### Why We Chose These Models for Training on the Original Data
## Why We Chose These Models for Training on the Original Data
The water potability dataset consists of various features such as pH, Hardness, Solids, Chloramines, Sulfate, Conductivity, Organic Carbon, Trihalomethanes, and Turbidity, which are essential in determining whether water is potable or not. When selecting machine learning models for training on this dataset, we considered the characteristics of the data, such as the distribution of the features, the relationships between features, and the class imbalance in the target variable. Below is an explanation of why each model was chosen based on the summary statistics of the dataset.
The water potability dataset consists of various features such as pH, Hardness, Solids, Chloramines, Sulfate, Conductivity, Organic Carbon, Trihalomethanes, and Turbidity, which are essential in determining whether water is potable or not. When selecting machine learning models for training on this dataset, we considered the characteristics of the data, such as the distribution of the features, the relationships between features, and the class imbalance in the target variable. Below is an explanation of why each model was chosen based on the summary statistics of the dataset.
