week-4

Week 4: Dynamic Pricing and Demand Forecasting

Table of Contents

1. Introduction
2. Why Dynamic Pricing and Demand Forecasting?
3. Workflow Overview
4. Detailed Task Breakdown
   • 4.1. Set Up Google Colab
   • 4.2. Load and Explore the Dataset
   • 4.3. Data Preprocessing
   • 4.4. Feature Engineering
   • 4.5. Implement Demand Forecasting Models
   • 4.6. Develop Dynamic Pricing Strategies
   • 4.7. Model Evaluation
   • 4.8. Simulate Pricing Scenarios
   • 4.9. Create an Interactive Dashboard (Optional)
   • 4.10. Develop a Real-time Pricing API (Optional)
5. Deliverables
6. Submission Guidelines
7. Resources

1. Introduction

Welcome to Week 4 of the AI/ML Development Track. This week, you'll focus on developing dynamic pricing models and demand forecasting techniques for our Point of Sale (PoS) application. You'll use machine learning to predict demand and optimize pricing strategies to maximize revenue and customer satisfaction.

2. Why Dynamic Pricing and Demand Forecasting?

Dynamic pricing and demand forecasting are crucial for businesses to optimize their revenue and inventory management. By accurately predicting demand and adjusting prices in real-time, companies can:

• Maximize revenue by setting optimal prices based on current market conditions
• Improve inventory management by anticipating demand fluctuations
• Enhance customer satisfaction by offering competitive prices
• Respond quickly to market changes and competitor actions

Machine learning models can analyze vast amounts of data to identify patterns and trends that humans might miss, leading to more accurate forecasts and pricing decisions.

3. Workflow Overview

1. Set up Google Colab for the project
2. Load and explore a suitable dataset
3. Preprocess data and engineer relevant features
4. Implement demand forecasting models
5. Develop dynamic pricing strategies
6. Evaluate models and pricing strategies
7. Simulate various pricing scenarios
8. (Optional) Create an interactive dashboard
9. (Optional) Develop a real-time pricing API

4. Detailed Task Breakdown

4.1 Set Up Google Colab

• Access Google Colab and create a new notebook
• Ensure GPU support is enabled if needed
• Google Colab Quick Start Guide

4.2 Load and Explore the Dataset

• Find a suitable dataset that includes historical sales data, pricing information, and relevant features
• Use pandas to load and initially explore the dataset
• Kaggle Datasets
• UCI Machine Learning Repository

Suggested datasets:

• Online Retail II UCI
• Brazil E-Commerce Public Dataset by Olist

4.3 Data Preprocessing

• Handle missing values and outliers
• Encode categorical variables
• Normalize or standardize numerical features
• Pandas Data Cleaning Tutorial
• Scikit-learn Preprocessing Guide

4.4 Feature Engineering

• Create time-based features (e.g., day of week, month, season)
• Develop price-related features (e.g., price elasticity, relative price to competitors)
• Generate demand-related features (e.g., rolling averages, lag features)
• Feature Engineering Techniques

4.5 Implement Demand Forecasting Models

Implement and compare multiple algorithms for demand forecasting:

• ARIMA/SARIMA: ARIMA Models Tutorial
• Prophet: Facebook Prophet Tutorial
• XGBoost: XGBoost for Time Series
• LSTM Neural Networks: LSTM for Time Series in Keras

4.6 Develop Dynamic Pricing Strategies

Implement pricing optimization techniques:

• Price Elasticity of Demand: Calculating Price Elasticity
• Reinforcement Learning for Pricing: RL for Dynamic Pricing
• Competitor-based Pricing: Analyze and respond to competitor prices
• Time-based Pricing: Adjust prices based on time of day, week, or season

4.7 Model Evaluation

• Use appropriate metrics like MAE, RMSE, and MAPE for demand forecasting
• Evaluate pricing strategies using simulated scenarios and A/B testing
• Time Series Cross-validation

4.8 Simulate Pricing Scenarios

• Create a simulation environment to test different pricing strategies
• Analyze the impact of price changes on demand and revenue
• SimPy for Discrete Event Simulation

4.9 Create an Interactive Dashboard (Optional)

• Use Streamlit or Dash to create a dashboard for visualizing demand forecasts and pricing recommendations
• Streamlit Documentation
• Plotly Dash Tutorial

4.10 Develop a Real-time Pricing API (Optional)

• Use FastAPI to create an API for real-time pricing recommendations
• FastAPI Tutorial

5. Deliverables

1. Google Colab notebook with your entire dynamic pricing and demand forecasting pipeline
2. A concise Markdown report discussing your approach, challenges, and results
3. (Optional) Python script for the interactive dashboard
4. (Optional) Python script for the real-time pricing API

6. Submission Guidelines

1. Share your Google Colab notebook as a link or .ipynb file
2. Submit your report as a .md file
3. (Optional) Submit your dashboard script as a .py file
4. (Optional) Submit your API script as a .py file

7. Resources

• Time Series Forecasting with Python
• Dynamic Pricing Strategies for E-Commerce
• Reinforcement Learning for Dynamic Pricing
• Scikit-learn Documentation
• Pandas Time Series Functionality