Insurance Cost Prediction Project

This project explores building a machine learning model to predict insurance costs based on various factors.

Functionality

The code performs the following tasks:

Data Collection & Analysis:

• Loads insurance data from a CSV file.
• Provides basic information about the data (dimensions, missing values, etc.).
• Analyzes the distribution of features through visualizations (age, gender, BMI, etc.).

Data Pre-Processing:

• Encodes categorical features (sex, smoker, region) into numerical values.
• Splits the data into features (X) and target variable (charges) (Y).
• Splits the data further into training and testing sets.

Model Training:

• Implements a Linear Regression model to predict insurance charges.
• Trains the model on the training data.

Model Evaluation:

• Evaluates the model's performance on both training and testing data using R-squared score.

Predictive System:

• Demonstrates how to use the trained model to predict insurance costs for a new data point.

Dependencies

• numpy
• pandas
• matplotlib.pyplot
• seaborn
• scikit-learn

Usage

1. Clone the repository.
2. Ensure the required libraries are installed:

   pip install numpy pandas matplotlib seaborn scikit-learn

3. The code is written in a single Python file (*.py). Run the script to execute the data analysis, model training, and prediction functionalities.

Future Enhancements

• Explore other machine learning models beyond Linear Regression (e.g., Random Forest, Gradient Boosting).
• Implement hyperparameter tuning to improve model performance.
• Develop a user-friendly interface for interactive cost prediction.