Machine Learning Notebooks for various Algorithms with data file
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Updated
May 19, 2024 - Jupyter Notebook
Machine Learning Notebooks for various Algorithms with data file
The following sections are elaborated in this Jupyter notebook: A discussion about each stage of the CRISP-DM cycle corresponding to this application. Where applicable, the code corresponding to each stage should follow the explanation of that stage. You should present an analysis of the nature of the model fit and recommend a course
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.
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.
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.
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
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 Jupyter notebook and report for ECM3420 focused on predicting the stock price using technical analysis
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.
DEEP LEARNING BASED STOCK PREDICTION THAT PROVIDES A ESTIMATED STOCK SPIKES.
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
Series of notebooks, which are my submissions to a online Hackathon : Code-2-ML
Google Colab notebooks and sample datasets for the intensive Crash Course in Deep Learning at Kaunas University of Applied Sciences, Kaunas, Lithuania
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.
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.
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.
Algo Trading - Notebooks: Start to End of Algo Trading Development. Data:Stocks up till 2019 with all daily close data.
Predicting opening price, closing price - all in Jupyter Notebooks
This is a simple jupyter notebook for stock price prediction. As a model I've used the linear, ridge and lasso model.
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