scikitlearn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "25a6342c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d48b4536",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "feature_names = iris.feature_names ##features or characteristics of the datasets\n",
    "target_names = iris.target_names ##types or variances in the datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2c85ce4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(105, 4)\n",
      "(45, 4)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fc004ca9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train, y_train)\n",
    "prediction = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fe66140b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9777777777777777\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.accuracy_score(prediction, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6f897c2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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