Logistic regression can help to predict a value whether it would happen or no. I'd like to know how can I do that using sklearn.
I'd like to know the probability if this event would happen or no.
I have a huge dataset (20K lines and 20 columns). My data has 19 columns as predictors and last column as target (values between 0-10). To simplify work, I am using random data to understand how can I interpret data.
A,B,C : Predictors
target: as a target
from sklearn import linear_model
import pandas as pd
dataset = pd.DataFrame({'A':np.random.rand(100)*1000, 'B':np.random.rand(100)*100, 'C':np.random.rand(100)*10, 'target':np.random.rand(100)})
predictors= dataset.ix[:,['A','B','C']].values
target = dataset.ix[:,['target']].values
lr = linear_model.LogisticRegression()
lr.fit(predictors, target)
linear_model.LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
Now, should I plot (lr.predict_proba) to get probability of every element ?
what should I do in order to have probability of every line.