Analyzing and transforming a UK-based retail dataset (2010-2011) into a customer-centric format for customer segmentation using K-means clustering. Implementing a personalized recommendation system to enhance marketing strategies and boost sales.

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This code is a Recommendation system for a Retail Store achieved through Customer Segmentation

Smart Recommender System is a retail customer recommendation project that combines RFM Analysis and Collaborative Filtering to provide personalized product suggestions. The system includes customer segmentation, KMeans clustering, PCA visualization, and interactive Streamlit interface for exploring data and recommendations.

Conducting a comprehensive analysis and transformation of a UK-based retail dataset (2010-2011) into a customer-centric format to facilitate customer segmentation using various clustering algorithms. Implementing a personalized recommendation system aimed at enhancing marketing strategies and driving sales growth.

Retail Customer Segmentation & Recommendation System

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This project delves into the dynamics of online retail through a thorough analysis of transactional data. Leveraging Python, we aim to optimize marketing strategies and sales by segmenting customers based on their behavior using K-means clustering. Additionally, we intend to develop a recommendation system to further enhance sales.

This project focuses on leveraging data science techniques to derive actionable business insights from a comprehensive online retail transaction dataset. The core objective was to develop two interconnected and practical tools: a customer segmentation model and a product recommendation system.

This project demonstrates an end-to-end customer analytics workflow used in retail banking to segment customers based on financial behavior and convert insights into actionable business strategies.

Customer segmentation and product recommendation system for retail banking using real public bank marketing data.

Retail-Pulse is a customer segmentation and product recommendation system designed to analyze consumer behavior, segment customers, and provide personalized product recommendations.

Customer Segmentation & Product Recommendation System: A machine learning project utilizing KMeans clustering for RFM-based customer segmentation and Cosine Similarity for personalized product recommendations using online retail data.

Online Retail Customer Segmentation and Recommendation System using Python, implementing RFM Analysis and KMeans Clustering to analyze customer behavior and provide personalized product recommendations.

RetailIQ — A Smart Retail Analytics System built with React, Vite, and Tailwind CSS for real-time retail insights, customer segmentation, sales forecasting, and product recommendations.

🛍️ Customer Segmentation & Product Recommendation System This project applies Machine Learning to analyze retail transactions, segment customers into groups, and provide personalized product recommendations based on their shopping behavior.

This project is a customer segmentation and recommendation system for an online retail store. The dataset used in this project contains transaction data for an online retail store.

E-commerce recommendation system using real transactional retail data, combining RFM customer segmentation with popularity-based, item-to-item, and user-based collaborative filtering methods.

Built a personalized recommendation system for retail using RFM customer segmentation and association rules, with an interactive web interface for product browsing and real-time suggestions.

Customer Segmentation and Product Recommendation System using Python and machine learning. The project applies RFM analysis and K-Means clustering on online retail transaction data to identify customer segments and generate product recommendations. Built as an industry-oriented BCA academic project with Power BI insights.

A project that integrates customer segmentation using K-Means clustering with a recommendation system using Neural Collaborative Filtering (NCF) to enhance personalized customer experiences. The project is built using Python and applied to the Online Retail II dataset.

Conducting a comprehensive analysis and transformation of a UK-based retail dataset (2010-2011) into a customer-centric format to facilitate customer segmentation using various clustering algorithms. Implementing a personalized recommendation system aimed at enhancing marketing strategies and driving sales growth.

Shopper Spectrum: Customer Segmentation & Product Recommendation System This project focuses on analyzing real-world e-commerce transaction data to uncover valuable insights into customer behavior and purchasing patterns. It transforms raw retail data into actionable business intelligence using data analytics and machine learning techniques.

Deep dive into online retail by analyzing a 2010-2011 transactional dataset from a UK-based retailer. We aim to amplify the efficiency of marketing strategies and boost sales through customer segmentation via K-means clustering. Furthermore, we develop a recommendation system that will suggest top-selling products to customers within each segment.

This repository presents a Data Science project developed as part of a 6-month Bootcamp in the field. The project utilizes retail data to perform data cleaning and preprocessing, exploratory data analysis (EDA), RFM analysis, and training of three models: Customer Segmentation, Recommendation System, and Sales Prediction

Customer churn or customer attrition occurs when certain customers are no longer loyal to a firm. In retail businesses, the event of churn is said to occur, if a customer's transactions terminates after a certain duration. High churn rates incur humungous losses for the businesses as it is observed that acquiring new buyers is costlier than retaining the current customer base. Hence, for calculating customer churn of companies, they should be able to monitor churn rates. These churn rates give an organization various factors to be considered to determine their customer retention success rates and identify strategies for improvement. Customer churn is predicted using Pareto/NBD model. Once the customers who are likely to churn are predicted, they need to be differentiated based on their previous purchasing history. Natural Language Processing is used to model product categorization. Semi- supervised learning does customer segmentation. This consists of assigning a score by RFM model and segmenting using k-means clustering. The prediction of clusters is then done using algorithms like logistic regression, SVM and SGD classifier. These methods are collectively used to build a suitable recommendation system, which is targeted to make the churn customers who were valuable to the company.