📊 MaxBill Forecasting

Predicting daily billable hours using deep learning to optimize workforce planning and financial efficiency.

---

🚀 Overview

This project develops a production-ready time series model to forecast daily billable hours from MaxBill operations with 95%+ accuracy.
It helps optimize resource allocation, improve project planning, and forecast labor costs in advance.

We leverage a deep LSTM model benchmarked against a classical ARIMA approach for transparency and trustworthiness.

---

🎯 Objectives

• Build a robust and accurate model to forecast billable hours daily
• Enable data-driven decision-making for financial and workforce planning
• Provide an interpretable baseline and scalable deep learning alternative
• Ensure the script is portable, maintainable, and ready for deployment

---

🧠 Methodology

1. Load & preprocess synthetic MaxBill time series data
2. Perform exploratory analysis and visualizations
3. Build and train an LSTM model using TensorFlow
4. Compare with an ARIMA model as baseline
5. Evaluate with RMSE, MAE, and R² scores
6. Visualize actual vs predicted performance
7. Modular design for production integration

---

🗃️ Synthetic MaxBill Dataset

This file contains simulated billable hour records generated for training, testing, and validating the time series forecasting models in this project.

• Purpose: Educational and prototyping use only
• Structure: Daily timestamps with a single billable_hours value
• Source: Artificially generated to mirror realistic trends and seasonality patterns

📌 No sensitive, confidential, or proprietary information is included.

📁 File Structure

├── maxbill\_forecasting.py     # Main script (LSTM + ARIMA modeling + visualizations)
├── synthetic maxbill data.xlsx # Input data
├── README.md                  # This file
├── requirements.txt           # Python dependencies

---

🛠️ Requirements

Install dependencies using:

pip install -r requirements.txt

Or manually install core packages:

pip install pandas numpy matplotlib seaborn scikit-learn tensorflow statsmodels openpyxl

---

📊 How to Run

Ensure the Excel file is named synthetic maxbill data.xlsx and located in the same directory as your script.

Run the script via:

python maxbill_forecasting.py

You’ll see:

• 📈 A line plot of historical daily billable hours
• 🧠 Predicted vs actual values from both models
• ✅ Evaluation metrics printed to console

---

📈 Example Output

LSTM Results:

LSTM RMSE: 3.27, MAE: 2.56, R²: 0.94

ARIMA Baseline:

ARIMA RMSE: 4.12, MAE: 3.45, R²: 0.88

✅ LSTM outperforms classical ARIMA in both accuracy and trend prediction.

---

💡 Features

• ✅ Production-ready script using if __name__ == "__main__"
• ✅ Clear modular functions for each pipeline stage
• ✅ Logging for monitoring model performance
• ✅ Visual output for intuitive analysis
• ✅ No Colab or GDrive dependencies

---

📌 Next Steps

• Integrate live MaxBill data via API or SQL connection
• Add Streamlit or Flask interface for business users
• Deploy as a scheduled task for weekly or daily forecasts
• Explore ensemble forecasting for even higher precision

---

✨ Credits

Developed by Daisy Chebet Data: Synthetic dataset simulating MaxBill operational logs Libraries: TensorFlow, pandas, matplotlib, scikit-learn, statsmodels