A Review on Python for Data Science, Machine Learning and IOT P.N.Siva Jyothi, Rohita Yamaganti

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International Journal of Scientific & Engineering Research Volume 10, Issue 12, December-2019 851 ISSN 2229-5518 A Review on Python for Data Science, Machine Learning and IOT P.N.Siva jyothi, Rohita Yamaganti

Abstract- Python is an object oriented, scripted and interpreted language for both learning and real world programming. Python is a powerful high-level language created by Guido van Rossum. In this paper, we will provide an introduction to the main Python software tools used for Data science, Machine learning techniques and IOT. Briefly, this paper will first introduce Python as a language, and give introduction about Data science, Machine learning and IOT, and then describe packages that are popular in the Data science and Machine learning communities, such as NumPy, SciPy, TensorFlow, Keras ,Matplotlib etc. From there, we will move to show the importance of python for building IOT applications. We will use different code examples throughout. To aid the learning experience, execute following examples contained in this paper interactively using Jupiter notebooks . Keywords: Machine learning · Data Science · IOT · Tools · Languages · Python

Introduction

1.1 Introduction to python Python is a general-purpose, high-level programming language which became popular in the recent times .It allows programmer to write the code in fewerIJSER lines that is not In this paper we wish to give brief possible with other languages. The idea of python in the area of Data important feature in Python science, IOT and Machine learning. programming is it supports multiple Python is known to have an programming paradigms. Python abundance of libraries that assist provides a large set of with data analysis and scientific comprehensive standard library computing. For example, we can which is extensible. The main build python application which features of Python are Simple and helps data analysts to analyze large easy to learn, Freeware and open amounts of data for scientific source, High level , Simple and easy computing. The prerequisites for to learn, Freeware and open source, this paper are basic under-standing High level programming language, of statistics, as well as some Platform independent, Portability, experience in any C-style language. Dynamically typed, Both procedure Some knowledge of Python is useful oriented and Object oriented, but not a must. Interpreted, Extensible, Embedded, Extensive Library. An accompanying Github repository is provided to aid the tutorial. It contains a number of notebooks of

International Journal of Scientific & Engineering Research Volume 10, Issue 12, December-2019 852 ISSN 2229-5518 python code snippets for reference. uncertainty. Python provides It helps to go through number of various predefined modules to work examples related to different on Data science projects. modules of Python. 1.3 Introduction to Machine Https://github.com/mdbloice/MLDS Learning

The term machine learning refers to the automated detection of meaningful patterns in data. In the past couple of decades it has become a common tool in almost any task that requires information extraction

from large data sets. We are surrounded by a machine learning based technology: search engines 1.2 Introduction to Data Science learn how to bring us the best Data science is a multi- results (while placing profitable disciplinary area that uses scientific ads), anti-spam software learns to methods, procedures, tools and filter our email messages, and credit systems to extract knowledge and card transactions are secured by get insights into structured and software that learns how to detect unstructured data. Data science is frauds. Digital cameras learn to related to data analytics, data detect faces and intelligent personal mining and big data. It understands assistance applications on smart- the phenomenon of the data. It phones learn to recognize voice employs techniquesIJSER and theories commands. drawn from many fields within the Cars are equipped with context of mathematics, statistics, accident prevention systems that are computer science, and information built using machine learning science. algorithms. Machine learning is also widely used in scientific applications such as bioinformatics, medicine, and astronomy. One common feature of all of these applications is that, in contrast to more traditional uses of computers, in these cases, due to the complexity of the patterns that need to be

detected, a human programmer Statistics is one of the most cannot provide an explicit, fine- important disciplines to provide detailed specification of how such tools and methods to find structure tasks should be executed. Taking in and to give deeper insight into example from intelligent beings, data, and the most important many of our skills are acquired or discipline to analyze and quantify refined through learning from our

International Journal of Scientific & Engineering Research Volume 10, Issue 12, December-2019 853 ISSN 2229-5518 experience (rather than following electromechanical systems (MEMS) explicit instructions given to us). and the internet. Machine learning tools are concerned with endowing programs with the ability to “learn “and adapt.

