Projects using NLTK in Bengaluru
Projects using NLTK in Bengaluru
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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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Sowmya Lanka
What I built Developed a Natural Language Processing (NLP) solution to detect DI Flag issues by analyzing and classifying text descriptions. How it works Cleaned text data by removing hashtags, HTML tags, special characters, and numeric values. Applied text preprocessing techniques including tokenization, stop-word removal, stemming, and lemmatization. Converted text into numerical features using Bag of Words (BoW) and TF-IDF. Trained and evaluated the model using accuracy and confusion matrix metrics. Performed 30 days of validation testing before production deployment. Technologies Python · NLP · Scikit-learn · TF-IDF · Bag of Words · Pandas · NumPy Outcome Built a text classification solution that helped identify DI Flag issues from descriptions and validated the model's performance before deployment.
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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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Shraddha Ruidas
LLM_detect_TfIdf_ensembleclassifier
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Sowmya Lanka
What I built Developed an automated clinical text summarization solution using Google Gemini AI and prompt engineering to generate concise summaries from lengthy clinical chart notes, transcripts, and unstructured medical text. How it works Processed lengthy clinical notes and transcripts. Used prompt engineering to guide Gemini in identifying important medical information. Extracted key details such as symptoms, diagnoses, medications, and treatment plans. Generated concise, readable summaries from the original clinical text. Technologies Google Gemini AI · Python · Prompt Engineering · NLP · Generative AI Outcome Reduced clinical chart review time by approximately 30%, helping healthcare professionals identify important information more quickly and efficiently.::
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