https://github.com/GokulSuseendran/Insurance-Fraud-Prediction

The objective of the project is to predict the risk of auto Insurance fraud using Logistic Regression.
https://github.com/GokulSuseendran/Insurance-Fraud-Prediction

logistic-regression msexcel pca-analysis r rshinyapp

Last synced: 5 months ago
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The objective of the project is to predict the risk of auto Insurance fraud using Logistic Regression.

• Host: GitHub
• URL: https://github.com/GokulSuseendran/Insurance-Fraud-Prediction
• Owner: GokulSuseendran
• Created: 2021-01-04T18:41:41.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2021-01-22T05:27:30.000Z (over 4 years ago)
• Last Synced: 2024-08-13T07:11:15.603Z (8 months ago)
• Topics: logistic-regression, msexcel, pca-analysis, r, rshinyapp
• Language: R
• Homepage:
• Size: 683 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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• jimsghstars - GokulSuseendran/Insurance-Fraud-Prediction - The objective of the project is to predict the risk of auto Insurance fraud using Logistic Regression. (R)

README

        
# Insurance-Fraud-Prediction

Objective: To predict the risk of Auto Insurance Fraud

Data Description: The data obtained can be found in the insurance_claims.csv file and it contains 1000 rows and 44 columns. The data can be split into 3 sections, namely:

  1. Details about the Policy: policy_day, policy_month, policy_year, policy_state, policy_deductible, policy_annual_premium, and so on

  2. Details about the Insured: insured_name, insured_state, insured_occupation, insured_hobbies, and so on

  3. Details about the Claim: incident_type, collision_type, incident_severity, authorities_contacted, incident_state, and so on The target variable is the binary variable, fraud_reported

A Logistic Regression Model was built using the above data, to predict if a claim has a risk of being fraudulent or not. A RShiny app was built for the same.