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This repository contains the code for a competitive assignment where we used Bayesian Networks for Medical Diagnosis as part of COL333- Principles of Artificial Intelligence

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ASSIGNMENT 4 : Medical Diagnosis Using Bayesian Networks

Goal: The goal of this assignment is to get experience with learning of Bayesian Networks and understanding their value in the real world.

Scenario: Medical diagnosis. Some medical researchers have created a Bayesian network that models the inter-relationship between (some) diseases and observed symptoms. Our job is to learn parameters for the network based on health records. Unfortunately, as it happens in the real world, certain records have missing values. We need to do our best to compute the parameters for the network, so that it can be used for diagnosis later on.

Problem Statement: We are given the Bayesian Network created by the researchers.

Such networks can be represented in many formats. We will use the .bif format. BIF stands for Bayesian Interchange Format.

The goal of the assignment is to learn the Bayes net from a healthcare dataset.

Rest necessary details are provided in the aassignment pdf!

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This repository contains the code for a competitive assignment where we used Bayesian Networks for Medical Diagnosis as part of COL333- Principles of Artificial Intelligence

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