Help & Supportcareers@gravityer.com
Python Engineer with less than a year in Django, Machine Learning, and AI projects.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Aspiring Python Developer with a strong foundation in Python, databases, and software development fundamentals. Experienced in academic projects and collaborative coding environments. Seeking an opportunity to apply skills, gain industry exposure, and grow as a developer.
M.S.Ramaiah Institute of Technology
Bachelor of Engineering · Information Science and Engineering
August 1, 2023 – Present
P.V.P. Polytechnic
Diploma · Computer Science and Engineering
August 1, 2020 – June 30, 2023
Expense Tracker with ML-Based Prediction
June 28, 2026 – Present
Developed a full-stack expense tracking web application using Django framework following MVC architecture. Designed REST-like functionality handling HTTP methods (GET, POST) for expense CRUD operations. Implemented data visualization using Chart.js to display monthly trends and category-wise spending. Built a Random Forest ML model to predict next month's expenses using historical and real-time data. Ensured user-specific data security by filtering data per authenticated user. Integrated CSV-based historical data with live database inputs for improved prediction accuracy.
AI-Powered NoSQL / MongoDB to SQL Converter
June 28, 2026 – Present
Built a Generative AI-powered web application to convert NoSQL and MongoDB JSON data into structured SQL queries. Implemented schema inference to automatically generate CREATE TABLE and INSERT statements. Integrated LLaMA 3.1 via Groq API using prompt engineering for intelligent SQL generation. Developed an interactive Streamlit UI with JSON input, file upload, and downloadable SQL output. Supported multiple SQL dialects including MySQL, PostgreSQL, and SQLite.
EARLY DETECTION OF BANKRUPTCY USING MACHINE LEARNING
June 28, 2026 – Present
Developed a predictive model to identify potential bankruptcies using financial ratios and historical company data. Collected and prepared historical company financial statements and computed key financial ratios (liquidity, leverage, profitability, efficiency) to capture the financial stability and operational performance of firms. Engineered features by transforming raw financial variables into predictive signals, handling missing values, outliers, and skewed distributions to improve model reliability. Selected LightGBM as the core classification model due to its gradient boosting strategy, ability to handle non-linear relationships, and superior performance on structured financial datasets. Evaluated model performance using accuracy, precision, ROC, and AUC to assess prediction reliability.
Cultural Fit Analysis
The candidate's academic projects demonstrate a breadth of technical interests, from full-stack web development to machine learning and generative AI. This diversity suggests adaptability and a willingness to explore different domains, which can be a positive cultural fit for dynamic environments. However, the lack of professional experience or collaborative project details limits the assessment of cultural alignment with a specific team or company values.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex problems and integrate various technologies. The academic nature of projects suggests a learning-oriented individual. However, without professional experience or psychometric test results, it's difficult to assess operational fit, teamwork, or stress handling capabilities.