Data Preprocessing and Handling for AI Questions

Covers the end to end preparation of raw data for analysis and modeling in machine learning and artificial intelligence. Topics include data collection and ingestion, data quality assessment, detecting and handling missing values with deletion or various imputation strategies, identifying and treating outliers, removing duplicates, and standardizing formats such as dates and categorical labels. Includes data type conversions, categorical variable encoding, feature scaling and normalization, standardization to zero mean and unit variance, and guidance on when each is appropriate given model choice. Covers feature engineering and selection, addressing class imbalance with sampling and weighting methods, and domain specific preprocessing such as data augmentation for computer vision and text preprocessing for natural language processing. Emphasizes correct order of operations, reproducible pipelines, splitting data into training validation and test sets, cross validation practices, and documenting preprocessing decisions and their impact on model performance. Also explains which models are sensitive to feature scale, common pitfalls, and evaluation strategies to ensure preprocessing does not leak information.

MediumTechnical

67 practiced

Describe a practical text preprocessing pipeline for a binary classification problem (e.g., spam detection). Include steps such as tokenization, lowercasing, punctuation handling, stopword removal, lemmatization/stemming, n-grams, vectorization (TF-IDF, hashing), and mention libraries you'd use (spaCy, scikit-learn). Indicate when raw text features might still be useful.

MediumTechnical

73 practiced

Describe how preprocessing steps (imputation, scaling, encoding) should be integrated into cross-validation to avoid data leakage. Provide an example design using scikit-learn's Pipeline and ColumnTransformer and explain why computing means or encodings on the whole dataset before CV is a mistake.

HardTechnical

74 practiced

For building a transformer-based classifier on long customer support tickets, describe preprocessing choices: tokenizer selection (BPE/subword), truncation vs sliding window strategies for long docs, special token handling, how to handle metadata fields, and how to ensure tokenization/versioning consistency between training and production.

EasyTechnical

73 practiced

Describe a step-by-step approach to standardize and normalize date and time data coming from multiple sources in Excel or Power BI. Cover parsing ambiguous formats (MM/DD/YYYY vs DD/MM/YYYY), timezone normalization to UTC, daylight saving considerations, and how you'd implement these steps in Power Query (or Excel functions).

HardTechnical

91 practiced

Describe methods to detect and mitigate label noise in a large dataset: ensemble disagreement/uncertainty, confident learning, human-in-the-loop relabeling for samples with high model disagreement, and robust loss functions. Which methods are practical if you have limited labeling budget?

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