1. Temple Analytics Challenge
Presented by:
Daniel Franks, Melissa Grossman,
Matthew Mayberry, & Reith Nolan

2. The Problem
Can small independent pharmacies compete with the big chains?

3. Our Focus
Are there specific products that a pharmacy should always have in stock? How do we identify those products?

4. Problem Statement and Analysis Proposal
Question: Are there specific products that a pharmacy should always have in stock? How do we identify those products?
Answer:
If we identify the most frequently sold items in every month, we can then draw conclusions about the items that are sold throughout the year.
The most frequently sold items in each month should be kept well stock for the corresponding month and stores should consider raising the price on these items.

5. Plan for Success Discussed problem and possible solutions
Decided on monthly analysis of product sales
Discussed how code and analysis will be managed
Github and Google Drive to manage code and presentation
Discussed code structure and framework for software
CSV file management using dictionaries and Counter
Developed code
Python script
Inputs - product sales database CSV and a product key/name CSV
Outputs - CSV with separated monthly top product sales
Analysed outputted CSV using excel and Draw.IO to visualize data

6. Data: csv Files
Before Manipulation
After Manipulation

7. Data: Product Name Key CSV

8. Code: Python

9. Code: Python

10. Code: Python

11. Code: Python

12. Code: Output

13. Execution of the Script
Exported.csv

14. Results
Top 10 Products Sold July June 2016

15. Tools
MobaXterm: This facilitated use of linux through neuronix and its text editing capacities.
Python: The entirety of our analysis was written in python.
Microsoft Excel: This was used to create charts and graphs with the data output from python.
Draw.io: We used this to create our Temple Analytics Challenge graphic image.
Google Drive (Sheets, Slides): To facilitate group access to the documents and files used to create this presentation.
GitHub: Version Control and Distribution of working code to team members

16. Issues Deciding how to approach the problem using the dataset given.
Deciding analysis to determine how pharmacies can maximize profit and sales using the given data
Comparison of monthly data to see products
A big problem was inability to see results and determine if method was valid until code was developed
The code development was fairly straightforward
Optimization was not needed as larger dataset was analyzed in under 30 seconds.
Code development took approximately 14 hours of total team time and 4 versions
Missing product keys and generalized product names made analysis more difficult
Required googling missing product keys and general names in order update product keys and data
Validation
Dataset was not checked for errors
Due to the large number of sales in the larger database, the percent of error is small for the large dataset used