movie_recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\"><font size=\"5\">BASIC MOVIE RECOMMENDER SYSTEM</font></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, I will try to recommend other movies to my girlfriend based on her ratings of her 10 favorites movies. The recommendation engine is very basic. It is purely based on the genres of the movies. This project is inspired from one of the hands-on of the <a href=\"https://www.coursera.org/professional-certificates/ibm-data-science\"> IBM Data Science Professional certificate</a> from Coursera. More precisely, it is similar to the lab entitled Content-based Recommendation Systems of the Machine Learning Course of that track."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Table of contents</h1>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ol>\n",
    "        <li><a href=\"#ref1\">Acquiring the Data</a></li>\n",
    "        <li><a href=\"#ref2\">Preprocessing</a></li>\n",
    "        <li><a href=\"#ref3\">Collaborative Filtering</a></li>\n",
    "    </ol>\n",
    "</div>\n",
    "<br>\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref1\"></a>\n",
    "# Acquiring the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "#import matplotlib.pyplot as plt\n",
    "#%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
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       "<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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df=pd.read_csv('movies.csv')\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will be reusing the dataset that was provided in the hands-on activity. It was not specified where the data were coming from.The data quality is quite good. The dataset contains movies from 1874 to 2016. For each movie is associated to one or several genres."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref2\"></a>\n",
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   movieId                        title  \\\n",
       "0        1                    Toy Story   \n",
       "1        2                      Jumanji   \n",
       "2        3             Grumpier Old Men   \n",
       "3        4            Waiting to Exhale   \n",
       "4        5  Father of the Bride Part II   \n",
       "\n",
       "                                        genres  year  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  1995  \n",
       "1                   Adventure|Children|Fantasy  1995  \n",
       "2                               Comedy|Romance  1995  \n",
       "3                         Comedy|Drama|Romance  1995  \n",
       "4                                       Comedy  1995  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using regular expressions to find a year stored between parentheses\n",
    "#We specify the parantheses so we don't conflict with movies that have years in their titles\n",
    "movies_df['year'] = movies_df.title.str.extract('(\\(\\d\\d\\d\\d\\))',expand=False)\n",
    "#Removing the parentheses\n",
    "movies_df['year'] = movies_df.year.str.extract('(\\d\\d\\d\\d)',expand=False)\n",
    "#Removing the years from the 'title' column\n",
    "movies_df['title'] = movies_df.title.str.replace('(\\(\\d\\d\\d\\d\\))', '')\n",
    "#Applying the strip function to get rid of any ending whitespace characters that may have appeared\n",
    "movies_df['title'] = movies_df['title'].apply(lambda x: x.strip())\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With that, let's also split the values in the __Genres__ column into a __list of Genres__ to simplify future use. This can be achieved by applying Python's split string function on the correct column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>[Adventure, Children, Fantasy]</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>[Comedy, Romance]</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>[Comedy]</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   movieId                        title  \\\n",
       "0        1                    Toy Story   \n",
       "1        2                      Jumanji   \n",
       "2        3             Grumpier Old Men   \n",
       "3        4            Waiting to Exhale   \n",
       "4        5  Father of the Bride Part II   \n",
       "\n",
       "                                              genres  year  \n",
       "0  [Adventure, Animation, Children, Comedy, Fantasy]  1995  \n",
       "1                     [Adventure, Children, Fantasy]  1995  \n",
       "2                                  [Comedy, Romance]  1995  \n",
       "3                           [Comedy, Drama, Romance]  1995  \n",
       "4                                           [Comedy]  1995  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Every genre is separated by a | so we simply have to call the split function on |\n",
    "movies_df['genres'] = movies_df.genres.str.split('|')\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since keeping genres in a list format isn't optimal for the content-based recommendation system technique, we will use the One Hot Encoding technique to convert the list of genres to a vector where each column corresponds to one possible value of the feature. This encoding is needed for feeding categorical data. In this case, we store every different genre in columns that contain either 1 or 0. 1 shows that a movie has that genre and 0 shows that it doesn't. Let's also store this dataframe in another variable since genres won't be important for our first recommendation system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Romance</th>\n",
       "      <th>...</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>IMAX</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>War</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Western</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>(no genres listed)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
