Wine Quality Prediction using Machine Learning in Python

Wine Quality Prediction using Machine Learning in Python

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


Each project comes with 2-5 hours of micro-videos explaining the solution.

Code & Dataset

Get access to 50+ solved projects with iPython notebooks and datasets.

Project Experience

Add project experience to your Linkedin/Github profiles.

Customer Love

Read All Reviews

Camille St. Omer

Artificial Intelligence Researcher, Quora 'Most Viewed Writer in 'Data Mining'

I came to the platform with no experience and now I am knowledgeable in Machine Learning with Python. No easy thing I must say, the sessions are challenging and go to the depths. I looked at graduate... Read More

Dhiraj Tandon

Solution Architect-Cyber Security at ColorTokens

My Interaction was very short but left a positive impression. I enrolled and asked for a refund since I could not find the time. What happened next: They initiated Refund immediately. Their... Read More

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.

Similar Projects

This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.

In this machine learning project, we will build a predictive model to find out the sales of each product at a particular store.

Machine Learning Project - Work with KKBOX's Music Recommendation System dataset to build the best music recommendation engine.

Curriculum For This Mini Project