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.

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 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.

Customer Segmentation Anaylsis

Customer Personality Analysis Using Clustering

Conducted customer sales segmentation and affinity analysis on chip sales to identify groups to target for advertisements and promotions.

Data Scientist Bootcamp capstone project

Customer Segmentation using Clustering (Machine Learning)

Data Science portfolio with projects I worked on for self-learning purpose.

A dataset of Customer Profile going into a Mall Reference: https://www.kaggle.com/vjchoudhary7/customer-segmentation-tutorial-in-python

A Streamlit App for Customer Segmentation Project using Kmeans Clustering (Best Choice)

In this project, a RFM model is implemented to relate to customers in each segment. Assessed the Data Quality, performed EDA using Python .

The idea of this challenge was to cluster customers based on a given dataset to align the marketing efforts. Four customer groups were characterized based on income, buying power, credit score, and other criteria

SegmentWise: Unveiling Customer Insights for Exploratory Data Analysis (EDA) and Customer Segmentation

Data Analytics virtual internship programme by KPMG AU on Forage.

It is highly related to the Customer Segmentation problem, so with RFM Analysis itself as well

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.

Analyzed e-commerce data, applied K-means clustering for customer segmentation, and built a recommendation system, enhancing marketing and boosting 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.