# ===================================================================
# MODULE 9, LESSON 1: CAPSTONE PROJECT OVERVIEW
# ===================================================================

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta
import warnings
warnings.filterwarnings('ignore')

# Set style
sns.set_style("whitegrid")
np.random.seed(42)

print("="*70)
print("CAPSTONE PROJECT: DIGITAL TRANSFORMATION IN BANKING")
print("="*70)

# ----------------------------------------------------------------
# PART A: PROJECT CHARTER
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Project Charter")
print("-"*60)

project_charter = """
--- PROJECT CHARTER: DIGITAL LENDING TRANSFORMATION ---

1. PROJECT TITLE: Digital Lending Transformation - Project Atlas

2. EXECUTIVE SUMMARY:
   Atlantic Bank seeks to modernise its lending operations by implementing 
   a data-driven, AI-powered underwriting platform. The project will reduce 
   loan approval times from days to hours, improve risk assessment accuracy, 
   reduce operational costs, and enhance customer satisfaction.

3. PROJECT OBJECTIVES:
   - Build a unified data platform for lending analytics.
   - Develop predictive models for default prediction and loan pricing.
   - Implement an automated underwriting decision engine.
   - Ensure regulatory compliance (SR 11-7, Fair Lending, GDPR).
   - Achieve 80% automation of loan applications.

4. SCOPE:
   - In-Scope: Personal and small business loans.
   - Out-of-Scope: Commercial real estate, large corporate loans.

5. SUCCESS CRITERIA:
   - Loan approval time < 1 hour for 80% of applications.
   - Model AUC > 0.80, KS > 0.35.
   - 80% automation rate.
   - 100% regulatory compliance.

6. SPONSOR:
   - Executive Sponsor: CEO
   - Project Sponsor: Head of Retail Banking
   - Project Manager: [Name]

7. TEAM:
   - Data Science Lead: [Name]
   - Data Engineering Lead: [Name]
   - ML Engineering Lead: [Name]
   - Risk & Compliance Lead: [Name]
   - Change Management Lead: [Name]

8. TIMELINE:
   - Assessment: 4 weeks
   - Data Foundation: 8 weeks
   - Model Development: 12 weeks
   - Integration: 8 weeks
   - Deployment: 4 weeks
   - Change Management: 8 weeks
   - Total: 12 months

9. BUDGET:
   - Personnel: $3.5M
   - Infrastructure: $1.0M
   - Data & External: $0.5M
   - Total: $5.0M

10. RISKS:
    - Data quality issues (High)
    - Regulatory rejection (Medium)
    - Talent shortage (Medium)
    - Change resistance (Medium)
"""

print(project_charter)

# ----------------------------------------------------------------
# PART B: STAKEHOLDER ANALYSIS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Stakeholder Analysis")
print("-"*60)

stakeholders = pd.DataFrame({
    'Stakeholder': ['CEO', 'Head of Retail Banking', 'Chief Risk Officer', 
                   'Head of IT', 'Compliance Officer', 'Loan Officers', 
                   'Customers', 'Regulators'],
    'Role': ['Executive Sponsor', 'Business Owner', 'Risk Oversight', 
             'Technology', 'Compliance', 'End Users', 'Beneficiaries', 'External'],
    'Interest (1-10)': [9, 10, 9, 8, 9, 7, 8, 10],
    'Influence (1-10)': [10, 9, 9, 8, 8, 5, 3, 9],
    'Engagement Strategy': [
        'Monthly updates, strategic alignment',
        'Weekly status, demos, business case',
        'Validation checkpoints, risk reporting',
        'Technical design reviews, integration planning',
        'Regular compliance checkpoints, documentation',
        'Training, user feedback sessions',
        'Communication, transparency, NPS tracking',
        'Quarterly reporting, transparency'
    ]
})

print("Stakeholder Analysis:")
print(stakeholders.to_string(index=False))

# ----------------------------------------------------------------
# PART C: PROJECT TIMELINE
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Project Timeline")
print("-"*60)

# Create a simple Gantt chart
def create_project_gantt():
    """Create a Gantt chart for the project."""
    phases = ['Assessment', 'Data Foundation', 'Model Development', 
              'Integration', 'Deployment', 'Change Management', 'Optimisation']
    start_weeks = [0, 4, 12, 24, 32, 36, 44]
    durations = [4, 8, 12, 8, 4, 8, 999]  # 999 = ongoing
    
    fig, ax = plt.subplots(figsize=(14, 6))
    
    # Create bars
    for i, (phase, start, duration) in enumerate(zip(phases, start_weeks, durations)):
        if duration == 999:
            # Ongoing
            ax.barh(i, 52 - start, left=start, height=0.6, 
                    color='steelblue', edgecolor='black', alpha=0.6, hatch='///')
            ax.text(start + (52 - start)/2, i, f'{phase} (Ongoing)', 
                    ha='center', va='center', fontsize=10, color='white', fontweight='bold')
        else:
            ax.barh(i, duration, left=start, height=0.6, 
                    color='steelblue', edgecolor='black', alpha=0.8)
            ax.text(start + duration/2, i, f'{phase} ({duration} weeks)', 
                    ha='center', va='center', fontsize=10, color='white', fontweight='bold')
    
    ax.set_yticks(range(len(phases)))
    ax.set_yticklabels(phases)
    ax.set_xlabel('Weeks')
    ax.set_title('Project Timeline (12 months)')
    ax.set_xlim(0, 60)
    ax.grid(True, alpha=0.3, axis='x')
    
