# Machine-Learning Approach to Determine Surface Quality on a Reactor Pressure Vessel (RPV) [PERSON]
## 1 Introduction
### Data Imputation and Augmentation for Low Data Sets  
#### 1.2.1 Exploratory Data Analysis  
The first step is to look at the provided data and perform an Exploratory Data Analysis (EDA). This means examining the data to detect any missing values, to determine a relationship between the variables, and to take account of the data statistics such as mean, median, mode, and standard deviation [12]. There are two ways to perform EDA, graphically and non-graphically.  
Since the dataset is new, the user may not have any idea about the underlying relations or even the patterns in the data. As the exploration is performed, some ideas may lead to interesting insights and some ideas may just be a 'dead end'. There are generally two types of questions about the data, the type of variation that exists and the type of correlation that exists [13].  
Graphical data analysis is best done in the form of bar charts for categorical data and histograms for numerical data [14], which is often easier to visualise than having a large array of numbers. Non-graphical data analysis is done by looking at the data statistics that include the central tendency--such as the mean, median, and mode, the spread of the data by looking at the standard deviation, and the shape of the distribution [15]. EDA also allows for the examination of the presence of outliers, which also shows the quality of the data.  
Univariate analysis involves the analysis of a single feature. This analysis includes histograms, count-plots, boxplots, and violin-plots, and are known as summary plots since they show the frequency distribution of the data. 2D scatterplots and line-plots can also be used for univariate analysis by plotting the feature on the y-axis and their corresponding index numbers on the x-axis [16]. One very useful feature is the option of colour coding the data by groups in order to see the spread of each category.  
Bivariate analysis involves the analysis of two variables (X, Y) and is the best way to determine the relationship between two variables. 2D scatterplots and line-plots can be used by assigning both the X and Y axis as variables and the data points can be grouped by labels. Pair-plots can be very useful to observe the correlation between more than two features. The study in [17] displays a pair-plot for visualising the Iris-Dataset, where a number of 2D plots are provided in a matrix format displaying comparisons of different data variable comparisons, albeit sequentially, and each plot's comparing two variables to show the fitness of correlation between the two. Along the diagonal plots are histograms of the feature in their corresponding rows. This is a very powerful technique for visually displaying data correlations.  
Variables can have a positive or a negative correlation. In a positive correlation, both the variables move in the same direction. An increase in X will also show an increase in Y. For a negative correlation, an increase in X results in a decrease in Y and vice versa. The closer the points are to the line of fit, the stronger to the correlation. However, if the line of fit is completely vertical or horizontal or there is no line of best fit, then there is no correlation between the features [18].  
A similar way of showing correlations is with an Attribute Correlation Heat-Map. Among other methods, such as Kendall Tau and Spearman Rank, this method uses a Pearson correlation coefficient by default to calculate the linear correlation between two variables [19]. The resultant values range from \(-1\) to \(+1\), with \(+1\) showing the strongest positive correlation. These results are presented as a matrix that can be plotted as a heat-map to clearly visualise the strength of the correlations [20].