IPL_win_predictor

Here’s a well-structured version you can use:

About the Project

I was inspired to build the IPL Win Probability Prediction project because I wanted to understand how machine learning can be applied in sports analytics. As a cricket fan, I was curious about how match outcomes could be predicted in real time using data instead of intuition.

Through this project, I learned about data preprocessing, feature engineering, Logistic Regression, model evaluation, and building an end-to-end machine learning pipeline. I also gained practical experience in deploying a model using Streamlit to create an interactive web application.

I built the project by collecting historical IPL match data, cleaning and preparing the dataset, creating important features such as runs left and required run rate, training a classification model, and finally integrating the trained model into a web interface.

One of the main challenges I faced was handling missing values and correctly calculating match situation features like balls left and wickets remaining. Debugging errors during preprocessing and ensuring the model worked correctly in the Streamlit app also required patience and problem-solving skills.

Built With

• machine-learning
• python
• streamlit