Ground Water Quality Prediction

This project leverages Machine Learning techniques to determine the quality of ground water being considered as drikable. It starts with exploratory data analysis followed by visualizations and classification algorithms such as Logistic Regression, kNN, DecisionTree, Random Forest, XGBoost and so on. The performance of the models are evaluated using confusion metrics and compared to understand their suitability of their usage to solve the business problem.

Salient Features:
– Comprehensive data analysis and visualizations
– Clear explanation of data insights
– Applying multiple algorithms
– Comparison of model performance
– Recommendation of the best algorithm for the prediction task

Tech Stacks:
Numpy, Pandas, Scikit Learn, Matplotlib, Seaborn, Deep Learning Frameworks, CNN, Python

Industry:
Healthcare and Life Sciences