Accurate Feature Elimination approach in Ensemble Learning on NSL-KDD dataset

Project Code :TCMAPY490

Objective

The aim of this project is to gain insights by studying and comparing different concept learning algorithms against ensemble learning techniques.

Abstract

The term "learning" has numerous definitions. One of the most basic definitions is: the activity or process of acquiring information or skill through study, practice, instruction, or experience. There are different classifications of learning strategies, just as there are various meanings for learning. A couple examples include Ensemble Learning and Concept Learning. In terms of machine learning, "concept learning" can be defined as: The problem of searching through a predefined space of potential hypotheses for the hypothesis that best fits the training examples. Much of human learning involves acquiring general concepts from past experiences. For example, humans identify different vehicles among all the vehicles based on specific sets of features defined over a large set of features. This special set of features differentiates the subset of cars in a set of vehicles. This set of features that differentiate cars can be called a concept. Similarly, machines can learn from concepts to identify whether an object belongs to a specific category by processing past/training data to find a hypothesis that best fits the training examples

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

HARDWARE SPECIFICATIONS:

RAM: 8GB (min)

Processor: I3/Intel Processor.

SOFTWARE SPECIFICATIONS:

Operating System: Windows 7+               

IDE: Jupyter Notebook.

Libraries Used: Pandas, numpy, SKlearn, Pgmpy

Learning Outcomes

  • What is Machine Learning?
  • About Concept Learning.
  • About Ensemble learning.
  • Knowledge on Jupyter Notebook Editor.
  • About Pandas.
  • About Numpy.


Demo Video

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