https://github.com/sharmaroshan/Insurance-Claim-Prediction

In this Data set we are Predicting the Insurance Claim by each user, Machine Learning algorithms for Regression analysis are used and Data Visualization are also performed to support Analysis.
https://github.com/sharmaroshan/Insurance-Claim-Prediction

beginner classification data-analysis data-visualization eda evaluation-metrics finance machine-learning radar-chart

Last synced: about 2 months ago
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In this Data set we are Predicting the Insurance Claim by each user, Machine Learning algorithms for Regression analysis are used and Data Visualization are also performed to support Analysis.

• Host: GitHub
• URL: https://github.com/sharmaroshan/Insurance-Claim-Prediction
• Owner: sharmaroshan
• License: gpl-3.0
• Created: 2019-03-31T03:38:25.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2019-04-20T13:04:41.000Z (over 5 years ago)
• Last Synced: 2024-08-09T02:17:44.138Z (5 months ago)
• Topics: beginner, classification, data-analysis, data-visualization, eda, evaluation-metrics, finance, machine-learning, radar-chart
• Language: Jupyter Notebook
• Size: 566 KB
• Stars: 35
• Watchers: 4
• Forks: 41
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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• jimsghstars - sharmaroshan/Insurance-Claim-Prediction - In this Data set we are Predicting the Insurance Claim by each user, Machine Learning algorithms for Regression analysis are used and Data Visualization are also performed to support Analysis. (Jupyter Notebook)

README

        
# Insurance-Claim-Prediction

In this Data set we are Predicting the Insurance Claim by each user, Machine Learning algorithms for Regression analysis are used and Data Visualization are also performed to support Analysis.

# Content

This is "Sample Insurance Claim Prediction Dataset" which based on "[Medical Cost Personal Datasets][1]" to update sample value on top.

# age : 

    age of policyholder 

# sex: 

    gender of policy holder (female=0, male=1)

# 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 25 

# steps: 

    average walking steps per day of policyholder 

# children: 

    number of children / dependents of policyholder 

# smoker:  

    smoking state of policyholder (non-smoke=0;smoker=1)

# region: 

    the residential area of policyholder in the US (northeast=0, northwest=1, southeast=2, southwest=3) 

# charges: 

    individual medical costs billed by health insurance

# insuranceclaim: 

    yes=1, no=0