https://github.com/praveendecode/retail-grocery-industry
Explore Gala Groceries' data-driven journey from EDA to ML production in partnership with Cognizant. Optimizing perishable item stocking
https://github.com/praveendecode/retail-grocery-industry
classification exploratory-data-analysis machine-learning-algorithms ml mlmodel production python random-forest-classifier
Last synced: 30 days ago
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Explore Gala Groceries' data-driven journey from EDA to ML production in partnership with Cognizant. Optimizing perishable item stocking
- Host: GitHub
- URL: https://github.com/praveendecode/retail-grocery-industry
- Owner: praveendecode
- Created: 2023-11-07T12:41:23.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-07T13:09:05.000Z (over 1 year ago)
- Last Synced: 2025-02-09T13:35:03.756Z (3 months ago)
- Topics: classification, exploratory-data-analysis, machine-learning-algorithms, ml, mlmodel, production, python, random-forest-classifier
- Language: Jupyter Notebook
- Homepage:
- Size: 410 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Gala Groceries Data-Driven Stock Optimization Project
# Overview:
- Gala Groceries, in partnership with Cognizant, leveraged data-driven insights and machine learning to optimize perishable item stocking. This repository showcases the journey through exploratory data analysis (EDA), data modeling, model building, and machine learning production.
# Main Features of Project:
- EDA (Task 1): Thorough data exploration using visualizations and statistics to reveal insights from extensive datasets.
- Data Modeling and Strategy (Task 2): Developing machine learning models with diverse algorithms to enhance decision accuracy and efficiency.
- Model Building (Task 3): Building and fine-tuning machine learning models.
- Machine Learning Production (Task 4): Leading model deployment to production and generating detailed reports for informed business decisions and AI solution validation.
# Process Steps:
## Exploratory Data Analysis (EDA):
- Conducted comprehensive data exploration.
- Utilized visualizations and statistics to gain insights.
## Data Modeling and Strategy:
- Developed a data modeling strategy for stock optimization.
- Utilized diverse machine learning algorithms.
## Model Building:
- Built and fine-tuned machine learning models.
## Machine Learning Production:
- Deployed models to production.
- Generated detailed reports for business decision-making and AI solution validation.
# Conclusion:
The Gala Groceries Data-Driven Stock Optimization Project showcases how data-driven insights and machine learning can revolutionize perishable item stocking. This collaborative effort with Cognizant resulted in enhanced decision accuracy, minimized waste, and optimized stock levels, ultimately improving the customer experience.