A-B-Testing

A/B Testing Data Analytics Project

Overview: This project is designed to perform A/B testing for analyzing the impact of different variations (A and B) on user behavior. By comparing two versions of a feature or design, we assess which performs better based on statistical analysis of collected data. The project leverages various data science techniques, including hypothesis testing, data visualization, and metrics evaluation.

Features:

Data Preprocessing: Clean and prepare raw A/B test data for analysis. Statistical Testing: Implement hypothesis testing using t-tests, z-tests, or non-parametric tests. Performance Metrics: Calculate key performance indicators (KPIs) such as conversion rates, average revenue, etc. Visualization: Graphical representation of test results (e.g., histograms, confidence intervals). Reporting: Generate concise reports on the outcome of the A/B test, highlighting statistical significance and business impact.