Create app.py

app.py

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+import pandas as pd
+import numpy as np
+import seaborn as sns
+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('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,
+    inputs=["number", "number"],
+    outputs="text",
+    title="Defaulter Prediction"
+)
+
+# Launch the interface
+interface.launch()