Welcome to my Loan Default Risk Assessment Model project repository! Predicting loan default risk is a critical task in the world of finance, and we're here to make it even more intriguing! 🤩
My mission is to predict loan default risk and assist financial institutions in making sound lending decisions! 💰 Using advanced predictive modeling techniques, I aim to enhance the accuracy and reliability of loan default assessments, ultimately contributing to financial stability and responsible lending practices.
EDA helps uncover hidden insights in the data! Captivating count plots, mesmerizing histograms, and intriguing box plots were used to better understand the data. 📊
Data preprocessing is the key to a successful model! Outliers were treated and data was transformed using the Yeo-Johnson method. Categorical features were encoded with labels for the model's delight! 🧙♂️
Standard scaling is the secret weapon! It ensures that all features are on the same playing field, with mean 0 and standard deviation 1.
An army of models was trained to predict employee attrition! From Logistic Regression 📈 to XGBoost 🚀
The models are as good as they come! They were evaluated using a barrage of metrics, including accuracy, precision, recall, F1-score, and ROC AUC.
To make the model even more focused, Recursive Feature Elimination (RFE) was employed to choose the best features.