NLPRna.ipynb

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        "<a href=\"https://colab.research.google.com/github/miller00315/ia_studies/blob/main/NLPRna.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, Embedding\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.utils import pad_sequences"
      ]
    },
    {
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      "source": [
        "spam = pd.read_csv('spam.csv')\n",
        "\n",
        "spam.head()"
      ],
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            "text/plain": [
              "  Category                                            Message\n",
              "0      ham  Go until jurong point, crazy.. Available only ...\n",
              "1      ham                      Ok lar... Joking wif u oni...\n",
              "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3      ham  U dun say so early hor... U c already then say...\n",
              "4      ham  Nah I don't think he goes to usf, he lives aro..."
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              "      <td>ham</td>\n",
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              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
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              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
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              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "spam",
              "summary": "{\n  \"name\": \"spam\",\n  \"rows\": 5572,\n  \"fields\": [\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"spam\",\n          \"ham\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Message\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5157,\n        \"samples\": [\n          \"Also sir, i sent you an email about how to log into the usc payment portal. I.ll send you another message that should explain how things are back home. Have a great weekend.\",\n          \"Are you free now?can i call now?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 5
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "spam.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UpTm6Yk4cQGL",
        "outputId": "22aff251-a194-4b51-f73b-6f0b7ca5f9ec"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5572, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "labelEncoder = LabelEncoder()\n",
        "\n",
        "y = labelEncoder.fit_transform(spam['Category'])\n",
        "\n",
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QtIHXwHEc9g_",
        "outputId": "24b6833d-2067-4ccb-c04c-0a85e8dbc2cc"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 1 ... 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "messages = spam['Message'].values\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(messages, y, test_size=.3)"
      ],
      "metadata": {
        "id": "Xpxl5EMOdUAT"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "token = Tokenizer(num_words=1000)\n",
        "\n",
        "token.fit_on_texts(X_train)\n",
        "\n",
        "X_train = token.texts_to_sequences(X_train)\n",
        "\n",
        "token.fit_on_texts(X_test)\n",
        "\n",
        "X_test = token.texts_to_sequences(X_test)"
      ],
      "metadata": {
        "id": "gG_FcRhadqIo"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "Q841CYtZePzr",
        "outputId": "4d2fe7b9-82fb-46a5-9bf0-541b7f68e5ef"
      },
      "execution_count": 17,
