Drinking water is essential to maintain a healthy functioning of human system. The quality of drinking in some of the under developed countries are below the standards as recommended by World Health Organization. Drinking water which is not potable can lead to serious health effects and may affect the functioning of the human system
This case familiarizes the learner on how to use the concept of Naive Bayes classifier to create a prediction model based on water quality parameters to accurately predict the water quality.
Understand the concept of data cleaning such as missing value treatment and outlier treatment to improve predictive power of Machine learning models.
Deep-dive to the concept of multicollinearity to identify and remove highly correlated variables that reduces the statistical significance of the machine learning model.
The case helps the learner to train and apply Naïve bayes model to predict water potability, which is based on the Conditional probability that something with happen, given that something has already occurred.
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Difficulty: Advanced
Duration: Approximately 3 hours
