K-Nearest Neighbor algorithm is one of the simplest and still popular machine learning models out there. If a simple model can do the job, I would never go for a harder one. Simply because simple models are easier to explain to the stakeholders.
This tutorial will cover everything you need to know to use a KNN classifier and a KNN Regressor for your project. This tutorial starts with a overview on how K nearest Neighbors algorithm works, then developing a classifier in Python's sklearn library with KNN Classifier, and developing a regression model in Python's sklearn library with KNN regressor.
Both the models will include a detailed process on how you can choose the right K for your model.
The dataset for the KNN classification model is here:
github.com/rashida048/Machine...
The dataset for the KNN regression model is here:
github.com/rashida048/Machine...
Please feel free to check out the sklearn documentation on KNN Classifier for more details:
scikit-learn.org/stable/modul...
Please have a look at the sklearn documentation on KK Regressor if you are interested:
scikit-learn.org/stable/modul...
The complete code of the Classification and Regression model can be found in these links:
KNN Classifier:
github.com/rashida048/Machine...
KNN Regressor:
github.com/rashida048/Machine...
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