Feature scaling is a method used to normalize the range of independent variables or features of data. In data processing, it is also known as data normalization and is generally performed during the data preprocessing step.

The necessity of feature scaling in a dataset can be justified with the following points:

1. **Different Measurement Scales**: Features in the dataset can be measured in different units, such as age (in years), income (in dollars), and height (in cm or inches). These features have different scales and might have a large difference in their ranges. Algorithms that use Euclidean Distance measure are sensitive to these scales and can underperform if scales are not normalized.

2. **Speeds up Convergence**: Many machine learning algorithms use gradient descent as an optimization technique. Feature scaling can speed up the convergence of these algorithms.

3. **Prevents Overfitting**: Feature scaling helps to prevent overfitting by ensuring that all features contribute equally to the model.

4. **Improves Model Performance**: Some machine learning algorithms, like support vector machines (SVM) and k-nearest neighbors (KNN), perform better on scaled data.

5. **Helps in Visualization**: Feature scaling helps in data visualization as features will have the same scale on the graph.

Therefore, considering the above points, there is a need to do feature scaling in the provided customer dataset.

Question

Feature scaling is a method used to normalize the range of independent variables or features of data. In data processing, it is also known as data normalization and is generally performed during the data preprocessing step.

The necessity of feature scaling in a dataset can be justified with the following points:

1. **Different Measurement Scales**: Features in the dataset can be measured in different units, such as age (in years), income (in dollars), and height (in cm or inches). These features have different scales and might have a large difference in their ranges. Algorithms that use Euclidean Distance measure are sensitive to these scales and can underperform if scales are not normalized.

2. **Speeds up Convergence**: Many machine learning algorithms use gradient descent as an optimization technique. Feature scaling can speed up the convergence of these algorithms.

3. **Prevents Overfitting**: Feature scaling helps to prevent overfitting by ensuring that all features contribute equally to the model.

4. **Improves Model Performance**: Some machine learning algorithms, like support vector machines (SVM) and k-nearest neighbors (KNN), perform better on scaled data.

5. **Helps in Visualization**: Feature scaling helps in data visualization as features will have the same scale on the graph.

Therefore, considering the above points, there is a need to do feature scaling in the provided customer dataset.

Knowee AI · Accepted Answer