movie_recommendation_data_processing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) Importations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import os\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_progress_bar(n_tot, n_prog) :\n",
    "    \"\"\"\n",
    "    A snippet of code to dispmay a progress bar for long operations\n",
    "    \"\"\"\n",
    "    from time import sleep\n",
    "    progress = math.ceil((n_prog + 1) * 100 / n_tot)\n",
    "    sys.stdout.write('\\r')\n",
    "    sys.stdout.write(\"[%-100s] %d%%\" %('='*progress, progress))\n",
    "    sys.stdout.flush()\n",
    "    sleep(0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting current path\n",
    "path = os.getcwd()\n",
    "\n",
    "# Verifying data presence\n",
    "try :\n",
    "    data_raw = pd.read_csv(path + \"/data/movie_metadata.csv\", sep=',')\n",
    "except FileNotFoundError :\n",
    "    print(\"Please check if the file 'movie_metadata.csv' is in the 'data' folder at the current location\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) Data first observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5043, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not to big of a dataset, what's in it ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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>color</th>\n",
       "      <th>director_name</th>\n",
       "      <th>num_critic_for_reviews</th>\n",
       "      <th>duration</th>\n",
       "      <th>director_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>gross</th>\n",
       "      <th>genres</th>\n",
       "      <th>...</th>\n",
       "      <th>num_user_for_reviews</th>\n",
       "      <th>language</th>\n",
       "      <th>country</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>budget</th>\n",
       "      <th>title_year</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>movie_facebook_likes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Color</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>723.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>Joel David Moore</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>760505847.0</td>\n",
       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
       "      <td>...</td>\n",
       "      <td>3054.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>237000000.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>1.78</td>\n",
       "      <td>33000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Color</td>\n",
       "      <td>Gore Verbinski</td>\n",
       "      <td>302.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>563.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Orlando Bloom</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>309404152.0</td>\n",
       "      <td>Action|Adventure|Fantasy</td>\n",
       "      <td>...</td>\n",
       "      <td>1238.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>300000000.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Color</td>\n",
       "      <td>Sam Mendes</td>\n",
       "      <td>602.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>Rory Kinnear</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>200074175.0</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "      <td>...</td>\n",
       "      <td>994.0</td>\n",
       "      <td>English</td>\n",
       "      <td>UK</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>245000000.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2.35</td>\n",
       "      <td>85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Color</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>813.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>Christian Bale</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>448130642.0</td>\n",
       "      <td>Action|Thriller</td>\n",
       "      <td>...</td>\n",
       "      <td>2701.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>2.35</td>\n",
       "      <td>164000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Doug Walker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rob Walker</td>\n",
       "      <td>131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</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>12.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   color      director_name  num_critic_for_reviews  duration  \\\n",
       "0  Color      James Cameron                   723.0     178.0   \n",
       "1  Color     Gore Verbinski                   302.0     169.0   \n",
       "2  Color         Sam Mendes                   602.0     148.0   \n",
       "3  Color  Christopher Nolan                   813.0     164.0   \n",
       "4    NaN        Doug Walker                     NaN       NaN   \n",
       "\n",
       "   director_facebook_likes  actor_3_facebook_likes      actor_2_name  \\\n",
       "0                      0.0                   855.0  Joel David Moore   \n",
       "1                    563.0                  1000.0     Orlando Bloom   \n",
       "2                      0.0                   161.0      Rory Kinnear   \n",
       "3                  22000.0                 23000.0    Christian Bale   \n",
       "4                    131.0                     NaN        Rob Walker   \n",
       "\n",
       "   actor_1_facebook_likes        gross                           genres  \\\n",
       "0                  1000.0  760505847.0  Action|Adventure|Fantasy|Sci-Fi   \n",
       "1                 40000.0  309404152.0         Action|Adventure|Fantasy   \n",
       "2                 11000.0  200074175.0        Action|Adventure|Thriller   \n",
       "3                 27000.0  448130642.0                  Action|Thriller   \n",
       "4                   131.0          NaN                      Documentary   \n",
       "\n",
       "          ...          num_user_for_reviews language  country  content_rating  \\\n",
