diff --git a/README.md b/README.md
index 6a386b3..3841be9 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,9 @@
+#Update
+The code compares 5 different classifiers from Scikit.
+Results:  With the data given, Decision Tree, KN and MLP classifier give the most accurate predictions.
+
+
+
 # gender_classification_challenge
 
 ##Overview
diff --git a/demo.py b/demo.py
index ac513d8..f701deb 100644
--- a/demo.py
+++ b/demo.py
@@ -1,24 +1,65 @@
-from sklearn import tree
+# Author Jeevith. 
+# This code is in response to the code challenge for the video - https://www.youtube.com/watch?v=T5pRlIbr6gg  of Siralogy (Youtube). Thank you Sirajology for introducing such concepts in a easy manner from a newbie! 
 
-clf = tree.DecisionTreeClassifier()
+#Importing modules
+from sklearn import tree
+from sklearn import svm
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.gaussian_process import GaussianProcessClassifier
+from sklearn.neural_network import MLPClassifier
 
-## CHALLENGE - create 3 more classifiers...
-#1
-#2
-#3
 
-#[height, weight, shoe_size]
-X = [[181, 80, 44], [177, 70, 43], [160, 60, 38], [154, 54, 37], [166, 65, 40], [190, 90, 47], [175, 64, 39],
-     [177, 70, 40], [159, 55, 37], [171, 75, 42], [181, 85, 43]]
+#Feature set - Data: [height, weight, shoe_size]
+X = [[181, 80, 44], [177, 70, 43], [160, 60, 38], [154, 54, 37], [166, 65, 40], [190, 90, 47], [175, 64, 39], [177, 70, 40], [159, 55, 37], [171, 75, 42], [181, 85, 43]]
 
+#Label set - Gender
 Y = ['male', 'male', 'female', 'female', 'male', 'male', 'female', 'female', 'female', 'male', 'male']
 
 
-#CHALLENGE - ...and train them on our data
-clf = clf.fit(X, Y)
+# Calling decision tree classifier and fitting
+clf1 =tree.DecisionTreeClassifier()
+clfDT =clf1.fit(X, Y) 
+
+#Calling support vector machine and fitting
+clf2 = svm.SVC(probability=True)
+clfSVC =clf2.fit(X, Y)  
+
+#Calling KNeighbors classifier and fitting 
+clf3 = KNeighborsClassifier(n_neighbors=3)
+clfKN =clf3.fit(X, Y)  
+
+#Calling gaussian_process classifier and fitting 
+clf4 = GaussianProcessClassifier()
+clfGP = clf4.fit(X, Y)
+
+##Calling MLPClassifier and fitting 
+clf5 = MLPClassifier(learning_rate='constant', learning_rate_init=0.001,)
+clfMLP = clf5.fit(X, Y)
+
+test = [180, 80, 42]
+#Storing results 
+predictionDT = clfDT.predict (test) 
+predictionSVC = clfSVC.predict (test) 
+predictionKN = clfKN.predict (test) 
+predictionGP = clfGP.predict (test) 
+predictionMLP = clfMLP.predict (test) 
+
+#Storing probabilities
+probaDT = clfDT.predict_proba (test)
+probaSVC = clfSVC.predict_proba (test) 
+probaKN = clfKN.predict_proba (test) 
+probaGP = clfGP.predict_proba (test) 
+probaMLP = clfMLP.predict_proba (test)  
+
+
+print "DT Classifier test data {} is predicted as {} with probability of {}".format(test, predictionDT, probaDT)
+
+print "SV Classifier test data {} is predicted as {} with probability of {}".format(test, predictionSVC, probaSVC)
+
+print "KN classifier test data {} is predicted as {} with probability of {}".format(test, predictionKN, probaKN)
+
+print "GP classifier test data {} is predicted as {} with probability of {}".format(test, predictionGP, probaGP )
 
-prediction = clf.predict([[190, 70, 43]])
+print "MLPClassifier test data {} is predicted as {} with probability of {}".format(test, predictionDT, probaDT)
 
-#CHALLENGE compare their reusults and print the best one!
 
-print prediction