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1. IMDB MOVIE ANALYSIS
Project by-
Surendra upadhyay
DATA ANALYST

2. Project Description
• Dataset having 28 columns, 5044 Rows of different IMDB Movies.
• Defined a problem, clean the data as necessary remove null and
duplicate values.
• Explore the data set and derive insights
• Use root cause analysis Five 'Whys' approach.
• Create chart pivot table and graphs to answer given questions
• Give answer to asked questions and create reports for data-driven
decision

3. Approach
• Download and Data open in excel
• Find null and duplicate values and clean all data
• Data processing and solved as per asked problems.
• Use filter ,pivot table, sum if, average if, count if and other functions
to give answer of asked questions
• Create charts to for easy and meaningful data representation
• Create report in ppt format and submit the project

4. Tech-Stack Used

5. Insights and results
• 1. Clean the data-
28
5044
28
3744
0
1000
2000
3000
4000
5000
6000
ROWS COLUMNS
BEFORE CLEANIGN AFTER CLEANING

6. 2.Find the movies with the highest profit?

7. 3.Find IMDB Top 20.
The Shawshank Redemption 9.3 1689764
The Godfather 9.2 1155770
The Dark Knight 9 1676169
The Godfather: Part II 9 790926
The Lord of the Rings: The Return of the King 8.9 1215718
Pulp Fiction 8.9 1324680
Schindler's List 8.9 865020
The Good, the Bad and the Ugly 8.9 503509
Forrest Gump 8.8 1251222
Star Wars: Episode V - The Empire Strikes Back 8.8 837759
The Lord of the Rings: The Fellowship of the Ring 8.8 1238746
Inception 8.8 1468200
Fight Club 8.8 1347461
Star Wars: Episode IV - A New Hope 8.7 911097
The Lord of the Rings: The Two Towers 8.7 1100446
The Matrix 8.7 1217752
One Flew Over the Cuckoo's Nest 8.7 680041
Goodfellas 8.7 728685
City of God 8.7 533200
Seven Samurai 8.7 229012

8. 4.Find the best directors
8.7
8.6 8.6
8.5 8.5 8.5 8.5
8.4 8.4
8.4 8.4
8.3 8.3 8.3 8.3
8.2 8.2
8.2 8.2 8.2 8.2 8.2
7.8
7.9
8
8.1
8.2
8.3
8.4
8.5
8.6
8.7
8.8
AVERAGE OF MEAN_IMBD_SCORE
Average of mean_imbd_score Linear (Average of mean_imbd_score)

9. 5.Find popular genres
147 144
138 138
131
114
81
55
50
46 45 45 43 42 41 41
34 34 31 30 30
0
20
40
60
80
100
120
140
160
genres_count(TOP-20) Linear (genres_count(TOP-20))

10. 0.0
100.0
200.0
300.0
400.0
500.0
600.0
700.0
800.0
Albert
Finney
Wood
Harris
Doona
Bae
Violante
Placido
Elizabeth…
Robert…
Sasha
Roiz
Rumi
Hiiragi
Bunta
Sugawara
Shô
Kosugi
Daniel
Stern
Michael
O'Neill
Jeremy
Irvine
Radha
Mitchell
Linda
Cardellini
Sarah
Polley
Phoebe
Cates
Kevin
Spacey
Levon
Helm
Erick
Avari
Joe
Morton
Isaac
Hayes
Roxanne
McKee
Sam
Claflin
Xander
Berkeley
Fanny
Ardant
Erika…
Sissy
Spacek
John
Saxon
Djimon…
Bella
Thorne
Gary
Dourdan
Dianne
Wiest
Michael…
Raoul
Bova
John
Cameron…
Stephen
Root
Hugh
M.
Hefner
Daniel
Mays
Johannes…
Alice
Krige
Peter
Fonda
Harry
Styles
Dean
Stockwell
Craig
T.
Nelson
Divine
John
Gatins
Thomas
F.…
Steve
Oedekerk
Christina
Milian
Brian
Dennehy
Judith
Barsi
Callie
Thorne
Peter
McNamara
Doug
E.
Doug
Dyan
Cannon
George
Carlin
Q'orianka…
Drew
Fuller
Rosanna…
mean_num_ critic_for_reviews
mean_num_ critic_for_reviews
0.0
100000.0
200000.0
300000.0
400000.0
500000.0
600000.0
700000.0
800000.0
Mhairi
Calvey
Eva
Green
Jennifer…
Andrew…
Hattie…
Bérénice…
Adriana…
Colin
Salmon
Matthew…
Bob
Balaban
Patricia…
Craig
Parker
Maika…
Charlyne
Yi
Jon
Hamm
Jason
Mewes
Sean…
Gary
Oldman
Jackson…
Ari
Graynor
James…
Jeff
Bridges
Alona
Tal
Haley
Joel…
Alex
Veadov
Dianne
Wiest
Dean…
Tiago
Riani
Alyson…
Jessica
Paré
Shawna…
Madeline…
Romany…
W.
Earl…
Ray…
Catherine…
Alexa…
Judie…
Kelly
Carlson
John
de…
Angie…
Rosie
Perez
Christopher…
Greg…
Kevin…
Brenda…
Nicholas…
Lesley…
Richard…
Lena
Horne
Sheryl
Crow
Gillian
White
Jessica…
Tyler
James…
Mary…
Daniel…
Rachel
True
Jon
Gries
Lindsay
Kay…
Burt
Young
mean_num_voted_users
mean_num_voted_users
6.Find the critic-favorite and audience-favorite actors

11. SUM OF NUM_VOTED_USERS DECADE WISE
116387 804839 159517 6783362982551
8523299
19987476
69581866
170714771
120213161
0
20000000
40000000
60000000
80000000
100000000
120000000
140000000
160000000
180000000
Sum of num_voted_users
Sum of num_voted_users 2 per. Mov. Avg. (Sum of num_voted_users)
17
21
11
0
5
10
15
20
25
Brad Pitt Leonardo
DiCaprio
Meryl Streep
Count of Movie_Title ACTOR_1_NAME_
MOVIE_COUNT( 'Meryl Streep',
'Leonardo DiCaprio', and 'Brad Pitt’)
Count of movie_title

12. THE END
THANK YOU