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How to Impute Missing Values and Encode Target Variables Using Sklearn in Python

Imputation and Encoding Process

Condition for Imputing Missing Values and Encoding Target Variables

Description:
Imputation: Refers to the process of filling in missing values in a dataset. It is essential because machine learning models often require complete datasets without missing values.

Encoding: Refers to converting categorical variables into numerical representations because most machine learning models work with numeric data. This is done using Label Encoding or One-Hot Encoding.

Step-by-Step Process

Imputing Missing Values:
Use SimpleImputer from sklearn.impute to handle missing values. Common strategies for imputation include mean, median, or most_frequent.
Encoding Target Variables:
Label Encoding: Converts categories into numerical values (0, 1, 2...).
One-Hot Encoding: Creates binary columns for each category in the target variable.

Sample Source Code

# Code for Imputing Missing Values and Encoding Target Variables

import pandas as pd

from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split

data = {
'Age': [25, 30, 35, None, 40],
'Salary': [50000, None, 70000, 80000, None],
'Department': ['HR', 'IT', 'Finance', 'Marketing', 'HR'],
'Target': ['Yes', 'No', 'Yes', 'No', 'Yes']
}

df = pd.DataFrame(data)

print("Original Data:")
print(df)

# Impute missing values (Age and Salary columns)
imputer = SimpleImputer(strategy='mean')
df[['Age', 'Salary']] = imputer.fit_transform(df[['Age', 'Salary']])

# Display the DataFrame after imputation
print("\nData after Imputation:")
print(df)

# Encode the target variable ('Target' column) using Label Encoding
label_encoder = LabelEncoder()
df['Target'] = label_encoder.fit_transform(df['Target'])

# Display the DataFrame after encoding the target variable
print("\nData after Target Encoding:")
print(df)

# Splitting Data into Train and Test sets
X = df[['Age', 'Salary', 'Department']]
y = df['Target']

# Splitting the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

print("\nTraining Data:")
print(X_train)

print("\nTesting Data:")
print(X_test)

Screenshots

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