This project utilizes PCA, Hierarchical Clustering, K-Means, and KNN to cluster customers for targeted marketing. Kaggle dataset employed.
This project focuses on customer segmentation, a crucial task in marketing and business analysis. By employing various clustering techniques, including Principal Component Analysis (PCA), Hierarchical Clustering, K-Means, and K-Nearest Neighbors (KNN), we aim to group customers with similar characteristics and behaviors. These clusters can provide valuable insights for targeted marketing and improved customer service.
We utilized a Kaggle dataset containing customer-related data, such as demographics, purchase history, and behavioral attributes. The dataset (https://www.kaggle.com/dataset-link) is available on Kaggle and contains essential information for our clustering analysis.
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Principal Component Analysis (PCA): We applied PCA to reduce the dimensionality of the dataset while preserving as much variance as possible. This technique simplifies the data for subsequent clustering algorithms.
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Hierarchical Clustering: Hierarchical clustering is used to create a dendrogram that visualizes the hierarchical relationships between data points. This approach helps identify natural clusters within the data.
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K-Means Clustering: K-Means is a partitioning method that groups data points into K clusters based on similarity. We experimented with different values of K to find the optimal number of clusters.
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K-Nearest Neighbors (KNN): KNN is employed for customer classification based on similarity to other customers. This method allows for customer grouping and recommendation.
The repository is organized as follows:
CC_GENERAL/: Contains the dataset used for customer segmentation.Clustering_Project.ipynb/: Jupyter notebooks detailing the step-by-step process of data exploration, preprocessing, feature engineering, and clustering using PCA, Hierarchical Clustering, K-Means, and KNN.