https://github.com/nirab25/Insurance-Claim-Fraud-Detection
Insurance claim fraud detection using machine learning algorithms.
https://github.com/nirab25/Insurance-Claim-Fraud-Detection
balanced-random-forest descision-tree insurance-claims knn-classification knn-classifier linear-discriminant-analysis machine-learning mlp-classifier naive-bayes-classifier neural-network random-forest xgboost
Last synced: 5 months ago
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Insurance claim fraud detection using machine learning algorithms.
- Host: GitHub
- URL: https://github.com/nirab25/Insurance-Claim-Fraud-Detection
- Owner: nirab25
- Created: 2020-04-11T16:48:36.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2020-05-06T05:40:31.000Z (almost 5 years ago)
- Last Synced: 2024-08-09T02:17:31.110Z (8 months ago)
- Topics: balanced-random-forest, descision-tree, insurance-claims, knn-classification, knn-classifier, linear-discriminant-analysis, machine-learning, mlp-classifier, naive-bayes-classifier, neural-network, random-forest, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 422 KB
- Stars: 9
- Watchers: 2
- Forks: 4
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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- jimsghstars - nirab25/Insurance-Claim-Fraud-Detection - Insurance claim fraud detection using machine learning algorithms. (Jupyter Notebook)
README
# Insurance-Claim-Fraud-Detection
Claim fraud detection is a common problem in insurance industries. Machine learning algorithms are handy in detecting fraud claims. Sample dataset downloded from https://github.com/mwitiderrick/insurancedata/blob/master/insurance_claims.csv
The dataset is evaluated using cross-validated score, ROC Curve and AUC. The predictive power of each model expressed by ROC curves. For instance, Linear Discriminant Analysis and XGBOOST model has higher probability of accurate prediction of correct class members, and gaining high level of accuracy prediction probability as compared to Random Forest, KNN, Naive Bayes, Neural Network and SVM models.