## 4 Data Processing Workflow  
Several challenges must be addressed when using the proposed model to predict chlorine levels in water monitoring systems, including the data quality and availability; the machine learning algorithms require large amounts of accurate data to train the model. Ensuring that the data collected is high quality and available in sufficient quantities is crucial for developing an accurate prediction model. Furthermore, feature selection is important to identify the key factors that affect chlorine levels in the water, as these will become the input variables for the prediction model. Careful selection of the input variables is crucial for obtaining accurate predictions. Additionally, a simple model may not capture the complex relationships between the input variables and chlorine levels. At the same time, a too complex model may overfit the data, leading to poor predictions for new data. Finally, model evaluation is important in evaluating the model's performance using appropriate metrics such as mean absolute error and root means square error or R-squared. This helps to determine the predictions' accuracy and identify areas for improvement. The time of observation of the problem statement and combination of elements for the water quality domain must be comprehensive. Consider the chlorine level data set, which includes all qualities and characteristics of the water quality data set. It facilitates feature engineering and enables the selection of the best feature. Employ pre-processing techniques such as data cleaning to eliminate erroneous or inconsistent data or to fill in missing data in a variety of ways, for as by discarding missing values, filling them in indiscriminately, or predicting incomplete data. Finally, remove unnecessary entries from the pre-processed data. Pearson, spearman, and Kendall may correlate target class labels and characteristics. Correlation affects the model's performance. Dimension reduction is performed on the pre-processed dataset to extract vital elements/attributes from the current dataset. It reduces the dimension via matrix factorization. Feature extraction and feature selection are used to decrease the features in the following ways: feature extraction generates new features from current ones. The feature selection technique picks a subset of the current method. The aim is to eliminate inappropriate and noisy information, minimize computing time and complexity, and performance enhancement of machine learning algorithms. Three types of feature selection methods are distinguished. Filter method is expandable and performs quicker before classification; moreover, a Black Box Evaluator is utilized to determine the optimum subsection of features. It is more efficient than the filter technique. Filter technique is used more often than wrappers, which is quite costly. Finally, the hybrid approach bridges the gap between the filter and wrapper methods. The filter will provide the wrapper with a reduced feature set, allowing the wrapper to grow to more enormous datasets efficiently. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Chlorine\_Temp}{**Vary High**} & \multicolumn{1}{c}{**High**} & \multicolumn{1}{c}{**Moderate**} & \multicolumn{1}{c}{**Low**} & \multicolumn{1}{c}{**Very Low**} \\ \hline
**Very High** & Partial Useable & Non-Useable & Non-Useable & Non-Useable & Non-Useable \\ \hline
**High** & Partial Useable & Partial Useable & Non-Useable & Non-Useable & Non-Useable \\ \hline
**Moderate** & Useable & Useable & Useable & Useable & Useable \\ \hline
**Low** & Partial Useable & Non-Useable & Non-Useable & Non-Useable & Non-Useable \\ \hline
**Very Low** & Non-Useable & Non-Useable & Non-Useable & Non-Useable & Non-Useable \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fuzzy Associative Mapping for water quality prediction.