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rochitasundar / Predictive-maintenance-cost-minimization-using-ML-ReneWind

Star 10

The aim to decrease the maintenance cost of generators used in wind energy production machinery. This is achieved by building various classification models, accounting for class imbalance, and tuning on a user defined cost metric (function of true positives, false positives and false negatives predicted) & productionising the model using pipelines.

pipeline cross-validation classification-algorithims sklearn-metrics randomizedsearchcv train-validation-test smote-sampling random-under-sampling
  • Updated Jan 20, 2022
  • Jupyter Notebook

prakHr / NeuralNetworksAndFuzzyLogic

Star 5

[College Course] - Course: BITS F312 Neural Network and Fuzzy Logic

matlab logistic-regression ridge-regression keras-models kmeans-clustering multilayer-perceptron-network radial-basis-function extreme-learning-machine stochastic-gradient-descent maximum-likelihood-estimation maximum-a-posteriori-estimation iris-dataset cnn-architecture holdout likelihood-ratio-test kfold-cross-validation xor-neural-network stacked-autoencoder and-gate-implementation sklearn-metrics
  • Updated Nov 20, 2022
  • Jupyter Notebook

MYoussef885 / Gold_Price_Prediction

Star 2

The "Gold Price Prediction" project focuses on predicting the prices of gold using machine learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn (sklearn), Matplotlib, Seaborn, Random Forest Regressor, and others, this project provides a comprehensive solution for accurate price estimation.

python data-science machine-learning numpy pandas-dataframe seaborn matplotlib-pyplot google-colab-notebook sklearn-metrics
  • Updated Sep 18, 2023
  • Jupyter Notebook

MezbanS / Mercedes-Benz-Greener-Manufacturing

Star 1

Reduce the time that cars spend on the test bench. Work with a dataset representing different permutations of features in a Mercedes-Benz car to predict the time it takes to pass testing. Optimal algorithms will contribute to faster testing, resulting in lower carbon dioxide emissions without reducing Mercedes-Benz’s standards.

numpy sklearn pandas data-visualization pca-analysis xgboost dimensionality-reduction xgboost-regression r2-score sklearn-metrics sklearn-model
  • Updated Jan 18, 2023
  • HTML

shanuhalli / Assignment-Random-Forest

Star 1

Use Random Forest to prepare a model on fraud data. Treating those who have taxable income <= 30000 as "Risky" and others are "Good" and A cloth manufacturing company is interested to know about the segment or attributes causes high sale.

machine-learning counter pipeline random-forest feature-selection classification imbalanced-data smote adasyn imblearn sklearn-metrics smotetomek grid-search-cv standard-scaler assignment-15
  • Updated Feb 5, 2023
  • Jupyter Notebook

shanuhalli / Assignment-Neural-Networks

Star 1

Predict the Burned Area of Forest Fire with Neural Networks and Predicting Turbine Energy Yield (TEY) using Ambient Variables as Features.

machine-learning deep-learning neural-network tensorflow keras optimizer feature-selection outlier-detection confusion-matrix feature-engineering hyperparameter-tuning log-transformation label-encoder sklearn-metrics roc-auc-curve standard-scaler assignment-16
  • Updated Feb 8, 2023
  • Jupyter Notebook

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