       "      <td>1995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>[Adventure, Children, Fantasy]</td>\n",
       "      <td>1995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>[Comedy, Romance]</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>[Comedy]</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                        title  \\\n",
       "0        1                    Toy Story   \n",
       "1        2                      Jumanji   \n",
       "2        3             Grumpier Old Men   \n",
       "3        4            Waiting to Exhale   \n",
       "4        5  Father of the Bride Part II   \n",
       "\n",
       "                                              genres  year  Adventure  \\\n",
       "0  [Adventure, Animation, Children, Comedy, Fantasy]  1995        1.0   \n",
       "1                     [Adventure, Children, Fantasy]  1995        1.0   \n",
       "2                                  [Comedy, Romance]  1995        0.0   \n",
       "3                           [Comedy, Drama, Romance]  1995        0.0   \n",
       "4                                           [Comedy]  1995        0.0   \n",
       "\n",
       "   Animation  Children  Comedy  Fantasy  Romance  ...  Horror  Mystery  \\\n",
       "0        1.0       1.0     1.0      1.0      0.0  ...     0.0      0.0   \n",
       "1        0.0       1.0     0.0      1.0      0.0  ...     0.0      0.0   \n",
       "2        0.0       0.0     1.0      0.0      1.0  ...     0.0      0.0   \n",
       "3        0.0       0.0     1.0      0.0      1.0  ...     0.0      0.0   \n",
       "4        0.0       0.0     1.0      0.0      0.0  ...     0.0      0.0   \n",
       "\n",
       "   Sci-Fi  IMAX  Documentary  War  Musical  Western  Film-Noir  \\\n",
       "0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "1     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "2     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "3     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "4     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "\n",
       "   (no genres listed)  \n",
       "0                 0.0  \n",
       "1                 0.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Copying the movie dataframe into a new one since we won't need to use the genre information in our first case.\n",
    "moviesWithGenres_df = movies_df.copy()\n",
    "\n",
    "#For every row in the dataframe, iterate through the list of genres and place a 1 into the corresponding column\n",
    "for index, row in movies_df.iterrows():\n",
    "    for genre in row['genres']:\n",
    "        moviesWithGenres_df.at[index, genre] = 1\n",
    "#Filling in the NaN values with 0 to show that a movie doesn't have that column's genre\n",
    "moviesWithGenres_df = moviesWithGenres_df.fillna(0)\n",
    "moviesWithGenres_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref3\"></a>\n",
    "# Content-Based recommendation system"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's take a look at how to implement __Content-Based__ or __Item-Item recommendation systems__. This technique attempts to figure out what a user's favourite aspects of an item is, and then recommends items that present those aspects. In our case, we're going to try to figure out the input's favorite genres from the movies and ratings given.\n",
    "\n",
    "Let's begin by creating an input user to recommend movies to:\n",
    "\n",
    "Notice: To add more movies, simply increase the amount of elements in the __userInput__. Feel free to add more in! Just be sure to write it in with capital letters and if a movie starts with a \"The\", like \"The Matrix\" then write it in like this: 'Matrix, The' ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Iron Man</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Black Panther</td>\n",
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       "      <td>2</td>\n",
       "      <td>Ratatouille</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Fast and the Furious 5, The</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Harry Potter and the Deathly Hallows</td>\n",
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       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Dark Knight Rises, The</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  title  rating\n",
       "0                              Iron Man     4.5\n",
       "1                         Black Panther     4.0\n",
       "2                           Ratatouille     4.0\n",
       "3                        Now You See Me     3.5\n",
       "4           Fast and the Furious 5, The     4.0\n",
       "5  Harry Potter and the Deathly Hallows     2.5\n",
       "6           Place Beyond the Pines, The     3.0\n",
       "7                   Imitation Game, The     4.0\n",
       "8                              I, Robot     5.0\n",
       "9                Dark Knight Rises, The     5.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "natInput = [\n",
    "            {'title':'Avatar', 'rating':4.5},\n",
    "            {'title':'Darknight Rises, The ', 'rating':4.5},\n",
    "            {'title':'Pirates of the Caribbean: Dead Man\\'s Chest', 'rating':4},\n",
    "            {'title':\"Love Actually\", 'rating':3.5},\n",
    "            {'title':'Little Mermaid, The', 'rating':4.5},\n",
    "            {'title':'Fast and the Furious, The','rating':2.5},\n",
    "            {'title':'Inception','rating':2.5},\n",
    "            {'title':'Sister Act 2: Back in the Habit','rating':4},\n",
    "            {'title':'Ten Commandments, The':4.5},\n",
    "            {'title':'Freedom Writers','rating':4}\n",
    "         ] \n",
    "inputMovies = pd.DataFrame(natInput)\n",
    "inputMovies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add movieId to input user\n",
    "With the input complete, let's extract the input movie's ID's from the movies dataframe and add them into it.\n",
    "\n",
