Your submission will be evaluated using **Mean Squared Error (MSE):**

$$
MSE = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2
$$


where:  
- $$ \( y_i \) = actual \ number\  of\  booked\  nights\  in \ Q3 \ 2016  $$
- $$\( \hat{y}_i \) \ = \ predicted \ number \ of \ booked \ nights \  $$
- $$ \( n \) = \ total \ number \  of\  test \ properties\   $$

A **lower MSE** indicates a **more accurate model**.

## 📤 Submission File Format

For each `PropertyID` in the test set, you must predict the **total number of nights booked in Q3 2016**.  
Your submission file should be in **CSV format** and file should be named submission.csv.

 **📌 Submission File Example**

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### **📜 Submission Guidelines**
- The file **must** have a header row: `PropertyID_test,Pred`.
- Predictions should be **numeric values** representing the total number of nights booked in Q3.
- Each `PropertyID` from `PropertyID_test` must be included exactly **once**.
- Ensure there are **no missing values** in your predictions.
- The file should be saved as a **comma-separated values (.csv) file**.

### **🚨 Important Notes**
✅ The `PropertyID` column must match the test dataset.  
✅ The predictions should be **integers (no decimals)**.  
✅ The submission will be evaluated based on **Mean Squared Error (MSE)**.  

Make sure to **double-check your file format** before uploading! 🚀