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.

Machine learning fundamentals

A Deep Learning based Fashion Recommendation System using the ResNET50

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.

Machine Learning and Deep Learning Tutorial

Thesis project: topic categorization and sentiment analysis on twitter with Apache Spark

🚀 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.

Building a Machine Learning Library from scratch using Python3, based on SOTA library Scikit-learn

Free High-Quality Financial Data in Azure

A machine learning project predicting Titanic passenger survival using data preprocessing, feature engineering, and model optimization with Logistic Regression, Random Forest, and XGBoost.

My Python learning experience 📚🖥📳📴💻🖱✏

Machine Learning Projects in Python. Examples of popular machine learning algorithms with interactive Jupyter code explained

Custom classifiers to detect sexist language.

Improve access to healthcare services and reduce costs.

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.

A shoe👟 recommendation website.

This is the framework for supervised algorithms in mechine learning