# CPSC 444 Competition Overview and Assignment Instructions

Welcome to the Kaggle competition for **CPSC 444 at the University of Illinois**. This competition is designed to evaluate your programming skills and proficiency with spatial data analysis. The goal is to predict maize hybrid performance across diverse environments using course materials and additional data as needed. This assignment contributes **20% of your final grade** and is an essential component for successful completion of the course.

## Competition Goals
- **Objective**: Predict maize hybrid performance in varying environments using provided datasets and your understanding of R and spatial data analysis.
- **Skills Evaluated**: Competency in programming, spatial data analysis, model accuracy, and exploration of advanced techniques for improving predictive performance.

## Assignment Instructions
Please follow these steps to participate in the competition:

1. **Access Competition Instructions**: Visit the Competition Website [INSERT LINK] for detailed instructions and data downloads.
2. **Deadline**: Final submissions are due by **December 18th, 2024, 11:55 p.m. Central Time**. No extensions will be granted.
3. **Accuracy Requirement**: Your model must achieve a minimum accuracy of **70%** to pass this assignment.
4. **Extra Points**: Additional credit is available if you:
   - Use neural networks or advanced machine learning models.
   - Incorporate supplementary data sources.
   - Combine different analytical techniques effectively.
   - Achieve the top accuracy in the competition leaderboard.

## Assignment Submission
- **Document Preparation**: Compose a PDF document describing your analysis, methodology, and code. This document should include:
   - A clear description of your approach and any data processing steps.
   - Your code and results, are presented in a way that clearly supports your solution.
- **File Naming**: Name your document as `LAST NAME-kaggle.pdf`.
- **Upload**: On the assignment page, click the **Submit Assignment** button to upload your PDF before the deadline.

### Assignment Grading Criteria
Your submission will be graded on the following criteria:

- **Clarity**: Is your explanation well-organized, clear, and easy to follow?
- **Accuracy**: Does your solution adequately address the problem and achieve the minimum accuracy requirement?


### Important Notes
- This is an individual assignment; no team submissions are allowed.
- Use only the provided data and additional datasets that are publicly available or pre-approved by the instructor.
- The highest level of academic integrity is expected. Plagiarism or collaboration without authorization will result in disciplinary actions as per university policy.

Good luck, and we look forward to seeing your innovative solutions and improvements in this competitive challenge!