Logistic regression

Introduction

This guide provides steps to perform Logistic Regression analysis in Cycon ML/AI platform, and compares it with the code available within Kaggle platform.

Note

Name: Advertising CSV
Path: Tests/sampleCSV_MLA_Classification/advertising.csv
Kaggle: https://www.kaggle.com/code/parjanyaadityashukla/logistic-regression-project/notebook
Shape: (1000, 10)
Classes: Clicked on Ad - 0 or 1 indicated clicking on Ad
Purpose: whether a user clicks on an ad or not

Data

_images/LR1.png

Preprocessing

CyCon

_images/LR2.png

Kaggle

from sklearn.model_selection import train_test_split
X = ad_data[['Daily Time Spent on Site', 'Age', 'Area Income',
    'Daily Internet Usage','Male']]
ad_data.columns
y = ad_data['Clicked on Ad']
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.4)

Method

CyCon

_images/LR3.png

Kaggle

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

Result

CyCon

_images/LR4.png

Kaggle

_images/LR5.png