FinalProject_Group075.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What makes a high rating score on IMDb?  - COGS 108 Final Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TEAM NAME:  \n",
    "Team MRP - Movie Rating Predictors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TEAM MEMBERS: \n",
    "Shuibenyang Yuan XXXXXXXXX<br>\n",
    "Meliza Ramos Suarez XXXXXXXXX<br>\n",
    "Sutianyi Wen XXXXXXXXX<br>\n",
    "Froilan Grepo XXXXXXXXX<br>\n",
    "Cristina Ahamad XXXXXXXXX<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 DATA SCIENCE QUESTION(S): \n",
    "According to the data from “The statistic Portal”, the global box office revenue will reach 50 billion U.S. dollar in 2020 and 19% Americans go to movie theatres once a month. Because of the film industry's prosperity in recent years, there are nearly 2000 movies made worldwide each year. As a group of movie fans, we usually find that there are many boring movies existing in the market and we often need to check the IMDb rating score before buying the ticket. After looking into the data set found on the Imdb website, we noticed that each movie has 28 features including. So we are curious about which top 5 features are most related to a high Imdb score? \n",
    "\n",
    "Before actually starting process the data set, we made several assumptions based on our experience of watching a movie and search “what kind of movies are more likely to have high Imdb score ” online. The popularity of the movie director, poster color, advertisement budgeting, production budget and movie genre might be the top5 important feature. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Hypothesis: (will be change based on features)\n",
    "After the first meeting, we came up with some factors that may influence movie ratings.<br>\n",
    "### The director of the movie<br>\n",
    "    -The more famous the director is, the higher the rating will be<br>\n",
    "    -The larger number of the directors is, the higher the rating will be<br>\n",
    "    -The more experienced the director is, the higher the rating will be<br>\n",
    "### The color of the poster<br>\n",
    "    -The movies with light colors on posters are more likely to have a higher rating score than those with dark colors on posters.<br>\n",
    "### Advertisement strategies<br>\n",
    "    -How do producers advertise their movies will affect the rating score.<br>\n",
    "    -High exposure to media online tend to lead a rating score.<br>\n",
    "### The budget of the movie<br>\n",
    "    -The higher the budget is, the higher the rating score will be.<br>\n",
    "### The genre of the movie<br>\n",
    "    -Sci-fi movies are more likely to have higher ratings than Romance movies.<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Background:\n",
    "The dataset that we will be examining for this project consists of a collection of thousands of movies over a span of 100 years from 66 different countries. The data collected from these movies were used to determine how movie ratings are created through an abundant number of different ways such as social media, advertisement, the actors who are involved in the movie and many other things were analyzed as well. Through this dataset our group was able to analyze the different input that is taken from social media, advertisements, and all the different categories that are used to rate movies in order to come up with an overall rating for these movies.\n",
    " A similar project that our group found was “IMDB Exploratory Data Analysis Project” by Ilya Ezepov. Ezepov’s dataset project is very similar to the path our group has decided to take because Ezepov also explores movie ratings through a number of different categories but instead of figuring the exact movie rating through these categories, Ezepov takes a different approach of predicting movie ratings through movie characteristics and the overall production budget. Some of Ezepov’s findings consisted of sound and color being a huge hit in the movie industry, political and economical events impact the audience's movie choices, and economic events also impact the amount of views per movie. \n",
    "\tIn addition, another similar project that we found was “Movie Exploratory Analysis Using IMDB Datasets” by Yash Sharma. Sharma’s project explores the ideas of which country produces the most movies and what kind of movies are produced the most. Sharma’s project examines a similar dataset as the one our group has decided to analyze but takes a different approach while analyzing it. Some of the insights that Sharma was able to collect through the dataset used for his project consisted of Drama, Comedy, and Thriller are the top genres and that the US has the most thriving movie industry. Both of these projects are very similar to the approach our group has decided to take and can be a great resource for the creation of our final project. \n",
    "    \n",
    "    References Link:\n",
    "IMDB Exploratory Data Analysis Project - Ilya Ezepov:\n",
    "http://rstudio-pubs-static.s3.amazonaws.com/52740_40aabe898b7a46b99c2b3f4ca3042e8a.html\n",
    "Movie Exploratory Analysis Using IMDB Datasets - Yash Sharma: \n",
    "https://rpubs.com/yash91sharma/dw_project_ys\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 ETHICAL CONSIDERATIONS: \n",
    "### Permission to the dataset:\n",
    "The dataset we will use is allowed for public access, meaning no necessary permission is needed. The dataset is from data.world which is an open source dataset website. The dataset is provided by Chuan Sun who scraped tons of metadata using a combination of www.the-numbers.com, IMDB.com, and a Python library called \"scrapy\". \n",
    "### Privacy Concern: \n",
    "Although the dataset we use is completely public, we still have our privacy concerns regarding our dataset. For instance, the dataset reveals 2399 unique director names, thousands of actors/actresses and the number of likes on their Facebooks. In this case, we decide to comply with the Safe Harbor Method and anonymize our dataset.\n",
    "### Potential Biases:\n",
    "One of the potential biases of the dataset is the countries of the movies. Since the dataset only contains the information of about 66 countries, it can only apply to the IMDB score over those countries. Another potential bias is that among 47 distinct languages used in these films, English dominates other languages in terms of use, there is a possibility that majority of the critics and user base consist of \"Westerners\". The IMDB score itself can only represent the rating of movies with English-speaking users. The number of critics who rate the movie can also affect the IMDB score since each movies are reviewed by a different number of people.\n",
    "### Bias Considertation:\n",
    "There might be dataset bias since all the datasets are collected from Imdb’s official website. The \n",
    "scores of movies only reflects the Imdb users’ likeness but can’t be used to represent everyone’s \n",
    "opinion toward each movie.\n",
    "Moreover, the dataset might not be big enough to draw a solid conclusion about what 5 features \n",
    "are most important to a high Imdb Score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Data Description:\n",
    "Dataset Name: IMDB 5000 Movie Dataset<br>\n",
    "Dataset Link: https://data.world/popculture/imdb-5000-movie-dataset<br>\n",
    "Number of Observations: 5043<br>\n",
    "Number of Features: 29<br>\n",
    "Dataset Description:<br>\n",
    "This dataset shows there are many influential factors towards the IMDB rating of each movie. With 29 variables and 5043 describing (almost) every aspect of IMDB rating of different movies spanning across 100 years and 66 countries. There are 2399 unique director names, and thousands of actors/actresses. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Data Parsing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import patsy\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.lines as mlines\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = pd.read_csv('movie_metadata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>director_name</th>\n",
       "      <th>num_critic_for_reviews</th>\n",
       "      <th>duration</th>\n",
       "      <th>director_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>gross</th>\n",
       "      <th>genres</th>\n",
       "      <th>...</th>\n",
       "      <th>num_user_for_reviews</th>\n",
       "      <th>language</th>\n",
       "      <th>country</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>budget</th>\n",
       "      <th>title_year</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>movie_facebook_likes</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Color</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>723.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>Joel David Moore</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>760505847.0</td>\n",
       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
       "      <td>...</td>\n",
       "      <td>3054.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>237000000.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>1.78</td>\n",
       "      <td>33000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Color</td>\n",
       "      <td>Gore Verbinski</td>\n",
       "      <td>302.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>563.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Orlando Bloom</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>309404152.0</td>\n",
       "      <td>Action|Adventure|Fantasy</td>\n",
       "      <td>...</td>\n",
       "      <td>1238.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>300000000.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Color</td>\n",
       "      <td>Sam Mendes</td>\n",
       "      <td>602.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>Rory Kinnear</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>200074175.0</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "      <td>...</td>\n",
       "      <td>994.0</td>\n",
       "      <td>English</td>\n",
       "      <td>UK</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>245000000.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2.35</td>\n",
       "      <td>85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Color</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>813.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>Christian Bale</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>448130642.0</td>\n",
       "      <td>Action|Thriller</td>\n",
       "      <td>...</td>\n",
       "      <td>2701.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>2.35</td>\n",
       "      <td>164000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Doug Walker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rob Walker</td>\n",
       "      <td>131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 28 columns</p>\n",
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      "text/plain": [
       "   color      director_name  num_critic_for_reviews  duration  \\\n",
       "0  Color      James Cameron                   723.0     178.0   \n",
       "1  Color     Gore Verbinski                   302.0     169.0   \n",
       "2  Color         Sam Mendes                   602.0     148.0   \n",
       "3  Color  Christopher Nolan                   813.0     164.0   \n",
       "4    NaN        Doug Walker                     NaN       NaN   \n",
       "\n",
       "   director_facebook_likes  actor_3_facebook_likes      actor_2_name  \\\n",
       "0                      0.0                   855.0  Joel David Moore   \n",
       "1                    563.0                  1000.0     Orlando Bloom   \n",
       "2                      0.0                   161.0      Rory Kinnear   \n",
       "3                  22000.0                 23000.0    Christian Bale   \n",
       "4                    131.0                     NaN        Rob Walker   \n",
       "\n",
       "   actor_1_facebook_likes        gross                           genres  ...  \\\n",
       "0                  1000.0  760505847.0  Action|Adventure|Fantasy|Sci-Fi  ...   \n",
       "1                 40000.0  309404152.0         Action|Adventure|Fantasy  ...   \n",
       "2                 11000.0  200074175.0        Action|Adventure|Thriller  ...   \n",
       "3                 27000.0  448130642.0                  Action|Thriller  ...   \n",
       "4                   131.0          NaN                      Documentary  ...   \n",
       "\n",
       "  num_user_for_reviews language  country  content_rating       budget  \\\n",
       "0               3054.0  English      USA           PG-13  237000000.0   \n",
       "1               1238.0  English      USA           PG-13  300000000.0   \n",
       "2                994.0  English       UK           PG-13  245000000.0   \n",
       "3               2701.0  English      USA           PG-13  250000000.0   \n",
       "4                  NaN      NaN      NaN             NaN          NaN   \n",
       "\n",
       "   title_year actor_2_facebook_likes imdb_score  aspect_ratio  \\\n",
       "0      2009.0                  936.0        7.9          1.78   \n",
       "1      2007.0                 5000.0        7.1          2.35   \n",
       "2      2015.0                  393.0        6.8          2.35   \n",
       "3      2012.0                23000.0        8.5          2.35   \n",
       "4         NaN                   12.0        7.1           NaN   \n",
       "\n",
       "  movie_facebook_likes  \n",
       "0                33000  \n",
       "1                    0  \n",
       "2                85000  \n",
       "3               164000  \n",
       "4                    0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_column_type = pd.Series({'color' : 'categorical',\n",
    " 'director_name': 'nominal',\n",
    " 'num_critic_for_reviews' : 'numerical', \n",
    " 'duration': 'numerical',\n",
    "'director_facebook_likes' : 'numerical', \n",
    " 'actor_3_facebook_likes': 'numerical',\n",
    " 'actor_2_name': 'nominal',\n",
    "'actor_1_facebook_likes': 'numerical',\n",
    " 'gross': 'numerical',\n",
    " 'genres':'categorical',\n",
    " 'actor_1_name': 'nominal',\n",
    "'movie_title': 'nominal',\n",
    " 'num_voted_users': 'numerical',\n",
    " 'cast_total_facebook_likes': 'numerical',\n",
    "'actor_3_name': 'categorical',\n",
    " 'facenumber_in_poster': 'numerical',\n",
    " 'plot_keywords': 'nominal',\n",
    "'movie_imdb_link': 'nominal', \n",
    " 'num_user_for_reviews': 'numerical', \n",
    " 'language' : 'categorical',\n",
    " 'country': 'categorical',\n",
    "'content_rating': 'ordinal',\n",
    " 'budget': 'numerical', \n",
    " 'title_year': 'ordinal', \n",
    " 'actor_2_facebook_likes': 'numerical',\n",
    "'imdb_score': 'ordinal', \n",
    " 'aspect_ratio' : 'categorical',\n",
    " 'movie_facebook_likes' : 'numerical'}, name = 'type of data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Data Type Summery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>director_facebook_likes</th>\n",
       "      <td>numerical</td>\n",
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       "      <th>actor_3_facebook_likes</th>\n",
       "      <td>numerical</td>\n",
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       "      <th>actor_2_name</th>\n",
       "      <td>nominal</td>\n",
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       "      <th>actor_1_facebook_likes</th>\n",
       "      <td>numerical</td>\n",
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       "      <th>gross</th>\n",
       "      <td>numerical</td>\n",
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       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <td>categorical</td>\n",
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       "    <tr>\n",
       "      <th>actor_1_name</th>\n",
       "      <td>nominal</td>\n",
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       "      <th>movie_title</th>\n",
       "      <td>nominal</td>\n",
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       "    <tr>\n",
       "      <th>num_voted_users</th>\n",
       "      <td>numerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <td>numerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actor_3_name</th>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>facenumber_in_poster</th>\n",
       "      <td>numerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plot_keywords</th>\n",
       "      <td>nominal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_imdb_link</th>\n",
       "      <td>nominal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_user_for_reviews</th>\n",
       "      <td>numerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language</th>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>content_rating</th>\n",
       "      <td>ordinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>budget</th>\n",
       "      <td>numerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title_year</th>\n",
       "      <td>ordinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <td>numerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imdb_score</th>\n",
       "      <td>ordinal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aspect_ratio</th>\n",
       "      <td>categorical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_facebook_likes</th>\n",
       "      <td>numerical</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          type of data\n",
       "color                      categorical\n",
       "director_name                  nominal\n",
       "num_critic_for_reviews       numerical\n",
       "duration                     numerical\n",
       "director_facebook_likes      numerical\n",
       "actor_3_facebook_likes       numerical\n",
       "actor_2_name                   nominal\n",
       "actor_1_facebook_likes       numerical\n",
       "gross                        numerical\n",
       "genres                     categorical\n",
       "actor_1_name                   nominal\n",
       "movie_title                    nominal\n",
       "num_voted_users              numerical\n",
       "cast_total_facebook_likes    numerical\n",
       "actor_3_name               categorical\n",
       "facenumber_in_poster         numerical\n",
       "plot_keywords                  nominal\n",
       "movie_imdb_link                nominal\n",
       "num_user_for_reviews         numerical\n",
       "language                   categorical\n",
       "country                    categorical\n",
       "content_rating                 ordinal\n",
       "budget                       numerical\n",
       "title_year                     ordinal\n",
       "actor_2_facebook_likes       numerical\n",
       "imdb_score                     ordinal\n",
       "aspect_ratio               categorical\n",
