Wine Quality Data Prediction Algorithm

This project aims to analyse and build a model from the dataset of Wines to learn about prediction algorithm. The analysis includes visualising the distribution and correlations of alcohol, sulphates, citric acid, and other key metrics. The notebook provides various visulisations to help understand the patterns and relations in the data.

If .ipynb file fails to render, you can view it here

Python Libraries Used

• Pandas
• Numpy
• Matplotlib
• Seaborn
• Scikit-learn
• Plotly.js

Model

• Decision Tree
• Random Forest

Feature Engineering

• K-means Clustering
• Principal component analysis

Results

Can be found in my report showcasing my findings.

Built With

• jupyter-notebook