Final Project Submission

Please fill out:

Student name: Joseph Husney
Student pace: full time
Scheduled project review date/time: 8/06/2020 5:00 pm
Instructor name: James Irving
Blog post URL: https://jhusney1.github.io/reasons_to_use_webscraping_over_apis

Introduction

Microsoft sees all the big companies creating original video content, and they want to get in on the fun. They have decided to create a new movie studio, but the problem is they don’t know anything about creating movies. They have hired you to help them better understand the movie industry. Your team is charged with doing data analysis and creating a presentation that explores what type of films are currently doing the best at the box office. You must then translate those findings into actionable insights that the CEO can use when deciding what type of films they should be creating. We will analyze our findings through three visuals. Hopefully, this will shed some light on which kinds of movies microsoft should invest in.

Retrieve Data

Import necessary modules

import requests
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from pandasql import sqldf
pysqldf = lambda q: sqldf(q, globals())
from tqdm import tqdm

#Allowing pandas to display unlimited info
# pd.set_option('display.max_rows', 1000)
# pd.set_option('display.max_columns', 1000)

Importing dataframe

Before analyzing the data, it must first be retrieved. Inside the package given to us, there is a file that has all the movie info. We will use this info to get all kinds of details which we can further analyze. We will put this into a pandas dataframe so we can utilize all the tools pandas has to offer

# Load csv file to dataframe
df = pd.read_csv('zippedData/tmdb_5000_movies.csv')

# Preview data
df.head()

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	budget	genres	homepage	id	keywords	original_language	original_title	overview	popularity	production_companies	production_countries	release_date	revenue	runtime	spoken_languages	status	tagline	title	vote_average	vote_count
0	237000000	[{"id": 28, "name": "Action"}, {"id": 12, "nam...	http://www.avatarmovie.com/	19995	[{"id": 1463, "name": "culture clash"}, {"id":...	en	Avatar	In the 22nd century, a paraplegic Marine is di...	150.437577	[{"name": "Ingenious Film Partners", "id": 289...	[{"iso_3166_1": "US", "name": "United States o...	2009-12-10	2787965087	162.0	[{"iso_639_1": "en", "name": "English"}, {"iso...	Released	Enter the World of Pandora.	Avatar	7.2	11800
1	300000000	[{"id": 12, "name": "Adventure"}, {"id": 14, "...	http://disney.go.com/disneypictures/pirates/	285	[{"id": 270, "name": "ocean"}, {"id": 726, "na...	en	Pirates of the Caribbean: At World's End	Captain Barbossa, long believed to be dead, ha...	139.082615	[{"name": "Walt Disney Pictures", "id": 2}, {"...	[{"iso_3166_1": "US", "name": "United States o...	2007-05-19	961000000	169.0	[{"iso_639_1": "en", "name": "English"}]	Released	At the end of the world, the adventure begins.	Pirates of the Caribbean: At World's End	6.9	4500
2	245000000	[{"id": 28, "name": "Action"}, {"id": 12, "nam...	http://www.sonypictures.com/movies/spectre/	206647	[{"id": 470, "name": "spy"}, {"id": 818, "name...	en	Spectre	A cryptic message from Bond’s past sends him o...	107.376788	[{"name": "Columbia Pictures", "id": 5}, {"nam...	[{"iso_3166_1": "GB", "name": "United Kingdom"...	2015-10-26	880674609	148.0	[{"iso_639_1": "fr", "name": "Fran\u00e7ais"},...	Released	A Plan No One Escapes	Spectre	6.3	4466
3	250000000	[{"id": 28, "name": "Action"}, {"id": 80, "nam...	http://www.thedarkknightrises.com/	49026	[{"id": 849, "name": "dc comics"}, {"id": 853,...	en	The Dark Knight Rises	Following the death of District Attorney Harve...	112.312950	[{"name": "Legendary Pictures", "id": 923}, {"...	[{"iso_3166_1": "US", "name": "United States o...	2012-07-16	1084939099	165.0	[{"iso_639_1": "en", "name": "English"}]	Released	The Legend Ends	The Dark Knight Rises	7.6	9106
4	260000000	[{"id": 28, "name": "Action"}, {"id": 12, "nam...	http://movies.disney.com/john-carter	49529	[{"id": 818, "name": "based on novel"}, {"id":...	en	John Carter	John Carter is a war-weary, former military ca...	43.926995	[{"name": "Walt Disney Pictures", "id": 2}]	[{"iso_3166_1": "US", "name": "United States o...	2012-03-07	284139100	132.0	[{"iso_639_1": "en", "name": "English"}]	Released	Lost in our world, found in another.	John Carter	6.1	2124

# See how long dataframe is
df.shape

(4803, 20)

Data Cleaning

Upon inspecting the data, it became clear that there are many movies missing certain important data points. For instance, some movies don't have genre data. Others have a budget and/or revenue of zero. Here we will clean up the data by deleting those rows from the dataframe.

