Using our knowledge of the imbalanced-learn and scikit-learn libraries, we evaluated three machine learning models by using resampling to determine which is better at predicting credit risk. First, we used the oversampling RandomOverSampler and SMOTE algorithms, and then we used the undersampling ...
In this work, in situ weather data and module temperature are used to train different machine learning algorithms and predict the module's back temperature, and then the power produced by the PV module. A ten-days dataset was utilized for the training, cross-validation, and testing of differen...
We are implementing various machine learning algorithms for building a predictive model for houses. We have considered housing data of 2000 properties. In this paper, We will be comparing the algorithms on the basis of parameters such as MAE, RMSE, MSE, accuracy.P. Ambalkar...
Machine learningDownscaling techniqueThis study aimed to develop and assess the feasibility of different machine learning algorithms for predicting ore production in open-pit mines based on a truck-haulage system with the support of the Internet of Things (IoT). Six machine learning algorithms, namely...
Machine Learning Models: We have tried boosted algorithms (XGBoost, LightGBM, Catboost) and fully connected neural network (FCNN) in this project. There are some technical differences in the application of different algorithms that needs to noticed: ...
An attempt has been made to predict the order lead time or delivery time for a restaurant using several features. Different supervised state-of-the-art regression machine learning algorithms were used and their accuracies were compared on the given dataset. 展开 ...
(Q2) of 0.787. Combining the analysis of 52 elements and the stable carbon isotope ratio with machine learning algorithms enables effective tracing and origin prediction of beef from different regions. Key factors influencing beef origin were identified as Fe, Cs, As, δ13C, Co, V, Sc, Rb,...
We propose sliding window-based data pre-processing techniques and various machine learning algorithms for classifying types of faults. The performance of the proposed scheme and other machine learning approaches are compared based on specificity, precision, recall, accuracy, F1-score, and AUC-ROC ...
The dataset for the shear capacity is right-skewed which is not suitable for machine/deep learning model and hence the input dataset was normalized through a standard scalar function available in one of the python libraries so that various machine learning algorithms can be implemented. The dataset...
The repository is a collection of open-source implementations of a variety of algorithms implemented in C and licensed underGPLv3 License. The algorithms span a variety of topics from computer science, mathematics and statistics, data science, machine learning, engineering, etc.. The implementations ...