Machine Learning Experiments
A collection of ML experiments in Jupyter notebooks from my Semester 6 ML Lab course, covering the full pipeline from basic statistics to ensemble classifiers and model comparison.
| # | Experiment | Highlights |
|---|---|---|
| 01 | Basic Statistical Measures | Mean, variance, covariance, covariance matrix with NumPy |
| 02 | Statistical Measures with Algorithm Steps | Stepwise statistical implementations |
| 03 | EDA and Visualization | Book dataset: bar, scatter, box, histogram, line plots |
| 04 | Multiple Linear Regression | House price prediction with MSE and R2 evaluation |
| 05 | Polynomial Regression | Temperature prediction with degree-2 feature expansion |
| 06 | Logistic Regression | Binary purchase prediction with encoding and scaling |
| 07 | Decision Tree (ID3) | Custom ID3 from scratch with entropy and information gain |
| 08 | K-Nearest Neighbors | Custom KNN on Iris dataset with Euclidean distance |
| 09 | Random Forest | Diabetes outcome prediction with feature importance |
| 10 | Multi-Model Classification | DT, RF, SVM, Naive Bayes, KNN comparison on mobile data |
| 11 | Case Study | Full pipeline: cleaning, DT, RF, SVM with metric comparison |