**Instructions**

1. You **must not perform any data cleaning or transformation**. The dataset is already clean and ready to use.
2. Use **any classification algorithm** seen during this or previous modules (KNN, Logistic Regression, Linear/Multiple Regression, KMeans (if creatively adapted), etc.).
3. **Do not waste time** showing how you imported the dataset or explored the DataFrame. Go **straight to your model** in your presentation.
4. You **must reach at least 85% accuracy** in your final model.
5. The Private Leaderboard position will account for 60% of the final grade.

**Presentation**
1. After the deadline, students will present and justify their model selection and configuration.
Each group must answer the following questions during the presentation:
- What model did you choose and why?
- How does your model work (basic explanation)?
- How did you tune hyperparameters?
- What would you try next if you had more time?

Please be concise. No need to show how you cleaned or imported the dataset.
Focus directly on the results and interpretation of your model.
Each student has **5 minutes** to present.
Please **turn on your camera** during the presentation if possible.
The presentation will account for 40% of the final grade.