Insurance Claim Prediction app

Overview

This application predicts whether a customer will make an insurance claim based on their profile and vehicle details. The app leverages machine learning models, logged and managed via MLflow, to ensure version control and easy deployment. The predictions are served through a Flask web application, which provides a simple user interface for file uploads and displays the prediction results.

Features

• Predict Insurance Claims: Upload customer and vehicle details in a CSV file to get predictions on insurance claims.
• Model Versioning with MLflow: Models are logged, tracked, and managed using MLflow.
• Easy Deployment: The app is designed to be deployed on local servers, with simple integration into cloud environments if needed.

Installation

Prerequisites

• Python 3.7 or later
• pip for package manaegement
• MLflow installed and configured
• Flask installed

Setup Instructions

1. Clone the repository:

   git clone https://github.com/yourusername/insurance-claim.git
   cd insurance-claim-prediction

2. Create a virtual environment:

python -m venv newvenv
source newvenv/bin/activate  # On Windows use `newvenv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Set up MLflow: Ensure that MLflow is running on your machine you can specify the port you want mlflow to run on (optional)

mlflow ui # to start mlflow ui or
mlflow ui --port 8080 # to specify the port

Configure the tracking URI in your Flask app or environment variables:

mlflow.set_tracking_uri("http://127.0.0.1:8080")

5. Run the Flask app:

python app.py

• The app will be accessible at http://127.0.0.1:5050 as specified in the app.py file.**

**Usage: **

• Access the Web Interface:
• Navigate to http://127.0.0.1:5000 in your web browser.
• Upload a CSV File:
• Prepare a CSV file with the required customer and vehicle details. Check mlartifacts/columns.txt for required details.
• Upload the file via the provided form on the web interface.

View Predictions:

After uploading the file, the app will return predictions indicating whether each customer is likely to make a claim. Model Details

• Model Training: The model was trained using historical insurance claim data, with features including customer demographics, vehicle specifications, and other relevant factors.
• Model Logging with MLflow: During training, the model was logged using MLflow, capturing metrics, parameters, and artifacts.
• Model Deployment: The model can be loaded directly from the MLflow tracking server or from a local pickle file.

Project Structure

insurance-claim-prediction-app/
│
├── app.py                      # Main Flask application
├── templates/
│   └── temp.html               # HTML template for file upload
├── mlartifacts/                # Directory for storing MLflow artifacts
│   └── 1909189155/            # Directory for a specific model run
│       ├── model.pkl           # Serialized model file (if using locally)
├── newvenv/                    # Virtual environment (optional)
├── requirements.txt            # Python dependencies
└── README.md                   # This README file

Contributing We welcome contributions to enhance the functionality and performance of the app. Please follow these steps:

Contact For questions, suggestions, or feedback, please open an issue or contact the project maintainers at olusimeon4@gmail.com.