Nagaladoddi Mounika
I build AI-powered dashboards and automation tools that turn
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Built a web-based Leave Management System to simplify employee leave requests and management.
The application provides a structured interface for submitting and managing leave requests, with a FastAPI backend connected to PostgreSQL for data storage and a Streamlit frontend for user interaction.
Built with: Python, FastAPI, PostgreSQL, Streamlit, and REST APIs.
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Built a machine learning application that analyzes Amazon customer reviews and predicts whether the sentiment is positive or negative.
The project uses TF-IDF to convert review text into numerical features and Logistic Regression for sentiment classification. The trained model is integrated into a Streamlit application so users can enter a review and receive a sentiment prediction.
Built with: Python, TF-IDF, Logistic Regression, Machine Learning, and Streamlit.
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Built an AI-powered Social Media Post Analyzer that analyzes social media content and generates structured insights using LLMs.
The application uses LangChain and Pydantic to process the input and return structured results through an interactive Streamlit interface.
Built with: Python, LangChain, Pydantic, Hugging Face, Streamlit, and LangSmith.
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Built an AI-powered Historical Event Analysis Assistant using RAG (Retrieval-Augmented Generation).
The application allows users to ask questions about historical documents and generates answers based on relevant retrieved content rather than relying only on the LLM’s general knowledge.
Built with: Python, LangChain, RAG, Qdrant/FAISS, BM25, Hugging Face embeddings, Groq, and Streamlit.
The project demonstrates document processing, semantic and keyword retrieval, context-based generation, and an interactive Streamlit interface.
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