       "0         ...                        3054.0  English      USA           PG-13   \n",
       "1         ...                        1238.0  English      USA           PG-13   \n",
       "2         ...                         994.0  English       UK           PG-13   \n",
       "3         ...                        2701.0  English      USA           PG-13   \n",
       "4         ...                           NaN      NaN      NaN             NaN   \n",
       "\n",
       "        budget  title_year actor_2_facebook_likes imdb_score  aspect_ratio  \\\n",
       "0  237000000.0      2009.0                  936.0        7.9          1.78   \n",
       "1  300000000.0      2007.0                 5000.0        7.1          2.35   \n",
       "2  245000000.0      2015.0                  393.0        6.8          2.35   \n",
       "3  250000000.0      2012.0                23000.0        8.5          2.35   \n",
       "4          NaN         NaN                   12.0        7.1           NaN   \n",
       "\n",
       "  movie_facebook_likes  \n",
       "0                33000  \n",
       "1                    0  \n",
       "2                85000  \n",
       "3               164000  \n",
       "4                    0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First four lines are whole, the fifth is very incomplete/gappy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Closer look to each feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "color : -'Color'- \n",
      "director_name : -'James Cameron'- \n",
      "num_critic_for_reviews : -723.0- \n",
      "duration : -178.0- \n",
      "director_facebook_likes : -0.0- \n",
      "actor_3_facebook_likes : -855.0- \n",
      "actor_2_name : -'Joel David Moore'- \n",
      "actor_1_facebook_likes : -1000.0- \n",
      "gross : -760505847.0- \n",
      "genres : -'Action|Adventure|Fantasy|Sci-Fi'- \n",
      "actor_1_name : -'CCH Pounder'- \n",
      "movie_title : -'Avatar\\xa0'- \n",
      "num_voted_users : -886204- \n",
      "cast_total_facebook_likes : -4834- \n",
      "actor_3_name : -'Wes Studi'- \n",
      "facenumber_in_poster : -0.0- \n",
      "plot_keywords : -'avatar|future|marine|native|paraplegic'- \n",
      "movie_imdb_link : -'http://www.imdb.com/title/tt0499549/?ref_=fn_tt_tt_1'- \n",
      "num_user_for_reviews : -3054.0- \n",
      "language : -'English'- \n",
      "country : -'USA'- \n",
      "content_rating : -'PG-13'- \n",
      "budget : -237000000.0- \n",
      "title_year : -2009.0- \n",
      "actor_2_facebook_likes : -936.0- \n",
      "imdb_score : -7.9- \n",
      "aspect_ratio : -1.78- \n",
      "movie_facebook_likes : -33000- \n"
     ]
    }
   ],
   "source": [
    "for feat in data_raw.columns :\n",
    "    print(\"%s : -%a- \" % (feat, data_raw.loc[0,feat]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wait, what's this '\\xa0' at the end of the movie's title ? Let's take some random samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "movie : -Mystery, Alaska -\n",
      "movie : -Eternal Sunshine of the Spotless Mind -\n",
      "movie : -This Is It -\n",
      "movie : -The Illusionist -\n",
      "movie : -Dancer in the Dark -\n"
     ]
    }
   ],
   "source": [
    "rands = np.random.randint(0,4000,5)\n",
    "for rand_samp in rands :\n",
    "    print(\"movie : -%s-\" % data_raw.loc[rand_samp, \"movie_title\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Weird, there is a space at the end of the name though"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mystery, Alaska\\xa0'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw.loc[rands[0], \"movie_title\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright, most-likely an encoding error, let's strip those"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_raw.loc[:, \"movie_title\"] = data_raw.loc[:, \"movie_title\"].apply(lambda x : x.strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mystery, Alaska'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw.loc[rands[0], \"movie_title\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Completely empty columns ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is 0 empty columns in this dataset\n"
     ]
    }
   ],
   "source": [
    "empty_col = data_raw.isnull().all(axis=0).sum()\n",
    "print(\"There is %i empty columns in this dataset\" % empty_col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Completely empty entries ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is 0 empty entry in this dataset\n"
     ]
    }
   ],
   "source": [
    "empty_entry = data_raw.isnull().all(axis=1).sum()\n",
    "print(\"There is %i empty entry in this dataset\" % empty_entry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good news"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obvious duplicates ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45, 28)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw[data_raw.duplicated()].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop obvious duplicates if any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4998, 28)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw = data_raw.drop_duplicates(keep='first')\n",
    "data_raw.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Movies duplicates ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82 duplicates sharing movie title\n"
     ]
    }
   ],
   "source": [
    "dup = data_raw[data_raw.duplicated(subset=[\"movie_title\"])]\n",
    "n_dup = dup.shape[0]\n",
    "print(\"%i duplicates sharing movie title\" % n_dup)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are some of those remakes ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79 duplicates sharing movie title and year, therefore 3 potential remakes\n"
     ]
    }
   ],
   "source": [
