ML3.ipynb

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        "id": "3jfYtFz0YyuF"
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      "source": [
        "\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Load the iris dataset\n",
        "iris = load_iris()\n",
        "X = iris.data\n",
        "y = iris.target\n"
      ],
      "metadata": {
        "id": "paZclbjEbXVK"
      },
      "execution_count": 3,
      "outputs": []
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      "source": [
        "\n",
        "# Split the dataset into training, validation, and testing sets\n",
        "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
        "\n",
        "# Normalize the dataset\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_val = scaler.transform(X_val)\n",
        "X_test = scaler.transform(X_test)\n"
      ],
      "metadata": {
        "id": "PiKC7F3Kb68l"
      },
      "execution_count": 4,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "\n",
        "# Find the best value for k using the validation set\n",
        "best_k = 0\n",
        "best_score = 0\n",
        "for k in range(1, 21):\n",
        "    clf = KNeighborsClassifier(n_neighbors=k)\n",
        "    clf.fit(X_train, y_train)\n",
        "    score = accuracy_score(y_val, clf.predict(X_val))\n",
        "    if score > best_score:\n",
        "        best_k = k\n",
        "        best_score = score\n"
      ],
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      "execution_count": 5,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "\n",
        "# Train the model using the best value for k\n",
        "clf = KNeighborsClassifier(n_neighbors=best_k)\n",
        "clf.fit(X_train, y_train)\n",
        "\n"
      ],
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      "execution_count": 6,
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              "KNeighborsClassifier(n_neighbors=1)"
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      "cell_type": "code",
      "source": [
        "# Make predictions on the training, validation, and testing sets\n",
        "y_train_pred = clf.predict(X_train)\n",
        "y_val_pred = clf.predict(X_val)\n",
        "y_test_pred = clf.predict(X_test)\n",
        "\n",
        "# Calculate the training, validation, and testing accuracy\n",
        "train_acc = accuracy_score(y_train, y_train_pred)\n",
        "val_acc = accuracy_score(y_val, y_val_pred)\n",
        "test_acc = accuracy_score(y_test, y_test_pred)\n",
        "\n",
        "# Calculate the confusion matrix for the testing set\n",
        "confusion_mat = confusion_matrix(y_test, y_test_pred)\n"
      ],
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      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Display the results\n",
        "print(\"Best value for k:\", best_k)\n",
        "print(\"Training Accuracy:\", train_acc)\n",
        "print(\"Validation Accuracy:\", val_acc)\n",
        "print(\"Testing Accuracy:\", test_acc)\n",
        "print(\"Confusion Matrix:\\n\", confusion_mat)\n"
      ],
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      "outputs": [
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          "text": [
            "Best value for k: 1\n",
            "Training Accuracy: 1.0\n",
            "Validation Accuracy: 1.0\n",
            "Testing Accuracy: 0.9565217391304348\n",
            "Confusion Matrix:\n",
            " [[6 0 0]\n",
            " [0 9 1]\n",
            " [0 0 7]]\n"
          ]
        }
      ]
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