An end-to-end Machine Learning project that predicts individual insurance charges using the Linear Regression algorithm. The project demonstrates the complete ML workflow, from data exploration and preprocessing to model training, prediction, and evaluation using Python and Scikit-learn.
The objective of this project is to build a regression model capable of predicting medical insurance charges based on demographic and lifestyle factors such as age, BMI, smoking status, gender, region, and number of children.
The dataset contains information about individuals, including:
- Age
- Sex
- BMI (Body Mass Index)
- Number of Children
- Smoking Status
- Region
- Medical Insurance Charges (Target Variable)
- Python
- Jupyter Notebook
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit-learn
- Data Loading
- Exploratory Data Analysis (EDA)
- Data Cleaning
- Feature Encoding
- Train-Test Split
- Linear Regression Model
- Model Prediction
- Model Evaluation
Algorithm Used:
- Linear Regression
The model performance was evaluated using:
- R² Score (Coefficient of Determination)
---Adjusted R2
Insurance-Charges-Prediction/
│
├── Linear_Regression_Model.ipynb
├── insurance modified.csv
├── requirements.txt
├── README.md
Clone the repository:
git clone https://github.com/yourusername/Insurance-Charges-Prediction.gitInstall the required libraries:
pip install -r requirements.txtLaunch Jupyter Notebook and open:
Linear_Regression_Model.ipynb
- Complete data preprocessing pipeline
- Data visualization using Matplotlib and Seaborn
- Linear Regression model implementation
- Prediction of medical insurance charges
- Performance evaluation using R² Score
- Well-structured and reproducible notebook
This project is intended for educational and portfolio purposes.