{ "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": "markdown", "source": [ "# 🟦 PHASE 3 — IMPORT LIBRARIES\n", "# ✅ STEP 3: Install & Import Libraries\n" ], "metadata": { "id": "1MiZZoYwfKwb" } }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "xEBWIru6ec-0" }, "outputs": [], "source": [ "# Core libraries\n", "import numpy as np\n", "import pandas as pd\n", "\n", "# Visualization\n", "import matplotlib.pyplot as plt\n", "\n", "# Machine Learning\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "\n", "# Models\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.naive_bayes import MultinomialNB\n", "from sklearn.svm import LinearSVC\n", "\n", "# Evaluation\n", "from sklearn.metrics import (\n", " accuracy_score,\n", " confusion_matrix,\n", " classification_report,\n", " ConfusionMatrixDisplay\n", ")\n", "\n", "# Save model\n", "import pickle" ] }, { "cell_type": "markdown", "source": [ "# 🟦 PHASE 4 — LOAD DATASET\n", "# ✅ STEP 4: Upload Dataset" ], "metadata": { "id": "i9z1II4EfXfF" } }, { "cell_type": "code", "source": [ "import kagglehub\n", "\n", "# Download latest version\n", "path = kagglehub.dataset_download(\"shantanudhakadd/email-spam-detection-dataset-classification\")\n", "\n", "print(\"Path to dataset files:\", path)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "odx0Nlo9fXGX", "outputId": "2ce9dfb3-d16b-4fcf-9a3f-3117bd57394b" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Downloading to /root/.cache/kagglehub/datasets/shantanudhakadd/email-spam-detection-dataset-classification/1.archive...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 211k/211k [00:00<00:00, 69.1MB/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Extracting files...\n", "Path to dataset files: /root/.cache/kagglehub/datasets/shantanudhakadd/email-spam-detection-dataset-classification/versions/1\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] } ] }, { "cell_type": "markdown", "source": [ "# ✅ STEP 5: Explore Dataset" ], "metadata": { "id": "cjiYts4DgJDG" } }, { "cell_type": "code", "source": [ "data = pd.read_csv('/content/spam.csv', encoding='latin1')\n", "data.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "ehU4MnUVgFxn", "outputId": "d94e81e5-b2bf-4220-d6ff-f2e5b1df89c1" }, "execution_count": 7, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " v1 v2 Unnamed: 2 \\\n", "0 ham Go until jurong point, crazy.. Available only ... NaN \n", "1 ham Ok lar... Joking wif u oni... NaN \n", "2 spam Free entry in 2 a wkly comp to win FA Cup fina... NaN \n", "3 ham U dun say so early hor... U c already then say... NaN \n", "4 ham Nah I don't think he goes to usf, he lives aro... NaN \n", "\n", " Unnamed: 3 Unnamed: 4 \n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
v1v2Unnamed: 2Unnamed: 3Unnamed: 4
0hamGo until jurong point, crazy.. Available only ...NaNNaNNaN
1hamOk lar... Joking wif u oni...NaNNaNNaN
2spamFree entry in 2 a wkly comp to win FA Cup fina...NaNNaNNaN
3hamU dun say so early hor... U c already then say...NaNNaNNaN
4hamNah I don't think he goes to usf, he lives aro...NaNNaNNaN
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", "
\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "data", "summary": "{\n \"name\": \"data\",\n \"rows\": 5572,\n \"fields\": [\n {\n \"column\": \"v1\",\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\": \"v2\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5169,\n \"samples\": [\n \"Did u download the fring app?\",\n \"Pass dis to all ur contacts n see wat u get! Red;i'm in luv wid u. Blue;u put a smile on my face. Purple;u r realy hot. Pink;u r so swt. Orange;i thnk i lyk u. Green;i realy wana go out wid u. Yelow;i wnt u bck. Black;i'm jealous of u. Brown;i miss you Nw plz giv me one color\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Unnamed: 2\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 43,\n \"samples\": [\n \" GOD said\",\n \" SHE SHUDVETOLD U. DID URGRAN KNOW?NEWAY\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Unnamed: 3\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 10,\n \"samples\": [\n \" \\\\\\\"OH No! COMPETITION\\\\\\\". Who knew\",\n \" why to miss them\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Unnamed: 4\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"GNT:-)\\\"\",\n \" one day these two will become FREINDS FOREVER!