Commonly used data analysis methods** are as follows:
CRISP-DM: CRISP-DM (Cross-Industry Standard Process for Data Mining) is a classic data mining process** that includes six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. CRISP-DM emphasizes the iterative and interactive nature of data mining projects, which is suitable for a variety of data mining tasks.
KDD: KDD (Knowledge Discovery in Databases) is a kind of data mining**, including data selection, data preprocessing, data transformation, data mining, pattern evaluation, and pattern interpretation. KDD emphasizes the systematic management and control of all aspects of the data mining process.
SEMMA: SEMMA (Sample, Explore, Modify, Model, Assess) is a data mining method proposed by SAS, including sample selection, exploratory data analysis, data processing, modeling and evaluation. The semma party emphasized the importance of sample selection and data processing in the data mining process.
osemn: Osemn (obtain, scrub, explore, model, interpret) is a commonly used data analysis method**, including data acquisition, data cleaning, data exploration, modeling, and interpretation. OSEMN emphasized the importance of data quality and data exploration in the data analysis process.
TDSP: TDSP (Team Data Science Process) is a kind of data science project proposed by Microsoft**, including business understanding, data acquisition and preparation, model development, model deployment and model management. TDSP emphasized the importance of data science teamwork and project management.
`python
Import the httprequest library.
import httprequest
Create an httprequest object.
http=httprequest.httprequest()
Set the URL you want to **
url=""ï¼›Crawler IP acquisition.
Use the contents of the http object **url.
response=http.get(url)
Print the contents of **.
print(response.read())
The above are some commonly used data analysis parties, each of which has its own characteristics and applicable scenarios, and can be applied according to specific data analysis tasks and project requirements. In addition, there are some other data analysis parties**, such as BDAS (Business Data Analytics Solution), DMBOK (Data Management Body of Knowledge), etc., which can also be selected and applied according to the actual situation.