## 5 Conclusions  
It is important to note that the primary goal of this project was not the development of specific models but rather the formulation of approaches in the form of recommendations for data acquisition, preparation for modeling, and subsequent model selection. Each modification of the technological scheme parameters (for instance, changes in treatment duration) necessitates retraining the model. The recommendations can be articulated as follows:  
1. It is essential to ensure complete adherence to the technological parameters of the investigated treatment facilities during laboratory modeling. First and foremost, this includes aligning the average values of incoming pollutant concentrations, dissolved oxygen concentrations, and temperature. The proportion of carbon dioxide is not regulated; therefore, in cases of significant discrepancies in values, it is recommended to exclude this parameter from the modeling process. It is advisable to use analytical equipment that is similar to that used at the facility. Additionally, it is recommended to automate the collection of sensor readings, focusing on the longest measurement intervals. 2. It is recommended to incorporate a new feature into the model, the carbon-to-nitrogen (C/N) ratio. This feature holds significant importance for nearly all target variables. 3. It is recommended to remove all complete duplicates from the training dataset to prevent data leakage during cross-validation. Missing values in quantitative features should be handled using the Pairwise Deletion strategy. In cases where time series data are present in the dataset, missing values can be filled with the average of the two adjacent values. For categorical features, it is advisable to impute missing values using the mode. 4. It is recommended not to remove outliers that fall outside the range of the interquartile range, as outliers are significant indicators for the occurrence of emergency situations. Before training models on the training dataset, statistical tests for the comparison of sample means should be conducted. Student's \(t\)-test can be applied when there are more than 50 observations in the sample. Additionally, a correlation analysis should be performed primarily to identify multicollinearity among predictors. If the values in the samples are not suitable for statistical analyses (for example, according to Student's \(t\)-test), it is advisable to conduct a bootstrap analysis, as water treatment efficiency indicators often exhibit exponential distribution. 5. It is recommended to apply oversampling methods or stratification of target categorical features. In the present project, high-quality predictions were achieved despite class imbalance; however, verification is necessary for each specific case. Feature scaling should be performed using robust methods, such as the RobustScaler. 6. For the examined set of features, gradient-boosting methods are the most preferred models for both regression and classification tasks, with CatBoost identified as the optimal model based on the results of the study. Each target feature has its own optimal set of hyperparameters; therefore, it is recommended to utilize cross-validation procedures for hyperparameter tuning during modeling. 7. The most objective metrics for model evaluation in this project are proposed to be SMAPE for regression and ROC-AUC for classification. It is recommended to assess model adequacy by comparing these metrics against a constant model during the model-building process. 8. The necessary sets of features for use as predictors (minimum quantity) are presented in Table 2 for each of the target metrics. These features were selected based on the evaluation of SHAP values and are intended to enhance the models by eliminating noise. 9. In addressing classification tasks related to system failure detection, it is essential to prevent the occurrence of false negative predictions. This means excluding the oversight of forecasting emergency situations, which are encoded as 0 by the LabelEncoder.