      "outputs": [
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[53, 334, 940], [362, 79, 4, 283, 519, 123, 55, 310, 253, 6, 44, 80, 413, 20, 233, 95], [6, 123], [365, 516, 481, 326, 410, 348, 41, 50, 136, 13, 108, 17, 50, 18, 2, 559, 19], [124, 22, 3, 1, 81, 3], [211, 1, 222, 30, 23, 1, 341, 370, 122, 52, 115, 763, 95, 1, 57, 2, 53, 1, 57, 52, 115, 7, 19, 30, 25, 650], [630, 49, 27, 1, 35, 5, 225, 10, 197, 127, 24, 164], [6, 246, 87, 41, 336, 663, 420, 179, 10, 23, 1, 444, 953, 487, 6], [101, 174, 597, 27, 6, 383, 31, 1, 44, 337, 5, 12, 4, 665, 597, 1, 9, 4, 472, 101, 398, 53, 260, 602, 4, 86, 11, 506], [103, 10, 208, 54, 13, 677, 8], [25, 278, 136, 240, 587, 7], [165, 1, 69, 3, 13, 746, 9], [46, 79, 139, 63, 72, 275], [84, 66, 10, 43, 230, 18, 5, 126], [74, 87, 2, 233], [30, 396, 920, 11, 727, 500, 627, 23, 6, 242, 20, 177, 10, 41, 336, 28, 656, 4, 25], [419, 139, 68, 225, 10, 46, 79], [113, 3, 51, 54, 1, 668], [9, 88, 363, 882], [669, 204, 354, 348, 108, 8, 13, 17, 478, 447, 352, 17, 45, 803, 261, 660, 761, 873], [13, 323, 111, 89, 75, 48, 448, 47, 13, 323, 721, 8, 977, 48, 448, 47, 13, 622, 8, 48, 47], [281, 176, 249, 238], [600, 31, 230, 221, 166, 10, 51, 43, 230, 575], [46, 1, 222, 56, 796], [8, 64, 4, 699, 25, 39, 259, 16, 4, 132, 115, 7, 52, 5, 268, 9, 13, 820], [61, 46, 382, 16, 451], [37, 1, 785, 5, 8, 5, 254, 21, 48, 47, 28, 48, 47, 39, 434, 8, 160, 16, 3, 621, 13, 297, 61, 452, 7, 16, 3, 691, 31, 3, 22, 162, 15, 434, 29, 31, 678, 34, 27, 5, 820], [3, 32, 13, 235, 865, 5, 243, 12, 7, 99, 98, 6, 748], [1, 32, 59, 151], [32, 238, 33, 2, 270], [529, 8, 6, 42, 20, 55], [70, 10, 13, 622, 7], [348, 3, 22, 350, 4, 348, 17, 368, 548, 865, 334], [55, 6, 179, 44, 86, 182, 6, 79, 24, 1, 484, 501, 477, 20, 177, 969], [271, 697, 689, 419, 26], [307, 165, 283, 42, 101, 6, 107, 51, 20, 439, 168, 25], [192, 99, 17, 45, 13, 368, 13, 898, 41, 299, 29, 380, 149, 591, 453, 592, 178, 327], [237, 5, 35, 21, 44, 113, 230, 4, 77, 704, 816, 734, 98, 3, 12, 254, 63, 254, 229], [30, 688, 2, 14, 1, 34, 141, 11, 120, 530, 10, 594, 198, 67, 4, 289, 265, 39, 259, 39, 817, 10, 9, 171, 9, 57, 121], [607, 623, 212], [5, 1, 34, 136, 2, 946], [7, 203, 198, 119, 206, 4, 840, 536, 23, 622, 25, 66, 119, 53, 15, 5, 825, 449, 12, 919], [204, 108, 8], [49, 59, 1, 137, 72, 291, 1, 996, 11, 502], [672, 71, 4, 931, 427, 227, 3, 16, 89, 417, 2, 146, 844, 2, 117, 17, 117, 390, 504, 862, 605, 67], [192, 99, 17, 45, 4, 368, 695, 149, 29, 4, 41, 299, 591, 453, 178, 327, 592, 342, 309, 261], [260, 50, 510, 496, 17, 563, 395, 33, 4, 220, 189, 115, 34, 66, 2, 799, 269], [183, 46, 281, 301, 2, 70, 36, 8, 678, 33, 14, 151], [5, 897, 8, 7, 30, 1, 16], [273, 27, 3, 56, 1, 16, 4, 7, 28, 11, 280], [37, 130, 235, 41, 38, 534, 189, 115, 34, 78, 2, 358, 235, 50, 23, 33, 19, 7, 103, 38, 223, 195, 235, 261, 104], [99, 17, 71, 64, 8, 121, 192, 155, 264, 12, 3], [3, 32, 36, 39, 411, 322, 14], [271, 361, 95, 408, 387], [515, 109, 719, 109, 6, 97, 25, 24, 1, 26, 470, 90, 33, 270, 35, 37, 133, 6, 97, 217, 11, 109, 190, 703], [113, 392, 92, 4, 283, 412, 31, 3, 325, 73, 2, 224], [110, 421, 34, 65, 87, 45, 140, 7, 1, 57, 2, 98, 6, 1, 141, 1, 438, 6, 206, 18, 5, 108, 95, 31, 6, 22, 42, 148, 10, 4, 17, 339], [144, 948, 8, 170, 23, 61, 5, 126, 40, 997], [291, 46], [50, 510, 411, 2, 483, 411, 33, 358, 115, 50, 70, 2, 185, 269, 115, 96, 263], [752, 402, 1, 432, 4, 15, 897, 2, 180, 175, 34, 76, 92, 432, 4, 842, 29, 98, 3, 151], [1, 2, 148, 18, 39, 343, 300], [26, 