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imbalanced-data-handling

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chatterjee007-dev / Machine_Learning

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Showcasing impactful machine learning projects tackling real-world issues with Decision Trees, SVM, Time Series, Random Forest, Logistic Regression, and Gradient Boosting. Demonstrates advanced preprocessing, feature engineering, and optimization techniques.

random-forest clustering svm linear-regression scikit-learn pandas seaborn pca logistic-regression matplotlib data-preprocessing feature-engineering predictive-modeling decision-tree gradient-boosting k-means-clustering ensemble-models real-world-problem-solving model-optimization imbalanced-data-handling
  • Updated Nov 21, 2024
  • Jupyter Notebook

nashish109 / smart-ecommerce-fraud-detection

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AI-powered system to detect fraudulent transactions in e-commerce using machine learning. Includes data preprocessing, feature engineering, and classification models like Random Forest and XGBoost. Achieved high accuracy with interpretable results for real-time detection.

feature-engineering machine-learning-models graph-attention-networks data-preprocessing-and-cleaning imbalanced-data-handling model-evaluation-and-metrics smote-sampling-technique classification-report-analysis ensemble-learning-with-xgboost
  • Updated May 1, 2025
  • Jupyter Notebook

SunnyRao07 / cnn-vs-mobilenet-image-classification

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Deep learning project comparing CNN and MobileNetV2 for image classification on a small, imbalanced dataset. Covers preprocessing, augmentation, training, evaluation, and performance analysis. Tools: Python, TensorFlow, Keras, Scikit-learn, Matplotlib

imbalanced-data-handling cnn-and-mobilenetv2-modeling transfer-learning-and-fine-tuning image-preprocessing-and-augmentation model-training-and-regularization evaluation-with-classification-metrics
  • Updated May 26, 2025
  • Jupyter Notebook

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