# Product Feature Optimization Engine

An A/B testing and statistical decision-making system designed to evaluate whether new

product features deliver real, measurable improvements.

## 🚀 Overview

This project simulates a real-world A/B experiment where users are randomly split into

control (A) and treatment (B) groups to evaluate the impact of a new product feature.

## 🧪 Experiment Design

- Control Group (A): Existing feature

- Treatment Group (B): New feature

- Metric: Conversion Rate

## 📊 Analysis

- Computed conversion rates for both groups

- Formulated null and alternative hypotheses

- Applied two-sample t-test for statistical significance

## 📈 Results

- Treatment group showed higher conversion rate

- p-value ≪ 0.05

- Statistically significant improvement

## ✅ Decision

Ship the new feature.

## 🛠 Tech Stack

- Python

- Pandas, NumPy

- SciPy

- Matplotlib

## 📌 Use Case

Designed for product teams to validate feature rollouts using statistically sound methods.