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Industry
retail-and-cpg
Skills
approach
data-understanding
data-modelling
cloud-management
ml-modelling
ai-modelling
git-version-control
data-storage
model-deployment
data-quality
data-wrangling
data-visualization
search-optimization-service
performance-tuning
data-governance
data-protection-sharing
access-control-security
code-versioning
programming
network-management
Tools
google-cloud
python
github
mlflow
excel
Learning Objectives
Demonstrating how to integrate and preprocess multiple datasets related to pricing, sales, and customer behavior for a regression problem.
Apply feature engineering techniques to create meaningful predictors for revenue optimization.
Interpret model results and optimization outputs to provide actionable insights for business decision-making.
Perform Exploratory Data Analysis (EDA) to identify key insights from pricing, competitor, and inventory data.
Build and evaluate machine learning models to optimize pricing strategies.
Overview
Prerequisites
- Basic knowledge of Python programming (e.g., variables, loops, functions and data structures).
- Experience with libraries such as Pandas, Matplotlib/Seaborn, and Scikit-learn. Knowledge of optimization libraries (e.g., Scipy or Pulp) is a plus.
- Familiarity with foundational concepts in machine learning (e.g., regression models, train-test splits, model evaluation metrics).
- Understanding of optimization principles
- Understanding of managing MLlifecycle using MLFlow
- Working knowledge of Inference Endpoints
- Building ML Solutions on Google Cloud Platform