Project Name: TMDb_Movies_data_analysis

Introduction

This is an analysis done on TMDb movies dataset of 1960 to 2015

The dataset contains information about 10,000 movies collected from TMDb Database including user ratings, budget for each movie and revenue.

Below is the list of columns names in our dataset and their significance

id - This is a unique identifier for each movie

popularity - A numeric quantity specifying the movie popularity

budget - The cost in which the movie was made

revenue - The worldwide revenue generated from the movie

original_title - The title of the movie before translation or adaptaion

cast - The name of lead and supporting actors

homepage - A link to the homepage of the movie

director - The directors of each movie

tagline - The movie’s tagline

overview - A brief description of the movie

runtime - The running time of the movie in minutes

genres - The genre of the movies; Action, Drama,Adventure etc..

Production_companies - The production house of the movie

release_date - The date the movie was released

vote_count - The count of votes the movie received

vote_average - Average rating the movie received

release_year - The year the movie was released

Research Question(s) for Analysis

This dataset will be analysed to answer the foll0ing the following questions;

  1. What year did TMDb Movies made the highest profit?
  2. Which TMDb movie has the highest profit expressed as a percentage of its budget?
  3. What is the correlation between the attributes of our TMDb movies dataset?

Objective of Analysis

The objective of our analysis are;

  1. To identify the year TMDb movies made the highest profit
  2. To determine the movie that has the highest percentage, when profit is expressed as a percentage of its budget.
  3. To ascertain the correlation between different attributes of TMDb dataset.

Exploratory Analysis

The Top Five Years With the Highest Profit

The First Five Movies with the Highest Profit relative to the budget

Correlation of TMDb Movies dataset attributes

Conclusion and Findings

  1. From our analysis we were able to ascertain that TMDb movies generated the highest profit in 2015.
  2. Also, we discovered that From Prada to Nada movie has the highest profit in relative to budget.
  3. We found that; revenue and popularity is highly correlated with vote_counts. revenue is highly correlated with budget. vote_counts is highly correlated with profit.

Limitations

The filtered dataset contained vast number of Null values of which certain revenue values where dropped when budget is zero and budget when revenue is zero as well. Also, I dropped many columns from the dataset which where not needed for my analysis.