Medical Cost Prediction
Project Overview
This data science project aims to predict individual medical costs using a dataset containing various attributes related to health insurance. The project focuses on analyzing features such as age, gender, BMI, number of children, smoking status, region, and predicting the corresponding medical costs.
About the Dataset:
The dataset used in this project provides information about health insurance beneficiaries and their medical costs.
Data Dictionary
The aim of this analysis is to predict the medical expense based on the patients'information. The dataset used for this analysis is Insurance dataset from Kaggle. The dataset contains 1338 observations and 7 variables. The variables are as follows:
| Variable | Description |
|---|---|
| age | age of primary beneficiary |
| bmi | body mass index |
| children | number of children covered by health insurance |
| smoker | smoking |
| region | the beneficiary's residential area in the US |
| charges | individual medical costs billed by health insurance |
Impact
Accurate medical cost prediction has significant implications for various stakeholders, including insurance companies, healthcare providers, and individuals. A reliable predictive model can assist insurance companies in assessing risks, determining appropriate premium rates, and managing resources efficiently. Healthcare providers can benefit from cost estimation to optimize resource allocation and budget planning. Additionally, individuals can gain insights into their potential medical expenses and make informed decisions regarding health insurance coverage.
By leveraging machine learning techniques, this project aims to provide valuable insights into medical cost prediction and contribute to more accurate financial planning in the healthcare industry.
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