---
title: TurnoverForecasting
emoji: 📊
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 5.22.0
app_file: app.py
pinned: false
license: mit
short_description: Forecasting SAP SE Revenue with AI
---

# 📊 AI-Powered Turnover Forecasting for SAP SE

## 🚀 Project Overview

This project delivers **AI-driven revenue forecasting** for **SAP SE** using a **univariate SARIMA model**.
It shows how accurate forecasts can be built from limited data (just historical turnover).

---

## 🏢 Why SAP SE?

- SAP SE is a **global leader in enterprise software**
- Revenue forecasts support **strategic planning & growth**
- Perfect case for **AI-powered financial forecasting**

---

## 🧠 Model Details

- **Model type**: SARIMA (Seasonal ARIMA)
- **Trained on**: SAP SE revenue from Top 12 German Companies Dataset (Kaggle)
- **SARIMA Order**: (3, 1, 5)
- **Seasonal Order**: (0, 1, 0, 12)
- **Evaluation Metric**: MAE (Mean Absolute Error)
- **Validation**: Walk-forward validation with test set (last 10%)

---

## ⚙️ How to Use

```python
import pickle

with open("sarima_sap_model.pkl", "rb") as f:
    model = pickle.load(f)

forecast = model.forecast(steps=4)
print(forecast)
```

## 📌 Intended Use & Limitations
👍 Forecast SAP SE revenue for next 1–6 quarters  
📈 Great for univariate, seasonal time series  
🚫 Not suitable for multivariate or non-seasonal data  
⚠️ Requires careful preprocessing (e.g., stationarity)

👨‍💻 Author: Pranav Sharma