This Jupyter Notebook is running different ML models on the well known Iris dataset.
Project is created with:
- Python 3.6
- Anaconda 4.6.12
- jupyter 1.0.0
- jupyter-client 5.2.4
- jupyter-console 6.0.0
- jupyter-core 4.4.0
- Markdown 3.0.1
- matplotlib 3.0.3
- notebook 5.7.8
- numpy 1.16.2
- pandas 0.24.2
- pip 19.0.3
- scikit-learn 0.20.3
- sklearn 0.0
To run this project:
- 1.) Download the Iris.ipynb
- 2.) Start your Jupyter Notebook using anaconda prompt and 'Jupyter notebook'
- 3.) Load Iris.ipynb into Jupyter Notebook
- 4.) Run and execute code block by block or entire file, it will access dataset within the notebook
I wanted to learn more about Sklearn and how to further my ability to do exploratory data analysis. This would also be a great time to learn how to implement several models to determine their viability for the given business problem. The tururial I followed was from Jason Brownlee (https://machinelearningmastery.com/machine-learning-in-python-step-by-step/) and gave me great insight into how some machine learning specialists are exploring data and how I can use this in my future projects when I go to explore different datasets.
Contributions are welcome on how to improve accurazy outside of the standard sklearn models.