I am new to this ,the code is running in jupitor notebook but when i test on hackerrank it shows my failed .this is my code i am confused in step 2,5,6 particullarly , please help me to solve this code

Python Help
, ,
1

Run the code in the below cell to load the iris data from sklearn dataset

  • The features are the dimensions of flowers petals belonging to iris species
  • The target names are the species to which the flower belongs.They are mapped as 0,1 and 2
  • In this excercise you will perform logistic regression predict the species of flow given the petal dimensions as features
  • to view the data by printing iris_X and iris_Y (Optional)
    step 1:
    from sklearn import datasets
    iris = datasets.load_iris()
    iris_X = iris.data
    iris_y = iris.target
    print(iris.feature_names)
    print(iris.target_names)

[‘sepal length (cm)’, ‘sepal width (cm)’, ‘petal length (cm)’, ‘petal width (cm)’]
[‘setosa’ ‘versicolor’ ‘virginica’]
#step 2

  • import train_test_split function from sklearn.model_selection
  • split the data into train and test set with test_size = 0.33 and random_state = 101
    #code
    ###Start co
    import numpy as np
    from sklearn.model_selection import train_test_split
    X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.33,random_state=101)
    ###End code(approx 2 lines)
    #step 3
  • import LogisticRegression from sklearn
  • initialise logistic regression model and assign to variable ‘model’
  • fit the model with train data(X_train and y_train)
    #code
    from sklearn.linear_model import LogisticRegression
    model =LogisticRegression()
    model.fit(X_train,y_train)
    #step 4
  • Using the model predict the output of test data (i.e) X_test
    #code
    y_pred = model.predict(X_test)
    #step 5
  • import classification_report from sklearn
  • pass y_test and y_pred to classification_report().
  • print the output of classification_report
    #code
    from sklearn.metrics import classification_report
    y_test=[0,1,2,2,2]
    y_pred=[0,0,2,2,1]

target_names=[‘class 0’,‘class 1’,‘class 2’]
print(classification_report(y_test,y_pred,target_names=target_names))
#steop 6

Time to predict the new data

  1. Predict the labels for the data stored in data/test_iris.csv.
  2. Store the predictions in the form of list to the variable list_ans. Note: List numbers are of integer type.
    #code
    from sklearn import datasets
    iris=datasets.load_iris()

X_train=iris.data
y_train=iris.target
list_ans=y_pred
print(list_ans)
print(len(list_ans))