feature selection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sys\n",
    "import csv\n",
    "from time import strptime\n",
    "import json\n",
    "from datetime import datetime as dt\n",
    "from datetime import date\n",
    "from statistics import mean\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/stas/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "csv.field_size_limit(sys.maxsize)\n",
    "np.set_printoptions(threshold=sys.maxsize)\n",
    "pd.options.display.max_columns = None\n",
    "pd.options.display.max_rows = 10000\n",
    "pd.set_option('display.max_colwidth', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_work_cols(df):\n",
    "    return df[[col for col in df.columns if not 'META' in col and not 'PROCESS' in col]]\n",
    "\n",
    "def prepare_X_y(df):\n",
    "    dff = get_work_cols(df)\n",
    "    min_prof = dff['profitability'].min()\n",
    "    dff['profitability_moved'] = dff['profitability'] - min_prof + 1\n",
    "    dff = dff.drop(['revenue','profitability'], axis=1)\n",
    "    \n",
    "    X = dff.drop('profitability_moved', axis=1)\n",
    "    X.fillna(X.mean(), inplace=True)\n",
    "    y = dff['profitability_moved']\n",
    "    \n",
    "    return X, y\n",
    "\n",
    "def normalize_df(df):\n",
    "    return (df - df.min())/(df.max() - df.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('15 topics.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['budget',\n",
       " 'META__id',\n",
       " 'PROCESS__original_language',\n",
       " 'revenue',\n",
       " 'runtime',\n",
       " 'spoken_languages',\n",
       " 'META__year',\n",
       " 'META__month',\n",
       " 'META__day',\n",
       " 'weekend',\n",
       " 'META__collection_name',\n",
       " 'META__cast_1_name',\n",
       " 'META__cast_2_name',\n",
       " 'META__cast_3_name',\n",
       " 'META__cast_4_name',\n",
       " 'META__cast_5_name',\n",
       " 'META__cast_6_name',\n",
       " 'META__cast_7_name',\n",
       " 'META__cast_8_name',\n",
       " 'META__crew__sound__music_editor',\n",
       " 'META__crew__sound__original_music_composer',\n",
       " 'META__crew__sound__sound_designer',\n",
       " 'META__crew__sound__sound_effects_editor',\n",
       " 'META__crew__sound__sound_re_recording_mixer',\n",
       " 'META__crew__sound__supervising_sound_editor',\n",
       " 'META__crew__directing__director__1',\n",
       " 'META__crew__directing__script_supervisor',\n",
       " 'META__crew__production__casting',\n",
       " 'META__crew__production__executive_producer__1',\n",
       " 'META__crew__production__producer__1',\n",
       " 'META__crew__production__producer__2',\n",
       " 'META__crew__editing__editor__1',\n",
       " 'META__crew__costume__costume_designer',\n",
       " 'META__crew__costume__costume_supervisor',\n",
       " 'META__crew__costume__makeup_artist',\n",
       " 'META__crew__crew__stunt_coordinator',\n",
       " 'META__crew__writing__screenplay__1',\n",
       " 'META__crew__art__art_direction',\n",
       " 'META__crew__art__production_design',\n",
       " 'META__crew__art__property_master',\n",
       " 'META__crew__art__set_decoration',\n",
       " 'META__crew__visualeffects__visual_effects_supervisor',\n",
       " 'META__crew__camera__director_of_photography',\n",
       " 'META__crew__camera__steadicam_operator',\n",
       " 'META__crew__camera__still_photographer',\n",
       " 'genre__war',\n",
       " 'genre__western',\n",
       " 'genre__mystery',\n",
       " 'genre__music',\n",
       " 'genre__crime',\n",
       " 'genre__romance',\n",
       " 'genre__action',\n",
       " 'genre__adventure',\n",
       " 'genre__thriller',\n",
       " 'genre__animation',\n",
       " 'genre__family',\n",
       " 'genre__drama',\n",
       " 'genre__comedy',\n",
       " 'genre__documentary',\n",
       " 'genre__history',\n",
       " 'genre__fantasy',\n",
       " 'genre__horror',\n",
       " 'META__production_company_1',\n",
       " 'META__production_company_2',\n",
       " 'META__production_company_3',\n",
       " 'country__rs',\n",
       " 'country__su',\n",
       " 'country__do',\n",
       " 'country__bo',\n",
       " 'country__ph',\n",
       " 'country__pe',\n",
       " 'country__id',\n",
       " 'country__gr',\n",
       " 'country__hn',\n",
       " 'country__nl',\n",
       " 'country__ee',\n",
       " 'country__ug',\n",
       " 'country__uy',\n",
       " 'country__ir',\n",
       " 'country__lb',\n",
       " 'country__pk',\n",
       " 'country__ua',\n",
       " 'country__eg',\n",
       " 'country__is',\n",
       " 'country__co',\n",
       " 'country__sk',\n",
       " 'country__bw',\n",
       " 'country__es',\n",
       " 'country__se',\n",
       " 'country__jp',\n",
       " 'country__np',\n",
       " 'country__tw',\n",
       " 'country__ie',\n",
       " 'country__lu',\n",
       " 'country__us',\n",
       " 'country__vn',\n",
       " 'country__ro',\n",
       " 'country__mn',\n",
       " 'country__cl',\n",
       " 'country__hu',\n",
       " 'country__br',\n",
       " 'country__ae',\n",
       " 'country__kr',\n",
       " 'country__be',\n",
       " 'country__ca',\n",
       " 'country__sg',\n",
       " 'country__bg',\n",
       " 'country__de',\n",
       " 'country__pl',\n",
       " 'country__mv',\n",
       " 'country__cn',\n",
       " 'country__in',\n",
       " 'country__th',\n",
       " 'country__fr',\n",
       " 'country__ru',\n",
       " 'country__dk',\n",
       " 'country__af',\n",
       " 'country__xg',\n",
       " 'country__it',\n",
       " 'country__ge',\n",
       " 'country__tr',\n",
       " 'country__za',\n",
       " 'country__ve',\n",
       " 'country__lk',\n",
       " 'country__hr',\n",
       " 'country__ng',\n",
       " 'country__ar',\n",
       " 'country__ba',\n",
       " 'country__il',\n",
       " 'country__mx',\n",
       " 'country__bd',\n",
       " 'country__my',\n",
       " 'country__nz',\n",
       " 'country__no',\n",
       " 'country__cz',\n",
       " 'country__hk',\n",
       " 'country__au',\n",
       " 'country__gb',\n",
       " 'country__lv',\n",
       " 'country__fi',\n",
       " 'profitability',\n",
       " 'day_sin',\n",
       " 'day_cos',\n",
       " 'month_sin',\n",
       " 'month_cos',\n",
       " 'cast_1_avg_profit',\n",
       " 'cast_1_avg_budget',\n",
       " 'cast_1_experience',\n",
       " 'cast_1_avg_profitability',\n",
       " 'cast_1_movies_before',\n",
       " 'cast_2_avg_profit',\n",
       " 'cast_2_avg_budget',\n",
       " 'cast_2_experience',\n",
       " 'cast_2_avg_profitability',\n",
       " 'cast_2_movies_before',\n",
       " 'cast_3_avg_profit',\n",
       " 'cast_3_avg_budget',\n",
       " 'cast_3_experience',\n",
       " 'cast_3_avg_profitability',\n",
       " 'cast_3_movies_before',\n",
       " 'cast_4_avg_profit',\n",
       " 'cast_4_avg_budget',\n",
       " 'cast_4_experience',\n",
       " 'cast_4_avg_profitability',\n",
       " 'cast_4_movies_before',\n",
       " 'cast_5_avg_profit',\n",
       " 'cast_5_avg_budget',\n",
       " 'cast_5_experience',\n",
       " 'cast_5_avg_profitability',\n",
       " 'cast_5_movies_before',\n",
       " 'cast_6_avg_profit',\n",
       " 'cast_6_avg_budget',\n",
       " 'cast_6_experience',\n",
       " 'cast_6_avg_profitability',\n",
       " 'cast_6_movies_before',\n",
       " 'cast_7_avg_profit',\n",
       " 'cast_7_avg_budget',\n",
       " 'cast_7_experience',\n",
       " 'cast_7_avg_profitability',\n",
       " 'cast_7_movies_before',\n",
       " 'cast_8_avg_profit',\n",
       " 'cast_8_avg_budget',\n",
       " 'cast_8_experience',\n",
       " 'cast_8_avg_profitability',\n",
       " 'cast_8_movies_before',\n",
       " 'cast_1_male',\n",
       " 'cast_1_female',\n",
       " 'cast_2_male',\n",
       " 'cast_2_female',\n",
       " 'cast_3_male',\n",
       " 'cast_3_female',\n",
       " 'cast_4_male',\n",
       " 'cast_4_female',\n",
       " 'cast_5_male',\n",
       " 'cast_5_female',\n",
       " 'cast_6_male',\n",
       " 'cast_6_female',\n",
       " 'cast_7_male',\n",
       " 'cast_7_female',\n",
       " 'cast_8_male',\n",
       " 'cast_8_female',\n",
       " 'crew__sound__music_editor_avg_profit',\n",
       " 'crew__sound__music_editor_avg_budget',\n",
       " 'crew__sound__music_editor_movies_before',\n",
       " 'crew__sound__original_music_composer_avg_profit',\n",
       " 'crew__sound__original_music_composer_avg_budget',\n",
       " 'crew__sound__original_music_composer_movies_before',\n",
       " 'crew__sound__sound_designer_avg_profit',\n",
       " 'crew__sound__sound_designer_avg_budget',\n",
       " 'crew__sound__sound_designer_movies_before',\n",
       " 'crew__sound__sound_effects_editor_avg_profit',\n",
       " 'crew__sound__sound_effects_editor_avg_budget',\n",
       " 'crew__sound__sound_effects_editor_movies_before',\n",
       " 'crew__sound__sound_re_recording_mixer_avg_profit',\n",
       " 'crew__sound__sound_re_recording_mixer_avg_budget',\n",
       " 'crew__sound__sound_re_recording_mixer_movies_before',\n",
       " 'crew__sound__supervising_sound_editor_avg_profit',\n",
       " 'crew__sound__supervising_sound_editor_avg_budget',\n",
       " 'crew__sound__supervising_sound_editor_movies_before',\n",
       " 'crew__directing__director__1_avg_profit',\n",
       " 'crew__directing__director__1_avg_budget',\n",
       " 'crew__directing__director__1_movies_before',\n",
       " 'crew__directing__script_supervisor_avg_profit',\n",
       " 'crew__directing__script_supervisor_avg_budget',\n",
       " 'crew__directing__script_supervisor_movies_before',\n",
       " 'crew__production__casting_avg_profit',\n",
       " 'crew__production__casting_avg_budget',\n",
       " 'crew__production__casting_movies_before',\n",
       " 'crew__production__executive_producer__1_avg_profit',\n",
       " 'crew__production__executive_producer__1_avg_budget',\n",
       " 'crew__production__executive_producer__1_movies_before',\n",
       " 'crew__production__producer__1_avg_profit',\n",
       " 'crew__production__producer__1_avg_budget',\n",
       " 'crew__production__producer__1_movies_before',\n",
       " 'crew__production__producer__2_avg_profit',\n",
       " 'crew__production__producer__2_avg_budget',\n",
       " 'crew__production__producer__2_movies_before',\n",
       " 'crew__editing__editor__1_avg_profit',\n",
       " 'crew__editing__editor__1_avg_budget',\n",
       " 'crew__editing__editor__1_movies_before',\n",
       " 'crew__costume__costume_designer_avg_profit',\n",
       " 'crew__costume__costume_designer_avg_budget',\n",
       " 'crew__costume__costume_designer_movies_before',\n",
       " 'crew__costume__costume_supervisor_avg_profit',\n",
       " 'crew__costume__costume_supervisor_avg_budget',\n",
       " 'crew__costume__costume_supervisor_movies_before',\n",
       " 'crew__costume__makeup_artist_avg_profit',\n",
       " 'crew__costume__makeup_artist_avg_budget',\n",
       " 'crew__costume__makeup_artist_movies_before',\n",
       " 'crew__crew__stunt_coordinator_avg_profit',\n",
       " 'crew__crew__stunt_coordinator_avg_budget',\n",
       " 'crew__crew__stunt_coordinator_movies_before',\n",
       " 'crew__writing__screenplay__1_avg_profit',\n",
       " 'crew__writing__screenplay__1_avg_budget',\n",
       " 'crew__writing__screenplay__1_movies_before',\n",
       " 'crew__art__art_direction_avg_profit',\n",
       " 'crew__art__art_direction_avg_budget',\n",
       " 'crew__art__art_direction_movies_before',\n",
       " 'crew__art__production_design_avg_profit',\n",
       " 'crew__art__production_design_avg_budget',\n",
       " 'crew__art__production_design_movies_before',\n",
       " 'crew__art__property_master_avg_profit',\n",
       " 'crew__art__property_master_avg_budget',\n",
       " 'crew__art__property_master_movies_before',\n",
       " 'crew__art__set_decoration_avg_profit',\n",
       " 'crew__art__set_decoration_avg_budget',\n",
       " 'crew__art__set_decoration_movies_before',\n",
       " 'crew__visualeffects__visual_effects_supervisor_avg_profit',\n",
       " 'crew__visualeffects__visual_effects_supervisor_avg_budget',\n",
       " 'crew__visualeffects__visual_effects_supervisor_movies_before',\n",
       " 'crew__camera__director_of_photography_avg_profit',\n",
       " 'crew__camera__director_of_photography_avg_budget',\n",
       " 'crew__camera__director_of_photography_movies_before',\n",
       " 'crew__camera__steadicam_operator_avg_profit',\n",
       " 'crew__camera__steadicam_operator_avg_budget',\n",
       " 'crew__camera__steadicam_operator_movies_before',\n",
       " 'crew__camera__still_photographer_avg_profit',\n",
       " 'crew__camera__still_photographer_avg_budget',\n",
       " 'crew__camera__still_photographer_movies_before',\n",
       " 'cast_avg_budget',\n",
       " 'cast_avg_profit',\n",
       " 'cast_avg_experience',\n",
       " 'cast_avg_movies_before',\n",
       " 'year_avg_profitability',\n",
       " 'year_avg_revenue',\n",
       " 'production_company_1_avg_profit',\n",
       " 'production_company_1_avg_profitability',\n",
       " 'production_company_1_avg_revenue',\n",
       " 'production_company_2_avg_profit',\n",
       " 'production_company_2_avg_profitability',\n",
       " 'production_company_2_avg_revenue',\n",
       " 'production_company_3_avg_profit',\n",
       " 'production_company_3_avg_profitability',\n",
       " 'production_company_3_avg_revenue',\n",
       " 'collection_name_avg_profit',\n",
       " 'collection_name_avg_revenue',\n",
       " 'competition',\n",
       " 'rating__g',\n",
       " 'rating__pg',\n",
       " 'rating__pg-13',\n",
       " 'rating__r',\n",
       " 'rating__nc-17',\n",
       " 'topic_0',\n",
       " 'topic_1',\n",
       " 'topic_2',\n",
       " 'topic_3',\n",
       " 'topic_4',\n",
       " 'topic_5',\n",
       " 'topic_6',\n",
       " 'topic_7',\n",
       " 'topic_8',\n",
       " 'topic_9',\n",
       " 'topic_10',\n",
       " 'topic_11',\n",
       " 'topic_12',\n",
       " 'topic_13',\n",
       " 'topic_14',\n",
       " 'META__original_title',\n",
       " 'META__title',\n",
       " 'META__keywords',\n",
       " 'META__plot',\n",
       " 'META__topic_dominant',\n",
       " 'META__topic_words']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "budget\n",
      "nan: 0\t0.0%\n",
      "\n",
      "revenue\n",
      "nan: 0\t0.0%\n",
      "\n",
      "runtime\n",
      "nan: 0\t0.0%\n",
      "\n",
      "spoken_languages\n",
      "nan: 0\t0.0%\n",
      "\n",
      "weekend\n",
      "nan: 0\t0.0%\n",
      "\n",
      "genre__war\n",
      "nan: 0\t0.0%\n",
