Credit Risk Report

Overview of the Analysis

The goal is to create a Machine Learning model using historcial lending activity to predict credit worthiness of borrowers.
Following data points are needed to build this model - loan_size,interest_rate,borrower_income,debt_to_income,num_of_accounts,derogatory_marks,total_debt,loan_status
'loan_status' provided in the given data set indicates whether the loan is a 'Healthy loan (0)' or a 'High-risk Loan (1)'
The data provided in the csv file was loaded into a Pandas DataFrame.
The data was then split into training and test set.
- The target column 'loan_status' was pulled into a seried named 'y'
- All the other features except 'loan_status' were pulled into a dataframe named 'X'
- Scikit-Learn 'train_test_split' function was used to split X and y to train and test data sets named X_train, y_train and X_test, y_test.
Logistic Regression model from Scikit Learn was chosen to train and predict the loan status
- LogisticRegression model was created using solver named 'lbfgs' and random_state=1

LogisticRegression Model created was then fitted using training dataset - X_train and y_train.
The model is then used to predict loan_status of test dataset 'X_test'

Logistic Regression Model :
- Classification Report
  - Accuracy (0.99),
  - Loan Status 0 (Healthy Loan) - Precision (1.00), and Recall (1.00).
  - Loan Status 1 (High-Risk Loan) - Precision (0.87), and Recall (0.89).

The Logistic Regression Model has performed well to predict the Loan Status with an accuracy score of 0.99 as reported in the classification report.
The precision and recall score for Loan Status '0' are both 1, which means the model was able to correctly predict 'Healty Loan' labels.
The precision and recall score for Loan Status '1' are .87 & .89 respectievely so the model was not able to predict 'High-Risk Loan' as accurate as 'healthy loan' but it still has a good success rate.

Recommends Logistics Regression Model for predicting credit worthiness of borrowers