/PCA-Principle-Component-Analysis-For-Wine-dataset

PCA(Principle Component Analysis) For Wine dataset in ML

Primary LanguageJupyter NotebookMIT LicenseMIT

PCA-Principle-Component-Analysis-For-Wine-dataset-

PCA(Principle Component Analysis) For Wine dataset in ML

Requirements

  1. import numpy as np

  2. import pandas as pd

  3. import matplotlib.pyplot as plt

  4. sklearn

  5. Wine dataset

This Program is About Principal Componenet analysis of Wine dataset.

I have used Jupyter console.

Along with Clustering Visualization Accuracy using Classifiers Such as Logistic regression, KNN, Support vector Machine, Gaussian Naive Bayes, Decision tree and Random forest Classifier is provided. To know the exactness in Accuracy Cohen Kappa is used.

For more information, Cite this paper if referred.

http://www.ijitee.org/wp-content/uploads/papers/v9i7/G5943059720.pdf

https://www.researchgate.net/profile/Ayantika_Nath2/publication/341671505_Clustering_Visualization_and_Class_Prediction_using_Flask_of_Benchmark_Dataset_for_Unsupervised_Techniques_in_ML/links/5ece482292851c9c5e5f8695/Clustering-Visualization-and-Class-Prediction-using-Flask-of-Benchmark-Dataset-for-Unsupervised-Techniques-in-ML.pdf

https://www.researchgate.net/profile/Ayantika_Nath2/publication/341150281_Clustering_Using_Dimensional_Reduction_Techniques_for_Energy_Efficiency_in_WSNs_A_Review/links/5eb10592299bf18b9595b113/Clustering-Using-Dimensional-Reduction-Techniques-for-Energy-Efficiency-in-WSNs-A-Review.pdf

Citing the paper(if referred) is mandatory since the paper has copyrights.

Enjoy Coding

PCA Cluster

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