get_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BBA7w2XejewU"
   },
   "source": [
    "## Get the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The IMDb movies dataset has been downloaded from [Kaggle](https://www.kaggle.com/orgesleka/imdbmovies) and extracted to data/imdb.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TrI_QKA2cY5c"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# read the data\n",
    "IMDB_FILEPATH = 'data/imdb.csv'\n",
    "imdb_data = pd.read_csv(IMDB_FILEPATH, escapechar='\\\\')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 874
    },
    "colab_type": "code",
    "id": "RQcQ-CQOcgz3",
    "outputId": "af7af01b-499f-4b42-88c7-5240b96bb8cb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14761 entries, 0 to 14760\n",
      "Data columns (total 44 columns):\n",
      "fn                  14761 non-null object\n",
      "tid                 14761 non-null object\n",
      "title               14761 non-null object\n",
      "wordsInTitle        14748 non-null object\n",
      "url                 14761 non-null object\n",
      "imdbRating          13551 non-null float64\n",
      "ratingCount         13551 non-null float64\n",
      "duration            13745 non-null float64\n",
      "year                14731 non-null float64\n",
      "type                14761 non-null object\n",
      "nrOfWins            14761 non-null int64\n",
      "nrOfNominations     14761 non-null int64\n",
      "nrOfPhotos          14761 non-null int64\n",
      "nrOfNewsArticles    14761 non-null int64\n",
      "nrOfUserReviews     14761 non-null int64\n",
      "nrOfGenre           14761 non-null int64\n",
      "Action              14761 non-null int64\n",
      "Adult               14761 non-null int64\n",
      "Adventure           14761 non-null int64\n",
      "Animation           14761 non-null int64\n",
      "Biography           14761 non-null int64\n",
      "Comedy              14761 non-null int64\n",
      "Crime               14761 non-null int64\n",
      "Documentary         14761 non-null int64\n",
      "Drama               14761 non-null int64\n",
      "Family              14761 non-null int64\n",
      "Fantasy             14761 non-null int64\n",
      "FilmNoir            14761 non-null int64\n",
      "GameShow            14761 non-null int64\n",
      "History             14761 non-null int64\n",
      "Horror              14761 non-null int64\n",
      "Music               14761 non-null int64\n",
      "Musical             14761 non-null int64\n",
      "Mystery             14761 non-null int64\n",
      "News                14761 non-null int64\n",
      "RealityTV           14761 non-null int64\n",
      "Romance             14761 non-null int64\n",
      "SciFi               14761 non-null int64\n",
      "Short               14761 non-null int64\n",
      "Sport               14761 non-null int64\n",
      "TalkShow            14761 non-null int64\n",
      "Thriller            14761 non-null int64\n",
      "War                 14761 non-null int64\n",
      "Western             14761 non-null int64\n",
      "dtypes: float64(4), int64(34), object(6)\n",
      "memory usage: 5.0+ MB\n"
     ]
    }
   ],
   "source": [
    "imdb_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "id": "EdSGZMqplOh2",
    "outputId": "2e7752d9-e521-47f8-8dbb-9f5fe4e368ed"
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   "outputs": [
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       "      <th></th>\n",
       "      <th>fn</th>\n",
       "      <th>tid</th>\n",
       "      <th>title</th>\n",
       "      <th>wordsInTitle</th>\n",
       "      <th>url</th>\n",
       "      <th>imdbRating</th>\n",
       "      <th>ratingCount</th>\n",
       "      <th>duration</th>\n",
       "      <th>year</th>\n",
       "      <th>type</th>\n",
       "      <th>...</th>\n",
       "      <th>News</th>\n",
       "      <th>RealityTV</th>\n",
       "      <th>Romance</th>\n",
       "      <th>SciFi</th>\n",
       "      <th>Short</th>\n",
       "      <th>Sport</th>\n",
       "      <th>TalkShow</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>titles01/tt0012349</td>\n",
       "      <td>tt0012349</td>\n",
       "      <td>Der Vagabund und das Kind (1921)</td>\n",
       "      <td>der vagabund und das kind</td>\n",
       "      <td>http://www.imdb.com/title/tt0012349/</td>\n",
       "      <td>8.4</td>\n",
       "      <td>40550.0</td>\n",
       "      <td>3240.0</td>\n",
       "      <td>1921.0</td>\n",
       "      <td>video.movie</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>titles01/tt0015864</td>\n",
       "      <td>tt0015864</td>\n",
       "      <td>Goldrausch (1925)</td>\n",
       "      <td>goldrausch</td>\n",
       "      <td>http://www.imdb.com/title/tt0015864/</td>\n",
       "      <td>8.3</td>\n",
