CatBoost

Collect transactional data (amount, time, location, user info).

Prepare features, including categorical features like transaction type and merchant category.

Train a CatBoost model to classify transactions as fraudulent or legitimate.

Deploy the model to score incoming transactions in real-time.

Flag suspicious transactions for manual review.

Collect customer data (demographics, usage, engagement, support interactions).

Prepare features, including categorical features like subscription plan and customer segment.

Train a CatBoost model to predict churn probability.

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

Monitor the impact of retention efforts on churn rate.

Collect historical sales data (price, demand, promotions, seasonality).

Prepare features, including categorical features like product category and competitor pricing.

Train a CatBoost model to predict demand at different price points.

Use the model to identify the price that maximizes revenue.

Implement dynamic pricing based on market conditions.

Collect patient data (symptoms, medical history, lab results, imaging scans).

Prepare features, including categorical features like age group and pre-existing conditions.

Train a CatBoost model to predict the probability of different diagnoses.

Provide doctors with a list of potential diagnoses, ranked by probability.

Support clinical decision-making with data-driven insights.