- Built a supervised ML classification model to predict why 20% of patients fail to appear to their scheduled appointment.
- Wrote a code using Sklearn’s OneHotEncoder to preprocess the categorical features and convert them to binary vectors.
- Used a Random Forest model and hyperparameter tuning to improve the ROC score to 0.74.
Home ML Projects
Machine Learning Capstone Projects
Predicting Medical Appointment No-Shows
Analyzing CalCOFI's Oceanographic Trends to Predict Climate Change
- Built a Supervised ML Regression model to understand oceanographic trends and predict temperature changes.
- Created a pipeline that fills missing values and scales numeric features.