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Hello, I'm
Mathematics graduate who builds and deploys practical data, machine learning, and AI applications — from model training all the way to live web apps.
I’m a Mathematics graduate making the transition into Data Science and AI/ML engineering. My work centers on building complete applications — cleaning and analyzing data, training models, and then turning those models into usable web tools that people can actually interact with.
During a 3-month Data Science internship I developed and deployed real-world applications for property data, image processing, and AI-assisted inspection workflows. I prefer taking projects all the way to a live environment on Vercel rather than leaving them as notebooks. Earlier experience analyzing recruitment data in Excel also taught me the value of clear, structured reporting.
Practical data and AI applications I built and deployed
Real estate AI application combining machine learning-based property price prediction, property analytics, and AI-assisted image analysis. Includes image upload, mortgage calculation, voice search, and property information workflows. Deployed as a live web application on Vercel.
AI-assisted property inspection web application for property image processing, address verification, and structured inspection workflows. Implemented image upload, AI-assisted image analysis, and property information processing. Deployed as a live web application on Vercel.
Flask-based stock scanner that automatically collects market data and screens stocks using rule-based filters on price range, prediction score, and analyst rating. Includes an investment recommendation API with live USD-INR conversion, tax and fee calculations. Deployed on Vercel.
Built and compared Linear Regression and Random Forest models to predict residential property prices on the Kaggle House Prices dataset (1,460 houses, 9 selected features). Random Forest selected as final model with R² of 0.8877 (vs 0.7907 for Linear Regression).
Interactive Power BI dashboard with KPI cards for Total Sales, Total Profit, Total Quantity, and Profit Margin. Includes donut chart for sales by category, bar chart for sales by region, and line chart for monthly sales trend. Designed with a professional dark theme.
Probability & Statistics, Statistical Analysis, Linear Algebra, Data Modeling, Quantitative Analysis, Mathematical Optimization, Numerical Methods, Calculus
Python, MySQL, MongoDB, NumPy, Pandas, Matplotlib, Scikit-Learn, TensorFlow, Keras, Git
Python, C, C++, MySQL, HTML5, CSS3, MS Office, Internet Applications, Digital Marketing
Exploratory Data Analysis, Risk Profiling, AI-assisted Delinquency Prediction, Business Reporting, Data Storytelling, AI-driven Collections Strategy
45 WPM
Open to Data Science and AI/ML opportunities
gayathirirajkumar95@gmail.com
linkedin.com/in/gayathiri23
github.com/Gayathiri2303
Nagercoil, Tamil Nadu
+91 78260 04824