TMDB 데이터 전처리.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "pd.set_option('display.max_rows', 200)\n",
    "pd.set_option('display.max_columns', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv(\"train_columns_remove.csv\", engine='python', header=None)\n",
    "df_test = pd.read_csv(\"test_columns_remove.csv\", engine='python', header=None)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터셋의 칼럼리스트는 csv에서 바로 가져왔습니다 ㅎㅅㅎ;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train과 test 간의 데이터프레임 columns 수 맞춰주기 위해 일단 공갈 revenue를 df_test에 넣어줍니다\n",
    "\n",
    "df_test.insert(len(df_test.columns), 'revenue', np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_list = \"\"\"id,belongs_to_collection,budget,genres,homepage,imdb_id,original_language,original_title,overview,popularity,poster_path,production_companies,production_countries,release_date,runtime,spoken_languages,status,tagline,title,Keywords,cast,crew,revenue\"\"\".split(\",\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.columns = column_list\n",
    "df_test.columns = column_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "쓸모 없는 피쳐 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train.drop(['id', 'belongs_to_collection', 'homepage', 'imdb_id', 'original_language', 'original_title', 'overview', 'poster_path', 'production_companies', 'release_date', 'runtime', 'status', 'tagline', 'Keywords', 'crew'], axis=1)\n",
    "df_test = df_test.drop(['id', 'belongs_to_collection', 'homepage', 'imdb_id', 'original_language', 'original_title', 'overview', 'poster_path', 'production_companies', 'release_date', 'runtime', 'status', 'tagline', 'Keywords', 'crew'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test 데이터 null이 있는 행은 제거\n",
    "\n",
    "df_train = df_train.loc[~df_train['genres'].isna()]\n",
    "df_train = df_train.loc[~df_train['production_countries'].isna()]\n",
    "df_train = df_train.loc[~df_train['spoken_languages'].isna()]\n",
    "df_train = df_train.loc[~df_train['cast'].isna()]\n",
    "\n",
    "\n",
    "\n",
    "# Test 데이터 null이 있는 행은 제거\n",
    "\n",
    "df_test = df_test.loc[~df_test['genres'].isna()]\n",
    "df_test = df_test.loc[~df_test['production_countries'].isna()]\n",
    "df_test = df_test.loc[~df_test['spoken_languages'].isna()]\n",
    "df_test = df_test.loc[~df_test['cast'].isna()]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# null 제거 뒤 reindex\n",
    "\n",
    "df_train = df_train.reset_index().drop('index', axis = 1)\n",
    "df_test = df_test.reset_index().drop('index', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터가 모두 스트링 타입으로 기입되어 있다. 고유 데이터 타입으로 전환\n",
    "\n",
    "import ast\n",
    "\n",
    "def literal_return(val):\n",
    "    try:\n",
    "        return ast.literal_eval(val)\n",
    "    except (ValueError, SyntaxError) as e:\n",
    "        return val\n",
    "\n",
    "\n",
    "df_train['genres'] = df_train['genres'].apply(literal_return)\n",
    "df_train['production_countries'] = df_train['production_countries'].apply(literal_return)\n",
    "df_train['spoken_languages'] = df_train['spoken_languages'].apply(literal_return)\n",
    "df_train['cast'] = df_train['cast'].apply(literal_return)\n",
    "\n",
    "\n",
    "df_test['genres'] = df_test['genres'].apply(literal_return)\n",
    "df_test['production_countries'] = df_test['production_countries'].apply(literal_return)\n",
    "df_test['spoken_languages'] = df_test['spoken_languages'].apply(literal_return)\n",
    "df_test['cast'] = df_test['cast'].apply(literal_return)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 피쳐 다루기\n",
    "\n",
    "### production countries\n",
    "\n",
    "1. dict 벗겨내서 첫 번재 국가로 변환\n",
    "\n",
    "2. 북미, 유럽, 기타 국가로 카테고리 지정 (value_counts 를 통해 자주 나오는 국가만 임의로 취합했습니다)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['production_countries'] = pd.Series(item[0]['iso_3166_1'] for item in df_train['production_countries'])\n",
    "\n",
    "df_test['production_countries'] = pd.Series(item[0]['iso_3166_1'] for item in df_test['production_countries'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categrize_country(country):\n",
