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LOGISTIC REGRESSION

 INTRODUCTION: It is a statistic method that is used for building machine learning models where the dependent variable is binary. It is used to describe data and the relationship between one dependent variable and one or more independent variables. The name "logistic regression" is derived from the concept of the logistic function that is uses. The logistic function is also known as the sigmoid function. The value of this logistic function lies between zero(0) and one(1). It is Supervised Classification Algorithm. The value of the logistic regression must be between 0 and 1, which cannot go beyond this limit, so it forms a curve like the "S" form. The S-form curve is called the Sigmoid function or the logistic function.   LOGISTIC REGRESSION EQUATION: The Logistic regression equation can be obtained from the Linear Regression equation. The mathematical steps to get Logistic Regression equations are given below: The equation of the straight line can be written as:   ...
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PYTHON PROGRAMMING LANGUAGE



INTRODUCTION:
                                 Python is a high-level, general-purpose and a very popular programming language. Python programming language is being used in web development, Machine Learning applications, along with all cutting edge technology in Software Industry. Python is a cross-platform programming language, which means that it can run on multiple platforms like Windows, macOS, Linux, and has even been ported to the Java and .NET virtual machines.

Python is currently the most widely used multi-purpose, high-level programming language. Python allows programming in Object-Oriented and Procedural paradigms. Python programs generally are smaller than other programming languages like Java. Programmers have to type relatively less and indentation requirement of the language, makes them readable all the time. Python language is being used by almost all tech-giant companies like – Google, Amazon, Facebook, Instagram, Dropbox, Uber… etc. The biggest strength of Python is huge collection of standard library which can be used for the following:
  • Machine Learning
  • GUI Applications 
  • Web frameworks 
  • Image processing 
  • Web scraping 
  • Test frameworks
  • Multimedia
  • Scientific computing
  • Text processing and many more.


ADVANTAGES:
  • Presence of third-party modules 
  • Extensive support libraries(NumPy for numerical calculations, Pandas for data analytics etc.) .
  • Open source and community development 
  • Versatile, Easy to read, learn and write.
  • User-friendly data structures. 
  • High-level language. 
  • Dynamically typed language(No need to mention data type based on the value assigned, it takes data type).
  • Object-oriented language.
  • Portable and Interactive.
  • Ideal for prototypes – provide more functionality with less coding.
  • Highly Efficient(Python’s clean object-oriented design provides enhanced process control, and the language is equipped with excellent text processing and integration capabilities, as well as its own unit testing framework, which makes it more efficient).
  • (IoT)Internet of Things Opportunities.
  • Interpreted Language.
  • Portable across Operating systems.
ORGANIZATIONS:
  • Google(Components of Google spider and Search Engine) 
  • Yahoo(Maps) 
  • YouTube 
  • Mozilla 
  • Dropbox 
  • Microsoft 
  • Cisco 
  • Spotify 
  • Quora

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