This project involved analyzing the 2025 IPL Auction dataset using Decision Tree modeling to predict whether a player is “Capped” or “Uncapped” based on their base price and winning bid. The analysis was conducted as part of an academic assignment for MIS2502, focused on applying data analysis techniques to a new dataset.
My role included data preprocessing, building the decision tree model, and extracting meaningful insights. I consolidated multiple team-specific datasets into a unified format, selected relevant features, and implemented the model using Python and Jupyter Notebook. Key deliverables included a detailed Word document summarizing the findings, a visual representation of the decision tree, predictions for test examples, and the Jupyter Notebook used for the analysis.
Through this activity, I developed a deeper understanding of Decision Tree algorithms, feature selection, and the trade-offs involved in tuning model parameters like minimum split. This hands-on experience reinforced my ability to handle real-world datasets, draw insights from data, and effectively communicate results through structured documentation and visualizations.