    "    if country in (['US', 'CA']):\n",
    "        return 'north_america'\n",
    "    elif country in (['GB', 'FR', 'DE', 'RU', 'ES', 'IT']):  # 영 프 독 러 스페인 이탈리아\n",
    "        return 'europe'\n",
    "    elif country in (['IN', 'in']):\n",
    "        return 'india'\n",
    "    else:\n",
    "        return 'etc'\n",
    "\n",
    "\n",
    "# 카테고리로 구분하는 칼럼 만들기\n",
    "\n",
    "df_train['production_counties_cate'] = df_train['production_countries'].apply(categrize_country)\n",
    "\n",
    "df_test['production_counties_cate'] = df_test['production_countries'].apply(categrize_country)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## spoken_languages\n",
    "\n",
    "1. production countries 칼럼과 비슷하지만 다름 (소문자 사용)\n",
    "2. 절대다수 영어이기 때문에 영어와 그 외로 구분"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['spoken_languages'] = pd.Series(item[0]['iso_639_1'] for item in df_train['spoken_languages'])\n",
    "\n",
    "df_test['spoken_languages'] = pd.Series(item[0]['iso_639_1'] for item in df_test['spoken_languages'])\n",
    "\n",
    "\n",
    "\n",
    "def categrize_language(lan):\n",
    "    if lan == 'en':\n",
    "        return 'en'\n",
    "    else:\n",
    "        return 'etc'\n",
    "\n",
    "\n",
    "\n",
    "# 카테고리로 구분하는 칼럼 만들기\n",
    "\n",
    "df_train['spoken_languages_cate'] = df_train['spoken_languages'].apply(categrize_language)\n",
    "\n",
    "\n",
    "df_test['spoken_languages_cate'] = df_test['spoken_languages'].apply(categrize_language)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 장르 dict 벗겨내서 인코딩 칼럼으로 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 장르를 원핫인코딩 형태로 칼럼으로 전환 (더미변수)\n",
    "\n",
    "\n",
    "for num, temp_list in enumerate(df_train['genres']):\n",
    "    for genre_dict in temp_list:\n",
    "        try:\n",
    "            df_train.insert(0, f\"genre_{genre_dict['name']}\", np.nan)\n",
    "            df_train.loc[df_train.index == num, f\"genre_{genre_dict['name']}\"] = 1\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "        \n",
    "for num, temp_list in enumerate(df_test['genres']):\n",
    "    for genre_dict in temp_list:\n",
    "        try:\n",
    "            df_test.insert(0, f\"genre_{genre_dict['name']}\", np.nan)\n",
    "            df_test.loc[df_test.index == num, f\"genre_{genre_dict['name']}\"] = 1\n",
    "        except:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>genre_TV Movie</th>\n",
       "      <th>genre_History</th>\n",
       "      <th>genre_Documentary</th>\n",
       "      <th>genre_Western</th>\n",
       "      <th>genre_War</th>\n",
       "      <th>genre_Fantasy</th>\n",
       "      <th>genre_Animation</th>\n",
       "      <th>genre_Foreign</th>\n",
       "      <th>genre_Mystery</th>\n",
       "      <th>genre_Science Fiction</th>\n",
       "      <th>genre_Crime</th>\n",
       "      <th>genre_Adventure</th>\n",
       "      <th>genre_Music</th>\n",
       "      <th>genre_Horror</th>\n",
       "      <th>genre_Action</th>\n",
       "      <th>genre_Thriller</th>\n",
       "      <th>genre_Romance</th>\n",
       "      <th>genre_Family</th>\n",
       "      <th>genre_Drama</th>\n",
       "      <th>genre_Comedy</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>popularity</th>\n",
       "      <th>production_countries</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>title</th>\n",
       "      <th>cast</th>\n",
       "      <th>revenue</th>\n",
       "      <th>production_counties_cate</th>\n",
       "      <th>spoken_languages_cate</th>\n",
       "      <th>budget_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14000000</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>6.575393</td>\n",
       "      <td>US</td>\n",
       "      <td>en</td>\n",
       "      <td>Hot Tub Time Machine 2</td>\n",
       "      <td>[{'cast_id': 4, 'character': 'Lou', 'credit_id...</td>\n",
       "      <td>12314651</td>\n",
       "      <td>north_america</td>\n",
