KaggleXProject

Streamlining Life Insurance Applications with Predictive Modeling

This repository contains a Python application that uses an XGBoost classifier to make predictions based on a CSV dataset. The application is designed to run on your local machine and use it for making predictions.

Prerequisites

Before you can run this application, you'll need to have the following installed on your local machine:

• Python (version 3.8 or later)
• Anaconda or virtual environment for managing Python packages
• XGBoost (Python package)
• Jupyter Notebook (optional but recommended for exploring the model)

Getting Started

1. Clone this repository to your local machine.

   git clone https://github.com/phyllisgitu/KaggleXProject.git

2. cd KaggleXProject
3. pip install -r requirements.txt
4. python app.py

Then, you can access the web application at http://127.0.0.1:8080/

Project Structure

• sample.csv/: This file contains sample csv data.
• life_insurance_classifier_model.pkl/: This file stores the trained XGBoost model.
• app.py: Python script for making predictions using the trained model.
• requirements.txt: List of Python packages required for this project.
• templates: Stores the html file.
• static: stores the css style files.

Acknowledgments

1. KaggleX BIPOC Mentorship Program.
2. Dr Romani Ibrahim Ph.D Information Systems

Feel free to reach out to me if you have any questions or need further assistance.