k_nearest_neighbor.ipynb

{
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  "metadata": {
    "colab": {
      "name": "k_nearest_neighbor.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyO/wJsrEkBpUFM477K0Djah",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mahendrasaikumargandham/machine-learning-algorithms/blob/main/k_nearest_neighbor.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt \n",
        "import numpy as np\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n"
      ],
      "metadata": {
        "id": "d5wFy12SFGFL"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iris = load_iris()"
      ],
      "metadata": {
        "id": "qaMOKz_uGSLR"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(iris.target_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5hMXclFOGi_u",
        "outputId": "f020dfa9-b748-4805-fd09-a01282f6a473"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['setosa' 'versicolor' 'virginica']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = iris.data\n",
        "y = iris.target\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 41)"
      ],
      "metadata": {
        "id": "XFo7_1wLGrPC"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors = 5)\n",
        "knn.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZXTz9CeVG-Yo",
        "outputId": "f89f80e0-b3f9-4024-be0c-c1d7ac6ccf42"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = knn.predict(X_test)"
      ],
      "metadata": {
        "id": "CPzI7R2ZHTPx"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn.score(X_train, y_train), knn.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KD5lEdVsHqqD",
        "outputId": "e403dfec-b3d3-4d87-840c-c1e47512c1be"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9833333333333333, 0.9666666666666667)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "diYIkptiIBvV",
        "outputId": "16d1b146-9a29-4086-ff82-0da9f455e47c"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 9,  0,  0],\n",
              "       [ 0, 10,  1],\n",
              "       [ 0,  0, 10]])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZsRozrhaIQRY",
        "outputId": "1d8715cd-acc1-4a7d-e951-f4cfd22891aa"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00         9\n",
            "           1       1.00      0.91      0.95        11\n",
            "           2       0.91      1.00      0.95        10\n",
            "\n",
            "    accuracy                           0.97        30\n",
            "   macro avg       0.97      0.97      0.97        30\n",
            "weighted avg       0.97      0.97      0.97        30\n",
            "\n"
          ]
        }
      ]
    }
  ]
}