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Python Top Libraries You Need to Create ML Model

Creating a Model of Machine Learning in Python, you need two libraries. One is 'NUMPY' and the other one is 'PANDA'.


For this project, we are using Python Libraries to Create a Model.
What Are Key Libraries You Need I have explained in the below steps. You need Two.
NUMPY - It has the capabilities of CalculationsPANDA - It has the capabilities of Data processing. To Build a model of Machine learning you need the right kind of data. So, to use data for your project, the Data should be refined. Else, it will not give accurate results. Data AnalysisData Pre-processing How to Import Libraries in Pythonimportnumpy as np # linear algebra
importpandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

How to Check NUMPY/Pandas installed After '.' you need to give double underscore on both the sides of version. 
How Many Types of Data You Need You need two types of data. One is data to build a model and the other one is data you need to test the model. Data to build…

How to Show Data science Project in Resume

In any project, the Data analyst role is to deal with data. The data for data science projects come from multiple sources. This post will explain how to put in data science project in Resume.
data science project for resume

Data Science project for Resume

The first step for an interview of any project is you need Resume. You need to tell clearly about your resume.

In interviews, you will be asked questions about your project. So the second step is you need to be in a position explain about project.

The third point is you need to explain the roles you performed in your data science project. If you mention the roles correctly, then, you will have 100% chance to shortlist your resume. Based on your experience your resume can be 1 page or 2 pages.

How to show Technologies used in Data science projects

In interviews, again they will be asked how you used different tools to complete your data science project.

So, you need to be in a position to explain about how you used different options present in the tools. Sometimes, in interviews, they may ask about specific role in Tools specifically you used. You should be in a position to answer these questions too.

4 Key Points You Need to Present in Resume

  1. Write clear description
  2. Write specific role
  3. Explain Tools
  4. Write about data flow

1. Write Clear Description

In any data science project, you will find few things like Client name, the expectation of client, and what you are going to deliver. These things you need to present clearly in Resume.

2. Write Specific Role

To convince your interviewer, you need to tell about your team roles and your specific role. In general, you can find the following roles.
  • Architect
  • Data scientist
  • Business Analyst
  • Development team
  • Testing Team
  • Integration testing team
  • Production release team

3. Explain Tools

You need to present all the Tools your project is using, and your specific tools. Then, in face to face interview, you need to tell what options you used to achieve what.

For example, I used some integration tool, to receive data to the development region, and to send out after unit testing.

If you explain, these key points, I can say, 100% sure, you will be selected.

4. Write about data flow

You need to explain how data is coming, is it in sequential data set, or document data. Something you need to tell clearly.

You also need to tell, after unit testing, which form you will send the data to next region. If you know this flow correctly, then you can convince easily your interviewer.

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