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CaféCast ☕📊

CaféCast is a data-focused platform designed to help users explore and analyze datasets using AI-driven insights. This project currently focuses on manual data cleaning, shaping, and model training workflows, with plans to evolve into a fully interactive and user-friendly platform.

Features 🚀

  • Cross-Platform Model Execution:
    • Supports TensorFlow (CPU) for macOS users to ensure compatibility and efficient local execution.
    • Utilizes PyTorch (CUDA) for Windows users with GPU support for accelerated training and predictions.
    • Automatic detection of the operating system to ensure the correct model implementation is selected.
  • Data Cleaning and Shaping:
    • Manual workflows for preparing datasets for analysis.
  • Model Training:
    • Advanced LSTM-based models with Bayesian optimization and iterative tuning for precise forecasts.
    • Time Series Transformers for capturing complex temporal dependencies.
    • Classical ARIMA modeling for interpretable, short-term linear trend forecasts.
  • Enhanced Model Flexibility:
    • Configurable hyperparameters to suit the unique characteristics of each dataset.
    • Dynamic support for both platform-optimized and manually fine-tuned workflows.
  • Data Visualization: Visual insights into sales trends, seasonality, and demand patterns.

Purpose and Motivation 🎯

The primary objective of CaféCast is to test my knowledge, challenge myself, and practice advanced machine learning techniques. Through this project, I aim to:

  • Deepen Understanding: Dive into various machine learning models, including LSTM, Transformers, and ARIMA.
  • Refine Skills: Gain hands-on experience in hyperparameter fine-tuning using Bayesian optimization and manual iterative tuning.
  • Explore Interpretability: Learn and apply techniques like SHAP values and attention mechanisms to make models more transparent.
  • Emphasize Learning: Approach this as a learning process, with the goal of improving my practical skills in applying machine learning models to real-world forecasting tasks.

This project is a testament to continuous learning and experimentation in the field of AI and machine learning.

Models and Methodologies 📘

1. LSTM with Bayesian Optimization

  • Automatically tunes hyperparameters, including:
    • Learning rate
    • Number of layers and neurons per layer
    • Dropout rates
  • Strikes a balance between exploration and exploitation to achieve efficient and optimal configurations.

2. Iterative LSTM Tuning

  • A hands-on approach for refining models through:
    • Adjustments to sequence length and batch size
    • Monitoring validation error trends
    • Incremental parameter tuning based on observed performance

3. Time Series Transformers

  • Uses self-attention mechanisms to:
    • Capture long-term dependencies and seasonality
    • Model complex temporal patterns in sales data
  • Supports multi-target predictions for key metrics like sales and revenue.

4. ARIMA Model

  • A classical time series model for capturing linear trends and seasonality.
  • Complements deep learning models for hybrid forecasting strategies.

Recent Enhancements 🌟

  1. Platform-Specific Execution:

    • macOS: TensorFlow-based models optimized for CPU execution.
    • Windows: PyTorch-based models leveraging CUDA for GPU acceleration.
    • Automatic logging to indicate which implementation is running.
    • Note: this is all for training of the model, mainly just convenience for me :D

  2. Broader Prediction Capabilities:

    • Added support for handling multiple predictions across various datasets.
    • Enhanced flexibility to adapt to different temporal forecasting requirements.

  3. Improved Model Interpretability:

    • SHAP values for LSTM and ARIMA models to explain predictions.
    • Attention weights in Transformer models to provide insights into feature importance.

Future Expansions 🚀

CafeCast is set to evolve into a more interactive and user-friendly platform with the following key features:

1. User-Focused Frontend Deployment

  • A dedicated web-based interface for seamless interaction.
  • Users can upload datasets directly through the frontend, removing the need for manual backend processes.
  • A clean and intuitive design that prioritizes simplicity and accessibility.
  • Technologies considered include React, Next.js, or other modern web frameworks for dynamic and responsive user experiences.

2. AI-Driven Q&A Functionality

  • Users will be able to ask questions about their uploaded data through the frontend.
  • The platform will leverage pre-trained models saved on the backend to provide accurate and meaningful answers.
  • Natural language processing (NLP) capabilities will interpret user queries and deliver real-time responses.
  • Support for follow-up queries to explore datasets more deeply.

3. Automated Data Cleaning and Preprocessing

  • Automate the current manual data cleaning workflow.
  • The platform will:
    • Validate datasets upon upload.
    • Detect and resolve missing values, outliers, and inconsistencies automatically.
    • Present a preview of cleaned data for user confirmation.

4. Model Training and Customization

  • Allow users to train or fine-tune models directly through the platform using their uploaded datasets.
  • Include visualization tools for monitoring training progress, accuracy, and performance metrics.
  • Ensure that users can easily customize their models to suit specific use cases.

Tech Stack 🛠️

  • Programming Language: Python
  • Core Libraries:
    • NumPy, Pandas for data manipulation
    • Matplotlib for visualization
    • TensorFlow (CPU) for macOS
    • PyTorch (CUDA) for Windows
    • scikit-learn and statsmodels for preprocessing and ARIMA modeling
  • Optimization Techniques:
    • Bayesian Optimization for automated hyperparameter tuning
    • Iterative tuning for manual refinement of models
  • Model Interpretability: SHAP, Attention Mechanisms
  • Environment: Tested on macOS and Windows with hardware-optimized configurations

Installation 💻

  1. Clone the repository: 
    git clone https://github.com/freddysongg/cafecast.git
cd cafecast
  2. Create a virtual environment: 
    python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
  3. Install dependencies: 
    pip install -r requirements.txt # On Windows: add in pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124 for Torch-CUDA compatability

Usage 💡

  1. Run the Application: 
    python src/main.py
  2. Menu Options: 1: Run LSTM Model 2: Run Time Series Transformer Model 3: Run Bayesian LSTM Optimization 4: Clear LSTM Model Parameters 5: Clear Transformer Model Parameters 6: Run ARIMA Model
  3. Run Forecasts: Generate detailed predictions for various time frames and visualize results.
  4. Analyze Results: Use visualizations and SHAP-based interpretability tools to gain insights into trends and model behavior.

Project Structure 📂

cafecast/
├── data/               # Sample datasets and preprocessing scripts
├── logs/               # Logging files for training and debugging
├── models/             # Model definitions and training scripts
├── params/             # Hyperparameter files and configurations
├── src/                # Main application code and model implementations
├── venv/               # Virtual environment for dependency management
├── requirements.txt    # Project dependencies
└── README.md           # Project documentation

License 📜

This project is licensed under the MIT License. See the LICENSE file for details.