    "We can achieve this by first filtering out the rows that contain the input movie's title and then merging this subset with the input dataframe. We also drop unnecessary columns for the input to save memory space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "       movieId                        title  \\\n",
       "7962      8644                     I, Robot   \n",
       "11624    50872                  Ratatouille   \n",
       "12646    59315                     Iron Man   \n",
       "18355    91529       Dark Knight Rises, The   \n",
       "20807   101525  Place Beyond the Pines, The   \n",
       "21167   102903               Now You See Me   \n",
       "\n",
       "                                      genres  year  \n",
       "7962   [Action, Adventure, Sci-Fi, Thriller]  2004  \n",
       "11624           [Animation, Children, Drama]  2007  \n",
       "12646            [Action, Adventure, Sci-Fi]  2008  \n",
       "18355       [Action, Adventure, Crime, IMAX]  2012  \n",
       "20807                         [Crime, Drama]  2012  \n",
       "21167             [Crime, Mystery, Thriller]  2013  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filtering out the movies by title\n",
    "inputId = movies_df[movies_df['title'].isin(inputMovies['title'].tolist())]\n",
    "inputId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>5.0</td>\n",
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       "      <td>4</td>\n",
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       "      <td>5</td>\n",
       "      <td>102903</td>\n",
       "      <td>Now You See Me</td>\n",
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       "      <td>2013</td>\n",
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      ],
      "text/plain": [
       "   movieId                        title  \\\n",
       "0     8644                     I, Robot   \n",
       "1    50872                  Ratatouille   \n",
       "2    59315                     Iron Man   \n",
       "3    91529       Dark Knight Rises, The   \n",
       "4   101525  Place Beyond the Pines, The   \n",
       "5   102903               Now You See Me   \n",
       "\n",
       "                                  genres  year  rating  \n",
       "0  [Action, Adventure, Sci-Fi, Thriller]  2004     5.0  \n",
       "1           [Animation, Children, Drama]  2007     4.0  \n",
       "2            [Action, Adventure, Sci-Fi]  2008     4.5  \n",
       "3       [Action, Adventure, Crime, IMAX]  2012     5.0  \n",
       "4                         [Crime, Drama]  2012     3.0  \n",
       "5             [Crime, Mystery, Thriller]  2013     3.5  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Then merging it so we can get the movieId. It's implicitly merging it by title.\n",
    "inputMovies = pd.merge(inputId, inputMovies)\n",
    "#Dropping information we won't use from the input dataframe\n",
    "#inputMovies = inputMovies.drop('genres', 1).drop('year', 1)\n",
    "#Final input dataframe\n",
    "#If a movie you added in above isn't here, then it might not be in the original \n",
    "#dataframe or it might spelled differently, please check capitalisation.\n",
    "inputMovies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to start by learning the input's preferences, so let's get the subset of movies that the input has watched from the Dataframe containing genres defined with binary values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                        title  \\\n",
       "7962      8644                     I, Robot   \n",
       "11624    50872                  Ratatouille   \n",
       "12646    59315                     Iron Man   \n",
       "18355    91529       Dark Knight Rises, The   \n",
       "20807   101525  Place Beyond the Pines, The   \n",
       "21167   102903               Now You See Me   \n",
       "\n",
       "                                      genres  year  Adventure  Animation  \\\n",
       "7962   [Action, Adventure, Sci-Fi, Thriller]  2004        1.0        0.0   \n",
       "11624           [Animation, Children, Drama]  2007        0.0        1.0   \n",
       "12646            [Action, Adventure, Sci-Fi]  2008        1.0        0.0   \n",
       "18355       [Action, Adventure, Crime, IMAX]  2012        1.0        0.0   \n",
       "20807                         [Crime, Drama]  2012        0.0        0.0   \n",
       "21167             [Crime, Mystery, Thriller]  2013        0.0        0.0   \n",
       "\n",
       "       Children  Comedy  Fantasy  Romance  ...  Horror  Mystery  Sci-Fi  IMAX  \\\n",
       "7962        0.0     0.0      0.0      0.0  ...     0.0      0.0     1.0   0.0   \n",
       "11624       1.0     0.0      0.0      0.0  ...     0.0      0.0     0.0   0.0   \n",
       "12646       0.0     0.0      0.0      0.0  ...     0.0      0.0     1.0   0.0   \n",
       "18355       0.0     0.0      0.0      0.0  ...     0.0      0.0     0.0   1.0   \n",
       "20807       0.0     0.0      0.0      0.0  ...     0.0      0.0     0.0   0.0   \n",
       "21167       0.0     0.0      0.0      0.0  ...     0.0      1.0     0.0   0.0   \n",
       "\n",
       "       Documentary  War  Musical  Western  Film-Noir  (no genres listed)  \n",
       "7962           0.0  0.0      0.0      0.0        0.0                 0.0  \n",
       "11624          0.0  0.0      0.0      0.0        0.0                 0.0  \n",
       "12646          0.0  0.0      0.0      0.0        0.0                 0.0  \n",
       "18355          0.0  0.0      0.0      0.0        0.0                 0.0  \n",
       "20807          0.0  0.0      0.0      0.0        0.0                 0.0  \n",
       "21167          0.0  0.0      0.0      0.0        0.0                 0.0  \n",
       "\n",
       "[6 rows x 24 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filtering out the movies from the input\n",
