ayda-darvishan Goto Github PK

ensemble-learning

flow-trace-back-algorithm

This algorithm is designed to sort out orders from network flow results.

ml-regression-model--aws

We analyze the data of ReCell case study, build a linear regression model to predict the price of a used phone, measure the performance of the model, check linear regression assumptions, and identify factors that significantly influence the target variable.

network-distances--gmap-api

Using GMAP API to calculate supply-demand distances and durations for a real-world facility location problem.

tuning-ml-classifiers

The project includes building seven different machine learning classifiers (including Linear Regression, Decision Tree, Bagging, Random Forest, Gradient Boost, AdaBoost, and XGBoost) using Original, OverSampled, and Undersampled data of ReneWind case study, tuning hyperparameters of the models, performance comparisons, and pipeline development for productionizing the final model.

unsupervised-learning--data-clustering

We apply and compare K-means and Hierarchical Clustering methods on the data set of the Trade & Ahead case study.