Mushroom Classification Using Different Classifiers

Mushroom Classification Using Different Classifiers

Introduction to classification using Decision Tree, Logistic Regression, KNN, SVM, Naive Bayes, Random Forest Classifiers with Python. In this project, we will examine the data and build different machine learning models that will detect if the mushroom is edible or poisonous by its specifications like cap shape, cap color, gill color, etc. using different classifiers.

“Nature alone is antique and the oldest art a mushroom.” ~ Thomas Carlyle

Mushrooms!! Creamy Mushroom Bruschetta, Mushroom Risotto, Mushroom pizza, Mushrooms in a burger, and what not! Just by hearing the names of these dishes, people be drooling! Their flavor is one reason that takes the dish to the next level!

But have you ever wondered if the mushroom you eat is healthy for you? From over 50,000 species of mushrooms only in North America, how will you classify the mushroom as edible or poisonous? Poisonous mushroomscan be hard to identify in the wild!

Let’s build different *machine-learning models *to classify the mushrooms into edible and poisonous!

Introduction

In this project, we will examine the data and build different machine learning models that will detect if the mushroom is edible or poisonous by its specifications like cap shape, cap color, gill color, etc. using different classifiers.

Dataset

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

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

  • NumPy
  • Pandas
  • Seaborn
  • Matplotlib
  • Graphviz
  • Scikit-learn

We’ll use the specifications like cap shape, cap color, gill color, etc. to classify the mushrooms into edible and poisonous.

artificial-intelligence machine-learning mushroomdataset model-comparison data-visualization

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