https://github.com/jdede1/data-analysis-visualization-assignment-5
INFO 526 — Data Analysis and Visualization, Assignment 5 (Dashboard Reports — Iowa Liquor Sales). Part of the Master’s in MIS/ML program at the University of Arizona. Includes positive and negative dashboards showing key KPIs: top products/vendors driving sales vs bottom products/vendors hindering sales.
https://github.com/jdede1/data-analysis-visualization-assignment-5
dashboard data-visualization matplotlib pandas seaborn
Last synced: 2 months ago
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INFO 526 — Data Analysis and Visualization, Assignment 5 (Dashboard Reports — Iowa Liquor Sales). Part of the Master’s in MIS/ML program at the University of Arizona. Includes positive and negative dashboards showing key KPIs: top products/vendors driving sales vs bottom products/vendors hindering sales.
- Host: GitHub
- URL: https://github.com/jdede1/data-analysis-visualization-assignment-5
- Owner: JDede1
- Created: 2025-10-03T04:46:11.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-10-03T05:11:59.000Z (9 months ago)
- Last Synced: 2025-10-03T07:09:09.043Z (9 months ago)
- Topics: dashboard, data-visualization, matplotlib, pandas, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 939 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# INFO 526 — Data Analysis and Visualization
## Assignment 5: Dashboard Reports — Iowa Liquor Sales
### Course
INFO 526 — Data Analysis and Visualization
Master’s in MIS/ML program, University of Arizona
---
### Objective
To create **static dashboard reports** that summarize KPIs for Iowa liquor sales using the `Smaller_Iowa_Liquor_Sales.csv` dataset.
The goal is to help a fictional distillery decide which alcohol products to prioritize for maximizing sales and which to avoid due to poor performance.
Each dashboard presents **two graphics side by side** with captions to provide a coherent narrative.
---
### Methods
1. **Data Cleaning**
- Dropped unnecessary location columns (`Address, City, Zip.Code, County`).
- Converted `Date` to datetime.
- Removed rows with missing `Category.Name`.
- Standardized column names (e.g., `Category.Name` → `Category_Name`).
- Converted key string columns (`Category_Name, Vendor_Name, Item_Description`) to categorical type.
- Ensured numeric columns are positive and properly typed.
2. **Dashboard Construction**
- **Positive Dashboard:** Highlights best-performing products and vendors.
- **Negative Dashboard:** Highlights underperforming products and vendors.
- Each dashboard contains **two charts** side by side with a narrative caption.
---
### Deliverables
#### Positive Dashboard
- **Chart 1:** Top 20 Best-Selling Products (by bottles sold).
- **Chart 2:** Top 10 Best-Selling Vendors (by bottles sold).
- **Narrative:** Shows the strongest opportunities for the distillery, identifying both leading products and the vendors driving sales growth.
---
#### Negative Dashboard
- **Chart 1:** Bottom 20 Least-Selling Products (by bottles sold).
- **Chart 2:** Bottom 10 Weakest Vendors (by bottles sold).
- **Narrative:** Identifies products and suppliers that contribute minimally to sales and should not be prioritized by the distillery.
---
### Repo Structure
```
data-analysis-visualization-assignment-5/
├── .gitignore
├── Dataset/ # <- Empty folder, dataset must be added manually
├── README.md
├── notebooks/
│ ├── Negative_Dashboard.png
│ ├── Positive_Dashboard.png
│ └── Week_6_Graded_Assessment_5.ipynb
└── requirements.txt
```
---
### Dataset Instructions
The Iowa Liquor Sales dataset (`Smaller_Iowa_Liquor_Sales.csv`) is **too large to host on GitHub** (174 MB exceeds the 100 MB limit).
To run the analysis:
1. **Download the dataset** from the course materials or the official Iowa data portal (if provided by the instructor).
2. Place the file in the following location in your repo:
```
Dataset/Smaller_Iowa_Liquor_Sales.csv
```
3. Open and run the Jupyter notebook `Week_6_Graded_Assessment_5.ipynb` to regenerate the dashboards.
---
### Key Insights
* **Positive KPIs:** Whiskey, Vodka, and Rum categories dominate sales, with top vendors like Jim Beam and Diageo driving large volumes.
* **Negative KPIs:** Certain niche products and vendors contribute negligible sales and should be avoided to optimize production.
---