3, 27, 554], [12, 15, 35, 5, 15, 52, 21, 3, 34, 107, 16, 4], [192, 13, 93, 37, 191, 4, 685, 357, 553, 868, 146, 18, 726, 39, 8, 5, 422, 527, 2, 433, 3, 17, 801, 352], [3, 331, 73, 2, 53, 133, 34, 9, 941, 7, 34, 465, 18, 25, 14, 31, 3, 26, 1, 120, 16, 116, 9, 7, 255], [542, 265, 34, 4, 604], [84, 1, 32, 70, 9, 48, 47, 201], [60, 94, 20, 740, 90, 40, 991, 278, 32, 176, 626, 20, 137, 6, 99, 252, 128, 38, 750, 104, 202], [14, 244, 140, 645, 433, 4, 28, 5, 243, 302], [220, 10, 38, 832, 622, 238], [291, 199, 79, 31, 6, 253, 242, 143, 46], [4, 329, 15, 39, 215, 74, 91, 14, 379, 141, 6, 222, 324, 238, 183], [258, 221, 65, 63, 2, 488, 240, 26, 44, 41, 50, 514, 17, 6, 2, 378], [23, 6, 242, 20, 59, 12, 83, 336, 224, 4, 25], [291, 143, 28, 1, 544, 691, 20, 996, 278, 881], [317, 33, 973, 73, 364, 220, 910, 2, 38, 93, 423, 5, 427, 78, 882, 2, 19, 790, 318, 232, 128], [110, 705, 107, 10, 168, 31, 216], [84, 24, 1, 73, 2, 603, 5, 88, 1, 317, 33, 29, 427], [30, 495, 2, 36, 207], [99, 17, 83, 227, 212, 18, 490, 71, 3, 16, 191, 4, 272, 380, 149, 29, 695, 146], [109, 585, 16, 13, 243, 18, 175, 277, 5, 18, 13, 20, 322, 4, 20, 41, 130, 135, 188], [1, 184, 182, 5, 108, 147, 19, 112, 70, 4, 155], [374, 41, 5, 63, 61, 38, 11, 421, 7, 52, 1, 73, 8, 6], [168, 292, 89, 42, 20, 116, 635, 512, 538, 61, 373, 73, 557, 28, 468, 29, 29, 684], [927, 8, 796, 55, 796, 8, 5, 15, 927, 31, 6, 908, 3, 435, 48, 47, 251, 604, 301, 2, 547], [375, 76, 735, 3, 42, 43, 1, 33, 114, 149], [271, 141, 72, 26, 470, 5, 532, 945], [192, 39, 8, 83, 422, 527, 2, 193, 6, 13, 146, 45, 670, 8, 92, 453, 2, 117, 17, 19], [116, 324, 19, 153, 183], [58, 37, 81, 122, 250, 704, 144, 99, 103, 106, 158, 1, 244, 551, 106], [16, 6, 89, 255, 337, 11, 323, 31, 23, 153, 65, 4, 845, 43, 1, 11, 721, 6, 137, 1, 120, 33, 41, 21], [155, 114, 66, 446, 279, 446, 158, 446, 220, 469, 446, 446, 6, 4, 329, 313, 310, 394, 8, 446, 220, 907, 188], [962, 33, 35, 5, 230], [128, 439, 6, 138, 6, 154, 267, 27, 6, 671, 8, 138, 364, 15, 267, 143, 138, 279, 15, 4, 409, 144, 7, 6, 671, 125, 8, 285, 409, 267, 101, 8, 138, 959, 125, 77, 26, 138, 279, 15, 4, 308, 338, 25, 209, 401, 719, 719, 138, 86, 1, 401, 178], [23, 172, 438, 3], [291, 46], [139, 23, 207, 92, 413, 733, 29, 40, 579, 44, 41, 336, 79, 1, 419], [425, 256, 874, 75, 9, 4, 283], [110, 705, 1, 71, 10, 4, 43, 22, 6, 50], [30, 74, 41, 335, 19, 487, 11, 525, 55, 627, 285, 1, 44, 20, 922, 79], [10, 203, 88, 1, 222, 670, 127, 214, 567, 127, 109], [110, 421, 168, 458, 18, 5, 367, 28, 5, 7, 1, 344, 36, 80, 454, 85, 345, 900, 317, 27, 102, 17, 10, 102, 87, 684]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = pad_sequences(X_train, padding='post', maxlen=500)\n",
        "\n",
        "X_test = pad_sequences(X_test, padding='post', maxlen=500)"
      ],
      "metadata": {
        "id": "jnTP2a9yeqXT"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GaQayLEhfK3D",
        "outputId": "9fb917e4-9c71-4aa4-8965-4c03eaf04bdd"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 82, 168, 239, ...,   0,   0,   0],\n",
              "       [429,   3,   7, ...,   0,   0,   0],\n",
              "       [848,  23,  49, ...,   0,   0,   0],\n",
              "       ...,\n",
              "       [ 30,  74,  41, ...,   0,   0,   0],\n",
              "       [ 10, 203,  88, ...,   0,   0,   0],\n",
              "       [110, 421, 168, ...,   0,   0,   0]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(token.word_index)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wl3UMRkegI8p",
