Projects using NLTK
Projects using NLTK
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Venkata Anirudh Parakala
Geographical Understanding of Twitter Health Topics using NLP
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Krutarth Oza
Product Recommendation Application
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Bulbul Gupta
I recently built a chatbot that can answer user questions using natural language processing. Features: • User question → AI response • Fast and lightweight chatbot • Built with Python and NLP tools Next step: I'm exploring how to connect this with company knowledge bases and automation workflows.
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Laksmi Wulandiari
Hotel Review Sentiment Analysis
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PATHAKHRK INC
AI Website Chatbot - Lead Capture & Sales Qualification
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Ratnapriya Lal
Summary of the work I've done in Data Science / ML
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Aditya Singh
Role: AI Automation Developer Description: Developed an automated system to process and analyze 1,000+ financial news reports using Natural Language Processing (NLP). The system performs sentiment-based investment scoring, allowing for rapid decision-making in volatile markets. Tools: Python (NLTK, Pandas), Jupyter Notebook. Impact: Reduced manual analysis time by 90% through automation.
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Arham Malik
This was a really cool EdTech project. The goal was to automate grading and student verification. I built a Flask backend that hooks into NLP models, speech to text, text to speech, and even computer vision with OpenCV. Students can give live demos, and the system checks their face, asks them questions out loud, and grades everything automatically. No more manual grading or worrying about cheating. It’s 100% automated, and teachers love how transparent it is. The AI responds in under a second, so it feels almost magical. For more details, please visit: https://arham-nexus.vercel.app/work/evaluasysai
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Ciro
Sentiment Analysis of IMDB Movie Reviews
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USMAN AKINTOBI
Context-Aware Fintech User Feedback Classification & Urgency Detection Most feedback tools stop at sentiment; positive or negative. This project goes further by building a decision-driven NLP system that classifies fintech user feedback by issue type and detects operational urgency, enabling smarter triage and escalation. What was built: Two machine learning models trained on manually collected and annotated app store reviews from real fintech platforms like Revolut, Wise, and Monzo: Issue Classifier: categorizes feedback into 9 classes including transaction issues, account security, KYC, refunds, app performance, and fraud Urgency Detector: binary classifier that flags high-risk feedback requiring immediate attention, optimized for recall to minimize missed critical cases
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Axel Gusto
MIND — AI Medical Billing Assistant MIND automates medical billing coding for French physicians. The system reads clinical notes and suggests the correct CCAM codes in real time — eliminating manual lookup and reducing billing errors. Built solo from scratch: — NLP pipeline for clinical note analysis — Secure architecture for sensitive medical data — Go-to-market strategy targeting independent practitioners Stack: Python, NLP, cloud-native, HIPAA & GDPR-compliant data architecture. Role: Sole founder — product, engineering, and distribution.
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Ashkan Kardan
Social Media Reviews Sentiment Analysis Machine Learning Model
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Chirag Patankar
Toxic Comment Classifier and Moderator App
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Varshith Gaddam
Voice-Based Cognitive Decline Pattern Detection
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William Alabi
English-to-Hebrew Translation with Gemma 2
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Sinchana T
Audio / Text to Indian Sign Language Converter Built a web-based accessibility application that converts audio and text into Indian Sign Language (ISL) to help improve communication for people with hearing impairments. The system uses the Web Speech API for speech-to-text conversion and applies NLP techniques to process and simplify text before mapping it to corresponding ISL gesture animations for clear visual output. Tech stack: Django, JavaScript, HTML, CSS, NLTK Focused on accessibility, inclusivity, and real-world usability.
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