Summary
Built K Means Clustering, Hierarchical Clustering (AgglomerativeClustering) models
Performed Exploratory Data Analysis (EDA), Quantitative Data Analysis, Data Cleaning, Detect Missing Values and Outliers, Outliers removal with IQR and Zscore, Data Pre-Processing, Cluster Analysis, Built Models
Used tools are pandas, numpy, sklearn, scipy, matplotlib, seaborn, yellowbrikcs.
Performed Cluster Analysis with Hopkins, Elbow Method, Silhouette Score, Dendogram.
Customer Segmentation with Cluster Analysis: A Dive into Unsupervised Learning
In today’s data-driven world, understanding customer segments can provide businesses with critical insights to tailor marketing strategies and improve customer satisfaction. For this project, I applied unsupervised learning techniques to perform customer segmentation, utilizing K-Means Clustering and Hierarchical Clustering (AgglomerativeClustering) models.
Here’s an overview of the project steps:
Data Exploration and Preparation
Conducted Exploratory Data Analysis (EDA) and Quantitative Data Analysis to understand customer patterns.
Cleaned the data, addressed missing values, and detected outliers, which were then handled using IQR and Z-score techniques.
Data Pre-Processing and Clustering
Performed data pre-processing to optimize the dataset for clustering, applying Hopkins for clustering tendency assessment and using the Elbow Method, Silhouette Score, and Dendrogram for optimal cluster selection.
Model Building and Cluster Analysis
Built models using K-Means and Hierarchical Clustering, analyzing clusters to identify unique customer groups based on purchasing behavior.
Tools Used
Employed a range of tools, including pandas, numpy, sklearn, scipy, matplotlib, seaborn, and yellowbricks, for robust analysis and visualization.
Through this project, I created clear customer segments, offering valuable insights into unique customer profiles. Cluster analysis like this supports businesses in building targeted marketing strategies and better understanding customer needs, highlighting the impact of unsupervised learning in driving data-driven decisions.
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