how to predict the output value using logistic regression?

Question

I simulated successfully my classification function to predict the single value of output binary by ANN utilizing pandas and sklearn libraries. Now I want to simulate my model to predict another feature which is not binary, as the input columns are (0,1,4,6,7,8,11,12,13,14) and the output column is (15) of my data set. A typical example of the input data is [4096,0.07324,1.7,20,5.2,64,0.142,0.5,35,30,584.232] as some values are float. How can I predict 584.232 by the first ten numbers utilizing logistic regression? thank you all.

dataset = pd.read_csv("DataSet.csv")
X = dataset.iloc[:, [0,1,4,6,7,8,11,12,13,14]].values
y = dataset.iloc[:, 15].values

for avoiding type error, I converted the input values into float using the following way:

dataset['ColumnsName'] = dataset['ColumnsName'].astype(float)
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import ColumnTransformer
labelEncoder_X_delay_1 = LabelEncoder()
X[:, 1] = labelEncoder_X_1.fit_transform(X[:, 1])
labelEncoder_X_delay_2 = LabelEncoder()
X[:, 2] = labelEncoder_X_2.fit_transform(X[:, 2])
# normalizing the input
X = X.T
X = X / np.amax(X, axis=1)
X = X.T
# splitting the dataset into the training set and test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = 
train_test_split(X, y, test_size = 0.2, random_state = 0)
# feature scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# fitting logestic regression to the training set
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state = 0)
classifier.fit(X_train, y_train)

but after compiling the code up to now, it gives the error:

from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state = 0)
classifier.fit(X_train, y_train)
Traceback (most recent call last):

  File "<ipython-input-5-f18c8875152f>", line 3, in <module>
    classifier.fit(X_train, y_train)

  File "C:\Users\ali\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py", line 1528, in fit
    check_classification_targets(y)

  File "C:\Users\ali\anaconda3\lib\site-packages\sklearn\utils\multiclass.py", line 169, in check_classification_targets
    raise ValueError("Unknown label type: %r" % y_type)

ValueError: Unknown label type: 'continuous'

I have already converted the predefined columns from string to float!

Answer 1

dataset = pd.read_csv("DataSet.csv")
X = dataset.iloc[:, [0,1,4,6,7,8,11,12,13,14]].values
y = dataset.iloc[:, 15].values

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import ColumnTransformer
labelEncoder_X_delay_1 = LabelEncoder()
X[:, 1] = labelEncoder_X_1.fit_transform(X[:, 1])
labelEncoder_X_delay_2 = LabelEncoder()
X[:, 2] = labelEncoder_X_2.fit_transform(X[:, 2])
# normalizing the input
X = X.T
X = X / np.amax(X, axis=1)
X = X.T
# splitting the dataset into the training set and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
# Activation Function
model = Sequential()
model.add(Dense(6, input_dim=9, activation= "relu"))
model.add(Dense(6, activation= "relu"))
model.add(Dense(6, activation= "relu"))
model.add(Dense(1))

# splitting the dataset into the training set and test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = 
train_test_split(X, y, test_size = 0.2, random_state = 0)
# feature scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# fitting logestic regression to the training set
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state = 0)
classifier.fit(X_train, y_train)