End to End MLOps Pipeline on Optimizing Revenue through Dynamic Pricing using Databricks

Learning Objectives

Demonstrating how to integrate and preprocess multiple datasets related to pricing, sales, and customer behavior for a regression problem.

Perform Exploratory Data Analysis (EDA) to identify key insights from pricing, competitor, and inventory data.

Apply feature engineering techniques to create meaningful predictors for revenue optimization.

Build and evaluate machine learning models to optimize pricing strategies.

Interpret model results and optimization outputs to provide actionable insights for business decision-making.

Implement MLFlow for Tracking Experiment and Model Artifacts

Deploying the Model for real-time Dynamic Pricing Inference

GlobalMart, a leading retailer in the fast-moving consumer goods (FMCG) sector, offers a wide range of products to its diverse customer base. Among its extensive portfolio, Tide stands out as one of the prominent detergent brands.

However the product faces increasing competition in the detergent market due to aggressive pricing strategies by rival brands. This has led to fluctuating customer demand and inventory inefficiencies.

The company seeks to implement a data-driven Dynamic Pricing Strategy to optimize Tide’s pricing decisions.

Your task as a data scientist is to analyze sales, competitor, customer behavior, and inventory data to :

Identify key factors influencing Tide's sales and demand. Optimize pricing to maximize revenue while maintaining demand and inventory balance. By solving this challenge, you will enable GlobalMart to regain its competitive edge, improve profitability, and align pricing with market dynamics.

End to End MLOps Pipeline on Optimizing Revenue through Dynamic Pricing using Databricks

Learning Objectives

Overview

Prerequisites