This project was completed with the intention of helping Tesla stock investors better understand how to make decisions where the stock market is very volatile by training different models through historical and social media data analytics. Behavioral economics shows that public emotions can profoundly affect individual behavior and decision making. In order for investors to utilize it, business analysts must understand the behaviors and attitudes of the public within the finance context. Nowadays, social media perfectly tracked by data reflects the public emotions and sentiment about stock movement. Also, tremendous stock marketing news can be used to capture a trend of stock movement. The fundamental trading and decision making for main techniques rely on expert training and prediction. This article concentrated on tweets and stock news, and I applied sentiment analysis and machine learning models, especially, XGBoost to tweets and news extracted from Elon Musk tweets, Nasdaq and New York Times News about Tesla. Only by understanding the values and priorities of the public sentiment of Tesla stock will investors be able to make significant decisions. In addition, I conducted two models- ARIMA and RNN(LSTM) in forecasting the Tesla stock price. I compare their results with the prediction performances of the classical ARIMA and RNN.

Stock price prediction using LSTM on historical ETF data, with performance evaluation and visualizations. The model forecasts future prices based on past trends.

LSTM - Time Series - Stock Price Prediction With 10 years of Amazon.com, INC. Stock Price Data(Open Stock Price, Source: Yahoo Finance), using 120 timesteps(6 months), 4 layers and a dropout rate of 0.25, my LSTM Regressor is able to predict the trend for April 2018(except for the sudden violations). #RNN#LSTM#TIME SERIES#STOCK_PRICE_PREDICTION#AMAZON

Stock Trend Prediction is a project that leverages Long Short-Term Memory (LSTM) neural networks to predict future stock prices using historical data. By integrating advanced machine learning techniques with a user-friendly web interface, it provides insights into stock trends and forecasts.

Our app uses LSTM neural networks to analyze stock market trends and predict future prices. With a user-friendly interface, investors can input stock symbols and receive accurate predictions, aiding them in making informed investment decisions

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Stock Price Prediction with LSTM is a machine learning project that uses a Long Short-Term Memory (LSTM) neural network to forecast stock prices based on historical data. The notebook demonstrates data preprocessing, model training, and evaluation to predict future stock trends.

This Stock Trend Prediction app uses a pre-trained LSTM model to forecast stock prices based on historical data from Yahoo Finance. Built with Streamlit, it visualizes Closing Prices, 100MA, 200MA, and predicted vs. actual prices in an interactive interface.

This project integrates financial news sentiment analysis with stock trend prediction using an LSTM (Long Short-Term Memory) neural network. It utilizes FinBERT, a financial sentiment analysis model, to extract sentiment scores from financial news and combines them with stock price trends to forecast future stock movements.

Performed Stock Price Prediction task using LSTM, Prophet, and ETS Models: A deep research and implementation of advanced time-series forecasting with Tesla stock data, featuring preprocessing, trend analysis, and performance metrics (RMSE, MAE).

An AI that predicts stock trends integrated into a server made with Django. Uses LSTM and takes volume and price trends into account to make predictions upto 50 days into the future. Made for ICS4U.

This Stock Forecasting App uses machine learning to predict stock prices. With Linear Regression and LSTM models, it analyzes historical data to forecast trends, offering an interactive Streamlit interface for visualizing predictions and selecting models.

Stock Trend Prediction with LSTM is a powerful tool designed to empower users with insights into the dynamic world of stock market trends. Leveraging cutting-edge technologies such as Long Short-Term Memory (LSTM) networks and real-time data from Yahoo Finance, this project enables users to forecast future price movements of stocks with precision.

A web application for stock price prediction using Prophet (Bayesian model) and LSTM (Deep Learning). Built with Streamlit, Plotly, and OpenAI for sentiment analysis, it visualizes historical trends, predicts future stock movements, and analyzes financial news sentiment.

TradeVision is a real-time stock analyzer using LSTM for price prediction. It fetches live stock data via yfinance and predicts future trends. Latest news is analyzed using VADER and summarized with Gemini AI. Interactive Plotly charts show actual vs predicted prices and sentiment insights.

An LSTM-based model for forecasting stock prices using historical data, capturing trends and patterns for accurate predictions. Useful in financial forecasting, with options to explore other methods like ARIMA, GRU, and Transformers.

The Stock Price Prediction Web App uses an LSTM model to forecast stock prices based on historical data from Yahoo Finance. Built with Streamlit, Pandas, and Keras, it offers interactive visualizations like candlestick charts, moving averages, and Bollinger Bands. Users can explore trends, analyze trading volume, and predict future stock movements

An AI-powered stock analysis app combining LSTM-based price prediction with LLM-generated news summaries. Users select a stock to view dynamic forecasts and AI-curated insights from the latest news, enabling data-driven investment decisions based on both market trends and sentiment.

This project applies Deep Learning with LSTM (Long Short-Term Memory) networks to predict stock prices based on historical data. By learning sequential patterns in time-series data, the model forecasts future trends, helping investors and analysts make informed decisions in stock market prediction.

StoxAI is an AI-powered stock market prediction platform that combines deep learning (LSTM) with real-time news sentiment analysis to forecast price trends. It analyzes historical market data and financial news to deliver smarter insights, multi-timeframe predictions, and data-driven investment support.

Developed an end-to-end stock price prediction system using multi-layer LSTMs with yFinance data ingestion, MinMax-scaled sequences, and trend-based MA analysis. Integrated an interactive Streamlit app for real-time visualization, insights, and accurate market forecasting.

An end-to-end time series forecasting system that predicts stock closing prices using LSTM neural networks. Historical data is fetched via Yahoo Finance, preprocessed for sequence modeling, and deployed as an interactive Flask web application with real-time prediction and visualization of future price trends.

This project combines an LSTM model for predicting stock prices over the next 7 days with sentiment analysis to evaluate market trends. It also provides actionable recommendations based on predictions and sentiment, making it ideal for financial forecasting and decision-making.

This project is a web-based stock market prediction system that allows users to input a stock symbol and date range to analyze historical data and predict the next day’s price using an LSTM (Long Short-Term Memory) neural network. It visualizes both the historical trends and the predicted future price with charts and accuracy metrics.

An interactive AI-based web application that forecasts future stock prices of NIFTY50 companies using LSTM (Long Short-Term Memory) neural networks. Built with Streamlit, TensorFlow/Keras, and Plotly, this tool visualizes historical trends and provides future predictions with options to download and analyze the data.

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"A simple project to predict stock price trends using LSTM with moving averages and RSI indicators."

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Predict future stock prices using past trends.

This project predicts future stock prices using an LSTM (Long Short-Term Memory) deep learning model trained on historical stock price data. It also provides interactive visualizations with moving average indicators via a Flask web application.