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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
```text
Insurance Claim Data
↓
Data Cleaning
↓
Missing Value Handling
↓
Feature Engineering
↓
Categorical Encoding
↓
SMOTE Class Balancing
↓
Decision Tree Classifier
↓
Fraud / Non-Fraud Prediction |