diff --git a/graphs/VisualizeGraph.py b/graphs/VisualizeGraph.py
new file mode 100644
index 000000000000..7c6c1a321447
--- /dev/null
+++ b/graphs/VisualizeGraph.py
@@ -0,0 +1,194 @@
+import networkx as nx
+import matplotlib.pyplot as plt
+
+
+# For tutorial of networkx visit https://networkx.org/documentation/latest/tutorial.html
+
+
+
+def undirected_visualize_from_adjmat(adjmat):
+    size = len(adjmat)
+
+    # Create a graph object
+    G = nx.Graph()
+
+    for i in range(size):
+        for j in range(size):
+            if adjmat[i][j] != 0:
+                # Add edge to the graph object
+                G.add_edge(i, j, weight=adjmat[i][j])
+
+
+    # Specify the layout of graph
+    pos = nx.spring_layout(G)
+
+    plt.figure(figsize=(10, 10))
+    
+    # Draw the vertices
+    nx.draw_networkx_nodes(G, pos, node_size=700, node_color='skyblue', alpha=0.9)
+    
+    # Draw the edges
+    nx.draw_networkx_edges(G, pos, width=2, alpha=0.5, edge_color='gray')
+    
+    # Draw the Vertices Labels
+    nx.draw_networkx_labels(G, pos, font_size=12, font_color='black', font_family='sans-serif')
+
+    # Draw the weights of edges
+    edge_labels = nx.get_edge_attributes(G, 'weight')
+    nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_color='black', font_size=10)
+
+    plt.title("Undirected Graph Visualization", size=15)
+    plt.axis('off')
+    plt.show()
+
+
+
+
+def directed_visualize_from_adjmat(adjmat):
+    size = len(adjmat)
+
+    # Create a Digraph object
+    G = nx.DiGraph()
+
+    for i in range(size):
+        for j in range(size):
+            if adjmat[i][j] != 0:
+                # Add edges to the graph object
+                G.add_edge(i, j, weight=adjmat[i][j])
+
+    # Specify the layout to use while plotting the graph 
+    pos = nx.spring_layout(G)
+
+    plt.figure(figsize=(10, 10))
+    
+    # Draw the vertices
+    nx.draw_networkx_nodes(G, pos, node_size=700, node_color='skyblue', alpha=0.9)
+    
+    # Draw the labels for vertices
+    nx.draw_networkx_edges(G, pos,arrowstyle='-|>', arrowsize=30, width=2, alpha=0.5, edge_color='gray')
+    
+    # Draw the weights
+    nx.draw_networkx_labels(G, pos, font_size=12, font_color='black', font_family='sans-serif')
+
+    # Draw the weights
+    edge_labels = nx.get_edge_attributes(G, 'weight')
+    nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_color='black', font_size=10)
+
+    plt.title("Directed Graph Visualization", size=15)
+    plt.axis('off')
+    plt.show()
+
+
+def visualize_from_adjmat(adjmat,isdirected):
+    if isdirected:
+        directed_visualize_from_adjmat(adjmat)
+    else:
+        undirected_visualize_from_adjmat(adjmat)
+
+
+
+
+def edges_to_adjacency_matrix(vertex_count, edge_count, is_directed):
+    '''
+    Takes input in form of edges and convert it to adjacency matrix.
+    '''
+
+    adjacency_matrix = [[0] * vertex_count for _ in range(vertex_count)]
+    for _ in range(edge_count):
+        u, v, weight = map(int, input().split())
+        adjacency_matrix[u][v] = weight
+        if not is_directed:
+            adjacency_matrix[v][u] = weight
+    return adjacency_matrix
+
+def matinput(vertex_count, edge_count, is_directed):
+    '''
+    Takes input in form of adjacency matrix and convert it to adjacency matrix.
+    '''
+
+    adjacency_matrix = []
+    print("Enter the adjacency matrix row by row:")
+    for _ in range(vertex_count):
+        row = list(map(int, input().split()))
+        adjacency_matrix.append(row)
+    return adjacency_matrix
+
+def list_to_adjacency_matrix(vertex_count, edge_count, is_directed):
+    '''
+    Takes input in form of adjacency list and convert it to adjacency matrix.
+    '''
+
+    adjacency_matrix = [[0] * vertex_count for _ in range(vertex_count)]
+    print("Enter the adjacency list:")
+    for u in range(vertex_count):
+        print("Neighbours of ",u,end=": ")
+        neighbours = input().split()
+        for neigh in neighbours:
+            v,weight = map(int,neigh.split(','))
+            adjacency_matrix[u][v] = weight
+            if not is_directed:
+                adjacency_matrix[v][u] = weight
+    return adjacency_matrix
+
+
+def main():
+    vertex_count = int(input("Enter the number of vertices: "))
+    edge_count = int(input("Enter the number of edges: "))
+    is_directed = int(input("Is the graph Directed?? 1 or 0 :"))
+
+    print("Enter the type of input:")
+    print("1: Edge List")
+    print("2: Adjacency Matrix")
+    print("3: Adjacency List")
+    input_type = int(input())
+
+    if input_type == 1:
+        print("The expected input is of the form u v w where w is the weight.")
+        adjacency_matrix = edges_to_adjacency_matrix(vertex_count, edge_count, is_directed)
+    elif input_type == 2:
+        print("Expected input is of the form V columns and V rows with the weights of the edges.")
+        adjacency_matrix = matinput(vertex_count, edge_count, is_directed)
+    elif input_type == 3:
+        print("Expected input is of the form for neighbour vertex v,w")
+        adjacency_matrix = list_to_adjacency_matrix(vertex_count, edge_count, is_directed)
+    else:
+        print("Enter valid input.")
+        return
+
+    visualize_from_adjmat(adjacency_matrix, is_directed)
+
+if __name__ == "__main__":
+    main()
+
+
+
+'''
+Example Inputs:
+
+1) Edge List:
+
+The expected input is of the form u v w where w is the weight.
+0 1 1
+0 2 1
+0 3 1
+3 1 1
+3 2 1
+
+2) Adj Mat:
+
+Expected input is of the form V columns and V rows with the weights of the edges.
+Enter the adjacency matrix row by row:
+0 1 1 1
+1 0 0 1
+1 0 0 1
+1 1 1 0
+
+3) Adj List:
+Expected input is of the form for neighbour vertex v,w
+Enter the adjacency list:
+Neighbours of  0: 1,2 2,3 3,-3
+Neighbours of  1: 2,3
+Neighbours of  2: 3,-3
+Neighbours of  3: 
+
+'''
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