Neural Network sms text classification.ipynb

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          "text": [
            "Requirement already satisfied: tensorflow-datasets in /usr/local/lib/python3.7/dist-packages (4.0.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets) (1.15.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets) (4.41.1)\n",
            "Requirement already satisfied: tensorflow-metadata in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets) (1.1.0)\n",
            "Requirement already satisfied: importlib-resources in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets) (5.2.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets) (1.19.5)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets) (0.3.4)\n",
            "Requirement already satisfied: attrs>=18.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets) (21.2.0)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets) (0.12.0)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets) (0.16.0)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets) (3.17.3)\n",
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            "Requirement already satisfied: promise in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets) (2.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets) (2.23.0)\n",
            "Requirement already satisfied: termcolor in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets) (1.1.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->tensorflow-datasets) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->tensorflow-datasets) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->tensorflow-datasets) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->tensorflow-datasets) (2021.5.30)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from importlib-resources->tensorflow-datasets) (3.5.0)\n",
            "Requirement already satisfied: googleapis-common-protos<2,>=1.52.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-metadata->tensorflow-datasets) (1.53.0)\n"
          ]
        }
      ],
      "source": [
        "# import libraries\n",
        "import pandas as pd\n",
        "!pip install tensorflow-datasets\n",
        "import tensorflow_datasets as tfds\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.preprocessing.text import one_hot\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Flatten, Embedding, Dense\n",
        "from tensorflow.keras.callbacks import EarlyStopping"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lMHwYXHXCar3",
        "outputId": "43855dc0-837c-47e7-e943-136578fd4ab0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2021-08-10 10:04:07--  https://cdn.freecodecamp.org/project-data/sms/train-data.tsv\n",
            "Resolving cdn.freecodecamp.org (cdn.freecodecamp.org)... 172.67.70.149, 104.26.3.33, 104.26.2.33, ...\n",
            "Connecting to cdn.freecodecamp.org (cdn.freecodecamp.org)|172.67.70.149|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 358233 (350K) [text/tab-separated-values]\n",
            "Saving to: ‘train-data.tsv’\n",
            "\n",
            "\rtrain-data.tsv        0%[                    ]       0  --.-KB/s               \rtrain-data.tsv      100%[===================>] 349.84K  --.-KB/s    in 0.06s   \n",
            "\n",
            "2021-08-10 10:04:07 (5.29 MB/s) - ‘train-data.tsv’ saved [358233/358233]\n",
            "\n",
            "--2021-08-10 10:04:07--  https://cdn.freecodecamp.org/project-data/sms/valid-data.tsv\n",
            "Resolving cdn.freecodecamp.org (cdn.freecodecamp.org)... 172.67.70.149, 104.26.3.33, 104.26.2.33, ...\n",
            "Connecting to cdn.freecodecamp.org (cdn.freecodecamp.org)|172.67.70.149|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 118774 (116K) [text/tab-separated-values]\n",
            "Saving to: ‘valid-data.tsv’\n",
            "\n",
            "valid-data.tsv      100%[===================>] 115.99K  --.-KB/s    in 0.03s   \n",
            "\n",
            "2021-08-10 10:04:07 (3.68 MB/s) - ‘valid-data.tsv’ saved [118774/118774]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# get data files\n",
        "!wget https://cdn.freecodecamp.org/project-data/sms/train-data.tsv\n",
