BankNote_Authentication.ipynb

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    "colab": {
      "name": "BankNote Authentication.ipynb",
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      "authorship_tag": "ABX9TyM8KtCYL/TKfNA+rj6qZjp5",
      "include_colab_link": true
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Kushagratandon12/Machine-Learning-Projects/blob/master/BankNote_Authentication.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PlsYvutj6eK2"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np "
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2B2yQ2kV7Xp9"
      },
      "source": [
        "data = pd.read_csv('/content/BankNote_Authentication.xls')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EWoJp4qX7oyf"
      },
      "source": [
        "data = data.sample(frac=1).reset_index(drop=True)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
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      "metadata": {
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          "height": 206
        },
        "id": "ckjYxnWJ79Ld",
        "outputId": "1bf15792-679a-4b98-ca01-a7fbc7d38676"
      },
      "source": [
        "data.head(5)"
      ],
      "execution_count": 4,
      "outputs": [
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              "      <th>curtosis</th>\n",
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              "      <td>-1.97020</td>\n",
              "      <td>0.65472</td>\n",
              "      <td>1</td>\n",
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              "      <th>4</th>\n",
              "      <td>5.59100</td>\n",
              "      <td>10.46430</td>\n",
              "      <td>-4.38390</td>\n",
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              "      <td>0</td>\n",
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            "text/plain": [
              "   variance  skewness  curtosis  entropy  class\n",
              "0  -0.49241   0.89392  -1.62830 -0.56854      1\n",
              "1   0.92970  -3.79710   4.64290 -0.29570      0\n",
              "2   1.86640   7.77630  -0.23849 -2.96340      0\n",
              "3   1.56310   0.89599  -1.97020  0.65472      1\n",
              "4   5.59100  10.46430  -4.38390 -4.33790      0"
            ]
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      "source": [
        "data.tail(5)"
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      "execution_count": 5,
      "outputs": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>variance</th>\n",
              "      <th>skewness</th>\n",
              "      <th>curtosis</th>\n",
              "      <th>entropy</th>\n",
              "      <th>class</th>\n",
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              "      <th>1367</th>\n",
              "      <td>-1.8391</td>\n",
              "      <td>-9.088300</td>\n",
              "      <td>9.2416</td>\n",
              "      <td>-0.10432</td>\n",
              "      <td>1</td>\n",
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              "      <td>8.9494</td>\n",
              "      <td>0.68058</td>\n",
              "      <td>1</td>\n",
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              "      <td>10.145700</td>\n",
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              "      <td>2.2634</td>\n",
              "      <td>-4.486200</td>\n",
              "      <td>3.6558</td>\n",
              "      <td>-0.61251</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "      variance   skewness  curtosis  entropy  class\n",
              "1367   -1.8391  -9.088300    9.2416 -0.10432      1\n",
              "1368   -1.7279  -6.841000    8.9494  0.68058      1\n",
              "1369   -2.2625  -0.099335    2.8127  0.48662      1\n",
              "1370    3.8117  10.145700   -4.0463 -4.56290      0\n",
              "1371    2.2634  -4.486200    3.6558 -0.61251      0"
            ]
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tdap_PJI8c3B"
      },
      "source": [
        "train_df = data[0:800].reset_index(drop=True)\n",
        "validation_df = data[801:1200].reset_index(drop=True)\n",
        "test_df = data[1200::].reset_index(drop=True)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7cziUD0s9ElE",
        "outputId": "ee9b79d9-6da1-4fbf-99d5-bea4d4e1bb80"
      },
      "source": [
        "train_df['class'].value_counts()\n",
        "validation_df['class'].value_counts()\n",
        "test_df['class'].value_counts()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    96\n",
              "1    76\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BcDkX-O-_alq"
      },
      "source": [
        "train_Y =train_df['class']\n",
        "train_X = train_df.loc[:, train_df.columns != 'class']"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yy1GGRu5DBPg"
      },
