📚 Project Overview
The IPL Winning Team Prediction project uses historical match data and machine learning to predict which team is likely to win upcoming matches. Built to combine data science with sports analytics, it offers cricket fans and analysts a data-backed perspective on match outcomes.

📊 Data Analysis & Preparation
Collected and cleaned IPL datasets, then explored key variables like toss decision, venue, team form, and head-to-head stats. Performed feature engineering to capture hidden patterns that could influence predictions, ensuring data quality and relevance.

🤖 Model Building & Evaluation
Implemented classification models using Python’s scikit-learn to predict match outcomes. Compared models based on accuracy and precision, refining hyperparameters to improve results. Followed best practices in splitting data, avoiding overfitting, and validating predictions.

📈 Visualization & Insights
Used Matplotlib and Seaborn to visualize winning trends, team performance, and other impactful features. These visualizations helped interpret model results and provide context to predictions, making the project more insightful and user-friendly.

⚙️ Key Features

• Predicts match winners based on past IPL data

• Multiple classification models with accuracy tracking

• Feature engineering for better prediction quality

• Data visualization to support insights and storytelling

• Clean, modular code for reproducibility

🧠 Learnings & Impact
This project strengthened my skills in sports analytics, classification algorithms, and feature engineering. It also improved my understanding of balancing data-driven predictions with real-world game dynamics.

🛠 Tech Stack & Tools
Languages & Libraries: Python, scikit-learn, Pandas, NumPy, Matplotlib, Seaborn
Version Control: Git & GitHub
Development Environment: Jupyter Notebook & VS Code

🔗 Project Repository
View on GitHub → IPL Winning Team Prediction