MusicRecommendation

Recommendation System

Problem Definition

• The context - Why is this problem important to solve?
• The objectives - What is the intended goal?
• The key questions - What are the key questions that need to be answered?
• The problem formulation - What is it that we are trying to solve using data science? Data Exploration
• Data Description - What is the background of this data? What does it contain?
• Observations & Insights - What are some key patterns in the data? What does it mean for the problem formulation? Are there any data treatments or pre-processing required? Proposed approach
• Potential techniques - What different techniques should be explored?
• Overall solution design - What is the potential solution design?
• Measures of success - What are the key measures of success to compare potential techniques?

• Refined insights - What are the most meaningful insights from the data relevant to the problem?
• Comparison of various techniques and their relative performance - How do different techniques perform? Which one is performing relatively better? Is there scope to improve the performance further?
• Proposal for the final solution design - What model do you propose to be adopted? Why is this the best solution to adopt?

• Executive Summary - What are the key takeaways? What are the key next steps?
• Problem and solution summary - What problem was being solved? What are the key points that describe the final proposed solution design? Why is this a 'valid' solution that is likely to solve the problem?
• Recommendations for implementation - What are some key recommendations to implement the solutions? What are the key actionable for stakeholders? What is the expected benefit and/or costs? What are the key risks and challenges? What further analysis needs to be done or what other associated problems need to be solved?

1. Import package
2. Load dataset
   • Data Ingestion
   • Data Storage
   • Data Processing
   • Processing Orchestration
   • Data Hosting
3. Data Preparation
4. Split data, declare model class
5. Train Model
   • Basic Models, Deep Learning Model
   • Model Validation
6. Test and evaluate model
   • Frequentist AB Testing
   • Bayesian AB Testing
   • Multi-Armed Bandit
   • Impact Estimation
7. Productionization
8. Hosting
   • Model Hosting

Data Support