      "source": [
        "validation_Y =validation_df['class'].reset_index(drop=True)\n",
        "validation_X = validation_df.loc[:, validation_df.columns != 'class']"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qTppQmGb_1mR"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.optimizers import Adam , SGD , RMSprop"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "icIP_ULFAbz5"
      },
      "source": [
        "model = tf.keras.models.Sequential([                    \n",
        "        tf.keras.layers.Dense(4,activation='relu'),\n",
        "        tf.keras.layers.Flatten(),\n",
        "        tf.keras.layers.Dense(1,activation='sigmoid')\n",
        "])"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gO_ZlhS5BpkX"
      },
      "source": [
        "adam = Adam(0.001)\n",
        "model.compile(optimizer='adam',loss=tf.keras.losses.binary_crossentropy,metrics=['accuracy'])"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hvcr8lGMCXKj",
        "outputId": "2ddff981-0e8a-4fb9-ded0-552a4e07ed69"
      },
      "source": [
        "history=model.fit(x=train_X,\n",
        "                  y=train_Y,\n",
        "                  epochs=70,\n",
        "                  validation_data =(validation_X,validation_Y))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/70\n",
            "25/25 [==============================] - 1s 11ms/step - loss: 0.8952 - accuracy: 0.4536 - val_loss: 0.8315 - val_accuracy: 0.4762\n",
            "Epoch 2/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.8768 - accuracy: 0.4690 - val_loss: 0.7875 - val_accuracy: 0.4937\n",
            "Epoch 3/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.8136 - accuracy: 0.4843 - val_loss: 0.7497 - val_accuracy: 0.5088\n",
            "Epoch 4/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7596 - accuracy: 0.5052 - val_loss: 0.7137 - val_accuracy: 0.5238\n",
            "Epoch 5/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.7315 - accuracy: 0.5123 - val_loss: 0.6806 - val_accuracy: 0.5363\n",
            "Epoch 6/70\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.6740 - accuracy: 0.5422 - val_loss: 0.6479 - val_accuracy: 0.5739\n",
            "Epoch 7/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6285 - accuracy: 0.6026 - val_loss: 0.6152 - val_accuracy: 0.7043\n",
            "Epoch 8/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.6077 - accuracy: 0.7227 - val_loss: 0.5842 - val_accuracy: 0.7293\n",
            "Epoch 9/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.5749 - accuracy: 0.7300 - val_loss: 0.5551 - val_accuracy: 0.7794\n",
            "Epoch 10/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.5407 - accuracy: 0.7741 - val_loss: 0.5274 - val_accuracy: 0.8296\n",
            "Epoch 11/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.5261 - accuracy: 0.8024 - val_loss: 0.5011 - val_accuracy: 0.8496\n",
            "Epoch 12/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.4921 - accuracy: 0.8314 - val_loss: 0.4778 - val_accuracy: 0.8596\n",
            "Epoch 13/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.4497 - accuracy: 0.8698 - val_loss: 0.4546 - val_accuracy: 0.8722\n",
            "Epoch 14/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.4505 - accuracy: 0.8579 - val_loss: 0.4333 - val_accuracy: 0.8772\n",
            "Epoch 15/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.4069 - accuracy: 0.9071 - val_loss: 0.4132 - val_accuracy: 0.8872\n",
            "Epoch 16/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3910 - accuracy: 0.9102 - val_loss: 0.3945 - val_accuracy: 0.8897\n",
            "Epoch 17/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3744 - accuracy: 0.9292 - val_loss: 0.3764 - val_accuracy: 0.9123\n",
            "Epoch 18/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3547 - accuracy: 0.9373 - val_loss: 0.3596 - val_accuracy: 0.9173\n",
            "Epoch 19/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3346 - accuracy: 0.9431 - val_loss: 0.3446 - val_accuracy: 0.9248\n",
            "Epoch 20/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3237 - accuracy: 0.9495 - val_loss: 0.3292 - val_accuracy: 0.9298\n",
            "Epoch 21/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3201 - accuracy: 0.9473 - val_loss: 0.3160 - val_accuracy: 0.9373\n",
            "Epoch 22/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.3055 - accuracy: 0.9417 - val_loss: 0.3030 - val_accuracy: 0.9424\n",
            "Epoch 23/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2811 - accuracy: 0.9560 - val_loss: 0.2903 - val_accuracy: 0.9449\n",
            "Epoch 24/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2703 - accuracy: 0.9580 - val_loss: 0.2785 - val_accuracy: 0.9474\n",
            "Epoch 25/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2532 - accuracy: 0.9646 - val_loss: 0.2678 - val_accuracy: 0.9499\n",
