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Keyword Rank Prediction with Markovchain

Markov chains are mathematical systems that hop from one “state” (a situation or set of values) to another. Also, a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. The state space, or set of all possible states, can be anything: letters, numbers, weather conditions, baseball scores, or stock performances. Markov chains may be modelled by finite state machines, and random walks provide a prolific example of their usefulness in mathematics.

In this model we have made a prediction of certain keywords and its ranking.

Before using:

Cannot predict what rank will come. Need to use SEO Quake to check keyword rank normally.

Checking keyword’s rank:

Analysis:

• collect all the keywords & it’s previous ranks into a file

• compare current rank vs previous ranks

• predict future rank based on current rank

Loading libraries:

Fetching the rank and the keyword then placing it to a specific variable:

Condition if the value of a keyword is higher or lower, assigning the value accordingly:

Output:

Keyword:-

mini dv conversion service

Using prediction model

Showing Output of the keyword is between 2 to 4. Between the range. So it Satisfy the algorithm.

Keyword:-

mini dv conversion service

Using prediction model

Showing Output of the keyword is between 16 to 26. Between the range. So it Satisfy the algorithm.

Keyword:-

vhs to dvd converter software

Using prediction model

So it Satisfy the algorithm.

Keyword:-

vhs to digital

Using prediction model

Showing Output of the keyword is between 1 to 4. Between the range. So it Satisfy the algorithm.

Output:

Keyword:-

vhs into dvd

Using prediction model

Showing Output of the keyword is between 7 to 11. Between the range. So it Satisfy the algorithm.

Keyword:-

change vhs to dvd

Using prediction model

Showing Output of the keyword is between 9 to 13. Between the range. So it Satisfy the algorithm.

Keyword:-

video tape to digital

Using prediction model

Use in SEO:

An obvious use is to know the fact what would we the rank of the keyword after some changed factors are implemented, this could help in keyword analysis and to see what will be position after the changes are made. This will also help in filtering out the irrelevant factors which blocks out then ranking factors.

As we all know applying machine learning in SEO will help in many analysis as well as predictions. By using machine learning we can identify ranking potential of keywords what keyword will do good in SERP judging by many factors.

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