deep_learning_model_implementation.ipynb

basic_deep_learning_model_implementation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "name": "deep_learning_model_implementation.ipynb",
      "authorship_tag": "ABX9TyPsKsWL07TCqX0puJVCJwQ3",
      "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/gist/ShaonBhattaShuvo/17b2b3c54985956be7afb40b200ded35/basic_deep_learning_model_implementation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\"\"\"\n",
        "@author: Shaon Bhatta Shuvo\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "4OUNOzaeBrcU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Importing Libraries"
      ],
      "metadata": {
        "id": "dMx-uHrTDCcB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import sklearn.datasets as skd\n",
        "import tensorflow.keras as tfk\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics \n",
        "from mlxtend.plotting import plot_decision_regions"
      ],
      "metadata": {
        "id": "0BR7fBM7BuDp"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Generating synthetic non-linear data to solve classification problem\n"
      ],
      "metadata": {
        "id": "82678TC2Cfns"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X,y = skd.make_circles(n_samples=100, shuffle=False, noise=None, random_state=None, factor=0.6)\n",
        "#print(f\"X={X} \\n y={y}\")"
      ],
      "metadata": {
        "id": "2EX2NkfVCc_K"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Finding and counting unique elements. \n"
      ],
      "metadata": {
        "id": "z-HNwEMFEjDk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "unique_elements, counts_elements = np.unique(y, return_counts=True)\n",
        "print(np.asarray((unique_elements, counts_elements)))"
      ],
      "metadata": {
        "id": "t0P0-F2_FQc6",
        "outputId": "5f0a08a6-960e-497d-bac7-c12ef113e59a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0  1]\n",
            " [50 50]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Visualizing the synthetic dataset of Class 1 and Class 0: \n"
      ],
      "metadata": {
        "id": "DztimyAXHEtk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(X[:, 0][y == 0], X[:, 1][y == 0], 'g^', label='Class: 0')\n",
        "plt.plot(X[:, 0][y == 1], X[:, 1][y == 1], 'ro' , label=\"Class: 1\")\n",
        "plt.title(\"Visualizing the synthetic dataset of class 1 and 0\")\n",
        "plt.xlabel(\"X1\")\n",
        "plt.ylabel(\"X2\")\n",
        "plt.legend()\n",
        "plt.show()  "
      ],
      "metadata": {
        "id": "LTw7Od8LGqhP",
        "outputId": "e841a236-886e-45f4-99b9-2e35693ec677",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Splitting the dataset into train and test sets. "
      ],
      "metadata": {
        "id": "4e-w0BsrIaVu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
      ],
      "metadata": {
        "id": "7J3lducHIZTc"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Frequency of unique class of elements in the test set:"
      ],
      "metadata": {
        "id": "h1KcUeaRI5P3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "unique_elements_test, count_elements_test=np.unique(y_test, return_counts=True)\n",
        "print(unique_elements_test, count_elements_test)"
      ],
      "metadata": {
        "id": "rEqdbwnbJF-i",
        "outputId": "71f86230-0ebf-4207-aaa5-2636dd62c802",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1] [10 10]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Creating validation set  by copying last 10 elements from the training set\n",
        "\n"
      ],
      "metadata": {
        "id": "RyBWd7LiKSZe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_val = X_train[70:]\n",
        "y_val = y_train[70:]"
      ],
      "metadata": {
        "id": "KRX-qL2fKYBm"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Removing the validation set (last 10 elements) from training set\n"
      ],
      "metadata": {
        "id": "_SA37M30KeK2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train[:70]\n",
        "y_train = y_train[:70]"
      ],
      "metadata": {
        "id": "XMrAxW3QLFk9"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Building a simple deep neural model\n"
      ],
      "metadata": {
