Logistic Regression, way-to-ds-ep05


Conventional Methods – Logistic Regression


It is used to do classification solving by regression which gives 0 or 1 outcome. This is achieved by applying a thredhold classifier.

Steps with code example

Step 1: Data Preprocessing

Importing the libraries

import pandas as pd
import numpy as np

Importing the dataset

dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values

Splitting the dataset into the Training set and Test set

from sklearn.cross_validation import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(sel_X, Y, test_size = 0.2, random_state = 0)

Do features scaling

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

Step 2: Fitting Multiple Linear Regression to the Training set

from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(X_train, y_train)

Step 3: Predicting the Test set results

y_pred = regressor.predict(X_test)

Step 4: Evaluate the prediction

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)