style: format code with Autopep8, Black, ClangFormat, dotnet-format, Go fmt, Gofumpt, isort, PHP CS Fixer, Prettier, RuboCop, Rustfmt, Scalafmt, StandardJS, StandardRB and Yapf

This commit fixes the style issues introduced in e8c6c5d according to the output
from Autopep8, Black, ClangFormat, dotnet-format, Go fmt, Gofumpt, isort, PHP CS
Fixer, Prettier, RuboCop, Rustfmt, Scalafmt, StandardJS, StandardRB and Yapf.

Details: None

Author: deepsource-autofix[bot]

generate_recommendations.py

@@ -2,42 +2,48 @@
 from sklearn.metrics.pairwise import cosine_similarity
 
 # Load user data
-user_data = pd.read_csv('user_data.csv')
+user_data = pd.read_csv("user_data.csv")
 
 # Load service data
-service_data = pd.read_csv('service_data.csv')
+service_data = pd.read_csv("service_data.csv")
 
 # Merge user and service data
-merged_data = pd.merge(user_data, service_data, on='service_id')
+merged_data = pd.merge(user_data, service_data, on="service_id")
 
 # Create user-service matrix
-user_service_matrix = merged_data.pivot_table(index='user_id', columns='service_id', values='rating')
+user_service_matrix = merged_data.pivot_table(index="user_id",
+                                              columns="service_id",
+                                              values="rating")
 
 # Calculate similarity between users
 user_similarity = cosine_similarity(user_service_matrix)
 
+
 # Function to generate recommendations for a user
 def generate_recommendations(user_id, top_n=5):
     # Get index of the user
     user_index = user_service_matrix.index.get_loc(user_id)
-    
+
     # Calculate similarity scores with other users
     similarity_scores = user_similarity[user_index]
-    
+
     # Get top similar users
-    top_similar_users = similarity_scores.argsort()[:-top_n-1:-1]
-    
+    top_similar_users = similarity_scores.argsort()[:-top_n - 1:-1]
+
     # Get services rated by similar users
-    services_rated_by_similar_users = user_service_matrix.iloc[top_similar_users].dropna(axis=1)
-    
+    services_rated_by_similar_users = user_service_matrix.iloc[
+        top_similar_users].dropna(axis=1)
+
     # Calculate average rating for each service
     service_avg_ratings = services_rated_by_similar_users.mean()
-    
+
     # Sort services based on average ratings
-    recommended_services = service_avg_ratings.sort_values(ascending=False)[:top_n]
-    
+    recommended_services = service_avg_ratings.sort_values(
+        ascending=False)[:top_n]
+
     return recommended_services
 
+
 # Generate recommendations for a user
 user_id = 1
 recommendations = generate_recommendations(user_id, top_n=5)