    "userMovies = moviesWithGenres_df[moviesWithGenres_df['movieId'].isin(inputMovies['movieId'].tolist())]\n",
    "userMovies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We'll only need the actual genre table, so let's clean this up a bit by resetting the index and dropping the movieId, title, genres and year columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
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      "text/plain": [
       "   Adventure  Animation  Children  Comedy  Fantasy  Romance  Drama  Action  \\\n",
       "0        1.0        0.0       0.0     0.0      0.0      0.0    0.0     1.0   \n",
       "1        0.0        1.0       1.0     0.0      0.0      0.0    1.0     0.0   \n",
       "2        1.0        0.0       0.0     0.0      0.0      0.0    0.0     1.0   \n",
       "3        1.0        0.0       0.0     0.0      0.0      0.0    0.0     1.0   \n",
       "4        0.0        0.0       0.0     0.0      0.0      0.0    1.0     0.0   \n",
       "5        0.0        0.0       0.0     0.0      0.0      0.0    0.0     0.0   \n",
       "\n",
       "   Crime  Thriller  Horror  Mystery  Sci-Fi  IMAX  Documentary  War  Musical  \\\n",
       "0    0.0       1.0     0.0      0.0     1.0   0.0          0.0  0.0      0.0   \n",
       "1    0.0       0.0     0.0      0.0     0.0   0.0          0.0  0.0      0.0   \n",
       "2    0.0       0.0     0.0      0.0     1.0   0.0          0.0  0.0      0.0   \n",
       "3    1.0       0.0     0.0      0.0     0.0   1.0          0.0  0.0      0.0   \n",
       "4    1.0       0.0     0.0      0.0     0.0   0.0          0.0  0.0      0.0   \n",
       "5    1.0       1.0     0.0      1.0     0.0   0.0          0.0  0.0      0.0   \n",
       "\n",
       "   Western  Film-Noir  (no genres listed)  \n",
       "0      0.0        0.0                 0.0  \n",
       "1      0.0        0.0                 0.0  \n",
       "2      0.0        0.0                 0.0  \n",
       "3      0.0        0.0                 0.0  \n",
       "4      0.0        0.0                 0.0  \n",
       "5      0.0        0.0                 0.0  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Resetting the index to avoid future issues\n",
    "userMovies = userMovies.reset_index(drop=True)\n",
    "#Dropping unnecessary issues due to save memory and to avoid issues\n",
    "userGenreTable = userMovies.drop('movieId', 1).drop('title', 1).drop('genres', 1).drop('year', 1)\n",
    "userGenreTable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're ready to start learning the input's preferences!\n",
    "\n",
    "To do this, we're going to turn each genre into weights. We can do this by using the input's reviews and multiplying them into the input's genre table and then summing up the resulting table by column. This operation is actually a dot product between a matrix and a vector, so we can simply accomplish by calling Pandas's \"dot\" function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adventure             14.5\n",
       "Animation              4.0\n",
       "Children               4.0\n",
       "Comedy                 0.0\n",
       "Fantasy                0.0\n",
       "Romance                0.0\n",
       "Drama                  7.0\n",
       "Action                14.5\n",
       "Crime                 11.5\n",
       "Thriller               8.5\n",
       "Horror                 0.0\n",
       "Mystery                3.5\n",
       "Sci-Fi                 9.5\n",
       "IMAX                   5.0\n",
       "Documentary            0.0\n",
       "War                    0.0\n",
       "Musical                0.0\n",
       "Western                0.0\n",
       "Film-Noir              0.0\n",
       "(no genres listed)     0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dot produt to get weights\n",
    "userProfile = userGenreTable.transpose().dot(inputMovies['rating'])\n",
    "#The user profile\n",
    "userProfile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have the weights for every of the user's preferences. This is known as the User Profile. Using this, we can recommend movies that satisfy the user's preferences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Adventure  Animation  Children  Comedy  Fantasy  Romance  Drama  \\\n",
       "movieId                                                                    \n",
       "1              1.0        1.0       1.0     1.0      1.0      0.0    0.0   \n",
       "2              1.0        0.0       1.0     0.0      1.0      0.0    0.0   \n",
       "3              0.0        0.0       0.0     1.0      0.0      1.0    0.0   \n",
       "4              0.0        0.0       0.0     1.0      0.0      1.0    1.0   \n",
       "5              0.0        0.0       0.0     1.0      0.0      0.0    0.0   \n",
       "\n",
       "         Action  Crime  Thriller  Horror  Mystery  Sci-Fi  IMAX  Documentary  \\\n",
       "movieId                                                                        \n",
       "1           0.0    0.0       0.0     0.0      0.0     0.0   0.0          0.0   \n",
       "2           0.0    0.0       0.0     0.0      0.0     0.0   0.0          0.0   \n",
       "3           0.0    0.0       0.0     0.0      0.0     0.0   0.0          0.0   \n",
       "4           0.0    0.0       0.0     0.0      0.0     0.0   0.0          0.0   \n",
       "5           0.0    0.0       0.0     0.0      0.0     0.0   0.0          0.0   \n",
       "\n",
       "         War  Musical  Western  Film-Noir  (no genres listed)  \n",
       "movieId                                                        \n",
       "1        0.0      0.0      0.0        0.0                 0.0  \n",
       "2        0.0      0.0      0.0        0.0                 0.0  \n",
       "3        0.0      0.0      0.0        0.0                 0.0  \n",
       "4        0.0      0.0      0.0        0.0                 0.0  \n",
