ML_Classifier_iris.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "from sklearn import datasets\r\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "iris = datasets.load_iris()\r\n",
    "features = iris.data\r\n",
    "labels = iris.target"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "classifier = KNeighborsClassifier()\r\n",
    "classifier.fit(features, labels)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "predict = classifier.predict([[1, 2, 3, 4]])\r\n",
    "# print(predict)\r\n",
    "if predict == [0]:\r\n",
    "    print(\"Iris setosa\")\r\n",
    "elif predict == [1]:\r\n",
    "    print(\"Iris virginica\")\r\n",
    "else:\r\n",
    "    print(\"Iris versicolor\")"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Iris virginica\n"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}