Anshul Muntha

"Driven Data Analyst Intern with experience at DigiPub Apex CoVantage AP Private Limited, skilled in transforming complex datasets into actionable insights and optimizing SQL queries. Proficient in predictive modeling, workflow analysis, and data mining to support strategic decisions. Strong technical aptitude in data analytics, visualization, and programming languages. Highly motivated, detail-oriented, and eager to contribute to team success in an entry-level data analyst role”

Introduction and Programming with IOT Boards from POHANG UNIVERSITY OF SCIENCE AND TECHNOLOGY

Document summarisation using A.I:

• Document summarisation system that can utilise langChain , natural language processing , Retrieval augmented generation(RAG) to generate concise and meaningful summaries of lengthy text documents.
• The system has potential applications in various domains and can survive as a foundation for future research in the field of text summarisation and natural language processing.

Library Database Management System:

• Developed and maintained a Library Database Management System, including the design of database schemas, implementation of SQL queries, and optimization of data retrieval processes to improve cataloging, tracking, and managing library resources efficiently.

Titanic Survival Prediction Using Machine learning:

• Implemented Prediction model using machine learning techniques. This involved data preprocessing, feature engineering, and training models using algorithms like logistic regression and random forests to predict passenger survival with high accuracy.

Heart disease prediction using machine learning:

• Prediction model using machine learning techniques. This project involved data preprocessing, feature selection, and the application of algorithms such as logistic regression and decision trees to predict the likelihood of heart disease, aiming to support early diagnosis and preventive healthcare.

SuperStore Sales Forecast using machine learning:

• This project involved analyzing historical sales data, applying time series analysis, and building predictive models to forecast future sales trends, helping to optimize inventory management and improve business decision-making.