This project developed a Random Forest Classifier to predict customer churn for Beta Bank, achieving an F1 score of 0.61 and a strong AUC-ROC score despite class imbalance. By targeting likely-to-leave customers, the model provides a tool for optimizing retention strategies and aligning predictions with actual churn trends. This approach offers Beta Bank a data-driven solution to reduce customer attrition and secure its future.

👀 Supervised Learning 🧼 Feature Prep including One-Hot, Label, and Ordinal Encoding ⚖️ Feature Scaling & Class-Imbalance Handling 🤔 Confusion Matrices, Precision, Recall, and F1 Score ↕️ Imbalanced Classification with Upsampling or Downsampling 🪨 ROC-Curve, PR Curve, True Positive Rate, and False Positive Rate 💯 Regression Metrics

• This project uses pandas, numpy, train_test_split, DecisionTreeClassifier, RandomForestClassifier, LogisticRegression, f1_score, roc_auc_score, accuracy_score, matplotlib.pyplot, shuffle, and StandardScaler. It requires python 3.9.6.