📈 Insurance Price Prediction using Machine Learning

This project predicts insurance costs using a machine learning model based on features such as age, sex, BMI, number of children, smoking status, and region. The model uses Linear Regression from scikit-learn and includes optimization techniques to improve accuracy.

📝 Dataset

The dataset consists of:

• Age: Age of the primary beneficiary
• Sex: Gender of the beneficiary (male/female)
• BMI: Body Mass Index
• Children: Number of dependents covered by health insurance
• Smoker: Smoking status (yes/no)
• Region: Residential area in the US (northeast, northwest, southeast, southwest)
• Charges: Actual medical costs billed to health insurance

🚀 Project Overview

1. Data Preprocessing:

   • Encoding categorical variables using One-Hot Encoding
   • Data normalization and scaling
   • Splitting the dataset into training and testing sets

2. Model Building:

   • Linear Regression model using scikit-learn
   • Model training and prediction

3. Model Evaluation:

   • Performance metrics: Mean Squared Error (MSE) and R² Score
   • Model optimization to improve prediction accuracy

4. Results:

   • Achieved strong predictive accuracy
   • Successfully minimized prediction errors

🛠️ Tech Stack

• Python: Core programming language
• Pandas: Data manipulation and analysis
• NumPy: Numerical operations
• Scikit-learn: Machine learning model implementation
• Matplotlib/Seaborn: Data visualization (if applicable)

💻 How to Run

1. Clone the repository:

git clone https://github.com/yourusername/insurance-price-prediction.git
cd insurance-price-prediction