Medical Health Insurance Cost Prediction

This dataset contains 1338 rows of insured data, where the Insurance charges are given against the following attributes of the insured: Age, Sex, BMI, Number of Children, Smoker and Region. The attributes are a mix of numeric and categorical variables. There are no missing or undefined values in the dataset.

This project focuses on predicting medical health insurance costs using various regression models. The goal is to create a model that can accurately estimate the insurance costs for individuals based on their attributes. The project employs exploratory data analysis, data preprocessing, and several regression techniques to achieve this.

In this project, we aim to predict medical health insurance costs for individuals based on various factors such as age, sex, BMI, number of children, smoking habits, and region. The project involves the following steps:

The project begins by loading the dataset containing information about individuals and their insurance costs.

We analyze the dataset to gain insights into the data distribution, relationships between variables, and identify potential patterns.

Data preprocessing steps are performed, including handling missing values, encoding categorical variables, and scaling numerical features.

Several regression models are implemented, including Multiple Linear Regression, Lasso Regression, Ridge Regression, ElasticNet Regressor, Random Forest Regressor, and Polynomial Regression.

The models are evaluated using metrics such as Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error. The performance of each model is compared.

The project concludes with a summary of key insights obtained from the analysis and suggestions for further improvement.