       "5        0.0      0.0      0.0        0.0                 0.0  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now let's get the genres of every movie in our original dataframe\n",
    "genreTable = moviesWithGenres_df.set_index(moviesWithGenres_df['movieId'])\n",
    "#And drop the unnecessary information\n",
    "genreTable = genreTable.drop('movieId', 1).drop('title', 1).drop('genres', 1).drop('year', 1)\n",
    "genreTable.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the input's profile and the complete list of movies and their genres in hand, we're going to take the weighted average of every movie based on the input profile and recommend the top twenty movies that most satisfy it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId\n",
       "1    0.274390\n",
       "2    0.225610\n",
       "3    0.000000\n",
       "4    0.085366\n",
       "5    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Multiply the genres by the weights and then take the weighted average\n",
    "recommendationTable_df = ((genreTable*userProfile).sum(axis=1))/(userProfile.sum())\n",
    "recommendationTable_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId\n",
       "115479    0.79878\n",
       "59844     0.72561\n",
       "79132     0.72561\n",
       "81132     0.72561\n",
       "67070     0.72561\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort our recommendations in descending order\n",
    "recommendationTable_df = recommendationTable_df.sort_values(ascending=False)\n",
    "#Just a peek at the values\n",
    "recommendationTable_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(recommendationTable_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
       "        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>recommendation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>115479</td>\n",
       "      <td>0.79878</td>\n",
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       "    <tr>\n",
       "      <td>59844</td>\n",
       "      <td>0.72561</td>\n",
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       "    <tr>\n",
       "      <td>79132</td>\n",
       "      <td>0.72561</td>\n",
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       "    <tr>\n",
       "      <td>81132</td>\n",
       "      <td>0.72561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67070</td>\n",
       "      <td>0.72561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         recommendation\n",
       "movieId                \n",
       "115479          0.79878\n",
       "59844           0.72561\n",
       "79132           0.72561\n",
       "81132           0.72561\n",
       "67070           0.72561"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommended_movies=recommendationTable_df.to_frame('recommendation')\n",
    "recommended_movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "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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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>recommendation</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>115479</td>\n",
       "      <td>0.798780</td>\n",
       "      <td>Whip Hand, The</td>\n",
       "      <td>[Action, Adventure, Crime, Drama, Sci-Fi, Thri...</td>\n",
       "      <td>1951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>59844</td>\n",
       "      <td>0.725610</td>\n",
       "      <td>Honor Among Thieves (Adieu l'ami) (Farewell, F...</td>\n",
       "      <td>[Action, Adventure, Crime, Drama, Mystery, Thr...</td>\n",
       "      <td>1968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>79132</td>\n",
       "      <td>0.725610</td>\n",
       "      <td>Inception</td>\n",
       "      <td>[Action, Crime, Drama, Mystery, Sci-Fi, Thrill...</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>81132</td>\n",
       "      <td>0.725610</td>\n",
       "      <td>Rubber</td>\n",
       "      <td>[Action, Adventure, Comedy, Crime, Drama, Film...</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>67070</td>\n",
       "      <td>0.725610</td>\n",
       "      <td>Army of One (Joshua Tree)</td>\n",
       "      <td>[Action, Adventure, Crime, Drama, Mystery, Thr...</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>26701</td>\n",
       "      <td>0.713415</td>\n",
       "      <td>Patlabor: The Movie (Kidô keisatsu patorebâ: T...</td>\n",
       "      <td>[Action, Animation, Crime, Drama, Film-Noir, M...</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>91054</td>\n",
       "      <td>0.713415</td>\n",
       "      <td>Batman</td>\n",
       "      <td>[Action, Adventure, Crime, Sci-Fi, Thriller]</td>\n",
       "      <td>1943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2625</td>\n",
       "      <td>0.713415</td>\n",
       "      <td>Black Mask (Hak hap)</td>\n",
       "      <td>[Action, Adventure, Crime, Sci-Fi, Thriller]</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>91056</td>\n",
       "      <td>0.695122</td>\n",
       "      <td>Batman and Robin</td>\n",
       "      <td>[Action, Adventure, Crime, Drama, Sci-Fi]</td>\n",
       "      <td>1949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>26504</td>\n",
       "      <td>0.689024</td>\n",
       "      <td>Cloak &amp; Dagger</td>\n",
       "      <td>[Action, Adventure, Children, Crime, Mystery, ...</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>105974</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>Assault on a Queen</td>\n",
       "      <td>[Action, Adventure, Crime, Drama, Thriller]</td>\n",
       "      <td>1966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>122339</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>Walking Tall: The Payback</td>\n",
       "      <td>[Action, Adventure, Crime, Drama, Thriller]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>459</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>Getaway, The</td>\n",
