/machine_learning_models

This repository is consists with a Google Colab script which I used to classify my research dataset. More in the Readme.

Primary LanguageJupyter Notebook

Classification Models

This repository is consists with a Google Colab script which I used to classify my research dataset.

The research was done to find the user preferences on various color palettes on websites.

Dataset was created after a survey and the survey was done with the help of a Google Form.

In this script you can find the codes for,

  1. Random Over Sampling using "imbalanced-learn" library. Documentation
  2. Dataset manipulation using "scikit-learn" library. Documentation
  3. Support Vector Machine (SVM) Classifier package of scikit-learn library.
  4. Decision Tree Classifier of scikit-learn library.
    • Printing Decision Tree in textual format
    • Printing Decision Tree in graphical format
  5. Naive Bayes Classifier
    • Gaussian Naive Bayes
    • Categorical Naive Bayes
  6. XGBoost Classifier
    • Printing Tree in graphical format
  7. Random Forest Classifier

Required datasets are uploaded to this repository itself. Feel free to contact me for any references.

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