Get rid of rows without genre data

# See if there are any null values for genre
df.isna().sum()

budget                     0
genres                     0
homepage                3091
id                         0
keywords                   0
original_language          0
original_title             0
overview                   3
popularity                 0
production_companies       0
production_countries       0
release_date               1
revenue                    0
runtime                    2
spoken_languages           0
status                     0
tagline                  844
title                      0
vote_average               0
vote_count                 0
dtype: int64

# No null values - this code isn't necessary for now
# df = df[~df['genres'].isna()]

Get rid of rows with budget or revenue of zero

# Find out how many rows we are dropping
indexNames1 = df[ df['budget'] == 0 ].index
indexNames2 = df[ df['revenue'] == 0 ].index
len(indexNames1), len(indexNames2)

(1037, 1427)

# Drop rows
indexNames = df[ df['budget'] == 0 ].index
df.drop(indexNames , inplace=True)


indexNames = df[ df['revenue'] == 0 ].index
df.drop(indexNames , inplace=True)

# See shape after dropping all those rows
df.shape

(3229, 20)

Now we will discuss three questions that will shed some light on which movies microsoft should invest in

Question 1: What is the domestic average movie profit categorized by genre?

# Check type of genre column in order to manipulate data
type(df['genres'][0])

str

# Convert to list of dictionaries that it originally was.
import ast
df['genres'] = df['genres'].map(ast.literal_eval)

type(df['genres'][0][0])

dict

def seperate_genres(genre_list):
    genres = []
    for genre in genre_list:
            genres.append(genre['name'])
    return genres

# Make separate column for genres as a list of genre names
df['genre'] = df['genres'].map(seperate_genres)

genre_df = df.explode('genre')

genre_df['profit'] = genre_df['revenue'] - genre_df['budget']

genre_df[['title','budget','revenue', 'profit']].head()

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	title	budget	revenue	profit
0	Avatar	237000000	2787965087	2550965087
0	Avatar	237000000	2787965087	2550965087
0	Avatar	237000000	2787965087	2550965087
0	Avatar	237000000	2787965087	2550965087
1	Pirates of the Caribbean: At World's End	300000000	961000000	661000000

sns.barplot(y= 'genre', x='profit', data=genre_df, ci=68,palette="Blues_d")

<matplotlib.axes._subplots.AxesSubplot at 0x23be6e34320>

Conclusion: The 5 most profitable genres are Animation, Adventure, Fantasy, Family, and Science Fiction. We recommend that microsoft invest in animated movies.

Question 2: Is there an optimal runtime (in terms of profit) for movies domestically. If so, what is it?

df_runtime = df.copy()

df_runtime['profit'] = df_runtime['revenue'] - df_runtime['budget']
df_runtime['profit_margin'] = (df_runtime['profit'] / df_runtime['budget'])*100

df_runtime['runtime']

0       162.0
1       169.0
2       148.0
3       165.0
4       132.0
        ...  
4773     92.0
4788     93.0
4792    111.0
4796     77.0
4798     81.0
Name: runtime, Length: 3229, dtype: float64

# slice out 50 most profitable movies to look at
df_runtime = df_runtime.sort_values('profit',ascending=False).head(50)

df_runtime[['title', 'profit', 'runtime']]

