Player Selection Predictive Model
A predictive analytics solution developed for sports institutions to assist in selecting players for national-level competitions. The model analyzes various features including fitness parameters, behavioral aspects, and physical measurements to predict whether a player will be selected. Built using a comprehensive dataset of 18,659 training samples across 30 features, the model helps coaches and selectors make data-driven decisions in player selection.
Problem Statement
Sports institutions need to select players for national-level competitions based on multiple criteria including fitness levels, behavioral aspects, and physical parameters. Manual selection can be subjective and may miss important patterns in the data.
My Approach
Developed a machine learning pipeline that processes player data with 30 features, performs feature engineering and selection, and trains predictive models to classify whether a player will be selected. The solution generates submission predictions for evaluation.
Key Outcomes
- Built predictive model for player selection classification
- Processed dataset of 18,659 samples with 30 features
- Automated prediction generation for player selection
- Enabled data-driven decision making for sports selectors
Tech Stack
Tags
Project Info
- Status
- Completed
- Category
- Personal
- Created
- 4 years ago
- Ended
- Dec 2021