Data Science - Algorithms

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Data Science

• Explores the study and construction of Algorithms 

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Introduction

• In present reality, each assignment is being
  robotized. Gone are the days when you needed to
  stroll for twenty days or a ride a horse for
  miles to get to a town or even do manual work,
  for example, carrying heavy logs.
• The term Data Science has developed as a result
  of the advancement of scientific measurements,
  data examination, and enormous data.
• Data Science explores the study and construction
  of algorithms that can learn from and make
  predictions on data.

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• Here are several Algorithms each in data science,
  for example, you should know today with the goal
  that our future can be more splendid.
• Decision Tree
• Linear Regression
• Logistic Regression
• Support Vector Machine
• Naïve Bayes
• Gradient Boosting Algorithm

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Decision Tree

• A Decision tree is a Decision support device that
  uses a tree-like diagram or model of choices and
  their potential results, including chance event
  results, asset expenses, and utility.
• It is one approach to show a algorithm that just
  contains contingent control explanations.

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Linear Regression

• In Statistics, linear regression is a direct way
  to deal with demonstrating the connection between
  a scalar reaction and at least one logical
  factors.
• A linear regression line has an equation of the
  form Y a bX, where X is the explanatory
  variable and Y is the dependent variable. The
  slope of the line is b, and a is the intercept.

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Logistic regression

• Logistics Regression is a factual model that in
  its essential structure utilizes a calculated
  capacity to demonstrate a paired dependent
  variable, albeit a lot increasingly complex
  expansions exist.
• In regression analysis, logistic regression is
  assessing the parameters of a logistic model.

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Support Vector Machine

• Support Vector Machines are based on the concept
  of decision planes that define decision
  boundaries.
• In this example, the objects belong either to
  class GREEN or RED. The separating line defines a
  boundary on the right side of which all objects
  are GREEN and to the left of which all objects
  are RED.
• Any new object (white circle) falling to the
  right is labeled, i.e., classified, as GREEN (or
  classified as RED should it fall to the left of
  the separating line).

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Naïve Bayes

• Naive Bayes Classifiers rely on the Bayes
  Theorem, which is based on conditional
  probability or in simple terms, the likelihood
  that an event (A) will happen given that another
  event (B) has already happened.
• Essentially, the theorem allows a hypothesis to
  be updated each time new evidence is introduced.

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Gradient Boosting Algorithm

• Gradient boosting is a machine learning technique
  for regression and classification problems, which
  produces a prediction model in the form of an
  ensemble of weak prediction models, typically
  decision trees. 

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