       "movie_facebook_likes         numerical"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_column_type.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1a787240>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEpCAYAAACDc9l6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAEwBJREFUeJzt3XuQZGV9xvHvAyt4A8EwMSriojEkiBh0jApECogGA/GSgIEqvOuqiZFoSsUyVV6SlJSIiWKibiFKlGgSvKFGhUIFjYruIsrNWxQURRkEFUEj4C9/9Blndtnd2b7snOl3v5+qqZlzuod+qmt49vR73vOeVBWSpOm3Q98BJEmTYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGrFqOV9sjz32qNWrVy/nS0rS1Fu/fv11VTWz1POWtdBXr17NunXrlvMlJWnqJblqa57nkIskNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpEct6YdG2sPrEj/QdYatcedKRfUeQ1DiP0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUiCULPcnpSa5NcumifScn+WqSryR5f5Ldtm1MSdJStuYI/R3AERvtOxfYr6r2B74OvGzCuSRJQ1qy0KvqAuD6jfadU1W3dpufB/bcBtkkSUOYxBj6M4CPbu7BJGuSrEuybm5ubgIvJ0nalLEKPcnLgVuBMzf3nKpaW1WzVTU7MzMzzstJkrZg5DsWJXkqcBRweFXV5CJJkkYxUqEnOQJ4KXBIVd082UiSpFFszbTFdwOfA/ZJcnWSZwJvAnYBzk1ycZK3bOOckqQlLHmEXlXHbWL327ZBFknSGLxSVJIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGLFnoSU5Pcm2SSxftu3uSc5N8o/u++7aNKUlaytYcob8DOGKjfScC51XVA4Dzum1JUo+WLPSqugC4fqPdjwfO6H4+A3jChHNJkoY06hj6ParqGoDu+29u7olJ1iRZl2Td3NzciC8nSVrKNj8pWlVrq2q2qmZnZma29ctJ0nZr1EL/YZJ7AnTfr51cJEnSKEYt9LOBp3Y/PxX44GTiSJJGtTXTFt8NfA7YJ8nVSZ4JnAQ8Osk3gEd325KkHq1a6glVddxmHjp8wlkkSWPwSlFJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGjFWoSd5YZLLklya5N1J7jipYJKk4Yxc6EnuDbwAmK2q/YAdgWMnFUySNJxxh1xWAXdKsgq4M/D98SNJkkYxcqFX1feA1wHfAa4BflJV52z8vCRrkqxLsm5ubm70pJKkLRpnyGV34PHA3sC9gLskOX7j51XV2qqararZmZmZ0ZNKkrZonCGXPwK+XVVzVXUL8D7gwMnEkiQNa5xC/w7wiCR3ThLgcOCKycSSJA1rnDH0C4GzgIuAS7r/1toJ5ZIkDWnVOL9cVa8AXjGhLJKkMXilqCQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNGKvQk+yW5KwkX01yRZJHTiqYJGk4q8b8/TcAH6uqo5PsBNx5ApkkSSMYudCT7Ao8CngaQFX9EvjlZGJJkoY1zhH6/YA54O1JHgysB06oqpsWPynJGmANwF577TXGy2k5rD7xI31HWNKVJx3Zd4StMg3vJUzP+6mljTOGvgp4CPDmqjoAuAk4ceMnVdXaqpqtqtmZmZkxXk6StCXjFPrVwNVVdWG3fRaDgpck9WDkQq+qHwDfTbJPt+tw4PKJpJIkDW3cWS5/DZzZzXD5FvD08SNJkkYxVqFX1cXA7ISySJLG4JWiktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpEWMXepIdk3wpyYcnEUiSNJpJHKGfAFwxgf+OJGkMYxV6kj2BI4HTJhNHkjSqcY/Q/xl4CfCrCWSRJI1h5EJPchRwbVWtX+J5a5KsS7Jubm5u1JeTJC1hnCP0g4DHJbkSeA9wWJJ3bfykqlpbVbNVNTszMzPGy0mStmTkQq+ql1XVnlW1GjgW+ERVHT+xZJKkoTgPXZIasWoS/5Gq+hTwqUn8tyRJo/EIXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJasRE1nKRpG1t9Ykf6TvCVrnypCN7e22P0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEaMXOhJ7pPkk0muSHJZkhMmGUySNJxxFue6FfjbqrooyS7A+iTnVtXlE8omSRrCyEfoVXVNVV3U/XwjcAVw70kFkyQNZyJj6ElWAwcAF27isTVJ1iVZNzc3N4mXkyRtwtiFnuSuwHuBv6mqn278eFWtrarZqpqdmZkZ9+UkSZsxVqEnuQODMj+zqt43mUiSpFGMM8slwNuAK6rq9ZOLJEkaxThH6AcBTwYOS3Jx9/UnE8olSRrSyNMWq+ozQCaYRZI0Bq8UlaRGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRYxV6kiOSfC3JN5OcOKlQkqThjVzoSXYE/gV4LLAvcFySfScVTJI0nHGO0P8A+GZVfauqfgm8B3j8ZGJJkoaVqhrtF5OjgSOq6lnd9pOBh1fV8zd63hpgTbe5D/C10eMumz2A6/oO0RDfz8nxvZysaXk/71tVM0s9adUYL5BN7Lvdvw5VtRZYO8brLLsk66pqtu8crfD9nBzfy8lq7f0cZ8jlauA+i7b3BL4/XhxJ0qjGKfQvAg9IsneSnYBjgbMnE0uSNKyRh1yq6tYkzwc+DuwInF5Vl00sWb+maohoCvh+To7v5WQ19X6OfFJUkrSyeKWoJDXCQpekRljoktQIC12SGjHOhUVNSHL3LT1eVdcvV5Zpl+TPtvR4Vb1vubJI26PtvtCB9QyucN3cla/3W944U+1Pt/BYARb6EJKcyiauvp5XVS9YxjhTLcklbPq9DFBVtf8yR9omtvtCr6q9+87Qiqp6et8ZGrOu7wANOarvAMvBeeiLJNkdeABwx/l9VXVBf4mmV5IjgQey4Xv56v4SSe3b7o/Q5yV5FnACgzVpLgYeAXwOOKzPXNMoyVuAOwOHAqcBRwNf6DXUFEsyA7yUwX0HFv8D6d/mkJI8AjgV+D1gJwZXud9UVbv2GmxCnOWy4ATgYcBVVXUocAAw12+kqXVgVT0FuKGqXgU8kg0XctNwzgSuAPYGXgVcyWAtJQ3vTcBxwDeAOwHPYlDwTbDQF/yiqn4BkGTnqvoqg/XbNbyfd99vTnIv4BYGZaTR/EZVvQ24parOr6pnMPgEqRFU1TeBHavqtqp6O4NPkk1wyGXB1Ul2Az4AnJvkBlwOeFQf7t7Lk4GLGMwuOK3fSFPtlu77Nd25ie8zGBrU8G7uVoe9OMlrgWuAu/ScaWI8KboJSQ4B7gZ8rLu9nkaUZGfgjlX1k76zTKskRwGfZjBsdSqwK/CqqnK56iEluS/wQwbj5y9k8P/5v3ZH7VPPQu90J0suq6obu+1dgH2r6sJ+k02nJAcCq1n0KbCq/q23QNJ2wDH0BW8GfrZo+6Zun4aU5J3A64CDGZxofhjQzG2+lluSM7ohrPnt3ZOc3memaZXkoCTnJvl6km/Nf/Wda1IcQ1+QWvRxpap+lcT3ZzSzDD7d+PFvMvavqh/Pb1TVDUkO6DPQFHsbg6GW9cBtPWeZOI/QF3wryQuS3KH7OgFo5l/uZXYp8Ft9h2jIDt1Fb8Cv1x/yYGM0P6mqj1bVtVX1o/mvvkNNin8UC54LvBH4OwazMs4D1vSaaHrtAVye5AvA/83vrKrH9Rdpqp0CfDbJWd32McA/9phnmn0yyckM1hVa/Ld5UX+RJseTopq4bpbQ7VTV+cudpRVJ9mVw1XKA86rq8p4jTaUkn9zE7mrlqtvtvtCTvKSqXru5le1c0U59SbJrVf10c0s8u7SzNuaQy+CSanBlu7El+UxVHZzkRjb8x3F+idIm1stYRv/OYJXA+SWe5wWXdh5KkuOr6l1JXrSpx6vq9cudaVvY7gu9qj6UZEdgv6p6cd95pllVHdx936XvLC2oqqOSBDikqr7Td54pN381aNN/m9v9kMu8JJ9oZRxtJehmZdyHDS8sauLE03JLsr6qHtp3Dq182/0R+iJfSnI28F8MLioCvG3aKJL8PfA0BtM+f9XtLlyKeFSfT/KwqnKFxREleeOWHm/lXJmFvuDuwI/YsHS8bdpongTc33VwJuZQ4DlJrmJwsNHUbdOWyfru+0EM1pX/j277mEWPTT2HXDRxSd4LPK+qru07Swu6BaVup6quWu4s066btviYqrql274DcE53D4Sp5xF6J8nvMFi75R5VtV+S/YHHVdU/9BxtGr2GwRDWpXhh0diq6qokDwb+sNv16ar6cp+Zpti9GJwYnZ/yedduXxM8Qu8kOR94MfDWqjqg23dpVe3Xb7Lpk+Qy4K3AJSyMoXth0Yi6ZSiezcLw3xOBtVXVzJ12lkuSpwOvBOYvMDoEeGVVndFbqAmy0DtJvlhVD0vypUWFfnFV/X7f2aZNkvOrapNXi2p4Sb4CPLKqbuq27wJ8zjH04XRTQPdkcMOQh3e7L6yqH/SXarIccllwXZL7013AkeRoBncz0fDWJ3kNcDYNrpfRg7DhyoC3dfs0hKqqJB/opoB+sO8824KFvuCvgLXA7yb5HvBt4Ph+I02t+aVdF9/30mmLo3s7cGGS93fbT2CwDKyG1/QUUIdcNtJ9nN1h/s5F0kqQ5CEMbhgS4IKq+lLPkaZSkssZ3Pz9ShqcAmqhd7o7wjyF2982rYkLDpZTkrsBrwAe1e06H3i19xUdzWYW57pxfuqdtl43BXR3FmYMXQD8uJUpoN7gYsF/MyjzSxhcaDD/peGdDtzI4AKjJwE/ZTBsoNFcBMwBXwe+0f387SQXJXFJgOE8AXgngzX7Z7qfm5lO6xF6J8lFVfWQvnO0YFOzg5wxNLokbwHeX1Uf77YfAxwB/Cfwhqp6+JZ+XwtanzHkEfqCdyZ5dpJ7Jrn7/FffoabUz5McPL+R5CDg5z3mmXaz82UOUFXnAI+qqs8DO/cXayo1PWPIWS4LfgmcDLychbWnXXN6NM8DzujG0gFuAJ7aY55pd32SlwLv6bb/ArihW/b5V5v/NW1C0zOGHHLpJPlf4OFVdV3fWaZdkp2Bo4H7A7sBP2Ewk+DVvQabUkn2YHCSef5Tz2eAVzN4X/eqqm/2lW0atTxjyELvdEvnHltVN/edZdol+RjwYwYn83798baqTuktVAOS3LWqftZ3Dq1cDrksuA24uFuNbfHVjU5bHN6eVXVE3yFakeRA4DQGC0nt1S3U9Zyq+st+k2mlsdAXfKD70vg+m+RBVXVJ30Ea8U/AHzNYSoGq+nKSR235V7Q9stA7ray2tkIcDDwtybcZfNpp6mq8PlTVdwdrS/3abZt7rrZfFnqnK5/bnVCoKme5DO+xfQdozHe7YZdKshPwAuCKnjNpBbLQF8wu+vmODG5N5Tz0EbRyGfUK8lzgDcC9gauBcwDHz3U7znLZgiSfqaqDl36mtO0kOaiq/mepfZJH6J1ubuq8HRgcse/SUxxpsVOBjZel2NQ+becs9AWnsDCGfiuD5TWP6S2NtntJHgkcCMwkedGih3YFduwnlVYyC33BY4E/Z8Plc49lcEWe1IedGMw9X8WGnxZ/yuBKXGkDjqF3vLpRK1WS+3qiWVvDI/QFXt2olermJCcDD2QwAwuAqvKWftqAy+cu+GySB/UdQtqEM4GvAnsDr2JwfqfJe2JqPA65dLp7Df42g5tDe3WjVowk66vqoUm+Mv/3mOT8qjqk72xaWRxyWeDVjVqp5u8dek2SI4HvA3v2mEcrlEfo0gqX5Cjg08B9GMw/3xV4ZVV9qNdgWnEcQ5dWvmMYHHxdWlWHAo8GnthzJq1AFrq08u1fVT+e36iq64EDesyjFcpCl1a+HZLsPr/R3bzc81+6Hf8opJXvFAbTas9isDzFk4B/7DeSViJPikpTIMm+wGEMptOeV1WX9xxJK5CFLkmNcAxdkhphoUtSIyx0SWqEhS5Jjfh/QXgiF4sx08IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_column_type.value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that there are over 46% of columns are numerical, and 21.4% nominal columns, and 21.4% categorical columns, and 10.7% ordinal columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1bdfe2e8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_map = raw_data.corr()\n",
    "plt.subplots(figsize=(16,16))\n",
    "sns.heatmap(corr_map, vmax=0.9, square=True, fmt = '.3f', annot = True, linewidths = .5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, light color indicates high correlation, and dark color indicates low correlation. We can ignore the white diagonal line, because it's the correlation between the same variables, which must be 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 The Target Data\n",
    "our target data is imdb_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5043.000000\n",
       "mean        6.442138\n",
       "std         1.125116\n",
       "min         1.600000\n",
       "25%         5.800000\n",
       "50%         6.600000\n",
       "75%         7.200000\n",
       "max         9.500000\n",
       "Name: imdb_score, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data['imdb_score'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1ce3f9b0>"
      ]
     },
     "execution_count": 16,
     "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(raw_data['imdb_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=463.28010571594075, pvalue=2.5119052637720155e-101)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.normaltest(raw_data['imdb_score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on these results, and using an alpha significance value of 0.01: \n",
    "From the distribution, we can see the distribution of imdb_score rating is not normally distributed and skewd to left."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Handling Duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 45 duplicated rows in the dataset, and we will drop them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = raw_data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Data preprocessing for numerical data\n",
    "we will not consider the features with low correlation to imdb score based on heap map above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num_critic_for_reviews       numerical\n",
       "duration                     numerical\n",
       "director_facebook_likes      numerical\n",
       "actor_3_facebook_likes       numerical\n",
       "actor_1_facebook_likes       numerical\n",
       "gross                        numerical\n",
       "num_voted_users              numerical\n",
       "cast_total_facebook_likes    numerical\n",
       "facenumber_in_poster         numerical\n",
       "num_user_for_reviews         numerical\n",
       "budget                       numerical\n",
       "actor_2_facebook_likes       numerical\n",
       "movie_facebook_likes         numerical\n",
       "Name: type of data, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_column_type[~data_column_type.isin(['categorical', 'nominal', 'ordinal'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have gross and budget information. So let’s add two colums: profit and percentage return on investment for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['profit'] = data['gross'] - data['budget']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['percentage_return'] = data['profit'] / data['budget']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Data preprocessing for categorical, nominal, ordinal data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "non_numeric = data_column_type[data_column_type.isin(['categorical', 'nominal', 'ordinal'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['color', 'director_name', 'actor_2_name', 'genres', 'actor_1_name',\n",
       "       'movie_title', 'actor_3_name', 'plot_keywords', 'movie_imdb_link',\n",
       "       'language', 'country', 'content_rating', 'title_year', 'imdb_score',\n",
       "       'aspect_ratio'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_numeric.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color                 2\n",
       "director_name      2398\n",
       "actor_2_name       3032\n",
       "genres              914\n",
       "actor_1_name       2097\n",
       "movie_title        4917\n",
       "actor_3_name       3521\n",
       "plot_keywords      4760\n",
       "movie_imdb_link    4919\n",
       "language             47\n",
       "country              65\n",
       "content_rating       18\n",
       "title_year           91\n",
       "imdb_score           78\n",
       "aspect_ratio         22\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[non_numeric.index].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3.1 Color, language, country, content_rating, aspect_ratio can be directly hot-encoded\n",
    "\n",
    "we will hot encode columns in sklearn pipeline later on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "hot_encoding_columns = ['color','language', 'country', 'aspect_ratio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>language</th>\n",
       "      <th>country</th>\n",
       "      <th>aspect_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Color</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>1.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Color</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>2.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Color</td>\n",
       "      <td>English</td>\n",
       "      <td>UK</td>\n",
       "      <td>2.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Color</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>2.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   color language country  aspect_ratio\n",
       "0  Color  English     USA          1.78\n",
       "1  Color  English     USA          2.35\n",
       "2  Color  English      UK          2.35\n",
       "3  Color  English     USA          2.35\n",
       "4    NaN      NaN     NaN           NaN"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[hot_encoding_columns].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3.2 Name columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we will drop the name columns since there are numerical columns 'facebook_likes' corresponding to the name columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "name_columns = ['director_name', 'actor_1_name', 'actor_2_name', 'actor_3_name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "popularity = ['director_facebook_likes', 'actor_1_facebook_likes', \\\n",
    "              'actor_2_facebook_likes', 'actor_3_facebook_likes']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3.3 Special columns\n",
    "Following columns need to be parsed into different words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "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>plot_keywords</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>avatar|future|marine|native|paraplegic</td>\n",
       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>goddess|marriage ceremony|marriage proposal|pi...</td>\n",
       "      <td>Action|Adventure|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bomb|espionage|sequel|spy|terrorist</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>deception|imprisonment|lawlessness|police offi...</td>\n",
       "      <td>Action|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       plot_keywords  \\\n",
       "0             avatar|future|marine|native|paraplegic   \n",
       "1  goddess|marriage ceremony|marriage proposal|pi...   \n",
       "2                bomb|espionage|sequel|spy|terrorist   \n",
       "3  deception|imprisonment|lawlessness|police offi...   \n",
       "4                                                NaN   \n",
       "\n",
       "                            genres  \n",
       "0  Action|Adventure|Fantasy|Sci-Fi  \n",
       "1         Action|Adventure|Fantasy  \n",
       "2        Action|Adventure|Thriller  \n",
       "3                  Action|Thriller  \n",
       "4                      Documentary  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "to_parse = ['plot_keywords', 'genres']\n",
    "display(data[to_parse].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use sklearn CountVectorizer to make transform them into matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "vector = CountVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<4998x6109 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 34503 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector.fit_transform(data['plot_keywords'].str.replace('|', ',').fillna(' '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<4998x30 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 15002 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector.fit_transform(data['genres'].str.replace('|', ',').fillna(' '))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For 'movie_title' column: we also will use countvectorizer to transform it into matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                              Avatar \n",
       "1            Pirates of the Caribbean: At World's End \n",
       "2                                             Spectre \n",
       "3                               The Dark Knight Rises \n",
       "4    Star Wars: Episode VII - The Force Awakens    ...\n",
       "Name: movie_title, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['movie_title'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<4998x4775 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 13181 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector.fit_transform(data['movie_title'].str.replace(' ',',').fillna(' '))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It will transform into matrix the count of word by word. Since they are large matrcies we will use directly preprocess in sklearn pipeline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3.4 Sort out content rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PG-13', nan, 'PG', 'G', 'R', 'TV-14', 'TV-PG', 'TV-MA', 'TV-G',\n",
       "       'Not Rated', 'Unrated', 'Approved', 'TV-Y', 'NC-17', 'X', 'TV-Y7',\n",
       "       'GP', 'Passed', 'M'], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['content_rating'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c0b54b890>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0FsFsoKMDSTeuLh3jtdaNEhdI5iGvj0TEmSPK30uaIfM/q65jL1Xx2ZQ0DIw6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\n9AsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c0b53bf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['content_rating'].value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that content rating is messy, and it is different by time. So we will standardize the content ratings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the history of naming these different content ratings, we find M = GP = PG, X = NC-17. We want to replace M and GP with PG, replace X with NC-17, because these two are what we use nowadays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We transform TV rating to corresponding Film rating."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to replace “Approved”, “Not Rated”, “Passed”, “Unrated” with the most common rating “R”."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_content_rating(x):\n",
    "    if x == 'M' or x == 'GP' or x == 'TV-PG' or x == 'TV-Y7':\n",
    "        return 'PG'\n",
    "    elif x == 'N' or x == 'NC-17' or x == 'X' or x == 'TV-MA':\n",
    "        return 'NC-17'\n",
    "    elif x == 'Approved' or x == 'Not Rated' or x == 'Passed' or x == 'Unrated':\n",
    "        return 'R'\n",
    "    elif x == 'TV-G' or x == 'G' or x == 'TV-Y':\n",
    "        return 'G'\n",
    "    elif x == 'TV-14' or x == 'PG-13':\n",
    "        return 'PG-13'\n",
    "    else:\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['content_rating'] = data['content_rating'].apply(filter_content_rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R        2338\n",
       "PG-13    1474\n",
       "PG        723\n",
       "G         123\n",
       "NC-17      39\n",