\\\"\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 7 } ] }, { "source": [ "from google.colab import sheets\n", "sheet = sheets.InteractiveSheet(df=data)" ], "cell_type": "code", "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "https://docs.google.com/spreadsheets/d/1-QfClpXynf8Fjend9vXSHuj7y5Eesc7I8vDU33ADRpA/edit#gid=0\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {} } ], "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/", "height": 639 }, "id": "0_k8GA9Wgdkw", "outputId": "10b43d77-3d41-408b-89ab-51b6be5155b0" } }, { "cell_type": "code", "source": [ "data.info()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e863vg5ZgNuQ", "outputId": "ec289a0e-6449-41b1-e888-3ba10422198e" }, "execution_count": 8, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "RangeIndex: 5572 entries, 0 to 5571\n", "Data columns (total 5 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 v1 5572 non-null object\n", " 1 v2 5572 non-null object\n", " 2 Unnamed: 2 50 non-null object\n", " 3 Unnamed: 3 12 non-null object\n", " 4 Unnamed: 4 6 non-null object\n", "dtypes: object(5)\n", "memory usage: 217.8+ KB\n" ] } ] }, { "cell_type": "code", "source": [ "data['v1'].value_counts()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 178 }, "id": "RZUSeVpygvPY", "outputId": "48c9d7aa-c41f-42cc-f333-439de80d5062" }, "execution_count": 11, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "v1\n", "ham 4825\n", "spam 747\n", "Name: count, dtype: int64" ], "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
count
v1
ham4825
spam747
\n", "

" ] }, "metadata": {}, "execution_count": 11 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 389 }, "id": "6de4bd0f", "outputId": "0473d9f6-0a58-45c3-f034-5ab59c41519d" }, "source": [ "import pandas as pd\n", "import os\n", "import kagglehub\n", "\n", "# Ensure the dataset is downloaded just before trying to access it\n", "# This makes the cell self-contained for data loading\n", "print(\"Downloading dataset from Kagglehub...\")\n", "dataset_root_path = kagglehub.dataset_download(\"shantanudhakadd/email-spam-detection-dataset-classification\")\n", "print(f\"Dataset downloaded to: {dataset_root_path}\")\n", "\n", "# Now, verify the path and list its contents to find spam.csv\n", "if not os.path.exists(dataset_root_path):\n", " print(f\"Error: The downloaded directory '{dataset_root_path}' does not exist.\")\n", "elif not os.path.isdir(dataset_root_path):\n", " print(f\"Error: '{dataset_root_path}' is not a directory.\")\n", "else:\n", " print(f\"Contents of {dataset_root_path}:\")\n", " # Search for spam.csv within this directory and its subdirectories\n", " found_file_path = None\n", " for dirpath, dirnames, filenames in os.walk(dataset_root_path):\n", " if 'spam.csv' in filenames:\n", " found_file_path = os.path.join(dirpath, 'spam.csv')\n", " break\n", "\n", " if found_file_path:\n", " print(f\"Found 'spam.csv' at: {found_file_path}\")\n", " try:\n", " data = pd.read_csv(found_file_path, encoding='latin1')\n", " display(data.head())\n", " except Exception as e:\n", " print(f\"An error occurred while reading the CSV: {e}\")\n", " else:\n", " print(f\"Error: 'spam.csv' not found within '{dataset_root_path}' or its subdirectories.\")" ], "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Downloading dataset from Kagglehub...\n", "Downloading to /root/.cache/kagglehub/datasets/shantanudhakadd/email-spam-detection-dataset-classification/1.archive...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 211k/211k [00:00<00:00, 41.2MB/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Extracting files...