Scaling Numerical Features Using RobustScaler in Pandas

Last update on December 21 2024 07:41:42 (UTC/GMT +8 hours)

Pandas: Machine Learning Integration Exercise-15 with Solution

Write a Pandas program to scale numerical features using Scikit-learn's RobustScaler.

This exercise shows how to scale numerical features using Scikit-learn's RobustScaler to reduce the effect of outliers.

Sample Solution :

Code :

import pandas as pd
from sklearn.preprocessing import RobustScaler

# Load the dataset
df = pd.read_csv('data.csv')

# Initialize the RobustScaler
scaler = RobustScaler()

# Apply RobustScaler to the 'Age' and 'Salary' columns
df[['Age', 'Salary']] = scaler.fit_transform(df[['Age', 'Salary']])

# Output the scaled dataset
print(df)

Output:

   ID      Name  Age  Gender    Salary  Target
0   1      Sara -0.8  Female -0.666667       0
1   2    Ophrah  0.2    Male  0.000000       1
2   3    Torben -1.4    Male  0.666667       0
3   4  Masaharu  1.2    Male  1.333333       1
4   5      Kaya  NaN  Female -0.333333       0
5   6   Abaddon  0.0    Male       NaN       1

Explanation:

• Loaded the dataset using Pandas.
• Initialized RobustScaler to scale features while reducing the influence of outliers.
• Applied RobustScaler to the 'Age' and 'Salary' columns.
• Displayed the scaled dataset.

Python-Pandas Code Editor:

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