# Innovation in Company Labor Productivity Management: Data Science Methods Application
## 3 Experimental Results
### Clustering and Classification of Companies' Employers  
In order to smooth out the identified data inhomogeneities, as well as to order complex factors interactions we design the technique for dividing personnel into homogeneous groups. This provides a detailed data study and identify patterns in the obtained homogeneous groups. For these we examine various methods. Clustering has two stages--qualitative analysis using hierarchical methods and analysis using the \(k\)-means method [56; 57; 58]. Analysis of various partitions of the original sample by the method of hierarchical classification showed that it is possible to form from three to six clusters. For the more reasonable grouping of objects (personnel), we use \(k\)-means clustering method with quantitative criteria to assess the clusterization quality. Table 5 shows the clustering results. The most numerous cluster (229 employees) is the second, including for about a third of all respondents. These are mostly married women with higher education, above average age with two or more children. Employees in this cluster have significant health problems, some of them have chronic diseases, bad habits and at the same time, have the lowest labor productivity in the sample. The first cluster includes mainly married men with higher education, having one child, taking care of their health, has good nutrition and no health problems. The average age of workers in this group is 34.7 years; their average individual productivity is quite high, equal \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Predictor** & **Regression** & **Regression** & & \\
**(Regressor)** & **Coefficient** & **Coefficient Error** & **T-Statistics** & _p_**-Value** \\ \hline age & \(-5.232\) & \(2.2219\) & \(-2.35490\) & \(0.028854\) \\ mar’chron\_dis & \(-584.479\) & \(120.7443\) & \(-4.84064\) & \(0.000099\) \\ gender’bad\_health & \(254.329\) & \(76.3480\) & \(3.33118\) & \(0.003329\) \\ mar’bad\_health & \(111.577\) & \(49.7048\) & \(2.24479\) & \(0.036250\) \\ edu’bad\_health & \(121.695\) & \(55.9648\) & \(2.17450\) & \(0.041843\) \\ chron\_dis’bad\_health & \(560.109\) & \(103.1680\) & \(5.42909\) & \(0.000026\) \\ gender’health\_ass & \(-229.364\) & \(78.0956\) & \(-2.93696\) & \(0.008152\) \\ edu’health\_ass & \(-108.392\) & \(43.4358\) & \(-2.49546\) & \(0.021441\) \\ chron\_dis’health\_ass & \(-488.851\) & \(105.3426\) & \(-4.64059\) & \(0.000158\) \\ mar’bad\_hab & \(516.338\) & \(108.2071\) & \(4.77176\) & \(0.000116\) \\ chron\_dis’bad\_hab & \(578.543\) & \(141.1459\) & \(4.09890\) & \(0.000558\) \\ bad\_health’bad\_hab & \(-476.916\) & \(95.9670\) & \(-4.96958\) & \(0.000074\) \\ health\_ass’bad\_hab & \(346.146\) & \(66.1466\) & \(5.23302\) & \(0.000040\) \\ gender’edu’bad\_health & \(79.240\) & \(34.3925\) & \(2.30400\) & \(0.032080\) \\ gender’mar’health\_ass & \(-162.445\) & \(62.3059\) & \(-2.60721\) & \(0.016862\) \\ gender’edu’health\_ass & \(-84.053\) & \(34.6773\) & \(-2.42387\) & \(0.024960\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Regression results for labor productivity modeling (only significant regressors are included). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **1** & **2** & **3** & **4** \\ \hline age & \(34.7\) & \(46.3\) & \(35.6\) & \(33.1\) \\ gender & \(1\) & \(0\) & \(1\) & \(0\) \\ mar & \(1\) & \(1\) & \(1\) & \(0\) \\ edu & \(1\) & \(1\) & \(0\) & \(1\) \\ child & \(1\) & \(2\) & \(2\) & \(0\) \\ chron\_dis & \(0\) & \(1\) & \(1\) & \(0\) \\ bad\_health & \(0\) & \(1\) & \(1\) & \(1\) \\ health\_self-ass & \(0\) & \(1\) & \(0\) & \(1\) \\ nurt & \(0\) & \(1\) & \(0\) & \(1\) \\ bad\_hab & \(0\) & \(1\) & \(1\) & \(0\) \\ labor\_pert & \(301.6\) & \(221.4\) & \(290.8\) & \(227.8\) \\ cluster size & \(191\) & \(229\) & \(184\) & \(98\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Clusters’ centers (\(K\)-means method). to 301.6 rub. per hour. Employers in this cluster can be designated as \"healthy\" and do not require special decisions about their health by the company.