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financial-modelling

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Experience the convergence of reinforcement learning and finance in this project, which implements a Q-learning agent for option pricing under the Black–Scholes model. Leveraging Monte Carlo simulation, B-spline basis functions, and a variance-based reward, the agent learns optimal hedging strategies to generate accurate, risk-neutral option price.

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  • Jupyter Notebook

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

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  • Python

Python desktop application for multi-asset portfolio optimization and management. Features machine learning-enhanced strategies like Minimum Variance and High Yield Equity, alongside robust backtesting tools. Includes a Tkinter GUI for data fetching, portfolio construction, and performance visualization using Yahoo Finance and SQLite.

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  • Python

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

  • Updated
  • Python

Advanced cross-market financial models (India & US) built for long-term sector analysis (2020-2030), including ROCE-PE relationships, CAS Scoring architecture, RAPI framework, heatmaps, and valuation-efficiency insights. Designed for investment research, Portfolio strategy, and global sector benchmarking.

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