Steps of the Modeling Approach

1. Import Libraries and Dataset

2. Exploratory Data Analysis and Feature Engineering

3. Correlation Analysis

4. Data Preprocessing and Setting the Machine Learning Environment

5. Modeling and Assessing Model Performances

   5.1 Model 1: Using Random Forest to predict credit score

   5.2 Setting the Hyperparameters and Creating the Classifier

   5.3 Printing the Model Output

   5.4 Refitting the Model with the best Model Parameters

   5.5 Testing the Model (predicting using the best refit model)

   5.6 Model 2 (Gaussian Naive Bayes)

   5.7 Predicting Probability and the ROC Curve

   5.8 Pipeline of Models

   5.9 Implementing Pipeline of Models

   Setting the Hyperparameters Creating the classifier (Grid Searching)