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A Practical Guide to Machine Learning with Python

Logistic Regression Steps: 1 to 4

Explore how to prepare data for logistic regression by importing libraries and datasets, removing irrelevant variables, and transforming categorical data using one-hot encoding. Understand these fundamental steps to build accurate classification models in Python.

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 ...