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All Journal International Journal Of Science, Technology & Management (IJSTM)
Mburu, Lucy
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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Application Of K-Means Clustering For Customer Segmentation In Grocery Stores In Kenya Omol, Edwin; Onyangor, Dorcas; Mburu, Lucy; Abuonji, Paul
International Journal of Science, Technology & Management Vol. 5 No. 1 (2024): January 2024
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v5i1.1024

Abstract

The retail industry, particularly in the context of grocery stores, plays a vital role in meeting consumers' daily needs. To optimize marketing strategies and enhance customer satisfaction, understanding customer behavior and preferences is crucial. Customer segmentation, a powerful market research technique, enables businesses to group customers with shared characteristics into distinct segments, allowing targeted and personalized approaches. This article explores the application of the K-means clustering algorithm for customer segmentation in grocery stores within the unique context of Kenya. By leveraging transactional and demographic data from diverse grocery stores across Kenya, the study aims to identify homogeneous customer groups with similar purchasing behaviors and preferences. The data collection process involved obtaining consent from store owners and ensuring data privacy and security. Following data preprocessing, K-means clustering was applied, and various validation techniques were utilized to determine the optimal number of clusters. The results yielded valuable insights into customer segments, aiding the identification of key customer groups and their distinct preferences.
Anomaly Detection In IoT Sensor Data Using Machine Learning Techniques For Predictive Maintenance In Smart Grids Omol, Edwin; Mburu, Lucy; Onyango, Dorcas
International Journal of Science, Technology & Management Vol. 5 No. 1 (2024): January 2024
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v5i1.1028

Abstract

The proliferation of Internet of Things (IoT) devices in the smart grid infrastructure has enabled the generation of massive amounts of sensor data. This wealth of data presents an opportunity to implement sophisticated data analytics techniques for predictive maintenance in smart grids. Anomaly detection using machine learning algorithms has emerged as a promising approach to identifying irregular patterns and deviations in sensor data, leading to proactive maintenance strategies. This article explores theapplication of machine learning techniques for anomaly detection in IoT sensor data to enable predictive maintenance in smart grids. We delve into various machine learning algorithms, including Isolation Forest, One-Class SVM, Autoencoders, and Random Forest, assessing their capabilities in identifying anomalies in large-scale data streams. The study also reviews the Performance Evaluation and Model Selection techniques for Anomaly Detection in IoT Sensor Data, possible integration and deployment challenges, and critique of the few selected studies. Explicitly, this scholarly inquiry questions the profound significance of predictive maintenance within the context of Smart Grids. It elucidates distinct categories of anomalies inherent within IoT Sensor Data.Furthermore, the article expounds upon various classes of Machine Learning Algorithms while also clarifying the criteria employed for their selection. Notably, the study probes the potential hindrances that could emerge during the deployment and integration of Machine Learning Techniques specifically aimed at Anomaly Detection in IoT Sensor Data. In addition, the research sheds light on the aspects that might have been inadvertently overlooked within the existing corpus of literature.
Co-Authors Abuonji, Paul Omol, Edwin Onyango, Dorcas Onyangor, Dorcas