https://github.com/abhi227070/customer-segmentation-using-kmeans
Utilizing KMeans clustering, this project segments customers for targeted marketing and analysis. Developed on Google Colab, it imports datasets from Kaggle, performs data analysis, preprocessing, and model building, providing actionable insights for businesses.
https://github.com/abhi227070/customer-segmentation-using-kmeans
clustering kmeans-clustering machine-learning machine-learning-algorithms machinelearning python3 unsupervised-clustering unsupervised-learning unsupervised-machine-learning
Last synced: 4 months ago
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Utilizing KMeans clustering, this project segments customers for targeted marketing and analysis. Developed on Google Colab, it imports datasets from Kaggle, performs data analysis, preprocessing, and model building, providing actionable insights for businesses.
- Host: GitHub
- URL: https://github.com/abhi227070/customer-segmentation-using-kmeans
- Owner: abhi227070
- Created: 2023-08-14T08:05:33.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-26T16:15:01.000Z (about 1 year ago)
- Last Synced: 2024-03-26T17:32:57.424Z (about 1 year ago)
- Topics: clustering, kmeans-clustering, machine-learning, machine-learning-algorithms, machinelearning, python3, unsupervised-clustering, unsupervised-learning, unsupervised-machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 82 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Customer Segmentation Using KMeans
This project implements KMeans clustering to segment customers based on their annual income and spending score. The goal is to identify different types of customers visiting a place and target specific customer groups for business analysis and marketing strategies.
## Table of Contents
- [Introduction](#introduction)
- [Usage](#usage)
- [Code Overview](#code-overview)
- [Conclusion](#conclusion)
- [License](#license)
## Introduction
Customer segmentation is a crucial task for businesses to understand their customer base and tailor their marketing efforts accordingly. This project utilizes the KMeans clustering algorithm to segment customers into distinct groups based on their annual income and spending score.
## Usage
This project is implemented in a Jupyter Notebook environment. To run the code:
1. Open the provided Jupyter Notebook file.
2. Execute each cell in the notebook sequentially to import dependencies, load the dataset, preprocess the data, build the KMeans model, and visualize the customer segmentation clusters.
3. Analyze the generated scatterplot to identify different customer segments and draw conclusions about their behavior.
4. Save or export the notebook as needed for future reference.
## Code Overview
The main components of the code include:
1. Loading and preprocessing the dataset: Importing the dataset from a CSV file and preprocessing the data to prepare it for clustering.
2. Building the KMeans model: Using the KMeans algorithm to cluster customers into groups based on their annual income and spending score.
3. Visualizing the clusters: Plotting a scatterplot to visualize the segmented customer clusters and identifying target customer groups for business analysis.
## Conclusion
From the analysis and visualization of customer segmentation clusters, we can draw conclusions about the different types of customers visiting a place. By targeting specific customer groups with tailored marketing strategies, businesses can optimize their resources and improve customer engagement and satisfaction.
## License
This project is licensed under the [MIT License](LICENSE).