https://github.com/divakarkumarp/medical-insurance-cost-prediction

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Host: GitHub
URL: https://github.com/divakarkumarp/medical-insurance-cost-prediction
Owner: divakarkumarp
Created: 2022-10-25T12:28:42.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2022-10-25T12:39:51.000Z (over 2 years ago)
Last Synced: 2025-01-22T08:13:25.269Z (4 months ago)
Language: Jupyter Notebook
Size: 1.54 MB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

## Medical-Insurance-Cost-Prediction

### Table of Content

* [Dataset Information](#dataset-information)
* [Overview](#overview)
* [Technologies Used](#technologies-used)

Problem Statement

Understanding the relation between the various factor like bmi, number of children or smoker affecting the Hosiptalization charges. Predicting the hospitalization by understanding patterns from other parameters.

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### Dataset information:

age : age of primary beneficiary

sex : insurance contractor gender, female, male

bmi : Body mass index, providing an understanding of body, weights that are relatively high or low relative to height,objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9

children : Number of children covered by health insurance / Number of dependents

smoker : Smoking

region : the beneficiary's residential area in the US, northeast, southeast, southwest, northwest.

charges : Individual medical costs billed by health insurance

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### Overview:

A Medical Insurance Company Has Released Data For Almost 1000 Customers. Create A Model That Predicts The Yearly Medical Cover Cost. The Data Is Voluntarily Given By Customers.

Technology and tools wise this project covers,

1.Python

2.Numpy and Pandas for data cleaning

3.Data visualization

4.Sklearn for model building

5.Jupiter Notebook

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### Technologies Used:

![](https://forthebadge.com/images/badges/made-with-python.svg)

[](https://numpy.org) [](https://pandas.pydata.org) [](https://seaborn.pydata.org) [](https://matplotlib.org) [](https://jupyter.org/)