Developed a lightweight algorithmic trading bot that uses historical financial data to automate trading decisions using moving average crossover strategies. The bot is deisgned for small-scale algorithmic trading and integrates risk management techniques to optimize positions and minimize losses.

Features: Market data integration, signal generation, automated portfolio updates, risk management

Tools: Python, Pandas, NumPy, yFinance, Object-Oriented Programming

TidyTable is a Python-based automation tool that speeds up the data cleaning process for large datasets. It handles missing values, detects outliers, and standardizes data formats, reducing manual cleaning time while maintaining data integrity.

Features: Automated cleaning, format standardization, outlier detection.

Tools: Python, Pandas, NumPy, FastAPI, PostgreSQL, Regular Expressions.

A minimalist web application for tracking daily habits without the friction of account creation. The app uses local storage for data persistence, allows users to maintain streaks, and ensures user privacy while providing a simplified habit-tracking experience.

Features: Streak tracking, daily logs, anonymous usage, data persistence.

Tools: Next.js, TypeScript, Tailwind CSS, Local Storage API.

Developed a simple logistic regression model to predict loan default probabilities of credit card clients using a synthetic dataset of 30,000+ records. The model includes comprehensive data preprocessing, feature engineering, and handling of imbalanced classes using SMOTENC.

Dataset: 30,000+ synthetic records.

Tools: Python, SMOTENC, Pandas, Scikit-learn.