Part of the Machine Learning Nanodegree Program.
Investigated factors that affect the likelihood of charity donations being made based on real census data. Developed a naive classifier to compare testing results to. Trained and tested several supervised machine learning models on preprocessed census data to predict the likelihood of donations. Selected the best model based on accuracy, a modified F-scoring metric, and algorithm efficiency.
- Naive Bayes
- sklearn
- Regression vs Classification type problems
- Model Fitting and Prediction
- Decision Trees
- Regression
- Neural Networks
- Support Vector Machines
- K-Nearest Neighbors
- Adaboosting