A case study done on the 1000 movies list

For this project I have used dataset from kaggle 'IMDB-Movie-Data.csv'. It contains features like Title, Genre, Description, Director, Rating etc.

Firstly, I have loaded all the necessary libraries. Then loaded the data using panda. In this project I will visualize different insights like classify movies based on Ratings[Excellent,Good and Average], find average rating of movies year-wise, display top 10 highest revenue movie titles etc.

Steps performed in this project:

1. Display Top 10 Rows of The Dataset

2. Check Last 10 Rows of The Dataset

3. Find Shape of Our Dataset (Number of Rows And Number of Columns)

4. Getting Information About Our Dataset Like Total Number Rows, Total Number of Columns, Datatypes of Each Column And Memory Requirement

5. Check Missing Values In The Dataset

6. Drop All The Missing Values

7. Check For Duplicate Data

8. Get Overall Statistics About The DataFrame

9. Display Title of The Movie Having Runtime Greater Than or equal to 180 Minutes

10. In Which Year There Was The Highest Average Voting?

11. In Which Year There Was The Highest Average Revenue?

12. Find The Average Rating For Each Director

13. Display Top 10 Lengthy Movies Title and Runtime

14. Display Number of Movies Per Year

15. Find Most Popular Movie Title (Highest Revenue)

16. Display Top 10 Highest Rated Movie Titles And its Directors

17. Display Top 10 Highest Revenue Movie Titles

18. Find Average Rating of Movies Year Wise

19. Does Rating Affect The Revenue?

20. Classify Movies Based on Ratings [Excellent, Good, and Average]

21. Count Number of Action Movies