        "id": "GiAVqNJhMegW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2 input -> [50 units in layer1] ->[100 units in layer2] ->[100 units in layer3] -> 1 output"
      ],
      "metadata": {
        "id": "70NVzWnLOwB0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tfk.Sequential()\n",
        "model.add(tfk.layers.Dense(50,input_shape=(2,), activation='relu')) #First Hidden Layer\n",
        "model.add(tfk.layers.Dense(100, activation='relu')) #Second Hidden Layer\n",
        "model.add(tfk.layers.Dense(100, activation='relu')) #Third Hidden Layer\n",
        "model.add(tfk.layers.Dense(1, activation='sigmoid')) #Output Layer"
      ],
      "metadata": {
        "id": "RvdWsTobMdYs"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Compiling the model for binary classification \n",
        "[Use loss = categorical_crossentropy for multiclass prediction.] "
      ],
      "metadata": {
        "id": "s1qTAZLBPF1a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) \n"
      ],
      "metadata": {
        "id": "IlbVK1_eOpUC"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model Overview"
      ],
      "metadata": {
        "id": "BId9DTE7P9ea"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "YgzYJIKuPzrU",
        "outputId": "88ff7cd9-25fc-4cf4-afc8-d3fc4c343bf5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_16 (Dense)            (None, 50)                150       \n",
            "                                                                 \n",
            " dense_17 (Dense)            (None, 100)               5100      \n",
            "                                                                 \n",
            " dense_18 (Dense)            (None, 100)               10100     \n",
            "                                                                 \n",
            " dense_19 (Dense)            (None, 1)                 101       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 15,451\n",
            "Trainable params: 15,451\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Train the model"
      ],
      "metadata": {
        "id": "96vaoCCDPt21"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "training = model.fit(X_train,y_train, epochs = 50, batch_size =10, validation_data =(X_val,y_val))\n"
      ],
      "metadata": {
        "id": "Ek82CaNgQ_DO",
        "outputId": "a0d309f7-6d88-494a-d2a8-2fc227499865",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "7/7 [==============================] - 5s 55ms/step - loss: 0.6785 - accuracy: 0.5000 - val_loss: 0.6604 - val_accuracy: 0.5000\n",
            "Epoch 2/50\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.6673 - accuracy: 0.5000 - val_loss: 0.6517 - val_accuracy: 0.5000\n",
            "Epoch 3/50\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.6570 - accuracy: 0.5000 - val_loss: 0.6463 - val_accuracy: 0.5000\n",
            "Epoch 4/50\n",
            "7/7 [==============================] - 0s 33ms/step - loss: 0.6519 - accuracy: 0.5000 - val_loss: 0.6395 - val_accuracy: 0.5000\n",
            "Epoch 5/50\n",
            "7/7 [==============================] - 0s 24ms/step - loss: 0.6451 - accuracy: 0.5000 - val_loss: 0.6344 - val_accuracy: 0.5000\n",
            "Epoch 6/50\n",
            "7/7 [==============================] - 0s 23ms/step - loss: 0.6365 - accuracy: 0.5000 - val_loss: 0.6283 - val_accuracy: 0.5000\n",
            "Epoch 7/50\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.6276 - accuracy: 0.5000 - val_loss: 0.6193 - val_accuracy: 0.5000\n",
            "Epoch 8/50\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.6162 - accuracy: 0.6286 - val_loss: 0.6047 - val_accuracy: 0.7000\n",
            "Epoch 9/50\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.6000 - accuracy: 0.7286 - val_loss: 0.5887 - val_accuracy: 0.7000\n",
            "Epoch 10/50\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.5822 - accuracy: 0.7143 - val_loss: 0.5672 - val_accuracy: 0.7000\n",
            "Epoch 11/50\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.5611 - accuracy: 0.8143 - val_loss: 0.5404 - val_accuracy: 0.8000\n",
            "Epoch 12/50\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.5293 - accuracy: 0.8429 - val_loss: 0.5077 - val_accuracy: 0.8000\n",
