# ====================================================================
# Week 12: Capstone Project
# Kmex Consult - R Data Science Course
# Open in RStudio and run each block with Ctrl+Enter (Cmd+Enter on Mac).
# ====================================================================

# Integrating all R skills into a professional project

# ====================================================================
# WEEK 12 OVERVIEW
# ====================================================================
# This is your chance to demonstrate complete mastery of R data science.
# PROJECT GOALS:
# ✓ End-to-end analysis (raw data to insights)
# ✓ Professional quality code
# ✓ Multiple analytical approaches
# ✓ Clear communication
# ✓ Reproducible results
# ✓ Portfolio-worthy deliverable
# CAPSTONE WORKFLOW:
# WEEK 12 SCHEDULE:
# MONDAY-TUESDAY (Days 1-2):
# - Choose dataset (provided or self-selected)
# - Exploratory Data Analysis
# - Initial visualization
# - Data cleaning plan
# WEDNESDAY-THURSDAY (Days 3-4):
# - Complete data cleaning
# - Statistical analysis
# - Build visualizations
# - Document findings
# FRIDAY-SATURDAY (Days 5-6):
# - Build ML models
# - Evaluate and compare
# - Tune best model
# - Write report
# SUNDAY-MONDAY (Days 7-8):
# - Finalize report
# - Create presentation
# - Practice presentation
# - Submit all deliverables
# DELIVERABLES CHECKLIST:
# 1. CLEANED DATASET
# ☑ CSV file with processed data
# ☑ Data dictionary (what each variable means)
# ☑ Documentation of cleaning decisions
# 2. R SCRIPTS
# ☑ 01_load_explore.R - Data loading and EDA
# ☑ 02_clean.R - Cleaning code
# ☑ 03_analysis.R - Statistical analysis
# ☑ 04_models.R - ML models
# ☑ 05_report.R - Generate report
# ☑ README.md - How to run everything
# 3. REPORTS/VISUALIZATIONS
# ☑ EDA Report (10-15 visualizations)
# ☑ Statistical Analysis Results
# ☑ Model Performance Comparisons
# ☑ Final Insights Document
# 4. PROFESSIONAL REPORT
# ☑ Executive Summary (1 page)
# ☑ Problem Statement
# ☑ Data Description
# ☑ Methodology
# ☑ Results and Findings
# ☑ Conclusions and Recommendations
# ☑ Appendix (code, tables)
# 5. PRESENTATION
# ☑ 10-15 minute presentation
# ☑ Slides with key findings
# ☑ Clear story arc
# ☑ Professional appearance
# ☑ Practice beforehand
# PROJECT IDEAS:
# Dataset Options:
# - Housing Prices (kaggle.com/c/house-prices-advanced-regression-techniques)
# - Titanic Survival (kaggle.com/c/titanic)
# - Iris Classification (built-in, classic)
# - Customer Churn (find on Kaggle)
# - Heart Disease (UCI ML repository)
# - Stock Market Data (get with yfinance)
# - Business Data (find internal sources)
# YOUR REPOSITORY STRUCTURE:


capstone_project/
├── README.md (Project overview)
├── data/
│ ├── raw/
│ │ └── dataset.csv (Original data)
│ └── processed/
│ └── clean_data.csv (Cleaned data)
├── notebooks/
│ ├── 01_exploration.Rmd
│ ├── 02_analysis.Rmd
│ └── 03_models.Rmd
├── scripts/
│ ├── functions.R (Custom functions)
│ └── config.R (Settings)
├── results/
│ ├── figures/
│ │ ├── distribution.png
│ │ ├── correlation.png
│ │ └── ...
│ └── tables/
│ └── model_comparison.csv
├── report/
│ ├── final_report.docx
│ ├── presentation.pptx
│ └── data_dictionary.csv
└── .gitignore

BEST PRACTICES:

Code Quality:
✓ Use meaningful variable names
✓ Comment complex logic
✓ Follow consistent style
✓ DRY principle (Don't Repeat Yourself)
✓ Use functions for reusable code
✓ Handle errors gracefully

Reproducibility:
✓ Set seed: set.seed(42)
✓ Relative paths: "data/raw/file.csv"
✓ Document dependencies: library() calls
✓ Specify versions if needed
✓ No hardcoded file paths
✓ Can run entire analysis fresh

Documentation:
✓ README explains purpose
✓ Code comments explain why
✓ Data dictionary explains what
✓ Report explains findings
✓ Presentation tells story

GRADING RUBRIC (100 points):

Data Preparation (15%):
- Cleaning documented
- Handling of missing data justified
- Appropriate transformations
- Final dataset quality

Exploratory Analysis (15%):
- Thorough visualization
- Statistical summaries
- Insights documented
- Pattern identification

Statistical Analysis (15%):
- Appropriate tests chosen
- Assumptions checked
- Results interpreted correctly
- Effect sizes reported

Machine Learning (20%):
- Multiple models compared
- Evaluation metrics used
- Model selection justified
- Interpretable results

Code Quality (15%):
- Clean, readable code
- Well organized
- Reproducible
- Commented appropriately

Report & Presentation (20%):
- Professional writing
- Clear visualizations
- Compelling story
- Actionable recommendations
- Effective delivery

FINAL TIPS:

1. START EARLY - Don't wait until Sunday!
2. EXPLORE THOROUGHLY - You'll find better insights
3. CLEAN CAREFULLY - 70% of time here
4. TRY MULTIPLE MODELS - Compare, don't just use one
5. INTERPRET RESULTS - Numbers mean nothing without context
6. TELL A STORY - Guide reader through your analysis
7. ASK QUESTIONS - What surprises you in the data?
8. VISUALIZE WELL - Graphics communicate better than tables
9. DOCUMENT DECISIONS - Why you chose each approach
10. PRACTICE PRESENTATION - Rehearse beforehand

COMMON MISTAKES TO AVOID:

✗ Using test data during training
✗ Ignoring class imbalance
✗ Over-interpreting results
✗ No cross-validation
✗ Forgetting to scale features
✗ Not checking model assumptions
✗ Perfect accuracy (sign of data leakage)
✗ Too much jargon in report
✗ Weak visualizations
✗ No business interpretation

WHAT "EXCELLENT" LOOKS LIKE:

1. Insightful findings that surprise/inform
2. Multiple valid approaches compared
3. Clear decision trail documented
4. Professional presentation
5. Reproducible code
6. Actionable recommendations
7. Sophisticated analysis
8. Well-written report
9. Compelling presentation
10. Portfolio-ready quality

You're ready! Good luck with your capstone project!