15. A data analyst wants to train a machine learning model. While conducting exploratory data analysis, the analyst noticed that there is a categorical column in the data called `size`, which contains the categories "big," "medium," and "small." The analyst wants to encode these categories into numerical values to avoid errors when training the machine learning model. The analyst is unsure which encoding method to use for this variable and has reached out to you for help. Which of the following encoding methods would you suggest as the most appropriate in this situation?Count Frequency encodingOrdinal encodingOne Hot EncodingNone of the above

Which of the following is a method for handling categorical variables in a supervised learning model?Review LaterOne-hot encodingLabel encodingbinary encodingall the above

Which of the following is a valid method for handling categorical data in ML?*1 pointo A) One-hot encodingo B) Mean normalizationo C) Log transformationo D) Principal Component Analysis

Based on the above identified features, encode the categorical values. Fill in the neccessary dictionaries below.

A data analyst wants to train a machine learning model to predict the salary of new hires. The training data contains both categorical and numeric features. Which of the following statements is correct regarding categorical and numeric features?Box Cox tranformation and Yeo Johnson transformation can be used on both numeric and categorical featuresBox cox tranformation is used on categorical features while Standard Scaler is used on numerical featuresMin Max Scaler is used on both numeric and categorical columnsYeo Johnson transfomation is used on numeric features while One hot encoder is used on categorical columns

Which of the following datatype is most appropriate for storing a string of up to 255 characters?