Issue
I want to fit a logistic regression model that predicts Y using X1 and X2. What I know is that we use the following method:
x_train, x_test, y_train, y_test = train_test_split(X,Y,test_size)
and then
model = LogisticRegression()
model.fit(x_train,y_train)
To predict Y using X, I don't know how to train the data using more than one predictor. Any help, please?
Solution
If there are 2 features X1
and X2
, then the training data X
will have 2 columns. For example if data has 1000 X1
and 1000 X2
, then the shape of X
should be (1000 x 2)
For example you have a csv file with 3 columns: X1
, X2
, y
import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
df = pd.read_csv('my_file.csv')
X = df[['X1', 'X2']]
Y = df['y']
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)
model = LogisticRegression()
model.fit(x_train,y_train)
y_pred = model.predict(x_test)
acc = accuracy_score(y_test, y_pred)
Answered By - perpetual student
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