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Data Interview Question

Reducing Order Errors

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Approach to Designing a System to Reduce Order Errors

1. Problem Understanding and Framing

  • Objective: Develop a machine learning model to minimize incorrect or missing orders on the DoorDash platform.
  • Types of Errors:
    • Incorrect orders (wrong items, wrong address, wrong time)
    • Missing orders (orders that are not fulfilled)
  • Stakeholders: Customers, Restaurants, DoorDash Operations
  • Key Metrics: Customer satisfaction, reduction in customer complaints, improvement in order accuracy, reduction in customer churn

2. Data Collection and Exploration

  • User Data: Order history, payment methods, addresses, user interactions
  • Restaurant Data: Menu items, operating hours, location, historical order accuracy
  • Contextual Data: Time of order, device used, location
  • Labeling:
    • 1: Wrong or missing orders
    • 0: Correct orders

3. Feature Engineering

  • Categorical Features:
    • User demographics, restaurant type
  • Continuous Features:
    • Order value, frequency of orders
  • Derived Features:
    • Time since last order, number of items in cart, anomalies in order patterns

4. Model Selection and Training

  • Classification Models:
    • Random Forest: Good for handling class imbalance and feature selection
    • XGBoost: Effective for large datasets with gradient boosting
    • Neural Networks: Useful if the dataset is extensive and requires complex pattern recognition
  • Training Strategy:
    • Train/validation/test split
    • Hyperparameter optimization (HPO)
    • Cross-validation

5. Model Evaluation

  • Metrics:
    • Recall: Proportion of true anomalies detected
    • Precision: Proportion of identified anomalies that are true anomalies
    • F1 Score: Balances recall and precision to provide an overall performance measure
    • AUC-ROC: To determine the optimal threshold for anomaly detection

6. Deployment and Monitoring

  • Real-time Anomaly Detection:
    • Implement a system to flag potential incorrect orders during the checkout process
    • Provide warnings or confirmation prompts for unusual orders
  • Feedback Loop:
    • Collect data on flagged orders and user interactions to continuously improve model accuracy
  • A/B Testing:
    • Gradual deployment to assess impact on order accuracy and customer satisfaction

7. Additional Considerations

  • Model Interpretability:
    • Ensure stakeholders understand the model's decision-making process
  • Operationalization:
    • Integrate with existing DoorDash systems for seamless real-time intervention
  • Customer Experience:
    • Balance between preventing errors and maintaining a smooth user experience

This approach ensures a comprehensive strategy to tackle the issue of order errors, leveraging data-driven insights and machine learning to enhance the reliability and efficiency of the platform.