Stock Price Prediction README

Overview

This project aims to predict stock prices based on historical data using machine learning and time series forecasting techniques. This README provides an overview of the project, including data collection, preprocessing, model selection, training, and evaluation steps.

Table of Contents

1. Problem Statement
2. Data Collection
3. Data Preprocessing
4. Feature Engineering
5. Model Selection
6. Model Training
7. Evaluation
8. Usage
9. Contributing
10. License

Problem Statement

Predicting stock prices is a complex task with various factors affecting price movements. This project aims to build a predictive model that can forecast stock prices based on historical data, helping investors make informed decisions.

Data Collection

• Historical stock market data is collected from reliable sources such as [source1] and [source2].
• Features include date, open price, close price, volume, and additional relevant indicators.
• Data consistency and accuracy are ensured.

Data Preprocessing

• Missing data is handled using interpolation/imputation techniques.
• Numerical features are normalized or scaled.
• Categorical features are converted into numerical representations.
• Exploratory data analysis (EDA) is performed to understand data distributions and identify outliers.

Feature Engineering

• Additional features are created, including moving averages (e.g., 20-day, 50-day, 200-day), technical indicators (e.g., Bollinger Bands, RSI), and lagged variables.
• External factors like economic indicators or news sentiment analysis are considered if relevant.

Model Selection

• Time series forecasting models like ARIMA, LSTM, and GRU are considered.
• Ensemble methods and hybrid models are explored for improved accuracy.

Model Training

• Data is split into training, validation, and test sets.
• The selected model is trained using the training data.
• Hyperparameters are tuned using the validation set to optimize performance.
• Techniques to prevent overfitting are implemented.

Evaluation

• The model's performance is evaluated using time series forecasting metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and directional accuracy.
• Backtesting is performed to assess historical performance.

Usage

1. Clone the repository.
2. Install the required libraries mentioned in the requirements.txt file.
3. Run the Jupyter Notebook or Python script for stock price prediction.
4. View the model's predictions and evaluation results.

Contributing

Contributions are welcome! If you have ideas for improvements or new features, please open an issue or submit a pull request.

License

This project is licensed under the [License Name] License - see the LICENSE.md file for details.

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