        "outputId": "e3e85f82-55d0-45f0-ca12-d14e60545287"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9004"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Embedding(input_dim=len(token.word_index), output_dim=50, input_length=500))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(units=10, activation='relu'))\n",
        "\n",
        "model.add(Dropout(.1))\n",
        "\n",
        "model.add(Dense(units=1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "7Tv2Q8jIfsEc"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "oXi1Ce5PhHGf"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bo7ZStT5iAIf",
        "outputId": "40060ca9-f63f-44ca-afb6-42edd3162892"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 500, 50)           450200    \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 25000)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 10)                250010    \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 10)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 700221 (2.67 MB)\n",
            "Trainable params: 700221 (2.67 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, epochs=20, batch_size=10, verbose=True, validation_data=(X_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vYU04TcUiOuk",
        "outputId": "3da3d2a2-809e-444f-c471-75bc69262be4"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "390/390 [==============================] - 6s 13ms/step - loss: 0.0842 - accuracy: 0.9028 - val_loss: 0.1007 - val_accuracy: 0.8744\n",
            "Epoch 2/20\n",
            "390/390 [==============================] - 5s 12ms/step - loss: 0.0364 - accuracy: 0.9841 - val_loss: 0.1048 - val_accuracy: 0.8756\n",
            "Epoch 3/20\n",
            "390/390 [==============================] - 4s 11ms/step - loss: 0.0264 - accuracy: 0.9908 - val_loss: 0.1119 - val_accuracy: 0.8672\n",
            "Epoch 4/20\n",
            "390/390 [==============================] - 5s 13ms/step - loss: 0.0201 - accuracy: 0.9931 - val_loss: 0.1132 - val_accuracy: 0.8612\n",
            "Epoch 5/20\n",
            "390/390 [==============================] - 4s 10ms/step - loss: 0.0154 - accuracy: 0.9951 - val_loss: 0.1248 - val_accuracy: 0.8535\n",
            "Epoch 6/20\n",
            "390/390 [==============================] - 5s 13ms/step - loss: 0.0119 - accuracy: 0.9956 - val_loss: 0.1221 - val_accuracy: 0.8600\n",
            "Epoch 7/20\n",
            "390/390 [==============================] - 7s 18ms/step - loss: 0.0098 - accuracy: 0.9954 - val_loss: 0.1228 - val_accuracy: 0.8666\n",
            "Epoch 8/20\n",
            "390/390 [==============================] - 4s 11ms/step - loss: 0.0078 - accuracy: 0.9969 - val_loss: 0.1231 - val_accuracy: 0.8696\n",
            "Epoch 9/20\n",
            "390/390 [==============================] - 5s 13ms/step - loss: 0.0061 - accuracy: 0.9977 - val_loss: 0.1258 - val_accuracy: 0.8654\n",
            "Epoch 10/20\n",
            "390/390 [==============================] - 5s 12ms/step - loss: 0.0055 - accuracy: 0.9972 - val_loss: 0.1272 - val_accuracy: 0.8612\n",
            "Epoch 11/20\n",
            "390/390 [==============================] - 4s 11ms/step - loss: 0.0036 - accuracy: 0.9962 - val_loss: 0.1293 - val_accuracy: 0.8630\n",
            "Epoch 12/20\n",
            "390/390 [==============================] - 5s 12ms/step - loss: 0.0021 - accuracy: 0.9979 - val_loss: 0.1304 - val_accuracy: 0.8636\n",
