Keywords: water potability
Maintaining potable water is a crucial aspect of public health and safety. This study focuses on utilizing Artificial Neural Networks (ANN) to predict the potability of water samples. The challenges in water potability prediction include the complex relationships between different water quality indicators and the need for accurate classification of potable and non-potable water samples. The goal of this project is to predict the potabality of the water using ANN. The chosen method to solve the problem involves training an ANN model on a dataset containing water quality parameters such as pH, turbidity, hardness, and chemical concentrations. The model will learn the patterns and correlations in the data, allowing it to classify water samples as potable or non-potable.
Aim
To Find the Water Potability using DL techniques
Objective
Develop a deep learning model to determine water potability by analyzing water quality parameters.
Train the model using labeled potability data to accurately classify water as potable or non-potable.
ANN Technique used
Jupyter, Google Collab
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