The ML program introduces students to the fundamentals of machine learning using Python. The course covers supervised and unsupervised learning, data preprocessing, and model evaluation. Students practice using libraries like NumPy, Pandas, Scikit-learn, and Matplotlib, creating mini-projects such as house price prediction, spam detection, and simple AI chatbots. It provides a solid foundation for higher studies or early career opportunities in data analysis and AI development.
| S.NO | PROJECT NAME | YEAR |
|---|---|---|
| 1 | Early Detection of Kidney Stones Using K Nearest Neighbors with Data Preparation Techniques | 2025 - 2026 |
| 2 | Optimizing House Tax Prediction Using Random Forest Regressor Algorithm and GridSearchCV for Regularization Techniques | 2025 - 2026 |
| 3 | Classification of High-Risk Patients for Priority Scheduling Using AdaBoost | 2025 - 2026 |
| 4 | Analysing Eye and Face Detection for Interactive Education Based on Haar Cascade Classifier Algorithm and Image Processing Techniques | 2025 - 2026 |
| 5 | Predicting Reservation in Aero Systems Using AdaBoost and Classification Techniques | 2025 - 2026 |
| 6 | Multiclass Currency Authentication Using Support Vector Machine Algorithm and Classification Techniques of Oriented Gradients | 2025 - 2026 |
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