      "\n",
      "genre__western\n",
      "nan: 0\t0.0%\n",
      "\n",
      "genre__mystery\n",
      "nan: 0\t0.0%\n",
      "\n",
      "genre__music\n",
      "nan: 0\t0.0%\n",
      "\n",
      "genre__crime\n",
      "nan: 0\t0.0%\n",
      "\n",
      "genre__romance\n",
      "nan: 0\t0.0%\n",
      "\n",
      "genre__action\n",
      "nan: 0\t0.0%\n",
      "\n",
      "genre__adventure\n",
      "nan: 0\t0.0%\n",
      "\n",
      "genre__thriller\n",
      "nan: 0\t0.0%\n",
      "\n",
      "genre__animation\n",
      "nan: 0\t0.0%\n",
      "\n",
      "genre__family\n",
      "nan: 0\t0.0%\n",
      "\n",
      "genre__drama\n",
      "nan: 0\t0.0%\n",
      "\n",
      "genre__comedy\n",
      "nan: 0\t0.0%\n",
      "\n",
      "genre__documentary\n",
      "nan: 0\t0.0%\n",
      "\n",
      "genre__history\n",
      "nan: 0\t0.0%\n",
      "\n",
      "genre__fantasy\n",
      "nan: 0\t0.0%\n",
      "\n",
      "genre__horror\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__rs\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__su\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__do\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__bo\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__ph\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__pe\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__id\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__gr\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__hn\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__nl\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__ee\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__ug\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__uy\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__ir\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__lb\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__pk\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__ua\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__eg\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__is\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__co\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__sk\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__bw\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__es\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__se\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__jp\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__np\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__tw\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__ie\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__lu\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__us\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__vn\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__ro\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__mn\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__cl\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__hu\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__br\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__ae\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__kr\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__be\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__ca\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__sg\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__bg\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__de\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__pl\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__mv\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__cn\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__in\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__th\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__fr\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__ru\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__dk\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__af\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__xg\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__it\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__ge\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__tr\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__za\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__ve\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__lk\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__hr\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__ng\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__ar\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__ba\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__il\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__mx\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__bd\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__my\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__nz\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__no\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__cz\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__hk\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__au\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__gb\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__lv\n",
      "nan: 0\t0.0%\n",
      "\n",
      "country__fi\n",
      "nan: 0\t0.0%\n",
      "\n",
      "profitability\n",
      "nan: 0\t0.0%\n",
      "\n",
      "day_sin\n",
      "nan: 0\t0.0%\n",
      "\n",
      "day_cos\n",
      "nan: 0\t0.0%\n",
      "\n",
      "month_sin\n",
      "nan: 0\t0.0%\n",
      "\n",
      "month_cos\n",
      "nan: 0\t0.0%\n",
      "\n",
      "cast_1_avg_profit\n",
      "nan: 1693\t0.22591406458500132%\n",
      "\n",
      "cast_1_avg_budget\n",
      "nan: 1693\t0.22591406458500132%\n",
      "\n",
      "cast_1_experience\n",
      "nan: 1\t0.00013344008540165466%\n",
      "\n",
      "cast_1_avg_profitability\n",
      "nan: 1693\t0.22591406458500132%\n",
      "\n",
      "cast_1_movies_before\n",
      "nan: 1693\t0.22591406458500132%\n",
      "\n",
      "cast_2_avg_profit\n",
      "nan: 2180\t0.2908993861756072%\n",
      "\n",
      "cast_2_avg_budget\n",
      "nan: 2180\t0.2908993861756072%\n",
      "\n",
      "cast_2_experience\n",
      "nan: 14\t0.0018681611956231651%\n",
      "\n",
      "cast_2_avg_profitability\n",
      "nan: 2180\t0.2908993861756072%\n",
      "\n",
      "cast_2_movies_before\n",
      "nan: 2180\t0.2908993861756072%\n",
      "\n",
      "cast_3_avg_profit\n",
      "nan: 2562\t0.3418734987990392%\n",
      "\n",
      "cast_3_avg_budget\n",
      "nan: 2562\t0.3418734987990392%\n",
      "\n",
      "cast_3_experience\n",
      "nan: 37\t0.004937283159861222%\n",
      "\n",
      "cast_3_avg_profitability\n",
      "nan: 2562\t0.3418734987990392%\n",
      "\n",
      "cast_3_movies_before\n",
      "nan: 2562\t0.3418734987990392%\n",
      "\n",
      "cast_4_avg_profit\n",
      "nan: 2944\t0.3928476114224713%\n",
      "\n",
      "cast_4_avg_budget\n",
      "nan: 2944\t0.3928476114224713%\n",
      "\n",
      "cast_4_experience\n",
      "nan: 76\t0.010141446490525754%\n",
      "\n",
      "cast_4_avg_profitability\n",
      "nan: 2944\t0.3928476114224713%\n",
      "\n",
      "cast_4_movies_before\n",
      "nan: 2944\t0.3928476114224713%\n",
      "\n",
      "cast_5_avg_profit\n",
      "nan: 3408\t0.45476381104883906%\n",
      "\n",
      "cast_5_avg_budget\n",
      "nan: 3408\t0.45476381104883906%\n",
      "\n",
      "cast_5_experience\n",
      "nan: 142\t0.01894849212703496%\n",
      "\n",
      "cast_5_avg_profitability\n",
      "nan: 3408\t0.45476381104883906%\n",
      "\n",
      "cast_5_movies_before\n",
      "nan: 3408\t0.45476381104883906%\n",
      "\n",
      "cast_6_avg_profit\n",
      "nan: 3769\t0.5029356818788364%\n",
      "\n",
      "cast_6_avg_budget\n",
      "nan: 3769\t0.5029356818788364%\n",
      "\n",
      "cast_6_experience\n",
      "nan: 242\t0.03229250066720043%\n",
      "\n",
      "cast_6_avg_profitability\n",
      "nan: 3769\t0.5029356818788364%\n",
      "\n",
      "cast_6_movies_before\n",
      "nan: 3769\t0.5029356818788364%\n",
      "\n",
      "cast_7_avg_profit\n",
      "nan: 4204\t0.5609821190285562%\n",
      "\n",
      "cast_7_avg_budget\n",
      "nan: 4204\t0.5609821190285562%\n",
      "\n",
      "cast_7_experience\n",
      "nan: 378\t0.05044035228182546%\n",
      "\n",
      "cast_7_avg_profitability\n",
      "nan: 4204\t0.5609821190285562%\n",
      "\n",
      "cast_7_movies_before\n",
      "nan: 4204\t0.5609821190285562%\n",
      "\n",
      "cast_8_avg_profit\n",
      "nan: 4569\t0.6096877502001601%\n",
      "\n",
      "cast_8_avg_budget\n",
      "nan: 4569\t0.6096877502001601%\n",
      "\n",
      "cast_8_experience\n",
      "nan: 529\t0.07058980517747532%\n",
      "\n",
      "cast_8_avg_profitability\n",
      "nan: 4569\t0.6096877502001601%\n",
      "\n",
      "cast_8_movies_before\n",
      "nan: 4569\t0.6096877502001601%\n",
      "\n",
      "cast_1_male\n",
      "nan: 0\t0.0%\n",
      "\n",
      "cast_1_female\n",
      "nan: 0\t0.0%\n",
      "\n",
      "cast_2_male\n",
      "nan: 0\t0.0%\n",
      "\n",
      "cast_2_female\n",
      "nan: 0\t0.0%\n",
      "\n",
      "cast_3_male\n",
      "nan: 0\t0.0%\n",
      "\n",
      "cast_3_female\n",
      "nan: 0\t0.0%\n",
      "\n",
      "cast_4_male\n",
      "nan: 0\t0.0%\n",
      "\n",
      "cast_4_female\n",
      "nan: 0\t0.0%\n",
      "\n",
      "cast_5_male\n",
      "nan: 0\t0.0%\n",
      "\n",
      "cast_5_female\n",
      "nan: 0\t0.0%\n",
      "\n",
      "cast_6_male\n",
      "nan: 0\t0.0%\n",
      "\n",
      "cast_6_female\n",
      "nan: 0\t0.0%\n",
      "\n",
      "cast_7_male\n",
      "nan: 0\t0.0%\n",
      "\n",
      "cast_7_female\n",
      "nan: 0\t0.0%\n",
      "\n",
      "cast_8_male\n",
      "nan: 0\t0.0%\n",
      "\n",
      "cast_8_female\n",
      "nan: 0\t0.0%\n",
      "\n",
      "crew__sound__music_editor_avg_profit\n",
      "nan: 6249\t0.8338670936749399%\n",
      "\n",
      "crew__sound__music_editor_avg_budget\n",
      "nan: 6249\t0.8338670936749399%\n",
      "\n",
      "crew__sound__music_editor_movies_before\n",
      "nan: 6249\t0.8338670936749399%\n",
      "\n",
      "crew__sound__original_music_composer_avg_profit\n",
      "nan: 3965\t0.5290899386175607%\n",
      "\n",
      "crew__sound__original_music_composer_avg_budget\n",
      "nan: 3965\t0.5290899386175607%\n",
      "\n",
      "crew__sound__original_music_composer_movies_before\n",
      "nan: 3965\t0.5290899386175607%\n",
      "\n",
      "crew__sound__sound_designer_avg_profit\n",
      "nan: 6642\t0.8863090472377902%\n",
      "\n",
      "crew__sound__sound_designer_avg_budget\n",
      "nan: 6642\t0.8863090472377902%\n",
      "\n",
      "crew__sound__sound_designer_movies_before\n",
      "nan: 6642\t0.8863090472377902%\n",
      "\n",
      "crew__sound__sound_effects_editor_avg_profit\n",
      "nan: 6606\t0.8815052041633307%\n",
      "\n",
      "crew__sound__sound_effects_editor_avg_budget\n",
      "nan: 6606\t0.8815052041633307%\n",
      "\n",
      "crew__sound__sound_effects_editor_movies_before\n",
      "nan: 6606\t0.8815052041633307%\n",
      "\n",
      "crew__sound__sound_re_recording_mixer_avg_profit\n",
      "nan: 6018\t0.8030424339471577%\n",
      "\n",
      "crew__sound__sound_re_recording_mixer_avg_budget\n",
      "nan: 6018\t0.8030424339471577%\n",
      "\n",
      "crew__sound__sound_re_recording_mixer_movies_before\n",
      "nan: 6018\t0.8030424339471577%\n",
      "\n",
      "crew__sound__supervising_sound_editor_avg_profit\n",
      "nan: 6171\t0.8234587670136109%\n",
      "\n",
      "crew__sound__supervising_sound_editor_avg_budget\n",
      "nan: 6171\t0.8234587670136109%\n",
      "\n",
      "crew__sound__supervising_sound_editor_movies_before\n",
      "nan: 6171\t0.8234587670136109%\n",
      "\n",
      "crew__directing__director__1_avg_profit\n",
      "nan: 3446\t0.4598345342941019%\n",
      "\n",
      "crew__directing__director__1_avg_budget\n",
      "nan: 3446\t0.4598345342941019%\n",
      "\n",
      "crew__directing__director__1_movies_before\n",
      "nan: 3446\t0.4598345342941019%\n",
      "\n",
      "crew__directing__script_supervisor_avg_profit\n",
      "nan: 5802\t0.7742193755004003%\n",
      "\n",
      "crew__directing__script_supervisor_avg_budget\n",
      "nan: 5802\t0.7742193755004003%\n",
      "\n",
      "crew__directing__script_supervisor_movies_before\n",
      "nan: 5802\t0.7742193755004003%\n",
      "\n",
      "crew__production__casting_avg_profit\n",
      "nan: 4109\t0.548305310915399%\n",
      "\n",
      "crew__production__casting_avg_budget\n",
      "nan: 4109\t0.548305310915399%\n",
      "\n",
      "crew__production__casting_movies_before\n",
      "nan: 4109\t0.548305310915399%\n",
      "\n",
      "crew__production__executive_producer__1_avg_profit\n",
      "nan: 5604\t0.7477982385908727%\n",
      "\n",
      "crew__production__executive_producer__1_avg_budget\n",
      "nan: 5604\t0.7477982385908727%\n",
      "\n",
      "crew__production__executive_producer__1_movies_before\n",
      "nan: 5604\t0.7477982385908727%\n",
      "\n",
      "crew__production__producer__1_avg_profit\n",
      "nan: 3829\t0.5109420870029356%\n",
      "\n",
      "crew__production__producer__1_avg_budget\n",
      "nan: 3829\t0.5109420870029356%\n",
      "\n",
      "crew__production__producer__1_movies_before\n",
      "nan: 3829\t0.5109420870029356%\n",
      "\n",
      "crew__production__producer__2_avg_profit\n",
      "nan: 6675\t0.8907125700560449%\n",
      "\n",
      "crew__production__producer__2_avg_budget\n",
      "nan: 6675\t0.8907125700560449%\n",
      "\n",
      "crew__production__producer__2_movies_before\n",
      "nan: 6675\t0.8907125700560449%\n",
      "\n",
      "crew__editing__editor__1_avg_profit\n",
      "nan: 3216\t0.4291433146517214%\n",
      "\n",
      "crew__editing__editor__1_avg_budget\n",
      "nan: 3216\t0.4291433146517214%\n",
      "\n",
      "crew__editing__editor__1_movies_before\n",
      "nan: 3216\t0.4291433146517214%\n",
      "\n",
      "crew__costume__costume_designer_avg_profit\n",
      "nan: 4362\t0.5820656525220176%\n",
      "\n",
      "crew__costume__costume_designer_avg_budget\n",
      "nan: 4362\t0.5820656525220176%\n",
      "\n",
      "crew__costume__costume_designer_movies_before\n",
      "nan: 4362\t0.5820656525220176%\n",
      "\n",
      "crew__costume__costume_supervisor_avg_profit\n",
      "nan: 6721\t0.8968508139845209%\n",
      "\n",
      "crew__costume__costume_supervisor_avg_budget\n",
