Data Science: Credit Card Fraud Detection - Model Building

Import and configure libraries for data handling, visualization, and model evaluation, including numpy, pandas, seaborn, matplotlib, and scikit-learn; prepare classifiers like random forest and logistic regression with grid search.

Import data from the source on your local machine or Google Colab. Mount Drive, authorize with a one-time key, load the dataset from G-Drive, and print the first five rows.

Learn to inspect the imported credit card fraud dataset, verify shape (about 84,000 rows by 31 columns), check data types and nulls, and identify the target variable with descriptive statistics.

Analyze the class distribution of the target variable by computing value counts for legitimate and fraud transactions, then visualize with pie and bar charts to show data imbalance.

Analyze feature correlations and visualize them with a heat map to reveal strong and weak relationships, guiding feature engineering for credit card fraud detection model building.

Perform feature engineering on time-based data by computing a time delta. Derive three time features: time_of_day, time_hour, and time_minute, and drop the time feature to prepare for model training.

Separate features from the target and perform a train-test split with 20% test size using train_test_split, then verify the resulting X and y distributions.

Plot the distribution of each feature by comparing normal and fraud records with green and red histograms, preparing for the upcoming model-building session in credit card fraud detection.

Master evaluation for credit card fraud detection models using confusion matrices, classification reports, and auc-roc, detailing precision, recall, f1-score, and thresholds to guide model evolution.

Create a common function to plot confusion matrices, avoiding repeated code, and store results in a dataframe with methodology, model accuracy, and value threshold for model comparison.

Explore logistic regression, knn, decision trees, random forest, xgboost, and svm as foundational models for credit card fraud detection, and learn to implement their common functions in upcoming sessions.

Develop a common function to build and evaluate logistic regression models, handling both L1 and L2 regularization, cross-validation, and model evolution with accuracy, confusion matrices, and classification reports.

Create a common function to fit and predict on a five-neighbor KNN model, store results, and compute accuracy, confusion matrix, classification report, and ROC metrics from predicted probabilities and thresholds.

Explore building credit card fraud detection tree models using a common function to fit and predict with two criteria (gini and entropy), and evaluate with confusion matrix and classification report.

Create and evaluate a random forest classifier with 100 trees, bootstrap enabled, and square-root features; fit on data, predict, and assess via confusion matrix, classification report, and probability thresholds.

Create a reusable function to fit and predict with an XGBoost model, evaluate on the test data via confusion matrix, classification report, and predicted probabilities, and compute decision thresholds.

Develop a reusable function to fit and predict on an svm model, print the confusion matrix and classification report, and compute probability values and thresholds for evaluation across methodologies.

Explore repeated k-fold cross-validation and stratified k-fold, two robust techniques for classification and regression, that shuffle data and preserve class proportions to build reliable models.

Apply repeated k-fold cross-validation with train-test splits to compare logistic regression (L1/L2), decision trees, random forest, and boosting on fraud detection data, using confusion matrices and classification reports.

Apply stratified k-fold cross-validation to credit card fraud detection, compare logistic regression with l1 and l2 regularization against tree-based and x ebos models, and evaluate with confusion metrics.

Optimize logistic regression with L2 regularization by tuning hyperparameters and evaluating accuracy and threshold. Visualize coefficient importance with a bar chart to identify key features and their impact on predictions.

Explore oversampling techniques to balance fraud and non-fraud transactions, including random oversampler, SMOTE, and ADASYN, and see how synthetic minority samples improve model performance.

Explore random oversampling with stratified k-fold cross-validation to balance the minority class, applying a 0.5 sampling strategy on training data while comparing logistic regression, decision tree, and random forest models.

Explore oversampling with SMOTE and model evaluation on balanced fraud data, using stratified sampling and random state, and show that boosting models yield the best accuracy and ROC value.

Explore oversampling techniques, including ADASYN and stratified random oversampling, to balance data, evaluate models, and identify the best approach for fraud detection.

Perform hyperparameter tuning on the boosted model using randomized cross-validation with stratified sampling to explore learning rate, max depth, and other parameters, with binary logistic objective to maximize accuracy.

Extract the most important features from the XGBoost model using feature_importance after hyperparameter tuning, and identify the top variables by descending importance. Compute ROC AUC and thresholds to evaluate performance.

Explore models for credit card fraud detection using stratified k fold cross-validation and oversampling; XGBoost with random oversampling gives top ROC, while logistic regression with L2 often performs best.