Description

This project delves into the intricate world of customer behavior, aiming to discern distinct customer segments founded on their credit card spending patterns. Employing a diverse array of unsupervised machine learning algorithms, such as K-Means, Hierarchical, and DBSCAN, the initiative identified and categorized unique customer groups. An in-depth Exploratory Data Analysis (EDA) laid the foundation, offering crucial insights into the dataset. To guarantee pristine data quality, various cleaning techniques were employed, transforming skewed distributions to align more with normal ones. Enhancing efficiency further, PCA (Principal Component Analysis) was incorporated for dimensionality reduction. This systematic approach culminated in a profound comprehension of underlying data patterns, paving the way for more tailored and effective marketing strategies.

Project Repository

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A work by Anuj Shah