Recently, I’m getting many questions about the methodology that we used.
We use CRISP-DM as everyone else. So what is this CRISP-DM. I’ll try to explain
briefly. CRISP-DM is Cross Industry Standart Process for Data Mining.
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| CRISP-DM Approach |
In seperately;
1.
Business Understanding
2.
Data Understanding
3.
Data Preparation
4.
Modeling
5.
Evaluation
6.
Deployment
This cycle continues with this sort. Firstly, we apply
Business Understanding. What exactly is your business needs, what we want from
us? We look for answers to these questions, Business Understanding and Data
Understanding should be evaluated together. The first two parts are the subject
of Data Science. Our role begins with Data Preparation. I want to tell you with
data preparation, Where and how to get data? When we decide how to get data, we
need to crop/clean the data in a way that we can use. It will take us a long
time to adapt the data to our ML/DL model. In this part, we must be patient J
The ML/DL subjects we read always correspond to the
modelling. The main issues are prediction, classification, clustering, ARM,
Reinforcement Learning, Natural Language Processing, Deep Learning and etc. We
use all these algorithms in modelling. In later times, I will discuss all these
algorithms in more detail.
We want to find hot to evaluate this model in the Evaluation
section. Also we compare the models that we used in this section. The decision
of which model we will continue with is based on the accuracy rate or any other
criteria that you determine.
At the end of the project, you deploy your project to the
server.