K_nn_on_iris_dataset.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNb1aghTv3vmj90WT6MeR54",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sijuswamy/intel-unnati-training/blob/main/K_nn_on_iris_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading necessary libraries and data"
      ],
      "metadata": {
        "id": "qGBi4g1PHXu5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "coDt-d1YTIE6"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "# loading k-nn classifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Load the Iris dataset\n",
        "iris = load_iris()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "OabXM3gHIOh1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a k-NN classifier object with k=3\n",
        "knn = KNeighborsClassifier(n_neighbors=3)\n",
        "\n",
        "# Fit the classifier to the training data\n",
        "knn.fit(X_train, y_train)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "hCGYauvqTVbB",
        "outputId": "fe1425c8-8610-4e81-aa97-514e71380540"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=3)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict the target values for the testing data\n",
        "y_pred = knn.predict(X_test)\n",
        "\n",
        "# Calculate the accuracy score of the classifier\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "print('Accuracy: %.3f' % accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0pFWHu7tTXUa",
        "outputId": "91194f63-bc25-4a22-dc2b-67b4fb039854"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 1.000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict the target values for the training data\n",
        "y_pred = knn.predict(X_train)\n",
        "\n",
        "# Calculate the accuracy score of the classifier\n",
        "accuracy = accuracy_score(y_train, y_pred)\n",
        "\n",
        "print('Accuracy: %.3f' % accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s0j8ofPHHUMR",
        "outputId": "5569d840-caa6-48da-ac11-d382bcbd9af7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.950\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "y_pred = knn.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ZnmdLTpTzPN",
        "outputId": "25d70e0b-d329-4d78-bb88-d3c5b350c377"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[10  0  0]\n",
            " [ 0  9  0]\n",
            " [ 0  0 11]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot confusion matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# confusion matrix sns heatmap \n",
        "ax = plt.axes()\n",
        "df_cm = cm\n",
        "sns.heatmap(df_cm, annot=True, annot_kws={\"size\": 30}, fmt='d',cmap=\"Blues\", ax = ax )\n",
        "ax.set_title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "DpsQGLqTT8Dm",
        "outputId": "fcb3be37-a41d-4bf1-e56b-a74dba90647e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
        "print(\"Precision:\",metrics.precision_score(y_test, y_pred,average=None))\n",
        "print(\"Recall:\",metrics.recall_score(y_test, y_pred,average=None))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VQqBpistUH2I",
        "outputId": "b01fb4b4-1af0-4349-fa25-80f9a8b4590e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 1.0\n",
            "Precision: [1. 1. 1.]\n",
            "Recall: [1. 1. 1.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This code will perform k-NN classification on the Iris dataset with k=3, split the data into training and testing sets, fit the classifier to the training data, and predict the target values for the testing data. Finally, it will calculate and print the accuracy score of the classifier.\n"
      ],
      "metadata": {
        "id": "4RxgDMnHTOU1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "parameters = {\"n_neighbors\": range(1, 50)}\n",
        "gridsearch = GridSearchCV(KNeighborsClassifier(), parameters)\n",
        "gridsearch.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        },
        "id": "oKuZf42WTNc4",
        "outputId": "d0bc9e19-fff9-4904-cff7-0ca0cdf21bc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=KNeighborsClassifier(),\n",
              "             param_grid={'n_neighbors': range(1, 50)})"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=KNeighborsClassifier(),\n",
              "             param_grid={&#x27;n_neighbors&#x27;: range(1, 50)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=KNeighborsClassifier(),\n",
              "             param_grid={&#x27;n_neighbors&#x27;: range(1, 50)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gridsearch.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PuS-umxpVDiE",
        "outputId": "4e1037cc-4122-437d-f89b-6d9687784483"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_neighbors': 3}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    }
  ]
}