    "dups = data_raw[data_raw.duplicated(subset=[\"movie_title\", \"title_year\"])]\n",
    "n_dups = dups.shape[0]\n",
    "print(\"%i duplicates sharing movie title and year, therefore %i potential remakes\" % (n_dups, n_dup - n_dups))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Out of the Blue', 1980.0)\n",
      "('Out of the Blue', 2006.0)\n",
      "('The Dead Zone', 1983.0)\n",
      "('The Host', 2006.0)\n",
      "('The Host', 2013.0)\n"
     ]
    }
   ],
   "source": [
    "dup_all = data_raw[data_raw.duplicated(subset=[\"movie_title\"], keep=False)]\n",
    "# Grouping by title and year\n",
    "grouped = dup_all.groupby([\"movie_title\", \"title_year\"])\n",
    "# Looking for singletons : potential remakes\n",
    "for name,group in grouped:\n",
    "    if len(group.index) < 2 :\n",
    "        print(name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quick online check indicates that those are not remakes, but completely different movies. We'll keep them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove the duplicates of movie title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4919, 28)\n"
     ]
    }
   ],
   "source": [
    "data_raw = data_raw.drop_duplicates(subset=[\"movie_title\", \"title_year\"], keep='first')\n",
    "print(data_raw.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at our features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "color\n",
      "director_name\n",
      "num_critic_for_reviews\n",
      "duration\n",
      "director_facebook_likes\n",
      "actor_3_facebook_likes\n",
      "actor_2_name\n",
      "actor_1_facebook_likes\n",
      "gross\n",
      "genres\n",
      "actor_1_name\n",
      "movie_title\n",
      "num_voted_users\n",
      "cast_total_facebook_likes\n",
      "actor_3_name\n",
      "facenumber_in_poster\n",
      "plot_keywords\n",
      "movie_imdb_link\n",
      "num_user_for_reviews\n",
      "language\n",
      "country\n",
      "content_rating\n",
      "budget\n",
      "title_year\n",
      "actor_2_facebook_likes\n",
      "imdb_score\n",
      "aspect_ratio\n",
      "movie_facebook_likes\n"
     ]
    }
   ],
   "source": [
    "labels = data_raw.columns\n",
    "for lab in labels : print(lab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4) Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color                         19\n",
       "director_name                102\n",
       "num_critic_for_reviews        49\n",
       "duration                      15\n",
       "director_facebook_likes      102\n",
       "actor_3_facebook_likes        23\n",
       "actor_2_name                  13\n",
       "actor_1_facebook_likes         7\n",
       "gross                        863\n",
       "genres                         0\n",
       "actor_1_name                   7\n",
       "movie_title                    0\n",
       "num_voted_users                0\n",
       "cast_total_facebook_likes      0\n",
       "actor_3_name                  23\n",
       "facenumber_in_poster          13\n",
       "plot_keywords                152\n",
       "movie_imdb_link                0\n",
       "num_user_for_reviews          21\n",
       "language                      12\n",
       "country                        5\n",
       "content_rating               301\n",
       "budget                       485\n",
       "title_year                   106\n",
       "actor_2_facebook_likes        13\n",
       "imdb_score                     0\n",
       "aspect_ratio                 326\n",
       "movie_facebook_likes           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some looks related(names and facebook likes for instance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove features not of interest for our prediction tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pop the facebook likes as it's complecated to link those to their owner in our data\n",
    "data_raw = data_raw.drop(['director_facebook_likes'], axis=1)\n",
    "data_raw = data_raw.drop(['cast_total_facebook_likes'], axis=1)\n",
    "data_raw = data_raw.drop(['actor_1_facebook_likes', 'actor_2_facebook_likes', 'actor_3_facebook_likes'], axis=1)\n",
    "# Drop the budget, which won't be on the same scale according to the time period or the country (currency)\n",
    "data_raw = data_raw.drop(['budget'], axis=1)\n",
    "# Dropping other features\n",
    "data_raw = data_raw.drop(['aspect_ratio'], axis=1)\n",
    "data_raw = data_raw.drop(['facenumber_in_poster'], axis=1)\n",
    "# Dropping movie gross, as this is not a criteria for our recommendation tool (could be though)\n",
    "data_raw = data_raw.drop(['gross'], axis=1)\n",
    "# Content rating should more be used as a main filter, not as a recommendation parameter\n",
    "data_raw = data_raw.drop(['content_rating'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4919, 18)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color                      19\n",
       "director_name             102\n",
       "num_critic_for_reviews     49\n",
       "duration                   15\n",
       "actor_2_name               13\n",
       "genres                      0\n",
       "actor_1_name                7\n",
       "movie_title                 0\n",
       "num_voted_users             0\n",
       "actor_3_name               23\n",
       "plot_keywords             152\n",
       "movie_imdb_link             0\n",
       "num_user_for_reviews       21\n",
       "language                   12\n",
       "country                     5\n",
       "title_year                106\n",
       "imdb_score                  0\n",
       "movie_facebook_likes        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will tend to reduce the missing values to zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_values(df) :\n",
    "    \"\"\"\n",
