spam-ham-detection-using-tensorflow.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix, classification_report\n\nimport tensorflow as tf\nimport warnings\nwarnings.filterwarnings('ignore')","metadata":{"execution":{"iopub.status.busy":"2022-12-12T10:57:19.861797Z","iopub.execute_input":"2022-12-12T10:57:19.862282Z","iopub.status.idle":"2022-12-12T10:57:27.152845Z","shell.execute_reply.started":"2022-12-12T10:57:19.862188Z","shell.execute_reply":"2022-12-12T10:57:27.151320Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"data = pd.read_csv('/kaggle/input/spam-email/spam.csv')","metadata":{"execution":{"iopub.status.busy":"2022-12-12T10:57:27.154884Z","iopub.execute_input":"2022-12-12T10:57:27.155503Z","iopub.status.idle":"2022-12-12T10:57:27.197430Z","shell.execute_reply.started":"2022-12-12T10:57:27.155465Z","shell.execute_reply":"2022-12-12T10:57:27.196059Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"data.head()","metadata":{"execution":{"iopub.status.busy":"2022-12-12T10:57:27.199190Z","iopub.execute_input":"2022-12-12T10:57:27.199860Z","iopub.status.idle":"2022-12-12T10:57:27.222106Z","shell.execute_reply.started":"2022-12-12T10:57:27.199822Z","shell.execute_reply":"2022-12-12T10:57:27.221204Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"  Category                                            Message\n0      ham  Go until jurong point, crazy.. Available only ...\n1      ham                      Ok lar... Joking wif u oni...\n2     spam  Free entry in 2 a wkly comp to win FA Cup fina...\n3      ham  U dun say so early hor... U c already then say...\n4      ham  Nah I don't think he goes to usf, he lives aro...","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>Category</th>\n      <th>Message</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>ham</td>\n      <td>Go until jurong point, crazy.. Available only ...</td>\n    </tr>\n    <tr>\n      <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    </tr>\n    <tr>\n      <th>3</th>\n      <td>ham</td>\n      <td>U dun say so early hor... U c already then say...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ham</td>\n      <td>Nah I don't think he goes to usf, he lives aro...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"data['Category'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2022-12-12T10:57:27.224375Z","iopub.execute_input":"2022-12-12T10:57:27.225381Z","iopub.status.idle":"2022-12-12T10:57:27.242453Z","shell.execute_reply.started":"2022-12-12T10:57:27.225340Z","shell.execute_reply":"2022-12-12T10:57:27.241148Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"ham     4825\nspam     747\nName: Category, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"data.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2022-12-12T10:57:27.244137Z","iopub.execute_input":"2022-12-12T10:57:27.245159Z","iopub.status.idle":"2022-12-12T10:57:27.455430Z","shell.execute_reply.started":"2022-12-12T10:57:27.245114Z","shell.execute_reply":"2022-12-12T10:57:27.453734Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"Category    0\nMessage     0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"def get_seq(texts, tokenizer, train = True, max_seq_length = None):\n    seq = tokenizer.texts_to_sequences(texts)\n    if train == True:\n        max_seq_length = np.max(list(map(len, seq)))\n    seq = tf.keras.preprocessing.sequence.pad_sequences(seq, maxlen = max_seq_length, padding = 'post')\n    return seq","metadata":{"execution":{"iopub.status.busy":"2022-12-12T11:04:43.180745Z","iopub.execute_input":"2022-12-12T11:04:43.181163Z","iopub.status.idle":"2022-12-12T11:04:43.187774Z","shell.execute_reply.started":"2022-12-12T11:04:43.181129Z","shell.execute_reply":"2022-12-12T11:04:43.186832Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"def preprocessing(df):\n    df = df.copy()\n    df['Category'] = df['Category'].replace({\"ham\" : 0, \"spam\" : 1})\n    