Olyphaunt Solutions: Research & Development - Part 1

Olyphaunt Solutions: Research & Development

Using an Industry Standard Methodology for Data Mining:

Olyphaunt Solutions has been consistently doing research in the field of data analysis and applying it to solve real-world problems in the industry.

We are a team wherein we inculcate “Learning” as a strong value of our organization.

In this blog we would like to describe how we use an industry standard methodology for data analysis.?

CRISP-DM stands for Cross Industry Standard Process for Data Mining. It is a process model that serves as a base for a data science project. This framework methodology consists of 6 phases. The sequence of the phases is not strict and moving back and forth between different phases is usually required [1].

Business Understanding: This phase focuses on understanding the objectives and requirements of the project. It helps to ensure that everyone is on the same page before expending valuable resources.

Data Understanding: This phase focuses on identifying, collecting, and analysing the datasets which will accomplish the project goals. This involves taking a closer look at the data available for mining.

Data Preparation: We need to prepare the data in such a way that the machine can understand and perform various machine learning models on it. This is done by Data Pre-processing techniques that include Data Cleaning, Data Deduplication, Label Encoding, and Feature Scaling.

Modelling: This is an important phase to determine and identify the right Machine Learning model to be applied. This includes the decision between choosing Supervised or Unsupervised Learning, and Regression or Classification based ML models. In Supervised Learning, the machine learns under supervision. It contains a model that can predict with the help of a labelled dataset. In Unsupervised Learning, the machine uses unlabeled data and learns on itself without any supervision. The machine tries to find a pattern in the unlabeled data and gives a response. After selecting the appropriate model for the project, we split the data into train and test sets based on the modelling approach.?

Evaluation: This phase looks more broadly at selecting the model that best meets the business requirements by assessing/comparing the predictions and accuracies of Machine Learning models that have been applied. If the accuracies/predictions do not meet the business standards, we need to go back to the first phase, i.e., the Business Understanding phase to rework on the mistakes that have been made.

Deployment: This phase includes completing wrap-up tasks by conducting a project review and creating the final report. The ML model developed can be provided as an input to different kinds of data as per the needs of the business organization.

How can Olyphaunt Solutions help:

At Olyphaunt Solutions, we specialize in designing and developing end-to-end solutions based on AI/ML and IoT technologies. We use CRISP-DM for Data Analysis and Machine Learning Projects. For example, see [2] and [3]. Our expertise in Artificial Intelligence, Machine Learning, Sensors, and Internet of Things (IoT), is available for delivering scalable, robust solutions for industrial needs. For more information, please contact us.?

?References

[1] Shearer C, CRISP-DM model: the new blueprint for data mining, J Data Warehousing (2000).

[2] Using Machine Learning to Predict Outcome for Covid-19 Patients,https://olyphaunt.com/blog/

[3] Using Machine Learning to Predict Rise Covid-19 Cases,https://olyphaunt.com/blog/

?Shaunak Bachal (ML Engineer)

Olyphaunt Solutions Pvt. Ltd.

For more information, please contact us on [email protected]