Below is given the link for a data set from the UCI ML repository.

Caesarian Section Classification Dataset

https://archive.ics.uci.edu/ml/datasets/Caesarian+Section+Classification+Dataset

Download the data set. You are required to do the following:

Question 1: Python

1. Construct a Bayesian Belief Network for the given data.

Use appropriate methods to predict the following:

2. Predict the probability of a patient having the following properties : Age is 22,first delivery with delivery time 2 and with no heart problem or blood pressure and having normal delivery
3. Predict the type of delivery for the lady with the following characteristics: Age is 26, admitted for second delivery with time of delivery 0, having blood pressure and no heart problem.
4. Infer the probability for the data : Age: 36, 4^th delivery, with time of delivery 1 and has blood pressure and no heart problem to have normal delivery.

Question 2: Prolog

1. Use Any of the decision tree algorithms to build a decision tree for the given data
2. Create rules from the decision tree.
3. Code the rules into a Prolog Knowledge base
4. Get user data as input and give the type of delivery as output.

Topics

1. Bayesian Belief Networks

2. Prolog

INSTRUCTIONS:

1. You are required to submit three files
   1. The executable python code for Bayesian inference.
   2. The executable Prolog file.
   3. A word file containing all the theory part wherever necessary to describe your approach and algorithms.
2. Do not include text in the coding except comments. Coding must be readily executable.
3. Wherever necessary pre-process the data as required by the implementation.