Balancing Imbalanced Datasets with SMOTE: A Python Example Using imbalanced-learn

Python

In this example we're using the SMOTE class from the imblearn library to oversample the minority class in our dataset. First we load our dataset and separate the features and target variable. Then we create a SMOTE object and use its fit_resample() method to perform the oversampling. This method takes the features and target variable as input and returns the resampled data.

 1|  from imblearn.over_sampling import SMOTE
 2|  import pandas as pd
 3|  
 4|  # Load dataset
 5|  data = pd.read_csv('titanic.csv')
 6|  
 7|  # Separate features and target variable
 8|  X = data.drop('Survived', axis=1)
 9|  y = data['Survived']
10|  
11|  # Perform SMOTE to oversample the minority class
12|  smote = SMOTE()
13|  X_resampled, y_resampled = smote.fit_resample(X, y)
Greg He
Python | Imbalanced-learn | Smote
Did you find this snippet useful?

Sign up for free to to add this to your code library

Remove Stop Words from Text in DataFrame Column
Python

Pandas | Nltk | Nlp

133
LightGBM Custom Loss Function
Python

LightGBM | Loss function

121
Tuning XGBoost Hyperparameters with Grid Search
Python

XGBoost | Tuning

116
How to Convert DataFrame Values Into Percentages
Python

Lambda | Percentages | Pipe

109
How to Scale Data Using Standard Scaler But Keep Column Names
Python

Scaler | Standard

78
LightGBM Hyperparameter Tuning with GridSearch
Python

Python | LightGBM | Hyperparameter tuning | Gridsearch

77
Calculating Root Mean Squared Error (RMSE) with Sklearn and Python
Python

Rmse

77
Dynamically Create Columns in Pandas Dataframe
Python

Pandas | Columns

61
How to Train a Catboost Classifier with GridSearch Hyperparameter Tuning
Python

Catboost | Hyperparameter tuning

60
Overwriting a Python Logging File Instead of Appending
Python

Logging

55