            "Epoch 26/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2499 - accuracy: 0.9642 - val_loss: 0.2577 - val_accuracy: 0.9499\n",
            "Epoch 27/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2313 - accuracy: 0.9646 - val_loss: 0.2482 - val_accuracy: 0.9524\n",
            "Epoch 28/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2187 - accuracy: 0.9779 - val_loss: 0.2387 - val_accuracy: 0.9524\n",
            "Epoch 29/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2129 - accuracy: 0.9755 - val_loss: 0.2298 - val_accuracy: 0.9524\n",
            "Epoch 30/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2126 - accuracy: 0.9796 - val_loss: 0.2210 - val_accuracy: 0.9524\n",
            "Epoch 31/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1997 - accuracy: 0.9851 - val_loss: 0.2134 - val_accuracy: 0.9549\n",
            "Epoch 32/70\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.1869 - accuracy: 0.9870 - val_loss: 0.2053 - val_accuracy: 0.9549\n",
            "Epoch 33/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1912 - accuracy: 0.9860 - val_loss: 0.1987 - val_accuracy: 0.9574\n",
            "Epoch 34/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1779 - accuracy: 0.9857 - val_loss: 0.1913 - val_accuracy: 0.9624\n",
            "Epoch 35/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1719 - accuracy: 0.9891 - val_loss: 0.1851 - val_accuracy: 0.9624\n",
            "Epoch 36/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1697 - accuracy: 0.9867 - val_loss: 0.1791 - val_accuracy: 0.9674\n",
            "Epoch 37/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1648 - accuracy: 0.9911 - val_loss: 0.1734 - val_accuracy: 0.9724\n",
            "Epoch 38/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1549 - accuracy: 0.9846 - val_loss: 0.1684 - val_accuracy: 0.9674\n",
            "Epoch 39/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1418 - accuracy: 0.9915 - val_loss: 0.1633 - val_accuracy: 0.9724\n",
            "Epoch 40/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1476 - accuracy: 0.9879 - val_loss: 0.1586 - val_accuracy: 0.9724\n",
            "Epoch 41/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1452 - accuracy: 0.9907 - val_loss: 0.1536 - val_accuracy: 0.9724\n",
            "Epoch 42/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1383 - accuracy: 0.9901 - val_loss: 0.1496 - val_accuracy: 0.9724\n",
            "Epoch 43/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1362 - accuracy: 0.9864 - val_loss: 0.1456 - val_accuracy: 0.9724\n",
            "Epoch 44/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1311 - accuracy: 0.9891 - val_loss: 0.1418 - val_accuracy: 0.9749\n",
            "Epoch 45/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1410 - accuracy: 0.9841 - val_loss: 0.1380 - val_accuracy: 0.9749\n",
            "Epoch 46/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1247 - accuracy: 0.9925 - val_loss: 0.1349 - val_accuracy: 0.9749\n",
            "Epoch 47/70\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.1140 - accuracy: 0.9949 - val_loss: 0.1316 - val_accuracy: 0.9774\n",
            "Epoch 48/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1241 - accuracy: 0.9920 - val_loss: 0.1282 - val_accuracy: 0.9774\n",
            "Epoch 49/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1176 - accuracy: 0.9861 - val_loss: 0.1253 - val_accuracy: 0.9825\n",
            "Epoch 50/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1085 - accuracy: 0.9896 - val_loss: 0.1223 - val_accuracy: 0.9825\n",
            "Epoch 51/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1059 - accuracy: 0.9906 - val_loss: 0.1195 - val_accuracy: 0.9825\n",
            "Epoch 52/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0994 - accuracy: 0.9876 - val_loss: 0.1170 - val_accuracy: 0.9825\n",
            "Epoch 53/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1030 - accuracy: 0.9899 - val_loss: 0.1143 - val_accuracy: 0.9850\n",
            "Epoch 54/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0959 - accuracy: 0.9959 - val_loss: 0.1119 - val_accuracy: 0.9850\n",
            "Epoch 55/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1001 - accuracy: 0.9931 - val_loss: 0.1094 - val_accuracy: 0.9875\n",
            "Epoch 56/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0988 - accuracy: 0.9967 - val_loss: 0.1070 - val_accuracy: 0.9875\n",
            "Epoch 57/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0963 - accuracy: 0.9942 - val_loss: 0.1047 - val_accuracy: 0.9875\n",
            "Epoch 58/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0819 - accuracy: 0.9938 - val_loss: 0.1025 - val_accuracy: 0.9875\n",
            "Epoch 59/70\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.0939 - accuracy: 0.9963 - val_loss: 0.1001 - val_accuracy: 0.9875\n",
