Create README.md

README.md

@@ -1,3 +1,57 @@
 ---
 license: mit
+pipeline_tag: text-classification
 ---
+# Nexus Bank Loan Default Prediction Model
+
+This is a machine learning model to predict loan defaulters for Nexus Bank.
+
+## Usage
+
+To use the model, you can input the salary and number of dependents of a customer, and it will predict whether they are likely to default on their loan.
+
+## Dependencies
+
+- pandas
+- numpy
+- seaborn
+- matplotlib
+- scikit-learn
+- gradio
+
+## Data Source
+
+The data used for training this model was obtained from Nexus Bank.
+
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+%matplotlib inline
+nexus_bank = pd.read_csv('nexus_bank_dataa.csv')
+nexus_bank.head()
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.15, random_state=90)
+from sklearn.neighbors import KNeighborsClassifier
+knn_classifier =KNeighborsClassifier()
+knn_classifier.fit(X_train,y_train)
+knn_predict = knn_classifier.predict(X_test)
+knn_predict
+import gradio as gr
+
+# Prediction function
+def predict_defaulter(salary, dependents):
+    input_data = [[salary, dependents]]
+    knn_predict = knn_classifier.predict(input_data)
+    return "Yes! its Defaulter" if knn_predict[0] == 1 else "No! its not Defaulter"
+
+# Interface
+interface = gr.Interface(
+    fn=predict_defaulter,
+    inputs=["number", "number"],
+    outputs="text",
+    title="Defaulter Prediction"
+)
+
+# Launch the interface
+interface.launch()