      "nan: 6721\t0.8968508139845209%\n",
      "\n",
      "crew__costume__costume_supervisor_movies_before\n",
      "nan: 6721\t0.8968508139845209%\n",
      "\n",
      "crew__costume__makeup_artist_avg_profit\n",
      "nan: 6448\t0.8604216706698692%\n",
      "\n",
      "crew__costume__makeup_artist_avg_budget\n",
      "nan: 6448\t0.8604216706698692%\n",
      "\n",
      "crew__costume__makeup_artist_movies_before\n",
      "nan: 6448\t0.8604216706698692%\n",
      "\n",
      "crew__crew__stunt_coordinator_avg_profit\n",
      "nan: 6376\t0.8508139845209501%\n",
      "\n",
      "crew__crew__stunt_coordinator_avg_budget\n",
      "nan: 6376\t0.8508139845209501%\n",
      "\n",
      "crew__crew__stunt_coordinator_movies_before\n",
      "nan: 6376\t0.8508139845209501%\n",
      "\n",
      "crew__writing__screenplay__1_avg_profit\n",
      "nan: 5452\t0.7275153456098212%\n",
      "\n",
      "crew__writing__screenplay__1_avg_budget\n",
      "nan: 5452\t0.7275153456098212%\n",
      "\n",
      "crew__writing__screenplay__1_movies_before\n",
      "nan: 5452\t0.7275153456098212%\n",
      "\n",
      "crew__art__art_direction_avg_profit\n",
      "nan: 5497\t0.7335201494528957%\n",
      "\n",
      "crew__art__art_direction_avg_budget\n",
      "nan: 5497\t0.7335201494528957%\n",
      "\n",
      "crew__art__art_direction_movies_before\n",
      "nan: 5497\t0.7335201494528957%\n",
      "\n",
      "crew__art__production_design_avg_profit\n",
      "nan: 4252\t0.5673872431278356%\n",
      "\n",
      "crew__art__production_design_avg_budget\n",
      "nan: 4252\t0.5673872431278356%\n",
      "\n",
      "crew__art__production_design_movies_before\n",
      "nan: 4252\t0.5673872431278356%\n",
      "\n",
      "crew__art__property_master_avg_profit\n",
      "nan: 6524\t0.870563117160395%\n",
      "\n",
      "crew__art__property_master_avg_budget\n",
      "nan: 6524\t0.870563117160395%\n",
      "\n",
      "crew__art__property_master_movies_before\n",
      "nan: 6524\t0.870563117160395%\n",
      "\n",
      "crew__art__set_decoration_avg_profit\n",
      "nan: 5130\t0.6845476381104884%\n",
      "\n",
      "crew__art__set_decoration_avg_budget\n",
      "nan: 5130\t0.6845476381104884%\n",
      "\n",
      "crew__art__set_decoration_movies_before\n",
      "nan: 5130\t0.6845476381104884%\n",
      "\n",
      "crew__visualeffects__visual_effects_supervisor_avg_profit\n",
      "nan: 6626\t0.8841740058713637%\n",
      "\n",
      "crew__visualeffects__visual_effects_supervisor_avg_budget\n",
      "nan: 6626\t0.8841740058713637%\n",
      "\n",
      "crew__visualeffects__visual_effects_supervisor_movies_before\n",
      "nan: 6626\t0.8841740058713637%\n",
      "\n",
      "crew__camera__director_of_photography_avg_profit\n",
      "nan: 3162\t0.42193755004003203%\n",
      "\n",
      "crew__camera__director_of_photography_avg_budget\n",
      "nan: 3162\t0.42193755004003203%\n",
      "\n",
      "crew__camera__director_of_photography_movies_before\n",
      "nan: 3162\t0.42193755004003203%\n",
      "\n",
      "crew__camera__steadicam_operator_avg_profit\n",
      "nan: 6716\t0.8961836135575126%\n",
      "\n",
      "crew__camera__steadicam_operator_avg_budget\n",
      "nan: 6716\t0.8961836135575126%\n",
      "\n",
      "crew__camera__steadicam_operator_movies_before\n",
      "nan: 6716\t0.8961836135575126%\n",
      "\n",
      "crew__camera__still_photographer_avg_profit\n",
      "nan: 5935\t0.7919669068588204%\n",
      "\n",
      "crew__camera__still_photographer_avg_budget\n",
      "nan: 5935\t0.7919669068588204%\n",
      "\n",
      "crew__camera__still_photographer_movies_before\n",
      "nan: 5935\t0.7919669068588204%\n",
      "\n",
      "cast_avg_budget\n",
      "nan: 592\t0.07899653055777955%\n",
      "\n",
      "cast_avg_profit\n",
      "nan: 592\t0.07899653055777955%\n",
      "\n",
      "cast_avg_experience\n",
      "nan: 1\t0.00013344008540165466%\n",
      "\n",
      "cast_avg_movies_before\n",
      "nan: 592\t0.07899653055777955%\n",
      "\n",
      "year_avg_profitability\n",
      "nan: 0\t0.0%\n",
      "\n",
      "year_avg_revenue\n",
      "nan: 0\t0.0%\n",
      "\n",
      "production_company_1_avg_profit\n",
      "nan: 2551\t0.340405657859621%\n",
      "\n",
      "production_company_1_avg_profitability\n",
      "nan: 2551\t0.340405657859621%\n",
      "\n",
      "production_company_1_avg_revenue\n",
      "nan: 2551\t0.340405657859621%\n",
      "\n",
      "production_company_2_avg_profit\n",
      "nan: 3604\t0.4809180677875634%\n",
      "\n",
      "production_company_2_avg_profitability\n",
      "nan: 3604\t0.4809180677875634%\n",
      "\n",
      "production_company_2_avg_revenue\n",
      "nan: 3604\t0.4809180677875634%\n",
      "\n",
      "production_company_3_avg_profit\n",
      "nan: 5049\t0.6737389911929543%\n",
      "\n",
      "production_company_3_avg_profitability\n",
      "nan: 5049\t0.6737389911929543%\n",
      "\n",
      "production_company_3_avg_revenue\n",
      "nan: 5049\t0.6737389911929543%\n",
      "\n",
      "collection_name_avg_profit\n",
      "nan: 6773\t0.903789698425407%\n",
      "\n",
      "collection_name_avg_revenue\n",
      "nan: 6773\t0.903789698425407%\n",
      "\n",
      "competition\n",
      "nan: 0\t0.0%\n",
      "\n",
      "rating__g\n",
      "nan: 0\t0.0%\n",
      "\n",
      "rating__pg\n",
      "nan: 0\t0.0%\n",
      "\n",
      "rating__pg-13\n",
      "nan: 0\t0.0%\n",
      "\n",
      "rating__r\n",
      "nan: 0\t0.0%\n",
      "\n",
      "rating__nc-17\n",
      "nan: 0\t0.0%\n",
      "\n",
      "topic_0\n",
      "nan: 0\t0.0%\n",
      "\n",
      "topic_1\n",
      "nan: 0\t0.0%\n",
      "\n",
      "topic_2\n",
      "nan: 0\t0.0%\n",
      "\n",
      "topic_3\n",
      "nan: 0\t0.0%\n",
      "\n",
      "topic_4\n",
      "nan: 0\t0.0%\n",
      "\n",
      "topic_5\n",
      "nan: 0\t0.0%\n",
      "\n",
      "topic_6\n",
      "nan: 0\t0.0%\n",
      "\n",
      "topic_7\n",
      "nan: 0\t0.0%\n",
      "\n",
      "topic_8\n",
      "nan: 0\t0.0%\n",
      "\n",
      "topic_9\n",
      "nan: 0\t0.0%\n",
      "\n",
      "topic_10\n",
      "nan: 0\t0.0%\n",
      "\n",
      "topic_11\n",
      "nan: 0\t0.0%\n",
      "\n",
      "topic_12\n",
      "nan: 0\t0.0%\n",
      "\n",
      "topic_13\n",
      "nan: 0\t0.0%\n",
      "\n",
      "topic_14\n",
      "nan: 0\t0.0%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "work_df = get_work_cols(df)\n",
    "for col in list(work_df.columns):\n",
    "    print(col)\n",
    "    col_nan = len([x for x in work_df[col] if pd.isna(x)])\n",
    "    print(f'nan: {col_nan}\\t{col_nan/work_df[col].shape[0]}%\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6721\t0.8968508139845209\tcrew__costume__costume_supervisor_avg_profit\n",
      "6721\t0.8968508139845209\tcrew__costume__costume_supervisor_avg_budget\n",
      "6721\t0.8968508139845209\tcrew__costume__costume_supervisor_movies_before\n",
      "6716\t0.8961836135575126\tcrew__camera__steadicam_operator_avg_profit\n",
      "6716\t0.8961836135575126\tcrew__camera__steadicam_operator_avg_budget\n",
      "6716\t0.8961836135575126\tcrew__camera__steadicam_operator_movies_before\n",
      "6675\t0.8907125700560449\tcrew__production__producer__2_avg_profit\n",
      "6675\t0.8907125700560449\tcrew__production__producer__2_avg_budget\n",
      "6675\t0.8907125700560449\tcrew__production__producer__2_movies_before\n",
      "6642\t0.8863090472377902\tcrew__sound__sound_designer_avg_profit\n",
      "6642\t0.8863090472377902\tcrew__sound__sound_designer_avg_budget\n",
      "6642\t0.8863090472377902\tcrew__sound__sound_designer_movies_before\n",
      "6626\t0.8841740058713637\tcrew__visualeffects__visual_effects_supervisor_avg_profit\n",
      "6626\t0.8841740058713637\tcrew__visualeffects__visual_effects_supervisor_avg_budget\n",
      "6626\t0.8841740058713637\tcrew__visualeffects__visual_effects_supervisor_movies_before\n",
      "6606\t0.8815052041633307\tcrew__sound__sound_effects_editor_avg_profit\n",
      "6606\t0.8815052041633307\tcrew__sound__sound_effects_editor_avg_budget\n",
      "6606\t0.8815052041633307\tcrew__sound__sound_effects_editor_movies_before\n",
      "6524\t0.870563117160395\tcrew__art__property_master_avg_profit\n",
      "6524\t0.870563117160395\tcrew__art__property_master_avg_budget\n",
      "6524\t0.870563117160395\tcrew__art__property_master_movies_before\n",
      "6448\t0.8604216706698692\tcrew__costume__makeup_artist_avg_profit\n",
      "6448\t0.8604216706698692\tcrew__costume__makeup_artist_avg_budget\n",
      "6448\t0.8604216706698692\tcrew__costume__makeup_artist_movies_before\n",
      "6376\t0.8508139845209501\tcrew__crew__stunt_coordinator_avg_profit\n",
      "6376\t0.8508139845209501\tcrew__crew__stunt_coordinator_avg_budget\n",
      "6376\t0.8508139845209501\tcrew__crew__stunt_coordinator_movies_before\n",
      "6249\t0.8338670936749399\tcrew__sound__music_editor_avg_profit\n",
      "6249\t0.8338670936749399\tcrew__sound__music_editor_avg_budget\n",
      "6249\t0.8338670936749399\tcrew__sound__music_editor_movies_before\n",
      "6171\t0.8234587670136109\tcrew__sound__supervising_sound_editor_avg_profit\n",
      "6171\t0.8234587670136109\tcrew__sound__supervising_sound_editor_avg_budget\n",
      "6171\t0.8234587670136109\tcrew__sound__supervising_sound_editor_movies_before\n",
      "6018\t0.8030424339471577\tcrew__sound__sound_re_recording_mixer_avg_profit\n",
      "6018\t0.8030424339471577\tcrew__sound__sound_re_recording_mixer_avg_budget\n",
      "6018\t0.8030424339471577\tcrew__sound__sound_re_recording_mixer_movies_before\n",
      "5935\t0.7919669068588204\tcrew__camera__still_photographer_avg_profit\n",
      "5935\t0.7919669068588204\tcrew__camera__still_photographer_avg_budget\n",
      "5935\t0.7919669068588204\tcrew__camera__still_photographer_movies_before\n",
      "5802\t0.7742193755004003\tcrew__directing__script_supervisor_avg_profit\n",
      "5802\t0.7742193755004003\tcrew__directing__script_supervisor_avg_budget\n",
      "5802\t0.7742193755004003\tcrew__directing__script_supervisor_movies_before\n",
      "5604\t0.7477982385908727\tcrew__production__executive_producer__1_avg_profit\n",
      "5604\t0.7477982385908727\tcrew__production__executive_producer__1_avg_budget\n",
      "5604\t0.7477982385908727\tcrew__production__executive_producer__1_movies_before\n",
      "5497\t0.7335201494528957\tcrew__art__art_direction_avg_profit\n",
      "5497\t0.7335201494528957\tcrew__art__art_direction_avg_budget\n",
      "5497\t0.7335201494528957\tcrew__art__art_direction_movies_before\n",
      "5452\t0.7275153456098212\tcrew__writing__screenplay__1_avg_profit\n",
      "5452\t0.7275153456098212\tcrew__writing__screenplay__1_avg_budget\n",
      "5452\t0.7275153456098212\tcrew__writing__screenplay__1_movies_before\n",
      "5130\t0.6845476381104884\tcrew__art__set_decoration_avg_profit\n",
      "5130\t0.6845476381104884\tcrew__art__set_decoration_avg_budget\n",
      "5130\t0.6845476381104884\tcrew__art__set_decoration_movies_before\n",
      "4362\t0.5820656525220176\tcrew__costume__costume_designer_avg_profit\n",
      "4362\t0.5820656525220176\tcrew__costume__costume_designer_avg_budget\n",
      "4362\t0.5820656525220176\tcrew__costume__costume_designer_movies_before\n",
      "4252\t0.5673872431278356\tcrew__art__production_design_avg_profit\n",
      "4252\t0.5673872431278356\tcrew__art__production_design_avg_budget\n",
      "4252\t0.5673872431278356\tcrew__art__production_design_movies_before\n",
      "4109\t0.548305310915399\tcrew__production__casting_avg_profit\n",
      "4109\t0.548305310915399\tcrew__production__casting_avg_budget\n",
      "4109\t0.548305310915399\tcrew__production__casting_movies_before\n",
      "3965\t0.5290899386175607\tcrew__sound__original_music_composer_avg_profit\n",
      "3965\t0.5290899386175607\tcrew__sound__original_music_composer_avg_budget\n",
      "3965\t0.5290899386175607\tcrew__sound__original_music_composer_movies_before\n",
      "3829\t0.5109420870029356\tcrew__production__producer__1_avg_profit\n",
      "3829\t0.5109420870029356\tcrew__production__producer__1_avg_budget\n",
      "3829\t0.5109420870029356\tcrew__production__producer__1_movies_before\n",
      "3446\t0.4598345342941019\tcrew__directing__director__1_avg_profit\n",
      "3446\t0.4598345342941019\tcrew__directing__director__1_avg_budget\n",
      "3446\t0.4598345342941019\tcrew__directing__director__1_movies_before\n",
      "3216\t0.4291433146517214\tcrew__editing__editor__1_avg_profit\n",
      "3216\t0.4291433146517214\tcrew__editing__editor__1_avg_budget\n",
      "3216\t0.4291433146517214\tcrew__editing__editor__1_movies_before\n",
      "3162\t0.42193755004003203\tcrew__camera__director_of_photography_avg_profit\n",
      "3162\t0.42193755004003203\tcrew__camera__director_of_photography_avg_budget\n",
      "3162\t0.42193755004003203\tcrew__camera__director_of_photography_movies_before\n"
     ]
    }
   ],
   "source": [
    "cast_cols = [c for c in list(work_df.columns) if 'crew__' in c]\n",
    "crew_count = {}\n",
    "for col in cast_cols:\n",
    "    crew_count[col] = len([p for p in work_df[col] if pd.isna(p)])\n",
    "\n",
    "sorted_crew = sorted(crew_count, key=lambda job: crew_count[job], reverse=True)\n",
    "for job in sorted_crew:\n",
    "    print(f'{crew_count[job]}\\t{crew_count[job]/work_df.shape[0]}\\t{job}')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using Pearson Correlation\n",
    "# plt.figure(figsize=(12,10))\n",
    "cor = work_df.corr()\n",
    "# sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "genre__drama   -0.171316\n",
       "rating__r      -0.127603\n",
       "topic_7        -0.153649\n",
       "Name: revenue, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor_target = cor[\"revenue\"]\n",
    "#Selecting highly correlated features\n",
    "relevant_features = cor_target[cor_target<-0.1]\n",
    "relevant_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "genre__horror                                                0.056003\n",
       "profitability                                                1.000000\n",
       "crew__production__executive_producer__1_movies_before        0.061414\n",
       "crew__costume__costume_supervisor_avg_profit                 0.080169\n",
       "crew__costume__makeup_artist_avg_budget                      0.053284\n",
       "crew__art__property_master_avg_profit                        0.063587\n",
       "crew__visualeffects__visual_effects_supervisor_avg_profit    0.088470\n",
