## 3 Research Methodology
### Dataset
#### 3.4.3 Dataset Preprocessing  
This study section outlines the various steps involved in data preparation, including data preprocessing before the final analysis. The process begins with data cleaning to ensure the correctness of values, followed by data transformation and the normalization of attributes to make them compatible with machine learning algorithms. The data \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Type** & **Description** \\ \hline Location & Text & Geographical Location Where the Sample Was Collected \\ Sample Code & Text & Unique Identifier for Each Sample Collected \\ Day & Text & Specific Day on Which the Data Were Recorded \\ Description & Text & Additional Details or Context About the Sample \\ State & Text & Condition Or State of The Sample at The Time of Collection \\ Analysis & Text & Type Of Analysis Performed on The Sample \\ Parameter & Text & Specific Measured Parameters (e.g., Water Temperature, Ph) \\ Value & Double & Numerical Value of The Measured Parameter \\ Unit & Text & Unit Of Measurement for The Parameter Value \\ Limit & Double & Threshold Critical Level Values for Certain Parameters \\ Aa\_X & Text & Additional Coordinate or Identifier Associated with The Sample \\ Month & Text & Month Of Data Collection, Providing Temporal Context \\ Cell & Text & Section or Cell (Cage) within a Location Where the Sample Was Taken \\ Portion & Text & Portion of the Sample or Area Being Analyzed \\ Fishno & Integer & Identifier for an Individual Fish Within a Sample \\ Mab & Text & Code or Identifier Possibly Related to a Management Area \\ Fish\_No & Integer & Count of Fish Within a Sample or Study Area \\ Deaths & Integer & Number of Fish Deaths Recorded Within the Sample \\ Corrections & Integer & Adjustments Made to the Data (e.g., Due to Errors) \\ Nofishing & Text & Information About Areas or Periods Where No Fishing (No Harvesting) Occurred \\ Damage & Text & Notes on Any Damage Observed in the Sample \\ Kg\_Fishing & Double & Weight of Harvested Fish (in Kilograms) \\ Sample & Text & Further Identification or Notes About the Sample \\ Medication & Text & Details on Any Medication Administered to Fish \\ Food & Text & Information on the Type of Food Provided to the Fish \\ Food\_Model & Text & Description of the Feeding Model or Regime Used \\ Sfrperement & Double & Percentage (\%) of a Specific Food Ratio or Supplement in the Fish Diet \\ Temp & Double & Water Temperature at the Time of Sampling \\ Vol & Double & Volume of Water or Sample Analyzed \\ i\_f & Text & Specific Index or Factor Relevant to the Sample \\ Medicine & Text & Details on Any Additional Medicine Used \\ Aa\_Y & Text & Coordinate or Identifier Potentially Linked to Location Data \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Caged fish farming parameters. preprocessing phase is crucial for improving the ML models' prediction accuracy and overall performance. A series of steps were included in the data preprocessing phase. Data preprocessing steps refer to the following:  
1. Data export and loading from the CSV files into Pandas DataFrames 1.5.0, which are used for data analysis and processing in the Python 3.10 programming language. 2. Data completeness checks identify inconsistent or missing data such as null or NaN values. A data frame is analyzed to reveal whether there are any anomalies or values that need replacement. 3. Data cleansing is used to identify and eliminate data inconsistencies. This may include low or high values, exclusions, or correcting certain typos. 4. Data transformation is conducted in the data preprocessing phase, such as discretization or normalization. After data processing, the two datasets are merged using the Pandas 1.5.0'merge' function. The resulting combined dataset includes a column with microbiological and physicochemical data. It is stored in the Apache Cassandra database, which, with its NoSQL structure, provides an ideal environment for analyzing large datasets.