Customer Segmentation Analysis

Abhijeet Parashar

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Data Scientist

ML Engineer

Data Analyst

Jupyter Notebook

pandas

scikit-learn

• Utilized a dataset of customer transactions, focusing on key features such as purchase behavior, demographics, and transaction frequency to group customers into meaningful segments for targeted marketing strategies and further behavioral analysis.
• Conducted data cleansing, preprocessing, exploratory data analysis, and feature engineering, followed by applying K-Means and Hierarchical Clustering algorithms to identify the most optimal customer groups, resulting in 3 distinct clusters for targeted marketing.
• K-Means clustering achieved a higher silhouette score (0.714) and lower Davies-Bouldin Index (0.706), indicating more distinct and well-separated clusters compared to Hierarchical Clustering, thus demonstrating superior clustering performance.
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Posted Sep 12, 2024

Segmented customers into 3 clusters using K-Means, outperforming Hierarchical Clustering.

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Data Scientist

ML Engineer

Data Analyst

Jupyter Notebook

pandas

scikit-learn

Abhijeet Parashar

Pro Data Scientist: Python, ML, Visualization Expert


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