Flowers Classificator Using K-Means
1. Exploring the dataset
2. Constructing our ML Model
As we could see in the previous section, the most significant column is the petal_lenght.
Also, the petal length and petal width are very correlated (0.96), so we could easily choose to work with only one of them.
A candidate for improving our predictions would be the sepal_length, as we can see in the pair plot of petal_length x sepal_length.
2.1. Using the Elbow Method to find the best K
2.2. Using the best k to create the model
/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation. FutureWarning