Wine-Quality-Prediction

This project aims to to predict the quality of wine using linear regression.

Dataset

1. The dataset used for this project can be found on Kaggle under the name "Red Wine Quality".
2. The main aim of the red wine quality dataset is to predict which of the physiochemical features make good wine. With 11 variables and 1 output variable (quality) given.

Prerequisites

1. Python 3.6 or above
2. NumPy
3. Pandas
4. Matplotlib
5. Scikit-learn

Steps to predict the quality of wine

1. Define the Problem
2. Data Gathering
3. Data Cleaning
4. Data Exploration and Visualization
5. Train the algorithm

Usage

1. Download the datset from kaggle and save it in the project directory.
2. Run the redwine_quality_prediction.ipynb file, which contains the code for data preprocessing, model training, and evaluation.
3. The script will load the dataset, preprocess the data, split it into training and testing sets, train a linear regression model, and evaluate its performance.
4. After the model training is completed, it will predict quality of the wine.

License

The dataset used in this project is subject to the licensing terms provided by Kaggle. Please refer to the dataset's license for more details.

Contributing

Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request.

Contact

For any questions or inquiries, please contact [[email protected]].