        "!wget https://cdn.freecodecamp.org/project-data/sms/valid-data.tsv\n",
        "\n",
        "train_file_path = \"train-data.tsv\"\n",
        "test_file_path = \"valid-data.tsv\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Pd2AYGwU3-WL"
      },
      "outputs": [],
      "source": [
        "names = [\"class\", \"message\"]"
      ]
    },
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      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
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          "height": 419
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        "id": "g_h508FEClxO",
        "outputId": "0244cafa-fb7e-4523-b2af-9f1c36587351"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>ahhhh...just woken up!had a bad dream about u ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>you can never do nothing</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>ham</td>\n",
              "      <td>now u sound like manky scouse boy steve,like! ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>mum say we wan to go then go... then she can s...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>never y lei... i v lazy... got wat? dat day ü ...</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>4174</th>\n",
              "      <td>ham</td>\n",
              "      <td>just woke up. yeesh its late. but i didn't fal...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4175</th>\n",
              "      <td>ham</td>\n",
              "      <td>what do u reckon as need 2 arrange transport i...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4176</th>\n",
              "      <td>spam</td>\n",
              "      <td>free entry into our £250 weekly competition ju...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4177</th>\n",
              "      <td>spam</td>\n",
              "      <td>-pls stop bootydelious (32/f) is inviting you ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4178</th>\n",
              "      <td>ham</td>\n",
              "      <td>tell my  bad character which u dnt lik in me. ...</td>\n",
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              "<p>4179 rows × 2 columns</p>\n",
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            "text/plain": [
              "     class                                            message\n",
              "0      ham  ahhhh...just woken up!had a bad dream about u ...\n",
              "1      ham                           you can never do nothing\n",
              "2      ham  now u sound like manky scouse boy steve,like! ...\n",
              "3      ham  mum say we wan to go then go... then she can s...\n",
              "4      ham  never y lei... i v lazy... got wat? dat day ü ...\n",
              "...    ...                                                ...\n",
              "4174   ham  just woke up. yeesh its late. but i didn't fal...\n",
              "4175   ham  what do u reckon as need 2 arrange transport i...\n",
              "4176  spam  free entry into our £250 weekly competition ju...\n",
              "4177  spam  -pls stop bootydelious (32/f) is inviting you ...\n",
              "4178   ham  tell my  bad character which u dnt lik in me. ...\n",
              "\n",
              "[4179 rows x 2 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {
            "tags": []
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      ],
      "source": [
        "train_file = pd.read_csv(train_file_path, sep='\\t', names=names)\n",
        "train_file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
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        "id": "zOMKywn4zReN",
        "outputId": "8f4d38a3-359e-4038-fb3f-abad5de4d983"
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      "outputs": [
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              "      <td>ham</td>\n",
              "      <td>i am in hospital da. . i will return home in e...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>not much, just some textin'. how bout you?</td>\n",
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              "      <th>2</th>\n",