\n", "Dataset downloaded to: /root/.cache/kagglehub/datasets/shantanudhakadd/email-spam-detection-dataset-classification/versions/1\n", "Contents of /root/.cache/kagglehub/datasets/shantanudhakadd/email-spam-detection-dataset-classification/versions/1:\n", "Found 'spam.csv' at: /root/.cache/kagglehub/datasets/shantanudhakadd/email-spam-detection-dataset-classification/versions/1/spam.csv\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ " v1 v2 Unnamed: 2 \\\n", "0 ham Go until jurong point, crazy.. Available only ... NaN \n", "1 ham Ok lar... Joking wif u oni... NaN \n", "2 spam Free entry in 2 a wkly comp to win FA Cup fina... NaN \n", "3 ham U dun say so early hor... U c already then say... NaN \n", "4 ham Nah I don't think he goes to usf, he lives aro... NaN \n", "\n", " Unnamed: 3 Unnamed: 4 \n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
v1v2Unnamed: 2Unnamed: 3Unnamed: 4
0hamGo until jurong point, crazy.. Available only ...NaNNaNNaN
1hamOk lar... Joking wif u oni...NaNNaNNaN
2spamFree entry in 2 a wkly comp to win FA Cup fina...NaNNaNNaN
3hamU dun say so early hor... U c already then say...NaNNaNNaN
4hamNah I don't think he goes to usf, he lives aro...NaNNaNNaN
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", "
\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "repr_error": "Out of range float values are not JSON compliant: nan" } }, "metadata": {} } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "a3a14b0c", "outputId": "55e36758-4779-47b6-ef08-c745fadc5a19" }, "source": [ "display(data.head())" ], "execution_count": 7, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " v1 v2 Unnamed: 2 \\\n", "0 ham Go until jurong point, crazy.. Available only ... NaN \n", "1 ham Ok lar... Joking wif u oni... NaN \n", "2 spam Free entry in 2 a wkly comp to win FA Cup fina... NaN \n", "3 ham U dun say so early hor... U c already then say... NaN \n", "4 ham Nah I don't think he goes to usf, he lives aro... NaN \n", "\n", " Unnamed: 3 Unnamed: 4 \n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
v1v2Unnamed: 2Unnamed: 3Unnamed: 4
0hamGo until jurong point, crazy.. Available only ...NaNNaNNaN
1hamOk lar... Joking wif u oni...NaNNaNNaN
2spamFree entry in 2 a wkly comp to win FA Cup fina...NaNNaNNaN
3hamU dun say so early hor... U c already then say...NaNNaNNaN
4hamNah I don't think he goes to usf, he lives aro...NaNNaNNaN
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", "
\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "repr_error": "Out of range float values are not JSON compliant: nan" } }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "# 🟦 PHASE 5 — ADVANCED PREPROCESSING\n", "# ✅ STEP 6: Handle Missing Values" ], "metadata": { "id": "10jAJTDxqOm9" } }, { "cell_type": "code", "source": [ "data = data.fillna('')" ], "metadata": { "id": "stsv3W6yqIft" }, "execution_count": 11, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 240 }, "id": "9a155429", "outputId": "4e6c71e4-a7dc-4283-94be-70323f97a201" }, "source": [ "data.isnull().sum()" ], "execution_count": 8, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "v1 0\n", "v2 0\n", "Unnamed: 2 5522\n", "Unnamed: 3 5560\n", "Unnamed: 4 5566\n", "dtype: int64" ], "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
0
v10
v20
Unnamed: 25522
Unnamed: 35560
Unnamed: 45566
\n", "

" ] }, "metadata": {}, "execution_count": 8 } ] }, { "cell_type": "markdown", "source": [ "# ✅ STEP 7: Label Encoding" ], "metadata": { "id": "-a-cGlSHs_yk" } }, { "cell_type": "code", "source": [ "data.loc[data['v1'] == 'spam', 'v1'] = 0\n", "data.loc[data['v1'] == 'ham', 'v1'] = 1\n", "data['v1'] = data['v1'].astype(int)" ], "metadata": { "id": "gr0EWSu9tHbe" }, "execution_count": 22, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 240 }, "id": "958f0a40", "outputId": "4afc8fac-fd97-4530-f04c-c75b37a4e9f1" }, "source": [ "data.isnull().sum()" ], "execution_count": 13, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "v1 0\n", "v2 0\n", "Unnamed: 2 0\n", "Unnamed: 3 0\n", "Unnamed: 4 0\n", "dtype: int64" ], "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
0
v10
v20
Unnamed: 20
Unnamed: 30
