Inspiration
I've always been interested in sports and how data is being used to improve player performance and decision-making. I wanted to build a project that combined my interest in AI with sports analytics and helped turn raw player statistics into something more meaningful.
What it does
PLAYERIQ is an AI-powered sports analytics dashboard that predicts player performance using machine learning. Users can upload a CSV dataset, compare players, view rankings, explore visualizations, and get performance predictions based on player statistics.
How we built it
I built PLAYERIQ using Python and Streamlit for the web application. I used Pandas to process the data, Scikit-learn to build a Linear Regression model for predictions, and Plotly to create interactive charts and graphs.
Challenges we ran into
One of the biggest challenges was understanding how to prepare the data for the machine learning model and making sure the predictions worked correctly. I also spent time improving the dashboard so that it was clean, easy to use, and visually appealing.
Accomplishments that we're proud of
I'm proud that I built and deployed a complete AI-powered web application on my own. It was my first time combining machine learning, data visualization, GitHub, and Streamlit into one project, and seeing it work online was a great achievement.
What we learned
This project helped me understand the complete development process—from working with datasets and training a machine learning model to deploying a live web application. I also became more comfortable using Git and GitHub for version control.
What's next for PLAYERIQ: AI Sports Performance Analytics
I plan to improve PLAYERIQ by supporting more sports, using more advanced machine learning models, adding team-level analytics, and providing deeper insights to make the predictions even more useful.
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