mlkit-learn: lightweight machine learning algorithms for learning.
mlkit-learn is a lightweight machine learning library designed to be interactive, easy-to-understand, and educational. It implements all of the classic machine learning algorithms from regression to gradient boosting trees. With only two lines of code, users can witness popular Kaggle datasets being preprocessed and predicted in action.
pip install mklearn
- download train.csv and put in the directory of your choosing.
- run the following code.
from mklearn import knn knn.demo(5) # demo(k [the number of nearest neighbour], dir [default: current directory], row [default: first 5000 rows]
model = KNNClassifier(k) model.fit(train_x, train_y) size = test_x.shape predictions =  for i in range(size): result = classifier.predict(test_x[i]) predictions.append(result)
model = KNNRegressor(k) model.fit(train_x, train_y) size = test_x.shape predictions =  for i in range(size): result = classifier.predict(test_x[i]) predictions.append(result)
Multivariate Linear Regression
- download kc_house_data.csv and put in the directory of your choosing.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
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