            "Epoch 60/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0907 - accuracy: 0.9920 - val_loss: 0.0981 - val_accuracy: 0.9925\n",
            "Epoch 61/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0719 - accuracy: 0.9959 - val_loss: 0.0959 - val_accuracy: 0.9925\n",
            "Epoch 62/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0830 - accuracy: 0.9956 - val_loss: 0.0937 - val_accuracy: 0.9925\n",
            "Epoch 63/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0862 - accuracy: 0.9947 - val_loss: 0.0917 - val_accuracy: 0.9925\n",
            "Epoch 64/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0710 - accuracy: 0.9979 - val_loss: 0.0896 - val_accuracy: 0.9925\n",
            "Epoch 65/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0682 - accuracy: 0.9980 - val_loss: 0.0878 - val_accuracy: 0.9925\n",
            "Epoch 66/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0713 - accuracy: 0.9970 - val_loss: 0.0859 - val_accuracy: 0.9925\n",
            "Epoch 67/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0678 - accuracy: 0.9975 - val_loss: 0.0840 - val_accuracy: 0.9925\n",
            "Epoch 68/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0728 - accuracy: 0.9944 - val_loss: 0.0822 - val_accuracy: 0.9925\n",
            "Epoch 69/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0712 - accuracy: 0.9944 - val_loss: 0.0804 - val_accuracy: 0.9925\n",
            "Epoch 70/70\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.0685 - accuracy: 0.9960 - val_loss: 0.0785 - val_accuracy: 0.9925\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "goNMfXNyDJr1",
        "outputId": "ead1ae88-3ade-4ba7-9328-71c754b8189f"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(len(acc))\n",
        "\n",
        "plt.plot(epochs, loss, 'r', label='Training Loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation Loss')\n",
        "plt.title('Training and validation Loss')\n",
        "plt.legend(loc=0)\n",
        "plt.figure()\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cDrB3mJmEFd2"
      },
      "source": [
        "testing_data = test_df.loc[:, test_df.columns != 'class']"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4a-Xj_fqEuTa"
      },
      "source": [
        "pred = model.predict(testing_data)\n",
        "for x in range(len(pred)):\n",
        "  if pred[x]<0.5:\n",
        "    pred[x]=0\n",
        "  else:\n",
        "    pred[x]=1\n",
        "# print(pred)\n",
        "test_df['Predicted'] = pred"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "n89qPKaJK3TC",
        "outputId": "e7d8d8bd-88b7-4b6e-b66c-0700502742ef"
      },
      "source": [
        "test_df"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>variance</th>\n",
              "      <th>skewness</th>\n",
              "      <th>curtosis</th>\n",
              "      <th>entropy</th>\n",
              "      <th>class</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.11783</td>\n",
              "      <td>-1.578900</td>\n",
              "      <td>8.03000</td>\n",
              "      <td>-0.028031</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.30660</td>\n",
              "      <td>0.252440</td>\n",
              "      <td>0.76230</td>\n",
              "      <td>1.775800</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.01820</td>\n",
              "      <td>9.109000</td>\n",
              "      <td>-0.62064</td>\n",
              "      <td>-1.712900</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.13150</td>\n",
              "      <td>7.921200</td>\n",
              "      <td>1.09300</td>\n",
              "      <td>-2.844400</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.09720</td>\n",
              "      <td>0.469720</td>\n",
              "      <td>1.66710</td>\n",
              "      <td>0.915930</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>-1.83910</td>\n",
              "      <td>-9.088300</td>\n",
              "      <td>9.24160</td>\n",
              "      <td>-0.104320</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>-1.72790</td>\n",
              "      <td>-6.841000</td>\n",
              "      <td>8.94940</td>\n",
              "      <td>0.680580</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>-2.26250</td>\n",
              "      <td>-0.099335</td>\n",
              "      <td>2.81270</td>\n",
              "      <td>0.486620</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>3.81170</td>\n",
              "      <td>10.145700</td>\n",
              "      <td>-4.04630</td>\n",
              "      <td>-4.562900</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>2.26340</td>\n",
              "      <td>-4.486200</td>\n",
              "      <td>3.65580</td>\n",
              "      <td>-0.612510</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>172 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     variance   skewness  curtosis   entropy  class  Predicted\n",
