## 3 Materials and Methods
### Data Pre-Processing Using Min-Max Normalization  
Before visualization, the data exhibited variations in nutrient concentrations that prompted the need for exploration. The raw data contained outliers, which are data points significantly different from the majority of the observations. These outliers, if not addressed, can impact the understanding of the overall nutrient distribution and make it challenging to discern patterns and trends in the data. The Min-Max normalization method is applied to rescale the input features between 0 and 1 during the pre-processing phase. This normalization technique is suitable for the prediction models of this study because it helps to ensure that all the input features are on the same scale and have the same range, which helps the linear regression models of this study converge faster and boost their performance. This approach removes noises from data and prevents the big scales from data by giving the range of \([0,1]\). Equation (1) shows the formula of the Min-MAX method. \[X_{Norm}=\frac{(X-X_{Min})}{(X_{Max}-X_{Min})} \tag{1}\]  
where \(X\) is the original value of a data point, \(X_{Min}\) is the minimum value in the dataset, \(X_{Max}\) is the maximum value in the dataset, and \(X_{Norm}\) is the normalized value of the data point. This formula ensures that the minimum value in the dataset is scaled to 0 and the maximum value is scaled to 1, with all other values falling between these two limits. By applying a pre-processing method to the dataset, we can improve the stability and performance of the regression models. Once this stage is complete, we can proceed to the next stage, where we design a regression model based on the different variables in the dataset. This stage involves selecting an appropriate regression method and specifying the independent and dependent variables. Finally, we analyze the model and provide informa  
Figure 3: Rice nutrient data: (**a**) original data and (**b**) min–max normalized data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **N (\%)** & **P (\%)** & **K (\%)** & **Mg (\%)** & **Ca (\%)** \\ \hline MIN & 0.15 & 0.15 & 1.61 & 0.09 & 0.16 \\ MAX & 4.59 & 28.00 & 3.89 & 0.20 & 0.38 \\ STDEV & 0.77 & 1.48 & 0.45 & 0.02 & 0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Value distribution for the nutrients. tion on its performance and accuracy. Figure 3 illustrates the rice nutrients data before and after applying the Min-Max normalization method. The visual representation of the data highlights the impact of normalization on the distribution of nutrient concentrations. The dataset under analysis consists of nutrient concentration data for rice samples, including attributes like nitrogen (N%), phosphorus (P%), potassium (K%), magnesium (Mg%), and calcium (Ca%). Prior to visualization, the data exhibited variations in nutrient concentrations that prompted the need for exploration. The raw data contained outliers, which are data points significantly different from the majority of the observations. These outliers, if not addressed, can impact the understanding of the overall nutrient distribution and make it challenging to discern patterns and trends in the data. Therefore, to gain a deeper understanding of the nutrient concentration data and visualize its distribution, we employed box plots both before and after applying Min-Max normalization. The original box plots revealed the presence of outliers in the dataset, which was affecting the clarity of the distribution. To address this issue, Min-Max normalization was applied to scale the data. The box plots after normalization effectively showcased the distribution of nutrient concentrations without displaying outliers. This approach allows for a more accurate and informative representation of the data, aiding in the identification of central tendencies and variations while providing a clearer view of the data's overall structure. The use of box plots before and after normalization aids in the assessment of data quality and the impact of data pre-processing techniques.