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AliAmini93 / Fault-Detection-in-DC-microgrids

Star 35

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 20

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 / 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

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

Star 9

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

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

grknc / Customer-Churn-Analyzer-with-ML

Star 5

Telco Churn Analysis and Modeling is a comprehensive project focused on understanding and predicting customer churn in the telecommunications industry. Utilizing advanced data analysis and machine learning techniques, this project aims to provide insights into customer behavior and help develop effective strategies for customer

prediction xgboost predictive-modeling random-forest-classifier customer-churn-prediction customer-churn xgboost-classifier lightgbm-classifier customer-churn-prediction-with-machine-learning
  • Updated Jan 23, 2024
  • Jupyter Notebook

emykes / Flu_Vaccination_ML

Star 5

The aim of this study is to predict how likely individuals are to receive their H1N1 flu vaccine. We believe the prediction outputs (model and analysis) of this study will give public health professionals and policy makers, as an end user, a clear understanding of factors associated with low vaccination rates. This in turn, enables end users to …

machine-learning random-forest logistic-regression decision-tree-classifier gradient-boosting knearest-neighbor-classifier xgboost-classifier
  • Updated Feb 3, 2022
  • Jupyter Notebook

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