From 7fac851fa3bc6d969d140a78e84c09eac115b88c Mon Sep 17 00:00:00 2001 From: swati-singh23 <139169646+swati-singh23@users.noreply.github.com> Date: Tue, 4 Jun 2024 19:32:09 +0530 Subject: [PATCH] First Commit --- Fake News Detection Using NLP and ML.ipynb | 788 +++++++++++++++++++++ news.zip | Bin 0 -> 12046622 bytes 2 files changed, 788 insertions(+) create mode 100644 Fake News Detection Using NLP and ML.ipynb create mode 100644 news.zip diff --git a/Fake News Detection Using NLP and ML.ipynb b/Fake News Detection Using NLP and ML.ipynb new file mode 100644 index 000000000..d4a3c2c45 --- /dev/null +++ b/Fake News Detection Using NLP and ML.ipynb @@ -0,0 +1,788 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "17b4b6f2", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd # Import the pandas library for data manipulation\n", + "from sklearn.feature_extraction.text import TfidfVectorizer # Import TF-IDF vectorizer for text feature extraction\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "73f10b99", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Read the CSV file containing news articles and labels into a pandas DataFrame\n", + "dataframe = pd.read_csv('news.csv')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5c2ea598", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0titletextlabel
08476You Can Smell Hillary’s FearDaniel Greenfield, a Shillman Journalism Fello...FAKE
110294Watch The Exact Moment Paul Ryan Committed Pol...Google Pinterest Digg Linkedin Reddit Stumbleu...FAKE
23608Kerry to go to Paris in gesture of sympathyU.S. Secretary of State John F. Kerry said Mon...REAL
310142Bernie supporters on Twitter erupt in anger ag...— Kaydee King (@KaydeeKing) November 9, 2016 T...FAKE
4875The Battle of New York: Why This Primary MattersIt's primary day in New York and front-runners...REAL
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" + ], + "text/plain": [ + " Unnamed: 0 title \\\n", + "0 8476 You Can Smell Hillary’s Fear \n", + "1 10294 Watch The Exact Moment Paul Ryan Committed Pol... \n", + "2 3608 Kerry to go to Paris in gesture of sympathy \n", + "3 10142 Bernie supporters on Twitter erupt in anger ag... \n", + "4 875 The Battle of New York: Why This Primary Matters \n", + "\n", + " text label \n", + "0 Daniel Greenfield, a Shillman Journalism Fello... FAKE \n", + "1 Google Pinterest Digg Linkedin Reddit Stumbleu... FAKE \n", + "2 U.S. Secretary of State John F. Kerry said Mon... REAL \n", + "3 — Kaydee King (@KaydeeKing) November 9, 2016 T... FAKE \n", + "4 It's primary day in New York and front-runners... REAL " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Print the first few rows of the DataFrame to get a glimpse of the data\n", + "dataframe.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9c9b8a3c", + "metadata": {}, + "outputs": [], + "source": [ + "# Extract the text of the news articles and the corresponding labels (fake or real)\n", + "x = dataframe['text']\n", + "y = dataframe['label']" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a2833937", + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data into training and testing sets for model evaluation (20% for testing)\n", + "from sklearn.model_selection import train_test_split\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "875b59aa", + "metadata": {}, + "outputs": [], + "source": [ + "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9a64988e", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a TF-IDF vectorizer to convert text into numerical features\n", + "# - stop_words='english': Remove common English words (e.g., the, a, an) that don't contribute much meaning\n", + "# - max_df=0.7: Keep only words that appear in no more than 70% of the documents (helps filter out very frequent or infrequent words)\n", + "tfvect = TfidfVectorizer(stop_words='english', max_df=0.7)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1ce0ad67", + "metadata": {}, + "outputs": [], + "source": [ + "# Fit the TF-IDF vectorizer to the training data (learn word importance)\n", + "tfid_x_train = tfvect.fit_transform(x_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "898a8fa3", + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the testing data using the fitted vectorizer (convert text to numerical features)\n", + "tfid_x_test = tfvect.transform(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "73f657aa", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a machine learning model for fake news detection (Passive Aggressive Classifier is used here)\n", + "# - max_iter=50: Set the maximum number of training iterations to 50\n", + "from sklearn.linear_model import PassiveAggressiveClassifier\n", + "\n", + "classifier = PassiveAggressiveClassifier(max_iter=50)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6ff4fe5d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
PassiveAggressiveClassifier(max_iter=50)
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On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "PassiveAggressiveClassifier(max_iter=50)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Train the model on the transformed training data and labels\n", + "classifier.fit(tfid_x_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "a73780bf", + "metadata": {}, + "outputs": [], + "source": [ + "# Use the trained model to predict labels (fake or real) for the testing data\n", + "y_pred = classifier.predict(tfid_x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "8cf63482", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate the accuracy of the model (percentage of correct predictions)\n", + "from sklearn.metrics import accuracy_score" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "2f28cbf9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 93.45%\n" + ] + } + ], + "source": [ + "score = accuracy_score(y_test, y_pred)\n", + "print(f'Accuracy: {round(score*100,2)}%')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "ad5db448", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[572 43]\n", + " [ 40 612]]\n" + ] + } + ], + "source": [ + "# Create a confusion matrix to see how many predictions were correct or incorrect for each class (fake and real)\n", + "from sklearn.metrics import confusion_matrix\n", + "cf = confusion_matrix(y_test, y_pred, labels=['FAKE', 'REAL'])\n", + "print(cf)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c41b00a9", + "metadata": {}, + "outputs": [], + "source": [ + "# Function to predict if a new piece of news is fake or real\n", + "def fake_news_det(news):\n", + " # Prepare the input news article as a list for the model\n", + " input_data = [news]\n", + "\n", + " # Transform the input data using the fitted vectorizer\n", + " vectorized_input_data = tfvect.transform(input_data)\n", + "\n", + " # Make a prediction using the trained model\n", + " prediction = classifier.predict(vectorized_input_data)\n", + "\n", + " # Print the predicted label (fake or real)\n", + " print(prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "6643e954", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['REAL']\n" + ] + } + ], + "source": [ + "# Example usage: Classify a news article\n", + "fake_news_det('U.S. Secretary of State John F. Kerry said Monday that he will stop in Paris later this week, amid criticism that no top American officials attended Sunday’s unity march against terrorism.')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "9351d1aa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['FAKE']\n" + ] + } + ], + "source": [ + "fake_news_det(\"\"\"Go to Article President Barack Obama has been campaigning hard for the woman who is supposedly going to extend his legacy four more years. The only problem with stumping for Hillary Clinton, however, is she’s not exactly a candidate easy to get too enthused about. \"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "655b29d1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/news.zip b/news.zip new file mode 100644 index 0000000000000000000000000000000000000000..02985c24e68b6195263c434b8eb560a4fbd02e2f GIT binary patch literal 12046622 zcmV(*K;FMlO9KQH000080M>DWSnL#TWvtM*0KsC^0SEv90B&V>b1q|ZcHF(&ZX`#x 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