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K-nearest neighbors (KNN) is a type of supervised learning algorithm used for both regression and classification. KNN tries to predict the correct class for the test data by calculating the distance between the test data and all the training points. Then select the K number of points which is closet to the test data.

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Salary_Prediction_and_Analysis

  • K-nearest neighbors (KNN) is a type of supervised learning algorithm used for both regression and classification.
  • KNN tries to predict the correct class for the test data by calculating the distance between the test data and all the training points.
  • Then select the K number of points which is closet to the test data.

KneighborPic

Creator: Anandan Raju

Synopsis

  1. Import Packages
  2. Load Dataset
  3. Summarize DataSet
  4. Mapping Salary Data to Binary Value
  5. Segregate Dataset into X & Y
  6. Feature Selection
  7. Splitting Dataset into Train & Test
  8. Feature Scaling
  9. Training
  10. Prediction
  11. Accuracy Score
  12. Prediction Output

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K-nearest neighbors (KNN) is a type of supervised learning algorithm used for both regression and classification. KNN tries to predict the correct class for the test data by calculating the distance between the test data and all the training points. Then select the K number of points which is closet to the test data.

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