Customer Segmentation App 🧑‍🤝‍🧑|👨‍👨‍👦|👨‍👩‍👧‍👦

About

Welcome to our Customer Segmentation App, a dynamic tool developed utilizing Streamlit, the renowned open-source Python framework. This application facilitates the detailed analysis and segmentation of customer data, allowing businesses to fine-tune their strategies and grasp the nuances of various market segments more effectively.

Live Link

Experience the live version hosted on Streamlit

Explore the Customer Segmentation App

Project Specifications

Our project is crafted using the following reliable libraries and frameworks:

• Visualization Tools: matplotlib, plotly, seaborn, squarify
• Framework: streamlit
• Additional Utilities: import-ipynb, utils

Video Demonstration

Witness the app in action through our video demonstration!

CustomerSegmentation_Streamlit.mp4

Important Information

Streamlit Application

Dive into the world of customer segmentation with our Streamlit application, which employs RFM analysis and the KMeans algorithm for meticulous segmentation.

Highlight Features

Explore the comprehensive sections of our application:

• Business Understanding: Gain insights into the business objective and learn the perks of customer segmentation.
• Data Understanding: Upload fresh data or opt for a sample file to delve into data exploration.
• Data Preparation: Use handy tools to cleanse the data and delve into exploratory data analysis with our visualization tools.
• Modeling and Evaluation: Engage with the RFM analysis and KMeans algorithm to identify the best clustering strategy and analyze the results through vivid visualizations.
• Predict: Input new data and get predictions on the potential cluster for new customers while having the facility to download the results.
• Feedback: Share your valuable feedback for the continual enhancement of the application.

Libraries in Use

Leveraging the power of the following libraries for a seamless user experience:

• Data Handling: pandas
• Visualization: matplotlib, seaborn, plotly.express, squarify
• Machine Learning: scikit-learn (KMeans)
• Framework: streamlit
• Others: os, datetime, pickle, base64

Guide to Users

Navigate through the app effortlessly:

• Start: Choose an option from the sidebar menu.
• Journey: Follow the structured path from understanding the business perspective to predicting the clusters for new data inputs.

Community Contribution

Your insights can shape the future of this app. Contribute through the feedback section.

Thank You!

Thank you. I hope you liked the project. If you really did then don't forget to give a star⭐ to this repo. It would mean a lot.