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Week 5: Handling Missing Values, Resampling, and Data Encoding in Data Science
- Week 5 focuses on critical preprocessing techniques in data science: handling missing values (MCAR, MAR, MNAR), resampling techniques (undersampling, oversampling, SMOTE), and data encoding.
- Missing values are gaps in datasets categorized into MCAR, MAR, and MNAR types, impacting analyses and models.
- Handling strategies include deleting rows with missing values, deleting columns with duplicates, and imputation methods like mean, median, or mode.
- Resampling techniques like undersampling, oversampling, and SMOTE are used to balance datasets for machine learning models.
- SMOTE is Synthetic Minority Over-sampling Technique that generates synthetic samples for the minority class to address class imbalance.
- SMOTE creates new samples based on existing minority class samples by interpolating between them, improving dataset balance.
- Encoding methods covered include Nominal/One-Hot, Label, Ordinal, and Target-Guided Ordinal Encoding.
- Preprocessing techniques are crucial for robust machine learning models, aiding in handling missing data, balancing datasets, and encoding categorical variables.
- Stay tuned for Week 6 where Machine Learning and AI topics will be explored further.
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