Using Transformer deep learning architecture to predict stock prices.
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Updated
Nov 26, 2024 - Jupyter Notebook
Using Transformer deep learning architecture to predict stock prices.
Reproduction of code described in the paper "Stock Market Prediction Based on Generative Adversarial Network" by Kang Zhang et al.
Stock Market Forecasting with CoreML in Swift
The main goal of this project is to predict future stock prices using a regression method. I have used two algorithms in this project to build a predictive model, i.e. PSO(Particle Swarm Optimization) and SVM(Support Vector Machine). PSO algorithm is a genetic population-based optimization algorithm that selects the future number using the paramet
web application for finding the best portfolio distribution for the total amount invested by investors in order to maximize their gains and minimize the risks.
Hidden Markov Model
This Streamlit-based web application predicts the closing price of stocks using historical data and a Random Forest model. Users can select any stock symbol, specify a date range, and enter stock features (Open, High, Low, Volume) to predict the closing price.
I use this repository to publish a daily Python-based code. The goal is to learn Machine Learning/AI modelling by using daily posts
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