Insurance Charges Prediction

Introduction

This project aims to predict insurance charges based on relevant features using machine learning models. The primary goal is to demonstrate the implementation of linear regression and random forest regression for predictive analysis.

Problem Statement

Health insurance charges can vary widely based on several factors such as age, BMI, and smoking habits. Predicting these charges accurately can assist insurance providers in setting appropriate pricing for different individuals.

Solution

The project utilizes two main machine learning models:

1. Linear Regression: A basic regression model that establishes a linear relationship between input features and insurance charges.
2. Random Forest Regression: A more advanced ensemble learning model that can capture complex patterns in the data.

Steps to Solve the Problem

1. Data Collection: Obtain a dataset containing information about individuals and their corresponding insurance charges.
2. Data Preprocessing: Clean and preprocess the data, handle missing values, and encode categorical variables.
3. Exploratory Data Analysis (EDA): Understand the distribution of features and relationships between variables.
4. Model Development: Implement linear regression and random forest regression models using a suitable machine learning library.
5. Model Training: Train the models on the training dataset.
6. Model Evaluation: Evaluate the models using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared.
7. Results Comparison: Compare the performance of linear regression and random forest regression.

Libraries Used

• scikit-learn: Utilized for implementing machine learning models and evaluating their performance.
• matplotlib and seaborn: Used for data visualization and model performance visualization.