       "year_avg_profitability                                       0.066297\n",
       "production_company_3_avg_profit                              0.063422\n",
       "production_company_3_avg_profitability                       0.099489\n",
       "production_company_3_avg_revenue                             0.057613\n",
       "collection_name_avg_profit                                   0.104383\n",
       "collection_name_avg_revenue                                  0.085449\n",
       "Name: profitability, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor_target = abs(cor[\"profitability\"])\n",
    "#Selecting highly correlated features\n",
    "relevant_features = cor_target[cor_target>0.05]\n",
    "relevant_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/stas/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
       "                    max_features='auto', max_leaf_nodes=None,\n",
       "                    min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                    min_samples_leaf=1, min_samples_split=2,\n",
       "                    min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "                    oob_score=False, random_state=None, verbose=0,\n",
       "                    warm_start=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = work_df.loc[(work_df.profitability < 1000) & (work_df.profitability > -1000)].reset_index(drop=True)\n",
    "test_df = normalize_df(test_df)\n",
    "X, y = prepare_X_y(test_df)\n",
    "\n",
    "model = ExtraTreesRegressor()\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtering methods (univariative) (remove constant and quaziconstant)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.feature_selection import VarianceThreshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "work_df = get_work_cols(df)\n",
    "X = work_df.drop(['profitability', 'revenue'], axis=1)\n",
    "y = work_df['profitability']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.01, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7419, 272)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "# constant_filter = VarianceThreshold(threshold=0)\n",
    "# X_train.fillna(X_train.median(), inplace=True)\n",
    "# constant_filter.fit(X_train)\n",
    "# X_train.columns[[not temp for temp in constant_filter.get_support()]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "#quazi constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VarianceThreshold(threshold=0.01)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qzf = VarianceThreshold(threshold=0.01)\n",
    "qzf.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "qzf.get_support().sum()\n",
    "X_train_qzf = X_train.columns[[not temp for temp in qzf.get_support()]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "T_T = X_train_qzf.T\n",
    "X_train_T = pd.DataFrame(T_T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_T.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "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>topic_0</th>\n",
       "      <th>topic_1</th>\n",
       "      <th>topic_2</th>\n",
       "      <th>topic_3</th>\n",
       "      <th>topic_4</th>\n",
       "      <th>topic_5</th>\n",
       "      <th>topic_6</th>\n",
       "      <th>topic_7</th>\n",
       "      <th>topic_8</th>\n",
       "      <th>topic_9</th>\n",
       "      <th>topic_10</th>\n",
       "      <th>topic_11</th>\n",
       "      <th>topic_12</th>\n",
       "      <th>topic_13</th>\n",
       "      <th>topic_14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>7494.000000</td>\n",
       "      <td>7494.000000</td>\n",
       "      <td>7494.000000</td>\n",
       "      <td>7494.000000</td>\n",
       "      <td>7494.000000</td>\n",
       "      <td>7494.000000</td>\n",
       "      <td>7494.000000</td>\n",
       "      <td>7494.000000</td>\n",
       "      <td>7494.000000</td>\n",
       "      <td>7494.000000</td>\n",
       "      <td>7494.000000</td>\n",
       "      <td>7494.000000</td>\n",
       "      <td>7494.000000</td>\n",
       "      <td>7494.000000</td>\n",
       "      <td>7494.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>0.000602</td>\n",
       "      <td>0.002216</td>\n",
       "      <td>0.250307</td>\n",
       "      <td>0.000602</td>\n",
       "      <td>0.011572</td>\n",
       "      <td>0.042851</td>\n",
       "      <td>0.021415</td>\n",
       "      <td>0.445113</td>\n",
       "      <td>0.002056</td>\n",
       "      <td>0.024122</td>\n",
       "      <td>0.008777</td>\n",
       "      <td>0.000322</td>\n",
       "      <td>0.002248</td>\n",
       "      <td>0.146274</td>\n",
       "      <td>0.021869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.004748</td>\n",
       "      <td>0.009481</td>\n",
       "      <td>0.152227</td>\n",
       "      <td>0.004783</td>\n",
       "      <td>0.036124</td>\n",
       "      <td>0.064195</td>\n",
       "      <td>0.045885</td>\n",
       "      <td>0.182993</td>\n",
       "      <td>0.011790</td>\n",
       "      <td>0.047353</td>\n",
       "      <td>0.030778</td>\n",
       "      <td>0.003539</td>\n",
       "      <td>0.010226</td>\n",
       "      <td>0.113032</td>\n",
       "      <td>0.051723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016253</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.036149</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",
       "      <td>25%</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.132835</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.305684</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>0.064435</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.209758</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.439444</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>0.109348</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.332415</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052245</td>\n",
       "      <td>0.019259</td>\n",
       "      <td>0.585131</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021570</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.198532</td>\n",
       "      <td>0.014959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>0.081906</td>\n",
       "      <td>0.153094</td>\n",
       "      <td>0.871903</td>\n",
       "      <td>0.084485</td>\n",
       "      <td>0.398342</td>\n",
       "      <td>0.539035</td>\n",
       "      <td>0.486393</td>\n",
       "      <td>0.907526</td>\n",
       "      <td>0.174178</td>\n",
       "      <td>0.392833</td>\n",
       "      <td>0.451016</td>\n",
       "      <td>0.090902</td>\n",
       "      <td>0.221828</td>\n",
       "      <td>0.701504</td>\n",
       "      <td>0.563723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           topic_0      topic_1      topic_2      topic_3      topic_4  \\\n",
       "count  7494.000000  7494.000000  7494.000000  7494.000000  7494.000000   \n",
       "mean   0.000602     0.002216     0.250307     0.000602     0.011572      \n",
       "std    0.004748     0.009481     0.152227     0.004783     0.036124      \n",
       "min    0.000000     0.000000     0.016253     0.000000     0.000000      \n",
       "25%    0.000000     0.000000     0.132835     0.000000     0.000000      \n",
       "50%    0.000000     0.000000     0.209758     0.000000     0.000000      \n",
       "75%    0.000000     0.000000     0.332415     0.000000     0.000000      \n",
       "max    0.081906     0.153094     0.871903     0.084485     0.398342      \n",
       "\n",
       "           topic_5      topic_6      topic_7      topic_8      topic_9  \\\n",
       "count  7494.000000  7494.000000  7494.000000  7494.000000  7494.000000   \n",
       "mean   0.042851     0.021415     0.445113     0.002056     0.024122      \n",
       "std    0.064195     0.045885     0.182993     0.011790     0.047353      \n",
       "min    0.000000     0.000000     0.036149     0.000000     0.000000      \n",
       "25%    0.000000     0.000000     0.305684     0.000000     0.000000      \n",
       "50%    0.018058     0.000000     0.439444     0.000000     0.000000      \n",
       "75%    0.052245     0.019259     0.585131     0.000000     0.021570      \n",
       "max    0.539035     0.486393     0.907526     0.174178     0.392833      \n",
       "\n",
       "          topic_10     topic_11     topic_12     topic_13     topic_14  \n",
       "count  7494.000000  7494.000000  7494.000000  7494.000000  7494.000000  \n",
       "mean   0.008777     0.000322     0.002248     0.146274     0.021869     \n",
       "std    0.030778     0.003539     0.010226     0.113032     0.051723     \n",
       "min    0.000000     0.000000     0.000000     0.000000     0.000000     \n",
       "25%    0.000000     0.000000     0.000000     0.064435     0.000000     \n",
       "50%    0.000000     0.000000     0.000000     0.109348     0.000000     \n",
       "75%    0.000000     0.000000     0.000000     0.198532     0.014959     \n",
       "max    0.451016     0.090902     0.221828     0.701504     0.563723     "
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "work_df[[c for c in work_df.columns if 'topic_' in c]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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>budget</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>7.494000e+03</td>\n",
       "      <td>7.494000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>-2.066076e-16</td>\n",
       "      <td>-8.262228e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>1.000067e+00</td>\n",
       "      <td>1.000067e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-7.147514e-01</td>\n",
       "      <td>-4.776370e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>-6.097373e-01</td>\n",
       "      <td>-4.521568e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>-3.603194e-01</td>\n",
       "      <td>-3.513691e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>2.041527e-01</td>\n",
       "      <td>-2.528812e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>9.261961e+00</td>\n",
       "      <td>1.668389e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             budget       revenue\n",
       "count  7.494000e+03  7.494000e+03\n",
       "mean  -2.066076e-16 -8.262228e-17\n",
       "std    1.000067e+00  1.000067e+00\n",
       "min   -7.147514e-01 -4.776370e-01\n",
       "25%   -6.097373e-01 -4.521568e-01\n",
       "50%   -3.603194e-01 -3.513691e-01\n",
       "75%    2.041527e-01 -2.528812e-03\n",
       "max    9.261961e+00  1.668389e+01"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scale = StandardScaler()\n",
    "b_r = scale.fit_transform(work_df[['budget','revenue']])\n",
    "scaled = pd.DataFrame(b_r)\n",
    "scaled.columns = ['budget', 'revenue']\n",
    "scaled.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'budget')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(pd.DataFrame(b_r).iloc[:,0], bins = 50, hist = True).set_title('budget')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'revenue')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(pd.DataFrame(b_r).iloc[:,1], bins = 50, hist = True).set_title('revenue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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>budget</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>7.494000e+03</td>\n",
       "      <td>7.494000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>-2.174965e-16</td>\n",
       "      <td>-8.140747e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>1.000067e+00</td>\n",
       "      <td>1.000067e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-7.147514e-01</td>\n",
       "      <td>-4.776370e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>-6.097373e-01</td>\n",
       "      <td>-4.521568e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>-3.603194e-01</td>\n",
       "      <td>-3.513691e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>2.041527e-01</td>\n",
       "      <td>-2.528812e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>9.261961e+00</td>\n",
       "      <td>1.668389e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             budget       revenue\n",
       "count  7.494000e+03  7.494000e+03\n",
       "mean  -2.174965e-16 -8.140747e-17\n",
       "std    1.000067e+00  1.000067e+00\n",
       "min   -7.147514e-01 -4.776370e-01\n",
       "25%   -6.097373e-01 -4.521568e-01\n",
       "50%   -3.603194e-01 -3.513691e-01\n",
       "75%    2.041527e-01 -2.528812e-03\n",
       "max    9.261961e+00  1.668389e+01"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "minmax = MinMaxScaler()\n",
    "stdscale = StandardScaler()\n",
    "arr1 = minmax.fit_transform(work_df[['budget','revenue']])\n",
    "scaled = pd.DataFrame(arr1)\n",
    "scaled.columns = ['budget', 'revenue']\n",
    "\n",
    "arr2 = stdscale.fit_transform(scaled)\n",
    "scaled = pd.DataFrame(arr2)\n",
    "scaled.columns = ['budget', 'revenue']\n",
