117 skills found · Page 1 of 4
saccofrancesco / DeepshotAI model predicting NBA game outcomes using advanced stats and trends
phunterlau / Kaggle HiggsMy winning solution for Kaggle Higgs Machine Learning Challenge (single classifier, xgboost)
MohamedMostafa010 / ExeRayExeRay AI detects malicious Windows executables using ML. Analyzes entropy, imports, and metadata for rapid classification, aiding incident response. Built with Python and scikit-learn.
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.
rajatsen91 / CCITClassifier Conditional Independence Test: A CI test that uses a binary classifier (XGBoost) for CI testing
jacobmontiel / AdaptiveXGBoostClassifierImplementation of the Adaptive XGBoost classifier for evolving data streams
jrothschild33 / Fudan DataMining2020 Spring Fudan University Data Mining Course HW by prof. Zhu Xuening. 复旦大学大数据学院2020年春季课程-数据挖掘（DATA620007）包含数据挖掘算法模型：Linear Regression Model、Logistic Regression Model、Linear Discriminant Analysis、K-Nearest Neighbour、Naive Bayes Classifier、Decision Tree Model、AdaBoost、Gradient Boosting Decision Tree(GBDT)、XGBoost、Random Forest Model、Support Vector Machine、Principal Component Analysis(PCA)
AliAmini93 / Fault Detection In DC MicrogridsUsing DIgSILENT, a smart-grid case study was designed for data collection, followed by feature extraction using FFT and DWT. Post-extraction, feature selection. CNN-based and extensive machine learning techniques were then applied for fault detection.
Soumilgit / XYZ Bank Customer Churn PredictorModular full-stack ML project leveraging Groq API, Streamlit, Supabase, JSON, SciPy, SciKit-Learn, Plotly & EmailJS, alongside libraries - NumPy, Pandas, Utils, OS, Base64, Re, Pillow & DateTime.
rohanmistry231 / Parkinsons Disease ClassificationA Python-based machine learning project for classifying Parkinson's disease using patient data and algorithms like XGBoost and Random Forest. Includes data preprocessing, feature analysis, and model evaluation with Scikit-learn and Pandas for accurate predictions.
ShivamVadalia / Underwater Waste Detection Using YoloV8 And Water Quality AssessmentNeural Ocean is a project that addresses the issue of growing underwater waste in oceans and seas. It offers three solutions: YoloV8 Algorithm-based underwater waste detection, a rule-based classifier for aquatic life habitat assessment, and a Machine Learning model for water classification as fit for drinking or irrigation or not fit.
MMBazel / Classifying Sales CallsTurning salesforce lead, oppty, & sales activities data => Sales predictions using pandas, Scikit-learn, SQLAlchemy, Redshift, XGBoost Classifier
echoCodeScript / Infant Cry Classification ML ModelThis repository contains CryMLClassifier, a machine learning model that classifies baby cries into five categories. It utilizes 193 features extracted from the cry audio data, achieving high accuracy with Random Forest and XGBoost algorithms. The repository includes a "features_extraction" folder for feature extraction code samples.
jonaac / Deep Xgboost Image ClassifierNo description available
amzn / Confident Sinkhorn AllocationPseudo-labeling for tabular data
DandiMahendris / Auto Insurance Fraud DetectionThis research goal is to build binary classifier model which are able to separate fraud transactions from non-fraud transactions.
ali-ghorbani-k / Credit Risk ManagementA binary classification model is developed to predict the probability of paying back a loan by an applicant. Customer previous loan journey was used to extract useful features using different strategies such as manual and automated feature engineering, and deep learning (CNN, RNN). Various machine learning algorithms such as Boosted algorithms (XGBoost, LightGBM, CatBoost) and Deep Neural Network are used to develop a binary classifier and their performances were compared.
virajbhutada / Telecom Customer Churn PredictionPredict and prevent customer churn in the telecom industry with our advanced analytics and Machine Learning project. Uncover key factors driving churn and gain valuable insights into customer behavior with interactive Power BI visualizations. Empower your decision-making process with data-driven strategies and improve customer retention.
mirekphd / Gbdt Algos Performance TestsA systematic CPU/GPU performance study of lightgbm and xgboost classifiers for different data shapes and hardware setups.
ashishrana1501 / Forest Fire PredictionAlgerian Forest Fire Prediction