    "    Function for dealing with each column's missing values in an appropriate way\n",
    "    Some errors are therefore induced to the database, but those are limited and \n",
    "    carefully taken into account\n",
    "    \"\"\"\n",
    "    \n",
    "    from sklearn.linear_model import LinearRegression\n",
    "    from sklearn import preprocessing\n",
    "    from scipy.optimize import curve_fit\n",
    "    \n",
    "    temp_df = df.copy()\n",
    "    n_rows_init = df.shape[0]\n",
    "    \n",
    "    for label in df.columns :    \n",
    "        \n",
    "        if label == 'color' :\n",
    "            print(\"Removing rows with color and title year missing\")        \n",
    "            # if the title year is missing as well as the color type : drop the row\n",
    "            temp_df = temp_df.dropna(how='all', subset=[label, 'title_year'])\n",
    "            \n",
    "            print(\"Imputing missing color values according to title_year (<1970 : B&W)\")   \n",
    "            # if the year is >= 1970 : color, else B&W\n",
    "            temp_df.loc[(temp_df[label].isna()) & (temp_df['title_year'] >= 1970), label] = \"Color\"\n",
    "            temp_df.loc[(temp_df[label].isna()) & (temp_df['title_year'] < 1970), label] = \"Black and White\"\n",
    "            \n",
    "        elif label == 'director_name' :\n",
    "            print(\"Removing rows with missing director's name\")   \n",
    "            # Remove those entries, sadly can't impute director's name\n",
    "            temp_df = temp_df.dropna(subset=[label])\n",
    "            \n",
    "        elif label in['actor_1_name', 'actor_2_name', 'actor_3_name'] :\n",
    "            print(\"Removing rows with missing actor's name\")   \n",
    "            # Remove those entries, sadly can't impute actor's name\n",
    "            temp_df = temp_df.dropna(subset=[label])\n",
    "            \n",
    "        elif label == 'num_critic_for_reviews' :\n",
    "            print(\"Imputing missing values of num_critic_for_reviews from linear regression with movie_facebook_likes\")   \n",
    "            # R2 score of 0.61\n",
    "            \n",
    "            clean_temp_df = df.dropna()\n",
    "            # Implementing linear regression\n",
    "            x = clean_temp_df['movie_facebook_likes'].values\n",
    "            y = clean_temp_df[label].values\n",
    "            x = x.reshape(len(x), 1)\n",
    "            y = y.reshape(len(x), 1)\n",
    "            regr = LinearRegression()\n",
    "            regr.fit(x, y)\n",
    "            # Imputing values\n",
    "            x_pred = temp_df.loc[temp_df[label].isna(), \"movie_facebook_likes\"].values\n",
    "            x_pred = x_pred.reshape(len(x_pred), 1)\n",
    "            temp_df.loc[temp_df[label].isna(), label] = regr.predict(x_pred)\n",
    "\n",
    "        elif label == 'duration' :\n",
    "            print(\"Replacing missing duration by average of durations\")\n",
    "            mean = temp_df[label].mean()\n",
    "            temp_df.loc[temp_df[label].isna(), label] = mean\n",
    "\n",
    "        elif label == 'gross' :\n",
    "            print(\"Replacing missing gross values by average of gross values <= 5e8 $\")\n",
    "            mean = temp_df[label][temp_df[label] <= 5e8].mean()\n",
    "            temp_df.loc[temp_df[label].isna(), label] = mean\n",
    "\n",
    "        elif label == 'genres' :\n",
    "            print(\"Removing rows with missing genres\")   \n",
    "            # Remove those entries, sadly can't impute genres of a movie\n",
    "            temp_df = temp_df.dropna(subset=[label])\n",
    "\n",
    "        elif label == 'movie_title' :\n",
    "            print(\"Removing rows with missing movie_title\")   \n",
    "            # Remove those entries, sadly can't impute movie_title\n",
    "            temp_df = temp_df.dropna(subset=[label])\n",
    "\n",
    "        elif label == 'num_voted_users' :\n",
    "            print(\"Replacing missing num_voted_users by average of num_voted_users\")\n",
    "            # Not mandatory here, but might need another look if new entries in the database\n",
    "            mean = temp_df[label].mean()\n",
    "            temp_df.loc[temp_df[label].isna(), label] = mean\n",
    "\n",
    "        elif label == 'num_user_for_reviews' :\n",
    "            print(\"Imputing missing values of num_user_for_reviews from linear regression with num_voted_users\")   \n",
    "            # R2 score of 0.60\n",
    "            \n",
    "            clean_temp_df = df.dropna()\n",
    "            # Implementing linear regression\n",
    "            x = clean_temp_df['num_voted_users'].values\n",
    "            y = clean_temp_df[label].values\n",
    "            x = x.reshape(len(x), 1)\n",
    "            y = y.reshape(len(x), 1)\n",
    "            regr = LinearRegression()\n",
    "            regr.fit(x, y)\n",
    "            # Imputing values\n",
    "            x_pred = temp_df.loc[temp_df[label].isna(), \"num_voted_users\"].values\n",
    "            x_pred = x_pred.reshape(len(x_pred), 1)\n",
    "            temp_df.loc[temp_df[label].isna(), label] = regr.predict(x_pred)\n",
    "\n",
    "        elif label == 'language' :\n",
    "            print(\"Removing rows with language and country missing\")        \n",
    "            # if the language is missing as well as the country : drop the row\n",
    "            temp_df = temp_df.dropna(how='all', subset=[label, 'country'])\n",
    "            \n",
    "            print(\"Imputing missing language values according to country (english only)\")   \n",
    "            # if the year is >= 1970 : color, else B&W\n",
    "            eng_countries = [\"UK\", \"USA\", \"Ireland\", \"New Zealand\", \"Australia\", \"Canada\"]\n",
