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‎Email Spam Classifer(LSTM Networks)/EmailSpamClassifier(LSTM_Network).ipynb‎

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "code",
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.feature_extraction.text import CountVectorizer\n",
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"from tensorflow.keras.models import Sequential\n",
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"from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout\n",
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"from tensorflow.keras.preprocessing.text import Tokenizer\n",
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"from tensorflow.keras.preprocessing.sequence import pad_sequences"
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],
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"metadata": {
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"id": "q9n8UiMR74n3"
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},
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"execution_count": 12,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"data = pd.read_csv('spam_ham_dataset.csv')\n",
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"X = data['text']\n",
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"y = data['label']\n",
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"y = y.map({'ham': 0, 'spam': 1})"
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],
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"metadata": {
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"id": "mzWr2hfq773m"
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},
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"execution_count": 13,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, random_state=42)"
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],
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"metadata": {
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"id": "DUbwo5FV7-LB"
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},
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"execution_count": 14,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"tokenizer = Tokenizer()\n",
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"tokenizer.fit_on_texts(X_tr)\n",
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"X_tr_seq = tokenizer.texts_to_sequences(X_tr)\n",
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"X_te_seq = tokenizer.texts_to_sequences(X_te)\n",
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"max_seq_len = 100\n",
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"X_tr_pad = pad_sequences(X_tr_seq, maxlen=max_seq_len, padding='post')\n",
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"X_te_pad = pad_sequences(X_te_seq, maxlen=max_seq_len, padding='post')"
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],
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"metadata": {
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"id": "Em0mEiBR8BPb"
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},
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"execution_count": 15,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"vocab_size = len(tokenizer.word_index) + 1\n",
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"model = Sequential()\n",
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"model.add(Embedding(input_dim=vocab_size, output_dim=128, input_length=max_seq_len))\n",
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"model.add(LSTM(64, return_sequences=True))\n",
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"model.add(Dropout(0.5))\n",
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"model.add(LSTM(64))\n",
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"model.add(Dense(1, activation='sigmoid'))\n",
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"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
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"model.fit(X_tr_pad, y_tr, epochs=10, batch_size=32, validation_split=0.2)"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "s_M5u0tM8Fkb",
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"outputId": "93d7c7f8-40b7-44be-b425-8f57dcc18190"
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},
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"execution_count": 16,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Epoch 1/10\n",
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"104/104 [==============================] - 35s 297ms/step - loss: 0.5534 - accuracy: 0.7482 - val_loss: 0.5267 - val_accuracy: 0.7705\n",
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"Epoch 2/10\n",
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"104/104 [==============================] - 28s 272ms/step - loss: 0.5402 - accuracy: 0.7582 - val_loss: 0.5110 - val_accuracy: 0.7742\n",
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"Epoch 3/10\n",
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"104/104 [==============================] - 29s 277ms/step - loss: 0.4195 - accuracy: 0.8210 - val_loss: 0.2600 - val_accuracy: 0.9118\n",
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"Epoch 4/10\n",
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"104/104 [==============================] - 28s 271ms/step - loss: 0.2672 - accuracy: 0.8951 - val_loss: 0.1177 - val_accuracy: 0.9614\n",
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"Epoch 5/10\n",
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"104/104 [==============================] - 28s 268ms/step - loss: 0.0643 - accuracy: 0.9797 - val_loss: 0.1393 - val_accuracy: 0.9650\n",
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"Epoch 6/10\n",
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"104/104 [==============================] - 28s 268ms/step - loss: 0.0444 - accuracy: 0.9879 - val_loss: 0.1399 - val_accuracy: 0.9710\n",
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"Epoch 7/10\n",
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"104/104 [==============================] - 29s 280ms/step - loss: 0.0451 - accuracy: 0.9912 - val_loss: 0.1501 - val_accuracy: 0.9674\n",
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"Epoch 8/10\n",
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"104/104 [==============================] - 28s 268ms/step - loss: 0.0311 - accuracy: 0.9946 - val_loss: 0.1582 - val_accuracy: 0.9686\n",
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"Epoch 9/10\n",
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"104/104 [==============================] - 29s 279ms/step - loss: 0.0275 - accuracy: 0.9952 - val_loss: 0.1492 - val_accuracy: 0.9710\n",
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"Epoch 10/10\n",
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"104/104 [==============================] - 29s 283ms/step - loss: 0.0249 - accuracy: 0.9955 - val_loss: 0.1553 - val_accuracy: 0.9698\n"
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]
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},
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"<keras.callbacks.History at 0x78bd259d00a0>"
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]
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},
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"metadata": {},
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"execution_count": 16
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"model.summary()"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "95K3lpbr8SkX",
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"outputId": "72b34217-6840-4f9e-f435-64c8861c5222"
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},
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"execution_count": 17,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Model: \"sequential_2\"\n",
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"_________________________________________________________________\n",
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" Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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" embedding_2 (Embedding) (None, 100, 128) 6625664 \n",
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" \n",
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" lstm_4 (LSTM) (None, 100, 64) 49408 \n",
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" \n",
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" dropout_2 (Dropout) (None, 100, 64) 0 \n",
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" \n",
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" lstm_5 (LSTM) (None, 64) 33024 \n",
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" \n",
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" dense_2 (Dense) (None, 1) 65 \n",
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" \n",
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"=================================================================\n",
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"Total params: 6,708,161\n",
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"Trainable params: 6,708,161\n",
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"Non-trainable params: 0\n",
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"_________________________________________________________________\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "JjiVnmmi5Mm6",
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"outputId": "dcd0456d-87ea-454d-a363-1ad8193eb132"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"33/33 [==============================] - 1s 42ms/step - loss: 0.1547 - accuracy: 0.9700\n",
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"Test Loss: 0.1547\n",
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"Test Accuracy: 0.9700\n"
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]
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}
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],
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"source": [
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"loss, acc = model.evaluate(X_te_pad, y_te)\n",
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"print(f\"Test Loss: {loss:.4f}\")\n",
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"print(f\"Test Accuracy: {acc:.4f}\")\n"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"email_text = input(\"Enter an email text: \")\n",
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"\n",
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"sequence = tokenizer.texts_to_sequences([email_text])\n",
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"padded_sequence = pad_sequences(sequence, maxlen=100, padding='post')\n",
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"prediction = model.predict(padded_sequence)\n",
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"\n",
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"if prediction > 0.5:\n",
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" print(\"Prediction: Spam\")\n",
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"else:\n",
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" print(\"Prediction: Ham\")"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "WvGAXfeW7f3h",
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"outputId": "084ba5c2-4e54-47af-d239-b83a6485b511"
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},
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"execution_count": 19,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Enter an email text: you won 1 mill\n",
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"1/1 [==============================] - 1s 855ms/step\n",
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"Prediction: Spam\n"
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]
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}
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]
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}
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]
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}

