[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126142-en":3,"doc-seo-126142-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126142,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Use of Machine Learning Application for Business - A Customer Segmentation Project Using K-Means Clustering","Customer segmentation is essential for understanding customer behaviour and tailoring marketing strategies. This project develops a customer segmentation model using K-Means clustering, an unsupervised machine learning algorithm, to group customers according to purchasing history. A fictitious e-commerce dataset of 5000 customers is collected with features including age, gender, annual income, and spending score. Data preprocessing handles missing values and standardizes inputs, after which K is selected via the elbow method and model quality is assessed using the Silhouette score.","Use of Machine Learning Application for Business  \nPerspective  \nDr. Rajeshri Pravin Shinkar  \nAssistant Prof.  \nDepartment of Computer Science,  \nSIES (Nerul) College of Arts, Science & Commerce Autonomous  \n[rajeshriyeola@gmail.com](rajeshriyeola@gmail.com)  \nAbstract : 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.  \nThe project begins with the collection of a fictitious ecommerce 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.  \nFeature 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.  \nThe 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.  \nThe 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  \nKeywords : machine learning, k means , clustering  \nI. INTRODUCTION  \nCustomer classification and segmentation are vital for businesses to understand their customers' behaviour, preferences, and needs. Effective customer segmentation allows businesses to tailor their marketing strategies, improve customer satisfaction, and optimize resource allocation. In this project, the focus is on using the K-Means clustering algorithm for customer classification based on their purchasing behaviour.  \nThe project aims to develop a customer segmentation model that can accurately categorize customers into distinct groups, enabling businesses to gain insights into their target audience and enhance their marketing efforts. By leveraging the power of machine learning and data analysis techniques,  \nthe project seeks to provide actionable information for businesses to make informed decisions and improve their customer relationships.  \nThe project begins by collecting a fictitious e-commerce dataset containing customer information and purchase history. The dataset includes features such as customer ID, age, gender, annual income, and spending score. These features are essential indicators of customer behaviour and play a crucial role in customer segmentation.  \nData preprocessing techniques are applied to the dataset to ensure its quality and suitability for analysis. Missing values, if any, are handled through imputation using appropriate methods such as mean or median substitution. Additionally, the data is standardized to eliminate any bias caused by different scales or units, enabling fair and accurate analysis.  \nFeature extraction is performed to select the relevant attributes that best capture the customers' purchasing behaviour. Features such as age, gender, annual income, and spending score are chosen as they provide valuable insights into customers' preferences, spending patterns, and purchasing power.  \nThe K-Means ","cbCaih0nKPemcba0","https://ap.wps.com/l/cbCaih0nKPemcba0","pdf",1977408,5,1,6,"English","en",105,"# Introduction\n## Project objective\n## Dataset and feature extraction\n## Data preprocessing\n## K-Means clustering approach\n## Choosing K with the elbow method\n## Model evaluation with Silhouette score","[{\"question\":\"What is the main goal of this project?\",\"answer\":\"The project aims to build a customer segmentation model that classifies customers into distinct groups based on purchasing behaviour using K-Means clustering.\"},{\"question\":\"What dataset and features are used for customer segmentation?\",\"answer\":\"A fictitious e-commerce dataset of 5000 customers is used, including customer ID, age, gender, annual income, and spending score.\"},{\"question\":\"How is the optimal number of clusters (K) selected?\",\"answer\":\"The elbow method is used by plotting the sum of squared distances for different K values and selecting the point where the curve shows diminishing returns.\"}]","Use of Machine Learning Application for Business - A Customer Segmentation Project Using K-Means Clustering | PDF",1785903382,15,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"use-of-machine-learning-application-for-business-a-customer-segmentation-project-using-k-means-clustering","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/use-of-machine-learning-application-for-business-a-customer-segmentation-project-using-k-means-clustering/126142/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What is the main goal of this project?","Question",{"text":77,"@type":78},"The project aims to build a customer segmentation model that classifies customers into distinct groups based on purchasing behaviour using K-Means clustering.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What dataset and features are used for customer segmentation?",{"text":82,"@type":78},"A fictitious e-commerce dataset of 5000 customers is used, including customer ID, age, gender, annual income, and spending score.",{"name":84,"@type":75,"acceptedAnswer":85},"How is the optimal number of clusters (K) selected?",{"text":86,"@type":78},"The elbow method is used by plotting the sum of squared distances for different K values and selecting the point where the curve shows diminishing returns.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":113,"slug":114},"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":20,"slug":138},19,"General","general"]