rahulvyasm/medical_insurance_data

Dataset Card for Medical Insurance Cost Prediction

The medical insurance dataset encompasses various factors influencing medical expenses, such as age, sex, BMI, smoking status, number of children, and region. This dataset serves as a foundation for training machine learning models capable of forecasting medical expenses for new policyholders.

Its purpose is to shed light on the pivotal elements contributing to increased insurance costs, aiding the company in making more informed decisions concerning pricing and risk assessment.

Dataset Description

The dataset contains 2.7K rows and 7 columns Columns include

1. Age
2. Sex
3. BMI (Body Mass Index)
4. Children
5. Smoker
6. Region
7. Charges

Introduction

Healthcare costs are a significant concern for individuals and families worldwide. Predicting medical insurance costs accurately can help insurance companies determine premiums and assist individuals in planning their healthcare expenses. This project focuses on building machine learning models to predict insurance costs based on demographic and health-related attributes.

Problem Statement

1. What are the most important factors that affect medical expenses?
2. How well can machine learning models predict medical expenses?
3. How can machine learning models be used to improve the efficiency and profitability of health insurance companies?

Features

• Data Exploration: Explore the dataset to understand its structure, identify missing values, and analyze the distribution of features.
• Data Preprocessing: Prepare the data by handling categorical variables, renaming columns, and scaling numerical features.
• Model Training: Utilize linear regression and ridge regression models to train predictive models on the prepared dataset.
• Pipeline Construction: Construct a data preprocessing pipeline to streamline the process of transforming input data for model training.
• Model Evaluation: Evaluate model performance using metrics such as R-squared score and mean squared error to assess predictive accuracy.
• Model Serialization: Save trained models and pipelines to disk using the pickle library for future use.

Technologies Used

• Python: Programming language used for data manipulation, analysis, and model implementation.
• Libraries: NumPy, Pandas, Seaborn, Matplotlib, and Scikit-learn for data handling, visualization, and machine learning tasks.
• Machine Learning Models: Linear Regression, Ridge Regression
• Pickle: Python library used for serializing trained models and pipelines to disk.

Dataset Sources

From multiple online and offline datasets

Problem Statement

1. What are the primary factors influencing medical expenses?
2. How accurate are machine learning models in predicting medical expenses?
3. In what ways can machine learning models enhance the efficiency and profitability of health insurance companies?