    "            temp_df.loc[(temp_df[label].isna()) & (temp_df['country'].isin(eng_countries)), label] = \"English\"\n",
    "            \n",
    "            print(\"Removing remaining rows with language missing\") \n",
    "            temp_df = temp_df.dropna(subset=[label])\n",
    "\n",
    "        elif label == 'plot_keywords' :\n",
    "            print(\"Removing rows with missing plot_keywords\")   \n",
    "            # Remove those entries, sadly can't impute plot_keywords\n",
    "            temp_df = temp_df.dropna(subset=[label])\n",
    "            \n",
    "        elif label == 'country' :\n",
    "            print(\"Removing rows with missing country\")   \n",
    "            # Remove those entries, sadly can't impute country\n",
    "            temp_df = temp_df.dropna(subset=[label])\n",
    "            \n",
    "        elif label == 'title_year' :\n",
    "            print(\"Imputing missing values of title_year from logarithmic regression with movie_imdb_link\")   \n",
    "            # R2 score of 0.85\n",
    "            # Spearman of 0.98\n",
    "            \n",
    "            link_df = temp_df.loc[:, ['movie_imdb_link']]\n",
    "            \n",
    "            train_year_df = temp_df.dropna().loc[:, ['title_year']]\n",
    "            train_link_df = temp_df.dropna().loc[:, ['movie_imdb_link']]\n",
    "            \n",
    "            # Encoding categorial column\n",
    "            le = preprocessing.LabelEncoder()\n",
    "            le = le.fit(link_df.values.reshape(-1))\n",
    "            temp_df_encoded = link_df.apply(le.transform)\n",
    "            \n",
    "            x = train_link_df.apply(le.transform).values\n",
    "            y = train_year_df.values\n",
    "\n",
    "            data = np.column_stack([x, y])\n",
    "            data = data[data[:,0].argsort()]\n",
    "            x_data = data[:,0].transpose()\n",
    "            y_data = data[:,1].transpose()\n",
    "            \n",
    "            # define a function for fitting\n",
    "            def func(x, a, b, c, d):\n",
    "                return a * np.exp(-b * (x - c)) + d\n",
    "\n",
    "            # fit data and get fit parameters\n",
    "            popt, pcov = curve_fit(func, x_data, y_data)\n",
    "\n",
    "            # Imputing values\n",
    "            x_pred = temp_df_encoded.loc[temp_df[label].isna(), \"movie_imdb_link\"].values\n",
    "            x_pred = x_pred.reshape(len(x_pred), 1)\n",
    "            temp_df.loc[temp_df[label].isna(), label] = func(x_pred, *popt)\n",
    "\n",
    "        elif label == 'imdb_score' :\n",
    "            print(\"Replacing missing imdb_score by average of imdb_score\")\n",
    "            # Not mandatory here, but might need another look if new entries in the database\n",
    "            mean = temp_df[label].mean()\n",
    "            temp_df.loc[temp_df[label].isna(), label] = mean\n",
    "            \n",
    "    n_rows_end = temp_df.shape[0]\n",
    "    rows_deleted = n_rows_init - n_rows_end\n",
    "    print(\"%i rows have been deleted over %i\" % (rows_deleted, n_rows_init))\n",
    "    \n",
    "    return temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing rows with color and title year missing\n",
      "Imputing missing color values according to title_year (<1970 : B&W)\n",
      "Removing rows with missing director's name\n",
      "Imputing missing values of num_critic_for_reviews from linear regression with movie_facebook_likes\n",
      "Replacing missing duration by average of durations\n",
      "Removing rows with missing actor's name\n",
      "Removing rows with missing genres\n",
      "Removing rows with missing actor's name\n",
      "Removing rows with missing movie_title\n",
      "Replacing missing num_voted_users by average of num_voted_users\n",
      "Removing rows with missing actor's name\n",
      "Removing rows with missing plot_keywords\n",
      "Imputing missing values of num_user_for_reviews from linear regression with num_voted_users\n",
      "Removing rows with language and country missing\n",
      "Imputing missing language values according to country (english only)\n",
      "Removing remaining rows with language missing\n",
      "Removing rows with missing country\n",
      "Imputing missing values of title_year from logarithmic regression with movie_imdb_link\n",
      "Replacing missing imdb_score by average of imdb_score\n",
      "260 rows have been deleted over 4919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\simon\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:145: RuntimeWarning: overflow encountered in multiply\n"
     ]
    }
   ],
   "source": [
    "# Run missing values function\n",
    "data_no_mv = data_raw.copy()\n",
    "data_no_mv = missing_values(data_no_mv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of this for 260 values, still : no missing values at the end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color                     0\n",
       "director_name             0\n",
       "num_critic_for_reviews    0\n",
       "duration                  0\n",
       "actor_2_name              0\n",
       "genres                    0\n",
       "actor_1_name              0\n",
       "movie_title               0\n",
       "num_voted_users           0\n",
       "actor_3_name              0\n",
       "plot_keywords             0\n",
       "movie_imdb_link           0\n",
       "num_user_for_reviews      0\n",
       "language                  0\n",
       "country                   0\n",
       "title_year                0\n",
       "imdb_score                0\n",
       "movie_facebook_likes      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_no_mv.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we can drop the 'movie_imdb_link' feature\n",
