🛍️ Customer Segmentation using K-Means Clustering

This project is a customer segmentation analysis using K-means clustering. The goal is to divide customers into distinct groups based on their annual income and spending score to help businesses make targeted marketing decisions. This repo contain following

• Unsuervised Learning K-means Clustering in Detail
• Implementing k-means clustering using
  • Built from scratch
  • using scikit library
• Building a project with customer segmentation
• Streamit Lit App Link

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🧑‍💻 K-Means Clustering Algorithm

K-means clustering is an unsupervised machine learning algorithm used to partition data into K distinct clusters. The objective of the algorithm is to minimize the within-cluster variance, so the points within each cluster are as similar as possible. For detailed checkout here

🧑‍💻 Implementation

1. K-Means Clustering from Scratch We start by implementing K-means clustering from scratch to understand the internal workings of the algorithm here

2. Using Built-in Library (Scikit-Learn) To simplify the clustering process, we also use the built-in KMeans algorithm from Scikit-Learn here

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Mini Project - Customer Segmenation

In this project, we use K-means clustering to perform customer segmentation. The dataset contains 200 rows and 5 columns representing customer data, including:

• Annual Income (in thousands): Customer's annual income in thousands of dollars.
• Spending Score (1 to 100) :A score assigned to customers based on their spending behavior.
• Age : Customer’s age.
• Gender : Gender of the customer (optional for segmentation).
• Customer ID : Unique identifier for each customer.

📊 Steps Performed:

• Exploratory Data Analysis (EDA)
• Visualization of clusters
• K-means clustering implementation
• Model Saving for prediction

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🚀 Streamlit UI

We created a Streamlit app to provide an interactive user interface for predicting customer clusters. Users can input their annual income and spending score, and the app will display:

• The predicted cluster
• The cluster meaning (e.g., high spenders, low spenders)
• Visualizations of the cluster distribution
• Recommended actions based on the cluster

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🛠️ Technologies Used

• Python: Programming language for implementing machine learning algorithms.
• Scikit-Learn: Library for machine learning models (K-means clustering).
• Streamlit: Framework to create the interactive web app.
• Matplotlib: Library for visualizations.