73 lines
1.8 KiB
Markdown
73 lines
1.8 KiB
Markdown
# Insurance Premium Prediction Model
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A deep learning model for predicting insurance premiums using PyTorch for Kaggle's [Regression with an Insurance Dataset](https://www.kaggle.com/competitions/playground-series-s4e12) competition.
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## Overview
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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.
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## Features
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- Multi-layer neural network architecture
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- Data preprocessing pipeline for both numerical and categorical features
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- Early stopping mechanism to prevent overfitting
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- CUDA support for GPU acceleration
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- Comprehensive training and validation loops
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- Progress tracking with tqdm
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## Project Structure
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```plaintext
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.
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│
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├── data/
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│ ├── train.csv
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│ ├── test.csv
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│ └── sample_submission.csv
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│
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├── model/
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│ └── best_model.pth
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│
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└── results/
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└── submission.csv
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```
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## Requirements
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- Python 3.11+
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- PyTorch
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- pandas
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- scikit-learn
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- tqdm
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## Usage
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1. Place your data files in the `data/` directory:
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- `train.csv`: Training data
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- `test.csv`: Test data
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- `sample_submission.csv`: Sample submission format
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2. Run the training script `insurance_regression.ipynb`
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3. Find predictions in `results/submission.csv`
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## Data Preprocessing
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The model includes comprehensive preprocessing steps:
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- Missing value imputation
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- Categorical feature encoding
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- Numerical feature standardization
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- Feature scaling and normalization
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## Hardware Requirements
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- Supports both CPU and CUDA GPU execution
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- CUDA GPU recommended for faster training
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## License
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Copyright © [Xi Xu](https://xi-xu.me)
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Licensed under the [GPL-3.0](LICENSE) license.
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