## 3 Methods
### Data Preprocessing  
The preprocessing of data helps enhance the quality of the data and to extract meaningful insights. The data acquired from machines are normally fuzzy, biased and noisy. The preprocessing of raw data can improve the efficiency and accuracy of the ML workflows. This is why data cleaning, data integration, and feature transformation and selection are required before data can be used. Data cleaning involves such operations as improving bad data, reducing the unnecessary elements of data, and filtering out some incorrect data that do not belong to the data set. The authors of [35] proposed different techniques such as the classic maximum likelihood procedures, like Expectation-Maximization or Multiple-Imputation for the treatment of missing and noisy data. Other authors proposed advanced ensemble missing data techniques (MDTs) [36] to improve prediction model and authors of [37] evaluate four MDTs techniques: listwise deletion (LD), mean imputation (MI), similar response pattern imputation (SRPI) and full information maximum likelihood (FMIL). The majority of authors agree to suggest using FMIL if there is enough data to afford; MI and SRPI when there is a scarcity of data. Not to use LD if data is suspect missing completely at random (MCAR). Feature scaling which is also known as data standardisation, is another pre-processing step. It refers to the standardisation of the range of features in a data set, which means adjusting the values of numerical columns measured on different scales to a formal common scale, without changing the ranges of the values or losing information. Data normalisation means re-scaling the dimensions of data and avoiding over-weighting values. It helps to improve the overall quality of a data set [38]. Scaling intervals of [0, 1] and [-1, 1] are normally used, as shown in Equations (1) and (2)  
\[[0,1]interval=\frac{actualValue-min(allValues)}{max(allValues)-min(allValues))} \tag{1}\]  
\[[-1,1]interval=\frac{actualValue-(max(allValues)+min(allValues))/2}{(max(allValues )-min(allvalues))/2}. \tag{2}\]  
Feature selection considers data composed of irrelevant and/or redundant features that could influence the performance of the trading activity to a great extent. Different feature selection such as multicollinearity, correlation coefficients and Variance Inflation Factors (VIF) are proposed by authors [39; 40] to improve performance of the ML model outputs. According to the authors [39] the most commonly used techniques for numerical input and output models are correlation coefficients, such as Pearson's for a linear correlation, or rank-based methods for a nonlinear correlation. For data with numerical input and categorical output, the most common used techniques are correlation based multicollinearity coefficients and ANOVA correlation coefficients. The techniques adopted in the present framework are correlation coefficients if data coming from robot and predicting variables is numerical.To predict categorical variables of the robot multicollinearity feature selection technique is used. Data variables-collected from a cobot during human-robot collaborative applications have been evaluated using a correlation matrix and the simplest way to detect collinearity is to look at the correlation matrix of the predictors. An element of this matrix that is large in absolute value indicates a pair of highly correlated variables, and therefore a collinearity problem in the data. Data transformation is the process where format is conveniently converted from numerical to categorical and redundant data are removed [41]. The above mentioned data pre-processing techniques have been implemented in this study to extract significant features from a cobot dataset. The collected features and variables have then been passed onto the next steps of the framework. Correlation analyses of cobot variables during HRC. In order t o evaluate the significance relation between predictors \(Xp\) and response \(Y\), correlation analysis was performed.