Predict the quality of wine using various ML classifiers

[ShashankP19]

Description

Given dataset with different features of wine, predict its quality. The quality is an integral score between 0 and 10.
The dataset can be found here
The given features are

1. fixed acidity
2. volatile acidity
3. citric acid
4. residual sugar
5. chlorides
6. free sulfur dioxide
7. total sulfur dioxide
8. density
9. pH
10. sulphates
11. alcohol

Details

• Technical Specifications: python, scikit-learn, pandas, numpy
• Type of issue: Multiple issues
• Time Limit: 3 days for each classifier implementation

Issue requirements / progress

Train the model on train data. Predict target values using test data. Find accuracy of the model comparing with actual test data targets
Note: Each pull request should be a solution using only one model.

• Using K Neighbors Classifier
• Using Decision Tree Classifier
• Using Gaussian Naive Bayes
• Using Support Vector Classifier
• Using Gaussian Process Classifier
• Using Random Forest Classifier
• Using Multi-layer Perceptron (MLP) Classifier
• Using AdaBoost Classifier
• Using Quadratic Discriminant Analysis

Resources

• To help you get started, the starter code wine_starter_code.ipynb is present in /machine_learning/wine_quality
• Jupyter notebook installation
• scikit-learn KNeighborsClassifier
• scikit-learn DecisionTreeClassifier
• scikit-learn GaussianNB
• scikit-learn SupportVectorClassifier
• scikit-learn GaussianProcessClassifier
• scikit-learn RandomForestClassifier
• scikit-learn MLPClassifier
• scikit-learn AdaBoostClassifier
• scikit-learn QuadraticDiscriminantAnalysis
• pandas tutorial
• numpy tutorial

Directory Structure

Place your solution file in path as follows.

• For K Neighbors Classifier /machine_learning/wine_quality/knn/<your_solution_file>
• For Decision Tree Classifier /machine_learning/wine_quality/dtc/<your_solution_file>
• For Gaussian Naive Bayes /machine_learning/wine_quality/gnb/<your_solution_file>
• For Support Vector Classifier /machine_learning/wine_quality/svc/<your_solution_file>
• For Using Gaussian Process Classifier /machine_learning/wine_quality/gpc/<your_solution_file>
• For Using Random Forest Classifier /machine_learning/wine_quality/rfc/<your_solution_file>
• For Using Multi-layer Perceptron (MLP) Classifier /machine_learning/wine_quality/mlp/<your_solution_file>
• For Using AdaBoost Classifier /machine_learning/wine_quality/abc/<your_solution_file>
• For Using Quadratic Discriminant Analysis /machine_learning/wine_quality/qda/<your_solution_file>

Note

Please claim the issue first by commenting here before starting to work on it.

[cemysf]

I would like to work on this

[ShashankP19]

@cemysf each model requires a new pull request. Choose one of the models that you would want to work on. What model do you choose?

[ThaysPrado]

I would like to work on this. I will use Decision Tree Classifier

[AMosa3d]

Hey, I would like to work on MLP

[ShashankP19]

@ThaysPrado You are assigned Decision Tree Classifier. Go ahead.

[ShashankP19]

@AMosa3d You are assigned MLP Classifier. Go ahead.

[bilalvur]

I would like to work on random forest.

[ShashankP19]

@bilalvur You are assigned to work on Random Forest Classifier. Go ahead.

[cemysf]

Sorry for the late reply, I would like to work on Support Vector Classifier

[ShashankP19]

@cemysf You are assigned to work on Support Vector Classifier. Go ahead.

[GajeshS]

Hi, I would like to work on Quadratic Discriminant Analysis

[ShashankP19]

@GajeshS You are assigned Quadratic Discriminant Analysis. Go ahead.