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https://github.com/devmunoz/avocado-price-predictor
Machine Learning Project
https://github.com/devmunoz/avocado-price-predictor
hyperparameter-tuning hyperparameters keras keras-tensorflow machine-learning pandas python temporal-series
Last synced: 15 days ago
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Machine Learning Project
- Host: GitHub
- URL: https://github.com/devmunoz/avocado-price-predictor
- Owner: devmunoz
- License: mit
- Created: 2024-11-25T11:17:54.000Z (27 days ago)
- Default Branch: master
- Last Pushed: 2024-11-26T11:19:48.000Z (26 days ago)
- Last Synced: 2024-11-26T12:26:14.383Z (26 days ago)
- Topics: hyperparameter-tuning, hyperparameters, keras, keras-tensorflow, machine-learning, pandas, python, temporal-series
- Language: Jupyter Notebook
- Homepage:
- Size: 5.72 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Avocado Price Predictor 🥑🤑
###### Last update: 12/2024
#### Machine Learning Project
## Description
This repository contains a hyperparameter processor to brute-force ML model training and select the best model based on the metrics.
The source data was extracted from the [Hass Avocado Board](https://hassavocadoboard.com/) website. The EDA and ETL processes have been omitted from this repository.
This repository is part of the original project carried out by [Patricia G-R Palombi](https://www.linkedin.com/in/patricia-g-r-palombi-269b78183/), [José Dos Reis - josedosr](https://github.com/josedosr), [Pamela Colman - pamve](https://github.com/pamve), and myself. If you want more information, please check the original project publication on LinkedIn [here](https://www.linkedin.com/posts/dmunoz-m_proyecto-02-avocado-temporal-series-activity-7165847101237542912-lvkd?utm_source=share&utm_medium=member_desktop).
## Installation and Execution
- **Prerequisites**:
- Install [Python](https://www.python.org/downloads/) and [Virtual Environment (venv)](https://docs.python.org/3/library/venv.html) on your machine.
- Clone this repository.
- [optional] Install [Jupyter Lab](https://jupyter.org/install).
- **Run the scripts**:
- Install the virtual environment:
```
python -m venv .venv
```
- Activate the virtual environment:
```
source .venv/bin/activate
```
- Install the requirements:
```
pip install -r requirements.txt
```
- **Usage** 😄:
```
usage: hyperparams_model_processer.py [-h] [-e] [-p]
Hyperparameter models executor
options:
-h, --help show this help message and exit
-e, --execute Build and execute models. WARNING! THIS CAN BE A VERY HEAVY PROCESS
-p, --plot Plot models' performance results
```
- **ALTERNATIVE:** Open the [.ipynb notebook](hyperparam_models_processer.ipynb) and just follow the content.
## Contribution
Feel free to improve or update the code.
## License
This project is licensed under the MIT License. See the LICENSE file for more details.