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pavankethavath / Microsoft-Classifying-Cybersecurity-Incidents-with-ML

Star 7

A machine learning pipeline for classifying cybersecurity incidents as True Positive(TP), Benign Positive(BP), or False Positive(FP) using the Microsoft GUIDE dataset. Features advanced preprocessing, XGBoost optimization, SMOTE, SHAP analysis, and deployment-ready models. Tools: Python, scikit-learn, XGBoost, LightGBM, SHAP and imbalanced-learn

machine-learning exploratory-data-analysis pandas feature-selection logistic-regression feature-engineering hyperparameter-tuning decision-tree-classifier gradientboosting smote dataanalysis correlation-analysis joblib shap modelselection xgboost-classifier randomforestclassifier lgbmclassifier randomsearchcv classificationreport
  • Updated Nov 27, 2024
  • Jupyter Notebook

fahadgorain / Bayesian-Hyper-Parameter-Optimization-for-Malware-Detection-Extended

Star 3

Selected Paper from the AI-CyberSec 2021 Workshop in the 41st SGAI International Conference on Artificial Intelligence (MDPI Journal Electronics)

python machine-learning-algorithms hyperparameter-optimization logistic-regression hyperparameter-tuning stochastic-gradient-descent knn-classification malware-detection hyperparameter-search random-forest-classifier gridsearchcv lightgbm-classifier randomsearchcv ahbo-tpe baysian-optimisation
  • Updated May 26, 2022
  • Jupyter Notebook

EimanTahir027 / Model-Tuning-and-Optimization

Star 2

Deep dive into tuning ML models like a pro. Master Grid Search, Random Search, and Bayesian Optimization, tame overfitting with regularization, validate like it matters, then automate it all with GridSearchCV & RandomizedSearchCV. Ends with a capstone project where you build and optimize a model from scratch..

cross-validation tuning regularization grid-search bayesian-optimization hyperparameter-tuning model-evaluation gridsearchcv randomsearch randomsearch-cv randomsearchcv
  • Updated Sep 14, 2026
  • Python

rajivaleaakash / Customer-Churn-Prediction

Star 1

A machine learning project focused on predicting customer churn using various data analysis and modeling techniques. The repository includes data preprocessing, feature engineering, exploratory data analysis (EDA), model training, evaluation, and visualization to help businesses identify customers at risk of leaving.

data-science machine-learning numpy scikit-learn pandas classification data-analysis pyhton churn-prediction gridsearchcv imblearn customer-churn randomsearchcv
  • Updated Mar 20, 2026
  • Jupyter Notebook

filsan-musa / Project-Twitter_Bot_Detection

Star 0

Using a synthetic dataset from Kaggle, generated with Python's Faker library to mimic real Twitter data, we train several classical machine learning models (ie. classical classification algorithms, as well as ensemble methods)to identify bots from real users.

logistic-regression data-preprocessing decision-trees feature-engineering ensemble-model gradient-boosting-classifier random-forest-classifier gridsearchcv randomsearchcv
  • Updated Aug 29, 2024
  • Jupyter Notebook

vicaaa12 / advanced-machine-learning

Star 0

Advanced Machine Learning

exploratory-data-analysis ensemble-learning outlier-detection confusion-matrix data-preprocessing hyperparameter-tuning model-comparison correlation-matrix feature-importance gradientboostinclassifier xgboost-classifier randomforest-classification missing-value-imputation randomsearchcv adaboost-classifier oversampling-technique undersampling-technique bagging-classifier model-performance-comparison
  • Updated Feb 23, 2024
  • Jupyter Notebook

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