Update app.py

app.py

@@ -5,28 +5,21 @@ import matplotlib.pyplot as plt
 from sklearn.model_selection import train_test_split
 from sklearn.neighbors import KNeighborsClassifier
 import gradio as gr
-
 # Load data
-nexus_bank = pd.read_csv('C:/Users/IT zone computer/nexus_bank_dataa.csv')
-
-
+nexus_bank = pd.read_csv('C:/Users/IT zone computer/Desktop/knn dataset/nexus_bank_dataa.csv')
 # Preprocessing
 X = nexus_bank[['salary', 'dependents']]
 y = nexus_bank['defaulter']
-
 # Train-test split
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=90)
-
 # Model training
 knn_classifier = KNeighborsClassifier()
 knn_classifier.fit(X_train, y_train)
-
 # Prediction function
 def predict_defaulter(salary, dependents):
     input_data = [[salary, dependents]]
     knn_predict = knn_classifier.predict(input_data)
     return "Yes! It's a Defaulter" if knn_predict[0] == 1 else "No! It's not a Defaulter"
-
 # Interface
 interface = gr.Interface(
     fn=predict_defaulter,
@@ -34,6 +27,5 @@ interface = gr.Interface(
     outputs="text",
     title="Defaulter Prediction"
 )
-
 # Launch the interface
 interface.launch()