Advanced Machine Learning Analysis in Retail
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Explore advanced machine learning techniques for retail analysis. Learn how to analyze customer data, optimize inventory, and enhance marketing strategies using AI-driven insights.
At a Glance
This project will help you apply Advanced Machine Learning methods to research trends and data in retail. Additionally, it will also guide you in analysing data from 45 stores located in different regions; each store contains a number of departments.
- Predict the department-wide sales for each store.
- Model the effects of markdowns on holiday weeks.
- Provide recommended actions based on the insights drawn, with priority placed on those having the largest business impact.
What you will learn
- Import Libraries/Define Auxiliary Functions
- Download and pre-preparation data
- Predict the department-wide sales
- Previous Data Analysis
- DataSet creation
- Data normalization
- Linear Regression
- Back Propagation Neural Network
- Long Short-Term Memory – LSTM
- Model the effects of markdowns on holiday weeks
- Preliminary analysis
- Linear Regression
- Back Propagation Neural Network
- Sensitivity analysis
- Recommendation for department
- Final Task
- SubTask 1. Sensitivity function
- SubTask 2. Sensitivity of Department
- SubTask 3. Sensitivity of 10 departments
Prerequisites
- Python – advanced level
- Pandas – basic level
- SeaBorn – basic level
- Scikit-learn – intermediate level
- keras – intermediate level
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