       "      <td>45319.0</td>\n",
       "      <td>5700.0</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>video.movie</td>\n",
       "      <td>...</td>\n",
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       "      <th>2</th>\n",
       "      <td>titles01/tt0017136</td>\n",
       "      <td>tt0017136</td>\n",
       "      <td>Metropolis (1927)</td>\n",
       "      <td>metropolis</td>\n",
       "      <td>http://www.imdb.com/title/tt0017136/</td>\n",
       "      <td>8.4</td>\n",
       "      <td>81007.0</td>\n",
       "      <td>9180.0</td>\n",
       "      <td>1927.0</td>\n",
       "      <td>video.movie</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>titles01/tt0017925</td>\n",
       "      <td>tt0017925</td>\n",
       "      <td>Der General (1926)</td>\n",
       "      <td>der general</td>\n",
       "      <td>http://www.imdb.com/title/tt0017925/</td>\n",
       "      <td>8.3</td>\n",
       "      <td>37521.0</td>\n",
       "      <td>6420.0</td>\n",
       "      <td>1926.0</td>\n",
       "      <td>video.movie</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>titles01/tt0021749</td>\n",
       "      <td>tt0021749</td>\n",
       "      <td>Lichter der Großstadt (1931)</td>\n",
       "      <td>lichter der gro stadt</td>\n",
       "      <td>http://www.imdb.com/title/tt0021749/</td>\n",
       "      <td>8.7</td>\n",
       "      <td>70057.0</td>\n",
       "      <td>5220.0</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>video.movie</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 44 columns</p>\n",
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      "text/plain": [
       "                   fn        tid                             title  \\\n",
       "0  titles01/tt0012349  tt0012349  Der Vagabund und das Kind (1921)   \n",
       "1  titles01/tt0015864  tt0015864                 Goldrausch (1925)   \n",
       "2  titles01/tt0017136  tt0017136                 Metropolis (1927)   \n",
       "3  titles01/tt0017925  tt0017925                Der General (1926)   \n",
       "4  titles01/tt0021749  tt0021749      Lichter der Großstadt (1931)   \n",
       "\n",
       "                wordsInTitle                                   url  \\\n",
       "0  der vagabund und das kind  http://www.imdb.com/title/tt0012349/   \n",
       "1                 goldrausch  http://www.imdb.com/title/tt0015864/   \n",
       "2                 metropolis  http://www.imdb.com/title/tt0017136/   \n",
       "3                der general  http://www.imdb.com/title/tt0017925/   \n",
       "4      lichter der gro stadt  http://www.imdb.com/title/tt0021749/   \n",
       "\n",
       "   imdbRating  ratingCount  duration    year         type  ...  News  \\\n",
       "0         8.4      40550.0    3240.0  1921.0  video.movie  ...     0   \n",
       "1         8.3      45319.0    5700.0  1925.0  video.movie  ...     0   \n",
       "2         8.4      81007.0    9180.0  1927.0  video.movie  ...     0   \n",
       "3         8.3      37521.0    6420.0  1926.0  video.movie  ...     0   \n",
       "4         8.7      70057.0    5220.0  1931.0  video.movie  ...     0   \n",
       "\n",
       "   RealityTV  Romance  SciFi  Short  Sport  TalkShow  Thriller  War  Western  \n",
       "0          0        0      0      0      0         0         0    0        0  \n",
       "1          0        0      0      0      0         0         0    0        0  \n",
       "2          0        0      1      0      0         0         0    0        0  \n",
       "3          0        0      0      0      0         0         0    0        0  \n",
       "4          0        1      0      0      0         0         0    0        0  \n",
       "\n",
       "[5 rows x 44 columns]"
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-vKnGyAekAjn"
   },
   "outputs": [],
   "source": [
    "# remove records with missing imdb rating\n",
    "imdb_data.dropna(subset=['imdbRating'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
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    "colab_type": "code",
    "id": "A5NrkJRTkjxo",
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       "13551"
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   "source": [
    "imdb_data.shape[0]"
   ]
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  {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>imdbRating</th>\n",
       "      <th>ratingCount</th>\n",
       "      <th>duration</th>\n",
       "      <th>year</th>\n",
       "      <th>nrOfWins</th>\n",
       "      <th>nrOfNominations</th>\n",
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       "      <th>Romance</th>\n",
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       "      <th>count</th>\n",
       "      <td>13551.000000</td>\n",
       "      <td>1.355100e+04</td>\n",
