In this project, we are trying to help the bakery to get higher profits based on data science technology. The instructions we give are mainly based on data analysis result and Apriori algorithm. Every businessmen including this bakery desire higher profits. There are two ways to gain profits: increasing revenues and decreasing expenses. Considering the relatively low-cost products, it is better to increase the sales by setting competitive prices in order to increase revenues. Given the dataset of transactions of a bakery from 30/10/2016 to 09/04/2017, we set our business goals, do analysis and data mining, therefore propose some advisable staregies according to the results. Finally, we estimate the increaced profits and loss based on hypothesis because do not have enough information about the bakery except transactions.
The ultimate goal of the bakery is to increase profits, namely to increase revenues and to reduce expenses. In order to raise revenues, we decide to boost sales rather than increasing prices because the products such as bread and coffee are price-sensitive. Details about our strategies will be illustrated in the analysis part below.
We choose python as our analysis tool because of the following reasons: 1. This is not a big data problem, the scale of data is acceptable with python. So we don’t need big data tools in this project. 2. The significant factor of choosing Python is the variety of data science libraries such as pandas, numpy, matplotlib and seaborn. 3. The widespread and involved community promotes easy access for us to find different solutions of similar problems. 4. Data visualization in python is beautiful and easy to operate.
It is written in python ver. 3.7.0. See the plan by jupyter notebook and choose Bakery Project Report.ipynb.
Document and plan is in ./docs/Bakery Project Report.pdf. Please check them for further details.
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├── README.md
├── docs
│ └── Bakery\ Project\ Report.pdf
├── notebook
│ ├── Bakery\ Project\ Report.ipynb
│ ├── notebook.tex
│ ├── output_11_0.png
│ ├── output_15_0.png
│ ├── output_17_1.png
│ ├── output_19_0.png
│ ├── output_27_0.png
│ └── output_9_0.png
└── src
├── bread.py
└── input
└── BreadBasket_DMS.csv