https://github.com/abeed04/iris-flower-classification

Simple classification approach to identify different species of Iris Flowers.
https://github.com/abeed04/iris-flower-classification

classification-algorithm exploratory-data-analysis logistic-regression machine-learning prediction

Last synced: 6 days ago
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Simple classification approach to identify different species of Iris Flowers.

• Host: GitHub
• URL: https://github.com/abeed04/iris-flower-classification
• Owner: abeed04
• License: bsd-3-clause
• Created: 2024-06-20T06:33:26.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-06-24T06:56:56.000Z (5 months ago)
• Last Synced: 2024-06-24T08:06:59.071Z (5 months ago)
• Topics: classification-algorithm, exploratory-data-analysis, logistic-regression, machine-learning, prediction
• Language: Jupyter Notebook
• Homepage:
• Size: 28.3 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Iris-Flower-Classification

![image](https://github.com/abeed04/Iris-Flower-Classification/assets/156498387/3d5eb0c6-f009-496b-9eac-48f8ef5bebfc)




This Project is thorugh application of machine learning with python programming. It focuses on IRIS flower classification using Machine Learning with scikit tools.




The data was classified into three categories:







1.Iris Versicolor




2.Iris Setosa




3.Iris Virginica







As the data was clean and equally distributed among all the classifications of flower, EDA part was easy to go.




After complteting EDA part, the data was split into training and testing(8:2) using the train test split approach.




Since the logistic regression technique works better when it comes to classification, it has been employed for this particular categorization.







PS: Please do not forget to drop a star if you like it!