To ensure equal participation and technical contribution, the project work was divided evenly among all three team members.

Each member contributed to:

• Data preprocessing
• Model implementation
• Performance evaluation
• Documentation
• Presentation preparation
• Poster creation
• Viva preparation
• GitHub maintenance

• Dataset merging (Fake.csv + True.csv)
• Label encoding
• Missing value handling
• Lowercase conversion
• Punctuation removal
• Stopword removal
• Tokenization
• Lemmatization
• TF-IDF Vectorization

Implemented:

• Logistic Regression
• K-Nearest Neighbors (KNN)
• Random Forest (Ensemble)

Worked on:

• Confusion Matrix
• Accuracy
• Precision
• Recall
• F1-Score

Explains:

• Dataset
• Data preprocessing pipeline
• TF-IDF Vectorization
• Logistic Regression
• KNN
• Random Forest

Implemented:

• Support Vector Machine (SVM)
• Decision Tree
• Naïve Bayes

Implemented:

• AdaBoost
• Gradient Boosting

Worked on:

• ROC Curve Analysis
• Training Time Comparison
• Model Performance Charts
• Feature Importance Analysis

Explains:

• SVM
• Decision Tree
• Naïve Bayes
• AdaBoost
• Gradient Boosting
• Feature Importance

Identified and analyzed a recent research paper related to:

Fake News Detection using Machine Learning and NLP

Prepared:

• Aim
• Objectives
• Problem Statement
• Methodology

Implemented:

• XGBoost

Developed:

• Streamlit-based web interface

Features:

• User news input
• Fake/Real prediction
• Confidence score output

Worked on:

• Final model comparison table
• Overfitting vs Underfitting Analysis
• Computational Complexity Comparison
• Final recommendation of best-performing model

Explains:

• Research paper
• XGBoost
• Deployment architecture
• Comparative analysis

FAKE-NEWS-DETECTION/
│
├── dataset/
│   ├── Fake.csv
│   ├── True.csv
│   └── processed_fake_news_dataset.csv
│
├── deployment/
│   ├── app.py
│   └── requirements.txt
│
├── models/
│
├── notebooks/
│   ├── 01_data_preprocessing.ipynb
│   ├── 02_logistic_regression.ipynb
│   ├── 03_knn.ipynb
│   ├── 04_random_forest.ipynb
│   ├── 05_svm.ipynb
│   ├── 06_decision_tree.ipynb
│   ├── 07_naive_bayes.ipynb
│   ├── 08_adaboost.ipynb
│   ├── 09_gradient_boosting.ipynb
│   ├── 10_xgboost.ipynb
│   └── 11_model_analysis.ipynb
│
├── poster/
├── presentation/
├── report/
├── screenshots/
│
├── README.md
└── requirements.txt

All team members actively contributed to:

• README.md
• Code comments
• Result interpretation

• PowerPoint preparation
• Visualizations
• Explanation flow

• Problem statement
• Methodology
• Results
• Conclusions

All members contributed through regular commits and collaborative development.

This project was developed through equal contribution and collaborative effort by all three team members. Every member actively participated in implementation, experimentation, analysis, documentation, deployment, and viva preparation.