Create README.md

README.md

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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