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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.
🚀 Complete ML Project: Salary Prediction using Linear Regression & Streamlit. 95.6% accuracy, interactive web interface, clean dataset, pre-trained model. Perfect for learning ML, web development, and practical HR applications.
🫀 A machine learning project using logistic regression to predict heart disease risk from clinical data. Built with Python, scikit-learn, and Jupyter notebooks. Achieves 85%+ accuracy on 303-patient dataset with 13 medical features. Complete ML pipeline from data exploration to model evaluation.
A machine learning project predicting Titanic passenger survival using data preprocessing, feature engineering, and model optimization with Logistic Regression, Random Forest, and XGBoost.
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.