https://github.com/marta-barea/mlp-boston-house-regression
A simple project to train and evaluate a multilayer perceptron model on the Boston House Prices data using TensorFlow, SciKeras, and Scikit-Learn
https://github.com/marta-barea/mlp-boston-house-regression
boston-housing-dataset boston-housing-price-prediction deep-learning multilayer-perceptron python regression-algorithms
Last synced: 4 months ago
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A simple project to train and evaluate a multilayer perceptron model on the Boston House Prices data using TensorFlow, SciKeras, and Scikit-Learn
- Host: GitHub
- URL: https://github.com/marta-barea/mlp-boston-house-regression
- Owner: Marta-Barea
- License: gpl-3.0
- Created: 2025-06-09T13:17:25.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-06-19T13:12:36.000Z (4 months ago)
- Last Synced: 2025-06-19T14:24:39.272Z (4 months ago)
- Topics: boston-housing-dataset, boston-housing-price-prediction, deep-learning, multilayer-perceptron, python, regression-algorithms
- Language: Python
- Homepage:
- Size: 142 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MLP Boston House Prices Regression
A simple project to train and evaluate a multilayer perceptron model on the Boston House Prices data using TensorFlow, SciKeras, and Scikit-Learn.
---
# Installation
1. Clone the repo
```bash
git clone https://github.com/yourusername/mlp-iris-classifier.git
cd mlp-boston-house-prices
```
2. Set up the Conda environment
It is included an `environment.yml` for Conda users:
```bash
conda env create -f environment.yml
conda activate mlp-boston-house-prices
```
# Dependencies
- Python 3.7+
- numpy, scikt-learn, tensorflow, scikeras, PyYAML, matplotlib.
You can also install them with:
```bash
pip install -r requirements.txt
```
# Usage
1. Verify the dataset
The [Boston House Prices](https://www.kaggle.com/datasets/vikrishnan/boston-house-prices) from Kaggle is already included under data/sonar.csv.
2. Adjust settings
Open `config.yaml`and tweak any values you like (seed, test_size, units, etc.)
3. Run the full pipeline
```bash
python run_all.py
```
This will:
- Train de MLP with standardized input data
- Save the model to `models` folder
- Evaluate and print train/test MAE and sample predictions
# Project Structure
```
mlp-iris-classifier/
│
├── config.yaml # Experiment settings
├── environment.yml # Conda environment spec
├── requirements.txt # Pinned pip dependencies (for Docker)
├── docker-compose.yml # Docker Compose setup
├── Dockerfile # Image build definition
├── .dockerignore # Exclude files from image context
├── .gitignore # Exclude files from Git tracking
│
├── data/
│ └── housing.csv # Boston House Prices data
│
├── models/ # (Auto-created) Trained model & params
│
├── reports/
│ └── figures # (Auto-created) Plots
│
├── src/
│ ├── config.py # Loads config.yaml
│ ├── data_loader.py # Reads & splits data
│ ├── model_builder.py # Defines the Keras MLP
│ ├── train.py # Hyperparameter search & model saving
│ └── evaluate.py # Loads model & prints metrics
│
└── run_all.py # Runs train.py then evaluate.py
```
# Dockerized Support
This project is fully containerized for portability and reproducibility.
## Docker Dependencies
Before using Docker, you need to have the following installed locally on your system:
- [Docker Engine](https://docs.docker.com/get-started/get-docker/)
- [Docker Compose](https://docs.docker.com/compose/install/)
✅ Note: These tools are required only if you want to run the project in a containerized environment. If you're using Conda, Docker is optional.
## How to Run
To build the image and run the project inside a container:
```bash
docker-compose up --build
```
This will:
- Build the Docker image using the included Dockerfile
- Run the run_all.py pipeline (training + evaluation)
- Save the best trained model in the models/ directory
- Save plots and metrics in the reports/ directory
✅ Note: Both models/ and reports/ are mounted to your host machine, so your outputs are preserved outside the container.