       "      <td>en</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   genre_TV Movie  genre_History  genre_Documentary  genre_Western  genre_War  \\\n",
       "0             NaN            NaN                NaN            NaN        NaN   \n",
       "\n",
       "   genre_Fantasy  genre_Animation  genre_Foreign  genre_Mystery  \\\n",
       "0            NaN              NaN            NaN            NaN   \n",
       "\n",
       "   genre_Science Fiction  genre_Crime  genre_Adventure  genre_Music  \\\n",
       "0                    NaN          NaN              NaN          NaN   \n",
       "\n",
       "   genre_Horror  genre_Action  genre_Thriller  genre_Romance  genre_Family  \\\n",
       "0           NaN           NaN             NaN            NaN           NaN   \n",
       "\n",
       "   genre_Drama  genre_Comedy    budget                          genres  \\\n",
       "0          NaN           1.0  14000000  [{'id': 35, 'name': 'Comedy'}]   \n",
       "\n",
       "   popularity production_countries spoken_languages                   title  \\\n",
       "0    6.575393                   US               en  Hot Tub Time Machine 2   \n",
       "\n",
       "                                                cast   revenue  \\\n",
       "0  [{'cast_id': 4, 'character': 'Lou', 'credit_id...  12314651   \n",
       "\n",
       "  production_counties_cate spoken_languages_cate  budget_level  \n",
       "0            north_america                    en             2  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 코미디 장르인 1번 영화는 genre_Comedy 칼럼에 1.0 있고 나머진 NaN\n",
    "# 장르가 여러개면 여러 칼럼에 1.0 표기됨\n",
    "\n",
    "df_train.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 영화예산 (budget)으로 등급 만들기\n",
    "-> 회귀에 사용해도 되지만, 캐스팅 배우를 등급 나누기 위해 만들었습니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 영화 예산을 자체적으로 3등분\n",
    "# 상위 25% 는 3점 ... 이런 식으로\n",
    "\n",
    "def budget_level(budget):\n",
    "    budget_list = df_train['budget'].tolist()\n",
    "    q1, q3 = np.percentile(budget_list, [25, 75])\n",
    "    if budget > q3:\n",
    "        return 3\n",
    "    elif q1 < budget <= q3:\n",
    "        return 2\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "df_train['budget_level'] = df_train['budget'].apply(budget_level)\n",
    "\n",
    "df_test['budget_level'] = df_test['budget'].apply(budget_level)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 출연 배우 (cast) 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 캐스팅 배우 전처리 방식 1 - 일단 규모로 파악하기\n",
    "\n",
    "df_train['casting_size'] = pd.Series(len(temp_list) for temp_list in df_train['cast'])\n",
    "\n",
    "df_test['casting_size'] = pd.Series(len(temp_list) for temp_list in df_test['cast'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "캐스팅 배우 전처리 방식 2 - 비싼 영화에 출연했는지 여부로 배우 등급 먹이기\n",
    "\n",
    "\n",
    "step 1. cast의 dict를 벗겨내서 주요 출연자만 5명 가량 뽑히도록 리스트를 만들어줍니다 (def main_cast)\n",
    "\n",
    "step 2. budget level에 따라 비싼 영화에 출연한 배우 리스트, 중급 영화에 출연한 배우 리스트 등을 만듭니다\n",
    "\n",
    "\n",
    "step 3. 주요 출연진이 어느 등급에 속했는지에 따라 점수를 매겨 합해 채점합니다\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# step 1 메인 출연진 뽑아내기\n",
    "\n",
    "def main_cast(casts):\n",
    "\n",
    "    temp_main_cast_list = []\n",
    "    \n",
    "    if 5 < len(casts) < 15:\n",
    "        for order in range(int(len(casts)*0.5)):\n",
    "            temp_main_cast_list.append(casts[order]['name'])\n",
    "    elif 15 < len(casts) < 20:\n",
    "        for order in range(int(len(casts)*0.2)):\n",
    "            temp_main_cast_list.append(casts[order]['name'])\n",
    "    elif 20 < len(casts):\n",
    "        for order in range(int(len(casts)*0.2)):\n",
    "            temp_main_cast_list.append(casts[order]['name'])\n",
    "    else:\n",
    "        for order in range(len(casts)):\n",
    "            temp_main_cast_list.append(casts[order]['name'])\n",
    "    return temp_main_cast_list\n",
    "\n",
    "df_train['main_cast'] =  df_train['cast'].apply(main_cast)\n",
    "\n",