       "      <td>12793.000000</td>\n",
       "      <td>13551.000000</td>\n",
       "      <td>13551.000000</td>\n",
       "      <td>13551.000000</td>\n",
       "      <td>13551.000000</td>\n",
       "      <td>13551.000000</td>\n",
       "      <td>13551.000000</td>\n",
       "      <td>13551.000000</td>\n",
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       "      <td>13551.000000</td>\n",
       "      <td>13551.000000</td>\n",
       "      <td>13551.000000</td>\n",
       "      <td>13551.000000</td>\n",
       "      <td>13551.000000</td>\n",
       "      <td>13551.000000</td>\n",
       "      <td>13551.000000</td>\n",
       "      <td>13551.000000</td>\n",
       "      <td>13551.000000</td>\n",
       "      <td>13551.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.865707</td>\n",
       "      <td>2.642480e+04</td>\n",
       "      <td>5626.405378</td>\n",
       "      <td>1989.476496</td>\n",
       "      <td>3.365951</td>\n",
       "      <td>4.914545</td>\n",
       "      <td>25.635377</td>\n",
       "      <td>269.551989</td>\n",
       "      <td>112.456719</td>\n",
       "      <td>2.295107</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003985</td>\n",
       "      <td>0.006937</td>\n",
       "      <td>0.137702</td>\n",
       "      <td>0.070770</td>\n",
       "      <td>0.034167</td>\n",
       "      <td>0.016899</td>\n",
       "      <td>0.012471</td>\n",
       "      <td>0.096303</td>\n",
       "      <td>0.034831</td>\n",
       "      <td>0.023910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.119515</td>\n",
       "      <td>6.506456e+04</td>\n",
       "      <td>2702.125762</td>\n",
       "      <td>22.302556</td>\n",
       "      <td>9.581572</td>\n",
       "      <td>16.831365</td>\n",
       "      <td>67.742465</td>\n",
       "      <td>1092.792558</td>\n",
       "      <td>236.940988</td>\n",
       "      <td>0.794300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063003</td>\n",
       "      <td>0.083001</td>\n",
       "      <td>0.344600</td>\n",
       "      <td>0.256449</td>\n",
       "      <td>0.181665</td>\n",
       "      <td>0.128898</td>\n",
       "      <td>0.110981</td>\n",
       "      <td>0.295017</td>\n",
       "      <td>0.183359</td>\n",
       "      <td>0.152773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1888.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.300000</td>\n",
       "      <td>6.025000e+02</td>\n",
       "      <td>4680.000000</td>\n",
       "      <td>1978.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.977000e+03</td>\n",
       "      <td>5760.000000</td>\n",
       "      <td>1998.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.600000</td>\n",
       "      <td>2.085850e+04</td>\n",
       "      <td>6720.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>120.500000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.900000</td>\n",
       "      <td>1.183395e+06</td>\n",
       "      <td>68400.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>226.000000</td>\n",
       "      <td>542.000000</td>\n",
       "      <td>2810.000000</td>\n",
       "      <td>32345.000000</td>\n",
       "      <td>4928.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         imdbRating   ratingCount      duration          year      nrOfWins  \\\n",
       "count  13551.000000  1.355100e+04  12793.000000  13551.000000  13551.000000   \n",
       "mean       6.865707  2.642480e+04   5626.405378   1989.476496      3.365951   \n",
       "std        1.119515  6.506456e+04   2702.125762     22.302556      9.581572   \n",
       "min        1.000000  5.000000e+00      2.000000   1888.000000      0.000000   \n",
       "25%        6.300000  6.025000e+02   4680.000000   1978.000000      0.000000   \n",
       "50%        7.000000  3.977000e+03   5760.000000   1998.000000      0.000000   \n",
       "75%        7.600000  2.085850e+04   6720.000000   2006.000000      3.000000   \n",
       "max        9.900000  1.183395e+06  68400.000000   2014.000000    226.000000   \n",
       "\n",
       "       nrOfNominations    nrOfPhotos  nrOfNewsArticles  nrOfUserReviews  \\\n",
       "count     13551.000000  13551.000000      13551.000000     13551.000000   \n",
       "mean          4.914545     25.635377        269.551989       112.456719   \n",
       "std          16.831365     67.742465       1092.792558       236.940988   \n",
       "min           0.000000      0.000000          0.000000         0.000000   \n",
       "25%           0.000000      0.000000          0.000000         8.000000   \n",
       "50%           0.000000      9.000000         13.000000        37.000000   \n",
       "75%           4.000000     29.000000        120.500000       114.000000   \n",
       "max         542.000000   2810.000000      32345.000000      4928.000000   \n",