    "scaled.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'budget')"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(pd.DataFrame(arr1).iloc[:,0], bins = 50, hist = True).set_title('budget')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'budget')"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(pd.DataFrame(arr2).iloc[:,0], bins = 50, hist = True).set_title('budget')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'revenue')"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(pd.DataFrame(arr1).iloc[:,1], bins = 50, hist = True).set_title('revenue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'revenue')"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(pd.DataFrame(arr2).iloc[:,1], bins = 50, hist = True).set_title('revenue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=1969.6745556617552, pvalue=0.0)"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.normaltest(work_df.runtime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'revenue')"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6mc0c+Q/0CcbQl8zvQoINWwemo1pmZlMu/4E+QQ+9o1LmxLldPOkeupnNELkP9MEJxtABli/sZsM299DNbGbIfaCP9tDHDrlAMo7uHrqZzRS5D/TBah0BZY3TQ1/QzY6Bqm90YWYzQu4Dfag2QqUsNE6gj850eXK7e+lmVnwFCPT6IR+Ijlp+YOqix9HNrPhyH+iD1ZFxPxAFWDo/ubjoya3uoZtZ8eU+0A/XQ+9qL3PC3E730M1sRsh9oA9WR8ad4TJq2YJuz3Qxsxkh94E+VKsfsnRuo+ULZrmHbmYzQqZAl3SRpHWS1kv68DjHPyjpUUkPSfqBpGWtr+r4Bqsjh9zcAuDGezZy4z0b2TFQZeveIb74r09w4z0bp6paZmZTrmmgSyoD1wEXA2cCl0s6c0yxfwdWRsRZwG3Ax1td0YkM1eqH3H6u0aLZHQD07dw/VVUyM5sWWXroLwXWR8TjETEM3Axc2lggIu6MiNFxjbuBk1tbzYkNVesH3SB6rOf0zqJcEj99avdUVcnMbFpkCfSTgE0N233pvolcAXx7vAOSrpK0WtLq/v7+7LU8jMHayGF76B2VMqf2zuKnT+/x3YvMrNCyBPp43d9xk1HSO4CVwF+Pdzwiro+IlRGxsre3N3stD2OoWh93DL3R6YvnsG3fMP17h1rynGZmx6Isgd4HLGnYPhnYPLaQpFcD/x24JCKmLDmHaiOHneUCcPri2QD89Kk9U1ElM7NpkSXQ7wVWSDpFUjtwGbCqsYCkc4HPkoT5ltZXc2JZeujzuts5YW4nP33a4+hmVlxNAz0iasB7gTuAtcAtEbFG0rWSLkmL/TXQA9wq6QFJqyY4Xcs1m+Uy6vTFs3ly2wA79g1PQa3MzKZeJUuhiLgduH3MvmsaHr+6xfXKZKQeDI/UJ1zLpdHpi+dw57p+fvjYFt5w7pRNwjEzmzK5vlJ0uJbcrWiitVwanXRcF7M7Knx/7ZSOCJmZTZlcB/pgdeK7FY1Vknje4tn8aF3/gT8EZmZFkutAHxrtoTeZ5TLq9MVz2DNU494N249mtczMpkWuA30yPXSA0xb10FEp8f21vzia1TIzmxa5DvTRHnqWWS4A7ZUS5522kB+s3eKrRs2scHIe6EkP/XBruYx14RmL2Lh9gPVb9h6tapmZTYtcB/pgdXI9dIALTz8ewLNdzKxwch3oB3roGcfQARbP7eQFJ83he48+fbSqZWY2LXId6Ad66BlnuYz6jXNO4v6NO/nKXRtaXykzs2mS6UrRY9VoDz3rLBdI7mTU2Vbm9MWz+eiqNTyxdYDTFvXwtpctPVrVNDObErnuoQ9Vs18p2qgk8daVS1jY08FNP9nIlt2DR6N6ZmZTKteBPngEPfRRHW1l3vVLyymVxOf/9Ql+1u9ZL2aWb7kO9AM99EmOoY+aP6udK3/5FOoRXH793TzuUDezHMt1oD+bHvqo4+d0cuV/eg4j9eB3/+F+Ruq+4MjM8inXgT50YJbLkQc6JKF+7aUvYN0v9vC1+/taUTUzsymX60AfrI3QXikhPbtAB3jdCxdz9pJ5fOJ7jx1YI8bMLE9yHehD1TqdldY0QRJXX3w6T+0a5Iv/tqEl5zQzm0o5n4dep6Ot3JJz3XjPRiC5Vd3ffP8xtuweZMXxsz0/3cxyI+c99BE621rbhNe/8ARmd1b44o83cPO9G3l6l+eom1k+5DvQa3U6Kq3poY9a0NPB+161gledvog1m3fzyo/fyR9/42E2bR9o6fOYmbVarodcBo9CDx2SK09ffcbxvGjpcWzetZ/bVvfx9fv7+NPfeCFvfrFvMG1mxyb30A9j/qx2/vwNL+Sf//ACzl1yHB+69UH+4NYHeXTz7gPz1Wsjdc+KMbNjQq576EO1ETpaNMtlIqMflr7+rBPobCtx23193HpfH93tZXo6KmzdO0SlVOL3fuU0fveCU6mO1LnpJxvp27GfS845kXOXzGvJtEozs2ZyHeiD1TqzO9um5LlKEq85czErl83nye372Lh9P9WROs8/cS79e4f4xPcf4x/ufpJqvc7OgSrt5RI3/HgDzz2+h2t+7fn88oqFU1JPM5u5chfoI/Xgh+u2cOEZx09JD32s42a1c9ysds5ZctxB+9c+tZvvPvo0i3s6uewlS1k0u4OHf76Lf/mPrbzj8/fw7lcs56pXPod6BCWJRbM7JnWnJTOzZjIFuqSLgL8FysDnIuIvxxzvAL4MvBjYBrw1Ija0tqqJW1Zv4uqvP8wHXr2CwWqdzhbNQ3+2zjhhDmecMOegfS9ZPp9zlszjO2ue5oYfb+CGH284cKwkWDynkzldbXS2lZnb1cbyBd0sWzCL5Qu7WTp/FhBs2rGf3furnDivi2Xzu5k/q33cPwT7h0doK8t/JMxmsKaBLqkMXAe8BugD7pW0KiIebSh2BbAjIk6TdBnwV8Bbj0aF37JyCfc9uYO/+f5/APCKUxccjadpmbZyiV8/60TOPmkuT+8eoiSoB+zaP8zOgSpDtTr7hmps2T3I3Y9vY6hWb3rOrrYyPZ0VZndUaK+U2LJniO37hulqK3P2krmctqiHbXuH6d8zxLzudpYt6GZWe5lf7B5i+8Awc7vaWNjTwWB1hM0797N93zDlkqiURXd7ct6ezgo9HRVmdVToqJRoK5fYsmeQx36xl+37hlk6v5vlC2bRO7uD47rbqJRL7Bmssnt/lT2DNfYO1ZjT1cZpi3pYclw3HW0lKiWxZc8QfTsGGBgeobu9THu5zMBwUn5edxunLOxhwax2dg5U2TEwTKUkOtvLdLWlX+1l5nS20dn2zJIP9XpQrdepjQS1keRxPZIPrYUY/QhDJFcE10bq7NpfZc9Qjfnd7Sye2zktHYN6PRiojjBcq1Muibaykn9LJUrPcn2imSBGX+OGz6jq6WSF0f+/iKA6EpRLyf8tJO/yh2ojtJdLVMolIoKhWjK5oau9TEelzEg92LW/ynCtzrzupNO1b6jGlj1DACya3UFXW5nNu/bz5LYBejoqLF84i5Lg/o07eXTzbk5Z2M2Ll83np0/v5tM//Bk/eWI7l5xzIr9z/qk89/jZR+X/JEsP/aXA+oh4HEDSzcClQGOgXwp8LH18G/B3khSj/+MtVC6Jj7/pLLraynzl7iePmR56M0sXzGLpglmHLRMR7BseYfveIbbtG0YSx6U/TDsHqmwfGGb/cI3Bap2h2giD1TrVkTqn9fYwb1kbe4ZqbNw2wAObdjK7o42ezgp9O/bzz49toTYS9HRW6G4vM1its2ewSqVUYl53Gz0dFYLkB3249sy5h2ojNC4+WVIyT7+no8L6LXvZtb86YVsqJVE7iitXtpdLSFCrR0tWyOyolGj22bVoHrJZP/+OSNYimug3REr+D8slZXreI63H2DoFkf4LjNmOiPTfZ55DJJ8vldKNkpLtsedLT3fgnM8cb6jz6DmlZx6nf5DrEdQjqUM9ku3R87aVRaVUolavUx1JdlbSDspwrX7gZ7itnNSzsdM0ej/i0e+D5GdreOTgjlVbWQeVAQ50zrJYNLuDS84+kW8//DRfv//nvP9Vp/HB1z4v2zdPgpplrqQ3AxdFxJXp9juBl0XEexvKPJKW6Uu3f5aW2TrmXFcBV6WbzwPWtagdC4GtTUvli9uUD0VsExSzXUVp07KI6B3vQJYe+nh/68f+FchShoi4Hrg+w3NOiqTVEbGy1eedTm5TPhSxTVDMdhWxTWNl+QStD1jSsH0ysHmiMpIqwFxgeysqaGZm2WQJ9HuBFZJOkdQOXAasGlNmFfBb6eM3A//vaIyfm5nZxJoOuURETdJ7gTtIpi1+ISLWSLoWWB0Rq4DPA1+RtJ6kZ37Z0az0OFo+jHMMcJvyoYhtgmK2q4htOkjTD0XNzCwffBWKmVlBONDNzAoiN4Eu6SJJ6yStl/ThcY53SPpqevweScunvpaTk6FN75bUL+mB9OvK6ajnZEj6gqQt6bUJ4x2XpE+mbX5I0oumuo5HIkO7LpC0q+G1umaq6zgZkpZIulPSWklrJP23ccrk7rXK2K5cvVaTEhHH/BfJh7E/A54DtAMPAmeOKfNfgc+kjy8Dvjrd9W5Bm94N/N1013WS7Xol8CLgkQmOvw74Nsm1Cy8H7pnuOreoXRcA35ruek6iPScAL0ofzwYeG+fnL3evVcZ25eq1msxXXnroB5YfiIhhYHT5gUaXAl9KH98GXKhjeyHyLG3KnYj4EYe/BuFS4MuRuBuYJ+mEqandkcvQrlyJiKci4v708R5gLXDSmGK5e60ytquw8hLoJwGbGrb7OPRFOlAmImrALuBYXrkrS5sA3pS+3b1N0pJxjudN1nbn0S9JelDStyU9f7ork1U6PHkucM+YQ7l+rQ7TLsjpa9VMXgK9ZcsPHEOy1PebwPKIOAv4Ps+8A8mzvL1OWd1PssbG2cD/Af5pmuuTiaQe4GvAByJi99jD43xLLl6rJu3K5WuVRV4CvYjLDzRtU0Rsi4ihdPPvSdabz7ssr2XuRMTuiNibPr4daJN0TN+mSlIbSej9Y0R8fZwiuXytmrUrj69VVnkJ9CIuP9C0TWPGKy8hGQ/Mu1XAu9IZFC8HdkXEU9NdqWdL0uLRz2wkvZTkd2vb9NZqYmldPw+sjYj/PUGx3L1WWdqVt9dqMnJxC7rIx/IDk5KxTe+XdAlQI2nTu6etwhlJuolkFsFCSX3AR4E2gIj4DHA7yeyJ9cAA8NvTU9PJydCuNwO/K6kG7AcuO8Y7FOcB7wQelvRAuu+PgaWQ69cqS7vy9lpl5kv/zcwKIi9DLmZm1oQD3cysIBzoZmYF4UA3MysIB7qZ2RRotsDbmLLLJP0gvUr8h5JOzvIcDnQzs6lxA3BRxrL/k2QdnbOAa4G/yPJNDnQrnPRCGP9s2zFlvAXeJJ0q6TuS7pP0L5JOTw+dCfwgfXwnGRfu8w+9FYKk5eka2J8iWavjnZLuknS/pFsl9Ui6WNItDd9zgaRvpo9fO7Z8un+DpP+R7n949BdO0sckfajhXI+ki0Eh6R2SfpKutf1ZSeWp+5+wnLkeeF9EvBj4EPCpdP+DwJvSx28AZktqutigA92K5HnAl4HXAFcAr46IFwGrgQ8C3wNeLmlWWv6twFfTdTw+Mk75UVvT/Z8m+aWbkKQz0vOeFxHnACPA21vUPiuQtNPwCuDW9KrWz5Ks5w7Jz9n5kv4dOB/4OckV44eVi0v/zTJ6MiLulvRrJG9Z/y1dsqMduCtdbuE7wK9Lug14PfCHJL8wh5RvOO/oAk/3AW9sUocLSRZRuzc9VxewpQVts+IpATvTP/wHiYjNpD9rafC/KSJ2NTuhA92KZF/6r4DvRcTl45T5KvB7JGOZ90bEnnShponKA4yueDnCM78zNQ5+h9vZ8Nxfioirj7ANNkNExG5JT0j6zYi4Nf05PCsiHkzfNW6PiDpwNfCFLOf0kIsV0d3AeZJOA5DULem56bEfktxK7j0k4d6s/EQ2pOdByb02T0n3/wB4s6RF6bH5kpa1olGWb+kCb3cBz5PUJ+kKkuG4KyQ9CKzhmQ8/LwDWSXoMOB74syzP4R66FU5E9Et6N3CTpI5090eAxyJiRNK3SFau/K1m5Q/zNF8jWVr2AZKlkB9Lz/WopI8A301n2lRJ3hE82cImWg4d5h3gIVMZI+I2kltpTopXWzQzKwgPuZiZFYQD3cysIBzoZmYF4UA3MysIB7qZWUE40M3MCsKBbmZWEP8fKxkGVLQuNJ0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(work_df.revenue, bins = 50, hist = True).set_title('revenue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "skew_feats = work_df.skew().sort_values(ascending=False)\n",
    "skewness = pd.DataFrame({'Skew':skew_feats})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Skew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>country__bw</td>\n",
       "      <td>86.567892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__su</td>\n",
       "      <td>86.567892</td>\n",
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       "    <tr>\n",
       "      <td>country__af</td>\n",
       "      <td>86.567892</td>\n",
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       "    <tr>\n",
       "      <td>country__do</td>\n",
       "      <td>86.567892</td>\n",
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       "    <tr>\n",
       "      <td>country__hn</td>\n",
       "      <td>86.567892</td>\n",
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       "    <tr>\n",
       "      <td>country__bd</td>\n",
       "      <td>86.567892</td>\n",
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       "    <tr>\n",
       "      <td>country__xg</td>\n",
       "      <td>86.567892</td>\n",
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       "    <tr>\n",
       "      <td>country__mn</td>\n",
       "      <td>86.567892</td>\n",
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       "    <tr>\n",
       "      <td>country__lv</td>\n",
       "      <td>86.567892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__mv</td>\n",
       "      <td>86.567892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__ng</td>\n",
       "      <td>86.567892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__np</td>\n",
       "      <td>86.567892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__ge</td>\n",
       "      <td>86.567892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__uy</td>\n",
       "      <td>61.200487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__vn</td>\n",
       "      <td>61.200487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__ug</td>\n",
       "      <td>61.200487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__lk</td>\n",
       "      <td>61.200487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__ve</td>\n",
       "      <td>61.200487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__bo</td>\n",
       "      <td>61.200487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__lb</td>\n",
       "      <td>61.200487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>profitability</td>\n",
       "      <td>51.068136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__hr</td>\n",
       "      <td>49.959980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__ba</td>\n",
       "      <td>49.959980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__ee</td>\n",
       "      <td>49.959980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_8_avg_profitability</td>\n",
       "      <td>47.252869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_1_avg_profitability</td>\n",
       "      <td>43.790908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__sk</td>\n",
       "      <td>43.257943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__is</td>\n",
       "      <td>38.683326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__co</td>\n",
       "      <td>38.683326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_5_avg_profitability</td>\n",
       "      <td>38.563113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_7_avg_profitability</td>\n",
       "      <td>36.786386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__pe</td>\n",
       "      <td>35.305804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__eg</td>\n",
       "      <td>35.305804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__cl</td>\n",
       "      <td>35.305804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__id</td>\n",
       "      <td>35.305804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__rs</td>\n",
       "      <td>32.680268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_2_avg_profitability</td>\n",
       "      <td>32.156559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__my</td>\n",
       "      <td>30.563459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__ua</td>\n",
       "      <td>30.563459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__bg</td>\n",
       "      <td>30.563459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__gr</td>\n",
       "      <td>28.809723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__tw</td>\n",
       "      <td>26.048912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__ph</td>\n",
       "      <td>24.934925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__il</td>\n",
       "      <td>23.951888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>production_company_1_avg_profitability</td>\n",
       "      <td>23.795339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__pl</td>\n",
       "      <td>22.289030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>production_company_2_avg_profitability</td>\n",
       "      <td>21.523944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>production_company_3_avg_profitability</td>\n",
       "      <td>21.268524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__ar</td>\n",
       "      <td>20.928477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__hu</td>\n",
       "      <td>20.334731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__sg</td>\n",
       "      <td>20.334731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__ae</td>\n",
       "      <td>19.788389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__lu</td>\n",
       "      <td>19.788389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__ro</td>\n",
       "      <td>19.283452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__fi</td>\n",
       "      <td>18.814932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__th</td>\n",
       "      <td>18.814932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__tr</td>\n",
       "      <td>18.378643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__za</td>\n",
       "      <td>17.971045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__no</td>\n",
       "      <td>17.589118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__br</td>\n",
       "      <td>17.230270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__pk</td>\n",
       "      <td>16.573145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>topic_11</td>\n",
       "      <td>14.862606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__nl</td>\n",
       "      <td>14.747996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__dk</td>\n",
       "      <td>14.326676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__cz</td>\n",
       "      <td>14.326676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__nz</td>\n",
       "      <td>13.938917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__mx</td>\n",
       "      <td>13.090204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__ir</td>\n",
       "      <td>12.937971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__kr</td>\n",
       "      <td>12.122110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__se</td>\n",
       "      <td>11.881872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__ie</td>\n",
       "      <td>11.337231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__be</td>\n",
       "      <td>10.129162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__hk</td>\n",
       "      <td>9.985353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>topic_0</td>\n",
       "      <td>9.855363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>topic_3</td>\n",
       "      <td>9.712303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__camera__still_photographer_avg_profit</td>\n",
       "      <td>8.726556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>genre__documentary</td>\n",
       "      <td>8.484241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>topic_8</td>\n",
       "      <td>8.213580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>rating__nc-17</td>\n",
       "      <td>8.072686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__cn</td>\n",
       "      <td>7.996967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>genre__western</td>\n",
       "      <td>7.679447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__jp</td>\n",
       "      <td>7.549290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__ru</td>\n",
       "      <td>7.549290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>topic_12</td>\n",
       "      <td>7.210553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__es</td>\n",
       "      <td>7.057896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__it</td>\n",
       "      <td>6.855599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__au</td>\n",
       "      <td>6.601968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_7_avg_profit</td>\n",
       "      <td>5.848810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>topic_1</td>\n",
       "      <td>5.811380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_4_avg_profit</td>\n",
       "      <td>5.526040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>topic_10</td>\n",
