The use of artificial intelligence has also begun to develop in the business environment. Data analysis enables companies and other institutions to operate more easily and efficiently. In this work, we create a model for forecasting the company's cash inflows. We design the model on the past invoices of the selected company, on which we test various machine learning algorithms. It turns out that the XGBoost algorithm returns the best results. The algorithm belongs to the machine learning tree algorithms and is used for both classification and regression modeling examples. The considered model is evaluated with different methods for evaluating machine learning models. These are metric auccuracy, AUC metric, precision metric, ROC curve, precision-recall curve, calibration curve and others. We additionally test the model on more recent data and compare the results of the estimates of this forecast with the estimates of the forecast carried out on the test data set.
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