       "\n",
       "          nrOfGenre  ...          News     RealityTV       Romance  \\\n",
       "count  13551.000000  ...  13551.000000  13551.000000  13551.000000   \n",
       "mean       2.295107  ...      0.003985      0.006937      0.137702   \n",
       "std        0.794300  ...      0.063003      0.083001      0.344600   \n",
       "min        0.000000  ...      0.000000      0.000000      0.000000   \n",
       "25%        2.000000  ...      0.000000      0.000000      0.000000   \n",
       "50%        2.000000  ...      0.000000      0.000000      0.000000   \n",
       "75%        3.000000  ...      0.000000      0.000000      0.000000   \n",
       "max        3.000000  ...      1.000000      1.000000      1.000000   \n",
       "\n",
       "              SciFi         Short         Sport      TalkShow      Thriller  \\\n",
       "count  13551.000000  13551.000000  13551.000000  13551.000000  13551.000000   \n",
       "mean       0.070770      0.034167      0.016899      0.012471      0.096303   \n",
       "std        0.256449      0.181665      0.128898      0.110981      0.295017   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "                War       Western  \n",
       "count  13551.000000  13551.000000  \n",
       "mean       0.034831      0.023910  \n",
       "std        0.183359      0.152773  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.000000      0.000000  \n",
       "50%        0.000000      0.000000  \n",
       "75%        0.000000      0.000000  \n",
       "max        1.000000      1.000000  \n",
       "\n",
       "[8 rows x 38 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data into training and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7Gq7P8VbmugN"
   },
   "outputs": [],
   "source": [
    "from preprocessing_utils import stratify_split_imdb\n",
    "\n",
    "training_set, test_set = stratify_split_imdb(imdb_data, test_size=0.2, drop_cat=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "1G4Sz3sgODIy",
    "outputId": "b7991ef3-ee77-4ab3-e555-7178b7c55c66"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10840, 45) (2711, 45)\n"
     ]
    }
   ],
   "source": [
    "print(training_set.shape, test_set.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 585
    },
    "colab_type": "code",
    "id": "Nx2GvzR0OwAj",
    "outputId": "cb16d4da-dd2d-4575-bad6-5c1c1524d304"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "fig = plt.figure(figsize=(6, 8))\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training Set')\n",
    "training_set['imdbRating_cat'].hist()\n",
    "plt.xlabel('Rating Buckets')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Test Set')\n",
    "test_set['imdbRating_cat'].hist()\n",
    "plt.xlabel('Rating Buckets')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rCHL_7YFj2TC"
   },
   "outputs": [],
   "source": [
    "training_set = training_set.drop(columns=['imdbRating_cat'])\n",
    "test_set = test_set.drop(columns=['imdbRating_cat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "                             fn        tid  \\\n",
       "11613  titles04/index.html.4791  tt0104070   \n",
       "12627  titles04/index.html.6413  tt0167038   \n",
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       "\n",
       "                                      title                   wordsInTitle  \\\n",
       "11613          Der Tod steht ihr gut (1992)          der tod steht ihr gut   \n",
       "12627  Belles zauberhafte Welt (Video 1998)  belles zauberhafte welt video   \n",
       "2775         Ehemänner und Ehefrauen (1992)        ehem nner und ehefrauen   \n",
       "470                    Ronde de nuit (1984)                  ronde de nuit   \n",
       "11480                    Miami Blues (1990)                    miami blues   \n",
       "\n",
       "                                        url  imdbRating  ratingCount  \\\n",
       "11613  http://www.imdb.com/title/tt0104070/         6.4      64312.0   \n",
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       "\n",
       "       duration    year         type  ...  News  RealityTV  Romance  SciFi  \\\n",
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       "11480    5820.0  1990.0  video.movie  ...     0          0        0      0   \n",
       "\n",
       "       Short  Sport  TalkShow  Thriller  War  Western  \n",
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       "\n",
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      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAINING_FILEPATH = 'data/training_set.csv'\n",
    "TEST_FILEPATH = 'data/test_set.csv'\n",
    "\n",
    "training_set.to_csv(TRAINING_FILEPATH, index_label='index')\n",
    "test_set.to_csv(TEST_FILEPATH, index_label='index')"
   ]
  }
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