Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/yuweaec/wine_quality_prediction
The Wine Quality Prediction project aims to predict the quality of wine based on its chemical properties using machine learning algorithms.
https://github.com/yuweaec/wine_quality_prediction
flask jupyter-notebook machine-learning python scikit-learn
Last synced: 2 months ago
JSON representation
The Wine Quality Prediction project aims to predict the quality of wine based on its chemical properties using machine learning algorithms.
- Host: GitHub
- URL: https://github.com/yuweaec/wine_quality_prediction
- Owner: YuweAEC
- Created: 2024-09-12T16:28:56.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-09-20T14:59:48.000Z (4 months ago)
- Last Synced: 2024-10-31T05:04:04.387Z (2 months ago)
- Topics: flask, jupyter-notebook, machine-learning, python, scikit-learn
- Language: Jupyter Notebook
- Homepage: https://yuweaec.github.io/wine_quality_prediction/
- Size: 325 KB
- Stars: 0
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# **Wine Quality Prediction**
## **Overview**
The **Wine Quality Prediction** project aims to predict the quality of red and white wines based on their chemical properties. Using a machine learning model, this project analyzes various features, such as acidity, alcohol content, and residual sugar, to predict a wine's quality on a scale from 0 to 10.
The dataset used for this project is the **Wine Quality Dataset**, which can be found on the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Wine+Quality).
---
## **Project Structure**
```
wine_quality_prediction/
│
├── data/
│ ├── winequality-red.csv # Wine dataset (red wine)
│
├── notebooks/
│ ├── data_analysis.ipynb # Jupyter notebook for EDA
│
├── src/
│ ├── __init__.py # Init file for the src module
│ ├── data_preprocessing.py # Script for data preprocessing and cleaning
│ ├── model_training.py # Script for training the model
│ ├── evaluate_model.py # Script for evaluating the model
│ ├── prediction.py # Script for making predictions
│
├── model/
│ ├── wine_quality_model.pkl # Trained model saved as a pickle file
│
├── static/ # Static folder for UI (optional)
│ └── css/
│
├── templates/ # HTML templates for web UI (optional)
│ └── index.html
│
├── app.py # Flask application for deploying the project
├── requirements.txt # Project dependencies
├── Dockerfile # Docker file for containerization (optional)
└── README.md # Documentation file
```
---
## **Installation**
### **1. Clone the Repository**
First, clone the project repository to your local machine:
```bash
git clone https://github.com/yourusername/wine_quality_prediction.git
cd wine_quality_prediction
```
### **2. Install Dependencies**
Make sure you have Python installed (preferably Python 3.8+). Then, install the project dependencies:
```bash
pip install -r requirements.txt
```
### **3. Download Dataset**
Download the wine quality dataset (red wine or white wine) from the UCI repository:
- Red Wine: [winequality-red.csv](https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv)
- White Wine: [winequality-white.csv](https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv)
Place the dataset file into the `data/` folder.
---
## **Usage**
### **1. Train the Model**
To train the model, use the following command:
```bash
python src/model_training.py
```
This will train the model using the RandomForestClassifier and save it in the `model/` directory as `wine_quality_model.pkl`.
### **2. Evaluate the Model**
To evaluate the model's performance, run:
```bash
python src/evaluate_model.py
```
This will output a classification report with metrics such as precision, recall, and F1-score.
### **3. Make Predictions**
To predict wine quality using custom data, edit the sample data in the `src/prediction.py` script and run:
```bash
python src/prediction.py
```
This will output the predicted wine quality based on the input features.
---
## **Files and Scripts**
- `src/data_preprocessing.py`: Contains functions for loading, cleaning, and preprocessing the dataset (standardization, train-test split, etc.).
- `src/model_training.py`: The script for training the machine learning model using RandomForest.
- `src/evaluate_model.py`: Used to evaluate the trained model on the test set and generate a performance report.
- `src/prediction.py`: Contains the logic to make predictions using the trained model.
- `notebooks/data_analysis.ipynb`: Jupyter notebook for exploratory data analysis (EDA) and data visualization.
- `app.py`: A simple Flask application to deploy the model as a web service (optional for deployment).
---
## **Technologies Used**
- **Python** (3.8+)
- **Pandas** (for data manipulation)
- **Scikit-learn** (for machine learning model building)
- **Flask** (for building a web app)
- **Jupyter Notebook** (for data exploration)
- **Docker** (optional, for containerization)
---
## **Future Enhancements**
- **Hyperparameter tuning**: Improve the model's performance by tuning hyperparameters.
- **Web deployment**: Host the Flask web app on platforms like Heroku or AWS.
- **Additional Models**: Experiment with other machine learning models like SVM, Gradient Boosting, and XGBoost.
- **UI/UX**: Add a graphical user interface to allow non-technical users to interact with the model.
---
## **Contributors**
- [Your Name](https://github.com/yourusername)
---
## **License**
This project is licensed under the MIT License - see the `LICENSE` file for details.
---