Logistic Regression with Python

For this lecture we will be working with the Titanic Data Set from Kaggle. This is a very famous data set and very often is a student's first step in machine learning!

We'll be trying to predict a classification- survival or deceased.
Let's begin our understanding of implementing Logistic Regression in Python for classification.

We'll use a "semi-cleaned" version of the titanic data set, if you use the data set hosted directly on Kaggle, you may need to do some additional cleaning not shown in this lecture notebook.

Import Libraries

Let's import some libraries to get started!

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

The Data

Let's start by reading in the titanic_train.csv file into a pandas dataframe.

train = pd.read_csv('titanic_train.csv')

train.head()