Skilled in the field of Data Analytics,Data Science,Gen AI.
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LSTM-Next-Word_Predictor
February 2, 2026 – Present
This LSTM based Model predicts the Next words for us based on the input context words provided by the user .
View ProjectTwitter-sentiment-Analysis-using-Deep-Learning
January 20, 2026 – Present
Using Deep Learning Recurrent Neural Network Predicting the Sentiment of the Tweets of users
View ProjectProduction-Grade-Email-Intelligence-Spam-Classification-Service
January 14, 2026 – Present
This project implements a production-grade Email Spam Classification service using classical machine learning models and a cloud-native deployment workflow. The system is designed to classify incoming email text as SPAM or NOT SPAM, with precision-focused model selection to minimize false positives — for business email filtering systems.
View ProjectCompany-Policy-Agentic-Q-A-System
January 13, 2026 – Present
This project implements a **Retrieval-Augmented Generation (RAG)** based question-answering system over internal company policy documents using **LangChain, Azure OpenAI, FAISS, and FastAPI**. The system allows users to query company policies and receive accurate, source-grounded answers.
View ProjectTitanic-Passenger-Survival-Chance-using-Deep-Learning
December 26, 2025 – Present
This application predicts that whether if a passenger is going to survive the titanic journey or not using Deep Leaning ANN .
View ProjectServerless-Gen-AI-Blog-Generation-System-using-AWS-Bedrock-AWS-Lambda-API-Gateway-with-S3-Bucket
December 10, 2025 – December 10, 2025
This project demonstrates a complete serverless Generative AI application pipeline powered by AWS Bedrock and Meta Llama 3.1. The system dynamically generates 150-word contextual blogs based on user-provided topics and stores them securely in AWS S3 for retrieval, indexing, or downstream content. Users communicates with an AWS API Gateway endpoint.
View ProjectFine-Tuning-Google-Gemma-2B-Using-LoRA-QLoRA
December 10, 2025 – December 10, 2025
This project demonstrates how to fine-tune the Google Gemma 2B Instruct model using LoRA (Low-Rank Adaptation) and QLoRA (Quantized LoRA) to efficiently adapt a large language model for specialized tasks. The goal was to explore Parameter Efficient Fine-Tuning (PEFT) techniques to: Reduce training cost Enable training on consumer-grade hardware
View ProjectNext-Word-Prediction-Model-using-LSTM
December 6, 2025 – December 8, 2025
Built a next-word prediction deep learning model using LSTM trained on real-world news text data. Preprocessed data, tokenized vocabulary, generated padded input sequences, and trained the model using sparse categorical cross-entropy for scalable memory usage. Integrated the model into Streamlit UI and designed deployment for AWS Lambda
View ProjectResearchFlow-Agentic-AI-Research-Content-Automation-System
December 3, 2025 – December 3, 2025
ResearchFlow is an advanced multi-agent AI system that autonomously performs web research, processes the information using a custom RAG pipeline, and generates publication-ready content (blog, LinkedIn post, newsletter, X thread) along with SEO metadata — all in a single run.
View ProjectDuplicate-Questions-Finder-System
April 29, 2024 – April 30, 2024
Created a Duplicate Questions Finder System using Machine Learning and NLP with Flask Web App and AWS and Azure Deployment
View ProjectCultural Fit Analysis
The candidate demonstrates a strong passion for AI/ML through a diverse portfolio of personal projects, indicating a self-starter mentality and continuous learning drive. The projects align well with the target role of Data Scientist, showcasing a proactive approach to skill development. However, the lack of team-based projects or professional experience makes it difficult to fully assess collaboration and broader cultural fit.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate a strong drive for practical application and learning new technologies, which are positive indicators for operational fit in a dynamic environment.