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Data Science with less than a year in Machine Learning & Generative AI
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Aspiring Data Scientist with a passion for building innovative AI solutions. Possessing strong skills in Python, Machine Learning, Deep Learning, and Generative AI, I am adept at developing data-driven applications. My project experience includes creating smart crop prediction systems and AI-powered portfolio websites, demonstrating practical application of advanced analytical techniques and LLM orchestration.
Innomatics Research Labs
Data Science
August 1, 2024 – June 30, 2025
NRI Institute of Technology
Bachelor of Technology
August 1, 2013 – June 30, 2017
Innomatics Research Labs
AI Intern
November 10, 2025 – February 26, 2026
Hyderābād, Telangana, India
Smart Crop Predictor with Alternative Suggestions and Economic Forecast
January 1, 2026 – Present
1. Developed a smart agriculture solution using machine learning to predict the most suitable crop based on soil nutrients and environmental conditions. 2. The system also recommends an alternative crop if the primary crop is not feasible and provides a detailed economic analysis for both. 3. Integrated data visualization, crop intelligence, and cost metrics into an interactive Streamlit web application to empower data-centric decisions, projected to increase crop yield by 10-15% for farmers. 4. Performed Exploratory Data Analysis (EDA) including univariate, bivariate, and multivariate analysis using matplotlib and seaborn. Outcome: 1. Helped simulate a decision-support tool for farmers to maximize crop yield and profit with data-driven recommendations. 2. Provided transparent cost insights to guide farming investment decisions more effectively. 3. Increased model interpretability by combining predictions with economic and visual insights (e.g., crop images). 4. Supports agricultural sustainability by suggesting alternative crops in case of risk or resource constraints.
AI-Generated Portfolio Website from Resume with LANGCHAIN
January 1, 2026 – Present
1. Created an AI-powered application that automatically creates a professional portfolio website from a user's resume (PDF/DOCX) using LangChain and Large Language Models. 2. Built an end-to-end automation pipeline that converts resume data into structured website content and produces HTML, CSS, and JavaScript code without manual intervention. 3. Delivered resume parsing using PyPDF2 and python-docx to extract structured information such as skills, experience, projects, education, and achievements. 4. Structured a two-stage LLM workflow where one model analyzes resume content and another brings about production-ready frontend code. 5. Created a Streamlit-based user interface for resume upload, website generation, and ZIP file download. 6. Automated website packaging and export using Python's zipfile module for easy deployment. 7. Demonstrated strong skills in prompt engineering, LLM orchestration, AI automation, and application design.
AI-Powered Agriculture Government Schemes Assistant using Retrieval-Augmented Generation (RAG)
January 1, 2026 – Present
1. Developed an LLM-powered question-answering system using Retrieval-Augmented Generation (RAG) to provide accurate information on Indian agriculture government schemes such as Pradhan Mantri Kisan Samman Nidhi, Pradhan Mantri Fasal Bima Yojana, and Soil Health Card Scheme. 2. Implemented multi-document ingestion and processing using LangChain document loaders and text splitting techniques to extract knowledge from multiple PDF documents. 3. Generated semantic embeddings using HuggingFace models and stored them in Chroma Vector Database for efficient similarity search and retrieval. 4. Integrated Google Gemini LLM API to generate context-aware responses using retrieved document chunks. 5. Built an interactive Streamlit-based chatbot interface enabling users to query agriculture schemes with natural language. 6. Applied vector similarity search and prompt engineering to improve response accuracy and reduce hallucinations. 7. Designed the system architecture including PDF ingestion → text chunking → embedding generation → vector storage → retrieval → LLM response generation.
View ProjectPython Completion Certification from Innomatics Research Labs
Innomatics Research Labs
June 1, 2026 – Present
Machine Learning Completion Certification from Innomatics Research Labs
Innomatics Research Labs
June 1, 2026 – Present
Nasscom Advanced Data Science with Python
Nasscom
June 1, 2026 – Present
The candidate scored 82% on the 'Data Scientist — Artificial Intelligence' test, indicating a strong grasp of the core concepts and practical applications relevant to the role.
Strengths
Limitations
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest in applying AI to real-world problems, particularly in agriculture and automation, which aligns with an innovative and impact-driven culture. The personal projects showcase initiative and self-directed learning. The internship experience, though future-dated, indicates a willingness to collaborate and learn within a team environment. The diversity of projects (crop prediction, website generation, government scheme assistant) suggests adaptability and a broad interest in AI applications.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to translate complex technical concepts into practical, user-centric solutions. The focus on 'decision-support tools' and 'transparent cost insights' suggests a problem-solving mindset and an understanding of business value. The psychometric test score (273/500) suggests potential areas for development in logical reasoning, work attitude, stress handling, or team collaboration, which would require further investigation during interviews.