Insurance Charges Prediction

This project builds a machine learning model to predict insurance charges based on various features such as age, BMI, smoking status, and region. The dataset is preprocessed, and a linear regression model is trained and validated.

---

Table of Contents

1. Project Overview
2. Dataset
3. Preprocessing
4. Model Training
5. Validation
6. Requirements
7. Usage
8. Results

---

Project Overview

The objective of this project is to predict the insurance charges of individuals based on demographic and health-related attributes. A linear regression model is used to understand the relationship between the input features and the target variable (charges).

---

Preprocessing

The dataset undergoes several preprocessing steps:

1. Missing Value Imputation:
   • Mean imputation for numerical columns (age, bmi, children, charges).
   • Mode imputation for categorical columns (sex, smoker, region).
2. Feature Encoding:
   • Convert categorical variables (sex, smoker) into numerical representations.
   • Convert region into dummy variables, excluding the first category to avoid multicollinearity.
3. Scaling:
   • StandardScaler is used to scale features for optimal model performance.

---

Model Training

A Linear Regression model is trained using the preprocessed dataset. The target variable is charges, and the features include:

• Numerical columns: age, bmi, children
• Encoded categorical columns: sex, smoker, region_<dummy_variables>

The model is evaluated on the training dataset using the R-squared metric.

---

Validation

A separate validation dataset (validation_dataset.csv) is used to evaluate the trained model. The same preprocessing steps are applied to this dataset before predictions are made.

Predictions are scaled back and adjusted:

• Minimum insurance charge is capped at 1000 for realistic predictions.