I completed this small project as part of Udacity's Machine Learning Nanodegree.
Given a dataset of the income of multiple individuals, find out whether an individual will donate to CharityML (a fictional charity organization) or not.
Considering this problem will have a binary resolve with respect to each individual (yes or no), I decided to try out 3 different algorithms:
- Naive Bayes (Base Model)
- Logistic Regression
- Support Vector Machines
- K Nearest Neighbours
Here's the result of the accuracy I achieved on the training set:
| Model | Accuracy % |
|---|---|
| Naive Bayes | ~25 |
| Logistic Regression | ~84 |
| Support Vector Machines | ~86 |
| KNN | ~87 |
I chose to go ahead with Logistic Regression since it gave the best results on the testing set.
I received a final accuracy score of 84.2% with this model on the testing set.