An End-to-End Machine Learning Pipeline that combines financial news sentiment analysis with historical stock price features to predict short-term market movements.

In the scope of the EPFL FIN-418 course in Machine learning for finance we develop a natural financial forecasting model. Using web scraping to collect news articles and NLP to predict financial returns based on these articles.

Predict trends, events, or sentiments using machine learning and NLP on news headlines and articles. This project extracts insights from textual data to support real-time forecasting in finance, politics, and public opinion.

Developed a market analysis software framework to support a consulting team in their financial research and investment strategy development. Achieved by integrating Cohere AI’s NLP capabilities for sentiment analysis and predictive forecasting along with Yahoo Finance API for real-time market data retrieval.

finance-ml → LSTM, GRU, DQN and NLP sentiment models applied to financial forecasting

Personal GitHub profile README showcasing projects in quantitative finance, econometrics, and machine learning (time series forecasting, derivatives pricing, and NLP pipelines).

Side project that forecasts Apple stock prices with GRU and live news sentiment. Merges finance, NLP, and time series in one notebook.

FinSense is an AI-powered personal finance management system that intelligently classifies transactions, forecasts budgets, and provides personalized financial insights. Built with FastAPI + React, it combines machine learning, time-series forecasting, and NLP for secure financial management.

JobMatch is an automated, data-driven platform that aggregates finance job postings, intelligently matches them against a candidate’s resume using advanced Natural Language Processing (NLP) techniques, and forecasts emerging job trends.

Interactive dashboard forecasting J.P. Morgan's $2.5T Sustainable Development Target using Python (Streamlit, Prophet) & NLP. Models Green Financing trends and simulates AI energy demand scenarios based on the 2024 Sustainability Report.

A smart finance tracker that manages expenses, budgets, and forecasts future spending using AI. Features expense tracking, AI budgeting, NLP chatbot, and real-time optimization to improve financial habits. Secure JWT authentication & MongoDB storage.

Unleashing ML's potential in quantitative finance. Cutting-edge models leveraging deep learning, reinforcement learning, NLP. Advanced algorithms extract insights from vast datasets: sentiment analysis, forecasting, optimization, risk management. Represents innovation's pinnacle, revolutionizing trading with machine intelligence.

SmartSpend is an end-to-end Personal Finance Analytics & Machine Learning system featuring EDA, rule-based and NLP-driven expense categorization, monthly spend forecasting using Random Forest, and anomaly detection with Isolation Forest. Built with Python and Scikit-learn.

Comprehensive portfolio of 50 data analysis projects across Power BI, Excel, SQL, Tableau, and Python. Covers dashboards, forecasting, NLP, ML models, and real-world datasets (finance, healthcare, social, e-commerce), showcasing end-to-end data insights, visualization, and business decision support.

This project leverages both machine learning and natural language processing (NLP) techniques to analyze and forecast stock movements. Historical stock data is collected from financial APIs (e.g., Yahoo Finance) to capture market patterns over time, while sentiment analysis is performed on news headlines and social media post.