metadata
license: mit
tags:
- machine-learning
- fraud-detection
- classification
- decision-tree
- scikit-learn
library_name: scikit-learn
🛡️ Insurance Fraud Detection
A machine learning classification model for predicting whether an insurance claim is Fraudulent or Non-Fraudulent.
The model uses a Decision Tree Classifier trained on insurance claim data after preprocessing, feature engineering, categorical encoding, and class balancing using SMOTE.
🤖 Model
Algorithm: Decision Tree Classifier
Task: Binary Classification
Classes:
- Fraudulent
- Non-Fraudulent
📊 Model Performance
The following models were evaluated during the project:
| Model | Accuracy |
|---|---|
| Logistic Regression | 73% |
| Decision Tree | 80% |
| Random Forest | 79% |
The Decision Tree Classifier was selected for the final application and achieved approximately 80% accuracy on the evaluation data.
Accuracy alone does not fully describe fraud-detection performance. Precision, recall, F1-score, and the confusion matrix should also be considered, particularly when the classes are imbalanced.
🔄 Machine Learning Workflow
Insurance Claim Data
↓
Data Cleaning
↓
Missing Value Handling
↓
Feature Engineering
↓
Categorical Encoding
↓
SMOTE Class Balancing
↓
Decision Tree Classifier
↓
Fraud / Non-Fraud Prediction