       "      <td>[Action, Adventure, Crime, Drama, Romance, Thr...</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>119603</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>Down Twisted</td>\n",
       "      <td>[Action, Adventure, Crime, Drama, Thriller]</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>122787</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>The 39 Steps</td>\n",
       "      <td>[Action, Adventure, Comedy, Crime, Drama, Thri...</td>\n",
       "      <td>1959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>49530</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>Blood Diamond</td>\n",
       "      <td>[Action, Adventure, Crime, Drama, Thriller, War]</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>4620</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>Lock Up</td>\n",
       "      <td>[Action, Adventure, Crime, Drama, Thriller]</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>64645</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>The Wrecking Crew</td>\n",
       "      <td>[Action, Adventure, Comedy, Crime, Drama, Thri...</td>\n",
       "      <td>1968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>96601</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>Icon</td>\n",
       "      <td>[Action, Adventure, Crime, Drama, Thriller]</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>6016</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>City of God (Cidade de Deus)</td>\n",
       "      <td>[Action, Adventure, Crime, Drama, Thriller]</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>78729</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>24: Redemption</td>\n",
       "      <td>[Action, Adventure, Crime, Drama, Thriller]</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>5018</td>\n",
       "      <td>0.664634</td>\n",
       "      <td>Motorama</td>\n",
       "      <td>[Adventure, Comedy, Crime, Drama, Fantasy, Mys...</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>198</td>\n",
       "      <td>0.664634</td>\n",
       "      <td>Strange Days</td>\n",
       "      <td>[Action, Crime, Drama, Mystery, Sci-Fi, Thriller]</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>108979</td>\n",
       "      <td>0.658537</td>\n",
       "      <td>Cowboy Bebop</td>\n",
       "      <td>[Action, Adventure, Animation, Crime, Sci-Fi]</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>8361</td>\n",
       "      <td>0.658537</td>\n",
       "      <td>Day After Tomorrow, The</td>\n",
       "      <td>[Action, Adventure, Drama, Sci-Fi, Thriller]</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>71999</td>\n",
       "      <td>0.658537</td>\n",
       "      <td>Aelita: The Queen of Mars (Aelita)</td>\n",
       "      <td>[Action, Adventure, Drama, Fantasy, Romance, S...</td>\n",
       "      <td>1924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>58025</td>\n",
       "      <td>0.658537</td>\n",
       "      <td>Jumper</td>\n",
       "      <td>[Action, Adventure, Drama, Sci-Fi, Thriller]</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>27618</td>\n",
       "      <td>0.658537</td>\n",
       "      <td>Sound of Thunder, A</td>\n",
       "      <td>[Action, Adventure, Drama, Sci-Fi, Thriller]</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>48774</td>\n",
       "      <td>0.658537</td>\n",
       "      <td>Children of Men</td>\n",
       "      <td>[Action, Adventure, Drama, Sci-Fi, Thriller]</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>141385</td>\n",
       "      <td>0.658537</td>\n",
       "      <td>Humanity's End</td>\n",
       "      <td>[Action, Adventure, Drama, Sci-Fi, Thriller]</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>91500</td>\n",
       "      <td>0.658537</td>\n",
       "      <td>Hunger Games, The</td>\n",
       "      <td>[Action, Adventure, Drama, Sci-Fi, Thriller]</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>91542</td>\n",
       "      <td>0.640244</td>\n",
       "      <td>Sherlock Holmes: A Game of Shadows</td>\n",
       "      <td>[Action, Adventure, Comedy, Crime, Mystery, Th...</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>112942</td>\n",
       "      <td>0.640244</td>\n",
       "      <td>Sky Murder</td>\n",
       "      <td>[Action, Adventure, Crime, Mystery, Thriller]</td>\n",
       "      <td>1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>8901</td>\n",
       "      <td>0.640244</td>\n",
       "      <td>Flashpoint</td>\n",
       "      <td>[Action, Adventure, Crime, Mystery, Thriller]</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>52722</td>\n",
       "      <td>0.634146</td>\n",
       "      <td>Spider-Man 3</td>\n",
       "      <td>[Action, Adventure, Sci-Fi, Thriller, IMAX]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>6934</td>\n",
       "      <td>0.634146</td>\n",
       "      <td>Matrix Revolutions, The</td>\n",
       "      <td>[Action, Adventure, Sci-Fi, Thriller, IMAX]</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>77561</td>\n",
       "      <td>0.634146</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>[Action, Adventure, Sci-Fi, Thriller, IMAX]</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>101076</td>\n",
       "      <td>0.634146</td>\n",
       "      <td>G.I. Joe: Retaliation</td>\n",
       "      <td>[Action, Adventure, Sci-Fi, Thriller, IMAX]</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>6365</td>\n",
       "      <td>0.634146</td>\n",
       "      <td>Matrix Reloaded, The</td>\n",
       "      <td>[Action, Adventure, Sci-Fi, Thriller, IMAX]</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>85261</td>\n",
       "      <td>0.628049</td>\n",
       "      <td>Mars Needs Moms</td>\n",
       "      <td>[Action, Adventure, Animation, Children, Comed...</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>95165</td>\n",
       "      <td>0.621951</td>\n",
       "      <td>Dragon Ball Z the Movie: The World's Strongest...</td>\n",
