Overview

Plagiarism Detection System: Built a machine learning model to identify textual similarities using TF-IDF and SVM

Key Features

• Text Preprocessing: Cleaned and prepared raw text data for analysis
• Feature Extraction: Used TF-IDF to convert text into numerical vectors
• Model Training: Implemented SVM classifier for plagiarism detection
• Model Persistence: Saved trained model and vectorizer using Pickle
• Prediction System: Enabled plagiarism detection on new/unseen text samples
• Notebook Workflow: Structured pipeline in Jupyter Notebook for easy experimentation

Technologies Used

• Programming Language: Python 3
• Development Environment: Jupyter Notebook
• Machine Learning: Scikit-learn (SVM, TF-IDF Vectorizer)
• Data Handling: Pandas, NumPy
• Model Storage: Pickle

Challenges and Learnings

• Text Vectorization: Learned effective feature extraction using TF-IDF
• Model Selection: Understood strengths of SVM for text classification
• Data Preprocessing: Handled noise and inconsistencies in text data
• Model Persistence: Implemented saving/loading models for reuse
• Evaluation: Gained insights into improving accuracy with better data

Outcome

• Functional Model: Developed a working plagiarism detection system
• Reusable Pipeline: Created a scalable ML workflow for text analysis
• Practical Understanding: Strengthened knowledge of NLP and ML concepts
• Future Scope Ready: Foundation built for advanced models like LSTM/BERT