Unnamed: 40
\n", "

" ] }, "metadata": {}, "execution_count": 13 } ] }, { "cell_type": "markdown", "source": [ "# ✅ STEP 8: Define Features" ], "metadata": { "id": "zO3fukFwteVT" } }, { "cell_type": "code", "source": [ "X = data['v2']\n", "Y = data['v1']" ], "metadata": { "id": "aMcQzcaPtaVr" }, "execution_count": 24, "outputs": [] }, { "cell_type": "markdown", "source": [ "# 🟦 PHASE 6 — TRAIN TEST SPLIT" ], "metadata": { "id": "j6ObT0ivtsiI" } }, { "cell_type": "code", "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "X_train, X_test, Y_train, Y_test = train_test_split(\n", " X,\n", " Y,\n", " test_size=0.2,\n", " random_state=42\n", ")" ], "metadata": { "id": "yZ80Uu4TtqF-" }, "execution_count": 25, "outputs": [] }, { "cell_type": "markdown", "source": [ "# 🟦 PHASE 7 — TF-IDF FEATURE EXTRACTION" ], "metadata": { "id": "gXgV9LVGt8nZ" } }, { "cell_type": "code", "source": [ "from sklearn.feature_extraction.text import TfidfVectorizer\n", "\n", "vectorizer = TfidfVectorizer(\n", " stop_words='english',\n", " lowercase=True\n", ")\n", "\n", "X_train_features = vectorizer.fit_transform(X_train)\n", "X_test_features = vectorizer.transform(X_test)" ], "metadata": { "id": "orDVkLF8t3g7" }, "execution_count": 26, "outputs": [] }, { "cell_type": "markdown", "source": [ "# 🎯 RESEARCHER EXPLANATION\n", "\n", "“TF-IDF converts textual emails into numerical vectors while reducing the importance of common words.”" ], "metadata": { "id": "qxLiD2jWuPMw" } }, { "cell_type": "markdown", "source": [ "# 🟦 PHASE 8 — MULTI-MODEL RESEARCH" ], "metadata": { "id": "kCPiq8cIuR2c" } }, { "cell_type": "markdown", "source": [ "# ✅ STEP 11: Train Multiple Models" ], "metadata": { "id": "JHFBUXvluaNM" } }, { "cell_type": "code", "source": [ "from sklearn.linear_model import LogisticRegression\n", "from sklearn.naive_bayes import MultinomialNB\n", "from sklearn.svm import LinearSVC\n", "from sklearn.metrics import accuracy_score\n", "\n", "models = {\n", " \"Logistic Regression\": LogisticRegression(),\n", " \"Naive Bayes\": MultinomialNB(),\n", " \"SVM\": LinearSVC()\n", "}\n", "\n", "results = {}\n", "\n", "for name, model in models.items():\n", "\n", " model.fit(X_train_features, Y_train)\n", "\n", " pred = model.predict(X_test_features)\n", "\n", " accuracy = accuracy_score(Y_test, pred)\n", "\n", " results[name] = accuracy\n", "\n", " print(f\"{name} Accuracy: {accuracy:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "a7oT6h1xuHYF", "outputId": "bc59bde2-c14c-4c73-fcb8-c78d8940f31b" }, "execution_count": 27, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Logistic Regression Accuracy: 0.9525\n", "Naive Bayes Accuracy: 0.9668\n", "SVM Accuracy: 0.9785\n" ] } ] }, { "cell_type": "markdown", "source": [ "# 🟦 PHASE 9 — VISUALIZATION" ], "metadata": { "id": "nqENGbpju_f_" } }, { "cell_type": "markdown", "source": [ "# ✅ STEP 12: Accuracy Comparison Graph" ], "metadata": { "id": "KhXEAyBEvCvt" } }, { "cell_type": "code", "source": [ "import matplotlib.pyplot as plt\n", "\n", "model_names = list(results.keys())\n", "accuracies = list(results.values())\n", "\n", "plt.figure(figsize=(8,5))\n", "\n", "plt.bar(model_names, accuracies)\n", "\n", "plt.xlabel(\"Models\")\n", "plt.ylabel(\"Accuracy\")\n", "\n", "plt.title(\"Model Comparison\")\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "_dNcosgXu9Fn", "outputId": "24735b16-b123-4d5e-c4d5-5ab14f743d17" }, "execution_count": 29, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "# 🟦 PHASE 10 — SELECT BEST MODEL" ], "metadata": { "id": "zORKNllbvRIk" } }, { "cell_type": "markdown", "source": [ "# ✅ STEP 13" ], "metadata": { "id": "a0KeAlUzvW7j" } }, { "cell_type": "code", "source": [ "best_model = LinearSVC()\n", "\n", "best_model.fit(X_train_features, Y_train)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 79 }, "id": "MOPEwwalvSNH", "outputId": "9a48a507-e6ed-4d68-e138-e87b18486411" }, "execution_count": 30, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "LinearSVC()" ], "text/html": [ "
