lavanyajoyce / bayesian-network

Implementation of Bayesian-Network

Aim :

To create a bayesian Network for the given dataset in Python

Algorithm:

Step 1:Import necessary libraries: pandas, networkx, matplotlib.pyplot, Bbn, Edge, EdgeType, BbnNode, Variable, EvidenceBuilder, InferenceController
Step 2:Set pandas options to display more columns
Step 3:Read in weather data from a CSV file using pandas
Step 4:Remove records where the target variable RainTomorrow has missing values
Step 5:Fill in missing values in other columns with the column mean
Step 6:Create bands for variables that will be used in the model (Humidity9amCat, Humidity3pmCat, and WindGustSpeedCat)
Step 7:Define a function to calculate probability distributions, which go into the Bayesian Belief Network (BBN)
Step 8:Create BbnNode objects for Humidity9amCat, Humidity3pmCat, WindGustSpeedCat, and RainTomorrow, using the probs() function to calculate their probabilities
Step 9:Create a Bbn object and add the BbnNode objects to it, along with edges between the nodes
Step 10:Convert the BBN to a join tree using the InferenceController
Step 11:Set node positions for the graph
Step 12:Set options for the graph appearance
Step 13:Generate the graph using networkx
Step 14:Update margins and display the graph using matplotlib.pyplot

Program:

''' Type your Code here '''

Output:

''' Show the output in the form screenshorts

Result:

Thus a Bayesian Network is generated using Python