KNN_iris_LGMVIP_Intern.ipynb

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      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.datasets import load_iris\n",
        " \n",
        "# Loading data\n",
        "iris = load_iris()\n",
        " \n",
        "# Create feature and target arrays\n",
        "X = iris.data\n",
        "y = iris.target\n",
        " \n"
      ]
    },
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      "source": [
        "# Split into training and test set\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42)\n",
        " \n",
        "knn = KNeighborsClassifier(n_neighbors=50)\n",
        " \n",
        "knn.fit(X_train, y_train)\n",
        " \n"
      ],
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              "KNeighborsClassifier(n_neighbors=50)"
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      "source": [
        "X_test.shape\n"
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              "(30, 4)"
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          "metadata": {},
          "execution_count": 3
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    },
    {
      "cell_type": "code",
      "source": [
        "# Predict on dataset which model has not seen before\n",
        "y_pred=knn.predict(X_test)\n",
        "y_pred"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "8dHSkO1w9GrM",
        "outputId": "7e4f1924-c46e-42bd-defa-9755f7f4be64"
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              "array([1, 0, 2, 1, 1, 0, 1, 2, 1, 1, 2, 0, 0, 0, 0, 1, 2, 1, 1, 2, 0, 1,\n",
              "       0, 2, 2, 2, 2, 2, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 4
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score,  confusion_matrix\n",
        "print(confusion_matrix(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K4LqtE3_-3YK",
        "outputId": "93cbea83-0bd9-4f45-9889-a2bfaf3302a0"
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      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[10  0  0]\n",
            " [ 0  9  0]\n",
            " [ 0  1 10]]\n"
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    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test,y_pred)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-OCdTEdAVs2",
        "outputId": "c42cae4e-e0c4-4132-9e09-f7b7f12133e1"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96.66666666666667"
            ]
          },
          "metadata": {},
          "execution_count": 6
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