As part of my portfolio, I created an interactive and advanced dashboard to explore and analyze the FIFA players dataset. Here's a glimpse of what it offers:
📊 Interactive Visualizations: Age vs. Overall Rating Scatter Plot: Discover the relationship between players' ages and their overall ratings. Wage Distribution by Club: Analyze wage distributions across various clubs. Top Players by Overall Rating: See the top 10 players based on overall ratings. Correlation Heatmap: Visualize correlations between attributes like age, overall rating, potential, and wage. Player Comparison: Compare multiple players across various attributes. Team Analysis: Detailed insights into selected teams, including age vs. overall rating and wage distribution by position. Player Attribute Radar Chart: Explore individual player attributes. Wage vs. Overall Rating Scatter Plot: Examine the relationship between players' wages and their overall ratings. Player Age Distribution by Position: Age distribution of players across different positions. Nationality Distribution: The nationality breakdown of players.
🔍 Advanced Filtering: The sidebar allows for detailed filtering based on clubs, nationalities, positions, age range, overall rating range, and wage range. This makes the analysis customizable and insightful.
🛠️ Tech Stack Used: Python Pandas for data manipulation Plotly for interactive visualizations Streamlit for building the interactive web app
💡 Why Streamlit? Streamlit is an amazing tool for creating interactive web applications quickly and easily. It allowed me to seamlessly integrate complex data visualizations and filters into a user-friendly dashboard.
💬 Personal Note: This project shares my passion for sports and my curiosity to learn how data can help in different ways. By merging these interests, I aim to uncover insights that enhance our understanding of the game and its players.