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Project Portal: Fake News Prediction Using Machine Learning with Python

Project Title

Fake News Prediction Using Machine Learning with Python

Domain

Python I Artificial Intelligence (AI) | Machine Learning (ML) | Natural Language Processing (NLP)

Project Level

Beginner to Intermediate

Project Objective

The rapid growth of social media and online news platforms has increased the spread of fake and misleading information. The objective of this project is to build an intelligent Machine Learning model capable of analyzing news articles and predicting whether a news article is Real or Fake.

Students will learn the complete Machine Learning workflow, including data collection, preprocessing, feature engineering, model training, testing, evaluation, and prediction. This project provides practical exposure to Artificial Intelligence and Natural Language Processing concepts used in real-world applications.

Session Access Link

Project Training Session

Session Link:
Click here to Watch your uploaded session

Learning Outcomes

After successful completion of this project, students will be able to:

  • Understand the fundamentals of Machine Learning.

  • Understand Fake News Detection systems.

  • Learn Natural Language Processing (NLP) concepts.

  • Perform data cleaning and preprocessing.

  • Work with large datasets.

  • Convert textual data into numerical features.

  • Train and evaluate machine learning models.

  • Measure model performance using different metrics.

  • Build a real-world prediction system.

  • Improve analytical and problem-solving skills.

Programming Language & Technologies Used

Programming Language

  • Python 3.x

Machine Learning Libraries

  • Scikit-learn

  • NumPy

  • Pandas

NLP Libraries

  • NLTK (Natural Language Toolkit)

Visualization Libraries

  • Matplotlib

  • Seaborn

Development Platforms

  • Google Colab

  • Jupyter Notebook

  • VS Code (Optional)

Data Storage

  • CSV Files

  • Google Drive

Project Workflow

Phase 1: Dataset Collection

Students will collect a Fake News Dataset containing:

  • News Title

  • News Content

  • Label (Fake/Real)

Dataset Format:

  • CSV File

Phase 2: Data Preprocessing

Students will perform:

  • Removal of duplicate records

  • Handling missing values

  • Removal of punctuation marks

  • Removal of special characters

  • Conversion to lowercase

  • Stop-word removal

  • Tokenization

  • Stemming/Lemmatization

Purpose:
To improve data quality and model performance.

Phase 3: Feature Extraction

Machine Learning algorithms cannot understand text directly.

Students will convert text into numerical format using:

TF-IDF Vectorization

(Term Frequency-Inverse Document Frequency)

Benefits:

  • Extracts important words.

  • Reduces the impact of common words.

  • Improves classification accuracy.

Phase 4: Model Building

Students will train Machine Learning models such as:

Logistic Regression

  • Fast and efficient.

  • High accuracy for text classification.

Passive Aggressive Classifier

  • Suitable for large-scale text classification.

  • Commonly used for Fake News Detection.

Decision Tree Classifier (Optional)

  • Easy to understand.

  • Useful for comparison.

Random Forest Classifier (Optional)

  • Ensemble learning method.

  • Improves prediction performance.

Phase 5: Model Evaluation

Students will evaluate model performance using:

  • Accuracy Score

  • Confusion Matrix

  • Precision

  • Recall

  • F1-Score

Expected Accuracy:
85%–98% (depending on dataset quality)

Phase 6: Prediction System

Students will create a system where users can:

  • Enter a news article.

  • Click Predict.

  • Receive output:

    • Real News

    • Fake News

Final Project Deliverables

Each student must submit:

Source Code

  • Python Files (.py)

  • Jupyter Notebook (.ipynb)

Dataset

  • CSV Dataset Files

Documentation

  • Project Report (PDF)

Screenshots

  • Dataset Import

  • Data Cleaning

  • Model Training

  • Accuracy Results

  • Prediction Results

Presentation

  • PPT (Minimum 10 Slides)

Additional Files

  • Trained Model (.pkl file)

  • Output Results

Google Drive Submission Process

Step 1:
Create a Google Drive Folder.

Folder Name Format:
FakeNewsPrediction_StudentName

Step 2:
Upload all project files.

Step 3:
Share the folder with:

support@corporatewebsolutions.in

Permission:
Viewer Access

Step 4:
Copy the shared Google Drive link.

Step 5:
Submit the link through the Google Form.

Status Report Submission Form

Google Form Link: https://forms.gle/RNLyVNgnsbeuffP27

Important Instructions

  1. All assignments must be completed and submitted on time.

  2. Students must submit original work only.

  3. Copying projects from online sources is strictly prohibited.

  4. Every student must maintain a project progress report.

  5. All project files must be uploaded to Google Drive before final submission.

  6. Ensure the shared folder is accessible before submitting the link.

  7. Late submissions may lead to reduced marks.

  8. Students should attend all project guidance sessions.