       "Name: content_rating, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['content_rating'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1dcb3e80>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAERCAYAAABy/XBZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAD7NJREFUeJzt3X+s3XV9x/Hny3Y4gxjKuBBWwDrXLOKcrKvI4paxkQGFLGXLWGBGG4YrbkA06kxdlmE0Jl38scTMkeCo1k1BMufspI41TKduU1sY44eM0GCFayutQtSNTAe+98f5Xnu4vffcH23P95bP85GcnO/3fT7nnPf5Jv2++v15U1VIktrznL4bkCT1wwCQpEYZAJLUKANAkhplAEhSowwASWqUASBJjTIAJKlRBoAkNcoAkKRGLe+7gVFOPvnkWrVqVd9tSNIx5c477/xWVU3MNW5JB8CqVavYtWtX321I0jElydfnM85dQJLUKANAkhplAEhSowwASWqUASBJjTIAJKlRBoAkNcoAkKRGLekLwY6EVZtu67sFAPZsvqTvFiTpGdwCkKRGGQCS1CgDQJIaZQBIUqMMAElqlAEgSY0yACSpUQaAJDXKAJCkRhkAktQoA0CSGmUASFKjDABJapQBIEmNMgAkqVEGgCQ1ygCQpEYZAJLUKANAkhplAEhSowwASWqUASBJjTIAJKlRBoAkNcoAkKRGzRkASc5I8tkkDyS5P8kbuvpJSXYkeah7XtHVk+T9SXYnuSfJmqHP2tCNfyjJhqP3syRJc5nPFsBTwJur6iXAucA1Sc4CNgF3VNVq4I5uHmAdsLp7bARugEFgANcDrwTOAa6fCg1J0vjNGQBVta+q7uqmvwc8AKwE1gNbu2FbgUu76fXAR2rgS8CJSU4DLgR2VNXjVfUEsAO46Ij+GknSvC3oGECSVcDPA18GTq2qfTAICeCUbthK4NGht012tdnqkqQezDsAkjwf+ATwxqr67qihM9RqRH3692xMsivJrgMHDsy3PUnSAs0rAJL8GIOV/0er6u+68mPdrh265/1dfRI4Y+jtpwN7R9SfoapurKq1VbV2YmJiIb9FkrQA8zkLKMBNwANV9b6hl7YBU2fybAA+NVR/bXc20LnAd7pdRLcDFyRZ0R38vaCrSZJ6sHweY14FvAa4N8ndXe2Pgc3ArUmuAh4BLute2w5cDOwGngSuBKiqx5O8E9jZjXtHVT1+RH6FJGnB5gyAqvoiM++/Bzh/hvEFXDPLZ20BtiykQUnS0eGVwJLUKANAkhplAEhSowwASWqUASBJjTIAJKlRBoAkNcoAkKRGGQCS1CgDQJIaZQBIUqMMAElqlAEgSY0yACSpUQaAJDXKAJCkRhkAktQoA0CSGmUASFKjDABJapQBIEmNMgAkqVEGgCQ1ygCQpEYZAJLUKANAkhplAEhSowwASWqUASBJjTIAJKlRBoAkNcoAkKRGGQCS1CgDQJIaZQBIUqPmDIAkW5LsT3LfUO3tSb6R5O7ucfHQa29LsjvJg0kuHKpf1NV2J9l05H+KJGkh5rMF8GHgohnqf15VZ3eP7QBJzgIuB17avecvkyxLsgz4ALAOOAu4ohsrSerJ8rkGVNXnk6ya5+etB26pqu8DX0uyGzine213VT0MkOSWbuxXF9yxJOmIOJxjANcmuafbRbSiq60EHh0aM9nVZqtLknqy2AC4AXgxcDawD3hvV88MY2tE/RBJNibZlWTXgQMHFtmeJGkuiwqAqnqsqp6uqh8CH+Tgbp5J4IyhoacDe0fUZ/rsG6tqbVWtnZiYWEx7kqR5mPMYwEySnFZV+7rZ3wSmzhDaBnwsyfuAnwRWA19hsAWwOsmLgG8wOFD8u4fTuBZu1abb+m4BgD2bL+m7BUnMIwCS3AycB5ycZBK4HjgvydkMduPsAa4GqKr7k9zK4ODuU8A1VfV09znXArcDy4AtVXX/Ef81kqR5m89ZQFfMUL5pxPh3Ae+aob4d2L6g7iRJR41XAktSowwASWqUASBJjTIAJKlRBoAkNcoAkKRGGQCS1CgDQJIaZQBIUqMMAElqlAEgSY0yACSpUQaAJDXKAJCkRhkAktQoA0CSGmUASFKjDABJapQBIEmNMgAkqVEGgCQ1ygCQpEYZAJLUKANAkhplAEhSowwASWqUASBJjTIAJKlRBoAkNcoAkKRGGQCS1CgDQJIaZQBIUqMMAElqlAEgSY0yACSpUXMGQJItSfYnuW+odlKSHUke6p5XdPUkeX+S3UnuSbJm6D0buvEPJdlwdH6OJGm+5rMF8GHgomm1TcAdVbUauKObB1gHrO4eG4EbYBAYwPXAK4FzgOunQkOS1I85A6CqPg88Pq28HtjaTW8FLh2qf6QGvgScmOQ04EJgR1U9XlVPADs4NFQkSWO02GMAp1bVPoDu+ZSuvhJ4dGjcZFebrX6IJBuT7Eqy68CBA4tsT5I0lyN9EDgz1GpE/dBi1Y1Vtbaq1k5MTBzR5iRJBy02AB7rdu3QPe/v6pPAGUPjTgf2jqhLknqy2ADYBkydybMB+NRQ/bXd2UDnAt/pdhHdDlyQZEV38PeCriZJ6snyuQYkuRk4Dzg5ySSDs3k2A7cmuQp4BLisG74duBjYDTwJXAlQVY8neSewsxv3jqqafmBZkjRGcwZAVV0xy0vnzzC2gGtm+ZwtwJYFdSdJOmq8EliSGmUASFKjDABJapQBIEmNMgAkqVEGgCQ1ygCQpEYZAJLUKANAkhplAEhSowwASWqUASBJjTIAJKlRBoAkNcoAkKRGGQCS1CgDQJIaZQBIUqMMAElq1Jx/E1h6Nlq16ba+WwBgz+ZL+m5BDXMLQJIaZQBIUqMMAElqlAEgSY0yACSpUQaAJDXKAJCkRhkAktQoA0CSGmUASFKjDABJapQBIEmNMgAkqVEGgCQ1ygCQpEYdVgAk2ZPk3iR3J9nV1U5KsiPJQ93ziq6eJO9PsjvJPUnWHIkfIElanCOxBfCrVXV2Va3t5jcBd1TVauCObh5gHbC6e2wEbjgC3y1JWqSjsQtoPbC1m94KXDpU/0gNfAk4MclpR+H7JUnzcLgBUMA/JbkzycaudmpV7QPonk/p6iuBR4feO9nVJEk9ONy/Cfyqqtqb5BRgR5L/GjE2M9TqkEGDINkIcOaZZx5me5Kk2RzWFkBV7e2e9wOfBM4BHpvatdM97++GTwJnDL39dGDvDJ95Y1Wtraq1ExMTh9OeJGmERQdAkuOTnDA1DVwA3AdsAzZ0wzYAn+qmtwGv7c4GOhf4ztSuIknS+B3OLqBTgU8mmfqcj1XVPybZCdya5CrgEeCybvx24GJgN/AkcOVhfLck6TAtOgCq6mHg5TPUvw2cP0O9gGsW+32SpCPLK4ElqVEGgCQ1ygCQpEYZAJLUKANAkhplAEhSowwASWqUASBJjTIAJKlRBoAkNcoAkKRGGQCS1CgDQJIaZQBIUqMMAElqlAEgSY0yACSpUQaAJDXKAJCkRhkAktQoA0CSGmUASFKjDABJapQBIEmNMgAkqVEGgCQ1ygCQpEYZAJLUqOV9NyCpX6s23dZ3CwDs2XxJ3y00xy0ASWqUASBJjTIAJKlRBoAkNcoAkKRGGQCS1KixB0CSi5I8mGR3kk3j/n5J0sBYrwNIsgz4APDrwCSwM8m2qvrqOPuQpJm0dk3EuLcAzgF2V9XDVfUD4BZg/Zh7kCQx/gBYCTw6ND/Z1SRJY5aqGt+XJZcBF1bV67r51wDnVNV1Q2M2Ahu72Z8BHhxbg7M7GfhW300sES6Lg1wWB7ksDloKy+KFVTUx16Bx3wtoEjhjaP50YO/wgKq6EbhxnE3NJcmuqlrbdx9LgcviIJfFQS6Lg46lZTHuXUA7gdVJXpTkOOByYNuYe5AkMeYtgKp6Ksm1wO3AMmBLVd0/zh4kSQNjvx10VW0Hto/7ew/Tktol1TOXxUEui4NcFgcdM8tirAeBJUlLh7eCkKRGGQCS1CgDQJIaZQDMU5JlSV7ddx/jluQ5SZ7TTR+XZE2Sk/ruS/1Isj7JNUPzX07ycPf47T5708IZANMkeUGStyX5iyQXZOA64GHgd/rub5ySXArsA76RZD3wBeA9wD1JfqPX5sbMFd+PvJVnXrvzXOAVwHnAH/TRUJ+S3JXkT5K8uO9eFmPsp4EeA/4aeAL4d+B1wB8BxwHrq+ruPhvrwfXAy4HnAf8JvKKqHkzyQuATwD/02dyYvZXBhYtTplZ8xwMfAv62j6Z6cFxVDd/P64tV9W3g20mO76upHq0ATgQ+m+SbwM3Ax6tq7+i3LQ0GwKF+qqpeBpDkrxjc0+PMqvpev231o6q+CZDkkap6sKt9fWq3UENc8Q2sGJ6pqmuHZue898yz0BNV9RbgLUl+GbgCuCvJA8DN3a1tlqzW/hHPx/9NTVTV08DXWl35w+AYQDf5e0O1ZQy2ilriim/gy0l+f3oxydXAV3roZ8moqi9U1R8yuMPxnwG/2HNLc/JCsGmSPA38z9Qsg90fT3bTVVUv6Ku3cUvyCuDeqvrfafVVwC9V1d/00VcfknwU+FxVfXBa/WrgvKq6op/OxivJKcDfA98H7urKv8Bgl9ilVfVYX731IcktVXX53COXJgNAmgdXfM+U5NeAl3az91fVP/fZjxbHANCiJPlMVa3ru49xc8Wn+UpyZVV9qO8+RjEANKska2Z7Cfh0VZ02zn76lOTHgdcDPw3cC9xUVU/125WWsu7EiTP77mMUzwLSKDuBf2Gwwp/uxDH30retDE4Q+AKwDngJ8MZeO1Lvktwz20vAqePsZTEMAI3yAHB1VT00/YUkj84w/tnsrKHTg2+i8TNe9COnAhcyuHZoWIB/G387C2MAaJS3M/upwtfNUn+2Gj49+Klkpo0iNejTwPNnukg0yefG387CeAxAmgdPD9azkReCaUGSfLrvHvpQVcuq6gXd44SqWj407cpfP5JkY989zJcBoIVa2XcD0hL3+r4bmC8DQAv1H303IC1xx8wBIo8BSNIRlOT0qprsu4/5cAtAs/Ie+NJoSd6U5KrhWlVNJrkuyZK/TsQtAM0qyb8Cl0/dBjnJ3cD5dPfAr6rz++xP6luS+4A1VfWDafXnAjur6uf66Wx+vA5Ao3gPfGm0mr7y74rfzzFwsYi7gDSK98CX5pDkkFs+zFRbigwAjeIf/5BGezdwW5JfSXJC9ziPwZ9LfU+/rc3NYwCalffAl+aWZB2wCfjZrnQfsLmqPtNfV/NjAGhO3gNfenbyILBm5T3wpdGS/OmIl6uq3jm2ZhbBLQDNKsnHeeY98PdU1ZI/t1kalyRvnqF8PHAV8BNV9fwxt7QgBoBmleTeoXvgLwe+UlWz/ZUwqWlJTgDewGDlfyvw3qra329Xo7kLSKN4D3xpDklOAt4EvJrBX45bU1XT/0DMkmQAaJSXJ/luNx3ged2898CXgCTvBn4LuBF4WVX9d88tLYi7gCRpkZL8kMFp0k8BwyvTY+I/SQaAJDXKK4ElqVEGgCQ1ygCQpEYZAJLUKANAkhr1/1i5NWDuk2jTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['content_rating'].value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After we standardize the content rating column, there are different five types of movies. R and PG-13 are the majority of the movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3.5 Drop Unnecessary Columns\n",
    "we suggest movie_imdb_link have no correlation with imdb score because link is not very informative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop('movie_imdb_link',axis =1 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 Transform target column: imdb_score\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we will do classification rather than regression. We will divide movies in to five categories. With following segement:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$ 1: [0-2), 2: [2-4), 3:[4-6), 4:[6-8), 5:[8-10) $ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trans(x):\n",
    "    if x<2:\n",
    "        return 1\n",
    "    elif x >= 2 and x < 4:\n",
    "        return 2\n",
    "    elif x >= 4 and x < 6:\n",
    "        return 3\n",
    "    elif x >= 6 and x < 8:\n",
    "        return 4\n",
    "    else:\n",
    "        return 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['imdb_score_5'] = data['imdb_score'].apply(trans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.5 Null value\n",
    "note that we have null value in our dataset, we need to elimunate the null values. <br>\n",
    "to proceed this process, we will impute null value with different decision based on different features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color                        0.003802\n",
       "director_name                0.020608\n",
       "num_critic_for_reviews       0.009804\n",
       "duration                     0.003001\n",
       "director_facebook_likes      0.020608\n",
       "actor_3_facebook_likes       0.004602\n",
       "actor_2_name                 0.002601\n",
       "actor_1_facebook_likes       0.001401\n",
       "gross                        0.174870\n",
       "genres                       0.000000\n",
       "actor_1_name                 0.001401\n",
       "movie_title                  0.000000\n",
       "num_voted_users              0.000000\n",
       "cast_total_facebook_likes    0.000000\n",
       "actor_3_name                 0.004602\n",
       "facenumber_in_poster         0.002601\n",
       "plot_keywords                0.030412\n",
       "num_user_for_reviews         0.004202\n",
       "language                     0.002401\n",
       "country                      0.001000\n",
       "content_rating               0.060224\n",
       "budget                       0.097439\n",
       "title_year                   0.021409\n",
       "actor_2_facebook_likes       0.002601\n",
       "imdb_score                   0.000000\n",
       "aspect_ratio                 0.065426\n",
       "movie_facebook_likes         0.000000\n",
       "profit                       0.228291\n",
       "percentage_return            0.228291\n",
       "imdb_score_5                 0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1dfb8c50>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,10))\n",
    "sns.distplot(data.isnull().mean(), bins = 25, kde = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1ddacb00>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,10))\n",
    "sns.heatmap(data.isnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that the profit and gross columns miss large amount of values. Other columns miss a few amount of values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we will impute the null data from distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.apply(lambda x: x.apply(lambda y: x.dropna().sample(1).iloc[0] if pd.isnull(y) else y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color                        0.0\n",
       "director_name                0.0\n",
       "num_critic_for_reviews       0.0\n",
       "duration                     0.0\n",
       "director_facebook_likes      0.0\n",
       "actor_3_facebook_likes       0.0\n",
       "actor_2_name                 0.0\n",
       "actor_1_facebook_likes       0.0\n",
       "gross                        0.0\n",
       "genres                       0.0\n",
       "actor_1_name                 0.0\n",
       "movie_title                  0.0\n",
       "num_voted_users              0.0\n",
       "cast_total_facebook_likes    0.0\n",
       "actor_3_name                 0.0\n",
       "facenumber_in_poster         0.0\n",
       "plot_keywords                0.0\n",
       "num_user_for_reviews         0.0\n",
       "language                     0.0\n",
       "country                      0.0\n",
       "content_rating               0.0\n",
       "budget                       0.0\n",
       "title_year                   0.0\n",
       "actor_2_facebook_likes       0.0\n",
       "imdb_score                   0.0\n",
       "aspect_ratio                 0.0\n",
       "movie_facebook_likes         0.0\n",
       "profit                       0.0\n",
       "percentage_return            0.0\n",
       "imdb_score_5                 0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1e591f28>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,10))\n",
    "sns.heatmap(data.isnull(), cbar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, our dataset does not have missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['color', 'director_name', 'num_critic_for_reviews', 'duration',\n",
       "       'director_facebook_likes', 'actor_3_facebook_likes', 'actor_2_name',\n",
       "       'actor_1_facebook_likes', 'gross', 'genres', 'actor_1_name',\n",
       "       'movie_title', 'num_voted_users', 'cast_total_facebook_likes',\n",
       "       'actor_3_name', 'facenumber_in_poster', 'plot_keywords',\n",
       "       'num_user_for_reviews', 'language', 'country', 'content_rating',\n",
       "       'budget', 'title_year', 'actor_2_facebook_likes', 'imdb_score',\n",
       "       'aspect_ratio', 'movie_facebook_likes', 'profit', 'percentage_return',\n",
       "       'imdb_score_5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 Analysis & Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1: Histogram of Movie Released"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1e6bf5c0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,10))\n",
    "data.title_year.plot(kind = 'hist', bins = int(data.title_year.max() - data.title_year.min()), edgecolor='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot Description\n",
    "From the graph, we noticed that most movies in the dataset were produced after 1980s. And the movies on the IMDB dataset increased dramatically between 1990s and 2000s. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will standardize years with different decades before we do bivariate analysis because of the distribution of the year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def standardize_year(x):\n",
    "    if x < 1990:\n",
    "        return 'before 1990'\n",
    "    elif 1990 <= x < 2000:\n",
    "        return '90s'\n",
    "    elif 2000 <= x < 2010:\n",
    "        return '00s'\n",
    "    else:\n",
    "        return '10s'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['standardized_year'] = data.title_year.apply(standardize_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1e625128>"
      ]
     },
     "execution_count": 56,
     "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": [
    "data.groupby('standardized_year').imdb_score.mean().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "standardized_year\n",
       "00s            6.372251\n",
       "10s            6.286891\n",
       "90s            6.545782\n",
       "before 1990    6.973333\n",
       "Name: imdb_score, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('standardized_year').imdb_score.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we make a hypothesis test with null hypothesis:<br>\n",
    "\n",
    "Decades of movies do not affect imdb score <br>\n",
    "\n",
    "We will use ANOVA test of mean to do hypothesis test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import f_oneway"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "_00s = data[data.standardized_year == '00s'].imdb_score\n",
    "_10s = data[data.standardized_year == '10s'].imdb_score\n",
    "_90s = data[data.standardized_year == '90s'].imdb_score\n",
    "_before90s = data[data.standardized_year == 'before 1990'].imdb_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=57.88635443595969, pvalue=8.87478092253908e-37)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_oneway(_00s, _10s, _90s, _before90s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on these results, and using an alpha significance value of 0.01: <br>\n",
    "we will reject our null hypothesis, and conclude that decades of movie will affect people's rating of movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Top 200 Movies based on Budget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "Top_200_profit = data[data.index.isin(data['profit'].sort_values(ascending = False)[:200].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "Top_200_profit['profit'] = Top_200_profit['profit'] / 1000000\n",
    "Top_200_profit['budget'] = Top_200_profit['budget'] / 1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1df48c18>"
      ]
     },
     "execution_count": 63,
     "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": [
    "Top_200_profit.plot.scatter(x = 'budget', y = 'imdb_score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary\n",
    "Through the graphing of this scatterplot, we are able to identify how movies with a certain budget were rated on a scale of 1-10. Some of the main analysis points that we can take from this scatter plot is that movies usually get rated between the 6-9 range on a scale of 1-10. In addition to this observation, we can also identify that the majority of movies have a relatively small production budget which does not necessarily dictate that the movie will be given a lower rating. As we can see through the scatterplot, a number of movies fell under the 50 budget but were rated between a 6-10. With this we can illustrate that movie budgets is not one of the main factors that weigh in when determining what rate a movie should receive.  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 Top 200 Movies based on Percentage Return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "Top_200_percentage_return = data[data.index.isin(data['percentage_return']\\\n",
    "                                                .sort_values(ascending = False)[0:200].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = Top_200_percentage_return['imdb_score']\n",
    "retur = Top_200_percentage_return['percentage_return']\n",
    "frame = { 'score': score, 'return': retur }\n",
    "score_vs_return_df = pd.DataFrame(frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1f29b3c8>"
      ]
     },
     "execution_count": 66,
     "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": [
    "score_vs_return_df.plot.scatter(x ='score', y = 'return')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot Descrption\n",
    "The plot contains 200 moveis with top percentage_return in the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "Top_200_percentage_return = Top_200_percentage_return.sort_values('percentage_return', ascending = False)[3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = Top_200_percentage_return['imdb_score']\n",
    "retur = Top_200_percentage_return['percentage_return']\n",
    "frame = { 'score': score, 'return': retur }\n",
    "score_vs_return_df = pd.DataFrame(frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1f36ae10>"
      ]
     },
     "execution_count": 69,
     "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": [
    "score_vs_return_df.plot.scatter(x = 'score', y = 'return')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot Description\n",
    "We dropped the outliar from the previous plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "Top_200_percentage_return = Top_200_percentage_return[Top_200_percentage_return['percentage_return'] <= 100]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1f4384a8>"
      ]
     },
     "execution_count": 71,
     "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": [
    "Top_200_percentage_return.plot.scatter(x = 'budget', y = 'percentage_return')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot Description\n",
    "The percentage_return is high for Low Budget Films and decreases as the budget of the movie increases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1f506c50>"
      ]
     },
     "execution_count": 72,
     "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": [
    "Top_200_percentage_return.plot.scatter(x = 'percentage_return', y = 'imdb_score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot Description\n",
    "Most of the movies have the percentage_return rate around 20% and the IMDB scores vary a lot as the percentage_return increase. It doesn't seem to have a strong corelation between the Percentage_return and imdb_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary\n",
    "In this part, we are interested if there exists a relationship between percentage_return and imdb_score. So we select 200 movies with the highest percentage_return from the dataset and plot a scatter plot(percentage_return v.s. IMDB_score) and drop the outliar(700% percentage_return). After that, we notice most movies' percentage_returns are below 100%. So we narrow down the scope further and analyze moveis with percentage_return less than 100%. It seems that there is a negtive correlation between percentage return and IMDB socre."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4 Directors with highest average IMDB score\n",
    "we will rank directors based on their facebook likes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "director_avg = data.groupby('director_name')['imdb_score', 'director_facebook_likes'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1f5c04a8>"
      ]
     },
     "execution_count": 74,
     "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": [
    "director_avg.plot.scatter(x = 'director_facebook_likes', y = 'imdb_score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1f66e898>"
      ]
     },
     "execution_count": 75,
     "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": [
    "director_avg[director_avg['director_facebook_likes'] > 2000].plot.scatter(x = 'director_facebook_likes', y = 'imdb_score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot Description\n",