    "data_no_mv = data_no_mv.drop(['movie_imdb_link'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5) Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if some outliers step out of the crowd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2880 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot by label\n",
    "fig = plt.figure(figsize=(20,40))\n",
    "num_df = data_no_mv.select_dtypes(['float64', 'int64'])\n",
    "for label, num in zip(num_df.columns, range(1,len(num_df.columns))) :    \n",
    "    data = data_no_mv[label].values    \n",
    "    ax = fig.add_subplot(11,3,num)\n",
    "    ax.plot(data)\n",
    "    ax.set_title(label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not so much : no treatment necessary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6) More Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove the space in front of \" Black and White\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_no_mv['color'] = data_no_mv['color'].str.strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reindex dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_no_mv = data_no_mv.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature engineering for genres : let's do some OneHot encoding manually. We expect one feature by genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 genres in the dataset\n"
     ]
    }
   ],
   "source": [
    "# Get splitted genres in a serie\n",
    "s = data_no_mv.genres.str.split(\"|\").values.tolist()\n",
    "# Get unique genres\n",
    "unique_genres = [genres_out for genres_in in s for genres_out in genres_in]\n",
    "unique_genres = set(unique_genres)\n",
    "unique_genres = list(unique_genres)\n",
    "print(\"%i genres in the dataset\" % len(unique_genres))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the genres df\n",
    "genres_df = pd.DataFrame(columns=unique_genres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def assign_genres(row, genre_column) :\n",
    "    \"\"\"\n",
    "    Short routine to populate the rows of the genre DF\n",
    "    \"\"\"\n",
    "    genre_found = 0\n",
    "    string_genres = row.loc['genres']\n",
    "    genres_list = string_genres.split(\"|\")\n",
    "    if genre_column in genres_list :\n",
    "        genre_found = 1\n",
    "    return genre_found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[====================================================================================================] 100%"
     ]
    }
   ],
   "source": [
    "# Assign genres to entries\n",
    "count = 0\n",
    "for genre in unique_genres :\n",
    "    genres_df.loc[:, genre] = data_no_mv.apply(assign_genres, axis=1, args=(genre,))\n",
    "    display_progress_bar(len(unique_genres), count)\n",
    "    count += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, have a look at our new genre df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Crime</th>\n",
       "      <th>Biography</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Family  Action  Animation  Western  Romance  Sport  Adventure  Comedy  War  \\\n",
       "0       0       1          0        0        0      0          1       0    0   \n",
       "1       0       1          0        0        0      0          1       0    0   \n",
       "2       0       1          0        0        0      0          1       0    0   \n",
       "3       0       1          0        0        0      0          0       0    0   \n",
       "4       0       1          0        0        0      0          1       0    0   \n",
       "\n",
       "   Drama    ...      History  Horror  Documentary  News  Music  Sci-Fi  \\\n",
       "0      0    ...            0       0            0     0      0       1   \n",
       "1      0    ...            0       0            0     0      0       0   \n",
       "2      0    ...            0       0            0     0      0       0   \n",
       "3      0    ...            0       0            0     0      0       0   \n",
       "4      0    ...            0       0            0     0      0       1   \n",
       "\n",
       "   Film-Noir  Thriller  Crime  Biography  \n",
       "0          0         0      0          0  \n",
       "1          0         0      0          0  \n",
       "2          0         1      0          0  \n",
       "3          0         1      0          0  \n",
       "4          0         0      0          0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genres_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sounds about right, let's check with our 5 first movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Action|Adventure|Fantasy|Sci-Fi\n",
       "1           Action|Adventure|Fantasy\n",
       "2          Action|Adventure|Thriller\n",
       "3                    Action|Thriller\n",
       "4            Action|Adventure|Sci-Fi\n",
       "Name: genres, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_no_mv.loc[:,\"genres\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature engineering for keywords : let's do some OneHot encoding manually. We expect one feature by keyword"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7963 keywords in the dataset\n"
     ]
    }
   ],
   "source": [
    "# Get splitted keywords in a serie\n",
    "s = data_no_mv.plot_keywords.str.split(\"|\").values.tolist()\n",
    "# Get unique keywords\n",
    "keywords = [keywords_out for keywords_in in s for keywords_out in keywords_in]\n",
    "unique_keywords = set(keywords)\n",
    "unique_keywords = list(unique_keywords)\n",
    "print(\"%i keywords in the dataset\" % len(unique_keywords))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['love', 'friend', 'murder', 'death', 'police', 'new york city',\n",
       "       'high school', 'alien', 'boy', 'school'], dtype='<U102')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get unique kw and counts\n",
    "kw_arr = np.array(keywords)\n",
