---
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()