              "      <td>ham</td>\n",
              "      <td>i probably won't eat at all today. i think i'm...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>don‘t give a flying monkeys wot they think and...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>who are you seeing?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1387</th>\n",
              "      <td>ham</td>\n",
              "      <td>true dear..i sat to pray evening and felt so.s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1388</th>\n",
              "      <td>ham</td>\n",
              "      <td>what will we do in the shower, baby?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1389</th>\n",
              "      <td>ham</td>\n",
              "      <td>where are you ? what are you doing ? are yuou ...</td>\n",
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              "    <tr>\n",
              "      <th>1390</th>\n",
              "      <td>spam</td>\n",
              "      <td>ur cash-balance is currently 500 pounds - to m...</td>\n",
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              "    <tr>\n",
              "      <th>1391</th>\n",
              "      <td>spam</td>\n",
              "      <td>not heard from u4 a while. call 4 rude chat pr...</td>\n",
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              "<p>1392 rows × 2 columns</p>\n",
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            "text/plain": [
              "     class                                            message\n",
              "0      ham  i am in hospital da. . i will return home in e...\n",
              "1      ham         not much, just some textin'. how bout you?\n",
              "2      ham  i probably won't eat at all today. i think i'm...\n",
              "3      ham  don‘t give a flying monkeys wot they think and...\n",
              "4      ham                                who are you seeing?\n",
              "...    ...                                                ...\n",
              "1387   ham  true dear..i sat to pray evening and felt so.s...\n",
              "1388   ham               what will we do in the shower, baby?\n",
              "1389   ham  where are you ? what are you doing ? are yuou ...\n",
              "1390  spam  ur cash-balance is currently 500 pounds - to m...\n",
              "1391  spam  not heard from u4 a while. call 4 rude chat pr...\n",
              "\n",
              "[1392 rows x 2 columns]"
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          "execution_count": 5,
          "metadata": {
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      ],
      "source": [
        "test_file = pd.read_csv(test_file_path, sep='\\t', names=names)\n",
        "test_file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "GO7UYNiu8yog"
      },
      "outputs": [],
      "source": [
        "train_message = train_file[\"message\"].values.tolist()\n",
        "train_label = np.array([0 if x==\"ham\" else 1 for x in train_file['class'].values.tolist()])\n",
        "test_message = test_file[\"message\"].values.tolist()\n",
        "test_label = np.array([0 if x==\"ham\" else 1 for x in test_file['class'].values.tolist()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Td-zUm4ANr9T"
      },
      "outputs": [],
      "source": [
        "vocabulary_dict = {}\n",
        "for messgae in train_message:\n",
        "  for vocabulary in messgae.split():\n",
        "    if vocabulary not in vocabulary_dict:\n",
        "      vocabulary_dict[vocabulary] = 1\n",
        "    else:\n",
        "      vocabulary_dict[vocabulary] += 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "y2EkA8czFqko"
      },
      "outputs": [],
      "source": [
        "VOCAB_SIZE = len(vocabulary_dict)\n",
        "MAX_LENGTH = len(max(train_message, key=lambda p: len(p.split())).split())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "JDtbI_Pg_zhj"
      },
      "outputs": [],
      "source": [
        "encoded_train_message = [one_hot(d, VOCAB_SIZE) for d in train_message]\n",
        "padded_train_message = pad_sequences(encoded_train_message, maxlen=MAX_LENGTH, padding='post')\n",