LinearSVC()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ] }, "metadata": {}, "execution_count": 30 } ] }, { "cell_type": "markdown", "source": [ "# 🟦 PHASE 11 — ADVANCED EVALUATION\n", "# ✅ STEP 14: Classification Report" ], "metadata": { "id": "uw3pM0Zuvb3v" } }, { "cell_type": "code", "source": [ "from sklearn.metrics import classification_report\n", "\n", "pred = best_model.predict(X_test_features)\n", "\n", "print(classification_report(Y_test, pred))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "B-oeHdqBvfot", "outputId": "6cc0ea91-4982-459d-b753-4b6fa8ee36d9" }, "execution_count": 32, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " 0 0.96 0.87 0.92 150\n", " 1 0.98 0.99 0.99 965\n", "\n", " accuracy 0.98 1115\n", " macro avg 0.97 0.93 0.95 1115\n", "weighted avg 0.98 0.98 0.98 1115\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "# ✅ STEP 15: Confusion Matrix" ], "metadata": { "id": "H4dyCPSdvqQI" } }, { "cell_type": "code", "source": [ "from sklearn.metrics import ConfusionMatrixDisplay\n", "import matplotlib.pyplot as plt\n", "\n", "ConfusionMatrixDisplay.from_estimator(\n", " best_model,\n", " X_test_features,\n", " Y_test\n", ")\n", "\n", "plt.title(\"Confusion Matrix\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 472 }, "id": "C6Z0q_ravoKk", "outputId": "a4ceb0c2-cd03-4ab6-f16d-cc6f60607561" }, "execution_count": 34, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "# 🟦 PHASE 12 — ADVANCED TESTING SYSTEM\n", "# ✅ STEP 16: Real-world Testing" ], "metadata": { "id": "23wz2n7kv0tR" } }, { "cell_type": "code", "source": [ "test_emails = [\n", "\n", " \"Congratulations! You won a free iPhone\",\n", "\n", " \"Meeting tomorrow at 10am\",\n", "\n", " \"Claim your reward now\",\n", "\n", " \"Project submission deadline reminder\",\n", "\n", " \"FREE MONEY!!! CLICK NOW\"\n", "]\n", "\n", "for email in test_emails:\n", "\n", " data_input = vectorizer.transform([email])\n", "\n", " prediction = best_model.predict(data_input)\n", "\n", " result = \"SPAM\" if prediction[0] == 0 else \"HAM\"\n", "\n", " print(email)\n", " print(\"Prediction:\", result)\n", " print(\"-\" * 50)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fAKYKmJnv10f", "outputId": "6cbac58e-1a20-4fe4-e5ea-98025cb327c1" }, "execution_count": 35, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Congratulations! You won a free iPhone\n", "Prediction: SPAM\n", "--------------------------------------------------\n", "Meeting tomorrow at 10am\n", "Prediction: HAM\n", "--------------------------------------------------\n", "Claim your reward now\n", "Prediction: SPAM\n", "--------------------------------------------------\n", "Project submission deadline reminder\n", "Prediction: HAM\n", "--------------------------------------------------\n", "FREE MONEY!!! CLICK NOW\n", "Prediction: HAM\n", "--------------------------------------------------\n" ] } ] }, { "cell_type": "markdown", "source": [ "# 🟦 PHASE 13 — ADVANCED AI FEATURES\n", "# ✅ STEP 17: Spam Risk Level" ], "metadata": { "id": "Dy2e0oTDwIJn" } }, { "cell_type": "code", "source": [ "def risk_level(message):\n", "\n", " spam_keywords = [\n", " \"free\",\n", " \"winner\",\n", " \"money\",\n", " \"offer\",\n", " \"click\",\n", " \"prize\"\n", " ]\n", "\n", " score = 0\n", "\n", " for word in spam_keywords:\n", " if word in message.lower():\n", " score += 1\n", "\n", " if score >= 3:\n", " return \"🔴 HIGH RISK\"\n", "\n", " elif score == 2:\n", " return \"🟠 MEDIUM RISK\"\n", "\n", " else:\n", " return \"🟢 LOW RISK\"" ], "metadata": { "id": "LB8ipnvjwN6o" }, "execution_count": 36, "outputs": [] }, { "cell_type": "markdown", "source": [ "# 🟦 PHASE 14 — SAVE MODEL\n", "# ✅ STEP 18" ], "metadata": { "id": "P0zOK9HEwRrC" } }, { "cell_type": "code", "source": [ "import pickle\n", "pickle.dump(best_model, open(\"spam_model.pkl\", \"wb\"))\n", "\n", "pickle.dump(vectorizer, open(\"vectorizer.pkl\", \"wb\"))" ], "metadata": { "id": "C9FmMiwKwXbd" }, "execution_count": 38, "outputs": [] }, { "cell_type": "markdown", "source": [ "# 🟦 PHASE 15 — BUILD ADVANCED GUI\n", "# ✅ STEP 19: Install Gradio" ], "metadata": { "id": "W0FXX_HrwmEJ" } }, { "cell_type": "code", "source": [ "!pip install gradio" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gL66AThawrk5", "outputId": "c6a0bf23-1ea2-4091-f33e-f734ed3f0a87" }, "execution_count": 39, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: gradio in /usr/local/lib/python3.12/dist-packages (5.50.0)\n", "Requirement already satisfied: aiofiles<25.0,>=22.