A Review on Classification Technique

Project Code :TCMAPY443

Objective

The main objective of this application is to investigate a specific problem of whether it is valuable or not to use machine learning techniques to predict whether the review is positive or negative.

Abstract

Classification techniques have shown recently their usefulness for complex process as high dimensionality diagnosis. Also the detail that no physical model for the process is mandatory, they enable to study the problem of sensor location. Preliminary made previously in the area of living process diagnosis have been the initial key point to extend its application to the medical diagnosis framework. In spite of the behavioral difference, both domains display many common practices. However, medical diagnosis recently has carried grave challenges such (gene expression profiling) and heterogeneity of data (symbolic histo-pathological factors). We show here that both challenges can be overawed and used in return to improve complex process diagnosis.

KEYWORDS: Data mining, Naïve Bayes Classifier, k-Nearest Neighbor Classifier.

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:

  • Processor: I3/Intel Processor
  •  RAM: 4GB (min)
  • Hard Disk: 128 GB
  • Key Board: Standard Window
  •  Keyboard. Mouse: Two or Three Button Mouse.
  • Monitor :Any.

SOFTWARE SPECIFICATIONS:

  • Operating System: Windows 7+
  • Technology: Python 3.6+
  •  IDE: PyCharm IDE
  •  Libraries Used: Pandas, NumPy, NLP, Scikit-Learn, Matplotlib.

Learning Outcomes


  • About Python.
  • About Jupyter Notebook.
  • About Pandas.
  • About Numpy.
  • About Machine Learning.
  • About Artificial Intelligent.
  • About how to use the libraries.
  • Project Development Skills:
    • Problem analyzing skills.
    • Problem solving skills.
    • Creativity and imaginary skills.
    • Programming skills.
    • Deployment.
    • Testing skills.
    • Debugging skills.
    • Project presentation skills.
    • Thesis writing skills

Demo Video

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