Machine Learning Projects: Fraud Detection and Insurance Premium Prediction

This repository contains two machine learning projects focused on real-world applications: fraud detection and insurance premium prediction.

Contents

1. fraudml.py: Script for fraud detection in financial transactions.
2. insurance.py: Script for predicting annual medical expenditure for insurance premium calculation.

Project Descriptions

Fraud Detection (fraudml.py)

This project focuses on detecting fraudulent transactions using machine learning techniques. It involves data preprocessing, feature engineering, and training models to identify potentially fraudulent activities in financial data.

Insurance Premium Prediction (insurance.py)

ACME Insurance Inc. tasked us with creating an automated system to estimate the annual medical expenditure for new customers. This prediction is used to determine the annual insurance premium offered to the customer. The system uses information such as age, sex, BMI, number of children, smoking habits, and region of residence.

Key aspects of this project:

• Predicts annual medical expenditure for insurance premium calculation
• Uses customer demographic and lifestyle data
• Aims to provide explainable predictions due to regulatory requirements
• Focuses on minimizing the loss (error) in predictions

Data

• For the fraud detection project, a dataset named Fraud.csv is used but not included in the repository due to its large size.
• The insurance prediction project likely uses a separate dataset (details to be added).

Setup and Execution

1. Ensure you have Python installed on your system.
2. Install the required libraries: numpy, pandas, seaborn, matplotlib, sklearn, and statsmodels.
3. Place the necessary data files in the same directory as the scripts.
4. Run the scripts using a Python interpreter or an IDE like VS Code.

Notes

• Both projects were developed and tested in Visual Studio Code.
• Some algorithms, particularly in the fraud detection project, may take time to render depending on your device configuration.
• While the implementations may not be 100% perfect, they represent my best effort to create functional systems based on my current understanding and skills.

Future Improvements

• Optimize code for faster execution
• Implement more advanced feature engineering techniques
• Explore other machine learning algorithms for comparison
• For the insurance project: further improve the model's explainability

I hope these projects demonstrate my enthusiasm for applying machine learning to real-world problems. I'm excited about the possibility of contributing to your community and further developing my skills in this field.

Adios!