This project show cases different ML techniques that can be used to classify data.

CharityML is a fictitious charity organization located in the heart of Silicon Valley that was established to provide financial support for people eager to learn machine learning. After nearly 32,000 letters were sent to people in the community, CharityML determined that every donation they received came from someone that was making more than $50,000 annually. To expand their potential donor base, CharityML has decided to send letters to residents of California, but to only those most likely to donate to the charity. With nearly 15 million working Californians, this project helps build an algorithm to best identify potential donors and reduce overhead cost of sending mail. The goal will be to evaluate and optimize several different supervised learners to determine which algorithm will provide the highest donation yield while also reducing the total number of letters being sent.

• Exploring data
• Data Preprocessing
• Applying ML classification algorithms - Random Forest, Decision Trees, Support Vector Machine, Ada Boost
• Evaluating model performance results using accuracy scores, f scores , precision score
• Improving results using GridSearch CV, get features importance
• Choosing the final model

There are multiple ways to run this project:

1. Jupyter notebook
2. Python file

The following python packages reqd to run this project:

1. Sklearn
2. Pandas, Numpy
3. Matplotlib
4. Python 3.0

After the environment set up, download the following files:

1. Census.csv
2. Test_census.csv.

Note: abc_results.csv, dtc_census.csv, rfc_census.csv, svc_census.csv are files generated as part of the project.