In this project, I applied supervised learning techniques on data collected for the U.S. census to help CharityML (a fictitious charity organization) identify people most likely to donate to their cause.
This project contains three files:
finding_donors.ipynb: This is the main file of my work on the project.census.csv: The project dataset. You'll load this data in the notebook.visuals.py: A Python file containing visualization code that is run behind-the-scenes. Do not modify
To construct a model that accurately predicts whether an individual makes more than $50,000, I chose three of the supervised learning models to test on the census data,
- Decision Tree
- Logistic Regression
- Random Forest
As a result, I believe when 100% of the training data is used,
Logistic Regression model is the most suitable for identifying individuals that make more than $50,000.
- It has the highest test F-score with about 0.68.
- It takes about 0.4 and 0.008 seconds in training and prediction respectively, which is a good timing compared to other models.
- It is the best model based on the characteristics of the dataset, because the dataset is Multivariate, it contains both categorical and numerical variables.
- The dependent variables are binary, it has two values =<50,000 and >50,000.
This project requires Python 3.x and the following Python libraries installed:
You will also need to have software installed to run and execute an iPython Notebook
It's recommend to install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.