Merge pull request #2273 from avinashkranjan/deepsource-transform-0863d605

format code with autopep8

Author: avinashkranjan

Job Recommendation Engine/Job Recommendation Engine.py

@@ -16,7 +16,7 @@
 # In[2]:
 
 
-data=pd.read_csv('naukri_com-jobs__20190701_20190830__30k_data.csv')
+data = pd.read_csv('naukri_com-jobs__20190701_20190830__30k_data.csv')
 
 
 # In[3]:
@@ -28,8 +28,10 @@
 # In[4]:
 
 
-user_profiles = data[['Uniq Id', 'Role Category', 'Location', 'Job Experience Required', 'Key Skills']]
-job_postings = data[['Uniq Id', 'Role Category', 'Location', 'Job Experience Required', 'Key Skills']]
+user_profiles = data[['Uniq Id', 'Role Category',
+                      'Location', 'Job Experience Required', 'Key Skills']]
+job_postings = data[['Uniq Id', 'Role Category',
+                     'Location', 'Job Experience Required', 'Key Skills']]
 
 
 # ### User Profile
@@ -48,7 +50,8 @@
 
 user_profiles_matrix = pd.get_dummies(user_profiles.drop('Uniq Id', axis=1))
 user_profiles_matrix = normalize(user_profiles_matrix)  # Normalize the matrix
-similarity_matrix = cosine_similarity(user_profiles_matrix, user_profiles_matrix)
+similarity_matrix = cosine_similarity(
+    user_profiles_matrix, user_profiles_matrix)
 
 
 # ### Job recommendation
@@ -59,26 +62,33 @@
 # Define the number of nearest neighbors to consider
 k = 5
 
+
 def get_job_recommendations(user_id):
     user_index = user_profiles[user_profiles['Uniq Id'] == user_id].index[0]
-    similar_users = similarity_matrix[user_index].argsort()[::-1][1:k+1]  # Exclude the user itself
+    similar_users = similarity_matrix[user_index].argsort(
+    )[::-1][1:k+1]  # Exclude the user itself
 
     # Get job postings from similar users
     recommended_roles = []
     for user in similar_users:
         similar_user_id = user_profiles.iloc[user]['Uniq Id']
-        similar_user_roles = data[data['Uniq Id'] == similar_user_id]['Role Category'].values
+        similar_user_roles = data[data['Uniq Id'] ==
+                                  similar_user_id]['Role Category'].values
         recommended_roles.extend(similar_user_roles)
 
     # Filter out already interacted job roles
-    user_interacted_roles = data[data['Uniq Id'] == user_id]['Role Category'].values
-    recommended_roles = list(set(recommended_roles) - set(user_interacted_roles))
+    user_interacted_roles = data[data['Uniq Id']
+                                 == user_id]['Role Category'].values
+    recommended_roles = list(set(recommended_roles) -
+                             set(user_interacted_roles))
 
     # Rank recommended roles based on frequency
-    recommended_roles = pd.Series(recommended_roles).value_counts().sort_values(ascending=False)
+    recommended_roles = pd.Series(
+        recommended_roles).value_counts().sort_values(ascending=False)
 
     return recommended_roles.index.tolist()
 
+
 # Example usage
 user_id = '9be62c49a0b7ebe982a4af1edaa7bc5f'
 recommended_roles = get_job_recommendations(user_id)
@@ -90,11 +100,4 @@ def get_job_recommendations(user_id):
 # In[ ]:
 
 
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 # In[ ]:
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