This project handle classification of the built in Iris flower data set, form the SKlearn library. The data set is arranged in a table and allows easy access to the information stored in it. Through Machine learning library SKlearn, it is relatively easy to analyze the information and predict results at a fairly accurate level. In this project I focused on two types of machine learning, from the SKlearn library:
- Logistic Regression
- K Neighbors classifier-KNN
I choosed to Splite the dataset into two pieces: a training set-60 % and a testing set-40 %. The random state was set to a fixed value - 4, in order to get a fixed result for the same parameters each time I run the program.
Conclusions
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Logistic Regression: accuracy score of 96.67%
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K Neighbors classifier: For KNN algorithm, I test the prediction accuracy for diffrent value K - neighbors, in order to find the optimum K value. After tuning the K value I got quite accurate prediction accuracy of 98.3%.
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For both algorithm the prediction, the prediction accuracy for the Setosa Iris flower was 100%.
**** To run the whole application, it required to install the Yellow Brick library