Published Paper Details:

The project "Customer Segmentation Using Machine Learning" is designed to address the crucial need for businesses to effectively understand and categorize their customer base. Customer segmentation, a fundamental aspect of modern marketing and business strategy, involves classifying customers into distinct groups based on shared characteristics and behaviors. This project leverages the power of machine learning, specifically the K-means clustering algorithm, to achieve this goal. In a rapidly evolving market, maintaining a diverse customer base is a challenging task. To overcome this complexity, businesses must focus on customer segmentation as it forms the cornerstone of informed decision-making. This project employs K-means clustering, an unsupervised machine learning algorithm, to categorize customers based on attributes such as age, annual income, spending habits, and more. Each resulting cluster represents a unique group of individuals with similar characteristics. The Mall Customers dataset serves as the foundation for this project, containing critical customer information. Using this dataset, our algorithm constructs a model that effectively clusters customers into segments. The implementation encompasses data gathering, preprocessing, feature extraction, K-means algorithm application, clustering, visualization, and suggested market strategies. The project concludes with the identification of the optimal number of clusters using the Elbow Method and presents visualizations of the clusters. By adopting this approach, businesses can make data-driven decisions, personalize marketing efforts, and enhance customer satisfaction. Customer segmentation through machine learning promises to revolutionize how businesses engage with their customer base, ensuring relevance and competitiveness in the dynamic business landscape.