Machine Learning Projects
Classic ML end to end: feature engineering, model comparison, cross-validation and imbalanced classification.
2 projects · GPU notebook · Premium only
California Housing Price Prediction
PROPredict median house values for 20,640 California districts from census features. Explore the data with pandas (distributions, correlations and a price map), engineer ratio features, then compare Linear Regression, Random Forest and Histogram Gradient Boosting with 5-fold cross-validated RMSE. Finish with permutation feature importance and a predicted-vs-actual diagnostic of the winning model.
Imbalanced Credit-Risk Classifier
PRODecide which loan applicants are bad credit risks when only 30% of them are, and when missing a bad applicant costs five times more than wrongly rejecting a good one. Build a ColumnTransformer + LogisticRegression pipeline, compare it with a class-weighted version using ROC and precision-recall curves, tune the decision threshold against the business cost matrix, and check whether the predicted probabilities are calibrated. The dataset is fetched from OpenML on first run.