    "df_test['main_cast'] =  df_test['cast'].apply(main_cast)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# step 2 배우 등급 나누기\n",
    "\n",
    "high_level_actor = []\n",
    "for actor_list in df_train.loc[df_train['budget_level'] == 3, 'main_cast']:\n",
    "    for actor in actor_list:\n",
    "        high_level_actor.append(actor)\n",
    "        \n",
    "mid_level_actor = []\n",
    "for actor_list in df_train.loc[df_train['budget_level'] == 2, 'main_cast']:\n",
    "    for actor in actor_list:\n",
    "        high_level_actor.append(actor)\n",
    "\n",
    "low_level_actor = []\n",
    "for actor_list in df_train.loc[df_train['budget_level'] == 1, 'main_cast']:\n",
    "    for actor in actor_list:\n",
    "        high_level_actor.append(actor)\n",
    "\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# step 3 총점 합하기\n",
    "\n",
    "def train_cast_point(main_cast_list):\n",
    "    final_score = []\n",
    "    if len(main_cast_list) == 0:\n",
    "        return 0\n",
    "    else:\n",
    "        for actor in main_cast_list:\n",
    "            if actor in high_level_actor:\n",
    "                final_score.append(3)\n",
    "            elif actor in mid_level_actor:\n",
    "                final_score.append(2)\n",
    "            else:\n",
    "                final_score.append(1)\n",
    "\n",
    "        return sum(final_score)\n",
    "\n",
    "df_train['cast_score'] = df_train['main_cast'].apply(train_cast_point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_test에서도 마찬가지로 진행\n",
    "high_level_actor = []\n",
    "for actor_list in df_test.loc[df_test['budget_level'] == 3, 'main_cast']:\n",
    "    for actor in actor_list:\n",
    "        high_level_actor.append(actor)\n",
    "        \n",
    "mid_level_actor = []\n",
    "for actor_list in df_test.loc[df_test['budget_level'] == 2, 'main_cast']:\n",
    "    for actor in actor_list:\n",
    "        high_level_actor.append(actor)\n",
    "\n",
    "low_level_actor = []\n",
    "for actor_list in df_test.loc[df_test['budget_level'] == 1, 'main_cast']:\n",
    "    for actor in actor_list:\n",
    "        high_level_actor.append(actor)\n",
    "\n",
    "def test_cast_point(main_cast_list):\n",
    "    final_score = []\n",
    "    if len(main_cast_list) == 0:\n",
    "        return 0\n",
    "    else:\n",
    "        for actor in main_cast_list:\n",
    "            if actor in high_level_actor:\n",
    "                final_score.append(3)\n",
    "            elif actor in mid_level_actor:\n",
    "                final_score.append(2)\n",
    "            else:\n",
    "                final_score.append(1)\n",
    "\n",
    "        return sum(final_score)\n",
    "\n",
    "\n",
    "\n",
    "df_test['cast_score'] = df_test['main_cast'].apply(test_cast_point)\n",
    "df_test = df_test.drop(['genres', 'title', 'cast', 'spoken_languages', 'production_countries'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 쓸모없어진 칼럼 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train.drop(['genres', 'title', 'cast', 'spoken_languages', 'production_countries'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'revenue'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# revenue 칼럼이 타겟으로 가야하는데 어중간한 위치에 속해있어서 마지막 칼럼으로 옮겨주는 작업\n",
    "\n",
    "df_train.columns[22]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = df_train.columns.tolist()\n",
    "cols = cols[:21] + cols[23:] + [cols[22]]\n",
    "df_train = df_train[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 공갈로 만들어둔 df_test의 revenue도 이제 제거\n",
    "\n",
    "df_test = df_test.drop('revenue', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>40000000</td>\n",
       "      <td>north_america</td>\n",
       "      <td>en</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>[Anne Hathaway, Julie Andrews, H√©ctor Elizond...</td>\n",
       "      <td>60</td>\n",
       "      <td>95149435</td>\n",
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       "      <th>2</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3300000</td>\n",
       "      <td>north_america</td>\n",
