Online Shopping Behavior Analysis

This project leverages machine learning techniques to predict whether a customer visiting a website will make a purchase, addressing traditional market constraints and enhancing the overall shopping experience.

Table of Contents

• Introduction
• Problem Statement
• Aim and Objectives
• Existing Solution
• Application
• System Architecture
• Dataset and Features
• Data Preprocessing
• ML Models Implemented
• Model Evaluation
• Web Application
• Tools and Technology
• Conclusion and Future Work

Introduction

In the ever-evolving landscape of e-commerce, traditional market approaches face significant limitations. This project aims to leverage machine learning techniques to predict whether a customer visiting a website will make a purchase, enhancing the overall shopping experience.

Problem Statement

Understanding customer behavior is crucial for businesses to optimize their marketing strategies and enhance customer satisfaction. By analyzing customer browsing and shopping data and predicting whether a customer will make a purchase through machine learning applications, one can increase the overall sales of an e-commerce business.

Aim and Objectives

1. Predict Customer Purchase Behavior.
2. Enhance Marketing Strategies.
3. Evaluate Multiple Machine Learning Algorithms.
4. Develop a Real-time Prediction Tool.
5. Visualize Model Performance.
6. Handle Data Preprocessing.

Existing Solution

Traditional e-commerce businesses face several limitations, such as limited personalization, manual decision-making, static product displays, static pricing strategies, ineffective marketing, limited insights, and missed opportunities for upselling and cross-selling.

Application

1. Personalized User Experience
2. Business Intelligence and Analytics
3. Targeted Marketing
4. Customer Retention
5. Enhanced Customer Support
6. Sales and Revenue Optimization

System Architecture

The system architecture includes a Flask backend running machine learning models and a web frontend using HTML, JavaScript, and CSS to handle user input and display predictions.

Dataset and Features

The dataset includes various features representing different aspects of customer interaction with an e-commerce website, such as:

• Administrative pages visited and duration
• Informational pages visited and duration
• Product-related pages visited and duration
• BounceRates, ExitRates, PageValues
• SpecialDay, Month, OperatingSystems, Browser, Region, TrafficType, VisitorType, Weekend

The target variable is Revenue, indicating whether a purchase was made or not.

Data Preprocessing

1. Handling Missing Values
2. Encoding Categorical Variables
3. Scaling Features

ML Models Implemented

1. K-Nearest Neighbors (KNN)
2. Naive Bayes
3. Support Vector Classifier (SVC)
4. Random Forest

Model Evaluation

The models are evaluated based on metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Detailed performance analysis and visualizations are provided for each model.

Web Application

A user-friendly web application is developed using Flask, allowing businesses to input customer data and receive immediate purchase predictions, facilitating real-time decision-making.

Tools and Technology

• Python
• Flask
• scikit-learn
• Pandas
• NumPy
• Matplotlib
• Seaborn
• HTML, CSS, JavaScript

Conclusion and Future Work

The project successfully demonstrates the application of machine learning techniques to predict online shopping behavior, providing actionable insights for e-commerce businesses. Future work includes enhancing the model's predictive accuracy, incorporating additional features, and exploring new machine learning algorithms.