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Python Package for Empirical Statistical Downscaling. pyESD is under active development and all colaborators are welcomed. The purpose of the package is to downscale any climate variables e.g. precipitation and temperature using predictors from reanalysis datasets (eg. ERA5) to point scale. pyESD adopts many ML and AL as the transfer function.
This is a repository to learn and get more computer vision skills, make robotics projects integrating the computer vision as a perception tool and create a lot of awesome advanced controllers for the robots of the future.
This repo attempts to utilise two powerful ensemble models, Random forest and Gradient Boosting to Predict the failure patterns of wind energy machinery
We use our customer geolocation data to perform a clustering algorithm to get several clusters in which the member data of each cluster are closest to each other using KMeans and Constrained-KMeans Algorithms.
The House Price Prediction System is a comprehensive project aimed at predicting housing prices based on various attributes using advanced data analysis and machine learning techniques.