Insurance Premium Prediction Model

A deep learning model for predicting insurance premiums using PyTorch for Kaggle's Regression with an Insurance Dataset competition.

Overview

This project implements a neural network model to predict insurance premium amounts based on various customer features. The model uses a multi-layer perceptron (MLP) architecture with dropout layers for regularization.

Features

• Multi-layer neural network architecture
• Data preprocessing pipeline for both numerical and categorical features
• Early stopping mechanism to prevent overfitting
• CUDA support for GPU acceleration
• Comprehensive training and validation loops
• Progress tracking with tqdm

Project Structure

insurance-premium/
│
├── data/
│   ├── train.csv
│   ├── test.csv
│   └── sample_submission.csv
│
├── model/
│   └── best_model.pth
│
└── results/
    └── submission.csv

Requirements

• Python 3.11+
• PyTorch
• pandas
• scikit-learn
• tqdm

Usage

1. Place your data files in the data/ directory:

   • train.csv: Training data
   • test.csv: Test data
   • sample_submission.csv: Sample submission format

2. Run the training script insurance_regression.ipynb

3. Find predictions in results/submission.csv

Data Preprocessing

The model includes comprehensive preprocessing steps:

• Missing value imputation
• Categorical feature encoding
• Numerical feature standardization
• Feature scaling and normalization

Hardware Requirements

• Supports both CPU and CUDA GPU execution
• CUDA GPU recommended for faster training

License

Copyright © Xi Xu. All rights reserved.

Licensed under the GPL-3.0 license.