11 skills found
Leci37 / TensorFlow Stocks Prediction Machine Learning RealTimePredict operation stocks points (buy-sell) with past technical patterns, and powerful machine-learning libraries such as: Sklearn.RandomForest , Sklearn.GradientBoosting, XGBoost, Google TensorFlow and Google TensorFlow LSTM..Real time Twitter:
Evovest / EvoTrees.jlBoosted trees in Julia
rohitinu6 / NeoLungLung Cancer Prediction using Machine Learning Algorithms
MegrezZhu / GradientBoostingDecisionTreegradient boosting decision tree
ndtands / TabularDataProblemClassification in TabularDataset
opennlp / Large Scale Text ClassificationLarge Scale benchmarking of state of the art text vectorizers
pavankethavath / Microsoft Classifying Cybersecurity Incidents With MLA 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
elcarpins / Li IonSOCalgorithm GradientBoostingThe available capacity of a battery, called the state of charge, is a fundamental characteristic for energy storage applications or electric vehicles. In order to model the state of charge of a lithium-ion battery using data-driven techniques, complex algorithms should be used so the dynamic behaviours of the battery are captured. An ensemble decision tree called gradient boosting tree was fitted with the information extracted from different experiments based on dynamic and constant discharge profiles at different temperatures and implemented in a laboratory. Despite the capacity and and the battery’s state of health being below its theoretical life expectancy, results of the model showed an adequate behaviour. The model’s performance was validated using previously unused data.
hayesall / MaliciousPortableExecutableDetectionDetermining whether a portable executable file (.exe) is malicious or benign with comparative results for multiple ML algorithms: AdaBoost, DT, GNB, GradientBoosting, KNN, RF.
matteochieregato / GradientboostingCovid19Repository for the paper "A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data"
mafrs47 / Lung Cancer PredictionThis project predicts lung cancer risks using machine learning models like Random Forest, Logistic Regression, and SVM. It analyzes patient data with features such as age, smoking habits, and symptoms. Data preprocessing, visualization, and performance evaluation ensure accurate predictions for early diagnosis.