            "Epoch 13/20\n",
            "390/390 [==============================] - 4s 11ms/step - loss: 0.0028 - accuracy: 0.9967 - val_loss: 0.1334 - val_accuracy: 0.8565\n",
            "Epoch 14/20\n",
            "390/390 [==============================] - 5s 13ms/step - loss: 0.0028 - accuracy: 0.9972 - val_loss: 0.1296 - val_accuracy: 0.8648\n",
            "Epoch 15/20\n",
            "390/390 [==============================] - 4s 11ms/step - loss: 0.0027 - accuracy: 0.9969 - val_loss: 0.1304 - val_accuracy: 0.8636\n",
            "Epoch 16/20\n",
            "390/390 [==============================] - 5s 12ms/step - loss: 0.0026 - accuracy: 0.9972 - val_loss: 0.1298 - val_accuracy: 0.8660\n",
            "Epoch 17/20\n",
            "390/390 [==============================] - 5s 13ms/step - loss: 0.0023 - accuracy: 0.9974 - val_loss: 0.1431 - val_accuracy: 0.8451\n",
            "Epoch 18/20\n",
            "390/390 [==============================] - 4s 11ms/step - loss: 0.0028 - accuracy: 0.9967 - val_loss: 0.1318 - val_accuracy: 0.8630\n",
            "Epoch 19/20\n",
            "390/390 [==============================] - 5s 12ms/step - loss: 0.0018 - accuracy: 0.9982 - val_loss: 0.1319 - val_accuracy: 0.8648\n",
            "Epoch 20/20\n",
            "390/390 [==============================] - 5s 13ms/step - loss: 0.0021 - accuracy: 0.9977 - val_loss: 0.1321 - val_accuracy: 0.8642\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7ac6a7de1570>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = model.evaluate(X_test, y_test)\n",
        "\n",
        "print(\"Losss\", loss)\n",
        "\n",
        "print('Accuracy', accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bwPuFfAsjxfb",
        "outputId": "a50c0ef2-0291-44c3-8412-9146d9900bdd"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "53/53 [==============================] - 0s 3ms/step - loss: 0.1321 - accuracy: 0.8642\n",
            "Losss 0.13207145035266876\n",
            "Accuracy 0.864234447479248\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_predicition = model.predict(X_test)\n",
        "\n",
        "\n",
        "print(new_predicition)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-14ywcTki0e",
        "outputId": "fbcebc6b-6dfa-492d-fbc5-e4262125d35e"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "53/53 [==============================] - 0s 3ms/step\n",
            "[[1.7195209e-11]\n",
            " [4.8800587e-07]\n",
            " [1.7262818e-04]\n",
            " ...\n",
            " [2.5697059e-06]\n",
            " [1.0064154e-12]\n",
            " [6.0891279e-12]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prev = (new_predicition > 0.5)\n",
        "\n",
        "print(prev)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2CN1XrAKku7V",
        "outputId": "97083de8-2c31-4c24-a7b8-9e96e413c010"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[False]\n",
            " [False]\n",
            " [False]\n",
            " ...\n",
            " [False]\n",
            " [False]\n",
            " [False]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, prev)\n",
        "\n",
        "\n",
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "foJWC9Jok299",
        "outputId": "a88dc5ad-b50a-493f-d9aa-6e3debaf0a32"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1431   27]\n",
            " [ 200   14]]\n"
          ]
        }
      ]
    }
  ]
}