-- Fraud Detection System: Predicting Fraudulent Transactions -- Problem Statement Fraudulent transactions pose a significant risk to financial institutions and consumers. The objective of this project was to build a robust machine learning solution to classify transactions as either fraudulent or normal. The dataset was highly imbalanced, with fraudulent transactions being a very small fraction of the total. The challenge was to handle this imbalance effectively while ensuring high accuracy and reliability of predictions.
Machine Learning Models Used:--
Artificial Neural Network (ANN):
A deep learning approach capable of capturing complex patterns in data. Designed and trained using a multi-layer perceptron structure to enhance predictive performance. Extra Tree Classifier:
An ensemble learning method that uses randomized decision trees for high-speed and accurate classification. Robust against overfitting and efficient for large datasets. SMOTE (Synthetic Minority Oversampling Technique):
Used to address the imbalanced dataset by creating synthetic samples for the minority class (fraudulent transactions). This technique helped ensure that both classes were adequately represented during model training. Results After applying SMOTE and using advanced cleaning techniques, both models showed significant improvement in handling imbalanced data. ANN delivered: Accuracy: 98% F1-Score: 94% (demonstrating balanced precision and recall) Extra Tree Classifier: Accuracy: 96% F1-Score: 92% The ANN model outperformed due to its ability to capture nonlinear relationships in the data, making it more reliable for fraud detection. Skills Gained Data Cleaning:
Mastered advanced techniques to preprocess data by handling missing values, outliers, and irrelevant features. Exploratory Data Analysis (EDA):
Performed comprehensive visualization and statistical analysis to uncover hidden trends and correlations. Resampling Techniques (SMOTE):
Learned to handle imbalanced datasets effectively, which is critical for real-world data scenarios. Model Implementation:
Gained expertise in implementing complex models like ANN and ensemble methods. Learned hyperparameter tuning for optimizing model performance.
Evaluation Metrics: Developed a strong understanding of metrics like F1-score, precision, recall, and AUC to evaluate model performance effectively.
Why This Project is Impressive This project showcases my ability to handle real-world challenges, such as imbalanced data, and my expertise in leveraging advanced techniques like SMOTE, deep learning, and ensemble models. The use of ANN and Extra Tree Classifiers highlights my capability to experiment with diverse algorithms, compare their performance, and select the best fit for the problem. Additionally, the project emphasizes critical skills such as data preprocessing, EDA, and an understanding of evaluation metrics, demonstrating my readiness to tackle data-driven challenges in professional settings.
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