128 skills found · Page 1 of 5
susanli2016 / Machine Learning With PythonPython code for common Machine Learning Algorithms
szilard / Benchm MlA minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
benedekrozemberczki / Awesome Gradient Boosting PapersA curated list of gradient boosting research papers with implementations.
Western-OC2-Lab / Intrusion Detection System Using Machine LearningCode for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
chasedehan / BoostARootaA fast xgboost feature selection algorithm
vlazovskiy / Route Optimizer Machine LearningRoute optimization solution which uses evolutionary algorithm with XGBoost model to optimize travel times.
TatevKaren / Data Science Popular AlgorithmsData Science algorithms and topics that you must know. (Newly Designed) Recommender Systems, Decision Trees, K-Means, LDA, RFM-Segmentation, XGBoost in Python, R, and Scala.
IntelPython / Scikit Learn Benchscikit-learn_bench benchmarks various implementations of machine learning algorithms across data analytics frameworks. It currently support the scikit-learn, DAAL4PY, cuML, and XGBoost frameworks for commonly used machine learning algorithms.
atif-hassan / FRUFSAn unsupervised feature selection technique using supervised algorithms such as XGBoost
jinlow / ForustA lightweight gradient boosted decision tree package.
ldv1 / LinXGBoostExtension of the awesome XGBoost to linear models at the leaves
OpenXAIProject / Automatic Stock TradingTrading Algorithm by XGBoost
Ekeany / XGBoost From ScratchThis repo contains a few tree based boosting algorithms implemented in python from scratch. This code relates to a medium.com article which I wrote explaining my journey to understanding how XGBoost works under the hood
harshilpatel1799 / IoT Network Intrusion Detection And Classification Using Explainable XAI Machine LearningThe continuing increase of Internet of Things (IoT) based networks have increased the need for Computer networks intrusion detection systems (IDSs). Over the last few years, IDSs for IoT networks have been increasing reliant on machine learning (ML) techniques, algorithms, and models as traditional cybersecurity approaches become less viable for IoT. IDSs that have developed and implemented using machine learning approaches are effective, and accurate in detecting networks attacks with high-performance capabilities. However, the acceptability and trust of these systems may have been hindered due to many of the ML implementations being ‘black boxes’ where human interpretability, transparency, explainability, and logic in prediction outputs is significantly unavailable. The UNSW-NB15 is an IoT-based network traffic data set with classifying normal activities and malicious attack behaviors. Using this dataset, three ML classifiers: Decision Trees, Multi-Layer Perceptrons, and XGBoost, were trained. The ML classifiers and corresponding algorithm for developing a network forensic system based on network flow identifiers and features that can track suspicious activities of botnets proved to be very high-performing based on model performance accuracies. Thereafter, established Explainable AI (XAI) techniques using Scikit-Learn, LIME, ELI5, and SHAP libraries allowed for visualizations of the decision-making frameworks for the three classifiers to increase explainability in classification prediction. The results determined XAI is both feasible and viable as cybersecurity experts and professionals have much to gain with the implementation of traditional ML systems paired with Explainable AI (XAI) techniques.
KSpiliop / Fraud DetectionTuning XGBoost hyper-parameters with Simulated Annealing
sohammanjrekar / Multiple Disease Prediction WebappDesigned web app employs the Streamlit Python library for frontend design and communicates with backend ML models to predict the probability of diseases. It's capable of predicting whether someone has Diabetes, Heart issues, Parkinson's, Liver conditions, Hepatitis, Jaundice, and more based on the provided symptoms, medical history, and results.
ojasphansekar / Zillow Home Value PredictionXGBoost, LightGBM, LSTM, Linear Regression, Exploratory Data Analysis
pankajrawat9075 / Fantasy Sports PredictionWe have used our skill of machine learning along with our passion for cricket to predict the performance of players in the upcoming matches using ML Algorithms like random-forest and XG Boost
sauravmishra1710 / Heart Failure Condition And Survival AnalysisPerform a survival analysis based on the time-to-event (death event) for the subjects. Compare machine learning models to assess the likelihood of a death by heart failure condition. This can be used to help hospitals in assessing the severity of patients with cardiovascular diseases and heart failure condition.
advikmaniar / ML Healthcare Web AppThis is a Machine Learning web app developed using Python and StreamLit. Uses algorithms like Logistic Regression, KNN, SVM, Random Forest, Gradient Boosting, and XGBoost to build powerful and accurate models to predict the status of the user (High Risk / Low Risk) with respect to Heart Attack and Breast Cancer.
