diff --git a/Code/prediction_scripts/item_based.py b/Code/prediction_scripts/item_based.py
index a1bb9907a..2f5f385a6 100644
--- a/Code/prediction_scripts/item_based.py
+++ b/Code/prediction_scripts/item_based.py
@@ -1,40 +1,42 @@
 import pandas as pd
-import warnings
 import os
 
 app_dir = os.path.dirname(os.path.abspath(__file__))
 code_dir = os.path.dirname(app_dir)
 project_dir = os.path.dirname(code_dir)
 
-#warnings.filterwarnings("ignore")
-
-
-def recommendForNewUser(user_rating):
-    ratings = pd.read_csv(project_dir + "/data/ratings.csv")
-    movies = pd.read_csv(project_dir + "/data/movies.csv")
+def recommend_for_new_user(user_rating):
+    ratings = pd.read_csv(os.path.join(project_dir, "data", "ratings.csv"))
+    movies = pd.read_csv(os.path.join(project_dir, "data", "movies.csv"))
     user = pd.DataFrame(user_rating)
-    userMovieID = movies[movies["title"].isin(user["title"])]
-    userRatings = pd.merge(userMovieID, user)
+    user_movie_id = movies[movies["title"].isin(user["title"])]
+    user_ratings = pd.merge(user_movie_id, user)
 
-    moviesGenreFilled = movies.copy(deep=True)
-    copyOfMovies = movies.copy(deep=True)
-    for index, row in copyOfMovies.iterrows():
-        copyOfMovies.at[index, "genres"] = row["genres"].split("|")
-    for index, row in copyOfMovies.iterrows():
+    movies_genre_filled = movies.copy(deep=True)
+    copy_of_movies = movies.copy(deep=True)
+    
+    for index, row in copy_of_movies.iterrows():
+        copy_of_movies.at[index, "genres"] = row["genres"].split("|")
+    
+    for index, row in copy_of_movies.iterrows():
         for genre in row["genres"]:
-            moviesGenreFilled.at[index, genre] = 1
-    moviesGenreFilled = moviesGenreFilled.fillna(0)
+            movies_genre_filled.at[index, genre] = 1
+    
+    movies_genre_filled = movies_genre_filled.fillna(0)
 
-    userGenre = moviesGenreFilled[moviesGenreFilled.movieId.isin(userRatings.movieId)]
-    userGenre.drop(["movieId", "title", "genres"], axis=1, inplace=True)
-    userProfile = userGenre.T.dot(userRatings.rating.to_numpy())
-    moviesGenreFilled.set_index(moviesGenreFilled.movieId)
-    moviesGenreFilled.drop(["movieId", "title", "genres"], axis=1, inplace=True)
+    user_genre = movies_genre_filled[movies_genre_filled.movieId.isin(user_ratings.movieId)]
+    user_genre.drop(["movieId", "title", "genres"], axis=1, inplace=True)
+    user_profile = user_genre.T.dot(user_ratings.rating.to_numpy())
+    
+    movies_genre_filled.set_index(movies_genre_filled.movieId)
+    movies_genre_filled.drop(["movieId", "title", "genres"], axis=1, inplace=True)
 
-    recommendations = (moviesGenreFilled.dot(userProfile)) / userProfile.sum()
-    joinMoviesAndRecommendations = movies.copy(deep=True)
-    joinMoviesAndRecommendations["recommended"] = recommendations
-    joinMoviesAndRecommendations.sort_values(
+    recommendations = (movies_genre_filled.dot(user_profile)) / user_profile.sum()
+    
+    join_movies_and_recommendations = movies.copy(deep=True)
+    join_movies_and_recommendations["recommended"] = recommendations
+    join_movies_and_recommendations.sort_values(
         by="recommended", ascending=False, inplace=True
     )
-    return [x for x in joinMoviesAndRecommendations["title"]][:201]
+    
+    return list(join_movies_and_recommendations["title"][:201])
diff --git a/Code/recommenderapp/app.py b/Code/recommenderapp/app.py
index b10b5f6a6..d9276ab6e 100644
--- a/Code/recommenderapp/app.py
+++ b/Code/recommenderapp/app.py
@@ -4,7 +4,7 @@
 import sys
 from utils import *
 sys.path.append("../../")
-from Code.prediction_scripts.item_based import recommendForNewUser
+from Code.prediction_scripts.item_based import recommend_for_new_user
 from search import Search
 
 app = Flask(__name__)
@@ -28,7 +28,7 @@ def predict():
         movie_with_rating = {"title": movie, "rating": 5.0}
         if (movie_with_rating not in training_data):
             training_data.append(movie_with_rating)
-    recommendations = recommendForNewUser(training_data)
+    recommendations = recommend_for_new_user(training_data)
     recommendations = recommendations[:10]
     resp = {"recommendations": recommendations}
     return resp