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A Practical Guide to Machine Learning with Python

Logistic Regression Steps: 1 to 4

This lesson will start to introduce the implementation steps (1-4) of logistic regression.

We'll cover the following...

1) Import libraries

The libraries used for this exercise are Pandas, Seaborn, Matplotlib, and Pyplot (a MATLAB-like plotting framework that combines Pyplot with NumPy), as well as Scikit-learn.

C++
#1. Import libraries
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split 
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report

Note: Codes of further steps won’t include codes of previous steps. They’re already ...