AI-Powered Loan Offers: Predictive Modeling & AI in Banking

Identify essential features including income, expenses, savings, credit score, age, and engagement that connect demographics and financial profile to a targeted loan offer model predicting acceptance and guiding recommendations.

Explore a data warehouse architecture for a loan offers modeling use case, mapping data sources, staging, data warehouses, data lakes, and data marts to support ETL, governance, and ML modeling.

Understand data sources for ai-powered loan offers by examining dim customers, dim financial profiles, loan portfolio, loan offers, and customer interactions to analyze credit score, income, and loan terms.

Create a data warehouse schema in MySQL Workbench, then populate seven tables including dim customers, dim financial profile, dim loan portfolio, and dim loan offers using provided SQL scripts.

Demonstrates loading data from multiple csv files into a MySQL data warehouse using Apache NiFi, building an ETL pipeline from csv sources to dw tables and validating with statistics.

Import core libraries for data manipulation and visualization, connect to a MySQL database, load customers, financial profiles, and loan offers into pandas, merge datasets, and inspect with head and describe.

Explore descriptive statistics, exploratory data analysis, and visualization to identify patterns, outliers, and relationships in numeric and categorical loan data, revealing predictive factors for loan acceptance.

Apply feature selection and engineering with a random forest to identify top predictors for loan acceptance, using one-hot encoding, smote balancing, and feature scaling.

Tune logistic regression via grid search over penalty, solver, and C; train the best model; evaluate with accuracy and roc auc, visualize the confusion matrix, and prep for decision trees.

Build a decision tree model with grid search CV to optimize maxdepth, min samples split, and classweight, evaluated by ROC AUC, accuracy, and a confusion matrix.

Explore random forests as an ensemble of decision trees that reduce overfitting via bootstrapping and random feature selection, for robust classification and regression tasks.

Tune a random forest with grid search and five-fold cross-validation, optimizing estimators, max_depth, min_samples_split, and class_weight. Evaluate ROC AUC and accuracy, and compare with gradient boosting via XGBoost and LightGBM.

Learn how XGBoost, a powerful extreme gradient boosting method for structured tabular data, builds sequential trees to minimize residuals and update predictions through learning rates for regression and classification.

train and evaluate an xgboost model for loan offers, using grid search to tune hyperparameters, and compare accuracy and roc auc against a random forest baseline.

Learn how to implement gradient boosting with Lightgbm for classification, tune hyperparameters with gridsearchcv, train the model, and evaluate using accuracy and ROC AUC, including a confusion matrix.

Explore artificial neural networks, neurons, inputs, hidden and output layers, weights and biases, activation functions (ReLU, Sigmoid), and forward/backward propagation for training.

Build and evaluate a TensorFlow‑Keras neural network with a 64‑32‑32‑16 architecture for binary classification, tracking training and validation performance, ROC AUC, and iterative optimizer and architecture refinements.

Explore item-based collaborative filtering using cosine similarity to predict user ratings and generate recommendations from a user-item ratings matrix, with practical Python and scikit-learn applications and evaluation metrics.

Apply item-based collaborative filtering with cosine similarity in Python to transform loan interactions into a user-item matrix, compute loan similarities, and generate top five loan recommendations.

Learn item-based collaborative filtering with the Pearson correlation coefficient to recommend loan offers from a user-item interaction matrix and a similarity matrix, culminating in top-n recommendations.

Install and run Apache NiFi on Windows, including Java setup, downloading the release, unzipping, starting NiFi with nifi.bat, and accessing the user interface at localhost:8443 to design data flows.

Install Anaconda and Python 3.8 across Windows, macOS, and Linux, following download, installer, and verification steps to run Python for AI-powered loan modeling.