Beginner-friendly collection of Python notebooks for various use cases of machine learning, deep learning, and analytics. For each notebook there is a separate tutorial on the relataly.com blog.

Predicting opening price, closing price - all in Jupyter Notebooks

In this Jupyter Notebook, I've used LSTM RNN with Technical Indicators namely Simple Moving Average (SMA), Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), and Bollinger Bands to predict the price of Bank Nifty.

Algo Trading - Notebooks: Start to End of Algo Trading Development. Data:Stocks up till 2019 with all daily close data.

Series of notebooks, which are my submissions to a online Hackathon : Code-2-ML

Machine Learning Notebooks for various Algorithms with data file

Utilizing linear regression for accurate forecasting, this Jupyter Notebook-based exploration encompasses theory, dataset analysis, and model implementation, offering a holistic journey from concept to conclusion.

DEEP LEARNING BASED STOCK PREDICTION THAT PROVIDES A ESTIMATED STOCK SPIKES.

This is a python program for finance. It shows how to commpute portfolio simple returns, get daily returns and volatility etc. The graphs are also present in the notebook.

In this repository I'm implementing PyTorch based Deep Neural Networks from basic ANN to Advanced Graph Neural Networks. Please suggest if you have any ideas

A collection of Jupyter Notebooks and python scripts used in my research regarding the influence of Twitter post sentiment on stock's rates of return

This notebook builds an artificial recurrent neural network called Long Short Term Memory (LSTM) to predict the adjusted closing price of the GOOGLE. Index by reiterating over the past 60 day stock price

A machine learning project for classifying stocks into their respective sectors using historical stock price data. Implemented in a Jupyter Notebook, the project includes data fetching, preprocessing, feature extraction, model training, and prediction.

This notebook builds an artificial recurrent neural network called Long Short Term Memory (LSTM) to predict the adjusted closing price of the NASDAQ Clean Edge Green Energy index by reiterating over the past 60 day stock price.

A data science project utilizing Jupyter Notebooks and Python to analyze historical Bitcoin price data and develop predictive models.

This is a simple jupyter notebook for stock price prediction. As a model I've used the linear, ridge and lasso model.

Google Colab notebooks and sample datasets for the intensive Crash Course in Deep Learning at Kaunas University of Applied Sciences, Kaunas, Lithuania

Analysis, a development of insights and a training of a prediction model for different stocks dataset such as APPLE, AMAZON or GOOGLE made in some notebooks with Spark in python.

This project predicts Apple Inc. (AAPL) stock prices using LSTM networks. It involves data preprocessing, model training, and evaluation to provide insights into future price movements. Users can explore and execute the provided notebooks for analysis.