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Mushroom dataset analysis and classification in python

Milind Soorya

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Introduction

Mushroom classification is a beginner machine learning problem and the objective is to correctly classify if the mushroom is edible or poisonous by its specifications like cap shape, cap color, gill color, etc. using different classifiers.

In this project I have used the following classifiers to make the prediction:-

  1. Logistic Regression
  2. KNN Logistic Regression,
  3. SVM,
  4. Naive Bayes
  5. Decision Tree,
  6. Random Forest Classifier

If you want to see the original notebook in Kaggle please visit kaggle-milindsoorya.

Dataset

The dataset used in this project contains 8124 instances of mushrooms with 23 features like cap-shape, cap-surface, cap-color, bruises, odor, etc.

you can download the dataset from kaggle if you want to follow along locally — mushroom-dataset

The python libraries and packages we’ll use in this project are namely:

  • NumPy
  • Pandas
  • Seaborn
  • Matplotlib
  • Graphviz

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