Problem Identification and Formulation
Learn how to identify and clearly formulate a machine learning problem worth solving.
Planning
Problem Definition
Scope
A well-defined problem is half solved. Your capstone should address a real-world challenge with clear inputs, desired outputs, and measurable success criteria. Consider business impact, technical feasibility, and data availability.
Milestone 1.1: Submit a 2-page problem statement including problem description, proposed solution approach, and expected outcomes.
# Problem formulation framework
problem_definition = {
"domain": "E-commerce recommendation system",
"problem_type": "Supervised learning - recommendation",
"input_data": "User behavior, product features, ratings",
"target_output": "Product recommendations ranked by relevance",
"business_impact": "Increase user engagement by 15%",
"success_metrics": ["Click-through rate", "Conversion rate", "User satisfaction"],
"constraints": ["Real-time inference < 100ms", "Cold start problem"],
"data_availability": "6 months of user interaction logs"
}
# Questions to validate your problem:
validation_checklist = [
"Is this problem valuable to solve?",
"Can machine learning provide a better solution?",
"Is sufficient data available or obtainable?",
"Are success criteria measurable?",
"Is the scope manageable for a capstone project?"
]
Business Case Development
Create a compelling business justification for your machine learning project.
Business Case Components:
• Current state analysis and pain points
• Proposed solution and benefits
• Cost-benefit analysis
• Risk assessment and mitigation
• Implementation timeline and resources
Deliverable: Business case document (3-4 pages) with executive summary, problem analysis, proposed solution, ROI projections, and implementation plan.
# Business case template
business_case = {
"executive_summary": {
"problem": "Manual fraud detection misses 20% of cases",
"solution": "ML-powered real-time fraud detection",
"expected_roi": "300% in first year"
},
"current_state": {
"annual_fraud_losses": "$2M",
"detection_accuracy": "80%",
"manual_review_cost": "$500K annually"
},
"proposed_solution": {
"ml_model_accuracy": "95% target",
"automation_level": "90% of cases",
"response_time": "< 100ms"
},
"financial_impact": {
"prevented_losses": "$1.6M annually",
"cost_savings": "$400K in manual review",
"implementation_cost": "$300K"
}
}
Success Metrics Definition
Define clear, measurable criteria for evaluating your project's success.
Success metrics should be SMART: Specific, Measurable, Achievable, Relevant, and Time-bound. Include both technical metrics (accuracy, precision, recall) and business metrics (ROI, user engagement, cost savings).
# Comprehensive success metrics framework
success_metrics = {
"technical_metrics": {
"model_performance": {
"accuracy": {"target": 0.92, "baseline": 0.85},
"precision": {"target": 0.90, "baseline": 0.80},
"recall": {"target": 0.88, "baseline": 0.75},
"f1_score": {"target": 0.89, "baseline": 0.77}
},
"system_performance": {
"inference_time": {"target": "< 50ms", "baseline": "500ms"},
"throughput": {"target": "1000 req/sec", "baseline": "100 req/sec"},
"uptime": {"target": "99.9%", "baseline": "95%"}
}
},
"business_metrics": {
"user_engagement": {
"click_through_rate": {"target": "15%", "baseline": "8%"},
"session_duration": {"target": "+25%", "baseline": "current"},
"user_retention": {"target": "80%", "baseline": "65%"}
},
"financial_impact": {
"cost_reduction": {"target": "40%", "measurement": "vs manual process"},
"revenue_increase": {"target": "12%", "timeframe": "6 months"},
"roi": {"target": "200%", "timeframe": "12 months"}
}
}
}
# Evaluation schedule
evaluation_timeline = {
"week_4": "Initial model baseline metrics",
"week_8": "Optimized model performance",
"week_10": "System integration testing",
"week_12": "Final business impact assessment"
}
Project Timeline and Milestones
Create a detailed project timeline with clear milestones and deliverables.
Timeline
Milestones
Deliverables
Week 1-2: Project planning and setup
Week 3-4: Data acquisition and exploration
Week 5-6: Data preprocessing and feature engineering
Week 7-8: Model development and experimentation
Week 9-10: Model optimization and validation
Week 11: Deployment and testing
Week 12: Documentation and presentation
import pandas as pd
from datetime import datetime, timedelta
# Project timeline with dependencies
milestones = [
{
"phase": "Planning",
"duration_weeks": 2,
"deliverables": [
"Problem statement document",
"Business case presentation",
"Project plan and timeline",
"Success metrics definition"
],
"success_criteria": "Stakeholder approval of project scope"
},
{
"phase": "Data Acquisition",
"duration_weeks": 2,
"deliverables": [
"Complete dataset with documentation",
"Exploratory data analysis report",
&