diff --git a/machine_learning/dbscan.py b/machine_learning/dbscan.py
index 3e044d8f6..566f8e217 100644
--- a/machine_learning/dbscan.py
+++ b/machine_learning/dbscan.py
@@ -2,8 +2,10 @@ import pandas as pd
 import math
 import matplotlib.pyplot as plt
 from typing import Dict, List
+
+
 class dbscan:
-    '''
+    """
     DBSCAN Algorithm :
     Density-Based Spatial Clustering Of Applications With Noise
     Refer this website for more details : https://en.wikipedia.org/wiki/DBSCAN
@@ -21,9 +23,10 @@ class dbscan:
     obj = dbscan.dbscan(minPts, radius, file)
     obj.print_dbscan()
     obj.plot_dbscan()
-    '''
-    def __init__(self, minPts : int, radius : int, file : str) -> None:
-        '''
+    """
+
+    def __init__(self, minPts: int, radius: int, file: str) -> None:
+        """
         Constructor
 
         Attributes:
@@ -48,13 +51,14 @@ class dbscan:
             6 | 4
             7 | 3
             -----
-        '''
+        """
         self.minPts = minPts
         self.radius = radius
         self.file = file
         self.dict1 = self.perform_dbscan()
+
     def perform_dbscan(self) -> Dict[int, List[int]]:
-        '''
+        """
         Parameters:
         -----------
         None
@@ -62,7 +66,7 @@ class dbscan:
         Return:
         --------
         Dictionary with points and the list of points that lie in its radius
-        '''
+        """
         data = pd.read_csv(self.file)
 
         minPts = self.minPts
@@ -71,50 +75,75 @@ class dbscan:
         dict1 = {}
         for i in range(len(data)):
             for j in range(len(data)):
-                dist = math.sqrt(pow(data['x'][j] - data['x'][i],2) + pow(data['y'][j] - data['y'][i],2))
+                dist = math.sqrt(
+                    pow(data["x"][j] - data["x"][i], 2)
+                    + pow(data["y"][j] - data["y"][i], 2)
+                )
                 if dist < e:
-                    if i+1 in dict1:
-                        dict1[i+1].append(j+1)
+                    if i + 1 in dict1:
+                        dict1[i + 1].append(j + 1)
                     else:
-                        dict1[i+1] = [j+1,]
+                        dict1[i + 1] = [
+                            j + 1,
+                        ]
 
         return dict1
+
     def print_dbscan(self) -> None:
-        '''
+        """
         Outputs:
         --------
         Prints each point and if it is a core or a noise (w/ border)
-        '''
+        """
         for i in self.dict1:
-            print(i," ",self.dict1[i], end=' ---> ')
+            print(i, " ", self.dict1[i], end=" ---> ")
             if len(self.dict1[i]) >= self.minPts:
                 print("Core")
             else:
                 for j in self.dict1:
-                    if i != j and len(self.dict1[j]) >= self.minPts and i in self.dict1[j]:
+                    if (
+                        i != j
+                        and len(self.dict1[j]) >= self.minPts
+                        and i in self.dict1[j]
+                    ):
                         print("Noise ---> Border")
                         break
                 else:
                     print("Noise")
+
     def plot_dbscan(self) -> None:
-        '''
+        """
         Output:
         -------
         A matplotlib plot that show points as core and noise along with the circle that lie within it.
-        '''
+        """
         data = pd.read_csv(self.file)
         e = self.radius
         for i in self.dict1:
             if len(self.dict1[i]) >= self.minPts:
-                plt.scatter(data['x'][i-1], data['y'][i-1], color='red')
-                circle = plt.Circle((data['x'][i-1], data['y'][i-1]), e, color='blue', fill=False)
+                plt.scatter(data["x"][i - 1], data["y"][i - 1], color="red")
+                circle = plt.Circle(
+                    (data["x"][i - 1], data["y"][i - 1]), e, color="blue", fill=False
+                )
                 plt.gca().add_artist(circle)
-                plt.text(data['x'][i-1], data['y'][i-1], 'P'+str(i), ha='center', va='bottom')
+                plt.text(
+                    data["x"][i - 1],
+                    data["y"][i - 1],
+                    "P" + str(i),
+                    ha="center",
+                    va="bottom",
+                )
             else:
-                plt.scatter(data['x'][i-1], data['y'][i-1], color='green')
-                plt.text(data['x'][i-1], data['y'][i-1], 'P'+str(i), ha='center', va='bottom')
-        plt.xlabel('X')
-        plt.ylabel('Y')
-        plt.title('DBSCAN Clustering')
-        plt.legend(['Core','Noise'])
+                plt.scatter(data["x"][i - 1], data["y"][i - 1], color="green")
+                plt.text(
+                    data["x"][i - 1],
+                    data["y"][i - 1],
+                    "P" + str(i),
+                    ha="center",
+                    va="bottom",
+                )
+        plt.xlabel("X")
+        plt.ylabel("Y")
+        plt.title("DBSCAN Clustering")
+        plt.legend(["Core", "Noise"])
         plt.show()