[Task 1] Implement Isolation Forest on the Shuttle Dataset

## Objective

Implement the Isolation Forest algorithm using scikit-learn on the Mammography dataset to understand the fundamentals of anomaly detection.

This is a learning exercise- focus on understanding the algorithm, experimenting with different settings, and analyzing the results.

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## Dataset

**Shuttle (ODDS Repository)**

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## Requirements

### Data Exploration
- Load and explore the dataset.
- Check for missing values.
- Perform any preprocessing you think is necessary.

### Model Implementation
- Train an Isolation Forest model.
- Explain the important hyperparameters you choose.
- Predict anomalies.

### Evaluation
Evaluate your model using the available labels.

Suggested metrics:
- Precision
- Recall
- F1-score
- ROC-AUC (optional)

### Experiments
Perform a few experiments to better understand the behavior of Isolation Forest.

Some ideas:
- Try different values of `contamination`.
- Experiment with different values of `n_estimators`.
- Change `max_samples`.
- Try the model with and without feature scaling.
- Compare with another anomaly detection algorithm (optional).

### Analysis
Write a short summary discussing:
- Your approach
- Important observations
- Which hyperparameters had the biggest impact
- Challenges faced (if any)

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## Deliverables

- Clean and well-documented code
- Jupyter Notebook (`.ipynb`)
- README describing your approach and findings

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**Note:** Please follow the repository's Pull Request template while submitting your solution.