Wine Quality Prediction
Machine learning
Data analysis
Python
Scikit-learn
RandomForestClassifier
Matplotlib
The Wine Quality Prediction Model is built using machine learning techniques to classify wines as good or bad based on various input parameters. It utilizes a dataset containing features like acidity levels, residual sugar, pH, alcohol content, etc. to train a machine learning algorithm.
- Algorithm used:
Random Forest Classifier - Accuracy:
93%
The dataset is preprocessed by handling missing values, feature scaling, and possibly feature engineering to improve the model's accuracy. After training on historical wine data, the model is capable of predicting the quality of new wines it hasn't seen before.
Github
DatasetFeature Description
- volatile acidity: Volatile acidity is the gaseous acids present in wine.
- fixed acidity: Primary fixed acids found in wine are tartaric, succinic, citric, and malic
- residual sugar: Amount of sugar left after fermentation.
- citric acid: It is weak organic acid, found in citrus fruits naturally.
- chlorides: Amount of salt present in wine.
- free sulfur dioxide: So2 is used for prevention of wine by oxidation and microbial spoilage.
- total sulfur dioxide: Total So2 present in the alcohol.
- pH: In wine pH is used for checking acidity.
- density: The weight of the alcohol per ml.
- sulphates: Added sulfites preserve freshness and protect wine from oxidation, and bacteria.
- alcohol: Percent of alcohol present in wine.
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