       "      <td>[Action, Adventure, Animation, Sci-Fi, Thriller]</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>2985</td>\n",
       "      <td>0.621951</td>\n",
       "      <td>RoboCop</td>\n",
       "      <td>[Action, Crime, Drama, Sci-Fi, Thriller]</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>37462</td>\n",
       "      <td>0.621951</td>\n",
       "      <td>Wedlock</td>\n",
       "      <td>[Action, Crime, Drama, Sci-Fi, Thriller]</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>519</td>\n",
       "      <td>0.621951</td>\n",
       "      <td>RoboCop 3</td>\n",
       "      <td>[Action, Crime, Drama, Sci-Fi, Thriller]</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>36509</td>\n",
       "      <td>0.615854</td>\n",
       "      <td>Cave, The</td>\n",
       "      <td>[Action, Adventure, Horror, Mystery, Sci-Fi, T...</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>8780</td>\n",
       "      <td>0.609756</td>\n",
       "      <td>Assassination Bureau, The</td>\n",
       "      <td>[Action, Adventure, Comedy, Crime, Sci-Fi]</td>\n",
       "      <td>1969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>144350</td>\n",
       "      <td>0.603659</td>\n",
       "      <td>Under the Mountain</td>\n",
       "      <td>[Action, Adventure, Children, Drama, Fantasy, ...</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>140295</td>\n",
       "      <td>0.603659</td>\n",
       "      <td>Mobile Suit Gundam III: Encounters in Space</td>\n",
       "      <td>[Action, Adventure, Animation, Drama, Sci-Fi, ...</td>\n",
       "      <td>1982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>74271</td>\n",
       "      <td>0.603659</td>\n",
       "      <td>Space Pirate Captain Harlock: Arcadia of My Yo...</td>\n",
       "      <td>[Action, Adventure, Animation, Drama, Sci-Fi]</td>\n",
       "      <td>1982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>91535</td>\n",
       "      <td>0.603659</td>\n",
       "      <td>Bourne Legacy, The</td>\n",
       "      <td>[Action, Adventure, Drama, Thriller, IMAX]</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movieId  recommendation  \\\n",
       "0    115479        0.798780   \n",
       "1     59844        0.725610   \n",
       "2     79132        0.725610   \n",
       "3     81132        0.725610   \n",
       "4     67070        0.725610   \n",
       "5     26701        0.713415   \n",
       "6     91054        0.713415   \n",
       "7      2625        0.713415   \n",
       "8     91056        0.695122   \n",
       "9     26504        0.689024   \n",
       "10   105974        0.682927   \n",
       "11   122339        0.682927   \n",
       "12      459        0.682927   \n",
       "13   119603        0.682927   \n",
       "14   122787        0.682927   \n",
       "15    49530        0.682927   \n",
       "16     4620        0.682927   \n",
       "17    64645        0.682927   \n",
       "18    96601        0.682927   \n",
       "19     6016        0.682927   \n",
       "20    78729        0.682927   \n",
       "21     5018        0.664634   \n",
       "22      198        0.664634   \n",
       "23   108979        0.658537   \n",
       "24     8361        0.658537   \n",
       "25    71999        0.658537   \n",
       "26    58025        0.658537   \n",
       "27    27618        0.658537   \n",
       "28    48774        0.658537   \n",
       "29   141385        0.658537   \n",
       "30    91500        0.658537   \n",
       "31    91542        0.640244   \n",
       "32   112942        0.640244   \n",
       "33     8901        0.640244   \n",
       "34    52722        0.634146   \n",
       "35     6934        0.634146   \n",
       "36    77561        0.634146   \n",
       "37   101076        0.634146   \n",
       "38     6365        0.634146   \n",
       "39    85261        0.628049   \n",
       "40    95165        0.621951   \n",
       "41     2985        0.621951   \n",
       "42    37462        0.621951   \n",
       "43      519        0.621951   \n",
       "44    36509        0.615854   \n",
       "45     8780        0.609756   \n",
       "46   144350        0.603659   \n",
       "47   140295        0.603659   \n",
       "48    74271        0.603659   \n",
       "49    91535        0.603659   \n",
       "\n",
       "                                                title  \\\n",
       "0                                      Whip Hand, The   \n",
       "1   Honor Among Thieves (Adieu l'ami) (Farewell, F...   \n",
       "2                                           Inception   \n",
       "3                                              Rubber   \n",
       "4                           Army of One (Joshua Tree)   \n",
       "5   Patlabor: The Movie (Kidô keisatsu patorebâ: T...   \n",
       "6                                              Batman   \n",
       "7                                Black Mask (Hak hap)   \n",
       "8                                    Batman and Robin   \n",
       "9                                      Cloak & Dagger   \n",
       "10                                 Assault on a Queen   \n",
       "11                          Walking Tall: The Payback   \n",
       "12                                       Getaway, The   \n",
       "13                                       Down Twisted   \n",
       "14                                       The 39 Steps   \n",
       "15                                      Blood Diamond   \n",
       "16                                            Lock Up   \n",
       "17                                  The Wrecking Crew   \n",
       "18                                               Icon   \n",
       "19                       City of God (Cidade de Deus)   \n",
       "20                                     24: Redemption   \n",
       "21                                           Motorama   \n",
       "22                                       Strange Days   \n",
