Used Copilot to create this Readme. Cuz why not ! :)
This application is designed to predict stock market prices using machine learning models. It is structured in a modular way, with different components responsible for data extraction, model creation, and model testing.
The data extraction process is likely handled by the data_extraction.py file. Although the exact details are not provided, it's reasonable to assume that this script is responsible for fetching stock market data from an external source.
The application uses machine learning models to predict stock prices. The details of the model creation process are not provided in the excerpts, but it's likely that this process involves training a model on historical stock market data.
The PmTesting class in app/Learningapp/modelTesting.py is responsible for testing the accuracy of the machine learning models. It provides methods for calculating model accuracy and for plotting the real stock price against the predicted stock price.
The TickerTesting class in the same file tests the models for multiple tickers. It reads the data for each ticker, loads the corresponding model, and calculates the model's accuracy. If the graph attribute is set to True, it also saves a graph of the real stock price vs the predicted stock price.
The main entry point of the application is likely the main.py file. The exact details of how to run the application are not provided in the excerpts, but it's likely that running main.py will execute the data extraction, model creation, and model testing processes.
app.pyandconfig.pylikely contain the main application logic and configuration settings, respectively.requirements.txtlists the Python packages that the application depends on.rec.jsonandOUTPUT_COM.txtmight be used for logging or storing intermediate results.test_model.h5is likely a pre-trained machine learning model that can be used for testing purposes.
The stock_market directory contains several Jupyter notebooks (check_test.ipynb, data_engg1.ipynb), which might be used for exploratory data analysis or model prototyping.
The stock_market directory also contains several CSV files with stock market data. The app directory contains a database subdirectory, which might be used for storing processed data.
This application is a comprehensive tool for predicting stock market prices. It uses machine learning models trained on historical data to make predictions, and provides tools for testing the accuracy of these models.