https://github.com/alphacrypto246/zoo-animal-classifier

A project that uses machine learning to classify animals into categories like Mammals, Birds, and Reptiles based on their characteristics.
https://github.com/alphacrypto246/zoo-animal-classifier

machine-learning machine-learning-algorithms random-forest scikit-learn

Last synced: 4 months ago
JSON representation

A project that uses machine learning to classify animals into categories like Mammals, Birds, and Reptiles based on their characteristics.

• Host: GitHub
• URL: https://github.com/alphacrypto246/zoo-animal-classifier
• Owner: alphacrypto246
• Created: 2024-12-23T10:44:51.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-12-23T10:54:17.000Z (6 months ago)
• Last Synced: 2025-01-01T00:37:50.119Z (6 months ago)
• Topics: machine-learning, machine-learning-algorithms, random-forest, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 1.36 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# ZOO Animal Classifier

## Overview

This project is designed to classify different animals based on various features such as habitat, physical characteristics, and behaviors. The goal is to build a machine learning model using the Random Forest classifier to predict the animal type from a set of predefined features.

## Dataset

The dataset contains 101 rows and 17 columns, with each row representing an animal. The features include information about the animal's habitat, physical traits, and behavior.

### Features

- `name`: Name of the animal

- `hair`: Indicates if the animal has hair (1 for yes, 0 for no)

- `features`: Various other binary features describing the animal

- `eggs`: Whether the animal lays eggs

- `milk`: Whether the animal produces milk

- `airborne`: Whether the animal can fly

- `aquatic`: Whether the animal lives in water

- `predator`: Whether the animal is a predator

- `toothed`: Whether the animal has teeth

- `backbone`: Whether the animal has a backbone

- `breathes`: Whether the animal breathes air

- `venomous`: Whether the animal is venomous

- `fins`: Whether the animal has fins

- `legs`: Number of legs the animal has

- `tail`: Whether the animal has a tail

- `domestic`: Whether the animal is domestic

- `catsize`: Size of the animal (small, medium, large)

- `type`: The animal type (e.g., mammal, bird, fish, etc.)

## Usage

### 1. Load the Dataset

The dataset is loaded and preprocessed using pandas.

### 2. Data Preprocessing

The dataset is cleaned by filling missing values and performing feature encoding. The target variable (`type`) is separated from the features.

### 3. Model Training

A Random Forest classifier is used to train the model on the preprocessed data. Cross-validation is performed to evaluate the model's performance.

### 4. Model Evaluation

The model is evaluated using various metrics such as accuracy, precision, recall, and F1-score. The model is also tested using cross-validation for a more robust evaluation.

### 5. Model Predictions

The trained model is used to predict the animal type based on new input data.

## Cross-validation

The model uses cross-validation to assess its performance. The dataset is split into multiple folds, and the model is trained on each fold to provide a more reliable accuracy score.

## Results

The model achieves high accuracy in classifying animals based on the features. The cross-validation scores demonstrate a stable and consistent performance across different splits of the dataset.