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.
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May 23, 2021 - Jupyter Notebook
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 shows how to perform customers segmentation using Machine Learning algorithms. Three techniques will be presented and compared: KMeans, Agglomerative Clustering ,Affinity Propagation and DBSCAN.
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.
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.
Customer Segmentation using Clustering (Machine Learning)
Data Scientist Bootcamp capstone project
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)
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.
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
It is highly related to the Customer Segmentation problem, so with RFM Analysis itself as well
Bank Campaign Optimization: Targeting Term Deposit Customers
SegmentWise: Unveiling Customer Insights for Exploratory Data Analysis (EDA) and Customer Segmentation
Data Analytics virtual internship programme by KPMG AU on Forage.
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.
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