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In this project, a RFM model is implemented to relate to customers in each segment. Assessed the Data Quality, performed EDA using Python and created Dashboard using Tableau.
This notebook provides some skills to perform financial analysis on economical data.
Customer Personality Analysis Using Clustering
Customer Segmentation Anaylsis
This project shows how to perform customers segmentation using Machine Learning algorithms. Three techniques will be presented and compared: KMeans, Agglomerative Clustering ,Affinity Propagation and DBSCAN.
A Streamlit App for Customer Segmentation Project using Kmeans Clustering (Best Choice)
RFM (Recency, Frequency, Monetary) analysis is a proven marketing model for behaviour based customer segmentation. It groups customers based on their transaction history in other terms– how recently (R), how often (F) and how much (M) did they buy.
This project focus on customer analysis and segmentation. Which help to generate specific marketing strategies targeting different groups. RFM Analysis, Cohort Analysis, and K-means Clusters were conducted on a UK-based online retail transaction dataset with 1,067,371 rows of records hosted on the UCI Machine Learning Repository.
This repository contains project materials for the Spring 2024 STAT 208 class, specifically for Team 8. All materials are the property of Team 8, University of California, Riverside, A. Gary Anderson School of Management. Thank you for viewing our repository.
Customer Segmentation using Clustering (Machine Learning)
Analyzing retail sales data to craft targeted marketing, elevate customer experiences, and forecast future sales.
The goal of the project is to group consumers into clusters using the elbow approach. The project also includes scatter plots to show the relationships between the variables and dataset's columns.
Can we use association mining and machine learning to understand groceries purchase? Can we predict products that a user will buy again, try for the first time or add to cart next during a session? Can we segment our customer base into several cohorts based on their preferred products and purchase behaviour?
Analyzing a dataset containing data on various customers' annual spending amounts of diverse product categories for internal structure. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.
Customer segmentation using Python and PowerBI
Data Science portfolio with projects I worked on for self-learning purpose.
Supply chain analytics is a valuable part of data-driven decision-making in various industries such as manufacturing, retail, healthcare, and logistics. It is the process of collecting, analyzing and interpreting data related to the movement of products and services from suppliers to customers.
A dataset of Customer Profile going into a Mall Reference: https://www.kaggle.com/vjchoudhary7/customer-segmentation-tutorial-in-python
Customer Segmentaton using RFM analysis
This project analyzes customer behavior in online retail using cohort analysis, **Recency, Frequency, Monetary (RFM)** metrics, and K-means clustering to segment customers. It identifies key groups like Best, At-Risk, and Average Customers, offering strategies to enhance engagement and drive loyalty.
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