https://github.com/macnianios/iris_machine_learning
a mini project on iris dataset, choosing the best machine learning model
https://github.com/macnianios/iris_machine_learning
decision-trees iris-classification kneighborsclassifier logistic-regression machinelearning python
Last synced: about 1 month ago
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a mini project on iris dataset, choosing the best machine learning model
- Host: GitHub
- URL: https://github.com/macnianios/iris_machine_learning
- Owner: macnianios
- Created: 2024-06-17T09:24:16.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-27T11:58:55.000Z (almost 2 years ago)
- Last Synced: 2025-10-17T17:05:04.004Z (8 months ago)
- Topics: decision-trees, iris-classification, kneighborsclassifier, logistic-regression, machinelearning, python
- Language: Jupyter Notebook
- Homepage:
- Size: 57.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# iris_machine_learning
a mini project on iris dataset, choosing the best machine learning model
Project Overview:
This is a classification project for the Iris species. Iris is a flower with three different species https://en.wikipedia.org/wiki/Iris_flower_data_set
Objectives:
• Try to find the best machine learning model to categorise the species of a flower into three categories
• Find the best parametres on this model.
• Generate random values for are features and give them to our model to categorize this "random" flowers into the categories.
#Running the Notebook in Google Colab
1. Open Google Colab: Go to https://colab.research.google.com/.
2. Upload the Notebook:
3. Click on "File" -> "Upload notebook" and select "Project_Capstone.ipynb" from your local machine.
4. Alternatively, you can upload the entire project directory (including "Project_Capstone.ipynb") to your Google Drive and open the notebook directly from there.
#Methods Used
• Data Manipulation (pandas,numpy)
• Data Visualization(seaborn,matplotlib)
• Machine Learning(Logistic Regression,Decision Tree, K-Neihgbors Classifier)
#DATA
• iris is a seaborn preloaded dataset
#Results
• The best model was K-Neighbors with k=7 with
Accuracy: 0.9777777777777777
Precision: 0.9761904761904763
Recall: 0.9722222222222222
f1 score: 0.9731615673644659