swalehmwadime/Capstone-Project

Healthcare Fraud Detection Analysis

This project aims to develop a robust system that leverages advanced machine learning algorithms to accurately identify and flag potential fraudulent activities in healthcare claims.

Project Description

This project uses a variety of machine learning algorithms to train models that can identify fraudulent healthcare claims. The models are trained on a dataset of historical healthcare claims data and publicly available data sources. The models are evaluated on their ability to identify fraudulent claims, and the performance of the models is reported using a variety of metrics.

Project Goals

The goals of this project are to:

• Develop a robust system for detecting healthcare fraud
• Identify fraudulent patterns in healthcare data
• Provide actionable insights that can be used to prevent fraud
• Facilitate decision-making processes related to fraud detection

Project Technology Stack

The following technologies are used in this project:

1. Python
2. NumPy
3. Pandas
4. Scikit-learn
5. sklearn
6. Matplotlib
7. Seaborn

Project Installation Instructions

To install the project, you will need to have Anaconda installed. Once you have Anaconda installed, you can create a virtual environment.

To set up the project environment, follow the steps below:

1. Install Anaconda

Please refer to this documentation:

• Windows Installation Guide

• macOS Installation Guide

2. Create a Virtual Environment

Once Anaconda is installed, you can create a virtual environment by following the instructions in the repository:

• Creating a Virtual Environment

Project Usage Instructions

To use the project, follow these steps:

• Fork and Clone the repository: git clone https://github.com/Eddie-254/Capstone-Project.git
• Navigate to the project directory: cd 'directory name'
• Launch Jupyter Notebook(from anaconda): jupyter notebook
• Open the project notebooks: HealthCare_cleaning& EDA.ipynb, HealthCare_modelling.ipynb
• Follow the instructions in the notebook to run the project and analyze the results.

Project Contributors

• [Sharon Chelangat](https://github.com/Chelangat-sharon)
• [Kinoti Martin](https://github.com/kinoti-m-martin)
• [Peter Onsomu](https://github.com/pkonsomu2020)
• [Swaleh Mwadime](https://github.com/swalehmwadime)
• [Victoria Nabea](https://github.com/VikkieN)
• [Edwin Nderitu](https://github.com/Eddie-254)

*** Feel free to contribute to the project by opening issues or submitting pull requests.