Use of Machine Learning Application for Business Perspective
Abstract
Customer segmentation plays a crucial role in understanding customer behaviour and tailoring marketing strategies. This project focuses on using K-Means clustering, a popular unsupervised machine learning algorithm, for customer classification based on their purchasing behaviour. The objective is to develop a customer segmentation model that can effectively group customers into distinct clusters to facilitate targeted marketing efforts.
The project begins with the collection of a fictitious e-commerce dataset consisting of 5000 customers with their purchase history. The dataset includes features such as customer ID, age, gender, annual income, and spending score. Data preprocessing techniques are applied to handle missing values and standardize the data, ensuring accurate and meaningful analysis.
Feature extraction involves selecting relevant features from the dataset, including age, gender, annual income, and spending score. These features provide valuable insights into customer behaviour and serve as the basis for customer segmentation.
The K-Means clustering algorithm is employed to classify customers into distinct clusters based on their purchasing behavior. The algorithm partitions the customers into K clusters by minimizing the sum of squared distances between the customers and their respective cluster centers. The optimal value of K is determined using the elbow method, a visual technique that identifies the point of maximum curvature in the sum of squared distances plot.
The effectiveness of the K-Means clustering model is evaluated using the Silhouette score. This score measures how well each customer fits into its assigned cluster, with values ranging from -1 to 1. A higher Silhouette score indicates better cluster cohesion and separation
References
[2 ] Expert Systems with Applications, vol. 100, Feb. 2018, "RetailBusiness Analytics: Customer Visit Segmentation Using MarketBasket Data."
[3] Tushar Kansal; Suraj Bahuguna; Vishal Singh; Tanupriya Choudhury, “Customer Segmentation using K-means Clustering,” IEEE, Jul. 2019.
[4] "CUSTOMER SEGMENTATION USING MACHINE LEARNING," IJCRT, AMAN BANDUNI and ILAVENDHAN A, vol. 05, 2018.
[5] K. Maheswari, “Finding Best Possible Number of Clusters using KMeans Algorithm,” International Journal of Engineering and Advanced Technology (IJEAT), vol. 9, no. 1S4, Dec. 2019.
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