basaktamer/Online_Shopping_Intention_Analysis_with_Clustering

Online Shopping Intention Analysis 🛒

📌 Project Overview

This project focuses on Unsupervised Learning to analyze and group online shopping sessions. By clustering visitors based on their real-time behavior (time spent on various page types), we can identify different user "intents"—distinguishing between casual browsers and high-intent shoppers.

📊 Metadata

• —Dataset: Online Shoppers Purchasing Intention Dataset
• —Size: 12,330 Sessions
• —Field: E-commerce / User Behavior Analytics
• —Language: Python
• —Libraries: Pandas, Scikit-Learn, Matplotlib, Seaborn

⚙️ Core Algorithm: K-Means Clustering

The project utilizes the K-Means algorithm to partition the sessions into distinct groups.

• —Process: It calculates the distance between data points and centroids to minimize within-cluster variance.
• —Features Used: Administrative_Duration, Informational_Duration, and ProductRelated_Duration.

📏 Key Metric: Silhouette Score

To ensure the clusters are mathematically sound, we use the Silhouette Score.

• —Purpose: It measures how similar an object is to its own cluster compared to other clusters.
• —Optimization: We iterate through different values of $k$ to find where the "numbers stop changing" and the score is highest, ensuring clear separation between segments.

💡 Business Insights

The clustering results provide actionable intelligence for e-commerce platforms:

1. 1.Targeted Marketing: Identify high-intent users who spend significant time on product pages to offer real-time discounts.
2. 2.UX Improvement: Recognize "Browser" segments that spend time on informational pages and provide them with better educational content or FAQs.
3. 3.Conversion Optimization: Understand the behavioral patterns that lead to a "Revenue" event without relying on pre-existing labels.

🚀 How to Run

1. 1.Clone the repository.
2. 2.Install dependencies: pip install -r requirements.txt.
3. 3.Run the Jupyter Notebook or the Streamlit app: streamlit run app.py.