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overfitting-analysis

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otuemre / FraudDetectAI

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FraudDetectAI is an advanced credit card fraud detection system built with XGBoost and Hybrid SMOTE Sampling (Oversampling + Undersampling). This project tackles highly imbalanced datasets, ensuring strong fraud detection accuracy while minimizing overfitting risks.

data-science credit-card-fraud cybersecurity feature-engineering hyperparameter-tuning imbalanced-data smote fraud-detection classification-models optuna xgboost-classifier financial-security shap-explainability overfitting-analysis
  • Updated Mar 5, 2025
  • Jupyter Notebook

mudassir-08 / LeNet-5-MNIST-CV

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This project implements the classical LeNet-5 CNN for MNIST digit classification using PyTorch. It covers a complete pipeline from data preprocessing to deployment. The model achieves ~98.8% test accuracy, showing the strong effectiveness of early CNN architectures for image classification.

convolutional-neural-networks pooling-layers stochastic-gradient-descent model-deployment learning-rate-scheduling model-generalization pytorch-deep-learning confusion-matrix-analysis real-time-inference overfitting-analysis mnist-digit-classification end-to-end-ml-pipeline lenet5-cnn feature-extraction-cnn hierarchical-feature-learning activation-functions-analysis tanh-vs-relu batch-size-generalization cnn-regularization class-wise-performance
  • Updated Apr 27, 2026
  • Jupyter Notebook

rajaramayan / autism-spectrum-detector2

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ASD early screening in children using ML & ANN — 9 classifiers (MLP-ANN, Random Forest, SVM, KNN, QDA, Naïve Bayes, LR), SMOTE, 7-metric evaluation, overfitting analysis. Streamlit web app deployment. MSc ISE thesis — Purbanchal University, Nepal.

python machine-learning deep-learning classification support-vector-machine artificial-neural-network smote mlp-classifier streamlit overfitting-analysis asd-detection
  • Updated May 15, 2026
  • Python

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