The motivation behind this document is its relevance to real-world applications. Most data used for decision-making day-to-day in the industry is structured and/or in the form of a time-series. This document explores the end-to-end process of using CRISP-DM to analyses and reveals insights from sales transactions.
CRISP-DM stands for the cross-industry process for data mining. This methodology provides a structured approach to planning a data mining project. It is a robust and well-proven methodology. This model is an idealized sequence of events. In practice, many of the tasks can be performed in a different order and it will often be necessary to backtrack to previous tasks and repeat certain actions .
You can download the jupyter notebook from this repositoty. The dataset instead can be download from:
https://www.kaggle.com/c/rossmann-store-sales/data
The image contain a Jupyter notebook base image with the libraries and the data to run the jupyter notebook. The size of the base docker images is 625 MB, after the installation of all libraries necesary to run the analysis the final size is 2.8 GB.
Download the image:
docker pull 25987908/crisp-dm_rossmann
Quick start:
docker run -p 8888:8888 25987908/crisp-dm_rossmann
Usually, the notebook will pop up in the default browser. If not, in the command line you will get a link with a token to open the notebook, just copy and paste the entire address including the token.
http://127.0.0.1:8888/?token=XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
Click in the folowing icon to open the notebook.---> 
CRISP-DMRossmann.pdf Only a PDF viewer is need it. :)
In Conclusion
The CRISP-DM, even today, remains as a dependable method to develop data science solutions for enterprise problems. Its BI-first approach also enables better sourcing of insights and other such data knowledge.
The flexible and iterative approach of the CRISP-DM also makes it a future-proof alternative for anyone looking to solve data science problems. Even as it is important to develop a unique method, it should also be kept in mind that using methods such as CRISP-DM bring an element of professionalism and uniformity to operational procedures.
- Business Understanding: determine business objectives; assess situation; determine data mining goals; produce project plan
- Data Understanding: collect initial data; describe data; explore data; verify data quality
- Data Preparation (generally, the most time-consuming phase): select data; clean data; construct data; integrate data; format data
- Modeling: select modeling technique; generate test design; build model; assess model
- Evaluation: evaluate results; review process; determine next steps
- Deployment: plan deployment; plan monitoring and maintenance; produce final report; review project
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