Wine Quality Prediction using Machine Learning in Python

In this project, we are going to predict different qualities of wine using different ML models.

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What will you learn

  • Importing necessary libraries and loading the dataset

  • Concatenating the dataset for better understanding of the dataset

  • Understanding different datatypes

  • Using info function for basic EDA and making necessary datatype conversions

  • Using seaborn for plotting graphs and understanding the skewness of some feature columns

  • Plotting box plot for understanding Outliers

  • Calculating the Outliers and handling them

  • Checking skewness of the target variable

  • Balancing the unbalanced target variables by replacing with closest neighbors

  • Splitting the dataset for Train and Test using train_test_split

  • Applying Logistic Regression for Prediction

  • Using Classification report and Confusion matrix for analysis of the prediction

  • Applying GridSearchCV on LogisticRegression for hyperparameter tuning

  • Using non-linear model Decision Tree for prediction

  • Applying GridSearchCV on Decision Tree for hyperparameter tuning

  • Using feature_importance function for selecting the best feature for Decision Tree

  • Applying SVC for classification

  • Defining different parameters for GridSearchCV and using different functions for classification

  • Selecting the best model on the basis of the scores and making the final predictions

Project Description

The inputs include objective tests (e.g. PH values) and the output is based on sensory data (median of at least 3 evaluations made by wine experts). Each expert graded the wine quality between 0 (very bad) and 10 (very excellent). The objective is to predict the wine quality classes correctly.

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