Banking Copilot Check out my live deployed project:
🏦 Banking Copilot
Project Overview This Streamlit-based banking dashboard provides a modern interface for money transfers and financial advice. The application features an AI-powered assistant to help with financial questions and transaction planning, all wrapped in a sleek custom dark theme with enhanced data visualization.
Technologies Used Streamlit: Interactive web app framework for Python LLM Integration: AI-powered financial advice copilot Custom CSS: Dark theme with glass morphism effects Data Visualization: Enhanced financial data displays Python: Core programming language Render: Cloud hosting platform for the application Key Features Banking Dashboard: View accounts, balances, and transaction history Money Transfers: Easily send money between accounts AI Financial Adviser: Get personalized financial advice using LLM technology Dark Mode Interface: Custom-designed UI with enhanced readability for financial data Voice Interface: Speech-to-text capability for natural interaction Design Highlights The application features a custom dark theme with a primary dark blue-gray palette (#1E1E2E), accent blue (#3B82F6), success green (#10B981), and other semantic colors optimized for financial interfaces.
Stocks Portfolio Optimizer Check out my live deployed project:
📊 Stocks Portfolio Optimizer
Project Overview The Stocks Portfolio Optimizer is an interactive web application that helps users create optimized investment portfolios based on Modern Portfolio Theory. This tool enables investors to select stocks, set investment constraints, and visualize the efficient frontier to identify optimal asset allocations that maximize returns while minimizing risk.
Technologies Used Python: Core programming language Streamlit: Interactive web app framework Financial Analysis: Historical stock data analysis and processing Optimization Algorithms: Portfolio optimization using mathematical models Data Visualization: Interactive charts and efficient frontier visualization Render: Cloud hosting platform for the application Key Features Stock Selection: Choose from a curated list of popular stocks or input custom tickers Portfolio Optimization: Find the optimal allocation of assets using Modern Portfolio Theory Efficient Frontier Visualization: Interactive chart showing risk-return tradeoffs Risk Analysis: Detailed breakdown of portfolio risk metrics Investment Constraints: Set minimum and maximum allocation constraints Performance Backtesting: Evaluate historical performance of optimized portfolios Modern Portfolio Theory This project implements Harry Markowitz’s Nobel Prize-winning Modern Portfolio Theory, which demonstrates that an optimized combination of assets can provide better risk-adjusted returns than individual assets.
News Classifier Check out my live deployed project:
👉 News Classifier
Project Overview This application uses Natural Language Processing (NLP) to automatically categorize Google News headlines into different topics. The system analyzes the content of headlines and assigns them to the most relevant category.
Technologies Used Flask: Lightweight web framework for Python Natural Language Processing: Text analysis and classification techniques Pre-trained Model: ML model trained on news headlines Render: Cloud hosting platform for the application How It Works The application fetches recent headlines from Google News Each headline is processed and analyzed using NLP techniques The pre-trained model predicts the most likely category Results are displayed in an intuitive interface Try It Yourself Visit news-classifier.
Hello World! Welcome to my new website! This is my first post using Hugo with the PaperMod theme.
Why I Created This Site I wanted a personal space on the web to:
Share my thoughts and learnings Showcase my projects Connect with like-minded people My News Classifier Project One of the projects I’m most excited about is my News Classifier application that uses Natural Language Processing to categorize news headlines. You can check it out at news-classifier.