🛍️ Customer Segmentation Using Unsupervised Learning

📌 Project Overview

This project applies Unsupervised Machine Learning techniques to segment customers based on their purchasing behavior and demographic characteristics. By understanding the distinct groups within a customer base, businesses can tailor their marketing strategies, optimize product recommendations, and improve overall customer retention. The project primarily leverages K-Means Clustering to identify these customer personas.

🎯 Objectives

• Analyze customer purchasing behavior and demographics.
• Perform Exploratory Data Analysis (EDA) to uncover trends.
• Apply feature engineering, data cleaning, and standardization.
• Implement K-Means clustering for robust customer segmentation.
• Visualize high-dimensional customer clusters using t-SNE.

📊 Dataset Information

The dataset (new.csv) contains comprehensive customer information, including:

• Demographics: Year of Birth, Education Level, Marital Status, Income.
• Family Structure: Number of children/teens at home.
• Spending Habits: Amounts spent on various product categories (Wines, Fruits, Meat, Fish, Sweets, Gold).
• Engagement: Web visits, campaign acceptance, and recent purchases.

(Note: The dataset is excluded from version control for privacy/space, but should be placed in the data/ directory to run the notebook).

🛠️ Technologies Used

• Programming Language: Python
• Data Manipulation: Pandas, NumPy
• Data Visualization: Matplotlib, Seaborn
• Machine Learning: Scikit-learn
• Environment: Jupyter Notebook

📂 Project Structure

Customer-Segmentation-Using-Unsupervised-Learning/
│
├── data/                  # Directory for the dataset (e.g., new.csv)
├── images/                # Saved visualizations and plots
├── notebooks/             # Jupyter notebooks for EDA and modeling
│   └── Customer_Segmentation_using_Unsupervised_Machine_Learning_in_Python.ipynb
├── results/               # Exported results and reports
├── .gitignore             # Ignored files and folders
├── requirements.txt       # Python dependencies
└── README.md              # Project documentation

🚀 How to Run

1. Clone the repository:

   git clone https://github.com/sashankabhiram/Customer-Segmentation-Using-Unsupervised-Learning.git
   cd Customer-Segmentation-Using-Unsupervised-Learning

2. Install the required libraries:

   pip install -r requirements.txt

3. Add the dataset: Ensure your dataset (new.csv) is placed inside the data/ folder.

4. Launch the Notebook:

   cd notebooks
   jupyter notebook

   Open Customer_Segmentation_using_Unsupervised_Machine_Learning_in_Python.ipynb and run all cells.

💡 Key Insights & Results

(Add your findings here!)

• Cluster 1 (e.g., High Spenders): ...
• Cluster 2 (e.g., Bargain Hunters): ...
• Cluster 3 (e.g., New Parents): ...

Visualizations such as the t-SNE scatter plot of customer segments can be saved in the images/ folder.

👨‍💻 Author

Muthyala Sashank Abhiram