iamharshvardhan / FIFA-2019-Data-Analysis

Explore the FIFA 2019 Players dataset in this data science project. Analyze player statistics, age, and ratings while creating visualizations to gain insights into virtual football's diverse world.

FIFA 2019 Players Data Analysis Project

Overview

In this data science project, we will explore the FIFA 2019 Players dataset to gain insights into the attributes and characteristics of football players from the popular video game FIFA 2019. We will perform data analysis and create data visualizations to better understand the player statistics and their distribution.

Dataset

The dataset we will be using is the FIFA 2019 Players dataset, which contains information on a wide range of attributes for over 18,000 football players. These attributes include player names, age, nationality, club, preferred positions, player ratings, skills, and more. The dataset can be found here.

Goals

1. Exploratory Data Analysis (EDA): We will start by conducting an EDA to understand the dataset's structure and the distribution of various player attributes. This will involve summary statistics, data cleaning, and identifying any missing values.

2. Data Visualization: We will create various visualizations to better understand the data. Some of the potential visualizations include:

   • Player Age Distribution: A histogram showing the distribution of player ages.
   • Player Rating Distribution: A histogram showing the distribution of player ratings.
   • Top Nationalities: A bar chart showing the top nationalities of players in the dataset.
   • Top Clubs: A bar chart displaying the top clubs with the most players in the dataset.
   • Player Position Distribution: A pie chart illustrating the distribution of player positions.

3. Analysis and Insights: Based on the exploratory data analysis and visualizations, we will draw insights and answer questions such as:

   • What is the typical age of FIFA 2019 players?
   • What is the distribution of player ratings?
   • Which countries contribute the most players to the dataset?
   • Which clubs have the highest representation in the dataset?
   • What are the most popular player positions?

4. Correlation Analysis: We will examine the correlation between various player attributes, such as age, rating, and skills, to identify any interesting patterns.

Tools and Libraries

We will use Python for this data science project, and some of the key libraries we'll be using include:

• Pandas: For data manipulation and cleaning.
• Matplotlib, Seaborn and Bokeh: For data visualization.
• Numpy: For numerical operations.
• Jupyter Notebook: For an interactive and organized analysis.

This is an incomplete project. If you want to add or modify this project according to your needs or test any ideas you have, feel free to do so. All meaningful contributions will be welcomed.