FIFA 2019

I’m excited to share one of my recent Data Analysis projects, where I explored the FIFA 2019 dataset to uncover insights about players, their attributes, performance, and market values.

📊 **What I worked on:

• Data cleaning & preprocessing

• Handling missing values using **realistic and meaningful values based on the available data**, instead of arbitrary replacements

• **Feature Engineering** to create and transform features for deeper analysis

• Exploratory Data Analysis (EDA)

• Statistical analysis

• Data visualization

• Player attributes, performance & market value analysis

• Interactive dashboard using Streamlit

• Detailed project report

🔍 One of the main challenges was handling missing data. Instead of simply filling missing values with arbitrary numbers, I analyzed the available data and used **realistic values that made sense within the context of each feature**.

I also applied **Feature Engineering** to transform the raw data and create more meaningful features that helped me gain deeper insights from the dataset.

This project gave me more practical experience with the complete Data Analysis workflow:

Data Collection → Data Cleaning → EDA → Feature Engineering → Analysis & Visualization → Dashboard

🛠️ **Tools & Technologies:**

Python | Pandas | NumPy | Matplotlib | Seaborn | Streamlit | Jupyter Notebook