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

13. A data analyst is developing a machine learning model to predict the sales revenue to be generated by each product being sold by a grocery shop. The training dataset has a lot of numeric variable that have different distributions as well las feature ranges. He was advised that he should use numeric transformations to transform the numeric features. Which of the following statements is/are true regarding some numeric transformations? Select all true.Yeo Johnson tranformation result in errors when the numeric feature contains negative valuesYeo Johnson transformation works on both negative and positive numbersBox Cox transformation result in errors when the numeric feature contains negative valuesBoth Cox Cox and Yeo Johnson transformation give error when the numeric values are floatsBoth Box Cox and Yeo Johnson transfomation transform the numeric fetures to normal distribution

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

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

Categorical DataIn which of the following situations will you have to deal with categorical data? (Please note that multiple options can be selected)You want to know whether or not the page load time affects the revenue.You want to know whether or not a more personalised search algorithm will have an impact on the conversion rate.You want to know whether or not employee efficiency is related to salary.You want to check whether or not altering the flow of your checkout funnel will lead to more purchases.

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