Research
Classical machine learning, studied properly
These six studies come from my computer engineering degree at Thapathali Campus. Each one takes a single algorithm, runs it on real datasets, and writes up what worked and what did not. They are small, but they are honest, and they are where I learned to measure before I believe.
A comparative study of Principal Component Analysis on different datasets
How much variance you can throw away before class separation collapses, measured on two classic datasets.
Classifying breast cancer as benign or malignant with decision trees
Gini impurity against entropy as the split criterion, with a look at depth and overfitting.
Obesity prediction with a Naive Bayes classifier
A Gaussian and hybrid Naive Bayes approach on mixed numeric and categorical features.
Student dropout prediction with K-Nearest Neighbours
Sweeping k and distance metrics to predict which students are likely to leave.
Digit classification with artificial neural networks
A plain feed-forward network first, then the same network with regularization to see what actually helps.
Speeding up KNN with KD-trees
Replacing brute-force neighbour search with a KD-tree and measuring where it pays off.