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	title	profit	runtime
0	Avatar	2550965087	162.0
25	Titanic	1645034188	194.0
28	Jurassic World	1363528810	124.0
44	Furious 7	1316249360	137.0
16	The Avengers	1299557910	143.0
7	Avengers: Age of Ultron	1125403694	141.0
124	Frozen	1124219009	102.0
546	Minions	1082730962	91.0
329	The Lord of the Rings: The Return of the King	1024888979	201.0
31	Iron Man 3	1015439994	130.0
52	Transformers: Dark of the Moon	928746996	154.0
29	Skyfall	908561013	143.0
26	Captain America: Civil War	903304495	147.0
506	Despicable Me 2	894761885	98.0
36	Transformers: Age of Extinction	881405097	165.0
42	Toy Story 3	866969703	103.0
12	Pirates of the Caribbean: Dead Man's Chest	865659812	151.0
675	Jurassic Park	857100000	127.0
197	Harry Potter and the Philosopher's Stone	851475550	152.0
330	The Lord of the Rings: The Two Towers	847287400	179.0
328	Finding Nemo	846335536	100.0
3	The Dark Knight Rises	834939099	165.0
32	Alice in Wonderland	825491110	108.0
65	The Dark Knight	819558444	152.0
233	Star Wars: Episode I - The Phantom Menace	809317558	136.0
504	The Secret Life of Pets	800958308	87.0
348	Ice Age: Dawn of the Dinosaurs	796686817	94.0
78	The Jungle Book	791550600	106.0
113	Harry Potter and the Order of the Phoenix	788212738	138.0
2967	E.T. the Extra-Terrestrial	782410554	115.0
325	Ice Age: Continental Drift	782244782	88.0
262	The Lord of the Rings: The Fellowship of the Ring	778368364	178.0
276	Harry Potter and the Chamber of Secrets	776688482	161.0
98	The Hobbit: An Unexpected Journey	771103568	169.0
565	Shrek 2	769838758	93.0
2912	Star Wars	764398007	121.0
114	Harry Potter and the Goblet of Fire	745921036	157.0
494	The Lion King	743241776	89.0
507	Independence Day	741969268	145.0
229	Star Wars: Episode III - Revenge of the Sith	737000000	140.0
788	Deadpool	725112979	108.0
183	The Hunger Games: Catching Fire	717423452	146.0
172	The Twilight Saga: Breaking Dawn - Part 2	709000000	115.0
22	The Hobbit: The Desolation of Smaug	708400000	161.0
19	The Hobbit: The Battle of the Five Armies	706019788	144.0
35	Transformers: Revenge of the Fallen	686297228	150.0
8	Harry Potter and the Half-Blood Prince	683959197	153.0
159	Spider-Man	682708551	121.0
77	Inside Out	682611174	94.0
17	Pirates of the Caribbean: On Stranger Tides	665713802	136.0

sns.jointplot("runtime", "revenue", data=df_runtime, kind="reg")

<seaborn.axisgrid.JointGrid at 0x23be7183cc0>

Conclusion: As illustrated in this plot, there is a small positive correlation between lengthy movies and how profitable they are. One thing to take note of is that out of the 50 most profitable movies, a big portion of them were around 100 minutes long or around 125-150 minutes long (as illustrated by the bars above the plot). Clearly, they are doing something right. Therefore, microsoft should make movies that are between 125 and 150 minutes long to mimic the most profitable movies.

Question 3: Which production company(s) are most successful in terms of domestic profit and therefore should be used?

# Ensure that our original dataset is unchanged
print(df.shape)
df.head()

(3229, 21)

