Build an end-to-end ML classification pipeline

Q: Build an end-to-end ML classification pipeline

This question evaluates a candidate's competency in building end-to-end tabular classification pipelines, including data loading and splitting, missing-value handling, categorical encoding, feature scaling, model training and comparison, hyperparameter tuning, metric-based evaluation, model persistence, and batch inference.

Q: How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

Q: What difficulty level is this interview question?

This is a medium difficulty ML System Design question, commonly asked during Technical Screen rounds at Nextdoor.

Q: What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at Nextdoor during technical interviews.

Question

End-to-End Tabular Classification Pipeline (Python)

Context

You are given a tabular dataset in a CSV file and asked to build an end-to-end machine learning pipeline for a classification problem. Assume the dataset contains a column named target (binary classification by default). You may extend to multiclass if desired.

Requirements

Load the data from CSV.
Create stratified train/validation/test splits (e.g., 60/20/20).
Handle missing values and encode categorical features.
Standardize numeric features.
Train a simple baseline model (e.g., Logistic Regression) and at least one stronger model (e.g., Gradient Boosting or a small neural network).
Tune key hyperparameters with cross-validation.
Report accuracy, precision, recall, and ROC-AUC on validation and test sets.
Persist the trained model and preprocessing steps.
Implement batch inference via a predict(input_csv_path, output_csv_path) function or CLI.

If you choose a neural network, include a correct training loop with optimizer initialization, forward pass, loss computation, backward pass, and optimizer step.

Deliverables

Clear, well-structured Python code (preferably using scikit-learn for classical models) with docstrings/comments.
A short explanation of design choices and how you would productionize this pipeline.

Build an end-to-end ML classification pipeline

Quick Overview

End-to-End Tabular Classification Pipeline (Python)

Context

Requirements

Deliverables

Solution

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