    "Here we took the director’s name, the movie rating the movie they directed received, and their facebook likes and combined the three different components in order to try and observe if there was a positive correlation between the movie director and the rating the movie received."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1f759978>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4zlzHwCX+sDZs2+O5n5eKdG+e+PuSORN54upF3HDWzIJjIL0MZbU06L0SxyHbZlhLqCSNnZANYdsiZKONWEpFqeUchoYP6Uwd2cLr75Vf/TFn8nDubdvKstUbCdkW0TxPDgHLCVH4YeTgUM4OUoD2zbuZNKKFBTNGY1vp57ctKxnjz2V4E9tzPSXc88JW7ly3GREnrNETi2U9cdjiJJkzue6smZ7NYKlJXa/wTtAWLHEMrVcYpZrx8WI882Ibw9a98R7O/dQ53ro339OkrVIRap0janiDb1ullVSmMn30QHbv7+Gqle1uMjQ3i44aw5827fR9jhvv25BsTsrXoPQPC6ZmfaaQnR4z/tJHZnDbmk5HWiHDkKZW0RhzKKm8P2nJDb0x57EoYDnJ3JBtJW8AsYw4zL+dcTQXnXh42rZcVSteXcP5OlbzxccTr5cqD1Goi7nYypvO7Xu5/em30rbd/tRbXHLiVGaMG5J3DYpSiFrniBre4P/l3f1lH+O1d/dz7o+fLrhfOGCx9i/vZsWmiyESNfzqmbe4/NRDdfud2/fytbva6I2R/IX/8NHXst6bGLzyzQde4mePv+aGYgyXLpzOp06Y4ikN0HrYUE7/3mN519QSDLD8orkMawmy50APX7rjheTNAJzSxROmjUp7Tz6PJFfXcK4/3FxeeMfWPVyw4qk0Y2zAd2mk13q6uiNs2LYneXMv5FW1uWJ1mbRt3q0GXymbWueIGt7gh2yIlFZY45tiJZJzcduazqSBvrdtK1+9s71gaCgcsDh/3iQ+9t21yRtNQkNn+aPO8bzY1xNjQMCiO5b74vTG48kyza7uSNYfnlfpYiGPJPMJJp9X7uWFLz2zlWX3b0y7oVy5sh2QrPLRYh57U9eT8OotkawnuVxe1RwPjaB82xXFD7UW62t4g19+QMffubyce5tERXx+EqGZXft6uPKu9Vkdq5mEbeHb583mq3e1eT5V5FOq9OqIBXKWJhb7h+dUCWU2VcU8Y+MdW/ew7P6Neb3yTC/cW5HTyvpF+33s9ZqklUour2rGuCFcMn8Ktz91KKxzyfwp6t0rFaOWYn0Nb/CHDwzxzt7ymq+KJZd5tm0hbElKnNybRLjihvs2FNXwFYkZ7mnbklbPnvZ6NJazMiatI9YSemOG685q5fSZ43P+YRUScksYcDHp64kbkk1VSS+aQ3mDQl555lNB1pOGiYNJt/h+H3tzCdANDNrEMXm9qhvP/iCXnDiVts27mTN5uBp7peLUSqyv4Q3++wdqY+y9CFoglsXXTjuK7zz0atbrtjg1/gOCFsbAP5w0lRtX5zb2FmTpYT78Uu4EsSXC4tseT/OcU6WZDx81iNWXnZylJ59Ldydh6FOljhMGPGBJWkdtKr0xkzVT14tivPJcTxpAzqePYpK5XrHSfHMMFKUZaXiDv79Cdfil0BuHkBi+89CrfOjwETzW2ZV8zQK+/NGj+Pis8fy+4x2Wr9nEL556M2cVkC1w4YemcMezb2W9FrDEMzzTEzMQM3ztrjaGDwyy+b0DLLt/IyZuiMQMIdtCBG4+9xhPvfoE+fR08hnwVBIzdTPF41Ip1ivP9aThta3YaptcN5LUOQa5uPaeF9MqdS6ZP4Ubz/5gwfcpSr1R7SHmHwB+k7JpOnCtMea71TxvLUkY3VRjD46n/v1HNjFt9CCWr9mUpn2TSTjgxOpBPA1+ocrTnhh89r/+nFVDn9CP+epd7TkTnPmqbgrp8KeSnKkb8943lEfV0wuvR1yvhLCfGuZSYqValqk0E1XttDXGvGKMmWOMmQMcB+wHflfNc9YTPTHDV+5sK1jbH4sb/uXONq6+e31WElqAhUeNLniufE54b8xwX/vWrLbtzu17+cWTb+QcDOKVoAUYGLSSEs2ps3NnjBvC1077QNb+IVv41WeP5/BRg3K2jpfSWl6KDHPm7IFC5CvLVJRGo5YhnVOBvxhj3qzkQccNDrK9u7eSh6woxSRnE6WZvR4llIb8cfxi+eYfXuE/HniF685q5aITDs8KU6SSSC6nJmgT3cXhgEXUwA1nz8pKAN/btpXvPPxqMneR4IRpI7n4P5/NGXYpVX7YKy5faRlmLctUmolaaulcCPy60gcNeU0XqVPCtvSZeNHB3jg90TjX/K6D//fQK57GfmDIZkDQYuniVm5cvYGDvfGkUFviphSJOse54b6N7NrXk/SWU8Mrmfe4xzq7ck6/8jMdK/MpIBGXD6ZoTydkmCtFoiwzFS3LVBqVmtgfEQkBS4C7PF67VETWici6nTv9e7JbKyCPXCt6YqaIaHh52AJnfHBcUuzLi9vWdHpu/9zJ03ji6kV0dfcUDEP1ROOcfuta7njaeWDzCq/kIlM+oZiwTC6BqQUzRqflOBIyzJVUHTzu8JGEAxYDAhbhgMW8w0dW7NiKUktq5XB+HHjeGLM98wVjzApjzDxjzLwxYwpXTGTSOP597jr+TPyodZ40fWTaNYgZeOTlnfz+nxdy4fGTPd/jpYgJsGT2BACW57ghZBKNwzX3dHDHM2/mlXbOJLVap1iRs1xPAV7qoJUcp5g4dyQa52A0TiTa9/N5FaVUamUv/44qhHMA+rAqs6KEAxahgMW/ffxoAh6e+YAcd4EnX3sv66khaFls23OAX3tU/ADEjbBk9mFp2xJhCseAZp/LeWrwLhdKhHe+9JEZhAOSlG8OWpIcLpMgVeoZ0jX1UxPAqUnVfE8B1dYiqbf5vIpSDlVP2orIQOBvgH+sxvGnjRzA6+8drMaha0o8HueBKxYyYlCISDSepoa5dHErk0cM5PO3rytKz8eZVCWeYxUBPnHsRFa1b2NQyKYnFufK0z7ApX99BOB43JnjAMMBiwVHjOHhjVkPaIBTSXTG9x4jHLABYdEHxvLwS9uxLeFAr4GUztxUqecEhcol8xn1amuR6AAUpZmousE3xuwHRhXcsURKUa6sRyyxuPv5Lfz8yTdcj/KQGiY4nua1ix1hMQvJO4rxslOOZPX6bRzwiMNfsWgGP37stbRmqu88/CqLjh7Lvp4YHVv3EEsxcEFbuPasVpat3khPjlMmbkI9rtLm7zvecV7w+OUEbGHNyzs45eixWSMTcxnpQka9mloktRa3UpRq0vCdtlt3Nb53DxCJxfnhnxxp5EQT0fJHOxk5KJQmQFbI2w/ZwvzpI/n2j7OlHj5/8jQmjxzo2Q17xvceI+ChB2QJTB4x0LMBqyVoEzMGMSZr7GIuEjNvv3FvR9Hll1DYqFdTi6SW4lZKfeNnJkM90vAGv1ilykbEFuGG1RvpSZEFXrZ6I09cvYibzz2Gr97VnjY8PWDBLefN5o0u7xkB//XUG4TsQ+MQEyS8fa+egYT2frYOjfDjT89lwrAWFt/2eMFHrZagzQH3qSRxfr+TfWolMFVv51bqg1L7ReqJhjf4fSedVhmCVu4u2YO9MQYE7bTPaImwYduepNe5Ydse3j/Qy9CWUFIErHP7Xs/j9cZMWnNXOCAYBBOP51yDo5k/7JDypgi9sTjXLp7JwqPGAmSFPM6fN4k7120haDmTtD585GjWvJxdclvNyT6KUklqPYqwWjS8wR8cgu4Gtvr5JPFFyPLG9/fE+Owv1nH9kplcdMLhSaObipeGu5cAWyxu+NJHjuDWR7zLMEO2JOPVS+ZMZO/BKDes3kgoYLHs/o0MGRBgyZyJniGPK049ijueeYvlazpzdgr3xGLsOdBDV3ekof7TKP2PWo8irBZFG3wREeAiYLox5kYRmQKMN8Y8W7XVFUFLMEh3T31KK4RsR4c+X7BDRNKqWFLJVZDTGzNc87sOMGTNm02QquE+ddRAPvWzZ7PuLiHb4odrs0cqgiN29vvLT052lHZ1R1h2vxNe6nFrYVM9HK+Qxw8e7cxZVWSLs5wv3fFCnzweN3osVqktzVKt5acO/wfAfJyaeoC9wPKKr8gn7+2rT2MP8Dd/NZZQgS6qsCtElo+WoEXYo8byhvs2ZDUApcoPzBg3hHPnTWbetFFcd1Zr1vujceNZWx+yhVvOPSZNPsBvPXq+ztugBQHbojdmCsopVINcXbuKkoti+kUaAT8hnROMMXNF5AUAY8wuVzKhTwlI/ZZm3t+xnVABYx6LG25YMovrVnXk9OjjJnswCkDQPvRI2dUdSYZQEvX7qV7zRSccDsa5SQRti5gxyfmxqWR69gn8eji5Om/DAeGyU45kxdrX0rz/Wj0eN0ssVqk9zVCt5cfg94qIU7IBiMgYvO1QTemtU2OfoCfPFUpoxC+ZM5HTZ41nw7b3AcPmXQdYtjp9FuzeSNQJ46QQM86Q8XvbtnLVyvak/k3CkGYasotOPJzTZ6UrXA4ZEEgfIr64lX09say4ut969Mz9e2JxLjtlRrKvYPmj6XmDWj0eN0ssVukbGr1ay4/B/x6Olv1YEfl34FzgG1VZlQ+OmTiYtq3dfb2MnIRs8Sx3DFqkedKjBofTpi95zZ5tf2sXdz53KPxw/rxJAK7Wi9c5sg1Z5h9sqtfSsXVP1o0mNa7u18PJt3+5zUylxuDzPaloXF9pdoo2+MaYO0TkORxdewE+YYx5qWorK5LuSJ8/ZOQlFjd8+dQZfH/NX4i5SVNb4HMfns62PQcYMSjkaVy8pjutWv922j53rtvC8VNHYWWNTXEo1mtOnOeCFU8VDHX49XBy7V9oYHo+o1tOPXSuJ5XHO99NHjP1aUQNv9JMiMlRIZK2k4gFrDfGzKrmYubNm2fWrVvn6z3Tvn5/0SqUfYUtjqRAJGoQ0lUzg7Yz3rCQwWrfvJuLf/oMeyOH5OLCtmDwbpgKByxuPrd4Q+h1/CHhAL/83Al55+FWkoQhT9T6X3fWzKwqpK7uCAtueiRNGmJA0OKJqxeV/IQAZB0TnHzDzecW/t0oSl8jIs8ZY+YV2q8oD98YExeRdhGZYozxlmDsI+rd2IOTVI65IZfM9fbGDFeuXM/wgU7jFODp4XqFIrzkDEI2XL7oKN/eaV+XnXkNTL/mng6Q9PBWpWLwqU8e7Zt3e0pHRKJGE7pKU+Enhn8YsEFEngX2JTYaY5ZUfFV1SObYvkoSica59PY/0xszWJY4VTYpHm7CG01U1QQti0g0hmVJmoEcGLL50cVzPZux8oVKksdf3JoVw8+1b6Xj3Ft2HfDU+Fl6TwfXr9pAwLKIG8O1Z7VW/MaUT8tfE7pKM+HH4N9QtVWUwcgWi/cOVC+OnwjBFGvsF84YxdrOLt/nOeg+AcRS5A+uuaeD9i27WdW+La2KZtaEYQwK2Y6GTQpOjkCyKmzyxbwzX1t6ZiuzJg7zNOjV1BKZNKKFXo+B6XED8ZRrcv2qDVx/1sw0Qbly66ETcf0rV67PahRrxOYaRclFUTH85M4i44APuT8+a4zZUcnFlBLDn3vDA1U1+H4I2RZP/esi/tDxDtfftyFN2KxSpMarV7VtTSYfD0ZjGGNoCQbSjHG+mDdkx65zxcMrFTvPxx1Pv+mEcQpw+2c+xMwJwyr+pNHVHeFXz7zFbWs2EbLthhXIUvofFY3huwc8H7gZeBTH8f2+iFxpjFlZ8iorQL0YewDbMqx5eQenzxrP5JED+cJ/P5dXt74ULEjqyacKqH3uF+vojZNMuiZiz14xb9uSZIdssfHwWtSvX3Ti4SBw7T0dBZ6opCr10KMGh7n81CP51AlTSqoeUpR6x09I5xrgQwmv3m28ehjIa/BFZDjwU2AWTnTkM8aYp0pbbjYtNhyoE33kA72Ga+7pQASWLm4lXoWU8v7eePIcCe+zbfOerEqdfCMA90WcQSenzxpfdDzcT1K3HON4+szx3HjfRmJ5NHgSye1qkXkzaQZZXEUBf1o6VkYIp6vI998K/MEYczQwG6ho7f7gAfUl+BmJxjnYG2fZ6o0sPbOVAUGroLxCKpY4ypYDQzahgMXZcw7L2idxjqvuXk/n9r3c+nD2sJOe2KERgEsXZ+voJCQVvPRBgKQeT4JitUTK1anJNVMXHGP//y6YU1MPO98AdUVpNPxYyz+IyP9yaBj5BcAD+d4gIkOBhcD/ATDG9FBhCfud++pzjHnQspg8ciArPn0cr+3cxzcfeJmDBebRJnRsnnn9vaTmzQMvvkM4YHmqTgYti8c73/UMf3zc6s0EAAAgAElEQVRmwdSkYUwkeVOllhNPAJkNUI93vsuCmx7x9GYLddoWq1OT7wnA60kiZAvfOX8O848YVfNwikoxKM2En07bK0Xkb4GTcWL4K4wxvyvwtunATuDnIjIbeA64whizL//biseiDgR9PDjQG+Xzt68jZDudm1GPCpRUEo1SI9yRhj0x4w4jh1zdBpFojHAOqc35RxwaIzxpRAuxjOR8ajgmEcIoxmDni50XYxwLhUdydcIunj0h16WrKn3dn6AolaToYIOITAN+b4z5ijHmX3A8/qkF3hYA5gI/NMYci1O///WM414qIutEZN3Ond6DMvJRL41XCZXhAUGLcEAQESJRJwwQicaxLMFDiRiAgUGbn1wyjyVzJuaQIXY6chMSyQkbb1nC9fdtxMo4btAWZk4Ylvy52HCMXwnkTAoZx2LDI0vmTOSJqxfxy8+dwBNXL+rTeHmzyOIqCvgL6dwFnJTyc8zd9iHv3QHYAmwxxjzj/rySDINvjFkBrACnLNPHegCYMXogm971nuFaS2698FjGDw3zRtd+BgRt/vW3L9IbOxRu6o0ZvvjX05k5YSj/cmcbqcU7cUwyEeltNBOywjOYP32kO8wkniyRDNpCAEdjPhY33HxutkEqRvisXG+2kKKmn/BItVQJS0koN4MsrqKAP4MfcGPwgBOPL6SHb4x5R0Q2i8gHjDGv4Aivbcz3Hr+cMH1knxv8S+ZPYc+BXr5yZxtB2yIaj3vW4P/nE6/z5NdP5dvnzclpFA81AbWnKWBGonGWP9rJ7MnHEbYtelJi+gMCNssvOpZhLaG8BqmQEfUrgexFPuPY1+GRckXX1NAr1aJWZb9+DP5OEVlijFkFICJnA+8W8b7LgTvcm8NrwD/4X2Zu/viK/zBQpQgHLL593mz2HOhNNgz1xHLXiAZyJEq9vPHhA4N84ZfPsz8j0QriaTRnThhWkT+USnizuYxjJW4opaKDT5R6pZZlv34M/hdwDPdtOEnbzcAlhd5kjGkDCnaAlUos2ndF+CJw9PghfOy7a4vavycWy0qU5mLmhGHEPRKtMycMrbrRrKY321fhEa22UeqRWjsifqp0/gKcKCKDcSQZ9lZ8NSXQtb+2ZZkCDA4fki/Ytudg0To7Fx1/eN5fYuZjXSK0Y4tFzBwy7KUYzXrqFO2L8Ei54aR6un5K81BrR8SPtMIVwM9xhpf/RETmAl83xjxY8VX54IMTqj/xSoCPzRzL506ezrQxg9P+4699tXg5oV//+S3mHj7C83HN67HOJM4ugEkvxSnGaCaMVMfWPVliY/2tU7SccJJ22irVotZ5LT8hnc8YY24VkY8BY3Fi8T8H+tTgWx6SupXGAF877ei0cYQJJgxrwbYkOc0qQcgCg9AbT028euurez3WXbmyHZC0hqtiH/VSB5oHbaE74oS96j12XW0vutQnI439K9Wi1nktPwY/YVnPAH5ujGkXqYG1LcCL2yoXWbIt8OqPCghpXaoJEp5fQJwa1VTiwHcvmM1VK19ME1DzelzzFDgTi8zJhcU86mUPNM/epx5j17Xyov2GkzT2r1SbWua1/GjpPCciD+IY/P8VkSHUQZNryK7McQKWsOLi4wh6dEfZtjAoZKfpy6R6fl6Tp0SEo8cPzRJQ83pc83qsi5l41lNDoUe9xJq8Bpr7OU6tqWe9mr4uJVX6B6MGh5k9eXjVnQg/Bv+zOE1THzLG7AdCpJRYisjMCq+tKCIVKtIxxvBPv3qej88an9a5GrDggnmTOfP7j/F3K57mpG/+kVVtWz27UlMZEHC0a4rp0vTq5rz53NncfK73e7u6I1niZuB4o4HMttsUBoXsqneK5lpbPsrt8K0m2mmrNBN+qnTiwPMpP3fhKGYm+G8cGYWaMm30QDbtKL/xKjF3dlX728ltC2eM4p9PPZILVjztVuI4d5ev3NnGH65YmHMsHhzyAieNaGHFp+cBJm+tfK7Husxt+UIfHVv3JOP1qYQDFtee5UzKquYjY6lhmXr3orXTVmkWKqkt3Cfx/KPHDq6IwfdibWcXj3V2Zen1ROOwbc/BtGSL18Spxzvf9WUAveLLqdvyJRDhkORxKl/8yHQ+d/L0tGO0b95dlOHyk0TNlXgePjBYsCmsLxuyFKU/UUmD3yc6Zk++vquqx8/9oUyW5wekfZ8YCVip6o58CUQvwgHh9JmH5VSqrOT8Wq+1RaKGL/zyeeLGFHx/PXvRWpapNAv1NT2kBEy89p22qWqUmV554vv2zbsrXt2RL/Sxa19P2rxZcAzuIDer7eWBX3NPB4PDNtF4ukEupRTRa21AUhqimJtdPerVaFmm0kz4SdoWoqKDTYpld41n2ib0cwr9Z69GXDpfAnFfTywpn5xcqy3JctJcSebuSIyDvXG+dlc7617von3zbjZs2+M7iZq6toHB7NKpSiRhS0kIl0s9J5QVxS++PPyUASgGeDx1AIox5sQKr60ojplY/U7bBLYF1y5uLepxvlpx6Vyhj0kjWhBLSNV5EEuSN5hcHniCnpjh3B8/7dw0BDIqQou6WR0aqv4+n799XVrTWLk3u74Kq9R7QllR/OBnAMoPcATUXgQ6gH8UkeXVWlixDG/Jq9BcFqe3jkubKBWLO4nRYj3Mag3y8KrZLVQ+mPr6oDzNC5GYIRI1GGMIB/yXIo4aHGbhUWNylpSWQl/W6WtZptJM+PHw/xqYZYwj4Sgiv8Ax/n3Kk6+/V7VjP/zydgYE7bRuVb9xeD9x6XKlBYqRXU68/oeOt/nhn17LeayWYIDlF81lWEuwpPV4JbSLrQ7KpK+7Xes5oawofvBj8F8BpgBvuj9PBtZXfEU+CdngoXpQoWNb9MT8dbuWSqVCFsUMORk1OMykES387PHXcl67hBRzOcYtca5yP1s9hFXqMaGsKH4pGNIRkftEZBUwCnhJRB4VkTXAS8CYai+wEAd6q3fsmDF89uSphANS0uN8IsnYuX0va1/dwdpXd3qGIfoiZDFqcJhbzptD5gz0sC0VDVtU4rNpWEVRKkMxHv4tVV9FGcydPJQ/v/V+RY8ZsoW4McQN/PKptwDh0oXT+dQJU4o2MgmvFkgrlwxY8J3z56R5uH0VskhNsoJhwrAW9vXEKhq2qNRn07CKopRPQYNvjPlTOScQkTdwNPRjQNQYU9HpVycdMbpsg98ScAaMXL7oKD4+azzb9hxMVpkkBpEvf7STT50wpajjpXq1mUTjTgdqah13X4YsEknWxLr39eQvN/SbZ6jkZ9OwiqKUR0GDLyJ7ydNwaowZWsR5TjHGFDP/1jf3rn+n7GP0xg22ZXH4qIHMGDeEfT0xvISfi/VKvbzaVGxJ93DrQVqgmDh7KbH4evhsiqI4FOPhDwEQkRuBd3BE0gS4CBhS1dUVwZhBAd7oKrxfPqJxQzR+aDjJoJCd5Z0f7I3nLWdMpVDNe8xke7h9GbIoppu0nI5TDccoSn3gp9P2Y8aYHxhj9hpj3jfG/BA4p4j3GeBBEXlORC7NfFFELhWRdSKybufOnT6W47BzX+Vm2iZiy4W6VguRmmQcEEy/xAELbj63cKduLSmmm7TcjtNa6X0ripIbP2WZMRG5CPgfHCP+d2QPevJigTFmm4iMBR4SkZeNMWsTLxpjVgArAObNm+dbgO3sY8Zz65rc9eR+OBiNJT3vfF2rxZDq1Q4K2WzbcwCQnKWOfSnQVUycvR5KIxVFKQ8/Hv6ngPOB7e7Xee62vBhjtrn/7gB+Bxzvf5m5mTZmcMWOFXf1BCpVBpjwameMG8LCo8ay8Kgxnsfo64lPxXzeSpdG9oUujqL0d/wMQHkDONvPwUVkEGAZY/a6358G3OhrhQVY/WL5SdsEMQO/euYtLj/1yJrGnfu6kxSKi7NX6pqo3LCi9A1FG3wRmQZcDkxNfZ8xZkmet40DfufOOg8AvzLG/KGkleZgz76DlTwct63pZPbk4cnQSy0Mbr2ES4r5vOVeE5UbVpS+w08M/x7gZ8B9FDm83BjzGjC7hHUVzas79lX0eJFonC/893PEKTy0w4tS9HD6U+liPTzNKEp/xY/BP2iM+V7VVlIif/NXY1n5wtuFd/TB/t7ih3akUk6oor+ULtbL04yi9Ef8JG1vFZHrRGS+iMxNfFVtZUUycbg/QzF3yjCCdnHjd1PLDgslGSulGdPspYuqi6MofYcfD/+DwKeBRRwK6Rj35z7Db6fti1vfT1bjFCLheRbjuWuoonj6y9OMotQbfgz+J4Hpxpg+GWWYiwlDQ7zRtb/o/XtjhY39oLBNzJ3zChSVZNRQhT9UF0dRao8fg98ODAd2VGktJbHrYGXE8BNGfumZrcyaOCzpeRY7jNwr8bp0cWsyJFQpqWH1ihVFKRU/Bn8c8LKI/BlIBqYLlGVWnU8ccxgvvb3X13uCtmAJhGzbMcwZRj4VP557aqiiY+selq3eWLFac61dVxSlXPwY/OuqtooyGBT2NYedoC18+7zZaTrwMycMy+kx+y2ZTGy/YMVTFas119p1RVEqgZ9O27J08avFvev9lWQuWzKTJXMm+vKY/SYZK53ArVVCWENGitLcFKOH/7gx5mQPXXwBTJF6+FXj7GMO489v7Cp6/+vu20DMwLL7N/rymP0kGSudwK1FQlhDRorS/BSswzfGnOz+O8QYMzTla0hfG/tSiEQN167qyBrp4qfmvhCVrjWvdu16X4u3KYpSG/wFwOsQvyEdgFgcYjk85kp5upWuNa9m7br2EChK/6DhDT7x8ssywwHxVXNfLJWuNa9W7br2EChK/8CPtEJd8lpX/olLtgWXzJ9CKIecQjhg8ZNL5rFkzsScU502bHu/qbXbVe5AUfoHDe/hf3zmOH757Jacr6+4+Di6e2L8z7Nv5dxn5oRhgOPpHoymPzHs64ny+dvXEbKbO5mpcgeK0vw0vId/ytHj8r7+eOe7XHlXG17jaAOWcO1ZrWnGzZj0bG7cOJLJ/SGZ2R/E2xSlP9PwBv/Bjdvzvv7zJ9/MMvaCY+zDAYtlqzeyqm0r4CQvW4L5H3r8DO5WFEWpJ2pi8EXEFpEXRGR1pY89NGz7fo8BonHDvp5YmtfulbzMRJOZiqI0KrXy8K8AXqrGgV9/r3xv2xJhw7Y9acnLQSGbkC2cP2+SJjNLQIeUK0r9UfWkrYhMAs4E/h34SqWPv/iD43n4pfIEPPf3xPj87eu4+dzZLJkzkb0Ho9yweiOhgMWq9m15xdWUbLRrV1Hqk1p4+N8FrqLIObjVwhZJeumXzJ9COJD+0SNRw1V3r6dz+16W3b+Rnmic7ogT8ll2/0Y19kWiXbuKUr9U1cMXkcXADmPMcyLykRz7XApcCjBlyhTf51j9YuGJVyFb+Onff4hhLcGk4R4YCvCjP72Wtl/QsmgrUv++mSlHRE27dhWlfql2SGcBsEREzgAGAENF5JfGmIsTOxhjVgArAObNm1fc7MEUpo0snEDtiRkmDBvAjHFDAMeg/fyJ1z32izNn8vB+3XVabjhGu3YVpX6pakjHGPOvxphJxpipwIXAI6nGvhK8HyksrRCyhX0ptZlbdh0gZGdX95wzdyIjBoX41jnHEA4IA4N2UnahP3inlRrErl27ilKfNHyn7Wmt4/jNutydtuB4+B1b9zB78nDA2wsFWNW+jd++sJXzj5sEiCsA7S3J0IxUKhyjXbuKUp/UrPHKGPOoMWZxpY87rCVY1H7L7t+Y9FQzyy8TJJK0tz/9FpFonP09MSLR/pN0rGQ4pp66drVEVFEcGr7Tdu2md4vaL7NDdsmciTxx9SJuWDKTwQWat/pLd20zhmPubdvKgpse4eKfPsOCmx5JdlUrSn+k4UM6sycNK2o/L0911OAwpxw9lm/c2+H7vc1KM4VjdBawoqTT8B7+6CEDsPKE2cO25E28enm1l8yf0lRerl/qKRxTDrnkrvvD05qieNHwHv6kES2YPMWckZghmEMLP4GXV3vFqUc1hZfbn9ESUUVJp+E9/La3dmWOp82iN2a4cuWhxGtXd4S1r+5k7as70hK5qV5tPXq5mnz0RzPmJBSlHBrewy8kj5wgbgxbdh3g8c53+eqdbURdxy9oC98+b3bda72oPk1pNFNOQlHKpeE9/OmjBha1X2/M0BuNcdXK9qSxT2xP9f7rEdWnKY96fFpTlL6g4Q3+vt7iNNkGBC3e6NqPLdkf2bakrufWavJRUZRK0PAhnYVHjuZ7j3QWte+cycOJmewbRKQ3VtdzazX5qChKJWh4D78YghZ86SMzGDEoxLWLZ5JatBOwwLKkrufWavJRUZRK0PAefjGdtr1x+O7Dr3LrH19FRGgJ2fREDZ89eSrzjxjF53+xLm1/Ezd1J+eryUdFUcql4T38QcHiPkLMQDTuJGm7IzF6YnF+/uQbDAzaRGLphZ2RmGFQyK67MkhNPiqKUg4N7+G/1rW/5PcGLSeROyBocTAl+TsgaPH7jnf4waOdWgapKErT0PAe/mmt40p+b2/cGXiSiTGG5Ws6tQxSUZSmouENfrHyyLY4CdqgLWmJzxnjhmQlRC875UhCdu4yyHoL9TQjeo0VpfI0fEinUNI2bAvXL5lJ64RhyTLGzMRnZkIUYPmj6aWeiTJI7XitPnqNFaU6VNXDF5EBIvKsiLSLyAYRuaHS5xg7OFRoEZw2c3wy2Zkr8Zm6PVcZJKAdr1VGu4oVpXpU28OPAIuMMd0iEgQeF5EHjDFPV+oEO7p78r5+ztyJJVW1LJkzkdbDhtK2eTdzJg9nxrghtG/eXZERgEpuKjVmUVGUbKpq8I0xBuh2fwy6X4XELX1RqNP2MwumlXRcr7DCghmjteO1ymhXsaJUj6onbUXEFpE2YAfwkDHmmUoef9qYweRSu7dFGDGoQMjHg1xhBUA7XquMdhUrSvWoetLWGBMD5ojIcOB3IjLLGJOcKSgilwKXAkyZMsX38bfsOoAlTmNVJuFgaaGAfGEF7XitPnqNFaU61KxKxxizW0QeBU4HOlK2rwBWAMybN893uKc3GvM09gCxuCkpFFAorJBI7CrVQ6+xolSealfpjHE9e0SkBfgo8HIlz/FGjk5bS+Das1rZsusAndv3Jmu689V3J14DDd0oitJ8VNvDPwz4hYjYODeXO40xqyt5Aq9OWQBj4Lp7OwjYjmxC2BbiOF20LcFAVn23V5L2iasXaVhBUZSmodpVOuuBY6t5jpznxhFLi7qhmVSBtL2RKABX3b2eBTNGA4fq6xNx+6vuXs8TVy9ido4biqIoSqPR8J22bW4IphRS5RK09ltRlGan4Q3+1CJn2nrREzuUiNXab0VRmp2GF08LBmyCdq5KfPK+du7cSYDWfiuK0j9oeA+/kBcuwM8uOY4v3vE8PRn1m/e2b+XuF7Ykk7da+60oSjPT8B4+OJU3uQgHbEYPGcAt581mQNBiUMhOvtYdiaWJc+lEKUVRmpmG9/C37DpASzCQrLzJJBGLnz15OAtmjGbNyzu4/r4NdEdiyX00QasoSn+g4T18r65YgJAtWbH4UYPDnHL0WKLx9CcCTdAqitIfaHiDP2pwmKVntma/IMLqy05OG5zR1R1hy64DLD2zVRO0iqL0Oxo+pAMwa+IwBofttDBN2LbY13Po58xO2qWLW5nlTsFSY68oSn+g4T18cMI6+cI0XnLHy1ZvVGOvKEq/oikMfr46+q7uCGte3kHASq/HT+2yVRRF6Q80RUgHvDXUE2EcWyQtvAOaqFUUpf/RNAYf0jXUU8M4qQwK28TipikStYkktIamFEUphqYy+Kl4Ta0aFLK54ayZnHL02IY3kF5yzqkVSYqiKJk0RQzfC6/6/JgxTWHsc83c9RrqoiiKkqBpDX61BNHyTcyqFYmnl1Q0Ca0oSiGqGtIRkcnA7cB4IA6sMMbcWs1zplJpQbR6CaMUmrmrKIriRbU9/CjwVWPMXwEnAl8SEY+22MqR6YFXShCtnsIoKuesKEopVHvE4dvA2+73e0XkJWAisLEa56umB+6VBO5L0TWVc1YUxS81q9IRkak4822fqcbxUz3w1Lm0C2aMrogxrMcwSmoZqqIoSiFqkrQVkcHA3cCXjTHvZ7x2qYisE5F1O3fuLPkcG7btwZLqddNqGEVRlEan6h6+iARxjP0dxpjfZr5ujFkBrACYN29e7kkmebi3bStXrWwnEq2u7LGGURRFaWSqXaUjwM+Al4wx36nGORKhnExjHw5UxwPXMIqiKI1KtUM6C4BPA4tEpM39OqOSJ/CqSR8YtPnJJfO081RRFCWFalfpPI4zR7xqeCVT4xhmThhazdMqiqI0HA3faZuaTB0UsgnZwtIzWzXsoiiKkkHDG3xwkqlLz2ylN24IBSyW3b+RVW1b+3pZiqIodUVTGPyu7gjL7t9ITzROdySmYmKKoigeNIXBVzExRVGUwjSFwa/HLlhFUZR6oykMvnbBKoqiFKZpJl5pF6yiKEp+msbgg3bBKoqi5KMpQjqKoihKYdTgK4qi9BPU4CuKovQT1OAriqL0E9TgK4qi9BPEmJJmjlQFEdkJvFnGIUYD71ZoOZVE1+UPXZc/dF3+aMZ1HW6MGVNop7oy+OUiIuuMMfP6eh2Z6Lr8oevyh67LH/15XRrSURRF6SeowVcUReknNJvBX9HXC8iBrssfui5/6Lr80W/X1VQxfEVRFCU3zebhK4qiKDloCoMvIqeLyCsi0ikiX6/xuSeLyBoReUlENojIFe7260Vkq4i0uV9npLznX921viIiH6vi2t4QkRfd869zt40UkYdEZJP77wh3u4jI99x1rReRuVVa0wdSrkmbiLwvIl/ui+slIv8pIjtEpCNlm+/rIyJ/7+6/SUT+vkrrullEXnbP/TsRGe5unyoiB1Ku249S3nOc+/vvdNcuVViX799bpf+/5ljXb1LW9IaItLnba3m9ctmGvvsbM8Y09BdgA38BpgMhoB1oreH5DwPmut8PAV4FWoHrga957N/qrjEMTHPXbldpbW8AozO2fQv4uvv914Gb3O/PAB4ABDgReKZGv7t3gMP74noBC4G5QEep1wcYCbzm/jvC/X5EFdZ1GhBwv78pZV1TU/fLOM6zwHx3zQ8AH6/Cunz93qrx/9VrXRmvfxu4tg+uVy7b0Gd/Y83g4R8PdBpjXjPG9AD/A5xdq5MbY942xjzvfr8XeAmYmOctZwP/Y4yJGGNeBzpxPkOtOBv4hfv9L4BPpGy/3Tg8DQwXkcOqvJZTgb8YY/I121Xtehlj1gLveZzPz/X5GPCQMeY9Y8wu4CHg9EqvyxjzoDEm6v74NDAp3zHctQ01xjxlHKtxe8pnqdi68pDr91bx/6/51uV66ecDv853jCpdr1y2oc/+xprB4E8ENqf8vIX8BrdqiMhU4FjgGXfTZe6j2X8mHtuo7XoN8KCIPCcil7rbxhlj3gbnDxIY2wfrSnAh6f8R+/p6gf/r0xfX7TM4nmCCaSLygoj8SUQ+7G6b6K6lFuvy83ur9fX6MLDdGLMpZVvNr1eGbeizv7FmMPhecbaalx6JyGDgbuDLxpj3gR8CRwBzgLdxHiuhtutdYIyZC3wc+JKILMyzb02vo4iEgCXAXe6merhe+ci1jlpft2uAKHCHu+ltYIox5ljgK8CvRGRoDdfl9/dW69/n35HuVNT8ennYhpy75lhDxdbWDAZ/CzA55edJwLZaLkBEgji/0DuMMb8FMMZsN8bEjDFx4CccCkPUbL3GmG3uvzuA37lr2J4I1bj/7qj1ulw+DjxvjNnurrHPr5eL3+tTs/W5ybrFwEVu2AE3ZNLlfv8cTnz8KHddqWGfqqyrhN9bLa9XAPhb4Dcp663p9fKyDfTh31gzGPw/A0eKyDTXa7wQWFWrk7sxwp8BLxljvpOyPTX+/UkgUUGwCrhQRMIiMg04EidZVOl1DRKRIYnvcZJ+He75E1n+vwfuTVnXJW6lwInAnsRjZ5VI87z6+nql4Pf6/C9wmoiMcMMZp7nbKoqInA5cDSwxxuxP2T5GRGz3++k41+c1d217ReRE92/0kpTPUsl1+f291fL/60eBl40xyVBNLa9XLttAX/6NlZOFrpcvnOz2qzh362tqfO6TcR6v1gNt7tcZwH8DL7rbVwGHpbznGnetr1BmJUCedU3HqYBoBzYkrgswCvgjsMn9d6S7XYDl7rpeBOZV8ZoNBLqAYSnban69cG44bwO9OF7UZ0u5Pjgx9U736x+qtK5OnDhu4m/sR+6+57i/33bgeeCslOPMwzHAfwFuw220rPC6fP/eKv3/1Wtd7vb/Ar6QsW8tr1cu29Bnf2PaaasoitJPaIaQjqIoilIEavAVRVH6CWrwFUVR+glq8BVFUfoJavAVRVH6CWrwFUVR+glq8JWKI45k7tdE5EYR+WgFjjdcRP6pEmtzj/fP4kjW3lF476KP+aiIlD2AOnHtitz3v0TkXPf7n4pIq/t9d7nrUJqTQF8vQGlejDHXem0XEdsYE/NxqOHAPwE/KPYNBc7xTziNQK/7WENdY4z5XF+vQal/1MNXKoKIXCPOUIuHgQ+421I90DdE5FoReRw4T0SOEJE/uEqej4nI0e5+48QZ8NHufp0EfBM4QpyBFTe7rec3i0iHOAMrLnDf+xFxBk78CqdT0WudP8LpQl4lIv8iIseLyJPiqCc+KSKJtdsicot7/PUicrm7/ThxVBafE5H/zZAWuNg9RoeIHO/uP1JE7nGP8bSIHJNve8ZaPy8iD4hISxHXP+sJQ0RGi8hTInKm+/OVIvJn95w3uNsGicj97rXuSFxLpTlRD18pGxE5DkcT5Vicv6nngec8dj1ojDnZfc8fcdreN4nICTje+yLge8CfjDGfdDVPBuMMiZhljJnjvvccHHXG2cBo4M8istY9x/Huvp7euzHmC+Lo0pxijHlXHKXEhcaYqBt++g+c9vtLcQZ3HOu+NlIcIazvA2cbY3a6xvHfcdreAcMfDGMAAAMZSURBVAYZY04SR5X0P4FZwA3AC8aYT4jIIhyd9Tl5tieu6WU4mimfMMZECv8W0hGRcThSB98wxjwkIqfh6MYcj9PCv8pd5xhgmzEmcVMY5vdcSuOgBl+pBB8GfmdcUS8RySWG9Rv39cHAScBdcmiKXNj9dxGOcBVuSGaPHNJYT3Ay8Gv39e0i8ifgQ8D7wLM+QzXDgF+IyJE4uidBd/tHcfRqou5a3hORWThG/CF33TaOhkuCX7v7rhWRoeKMITwZ5waCMeYRERnlGtVc2wE+jaMJ8wljTK+Pz5IgiKPR8iVjzJ/cbae5Xy+4Pw/GuQE8BtwiIjcBq40xj5VwPqVBUIOvVIpiRJn2uf9awO6Ex14C+WaN7svzmhfLgDXuE8VU4NGUc2R+JgE2GGPm5zhW5v75tMzzaZx34Hj7k4BS8gxRnCesjwEJgy/A/zXG/DhzZ/cJ7Qzg/4rIg8aYG0s4p9IAaAxfqQRrgU+KSIs4ksxn5dvZOEMgXheR8yA5vHm2+/IfgS+622035LIXZyZo6vkucF8fgzPTtFTJ5GHAVvf7/5Oy/UHgC+JoqiMiI3FUH8eIyHx3W1BEZqa8J5FLOBlH2naPu9aL3O0fAd51P3+u7eB44f+IE3aZUMJnMjhhpqPl0JDw/wU+4z5dISITRWSse/z9xphfArfgzIZVmhT18JWyMcY8LyK/wZF/fRMnTFCIi4Afisg3cEIQ/4MjWXsFsEJEPgvEgC8aY54SkSdEpANntN9VOMOm23GM21XGmHfETfz65Fs4IZ2vAI+kbP8pzmCM9SLSC/zEGHObOEno77nhlwDwXRy5XYBdIvIkMJRDcf3rgZ+LyHpgP4d00HNtB8AY87g45Zn3i8jfGGPe9fOhjDExEbkQuE9E3jfG/EBE/gp4yg1HdQMXAzOAm0UkjiMv/EU/51EaC5VHVhRF6SdoSEdRFKWfoCEdpSkRkcRUoUxONe5M00ZBRJYDCzI232qM+XlfrEdpXDSkoyiK0k/QkI6iKEo/QQ2+oihKP0ENvqIoSj9BDb6iKEo/QQ2+oihKP+H/A8JVCH4od6XzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "director_avg[director_avg['director_facebook_likes'] <= 2000]\\\n",
    ".plot.scatter(x = 'director_facebook_likes', y = 'imdb_score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "director_avg_2000 = director_avg[director_avg['director_facebook_likes'] <= 2000].sample(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1f816400>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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d8XhstXLippGNIulV8Vo6ZvZR4LsEk5d/28yOBi529zuH3szhW7t5R9HlpQJ+/tFzkgeLxFVPX0RqS5SLtu9z95eBNwHTgP8JXB5LqyJYNGtSweV/3b2/4PJcubXikzxYJM56+iJSO6IE/GzR+FOB77r7OopNN1UFrr7zqUi9bpI8WCTJX1YiMnKiXLRdY2Z3AocCnzaz8VCkJOUIKpbSASKnY/LTPADrNu+o+oug2S8r9eiRtFFHhWiiBPz3A4uAP7n7HjNrJUjrAGBm8919Y6UbOJhiKZ39Pc6GrTtZWOT+YrK9ZZJ2EVQjGyVtkvYZrQZRaun0uvvv3H1HeLvd3dfnrPIfFW9dGeYePJ7zlhSeavey2x8f0mCqahqYFUUc89eKVKOkfkZHWyXLG49aPv/SM17D5W9b0G9uWhj6hUtdBBWpbvqMDk0lA360Dv0VdMvaLXzuto109fRvwr7uniFduKymi6DtHZ2s27xDRy4iOarpM5oksU9gYmaTzOwmM3vSzJ4wsyWVfP7sqV3uJORZUQeVZVVLjx2VRxYprFo+o0kT5aLtYIp1fL8G+IW7n2VmjcDYCm6z5By2zQ31Qx44NdoXQTWYSqS00f6MJlGkgG9mbweOJ0jfPOjuP83e5+5vKLD+BGAZ8A/hOvsp/sUwJMXmsIXip3jlduUazfKuSR75KzJSVII5mrJTOmb2DYICao8BG4B/MrNrB3nYYcA24Ltm9nszu97Mxg25tQXknto1hRdtxzTUFT3FS0qaRDlKEam0sounmdlGYIGHDzCzOuAxd59f4jGLgYeBpe7+iJldA7zs7pfkrLMSWAkwe/bs1z333HNDeiGbXtjF2s07mNM6lob6TN/Re+7RPMDSK+4pWSCtmgZy5Bd4Uz9jESmk4sXTgKeA2UA2Is8C1hdfHYA2oM3dHwlv3wRcnLuCu68CVkFQLTNCe/oUGoCxcNakAcvPP2FuyTRJtQ3kUI5SRCpp0IBvZrcR5OwnAk+Y2aPh7WOBX5d6rLv/xcw2m9mr3P0p4CTg8eE3+4BiFzfnHTJhwPKv37uJ/N6j2TRJtV4kVY5SRCqlnCP8q4e5jQ8TTJzSCPyJnHIMlVDs4ubazTsGLG/M1LFy2WFce98mMmZ09QR18FtbmlhXYH1dJBWRWjJowHf3+4ezAXdfCwyaWxqqYhc3F82axL7unn7L93X38O5jZ3PQuEa+cPvjNNbXcdkdjzN+TD1L507RRVIRqWmD9tIxs11m9nKxn5FoZCnFBmBMHtc4YOCVu7N9934uu+Nx9nf30tHZ01eDA9BADhGpaeUc4Y8HMLNLgb8QFEkz4Byg+JRSI2jFohnMO2QCazfvYNGsScw9eDzrNu+guaGeXZ3dfes1N9QXTPVkUze6SCoitSxKL503u/uxObe/aWaPAFdWuE2R5fau2d/TywUnzuWUBa8omuoplbrRRVIRqVVRaun0mNk5ZpYxszozOwfoGfRRMcsvk9rZ3cu/3fUHTrnmAVYsPGRAimbuweOVuhGRVIpyhP9ugro41xD0bXwoXDaqitXS6eqFH6/ewv8+5UiOPay1X4pGqRsRSaOyA767PwucEV9Thmbm5GZ27+8uev/Vd/2B31y8fEBQV+pGRNKm7IBvZocS9Kmfk/s4d19R+WaVb/vu/fSWGJ/bkDH1pRcRIVpK52fAd4DbqILJy7NKTWIO0NPr6ksvIkK0gL/P3b8aW0uGqNgk5llnL56po3sREaL10rnGzD5nZkvM7OjsT2wtK9PkcY3UlZhN9z8feV7TA4qIEO0I/zXAe4DlHEjpeHh71LRt38uYhgx79hfuIdrdCxu3vsyyI6YOeRvVVDJZKk9/X0mLKAH/bcBh4axVVWPm5GY6uwYbDjD0+dWrrWSyVJb+vpImUVI664DSCfNRUlcip9OQMeZPnzik580f1JWtu6MUUW3Q31fSJsoR/sHAk2b2W6DvEzHa3TLbtu9lTH2Grp7+ffGb6oPvsqvOGjiKttxTeM0rW9v095W0iRLwPxdbK4Zh5uTmAWWQGzLGt89bzPzpEwrOaVvqFD73y2A488oqL1z9NG+wpE2UkbbDqosfp0JlkAsF+8FmtSr0ZXDlmUcNmFd2sACuvHAyZEtrR/37iiRVOVMcPujux5vZLvpf/TTA3X1CbK0rQ9v2vQPKIDc1ZAqelpc6hQcKfhk8dNFyHrpoedlH69U6VaIUprpKkibl1MM/Pvy/Kmrf55s5uZm9Xf3z97s7e9iwZScL8wZllTqFL/VlsHDWpLIDgfLCyaO6SpIWUXrpVC2zgb10Lr1944DeFsVmx2ptaapYPld5YRGpVlEu2laltu17acjU0dXT/8KtYdz75IuceOS0fkdvxU7hK5XPVV5YRKqV5V/wHE2LFy/21atXR3pMe0cnx11+D53dA+u5tTRl6O71SBdNK9W7Rr10RGSkmNkad1882Hqxp3TM7Fkze8zM1ppZtGhehtaWJq466ygaMgPTOrmTlJc7mKa1pSlSzj7u5xERqZSRSumc6O4vxfXk2TTNxq07+dO23Vx951N0dB5I8eiiqYhIjVy0heCIetkR0/i7hdPpzpsRRRdNRURGJuA7cKeZrTGzlXFvrFRPHBGRNBuJlM5Sd99qZtOAu8zsSXd/IHtn+CWwEmD27NnD3lh7Ryd/0zqO2y84nt37e3TRVEQkFHvAd/et4f8vmtlPgWOAB3LuXwWsgqCXznC2VaikQf7gKxGRtIo1pWNm48xsfPZ34E3Ahji2pVK3IiKlxZ3DPxh40MzWAY8Cd7j7L+LYUNv2vWTyRtzm1skZTHtHJ+s27yj7CyLq+iIioy3WlI67/wlYGOc2sjZs2cnuvGkOy+2dE7W6paphikgS1US3zPaOTi674/EByy85bV6k6pblpIKUOhKRpKqJgJ+tUJlrbGMdTfV1gwbiQo8tlQqKur6ISLWoiYBfqELlnv29fP62jSy94h5uXbsl0mNLpYJUDVNEkqomAn7uYKtxjZm+5eXU0ok6UEsDu0QkqRJfLTPXphd2ceu6rXznwWf6XcAd25jhunOPZtkR0/qtn1vREohU3VLVMEWkWpRbLTPx9fCzsj1nMmYDeuvs2d/DB25YzVVnLezrTTPcnjaaJUlEkqYmUjq5PWfyg31WZ7f3pXbU00ZE0qgmAn6hnjNN9UZTfeHeNOppIyJpVBMpnUI9Zzq7nYZM//Vye9Oop42IpE1NHOG3tjRxyenzBix3d5rqB/amUU8bEUmjmjjCB1gwfSLjGjP9cvjNDfVce87RTGxuGNCbpthk5iIitapmAv7Myc30+MCZruZPn1CyT70CvYikRU2kdEADokREBlMzR/igNI2ISCk1FfBBaRoRkWJqJqUjIiKl1UzA1wxUIiKl1URKRzNQiYgMLvFH+KqLIyJSnsQH/KHWxVEKSETSJvEpnaHMQKUUkIikUeKP8KMOuFIKSETSakSO8M0sA6wGtrj76ZV+/igDrrIpoH0cOCvIpoDUf19EatlIpXQ+CjwBTIhrA+UOuNIk5CKSVrGndMxsJnAacH3c2yqHau6ISFqNxBH+V4ALgfGF7jSzlcBKgNmzZ49Ac1RzR0TSKdYjfDM7HXjR3dcUW8fdV7n7YndfPHXq1Dib009rSxMLZ01SsBeR1Ig7pbMUWGFmzwI/BJab2fdi3qaIiBQQa8B390+7+0x3nwO8E7jH3c+Nc5vl0KArEUmjxA+8ikqDrkQkrUZs4JW73xdHH/woNOhKRNIs8SNtoxhq3R0RkVqQqoCvQVcikmapCvgadCUiaZa6i7YadCUiaVWTAb+9o7NkQNdE5yKSRjUX8NXtUkSksJrK4avbpYhIcTUV8Ddu3UmdWb9l6nYpIhKomZTOLWu3cOFN6+js9n7L1e1SRCRQE0f42VROfrBvqle3SxGRrJo4wi80beHYhgzXved1LDti5Eoui4hUs5o4wi80grYXZ/702GZUFBFJnJoI+BpBKyIyuJpI6YBG0IqIDKZmAj5oBK2ISCk1kdIREZHB1UzA17SFIiKl1URKR/VzREQGl/gjfNXPEREpT+IDvqYtFBEpT+IDvqYtFBEpT6wB38zGmNmjZrbOzDaa2RcqvQ0NuhIRKU/cF207geXu3mFmDcCDZvZzd3+4khvJHXQ1rjHD7v09tHd0KuiLiOSINeC7uwMd4c2G8MeLP2LoWluaeHDTS+qtIyJSROw5fDPLmNla4EXgLnd/JI7tqLeOiEhpsQd8d+9x90XATOAYM1uQe7+ZrTSz1Wa2etu2bUPejnrriIiUNmK9dNx9B3Af8Ja85avcfbG7L546dei169VbR0SktLh76Uw1s0nh783AycCTcWxLvXVEREqLu5fOIcC/m1mG4Mvlx+5+e1wbU4lkEZHi4u6lsx54bZzbyKcSySIihSV+pK2IiJRHAV9EJCUU8EVEUkIBX0QkJRTwRURSwoJyN9XBzLYBzw3jKaYAL1WoOUmlfRDQftA+yErDfvgbdx905GpVBfzhMrPV7r54tNsxmrQPAtoP2gdZ2g8HKKUjIpISCvgiIilRawF/1Wg3oApoHwS0H7QPsrQfQjWVwxcRkeJq7QhfRESKqImAb2ZvMbOnzGyTmV082u2Jk5nNMrN7zeyJcGL4j4bLDzKzu8zs6fD/yeFyM7OvhvtmvZkdPbqvoHLC2dR+b2a3h7cPNbNHwn3wIzNrDJc3hbc3hffPGc12V5KZTTKzm8zsyfA9sSRt7wUz+3j4WdhgZj8wszFpfC+UI/EBPyy9fC1wCjAPeJeZzRvdVsWqG/iEu78aeANwfvh6LwbudvfDgbvD2xDsl8PDn5XAN0e+ybH5KPBEzu0rgC+H+2A78P5w+fuB7e4+F/hyuF6tuAb4hbsfCSwk2B+peS+Y2QzgI8Bid18AZIB3ks73wuDcPdE/wBLglzm3Pw18erTbNYKv/xbgjcBTwCHhskOAp8LfvwW8K2f9vvWS/EMwZebdwHLgdsAIBtfU578vgF8CS8Lf68P1bLRfQwX2wQTgmfzXkqb3AjAD2AwcFP5tbwfenLb3Qrk/iT/C58AfPKstXFbzwtPR1wKPAAe7+58Bwv+nhavV6v75CnAhkJ3XshXY4e7d4e3c19m3D8L7d4brJ91hwDbgu2Fq63ozG0eK3gvuvgW4Gnge+DPB33YN6XsvlKUWAr4VWFbzXY/MrAW4GfiYu79catUCyxK9f8zsdOBFd1+Tu7jAql7GfUlWDxwNfNPdXwvs5kD6ppCa2w/h9YkzgEOB6cA4gtRVvlp/L5SlFgJ+GzAr5/ZMYOsotWVEmFkDQbD/vrv/JFz8gpkdEt5/CPBiuLwW989SYIWZPQv8kCCt8xVgkpllZ3HLfZ19+yC8fyLw15FscEzagDZ3fyS8fRPBF0Ca3gsnA8+4+zZ37wJ+AhxH+t4LZamFgP9b4PDwqnwjwQWbW0e5TbExMwO+Azzh7l/KuetW4L3h7+8lyO1nl58X9tB4A7Aze7qfVO7+aXef6e5zCP7e97j7OcC9wFnhavn7ILtvzgrXT/xRnbv/BdhsZq8KF50EPE6K3gsEqZw3mNnY8LOR3Qepei+UbbQvIlTiBzgV+APwR+BfRrs9Mb/W4wlOQdcDa8OfUwnykHcDT4f/HxSubwS9mP4IPEbQm2HUX0cF98cJwO3h74cBjwKbgBuBpnD5mPD2pvD+w0a73RV8/YuA1eH74WfA5LS9F4AvAE8CG4D/AJrS+F4o50cjbUVEUqIWUjoiIlIGBXwRkZRQwBcRSQkFfBGRlFDAFxFJCQV8EZGUUMCXijOzz5vZJ83sUjM7uQLPN8nMPlSJtoXP95GwlPD3K/ic95nZsCfKzu67Mtf9f2Z2Vvj79dkqsWbWMdx2SG2qH3wVkaFx988WWm5mGXfvifBUk4APAd8o9wGDbONDwCnu/kyENlQ1d//H0W6DVD8d4UtFmNm/WDAJzX8BrwqX5R6BPmtmnzWzB4G/N7NXmtkvzGyNmf3KzI4M1zvYzH5qZuvCn+OAy4FXmtlaM7sqLA1wVTjhxWNm9o7wsSdYMDnMfxKMJC3UzusIRmHeGk6ccYyZ/TqsNvnrbJkCCyZXuTp8/vVm9uFw+evM7P6w3b/M1qwJnRs+xwaWB5jvAAADzUlEQVQzOyZc/yAz+1n4HA+b2VGllue19QNm9nMzay5j/w84wzCzKWb2GzM7Lbz9KTP7bbjNL4TLxpnZHeG+3pDdl1KbdIQvw2ZmryOoafNagvfU7whK1Obb5+7Hh4+5G/iguz9tZscSHL0vB74K3O/ub7NgcpsWggqQC9x9UfjYMwlKCiwEpgC/NbMHwm0cE65b8Ojd3T9oZm8BTnT3l8xsArDM3bvD9NP/Ac4kmCDkUOC14X0HWVC07mvAGe6+LQyO/wq8L3z6ce5+nJktA/4vsIBg2P/v3f2tZrYcuCFse7Hl2X16AfAm4K3u3jn4X6E/MzuYoG7MZ9z9LjN7E8HEJ8cQlFi4NWznVGCru2e/FCZG3ZYkhwK+VMLfAj919z0AZlaseN2PwvtbCCoa3hjUuwKC+icQBP3zAMKUzE4Lp+jLcTzwg/D+F8zsfuD1wMvAoxFTNROBfzezwwlqFDWEy08GrvOwprq7/9XMFhAE8bvCdmcIarBn/SBc9wEzm2Bmk8K2nhkuv8fMWsOgWmw5wHsIqjq+1YMKkFE1ENTQOd/d7w+XvSn8+X14u4XgC+BXwNVmdgVBTaJfDWF7khAK+FIp5RRl2h3+X0cwQcWiUiuXUKimef42ynUZcG94RjEHuC9nG/mvyYCN7r6kyHPlr+8Ur79eqi77BoKj/ZkEM1pF1U1whvVmIBvwDfiiu38rf+XwDO1U4Itmdqe7XzqEbUoCKIcvlfAA8DYzazaz8cDflVrZgwlbnjGzv4e+ybUXhnffDfxzuDwTplx2AePztveO8P6pwDKCyodDMRHYEv7+DznL7wQ+aGFNdTM7iGBKwKlmtiRc1mBm83Mek72WcDxB6eGdYVvPCZefALwUvv5iyyE4Cv8ngrTL9CG8JidIMx1pZtkJUX4JvC88u8LMZpjZtPD597j79whmjqqJic2lMB3hy7C5++/M7EcEpZqfI0gTDOYc4Jtm9hmCFMQPgXUEE5OvMrP3Az3AP7v7b8zsITPbAPycYGrDJeH6Dlzo7n+x8MJvRFcSpHT+F3BPzvLrgSOA9WbWBXzb3b9uwUXor4bpl3qCiVc2ho/Zbma/JphrNpvX/zzBFITrgT0cqMVebDkA7v6gBd0z7zCzN7r7S1FelLv3mNk7gdvM7GV3/4aZvRr4TZiO6gDOBeYCV5lZL9BF+GUrtUnlkUVEUkIpHRGRlFBKR2qSmWVnfcp3kru3j3R7hsPMriWYxzfXNe7+3dFojySXUjoiIimhlI6ISEoo4IuIpIQCvohISijgi4ikhAK+iEhK/Ddqyf8HNSr92QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "director_avg_2000.plot.scatter(x = 'director_facebook_likes', y = 'imdb_score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary\n",
    "After plotting this information in a scatterplot, we were able to observe that there was a correlation between the actor’s name/social media likes and the rating a movie they produced received. Through the scatterplot, we are able to see that the movies that received a higher IMDB rating were directed by people that were very known or famous on social media based on their number of likes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.5 Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              country\n",
      "USA              3777\n",
      "UK                443\n",
      "France            154\n",
      "Canada            124\n",
      "Germany            96\n",
      "Australia          55\n",
      "India              34\n",
      "Spain              33\n",
      "China              28\n",
      "Japan              23\n",
      "Italy              23\n",
      "Hong Kong          17\n",
      "Mexico             17\n",
      "New Zealand        15\n",
      "South Korea        14\n",
      "Ireland            12\n",
      "Russia             11\n",
      "Denmark            11\n",
      "Brazil              8\n",
      "Norway              8\n",
      "South Africa        8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1f8e84e0>"
      ]
     },
     "execution_count": 80,
     "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": [
    "country_number_df = pd.DataFrame(data.country.value_counts())\n",
    "country_number_df = country_number_df.drop(country_number_df[country_number_df.country < 8].index)\n",
    "print(country_number_df)\n",
    "country_number_df.plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maybe drop countries with movies < 8?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = data.country.apply(lambda x: 'United Kingdom'if x == 'UK'\\\n",
    "                          else ('Russia' if x == 'Soviet Union' else \\\n",
    "                          ('Germany' if x == 'West Germany' else x))).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:New Line not found in regex\n",
      "WARNING:root:Official site not found in regex\n"
     ]
    }
   ],
   "source": [
    "import country_converter as coco\n",
    "some_names = ['United Rep. of Tanzania', 'DE', 'Cape Verde', '788', 'Burma', 'COG',\n",
    "              'Iran (Islamic Republic of)', 'Korea, Republic of',\n",
    "              \"Dem. People's Rep. of Korea\"]\n",
    "Continent = coco.convert(names=temp, to='Continent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['continent'] = pd.Series(Continent, index = data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1fa4dc88>"
      ]
     },
     "execution_count": 84,
     "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": [
    "data.continent.value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1fa43e80>"
      ]
     },
     "execution_count": 85,
     "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": [
    "data.groupby('continent')['imdb_score'].mean().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "by the plot we can see continent and imdb score are not very correlated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6 Number of Reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1fb27cc0>"
      ]
     },
     "execution_count": 86,
     "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": [
    "total_reviews = pd.DataFrame(columns=['reviews', 'imdb_score'])\n",
    "total_reviews['reviews'] = data['num_user_for_reviews'] + data['num_critic_for_reviews']\n",
    "total_reviews['imdb_score'] = data['imdb_score']\n",
    "\n",
    "sns.regplot(y='imdb_score', x='reviews', data=total_reviews)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Figure 4.6.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Under 1000 sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1fd85a20>"
      ]
     },
     "execution_count": 87,
     "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": [
    "reviews_under_1000 = total_reviews[total_reviews['reviews'] < 1000].sample(100)\n",
    "sns.regplot(y='imdb_score', x='reviews', data=reviews_under_1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Figure 4.6.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1000 & up sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1fe37208>"
      ]
     },
     "execution_count": 88,
     "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": [
    "reviews_1000_up = total_reviews[total_reviews['reviews'] >= 1000].sample(100)\n",
    "sns.regplot(y='imdb_score', x='reviews', data=reviews_1000_up)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Figure 4.6.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary\n",
    "Here we are interested in how the number of reviews by critics and IMDB users affects the movie's outcome. We summed the number of critic and user reviews to just see the impact of total reviews. Figure 4.6.1 plots all movies in our database and shows that movies with a larger number of reviews tend to have higher ratings. This is best seen in Figure 4.6.3 which takes into account only movies with 1000+ reviews. The linear regression line starts at an IMDB score of 7/10 and increases as the number of reviews gets larger, and in contrast, Figure 4.6.2 presents movies with less than 1000 reviews to have more dispersed ratings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.7 Duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1ff1f2b0>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7/X6gCrLSuOzcYhZML+TNqnpWV9byzMZq/rhpP1v2N7B4bikXTcrX2r/IWabQH8Kef/sgn39sA4VZaSxfMp9pxdmJLum0JUeSuHBSPhdOymffkWbW7qpj1eYDPLWhimnFWVwzezyfnDWessJRiS5VJBQU+kPUMxur+R+Pv8H7xuWw7NaLKcpOS3RJg1aSn0FJfgk//4eL+f2b+/nN+r18+9ntfPvZ7cyakMvVs8bz8VnjKMnr/UxhERk8hf4Q9MTavdz59JuUTxrNQ7eWk52ekuiS4mpUWjKfvriUT19cSvXRZn7/5n5WbKzmGyu38o2VW3nfuBwuO7eIy84dw+zSPCJDZIe1yEig0B9ifv7yTr76zBYunV7I0pvLyUgd2Sc7jc/L4HMLpvC5BVP4wZ/fYUt1A28faDw55s/oUalcMq2QD0wt4ANTCphUkKn9AP04k+cY6PyF4U+hP4Q8+EIF96/axsfOG8MPFs8J3dmthVlpLJhRxIIZRTS3dbL9UCOt7Z28VHGYFRujY/yNy01n/pQCutyZkJ/J2Jx0/RIQOQ0K/SHA3fnWqm385C+VXDunhPuvm0VyJNyXL85IjXDBhDxunDcRd6eypolXdtTxauVhXtxew+Hg2gDJSca43HQmjM6kND+D+VNGU1YwasicwyAy1Cj0E6yry7nnmc088spubpo3ka8tfL8CqwczY1pxNtOKs7l5/iTcnQdfqKTqyHGqjjRTdeQ463bV8Uql88S6KjJTI0wvzmL6mGymF2cxY0w2R461kZuZEtfB6ESGI4V+ArV3dvHlp97k6Q37WLJgCnddda62V8fAzBg9KpXRo1KZFZzs1dnlHGpsoSQvg7cPNPLOoUb+ur2GJ9dXnXxcaiSJwuxU8jNTGZ2ZSv6oVMbnpQcXlkkjNyNFf38Z8RT6CdLS3sntv9rAc1sP8T+vmMHtl01T4AxCJMkYl5vBornv3aF49Hgb7xxq4rFX93CwsYXDTa0cbGhl24FGOrr85L4CgNTkJIqy0ijOSeN4aydpKUmkJ0dIT0kiPSVCWkp0OjM1QnJSEinJSaREjNRIEinBLTXZTi5LO3mLkBIxvb8yJCj0E6ChpZ3P/mIda3fX8bVr3s/N8ycluqQRKy8zlYvLRvPOwab3zO9yp6mlg/KyfPbXt3CosZVDjS3UNLRS09TKoYZWaps6aenoorW9k46u6DWBntnY86JxsTHj5BdAlzvpyREyUiNkpETITI1ON7a0MzY3+stjbE46Y3PTSQn5vh2JP4X+WXaosYVblq2l4lAjP1w8h0/OGp/okkIpyYycjBTKy0b3urznoYkdnV20dHRxxcwxtLR30t7ZRXtnF20dTkfXu9Pvzu+irbOL1vYT/3bS2tFFa0cXm/bV09LRRXNbB42t7RxqbOF4WyerKw+/5zUjScak0ZmkJSdRlJ1OUXYaY3LSGJuTHvod/TJwCv2zaPfhY3xm2WscamjloVsuZsGMokSXJDFKjiSRFUlichyGi+jrWPerLxjHgfoWqutbOFDfzN66Ziprmli/+wjbDzbRGVyBNGLG2Nx0NlXXM6sklzkT85lenDXgAwB07H24KPTPktWVtXz+sQ0APPa5eVw4MT/BFclQk52eQnZ6CtPHvHeMpV+t2UNnl3PkWBv7G1rYd6SZfUeP88zG6pOBnZ+ZwtzJo5k7uYAjx9oYm5uuI5WkVwr9s+CXr+7mnhWbKSscxc8+U67BxeS0RZKMwuw0CrPTOL8kF4DFc0vZdTh6uOqanXW8trOOVZsPApCeksSk0aOYXDiKacVZjM3VxWwkSqF/BrV3dvG1323hkVd28+Fzivj+4jnkjLBxdCRxzIzJhdFgv768FIDqo81877l32Fl7jJ21x9h2sBE2Q2ZqhNWVtXxwaiGXTCvUcBYhptA/Qw43tfLF5a/zcsVhliyYwpevPFfDBcgZNz4vg9mlecwujZ6/0NDcTmVNE5U1x3h9z1FWvnUAgJK8DD44tYBLphfS2NI+4gb1k74p9M+A1ZW1fGn5Gxw93s791806uRYmcrblZKQwZ2I+cybms3huKTtrj/Fy5WFWV9Ty7JaD/CY4ea04O42pxVlMK8picuEo0lPCNe5TmCj046ilvZPvPfcOP32xksmFo/jFP8xl5vicRJclAkQ3B00pymJKURY3z59EZ5ezpbqBB1+ooLKmKRjK4jBJFv0lsL++mQ9OLeTCSXmhG/xvJFPox8n63Uf41yc3UllzjEUXl3L31TPJTNWfV4auSJJx/oTckyObdnR2safuOJU1TVQcauLHf6nkh89XkJ6SxMVlo/nQtEIamzsYl6cjg4YzpdIg1Ta1cv8ft/HE+r2Mz83gkf86V8ffy7CUHEk6+UvgipnR8wbW7Kjj5cpaXq6o5Zt/eBuAjJQIU4pGMbUoujmoICs1wZXL6VDoD9Dxtg4eXr2bH/+lgua2Tj57yWS+ePl07RALqd5OcBruJzdlp6fwkZlj+MjMMUD0bPJvr9pO5aEmKmua2FzdAEBuRgob9hzlokn5zC7NY8aYrH7PGNYJYYmj0D9NjS3tLH9tLz99sZLapjY+fE4RX/nETKYVZyW6NJEzqjg7/eSRQe5O3bE2KmqaqDzUxAvbDvHUhuhO4czUCLMm5DK7NJ85E/OYU5pHcY7OExgqFPoxeudgI79+bS9PrNtLU2sHH5xawE9vnsFFk3ofu0VkJDMzCrLSKMhKY97kAhbPLWVvXTOv7z3C63uO8vqeIzz00g7aO6NDRxRnp/G+cTnMHJ/DzHE51DS2UpCVqn0DCaDQP4V9R5t5dvMB/uONajbuPUpykvGJWeO47ZLJJ8dxF5Hol8DEgkwmFmSycHYJED2abXN1A2/sPcqW6ga27G/g5Rd3nByxNCVijM1JpzgnnaKsNAqz0qisaWLi6EyNLnoGxRT6ZnYl8H0gAvzM3b/ZY3ka8AhwEXAYuMHddwXL7gJuAzqBL7r7qrhVH2eNLe1s2HOU1RW1vFRRe3Kb5bljs/lfn3gf18wpoTArLcFVigwP6SkRLpqUz0WT3h1nqrWjk4pDTSx7aRcH6puprm9h24FG1rceAeCXa3aTnGRMHJ3JlKIsygoyGZ+XQUl+BiV5GUzIz9DFbgap39A3swjwIHAFUAWsNbMV7r6lW7PbgCPuPs3MFgH3ATeY2UxgEXAeMB54zsxmuHtnvDtyOlraO9l1+Bg7ao5ReaiJHbXH2LSvnoqaJtyjayBzJuZz51Xn8rHzxsZlZEURgbTkCOeNzw2+CN79Mmhu66S2qZVpxVnsqG2KfjZrmnipooaW9q73PMeo1Agl+RmMzc2I/kLITj35S6EwuF8wKnoltNRk/WLoKZY1/blAhbvvADCz5cBCoHvoLwTuCaafBH5k0a/ihcByd28FdppZRfB8r8Sn/Hc1trSzubqBppYOmlo7aGztoKmlg/rmdmqbWqlpDG5NrdQ2tRKMUgvAuNx0zh2bzSdnjWfOxDzKy/J1jL3IWZSRGqF0dCZ/f9GE98w/scN439HmYHTR5pPTBxpaqDjYSG1TG22dXb0+b3pKEjnpKeRkpJCTnkxuRnQk0xMXrslICW6pEdK7TWekREhLTiKSZCRHoldD6z6d3GN+r1dG85533zujs8vp6Ipeg6GjM3pdhszUZMbnZQz673kqsSRbCbC32/0qYF5fbdy9w8zqgYJg/qs9Hlsy4GpPobLmGIuWvvo385OTjKLsNIqy0xibm875JbmMzU0/eZzxlKJRCniRIar7DuO+9qO5Ow0tHdQ2tVLb2EptUxuHj7XS0NxOQ0sHDc3t1De309DSTm1TGztqj9Hc1klze2dwQRzv9XkT4eoLxvPDxXPO6GuY+6k7bGbXAx9z988G928G5rr7P3VrszloUxXcryS6Rn8v8Iq7/zKY/xCw0t2f6vEaS4Alwd1zgG391F0I1MbUw+FPfR15wtJPUF/Ppknu3u+ZobGs4lYB3UcMmwD0vFDoiTZVZpYM5AJ1MT4Wd18KLI2hFgDMbJ27l8fafjhTX0eesPQT1NehKJa9HGuB6WY22cxSie6YXdGjzQrglmD6OuB5j/6EWAEsMrM0M5sMTAdei0/pIiJyuvpd0w+20d8OrCJ6yOYyd99sZvcC69x9BfAQ8Giwo7aO6BcDQbsniO707QC+kOgjd0REwiymPZjuvhJY2WPe3d2mW4Dr+3jsN4BvDKLG3sS8KWgEUF9HnrD0E9TXIaffHbkiIjJy6MwFEZEQGVahb2b3mNk+M3sjuH2827K7zKzCzLaZ2ccSWWc8mNmVQV8qzOzORNcTb2a2y8zeCt7HdcG80Wb2JzN7J/g3v7/nGYrMbJmZHTKzTd3m9do3i/pB8D6/aWYXJq7y09dHX0fc59TMSs3sBTPbamabzeyfg/nD731192FzI3rW7x29zJ8JbATSgMlAJRBJdL2D6Gck6MMUIDXo28xE1xXnPu4CCnvM+xZwZzB9J3BfouscYN8WABcCm/rrG/Bx4A+AAfOBNYmuPw59HXGfU2AccGEwnQ1sD/oz7N7XYbWmfwonh3tw953AieEehquTQ1+4extwYuiLkW4h8HAw/TBwTQJrGTB3f5HoUWzd9dW3hcAjHvUqkGdm485OpYPXR1/7Mmw/p+6+3903BNONwFaiowsMu/d1OIb+7cHPpWXdfv73NlTEGRnu4SwZaf3pjQPPmtn64IxsgDHuvh+iHzKgOGHVxV9ffRup7/WI/ZyaWRkwB1jDMHxfh1zom9lzZrapl9tC4CfAVGA2sB/4zomH9fJUw/mwpJHWn958yN0vBK4CvmBmCxJdUIKMxPd6xH5OzSwLeAr4krs3nKppL/OGRF+H3Ehj7v6RWNqZ2b8DvwvuxjTcwzAy0vrzN9y9Ovj3kJn9lujP/INmNs7d9wc/hQ8ltMj46qtvI+69dveDJ6ZH0ufUzFKIBv5j7v50MHvYva9Dbk3/VHpsE7sWOHHEwEgb7iGWoS+GLTMbZWbZJ6aBjxJ9L7sP53EL8P8SU+EZ0VffVgCfCY72mA/Un9hcMFyNxM+pmRnRkQe2uvt3uy0afu9rovckn+Ye9EeBt4A3if5Rx3Vb9hWiRwNsA65KdK1x6OvHiR4hUAl8JdH1xLlvU4gexbER2Hyif0SH4/4z8E7w7+hE1zrA/v2a6GaNdqJrfLf11TeimwEeDN7nt4DyRNcfh76OuM8pcAnRzTNvAm8Et48Px/dVZ+SKiITIsNq8IyIig6PQFxEJEYW+iEiIKPRFREJEoS8iEiIKfQmNYPTHO+LwPHlm9vlu98eb2ZODfV6Rs0GhL9ILMzvV2ep5wMnQd/dqd7/uzFclMngKfRnRzOwrwdjtzwHnBPP+YmblwXShme0Kpm81s9+Y2TNEB4PLMrM/m9mGYOz/EyOdfhOYGowVf7+ZlZ0YT97M0s3s50H7183sw92e+2kz+2Mw9vq3zvKfQgQYgmPviMSLmV1EdAiLOUT/r28A1vfzsA8As9y9Lljbv9bdG8ysEHjVzFYQHTf9/e4+O3idsm6P/wKAu59vZucS/fKYESybHdTSCmwzsx+6e/eRGEXOOIW+jGSXAr919+MAQWD350/ufmJ8eAP+dzACaBfRoXHH9PP4S4AfArj722a2GzgR+n929/qgli3AJN47/K7IGafQl5Gut3FGOnh302Z6j2XHuk3fBBQBF7l7e7AZqGf7nnobUveE1m7TnejzJwmgbfoykr0IXGtmGcGonlcH83cBFwXTp9oBmwscCgL/w0TXzAEaiV4yr6/XvAkg2KwzkejgYiJDgkJfRiyPXt7ucaIjIj4F/Gew6NvAfzez1UDhKZ7iMaDcohduvwl4O3jew8DLwcV97u/xmB8DETN7K3jtW929FZEhQqNsioiEiNb0RURCRKEvIhIiCn0RkRBR6IuIhIhCX0QkRBT6IiIhotAXEQkRhb6ISIj8f1bSn1NT5Z6HAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "duration = data.duration.value_counts(sort=False).round(-1)\n",
    "duration = duration[duration != 0]\n",
    "sns.distplot(duration, bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Figure 4.7.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top 200 Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c2000d940>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VdikARDIymFecy26daSSSUhQIM0BtyFNWjKayNJddDe3Ezz0QkVSgQJjmegcGOXi6KwUDIY+27gGOt/WEXYqIBBQI01zdqU4GY556gVCSB6DrEURSiAJhmhs6wyhVrkEYMq8kNz6wrHEEkZShs4ymoeFn9/zkhQayMo3fHWjm6YMtIVb1UlmZGayYU6g5jURSiPYQprnG9l7mFeeSYaNNKxWutVUl7DqugWWRVKFAmMbcnRPtvcwLjtenmrWVxZzu7OfUmb6wSxERFAjTWnvPAD0Dg8wvyQ27lFGdnQpbA8siKUGBMI2daO8FYF5xagbCqvnFmKFbaoqkCAXCNNbYEQRCiu4hFOREWFpeoDONRFKEAmEaa2zrYVZ+FrlZmeM3DsnaqhJ260wjkZSgQJjG6lt7WDArP+wyzmltZQmN7b2c7tTAskjYFAjT1JneAdp6Blg4KzXPMBoyNLC8u0GHjUTCpkCYpupb43MEpfoewurK+JQaOtNIJHwKhGmqvrWbDItPIpfKSvKyWFyWr0AQSQEKhGmqvrWHucW5ZEdS/794bWWJprAQSQGp/2kh583dOdbazYIUHz8YsqaqmGMtPbR3D4RdisiMpkCYhpq7+ukdiKX8+MGQtZVDA8vaSxAJkwJhGjrW0g3AwnQJhKEpLBQIIqFSIExD9a09ZGdmMKc4J+xSEjK7IJuq0jxdsSwSMgXCNFTf2k1laV5KTnk9ljWVxdpDEAmZAmGa6YsO0tDem/IXpI10SVUJB5u6aO/RwLJIWBQI08y2I60Mxpzq8oKwSzkvr1g8C4Dnj7WFXInIzKVAmGZ+W3eaDIMlaRYI6xaWkmHxQBORcCgQppkn959mwaz8lJ7hdDQFORFWzS/mOQWCSGgSCgQz22hm+8yszszuGOX1HDO7P3h9q5lVD3vtzmD5PjO7YcR6mWa23cx+cqEdEWjvHuCF4+0sn1MYdikTsn7xLLYfbSU6GAu7FJEZKTJeAzPLBL4IvBGoB541sy3uvmdYs/cBre6+3Mw2A58A3mlmq4HNwBqgEnjUzC5y98FgvQ8DtUBx0no0g/3uwGncYXlFegTCvVuPvuR570CMrv5BPvvo/rNzML37ykVhlCYyIyWyh3AFUOfuB929H7gP2DSizSbgW8HjB4HrzMyC5fe5e5+7HwLqgu1hZguAtwBfu/BuCMCTdacpzImwcHZ6XJA20uKg7qPBhXUiMrUSCYQq4Niw5/XBslHbuHsUaAfKxln3s8DfAjo+kCRP1p3mqqWzycxIn+sPhivNz6IoN6JAEAlJIoEw2qeLJ9hm1OVm9gfAKXffNu6bm91mZjVmVtPU1DR+tTPUsZZujjR3c83y8rBLmTAzY9HsfI40d4VdisiMlEgg1AMLhz1fADSM1cbMIkAJ0HKOda8B3mZmh4kfgnqDmf33aG/u7ne7+wZ331BRUZFAuTPTr/fHw/LVaRwIED9s1No9QEevLlATmWrjDioDzwIrzGwJcJz4IPG7R7TZAtwKPAXcBDzu7m5mW4B7zezTxAeVVwDPuPtTwJ0AZnYt8Dfu/sdJ6M+0N3IgdsjdvzpIRWEOzxxqwdJoyoqRFpfFr5842tx9dtI7EZka4+4hBGMCHwAeIX5G0APuvtvMPm5mbwua3QOUmVkd8FfAHcG6u4EHgD3Aw8Dtw84wkiRpOtPHkZZu1i+eldZhADC/NJesTOOQDhuJTLlE9hBw94eAh0Ys++iwx73AzWOsexdw1zm2/Uvgl4nUIaN77mgrGQbrFpWGXcoFi2RksHh2AYeaFAgiU01XKqe5mDvbj7ayYk4RxblZYZeTFEsqCjjR0Ut3XzTsUkRmFAVCmtt/spOO3ijrg8nhpoOlwTxMOmwkMrUUCGlu25EW8rMzWTm/KOxSkqZqVh5ZmcbB0woEkamkQEhj9a3d7G7oYMPi2UQyps9/pcYRRMIxfT5FZpiYOz/e0UBBToRrL55+12cMjSO0dvWHXYrIjKFASFPbj7ZyrLWHjWvnpd1U14kYGkfYeqgl5EpEZg4FQhrq7ovy8O6TLJqdz7qF6X+q6WiGxhGePtgcdikiM4YCIc30DgzyzacO0zcwyNsuqyQjzS9EG8vQOMLvDpwOuxSRGSOhC9MkNfT0D/JfTx2hoa2HW65cfPaeAdPV8jmFPLz7BF/55QGK80a/xkL3SxBJHu0hpInmzj5u/cYzHGnu4ub1C1k1f/rfU2jozm91pzpDrkRkZlAgpIE9DR287Qu/ZcexNv5ow0Ium6bjBiPNK8mlICfC/lNnwi5FZEZQIKS4n+1s5A+//DuisRgP/NnVMyYMADLMWDGnkLpTncR85C04RCTZFAgpKhZzPv2LF3n/d55j5fwifvyBV8+oMBiyfE4hXf2DnGjvDbsUkWlPg8pTaKx7GQz37isX4e78w492ce/Wo9y0fgF3vWMtOZHpd61BIoaPI0z3QXSRsGkPIQV94uF93Lv1KO+/dhn/ftOlMzYMAIpzs5hXnKtxBJEpoEBIMV/91QG+8qsD/PFVi/jbGy5O+xveJMOKOYUcbu6mPxoLuxSRaU2BkEIONnXybw/v5S2Xzufjb1urMAgsn1vIYMw52KTTT0UmkwIhRXT1RXmg5hhLygr45B9eSkaGwmDIkrICsiMZ7D2hw0Yik0mBkALcne8/V09X/yCff9flFORorH+4SGYGK+YUsvdEB67TT0UmjQIhBWw/2sbeE2fYuGYea6tKwi4nJa2cV0xHb5RGnX4qMmkUCCHr7o/ys12NLJqdz9XLysIuJ2VdPK8IA/ae6Ai7FJFpS4EQsp/vOUl3/yCb1k3fmUuToTAnwoJZeRpHEJlECoQQ1bd28+yhFl61rIz5Jbroajwr5xdT39rDmd6BsEsRmZYUCCFxd37yQiOFORGuWzU37HLSwsp5RQDs016CyKRQIISktvEMR1u6uX7V3Gl5C8zJMK84l9K8LPY0ahxBZDIoEEIwGHMe2XOC8sIcXrF4VtjlpA0zY01lMXWnOukdGAy7HJFpR4EQgu1HW2k608cNa+aSqQvQzsvaqhKiMdfgssgkUCBMsYHBGI/WnmThrDxWz4C7niXbwtn5FOdG2HW8PexSRKadhALBzDaa2T4zqzOzO0Z5PcfM7g9e32pm1cNeuzNYvs/MbgiWLTSzJ8ys1sx2m9mHk9WhVFdzpJWO3ihvWjNPcxVNQIYZaypLePHkGfqiOmwkkkzjBoKZZQJfBN4MrAbeZWarRzR7H9Dq7suBzwCfCNZdDWwG1gAbgS8F24sCf+3uq4CrgNtH2ea0E43F+PWLTSyanc/S8oKwy0lbQ4eNdLaRSHIlsodwBVDn7gfdvR+4D9g0os0m4FvB4weB6yz+5+8m4D5373P3Q0AdcIW7N7r7cwDufgaoBaouvDupbfvRNtp7BnjDyjnaO7gAi8vyKcyJsKtBZxuJJFMigVAFHBv2vJ6Xf3ifbePuUaAdKEtk3eDw0uXA1tHe3MxuM7MaM6tpampKoNzUFB2M8asXm6gqzWNFcBcwmZiM4GyjfSc66OyLhl2OyLSRSCCM9qfsyCknx2pzznXNrBD4PvCX7j7qn3vufre7b3D3DRUVFQmUm5q27Gigpauf11+svYNkWLewlIFB52c7G8MuRWTaSCQQ6oGFw54vABrGamNmEaAEaDnXumaWRTwMvuPuP5hI8eliMOZ84Yk65hXnsnJ+UdjlTAuLZudTVpDNg9vqwy5FZNpIJBCeBVaY2RIzyyY+SLxlRJstwK3B45uAxz0+cf0WYHNwFtISYAXwTDC+cA9Q6+6fTkZHUtlDOxs52NTFtRdXaAK7JDEzXrF4FlsPtXC0uTvsckSmhXHvxOLuUTP7APAIkAl83d13m9nHgRp330L8w/3bZlZHfM9gc7DubjN7ANhD/Myi29190MxeDbwH2Glmzwdv9Xfu/lCyOziV7t169GXLYu785+P7qSjM0b0OkuzyhaU8WnuS7z9Xz0feeFHY5YikvYRuzRV8UD80YtlHhz3uBW4eY927gLtGLHuS0ccXpp29jR2c7Ojj5vULtHeQZKX52bxqWRnff66eD1+3QrcdFblAulJ5Erk7j+87xeyCbC5dUBp2OdPSTesXUN/aw9OHmsMuRSTtKRAm0YsnO2lo6+Xaiyo0Z9Ek2bhmPqX5WXzjt4fDLkUk7SkQJom788S+U5TmZbFukfYOJktedibvuWoxj9ae5GBTZ9jliKQ1BcIkOdDUxdGWbl57UQWRDP0zT6b3XL2YrIwM7nnyUNiliKQ1fVJNkif2naIoN8J63e9g0s0pyuUdl1fx4LZ6mjv7wi5HJG0pECbB4dNdHDrdxWtXVJCVqX/iqfCnr1lCXzTGfz/98lN/RSQx+rSaBE/sO0VBdiavrJ4ddikzxoq5RVy/ag5fe/Igp7WXIDIhCoQkO9bSzf5Tnbx6RQXZEf3zTqU7b1xF78Agn3x4b9iliKQlfWIl2RP7TpGXlclVS7R3MNWWVRTyP69ZwgM19Tx/rC3sckTSTkJXKktiGtp62HviDNevmkNOVuaEtjHa9BeSuA9et4Ifbj/Ox360ix/8xTUJXf8x3r/5u69clKzyRFKa9hCS6PG9p8iJZHD10vKwS5mxCnMi/P1bVrGjvp1/fag27HJE0or2EJLkhfo29jR2cN3KOeRlT2zvQJJj07oqth9t42tPHmJxWT7vubo67JJE0oICIUk+9fMXyc/O5Jrl2jtIBf/4B6s51tLNx7bspjQ/m7deVhl2SSIpT4eMkmDrwWZ+/WITr7uogtwJjh1IcmVmGJ9/1+WsW1jKB7+7nb99cIdutykyDgXCBXJ3PvXzfcwpyuGqpWVhlyPDFOREuO+2q7n99cv43rZ6rvuPX/LJh/dSd+pM2KWJpCQdMrpAP9t1gmcPt/LPb1+r+x2koOxIBv/7hpVce/EcvvhEHV/51QG+9MsDzCnK4dIFJVxSVUpLVx9Vs/IpzNGvg8xs+g24AL0Dg9z101pWziviXVcs4v5nj4VdkozhldWz+eb/uIJTHb08vPsEzx9tY0d9G4/tPYV7vE1pXhZVs/JYNa+YSxaUaNoRmXEUCBfgq786yPG2Hu677Srd7yBNzCnO5b1XV/Peq+PPz/QO8LnH9nO8tYf61h6OtXSzu6GDn+5s5JXVs3ndRRXhFiwyhRQIE3S8rYcv/6qOt1wyX2MHaawoN4ul5YUsLS8E4mNCB093sfVgM7/Z30TNkRYimcYtVy4ioj0GmeYUCBPg7tzx/RcwjDtvXBl2OZJEZsayikKWVRTS2N7DT3c28rEtu/nBc/X8xx9dxvI5RWGXKDJpFAgT8O2nj/Cb/af557evZcGs/LDLmdEmc6qP+SV5vO+aJRTnZfHRH+3ixs8/yV+/8SL+9DVLz/sQYSJ1aooMCZv2gc/TgaZO/uWhWl53UQW36Bd42jMz3npZJT//yOu49qIK/vVne7n5K7/T7TplWlIgnIeuvigf+u52crMy+eRNl2I6zXTGqCjK4avvWc9n37mOA01dvPlzv+GeJw8Ri3nYpYkkjQIhQdHBGB/87nZqGzv4zB+tY25xbtglyRQzM95+eRU//8hrefXycv7pJ3vYfPfT7D+pC91kelAgJMDd+diW3Ty+9xT/9Pa1vH7lnLBLkhDNLc7la7du4FM3X0btiQ7e9Nlf86HvbmffCQWDpDcNKo+jPxrj7364kwe31fPnr1vGLVcuDrskmWLnGhD+0BtW8GTdaR6tPcmWHQ0sn1PIm1bPZeX8YhbNzqc4N0LMnYa2Hrr7B+nuj9LdP0hXf5SBaAwzI8OM4rwIC2blcfG8Iu19ppiZdL8MBcI5NHf28f7vPMczh1r4y+tX8OHrVoRdkqSYgpwIN6yZx2feuY4f72jgkd0n+OqvDzKYwNhCJMNwIBZzHPjR8w0ALJydxxXVZbxh5Rxed3GFptSQKZPQT5qZbQQ+B2QCX3P3fxvxeg7wX8B6oBl4p7sfDl67E3gfMAh8yN0fSWSbYYoOxrj3maN8+hcv0t0/yOc2r2PTuqqwy5IUNrsgm1tfVc2tr6qmuz/KsZYejjR30TMwiJnx9IFm8nMyKciOkJ+dSX525OypqzF3OnoGWL94FjuPt1NzuJXH9p7k+8/Vk53c33iVAAAJr0lEQVSZwVXLynjjqjlcv3ou80vyQu6pAERjMXr6B4nGnGMt3WRmGMV5WRRkZ6b1ySbmfu6/ZMwsE3gReCNQDzwLvMvd9wxr8xfApe7+52a2GXiHu7/TzFYD3wWuACqBR4GLgtXOuc3RbNiwwWtqas6/lwlqbO/h/21v4Hs1xzh4uotXLSvjY29dw8XzErsYSbe/nLnGO2xwvtchRAdjbDvSyqO1J/nFnpMcbu4GYG1VMdetnMvVy8q4bEGpbsY0Sdyd1u4BDp3u4rtbj3K6q4/Tnf00d/bR0tVPXzQ26nqRDKOyNI/FZfksmp1PdVkBi8ryWVJewKLZ+aFMj29m29x9QyJtE9lDuAKoc/eDwcbvAzYBwz+8NwH/J3j8IPAFi8fkJuA+d+8DDplZXbA9Ethm0sRiTl80Rnd/lJ6BQXr6B+nsi3Kyo5fjbb3sbexg+7E26k7Fzy1fv3gWX9m4khvWzE3rtJf0FcnM4MqlZVy5tIy/u3EVB5o6+cWeUzxae5LPP76fzz22n0iGsWJuESvmFLK0ooDywhzKC7MpK8xhdkE2RbkRsjIyiGQaWZkZRDKMzAyblj/T7o57fG/LCb478S+cmMfbxGLQPRClszfKmb4oZ3qjtPcM0NjWQ2N7L8fbemhoi89r1d4zcHb7GQaz8rMpL8xhcVkBhTnxvbysTOPqZeVEB2N09A7Q2j3AsZZujrZ085MXGl+yDTOoLMljaUUBC2blU1GUQ0VhfJvlRTkU52aRm5VBblYmuZFMcrIyyMrMIMOYsv+zRAKhChg+jWc9cOVYbdw9ambtQFmw/OkR6w4dexlvm0mz+mMP0zsweqJDfHd/3cJS3nF5FW+5ZD7V5QWTVYrIeTMzls8pYvmcIt5/7TLauvvZdqSVmiOt1DZ2sO1IK1t2NCS8vd8HwwTrYeIfThN9z+Ef9iM//Mc5yJGwopwIlaV5VJbmcvmiUqrLClhaUcCu+g5mFWSPeXX6TesXjLnN9u4BDjd3cbi5i0Onuzh8Ov795w0naOnuT7j28sIcav7h+ol067wkEgij/SuM7MZYbcZaPtrprqP+05jZbcBtwdNOM9s3Rp0TdgTYDnwD+EDiq5UDp5NdS0imS19C68ctyd/GdPk/gTTqy67xm7ysL8n4vx/PEcD+ccKrJ3xqZCKBUA8sHPZ8ATDyz5GhNvVmFgFKgJZx1h1vmwC4+93A3QnUOaXMrCbR43Kpbrr0Zbr0A9SXVDWd+jKaRC5MexZYYWZLzCwb2AxsGdFmC3Br8Pgm4HGPj1ZvATabWY6ZLQFWAM8kuE0REZlC4+4hBGMCHwAeIX6K6NfdfbeZfRyocfctwD3At4NB4xbiH/AE7R4gPlgcBW5390GA0baZ/O6JiEiixj3tVEZnZrcFh7PS3nTpy3TpB6gvqWo69WU0CgQREQE0uZ2IiAQUCOfJzDaa2T4zqzOzO8KuZzxm9nUzO2Vmu4Ytm21mvzCz/cH3WcFyM7PPB317wcxeEV7lL2dmC83sCTOrNbPdZvbhYHna9cfMcs3sGTPbEfTl/wbLl5jZ1qAv9wcnXRCcmHF/0JetZlYdZv0jmVmmmW03s58Ez9OyHwBmdtjMdprZ82ZWEyxLu5+xiVAgnIdgGo8vAm8GVgPvCqbnSGXfBDaOWHYH8Ji7rwAeC55DvF8rgq/bgC9PUY2JigJ/7e6rgKuA24N//3TsTx/wBne/DFgHbDSzq4BPAJ8J+tJKfB4wgu+t7r4c+EzQLpV8GKgd9jxd+zHk9e6+btgppun4M3b+4lf96SuRL+Bq4JFhz+8E7gy7rgTqrgZ2DXu+D5gfPJ4P7Asef5X4nFIva5eKX8CPiM+Hldb9AfKB54hfrX8aiIz8eSN+Rt7VweNI0M7Crj2oZwHxD8k3AD8hfkFq2vVjWH8OA+UjlqX1z1iiX9pDOD+jTeORjtOgznX3RoDg+9Adf9Kmf8GhhsuBraRpf4LDLM8Dp4BfAAeANnePBk2G1/uS6WGAoelhUsFngb8FhuaHKSM9+zHEgZ+b2bZgpgRI05+x86WJ1s9PItN4pLO06J+ZFQLfB/7S3TvOMfFXSvfH49fkrDOzUuCHwKrRmgXfU7IvZvYHwCl332Zm1w4tHqVpSvdjhGvcvcHM5gC/MLO952ibDv1JmPYQzk8i03ikg5NmNh8g+H4qWJ7y/TOzLOJh8B13/0GwOG37A+DubcAviY+LlFp8+hd4ab1n+2IvnR4mbNcAbzOzw8B9xA8bfZb068dZ7t4QfD9FPKivIM1/xhKlQDg/02XKjeFTjdxK/Fj80PL3BmdOXAW0D+0mpwKL7wrcA9S6+6eHvZR2/TGzimDPADPLA64nPij7BPHpX+DlfRltephQufud7r7A3auJ/z487u63kGb9GGJmBWZWNPQYeBPxOe/S7mdsQsIexEi3L+BG4jf3OQD8fdj1JFDvd4FGYID4XzPvI37M9jFgf/B9dtDWiJ9FdQDYCWwIu/4RfXk18d3xF4Dng68b07E/wKXEJ9l9gfgHzkeD5UuJz/dVB3wPyAmW5wbP64LXl4bdh1H6dC3wk3TuR1D3juBr99DveDr+jE3kS1cqi4gIoENGIiISUCCIiAigQBARkYACQUREAAWCiIgEFAgy45nZ/zGzv0nCdkrN7C+GPa80swcvdLsiU0WBIHIehl19O5pS4GwguHuDu990jvYiKUWBIDOSmf29xe9r8ShwcbDsl2a2IXhcHkzHgJn9iZl9z8x+THzSs0Ize8zMngvmzd8UbPbfgGXBPPr/bmbVFtyHIrj/wTeC9tvN7PXDtv0DM3s4mGv/k1P8TyFylia3kxnHzNYTn2bhcuK/A88B28ZZ7WrgUndvCfYS3uHxifXKgafNbAvxOfLXuvu64H2qh61/O4C7X2JmK4kHy0XBa+uCWvqAfWb2n+4+fAZNkSmhQJCZ6DXAD929GyD4MB/PL9x9aBI2A/7FzF5LfMrnKmDuOOu/GvhPAHffa2ZHgKFAeMzd24Na9gCLeemUyiJTQoEgM9Voc7ZE+f1h1NwRr3UNe3wLUAGsd/eB4NDSyPYjjTlHN/E9gyGD6PdSQqIxBJmJfg28w8zygpkt3xosPwysDx6fazC4hPg9AAaCsYDFwfIzQNE53vMWgOBQ0SLid9cSSRkKBJlx3P054H7is6V+H/hN8NKngPeb2e+A8nNs4jvAhuAG7LcAe4PtNgO/NbNdZvbvI9b5EpBpZjuD9/4Td+9DJIVotlMREQG0hyAiIgEFgoiIAAoEEREJKBBERARQIIiISECBICIigAJBREQCCgQREQHg/wPMaI7wQWarrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "durationTop200 = data[data.index.isin(data['imdb_score'].sort_values(ascending = False)[:200].index)]\n",
    "duration = durationTop200.duration.round(-1)\n",
    "duration = duration[duration != 0]\n",
    "sns.distplot(duration, bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Figure 4.7.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary \n",
    "The duration of the movie also affects its outcome. Figure 4.7.1 shows the distribution of the total movie durations, with movies of 10-20 minutes having the largest amount. However, out of the top 200 scoring movies as in Figure 4.7.1, we see that most highly rated movies have a duration of 110-120 minutes as seen in the histogram above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.8 content_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c20122860>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.content_rating.value_counts().plot.pie(figsize=(5,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Figure 4.8.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top 200 Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c201c8b38>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "contentTop200 = data[data.index.isin(data['imdb_score'].sort_values(ascending = False)[:200].index)]\n",
    "contentTop200.content_rating.value_counts().plot.pie(figsize=(5,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Figure 4.8.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary \n",
    "Here we compare the ratings of the movies in our database. Figure 4.8.1 shows that almost half the movies have an R rating, with about a third being PG-13. The popularity of rated R movies is also seen in the top 200 scoring movies shown in Figure 4.8.2. Again, movies with an R rating take up half the chart, however these are the 200 most highly rated movies. PG-13 movies are no longer 1/3 of the total, with NC-17 and G ratings having a greater presence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.9 language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1fae2828>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pU/3l2TamDcgylHaSW3frvk19TgLHSzoB+Hr3eFugpfnhEyhjt89RXtfXUeYLmzBVifOBBkudP5dyQvY3lPn1NSW9tsFNe324R7u525j5K0lvAP5EKYHdmhu7ecELJR1Aub63tmH4Bkn3YeIQ1Ua0uRmuD3NF0I/3z7WSVqC0ljxc0t8on+mzSm1uZLtzNNEzbSnK4PJyyg++mZ5pQx+WKwLrAucw+ZeziQ/LoUGcKBfKfwG30mZfg28A7xvcmHfllt5i+1VVg41Q6Ym4gJbKVEr6r3HPt7Ybsvug3Ijy+3i27asqRxqrD69nHzICSHokpTSdgB+2uBtO0ik0fF0fkHRf4JOUntGi9Ireo4VShd1rOBW7kd4/fRhvDJO0B2UT2YMoi1anAafb/k3VYEMkLU3pGSzgMtu3Vo40lqTfjnnabqgXmUp/41Fu5TRvH94/U7yGA828lgOStgSeQXkNT7D9g8qRJpF0QVdK7WLbj+ve7ye0ck0f1YPXc/g+zcBfgLePnjyvSdK5jCnvaPud035jBZJOppRDPge4YfB8S+O3KcZHzYyLBiT9nFLG87c0dl0f1o2FB/eSP271XrJVU81rdOwGetbPkLGpORhYYFFtCUrP2yNs710v1YL6cr8bi5akl1AOqohyD3lU5UjzdQuSr2ViruhE4IvdSdnqJD1luq+3tJkDQNJlwPNs/7p7/FDgONuPrJtsstbvdwEkbUjZIHxPSnnpewAftn121WAjuko7f6Vs3nkjsBLw2cHvQAu6gzUHAo+hbIpamVKV4+KqwUb0Ya4IevP+WR64mXJdfwXl9/Lw2Z63XlwWzB883ddbKBHXtw/LPpB0oe11Z3ouFg+SNgEutH2DpO2B9YBPtvD+jv+MbpB+he1/SXoqpYrEVzzUf7AFU13fc11f/PRhvDFOt0NzR+AtlLKpS1bOs4Xtk4dO807S2A7XWET6+v5p1eBm0vbtkh5B2Xjy/ZY2nUg6x/YTJJ0O7EpZ4D2ntcmDWHQknWt7g8Emie65s2xvXDvbqIzfFp2pru8tXNclPdL2ZZqirVtLp/UlPYsyuf5gyuaywcaDe1cNNkLSGrZ/N/LchrZ/WilSb41ch24Dfm/7ilp5ptKH66WkC1jwRO88Sg/pD7Ry0k/SYBFgTdvvkfQg4H62z6kcLeYwSafb3mzosYDThp+LhSPpMbYvnflvxsKQtBQTByx+2dK9biy+FosF84E+LK5IWhO40vbN3eO7A6uO3nDUNsXi5CdaKlmn0rfvBuCrlIHx9sAKtrerGqwj6RO295yqFE9Lu3ElrQV8gLKjebjHZDMTmpIuBtahe18DXwJebHvazSg19OT17EPGCymnk9aglNc6BniE7efUzNVXXZml91J6RB9PeT/tafurVYMN6U5EHwpcTymFux6wt+0TqwYb0YfxBpSemJQT5isAPwbOAM6wfXnlXPvZ3qcPO1yHdZVsRq+ZX6mXaDL1pF90H94/kr5MqcBxbff4XsBHW3otJZ0HPBm4F3A2ZUL4RtuvqBpsiKTXAN+m/IwPpVyL3m37c1WDjdFt4PkQpWSiaKi61lQLfQONLfidTqlk80XKBokrgVfZXqdqsB5TKZM6el2vfpJ3HEmrMDln9bkDSV+wvXMfTutL+jXwUuASYH4LnVZOTA50nz/Pt/2n7vFmwGdsP7ZusgmSfgBsM/I5/g3bz6ybLP5TuntdUVpRQal2cjvwT2CjVubfJB1EeX9vYftR3e/mibY3rBxtklbHRZKOsP3SocpVk7RS2aRHOQcb2LekbNY6gpJ3G8ri5JtrZRunD/e7ks6knNr+X+BrLd3jDuvWffZlYpMe0Nyc8DbA8bavl/Quyvzge1u69xlofa4I2n7/aMG2JZPM9mfP4rZgPry4cgJwNI0trnRl6ja2fUv3eBngRw0OjoYXJw+j9D5tanGye6P/N6X/GJT+BgcNNiPUJml92+f1ZDfumZQe4R8HtqKcRJTtaUuazSZJ59teT9K7gT/ZPmTwXO1so3ryevYh4+Bnvhdwk+0D1ZV3rZ1tmHrSr3NQgUPSi4AXUsoundLSJLaki2yvI+mZwOuB/wEObe193ofxBsy/wTjd9l9rZ+k7lRKfT6XcBH0PeDZwpu2ta+YaJulISun9lzPUL9r2HlWDjejD+2fcZ01rnz9Dn5G7AXe3fUBrGfukW6jayu22fplKawt+zZd3HOjD+E2lN+tywOaUTQhbU6o07FQ12AhJzwc+Ctwf+Btl8vUXth9dNVjPSDqVsoh2x0x/tyaVcrOfpdxDrge8n3L9/GPVYEM0vhphM5+RrU0Mz6Qn18szbW867jlJl7SyoWNo/Db/93FwD1w727BWx0WS7mf7ypYrm0CvcvatHVVf7nfXAl5N2XjwU+BLbq/V02WUsfp5lM1FALiBlo0DmmjrtSnlwNdHgHfYfmLlaJP0Ya4I+vH+kbQ/ZdP1YTC/LPuKtg+YzRxLzfxXeuUO27d1O6Q+MVhcqR1qxFKDxXIA27d0i+atuc22Jb2AUvb6EE3R87iWbmH8490/zekWy5cEdra9fe08M7i77R9KUjdw21fSGZQF1VZcL+ntwA7Ak7vXdunKmabSh9ezDxlvlbQdpf/lVt1zLf7MP82C/TrXqppovMFr9xzg67avllQzzziDQM+hLJRfpAZD0o/xBpSTnS+XNCj5tzqwmhsp+SfpPpRrzqaUScMzgf1bukkbsjVlI+EFtneUtCpl8aIlD7O9jaQX2P6ypK9RFqRb04f3zxKS7mX7GgBJ96a9+yZJehLlJnKweNZURkl3A15C2RwxfHKhxdOxf21tUnjA9ua1MyysoQngm4GmegWP0Yfx28bdROHFtvdTqRzTYtuS91B6g59k+/GSNgdaqfo2tv3LgNtqA7MXcEy3cD7cJ/pT1RKNYfunknan9Ay+GdjS9t8rxxp1u6TV3VU56BatmjktZHtFmHpiuGK0qfTherni4NAKzK/OMljQv61erAXc2s1lGUDSygxVlGhIk+Mi21d2fzax4DyVHuXcsXaGO6kX97u2f9WdiD4X+BSwbjev9Y6Gxh3zbH+/dogZDBbyn0s5IPldSftWzDOVPswVQT/eP88c2RBxkKSfAFkwvwv6sLjyd0nPt300QLcgfVXlTOMMFie3BzZrcXFSPSgf4tJXcmVJywxvlGjQzZKWAH4l6Q3Anyill1qyLWUX1Ktt/6Vb+Plw5UxT6cPr2YeMOwKvA95n+7cqLS2aKR8+zPavJS3pUjLxUEln1c40xjHdLtKbgF27m/MmKnIMOU/SicCawNslrUibEwh9GG8AfIau5B9lMvt6yiJ6K1VtvkGpDvOS7vErgG9Syvm25ibbd0i6TdI9KCfomhlvdAb9vK5VKQn2F8pCZWv68P75KHCWpG9RJjRfSjlB15I9gbcDR9n+maSHANOdRK7hu5T+oecxtPjTqHMlfRP4DpMXqlqZ1ELSK8c974bK/fXh/mxYD8ZvN3V/3ijp/sA/KGOk1txq+x+SlpC0hO1TJH2odqjO4HNmFWBj4OTu8ebAqbS1AWE/ymf5PWlw/KsFW80tR7nGHyKpqZZzwDuBMyUNqvptBuxSMc9UmpgYXhg9uF6+FjhM0tKUzQe3ADtJWp62Xs9PAUcBq0p6H2Wh5V11I43V9LhIPah6AO2Wth+lhks1j2j+flfS4yhzmc8FfkCp1HB+N477MZXHHZpo9XSKpA93eYbf4y2VO/+TpM9T5oc+1G3GXqJypnH6MFcEPXj/UDY8voIyV2jKBtxZb020uC2Y92Fx5XXA4ZI+Tfmg/CNlwrA1g8XJnRpenDyEMeVDGvQ74EeSjqb0XAfA9seqJVrQnpQb3t0piypbAK1VFPiLpG8zsZP5KsqNRouafz3pQUbbP5f0NmD17vFvgQ/WTTXWjSqVQi5U6Z12JbB85UwLsL13N3l5XbeZ50bgBbVzjdgJWBe43PaN3QnkFnc992G8AfDEQck/ANvXqK2qNve2/Z6hx++V9MJqaaZ3rqR7Al+gjDv+CTRxUn/IwSp9EP+HUuZ8BeDddSON1fz7x/ZXVNoobUEZr7/Y9s8rx5rEpbXPad1EMLYvp3ymt+SBtp9VO8RCugdwI/CMoedMW4tpw5udlgWeBpwPNLNgTn/uz6Af47dju8+eD1N+1qbNEyvXSlqBsgnucEl/o5ETnYPTc5KOBdYenPqTdD/KxsKWrGJ7/dohpvGR2gEWlu3ju0WBjSif42+03eJBlSYmhhdC89dL22cDa3f3jxr5eX+9UqwF2D5c0nmUz3ABL2zxJDftj4vGVT14WNVE4x1Ag6XtxziMUqr5mQyVaq6aaLw+3O9+mjJn8A7bg42P2P5zd+q8to+OPN5g6N9Nuf9txUuBZwEfsX1tN3Z7a+VM4/Rhrgj68f55OfDJ7h8DP+qem1WLVQ/zPuluKGX7+tpZ+krST1rrWzGOSi+LBdhuvUxhUyTtTNkVfm/bD1XpCfM520+rHC3+QyRtRZmYWcb2mpLWpZRrbun0Qm/6dUpaDngTsLrtXbr30CNsH1s5GpIeafuyod2ukzS2y7U3uhMqGwM/7RbOVwZOdDv9Gz9CKVN2RPfU1sCjbbfUGmIBktYA7mH74spR4j9E0mG2d5jpuZpUyrEfAqxge3VJ6wCvtb1r5WjzSToYOND2JbWzLI4krQQc1tK4qC/3Z9Cf8dtAd6pmWdvzamcZ1W3cuZmJktIrAYe31GJF0qW2HzP0eAng4uHnausWIo+3ffKMfzmmJWnwu/gQ2/ursbZEA92Y8pPAJkxMDO9p+3f1Ui2ou17+jVIRqMnrpaR3jHvedmsVglDpx7uW7UO7+7MVusMBsZAknWt7A3U9jrvnzrK9ce1swyT9yPYmtXPMRNIFXUuVQc/opYETbLe0eBqLkKRlXVrdDj93n5bGbtC/62XmihYPi8WCuaQjbL9U0iWM6Us0+PBsgRrv5SfpTNubSrqeya9lc2VjJH2QUnan5fIhvSBpA0rZstHyiS29dy4EngD8ZLDYI+kS24+tm2yCpE/Y3nNMuTqAJsrU9SHjQLfzegvg1FZ/5gOS7k5ZiP5l7SxT6UqqnQe80vZjusw/tr1u5WhIOrhbxB9XTtit3Kj1abwB0J1Y2RZYD/gyXck/20dWzjUYY4hyOmVQdnQJ4J+NjTWa38whaXvbX5X0pnFfb6WiTZ/eP5LOt73e0OMlgUtsr10x1iTdhpitgaOHPiMnLQjVIulSyvt6KUploMspY/XB/UQzP+sBSQ+klPYcLFqcCexh+4qqwabRTWZeYvuRtbMM9O3+rJt4w431X5a0he2TNUX/bTdSErdPVCr8rUU5aWrKycRf296tarAhkq6hLETeSCknPbhm3rtqsBHqQSlkSQfRtSWy/ajuRNWJtltpSzQYW+xu++O1sywOVCrTDSxLKYf8MzfWo7k7ULMBZeP6w1XKNB/ZyqKqpL1sHyDpQMaP15uoZiTpdEqZ5i9SygpfCbzK9jpVg3WGPr+fAqxGo6XtBySdY/sJ3eu6K+U1PceNtNTpy/0uQHcw5QPA2kwub9/Eazkg6TjgBbZv6x6vBhzXUqWbHlwvm58rgt69f5poD7G4lGTfo/vzeVVTLJyme/nZ3rT7c8XaWRbC4PRCy+VDBhMxe7Hgm72lnIdTyppcQoP90jr/sn1L2SwOkpZizAC+ssO6P1suV9eHjAO32Z43+Jl3WvuZTzoJDzR7Eh54qO1tVXoHY/smjby4tdjepftz89pZZtCn8UazJf96MsYYeBOluslo6TJoZ8wxKInZ+uva/PtH0tuBdwB3l3Qd5X0DZeHi4GrBpmD7jyOX8VbKuD6A0l6jTw4FvgZs0z3evntuy2qJRoxsdlyCMhF3xNTfUUXz92fd2Gcf4A2U9/gSkm6jVENoYhM7ZYL9ZCb6bw9rpiRunzbb235Dt4Dx5O6pg2231t7rvrUDLKRxpZDXmvY7Zl/rbYlwaZH1AqDZBfM+bXa0/aHhxyqtyL5TKc50XgQ8ntJmY1CmuaUx/OBe8dyqKWa2A2WzzhsoVQ8eRDmc1orhz++WS9sPtF6quS/3u1DuH/ahXNs3p7Qja2LebcR3gG9Jegnl/XM08Ja6kRbQ+vXyzcDOtD1XBP16/zTRHmKxOGHeJ62c/FgY3Y7XVZl84vgP9RL1k6QTgW9SPnheR+kT/Xfbb5v2G2fRYLKjdo7pdCXqrqXckO9G2fX4c9vvrBpsBt2g80Epx3LnSToE+CGwN+XmZ3dgaduvqxpsxBQn4S9uaQIBSokyysLpj7oJpIcCX7f9hMrRJpG0MQtWYWmpP2svqMFSo+N018i1mLyh7PR6iSJA0gdsv712julI+hbwMcrCxUaUz8gNbL+sajAWPKHfB5IuHK24Mu65GiQ90/YJkp4y9PRtwO+BJ9WuGtI3kt4IPAfYZVDSUdJDgIMo5bCbXbyKxZ9Kq4WHMnlcdFa9RAtSD0ohq/G2RAOS3kepKvBN4IbB8w2dSruf7StVSrIvwPbvZzvTwureS+fabmozx9BJ3vO7383lKVXfmpo7GJB0D8oGqLQTjVgIks6zvf5wdUxJZ9h+8kzfO9skvZ7SI3wNSmuv1sYbvbpexl2nRtpDLBYnzMfsaJ7/JRrb2QycJemxbryXn6TdKDui/srEiWMD1S9KfSol0bmP7UMk7WH7NOA0SafVDjViH0lfpCxOtlomaG9KWY5LgNcC36OUX2qOpFOB51OusRcCf5d0mu2xv7Ozaard4QONDTx2o7QK+BelfOIJwHuqJhpv3En4Fu0DHA88SNLhlLKzr6qaaISkwyiThBcycVLSQBML5tOMNwBoabxh+w5JF0lavdXNbpJeQzl1/EDKz3wj4Me0sxN3vinK4s6jlEP+22znGafbWPZe4CbKe30dSh/Mr1YN1pnqBCJtjte/L2mz0Scb28zxOkrf0wcAVwAnAq+vmmjCKlON06HJsTrAVZK2p4w3ALYDWunh972uROb2tv80/IWuKkL1BfOe3Z+9EtjS9lWDJ2xf3v38T6Sh056S9qCcVLoe+AKlxcretk+sGmyM1o6JDDwAACAASURBVDfbd5/jHwJWoXzuNPfZI2knSmWbB1DueTcEzgaeWjHWODd2p7Uv7MYeVzJxeqkVnwKOonwevY+uLVHdSGMNNhns1/05GBs1MRbuFsuXBA6x/fTaeabTVRMYjDOXBO4HNNe/HDhC0ueBe0raGXg15freFJW2jYdSTiRK0rXAq22fVzdZIel5lLmhQVvJ5q7p0E554anMMH4zcDWl/dM1sxxtrG7z084seMCiidezc3N3eOFXkt4A/Iky9mjCyM9alNPlFwIbSdqosTF709fLKeaI5mtsTaUv759buz+vlfQYSnuINWY7xGKxYN6z0p6bAq+S9Fva7uW3B6VHRCsTRcP6VEoCJt7sV0p6LvBnyuJAS3YEHgkszeQNEs1c3G3fQflgbObDcRor2b6uWww61PY+klo5Yd5sKdxRtm+kLJg3XUUAuFTSy4ElVfoV7Q40tTMTwPYPJJ1PWZQUpTfrVTN822zbAFjbjZa/GYw3JO1PGbgdRnktX0Gbn0n3A34m6Rwmn1pppV3AHnSTwbY3l/RIJiYMW7MT8CTglO7xUymT2A+XtL/tw6b6xln0DNt7SXoRZQF1G0reJhbMezZef+vQvy8LPIHSTqmJCWyA7vr9ito5prAkpZRj8zvJhryaclr/45Qx8Fndcy24mFIu/seS3jxyoryV17hP92dLjxv/2P57d4qhJa+2/UlJz6RMtu5IWcBoasG85c32Qw4AtnIDrWmmsSdlLPxj20+W9GjaXOTdgdIWotVSyM22JRoYWrA4tvvTwN+BMweVL1rRlY6/UdJKtufVzjONrYf+/TbgL7ZbbIP5EUlbAtcBjwDebfsHlWON8yVgV9tnAEjalPL508p1/RPAiymbl5ucO+g0UV54GjON39YE/psyh9SC7wJnACfRTiuqUXsCy1HmBd9DuX/8r6qJJhv9WR81xfPV9eB6Oa510kBTayqdPrx/mmgPsViVZJd07zFPX2/71jHPV9GXUkaSTqHsvL+tdpa+63Y+nkG5kTwQuAewn+2jqwYbMlwqplWSNgH2ZcEdpA+pmWuc7hT3M4AvA++0/VM1WKK7dZIeTmllsAaTd781s2ABIGk5yqL+oC/VCcB7bd9cL9WCxp2WhLZOTEo6Etjd9pW1s0xH0k9sP3Gm52obKd87X1ftpDpJP7W9oaQLKb0m/6VGSiCPUukd/Brbf+0er0op3/sa4HQ3UPpe0s9sP1rSF4Bv2z5e0kW216mdbZSkdZjoI3u6G29bIulBwAG2t6udZaDlHeLqYUn2lg2VIXw4cDhwKfB62zfmtb7zpnvNWns9h8oRfpLS+ueoQanC2tmGSfo15XO8xc32AEj6ke1NaueYzsi46Am2b2n0570D8B0PlWiW9Dzbx07zbbNKpbXXgbYvHHpuX9v71ks1QdI+Y56+N2VRbV/b35jlSNOSdARlwewHTN6Eu3u1UB2VkuFTsn3dbGWZSXda/4TWT+vD+GtmS9fRbs76ad3BmmapkfLCd0W3ObyJfuatzhXEf1Z3nR++3726Ypzeyvtn4S0WJ8yHnE9ZlLyGsph2T8qp3r8BO7dQOsb277udeWvZPrSb7Fqhdq4xLgdOlXQck0t0Vy/NIelT0329hUH7sKEbx3nA5jWzTONsSWvb/nntINM4hLKD/Tza3Qk1sD9l0fTMbrH8IcCvKmeaRNJGlA0cjwKWoZwGu6GxElZHAp+jlN5v8mfe3fTuZ/uttH8SvvkTk8B9gZ93J6KHP3taORE9cLukVwDfoOwc3Y4Gf0dtnyZpNcrP2pQ+jn+pHGvYFZLuCXwH+IGkayhVWFq0xmCxvPM34OG2r5bUysbMoyVdRinJvms3xmxq4w7MLzG8MxM7rg+XdLDtAyvGmskVQPVNESNa3iHeyqnnGUmabgLQtptpBWP7/0l6EqX1wgWSXlk706gp7tPmUfrIfne280xhHUnjFlDEULnURpwn6UTKCa+3S1qRiRPcLfkj5efcsnMlfZMy5miqBZmkpbqDCld246JjgBMkXU05td+aA4E3S9pu6NT2/kyclm7BM4H1JX3M9qC10/MpG/Crsz22olJ3COgkyj1GS47r/mnRz5ho8TMwv+UPsHqNUOP06LQ+wDldKeSvU17HbSlzxOsB2D6/ZjhgL0rLmtNobM56RBPlhWcy3UbcVhbLO8dKeo7t79UOMkrStIfiWpvTmiLvPOBc4PMtHACS9FrK+OImyvh3cF1v6uCcpJUolZYGh5ROA/Zv8Drf7PtnQNLdKFWL1mDytWj/Wc2xmJ0w/xxwlO0TusfPAJ4FHAF8soWTX91O0g0o5c4fLun+wJGt7NIbmGLH65QD+9kkabiUyX6Ui9J8tr88u4nGk3Qg0/e6bWZhX9IvKH2Dm20V0OLpzT6TdC7wMsqi9AaUfo4Ps93Moq+k82yvXzvHTCSd3KcdwgONnphs+kT0gKQ1KL2DN6Fc539E6RX9u3qpFqTSFuLdwMmU6/pTKAP3L1UNNkb3s18JON72LbXzjJL0WcqE26AU8taUBYK3AsfarrohTqVP2kaUEn/XdRNyywMrNrZJApUWJU+yfUP3eHlK+dmWxhzDY7glgHWB39nevl6qyVreIS7p3n3Z+S/pzWOeXp7ShuE+tqtvbB53wlTSUynlUld2Q+0OJB1MafM0uFa+hLKY8SDgctt71srWR921fV3Ka3etpPsAD2itKkd3mvcRlAW1JhcuJB065mm3WpVD0tMo46Lj3FhZaZVe0TtRSg3va/vI1k7Cq7SheiqlKscfKG2AftpSxqm09lrGotXyaf1h3Qnuqbj2/Ee3meyfwCUMbSRrYc56WHc//m1KKftDKQfm/sf256sGGyHpLMpG3EkHlGx/u1qoMSRdTxmn39L900zvekl/p8wPfB34CSMbiBuc0/oksDIlL5RNMX8B7g7cw/YOtbINSPoVZd6gtVaSk0j6NqUC2GA9agdgHdvT9jifbUPvn39RNvM08/4ZkHQ8ZePG6LXoo7OaYzFbMD/X9gbjnmtlUqkrr/V44PzBIFgp1fxva/lmYmhhfxNgbeCb3eNtgPNsv7FKsDHUg1YBkj5IOQX9f0yeiKm9s3UBkg6gnP65CTgeWIeymNZEH1mYdG2cf/2RdJbtjWtnG5C0L+UU51FM/pk3NQkv6aPAWpTJ4eGb3uonVqYjScDFbrwdQ/z7JP0S2NhdidRuov0s24+om2z+IsDFbqCU+cLo3i8vBjal3FicSSl73sxAWtKPbT+pdo6ZqLQt2XCwa13SspRJ7GauRSObM2+jLJb/qFaecSS9l/J+bnaHeN90p3f3oCwEHQF81Pbf6qYCSS+0/Z0xz98LeK3tD1aINZakk4FndKdlkbQUpd/2lpQ+o2vXzNc33WfPK4CH2N5f0urAarbPqRxtkpY32/dBy3Ma42iiTcR9KRPtF1He983MaQ2/pt095dOB+7vBdm7DJG0BvKv2YuQoSWsBH6DMa82vxNHa6ynpZZTr5fslPRBY1Q1UGh02MsYc3EeolYM/fTFu/j/+fa2smfSZSvXJLSnVBx9H2UT4dds/qxpsCpJOt73ZuOfUtXqrlW0oz/HAi23fWDvLdMa9f/Ke+vdIurSFOcLFrST71ZLexkT5om2Ba7qLViuly26xbUmG+adqmtOVY9kLeDSTB8RNDdyZ5gR3bYMBr6RXAZvbvrV7/DnKxFETukWL41q4IM1gcLp8eFBs2ionPfAM23tJehGljOs2wClAMwvmwI2SlgEu7Bb4r6TsNGvJ4GZyuJR4c+V3KP3m/sHk30UzUW64CVOcmLyoXqIFqR+tAlDpI3sQZRLmMZIeBzzf9nsrRxt1BXD90OPrKbueq7N9h6SLJK1u+w+188ykG7udC8yzfZKk5SgnBK6f4Vtn04mSXgL8X0sL+WMcCvxE0lHd4xdS2q40w/aXu7Ewtv9eO88U9gDeIanZHeJ9oVIC902UhckvA+vZvqZuqgnjFsu7568Bmlks7zyAMp4clCBcnrJIdXv3uxp3zmcp8xhbUEpSXk85qbZhzVCj+rAw3m3O2okF5zeqnzAHVpb0pqm+2NJJ/c6VALavkvRM4EM00rZE0sOA1YD5pWZt79vNv2025TfOsm7z4OhY7d6U1kTNtdugjN32AT5OaTO4I7TVfkXSp4GlKT/n9wM3Utq7NXG9lPQC4IG2P9M9PodyutPA22pmGyZpe9tfneqa1ND16CRJz7DdzNzqOJJ+A5xNOb19utttg9l8qWaYtJFwTdvvUamaeL8WNhLavp1yYOp4lbLS21HaGOzvNluPrTw8F9Ntyrxv97VWKv69HThL0k+YfIiqqYocwE2SNrV9JoCkTSgH6JrS5brQ9g2StgfWAz7R2HzcWZIea/uSmiEWtwXzl1MGcd9h4vTPyykT7i+tmGvYESp9YO4paWfg1cAXKmca53DKiejnAa+jLFy1OmHYuvsDKwKDU7ErdM81oS+LFq5c7vZOWrr78zmUHYVXl3FdU3agLJq+gdIb/kGU05PNsL1m7QwLw/aOtTMspHOH/v02yu9mUycmgU+zYKuAtaomGu8LlI0cnwewfbGkr1EqS1Q3NMHxJ8rC5HcpkzEvAKrfTA65H/CzbsJoUJ3Btl9QMdNY3ZhtF8pk5kMpi0KfA55WM9eIN1EWp26XdBONLqDa/pikU5k4rb+j7Qvqpiq6SZh9KJ+NApaQdBtwoGe5b9ZM3FAZ7j6T9GHK+Odg4LG2/1k5Ut8dQNmMeSrlPbQZ8P5uk/hJNYP11BO7k7wXQNkk0W14bUpPNtsfBlxG6W29P2XC/RfTfsfsWZIyR9DcDeM4tp879O93UMbEb536O2bVJ4B32B6tenAckzfe1/a8kccG/uGuXU2D7m77h5LUVSHcV9IZjLRHrGzjkevl1Y1dL/ei3OcOLAOsT3nvH8pEK5PaBocoWh9nvh7YqwcbR9emHP55MvARSY8ELrL9orqxFtCXjbjDGwnfQynL/xna2RhzN+C5lMXyNYBP0dhBmiFvBs7sNnUIWBPYtRuzt1Lx4vOU9oKTWi806L+BL6v0Mhdl/edVVRONdxCwjqR1KJ9Jh1DGx2NbY1ayKfAqSVVbBi9WC+YuPQ12m+LLv57NLFOx/RFJWwLXUfp8vdv2DyrHGuc+tg+RtIdLn43TJDXRb0Ol58JgN+5ykq4bfIk2P9A/CFygiR5ATwH2rRdnrHGLFth+fr1IC5L0XBaciGlqArtzjKTLKDvKdu0mkW6unGnUC21/kpJrPwBJe1D6MjdB0tKUgcfgNMCpwOcH1Rpa0ZV7O5CJftZnAnvYvqJqsBF9KfNm+9eSlux26B6q0k+rNcvZPmdkI8xttcKMMZjg+E33z8B3K2SZzvCJNFEGx9tVyjKT1wNPoPQjw/avJK1SN9JkrS+gdqf7Xgc8jHLT+9lB2eaG7Em5lm9o+7cAkh4CHCTpjbY/XjVdyfNI25dJWm/c191gq5rGvZlyM/4u4J1D1/VW7yua1t0/fo9yvRRl4erP3ZdbWVDrk1u7anmD6nQr0+aEYR822z/M9jaSXtBVEfkacELtUJ0rG72nHav7PXwbC5bnbmGDxBq