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MohamedMostafa010 / ExeRay

Star 46

ExeShield AI detects malicious Windows executables using ML. Analyzes entropy, imports, and metadata for rapid classification, aiding incident response. Built with Python and scikit-learn.

python machine-learning ai random-forest detection scikit-learn machine-learning-algorithms malware python3 executable artificial-intelligence xgboost machinelearning exe malicious random-forest-classifier gridsearchcv benign aimodel xgboost-classifier
  • Updated Aug 1, 2025
  • Python

saccofrancesco / deepshot

Star 44

DeepShot is a machine learning model designed to predict NBA game outcomes using advanced team statistics and rolling averages. It combines historical performance trends with contextual game data to deliver highly accurate win predictions (71%)

python open-source nba data-science machine-learning ai sklearn basketball pandas xgboost nba-dataset nba-data ewma sports-analytics predictive-model xgboost-classifier nba-predictions nicegui sports-ai ml-in-sport
  • Updated Jul 21, 2025
  • Jupyter Notebook

AliAmini93 / Fault-Detection-in-DC-microgrids

Star 38

Using 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.

neural-network random-forest svm cnn feature-selection fft dwt data-augmentation mutual-information linear-discriminant-analysis pearson-correlation grid-search-hyperparameters xgboost-classifier anova-analysis digsilent
  • Updated Aug 25, 2024
  • Jupyter Notebook

ShivamVadalia / Underwater-Waste-Detection-Using-YoloV8-And-Water-Quality-Assessment

Star 30

Neural 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.

python machine-learning deep-learning pytorch statistical-analysis object-detection denoising-images dark-channel-prior xgboost-classifier rule-based-classifier yolov8
  • Updated Jun 12, 2023
  • Jupyter Notebook

xxl4tomxu98 / autoencoder-feature-extraction

Star 11

Use auto encoder feature extraction to facilitate classification model prediction accuracy using gradient boosting models

gradient-boosting-machine convolutional-autoencoder sequence-to-sequence variational-autoencoders autoencoder-neural-network autoencoder-classification autoencoderscompression xgboost-classifier light-gradient-boosting-machine autoencoder-denoising autoencoder-latent-layer histgram-gradient-boosting
  • Updated Apr 14, 2023
  • Jupyter Notebook

edaaydinea / OP2-Prediction-of-the-Different-Progressive-Levels-of-Alzheimer-s-Disease-with-MRI-data

Star 10

This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD) with MRI data.

data-science machine-learning deep-learning tensorflow neural-networks logistic-regression keras-neural-networks chi-square-test random-forest-classifier xgboost-classifier lightgbm-classifier smote-oversampler anova-analysis catboost-classifier multi-layer-perceptron-classifier
  • Updated Nov 6, 2022
  • Jupyter Notebook

edaaydinea / OP1-Prediction-of-the-Different-Progressive-Levels-of-Alzheimer-s-Disease

Star 10

This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD).

data-science machine-learning deep-neural-networks tensorflow neural-networks logistic-regression keras-neural-networks chi-square-test random-forest-classifier alzheimer-disease-prediction xgboost-classifier anova-test lightgbm-classifier catboost-classifier multi-layer-perceptron-classifier
  • Updated Nov 6, 2022
  • Jupyter Notebook

tanishq-ctrl / House-price-prediction-and-visualization

Star 9

This repository contains code and data for analyzing real estate trends, predicting house prices, estimating time on the market, and building an interactive dashboard for visualization. It is structured to cater to data scientists, real estate analysts, and developers looking to understand property market dynamics.

xgboost lightgbm xgboost-algorithm catboost machinelearningalgorithms xgboost-classifier r2score rmse-score lightgbm-regressor lightgbm-classifier catboostregressor datavisualization-project catboost-classifier housepriceprediction dataanlaytics
  • Updated Jan 16, 2025
  • Jupyter Notebook

virajbhutada / telecom-customer-churn-prediction

Star 8

Predict 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.

data-science machine-learning neural-network python-library machine-learning-algorithms eda pandas data-analysis logistic-regression powerbi roc-curve decision-tree-classifier churn-prediction machine-learning-models random-forest-classifier classification-models customer-churn-prediction customer-attrition-analysis xgboost-classifier powerbi-desktop
  • Updated Oct 30, 2024
  • HTML

Soumilgit / Datathon_Team-DataP1ac3X.c0m

Sponsor
Star 8

A modular bank customer churn predictor ML project leveraging Groq API, Streamlit, Supabase, SciPy, Plotly and EmailJS, alongside libraries - NumPy, Pandas, Utils, OS, Base64, Re, Pillow & DateTime.

css python html scikit-learn jupyter-notebook fintech openai naive-bayes-classifier decision-tree-classifier svm-classifier smote emailjs random-forest-classifier coreml streamlit xgboost-classifier supabase pillow-library groq-api qwen3-32b
  • Updated Jul 30, 2025
  • Jupyter Notebook

rochitasundar / Customer-profiling-using-ML-EasyVisa

Star 7

The aim is to find an optimal ML model (Decision Tree, Random Forest, Bagging or Boosting Classifiers with Hyper-parameter Tuning) to predict visa statuses for work visa applicants to US. This will help decrease the time spent processing applications (currently increasing at a rate of >9% annually) while formulating suitable profile of candidate…

decision-tree-classifier gradient-boosting-classifier f1-score random-forest-classifier grid-search-hyperparameters precision-recall accuracy-metrics xgboost-classifier stacking-ensemble adaboostclassifier bagging-ensemble grid-search-cross-validation
  • Updated Jan 20, 2022
  • Jupyter Notebook

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