Mehfooz08/Insurance_fraud_claim_Prediction

🛡️ 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