Oct 2024 – Dec 2024 · 2 mos · Navi Mumbai, Maharashtra, India · Remote

• 1) Machine Learning Integration :-
• Trained and implemented a spam classification model using scikit-learn.
• Serialized the trained model (spam.pkl) and vectorizer (vec.pkl) for reuse.
• 2) Web Interface Development :-
• Built an interactive front-end using Streamlit for real-time predictions.
• Enabled users to input raw email text and receive instant spam classification results.
• 3) Text Preprocessing & Vectorization :-
• Applied TF-IDF for feature extraction from email text.
• Ensured accurate preprocessing to handle real-world variations in input data.
• 4) User Feedback & Confidence Score :-
• Displayed classification results along with model confidence percentage.
• Enhanced user trust by showing prediction certainty.
• 5) Project Documentation & Deployment Readiness :-
• Wrote comprehensive README.md with clear setup, usage instructions, and example outputs.
• Included requirements.txt for easy environment setup and reproducibility.
• Major Successes:
• Functional End-to-End Spam Detection System
• Deployed a complete ML pipeline from training to real-time usage through a web interface.
• Accurate Classification
• The system correctly identifies common spam emails with high confidence (~90%) and genuine messages with low false positives.
• User-Centric Design
• Integrated a clean and responsive UI that simplifies spam detection for non-technical users.
• Portable & Reusable
• Model and vectorizer files are reusable across other Python applications or APIs.