https://github.com/fahrettinsolak/ai-customer-segmentation-project

This project demonstrates the use of K-Means clustering to segment customers based on their annual income and spending score. The dataset used is Avm_Customers.csv, which contains information about customers' annual income and their spending score.
https://github.com/fahrettinsolak/ai-customer-segmentation-project

artificial-intelligence deep-learning k-means machine-learning phyton

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This project demonstrates the use of K-Means clustering to segment customers based on their annual income and spending score. The dataset used is Avm_Customers.csv, which contains information about customers' annual income and their spending score.

• Host: GitHub
• URL: https://github.com/fahrettinsolak/ai-customer-segmentation-project
• Owner: Fahrettinsolak
• Created: 2024-10-15T21:54:07.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-10-15T22:01:47.000Z (2 months ago)
• Last Synced: 2024-10-17T10:37:28.964Z (2 months ago)
• Topics: artificial-intelligence, deep-learning, k-means, machine-learning, phyton
• Language: Jupyter Notebook
• Homepage:
• Size: 71.3 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# AI-Customer-Segmentation-Project

## K-Means Clustering of Customer Data

This project demonstrates the use of K-Means clustering to segment customers based on their annual income and spending score. The dataset used is `Avm_Customers.csv`, which contains information about customers' annual income and their spending score.

## Project Overview

1. **Data Exploration**: 

   - Load the dataset and preview the data.

   - Visualize the distribution of customers based on annual income and spending score.

2. **Data Preprocessing**:

   - Rename columns for simplicity.

   - Normalize the data using `MinMaxScaler`.

3. **Clustering**:

   - Determine the optimal number of clusters using the Elbow Method.

   - Apply K-Means clustering with the optimal number of clusters.

   - Analyze and visualize the clustering results.

4. **Visualization**:

   - Plot the clustered data points and cluster centroids.

## Files

- `Avm_Customers.csv`: The dataset used for clustering.

- `K-MeansClustering-Customer_Segmentation.ipynb`: The Python script implementing the K-Means clustering algorithm and visualizations.

## Installation

To run this project, you will need Python and the following libraries:

- `numpy`

- `pandas`

- `scikit-learn`

- `matplotlib`

You can install the required libraries using pip:

pip install numpy pandas scikit-learn matplotlib

## Usage

1. **Clone this repository:**

    ```bash

    git clone https://github.com/Semssolak/repository.git

    ```

2. **Navigate to the project directory:**

    ```bash

    cd repository

    ```

3. **Run the Python script:**

    ```bash

    python K-MeansClustering-Customer_Segmentation.ipynb

    ```

## Results

- **Elbow Method Plot**: This plot shows the distortion value (inertia) for different numbers of clusters, helping to determine the optimal number of clusters.

- **Clustered Data Plot**: A scatter plot visualizing the clusters, with different colors representing different clusters. The centroids of the clusters are marked with blue 'X' markers.