       "      <td>en</td>\n",
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       "      <td>51</td>\n",
       "      <td>[Miles Teller, J.K. Simmons, Melissa Benoist, ...</td>\n",
       "      <td>30</td>\n",
       "      <td>13092000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>india</td>\n",
       "      <td>en</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>[Vidya Balan, Nawazuddin Siddiqui, Parambrata ...</td>\n",
       "      <td>9</td>\n",
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       "      <th>4</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>4</td>\n",
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       "      <td>12</td>\n",
       "      <td>3923970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   genre_TV Movie  genre_History  genre_Documentary  genre_Western  genre_War  \\\n",
       "0             NaN            NaN                NaN            NaN        NaN   \n",
       "1             NaN            NaN                NaN            NaN        NaN   \n",
       "2             NaN            NaN                NaN            NaN        NaN   \n",
       "3             NaN            NaN                NaN            NaN        NaN   \n",
       "4             NaN            NaN                NaN            NaN        NaN   \n",
       "\n",
       "   genre_Fantasy  genre_Animation  genre_Foreign  genre_Mystery  \\\n",
       "0            NaN              NaN            NaN            NaN   \n",
       "1            NaN              NaN            NaN            NaN   \n",
       "2            NaN              NaN            NaN            NaN   \n",
       "3            NaN              NaN            NaN            NaN   \n",
       "4            NaN              NaN            NaN            NaN   \n",
       "\n",
       "   genre_Science Fiction  genre_Crime  genre_Adventure  genre_Music  \\\n",
       "0                    NaN          NaN              NaN          NaN   \n",
       "1                    NaN          NaN              NaN          NaN   \n",
       "2                    NaN          NaN              NaN          NaN   \n",
       "3                    NaN          NaN              NaN          NaN   \n",
       "4                    NaN          NaN              NaN          NaN   \n",
       "\n",
       "   genre_Horror  genre_Action  genre_Thriller  genre_Romance  genre_Family  \\\n",
       "0           NaN           NaN             NaN            NaN           NaN   \n",
       "1           NaN           NaN             NaN            1.0           1.0   \n",
       "2           NaN           NaN             NaN            NaN           NaN   \n",
       "3           NaN           NaN             1.0            NaN           NaN   \n",
       "4           NaN           1.0             NaN            NaN           NaN   \n",
       "\n",
       "   genre_Drama  genre_Comedy    budget production_counties_cate  \\\n",
       "0          NaN           1.0  14000000            north_america   \n",
       "1          1.0           NaN  40000000            north_america   \n",
       "2          NaN           NaN   3300000            north_america   \n",
       "3          NaN           NaN   1200000                    india   \n",
       "4          NaN           NaN         0                      etc   \n",
       "\n",
       "  spoken_languages_cate  budget_level  casting_size  \\\n",
       "0                    en             2            24   \n",
       "1                    en             3            20   \n",
       "2                    en             2            51   \n",
       "3                    en             2             7   \n",
       "4                   etc             1             4   \n",
       "\n",
       "                                           main_cast  cast_score   revenue  \n",
       "0  [Rob Corddry, Craig Robinson, Clark Duke, Adam...          12  12314651  \n",
       "1  [Anne Hathaway, Julie Andrews, H√©ctor Elizond...          60  95149435  \n",