       "23                                       Cowboy Bebop   \n",
       "24                            Day After Tomorrow, The   \n",
       "25                 Aelita: The Queen of Mars (Aelita)   \n",
       "26                                             Jumper   \n",
       "27                                Sound of Thunder, A   \n",
       "28                                    Children of Men   \n",
       "29                                     Humanity's End   \n",
       "30                                  Hunger Games, The   \n",
       "31                 Sherlock Holmes: A Game of Shadows   \n",
       "32                                         Sky Murder   \n",
       "33                                         Flashpoint   \n",
       "34                                       Spider-Man 3   \n",
       "35                            Matrix Revolutions, The   \n",
       "36                                         Iron Man 2   \n",
       "37                              G.I. Joe: Retaliation   \n",
       "38                               Matrix Reloaded, The   \n",
       "39                                    Mars Needs Moms   \n",
       "40  Dragon Ball Z the Movie: The World's Strongest...   \n",
       "41                                            RoboCop   \n",
       "42                                            Wedlock   \n",
       "43                                          RoboCop 3   \n",
       "44                                          Cave, The   \n",
       "45                          Assassination Bureau, The   \n",
       "46                                 Under the Mountain   \n",
       "47        Mobile Suit Gundam III: Encounters in Space   \n",
       "48  Space Pirate Captain Harlock: Arcadia of My Yo...   \n",
       "49                                 Bourne Legacy, The   \n",
       "\n",
       "                                               genres  year  \n",
       "0   [Action, Adventure, Crime, Drama, Sci-Fi, Thri...  1951  \n",
       "1   [Action, Adventure, Crime, Drama, Mystery, Thr...  1968  \n",
       "2   [Action, Crime, Drama, Mystery, Sci-Fi, Thrill...  2010  \n",
       "3   [Action, Adventure, Comedy, Crime, Drama, Film...  2010  \n",
       "4   [Action, Adventure, Crime, Drama, Mystery, Thr...  1993  \n",
       "5   [Action, Animation, Crime, Drama, Film-Noir, M...  1989  \n",
       "6        [Action, Adventure, Crime, Sci-Fi, Thriller]  1943  \n",
       "7        [Action, Adventure, Crime, Sci-Fi, Thriller]  1996  \n",
       "8           [Action, Adventure, Crime, Drama, Sci-Fi]  1949  \n",
       "9   [Action, Adventure, Children, Crime, Mystery, ...  1984  \n",
       "10        [Action, Adventure, Crime, Drama, Thriller]  1966  \n",
       "11        [Action, Adventure, Crime, Drama, Thriller]  2007  \n",
       "12  [Action, Adventure, Crime, Drama, Romance, Thr...  1994  \n",
       "13        [Action, Adventure, Crime, Drama, Thriller]  1987  \n",
       "14  [Action, Adventure, Comedy, Crime, Drama, Thri...  1959  \n",
       "15   [Action, Adventure, Crime, Drama, Thriller, War]  2006  \n",
       "16        [Action, Adventure, Crime, Drama, Thriller]  1989  \n",
       "17  [Action, Adventure, Comedy, Crime, Drama, Thri...  1968  \n",
       "18        [Action, Adventure, Crime, Drama, Thriller]  2005  \n",
       "19        [Action, Adventure, Crime, Drama, Thriller]  2002  \n",
       "20        [Action, Adventure, Crime, Drama, Thriller]  2008  \n",
       "21  [Adventure, Comedy, Crime, Drama, Fantasy, Mys...  1991  \n",
       "22  [Action, Crime, Drama, Mystery, Sci-Fi, Thriller]  1995  \n",
       "23      [Action, Adventure, Animation, Crime, Sci-Fi]  1998  \n",
       "24       [Action, Adventure, Drama, Sci-Fi, Thriller]  2004  \n",
       "25  [Action, Adventure, Drama, Fantasy, Romance, S...  1924  \n",
       "26       [Action, Adventure, Drama, Sci-Fi, Thriller]  2008  \n",
       "27       [Action, Adventure, Drama, Sci-Fi, Thriller]  2005  \n",
       "28       [Action, Adventure, Drama, Sci-Fi, Thriller]  2006  \n",
       "29       [Action, Adventure, Drama, Sci-Fi, Thriller]  2009  \n",
       "30       [Action, Adventure, Drama, Sci-Fi, Thriller]  2012  \n",
       "31  [Action, Adventure, Comedy, Crime, Mystery, Th...  2011  \n",
       "32      [Action, Adventure, Crime, Mystery, Thriller]  1940  \n",
       "33      [Action, Adventure, Crime, Mystery, Thriller]  1984  \n",
       "34        [Action, Adventure, Sci-Fi, Thriller, IMAX]  2007  \n",
       "35        [Action, Adventure, Sci-Fi, Thriller, IMAX]  2003  \n",
       "36        [Action, Adventure, Sci-Fi, Thriller, IMAX]  2010  \n",
       "37        [Action, Adventure, Sci-Fi, Thriller, IMAX]  2013  \n",
       "38        [Action, Adventure, Sci-Fi, Thriller, IMAX]  2003  \n",
       "39  [Action, Adventure, Animation, Children, Comed...  2011  \n",
       "40   [Action, Adventure, Animation, Sci-Fi, Thriller]  1990  \n",
       "41           [Action, Crime, Drama, Sci-Fi, Thriller]  1987  \n",
       "42           [Action, Crime, Drama, Sci-Fi, Thriller]  1991  \n",
       "43           [Action, Crime, Drama, Sci-Fi, Thriller]  1993  \n",
       "44  [Action, Adventure, Horror, Mystery, Sci-Fi, T...  2005  \n",
       "45         [Action, Adventure, Comedy, Crime, Sci-Fi]  1969  \n",
       "46  [Action, Adventure, Children, Drama, Fantasy, ...  2009  \n",
       "47  [Action, Adventure, Animation, Drama, Sci-Fi, ...  1982  \n",
       "48      [Action, Adventure, Animation, Drama, Sci-Fi]  1982  \n",
       "49         [Action, Adventure, Drama, Thriller, IMAX]  2012  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommended_movies=recommended_movies.merge(how='left', on='movieId',right=movies_df)\n",
    "recommended_movies.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "recommended_movies.to_csv('girlfriend_movies.csv')"
   ]
  }
 ],
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