# 🔮 Demand Forecasting App A powerful Streamlit-based web application for demand forecasting that combines the strengths of Prophet and N-Beats models, enhanced with Gemini AI for intelligent pattern detection and analysis. ## ✨ Features - **🔄 Multi-Model Forecasting**: - **Prophet**: Meta's time series forecasting tool that handles seasonality and holiday effects - **N-Beats**: Deep neural architecture specifically designed for time series forecasting with interpretable outputs - **📊 Interactive Analysis**: - Upload CSV files with historical sales data - Visualize forecasts with interactive charts - Compare different forecasting models - **🤖 AI-Powered Insights**: - Gemini Bot for image-based trend analysis (forecast visualizations) - Gemini Bot for data-based Q&A (CSV analysis) - **💾 Export Options**: - Download forecasts as CSV - Save forecast visualizations as images ## 🧠 Understanding the Models ### Prophet Prophet is a forecasting procedure developed by Meta (formerly Facebook) that: - Automatically detects seasonal patterns (daily, weekly, yearly) - Handles missing data and outliers gracefully - Accommodates holiday effects and special events - Uses a decomposable model with trend, seasonality, and holiday components - Is particularly effective for business forecasting with multiple seasonalities ### N-Beats (Neural Basis Expansion Analysis for Time Series) N-Beats is a deep learning approach that: - Uses deep neural networks specifically designed for time series - Provides interpretable forecasts through trend and seasonality decomposition - Often outperforms traditional statistical methods for complex patterns - Can capture non-linear relationships in your data - Works well even with limited historical data ## 🛠️ Tech Stack - **Backend**: Python - **ML Models**: Prophet, N-Beats (TensorFlow/Keras) - **AI Integration**: Google Gemini API - **Frontend**: Streamlit - **Data Processing**: Pandas, NumPy - **Visualization**: Matplotlib ## 🎬 Demo https://github.com/user-attachments/assets/9fc5bf11-8f60-4780-8e4d-2280cb09943a ## ⚙️ Installation 1. **Clone the repository**: ```bash git clone https://github.com/yourusername/demand-forecasting-app.git cd demand-forecasting-app ``` 2. **Create and activate a virtual environment**: ```bash python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate` ``` 3. **Install dependencies**: ```bash pip install -r requirements.txt ``` 4. **Set up environment variables**: - Create a `.env` file in the root directory - Add your Google API key: ``` GOOGLE_API_KEY=your_api_key_here ``` 5. **Run the application**: ```bash streamlit run multipage.py ``` ## 🚀 Usage ### Generate Forecasts 1. Upload a CSV file with `Date` and `Sales` columns 2. Select forecast end date 3. Choose between Prophet or N-Beats model 4. View and download forecasts ### Chat with Image 1. Upload forecast visualization images 2. Get AI-powered analysis of trends and patterns ### Chat with Data 1. Upload your sales data (CSV or Excel) 2. Ask questions about trends, anomalies, and insights ## 📋 Data Requirements CSV files must contain: - A `Date` column (formats supported: `DD-MM-YYYY`, `MM/DD/YYYY`, etc.) - A `Sales` column with numerical values

📝 Example CSV format

``` Date,Sales 01/01/2023,100 01/02/2023,150 01/03/2023,200 ```

## 📂 File Structure

View project structure

``` demand-forecasting-app/ ├── main.py # Main forecasting functionality ├── multipage.py # Multi-page app configuration ├── image_bot.py # Image analysis with Gemini ├── data_bot.py # Data analysis with Gemini ├── prophet_script.py # Prophet model utilities ├── nbeats.py # N-Beats model implementation ├── .env.example # Environment variables template ├── requirements.txt # Dependencies └── README.md # This file ```

## 📸 Screenshots

Project Map

Forecast Generation

Graph Analysis

Data Analysis

## 🤔 When to Use Each Model | Factor | Prophet | N-Beats | |--------|---------|---------| | **Data Size** | Works well with limited data | Better with more historical data | | **Seasonality** | Excellent at detecting multiple seasonal patterns | Good for complex, non-linear seasonality | | **Noise Handling** | Robust to missing data and outliers | May need cleaner data | | **Computation** | Faster training | More computationally intensive | | **Best For** | Business data with clear seasonal patterns | Complex relationships and patterns | ## 👤 Author For any questions or issues, please open an issue on GitHub: [@Siddharth Mishra](https://github.com/Sid3503) ---

Made with ❤️ and lots of ☕