Background

Lending services companies allow individual investors to partially fund personal loans as well as buy and sell notes backing the loans on a secondary market.

I used this data to create machine learning models to classify the risk level of given loans. Specifically, you will be comparing the Logistic Regression model and Random Forest Classifier.

This project was completed using the following workflow:

Retrieve the data

The data is located in the Challenge Files Folder:

lending_data.csv

Import the data using Pandas.

Consider the Models

Compare and consider two models on this data: a Logistic Regression, and a Random Forests Classifier.

Fit a LogisticRegression model and RandomForestClassifier model

Create a Logistic Regression model, fit it to the data, and print the model's score. Do the same for a Random Forest Classifier. Choose any starting hyperparameters you like. Which model performed better? How does that compare to beginning predictions? Write down results and thoughts.

Methods used:

Pandas

Train_test_split

LogisticRegression

RandomForestClassifier

StandardScaler

Confusion_matrix