            "Epoch 13/50\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4917 - accuracy: 0.8429 - val_loss: 0.4700 - val_accuracy: 0.8000\n",
            "Epoch 14/50\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4436 - accuracy: 0.9286 - val_loss: 0.4219 - val_accuracy: 1.0000\n",
            "Epoch 15/50\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.3912 - accuracy: 1.0000 - val_loss: 0.3596 - val_accuracy: 1.0000\n",
            "Epoch 16/50\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.3258 - accuracy: 1.0000 - val_loss: 0.3005 - val_accuracy: 1.0000\n",
            "Epoch 17/50\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.2668 - accuracy: 1.0000 - val_loss: 0.2442 - val_accuracy: 1.0000\n",
            "Epoch 18/50\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.2072 - accuracy: 1.0000 - val_loss: 0.1860 - val_accuracy: 1.0000\n",
            "Epoch 19/50\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.1543 - accuracy: 1.0000 - val_loss: 0.1371 - val_accuracy: 1.0000\n",
            "Epoch 20/50\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.1109 - accuracy: 1.0000 - val_loss: 0.0987 - val_accuracy: 1.0000\n",
            "Epoch 21/50\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.0804 - accuracy: 1.0000 - val_loss: 0.0721 - val_accuracy: 1.0000\n",
            "Epoch 22/50\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.0581 - accuracy: 1.0000 - val_loss: 0.0530 - val_accuracy: 1.0000\n",
            "Epoch 23/50\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.0411 - accuracy: 1.0000 - val_loss: 0.0396 - val_accuracy: 1.0000\n",
            "Epoch 24/50\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.0301 - accuracy: 1.0000 - val_loss: 0.0296 - val_accuracy: 1.0000\n",
            "Epoch 25/50\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.0228 - accuracy: 1.0000 - val_loss: 0.0234 - val_accuracy: 1.0000\n",
            "Epoch 26/50\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.0178 - accuracy: 1.0000 - val_loss: 0.0189 - val_accuracy: 1.0000\n",
            "Epoch 27/50\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.0141 - accuracy: 1.0000 - val_loss: 0.0151 - val_accuracy: 1.0000\n",
            "Epoch 28/50\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.0112 - accuracy: 1.0000 - val_loss: 0.0127 - val_accuracy: 1.0000\n",
            "Epoch 29/50\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.0094 - accuracy: 1.0000 - val_loss: 0.0109 - val_accuracy: 1.0000\n",
            "Epoch 30/50\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.0080 - accuracy: 1.0000 - val_loss: 0.0094 - val_accuracy: 1.0000\n",
            "Epoch 31/50\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.0069 - accuracy: 1.0000 - val_loss: 0.0082 - val_accuracy: 1.0000\n",
            "Epoch 32/50\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.0061 - accuracy: 1.0000 - val_loss: 0.0074 - val_accuracy: 1.0000\n",
            "Epoch 33/50\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 1.0000\n",
            "Epoch 34/50\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.0047 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 1.0000\n",
            "Epoch 35/50\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.0042 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 1.0000\n",
            "Epoch 36/50\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.0038 - accuracy: 1.0000 - val_loss: 0.0048 - val_accuracy: 1.0000\n",
            "Epoch 37/50\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.0035 - accuracy: 1.0000 - val_loss: 0.0044 - val_accuracy: 1.0000\n",
            "Epoch 38/50\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.0040 - val_accuracy: 1.0000\n",
            "Epoch 39/50\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.0037 - val_accuracy: 1.0000\n",
            "Epoch 40/50\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0034 - val_accuracy: 1.0000\n",
            "Epoch 41/50\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0032 - val_accuracy: 1.0000\n",
            "Epoch 42/50\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0030 - val_accuracy: 1.0000\n",
            "Epoch 43/50\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0028 - val_accuracy: 1.0000\n",
