Update README.md

README.md

@@ -1,6 +1,12 @@
 ---
 license: mit
 pipeline_tag: text-classification
+datasets:
+- HuggingFaceTB/cosmopedia
+metrics:
+- accuracy
+tags:
+- finance
 ---
 # Nexus Bank Loan Default Prediction Model
 
@@ -21,37 +27,4 @@ To use the model, you can input the salary and number of dependents of a custome
 
 ## 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()
+The data used for training this model was obtained from Nexus Bank.