Upload recommender_system.py

recommender_system.py

@@ -63,7 +63,7 @@ def recommend_books(df: pd.DataFrame, book_to_be_recommended: str) -> pd.DataFra
     # Create DataFrame with correlations
     correlations_df = pd.DataFrame({
         'Book-Title': correlations.index,
-        'Correlation': correlations.values,
+        'Correlation [%]': correlations.values,
     })
 
     # Merge correlations_df with average_ratings
@@ -73,6 +73,10 @@ def recommend_books(df: pd.DataFrame, book_to_be_recommended: str) -> pd.DataFra
     # Sort by correlation value
     correlations_df = correlations_df.sort_values('Correlation', ascending=False)
 
+    # convert correlation column to percentage and limit to two decimals
+    correlations_df['Correlation'] = correlations_df['Correlation'] * 100
+    correlations_df['Correlation'] = correlations_df['Correlation'].round(2)
+
     # Remove the book being recommended from the list
     correlations_df = correlations_df[correlations_df['Book-Title'] != book_to_be_recommended]
     correlations_df = correlations_df.head(10)