This project predicts startup profitability using Logistic Regression and Random Forest, analysing financial (funding amount, funding rounds, revenue), market (market share), and operational (startup age, employee count) factors. It evaluates AUC, accuracy, precision, recall, and F1-score, addressing underfitting, overfitting, and feature selection

Balance sheet forecasting tool for banks - capital management, liquidity management, and stress testing with Basel III compliance

A machine learning pipeline that combines financial fundamentals and historical stock trends to deliver more informed stock recommendations for London-listed companies.

This project predicts startup profitability using Logistic Regression and Random Forest, analysing financial (funding amount, funding rounds, revenue), market (market share), and operational (startup age, employee count) factors. It evaluates AUC, accuracy, precision, recall, and F1-score, addressing underfitting, overfitting, and feature selection

Simulation of a market making strategy using the Avellaneda–Stoikov model with EWMA volatility, Poisson fills, and risk controls.

Just a very simplistic tool to make very rough financial projections using Monte Carlo Simulations. I hope this can come in handy for British University Students.