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (24.1.0)\n", "Requirement already satisfied: anyio<5.0,>=3.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (4.13.0)\n", "Requirement already satisfied: brotli>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (1.2.0)\n", "Requirement already satisfied: fastapi<1.0,>=0.115.2 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.136.1)\n", "Requirement already satisfied: ffmpy in /usr/local/lib/python3.12/dist-packages (from gradio) (1.0.0)\n", "Requirement already satisfied: gradio-client==1.14.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (1.14.0)\n", "Requirement already satisfied: groovy~=0.1 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.1.2)\n", "Requirement already satisfied: httpx<1.0,>=0.24.1 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.28.1)\n", "Requirement already satisfied: huggingface-hub<2.0,>=0.33.5 in /usr/local/lib/python3.12/dist-packages (from gradio) (1.11.0)\n", "Requirement already satisfied: jinja2<4.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (3.1.6)\n", "Requirement already satisfied: markupsafe<4.0,>=2.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (3.0.3)\n", "Requirement already satisfied: numpy<3.0,>=1.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (2.0.2)\n", "Requirement already satisfied: orjson~=3.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (3.11.8)\n", "Requirement already satisfied: packaging in /usr/local/lib/python3.12/dist-packages (from gradio) (26.1)\n", "Requirement already satisfied: pandas<3.0,>=1.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (2.2.2)\n", "Requirement already satisfied: pillow<12.0,>=8.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (11.3.0)\n", "Requirement already satisfied: pydantic<=2.12.3,>=2.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (2.12.3)\n", "Requirement already satisfied: pydub in /usr/local/lib/python3.12/dist-packages (from gradio) (0.25.1)\n", "Requirement already satisfied: python-multipart>=0.0.18 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.0.26)\n", "Requirement already satisfied: pyyaml<7.0,>=5.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (6.0.3)\n", "Requirement already satisfied: ruff>=0.9.3 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.15.11)\n", "Requirement already satisfied: safehttpx<0.2.0,>=0.1.6 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.1.7)\n", "Requirement already satisfied: semantic-version~=2.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (2.10.0)\n", "Requirement already satisfied: starlette<1.0,>=0.40.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.52.1)\n", "Requirement already satisfied: tomlkit<0.14.0,>=0.12.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.13.3)\n", "Requirement already satisfied: typer<1.0,>=0.12 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.24.2)\n", "Requirement already satisfied: typing-extensions~=4.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (4.15.0)\n", "Requirement already satisfied: uvicorn>=0.14.