https://github.com/lijesh010/ml_project-iris_data_species_classification_algorithms
This repository contains a machine learning project focused on the classification of Iris flower species using different Classification algorithms.
https://github.com/lijesh010/ml_project-iris_data_species_classification_algorithms
decision-trees iris-dataset jupyter-notebook knearest-neighbor-algorithm logistic-regression machine-learning naive-bayes-algorithm python random-forest sklearn-library support-vector-machine
Last synced: 3 months ago
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This repository contains a machine learning project focused on the classification of Iris flower species using different Classification algorithms.
- Host: GitHub
- URL: https://github.com/lijesh010/ml_project-iris_data_species_classification_algorithms
- Owner: lijesh010
- Created: 2023-08-03T04:53:04.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-08-03T08:55:57.000Z (almost 2 years ago)
- Last Synced: 2025-02-03T14:12:41.380Z (5 months ago)
- Topics: decision-trees, iris-dataset, jupyter-notebook, knearest-neighbor-algorithm, logistic-regression, machine-learning, naive-bayes-algorithm, python, random-forest, sklearn-library, support-vector-machine
- Language: Jupyter Notebook
- Homepage:
- Size: 19.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Iris Flower Species Classification
This repository contains a Machine Learning project focused on classifying iris flower species using various classification algorithms. The project employs the popular scikit-learn library and explores Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, and Naive Bayes algorithms to create predictive models for classifying iris flowers into three different species: Setosa, Versicolour, and Virginica.
## Problem Description
The main objective of this project is to build a machine learning model that can accurately classify iris flowers into one of the three species based on four features: sepal length, sepal width, petal length, and petal width.
## Dataset
The Iris Flower dataset consists of 150 samples, each with four features: sepal length, sepal width, petal length, and petal width. These samples are labeled with the corresponding species they belong to: Setosa, Versicolour, or Virginica.
## Implementation
The project involves the following steps:
1. Data Preprocessing: The dataset is loaded and preprocessed to handle any missing values or anomalies.
2. Data Splitting: The dataset is divided into a training set and a test set. The training set is used to train the logistic regression model, while the test set is used to evaluate the model's performance.
3. Model Training: A classification ML model is trained using the features from the training dataset and their corresponding labels.
4. Model Evaluation: The accuracy of the trained model is calculated using the test dataset. This accuracy gives an indication of how well the model can classify new, unseen samples.
5. Prediction: The trained model is used to predict the species of iris flowers in the test dataset.