I aim to automate playlist creation for Moosic, a startup known for manual curation, using Machine Learning, while addressing skepticism about the ability of audio features to capture playlist "mood."

Prediction of optimal number of clusters of the given iris data and it's visualisation.

This repository provides a Python implementation of K-Means clustering for segmenting retail store customers based on their purchase behavior. The algorithm groups customers into clusters using features such as Annual Income and Spending Score, enabling data-driven decision-making for marketing strategies.

ML Engineering, AI & Data Science: student at Lambda School. Prev B2C Growth & Storytelling via Motion Picture & Still Photography

I cleaned and scaled data for 124 menu items, applying both hierarchical and K-means clustering to categorize these items based on customer preferences. This analysis allowed me to optimize the menu structure effectively. By leveraging the insights gained from clustering, I adjusted the menu offerings, which led to increased customer satisfaction.