diff --git a/algorithms/greedy/huffman_coding.py b/algorithms/greedy/huffman_coding.py
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+"""
+In regular text file each character would take up 1 byte (8 bits) i.e. there are 16 characters
+(including white spaces and punctuations) which normally take up 16 bytes.
+In the ASCII code there are 256 characters and this leads to the use of 8 bits 
+to represent each character but in any test file we do not have use all 256 characters.
+For example, in any English language text, generally the character ‘e’ appears more than the character ‘z’.
+To achieve compression, we can often use a shorter bit string to represent more frequently occurring characters.
+We do not have to represent all 256 characters, unless they all appear in the document.
+For optimal compression we use Huffman coding.
+
+Solution:
+A greedy algorithm constructs an optimal prefix code called Huffman code. 
+The algorithm builds the tree T corresponding to the optimal code in a bottom-up manner. 
+It begins with a set of |C| leaves (C is the number of characters) and perform |C| – 1 ‘merging’ 
+operations to create the final tree.
+In the Huffman algorithm ‘n’ denotes the number of set of characters, 
+z denotes the parent node and x & y are the left & right child of z respectively.
+"""
+# A Huffman Tree Node
+class node:
+    def __init__(self, freq, symbol, left=None, right=None):
+        # frequency of symbol
+        self.freq = freq
+
+        # symbol name (character)
+        self.symbol = symbol
+
+        # node left of current node
+        self.left = left
+
+        # node right of current node
+        self.right = right
+
+        # tree direction (0/1)
+        self.huff = ''
+
+# utility function to print huffman
+# codes for all symbols in the newly
+# created Huffman tree
+
+
+def print_nodes(node, val=''):
+    # huffman code for current node
+    new_val = val + str(node.huff)
+
+    # if node is not an edge node
+    # then traverse inside it
+    if(node.left):
+        print_nodes(node.left, new_val)
+    if(node.right):
+        print_nodes(node.right, new_val)
+
+        # if node is edge node then
+        # display its huffman code
+    if(not node.left and not node.right):
+        print(f"{node.symbol} -> {new_val}")
+
+
+# characters for huffman tree
+chars = ['a', 'b', 'c', 'd', 'e', 'f']
+
+# frequency of characters
+freq = [ 5, 9, 12, 13, 16, 45]
+
+# list containing unused nodes
+nodes = []
+
+# converting ccharacters and frequencies
+# into huffman tree nodes
+for x in range(len(chars)):
+    nodes.append(node(freq[x], chars[x]))
+
+while len(nodes) > 1:
+    # sort all the nodes in ascending order
+    # based on theri frequency
+    nodes = sorted(nodes, key=lambda x: x.freq)
+
+    # pick 2 smallest nodes
+    left = nodes[0]
+    right = nodes[1]
+
+    # assign directional value to these nodes
+    left.huff = 0
+    right.huff = 1
+
+    # combine the 2 smallest nodes to create
+    # new node as their parent
+    new_node = node(left.freq+right.freq, left.symbol+right.symbol, left, right)
+
+    # remove the 2 nodes and add their
+    # parent as new node among others
+    nodes.remove(left)
+    nodes.remove(right)
+    nodes.append(new_node)
+
+# Huffman Tree is ready!
+print_nodes(nodes[0])
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