2Lx590va5ktaY/TjjuaHWdwvp5q4y4a8kvYGyKbepcTDlerkEE9fL+9DW9XIZ28OVvs50aTV3dUuVR9311m69ckjr9z1DbqcsQN9O+X38K6XtYFN69Ho2u5FQ0pcp1Va+D+xn+9LKkaZl+3sq7TYeSRmzX+auZRpl81kLbrM9ZQWeVti+kLIQfY/u8XUzfEstt3WVE18AfLK7b/uvGb9rdj27dgBYzBbMuxKpb6Hs4pn//62Rgfugn8UJtp8OtLhIPmywIHVlt0j5Z+CBFfPM16MPcgBsHyrp+0yUFN/b9l9qZhqj6cEwzC9lvxylBNgXga1p66TkfLb3lvQh4LquBOUNlJOdLfkvFlwcf9WY52o6iHJa/7Pd4x26515TLdF4hwJfo5TeB9i+e27LaonGmKL03zzKyfP3uutzXVkfWgUAXCXpoUxMyGxNV56yBaMTHJKWb/HEiu3TJK1LqQb0UuC3lFPbLfqX7VsGi2kqfXmbKnvecom6zpcp48szKDdCj6IsULfklcCW3SZcAGxf3pUsO5FSirS2N1GqHXx0zNdabVXTLNtL1M6wOBizkWOwKLCapNWykePf9ingKGAVSe+j3P+8q26ksZrdbD9kML9xraTHAH+hzBu1oBcny4cMNkg8l/Y2SCw7zdfuPmspFj97UuZidqec6tyCifZpVUlaqtuA+RlKy4qVJe1HubdoaZ7rXsMPbL9h6OHKs5xlSpI+Nd3XWyqFLOlelGp0wxt3Tq+XaKzrKBuFPwZ8oZE5l7F68nq2vJFwB8oBtIcDu/dkE+76TKyhPU4Str9SN9Ikp0jaBTiGySXZr576W2afRlpYdD/7ecB53WJ6K66X9HbK7+qTu/fS0jN8z6yy/fsu16pUXLeWm25xeOdIuogy0XoeZfcWALbPqxZqhKSjgR1sz5vxL1ck6XmUCc0HUU5O3oOyQ+roab8xxpL0AODBTN7I0dTAQ9KDgbU80Zt1SdvN9GaVdLHtxw39uQKlT+szamcbJWls37EWBh6StqMsTm1KeY8PrAjc3m3oaYKki2yvM9NztUm60Pa6Mz1XW7cAfTtlcR8mSsJdB2xqe6sqwYZ016G/UkrUvRFYiXIKtYkqMQPdidODgY2BaygLva9o7bSIpCdRyiytYHv1rvTSa23vWjnXwym/f9sB/6BMur7F9oNr5ppO9/65lrKguhuwK/Bz2++sGmyIpIPoStTZflQ36XGi7VZK1F1i+7Hdvy8FnGN77CnpWiRdantsH9bpvhYx10k62PYuQxW1hrmVDex91JVvfRplwvWHtlspIz6fpLNtbyTpBMoi/5+Bb9l+aOVo80l6DWUx7XGUja0rUKr9Vd+oJ+nerU3+TkfSebbXH9yXd8+dZrt6SU9JXwdOtv2Fked3Ap5he9s6yeI/RdL5g/GkpEcDT6dcL09q6YSnpMOBU8f8br4WeKrtJqpszXTasJWqdd01fQ/Kwa4LgY2AH7c23uhOcm5KqbxzC3AWpZf5D6sGG9Gj1/MVwLaU3stfpttIaLuVlga9IekwSqu5C5lYQ3Njm2J+O+Zp237IrIeZRle1aAPKwj6UDYU/pZzeP9L2AbWyDZO0GmU94Ke2z1DpW//UFtYqBiTtRmn38lcmNsPYs1ySfXFbMD/P9vq1c0xH0hGUD54fMLn0dTMXpFi0upPG2wI/Y/KbvZly5xrqzWr7oV1Zls/ZbqY3q6Sf2H6ipLMpvSb/AVxqu7n+xpIOHHq4LGWS63zbW1eKNF+3ILkm8AFg76EvXQ9c7IbK40o6H9jG9m+6xw+hTL61tsByEvC/wNe7p7aj9ORt5v0DIOlHtjcZ99zwQlZt3S5hbLdyUmVKXfm8JVraXDRM0k8oN5FH235891z1RT9Jd1A27Ow02Awh6fLWbnyGqZR43Al4BmUS7gTgi25oID2YMJR0wdDPu5lNRsMTmuMet2C6TI3m3ZgFK2s1c8Mbc0t3nXyS7R/VzrI4GXfKwvYf6iVaUDbbzy0tb5CQtCqlKsMtlIM0UCaxlwFe1GClv16QdAxTVyr7vCfK+M664XFvy1RaOX2HckpyUHVlfeBulHZ5f62VrY9UqudtCJxte91uc9l+rW6K6fI9m1KtYRXbTVW86NPr2YeNhH0g6RfA2i3NZ/RVNx56ie1/do9XAL4FvIhyynztmvmGdeOkwYGKc2w31SJC0q8prReqVuNYrEqyU/oG70oZILdaquG47p+mdQtTnwSeRFnk/THwRtuXVw3WTy8EHmH7XzP+zXqa780KHCvpnsCHKTcYBr4w/bfUYXu34ceSVgIOqxRnku4E7O8p7+3WvZVSgudyymD4wcCr60Ya69XApymlek3ZNbxj1UTjrSDpibZ/AiDpCZTTNVC5/7ZKzaJ9gDdQftZLSLoNONAN9nSU9BvgbMrk8OnAz+smmprtP2pyr/Xbp/q7s+gllBPmp0g6HvgG7ZcjfSpw+OipkMa0XKIOSl+vQT8vAXfvHrdUpm444zAxfZnXWTfVqQAgC+ZRhe07JH2Efowxe2HklMXtdNdLyinpZtg+tvvXeZT2Wc3QSJnMUbY/NltZFiPv7e5v38zEBokmWqx0i44bS9qc0ksW4DjbJ1eMtTi4nFI2fLBBfFvKdenhlDmZHSrlglKCfcr3eSvv8W5RYmNJWwCP7p5u7ndT0ids7znFJgkaOvhzs+2bJSHpbi5tYR5RO9QoSd8G1gV+TZk72IE2W0v25fX8JPBN25+pnWUxcCmwGg21FhxHpY3O2kxuFdDa/e7qlI16A7cCD7Z9k6Rm1oIkvZSypnIq5Z7iQElvtf2tqsEm+yPlfqKqxW3BfFA65q1DzxmofmJJ0uq2/9BK+ZqF8DVKD6AXdY9fRhkcP3HK74ipXE7pCdHMRXKM5nuz2n5P96/flnQssKwbb20w5EZKL6DqJF3P+J9tSwsWA2dSXrdHUPJdVjfOZJIeaPuK7pTP80e+thXQ1OkfSu/3L3W7HUUpxf6a7pT0B6omK5NsmwAb2v4tzN+4dZCkN9puoW/wsLUpn4dPBj7S7XK+yPaLpv+2WffH7gSqVXrD7w5U34Ft+yjgqO5374WU8vurqpQUP8r2iVUDjvcq4HOS/kGZ7DgDONP2NVVTTdZ0r1vbS9bOMJM+ZByyATkVEO05UdJLKG2T8rt51+1B2XjdbM9TAElrUtqVrMHkk/AtLKysWDvA4mLo3meBDRLdvU8zbJ8CjGsREf+ex9vebOjxMZJOt72ZpJ9VS1UsSdkE3vrmWwC6BfKmFslHDA56fKRqipld0R2o+Q7wA0nXUKpdNEHShpSFnw9SDvxsT9k0vgplobJaVYYpNP16DjkfeJdKi7ejKIvn51bO1Ff3BX4u6RwmHzptYewGgKR9KAcX1ga+R6nScCbtbRD/GnC2pO92j7cCvt7Nd7V0uOadlDnXv8H8AxYnUU7Dt+Jy4FRJxzH593JWN78tViXZW6bJfXW+bfsltTNNZ1D+euS5s21vVCtTX3U7CtcBfsjkN3szZfjVj96sy1JybUpZ8D0TOKhm+a+pjOzGXYLy4X6k7bfVS9U/48rftlQSV9IvgWfa/t3I8ztS+ihVL0s4TnciRLavrZ1lQNIFwJa2rxp5fmVKD+amyux1m4o2BJ5CuSbdh9LS4LVVg42QdF9KtZhBL78TgT1anHiXdG9gG2BbN9YrbZik+1MWot8C3N92U5tPU6Ju7pB0JLC77aZPBcTc0m3MXJ5yGvom2tyQ2RsqPeG3dEMtk8aRdBFwCHAJQ5VNbJ9WLVQscn2994m7rivd+8xBOwiVvqfH2167dkn0luYHFgeDw161c9wZkp4CrET5nbxlpr8/G1TaCz7d9tWSNqNUVNuNctr8UW6gXeNUWnw9R3VzB4Oqdau7wVadret+zgtoaezWtQpYB7jA9jpdOfEv2m5qkx6ApA0oh4BEOVjR3EYOjbTiVGmndZEbac8J8zdJLMD2frOZo6lJvn+XpL1sH9D9+za2jxz62vttv6NeuvmGdztWP/G+EE6RtDflQ92UkkvHdR9KrZW5b93R3T8t25vSm/USSi/z42x/sW6kBXyF0md70B98O8ru122qJZra8G7c24Df276iVpi+kbQa8ABKud7HM3H9vAewXLVgC3ojZfftc2z/CkDS24GXUxZSm/L/27vvIMvKOo3j32eGNCi4YkBWiowBh+iAiohKUAElqcCISqkLWqAEFVfUFTCBsgZEl2QaXQUDjiIKomQXZUhDUkwE0VIRxTQKOMOzf7zn0rfv9DQT+z3n9vOp6pp7zq2ueqpnpu855/29v5+kdw8cA9CSlucrDy6WQ5ljLmnlGoEexl8pvy8/ApzZxgVogOZnemDtHIujua44vflqHUmvpHQU2By4hzKG4YqqoRpNQdkbgE0o/y5Pb/viSiy9vqK8NWj5roCYfGxnN+/y1YpdFovhPtsfrx1iPM1utFOBtW1Pl7QFsKft91WO1iWduveJ5eotwA9UxlIJ2BA4tNk9V7uLZid2lnfIN4BWb/ZqFnlutD0d2rXA12dq33Pz/YEzbJ9D6Zg5t2KuUca4j/x0S3+egzYBnkLpbNOmHbydYfsytXyeNfDPZuTTfElrAnfT3jW16yldGVaC1hYfXaAyb71/vMp3KuZZyEQvjC/KUCyYUyqKPtS8Pgb4at97LwLasGDuRbxuq/2bPw9p/uxdhL6WlrS574o2t+GXtBewbjP/5UxJB1NmUz1d0p9bNsfiyba37Du+pNnN0DqDF5iSpko60PYXa2XqmBdS2h+vS1mQ7Pkb7fh9DoDt7zTzaM6XtDel5fm2wI4ta9PcM6/v9WrAi2lBe+7GeJXLbaxqnknZWX4opa39lcDlti+qG6uQdArjXGu0qcNJh3wM+CVwGnDJ4O6qymZR5mRdQWlT9lRaMks0VohzgbVZuGDjucBvJj5OxGiS9gR6rXsv7WvfHEvuV83XKs1XW53c7Ai5kNEL+9fVi7SQMymj+04HsH2jpC8BWTBfTB2894nlpPm735SyQCXg1r5Ofx+rlwwonZVi+Wn9Zq9mAe2Gli5I9UyVtFJTxLwzI8/WoV1rMYP3kZtRxsG0kqQPAvtS7su/Ary3TZ0Tu0TdmGd9TTMq4EzgWuDvwJy6kRYm6U3AscDvKV22RHket0XNXINsHy1pX8qzTFEKeWZXjjVK02X0bcDTGD23fkK7UA5FS/b+FkCD7YBqtwfqy7GAslghYBplpjG0rE1db86K7d81xwdR2pzcARyXneVLrrmxOIFy4dH/n736xaek/wMOsH1XczwX2IkyA+qztltz8yHpc8Bptn/UHD8DOMj2oVWD9Wkq3g6j7I4+F/hec3w0MNf2XhXjdY6kLn7pLQAAEmhJREFUlzZVuK0maQdKJfaVwH5tHBMwFkmrAufafmELsvQ+Ixd6C1jNdht3mffaX+9GWZx8vO1plSMBD3129xxPuXh/SJsLudpM0tMoi0A7AJsCP7X9qrqpRrfWasYFzElryuEl6TzgHbZvHDg/Azi2jS3qYvKQdCJlAa1XJDoTuNb22+ul6j5Ja1CeGfy9dpaxSDoBeBXlAXavJbvbNGJF0tW2tx14djTX9la1s3VNV+99YtlI2p6ym/OhxT7bbZsjG8tIo8eJtrbdvaSLKdcbcxh5juC2PHOT9E5gd0pnsvWAbWxb0ibALNvPrhqw0bX7SElvAL42VnfCWDLNBrRdPTDPemCjWmtI2gBYc/AeuA0k/QJ4Rls7T45FZXzjH92yhWFJFwJfpoxAfANwEPAHT/CI2zZVNS2L8XZvt+Iv3vbU2hkW0+mUWac0c1ZOYGTOyhmUuZ2xZD5LWaz4KPB84DW0p23UKr3F8sYPmqKIPzXttdrkGcCrJfUqSNcDftLMNLHtNlRufQG4F/ghpeL+aMpukL1st6btUoecJ+kVLHxj3oYW4r05nab8f16VUjl8t0qv89YUQo1jdVpSNd6hz0igtKejfC7+glKN/SpaVOnavyAu6cgskC+7piBqPWB9yu+kR9E3p7Wyf/Ve2J7fG7cQQ2uDsR4U2L6meZAQUdPuwFa2HwSQNIvSojAL5ktB0nTK/cVazfE9wKtt31I12ML2ATZyS2edNu6RtDHN8yFJLwN+WzdStwzBvU8sJUlfADYG5lJ2z0H5t5AF8+GzpaS/0mz2al5DyzZ7UYrCe0QpaJ5ZKctCbL9f0kXAOsCFfYtSUyjP2NuiU/eRtk+TtGezXgFwme1vVQ3VXVMGWrD/kfLvs1UkPZHyDKbX6nxH25fXTbWQu4C/1A6xKJKeCZwI/Al4L+Xe4rHAFEmvtn1BzXwDHmP705KOaLr3XiZpwsdEDMuC+Xgf6Kst+ttiDJ2Ys9Ix02xfJEm27wSOk3QFAzv+Knl0/4HtN/YdPm6CszycF9UOsBg26qvO/BRNNantv9WN1VnfpFx0XEtfe8e2cMfmdPaKS5rDqZT/4++tl6h7el1YKBeb1wGvpHRheTxwM9DGHTatKBwcAj/o+/qE7V9XztNvy4Fr32l918VterAVy8d49zat6HIRk96/UR7IQCkuiqV3BvBm25cASHoepS3l9jVDjeEGyt9722Zf9juM8vN8iqTfALdTruNiMXXt3ieWqxnAZm3biRbLX1cK2V3mL28FvALYj/I7/bS6qUbrdcccOPezGlnG0an7yKajzXaMdDI6XNL2to+pGKurxppnfX7FPAtpWvDvT5lT31+s1bYF89uASyV9m9GjiT6y6G+ZUJ+gjDd9FHAxsJvtHzUdM88C2rRg3ivi+a2kPShz4ded6BBDsWDelQ/0jujKnJUuuU/SFODnkt5ImS/5+MqZeq6SdLDtM/tPSno9LdotCdAUGyDp8Yxubd+mmUX91ZkLJN2exfJlsq7tLhRKdMWL+17PB37f/K6PxXc6sIvtq9OFZfKQNJWyM+CttbOMJdfBk87Vi7h2ex2lwCyiphOA6yVdQnnYuiOQh5hL7xG9xXIA25e2sAsYwNrArZKuZvSDwj3rRRrN9m3ALs3Pb0ru0SKWyM3AE0hXhqhM0pOAAyi7yf9Iad0r28+vGqyjOngfuQdjdzLKteYScgfmWQN7A0+23boNVAN+1Xyt0ny1zUq2LwSQ9J5eMY/tW1vYVeJ9kh4FvAU4BViTMgJzQmUBNAadRWl3cA/wT0qrWZo5K61tL9FyR1JaHx9O2c25E2UGQxscBXyjaXt9XXPu6ZQWa3tXSzUGSXsCHwb+nbJ7YX3gJ8DTauYa0KnqzA64UtLmtm+qHWRIvG9w3rKkL7RhBnOHdKILS1/LTIDVW9xKrxOaAqita+eIaBwJzJZ0ICML5DMoN+f7VEsVAdg+S9KllLmiAP9p+3cVI3XdbZL+i9I6EcqO6Nsr5lmUNnROG5ekDwAfsv3n5vjRwFtsv6tusohOeCzwY0lzaGlRTEwat1KeU7/E9i8AJB1VN1JMsHQyWg6a68vP2f5637lDbJ9RMdag24CVaWHH0X62jweQtEY59N8rRxrUP0rwnwPvtaJzjKR1bf/a9nnNqb9Qxhoj6SUTnicddWJQM9ugN2dlXnPuScAjbV837jdHJ0naiZGF51tsX1wzz1gk3UApNvi+7a0lPR+YafuQh/nW6ChJPwY2oTwYvJ+Rxb42zKvvHEnX2d6m73gl4Ebbm1WM1SmSbqZUNM+XdCtwSG9+kqSbbU+vmzBWFEkfBjYFvgrM653vv8GMmEjNdVDvd04rr91icurbrWLgBy3crdIZzaLu8Yzs/rkcOM72vVWDjUHS2owUSswZmItZnaTrbW89cG7UtXFEjE3Sc8c638wXjZgwkvah7DDfntJG+GzgU7Y3rBosJoSkmZTxeKM6Gdk+u2qwDpJ0N2WM6GF9o39adV0k6RxgS+AiRhdrHV4t1BgkTacUt67VnLoHeLXtW+qlGiFpAeUZligj3P7RewtYzfbKtbL1SPop8ELbdwycfw3wLtsbT2ieLJhHrBiSzh3v/VTjLhlJ19ie0Sycb237QUlzbG9XO1usGJLWH+t8rz1/LB5Jx1Dm1QxeGD1A2SGd9lWLSdI7gd0pF8DrAdvYdtOFZZbtZ1cNGCuMpM+Ocdq2XzvhYSIiWkrS/1CKHfvnIf7S9mH1UsWKJmk/4CTgUso15nOAo21/rWaufpJuBLbttfWUNA24xnabupVFRMRiaMZr7E1pzb4TMAuY3Ws7HMNL0jqUAj0BV6WT0dKRdD2wF2VDwNdsnzRWcWFNksbqzmvbn5/wMOOQdCXwzr7Cg+cBH7C9fdVgHSJpd+BkYHfbP2/OHQO8gjJz/dcTmicL5hErhqQ/AHdRHhhdRfkwf0iqcZeMpO9TLohPBB5Dacu+bT6Ahl/L59Z3gqQplMrrLO4to3RhiYiIGJukW4Dpbh4yNNcfN2VRcslI+hbjtEhsW+F1U9C8a29XuaTHUbqCbVk32QhJbwP2BD5L+dm+FjjX9oeqBovogIFxT6tQWuTOy5inaANJawEvB/a3vVPtPLH8SRp313Oewyy53uK4pNWAU4FHApvbfkrlaA+RdITtkx/uXG2Sbhi85h3rXIxP0s7A6ZS1n/+gFMa8uEZnrSyYR6wgkqYCu1IqHrcAvg2c1ZaWHF0jaXXgPkrhwSuBNYEv9s0TjiGzqLn1eei6dCRda/vptXNEdJGkdYFTgGfTtBkGjpjoSteIiDaT9HXgqF43oKZb0Im2Z9ZN1i197Y/3BZ4A/G9zPBO4w/Y7qgRbBEk32d6873gKcEP/uTaQtBuwM+V+8kLb360cKaKTJO0NbNe230URMZwkXUK5B+9tRBu1mJVCiSUn6UzbB/cdHwa8xfZGFWONMlaL+LbtggeQNBu4jtKWHcqaxQzbe9dL1U2SdgC+AVwJ7Gf7vio5smAeseJJWpXygOMk4D22T6kcqTMGqpkfOt38eR/wS0rrk4smNFiscJlbv3xJ+iTwOdtX184S0TWSvgd8idE3QQfa3rVeqoiIdujbEf0oym6AOc1b2wI/tL1LrWxdJuly2zs+3LnaJJ1EKRDvb8V/k+231UsVESuSpB/ZfmbtHBEx/CRtB9xl+7fN8UHAS4E7gOOykWrpNB2BsP2H2ln6NbPqXwHsAFzR99YawIK23VdIejRwPCWvgMsp/y4nfGd0V/Wt/QhYFfgXsKA59kR3tMmCecQK1CyU70FZLN8AOBf4jO3f1Mw1LJpd/NMpO82n184Ty1fm1i9fkn4MPAm4E5jHyIXHFlWDRXSApLm2t3q4cxERk1HfjuhRpykPjmamO9DSkfQTYA/btzXHGwLfsf3UuskWJmlf+h4U2p5dORKwyOJrqPQALqKLmv/fPVOAGcBzbT+rUqSImEQkXQfsYvtPknYEzgbeBGwFPNX2y6oG7BBJAo4FDqP8Pp8CzAdOsf2emtl6mg5VGwInAG/ve+tvwI2251cJFpPGSrUDRAwrSbMoi7nnA8fbvrlypKFjewFwg6Ts2B9Of5b0SEp13hcl3U25kIuls1vtABEddo+kVzKye24m8MeKeSIiWsP2Zb3Xkrai7ArZD7gdOK1WriFwFHCppNua4w2A19eLs2i2vw58HUpRs6QDbX+xcixsr1E7Q8QQeEnf6/mUXZ171YkSEZPQ1L5d5PsDZ9g+BzhH0tyKubroSMqYue1s3w4gaSPgVElH2f5o1XRAM9rpTuBZzeL5pra/L2kaMI2ycF6dpHPHe9/2nhOVJZav7DCPWEEkPUjZxQmjq9pTzR4xDkmbAGsDc4F/UioeD6TMMP+27Wsrxus0SVsCz2kOr7B9Q808EV0haT3gE8CzKJ/pV1JmmN9ZNVhERAtIehJwACPFRF8G3mp7/arBhkDTsewpzeGttu+vmaefpDUpO5SeSOmk9r3m+Ghgru0sqEVERMQykXQzsJXt+ZJuBQ6xfXnvvXQcXXySrgd2tX3PwPnHARe2aT64pIOBQ4C1bG8saVPgNNs7V44GgKQ/AHdRNlVcxcj4WGB0QXF0SxbMIyKiVSSdB7zD9o0D52cAx9p+ydjfGeORdARwMM3uH2AfSmVuOjRERETEUmsKha8AXmf7F82522xvVDdZ90nanrKz/KHugLY/Xy1QH0nfBO4FfgjsDDwaWIVSUJYdXxEd13TyW+RDY9uHT2CciJikJL0T2B24B1gP2Ma2m802s2w/u2rADhmvwKBtxQdN94DtgKt6C/mSbrK9ed1kRTMmdldKwfAWwLeBs2zfUjVYLLO0ZI+IiLbZYHCxHMD2NZI2mPg4Q+N1wDNszwOQ9EHKA84smEcsgqR3j/O2bb93wsJERLTXSyk7zC+RdAFltqTG/5Z4OJK+AGxM6bq0oDltoBUL5sBGvYeWkj5F8yDbditaZUbEMrum7/XxlLm3ERETyvb7JV0ErEPZBd0r5JlCmWUei++BpXyvhvttP1DGroOklRiniGuiNWNiLwAuaDpCzaSMUnpPNiZ1WxbMIyKibVYb571pE5Zi+IiRh600r/MwO2J888Y49whKAcpjgCyYR8SkZ3s2MFvSI4C9KbO315Z0KjDb9oVVA3bXDGCzvgfDbfOv3gvbCyTdnsXyiOFhe1bvtaQj+48jIiaS7R+Nce5nNbJ03JaS/jrGeTH+s9gaLpP0DmCapF2BQ4FvVc40SrNQvgdlsXwD4OOMdPWMjkpL9oiIaBVJZwEX2z5z4PzrgBfY3r9Osm6T9GbgIGB2c2pv4HO2P1YvVUR3SFoDOIKyWP4V4MO2766bKiKinSStBbwc2N/2TrXzdJGkrwKH2/5t7SxjkbSAkcIyUQpb/9G8tu01a2WLiOVL0nW2t6mdIyIiJgdJUyjPXl5Aubb8LvCpthSSSpoFTAfOB862fXPlSLGcZME8IiJaRdLalEXdB4Brm9MzKDMR97H9u1rZuk7SNsAOlIvNy21fXzlSROs1iz5vBg4EZgEn2763bqqIiBh2ki4BtgLmAPf3ztves1qoiJiUsmAeERExQtKDjBSO9i+wpnC047JgHhERrSTp+ZRqPYBbbF9cM09XSVoNeAOwCXAT8Gnb8+umiugGSScB+wJnAJ+0/ffKkSIiYpKQ9Nyxztu+bKKzRMTkI+lvjCwCrE7pIAFZDIiIiBVE0lds7yfpJsaYWW57iwqxYhLJgnlERMQQk/RlyozJK4DdgDtsH1k3VUQ3NFXD9wPzSdVwRERERERERMQKIenTwGeA3zD2gvmdEx4qJpWVageIiIiIFWoz25vDQxeecyrniegM21NqZ4iIiMllYFfnqLdIsVZEREREDK8bgf8G1gG+DJxle27dSDGZZId5RETEEBucN5f5cxEREREREREREdFGktYHDmi+VgPOAs62/bOqwWLoZcE8IiJiiElaAMzrHQLTKPPnskspIiIiIiIiIiIiWknS1pQ27VvYnlo7Twy3tGSPiIgYYrmYjIiIiIiIiIiIiC6QtDLwIsoO852By4Djq4aKSSE7zCMiIiIiIiIiIiIiIiKiCkm7AjOBPYA5wNnAN2zPG/cbI5aTLJhHRERERERERERERERERBWSLgG+BJxj+0+188TkkwXziIiIiIiIiIiIiIiIiIiYlKbUDhAREREREREREREREREREVFDFswjIiIiIiIiIiIiIiIiImJSyoJ5RERERERERERERERERERMSlkwj4iIiIiIiIiIiIiIiIiISen/AXjegm+y+BAxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2520x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.language.value_counts().plot(kind = 'bar', figsize=(35,10), logy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c20b5aeb8>"
      ]
     },
     "execution_count": 94,
     "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": [
    "\n",
    "data['language'].value_counts().plot(kind = 'bar')\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot Description\n",
    "The plot demonstrates that most of the movies are in English"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary\n",
    "We don't further analyze the relationship between language and IMDB scores because English moveis are dominant in this dataset and it does not yield useful results. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.10 color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Color               4790\n",
       " Black and White     208\n",
       "Name: color, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['color'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "For this section, we decided to take the data and and illustrate the number of movies that were created in color versus the number of movies that were created in black and white, Through the data we were able to observe that 95.85% of the films were created in color compared to the 5% that were created in black and white."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Color               95.838335\n",
       " Black and White     4.161665\n",
       "Name: color, dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data['color'].value_counts()/data['color'].count())*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YY0kag/nH1GEFV1333mZV5uiTREeKSuXl5Tx34ix67h6EzWaTvf3227GFhYUHvIjY7/dj8eLFbQ0NDVvr6+u3aDQaacmSJZl7379w4cJBTqdTtn379u1Wq3Xz9ddf3/Lb+7jppptS3njjjfi1a9dW5OTk/G4YkXOOW265ZU9zc/Pm7du3b29qalLdfPPNvzoH9sEHH8StXLmyqrq6euuOHTu0zz77bAIAvPfee6bnn38++dNPP62srq7e9vXXX5sO9fWOHz/eXlpaagSA1atXG08++WT7pEmTfnVbfn5+r0aj2bfw6sEee38ffvhhXUpKiuett96qcjgcPz/wwAMtdXV1ygULFgy59dZbm7u6ujY9/PDDuy688MKcpqamARmNo19yckQYY5NUWn3lhHOWjLnin18qDbEW0ZGikt/vR0NDAxt/zhLRUULOokWLco1GY0FsbGzhunXrTLfddtvvigUAkpOT/ZdcckmX0WiUYmNjpbvuuqt5/fr1RgCor69Xrlmzxvzyyy/XWywWv1qt5nPmzNlXdJxzXH755emrVq0yrV27tjI1NfWAFyiPGjXKPX/+/B6tVstTU1N91157bcv//ve/Xx1p/OlPf2rJzs72JiUl+WfOnNm9adMmLQC8/fbbcQsXLmwbN26cy2QySQ8++OAhh+amTJli+/77740A8O233xqKiors06dPt+9/26RJk351Dulgj304y5Yti586dWr3woULu+VyOebPn98zatSo3uXLl5uP5POPFp2DIofEGGMKlfpGtd70wHl/fVU1fPJs0ZGiWlNTE5QqNU/OGUlXxP7GG2+8UT1v3jybz+fD66+/HnPaaacN27p167bMzMxflYjNZpMtWbIk45tvvjH19PQoAKC3t1fm8/lQW1urNJvNPovFcsAp8jabTf7GG29Y/v3vf9fGx8cfdBr97t27FUuWLMlcv369weFwyCVJgslk+tXH778Jok6nk5qbm5UAsGfPHuUJJ5zQu/d9Q4YM8Rzq654xY4bt+uuvz7ZarfJNmzYZpk+fXmc2myWr1aq0Wq3yjRs3Gq+77rrWI3nsw6mvr1d99tlnsUajcV8h+Xw+NmXKlAGZREFHUOSgGGNGjcH8WVz64AevfXsDlVMIqK6u5nGZQ0THCGkKhQKLFy/ukslk/Ouvv/7d+ZH7778/qbq6WvP999/vsNvtP69cubIcCBwdDR482Nvd3a3Y/3zR/kwmk/+dd96pXrp0afbKlSv1B8tw/fXXpzHG+JYtW7bb7fafX3zxxboj3dooKSnJ29jYqNr7/9XV1apDfXxeXp7HYrF4n3zySUtKSorHbDZLAFBYWGh/8sknLQ6HQzZt2rR+WTMxIyPDM3/+/HabzbZp75vT6fz5cLMDjxUVFDkgxthgtc5YOXLaWTP+/PoPytjUbNGRCIDyigqMnD6fjp4OQZIkvPbaazE2m00xevRo52/fb7PZ5BqNRkpISPC3tLTI77nnnn3nhrKysrxTpkzp/uMf/5hptVrlbrebffbZZ4b9P7+4uNj20ksv1Z133nm5q1atOmBJ2e12uV6vlxISEvx1dXXKJ554IvlI85977rkd77zzTsLGjRs1NptNdueddx72+q1x48bZXnjhhaQJEybsO5KZOHGi/YUXXkgaNWqUw2AwHNPGfwkJCd7q6up9119ddtll7V999VXM8uXLTT6fDw6Hg5WUlBgPNFGkP1BBkd+RK5RjVVr9lplX35909n3L5Eq1RnQkAsDtdqPNamXjFlwhOkpI2jvbzGg0Ft53331pzzzzTN2JJ574u51wb7311haXyyVLSEgoGD9+/IiZM2d27//+t99+u06pVPLhw4ePslgs+U888UTSb+9j/vz5Pc8///zOc845J3ft2rW6377//vvvb9q6davOZDIVnnHGGUPOPPPMziP9Os4999yeK6+8smXWrFnDcnJyRk2bNq3ncJ8zZcoUW0dHh2Ly5Mn7jpSmTp1q6+joUJx88snHPPx200037XnsscdSjEZjwd13352Um5vrfeedd6offvjhlPj4+IK0tLQxjz76aJIkSQPyool21CW/otLqZzPGPlhw7zLVmNMWiI4TVgZ6R92KigqUfPY5v31Vi5AjqKmyKmQNGf6r2w5yoW6/OdILdUlooh11Sb9R6wx/lMnl/7zo8eWKnHFTRcchv1FZWcmTh48VHeNXNlY19M8dcaCndTd327u8Q4cMKddoNBG9AgQ5MjTERwAAWmPMjXKl6p9X/usrKqcQVVVVzQpmXxCZ558YYEpKY9oYi6q8oiKvt7eXxpUJFRQBdOa4++VK1UN/emWtInV4oeg45AC6u7vhcPRizKxzRUcZUIb4RJgS0xWVVVUjuru7DYf/DBLJqKCinD424XGFSnPb0lfXKWgr9tBVW1sLY3ySpFAOyGSpkKI1xyI2dZCstrZuSEdHxyFXUSCRjQoqihliLf9UqnV/WfrqOkVc2iDRccghVFRUSFljpwp/vgZrUpXaYERcRo5sZ319Tnd390GvNyKhq29mn3Q89yH8F56IYYhLfE6l0/9x6avrFDHJGaLjkEOQJAl1dXWy8QsuE5rDxtXo7eoIWkmpdHrEpg2S1dbVDe3t7T2ipXhIaJAkiVmtVjOAbYf94EOgWXxRyGRJuQ/gS65ctkpusqSIjkMOo7W1FWAM2YWnCM2xRUoFrE0wtlmD+rgel1f27bffDY+Pj2tWKBQHXPuOhBwJwDafz3f58dwJFVSUiUsbdK3H2XvHkpdWy+nIKTzU1NQgJiVbguARDy9TYCPPBIJ96aQa+O7r5/kXz9zBPM7e8ZzzAy4CSyIPDfFFEUvWkLN7u9oeveix9+QpQ4XsXE2OQXl5OR82ZU5UP1cnnrdUfsqF11lUOsNaxtiArJxNQk9U/9JHk6TBIybZ2lv+O++2Z+Q5J00THYccIa/Xi6amJjbhnKtERxFuxlV3KwtnL8pU64xfMcbonFQUoIKKAqnDCobY2ls+KbrkJlXhnAi90DNCNTY2Qq3V8djULNFRhGOMYe6tT6tzJ5w6Uq0zrmCM0SmKCEcFFeGyxkxI7LE2fTPq1D8Ypl56C/28w0x1dTW35IyiFxV9ZDIZznvoNW3qiMJJKp3hVcYYfW8iGP3BimBDTz5N19lcvzptRGHivNufldNzOfxUVFRgzMzIXj3iaCmUKlzy9AqdMSF5rkyhXCo6Dxk4VFARKq+oWNlaV77CEJc09IK/v6OQyQ+4/xoJYQ6HA11dXeyEsxaLjhJyVFo9Lnl6hV6hVP2dMRZaK+iSfkMFFYHyioplbQ1VD3hdjml/fK5EodL+brsaEgbq6uqgj4mTNDpaku5AEjKHYMG9/9KotPpPGGOxovOQ/kcFFYEc3R2LeqzN15330GtyY/zv9lojYaKispKnjzmZnqOHMOa0s1nhnAti1Hrj23Q+KvLQL3+EGTF59uiOXXV/Hzf/MvmQCTNExyHHiHOO6upqNnbuxaKjhLzimx5Xx6ZkTZQrVTeJzkL6FxVUBMkrKo5r3VnxD31cQvzpf3mQTjqFsY6ODng9XgyfXCw6SshTKFW4+MkP9AqV+l7G2CTReUj/oYKKEHlFxfLu1t132ztaJ1z02HtKeRRsyxDJamtrYUpMlWQyeooeidjULCx88FWtUqNbwRiziM5D+gf99kcIj6P3rI7ddVfMv+N5eVz6YNFxyHHaUV7OcyecRs/PozBiyhxMOGeJUa03vs8YoxGECEBPgAiQV1ScY22oemz4KbNVBWecJzoOOU5+vx8N9Q1s/DlLREcJO7P+8qDKkjW0UKHW3ik6Czl+VFBhLq+o2GCtr3xaJpenz7/zeVr6JQI0NzdDoVJyWtD36MkVClz42Lt6mUx2C2NspOg85PhQQYWxvKJi1tvVfk1Pa9PMix5/T6HS0sajkaC6uprHZw4VHSNsmZPSccZ1D6vVeuObNNQX3qigwtukruaGq0+56DpZ6rAC0VlIPymvqMCIqWfRNT3H4aQFV8gSsoYOlimUfxadhRw7KqgwlVdUnNq1p/EODm6Z+kdaBDZSuN1uWFtb2fizrxAdJazJZDIsfOBlvVyu+CtjLFN0HnJs6A9bGMorKpZJft/lnc0Nk+ff8bxSqdaIjkT6SX19PbQGEzfEJYqOEvYs2cNQdOnNKrXe9DKtMhGeqKDC0zhrfdVZaSNOUI2YMkd0FtKPKquqeNLwQtExIkbRJTcpNEbzSQDOFJ2FHD0qqDCTV1Ssd/faltjaW8bMu/1Zuho3wlRVVrKC2Yvo1X4/UShVWHD3i3qVVv9P2oU3/FBBhZ/itsaa6RMXLpUlZOaKzkL6UU9PD3p7e1Ewi65l609DJszA4HFTjUq6NirsUEGFkbyi4vTu1qbFfq8ndfoVt9PPLsLU1tbCEJfIFSqV6CgR56xbn9aBsesZY7TMShihP3JhIjAxwn9RZ9POSWfd9rSSrnmKPBUVFVL22CIa3hsAMckZmHrpzUq1wfSU6CzkyFFBhY/Ctobq+YmDhmtHnfoH0VlIP+Oco7a2VjZu/qWio0SsUy64VgHOT6UVJsIHFVQYyCsq1nrdrst7rE0F8+/6h5JmzEae1tZWcACDTywSHSViqbR6TL30FpVab3pIdBZyZKigwsMZHbtqxw+ffAaSBueJzkIGQE1NDWJSsiTROSLdyQuXysH5aYwxeiKFASqoEJdXVJzk87rn2TpaR572p3tpWnmE2lFezodNnkPPxwGm1hlQdOnNSrXe9FfRWcjh0RMi9M1ub6gZPvTk05gle5joLGQA+Hw+NO3ezSace5XoKFFhYuAoaiZjbLjoLOTQqKBCWF5RcaLf65lm72jNP+1P99DRU4RqbGyEWqvjcWmDREeJCmq9EVMuuVFFR1GhjwoqtJ3e1lA9dPC4qSwphyYeRarq6mpuoXOLQTXxvKvlnEtnMMZoWCKEUUGFqLyiYosk+af1drWNnn7FHXT0FMEqKiowauY5NDUziDQGE4oW36BQ600Pis5CDo4KKnTN7Ni9M8uSPVyRMfJE0VnIAHE4HOjs7GQnzr1EdJSoM/H8vyg4l+Ywxmh3yBBFBRWC8oqKzZzzab2d1vzpV9xO695EsLq6OujNsZLGYBIdJepoDCaccuG1CrXOeLvoLOTAqKBC0+Se1t2pKq3eMOyUM0RnIQOosqpKShs9gZ6Hgpw0/zKF3+c9hzGmE52F/B49MUJMXlGxBsBsW3vLiKmX3qKSyehHFMmqq6pkJ5x5segYUcuclI7M0SdJABaIzkJ+j/76hZ5xXpcjxmXrzig4nbZdiGQdHR3weLzImzpXdJSodvJ5Sw0aY8x1onOQ36OCCiF5RcVyAHPbd9clDp00S1LrjaIjkQFUW1sLU2KqREfJYg2fUgzu9+cxxmiDtRBDz4zQkgMg3uPoHTNu/qU0tTzClZeX88HjT6XnoGAKpQpj514sU6g1V4jOQn6NnhyhZYKjp1Pn93lic8fPEJ2FDCBJklBfX8/Gn32l6CgEwLg/XKZiTHYFY0wuOgv5BRVUiMgrKlYDmNTV3JBecMb5TK5QiI5EBlBTUxPkCiXShheKjkIAJOeOQmxatgLALNFZyC+ooEJHHudc6e7tKThx7iX0Ki7C1dTU8LiMXC46B/nFxIVXGzVG8zWic5BfUEGFjik2a7NZY4xVpo6gV9WRrry8AiOmnUXLG4WQ/Fnnwud2T2WMWURnIQFUUCEgr6jYDCDf1tGaM27+pbRjboTzeDxobW1hE85ZIjoK2Y/GaMaIqWf6mUx2oegsJIAKKjSMkfw+mbO7I69w9iJqpwhXX18PrcHIDXGJoqOQ3yg4faFOY4ihCxBDBBWUYHlFxQzAjM6mhrik3JE8NjVLdCQywCqrqnjSUBrGDUU546bB47QXMMb0orMQKqhQkAwg09XbPahwzgW0MGwUqKqsZPlnnEdHyiFIrTciZcgYF4CporMQKqhQMBaA5HH0Ds45aZroLGSA2Ww22O12FM6+QHQUchAjT51nUGn1Z4nOQaighOob3pvi6On0gUGVOGiE6EhkgNXW1sIQZ+EKFR0sh6phk2bJmEx2pugchApKtDgACT3WppScE6dymr0X+SoqKnnWCVPoBx3CkoeMAWMyM2MsR3Q5fYiCAAAeHklEQVSWaEcFJdYgAPB7vUOHTZ5Na+9FOM45amtr2Lh5l4qOQg6BMYZhp5zBQatKCEcFJdZozrnbZevKzhk3VXQWMsCsViskzkHnGkPfiKI5Oq0p9hzROaIdFZQgfeefCns72xQqnUEWlzZIdCQywGpqahCTnEnLG4WB3PEz4HH2TmCM0clCgaigxEkCoLe17UkbMmEGnZOIAuXl5XzoKWfQzzoM6GPikZCZ6wEwSXSWaEYFJc4gAEzy+4YPnTiTli6PcD6fD7t27WInnU3LG4WLvKln6eVK1emic0QzKihxCrgkuRw9HRk54+icRKTbtWsX1Bodt2QNER2FHKHMMePlap1hqugc0YwKSoC+rd3H2Npb1Ia4RMmYkCw6Ehlg1dXVSBhM17mFk9ThhfA4e0cyuv5DGCooMVIBqJw9nQnpI0+kX/4oUFFRwUeddjb9rMOIyZIChUojA5ApOku0ooISYzAA5vN6ElOHFdL1TxHO6XSio6ODrn8KQ6nDC7wILEdGBKCCEmMEACc4T00cPFx0FjLA6urqoDfFco3BJDoKOUpZBRMNcqXqJNE5ohUVlBhZAHo9zt74xEFUUJGusqqKp46iv3HhKHVYvkytM0wQnSNaUUEFWV5RsQJAot/r8XpcDm1cOi33Femqq6rYCcUX0fmnMJQ4aAT8Pi+9ihSECir44gHA0d0Rb05