REPOSITORY OVERVIEWLive repository statistics
★ 455Stars
⑂ 126Forks
◯ 8Open issues
◉ 455Watchers
65/100
OPENREPOHUB HEALTH SIGNALHealthy signals
A transparent discovery signal based on current public GitHub metadata.
Recent activity35% weight
90 Community adoption25% weight
53 Maintenance state20% weight
85 License clarity10% weight
0 Project information10% weight
35 This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
README preview
Time-Series-Forecasting-of-Amazon-Stock-Prices-using-Neural-Networks-LSTM-and-GAN-
Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is used with multiple features to predict stock prices and then sentimental analysis is performed using news and reddit sentiments. GANs are used to predict stock data too where Amazon data is taken from an API as Generator and CNNs are used as discriminator.
IF THE NOTEBOOKS DONT OPEN ON GITHUB VIEW THEM ON THIS SITE:
https://nbviewer.jupyter.org/
Copy the Jupyter notebooks github URL in this site and view them
ALGORITHMICALLY RELATEDSimilar Open-Source Projects
Selected from shared topics, language and repository description—not editorial ratings.
This repository contains a Python-based sentiment analysis project using Jupyter Notebook to analyze customer reviews. The project leverages TextBlob for sentiment classification, pandas for data manipulation, and Matplotlib/Seaborn for visualizing insights. Key features include: Sentiment Classification,Statistical Analysis
47/100 healthActive repository
Jupyter NotebookNo license#customer-reviews#data-visualization#sentiment-analysis
⑂ 0 forks◯ 0 issuesUpdated Mar 31, 2026
The aim of this project is to perform data exploratory analysis and to use time series analysis in order to analyze data of Amazon Stocks to understand and forecast the trends. The time series analysis of Amazon index will be done over two parts. The first part will consist of the exploratory data analysis of available data of Amazon over the chosen time order to understand the reasons behind the recorded fluctuations, and the second part will consist of the time series analysis of the data. The data used for this analysis is from January 1, 2011 to present. The next step is to analyze the data and implement in Jupyter notebook using ARIMA
35/100 healthActive repository
Jupyter Notebook