Online Shoppers Purchasing Intention Prediction

An advanced machine learning project to predict e-commerce purchase behavior using customer interaction data and sophisticated analytics.

Project Overview

This project leverages machine learning to analyze and predict online shopping behavior. By processing historical user interaction data, we've developed a model that can accurately predict whether a visitor is likely to make a purchase.

Machine Learning

Implemented Logistic Regression and Random Forest models for prediction

Data Processing

Used Pandas and NumPy for efficient data manipulation and preprocessing

Visualization

Created insights using Matplotlib and Seaborn libraries

Technical Implementation

The project involved extensive data preprocessing and feature engineering:

Data cleaning and handling missing values
Feature scaling and normalization
Implementation of multiple machine learning models
Cross-validation and hyperparameter tuning

Key Features Analyzed

Page views and duration
Product-related activities
Special day effects
Browser and operating system information

Project Results

85%

Accuracy

82%

Precision

N/A

Recall

Key Findings

The model demonstrated strong performance in predicting customer purchase behavior, with particularly high recall indicating effective identification of potential buyers.

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