fixes #2

[Mukku27]

Update README and Improve ML Model Efficiency (#2)

Overview

This PR addresses issue #2 by making several enhancements to the repository. The updates include improvements to the machine learning model's efficiency, adding a detailed README.md for better documentation, and providing a requirements.txt file for easier environment setup.

Changes Made

1. Jupyter Notebook Enhancements (Fake_News_Detection.ipynb):

   • Hyperparameter Tuning: Added hyperparameter tuning to the Logistic Regression model to improve accuracy and efficiency. This includes optimizing parameters such as C (regularization strength) and max_iter (maximum number of iterations).
   • Comments for Better Readability: Added detailed comments throughout the notebook to explain each step, making the code easier to follow and understand.
   • Improved Data Preprocessing: Enhanced text preprocessing steps to include more robust text cleaning and feature extraction techniques.

2. Documentation (README.md):

   • Updated and expanded the README.md file to provide a comprehensive guide to the project. This includes sections on project introduction, dataset description, installation instructions, usage guidelines, model details, results, and contribution instructions.
   • The README now provides clear instructions for setting up the environment and running the notebook, making it easier for new contributors to get started.

3. Dependencies File (requirements.txt):

   • Added a requirements.txt file listing all necessary Python packages and their versions to ensure a smooth setup of the project environment.
   • This file simplifies the installation process, allowing users to quickly install all dependencies with a single command.

Impact

• The improvements to the ML model, particularly through hyperparameter tuning, have enhanced the model's performance, resulting in better test accuracy.
• The enhanced documentation and comments improve the project's usability and accessibility for both new and existing contributors.
• Providing a requirements.txt file ensures a consistent development environment, reducing potential setup issues.

How to Test

1. Setup:

   • Clone the repository and navigate to the project directory.
   • Create a virtual environment and install dependencies using pip install -r requirements.txt.

2. Run the Jupyter Notebook:

   • Launch Jupyter Notebook and open Fake_News_Detection.ipynb.
   • Execute each cell sequentially to train the model and observe the improved performance metrics.

3. Review Documentation:

   • Ensure that the README.md provides clear and accurate instructions for setting up and using the project.

Additional Notes

• Further optimization of the model may be possible with additional feature engineering or by exploring other algorithms.
• Contributors are encouraged to provide feedback or suggest further improvements to the project.

[beaubeas]

👍🏼

[Mukku27]

@TadeKDev if everything looks good, I would appreciate it if you could merge the PR.