1. Project motivations
2. Installations
3. File Descriptions
4. Results
5. Link to the blog post

FIFA 19 is one of the most popular football games. In this project we analyzed Fifa 19 players dataset and try to predict a player potential. The analysis uses CRISP-DM method for our analysis process. The process is as follows:

1. Business Understanding
2. Data Understanding
3. Data Preparation
4. Modeling
5. Evaluation
6. Deployment

The following are the questions we are interested in answering:

Q1: What's the ratio of total wages/ total potential for clubs.

Q2: Which are the high spending clubs? Which clubs are the most economical ？

Q3: What's the age distribution like? Is it related to player's overall rating? if yes, how is it related?

Q4: How is a player's skils set influence his potential? Can we predict a player's potential based on his skills' set?

1. Python
2. Libraries: sklearn, pandas, numpy, matplotlib, seaborn
3. Jupyter notebook

• data.csv: Contains the FIFA 19 dataset
• Data Analysis of blog post: jupyter notebook which contains the analysis of FIFA 19 dataset

1. Money plays a crucial role in a club’s performance. Big clubs have been and will continue to spend huge amount of money in order to compete to win trophies.

2. A lot of players ended the career after 26 years of age. But there are a few players who continue to play football even at the age of 40.

3. Ball control, reaction, and age are the main determinants of a player’s potential. People playing this game should focus on these factors to purchase players for their clubs.

The following is the link to the blog post of the analysis: Predicting a players potential on FIFA 19