        "encoded_test_message = [one_hot(d, VOCAB_SIZE) for d in test_message]\n",
        "padded_test_message = pad_sequences(encoded_test_message, maxlen=MAX_LENGTH, padding='post')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RvIykJLYLzUz",
        "outputId": "aa6eb2ba-6c69-4551-d3fb-9af250529f3a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "131/131 - 5s - loss: 0.2396 - acc: 0.9134 - val_loss: 0.0994 - val_acc: 0.9720\n",
            "Epoch 2/1000\n",
            "131/131 - 1s - loss: 0.0557 - acc: 0.9868 - val_loss: 0.0558 - val_acc: 0.9813\n",
            "Epoch 3/1000\n",
            "131/131 - 1s - loss: 0.0266 - acc: 0.9921 - val_loss: 0.0465 - val_acc: 0.9849\n",
            "Epoch 4/1000\n",
            "131/131 - 1s - loss: 0.0140 - acc: 0.9966 - val_loss: 0.0404 - val_acc: 0.9871\n",
            "Epoch 5/1000\n",
            "131/131 - 1s - loss: 0.0084 - acc: 0.9988 - val_loss: 0.0384 - val_acc: 0.9871\n",
            "Epoch 6/1000\n",
            "131/131 - 1s - loss: 0.0053 - acc: 0.9993 - val_loss: 0.0406 - val_acc: 0.9856\n",
            "Epoch 7/1000\n",
            "131/131 - 1s - loss: 0.0036 - acc: 0.9998 - val_loss: 0.0386 - val_acc: 0.9864\n",
            "Epoch 8/1000\n",
            "131/131 - 1s - loss: 0.0027 - acc: 0.9998 - val_loss: 0.0400 - val_acc: 0.9864\n",
            "Epoch 9/1000\n",
            "131/131 - 1s - loss: 0.0022 - acc: 0.9998 - val_loss: 0.0405 - val_acc: 0.9864\n",
            "Epoch 10/1000\n",
            "131/131 - 1s - loss: 0.0018 - acc: 0.9998 - val_loss: 0.0394 - val_acc: 0.9864\n",
            "Epoch 11/1000\n",
            "131/131 - 1s - loss: 0.0013 - acc: 0.9998 - val_loss: 0.0361 - val_acc: 0.9892\n",
            "Epoch 12/1000\n",
            "131/131 - 1s - loss: 0.0011 - acc: 0.9998 - val_loss: 0.0378 - val_acc: 0.9892\n",
            "Epoch 13/1000\n",
            "131/131 - 1s - loss: 7.8530e-04 - acc: 1.0000 - val_loss: 0.0429 - val_acc: 0.9871\n",
            "Epoch 14/1000\n",
            "131/131 - 1s - loss: 6.8651e-04 - acc: 0.9998 - val_loss: 0.0395 - val_acc: 0.9885\n",
            "Epoch 15/1000\n",
            "131/131 - 1s - loss: 5.2232e-04 - acc: 1.0000 - val_loss: 0.0411 - val_acc: 0.9885\n",
            "Epoch 16/1000\n",
            "131/131 - 1s - loss: 4.1682e-04 - acc: 1.0000 - val_loss: 0.0417 - val_acc: 0.9885\n",
            "Epoch 17/1000\n",
            "131/131 - 1s - loss: 3.4313e-04 - acc: 1.0000 - val_loss: 0.0418 - val_acc: 0.9885\n",
            "Epoch 18/1000\n",
            "131/131 - 1s - loss: 2.8415e-04 - acc: 1.0000 - val_loss: 0.0443 - val_acc: 0.9871\n",
            "Epoch 19/1000\n",
            "131/131 - 1s - loss: 2.4964e-04 - acc: 1.0000 - val_loss: 0.0447 - val_acc: 0.9864\n",
            "Epoch 20/1000\n",
            "131/131 - 1s - loss: 2.1204e-04 - acc: 1.0000 - val_loss: 0.0430 - val_acc: 0.9885\n",
            "Epoch 21/1000\n",
            "131/131 - 1s - loss: 1.8529e-04 - acc: 1.0000 - val_loss: 0.0449 - val_acc: 0.9878\n",
            "Epoch 22/1000\n",
            "131/131 - 1s - loss: 1.6317e-04 - acc: 1.0000 - val_loss: 0.0443 - val_acc: 0.9885\n",
            "Epoch 23/1000\n",
            "131/131 - 1s - loss: 1.4493e-04 - acc: 1.0000 - val_loss: 0.0445 - val_acc: 0.9885\n",
            "Epoch 24/1000\n",
            "131/131 - 1s - loss: 1.2489e-04 - acc: 1.0000 - val_loss: 0.0473 - val_acc: 0.9871\n",
            "Epoch 25/1000\n",
            "131/131 - 1s - loss: 1.1419e-04 - acc: 1.0000 - val_loss: 0.0470 - val_acc: 0.9871\n",
            "Epoch 26/1000\n",
            "131/131 - 1s - loss: 1.0146e-04 - acc: 1.0000 - val_loss: 0.0474 - val_acc: 0.9871\n",
            "Epoch 27/1000\n",
            "131/131 - 1s - loss: 9.1298e-05 - acc: 1.0000 - val_loss: 0.0469 - val_acc: 0.9885\n",
            "Epoch 28/1000\n",
            "131/131 - 1s - loss: 8.0863e-05 - acc: 1.0000 - val_loss: 0.0484 - val_acc: 0.9871\n",
            "Epoch 29/1000\n",
            "131/131 - 1s - loss: 7.4275e-05 - acc: 1.0000 - val_loss: 0.0487 - val_acc: 0.9871\n",
            "Epoch 30/1000\n",
            "131/131 - 1s - loss: 6.6754e-05 - acc: 1.0000 - val_loss: 0.0483 - val_acc: 0.9885\n",