X = df['Message']\n    Y = df['Category']\n    x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.30, shuffle = True, random_state = 1)\n    tokenizer = tf.keras.preprocessing.text.Tokenizer()\n    tokenizer.fit_on_texts(x_train)\n    print('Vocab Length : ' , len(tokenizer.word_index )+1)\n    x_train = get_seq(texts = x_train, tokenizer = tokenizer, train = True)\n    x_test = get_seq(texts = x_test, tokenizer = tokenizer, train = False, max_seq_length = x_train.shape[1])\n    print('Sequence Length', x_train.shape[1])\n    return x_train, x_test, y_train, y_test","metadata":{"execution":{"iopub.status.busy":"2022-12-12T11:05:07.036208Z","iopub.execute_input":"2022-12-12T11:05:07.036646Z","iopub.status.idle":"2022-12-12T11:05:07.046219Z","shell.execute_reply.started":"2022-12-12T11:05:07.036609Z","shell.execute_reply":"2022-12-12T11:05:07.044865Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"code","source":"x_train, x_test, y_train, y_test = preprocessing(data)\nx_train","metadata":{"execution":{"iopub.status.busy":"2022-12-12T11:05:08.381580Z","iopub.execute_input":"2022-12-12T11:05:08.381962Z","iopub.status.idle":"2022-12-12T11:05:08.640417Z","shell.execute_reply.started":"2022-12-12T11:05:08.381930Z","shell.execute_reply":"2022-12-12T11:05:08.639261Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stdout","text":"Vocab Length :  7383\nSequence Length 189\n","output_type":"stream"},{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"array([[  53,   21,   13, ...,    0,    0,    0],\n       [ 918,    5, 1123, ...,    0,    0,    0],\n       [1124,   58,   42, ...,    0,    0,    0],\n       ...,\n       [7377,    6, 7378, ...,    0,    0,    0],\n       [  68,  167,  222, ...,    0,    0,    0],\n       [ 104,  104,  206, ...,    0,    0,    0]], dtype=int32)"},"metadata":{}}]},{"cell_type":"markdown","source":"# Training","metadata":{}},{"cell_type":"code","source":"inputs = tf.keras.Input(shape = (189, ))\nx = tf.keras.layers.Embedding(\n    input_dim = 7383,\n    output_dim = 250\n)(inputs)\nx = tf.keras.layers.Flatten()(x)\nx = tf.keras.layers.Dense(128, activation = 'relu')(x)\nx = tf.keras.layers.Dense(128, activation = 'relu')(x)\noutputs = tf.keras.layers.Dense(1, activation = 'sigmoid')(x)\nmodel = tf.keras.Model(inputs = inputs, outputs = outputs)\nmodel.compile(\n    optimizer = 'adam',\n    loss = 'binary_crossentropy',\n    metrics = [\n        \"accuracy\",\n        tf.keras.metrics.AUC(name = 'auc')\n    ]\n)\nprint(model.summary())","metadata":{"execution":{"iopub.status.busy":"2022-12-12T11:12:15.171070Z","iopub.execute_input":"2022-12-12T11:12:15.171506Z","iopub.status.idle":"2022-12-12T11:12:15.274461Z","shell.execute_reply.started":"2022-12-12T11:12:15.171464Z","shell.execute_reply":"2022-12-12T11:12:15.272880Z"},"trusted":true},"execution_count":31,"outputs":[{"name":"stdout","text":"Model: \"model_1\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ninput_4 (InputLayer)         [(None, 189)]             0         \n_________________________________________________________________\nembedding_3 (Embedding)      (None, 189, 250)          1845750   \n_________________________________________________________________\nflatten_3 (Flatten)          (None, 47250)             0         \n_________________________________________________________________\ndense_5 (Dense)              (None, 128)               6048128   \n_________________________________________________________________\ndense_6 (Dense)              (None, 128)               16512     \n_________________________________________________________________\ndense_7 (Dense)              (None, 1)                 129       \n=================================================================\nTotal params: 7,910,519\nTrainable params: 7,910,519\nNon-trainable params: 0\n_________________________________________________________________\nNone\n","output_type":"stream"}]},{"cell_type":"code","source":"history = model.fit(\n    x_train,\n    y_train,\n    validation_split = 0.2,\n    batch_size = 32,\n    epochs = 100,\n    callbacks = [\n        tf.keras.callbacks.EarlyStopping(\n        monitor = 'val_loss',\n        patience = 3,\n        restore_best_weights = True)\n    ]\n)","metadata":{"execution":{"iopub.status.busy":"2022-12-12T11:15:14.991212Z","iopub.execute_input":"2022-12-12T11:15:14.991911Z","iopub.status.idle":"2022-12-12T11:15:36.685116Z","shell.execute_reply.started":"2022-12-12T11:15:14.991855Z","shell.execute_reply":"2022-12-12T11:15:36.684039Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stderr","text":"2022-12-12 11:15:15.110224: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n","output_type":"stream"},{"name":"stdout","text":"Epoch 1/100\n98/98 [==============================] - 7s 59ms/step - loss: 0.1670 - accuracy: 0.9381 - auc: 0.9427 - val_loss: 0.0710 - val_accuracy: 0.9808 - val_auc: 0.9839\nEpoch 2/100\n98/98 [==============================] - 5s 47ms/step - loss: 0.0194 - accuracy: 0.9949 - auc: 0.9988 - val_loss: 0.1028 - val_accuracy: 0.9756 - val_auc: 0.9709\nEpoch 3/100\n98/98 [==============================] - 5s 47ms/step - loss: 0.0041 - accuracy: 0.9990 - auc: 1.0000 - val_loss: 0.0818 - val_accuracy: 0.9808 - val_auc: 0.9753\nEpoch 4/100\n98/98 [==============================] - 5s 51ms/step - loss: 0.0020 - accuracy: 0.9997 - auc: 1.0000 - val_loss: 0.0897 - val_accuracy: 0.9795 - val_auc: 0.9714\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Results\n","metadata":{}},{"cell_type":"code","source":"results = model.evaluate(x_test, y_test, verbose = 0)\nprint(f'Accuracy : {round(results[1]*100, 4)}%')\nprint(f'AUC : {round(results[2],3)}')","metadata":{"execution":{"iopub.status.busy":"2022-12-12T11:17:48.382005Z","iopub.execute_input":"2022-12-12T11:17:48.382455Z","iopub.status.idle":"2022-12-12T11:17:48.811126Z","shell.execute_reply.started":"2022-12-12T11:17:48.382418Z","shell.execute_reply":"2022-12-12T11:17:48.809871Z"},"trusted":true},"execution_count":34,"outputs":[{"name":"stdout","text":"Accuracy : 97.8469%\nAUC : 0.988\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Confusion Matrix and Classification Report\n","metadata":{}},{"cell_type":"code","source":"y_pred = np.squeeze(model.predict(x_test) >= 0.5).astype(np.int)\ncm = confusion_matrix(y_test, y_pred,labels = [0,1] )\nclr = classification_report(y_test, y_pred, labels = [0,1], target_names = ['Ham', \"Spam\"])\nplt.figure(figsize = (6,6))\nsns.heatmap(cm, annot = True, fmt = 'g', cbar = False)\nplt.xticks(ticks = [0.5, 1.5], labels = ['Ham', \"Spam\"])\nplt.yticks(ticks = [0.5, 1.5], labels = ['Ham', \"Spam\"])\nplt.xlabel('Predicted')\nplt.ylabel('Actual')\nplt.show()\nprint(f'Classification Report ------------------->\\n{clr}')","metadata":{"execution":{"iopub.status.busy":"2022-12-12T11:31:39.417147Z","iopub.execute_input":"2022-12-12T11:31:39.418195Z","iopub.status.idle":"2022-12-12T11:31:40.041100Z","shell.execute_reply.started":"2022-12-12T11:31:39.418140Z","shell.execute_reply":"2022-12-12T11:31:40.039905Z"},"trusted":true},"execution_count":38,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x432 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"name":"stdout","text":"Classification Report ------------------->\n              precision    recall  f1-score   support\n\n         Ham       0.98      1.00      0.99      1442\n        Spam       0.99      0.85      0.92       230\n\n    accuracy                           0.98      1672\n   macro avg       0.98      0.93      0.95      1672\nweighted avg       0.98      0.98      0.98      1672\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"> Almost perfect recall for ham emails\n\n> Little less recall for spam as data was not equally distrubuted\n\n> Over all good model\n","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}