7. A data scientist is preparing the training data for a regression model that will estimate the resale value of a used car. The data contains the following set of key features: - Resale price ($1,500 - $50,000) - Build year (2005 - 2021) - Mileage (100 Km - 200,000 Km) - Transmission (Automatic/Manual) - Fuel Type (Petrol/Diesel) - Engine Size (1.3L - 2.5L) The dataset follows a nearly normal distribution and has few outliers. Which combination of methods is the most appropriate way of preparing the data effectively?Use a Max Absolute scaler on Build year, Mileage, and Engine Size. Perform one-hot encoding on Transmission and Fuel Type.Use a One-hot encoder on Build year, Mileage, and Engine Size. Perform Standard scaling on Transmission and Fuel Type.Use a row normalizer on Build year, Mileage, and Engine Size. Perform ordinal encoding on Transmission and Fuel Type.Use a Standard scaler on Build year, Mileage, and Engine Size. Perform one-hot encoding on Transmission and Fuel Type.
Question
- A data scientist is preparing the training data for a regression model that will estimate the resale value of a used car. The data contains the following set of key features: - Resale price (50,000) - Build year (2005 - 2021) - Mileage (100 Km - 200,000 Km) - Transmission (Automatic/Manual) - Fuel Type (Petrol/Diesel) - Engine Size (1.3L - 2.5L) The dataset follows a nearly normal distribution and has few outliers. Which combination of methods is the most appropriate way of preparing the data effectively?Use a Max Absolute scaler on Build year, Mileage, and Engine Size. Perform one-hot encoding on Transmission and Fuel Type.Use a One-hot encoder on Build year, Mileage, and Engine Size. Perform Standard scaling on Transmission and Fuel Type.Use a row normalizer on Build year, Mileage, and Engine Size. Perform ordinal encoding on Transmission and Fuel Type.Use a Standard scaler on Build year, Mileage, and Engine Size. Perform one-hot encoding on Transmission and Fuel Type.
Solution
The most appropriate way of preparing the data effectively is to use a Standard scaler on Build year, Mileage, and Engine Size. Perform one-hot encoding on Transmission and Fuel Type.
Here's why:
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Standard Scaler: This method standardizes features by removing the mean and scaling to unit variance. It's a common requirement for many machine learning estimators. Since the dataset follows a nearly normal distribution and has few outliers, using a Standard Scaler on numerical features like Build year, Mileage, and Engine Size is appropriate.
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One-hot encoding: This is a process of converting categorical data variables so they can be provided to machine learning algorithms to improve predictions. With one-hot, we convert each categorical value into a new categorical column and assign a binary value of 1 or 0. Transmission and Fuel Type are categorical features, so one-hot encoding is suitable.
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