            "Epoch 44/50\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0026 - val_accuracy: 1.0000\n",
            "Epoch 45/50\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0025 - val_accuracy: 1.0000\n",
            "Epoch 46/50\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000\n",
            "Epoch 47/50\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000\n",
            "Epoch 48/50\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000\n",
            "Epoch 49/50\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000\n",
            "Epoch 50/50\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Visulaizing the Training and Validation Sets Loss and Accuracy\n"
      ],
      "metadata": {
        "id": "zP8L177rRhNK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8,4))\n",
        "#Plot training and validation accuracy values\n",
        "#axes[0].set_ylim(0,1) #if we want to limit axis in certain range\n",
        "axes[0].plot(training.history['accuracy'], label='Train')\n",
        "axes[0].plot(training.history['val_accuracy'], label='Validation')\n",
        "axes[0].set_title('Model Accuracy')\n",
        "axes[0].set_xlabel('Epoch')\n",
        "axes[0].set_ylabel('Accuracy')\n",
        "axes[0].legend()\n",
        "#Plot training and validation loss values\n",
        "#axes[1].set_ylim(0,1)\n",
        "axes[1].plot(training.history['loss'], label='Train')\n",
        "axes[1].plot(training.history['val_loss'], label='Validation')\n",
        "axes[1].set_title('Model Loss')\n",
        "axes[1].set_xlabel('Epoch')\n",
        "axes[1].set_ylabel('Loss')\n",
        "axes[1].legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "BSY9kIGhRhw4",
        "outputId": "f1b11f1a-5558-4c22-e804-5207a0021446",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluating the performance on the Test set "
      ],
      "metadata": {
        "id": "-ro-lJG3SH_n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss_accuracy = model.evaluate(X_test, y_test, verbose=2)\n"
      ],
      "metadata": {
        "id": "LeC7WOB8SN9N",
        "outputId": "eb42ffba-35e4-42d1-d391-3fe08451cf20",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 - 0s - loss: 0.0017 - accuracy: 1.0000 - 162ms/epoch - 162ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualising the Training and Test set plot decision area"
      ],
      "metadata": {
        "id": "eyiPm6LRViEt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))\n",
        "\n",
        "# Create a meshgrid to visualize the decision boundaries\n",
        "xx, yy = np.meshgrid(np.linspace(X_train[:,0].min(), X_train[:,0].max(), 100),\n",
        "                     np.linspace(X_train[:,1].min(), X_train[:,1].max(), 100))\n",
        "\n",
        "# Compute predictions on the meshgrid points for training and testing data\n",
        "Z_train = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "Z_train = Z_train.reshape(xx.shape)\n",
        "Z_test = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "Z_test = Z_test.reshape(xx.shape)\n",
        "\n",
        "# Plot the decision boundaries\n",
        "axes[0].contourf(xx, yy, Z_train, alpha=0.4)\n",
        "axes[1].contourf(xx, yy, Z_test, alpha=0.4)\n",
        "\n",
        "# Plot the actual data points\n",
        "scatter_train = axes[0].scatter(X_train[:,0], X_train[:,1], c=y_train, edgecolors='k', s=20)\n",
        "scatter_test = axes[1].scatter(X_test[:,0], X_test[:,1], c=y_test, edgecolors='k', s=20)\n",
        "\n",
        "axes[0].set_title('NN Plot Decision Region (Training set)')\n",
        "axes[0].set_xlabel('x1')\n",
        "axes[0].set_ylabel('x2')\n",
        "axes[1].set_title('NN Plot Decision Region (Test set)')\n",
        "axes[1].set_xlabel('x1')\n",
        "axes[1].set_ylabel('x2')\n",
        "\n",
        "legend1 = axes[0].legend(*scatter_train.legend_elements(),\n",
        "                    title=\"Classes\")\n",
        "axes[0].add_artist(legend1)\n",
        "legend2 = axes[1].legend(*scatter_test.legend_elements(),\n",
        "                    title=\"Classes\")\n",
        "axes[1].add_artist(legend2)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "J6aCBUOTTv4P",
        "outputId": "694b1d41-0974-4664-ef3c-62105cae3452",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n",
