Predicting Student Exam Performance

Project Overview:

1. Collect or use a dataset with features like study_hours, sleep_hours, attendance_rate, and exam_score.
2. Task 1: Use Linear Regression to predict the exam_score based on study_hours.
3. Task 2: Use Decision Tree Classification to classify students as "Pass" or "Fail" based on a threshold score (e.g., 40 marks).
4. Bonus: Visualize how study hours impact performance using scatter plots and decision boundaries.

Branches for each member:

@GauravPatil04:

• feature/data-cleaning (shared)

• feature/linear-regression

• feature/scatter-plot

@sohamnr:

• feature/decision-tree

• feature/decision-boundary

• feature/final-report (shared)

⚠️ Important:

• Team members must work on their respective feature/* branches.
• All changes should be submitted via a Pull Request to the dev branch. Direct commits to the main branch are strictly prohibited.
• Merges to main will only occur after review, testing, and approval of all features from dev.