https://github.com/salihfurkaan/iris-flower-analysis

An analysis on Iris dataset of Scikit learn
https://github.com/salihfurkaan/iris-flower-analysis

data-science decision-trees iris-dataset linear-regression logistic-regression machine-learning machine-learning-algorithms scikitlearn-machine-learning support-vector-machines svm-classifier

Last synced: 4 months ago
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An analysis on Iris dataset of Scikit learn

• Host: GitHub
• URL: https://github.com/salihfurkaan/iris-flower-analysis
• Owner: salihfurkaan
• License: apache-2.0
• Created: 2024-05-05T21:44:50.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-09T09:53:19.000Z (about 1 year ago)
• Last Synced: 2024-12-28T05:16:28.554Z (6 months ago)
• Topics: data-science, decision-trees, iris-dataset, linear-regression, logistic-regression, machine-learning, machine-learning-algorithms, scikitlearn-machine-learning, support-vector-machines, svm-classifier
• Language: Jupyter Notebook
• Homepage:
• Size: 1.03 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Iris Flower Analysis and Machine Learning Models for Prediction




Click  here  to see the utilized dataset!







What is Iris?
 

Iris is a flowering plant genus of 310 accepted species with showy flowers. As well as being the scientific name, iris is also widely used as a common name for all Iris species, as well as some belonging to other closely related genera. A common name for some species is flags, while the plants of the subgenus Scorpiris are widely known as junos, particularly in horticulture. It is a popular garden flower.

  In this project; Versicolor, Virginica and Setosa were used





![images](https://github.com/salihfurkaan/iris-flower-analysis/assets/119700956/1cf5c3b6-c259-4384-80fd-d60b9be50984)




Used Python Libraries


* Scikit-learn for cross validation, ML models, dataset and calculating mean squared error

* Pandas for converting arrays into data frames and some operations

* Matplotlib for data visualization

* Seaborn for data visualization

* Numpy for array-wise operations




Used Machine Learning Models


* Linear Regression

* Logistic Regression

* Decision Tree

* Support Vector Machines

* Random Forest




Plots


![sepal_length_start](https://github.com/salihfurkaan/iris-flower-analysis/assets/119700956/7b1ba536-6915-4d33-af31-44d5cd8a7de2)




![petal](https://github.com/salihfurkaan/iris-flower-analysis/assets/119700956/3271bf95-fa41-4849-8009-08828d333176)




![lr](https://github.com/salihfurkaan/iris-flower-analysis/assets/119700956/bdfa399d-c0d3-4932-a5e0-abace0af6854)

![random_forest](https://github.com/salihfurkaan/iris-flower-analysis/assets/119700956/170931c0-bef2-4759-9e24-f2714fc789d4)

![pp](https://github.com/salihfurkaan/iris-flower-analysis/assets/119700956/466a6f2c-5236-4202-a79f-b7ccea71ba8b)

![decision_tree](https://github.com/salihfurkaan/iris-flower-analysis/assets/119700956/009d7f62-36bc-4211-b066-3030d9666348)