Model evaluation

Table of Content

1. Problem Statement
   
2. Data Loading and Description
   
3. Exploratory Data Analysis
   
4. Random Forest Classifier
   
5. Model evaluation
   
   • 5.1 Model evaluation using accuracy score
     
   • 5.2 Model Evaluation using confusion matrix
     
   • 5.3 Model evaluation using precision score
     
   • 5.4 Model evaluation using recall score
     
   • 5.5 Model evaluation using f1_score
     
   • 5.6 Model evaluation using ROC_AUC curve
     
   • 5.7 Choosing better model using precision score
     

1. Problem Statement

Given the credit card transaction dataset, make a model to predict transactions are fradulent or not using random forest algorithm. Evaluate the model using possible model evaluation techniques.