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	budget	genres	homepage	id	keywords	original_language	original_title	overview	popularity	production_companies	...	release_date	revenue	runtime	spoken_languages	status	tagline	title	vote_average	vote_count	genre
0	237000000	[{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...	http://www.avatarmovie.com/	19995	[{"id": 1463, "name": "culture clash"}, {"id":...	en	Avatar	In the 22nd century, a paraplegic Marine is di...	150.437577	[{"name": "Ingenious Film Partners", "id": 289...	...	2009-12-10	2787965087	162.0	[{"iso_639_1": "en", "name": "English"}, {"iso...	Released	Enter the World of Pandora.	Avatar	7.2	11800	[Action, Adventure, Fantasy, Science Fiction]
1	300000000	[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...	http://disney.go.com/disneypictures/pirates/	285	[{"id": 270, "name": "ocean"}, {"id": 726, "na...	en	Pirates of the Caribbean: At World's End	Captain Barbossa, long believed to be dead, ha...	139.082615	[{"name": "Walt Disney Pictures", "id": 2}, {"...	...	2007-05-19	961000000	169.0	[{"iso_639_1": "en", "name": "English"}]	Released	At the end of the world, the adventure begins.	Pirates of the Caribbean: At World's End	6.9	4500	[Adventure, Fantasy, Action]
2	245000000	[{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...	http://www.sonypictures.com/movies/spectre/	206647	[{"id": 470, "name": "spy"}, {"id": 818, "name...	en	Spectre	A cryptic message from Bond’s past sends him o...	107.376788	[{"name": "Columbia Pictures", "id": 5}, {"nam...	...	2015-10-26	880674609	148.0	[{"iso_639_1": "fr", "name": "Fran\u00e7ais"},...	Released	A Plan No One Escapes	Spectre	6.3	4466	[Action, Adventure, Crime]
3	250000000	[{'id': 28, 'name': 'Action'}, {'id': 80, 'nam...	http://www.thedarkknightrises.com/	49026	[{"id": 849, "name": "dc comics"}, {"id": 853,...	en	The Dark Knight Rises	Following the death of District Attorney Harve...	112.312950	[{"name": "Legendary Pictures", "id": 923}, {"...	...	2012-07-16	1084939099	165.0	[{"iso_639_1": "en", "name": "English"}]	Released	The Legend Ends	The Dark Knight Rises	7.6	9106	[Action, Crime, Drama, Thriller]
4	260000000	[{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...	http://movies.disney.com/john-carter	49529	[{"id": 818, "name": "based on novel"}, {"id":...	en	John Carter	John Carter is a war-weary, former military ca...	43.926995	[{"name": "Walt Disney Pictures", "id": 2}]	...	2012-03-07	284139100	132.0	[{"iso_639_1": "en", "name": "English"}]	Released	Lost in our world, found in another.	John Carter	6.1	2124	[Action, Adventure, Science Fiction]

5 rows × 21 columns

Find out about production company column (type, etc)

type(df['production_companies'][0][0])

str

# Convert to list of dicts
df['production_companies'] = df['production_companies'].map(ast.literal_eval)

type(df['production_companies'][0][0])

dict

list_of_dicts = df['production_companies'][0]

list_of_dicts

[{'name': 'Ingenious Film Partners', 'id': 289},
 {'name': 'Twentieth Century Fox Film Corporation', 'id': 306},
 {'name': 'Dune Entertainment', 'id': 444},
 {'name': 'Lightstorm Entertainment', 'id': 574}]

def seperate_production_companies(company_list):
    company_names = []
    for dict_ in company_list:
        company_names.append(dict_['name'])
    return company_names

df['production_companies'] = df['production_companies'].map(seperate_production_companies)

df['production_companies']

0       [Ingenious Film Partners, Twentieth Century Fo...
1       [Walt Disney Pictures, Jerry Bruckheimer Films...
2                        [Columbia Pictures, Danjaq, B24]
3       [Legendary Pictures, Warner Bros., DC Entertai...
4                                  [Walt Disney Pictures]
                              ...                        
4773              [Miramax Films, View Askew Productions]
4788                              [Dreamland Productions]
4792                                      [Daiei Studios]
4796                                          [Thinkfilm]
4798                                  [Columbia Pictures]
Name: production_companies, Length: 3229, dtype: object

production_company_df = df.explode('production_companies')