    "kw_unique, kw_count = np.unique(kw_arr, return_counts=True)\n",
    "# Sort by count\n",
    "kw_id_sort = np.argsort(-kw_count)\n",
    "# Get kw sorted by counts\n",
    "kw_unique_sorted = kw_unique[kw_id_sort]\n",
    "# First 10 kw\n",
    "kw_unique_sorted[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use the first 100 keywords sorted by counts to avoid too many features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the keywords dataframe\n",
    "n_first_kw = 100\n",
    "kw_df = pd.DataFrame(columns=kw_unique_sorted[:n_first_kw])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def assign_kw(row, kw_column) :\n",
    "    \"\"\"\n",
    "    Short routine to populate the rows of the genre DF\n",
    "    \"\"\"\n",
    "    kw_found = 0\n",
    "    string_kw = row.loc['plot_keywords']\n",
    "    kw_list = string_kw.split(\"|\")\n",
    "    if kw_column in kw_list :\n",
    "        kw_found = 1\n",
    "    return kw_found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[====================================================================================================] 100%"
     ]
    }
   ],
   "source": [
    "# Assign most common 100 keywords to entries\n",
    "count = 0\n",
    "for kw in kw_unique_sorted[:n_first_kw] :\n",
    "    kw_df.loc[:, kw] = data_no_mv.apply(assign_kw, axis=1, args=(kw,))\n",
    "    display_progress_bar(len(kw_unique_sorted[:n_first_kw]), count)\n",
    "    count += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have a look"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "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>love</th>\n",
       "      <th>friend</th>\n",
       "      <th>murder</th>\n",
       "      <th>death</th>\n",
       "      <th>police</th>\n",
       "      <th>new york city</th>\n",
       "      <th>high school</th>\n",
       "      <th>alien</th>\n",
       "      <th>boy</th>\n",
       "      <th>school</th>\n",
       "      <th>...</th>\n",
       "      <th>african american</th>\n",
       "      <th>gang</th>\n",
       "      <th>family relationships</th>\n",
       "      <th>monster</th>\n",
       "      <th>martial arts</th>\n",
       "      <th>priest</th>\n",
       "      <th>nudity</th>\n",
       "      <th>military</th>\n",
       "      <th>russian</th>\n",
       "      <th>based on true story</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>...</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   love  friend  murder  death  police  new york city  high school  alien  \\\n",
       "0     0       0       0      0       0              0            0      0   \n",
       "1     0       0       0      0       0              0            0      0   \n",
       "2     0       0       0      0       0              0            0      0   \n",
       "3     0       0       0      0       0              0            0      0   \n",
       "4     0       0       0      0       0              0            0      1   \n",
       "\n",
       "   boy  school         ...           african american  gang  \\\n",
       "0    0       0         ...                          0     0   \n",
       "1    0       0         ...                          0     0   \n",
       "2    0       0         ...                          0     0   \n",
       "3    0       0         ...                          0     0   \n",
       "4    0       0         ...                          0     0   \n",
       "\n",
       "   family relationships  monster  martial arts  priest  nudity  military  \\\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",
       "\n",
       "   russian  based on true story  \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 100 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kw_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature engineering for actors : let's do some OneHot encoding manually. We expect one feature by actor's name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DF of lists for treatment, string for unique actors\n",
    "actors_df_init = pd.DataFrame()\n",
    "actors_df_init['actors'] = data_no_mv.loc[:,'actor_1_name'] + \"|\" + data_no_mv.loc[:,'actor_2_name'] + \"|\" + data_no_mv.loc[:,'actor_3_name']\n",
    "actors_features = ['actor_1_name', 'actor_2_name', 'actor_3_name']\n",
    "actors_df_init_lists = pd.DataFrame()\n",
    "actors_df_init_lists['actors'] = data_no_mv.loc[:,actors_features].apply(lambda x: list(x), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5841 actors in the dataset\n"
     ]
    }
   ],
   "source": [
    "# Get splitted actors in a serie\n",
    "s = actors_df_init.actors.str.split(\"|\").values.tolist()\n",
    "# Get unique actors\n",
    "unique_actors = [actors_out for actors_in in s for actors_out in actors_in]\n",
    "unique_actors = set(unique_actors)\n",
    "unique_actors = list(unique_actors)\n",
    "print(\"%i actors in the dataset\" % len(unique_actors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign actors to entries (very, very long)\n",
    "# Verifying data presence\n",
    "try :\n",
    "    actors_df = pd.read_csv(path + \"/data/actors_df.csv\", sep=',')\n",
    "except FileNotFoundError :\n",
    "    actors_df = pd.DataFrame(columns=unique_actors)\n",
    "    count = 0\n",
    "    for actor in unique_actors :\n",
    "        actors_df.loc[:, actor] = actors_df_init_lists.apply(lambda row : 1 if actor in row.values[0] else 0, axis=1)\n",
    "        display_progress_bar(len(unique_actors), count)\n",
    "        count += 1\n",
    "    # Save actors df (long to compute) for next time\n",
    "    actors_df.to_csv(\"data/actors_df.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Nelson Ascencio</th>\n",
       "      <th>Kelly Rowan</th>\n",
       "      <th>Julianna Guill</th>\n",
       "      <th>Adrian Paul</th>\n",
       "      <th>Corbin Allred</th>\n",
