The Software Application for Ground Water Level Predictor aims to forecast the future availability of ground water based on historical data using machine learning models. This application utilizes algorithms such as Random Forest, Gradient Boosting Regressor, and Linear Regression to predict ground water levels. To ensure model transparency, explainable AI techniques like SHAP or LIME will be incorporated to interpret model predictions. The project includes an extensive Exploratory Data Analysis (EDA) phase to understand trends and correlations in the dataset, enhancing the model’s predictive performance. The user will input relevant parameters into the system, and the model will output the predicted amount of ground water availability. The application aims to assist in resource management, providing insights into future water usage and conservation strategies. The backend of the system is implemented in Python, while the frontend is built using HTML, CSS, and JavaScript to create an interactive and user-friendly interface.

Keywords: Ground Water Prediction, Machine Learning, Random Forest, Gradient Boosting, Linear Regression, Explainable AI, Water Resource Management.