Udacity Investigate a dataset

Project Overview

This was the first Udacity data analyst project I worked on where I investigated the TMDB Movie Dataset and communicated my findings.

The csv data set contains information on 10,000+ movies from 1960 through to 2015.

I used the following libraries to analyse the dataset:

• Pandas
• Numpy
• Matplotlib

The questions I looked to answer going into the analysis are as follows:

1. What were the most produced genres from year to year?
2. Which film studios have generated the most revenue(adjusted)?
3. What kinds of properties are associated with movies that have high revenues?
4. Do we see a relationship between revenue vs movie ratings and budget(adjusted)?
5. How has annual revenue(adjusted) changed year on year? Is it trending upwards?

Overall Conclusions:

1. Comedy and drama ranked as the most produced genre, year to year more than 70% of the time during the timeframe (1960 - 2015).
2. Universal was the leading film studio in revenue adjusted over the date period (1965 - 2015) versus competitors; Disney, Paramount & Columbia.
3. High grosing movies were often to more likely have a higher budget. High grossing movies often had a runtime between 100 to 200 minutes. High grossing were more likely to have a higher vote average. High grossing movies typically had higher popularity scores.
4. There appears to be a positive correlation between vote average and revenue adjusted. There appears to be a positive correlation between budget adjusted and revenue adjusted.
5. There is a clear trend which shows that revenue (adjusted) has been growing year on year within the movie industry.