Question 1

Figure from the original question paper You are working on a machine learning project and have received a dataset containing numeric and categorical features. The dataset has some missing values and potential outliers. Given the following data cleaning steps: 1. Use One-Hot Encoding for categorical variables. 2. Impute missing values with feature’s mean for numeric features. 3. Remove duplicates. 4. Standardize numeric features using Z-score normalization. 5. Identify and handle outliers using the IQR method. Which of the following represents the MOST appropriate sequence for preparing the data for a machine learning model? You’re working with a dataset that consists of training data (‘train\_data’) and test data (‘test\_data’). The dataset contains both numerical and categorical features. You decide to employ a combination of ‘StandardScaler’ (for numerical columns) and ‘OneHotEncoder’ (for categorical columns) from ‘scikit-learn’ using the ‘ColumnTransformer’ utility. Which of the following actions is MOST likely to introduce data leakage or potential modeling issues?