Зміст курсу
Data Science Interview Challenge
Data Science Interview Challenge
Challenge 1: Data Scaling
In the realm of data science and machine learning, data scaling is a critical preprocessing step. It primarily involves transforming the features (variables) of the dataset to a standard scale, ensuring that each feature has a similar scale or range. This is especially significant for algorithms that rely on distances or gradients, as it ensures that all features contribute equally to the outcome and the algorithm converges more efficiently.
Here's a demonstration of how the scaling utilities from scikit-learn modify the data distribution:
Swipe to show code editor
In this task, you will be working with the popular Iris dataset. Your objective is to apply two types of scalers to the data and compare the resulting datasets.
- Use the
StandardScalerclass to standardize the dataset, which means transforming it to have a mean of0and a standard deviation of1. - Use the
MinMaxScalerclass to rescale the dataset. Ensure that after scaling, the feature values lie between-1and1.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Challenge 1: Data Scaling
In the realm of data science and machine learning, data scaling is a critical preprocessing step. It primarily involves transforming the features (variables) of the dataset to a standard scale, ensuring that each feature has a similar scale or range. This is especially significant for algorithms that rely on distances or gradients, as it ensures that all features contribute equally to the outcome and the algorithm converges more efficiently.
Here's a demonstration of how the scaling utilities from scikit-learn modify the data distribution:
Swipe to show code editor
In this task, you will be working with the popular Iris dataset. Your objective is to apply two types of scalers to the data and compare the resulting datasets.
- Use the
StandardScalerclass to standardize the dataset, which means transforming it to have a mean of0and a standard deviation of1. - Use the
MinMaxScalerclass to rescale the dataset. Ensure that after scaling, the feature values lie between-1and1.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Challenge 1: Data Scaling
In the realm of data science and machine learning, data scaling is a critical preprocessing step. It primarily involves transforming the features (variables) of the dataset to a standard scale, ensuring that each feature has a similar scale or range. This is especially significant for algorithms that rely on distances or gradients, as it ensures that all features contribute equally to the outcome and the algorithm converges more efficiently.
Here's a demonstration of how the scaling utilities from scikit-learn modify the data distribution:
Swipe to show code editor
In this task, you will be working with the popular Iris dataset. Your objective is to apply two types of scalers to the data and compare the resulting datasets.
- Use the
StandardScalerclass to standardize the dataset, which means transforming it to have a mean of0and a standard deviation of1. - Use the
MinMaxScalerclass to rescale the dataset. Ensure that after scaling, the feature values lie between-1and1.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
In the realm of data science and machine learning, data scaling is a critical preprocessing step. It primarily involves transforming the features (variables) of the dataset to a standard scale, ensuring that each feature has a similar scale or range. This is especially significant for algorithms that rely on distances or gradients, as it ensures that all features contribute equally to the outcome and the algorithm converges more efficiently.
Here's a demonstration of how the scaling utilities from scikit-learn modify the data distribution:
Swipe to show code editor
In this task, you will be working with the popular Iris dataset. Your objective is to apply two types of scalers to the data and compare the resulting datasets.
- Use the
StandardScalerclass to standardize the dataset, which means transforming it to have a mean of0and a standard deviation of1. - Use the
MinMaxScalerclass to rescale the dataset. Ensure that after scaling, the feature values lie between-1and1.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів