An interactive Power BI dashboard that transforms school enrolment data into actionable insights using feature engineering, gender analytics, priority scoring, and school-level planning indicators to support data-driven education resource planning.
A Machine Learning web application that predicts whether a loan application is likely to be Approved or Rejected using an XGBoost Classifier. The application is built with Streamlit and includes business-rule validation for reliable predictions.
An end-to-end Machine Learning application that predicts house prices using XGBoost Regressor. The project follows a complete industry-standard ML workflow, including data preprocessing, exploratory data analysis, feature engineering, hyperparameter tuning, explainable AI (SHAP), and deployment with Streamlit.
FINGENIUS.AI is an intelligent, AI-powered stock market query system that combines natural language processing and real-time financial data analysis. The project involved building a multi-agent architecture with dedicated Query and Finance Analysis agents, reaching 92% accuracy in handling stock-related questions.
The Predicting Student Placement: CGPA and Resume Score project is designed to create a machine learning model that can effectively predict whether a student will be placed or not, based on their CGPA and resume score.
A Health based web applications which mainly focus on providing steps to be taken on crucial situations like Snakebite, drowning, coin swallow, etc.As it's a team project of two members (frontend and backend), I'm on the backend side.
This machine learning project predicts heart attack risk using a dataset of 170,501 records and 25 features, achieving 75% accuracy. Ongoing improvements focus on feature engineering, scaling, and model optimization to enhance accuracy and robustness. The goal is to refine feature selection, improve performance, and provide a user-friendly interface with detailed documentation.
Explore my freelance work portfolio including full-stack applications, backend services, and custom solutions developed for various clients.
View my professional certifications and completed courses in web development, machine learning, and other technical domains.
Hi, I’m Aditya 👋 I’m a Computer Science and Engineering graduate with a strong interest in Data, Machine Learning, and technology-driven social impact. I enjoy using technology and data to understand real-world problems, build practical solutions, and support better decision-making. My experience includes working at the intersection of technology, data, and grassroots development, where I have worked with field teams and stakeholders, supported technology adoption, and used data to identify insights and improve processes. Alongside my field experience, I have been building my technical skills in Python, SQL, Data Analysis, Machine Learning, Power BI, and web technologies. I believe that technology is most valuable when it solves meaningful problems. I’m particularly interested in opportunities where I can combine my technical knowledge, analytical thinking, and field experience to create solutions that have a measurable impact. I’m continuously learning, building projects, and improving my ability to turn data and technology into simple, useful, and scalable solutions.
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HTML
CSS
Python
SQL
Git
GitHub
Docker
AWS
Heroku
Bootstrap
Pandas
WordPress
Building robust, scalable, and efficient websites using the latest technologies to ensure optimal performance and user experience.
Web scraping extracts data from websites, allowing you to gather information about customers, such as their names, contact details, preferences, and purchase history. This data can be invaluable for tailoring your marketing
Backend services handle the server-side logic and data management for your personal applications. They process requests from frontend clients, interact with databases, perform calculations, and implement business logic.
Machine learning is a field of artificial intelligence that focuses on developing algorithms and models that allow computers to learn from data and improve their performance on a specific task without being explicitly programmed. It involves training computers on large datasets to identify patterns, make predictions, and solve complex problems.
