Normalizing numerical data using Min-Max scaling in Pandas

Last update on December 21 2024 07:41:36 (UTC/GMT +8 hours)

Pandas: Machine Learning Integration Exercise-7 with Solution

Write a Pandas program that normalizes numerical data using Min-Max scaling.

This exercise demonstrates how to normalize numerical features using Min-Max scaling.

Sample Solution :

Code :

import pandas as pd
from sklearn.preprocessing import MinMaxScaler

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

# Initialize the MinMaxScaler
scaler = MinMaxScaler()

# Apply Min-Max scaling 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  0.230769  Female  0.000000       0
1   2    Ophrah  0.615385    Male  0.333333       1
2   3    Torben  0.000000    Male  0.666667       0
3   4  Masaharu  1.000000    Male  1.000000       1
4   5      Kaya       NaN  Female  0.166667       0
5   6   Abaddon  0.538462    Male       NaN       1

Explanation:

• Loaded the dataset using Pandas.
• Initialized the MinMaxScaler from Scikit-learn.
• Applied Min-Max scaling to the 'Age' and 'Salary' columns, transforming them into a range between 0 and 1.
• Displayed the normalized dataset.

Python-Pandas Code Editor:

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