Analyze real census data and create a supervised machine learning model to predict potential donor for charity.
This project uses the following software and python libraries
You will also need to have software installed to run and execute a Jupyter Notebook
This project is designed to get acquainted with many supervised learning algorithms available in sklearn, and to also provide a method of evaluating each model's performance on a certain type of data. It is important in machine learning to understand how to select an appropriate machine learning algorithm to use for a given problem.
In this project, I applied supervised learning techniques on data collected for the U.S. census to help CharityML ( a fictious charity organization) identify people most likely to donate to their cause. Specificically the following items have been covered in the analysis:
- explore the data and learn how the census data is recorded
- apply a series of transformations and preprocessing techniques to manipulate the data into workable format.
- Evaluate several supervised learners on the data, and consider which is best suited for the solution.
- census.csv : U.S. census data
- finding_donors.ipynb : Jputer notebook used for the project
- finding_donors.html: report from the notebook in html format
Please see notebook for analysis and results.
Must give credit to UC Irvine for the data and Udacity for creating a beautiful learning experience. Find the Licensing for the data and other descriptive information from UC Irvine ML Repository.