Here’s a sample README file description for a "Shopping Mall Customer Segmentation" project using AI and ML with Python:

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Shopping Mall Customer Segmentation Using AI & ML

Overview

This project demonstrates how to perform customer segmentation using Machine Learning techniques. The goal is to segment shopping mall customers into distinct groups based on their purchasing behavior, helping businesses tailor their marketing strategies more effectively. We will use K-means clustering, one of the most popular unsupervised learning techniques, to achieve this.

Project Structure

customer-segmentation/
│
├── data/
│   ├── mall_customers.csv     # The dataset used for segmentation
├── notebooks/
│   ├── customer_segmentation.ipynb   # Jupyter notebook demonstrating step-by-step analysis
├── src/
│   ├── data_preprocessing.py   # Script for data loading and preprocessing
│   ├── clustering_model.py     # Script for building the K-means clustering model
│   └── visualize_clusters.py   # Script for visualizing the clustered results
├── requirements.txt            # Python packages required for running the project
├── README.md                   # Project description and usage instructions

Dataset

The dataset used for this project is mall_customers.csv. It contains the following features:

• CustomerID: Unique customer identifier.
• Gender: Gender of the customer.
• Age: Age of the customer.
• Annual Income (k$): Annual income of the customer in thousand dollars.
• Spending Score (1-100): The spending score assigned to the customer by the shopping mall, based on customer behavior and purchase data.

Approach

1. Data Exploration:

   • Analyze and visualize the features to understand patterns.

2. Data Preprocessing:

   • Handle missing values, normalize or scale the data as needed.

3. Clustering Algorithm:

   • Use K-means clustering to group customers based on their income and spending habits.
   • Determine the optimal number of clusters using Elbow Method.

4. Evaluation and Visualization:

   • Visualize the clustered groups using 2D plots.
   • Analyze the segments to derive business insights.

Requirements

To install the necessary dependencies, run the following command in the root directory of the project:

pip install -r requirements.txt

Key Libraries:

• pandas: Data manipulation and analysis.
• numpy: Numerical computing.
• matplotlib & seaborn: Data visualization.
• scikit-learn: Machine learning algorithms (K-means).
• plotly (optional): Interactive visualization.

Usage

1. Run Jupyter Notebook:

You can explore the entire segmentation process using the provided notebook.

jupyter notebook notebooks/customer_segmentation.ipynb

2. Run the Python Scripts:

If you prefer running Python scripts:

• Data Preprocessing:

  python src/data_preprocessing.py

• Build and Apply the Clustering Model:

  python src/clustering_model.py

• Visualize the Clusters:

  python src/visualize_clusters.py

3. Interpreting the Clusters:

The output will generate different customer segments that can be interpreted as follows:

• High Income, Low Spending
• Low Income, High Spending
• High Income, High Spending
• Moderate Income, Moderate Spending

These insights can be used to target specific customer segments with appropriate marketing strategies.

Future Work

• Improved Clustering Algorithms: Try hierarchical clustering, DBSCAN, or Gaussian Mixture Models for better results.
• Feature Engineering: Add more features like purchase history, customer preferences, etc., to create more detailed segments.
• AI Recommendations: Implement AI-based recommendations based on customer segments to suggest personalized products.

License

This project is open source and available under the MIT License.

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