Unlock the potential of agricultural production with innovative optimization techniques. Explore strategies, technologies, and practices to enhance crop yields, improve efficiency, and sustainably increase output. Revolutionize farming practices and cultivate a thriving agricultural ecosystem

A collection of essential machine learning algorithms implemented from scratch and with libraries. Ideal for students and beginners to understand core ML concepts through hands-on examples.

K-Means clustering algorithm implementation with OpenMP

This project uses K-means clustering to group countries based on socio-economic and health factors such as child mortality, exports, health spending, income, and GDP per capita. The workflow includes data preprocessing, clustering, and performance evaluation using silhouette scores.

Shopper Spectrum: Streamlit app for customer segmentation (RFM + KMeans) and product recommendations (collaborative filtering) using e-commerce data.

The U.S. Postal Service has been using machine learning and scanning technologies since 1999. Because its postal offices have to look at roughly half a billion pieces of mail every day, they have done extensive research and developed very efficient algorithms for reading and understanding addresses. And not only the post office, ATMs can recogni…

In this project, I have used several Python libraries to make a K-Nearest Neighbor classifier that is trained to predict whether a patient has breast cancer or not in general

Using k-fold cross validation to find the best classification model to predict employee attrition.