              "0    -0.11783  -1.578900   8.03000 -0.028031      0        0.0\n",
              "1    -1.30660   0.252440   0.76230  1.775800      1        1.0\n",
              "2     1.01820   9.109000  -0.62064 -1.712900      0        0.0\n",
              "3     1.13150   7.921200   1.09300 -2.844400      0        0.0\n",
              "4     4.09720   0.469720   1.66710  0.915930      0        0.0\n",
              "..        ...        ...       ...       ...    ...        ...\n",
              "167  -1.83910  -9.088300   9.24160 -0.104320      1        1.0\n",
              "168  -1.72790  -6.841000   8.94940  0.680580      1        1.0\n",
              "169  -2.26250  -0.099335   2.81270  0.486620      1        1.0\n",
              "170   3.81170  10.145700  -4.04630 -4.562900      0        0.0\n",
              "171   2.26340  -4.486200   3.65580 -0.612510      0        0.0\n",
              "\n",
              "[172 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "I6FFZoIjEzMK",
        "outputId": "065257df-7977-4ce0-bafe-cec1b08ebf46"
      },
      "source": [
        "test_df.head(5)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>variance</th>\n",
              "      <th>skewness</th>\n",
              "      <th>curtosis</th>\n",
              "      <th>entropy</th>\n",
              "      <th>class</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.64720</td>\n",
              "      <td>0.48213</td>\n",
              "      <td>4.7449</td>\n",
              "      <td>1.22500</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-2.66490</td>\n",
              "      <td>-12.81300</td>\n",
              "      <td>12.6689</td>\n",
              "      <td>-1.90820</td>\n",
              "      <td>1</td>\n",
              "      <td>0.999058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.44540</td>\n",
              "      <td>-8.43850</td>\n",
              "      <td>8.8483</td>\n",
              "      <td>0.96894</td>\n",
              "      <td>1</td>\n",
              "      <td>0.987296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.10648</td>\n",
              "      <td>-0.76771</td>\n",
              "      <td>7.7575</td>\n",
              "      <td>0.64179</td>\n",
              "      <td>0</td>\n",
              "      <td>0.001874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.13130</td>\n",
              "      <td>1.90370</td>\n",
              "      <td>7.5339</td>\n",
              "      <td>1.02200</td>\n",
              "      <td>0</td>\n",
              "      <td>0.002532</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   variance  skewness  curtosis  entropy  class  Predicted\n",
              "0   1.64720   0.48213    4.7449  1.22500      0   0.000482\n",
              "1  -2.66490 -12.81300   12.6689 -1.90820      1   0.999058\n",
              "2  -1.44540  -8.43850    8.8483  0.96894      1   0.987296\n",
              "3  -0.10648  -0.76771    7.7575  0.64179      0   0.001874\n",
              "4  -1.13130   1.90370    7.5339  1.02200      0   0.002532"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "klAyoT7QPgws"
      },
      "source": [
        "y_test = test_df['class']\n",
        "y_pred = test_df['Predicted'].astype(int)"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YG0ElMhaFHXS",
        "outputId": "1752f8a1-a932-4dd2-a176-b1ae32925bab"
      },
      "source": [
        "import itertools\n",
        "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
        "print(classification_report(y_test, y_pred))\n",
        "cnf_matrix = confusion_matrix(y_test,y_pred)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.99      0.99        96\n",
            "           1       0.99      1.00      0.99        76\n",
            "\n",
            "    accuracy                           0.99       172\n",
            "   macro avg       0.99      0.99      0.99       172\n",
            "weighted avg       0.99      0.99      0.99       172\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mCkenjugQGMy"
      },
      "source": [
        "classes = [0,1]"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "id": "J6Q0pO_kQDQY",
        "outputId": "a78e2d64-de1e-43f5-8500-1831c6106898"
      },
      "source": [
        "def plot_confusion_matrix(cm, classes, title='Confusion matrix', cmap=plt.cm.Blues):\n",
        "    cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "    plt.figure(figsize=(10,10))\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "    plt.tight_layout()\n",
        "\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "\n",
        "# plot normalized confusion matrix\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cnf_matrix, classes=classes, title='Normalized confusion matrix')\n",
        "plt.show()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}