Update app.py

app.py

@@ -10,10 +10,8 @@ from sklearn.metrics import accuracy_score, classification_report
 def load_data():
     df = pd.read_csv(r"C:\Users\pavilion\Desktop\INNOMATICS_PROJECTS(SL)\Student_Depresion_Prediction(Machine_Learning)\student_depression_dataset.csv")
     
-    # Drop unnecessary columns
     df.drop(columns=['id', 'City', 'Work Pressure', 'Job Satisfaction'], inplace=True, errors='ignore')
 
-    # Keep only 'Student' in Profession and then drop the column
     if 'Profession' in df.columns:
         df = df[df['Profession'].str.lower() == 'student']
         df.drop(columns=['Profession'], inplace=True, errors='ignore')