In this lesson, you will be extending your knowledge of simple linear regression, where you were predicting a quantitative response variable using a quantitative explanatory variable. That is, you were using an equation that looked like this: \hat{y} = b_0 + b_1x_1y^=b0+b1x1 In this lesson, you will learn about multiple linear regression. In these cases, […]

# Daily Archives: March 11, 2018

## Logistic Regression

Fitting Logistic Regression import numpy as np import pandas as pd import statsmodels.api as sm df = pd.read_csv(‘./fraud_dataset.csv’) df.head() 1. As you can see, there are two columns that need to be changed to dummy variables. Replace each of the current columns to the dummy version. Use the 1 for weekday and True, and 0 otherwise. Use the first […]