title']\n",
        "        #for the first movie in the list i.e closest print the title\n",
        "        if i==0:\n",
        "            print('Recommendations for {0}:\\n'.format(get_movie))\n",
        "        else :\n",
        "            #get the indiciees for the closest movies\n",
        "            indices_flat = indices.flatten()[i]\n",
        "            #get the title of the movie\n",
        "            get_movie = movie_list.loc[movie_list['movieId']==movie_wide.iloc[indices_flat,:].name]['title']\n",
        "            #print the movie\n",
        "            print('{0}: {1}, with distance of {2}:'.format(i,get_movie,distances.flatten()[i]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SlhhOXuVXbUw",
        "colab_type": "code",
        "outputId": "752ef577-b62b-4a9f-92cf-3604d3d08f9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        }
      },
      "source": [
        "print_similar_movies('Godfather, The (1972)')"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Recommendations for 843    Godfather, The (1972)\n",
            "Name: title, dtype: object:\n",
            "\n",
            "1: 1195    Godfather: Part II, The (1974)\n",
            "Name: title, dtype: object, with distance of 0.21758073000073486:\n",
            "2: 1187    Goodfellas (1990)\n",
            "Name: title, dtype: object, with distance of 0.387131730869816:\n",
            "3: 1173    Raiders of the Lost Ark (Indiana Jones and the...\n",
            "Name: title, dtype: object, with distance of 0.4108251974651552:\n",
            "4: 1169    One Flew Over the Cuckoo's Nest (1975)\n",
            "Name: title, dtype: object, with distance of 0.42684778479958396:\n",
            "5: 1171    Star Wars: Episode V - The Empire Strikes Back...\n",
            "Name: title, dtype: object, with distance of 0.42989690544457093:\n",
            "6: 257    Star Wars: Episode IV - A New Hope (1977)\n",
            "Name: title, dtype: object, with distance of 0.44739732816144273:\n",
            "7: 1182    Apocalypse Now (1979)\n",
            "Name: title, dtype: object, with distance of 0.447716892170405:\n",
            "8: 602    Fargo (1996)\n",
            "Name: title, dtype: object, with distance of 0.44911889048444265:\n",
            "9: 2772    American Beauty (1999)\n",
            "Name: title, dtype: object, with distance of 0.4503294367435229:\n",
            "10: 2486    Matrix, The (1999)\n",
            "Name: title, dtype: object, with distance of 0.46240871835669894:\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91AczyoXZFuV",
        "colab_type": "code",
        "outputId": "a81b79f8-4710-405e-a83b-2302efe0c66e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        }
      },
      "source": [
        "print_similar_movies('Toy Story (1995)')"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Recommendations for 0    Toy Story (1995)\n",
            "Name: title, dtype: object:\n",
            "\n",
            "1: 257    Star Wars: Episode IV - A New Hope (1977)\n",
            "Name: title, dtype: object, with distance of 0.433065437179161:\n",
            "2: 1242    Back to the Future (1985)\n",
            "Name: title, dtype: object, with distance of 0.43928379391297645:\n",
            "3: 3027    Toy Story 2 (1999)\n",
            "Name: title, dtype: object, with distance of 0.44279072388292107:\n",
            "4: 767    Independence Day (a.k.a. ID4) (1996)\n",
            "Name: title, dtype: object, with distance of 0.44663993835014426:\n",
            "5: 1184    Star Wars: Episode VI - Return of the Jedi (1983)\n",
            "Name: title, dtype: object, with distance of 0.45427731778870495:\n",
            "6: 352    Forrest Gump (1994)\n",
            "Name: title, dtype: object, with distance of 0.45743951986158915:\n",
            "7: 476    Jurassic Park (1993)\n",
            "Name: title, dtype: object, with distance of 0.45755152756874906:\n",
            "8: 360    Lion King, The (1994)\n",
            "Name: title, dtype: object, with distance of 0.46968728852403374:\n",
            "9: 583    Terminator 2: Judgment Day (1991)\n",
            "Name: title, dtype: object, with distance of 0.4706152247674611:\n",
            "10: 582    Aladdin (1992)\n",
            "Name: title, dtype: object, with distance of 0.47333610807958104:\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ETeuHbYcZHxx",
        "colab_type": "code",
        "outputId": "6e08b1a4-dc84-4b34-fa03-6b2812401763",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        }
      },
      "source": [
        "print_similar_movies('Matrix, The (1999)')"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Recommendations for 2486    Matrix, The (1999)\n",
            "Name: title, dtype: object:\n",
            "\n",
            "1: 2873    Fight Club (1999)\n",
            "Name: title, dtype: object, with distance of 0.3291742859646294:\n",
            "2: 1171    Star Wars: Episode V - The Empire Strikes Back...\n",
            "Name: title, dtype: object, with distance of 0.3339523024656883:\n",
            "3: 4897    Lord of the Rings: The Fellowship of the Ring,...\n",
            "Name: title, dtype: object, with distance of 0.34485104333777483:\n",
            "4: 2676    Sixth Sense, The (1999)\n",
            "Name: title, dtype: object, with distance of 0.348277809834007:\n",
            "5: 1173    Raiders of the Lost Ark (Indiana Jones and the...\n",
            "Name: title, dtype: object, with distance of 0.36086129150807933:\n",
            "6: 5853    Lord of the Rings: The Two Towers, The (2002)\n",
            "Name: title, dtype: object, with distance of 0.3672094973831135:\n",
            "7: 1242    Back to the Future (1985)\n",
            "Name: title, dtype: object, with distance of 0.3679395316152976:\n",
            "8: 1944    Saving Private Ryan (1998)\n",
            "Name: title, dtype: object, with distance of 0.3687028550766247:\n",
            "9: 2772    American Beauty (1999)\n",
            "Name: title, dtype: object, with distance of 0.3791914426660894:\n",
            "10: 1212    Terminator, The (1984)\n",
            "Name: title, dtype: object, with distance of 0.3812524669385232:\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}