Background: In today’s dynamic e-commerce landscape, harnessing the power ofmachine learning is critical for understanding customer behavior and driving business growth. Our research focuses on the use of various machine learning techniques,such as RFM analysis for customer segmentation, K-means clustering for targetedmarketing, and recommendation systems for personalized product suggestions. Weaim to improve clustering accuracy and overall e-commerce efficiency by leveragingoutlier detection algorithms such as isolation forest and validation metrics such assilhouette score. We strive to provide comprehensive insights and solutions for thee-commerce industry’s evolving needs using Python-based tools such as NumPy, Pandas, and scikit-learn, as well as collaborative platforms such as Jupyter Notebookand GitHub.

Objectives: The project examined customer behavior using historical e-commercedata, with a focus on gift products. Trends were identified to guide decision-makingby analyzing previous transactions and engagement metrics. Advanced clustering algorithms, such as RFM analysis, were used to effectively segment customers, allowingfor targeted marketing strategies. In addition, a personalized recommendation system was created to boost customer engagement and loyalty. Segmentation strategieswere optimised in order to identify high-value customers and align marketing effortswith peak engagement periods, thereby improving overall satisfaction and conversionrates.

Method: To gain a thorough understanding of customer behaviors and preferences, the researchers selected a variety of transactional datasets from e-commerceplatforms. After preprocessing steps such as handling missing values and encodingcategorical variables, they used machine learning algorithms to create recommendation systems and segmentation methods, each with its own configuration. Separatedatasets were trained, tested, and generalized to determine adaptability and robustness. Performance evaluation and visualization techniques provided informationabout model effectiveness. Model optimization focused on identifying performancegaps, whereas dataset analysis highlighted the impact of dataset characteristics onmodel performance and generalization capabilities. Ultimately, their goal was to improve customer segmentation, provide insightful historical data analysis, accuratelyassign clusters, and provide personalized product recommendations for e-commerce.

Results: The cluster analysis results were visually represented using radar chartsand histograms. Radar charts represented the characteristics of each cluster, withaxes representing different features and centroid values displaying customer behaviorwithin each group. Histograms showed feature distributions within clusters, allowingfor comparisons across groups and emphasizing differences in spread and central tendency. These visualizations provided valuable insights into cluster characteristics,allowing for more informed decisions about personalized marketing strategies andproduct recommendations.

Conclusions: In conclusion we demonstrated the effectiveness of machine learning inrefining client segmentation and providing specific recommendations in e-commerce.Transactional data analysis revealed separate consumer segments, each with theirown preferences. A recommendation system was developed using clustering algorithms, resulting in greater conversion rates, average order values, and customersatisfaction. A/B testing and consumer feedback verified the approach’s efficiencyand user approval.