---
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