As per the TIOBE index, Python was the programming language of the year in 2018. With a Because machine learning is rating of 10.020%, it is also the 3rd typically used to process large most popular language in volumes of data, you may want to 2019.Python is mostly used for choose a powerful low-level writing web applications, but it has language. However, if you’re only gained popularity in the IOT just beginning to explore this field, it system. It is an interpreted language might be better to start with Python. that offers readability with syntax Python is beginner-friendly, and can without compromising the size. This do the same thing that other coding language has a large number of languages can, but in fewer lines of libraries; it can get more stuff done code.If you are interested in with fewer codes. Python’s clean exploring machineIJSER learning with syntax is suitable for database Python, this paper will serve as your arrangement. In case your app guide. This is paper gives overview needs the data to be arranged in a of programming machine learning database format or use tables. using Python. Python is the right choice available. Python is the right choice, for data 1.3 Introduction to IOT analysis in IOT systems. The language is simple and can be easily The Internet of Things deployed. Its large community helps (IOT) is a scenario in which objects, in providing help and libraries as animals or people are provided with and when required. It is the ideal single identifiers and the capability language for data-intensive to automatically transfer and the applications. capability to automatically transfer data more to a network without 2. Objectives of Study requiring human-to-human or human-To-computer 1. To conceptualize the features of communication. IOT has evolved Python from the meeting of wireless 2. To investigate python modules Technologies, micro- for Data Science like Numpy

International Journal of Scientific & Engineering Research Volume 10, Issue 12, December-2019 854 ISSN 2229-5518 which is used for matrix and development of a wide range of vector manipulation,Scipy, the applications from simple text 2D plotting library Matplotlib etc processing to WWW browsers to 3. To focus on python modules for games. Machine learning like Tensor flow numerical computations for machine learning, Keras for neural networks and deep learning

Each will be discussed in this paper.

3. Related Works 3.1 Basic Features of Python Python is a general-purpose interpreted, interactive, object- oriented, and high-level programming language. It was created by Guido van Rossum 3.2 Python for Data Science during 1985- 1990. Like Perl, Python source code is also These are the most essential Data available under the GNU General Science libraries you have to Public License (GPL). This paper know: gives enough understanding on Python programming language.  Numpy IJSER Matplotlib   Python is Interpreted − Scipy Python is processed at runtime by the interpreter. You do not need to Numpy: Numpy will help us to compile your program before manage multi-dimensional arrays executing it. This is similar to PERL very efficiently. Maybe it is difficult and PHP. to do that directly, but since the  Python is Interactive − you concept is a crucial part of data can actually sit at a Python prompt science, many other libraries (well, and interact with the interpreter almost all of them) are built on directly to write your programs. Numpy. Simply to say, without  Python is Object-Oriented − Numpy it is difficult to use Pandas, Python supports Object-Oriented style or technique of programming that encapsulates code within objects.  Python is a Beginner's Language − Python is a great language for the beginner-level programmers and supports the

International Journal of Scientific & Engineering Research Volume 10, Issue 12, December-2019 855 ISSN 2229-5518 Matplotlib, Scipy or Scikit-Learn. usually it provides many functions like barchart,scatterplot,piechart,histogr am etc which are useful for projecting many dimensions of data.

3-dimensional Numpy array:

But on the other hand, it also has a few well-implemented methods. It’s quite to use Numpy random function, which is found slightly better than the random module of the standard library. And when it Scipy: comes to simple predictive analytics tasks like linear or polynomial Mathematics deals with a huge regression, Numpy polyfit function number of concepts that are very will be favorite. important but at the same time, complex and time-consuming. However, Python provides the full- fledged scipy library that resolves IJSERthis issue for us. In this scipy, we will be learning how to make use of this library along with a few functions and their examples.

Matplotlib:

Data visualization is very important. Data visualization helps us to better understand the data, discover things that wouldn’t discover in raw 3.3 Python for Machine learning format and communicate findings The following libraries are more efficiently to others. general-purpose libraries for The best and most well-known anything involving advanced data Python data visualization library is manipulation. This means they can Matplotlib. It is not easy to use,but all be used in implementing

