Weka Workbench

Import customer data with features like demographics, purchase history, and engagement metrics.

Preprocess the data to handle missing values and normalize features.

Apply classification algorithms like decision trees or logistic regression.

Evaluate the model's performance using metrics like precision, recall, and AUC.

Identify high-risk customers and target them with personalized offers.

Collect transaction data with features like amount, time, location, and merchant information.

Preprocess the data to handle imbalanced classes (more legitimate than fraudulent transactions).

Apply anomaly detection algorithms like isolation forest or one-class SVM.

Set a threshold for fraud score to flag suspicious transactions.

Alert the security team for further investigation.

Gather patient data with features like age, gender, symptoms, and test results.

Preprocess the data to handle missing values and normalize features.

Apply classification algorithms like support vector machines or neural networks.

Evaluate the model's performance using metrics like accuracy, sensitivity, and specificity.

Provide a list of possible diagnoses with probabilities to the doctor.

Collect a dataset of emails labeled as spam or not spam.

Extract features from the email content, such as keywords, sender information, and email headers.

Preprocess the data to remove stop words and apply stemming.

Apply classification algorithms like naive Bayes or support vector machines.

Filter incoming emails based on the model's predictions.

Collect sensor data from equipment, such as temperature, pressure, vibration, and operating hours.

Preprocess the data to handle missing values and smooth noisy data.

Apply regression algorithms like linear regression or neural networks.

Predict the remaining useful life (RUL) of the equipment.

Schedule maintenance when the RUL falls below a certain threshold.