Retail Demand Forecasting and Inventory Optimization

Overview

Goal: Forecast retail demand and optimize inventory to enhance efficiency and cost savings.

Tech Stack:

• Data Processing: Python (pandas, Dask)
• Forecasting Models: ARIMA, Prophet, LSTM
• Visualization: Excel, Python (matplotlib, seaborn), Power BI / Tableau
• Data Storage: Local storage or cloud-based (AWS S3)

Key Skills: Time series forecasting, data manipulation, visualization, ETL

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Weekly Deliverables

Week 1: Project Setup & Dataset Exploration

Tasks:

• Set up GitHub repository with README.
• Collect dataset from Kaggle or Google BigQuery.
• Conduct initial data exploration using Excel and Python.

Deliverables:

• GitHub repo with README, initial data exploration code, visualizations, and a summary update.

Links:

• Week 1 Note

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Week 2: Data Cleaning & Preprocessing

Tasks:

• Clean data (handle missing values, correct inconsistencies).
• Set up ETL pipeline for data extraction and transformation.

Deliverables:

• Data cleaning scripts, ETL pipeline, and quality report.
• GitHub update with data cleaning scripts and README updates.

Links:

• Week 2 Note

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Week 3: Exploratory Data Analysis (EDA)

Tasks:

• Conduct EDA to identify patterns, seasonality, trends, and outliers.
• Create visualizations in Excel and Python.

Deliverables:

• EDA summary report and visualizations.
• GitHub update with code, graphs, and README additions.

Links:

• Week 3 Note

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Week 4: Model Selection & Initial Forecasting Model

Tasks:

• Research potential models and build a simple baseline model.
• Evaluate baseline performance and document results.

Deliverables:

• Baseline model with evaluation metrics.
• GitHub update with model code, README explanation of model selection.

Links:

• Week 4 Note

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Week 5: Model Tuning and Optimization

Tasks:

• Use hyperparameter tuning for optimization.
• Implement advanced forecasting models (e.g., LSTM).

Deliverables:

• Optimized model with performance metrics.
• GitHub update with model tuning code and README updates.

Links:

• Week 5 Note

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Week 6: Inventory Optimization Insights

Tasks:

• Analyze forecasted demand for inventory adjustments.
• Create dashboard with Power BI / Tableau.

Deliverables:

• Inventory recommendations report, dashboard link.
• GitHub update with inventory optimization insights and links.

Links:

• Week 6 Note

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Week 7: Final Documentation & Project Polish

Tasks:

• Complete project documentation, code cleanup, and portfolio presentation.

Deliverables:

• Final README with video walkthrough link, polished code.

Links:

• Week 7 Note

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Notes and Resources

• GitHub Repository
• Data Sources
• Useful Libraries