This project is about predicting the quality of wine based on various features. The project uses several machine learning models from the sklearn library.
- Logistic Regression
- Stochastic Gradient Descent Classifier
- Decision Tree Classifier
- Random Forest Classifier
Each model is trained and evaluated on the wine quality dataset. The performance of the models can be compared to choose the best one for this prediction task.
The project requires the following Python libraries:
- streamlit
- pandas
- seaborn
- matplotlib
- sklearn
- io
- sys
You can install these dependencies using pip:
pip install -r requirements.txtmain.py: This is the main file of the project. It contains the following functions:
-
wine_quality_labelling(data): This function takes a DataFrame as input and maps the 'quality' column to 'bad' or 'good' based on the quality score. -
categorical_to_numerical(data): This function converts the categorical 'quality' column into numerical values using LabelEncoder. -
train_test_spliting(data): This function splits the data into training and testing sets. It also scales the features using StandardScaler.
To run the project, navigate to the project directory and run the following command:
streamlit run main.py