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    "info": {
        "author": "Ryan Han",
        "author_email": "ryan.han@uwaterloo.ca",
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            "License :: OSI Approved :: MIT License",
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        "description": "mlkit-learn\n===========\n![license](https://img.shields.io/github/license/ryanxjhan/mlkit-learn.svg)\n![pyv](https://img.shields.io/pypi/pyversions/mklearn.svg)\n![pypiv](https://img.shields.io/pypi/v/mklearn.svg?color=green)\n![format](https://img.shields.io/pypi/format/mklearn.svg)\n\nmlkit-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**.\n\n\n## Contents\n[Nearest Neighbor](#nearest-neighbor)\n\n[Multivariate Linear Regression](#multivariate-linear-regression)\n\n[Naive Bayes](#naive-bayes)\n\n[Decision Tree](#decision-tree)\n\n[Logistic Regression](#logistic-regression)\n\n\n## Install\n`pip install mklearn`\n\n\n## Demo\n\n\n### Nearest Neighbor\n#### Demo\n1. download [train.csv](https://www.kaggle.com/c/digit-recognizer/data) and put in the directory of your choosing.\n2. run the following code.\n```python\nfrom mklearn import knn\nknn.demo(5) # demo(k [the number of nearest neighbour], dir [default: current directory], row [default: first 5000 rows]\n```\n#### Use\n\n##### KNN Classifier\n\n```python\nmodel = KNNClassifier(k)\nmodel.fit(train_x, train_y)\nsize = test_x.shape[0]\npredictions = []\nfor i in range(size):\n    result = classifier.predict(test_x[i])\n    predictions.append(result)\n```\n\n##### KNN Regressor\n\n```python\nmodel = KNNRegressor(k)\nmodel.fit(train_x, train_y)\nsize = test_x.shape[0]\npredictions = []\nfor i in range(size):\n    result = classifier.predict(test_x[i])\n    predictions.append(result)\n```\n\n### Multivariate Linear Regression\n#### Demo\n1. download [kc_house_data.csv](https://www.kaggle.com/harlfoxem/housesalesprediction/download#kc_house_data.csv) and put in the directory of your choosing.\n### Naive Bayes\n### Decision Tree\n### Logistic Regression\n\n\n\n\n\n",
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