       "      <th>Tony Nappo</th>\n",
       "      <th>Amber Valletta</th>\n",
       "      <th>Eric Idle</th>\n",
       "      <th>Tim Daly</th>\n",
       "      <th>Ruth Sheen</th>\n",
       "      <th>...</th>\n",
       "      <th>Kelly Lynch</th>\n",
       "      <th>Rip Torn</th>\n",
       "      <th>Mackenzie Aladjem</th>\n",
       "      <th>Ella Wahlestedt</th>\n",
       "      <th>Jeff Garlin</th>\n",
       "      <th>Scott Adsit</th>\n",
       "      <th>Louise Fletcher</th>\n",
       "      <th>Rachel Crow</th>\n",
       "      <th>Hulk Hogan</th>\n",
       "      <th>Billy Brown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 5841 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Nelson Ascencio  Kelly Rowan  Julianna Guill  Adrian Paul  Corbin Allred  \\\n",
       "0                0            0               0            0              0   \n",
       "1                0            0               0            0              0   \n",
       "2                0            0               0            0              0   \n",
       "3                0            0               0            0              0   \n",
       "4                0            0               0            0              0   \n",
       "\n",
       "   Tony Nappo  Amber Valletta  Eric Idle  Tim Daly  Ruth Sheen     ...       \\\n",
       "0           0               0          0         0           0     ...        \n",
       "1           0               0          0         0           0     ...        \n",
       "2           0               0          0         0           0     ...        \n",
       "3           0               0          0         0           0     ...        \n",
       "4           0               0          0         0           0     ...        \n",
       "\n",
       "   Kelly Lynch  Rip Torn  Mackenzie Aladjem  Ella Wahlestedt  Jeff Garlin  \\\n",
       "0            0         0                  0                0            0   \n",
       "1            0         0                  0                0            0   \n",
       "2            0         0                  0                0            0   \n",
       "3            0         0                  0                0            0   \n",
       "4            0         0                  0                0            0   \n",
       "\n",
       "   Scott Adsit  Louise Fletcher  Rachel Crow  Hulk Hogan  Billy Brown  \n",
       "0            0                0            0           0            0  \n",
       "1            0                0            0           0            0  \n",
       "2            0                0            0           0            0  \n",
       "3            0                0            0           0            0  \n",
       "4            0                0            0           0            0  \n",
       "\n",
       "[5 rows x 5841 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actors_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assemble different DataFrames, shapes ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data :  (4659, 17)\n",
      "actors :  (4659, 5841)\n",
      "genres :  (4659, 24)\n",
      "keywords :  (4659, 100)\n"
     ]
    }
   ],
   "source": [
    "print(\"data : \", data_no_mv.shape)\n",
    "print(\"actors : \", actors_df.shape)\n",
    "print(\"genres : \", genres_df.shape)\n",
    "print(\"keywords : \", kw_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pop main df features\n",
    "data_new = data_no_mv.copy()\n",
    "data_new = data_new.drop(['genres'], axis=1)\n",
    "data_new = data_new.drop(['plot_keywords'], axis=1)\n",
    "data_new = data_new.drop(actors_features, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data :  (4659, 12)\n",
      "actors :  (4659, 5841)\n",
      "genres :  (4659, 24)\n",
      "keywords :  (4659, 100)\n"
     ]
    }
   ],
   "source": [
    "print(\"data : \", data_new.shape)\n",
    "print(\"actors : \", actors_df.shape)\n",
    "print(\"genres : \", genres_df.shape)\n",
    "print(\"keywords : \", kw_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data new :  (4659, 36)\n",
      "data new :  (4659, 136)\n",
      "data new :  (4659, 5977)\n"
     ]
    }
   ],
   "source": [
    "# Concatenate main df and genres df\n",
    "data_new = pd.concat([data_new, genres_df], axis=1)\n",
    "print(\"data new : \", data_new.shape)\n",
    "# Concatenate main df and plot_keywords df\n",
    "data_new = pd.concat([data_new, kw_df], axis=1)\n",
    "print(\"data new : \", data_new.shape)\n",
    "# Concatenate main df and actors df\n",
    "data_new = pd.concat([data_new, actors_df], axis=1)\n",
    "print(\"data new : \", data_new.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have a look at our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['color', 'director_name', 'num_critic_for_reviews', 'duration',\n",
       "       'movie_title', 'num_voted_users', 'num_user_for_reviews',\n",
       "       'language', 'country', 'title_year', 'imdb_score',\n",
       "       'movie_facebook_likes', 'Family', 'Action', 'Animation', 'Western',\n",
       "       'Romance', 'Sport', 'Adventure', 'Comedy', 'War', 'Drama',\n",
       "       'Mystery', 'Short', 'Fantasy'], dtype=object)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new.columns.values[:25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save reduced data for exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4659, 5977)\n"
     ]
    }
   ],
   "source": [
    "print(data_new.shape)\n",
    "data_new.to_csv(\"data/movie_recommendation_reduced_data.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save reduced data for deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_new.to_csv(\"data/movie_recommendation_tool_data.csv\")"
   ]
  }
 ],
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