Medical Cost Prediction Notebook

This repository contains a Python notebook for an end-to-end medical cost prediction project. The notebook includes the following steps:

1. Data Loading and Cleaning: The data is loaded and cleaned by removing null values, converting strings, and dropping unnecessary features.

2. Data Analysis: The correlation between the target variable (medical cost) and the features is analyzed and visualized.

3. Model Training: Different models including Linear Regression, Support Vector Regression, and K-Nearest Neighbors (KNN) are trained on the preprocessed data.

4. Error Calculation and Model Comparison: The errors of the different models are calculated and compared to choose the best performing model.

Team Members

This project is brought to you by our dedicated team:

• Muhannad Einooo
• Ziad Ahmed Eliwa
• Nourhan Mahmoud
• Adham Adel
• Abdelrahman Mohamed Ali

We hope you find this repository useful for understanding the process of predicting medical costs using machine learning models.