       "      <td>5.458936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__production__executive_producer__1_avg_profit</td>\n",
       "      <td>5.435363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>revenue</td>\n",
       "      <td>5.297126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>genre__war</td>\n",
       "      <td>5.021935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>genre__music</td>\n",
       "      <td>5.011428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_2_avg_profit</td>\n",
       "      <td>4.846296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__directing__script_supervisor_avg_profit</td>\n",
       "      <td>4.812757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_6_avg_profit</td>\n",
       "      <td>4.803592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>topic_4</td>\n",
       "      <td>4.608696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__ca</td>\n",
       "      <td>4.516493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>rating__g</td>\n",
       "      <td>4.399802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_3_avg_profit</td>\n",
       "      <td>4.328331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>genre__history</td>\n",
       "      <td>4.283270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_8_avg_profit</td>\n",
       "      <td>4.225660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__editing__editor__1_avg_profit</td>\n",
       "      <td>4.221567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__art__art_direction_avg_profit</td>\n",
       "      <td>4.156364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__costume__costume_designer_avg_profit</td>\n",
       "      <td>4.093029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>genre__animation</td>\n",
       "      <td>4.071396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__costume__makeup_artist_avg_profit</td>\n",
       "      <td>4.020596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__production__executive_producer__1_movies_before</td>\n",
       "      <td>3.867635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__de</td>\n",
       "      <td>3.801878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__sound__sound_re_recording_mixer_avg_profit</td>\n",
       "      <td>3.744505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>production_company_2_avg_profit</td>\n",
       "      <td>3.642598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>topic_14</td>\n",
       "      <td>3.595156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__camera__director_of_photography_avg_profit</td>\n",
       "      <td>3.585741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_1_avg_profit</td>\n",
       "      <td>3.584672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>production_company_1_avg_profit</td>\n",
       "      <td>3.567649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__directing__director__1_avg_profit</td>\n",
       "      <td>3.543053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__in</td>\n",
       "      <td>3.534700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__production__producer__1_avg_profit</td>\n",
       "      <td>3.487441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__fr</td>\n",
       "      <td>3.456289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>topic_6</td>\n",
       "      <td>3.380655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__art__production_design_avg_profit</td>\n",
       "      <td>3.261945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__sound__sound_designer_avg_profit</td>\n",
       "      <td>3.261534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>production_company_1_avg_revenue</td>\n",
       "      <td>3.239958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__sound__music_editor_avg_profit</td>\n",
       "      <td>3.226289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>production_company_3_avg_profit</td>\n",
       "      <td>3.219687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>production_company_2_avg_revenue</td>\n",
       "      <td>3.180541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__costume__makeup_artist_movies_before</td>\n",
       "      <td>3.169191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>genre__mystery</td>\n",
       "      <td>3.149711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_5_avg_profit</td>\n",
       "      <td>3.107859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__writing__screenplay__1_avg_profit</td>\n",
       "      <td>3.090229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>weekend</td>\n",
       "      <td>3.068831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__art__set_decoration_avg_profit</td>\n",
       "      <td>3.061764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>topic_9</td>\n",
       "      <td>3.024528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_8_movies_before</td>\n",
       "      <td>2.984202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__art__property_master_avg_profit</td>\n",
       "      <td>2.903865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__directing__director__1_movies_before</td>\n",
       "      <td>2.883752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__visualeffects__visual_effects_supervisor_movies_before</td>\n",
       "      <td>2.863533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>genre__fantasy</td>\n",
       "      <td>2.830624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>production_company_3_avg_revenue</td>\n",
       "      <td>2.820830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>budget</td>\n",
       "      <td>2.819117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__production__producer__2_movies_before</td>\n",
       "      <td>2.778709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>spoken_languages</td>\n",
       "      <td>2.766083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__camera__steadicam_operator_avg_profit</td>\n",
       "      <td>2.748012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__production__producer__2_avg_profit</td>\n",
       "      <td>2.710425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_avg_profit</td>\n",
       "      <td>2.696350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__art__art_direction_movies_before</td>\n",
       "      <td>2.670849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__production__casting_avg_profit</td>\n",
       "      <td>2.670599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__production__producer__1_movies_before</td>\n",
       "      <td>2.668082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__costume__costume_supervisor_avg_profit</td>\n",
       "      <td>2.652800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>genre__family</td>\n",
       "      <td>2.624368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__crew__stunt_coordinator_avg_profit</td>\n",
       "      <td>2.595903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__writing__screenplay__1_movies_before</td>\n",
       "      <td>2.591529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_7_movies_before</td>\n",
       "      <td>2.587607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__sound__sound_effects_editor_movies_before</td>\n",
       "      <td>2.561611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>topic_5</td>\n",
       "      <td>2.555755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>genre__horror</td>\n",
       "      <td>2.511681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_4_avg_budget</td>\n",
       "      <td>2.427378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_5_movies_before</td>\n",
       "      <td>2.394649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_8_experience</td>\n",
       "      <td>2.379599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_3_avg_budget</td>\n",
       "      <td>2.374433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__gb</td>\n",
       "      <td>2.351475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__visualeffects__visual_effects_supervisor_avg_profit</td>\n",
       "      <td>2.337573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__production__executive_producer__1_avg_budget</td>\n",
       "      <td>2.334379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__sound__supervising_sound_editor_movies_before</td>\n",
       "      <td>2.318987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__production__producer__1_avg_budget</td>\n",
       "      <td>2.312516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__directing__director__1_avg_budget</td>\n",
       "      <td>2.309207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__costume__makeup_artist_avg_budget</td>\n",
       "      <td>2.252647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_6_movies_before</td>\n",
       "      <td>2.250201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__sound__sound_effects_editor_avg_profit</td>\n",
       "      <td>2.182109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_8_avg_budget</td>\n",
       "      <td>2.181084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__editing__editor__1_avg_budget</td>\n",
       "      <td>2.179872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__sound__supervising_sound_editor_avg_profit</td>\n",
       "      <td>2.173438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__art__art_direction_avg_budget</td>\n",
       "      <td>2.161105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_4_movies_before</td>\n",
       "      <td>2.141828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__sound__supervising_sound_editor_avg_budget</td>\n",
       "      <td>2.077284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__editing__editor__1_movies_before</td>\n",
       "      <td>2.077065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_6_experience</td>\n",
       "      <td>2.071041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__sound__original_music_composer_avg_profit</td>\n",
       "      <td>2.047168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_7_experience</td>\n",
       "      <td>2.036447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__production__casting_movies_before</td>\n",
       "      <td>2.033038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_3_movies_before</td>\n",
       "      <td>2.024063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__art__property_master_movies_before</td>\n",
       "      <td>2.002930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_2_movies_before</td>\n",
       "      <td>1.992118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>genre__crime</td>\n",
       "      <td>1.977155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>collection_name_avg_profit</td>\n",
       "      <td>1.940923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_2_avg_budget</td>\n",
       "      <td>1.918474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__camera__still_photographer_movies_before</td>\n",
       "      <td>1.910052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__production__producer__2_avg_budget</td>\n",
       "      <td>1.907694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__art__production_design_avg_budget</td>\n",
       "      <td>1.880970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>genre__adventure</td>\n",
       "      <td>1.866157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>collection_name_avg_revenue</td>\n",
       "      <td>1.860169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__costume__costume_designer_avg_budget</td>\n",
       "      <td>1.843559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_7_avg_budget</td>\n",
       "      <td>1.841984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>year_avg_profitability</td>\n",
       "      <td>1.821491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_5_experience</td>\n",
       "      <td>1.806841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__sound__sound_designer_movies_before</td>\n",
       "      <td>1.799164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__production__casting_avg_budget</td>\n",
       "      <td>1.777919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__writing__screenplay__1_avg_budget</td>\n",
       "      <td>1.747988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>competition</td>\n",
       "      <td>1.738700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__sound__original_music_composer_movies_before</td>\n",
       "      <td>1.731574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_6_avg_budget</td>\n",
       "      <td>1.705066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_4_experience</td>\n",
       "      <td>1.701762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__costume__costume_designer_movies_before</td>\n",
       "      <td>1.700636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__art__set_decoration_avg_budget</td>\n",
       "      <td>1.679921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__camera__director_of_photography_avg_budget</td>\n",
       "      <td>1.657284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__crew__stunt_coordinator_avg_budget</td>\n",
       "      <td>1.652733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__costume__costume_supervisor_movies_before</td>\n",
       "      <td>1.635347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__art__property_master_avg_budget</td>\n",
       "      <td>1.620494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_3_experience</td>\n",
       "      <td>1.607510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__sound__sound_re_recording_mixer_movies_before</td>\n",
       "      <td>1.599974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_1_movies_before</td>\n",
       "      <td>1.595023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__crew__stunt_coordinator_movies_before</td>\n",
       "      <td>1.573760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>genre__romance</td>\n",
       "      <td>1.570670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__camera__director_of_photography_movies_before</td>\n",
       "      <td>1.564378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__art__set_decoration_movies_before</td>\n",
       "      <td>1.546785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_5_avg_budget</td>\n",
       "      <td>1.518773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_2_experience</td>\n",
