I have used many machine learning models throughout the course. What interested me the most is how I can train my own model and use it. As I browse the data sets in Kaggle. I found a really interesting one called Red Wine Quality. So I decided to train my own neural network and make a quality checker machine.
Context
This dataset is related to red variants of the Portuguese "Vinho Verde"(young wine) wine. They may be red, white, or rosé, and they are usually consumed soon after bottling.
Fixed Acidity - most acids involved with wine or fixed or nonvolatile
Volatile Acidity - the amount of acetic acid in wine, too high wine will taste like vinegar
Citric Acid - can add 'freshness' and flavor to wines
Residual Sugar - can add 'freshness' and flavor to wines
Chlorides - the amount of salt in the wine
Free Sulfur Dioxide - molecular to help protect the wine from oxidation and spoilage
Total Sulfur Dioxide - the amount of free and bound forms of S02
Density - the density of water is close to that of water
PH - acidic or basic a wine is on a scale from 0 (very acidic) to 14(very basic)
Sulphates - a wine additive which can contribute to sulfur dioxide gas (S02) levels
Alcohol - the percent alcohol content of the wine
Output variable (based on sensory data):