Stock Price Prediction predicts the stock price for next 5 years, data is fetched from Yahoo finance. The webapp uses Facebook Prophet model which has an MAPE of 10%.

📈💰 Predict stock prices with a linear regression model and 50DMA data 🚀 🎯 Make informed investment decisions 🧠💰 👩‍🏫👨‍🏫 Train a model and generate predictions 🔮 🤝 Contribute to the future of stock prediction 🚀

Time series model for predicting the stock price using LSTM. The dataset includes the closing price of the stock.

This was submitted on the 21st August 2020 as part of my dissertation project for the Master's in Computer Science degree at Newcastle University. In this project, I investigated the existing and new methods of predicting stock price. In particular, the ARIMA model is a robust and established model. On the other hand, the Prophet model, a newer …

Forecasting Nifty-50 stock prices and computing gains or losses for individual stocks, seamlessly integrated with Google Sheets.

Stock Market Analysis and Prediction

This repo is for the LinkedIn Learning course Recurrent Neural Networks

This project explores the application of machine learning models for predicting stock price movements using financial indicators and historical stock price data.

Use scikit learn, keras and tensor flow for some basic predictions

A comprehensive suite of Python-based machine learning models for predictive analytics, employing different evolutionary algorithms for data analysis across various topics.

In this project, I applied sentiment analysis using a statistical machine learning model to capture the correlation between the tweets extracted from Twitter and stock’s price market movements. My exploration sought to answer if daily stock prices behave in response to a positive, neutral, or negative sentiment scoring of respective tweets

Predicting Nifty-50 Stock Price and calculating profit or loss for each stock with google sheet integration

This project employs LSTM, a type of recurrent neural network, to forecast stock market indices using historical data with features like date, open, high, low, close, and volume. By training the model on this dataset, it learns patterns to predict future market trends, aiding investors, traders, and analysts in decision-making.