Paper PublishingA Study of Machine Learning Techniques on Clustering, Classification and Regression
Objective
The main objective of this application is to give a brief introduction about the machine learning algorithms on various techniques like classification, regression and clustering.
Abstract
ABSTRACT:
Machine learning is predominantly an area of Artificial Intelligence that has been a key component of digitalization solutions that has caught major attention in the digital arena. In this application, we intend to do a brief review of various machine learning algorithms which are most frequently used and therefore are the most popular ones. The author intends to highlight the merits and demerits of the machine learning algorithms from their application perspective to aid in informed decision-making towards selecting the appropriate learning algorithm to meet the specific requirement of the application.
Keywords—Gradient Descent, Logistic Regression, Support Vector Machine, K Nearest Neighbor, Decision Tree, Naïve Bayes, Random Forest, Linear Regression.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
SOFTWARE AND HARDWARE REQUIREMENTS:
Operating system : Windows 7 or 7+
Ram : 8 GB
Hard disc or SSD : More than 500 GB
Processor : Intel 3rd generation or high or Ryzen with 8 GB Ram
Software’s : Python 3.6 or high version, Visual studio, PyCharm.
Learning Outcomes
· What is Machine Learning.
· Abut Machine Learning algorithms.
· About Classification in machine learning.
· About Regression in machine learning.
· About clustering algorithm.
· Feature engineering techniques.
· About preprocessing techniques.
· Label encoding techniques.
· Plotting different graphs.
· Knowledge on PyCharm Editor.
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