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."

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.

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

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.