diff --git a/PageRank/Page Rank_stage_5.py b/PageRank/Page Rank_stage_5.py
new file mode 100644
index 0000000..09a6aad
--- /dev/null
+++ b/PageRank/Page Rank_stage_5.py	
@@ -0,0 +1,97 @@
+import numpy as np
+import numpy.linalg as la
+from io import StringIO
+
+def print_mat(mat):
+
+    stream = StringIO()
+    np.savetxt(stream, mat, fmt="%.3f")
+    print( stream.getvalue() )
+
+# -----------------------------------
+
+def get_convergent_vector(L, r_0, threshold=0.01):
+
+    '''
+    :param L: transition matrix
+    :param r_0: initial vector
+    :param threshold: parameter for convergence condition
+    :return: convergent vector
+    '''
+
+    r_cur = r_0
+
+    while True:
+
+        r_next = np.matmul(L, r_cur)
+
+        if la.norm(r_next - r_cur) < threshold:
+            # check convergence condition is met or not
+            break
+
+        r_cur = r_next
+
+    return r_cur
+
+# -----------------------------------
+def get_matrix_with_damping(matrix, damping=0.5):
+
+    # get the size of matrix
+    n, _ = matrix.shape
+    
+    return matrix * damping + ( 1 - damping ) * np.ones((n, n)) / n
+
+
+
+# -----------------------------------
+
+# get the size
+n = int( input() )
+
+# get the name of website
+websites = input().split()
+
+# get transition matrix
+matrix = [ [ 0.0 for x in range(n)] for y in range(n) ]
+
+for y in range(n):
+    matrix[y] = [ *map( float, input().split() ) ]
+
+# get the name of target website
+target = input()
+
+# convert to numpy array
+matrix = np.array(matrix)
+
+matrix_with_damping = get_matrix_with_damping(matrix, damping=0.5)
+
+r_0 = ( np.ones(n) / n) * 100
+
+# compute pagerank
+r_pagerank = get_convergent_vector(L=matrix_with_damping, r_0=r_0, threshold=0.01)
+
+# output result
+
+
+web_pagerank_dict = {}
+for idx in range(n):
+    web_pagerank_dict[ websites[idx] ] = r_pagerank[idx]
+
+# target is always on the top
+result = [ target ]
+
+# remove target from dictionary
+del web_pagerank_dict[target]
+
+# sorted by pagerank and name of website in ascending order
+for website in sorted(web_pagerank_dict, key=lambda w: (web_pagerank_dict[w], w), reverse=True):
+    result.append( website )
+
+# output top 5 results
+for idx, website in enumerate(result):
+
+    if idx < 5:
+        print(website)
+    else:
+        break
+