       "2  [Miles Teller, J.K. Simmons, Melissa Benoist, ...          30  13092000  \n",
       "3  [Vidya Balan, Nawazuddin Siddiqui, Parambrata ...           9  16000000  \n",
       "4  [Kim Kang-woo, Jo Jae-hyeon, Park Si-yeon, Kim...          12   3923970  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3.217680</td>\n",
       "      <td>north_america</td>\n",
       "      <td>en</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>[Dennis Hopper, Peter Bart, Warren Beatty, Car...</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   genre_Foreign  genre_Western  genre_Crime  genre_Music  genre_Action  \\\n",
       "0            NaN            NaN          NaN          NaN           NaN   \n",
       "1            NaN            NaN          NaN          NaN           NaN   \n",
       "2            NaN            NaN          NaN          NaN           NaN   \n",
       "3            NaN            NaN          NaN          NaN           NaN   \n",
       "4            NaN            NaN          NaN          NaN           NaN   \n",
       "\n",
       "   genre_Thriller  genre_Documentary  genre_History  genre_Mystery  genre_War  \\\n",
       "0             NaN                NaN            NaN            NaN        NaN   \n",
       "1             NaN                NaN            NaN            NaN        NaN   \n",
       "2             NaN                NaN            NaN            NaN        NaN   \n",
       "3             NaN                NaN            NaN            1.0        1.0   \n",
       "4             NaN                1.0            1.0            NaN        NaN   \n",
       "\n",
       "   genre_Drama  genre_Romance  genre_Comedy  genre_Science Fiction  \\\n",
       "0          NaN            NaN           NaN                    NaN   \n",
       "1          NaN            NaN           NaN                    1.0   \n",
       "2          NaN            1.0           1.0                    NaN   \n",
       "3          1.0            NaN           NaN                    NaN   \n",
       "4          NaN            NaN           NaN                    NaN   \n",
       "\n",
       "   genre_Horror  genre_Fantasy  genre_Family  genre_Animation  \\\n",
       "0           NaN            1.0           1.0              1.0   \n",
       "1           1.0            NaN           NaN              NaN   \n",
       "2           NaN            NaN           NaN              NaN   \n",
       "3           NaN            NaN           NaN              NaN   \n",
       "4           NaN            NaN           NaN              NaN   \n",
       "\n",
       "   genre_Adventure   budget  popularity production_counties_cate  \\\n",
       "0              1.0        0    3.851534                      etc   \n",
       "1              NaN    88000    3.559789            north_america   \n",
       "2              NaN        0    8.085194            north_america   \n",
       "3              NaN  6800000    8.596012            north_america   \n",
       "4              NaN  2000000    3.217680            north_america   \n",
       "\n",
       "  spoken_languages_cate  budget_level  casting_size  \\\n",
       "0                    en             1             7   \n",
       "1                    en             2            10   \n",
       "2                    en             1             9   \n",
       "3                   etc             2            23   \n",
       "4                    en             2             4   \n",
       "\n",
       "                                           main_cast  cast_score  \n",
       "0       [Craig Blair, Emily Bauer, Sarah Natochenny]           9  \n",
       "1  [Allison Hayes, William Hudson, Yvette Vickers...          15  \n",
       "2  [Meg Ryan, Matthew Broderick, Kelly Preston, T...          12  \n",
       "3  [Lubna Azabal, M√©lissa D√©sormeaux-Poulin, Ma...          12  \n",
       "4  [Dennis Hopper, Peter Bart, Warren Beatty, Car...          12  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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