{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load iris data\n",
    "iris = load_iris()\n",
    "iris_X = iris.data\n",
    "iris_y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split training data\n",
    "train_X, val_X, train_y, val_y = train_test_split(iris_X,\n",
    "                                                  iris_y,\n",
    "                                                  test_size=0.3,\n",
    "                                                  random_state=5566)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create KNN model\n",
    "model = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "# Train the model using the training sets\n",
    "model.fit(train_X, train_y)\n",
    "\n",
    "# Predict Output\n",
    "predicted = model.predict(val_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('val_y: ', val_y)\n",
    "print('pred_y:', predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# accuracy\n",
    "acc = metrics.accuracy_score(y_true=val_y, y_pred=predicted)\n",
    "print('accuracy: ', acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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