This project explores the use of machine learning models to predict loan repayment behavior using historical data from LendingClub.com. Focusing on the years 2007 to 2010, the analysis aims to help investors assess borrower risk and make more informed lending decisions. The dataset includes borrower profiles, loan characteristics, and repayment outcomes, allowing us to identify key factors that influence loan repayment. We employ **Decision Trees** and **Random Forests** to classify whether a borrower is likely to repay their loan in full. To address the class imbalance inherent in financial data, we apply the **Synthetic Minority Over-sampling Technique (SMOTE)**, which improves model performance by balancing the dataset. Our findings reveal that the Random Forest model outperforms the Decision Tree model, achieving higher accuracy and recall rates. The results demonstrate the practical application of predictive analytics in enhancing credit risk assessment, with implications for better investment strategies in peer-to-peer lending.

Un projet sur la prédiction de zones susceptibles d'être inondé, pour mieux se préparé à la planification de secours auprès des populations vulnérables.