Naive Bayes Net Implmentation

(P3.py) Project Goal: To build a classifier using the Naive Bayes model. The task is to predict whether a child will be recommended for a nursery. Predictions are made off of eight possible features which all cases have.

The label and each feature can take the following values:

• Nursery admission recommendation (L): { recommend, not-recom}
• Parents occupation (O): {usual, pretentious, great_pret}
• Childs Nursery (N): {proper, less_proper, improper, critical, very_crit}
• Family form (F): {complete, completed, incomplete, foster}
• Number of children (C): {1, 2, 3, more}
• Housing (H): {convenient, less_conv, critical}
• Finance (I): {convenient, inconv}
• Social (S): {non-prob, slightly_prob, problematic}
• Health (A): {recommended, priority, not_recom}

(train_data.dat): File used for training Bayes Net (val_data.dat): File used for validating the model to detect accuracy