Insurance Charge Predictor

Welcome to the Insurance Charge Predictor project! This application predicts insurance charges based on user-provided information using various machine learning models.

Key Features

• Data Preprocessing: Cleaning and preparing data.
• EDA: Understanding data patterns and relationships.
• Outlier Handling: Managing outliers to improve model performance.
• Machine Learning Algorithms: Linear Regression, Decision Tree Regressor, and Random Forest Regressor.
• Model Persistence: Models saved using Pickle.
• Deployment: Hosted using Streamlit for easy accessibility.

How It Works

Users input the following details:

• Age
• BMI
• Sex
• Number of Children
• Smoking Status
• Region in India

They can then select a machine learning model (Linear Regressor, Decision Tree Regressor, Random Forest Regressor) to predict the insurance charge.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/insurance-charge-predictor.git

2. Navigate to the project directory:

   cd insurance-charge-predictor

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the Streamlit application:

   streamlit run app.py

Usage

Open your browser and go to http://localhost:8501. Enter the required details, select the machine learning model, and click "Predict" to get the estimated insurance charge.

Models

• Linear Regression: Assumes a linear relationship between features and target.
• Decision Tree Regressor: Splits data into subsets based on feature values.
• Random Forest Regressor: Builds multiple decision trees and merges their predictions.

Thank you for using the Insurance Charge Predictor! We hope this tool provides valuable insights and predictions for your insurance needs.

URL: https://insurance-charge-predictor-gatfzeqefac8tz4ds3hc6x.streamlit.app/