{"repo":"AaravMehta-07/LSTM-Random-Forest-XGBoost-Stock-Predictor-with-Optuna","free":true,"listed":false,"github":"https://github.com/AaravMehta-07/LSTM-Random-Forest-XGBoost-Stock-Predictor-with-Optuna","clone":"git clone https://github.com/AaravMehta-07/LSTM-Random-Forest-XGBoost-Stock-Predictor-with-Optuna.git","description":"A hybrid AI-based stock market prediction system using LSTM, Random Forest, and XGBoost, built for real-world deployment with Optuna-powered tuning, feature-rich engineering, and ensemble prediction logic. Designed to optimize F1 score and accuracy, this system aims to generate reliable buy/sell signals on stocks.","language":"Python","stars":13,"topics":["deep-learning","ensemble-learning","financial-analysis","hyperparameter-optimization","investment","lstm","machine-learning","ml","optuna","python"],"license":"MIT","category":"machine-learning","readme_excerpt":"LSTM-Random-Forest-XGBoost-Stock-Predictor-with-Optuna A hybrid AI-based stock market prediction system using LSTM, Random Forest, and XGBoost, built for real-world deployment with Optuna-powered tuning, feature-rich engineering, and ensemble prediction logic. Designed to optimize F1 score and accuracy, this system aims to generate reliable buy/sell signals on stocks. still work under progress 📈 LSTM + Random Forest + XGBoost Stock Predictor --- 🚀 About the Project This project integrates: - 🔁 Recurrent Neural Networks (LSTM) for sequential financial patterns - 🌲 Random Forest for ensemble-based classification - ⚡ XGBoost for gradient boosting decision trees - 🎯 Optuna for automatic hyperparameter tuning (optional mode) - 📊 Backtesting Module to simulate trading performance ⚙️ Built by a Computer Engineering student to demonstrate real-world ML/AI skills in finance and time series prediction. --- 📌 Features - ✔️ Ensemble of 3 models: LSTM + RF + XGBoost - ✔️ Flag-based retraining (no need to retrain every time) - ✔️ Real stock data from Yahoo Finance - ✔️ Feature-rich engineering: RSI, Moving Averages, Volatility, Volume - ✔️ Backtesting for historical performance validation - ✔️ Soft voting for final trade signal (BUY / SELL) - ✔️ CLI-based output, no GUI bloat - ✔️ Saved model reuse ( models/ folder) --- 🧠 Technologies Used Category Stack ------------------ --------------------------------------- Language Python 3.10 ML Models RandomForestClassifier, XGBClassifier D","default_branch":null,"files":null,"tree":[],"storefront":"/r/AaravMehta-07","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AaravMehta-07/LSTM-Random-Forest-XGBoost-Stock-Predictor-with-Optuna/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}