FEATURE: Implement Machine Learning Models for Financial Trend and Stock Price Prediction

[sanchitc05]

Feature Summary

I'd like to contribute to FinVeda by implementing a machine learning module that can predict financial trends, stock prices, and customer behavior. This module will leverage popular deep learning libraries such as TensorFlow or PyTorch to create reliable and robust predictive models tailored to financial data.

Description

This module will enhance FinVeda's predictive capabilities, providing users with actionable insights into financial markets and customer patterns. It will also serve as a foundation for further enhancements in predictive finance.

Proposed Solution

Proposed Features

1. Data Collection and Preprocessing

   • Gather relevant financial datasets (e.g., stock price data, economic indicators, customer behavioral data) from available APIs and open datasets.
   • Perform preprocessing steps, including handling missing values, normalization, and feature engineering, to enhance model performance.

2. Model Development

   • Time Series Prediction: Implement models such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) for stock price and financial trend prediction.
   • Customer Behavior Prediction: Use classification models like Random Forest, XGBoost, or neural networks for customer behavior analytics.
   • Provide separate classes or functions for each type of prediction to keep the code modular.

3. Model Training and Evaluation

   • Train each model on financial data and optimize hyperparameters for accuracy and efficiency.
   • Implement evaluation metrics suitable for financial predictions, such as MSE (Mean Squared Error) for stock prices and accuracy/F1 score for customer behavior.

4. Documentation and Examples

   • Provide comprehensive documentation explaining each model, how it works, and how to use it in FinVeda.
   • Include example scripts demonstrating predictions for stock prices and customer behavior.

Libraries and Frameworks

• TensorFlow or PyTorch for model implementation
• Pandas and NumPy for data handling
• scikit-learn for preprocessing and evaluation metrics

Alternatives Considered

1. Use of Pre-trained Models

Instead of building models from scratch, pre-trained financial models from sources such as Hugging Face or TensorFlow Hub could be integrated. However, these models might lack flexibility for FinVeda’s specific requirements and may require extensive fine-tuning.

2. Simpler Statistical Models

Considered implementing traditional statistical methods, such as ARIMA or linear regression, for time series forecasting. Although these methods are computationally less intensive, they may not capture complex patterns as effectively as neural network-based models, especially for non-linear financial data.

3. Automated Machine Learning (AutoML) Tools

Using AutoML tools (e.g., Google AutoML or H2O.ai) could simplify model building and tuning. However, this may limit customization options, and the cost of certain AutoML services could be prohibitive if the project requires extensive experimentation.

Screenshots/Logs

No response

Additional Information

• [X] I have searched for existing feature requests
• [X] I am willing to help implement this feature
• [X] I can provide more details or clarification if needed

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