Wine Quality Prediction using Logistic Regression

This project involves predicting wine quality using logistic regression in Jupyter Notebook. Wine quality prediction is an important task in the field of wine production and quality control, as it helps assess the overall quality of wines based on various chemical properties. Through this project, we aim to explore and understand how logistic regression can be used for wine quality prediction.

Dataset

The dataset used for this project is the Wine Quality dataset. It contains various chemical properties of wines, such as acidity, pH, alcohol content, etc., along with their corresponding quality ratings. The task is to predict the quality rating of wines based on these features. Make sure to preprocess and clean the dataset before using it for modeling.

Getting Started

To get started with the project, follow the steps below:

1. Clone the repository:

git clone https://github.com/shaadclt/Wine-Quality-Prediction-LogisticRegression.git

2. Change into the project directory:

cd Wine-Quality-Prediction-LogisticRegression

3. Install the required dependencies:

4. Run Jupyter Notebook:

jupyter notebook

5. Open the Wine Quality Prediction.ipynb notebook in Jupyter.

6. Follow the instructions in the notebook to load the dataset, preprocess the data, train the logistic regression model, and make predictions.

Project Overview

The notebook provides an overview of the steps involved in wine quality prediction using logistic regression. The steps include:

1. Data Loading: Loading the dataset into a pandas DataFrame.
2. Data Preprocessing: Handling missing values, normalizing or standardizing features, and splitting the dataset into training and testing sets.
3. Logistic Regression Model: Training the logistic regression model on the preprocessed dataset.
4. Model Evaluation: Assessing the model performance using evaluation metrics such as accuracy, precision, recall, or F1-score.
5. Prediction: Using the trained model to make quality predictions on new wines.

The notebook includes explanations, code snippets, and visualizations to aid in understanding the wine quality prediction process using logistic regression.

Results and Insights

The project aims to predict wine quality using logistic regression. The results and insights gained from this project include:

• Evaluating the performance of the logistic regression model in terms of accuracy and other evaluation metrics.
• Understanding the important chemical properties or features that contribute to wine quality predictions.
• Applying the trained model to make quality predictions on new, unseen wines.

The insights gained from this project can aid in quality control processes in the wine industry and assist in producing high-quality wines.

Customization

You can customize the project by modifying the dataset, experimenting with different preprocessing techniques, trying other classification algorithms, or exploring additional features for wine quality prediction. This project serves as a starting point for wine quality prediction using logistic regression, and you can extend it further to suit your needs.

License

This project is licensed under the MIT License. See the LICENSE file for more information.

Acknowledgments

• This project is created for the purpose of exploring wine quality prediction using logistic regression in Jupyter Notebook.

Contributing

Contributions are welcome! If you find any issues, have suggestions for improvements, or want to add more features, please open an issue or submit a pull request.