International Journal of Scientific & Engineering Research Volume 10, Issue 12, December-2019 856 ISSN 2229-5518 machine learning, and many of the Built on top of Theano and higher level machine learning Tensorflow is Keras, a high-level libraries makes use of some or all of library for working with these libraries. Getting acquainted datasets. Keras is best known with them is highly recommended if for being one of the easiest you plan on getting anywhere with machine learning libraries out scientific Python programming. there because it is coded entirely This list is by no means in Python, while using either exhaustive; it is meant to be a Theano or Tensorflow as a back- starting point for you as you explore end. It is the most beginner- machine learning through Python! friendly library for machine Already we discussed Numpy, learning, and includes functions Matplotlib and Scipy which are used for creating training datasets and for machine learning too. But we more. Keras' neural networks will see other modules which are API was developed for fast used in machine learning. experimentation and is a good Tensor flow: choice for any deep learning Tensor flow is almost certainly the project that requires fast most well-known open source prototyping. machine learning library available for Python, and for good reason. It was developed by Google, and is used in nearly every Google application that utilizes machine learning. If you’veIJSER used Google Photos or voice search, then you’ve been using tensor flow. Tensorflow is extremely well documented and supported, and is optimized for speed. It is more difficult to learn, however, because it is actually a Python front-end coded on top of C or C++. 3.4 Python for IOT Some years ago python was only used for web applications; no one thought it would apply in iot development. But now developer’s uses python programming language for developing the IOT devices. With it’s efficient programming and easily syntax’s most of us Keras: looking towards python. Developers have to create iot devices to make life easy. The

International Journal of Scientific & Engineering Research Volume 10, Issue 12, December-2019 857 ISSN 2229-5518 small iot devices have low used by many research computational power and communities. memory, so developers choose python scripting language. Acknowledgement Nowadays most popular microcontrollers also use python We would like to thank language like Micropython Department of CSE,Sri Satya Sai board and software package and Institute of Technology and Medical other.Again, this is far from a Sciences for encouragement and comprehensive list of machine support in writing this paper. learning libraries. Python is a

versatile language, and there is a References library available for any

preference. The highlighted 1. Jordan, M.I., Mitchell, T.M.: Machine libraries are a great place to learning: trends, perspectives, and begin your journey. prospects. Science 349(6245), 255–260 (2015) Conclusion 2. Le Cun, Y., Bengio, Y., Hinton, G.: In this paper we have presented Deep learning. Nature 521(7553), 436– 444 (2015) usage of python as a tool in various 3. Holzinger, A., Dehmer, M., Jurisica, I.: research areas like Data Knowledge discovery and interactive Science,Machine learning and IOT. data mining in bioinformatics - state- Along with Python language, there of-the-art, future challenges and research direc-tions. BMC Bioinform. are many other languages are used 15(S6), I1 (2014) for Data science,MachineIJSER learning 4. Wolfram, S.: Mathematica: A System and for developing iot devices like for Doing Mathematics by Computer. Java, C++ etc. But right now most of Addi-son Wesley Longman Publishing Co., Inc., Boston (1991) the developers use python scripting 5. Engblom, S., Lukarski, D.: Fast language than Java, C++. Because of MATLAB compatible sparse assembly its easy syntax, secure coding, and on multicore computers. Parallel it’s simplicity. When it comes to Comput. 56, 1–17 (2016) https://www.researchgate.net/publicati robust and performance, developers on/330513589_Internet_of_Things_IOT choose Python. Iot, when integrated _Using_Raspberry_Pi with AI, will help developers to work with Python further. 6. Python Machine Learning: A Guide To Getting Started | Built In With respect to the future work 7. https://www.researchgate.net/publicati there is still huge space for this on/330513589_Internet_of_Things_IOT language to serve other upcoming _Using_Raspberry_Pi 8. https://www.techaheadcorp.com/blog/ research areas because of its top-6-programming-languages-for-iot- features like simplicity,extensive projects/ library,inbuilt and extensible 9. https://www.google.com/role-of- python-in-iot-development modules.In future we will propose python as a powerful tool which is 10. Https://nbviewer.jupyter.org/github/e hmatthes/intro_programming/blob/ma

International Journal of Scientific & Engineering Research Volume 10, Issue 12, December-2019 858 ISSN 2229-5518 ster/notebooks/introducing_functions.i pynb 11. Https://data36.com/python-libraries- packages-data-scienTists/

P.N.Siva Jyothi, pursuing Ph.D in SSSTUMS Sehore, Bhopal, MadhyaPradesh and received a degree in M.Tech in Computer Science and Engineering from university of JNTUH and currently working as Assistant Professor in Sreenidhi Institute of Science & Technology,Research interest includes Data warehousing, Data Science, Machine learning,IOT and Data mining.

Rohita Yamaganti pursuing Ph.D in SSSTUMS Sehore, Bhopal, MadhyaPradesh and received a degree in M.Tech in Computer Science and Engineering from university of JNTUH and currently working as Assistant Professor in SreenidhiInstitute of Science & Technology. Research interest includes Data warehousing, Data Science, Machine learning,IOT and Data mining.

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