github-actions[bot]
commented
3 months ago Thank you for submitting your pull request! 🙌 We'll review it as soon as possible. In the meantime, please ensure that your changes align with our [CONTRIBUTING.md]. If there are any specific instructions or feedback regarding your PR, we'll provide them here. Thanks again for your contribution! 😊
AI-Based Stock Predictor Overview The AI-Based Stock Predictor uses machine learning to forecast stock prices based on historical data. This project leverages advanced algorithms to analyze past market trends and provide predictions for future stock prices.
Features
Predict future stock prices using historical data Visualize predictions and stock trends Supports different machine learning models (e.g., LSTM, Random Forest) Evaluate model performance using metrics like MAE, RMSE. Requirements •Python 3.7+ •TensorFlow 2.x •NumPy •Pandas •Matplotlib •scikit-learn
Install the required packages using pip:
cd ai-stock-predictor
Prepare Your Data Place your historical stock data in a CSV file. The data should include columns like Date, Open, High, Low, Close, and Volume.
Training the Model Edit the config.py file to specify the path to your data file and adjust model parameters.
python predict_stock.py --input_path data/new_stock_data.csv This script will load the trained model and generate predictions, saving them to predictions.csv.
Visualizing Predictions To visualize the predictions, use: bash Copy code python visualize_predictions.py This script will create plots comparing the actual stock prices with the predicted values.
Code Structure train_model.py: Script to train the machine learning model. predict_stock.py: Script to make predictions using the trained model. visualize_predictions.py: Script to generate plots for predictions. config.py: Configuration file to set paths and model parameters. data/: Directory to store data files. models/: Directory to store the trained model. utils.py: Utility functions for data processing and evaluation. Example Usage After training the model, you can use the prediction script to forecast stock prices. Here’s an example of how to execute it: