Customer Segmentation using Machine Learning

Run This App

https://customer-segmentation-machine-learning-webapp-vjvaefg8lhaf7zn2.streamlit.app/

This repository contains the code and resources for a customer segmentation project using KMeans clustering algorithms. The project includes:

• Data preprocessing
• KMeans clustering
• Evaluation and visualization
• Deployment of a web application using Streamlit

Data Preprocessing

Algorithms Analysis

Deployment on Web

Prerequisites

• Python
• NumPy
• Pandas
• Scikit-learn
• Matplotlib
• Streamlit

Getting Started

1. Clone this repository:

git clone https://github.com/shubham5027/customer-segmentation.git

2. Navigate to the project directory:

cd customer-segmentation

3. Install the required libraries:

pip install -r requirements.txt

4. Run the Google Colab Customer_Segmentation.ipynb to perform data preprocessing, clustering, and evaluation.

5. To deploy the web application, run:

streamlit run _app.py

Data Preprocessing

The data preprocessing step involves:

• Loading the customer dataset
• Cleaning the data
• Feature engineering
• Scaling the data

KMeans Clustering

KMeans clustering algorithm is used to segment the customers into different groups based on their features. The algorithm is run with different values of k to select the optimal number of clusters.

Evaluation and Visualization

The evaluation step involves:

• Visualizing the clusters in 2D and 3D
• Calculating the Within-Cluster Sum of Squares (WCSS) for each value of k
• Selecting the optimal number of clusters based on the WCSS plot

References

• KMeans Clustering in Scikit-learn
• Streamlit Documentation

License

This project is licensed under the MIT License. See the LICENSE file for details.