## 4 Conclusions  
Using well-known machine learning algorithms, this study aims to predict the level of Almen intensity of steels by making use of the data on surface roughness, surface hardness, and residual stress obtained from experiment results. The gathered dataset was examined for this purpose by using methods based on neural networks, regression models, and boosting models. The results obtained from the study are as follows:  
1. When all samples used in the study were examined, the highest surface roughness values were obtained at 18-20 A intensity in the AISI 5410 (5.23 \(\upmu\)m), AISI 4140 (4.7 \(\upmu\)m), and AISI 1020 (5.2 \(\upmu\)m) material groups to which the traditional SP process was applied. The sample with AISI 8620 had the highest surface roughness rating (6.27 \(\upmu\)m) when the intensity was between 12 and 14 A. During the vibratory SP procedure, all samples showed the maximum surface roughness value when 4 mm balls were processed for 30 min. The roughness values obtained from the vibratory shot-peened samples were 2.7 \(\upmu\)m for AISI 5410, 2.9 \(\upmu\)m for AISI 4140, and 2.7 \(\upmu\)m for AISI 1020. 2. When comparing the samples that underwent SP to those that did not, a noteworthy improvement in hardness values was observed. The samples shot-peened with a strength of 24-26 A using the traditional SP technique had the highest hardness values. The hardness values obtained from the shot-peened and vibratory shot-peened samples were 251 HV-268 HV for AISI 8120, 345.7 HV-342 HV for AISI 5410, 347.3 HV-363 HV for AISI 4140, and 240.7 HV-276.10 HV for AISI 1020, respectively. This can be explained by an increase in plastic deformation leading to an increase in hardness and Almen intensity. Examining the SP procedure, the hardest samples were those that had vibratory SP at varying times and ball sizes; those that underwent processing for 60 min yielded the hardest values. 3. When the residual stress values of the samples peened at different Almen intensities were examined, it was determined that the highest value was obtained at 24-26 A intensity. Residual stress values obtained from shot-peened samples were \(-\)436.5 MPa for AISI 8120; 310.6 MPa for AISI 5410, 499.3 MPa for AISI 4140; and 437.5 MPa for AISI 1020, respectively. Residual stress values obtained from shot-peened samples were \(-\)436.5 MPa for AISI 8120; 310.6 MPa for AISI 5410, 499.3 MPa for AISI 4140; and 437.5 MPa for AISI 1020, respectively. The residual stress values of the samples subjected to shot peening were measured as \(-\)388,9 MPa for AISI 8120; 418,3 MPa for AISI 5410, 404,2 MPa for AISI 4140; and 477.5 MPa for AISI 1020. It was found that as the ball diameter rises in vibratory shot-peened materials, the compressive residual stress area grows. This suggests that one of the study's goals has been accomplished. When the results were examined, higher residual stress values were obtained in the shot-peened samples. 4. All of the machine learning methods produced materials as the most accurate prediction outcomes. 5. It was found that machine learning approaches can accurately predict the Almen intensity of steels when prediction results are compared. Therefore, it was determined that being able to predict the Almen intensity of materials without experimenting every time is advantageous in terms of time and cost. Using ML models to determine the Almen intensity, the performance of the algorithms was analyzed in terms of RMSE, R\({}^{2}\), and MAE values. After examining mean values, the DNN algorithm produced the best results, with an RMSE of 0.0731, R\({}^{2}\) of 0.9665, and MAE of 0.0613. The best outcomes were obtained by the XGBR, GBR, ANN, ABR, RFR, DTR, and SVR algorithms, in that order. M.I. : Conceptualization; Data curation; Software; Formal analysis; Investigation; Methodology; Writing--original draft. H.V.O. : Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Validation; Project administration; Supervision; Writing--original draft.