Sampling

Analysing different Sampling techniques and testing them on different Machine Learning models. The dataset is Imbalanced so we use SMOTE to convert the Imbalanced dataset to balanced dataset by simply oversampling the miniority class.

SMOTE: Synthetic Minority Oversampling Technique

Sampling techniques used :

1. Convenience Sampling
2. Simple Random Sampling
3. Systematic Sampling
4. Stratified Sampling
5. Cluster Sampling

Machine Learning Models used :

1. Logistic Regression
2. Support Vector Machine
3. K-Nearest Neighbors
4. Random Forest Classifier
5. Decision Tree Classifier

Data Set

Creditcard_data.csv