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5 Super SEO Blogger Tools

In this post, I have explained top blogging tools that need to be considered by every blogger. These tools help you write better SEO friendly blog posts.



1). Headline Analyzer The best tool is the EMV Headline Analyzer. When you enter the headline it analyzes it and gives you EMV ranking. When you get '50' and above it performs better SEO.

2). Headline Length Checker The usual headline length is 50 to 60 characters. Beyond that, the headline will get truncated and looks ugly for search engine users. The tool SERP Snippet Optimization Tool useful to know how it appears in the search results.

3). Free Submission to Search Engines The tool Ping-O-Matic is a nice free submission tool. After your blog post, you can submit your feed to Ping-O-Matic. It submits to search engines freely.

4). Spell and Grammar Check Another free tool is Grammarly, this tool checks your spelling and grammar mistakes. So that you can avoid small mistakes.

5). Keyword AnalyzerWordstream Keyword analyzer i…

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'.
ML model in python


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 Calculations
  • PANDA - 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.
  1. Data Analysis
  2. Data Pre-processing

How to Import Libraries in Python

import numpy as np # linear algebra
import pandas 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 to check numpy or pandas 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.
  1. Data to build Model
  2. Test data to evaluate the Model

Sample Data To Build Model

I have given a flow chart to build a model along with sample data.

References
  1. Sample ML Model Project using Python

Comments

  1. Big data solutions developer should understand the need of Data, and they should work to build more appropriate services to meet the requirements of their clients.


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Tokenization story you need Vault based Vs Vault-less

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Vault-Tokenization is a concept a Vault server create a new Token for each transaction when Customer uses Credit or Debit Card at Merchant outlets 
Let us see an example,  data analysis. Here, card numbers masked with other junk characters for security purpose.

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