Tell me in detail about a project from start to finish — how it was formed and how you solved it.

One of the projects I implemented was related to predicting customer churn for a large telecommunications company.

Problem formulation: The company wanted to reduce customer churn to increase profit and retain its audience. The goal was to build a model that would predict the likelihood of a customer leaving in the upcoming month.

Data collection: We obtained historical customer data: demographics, call history, service usage, support requests, payments, etc.

Preprocessing: We cleaned the data, handled missing values, and created new features (e.g., average number of calls per month, support request frequency).

Model selection: We used several algorithms: logistic regression, random forest, gradient boosting. We evaluated the quality using AUC-ROC, accuracy, and recall metrics.

Training and validation: We split the data into training and testing sets, performed cross-validation, and tuned hyperparameters.

Results: The gradient boosting model showed the best result with an AUC of about 0.85. This allowed us to identify a group of high-risk customers.

Implementation: We integrated the model into the CRM system so that managers could respond promptly and offer special conditions to such customers.

Outcome: The project helped reduce churn by 10% in the first 6 months after implementation, significantly improving the company's financial indicators.