SAHIL-AGARWAL-IN/Insurance-dataset-Lasso-Regression

# 🏥 Insurance Cost Prediction using Machine Learning ## 📌 Project Overview This project predicts medical insurance charges based on personal and lifestyle attributes such as age, BMI, smoking habits, number of children, and region. The project uses **Linear Regression** and **Lasso Regression** to analyze how different features impact insurance costs and to handle feature selection using regularization. --- ## 📂 Dataset **File:** `insurance.csv` ### Columns Description | Column | Description | |----------|------------| | age | Age of the insured person | | sex | Gender (male / female) | | bmi | Body Mass Index | | children | Number of dependents | | smoker | Smoking status (yes / no) | | region | Residential area | | charges | Medical insurance cost (target variable) | --- ## ⚙️ Technologies Used - Python - Pandas - NumPy - Matplotlib - Seaborn - Scikit-learn --- ## 🧠 Machine Learning Workflow 1. Load the dataset 2. Perform Exploratory Data Analysis (EDA) 3. Convert categorical variables using `pd.get_dummies()` 4. Split data into training and testing sets 5. Train models using: - Linear Regression - Lasso Regression 6. Evaluate model performance 7. Visualize predictions --- ## 🚀 How to Run the Project 1. Clone the repository: ```bash git clone github.com ``` 2. Navigate to the project directory 3. Install required dependencies: pip install pandas numpy matplotlib seaborn scikit-learn 4. Launch Jupyter Notebook 5. Open the notebook and run all cells --- ## 📊 Model Evaluation - The models are evaluated using: - Mean Squared Error (MSE) - R² Score - Lasso Regression helps reduce overfitting by shrinking less important feature coefficients to zero. --- ## 🔮 Future Improvements - Hyperparameter tuning for Lasso (alpha) - Try Ridge and ElasticNet Regression - Feature scaling - Deploy using Flask or Streamlit