       "      <td>1.508610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>rating__pg</td>\n",
       "      <td>1.503223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__sound__music_editor_movies_before</td>\n",
       "      <td>1.460703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__directing__script_supervisor_movies_before</td>\n",
       "      <td>1.438392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>topic_13</td>\n",
       "      <td>1.379067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_avg_movies_before</td>\n",
       "      <td>1.346072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__camera__steadicam_operator_movies_before</td>\n",
       "      <td>1.331029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__sound__music_editor_avg_budget</td>\n",
       "      <td>1.327058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__directing__script_supervisor_avg_budget</td>\n",
       "      <td>1.323953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_1_avg_budget</td>\n",
       "      <td>1.276114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_1_female</td>\n",
       "      <td>1.273273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__art__production_design_movies_before</td>\n",
       "      <td>1.257740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__camera__still_photographer_avg_budget</td>\n",
       "      <td>1.246558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__sound__sound_designer_avg_budget</td>\n",
       "      <td>1.235307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__visualeffects__visual_effects_supervisor_avg_budget</td>\n",
       "      <td>1.231560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__camera__steadicam_operator_avg_budget</td>\n",
       "      <td>1.222275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>runtime</td>\n",
       "      <td>1.209689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_8_female</td>\n",
       "      <td>1.197858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_1_experience</td>\n",
       "      <td>1.188545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__sound__sound_effects_editor_avg_budget</td>\n",
       "      <td>1.175484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>genre__action</td>\n",
       "      <td>1.172706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>genre__thriller</td>\n",
       "      <td>1.138986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__sound__original_music_composer_avg_budget</td>\n",
       "      <td>1.098719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_7_female</td>\n",
       "      <td>1.092502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>topic_2</td>\n",
       "      <td>1.079628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>rating__pg-13</td>\n",
       "      <td>1.058900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_6_female</td>\n",
       "      <td>0.966438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_5_female</td>\n",
       "      <td>0.929542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_avg_experience</td>\n",
       "      <td>0.902954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__costume__costume_supervisor_avg_budget</td>\n",
       "      <td>0.892454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>crew__sound__sound_re_recording_mixer_avg_budget</td>\n",
       "      <td>0.887151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_4_female</td>\n",
       "      <td>0.777877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_3_female</td>\n",
       "      <td>0.591210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>genre__comedy</td>\n",
       "      <td>0.576725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_avg_budget</td>\n",
       "      <td>0.551904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>rating__r</td>\n",
       "      <td>0.514884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_2_female</td>\n",
       "      <td>0.442905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>year_avg_revenue</td>\n",
       "      <td>0.349527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>month_sin</td>\n",
       "      <td>0.105477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>day_cos</td>\n",
       "      <td>0.091226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>topic_7</td>\n",
       "      <td>0.073827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_4_avg_profitability</td>\n",
       "      <td>0.066819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>genre__drama</td>\n",
       "      <td>0.037911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>day_sin</td>\n",
       "      <td>-0.047776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_8_male</td>\n",
       "      <td>-0.076934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>month_cos</td>\n",
       "      <td>-0.099386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_7_male</td>\n",
       "      <td>-0.160676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_2_male</td>\n",
       "      <td>-0.176323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_3_avg_profitability</td>\n",
       "      <td>-0.191896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_6_male</td>\n",
       "      <td>-0.212059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_3_male</td>\n",
       "      <td>-0.257838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_4_male</td>\n",
       "      <td>-0.305124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_5_male</td>\n",
       "      <td>-0.322288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_1_male</td>\n",
       "      <td>-0.763422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>country__us</td>\n",
       "      <td>-1.218285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast_6_avg_profitability</td>\n",
       "      <td>-5.361444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                   Skew\n",
       "country__bw                                                   86.567892\n",
       "country__su                                                   86.567892\n",
       "country__af                                                   86.567892\n",
       "country__do                                                   86.567892\n",
       "country__hn                                                   86.567892\n",
       "country__bd                                                   86.567892\n",
       "country__xg                                                   86.567892\n",
       "country__mn                                                   86.567892\n",
       "country__lv                                                   86.567892\n",
       "country__mv                                                   86.567892\n",
       "country__ng                                                   86.567892\n",
       "country__np                                                   86.567892\n",
       "country__ge                                                   86.567892\n",
       "country__uy                                                   61.200487\n",
       "country__vn                                                   61.200487\n",
       "country__ug                                                   61.200487\n",
       "country__lk                                                   61.200487\n",
       "country__ve                                                   61.200487\n",
       "country__bo                                                   61.200487\n",
       "country__lb                                                   61.200487\n",
       "profitability                                                 51.068136\n",
       "country__hr                                                   49.959980\n",
       "country__ba                                                   49.959980\n",
       "country__ee                                                   49.959980\n",
       "cast_8_avg_profitability                                      47.252869\n",
       "cast_1_avg_profitability                                      43.790908\n",
       "country__sk                                                   43.257943\n",
       "country__is                                                   38.683326\n",
       "country__co                                                   38.683326\n",
       "cast_5_avg_profitability                                      38.563113\n",
       "cast_7_avg_profitability                                      36.786386\n",
       "country__pe                                                   35.305804\n",
       "country__eg                                                   35.305804\n",
       "country__cl                                                   35.305804\n",
       "country__id                                                   35.305804\n",
       "country__rs                                                   32.680268\n",
       "cast_2_avg_profitability                                      32.156559\n",
       "country__my                                                   30.563459\n",
       "country__ua                                                   30.563459\n",
       "country__bg                                                   30.563459\n",
       "country__gr                                                   28.809723\n",
       "country__tw                                                   26.048912\n",
       "country__ph                                                   24.934925\n",
       "country__il                                                   23.951888\n",
       "production_company_1_avg_profitability                        23.795339\n",
       "country__pl                                                   22.289030\n",
       "production_company_2_avg_profitability                        21.523944\n",
       "production_company_3_avg_profitability                        21.268524\n",
       "country__ar                                                   20.928477\n",
       "country__hu                                                   20.334731\n",
       "country__sg                                                   20.334731\n",
       "country__ae                                                   19.788389\n",
       "country__lu                                                   19.788389\n",
       "country__ro                                                   19.283452\n",
       "country__fi                                                   18.814932\n",
       "country__th                                                   18.814932\n",
       "country__tr                                                   18.378643\n",
       "country__za                                                   17.971045\n",
       "country__no                                                   17.589118\n",
       "country__br                                                   17.230270\n",
       "country__pk                                                   16.573145\n",
       "topic_11                                                      14.862606\n",
       "country__nl                                                   14.747996\n",
       "country__dk                                                   14.326676\n",
       "country__cz                                                   14.326676\n",
       "country__nz                                                   13.938917\n",
       "country__mx                                                   13.090204\n",
       "country__ir                                                   12.937971\n",
       "country__kr                                                   12.122110\n",
       "country__se                                                   11.881872\n",
       "country__ie                                                   11.337231\n",
       "country__be                                                   10.129162\n",
       "country__hk                                                   9.985353 \n",
       "topic_0                                                       9.855363 \n",
       "topic_3                                                       9.712303 \n",
       "crew__camera__still_photographer_avg_profit                   8.726556 \n",
       "genre__documentary                                            8.484241 \n",
       "topic_8                                                       8.213580 \n",
       "rating__nc-17                                                 8.072686 \n",
       "country__cn                                                   7.996967 \n",
       "genre__western                                                7.679447 \n",
       "country__jp                                                   7.549290 \n",
       "country__ru                                                   7.549290 \n",
       "topic_12                                                      7.210553 \n",
       "country__es                                                   7.057896 \n",
       "country__it                                                   6.855599 \n",
       "country__au                                                   6.601968 \n",
       "cast_7_avg_profit                                             5.848810 \n",
       "topic_1                                                       5.811380 \n",
       "cast_4_avg_profit                                             5.526040 \n",
       "topic_10                                                      5.458936 \n",
       "crew__production__executive_producer__1_avg_profit            5.435363 \n",
       "revenue                                                       5.297126 \n",
       "genre__war                                                    5.021935 \n",
       "genre__music                                                  5.011428 \n",
       "cast_2_avg_profit                                             4.846296 \n",
       "crew__directing__script_supervisor_avg_profit                 4.812757 \n",
       "cast_6_avg_profit                                             4.803592 \n",
       "topic_4                                                       4.608696 \n",
       "country__ca                                                   4.516493 \n",
       "rating__g                                                     4.399802 \n",
       "cast_3_avg_profit                                             4.328331 \n",
       "genre__history                                                4.283270 \n",
       "cast_8_avg_profit                                             4.225660 \n",
