How to use ChatGPT for Banking Credit Analyst Job Role

python

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

# Load the dataset
file_path = 'path_to_your_uploaded_dataset.csv'
data = pd.read_csv(file_path)

# Data preprocessing
# Assuming 'default' is the target variable and the rest are features
X = data.drop('default', axis=1)
Y = data['default']

# Split the data into training and testing sets
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)

# Create and train the model
model = LogisticRegression()
model.fit(X_train, Y_train)

# Make predictions
Y_pred = model.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(Y_test, Y_pred)
conf_matrix = confusion_matrix(Y_test, Y_pred)
class_report = classification_report(Y_test, Y_pred)

# Output the results
print(f'Accuracy: {accuracy}')
print('Confusion Matrix:')
print(conf_matrix)
print('Classification Report:')
print(class_report)