The objective of this analysis was to develop a machine learning model capable of accurately predicting loan risk based on financial data. The data set comprised various financial metrics which are indicators of a loan's risk. The goal was to predict whether a loan would be "healthy" (low-risk) or "high-risk," aiding in the decision-making process for loan approval.
Two primary outcomes were identified in the dataset: label '0' for healthy loans and label '1' for high-risk loans. The value_counts method helped establish the distribution of these outcomes, indicating an imbalance that could potentially bias the predictive models.
The analysis involved several stages, including data preprocessing, feature selection, model training, and evaluation. The primary machine learning method used was LogisticRegression, which is suitable for binary classification tasks. To address the imbalance in the dataset, a resampling method may have been employed, though specifics on this were not provided.
Only one machine learning model's results were provided, which is the Logistic Regression model. Here are its results:
- Machine Learning Model 1: Logistic Regression
- The model showed a high precision score of 1.00 for healthy loans (label '0') and a good precision score of 0.85 for high-risk loans (label '1').
- The recall for healthy loans was nearly perfect at 0.99, while it was also quite high for high-risk loans at 0.91.
- The F1-scores were correspondingly high, 1.00 for healthy loans and 0.88 for high-risk loans.
- Overall accuracy was reported at 0.99.
The Logistic Regression model demonstrated strong performance, particularly in identifying healthy loans, with perfect precision and almost perfect recall. It also performed reasonably well in identifying high-risk loans but with some room for improvement, especially in terms of precision.
Based on the results, this model would be recommended for predicting loan health, especially if the priority is to minimize false positives in identifying healthy loans. However, if the cost of misclassifying high-risk loans as healthy is substantial, further tuning of the model or consideration of additional models might be warranted. The high overall accuracy suggests the model is robust, but the importance of predicting each class correctly depends on the business context and the implications of false negatives versus false positives.
Should there be a significant consequence for misclassifying high-risk loans, one could consider exploring more complex models or ensembles of models that might capture the nuances better, even though they might be more computationally expensive or require more data.