Fifa 19 EDA

FIFA 19 is a football simulation video game developed by EA Vancouver as part of Electronic Arts' FIFA series. The objective of this notebook is to analyse the dataset and see various trends (EDA) using Python and its libraries.

We will load the dataset with all the players and also load libraries to do all the analysis. This kernel is easily understandable to the beginner like me. This verbosity tries to explain everything I could possibly know. Once you get through the notebook, you can find this useful and straightforward. I attempted to explain things as simple as possible. In this notebook, I extensively use plotly along with seaborn and matplotlib for data visualization and EDA on FIFA 19 dataset.

Used Libraries

  • NumPy (Numerical Python)
  • Pandas
  • Matplotlib
  • Missingno
  • Plotly


    • We will use a dataset for FIFA 19 soccer game. Based on it we will tour through the process of data preparation, model assembly and model understanding. In each phase we show how to combine results from different methods of exploration. The main goal of this chapter is to show how different techniques complement each other. Some phases, like data preparation, are simplified in order to leave space for the method for visual exploration.

    Top Teams based on Rating

The dataset contains the detailed attributes for every player registered in the latest edition of FIFA 19 database. Data scraped from sofifa

Changes and Improvement suggestions are welcome. Feel free to suggest any new additions that you think are useful or drop a PR on the github project.