production_company_df

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	budget	genres	homepage	id	keywords	original_language	original_title	overview	popularity	production_companies	...	release_date	revenue	runtime	spoken_languages	status	tagline	title	vote_average	vote_count	genre
0	237000000	[{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...	http://www.avatarmovie.com/	19995	[{"id": 1463, "name": "culture clash"}, {"id":...	en	Avatar	In the 22nd century, a paraplegic Marine is di...	150.437577	Ingenious Film Partners	...	2009-12-10	2787965087	162.0	[{"iso_639_1": "en", "name": "English"}, {"iso...	Released	Enter the World of Pandora.	Avatar	7.2	11800	[Action, Adventure, Fantasy, Science Fiction]
0	237000000	[{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...	http://www.avatarmovie.com/	19995	[{"id": 1463, "name": "culture clash"}, {"id":...	en	Avatar	In the 22nd century, a paraplegic Marine is di...	150.437577	Twentieth Century Fox Film Corporation	...	2009-12-10	2787965087	162.0	[{"iso_639_1": "en", "name": "English"}, {"iso...	Released	Enter the World of Pandora.	Avatar	7.2	11800	[Action, Adventure, Fantasy, Science Fiction]
0	237000000	[{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...	http://www.avatarmovie.com/	19995	[{"id": 1463, "name": "culture clash"}, {"id":...	en	Avatar	In the 22nd century, a paraplegic Marine is di...	150.437577	Dune Entertainment	...	2009-12-10	2787965087	162.0	[{"iso_639_1": "en", "name": "English"}, {"iso...	Released	Enter the World of Pandora.	Avatar	7.2	11800	[Action, Adventure, Fantasy, Science Fiction]
0	237000000	[{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...	http://www.avatarmovie.com/	19995	[{"id": 1463, "name": "culture clash"}, {"id":...	en	Avatar	In the 22nd century, a paraplegic Marine is di...	150.437577	Lightstorm Entertainment	...	2009-12-10	2787965087	162.0	[{"iso_639_1": "en", "name": "English"}, {"iso...	Released	Enter the World of Pandora.	Avatar	7.2	11800	[Action, Adventure, Fantasy, Science Fiction]
1	300000000	[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...	http://disney.go.com/disneypictures/pirates/	285	[{"id": 270, "name": "ocean"}, {"id": 726, "na...	en	Pirates of the Caribbean: At World's End	Captain Barbossa, long believed to be dead, ha...	139.082615	Walt Disney Pictures	...	2007-05-19	961000000	169.0	[{"iso_639_1": "en", "name": "English"}]	Released	At the end of the world, the adventure begins.	Pirates of the Caribbean: At World's End	6.9	4500	[Adventure, Fantasy, Action]
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4773	27000	[{'id': 35, 'name': 'Comedy'}]	http://www.miramax.com/movie/clerks/	2292	[{"id": 1361, "name": "salesclerk"}, {"id": 30...	en	Clerks	Convenience and video store clerks Dante and R...	19.748658	View Askew Productions	...	1994-09-13	3151130	92.0	[{"iso_639_1": "en", "name": "English"}]	Released	Just because they serve you doesn't mean they ...	Clerks	7.4	755	[Comedy]
4788	12000	[{'id': 27, 'name': 'Horror'}, {'id': 35, 'nam...	NaN	692	[{"id": 237, "name": "gay"}, {"id": 900, "name...	en	Pink Flamingos	Notorious Baltimore criminal and underground f...	4.553644	Dreamland Productions	...	1972-03-12	6000000	93.0	[{"iso_639_1": "en", "name": "English"}]	Released	An exercise in poor taste.	Pink Flamingos	6.2	110	[Horror, Comedy, Crime]
4792	20000	[{'id': 80, 'name': 'Crime'}, {'id': 27, 'name...	NaN	36095	[{"id": 233, "name": "japan"}, {"id": 549, "na...	ja	キュア	A wave of gruesome murders is sweeping Tokyo. ...	0.212443	Daiei Studios	...	1997-11-06	99000	111.0	[{"iso_639_1": "ja", "name": "\u65e5\u672c\u8a...	Released	Madness. Terror. Murder.	Cure	7.4	63	[Crime, Horror, Mystery, Thriller]
4796	7000	[{'id': 878, 'name': 'Science Fiction'}, {'id'...	http://www.primermovie.com	14337	[{"id": 1448, "name": "distrust"}, {"id": 2101...	en	Primer	Friends/fledgling entrepreneurs invent a devic...	23.307949	Thinkfilm	...	2004-10-08	424760	77.0	[{"iso_639_1": "en", "name": "English"}]	Released	What happens if it actually works?	Primer	6.9	658	[Science Fiction, Drama, Thriller]
4798	220000	[{'id': 28, 'name': 'Action'}, {'id': 80, 'nam...	NaN	9367	[{"id": 5616, "name": "united states\u2013mexi...	es	El Mariachi	El Mariachi just wants to play his guitar and ...	14.269792	Columbia Pictures	...	1992-09-04	2040920	81.0	[{"iso_639_1": "es", "name": "Espa\u00f1ol"}]	Released	He didn't come looking for trouble, but troubl...	El Mariachi	6.6	238	[Action, Crime, Thriller]

10373 rows × 21 columns

production_company_df['production_companies'].value_counts()

Warner Bros.                              280
Universal Pictures                        273
Paramount Pictures                        245
Twentieth Century Fox Film Corporation    201
Columbia Pictures                         167
                                         ... 
Filmtribe                                   1
Seven Arts                                  1
Geisler-Roberdeau                           1
Lago Film                                   1
Novo RPI                                    1
Name: production_companies, Length: 3564, dtype: int64