M88oVdAlUJOvs7ITb7cbIU+eLjkKOQXxGLrwuZzxjTCs6SzSiggo+CwA47d0JiYNH0KvqCFdbWwuTJUWSyeipFo7kSiVMiakOAMNEZ4lG9KwJPgsA5nU6ElOHFdAMvghXXl4uDRp/Kj3Pwlhy7igGYKToHNGInjjBNwiAC4ylJuXm0RFUBJMkCTvr62XjF1wpOgo5DqnDC/QyuZzOQwlABRV82QAcPrfTQjP4IltzczPkcgXS8+gymnBmsqTKVFp9lugc0YgKKojyioplAFIkye902XsMCZlDRUciA6impobHpg+m7TXCnD7WAplcmSY6RzSiggquWAAyj9OhVWp0klJDE4MiWXlFBUZMnUvDuGHOEJcIDp4kOkc0ooIKLgsAeF0OndYU4xcdhgwcj8eDlj172Piz6fxTuNPHJkDy+RJE54hGVFDBFQOA+dwunT6Gft8jWUNDAzR6IzdZUkVHIcfJEJcIn8dlFp0jGlFBBZceAHwet94Qlyg6CxlAVVVVPGlovugYpB9ojDGQ/H4VY0wtOku0oYIKrjgAPp/XrTNaUuSiw5CBU1FZifzTz6fzTxGAMQa13uhC3xA9CR4qqOCKBeD1+7w6Y3wSrXEUoex2O2w9NlYwe5HoKKSf6MzxXgA07BFkVFDBFQPAy8C0aj1tvRCpamtrYYxL4CqNRnQU0k+M8YkAFVTQUUEFlwmAl8lkWrXeKDoLGSAVlZU8o3AyDe9FEGNCihxUUEFHBRVcegA+AGq1ziA6CxkgNTU1bNy8P4qOQfqRMSFZBToHFXRUUMGlAeDjnGvoCCoyuVwuSJKEnJOmi45C+hGTyRkAOioOMiqoIMkrKmYIFJSfS361SktHUJGIcw5zUian7TUiC5f8HABdXB9k9CwKHhWAfeuyMfoDFrGGnnI6vdKOMFySOABJdI5oQ38lg0e19x+MMb/P4xaZhQwgWt4o8kiBIygqqCCjggoeBfYeQTGZ1+dxiU1D+l3y0DE476HXYMmmzVcjjeSnghKBCip49h+/9vnpCCriqLQ65M86V3QMMgC45AfoHFTQUUEFjw99s4AYYz6flwqKkHBB56DEoIIKHt/+//Z5PMKCEEKOTt8RFBVUkFFBBY8ffUdQnHMPnYMiJHzQOSgxqKCCR0JgkgQDuI9m8RESPiS6DkoIKqggKSst4QC8ABjn3EOTJAgJHxIN8QlBBRVcPgS+5z6v2yk6CyHkCNnbW/wA2kXniDZUUMHlBSBjTObzul30aoyQMNHT2sQA7BadI9pQQQWXF4BMrlC4+l6REULCgL3TqgLQJDpHtKGCCi4vAJlKq+9q31VLR1CEhAGv2wWf26UE0CY6S7ShggouFwC5Smfo7trTQAuKEhIGbNYmKDXaTs45P/xHk/5EBRVcrQDUWoO5q7fDqpQkOogiJNT1WJshV6haReeIRlRQwdUMQCNXqnwKldprb98jOg8h5DB6rE1gDI2ic0QjKqjgsqLve67S6m2dTfWC4xBCDqentQlet6tOdI5oRAUVXF3ou9hPplB2djZTQRES6rpbdnm9LgcVlABUUMHVte9fnFu76AiKkJDX0VTnAk0xF4IKKri60Pc9lykUnW0N1b7DfDwhRLCu5gYJVFBCUEEFlxOBa6HkKo2+q62xmi7WJSSEcc7R3lCjBlAhOks0ooIKor4FY1sBaFQ6Q3dXM10LRUgos7e3wO/z+kFHUEJQQQVfCwCN1hTTaW/bo/C6aNFYQkJVc+UWqLS6crpIVwwqqOBrBqBRKFVejTGmq6lik+g8hJCDaK7cwj0u5/9E54hWVFDBtweAHADkSmV9w5bvBcchhBxM47b1vT63c4PoHNGKCir4mhDYWRdKlXZn7YZSj+A8hJCD2LV9IwewWXSOaEUFFXx7T7YyfZxlV8PWH2iiBCEhyGnrgr19jwrANtFZohUVVJCVlZa4ATQCMOjMcR1el5N3t+wSHYsQ8hu7tm+ASm8s55zT9YqCUEGJsRWAiTEGrTGmuXHbetF5CCG/0bj1B8nrdKwSnSOaUUGJUYO+iRKMsdqdP39L+24QEmJqNpTafR7Xt6JzRDMqKDH2jelpTDGNtRvWeEWGIYT8miRJ2LV9gwrAD6KzRDMqKDHaAfQCUJkSkne31pUrfV6azEdIqGgu3wTGWDvnnE4QC0QFJUDfkkcVAEwKlcaj1htszZVbRMcihPQpK/3YL/l874nOEe2ooMQpA6ADAIVKXV39v69oKRVCQsSWle/2et3OD0TniHZUUOI0oO+CXa0xtmzLyndojI+QENDduhtdzQ0KADRBQjAqKHF2IVBQspjk9Pq2hmq5rW2P6EyERL2KtZ9BoVJ/Sdc/iUcFJUhZaYkLwA4AMTK5wq8zx9aVr/tMdCxCot6Wle/aXPbut0TnIFRQov0PgAEAlBrdti1f0DAfISJ5nA7s3PydGsAXorMQKijRKgAwAIhJzqzauelbudthFxyJkOhVs34VVBrdNs55p+gshApKqLLSknYEzkUZVVqdU2uMaapY+6noWIREre2rV7hctu43ROcgAVRQ4n0HIBYAlGrtzxs//i8N8xEiAOccZd98JHEufSQ6CwmgghJv695/xKZl76jdWCp399pE5iEkKjVs+QGS39fJOa8SnYUEUEGJtxuAFYBepdG5tMaYXWWlH4vOREjU+d/bzzu9LuezonOQX1BBCda37NE3AOIAQKXR/7Thw5dpmI+QIHL2dGL76g+Z5Pe9JDoL+QUVVGjYjL6fRVz6oB2N29azjl21giMREj1+KnmNK5SqLzjnVtFZyC+ooEJDM4A9AAwKldqrM8Vu/PaNZ/yiQxESDTjnWPfaU70ue88TorOQX6OCCgF9w3xfoW+YLyY164cNK17mdE0UIQNv50/r4LR1dgFYIzoL+TUqqNCxHoAfgEJniu1S640NP3/yuuhMhES8b998xuFx9j7GOacdBUIMFVSIKCstsQEoBZAIAPqYhLVrXnnMQ88ZQgaOvdOKinWfy7gkvSI6C/k9KqjQ8g0AJQAWk5K509Xb46hZv1pwJEIi18YVL0typfIjWtooNFFBhZCy0pJdAMoBxDHGoDGY1q155VGack7IAJAkCd++8YzT3WujyREhigoq9HyGvhXOEzJyt+zc9B3r2F0nOBIhkaf6+6/gcTn2APhBdBZyYFRQoacMQCcAXd+U859oyjkh/Ytzjs+fvt3utvfcQ5MjQhcVVIgpKy3xAfgEQAIAxKRmfb9hxcuc1ucjpP9UrPsMHbvq2gDQxoQhjAoqNK0HIKFvyrlGb6xe8+pjkuhQhEQCSZJQ8tiNvW6H7f845zQ6EcKooELQb6ecx6Zmf7HutackW3uL2GCERIBtXy2Hvb21EcCHorOQQ6OCCl2rEJhyLtOZ47q0xpifVz53t090KELCmd/nw6dP3Opw9/ZcR+eeQh8VVIgqKy3ZDWAtgBQASMgasnrzF29z684KscEICWM/f/I6d/V2VwBYKToLOTwqqND2EQAGQKnS6p36mIS1nzx+s1d0KELCkc/rwRfP3OF023uupaOn8EAFFcLKSkvaAHwKIBkAErOH/W/nT+t89Zu/FxuMkDD04/svcZ/H/RPnfK3oLOTIUEGFvpUAPAA0cqXKp4+zrPzob9fSGn2EHAWP04Ev/3Gvy2Xvvl50FnLkqKBCXFlpiR3Ae/jlKGpzZ9PO3h20LTwhR+y7N5+RJMm/hnO+QXQWcuSooMLDWgAdAIxMJuPGhORPP/77DV6/jyb1EXI4nU31WLXsIbfb3nO16Czk6FBBhYGy0hIPgDfQt7pEXNqgaq/L0frDey/SOB8hh8A5x/L7l7i4JP2Nc14jOg85OlRQ4eNnAPXoW+k8Li17xRfP3uWjhWQJObjtqz5A47b1rT6P6yHRWcjRo4IKE2WlJX4AbwIwA2CGuESr3hy/5q3bL/ZKEq2CRMhvuew9+OCBq10eh/1CzjltWxOGqKDCSzmADei7eDcpJ+/b9sbqjv+99RwN9RHyG58+cYvX53Uvp2nl4YsKKoyUlZZwAK8B8AHQMZmMJ2Tkvrvyubv9bQ3VgtMREjpq1q/Gps/ftHsc9j+LzkKOHRVUmCkrLekE8B8Epp0zfWxCuz42YdVbt13glfy0MDMh7l4b3rrjYrfX6biAc94lOg85dlRQ4WkDAruApgJAUk7e953NjW3rXn+KTkaRqFfy6I0+r9PxAef8M9FZyPGhggpD+w31eQDoGZNxS1buu1+/+P/8rXXlgtMRIk71D6uweeU7NrfD9ifRWcjxo4IKU2WlJd0AXgKQBIDpzPGd+rjEr968lYb6SHTqsTbhjVvOd3udvQtpaC8yUEGFt58BfAsgDQCSBg//0dbW3PrNfx6hoT4SVXxeD16+Zp7H63Y9zDn/UnQe0j+ooMJY31DfmwCcAAyBob5h75b+5xFvzY/fiA1HSBB99PA1/s7ddd/73M77RWch/YcKKsyVlZb0AFiGwPbwMq0ppjs2Nfud124819fV3CA4HSEDb8OKl/mWL97pcNm753LOafQgglBBRYYtAEoBpANAXFp2rdYY881//jLX63U5xSYjZADt3vEzPn7keo/b2TuNc94tOg/pX1RQEaBvqO91ALsROJJCUk7et86ejtrl/2+Jj/aOIpGot6sdL18z1+t1uxZzSdouOg/pf1RQEaKstMQF4BkEtog3MMaQnDtqeeW6z21rX3uShj1IRJH8frx2wzk+r8vxb8nve1t0HjIwqKAiSFlpSSuAZwFYACgVKrU3KSfvla9f+H+eHWs+EZyOkP7z+TN3Snuqtm5z99poKaMIRgUVYcpKS7YDeBtABgCmNcV2J2Tmvv7W7Rd5mys3C05HyPHb+uV7+OHdF2wue/cszjnt2hnBqKAi0+cI7MKbAQDmpLRdJkvKipeWzvH2WJvFJiPkOFR8+wXevedyt8flmMY5bxWdhwwsKqgIVFZaIgF4FUAN+tbrs2QN3a5Uab576arTvU4bXWRPwk/txjV44+bzvJLfN4tL0s+i85CBRwUVocpKS9wInI/qARAPAMlDRn3j6u3Z/OJl070uG83IJeGjYet6vHrtfB+Tyc71edylovOQ4KCCimB96/U9AUCNvpl9qcPyP3H2dG558fJTqaRIWGiu3Ix/Xz3bx2Syy1z2ng9F5yHBQwUV4cpKS3YjUFKx+KWkShzdbVv/eeUMr8veIzghIQfXWleOf10508fArnHaul4VnYcEFxVUFCgrLdkB4HEAcQD0gZIq+Li3w7rtX1ee5nX32gQnJOT3OnbV4p+XTfdzzu9w2rr+IToPCT4qqCjRN/38cQTORwVKanj+x/b2lu1UUiTUdLfswguXTvNzLj3k7Ol8RHQeIgYVVBQpKy3ZhsBwXwL6NjpMHZ7/ka1tT9myq2Z53Q674ISEAN2tu/HCpVN9fq/7md6u9rtE5yHiUEFFmbLSkq0IHEklAND1ldSKntbdO5YtmeX1OHsFJyTRrLlyC545/yS/x+n4V29X+/Wi8xCxqKCiUF9JPYHAkkh9JVXwYXfLrvIXL53mtXfQ9Y8k+Cr/9yVevGyan8nY/b2d1qWi8xDxqKCiVFlpyRYATyKw+rmOMRlPG1HwgaO7/YenFo71NlduEZyQRJMfP/g3f/3Gc736mIT/s7W10KaDBADAaCuG6JZXVFwI4FoAVgC9ANC6s2KUva1l7sIHX1GOKCoWmo9ENs45vnjmTumH9150GxNSLmytK39fdCYSOqigCPKKikcD+AsAN4B2AOhu3Z3W1lB94bTLblMVXXKjjDEmNCOJPD6PG2/fudhfu6G0y2RJmd1cuXW96EwktFBBEQBAXlFxBoDrABgBNAGA09Zlaqkpu3jopFnms+/5l0KhUgvNSCKHs6cT//nzmb7O5vrGmOSMUxu3/VgnOhMJPVRQZJ+8ouIYAEsBDAHQAID7PG5lc9XWc81J6VmXPPOR0hBrERuShL2O3XVYtmSmz+/zbYhPH3xG7cY1tHoxOSCaJEH2KSst6QLwKIB1AAahb9PD9LwT3rB3tP749MKx3j3V28SGJGFt++oVePr8cX4u8ffThhdMpXIih0JHUOR38oqKGYDTAZwHoAWAAwBad1aMsbe1FM+97Wll4exFoPNS5Ej5PG6UPHqDf9Pnb3nNltT74jNy/l5WWuIXnYuENiooclB5RcUFAK4G4ATQAQA9bc3JHY21C7MKJuoX3PsvGvIjh9XWUIVXr1/gddm7W+PSBi3RmWI/LSstoT885LCooMgh5RUVZyIwecKAvskTfp9X0VJTNsPj7B17zn0vKWgqOjkQzjk2rPgPL3n0Rr8xPmldQtbQq8rXflohOhcJH1RQ5LD6Jk9cAWAUAiXlBoCu5oasjqb6c/Kmnqmee8tTCo3BJDImCSH2jla8e/el3sbtG1xxqVn/MFlS/9q3PxkhR4wKihyRvKJiGYAiABcA8ABoBQCfx6VqqdkxR5L8I85/+HXl4LFTRMYkIaDsm4/w7j2X+7RGc0VC5pBbFCr1Z2WlJZLoXCT8UEGRo5JXVJwC4HIAuQB2AfACQHtj7ZCulsY/jJ27WHnGtQ/JlWqNyJhEAFvbHpQ8dqOv8tsvvHFp2R+Yk9LvKistqRWdi4QvKihy1PKKihUAZgE4G4AdfatPeJy9upbaHfOUGl32wgdeUWblTxAZkwSJz+vButef4quXPeTXxcRXxacNflyl1b1eVlriFJ2NhDcqKHLM8oqKswBcCSAVgaMpP+ccbfVVo3ramucMmzRLPueGR5XmxDSxQcmAKV/3GT588Gqv5Pe1xaUN+kpnjnukb98xQo4bFRQ5LnlFxWoAcwEUA+gE0AUAPo9b1VpXMcXR0zF+ysX/JytafKNMqdGKjEr6kbW+Eiv++mfv7vJNnpjkjO9jUzLfAvBOWWlJj+hsJHJQQZF+kVdUPBSBo6l4AM0ITKSAo7sjpmN33RwuSVlzbvi7csyshZDJaAGTcOWy9+CrF+7zr3//JcmYkLzFkjXkM5lc8UpZaUm16Gwk8lBBkX7TdzQ1DcAfADAEikoCgM7mhqye1qY5uph4c/GNj6mGTpxJK1GEEUmS8NPHr+LTJ27xqbT6hviMnFK1zvASgO9pRQgyUKigSL/LKyqOAzAPwBQE9piyAoELN9sbqkfYOlpPt2QP1Zx50xOqjFHjREYlh+HzuPHzJ69j1b8e9HjdTntsava3xvik/wL4tKy0pFd0PhLZqKDIgMkrKh4M4HwEVkfv6nuDJPllrXUVBY7OthlJuSPlUy+9RTXslDNo6C+EuGzd+P69F/maVx73KZSqVqMlebvJkvoFY+ytstKSJtH5SHSggiIDqm/h2dEILDybCqANganpkPw+eVtDdZ7T1lWkVGlMUy65UTF27mKm1hkEJo5u3a27sfa/T/p/fH8Z1xjMdTHJGTv0sQmVAF4FsI3W0CPBRAVFgiKvqFgOYByAhQBiEFh81gYEhv66W3Zl2jusk90OW/a4+ZexUxb9RR6TkikwcXRprd2B1f/+m3f7qg+Yzhy/Iy5tULnGYGoA8AGAH8tKSzyiM5LoQwVFgqpvIsWJCExNT0LgHFUbAA4EZv11NTec7OjpKMw96VQU/fFmZeaY8TShYgBIfj9qflyNtf99wlO/6TvoYxM2x6fn1Cg12goAHwLYShMgiEhUUESIvrX9hgOYDWAkAksmtQLwAYE1/toaqgudPV2nmJPTVRMXXq3Km34WaHuP48M5x67tG/BTyX/9mz57i8sVim6N3rwjPjNnp1yh/BnAJwAqaSiPhAIqKCJcXlFxGoDpCMz6kyMw688JAFySWMfuuiEue/dYR0/n4NRh+f4Tii9Sj5w+D4a4RIGpw4t1ZwV+/uR1aeNHr/o8LodbrTdujknOaNaZYu0AvgPwRVlpSaPonITsjwqKhIy8omITgIkA5gAwYr+ZfwDg83qUXU31uS6HLd/Z3ZmTMnS0v7D4IvWo6fNgTEgWlDp0dbfuxubP3uYbVvzH2926W9IYzFtNlpSdhrjEXsaYF8DXAFaVlZZYRWcl5ECooEjIySsqVgLIR2D4b1DfzV0AetB3rsrv9Sg6mxty3b22fEd3R25S7kj/CcUXqkedOh8mS6qY4IJJfj+aKzaj6vuv+LZVH3haaspkOnNshT4mocqcmNbDZDI/gC0A1gDYUVZa4hIcmZBDooIiIS2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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = 'Color','Black and White'\n",
    "sizes =[4789, 209]\n",
    "colors = ['lightskyblue','gray']\n",
    "patches, texts = plt.pie(sizes,colors=colors,shadow=True,startangle=90,\n",
    "wedgeprops={\"edgecolor\":\"0\", 'linewidth':1,\n",
    "            'linestyle':'solid','antialiased':True })\n",
    "plt.legend(patches,labels,loc=\"best\",fontsize=12)\n",
    "plt.axis('equal')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot Description\n",
    "Through the plotting of the colored data we were also able to identify that the oldest black and white movie was created in 1916 while the newest black and white movie was created in 2015. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.11 genres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Noticing that many movies contain multiple genres type. But we are more interesting the main genres so we want to re-categrize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c2173cdd8>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2520x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "primary_genres = []\n",
    "sep = '|'\n",
    "for name in data['genres']:\n",
    "    name = name.split(sep,1)[0]\n",
    "    primary_genres.append(name)\n",
    "d={'genres':primary_genres,'imdb_score':data['imdb_score']}\n",
    "genres_df = pd.DataFrame(data=d)\n",
    "genres_df.genres.value_counts().plot(kind = 'bar', figsize=(35,10), logy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c215c7160>"
      ]
     },
     "execution_count": 125,
     "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": [
    "genres_df.groupby('genres').imdb_score.mean().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary\n",
    "\n",
    "By looking at the bar plot, even though the difference are not that drastic,\n",
    "we can still observe that genre categories can affect the IMDB score of a movie."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.11 Other features\n",
    "We have explored all features most correlated with sale imdb score.\n",
    "How about features haven't analyze yet?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to use PCA to analyze the reamaining data at once. It's difficult to throw categorical data into the PCA anaylsis so we ignore them here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['color', 'director_name', 'num_critic_for_reviews', 'duration',\n",
       "       'director_facebook_likes', 'actor_3_facebook_likes', 'actor_2_name',\n",
       "       'actor_1_facebook_likes', 'gross', 'genres', 'actor_1_name',\n",
       "       'movie_title', 'num_voted_users', 'cast_total_facebook_likes',\n",
       "       'actor_3_name', 'facenumber_in_poster', 'plot_keywords',\n",
       "       'num_user_for_reviews', 'language', 'country', 'content_rating',\n",
       "       'budget', 'title_year', 'actor_2_facebook_likes', 'imdb_score',\n",
       "       'aspect_ratio', 'movie_facebook_likes', 'profit', 'percentage_return',\n",
       "       'imdb_score_5', 'standardized_year', 'continent'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "Other_df = data[['actor_3_facebook_likes','actor_1_facebook_likes','gross',\n",
    "                'num_voted_users','cast_total_facebook_likes','facenumber_in_poster',\n",
    "                'title_year','actor_2_facebook_likes','aspect_ratio','movie_facebook_likes','profit','imdb_score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=1)\n",
    "principalComponents = pca.fit_transform(Other_df)\n",
    "principalDf = pd.DataFrame(data = principalComponents\n",
    "             , columns = ['principal component'])\n",
    "rating = Other_df[['imdb_score']]\n",
    "principalDf.reset_index(drop=True,inplace=True)\n",
    "rating.reset_index(drop=True,inplace=True)\n",
    "finalDf = pd.concat([principalDf, rating], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c22ad2f98>"
      ]
     },
     "execution_count": 106,
     "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": [
    "finalDf_greater_7= finalDf[finalDf['imdb_score']>7]\n",
    "sns.regplot(y='imdb_score', x='principal component', data=finalDf_greater_7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.90253037])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After using PDA to project all the numeric data into one feature, we noticed that the reduced dataframe contains the 90% information of the oreginal dataframe. We are interested if the reduced column can be used to predict the imdb socre but the regression plot suggest that there might not be a good linaer relationship between them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 Machine Learning: Classification & Feature Imporatance\n",
    "we will use random forest (parameter not tuned) with our classfication algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['color', 'director_name', 'num_critic_for_reviews', 'duration',\n",
       "       'director_facebook_likes', 'actor_3_facebook_likes', 'actor_2_name',\n",
       "       'actor_1_facebook_likes', 'gross', 'genres', 'actor_1_name',\n",
       "       'movie_title', 'num_voted_users', 'cast_total_facebook_likes',\n",
       "       'actor_3_name', 'facenumber_in_poster', 'plot_keywords',\n",
       "       'num_user_for_reviews', 'language', 'country', 'content_rating',\n",
       "       'budget', 'title_year', 'actor_2_facebook_likes', 'imdb_score',\n",
       "       'aspect_ratio', 'movie_facebook_likes', 'profit', 'percentage_return',\n",
       "       'imdb_score_5', 'standardized_year', 'continent'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data[['color', 'num_critic_for_reviews', 'director_facebook_likes',\\\n",
    "        'actor_3_facebook_likes', 'actor_1_facebook_likes', 'movie_title',\\\n",
    "         'facenumber_in_poster', 'genres','plot_keywords', 'language', 'content_rating', 'standardized_year'\\\n",
    "        , 'actor_2_facebook_likes', 'aspect_ratio', 'movie_facebook_likes', 'profit',\\\n",
    "        'profit', 'continent']]\n",
    "y = data['imdb_score_5']\n",
    "X['genres'] = X['genres'].str.replace('|', ',').fillna(' ')\n",
    "X['movie_title'] = X['movie_title'].str.replace(' ',',').fillna(' ')\n",
    "X['plot_keywords'] = X['plot_keywords'].str.replace(' ',',').fillna(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin, BaseEstimator\n",
    "\n",
    "# Changed the base classes here, see Point 3\n",
    "class PassthroughTransformer(BaseEstimator, TransformerMixin):\n",
    "\n",
    "    # I corrected the `fit()` method here, it should take X, y as input\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        self.X = X\n",
    "        return X\n",
    "\n",
    "    # I have corrected the output here, See point 2\n",
    "    def get_feature_names(self):\n",
    "        return self.X.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "catagorical_feat = ['color', 'language', 'content_rating', 'aspect_ratio', 'continent', 'standardized_year']\n",
    "remainder_feat = list(set(X.columns).difference(set(catagorical_feat)).difference(['genres','movie_title','plot_keywords']))\n",
    "preproc = ColumnTransformer(transformers=[('catagorical', OneHotEncoder(handle_unknown='ignore'), catagorical_feat),\\\n",
    "                                            ('genres', CountVectorizer(), 'genres'),\n",
    "                                         ('movie_title', CountVectorizer(), 'movie_title'),\n",
    "                                         ('plot_keywords', CountVectorizer(), 'plot_keywords'),\n",
    "                                         ('other', PassthroughTransformer(), remainder_feat)])\n",
    "clf = RandomForestClassifier()\n",
    "pl = Pipeline(steps=[('preprocessor', preproc),\n",
    "            ('clf', clf)\n",
    "            ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('preprocessor', ColumnTransformer(n_jobs=None, remainder='drop', sparse_threshold=0.3,\n",
       "         transformer_weights=None,\n",
       "         transformers=[('catagorical', OneHotEncoder(categorical_features=None, categories=None,\n",
       "       dtype=<class 'numpy.float64'>, handle_unknown='ignore',\n",
       "       n_v...obs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False))])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accuracy is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6712"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our pipeline works fine since there are five labels and we did not tune the parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('preprocessor', ColumnTransformer(n_jobs=None, remainder='drop', sparse_threshold=0.3,\n",
       "         transformer_weights=None,\n",
       "         transformers=[('catagorical', OneHotEncoder(categorical_features=None, categories=None,\n",
       "       dtype=<class 'numpy.float64'>, handle_unknown='ignore',\n",
       "       n_v...obs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False))])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest = pl.steps[1][1]\n",
    "importances = forest.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in forest.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "other__movie_facebook_likes\n",
      "other__num_critic_for_reviews\n",
      "other__profit\n",
      "genres__drama\n",
      "other__profit\n",
      "other__director_facebook_likes\n",
      "other__actor_1_facebook_likes\n",
      "other__actor_2_facebook_likes\n",
      "other__actor_3_facebook_likes\n",
      "other__facenumber_in_poster\n"
     ]
    }
   ],
   "source": [
    "for i in np.array(pl.steps[0][1].get_feature_names())[indices[:10]]:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In random forest classifier's feature importance, the cell above are random forest classifier weights the top 10 important features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7 Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through the analysing of the Dataset, “IMDB Exploratory Data Analysis Project” we were able to break down the movie data into many different categories that can potentially be a reason behind the reasoning for a specific movie rating. After reviewing the IMDB dataset and extracting information from it we were able to come up with an abundant number of different categories which could possibly explain the rating behind movies. Some of these categories consisted of the effect of movie advertising colors on posters or billboards, the director of the movie, the number of critic reviews, the duration of the movie, the number of Facebook likes, movie genre, the language of the movie, what country the movie was produced in, and the budget for the production of the movie. After extracting these different categories from the dataset we plotted all the data in bar charts, histograms, or scatterplots in order to get a better visualization of the data and try to find a positive correlation with these different categories and the rating a movie received."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8 Final Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through the analysis and break down of the IMDB dataset we were able to identify that for the most part IMDB ratings are normally distributed but tend to be skewed to the left towards the higher rating on the scale. In addition to this, some of the top categories we found to have a great influence on the rating a movie received consisted of the movie’s title, plot keywords, and the actor’s in the movie and if the movie was a rated-R movie followed by a PG-13 movie coming after it. To add some of the other analysis we were able to gather from the dataset were that the movies with the most highest ratings were those movies that were created in the United States followed by movies that were created in the UK. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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