https://github.com/onapte/ecommerce-conversion-optimization

E-Commerce A/B Tesing Analysis and Optimization
https://github.com/onapte/ecommerce-conversion-optimization

ab-testing data-analysis data-visualization exploratory-data-analysis predictive-modeling python

Last synced: 28 days ago
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E-Commerce A/B Tesing Analysis and Optimization

• Host: GitHub
• URL: https://github.com/onapte/ecommerce-conversion-optimization
• Owner: onapte
• Created: 2024-11-16T23:01:59.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-16T23:08:19.000Z (about 2 months ago)
• Last Synced: 2024-11-17T00:24:56.464Z (about 2 months ago)
• Topics: ab-testing, data-analysis, data-visualization, exploratory-data-analysis, predictive-modeling, python
• Language: Jupyter Notebook
• Homepage:
• Size: 4.04 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
E-Commerce A/B Testing Analysis

Overview

This project involves an A/B testing analysis using an e-commerce dataset to determine whether a new landing page leads to higher conversion rates compared to the old page. The analysis includes data preprocessing, exploratory data analysis (EDA), and building machine learning models (Logistic Regression and Support Vector Machine) to predict user conversions.

Dataset

The dataset was sourced from Kaggle and contains user interaction data, including:

    user_id: Unique identifier for each user.

    timestamp: Date and time of the user's interaction.

    group: Indicates whether the user was part of the "control" (old page) or "treatment" (new page) group.

    landing_page: The page shown to the user (either old or new).

    converted: Indicates whether the user converted (1) or not (0).