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.46.0)\n", "Requirement already satisfied: fsspec in /usr/local/lib/python3.12/dist-packages (from gradio-client==1.14.0->gradio) (2025.3.0)\n", "Requirement already satisfied: websockets<16.0,>=13.0 in /usr/local/lib/python3.12/dist-packages (from gradio-client==1.14.0->gradio) (15.0.1)\n", "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.12/dist-packages (from anyio<5.0,>=3.0->gradio) (3.13)\n", "Requirement already satisfied: typing-inspection>=0.4.2 in /usr/local/lib/python3.12/dist-packages (from fastapi<1.0,>=0.115.2->gradio) (0.4.2)\n", "Requirement already satisfied: annotated-doc>=0.0.2 in /usr/local/lib/python3.12/dist-packages (from fastapi<1.0,>=0.115.2->gradio) (0.0.4)\n", "Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx<1.0,>=0.24.1->gradio) (2026.4.22)\n", "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1.0,>=0.24.1->gradio) (1.0.9)\n", "Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1.0,>=0.24.1->gradio) (0.16.0)\n", "Requirement already satisfied: filelock>=3.10.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=0.33.5->gradio) (3.29.0)\n", "Requirement already satisfied: hf-xet<2.0.0,>=1.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=0.33.5->gradio) (1.4.3)\n", "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=0.33.5->gradio) (4.67.3)\n", "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas<3.0,>=1.0->gradio) (2.9.0.post0)\n", "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas<3.0,>=1.0->gradio) (2025.2)\n", "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas<3.0,>=1.0->gradio) (2026.1)\n", "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.12/dist-packages (from pydantic<=2.12.3,>=2.0->gradio) (0.7.0)\n", "Requirement already satisfied: pydantic-core==2.41.4 in /usr/local/lib/python3.12/dist-packages (from pydantic<=2.12.3,>=2.0->gradio) (2.41.4)\n", "Requirement already satisfied: click>=8.2.1 in /usr/local/lib/python3.12/dist-packages (from typer<1.0,>=0.12->gradio) (8.3.3)\n", "Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.12/dist-packages (from typer<1.0,>=0.12->gradio) (1.5.4)\n", "Requirement already satisfied: rich>=12.3.0 in /usr/local/lib/python3.12/dist-packages (from typer<1.0,>=0.12->gradio) (13.9.4)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas<3.0,>=1.0->gradio) (1.17.0)\n", "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer<1.0,>=0.12->gradio) (4.0.0)\n", "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer<1.0,>=0.12->gradio) (2.20.0)\n", "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=12.3.0->typer<1.0,>=0.12->gradio) (0.1.2)\n" ] } ] }, { "cell_type": "markdown", "source": [ "# ✅ STEP 20: Professional GUI" ], "metadata": { "id": "y_0qSDrxwuYu" } }, { "cell_type": "code", "source": [ "import gradio as gr" ], "metadata": { "id": "2ZbZg581wv-n" }, "execution_count": 40, "outputs": [] }, { "cell_type": "code", "source": [ "def predict_email(message):\n", "\n", " data_input = vectorizer.transform([message])\n", "\n", " prediction = best_model.predict(data_input)\n", "\n", " result = \"🚫 SPAM EMAIL\" if prediction[0] == 0 else \"📩 SAFE EMAIL\"\n", "\n", " risk = risk_level(message)\n", "\n", " return result, risk" ], "metadata": { "id": "wYT8DhOQw1uY" }, "execution_count": 41, "outputs": [] }, { "cell_type": "code", "source": [ "interface = gr.Interface(\n", "\n", " fn=predict_email,\n", "\n", " inputs=gr.Textbox(\n", " lines=6,\n", " placeholder=\"Enter Email Message\"\n", " ),\n", "\n", " outputs=[\n", " \"text\",\n", " \"text\"\n", " ],\n", "\n", " title=\"🧠 AI Smart Email Security System\",\n", "\n", " description=\"Research-Level Spam Detection System\",\n", "\n", " theme=\"soft\"\n", ")\n", "\n", "interface.launch()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 649 }, "id": "FyB6qCsiw4M0", "outputId": "5ccb7143-defc-4102-d71a-86c1089ffc46" }, "execution_count": 42, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "It looks like you are running Gradio on a hosted Jupyter notebook, which requires `share=True`. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n", "\n", "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n", "* Running on public URL: https://bffa29dddbd3c2ea2c.gradio.live\n", "\n", "This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "
" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [] }, "metadata": {}, "execution_count": 42 } ] }, { "cell_type": "markdown", "source": [ "# 🟦 PHASE 16 — DEPLOYMENT" ], "metadata": { "id": "8gXdkgPnxOY7" } } ] }