Technical Skills:

• Programming Languages: Python, HTML, CSS
• Machine Learning: Supervised Learning (Regression, Classification) Unsupervised Learning (Clustering, Dimensionality Reduction) Reinforcement Learning
• Generative AI: Prompting LLMs
• Data Science: Data Cleaning and Preprocessing Statistical Analysis Data Visualization
• Tools and Technologies: ML Libraries (TensorFlow, Keras, Scikit-Learn) Data Analysis Libraries (Pandas, NumPy) Data Visualization Tools (Power BI)
• Frameworks: Databases (SQL)

Education

• Btech -> Computer Science Engineering in AI & ML at Dayananda Sagar University (Sep 2020 - June 2024)
• II PUC -> PCMB at Swargarani PU College (May 2018 - June 2020)
• SSLC -> Science at G.S. English School (May 2007 - Mar 2018)

Work Experience

Artificial Intelligence Intern @ Pantech Solutions (March 2024 - June 2024)

• Collaborated with cross-functional teams on real-time AI projects.
• Gained hands-on experience in building and fine-tuning Gen AI models, ensuring high accuracy and performance across live applications.

Machine Learning Intern @ Take IT Smart OPC Pvt Ltd (February 2024 - March 2024)

• Conducted data collection, processing, and analysis for novel study evaluating the data to provide recommendations for optimizing communication strategies and tailoring product offerings to better meet customer needs.
• Worked on end-to-end project, building a model to predict whether a customer would be interested in client service or not. It will be extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimise its business model and revenue.

Data Science and Business Analytics Intern @ The Sparks Foundation (September 2023 - October 2023)

• Led testing efforts for real-time data automation projects to ensure flawless execution. • Defined, developed, and implemented real-time data based projects to automate and measure target audience movement.

Machine Learning Intern @ Codsoft (August 2023 - September 2023)

• Contributed my work towards Unsupervised Machine Learning projects.
• Led 3 premium projects with a team of 3 members

Projects

1. Generative AI-Powered Medical QA System with Document Retrieval

Tools Technologies: Langchain, Sentence Transformers, ChromaDB, LLaMA, PyPDF, Python

• Developed a Medical Assistance QA system that processes and retrieves relevant information from medical PDFs to answer user queries related to healthcare, focusing on conditions like heart disease.
• Employed LLaMA (LlamaCpp) to build a generative AI model capable of providing accurate and context-aware medical responses.
• Implemented a Retrieval-Augmented Generation (RAG) pipeline, combining document retrieval with LLaMA’s generative capabilities to deliver fact-based answers and LLM-driven responses allowing users to interact in a real-time query environment.

2. Website Performance Analysis

A comprehensive analysis of the website’s performance, based on:

• Session Analysis: Understanding traffic trends.
• User Engagement Analysis: Gauging the depth of user interaction.
• Channel Performance: Evaluating which channels are most effective.

• Website Traffic Forecasting: Predicting future traffic patterns

• Repository
• Article

3. Parkinson Disease Detection

Final year major project using LSTM and CNN to detect the disease at early stages.

• Team members: Keerthana MG, Nivedha S, Sahana R, Sindhu MG.
• Parkinsons disease (PD) is a challenging neurode-generative disorder to diagnose due to its variable symptoms and lack of definitive biomarkers. This project aims to detect this disease at early stages of patients using Gait analysis.
• Advancements in machine learning, particularly CNN and LSTM architectures, provide a unique opportunity to revolutionize PD diagnosis.
• Comparing with other models, our proposed model gained the accuracy of 95.6 percentage.

4. An Analytics Dashboard

An analytics dashboard is a data visualization tool that aggregates, displays, and analyzes key performance indicators (KPIs), metrics, and other key data points related to a business, department, or specific process.

• Repository
• Article

5. Statistical Modelling

Statistical modelling is a form of mathematical modelling that involves statistics to estimate or predict real-world behaviours, trends, and future outcomes based on data. It involves the construction of a statistical model, which is a formal representation of relationships between variables, typically expressed in the form of mathematical equations.

• Repository
• Article