We did perform an exploratory data analysis (EDA) on Iris dataset before, here this link: https://github.com/Aerospacerr/Iris-Dataset-Exploratory-Data-Analysis
Now we will implement classifier methods as a benchmark for Iris dataset. It will be a live script which I will try to update weekly with different methods and try to improve method's accuracy. Just for start, it includes K-means & Decision tree classifier and also a Deep Learning-Neural Network predictor for classification.
Iris dataset is the best known dataset in the machine learning literature. It contains 150 samples, 50 samples for each of the 3 classes. Class labels are given in the last column of the dataset as integer values and 0, 1, 2 refers to setosa, versicolor, virginica respectively.
- sepal length in cm
- sepal width in cm
- petal length in cm
- petal width in cm
- class: -- Iris Setosa -- Iris Versicolour -- Iris Virginica
Dataset link: https://archive.ics.uci.edu/ml/datasets/iris
Project is created with:
- PyCharm: 2021.3
- Pandas: 1.3.4
- Numpy
- Scikit
- Tensorflow
It will be a live document. As machine learning methods added, we try to update.