Understanding what influences scores on media and entertainment

Description

Metacritic is a platform for reviewing games, movies, TV shows and musics.

Using the "Metacritic Scores for Games, Movies, TV & Music" dataset, we have generate insights about the the correlation between the parameters and their influence over the scores given by the platform and the users.

Dataset Metadata

1. Games:

• metascore
• platform
• release_date
• sort_no
• summary
• title
• user_score

2. Movies:

• metascore
• rating
• release_date
• sort_no
• summary
• title
• user_score

3. Musics:

• artist
• metascore
• release_date
• sort_no
• summary
• title
• user_score

4. TV shows:

• metascore
• release_date
• sort_no
• summary
• title
• user_score

Gettins Started

You can try this code on your own by opening google colab, and chossing "File"> "Open notebook" > "GitHub" and inserting the URL for this project. Then you only need to select the notebook file that is shown there.

Tools

Python, Pandas, Data Visualization (Matplotlib, Seaborn, Pyplot)

Improvements and Reviews

• Finish the analysis on other types of media and entertainment
• There are few useful parameters for trying to create a predictive model for the scores, it would be good to collect more features.

Notes

This project was initially developed during ADA's Data Science Path course - Programming Techniques module (EDA with Data Visualization), along with collaborators.