Getting into the 🍷 spirits 😉 with my latest machine learning project predicting red wine quality! 🍇 I compared three different regression models - Linear Regression 📏, Decision Tree 🌳, and Random Forest 🌲 to see which performed best at predicting wine quality on a dataset of red wines 🍷. This is how I approached this project.
Dataset: First I downloaded the Red Wine Quality Dataset. Then I used Pandas library to view the data. The main values were 'fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol', 'quality'
Data Modelling: Then I performed Data Modelling by cleaning the data and checking for null values. I also performed Univariate & Bivariate Analysis.
Data Visualization: I visualized the data using Matplotlib.
I used 3 of Machine Learning Algorithms: Linear Regression, Decision Tree Regressor, & Ranodm Forest Regressor. and calculated the MSE and R2 metrics.
Features (X): fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH, sulphates, alcohol
Target Variable (y): quality