📊 Price Elasticity of Demand Analysis (Retail)

📌 Project Overview

This project analyzes price elasticity of demand using retail transaction data to understand how changes in price impact product demand and revenue. A log–log regression model is used to estimate elasticity, followed by pricing scenario simulations to support data-driven pricing decisions.

The project is built with a modular, industry-style pipeline separating data cleaning, modeling, and simulation logic.

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🎯 Objectives

• Measure price elasticity of demand using statistical modeling
• Analyze price–quantity relationships through EDA
• Simulate price changes to evaluate revenue impact
• Build a reusable, version-controlled analytics pipeline

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🗂️ Project Structure

Price_Elasticity_Analysis/
│
├── data/
│   └── supermarket_sales.csv
│
├── src/
│   ├── preprocessing.py        # Data loading, cleaning, renaming
│   ├── elasticity_model.py     # Log-log regression using OLS
│   └── simulation.py           # Price & revenue simulations
│
├── notebooks/
│   └── eda.ipynb               # Exploratory Data Analysis
│
├── requirements.txt
└── README.md

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📊 Exploratory Data Analysis (EDA)

EDA was performed to:

• Inspect price and quantity distributions
• Identify invalid or missing values
• Validate assumptions required for log–log regression
• Understand demand patterns before modeling

All EDA is documented in eda.ipynb.

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📈 Modeling Approach

• Used log–log regression to estimate price elasticity:

  [ \log(Q) = \beta_0 + \beta_1 \log(P) + \epsilon ]

• β₁ represents price elasticity of demand

• Model trained using Ordinary Least Squares (OLS) from statsmodels

• Input validation ensures all prices and quantities are positive

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🔄 Pricing & Revenue Simulation

Using the estimated elasticity:

• Simulated price changes (±5%, ±10%, ±20%)
• Estimated corresponding demand changes
• Calculated revenue impact for each scenario

This enables what-if pricing analysis for decision-making.

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🛠️ Tech Stack

• Python
• Pandas, NumPy
• Statsmodels
• Jupyter Notebook
• Git & GitHub

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✅ Key Outcomes

• Built an end-to-end pricing analytics pipeline
• Estimated demand sensitivity using statistical modeling
• Delivered actionable revenue insights through simulation
• Followed clean, modular, and reproducible code practices

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🚀 How to Run

pip install -r requirements.txt

Run EDA:

jupyter notebook notebooks/eda.ipynb

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📌 Notes

• No dashboard or deployment included
• Focused on modeling correctness and analytical clarity
• Designed to reflect real-world pricing analytics workflows