            "313/313 [==============================] - 1s 2ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prediction using Machine Learning \n",
        "Using non-linear SVM calssifier , use kernel=linear for linear classifier. \n",
        "\n"
      ],
      "metadata": {
        "id": "bbAI146sTZxw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import svm\n",
        "classifier = svm.SVC(kernel='rbf') #rbf = 'radial basis function' for non-linear classification\n",
        "classifier.fit(X_train,y_train)"
      ],
      "metadata": {
        "id": "7yC5Jsc_TeU0",
        "outputId": "5b01c94c-a3fc-4c44-f34e-53186f523563",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Predicting Teset set result for SVM\n"
      ],
      "metadata": {
        "id": "IzcCC_rrTjc-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = classifier.predict(X_test)"
      ],
      "metadata": {
        "id": "5oQasBIXTpgh"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Generating confusion matrics, details classification report\n"
      ],
      "metadata": {
        "id": "6X-v10C6T5Va"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm = metrics.confusion_matrix(y_test,y_pred)\n",
        "print(\"Confusion Matrix for SVM Clssifer:\\n \",cm)\n",
        "print( \"{0}\".format(metrics.classification_report(y_test,y_pred)))"
      ],
      "metadata": {
        "id": "fGrKvyfmT2o3",
        "outputId": "5a29ed32-6aba-4202-8f5f-5d1c90e83a8b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix for SVM Clssifer:\n",
            "  [[10  0]\n",
            " [ 0 10]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        10\n",
            "           1       1.00      1.00      1.00        10\n",
            "\n",
            "    accuracy                           1.00        20\n",
            "   macro avg       1.00      1.00      1.00        20\n",
            "weighted avg       1.00      1.00      1.00        20\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Visualising the Training and Test set plot decision area for SVM classifier\n"
      ],
      "metadata": {
        "id": "L9KZCV1PU0b4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Create the subplots\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))\n",
        "\n",
        "# Create a meshgrid to visualize the decision boundaries\n",
        "xx, yy = np.meshgrid(np.linspace(X_train[:,0].min(), X_train[:,0].max(), 100),\n",
        "                     np.linspace(X_train[:,1].min(), X_train[:,1].max(), 100))\n",
        "\n",
        "# Compute predictions on the meshgrid points for training and testing data\n",
        "Z_train = classifier.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "Z_train = Z_train.reshape(xx.shape)\n",
        "Z_test = classifier.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "Z_test = Z_test.reshape(xx.shape)\n",
        "\n",
        "# Simplified colors\n",
        "colors = ['blue', 'orange']\n",
        "\n",
        "# Plot the decision boundaries\n",
        "axes[0].contourf(xx, yy, Z_train, alpha=0.4, colors=colors)\n",
        "axes[1].contourf(xx, yy, Z_test, alpha=0.4, colors=colors)\n",
        "\n",
        "# Plot the actual data points\n",
        "scatter_train = axes[0].scatter(X_train[:,0], X_train[:,1], c=y_train, cmap='viridis', edgecolors='k', s=20)\n",
        "scatter_test = axes[1].scatter(X_test[:,0], X_test[:,1], c=y_test, cmap='viridis', edgecolors='k', s=20)\n",
        "\n",
        "axes[0].set_title('SVM Plot Decision Region (Training set)')\n",
        "axes[0].set_xlabel('x1')\n",
        "axes[0].set_ylabel('x2')\n",
        "axes[1].set_title('SVM Plot Decision Region (Test set)')\n",
        "axes[1].set_xlabel('x1')\n",
        "axes[1].set_ylabel('x2')\n",
        "\n",
        "# Add legend manually\n",
        "legend1 = axes[0].legend(*scatter_train.legend_elements(),\n",
        "                    title=\"Classes\")\n",
        "axes[0].add_artist(legend1)\n",
        "legend2 = axes[1].legend(*scatter_test.legend_elements(),\n",
        "                    title=\"Classes\")\n",
        "axes[1].add_artist(legend2)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "h_mis3V7U3OH",
        "outputId": "06974bc2-f364-41b5-f237-76808533075b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}