diff --git a/bayesian.py b/bayesian.py
new file mode 100644
index 0000000..d8b093e
--- /dev/null
+++ b/bayesian.py
@@ -0,0 +1,78 @@
+import bayespy as bp
+import numpy as np
+import csv
+from colorama import init
+from colorama import Fore, Back, Style
+init()
+# Define Parameter Enum values
+#Age
+ageEnum = {'SuperSeniorCitizen':0, 'SeniorCitizen':1, 'MiddleAged':2, 'Youth':3, 'Teen':4}
+# Gender
+genderEnum = {'Male':0, 'Female':1}
+# FamilyHistory
+familyHistoryEnum = {'Yes':0, 'No':1}
+# Diet(Calorie Intake)
+dietEnum = {'High':0, 'Medium':1, 'Low':2}
+# LifeStyle
+lifeStyleEnum = {'Athlete':0, 'Active':1, 'Moderate':2, 'Sedetary':3}
+# Cholesterol
+cholesterolEnum = {'High':0, 'BorderLine':1, 'Normal':2}
+# HeartDisease
+heartDiseaseEnum = {'Yes':0, 'No':1}
+#heart_disease_data.csv
+with open('heart_disease_data.csv') as csvfile:
+lines = csv.reader(csvfile)
+dataset = list(lines)
+data = []
+for x in dataset:
+data.append([ageEnum[x[0]],genderEnum[x[1]],familyHistoryEnum[x[2]],dietEnum[x[3]],lifeStyleEnum
+[x[4]],cholesterolEnum[x[5]],heartDiseaseEnum[x[6]]])
+# Training data for machine learning todo: should import from csv
+data = np.array(data)
+N = len(data)
+# Input data column assignment
+p_age = bp.nodes.Dirichlet(1.0*np.ones(5))
+age = bp.nodes.Categorical(p_age, plates=(N,))
+age.observe(data[:,0])
+p_gender = bp.nodes.Dirichlet(1.0*np.ones(2))
+gender = bp.nodes.Categorical(p_gender, plates=(N,))
+gender.observe(data[:,1])
+p_familyhistory = bp.nodes.Dirichlet(1.0*np.ones(2))
+familyhistory = bp.nodes.Categorical(p_familyhistory, plates=(N,))
+familyhistory.observe(data[:,2])
+p_diet = bp.nodes.Dirichlet(1.0*np.ones(3))
+diet = bp.nodes.Categorical(p_diet, plates=(N,))
+diet.observe(data[:,3])
+p_lifestyle = bp.nodes.Dirichlet(1.0*np.ones(4))
+lifestyle = bp.nodes.Categorical(p_lifestyle, plates=(N,))
+lifestyle.observe(data[:,4])
+p_cholesterol = bp.nodes.Dirichlet(1.0*np.ones(3))
+cholesterol = bp.nodes.Categorical(p_cholesterol, plates=(N,))
+cholesterol.observe(data[:,5])
+# Prepare nodes and establish edges
+# np.ones(2) -> HeartDisease has 2 options Yes/No
+# plates(5, 2, 2, 3, 4, 3) -> corresponds to options present for domain values
+p_heartdisease = bp.nodes.Dirichlet(np.ones(2), plates=(5, 2, 2, 3, 4, 3))
+heartdisease = bp.nodes.MultiMixture([age, gender, familyhistory, diet, lifestyle, cholesterol],
+bp.nodes.Categorical, p_heartdisease)
+heartdisease.observe(data[:,6])
+p_heartdisease.update()
+# Sample Test with hardcoded values
+#print("Sample Probability")
+#print("Probability(HeartDisease|Age=SuperSeniorCitizen, Gender=Female, FamilyHistory=Yes,
+DietIntake=Medium, LifeStyle=Sedetary, Cholesterol=High)")
+#print(bp.nodes.MultiMixture([ageEnum['SuperSeniorCitizen'], genderEnum['Female'],
+familyHistoryEnum['Yes'], dietEnum['Medium'], lifeStyleEnum['Sedetary'], cholesterolEnum['High']],
+bp.nodes.Categorical, p_heartdisease).get_moments()[0][heartDiseaseEnum['Yes']])
+# Interactive Test
+m = 0
+while m == 0:
+print("\n")
+res = bp.nodes.MultiMixture([int(input('Enter Age: ' + str(ageEnum))), int(input('Enter Gender: ' +
+str(genderEnum))), int(input('Enter FamilyHistory: ' + str(familyHistoryEnum))), int(input('Enter
+dietEnum: ' + str(dietEnum))), int(input('Enter LifeStyle: ' + str(lifeStyleEnum))), int(input('Enter
+Cholesterol: ' + str(cholesterolEnum)))], bp.nodes.Categorical,
+p_heartdisease).get_moments()[0][heartDiseaseEnum['Yes']]
+print("Probability(HeartDisease) = " + str(res))
+#print(Style.RESET_ALL)
+m = int(input("Enter for Continue:0, Exit :1 "))