Open pratikshabhujade opened 1 month ago
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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
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.
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:
https://github.com/user-attachments/assets/2b01d4d7-a769-4f79-a2a6-30ca515bbf82