       "crew__editing__editor__1_avg_profit                           4.221567 \n",
       "crew__art__art_direction_avg_profit                           4.156364 \n",
       "crew__costume__costume_designer_avg_profit                    4.093029 \n",
       "genre__animation                                              4.071396 \n",
       "crew__costume__makeup_artist_avg_profit                       4.020596 \n",
       "crew__production__executive_producer__1_movies_before         3.867635 \n",
       "country__de                                                   3.801878 \n",
       "crew__sound__sound_re_recording_mixer_avg_profit              3.744505 \n",
       "production_company_2_avg_profit                               3.642598 \n",
       "topic_14                                                      3.595156 \n",
       "crew__camera__director_of_photography_avg_profit              3.585741 \n",
       "cast_1_avg_profit                                             3.584672 \n",
       "production_company_1_avg_profit                               3.567649 \n",
       "crew__directing__director__1_avg_profit                       3.543053 \n",
       "country__in                                                   3.534700 \n",
       "crew__production__producer__1_avg_profit                      3.487441 \n",
       "country__fr                                                   3.456289 \n",
       "topic_6                                                       3.380655 \n",
       "crew__art__production_design_avg_profit                       3.261945 \n",
       "crew__sound__sound_designer_avg_profit                        3.261534 \n",
       "production_company_1_avg_revenue                              3.239958 \n",
       "crew__sound__music_editor_avg_profit                          3.226289 \n",
       "production_company_3_avg_profit                               3.219687 \n",
       "production_company_2_avg_revenue                              3.180541 \n",
       "crew__costume__makeup_artist_movies_before                    3.169191 \n",
       "genre__mystery                                                3.149711 \n",
       "cast_5_avg_profit                                             3.107859 \n",
       "crew__writing__screenplay__1_avg_profit                       3.090229 \n",
       "weekend                                                       3.068831 \n",
       "crew__art__set_decoration_avg_profit                          3.061764 \n",
       "topic_9                                                       3.024528 \n",
       "cast_8_movies_before                                          2.984202 \n",
       "crew__art__property_master_avg_profit                         2.903865 \n",
       "crew__directing__director__1_movies_before                    2.883752 \n",
       "crew__visualeffects__visual_effects_supervisor_movies_before  2.863533 \n",
       "genre__fantasy                                                2.830624 \n",
       "production_company_3_avg_revenue                              2.820830 \n",
       "budget                                                        2.819117 \n",
       "crew__production__producer__2_movies_before                   2.778709 \n",
       "spoken_languages                                              2.766083 \n",
       "crew__camera__steadicam_operator_avg_profit                   2.748012 \n",
       "crew__production__producer__2_avg_profit                      2.710425 \n",
       "cast_avg_profit                                               2.696350 \n",
       "crew__art__art_direction_movies_before                        2.670849 \n",
       "crew__production__casting_avg_profit                          2.670599 \n",
       "crew__production__producer__1_movies_before                   2.668082 \n",
       "crew__costume__costume_supervisor_avg_profit                  2.652800 \n",
       "genre__family                                                 2.624368 \n",
       "crew__crew__stunt_coordinator_avg_profit                      2.595903 \n",
       "crew__writing__screenplay__1_movies_before                    2.591529 \n",
       "cast_7_movies_before                                          2.587607 \n",
       "crew__sound__sound_effects_editor_movies_before               2.561611 \n",
       "topic_5                                                       2.555755 \n",
       "genre__horror                                                 2.511681 \n",
       "cast_4_avg_budget                                             2.427378 \n",
       "cast_5_movies_before                                          2.394649 \n",
       "cast_8_experience                                             2.379599 \n",
       "cast_3_avg_budget                                             2.374433 \n",
       "country__gb                                                   2.351475 \n",
       "crew__visualeffects__visual_effects_supervisor_avg_profit     2.337573 \n",
       "crew__production__executive_producer__1_avg_budget            2.334379 \n",
       "crew__sound__supervising_sound_editor_movies_before           2.318987 \n",
       "crew__production__producer__1_avg_budget                      2.312516 \n",
       "crew__directing__director__1_avg_budget                       2.309207 \n",
       "crew__costume__makeup_artist_avg_budget                       2.252647 \n",
       "cast_6_movies_before                                          2.250201 \n",
       "crew__sound__sound_effects_editor_avg_profit                  2.182109 \n",
       "cast_8_avg_budget                                             2.181084 \n",
       "crew__editing__editor__1_avg_budget                           2.179872 \n",
       "crew__sound__supervising_sound_editor_avg_profit              2.173438 \n",
       "crew__art__art_direction_avg_budget                           2.161105 \n",
       "cast_4_movies_before                                          2.141828 \n",
       "crew__sound__supervising_sound_editor_avg_budget              2.077284 \n",
       "crew__editing__editor__1_movies_before                        2.077065 \n",
       "cast_6_experience                                             2.071041 \n",
       "crew__sound__original_music_composer_avg_profit               2.047168 \n",
       "cast_7_experience                                             2.036447 \n",
       "crew__production__casting_movies_before                       2.033038 \n",
       "cast_3_movies_before                                          2.024063 \n",
       "crew__art__property_master_movies_before                      2.002930 \n",
       "cast_2_movies_before                                          1.992118 \n",
       "genre__crime                                                  1.977155 \n",
       "collection_name_avg_profit                                    1.940923 \n",
       "cast_2_avg_budget                                             1.918474 \n",
       "crew__camera__still_photographer_movies_before                1.910052 \n",
       "crew__production__producer__2_avg_budget                      1.907694 \n",
       "crew__art__production_design_avg_budget                       1.880970 \n",
       "genre__adventure                                              1.866157 \n",
       "collection_name_avg_revenue                                   1.860169 \n",
       "crew__costume__costume_designer_avg_budget                    1.843559 \n",
       "cast_7_avg_budget                                             1.841984 \n",
       "year_avg_profitability                                        1.821491 \n",
       "cast_5_experience                                             1.806841 \n",
       "crew__sound__sound_designer_movies_before                     1.799164 \n",
       "crew__production__casting_avg_budget                          1.777919 \n",
       "crew__writing__screenplay__1_avg_budget                       1.747988 \n",
       "competition                                                   1.738700 \n",
       "crew__sound__original_music_composer_movies_before            1.731574 \n",
       "cast_6_avg_budget                                             1.705066 \n",
       "cast_4_experience                                             1.701762 \n",
       "crew__costume__costume_designer_movies_before                 1.700636 \n",
       "crew__art__set_decoration_avg_budget                          1.679921 \n",
       "crew__camera__director_of_photography_avg_budget              1.657284 \n",
       "crew__crew__stunt_coordinator_avg_budget                      1.652733 \n",
       "crew__costume__costume_supervisor_movies_before               1.635347 \n",
       "crew__art__property_master_avg_budget                         1.620494 \n",
       "cast_3_experience                                             1.607510 \n",
       "crew__sound__sound_re_recording_mixer_movies_before           1.599974 \n",
       "cast_1_movies_before                                          1.595023 \n",
       "crew__crew__stunt_coordinator_movies_before                   1.573760 \n",
       "genre__romance                                                1.570670 \n",
       "crew__camera__director_of_photography_movies_before           1.564378 \n",
       "crew__art__set_decoration_movies_before                       1.546785 \n",
       "cast_5_avg_budget                                             1.518773 \n",
       "cast_2_experience                                             1.508610 \n",
       "rating__pg                                                    1.503223 \n",
       "crew__sound__music_editor_movies_before                       1.460703 \n",
       "crew__directing__script_supervisor_movies_before              1.438392 \n",
       "topic_13                                                      1.379067 \n",
       "cast_avg_movies_before                                        1.346072 \n",
       "crew__camera__steadicam_operator_movies_before                1.331029 \n",
       "crew__sound__music_editor_avg_budget                          1.327058 \n",
       "crew__directing__script_supervisor_avg_budget                 1.323953 \n",
       "cast_1_avg_budget                                             1.276114 \n",
       "cast_1_female                                                 1.273273 \n",
       "crew__art__production_design_movies_before                    1.257740 \n",
       "crew__camera__still_photographer_avg_budget                   1.246558 \n",
       "crew__sound__sound_designer_avg_budget                        1.235307 \n",
       "crew__visualeffects__visual_effects_supervisor_avg_budget     1.231560 \n",
       "crew__camera__steadicam_operator_avg_budget                   1.222275 \n",
       "runtime                                                       1.209689 \n",
       "cast_8_female                                                 1.197858 \n",
       "cast_1_experience                                             1.188545 \n",
       "crew__sound__sound_effects_editor_avg_budget                  1.175484 \n",
       "genre__action                                                 1.172706 \n",
       "genre__thriller                                               1.138986 \n",
       "crew__sound__original_music_composer_avg_budget               1.098719 \n",
       "cast_7_female                                                 1.092502 \n",
       "topic_2                                                       1.079628 \n",
       "rating__pg-13                                                 1.058900 \n",
       "cast_6_female                                                 0.966438 \n",
       "cast_5_female                                                 0.929542 \n",
       "cast_avg_experience                                           0.902954 \n",
       "crew__costume__costume_supervisor_avg_budget                  0.892454 \n",
       "crew__sound__sound_re_recording_mixer_avg_budget              0.887151 \n",
       "cast_4_female                                                 0.777877 \n",
       "cast_3_female                                                 0.591210 \n",
       "genre__comedy                                                 0.576725 \n",
       "cast_avg_budget                                               0.551904 \n",
       "rating__r                                                     0.514884 \n",
       "cast_2_female                                                 0.442905 \n",
       "year_avg_revenue                                              0.349527 \n",
       "month_sin                                                     0.105477 \n",
       "day_cos                                                       0.091226 \n",
       "topic_7                                                       0.073827 \n",
       "cast_4_avg_profitability                                      0.066819 \n",
       "genre__drama                                                  0.037911 \n",
       "day_sin                                                      -0.047776 \n",
       "cast_8_male                                                  -0.076934 \n",
       "month_cos                                                    -0.099386 \n",
       "cast_7_male                                                  -0.160676 \n",
       "cast_2_male                                                  -0.176323 \n",
       "cast_3_avg_profitability                                     -0.191896 \n",
       "cast_6_male                                                  -0.212059 \n",
       "cast_3_male                                                  -0.257838 \n",
       "cast_4_male                                                  -0.305124 \n",
       "cast_5_male                                                  -0.322288 \n",
       "cast_1_male                                                  -0.763422 \n",
       "country__us                                                  -1.218285 \n",
       "cast_6_avg_profitability                                     -5.361444 "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = work_df.budget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa75064f390>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa74fda8d90>"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(b**(0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa750556110>"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.log(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}