Wine Quality Prediction using RandomForest Classifier

Date created

27th Oct 2022

 

Project Title

Wine Quality Prediction using RandomForest Classifier

 

Description

A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.

For this project, we will be building a machine learning model to classify whether the wine is of Good or Bad quality based on certain chemical measurements included in the dataset. Our goal is to work through this notebook by collecting data, preprocessing it, splitting it into testing and training datasets, train the model and evaluate the accuracy of our model.

API

We have created an API using FastAPI for users to interact with. You can run the model using uvicorn on your local machine and test the API, refer to wine_quality_ml_api.py & api_testing.py.

StreamLit Web App

We also created a simple web app using Streamlit. You can run the app on your local machine using StreamLit and test the web app, refer to wine_quality_ml_streamlit.py.

 

Files used

We used the following dataset available on Kaggle to work on this project:

• Red Wine Quality

The datasets consists of several chemical predictor variables and one target variable, quality. Predictor variables includes the fixed acidity, citric acid, chlorides, density, pH, alcohol and so on.

 

Credits

Thanks to Kaggle for teaching me ML