We have created a stock market analysis app in which we took top companies stocks such as amazon, tesla, apple, microsoft and compared their past stock market exchanges with each other.Installed yfinance which updates us with the current status of stocks.Used regression and classification algorithms to predict the future of these companies.

Predicting NYSE with respect to time further ahead.

📈 Stock market information with accurate and detailed statistical simulations for stocks using ChatGPT.

Stock market forecasting using Stacked LSTM

Stock value alanatics program

Graduate capstone project to predict one-year price change of stocks using sentiment analysis of tweets.

This repository hosts a stock market prediction model for Tesla and Apple using Liquid Neural Networks. It showcases data-driven forecasting techniques, feature engineering, and machine learning to enhance the accuracy of financial predictions.

This repository contains my Social Networks Projects during University and the projects that I've implemented due to my interest in Social Networks. These projects include analysis of social networks like telegram and graph mining with NetworkX and Gephi and investigating the effect of social data on the stock market.

⚡ STOCK MARKET PREDICTION is a Deep Learning based web application using LSTM model and that is used to predict the future stock prices based on 10 years historical data

Stock market prediction of a stock using stacked LSTM

Stock Market Analysis Techniques, Algorithms, Sample Experiments

Repository for my academic project that aimed to forecast the evolution of a stock market price using neural networks and LSTM.

A Repository Submitted in Partial Fulfillment of the Requirements for the Course Prediction and Machine Learning (COE 005)

I got an internship from Code Clause for the role of Data Science Intern. I had been allocated with 4 tasks, and this repository consists all of them. Hope its helpful if you need any. Thank you.

Flask backend for stock price prediction app

Using article and company embeddings to calculate adjusted media sentiment.

MA thesis in Economics that I defended at University of Warsaw. The thesis has received a very good grade.