{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "gpuType": "T4" }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "id": "AS9GGbw-GkpF", "outputId": "e421cd35-5757-4929-cdd8-2ee6b43b65b4" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (2.2.2)\n", "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.12/dist-packages (1.6.1)\n", "Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)\n", "Requirement already satisfied: seaborn in /usr/local/lib/python3.12/dist-packages (0.13.2)\n", "Requirement already satisfied: numpy>=1.26.0 in /usr/local/lib/python3.12/dist-packages (from pandas) (2.0.2)\n", "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas) (2.9.0.post0)\n", "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)\n", "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)\n", "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (1.16.3)\n", "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (1.5.2)\n", "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (3.6.0)\n", "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.3.3)\n", "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (0.12.1)\n", "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (4.61.0)\n", "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.4.9)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (25.0)\n", "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (11.3.0)\n", "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (3.2.5)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n" ] } ], "source": [ "pip install pandas scikit-learn matplotlib seaborn\n" ] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "from sklearn.naive_bayes import MultinomialNB\n", "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "df = pd.read_csv(\"spam.csv\", encoding='latin-1')[['v1', 'v2']]\n", "df.columns = ['Label', 'Message']\n", "df['Label'] = df['Label'].map({'ham':0, 'spam':1})\n", "print(df.head())\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VBJ7x9bEG1Tu", "outputId": "b7df4aab-247e-4658-9883-67535ef47d7e" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " Label Message\n", "0 0 Go until jurong point, crazy.. Available only ...\n", "1 0 Ok lar... Joking wif u oni...\n", "2 1 Free entry in 2 a wkly comp to win FA Cup fina...\n", "3 0 U dun say so early hor... U c already then say...\n", "4 0 Nah I don't think he goes to usf, he lives aro...\n" ] } ] }, { "cell_type": "code", "source": [ "X = df['Message']\n", "y = df['Label']\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, test_size=0.2, random_state=42\n", ")\n" ], "metadata": { "id": "8UYri9Z0G1Wf" }, "execution_count": 10, "outputs": [] }, { "cell_type": "code", "source": [ "vectorizer = CountVectorizer()\n", "X_train_vec = vectorizer.fit_transform(X_train)\n", "X_test_vec = vectorizer.transform(X_test)\n" ], "metadata": { "id": "qRj-sxXsG1Y9" }, "execution_count": 11, "outputs": [] }, { "cell_type": "code", "source": [ "nb_model = MultinomialNB()\n", "nb_model.fit(X_train_vec, y_train)\n", "\n", "y_pred = nb_model.predict(X_test_vec)\n" ], "metadata": { "id": "ojif6K6vG1bx" }, "execution_count": 12, "outputs": [] }, { "cell_type": "code", "source": [ "\n", "accuracy = accuracy_score(y_test, y_pred)\n", "print(f\"Accuracy: {accuracy*100:.2f}%\")\n", "cm = confusion_matrix(y_test, y_pred)\n", "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Ham','Spam'], yticklabels=['Ham','Spam'])\n", "plt.title(\"Confusion Matrix\")\n", "plt.xlabel(\"Predicted\")\n", "plt.ylabel(\"Actual\")\n", "plt.show()\n", "\n", "print(classification_report(y_test, y_pred))\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 646 }, "id": "7DfI_PBVG1eW", "outputId": "2b81c19f-331c-497d-ef6c-fbc339618ca5" }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Accuracy: 98.39%\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " 0 0.98 1.00 0.99 965\n", " 1 0.99 0.89 0.94 150\n", "\n", " accuracy 0.98 1115\n", " macro avg 0.98 0.95 0.96 1115\n", "weighted avg 0.98 0.98 0.98 1115\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "new_messages = [\"Congratulations! You won a free ticket.\", \"Hey, are we still meeting today?\"]\n", "new_vec = vectorizer.transform(new_messages)\n", "predictions = nb_model.predict(new_vec)\n", "\n", "for msg, pred in zip(new_messages, predictions):\n", " label = \"Spam\" if pred == 1 else \"Ham\"\n", " print(f\"Message: '{msg}' => Prediction: {label}\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uxrPs_KQHOFB", "outputId": "7d6815c6-bde3-43ba-a0ee-69df94d03e0a" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Message: 'Congratulations! You won a free ticket.' => Prediction: Spam\n", "Message: 'Hey, are we still meeting today?' => Prediction: Ham\n" ] } ] }, { "cell_type": "code", "source": [ "\n", "import numpy as np\n", "feature_names = np.array(vectorizer.get_feature_names_out())\n", "top_spam_words = feature_names[np.argsort(nb_model.feature_log_prob_[1])[-10:]]\n", "print(\"Top spam words:\", top_spam_words)\n" ], "metadata": { "id": "EFbz0g7jHWT9" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "Nor4DHwPHWZI" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "4wwtu1YZICKW" }, "execution_count": null, "outputs": [] } ] }