A complete retail analytics case study completed as part of the Quantium Data Analytics Virtual Experience.
This project demonstrates how data analytics can be used to solve real business problems using Python. It covers customer analytics, retail performance analysis, trial store evaluation, statistical testing, and business recommendations.
The repository contains two end-to-end analytics tasks:
In this task, customer transaction data is analyzed to understand purchasing behavior and customer segments.
Key Highlights
- Data Cleaning & Preparation
- Exploratory Data Analysis (EDA)
- Customer Segmentation
- Brand Analysis
- Pack Size Analysis
- Sales Trends
- Business Insights & Recommendations
This task evaluates the performance of trial stores by selecting suitable control stores and measuring the impact of the trial.
Key Highlights
- Monthly KPI Creation
- Similarity Analysis
- Pearson Correlation
- Magnitude Distance Calculation
- Control Store Selection
- Sales Scaling
- Trial vs Control Comparison
- Statistical Significance Testing
- Business Recommendations
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- SciPy
- Jupyter Notebook
quantium-retail-analytics
│
├── Task-1
│ ├── Data
│ ├── Notebook
│ ├── Images
│ ├── Reports
│ └── README.md
│
├── Task-2
│ ├── Data
│ ├── Notebook
│ ├── Images
│ ├── Reports
│ └── README.md
│
└── README.md
- Data Cleaning
- Exploratory Data Analysis
- Customer Analytics
- Retail Analytics
- Feature Engineering
- Statistical Analysis
- Data Visualization
- Business Insights
- Python Programming
- 📘 Task 1: Customer Analytics
- 📗 Task 2: Trial Store Analysis
Detailed notebooks, reports, and visualizations are available inside their respective folders.
Mohan Kumar
- GitHub: https://github.com/Mohan81020
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