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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
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.