For some reasons i was out of touch with machine learning, datascience after i finished my certification
'Microsoft: DAT210x
Programming with Python for Data Science'
so it was a new start today after a long break. got confused from where to start, thought of revising the course but i wanted to dig my hands in prediction exercise rather going with theories about what is machine learning and detail explanation about different models.
Thought to run some code for iris data first. search on github and plenty of codes but all were talking different models. My goal was clear to find prediction from iris data. for that I need to
At the end of the tutorial did prediction with couple of random data for exampleas below using Support Vector Machine's algorithm.
Output: Prediction of species:['Iris-setosa' 'Iris-versicolor']
'Microsoft: DAT210x
Programming with Python for Data Science'
so it was a new start today after a long break. got confused from where to start, thought of revising the course but i wanted to dig my hands in prediction exercise rather going with theories about what is machine learning and detail explanation about different models.
Thought to run some code for iris data first. search on github and plenty of codes but all were talking different models. My goal was clear to find prediction from iris data. for that I need to
- Prepare Data.
- Evaluate Algorithms.
- Find more accurate Algorithms.
- Predictions
At the end of the tutorial did prediction with couple of random data for exampleas below using Support Vector Machine's algorithm.
#test predictions
X_new = [[5.2, 3.9,1.2, 0.2], [6.9,3.0,4.1,1.2]]
Y_new = svn.predict(X_new)
print('Prediction of species:{}'.format(Y_new))
Output: Prediction of species:['Iris-setosa' 'Iris-versicolor']
and really happy with the accuracy and predication.
So good luck to me for my self - learning journey as a DataScientist.
