This project aims to predict wine prices based on various features such as age, country of origin, grape variety, region, alcohol content, acidity, sugar level, and sulphates. By analyzing these features, we can gain insights into what factors influence wine prices and how to strategically focus on maximizing these factors for business success.
The dataset used for this project is available on Kaggle at the following link: https://www.kaggle.com/datasets/elvinrustam/wine-dataset
Please download the dataset and place it in the same directory as the Python script. Adjust the file path accordingly in the code.
- Python 3.x
- pandas
- numpy
- scikit-learn
- matplotlib
You can install the required dependencies using pip:
- Clone the repository or download the Python script and README.md file.
- Download the dataset from the provided Kaggle link and place it in the same directory as the Python script.
- Adjust the file path in the Python script to load the dataset.
- Run the Python script.
- The script will train a RandomForestRegressor model on the dataset and display the mean squared error as well as the feature importances.
- Interpret the results to identify the most important factors influencing wine prices.
wine_price_prediction.py: Python script containing the code for loading, preprocessing, training, and evaluating the model.README.md: Markdown file providing an overview of the project, instructions for usage, and dependencies.
- [Your Name]
- [Your Email]
This project is licensed under the MIT License.