Machine learning with KNN classifier

KNN is important classifier used in machine learning t for classifying the data.

  • Language used: Python,
  • Library used: Scikit, numpy, pandas, matplot
  • Interface: Anaconda Jupyter
  1. Save the data file in either excel or csv format. Re-direct python directory to file location.

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2.  Import file and import required libraries, Identify the key features and classes in your data-set, and scatter plot to check their co-variance.

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3. The output of print (df)

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4. Initiate X array for the feature data-set and Y array for class data-set.

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5. CASE I: In case I we shall use the entire data in data-set for training and testing again with the same data-set. Import KNN classifier and metrics. Calculate accuracy of prediction.

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6.The net accuracy of training and testing on the same data.

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7. CASE II: In case II we shall use the divide data-set into training and testing data-sets. Import KNN classifier,confusion matrix and metrics. Give test-size and random state as per required. Calculate accuracy of prediction.

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8. Confusion matrix and net accuracy calculated where the training and testing is done on split data.

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9. CASE III: In case III we shall use the cross-validation  training and testing. Import KNN classifier and metrics. Give test-size and random state as per required. Apart from accuracy score, cross-validation accuracy score, precision score, recall score, F1 score is also important parameters to assess the machine learned classifier.

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10. Many times it a question as to use which K value. Use the below code to figure out optimum value of k-value that gives maximum accuracy in predicting.

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About YANAMALA VIJAY RAJ

Mtech in Clinical Eng Jointly offered by Indian institute of technology Madras& Christian medical college Vellore& Sree chitra tirunal institute for medical sciences and technology Trivandrum.
This entry was posted in Machine Learning, Uncategorized and tagged , , . Bookmark the permalink.

2 Responses to Machine learning with KNN classifier

  1. mohammed says:

    wow…what a great article…great writer..

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