How to use JavaScript to predict Iris varieties
This article mainly introduces "how to use JavaScript to predict iris varieties". In daily operation, I believe many people have doubts about how to use JavaScript to predict iris varieties. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful to answer the doubts about "how to predict iris varieties with JavaScript"! Next, please follow the editor to study!
Import the module import pandas as pd that you need
From sklearn.svm import SVC
From sklearn.model_selection import train_test_split
From sklearn.metrics import accuracy_score
Read in data df = pd.read_csv (r "iris\ YT-Django-Iris-App-3xj9B0qqps-master\ iris.csv")
Split the data into training sets and test sets x = ['sepal_length','sepal_width','petal_length','petal_width']
X = df [x]
Y = df ['classification']
X_train, X_test, Y_train, Y_test = train_test_split.
The proportion of the training data set testing the data set is 8:2
Train the model and predict model = SVC (gamma='auto')
Model.fit (Xenopodium thunbergii)
Predictions = model.predict (X_test)
Input data prediction
Iris = [1, 1, 1, 1, 1]
Results = model.predict ([iris])
Print (results)
As a result, results is a list
Output model accuracy print (accuracy_score)
The result of running the code is 0.96666666666667.
Save the model pd.to_pickle (model,r "new_model.pickle")
If you need to use this model, you can read it directly
Model = pd.read_pickle (r "new_model.pickle")
Complete code
Import pandas as pd
From sklearn.svm import SVC
From sklearn.model_selection import train_test_split
From sklearn.metrics import accuracy_score
Df = pd.read_csv (r "iris\ YT-Django-Iris-App-3xj9B0qqps-master\ iris.csv")
Print (df.head ())
X = ['sepal_length','sepal_width','petal_length','petal_width']
X = df [x]
Y = df ['classification']
X_train, X_test, Y_train, Y_test = train_test_split.
Model = SVC (gamma='auto')
Model.fit (Xenopodium thunbergii)
Predictions = model.predict (X_test)
Print (accuracy_score (Yantitestprections))
Pd.to_pickle (model,r "new_model.pickle")
Model = pd.read_pickle (r "new_model.pickle")
Iris = [1, 1, 1, 1, 1]
Results = model.predict ([iris])
Print (results) at this point, the study on "how to use JavaScript to predict iris varieties" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!
