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francescopatane96 / Computer_aided_drug_discovery_kit

Star 17

This pipeline provides a way to perform pharmaceutical compounds virtual screening using similarity-based analysis, ligand-based and structure-based techniques. The pipeline contains a collections of modules to perform a variety of analysis.

machine-learning fingerprints drug-discovery drug-repurposing chemoinformatics padel descriptors cancer-research biotechnology chemical-descriptors randomforestclassifier computational-drug-discovery pharmacoinformatics padel-descriptors
  • Updated Aug 14, 2023
  • Jupyter Notebook

MattiaRaffa / RF-VDA-landslide-map

Star 13

Modello Random Forest per la creazione di una mappa di suscettibilità da frane superficiali // // Tesi di Laurea Magistrale in Scienze della Terra (Geologia Applicata) - Università degli Studi di Milano

python tree random-forest mapping random scikit-learn forest aspect gis geology scikitlearn-machine-learning landslide geologia geologic-mapping landslide-susceptibility-mapping susceptibility randomforestclassifier applied-geology oob-error geologia-applicata
  • Updated Apr 27, 2021
  • Python

emirhanai / Machine-Learning-Software-that-predicts-planets-based-on-their-distance-from-the-sun-number-of-sate

Star 13

Machine Learning Software that predicts planets based on their distance from the sun, number of satellites and various properties

machine-learning random-forest machine machine-learning-algorithms randomforest machine-learning-library machinelearning decision-trees decision-tree decision-tree-classifier decision-tree-algorithm machine-learning-models random-forest-regressor machine-learning-tutorials decision-tree-regression random-forest-classifier machine-learning-projects random-forest-regression randomforestregressor randomforestclassifier
  • Updated Aug 29, 2021
  • Python

FaezehAbedi2023 / Statistical-Analysis-in-Sensor-Data-Processing-with-Machine-Learning-Models

Star 4

This project develops an activity recognition model for a mobile fitness app using statistical analysis and machine learning. By processing smartphone sensor data, it extracts features to train models that accurately recognize user activities.

python numpy scikit-learn pandas matplotlib confusion-matrix histograms svm-classifier f1-score correlation-matrix decisiontreeclassifier accuracy-score randomforestclassifier smote-sampling precision-score recall-score smote-oversampler preprocessing-data
  • Updated Aug 6, 2024
  • Jupyter Notebook

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

Star 2

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

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