diff --git a/classification.py b/classification.py
index 56333bb..b501a0e 100644
--- a/classification.py
+++ b/classification.py
@@ -5,49 +5,32 @@
 ############### data ###############
 path = "C:\\ML\\Classification\\First_Project_Classification\\data.txt"
 data = pd.read_csv(path,header= None,names=["Exam1","Exam2","Admitted"])
-# print(data)
 
 data.insert(0,"Bais",1)
-# print(data)
 
 # separate featurs and output 
 col =data.shape[1] # output number of columns for data 
 x = data.iloc[ : , 0 : col - 1]
 y = data.iloc[ : , col - 1 : col]
 
-# print("x\n",x)
-# print("="*50)
-# print("y\n",y)
-
 # separate positive and negative data
 positive = data[data["Admitted"].isin([1])]
 negative = data[data["Admitted"].isin([0])]
 
-# print(positive)
-# print(negative)
-
 # draw positive and negative data
-# fig,ax = plt.subplots()
-# ax.scatter(positive["Exam1"],positive["Exam2"],marker='o',c='b') 
-
-# ax.scatter(negative["Exam1"],negative["Exam2"],marker='x',c='r')
-# plt.show()
+fig,ax = plt.subplots()
+ax.scatter(positive["Exam1"],positive["Exam2"],marker='o',c='b') 
+ax.scatter(negative["Exam1"],negative["Exam2"],marker='x',c='r')
+plt.show()
 
 # convert x , y to matrix
 x = np.matrix(x.values)
 y= np.matrix(y.values)
 theta = np.zeros((x.shape[1],1))
 
-# print(x)
-# print(f"y shape : {y.shape}") 
-# print(f"x shape : {x.shape}")
-# print(f"theta shape : {theta.shape}")
-
 # implement Sigmoid Function
 def Sigmoid(z):
     return 1 / (1 + np.exp(-z))
-# test = np.log(Sigmoid(x @ theta))
-# print (f"test {test.shape}")
 
 #implement Cost Function
 def Cost(theta,x, y):
@@ -67,27 +50,6 @@ def Gradient(theta,x,y):
     grad = (1/m) * (x.T @ error)
     return grad
 
-result = sc.optimize.fmin_tnc(func=Cost, x0=theta, args=(x, y), fprime=Gradient)
-
-
-newTheta = result[0]
-
-print(f"newtheta : {newTheta.shape}")
-print(f"x : {x.shape}")
-print(f"y : {y.shape}")
-
-
-
-# print(f"newTheta :\n{newTheta}")
-# print("============================")
-cost0 = Cost(theta,x,y)
-
-print(f"Cost Before = {cost0}")
-print("============================")
-cost1 = Cost(newTheta,x,y)
-
-print(f"Cost After: {cost1}")
-
 def predict(x, theta):
     p = Sigmoid(x @ newTheta)
     for i in range(96):
@@ -97,19 +59,37 @@ def predict(x, theta):
             p[0,i] = 0
     return p
 
-t = predict(x,newTheta)
+""""""""" Training Data """""""""
 
-print(t.reshape(96,1))
+result = sc.optimize.fmin_tnc(func=Cost, x0=theta, args=(x, y), fprime=Gradient)
 
-# p = Sigmoid(x @ newTheta)
 
-# print(p.shape)
+newTheta = result[0]
 
-# for i in range(96):
-#     if p[0,i] > 0.5:
-#         p[0,i] = 1
-#     else:
-#         p[0,i] = 0
+print(f"newtheta : {newTheta.shape}")
+print(f"x : {x.shape}")
+print(f"y : {y.shape}")
+
+cost0 = Cost(theta,x,y)
+print(f"Cost Before = {cost0}")
+print("============================")
 
-# print(p)
+cost1 = Cost(newTheta,x,y)
+print(f"Cost After: {cost1}")
 
+prediction_values = predict(x,newTheta)
+prediction_values = prediction_values.reshape(96,1)
+
+print(prediction_values.shape)
+print(y.shape)
+compare = np.zeros((96, 2))
+compare[:, 0] = prediction_values.flatten()
+compare[:, 1] = y.flatten()
+print(compare)
+a= 0
+for i in range(96):
+    if compare[i,0] == compare[i,1]:
+        a += 1
+accurecy = (a/96) * 100
+print(f"Accurecy : {accurecy}%")
+print("============================")