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Learn Challenge 1: Data Scaling | Scikit-learn
Data Science Interview Challenge

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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:

Task

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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.

  1. Use the StandardScaler class to standardize the dataset, which means transforming it to have a mean of 0 and a standard deviation of 1.
  2. Use the MinMaxScaler class to rescale the dataset. Ensure that after scaling, the feature values lie between -1 and 1.

Solution

# Import necessary libraries
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler, MinMaxScaler
import pandas as pd

# Load the iris dataset
iris = load_iris()
data = iris.data
feature_names = iris.feature_names

# Convert the dataset to a pandas DataFrame for easier operations
data_df = pd.DataFrame(data, columns=feature_names)

# 1. Scaling using StandardScaler
scaler_std = StandardScaler()
data_standardized = scaler_std.fit_transform(data_df)
data_standardized_df = pd.DataFrame(data_standardized, columns=feature_names)

# 2. Scaling using MinMaxScaler
scaler_minmax = MinMaxScaler(feature_range=(-1, 1))
data_minmax = scaler_minmax.fit_transform(data_df)
data_minmax_df = pd.DataFrame(data_minmax, columns=feature_names)

# Display the first few rows of transformed datasets
print("Original Data:\n", data_df.head())
print("\nStandardized Data:\n", data_standardized_df.head())
print("\nMin-Max Data:\n", data_minmax_df.head())

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How can we improve it?

Thanks for your feedback!

Section 7. Chapter 1
# Import necessary libraries
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler, MinMaxScaler
import pandas as pd

# Load the iris dataset
iris = load_iris()
data = iris.data
feature_names = iris.feature_names

# Convert the dataset to a pandas DataFrame for easier operations
data_df = pd.DataFrame(data, columns=feature_names)

# 1. Scaling using StandardScaler
scaler_std = ___()
data_standardized = scaler_std.___(data_df)
data_standardized_df = pd.DataFrame(data_standardized, columns=feature_names)

# 2. Scaling using MinMaxScaler
scaler_minmax = ___(feature_range=(___))
data_minmax = scaler_minmax.___(data_df)
data_minmax_df = pd.DataFrame(data_minmax, columns=feature_names)

# Display the first few rows of transformed datasets
print("Original Data:\n", data_df.head())
print("\nStandardized Data:\n", data_standardized_df.head())
print("\nMin-Max Data:\n", data_minmax_df.head())

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