---

👥 User Profiling and Segmentation

Deployment Can be found at 👉 https://user-profiling-and-segmentation.streamlit.app/

This repository contains a Jupyter Notebook for User Profiling and Segmentation using clustering and exploratory data analysis (EDA) techniques. The notebook processes user data to identify distinct groups based on behavior or characteristics — useful for targeted marketing, personalization, and user analytics.

---

📂 File Structure

• User_profiling_and_segmentation.ipynb — Main notebook containing the analysis, visualizations, and segmentation logic.
• README.md — Project overview and usage instructions.

---

🔍 Features

• Data Cleaning & Preprocessing
• Exploratory Data Analysis (EDA)
• Dimensionality Reduction using PCA
• User Segmentation using KMeans clustering
• Visualization of Segments
• Insights and Interpretation

---

🧰 Technologies Used

• Python 3
• Jupyter Notebook
• Pandas
• NumPy
• Seaborn
• Matplotlib
• Scikit-learn
• Plotly

---

🚀 How to Run

1. Clone the repository:

   git clone https://github.com/ashwathnakate/user-profiling-and-segmentation.git
   cd user-profiling

2. (Optional) Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use venv\Scripts\activate

3. Launch the notebook:

   jupyter notebook User_profiling_and_segmentation.ipynb

---

📦 Requirements

pandas
numpy
matplotlib
seaborn
scikit-learn
jupyter
plotly

---

📈 Sample Outputs

• Cluster plots showing user segments
• PCA-based visualizations for dimensionality reduction
• Summary statistics and feature distributions

---

👤 Author

GitHub

---

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.