Kanuri29/Bank_Risk_analysis

Customer Churn Prediction App

This is a Streamlit-based web application for predicting customer churn using XGBoost machine learning model.

Features

• —Upload CSV dataset for churn modeling
• —Automatic data preprocessing with OneHotEncoding for categorical features
• —Train XGBoost classifier on the dataset
• —Display model evaluation metrics: accuracy, confusion matrix, cross-validation scores
• —Interactive sidebar for inputting new customer data to predict churn probability

Dataset

The app expects a CSV file with the following columns:

• —CreditScore
• —Geography
• —Gender
• —Age
• —Tenure
• —Balance
• —NumOfProducts
• —HasCrCard
• —IsActiveMember
• —EstimatedSalary
• —Exited (target variable)

A sample dataset Churn_Modelling.csv is included in the repository.

Installation

1. 1.Clone the repository:

   git clone <repository-url>
   cd <project-directory>

1. 1.Install dependencies:

   pip install -r requirements.txt

Usage

Run the Streamlit app:

streamlit run app.py

Open the provided URL in your browser, upload the CSV file, and interact with the app.

Requirements

• —Python 3.7+
• —Streamlit
• —XGBoost
• —Scikit-learn
• —Pandas
• —Matplotlib
• —Seaborn

License

[Add license information if applicable]