            "Epoch 31/1000\n",
            "131/131 - 1s - loss: 6.0208e-05 - acc: 1.0000 - val_loss: 0.0495 - val_acc: 0.9878\n",
            "Epoch 32/1000\n",
            "131/131 - 1s - loss: 5.5236e-05 - acc: 1.0000 - val_loss: 0.0494 - val_acc: 0.9878\n",
            "Epoch 33/1000\n",
            "131/131 - 1s - loss: 4.9896e-05 - acc: 1.0000 - val_loss: 0.0498 - val_acc: 0.9878\n",
            "Epoch 34/1000\n",
            "131/131 - 1s - loss: 4.5089e-05 - acc: 1.0000 - val_loss: 0.0508 - val_acc: 0.9871\n",
            "Epoch 35/1000\n",
            "131/131 - 1s - loss: 4.1314e-05 - acc: 1.0000 - val_loss: 0.0513 - val_acc: 0.9871\n",
            "Epoch 36/1000\n",
            "131/131 - 1s - loss: 3.7793e-05 - acc: 1.0000 - val_loss: 0.0511 - val_acc: 0.9878\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00036: early stopping\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f428e218b10>"
            ]
          },
          "execution_count": 10,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = Sequential()\n",
        "embedding_layer = Embedding(VOCAB_SIZE, 100, input_length=MAX_LENGTH)\n",
        "model.add(embedding_layer)\n",
        "model.add(Flatten())\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "monitor = EarlyStopping(monitor='val_acc', min_delta=1e-4, patience=25, verbose=1, mode='max', restore_best_weights=True)\n",
        "model.fit(padded_train_message, train_label, validation_data=(padded_test_message, test_label), callbacks=[monitor], epochs=1000, verbose=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J9tD9yACG6M9",
        "outputId": "89977708-7483-4683-a8f4-e86e00aeedd1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0.0005038009, 'ham']\n"
          ]
        }
      ],
      "source": [
        "# function to predict messages based on model\n",
        "def predict_message(pred_text):\n",
        "  class_dict = {\n",
        "      0 : \"ham\", \n",
        "      1 : \"spam\",   \n",
        "      }\n",
        "  encoded_message = [one_hot(pred_text, VOCAB_SIZE)]\n",
        "  padded_message = pad_sequences(encoded_message, maxlen=MAX_LENGTH, padding='post')\n",
        "  prediction = [model.predict(padded_message)[0][0], class_dict[np.round(model.predict(padded_message)[0][0])]]\n",
        "  return prediction\n",
        "\n",
        "pred_text = \"how are you doing today?\"\n",
        "\n",
        "prediction = predict_message(pred_text)\n",
        "print(prediction)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dxotov85SjsC",
        "outputId": "4480bc6a-a413-4309-f88f-89fd68ff49b8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "You passed the challenge. Great job!\n"
          ]
        }
      ],
      "source": [
        "def test_predictions():\n",
        "  test_messages = [\"how are you doing today\",\n",
        "                   \"sale today! to stop texts call 98912460324\",\n",
        "                   \"i dont want to go. can we try it a different day? available sat\",\n",
        "                   \"our new mobile video service is live. just install on your phone to start watching.\",\n",
        "                   \"you have won £1000 cash! call to claim your prize.\",\n",
        "                   \"i'll bring it tomorrow. don't forget the milk.\",\n",
        "                   \"wow, is your arm alright. that happened to me one time too\"\n",
        "                  ]\n",
        "\n",
        "  test_answers = [\"ham\", \"spam\", \"ham\", \"spam\", \"spam\", \"ham\", \"ham\"]\n",
        "  passed = True\n",
        "\n",
        "  for msg, ans in zip(test_messages, test_answers):\n",
        "    prediction = predict_message(msg)\n",
        "    if prediction[1] != ans:\n",
        "      passed = False\n",
        "\n",
        "  if passed:\n",
        "    print(\"You passed the challenge. Great job!\")\n",
        "  else:\n",
        "    print(\"You haven't passed yet. Keep trying.\")\n",
        "\n",
        "test_predictions()\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "fcc_sms_text_classification.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}