‎Email Spam Classifer(LSTM Networks)/ReadMe.txt‎

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Description:
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The primary goal of this project is to develop a machine learning model that can automatically classify emails as either spam or legitimate (ham) based on their content. The LSTM model(Long Short-Term Memory), recurrent neural network (RNN) architecture, is employed to capture sequential patterns in the text data, making it well-suited for natural language processing tasks like this.
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Dataset
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The project employs a labeled dataset of emails, containing both spam and ham samples. The dataset is preprocessed to tokenize and pad the text data before feeding it into the LSTM model. Please replace `'spam_ham_dataset.csv'` in the code with your actual dataset path.
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Model Architecture
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The LSTM model architecture involves the following key components:
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- `Embedding` Layer: Converts the integer-encoded vocabulary into dense vectors of fixed size.
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- First `LSTM` Layer: Captures sequential patterns by returning sequences instead of a single output.
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- `Dropout` Layer: Helps prevent overfitting by randomly deactivating a fraction of input units during training.
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- Second `LSTM` Layer: Aggregates the output of the previous LSTM layer.
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- `Dense` Layer: Produces a single output unit with sigmoid activation for binary classification.
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The model is trained using binary cross-entropy loss and the Adam optimizer.
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Install required packages:
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pip install numpy pandas tensorflow
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