RNN - Stock Prediction Model using Attention Multilayer Recurrent Neural Networks with LSTM Cells

A hybrid deep learning approach for stock price prediction using CNN and LSTM models, leveraging multi-scale historical data to improve forecast accuracy.

A complete deep learning project for forecasting stock market trends using LSTM and GRU networks in PyTorch. Includes data preprocessing (SMA, RSI), model training notebooks, multi-stock evaluation (TSLA, AAPL, AMZN, GOOGL), and a deployed Streamlit web app for live predictions using Yahoo Finance.

This model predicts the opening price of the Google stock on a particular day by observing the opening price of the past 60 days.

Multi-model stock prediction system using ARIMA, LSTM, GRU, and CNN

Graph-based LSTM model for multi-stock price prediction using GNN and time-series data.

This repository contains Jupyter notebooks for stock market prediction using models like ARIMA, LSTM, Stacked LSTM, GRU, and Bidirectional LSTM. It includes single and multi-stock forecasting (e.g., NIFTY 50), a Streamlit app for interactive predictions, and pre-trained models. Run notebooks in Google Colab w.ith respect to the .ipynb files.

A modular, end-to-end stock price prediction system using LSTM models. Includes data downloading, preprocessing, model training, prediction for the next 7 days, and interactive visualizations via a multi-page Streamlit web app.

A step-by-step guide to mastering sequence prediction using TensorFlow and LSTM. This repository covers everything from basic linear predictions to advanced multi-step forecasting, many-to-many LSTM models, and real-world applications like stock price forecasting and sales prediction.

This project implements a deep learning model for stock price prediction using Long Short-Term Memory (LSTM) neural networks. The model analyzes historical stock market data and learns temporal patterns to predict future stock prices.

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Multi stage stock prediction machine learning model using the LSTM and MLP

Graph-Based LSTM model for multi-stock price prediction using GCN and time-series analysis

NVIDIA Stock price prediction using custom multi-layered LSTM Model. The high dimension deep learning model is developed with TensorFlow Library.

Developed LSTM-based forecasting model for stock price prediction. Achieved 85% directional accuracy using multi-variate time series analysis and technical indicators.

Project for forecasting Tesla (TSLA) stock prices using advanced LSTM neural networks. Includes a single-step ahead model and a multi-step stacked LSTM model for short and medium-term predictions. Data-driven insights for stock market enthusiasts and practitioners.

TATA Stock Price Prediction Using LSTM: Built a multi-layer LSTM model to predict TATA stock prices using historical NSE data, with data normalization, 60-day sequences, and Dropout regularization. Predicted prices are visualized and used to estimate potential profit/loss.

Project for forecasting Tesla (TSLA) stock prices using advanced LSTM neural networks. Includes a single-step ahead model and a multi-step stacked LSTM model for short and medium-term predictions. Data-driven insights for stock market enthusiasts and practitioners.

Built a stock price prediction dashboard using Python, LSTM, Dash, and Plotly. Trained models to forecast closing prices and integrated real-time interactive graphs for multi-stock comparison, volume tracking, and predictive insights.

Deep learning project for stock market prediction using historical price data. Compares single-stock and multi-stock time-series models (e.g., LSTM/Transformer) to test whether incorporating data from multiple correlated stocks improves prediction accuracy. Includes preprocessing, training, and evaluation pipelines in PyTorch.

A machine learning project to predict Tesla (TSLA) stock prices using a Long Short-Term Memory (LSTM) neural network. This project fetches historical stock data from Yahoo Finance, processes it into a windowed dataset, trains an LSTM model, and implements recursive multi-step predictions.

A deep learning project for forecasting Apple stock closing prices using historical multi-company stock data. This repo compares transformer-based models (BERT, RoBERTa) with an LSTM-based model for multi-step time series prediction. Implemented in PyTorch with recursive forecasting and performance evaluation across multiple lags.

An interactive Streamlit app that forecasts stock prices using LSTM and ARIMA models, then optimizes multi-asset portfolios with PyPortfolioOpt. Features include historical data analysis, price prediction, portfolio construction, and efficient frontier visualization.

A Streamlit-based web app for real-time stock data visualization and price prediction. Features include interactive charts, multi-timeframe analysis, and future price forecasting using an LSTM model. Supports US and Indian stocks. 🚀

A Python project for time-series forecasting of stock prices using an LSTM neural network. Combines historical stock data and sentiment features for next-day and multi-day predictions. Includes pre-trained models, feature scalers, and scripts for training, predicting, and saving results.

Previzia is a machine learning-powered stock price prediction app built with Python and Streamlit. It uses LSTM models to forecast future stock prices based on historical data. The app features an intuitive UI, real-time data visualization, and multi-feature analysis for improved prediction accuracy.

Hybrid CNN-Transformer model for diversified portfolio financial risk forecasting using real stock market data. Captures short-term volatility and long-term temporal dependencies. Includes multi-step prediction, LSTM baseline comparison, and portfolio risk estimation for deep learning research and academic use.

Built a time-series forecasting model for stock price prediction using Machine Learning and Deep Learning techniques. Implemented Linear Regression and multi-layer LSTM networks with MinMax scaling and 60-day sequence windows. Achieved an R² score of 0.88 and generated 30-day forward predictions with comprehensive visualization and model evaluation

This project predicts the next day’s Open, High, Low, Close, and Volume for NSE stocks using an LSTM/GRU model. It fetches the last 60 days of stock data via Yahoo Finance and supports symbols like RELIANCE, TCS, INFY, etc. Users can input any NSE stock and get accurate, multi-feature price predictions.