Predictive Modeling & ML Application
Building practical machine learning applications
About This Project
This project involves creating a predictive model for classification or regression tasks using real-world datasets such as housing prices or customer segmentation. The implementation includes comprehensive data preprocessing, feature selection techniques, and performance evaluation using appropriate metrics.
Core Concepts
- Regression and classification modeling
- Feature selection and engineering
- Data preprocessing techniques
- Model evaluation and validation
- Hyperparameter tuning
- Model deployment considerations
Key Knowledge/Skills
- Supervised/unsupervised learning
- Data preprocessing workflows
- Feature importance analysis
- Performance metrics (precision, recall)
- Model selection and evaluation
- Practical ML implementation
Coursework Covered
Machine Learning Principles and Practices