    # Mark milestones
    milestones = [4, 12, 24, 32, 36, 44]
    milestone_labels = ['Phase 1 Complete', 'Phase 2 Complete', 'Phase 3 Complete', 
                        'Phase 4 Complete', 'Phase 5 Complete', 'Phase 6 Complete']
    for m, label in zip(milestones, milestone_labels):
        ax.axvline(x=m, color='red', linestyle='--', alpha=0.5, linewidth=0.5)
        ax.text(m, -0.5, label, rotation=90, ha='center', va='top', fontsize=8, color='red')
    
    plt.tight_layout()
    plt.savefig('project_gantt.png', dpi=300, bbox_inches='tight')
    plt.show()

create_project_gantt()
print("Project Gantt chart saved as 'project_gantt.png'")

# ----------------------------------------------------------------
# PART D: DATA INVENTORY
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Data Inventory")
print("-"*60)

data_inventory = pd.DataFrame({
    'Dataset': ['loan_applications.csv', 'customer_data.csv', 'transactions.csv', 
                'macro_data.csv', 'loan_performance.csv'],
    'Description': ['Historical loan applications with default labels', 
                    'Customer demographics and behaviour',
                    'Customer transaction history (3 years)',
                    'Quarterly macroeconomic indicators',
                    'Historical loan performance data'],
    'Rows (estimate)': ['100,000', '50,000', '1,000,000', '40', '50,000'],
    'Key Fields': ['application_id, applicant_age, income, credit_score, dti, loan_amount, loan_term, employment_years, home_owner, marital_status, education, purpose, default',
                   'customer_id, age, income, credit_score, account_balance, transaction_count, avg_transaction_amount, tenure, churn',
                   'transaction_id, customer_id, date, amount, category, merchant, channel',
                   'date, gdp_growth, unemployment, inflation, interest_rate, consumer_confidence',
                   'loan_id, origination_date, maturity_date, current_balance, delinquency_status, default_date'],
    'Status': ['Available', 'Available', 'Available', 'Available', 'Available']
})

print("Data Inventory:")
print(data_inventory.to_string(index=False))

# ----------------------------------------------------------------
# PART E: SUCCESS CRITERIA AND KPIS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: Success Criteria and KPIs")
print("-"*60)

success_criteria = pd.DataFrame({
    'Category': ['Business', 'Business', 'Business', 'Model', 'Model', 'Model', 
                 'Operational', 'Operational', 'Risk', 'Compliance'],
    'Metric': ['Loan approval time', 'Loan volume growth', 'Customer NPS', 
               'AUC', 'KS Statistic', 'Calibration p-value',
               'Automation rate', 'Cost per loan', 'Default rate', 'Validation findings'],
    'Target': ['< 1 hour (80%)', '+20% in Year 1', '> 60',
               '> 0.80', '> 0.35', '> 0.05',
               '80%', '-30%', '< 3%', '0 critical findings'],
    'Measurement': ['System logs', 'Origination system', 'Surveys',
                    'Validation', 'Validation', 'Validation',
                    'Decision engine', 'Financial system', 'Portfolio monitoring', 'Audit reports']
})

print("Success Criteria and KPIs:")
print(success_criteria.to_string(index=False))

# ----------------------------------------------------------------
# PART F: SUMMARY AND NEXT STEPS
# ----------------------------------------------------------------

print("\n" + "="*70)
print("PART F: Summary and Next Steps")
print("="*70)

print("""
CAPSTONE PROJECT OVERVIEW – KEY TAKEAWAYS:

1. Case Study: Atlantic Bank – a mid-sized bank modernising its lending operations.
2. Business Problem: Manual, slow, inconsistent loan origination.
3. Objectives: Speed, accuracy, automation, compliance, customer satisfaction.
4. Stakeholders: CEO, business owners, risk, IT, compliance, end users.
5. Timeline: 12 months across 7 phases.
6. Success Criteria: Clear KPIs for business, model, operational, and compliance performance.
7. Data: Simulated datasets covering loan applications, customers, transactions, macro data, and loan performance.

NEXT STEPS (Lesson 2):
- Data ingestion and exploratory analysis.
- Data quality assessment and preprocessing.
- Feature engineering.
- Building initial baseline models.
- Data visualisation and insights.

PREPARATION FOR LESSON 2:
- Review the data dictionary.
- Set up your Python environment (pandas, numpy, matplotlib, seaborn, scikit-learn).
- Download the datasets (provided).
- Familiarise yourself with the project objectives.
""")

print("="*70)
print("END OF LESSON 1 – MODULE 9")
print("="*70)