# Filter to 50 most profitable movies
production_company_df = production_company_df[production_company_df.groupby('production_companies')['production_companies'].transform('count').ge(30)]

fig_dims = (15,8)
fig, ax = plt.subplots(figsize=fig_dims)
sns.barplot(x = "revenue", y = "production_companies", ax=ax, data=production_company_df)

<matplotlib.axes._subplots.AxesSubplot at 0x23be8a35978>

Conclusion: The top production companies in terms of revenue are DreamWorks animation, Lengendary Pictures, Amblin Entertainment, Walt Disney Pictures, and Dune entertainment. Therefore, microsoft should consider consulting with these companies to help them produce their movies

Final Conclusion

Bottom line, it's a risky business to get involved with something new without any experience. Microsoft did what they can by getting the data they needed to see how to get started with the movie business

Some questions that have been cleared up for them are as follows: Question 1: What is the domestic average movie profit categorized by genre? Question 2: Is there an optimal runtime (in terms of profit) for movies domestically. If so, what is it? Question 3: Which production company(s) are most successful in terms of domestic profit and therefore should be used?

After analyzing the data, it seems clear that microsoft should make animated movies around 150 minutes long using Dreamworks Animation as their production company.

Although microsoft now has a better idea about which movies are most profitable, there are many other ideas that can be researched to further their investigation to get a clearer picture. One idea would be to analyze the MPAA ratings and see if there is a correlation between certain ratings and the profit. Another idea would be to see if it's worthwhile to hire certain actors and actresses to gain popularity and in turn, profit. These are just some ideas that I plan to get my hands with in the future.

Name		Name	Last commit message	Last commit date
Latest commit History 24 Commits
zippedData		zippedData
.gitignore		.gitignore
.learn		.learn
CONTRIBUTING.md		CONTRIBUTING.md
InfoFromCSV.ipynb		InfoFromCSV.ipynb
LICENSE.md		LICENSE.md
Mod 1 Presentation.pdf		Mod 1 Presentation.pdf
README.md		README.md
README_original.md		README_original.md
awesome.gif		awesome.gif
module1_project_rubric.pdf		module1_project_rubric.pdf
movie_df.csv		movie_df.csv
output_28_1.png		output_28_1.png
output_36_1.png		output_36_1.png
output_53_1.png		output_53_1.png
student.ipynb		student.ipynb

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Final Project Submission

Introduction

Retrieve Data

Import necessary modules

Importing dataframe

Data Cleaning

Get rid of rows without genre data

Get rid of rows with budget or revenue of zero

Now we will discuss three questions that will shed some light on which movies microsoft should invest in

Question 1: What is the domestic average movie profit categorized by genre?

Conclusion: The 5 most profitable genres are Animation, Adventure, Fantasy, Family, and Science Fiction. We recommend that microsoft invest in animated movies.

Question 2: Is there an optimal runtime (in terms of profit) for movies domestically. If so, what is it?

Question 3: Which production company(s) are most successful in terms of domestic profit and therefore should be used?

Find out about production company column (type, etc)

Conclusion: The top production companies in terms of revenue are DreamWorks animation, Lengendary Pictures, Amblin Entertainment, Walt Disney Pictures, and Dune entertainment. Therefore, microsoft should consider consulting with these companies to help them produce their movies

Final Conclusion

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Get rid of rows without genre data

Get rid of rows with budget or revenue of zero

Now we will discuss three questions that will shed some light on which movies microsoft should invest in

Question 1: What is the domestic average movie profit categorized by genre?

Conclusion: The 5 most profitable genres are Animation, Adventure, Fantasy, Family, and Science Fiction. We recommend that microsoft invest in animated movies.

Question 2: Is there an optimal runtime (in terms of profit) for movies domestically. If so, what is it?

Question 3: Which production company(s) are most successful in terms of domestic profit and therefore should be used?

Find out about production company column (type, etc)

Conclusion: The top production companies in terms of revenue are DreamWorks animation, Lengendary Pictures, Amblin Entertainment, Walt Disney Pictures, and Dune entertainment. Therefore, microsoft should consider consulting with these companies to help them produce their movies

Final Conclusion

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