Logistic Regression in Python – Quick Guide ”; Previous Next Logistic Regression in Python – Introduction Logistic Regression is a statistical method of classification of objects. This chapter will give an introduction to logistic regression with the help of some examples. Classification To understand logistic regression, you should know what classification means. Let us consider the following examples to understand this better − A doctor classifies the tumor as malignant or benign. A bank transaction may be fraudulent or genuine. For many years, humans have been performing such tasks – albeit they are error-prone. The question is can we train machines to do these tasks for us with a better accuracy? One such example of machine doing the classification is the email Client on your machine that classifies every incoming mail as “spam” or “not spam” and it does it with a fairly large accuracy. The statistical technique of logistic regression has been successfully applied in email client. In this case, we have trained our machine to solve a classification problem. Logistic Regression is just one part of machine learning used for solving this kind of binary classification problem. There are several other machine learning techniques that are already developed and are in practice for solving other kinds of problems. If you have noted, in all the above examples, the outcome of the predication has only two values – Yes or No. We call these as classes – so as to say we say that our classifier classifies the objects in two classes. In technical terms, we can say that the outcome or target variable is dichotomous in nature. There are other classification problems in which the output may be classified into more than two classes. For example, given a basket full of fruits, you are asked to separate fruits of different kinds. Now, the basket may contain Oranges, Apples, Mangoes, and so on. So when you separate out the fruits, you separate them out in more than two classes. This is a multivariate classification problem. Logistic Regression in Python – Case Study Consider that a bank approaches you to develop a machine learning application that will help them in identifying the potential clients who would open a Term Deposit (also called Fixed Deposit by some banks) with them. The bank regularly conducts a survey by means of telephonic calls or web forms to collect information about the potential clients. The survey is general in nature and is conducted over a very large audience out of which many may not be interested in dealing with this bank itself. Out of the rest, only a few may be interested in opening a Term Deposit. Others may be interested in other facilities offered by the bank. So the survey is not necessarily conducted for identifying the customers opening TDs. Your task is to identify all those customers with high probability of opening TD from the humongous survey data that the bank is going to share with you. Fortunately, one such kind of data is publicly available for those aspiring to develop machine learning models. This data was prepared by some students at UC Irvine with external funding. The database is available as a part of UCI Machine Learning Repository and is widely used by students, educators, and researchers all over the world. The data can be downloaded from here. In the next chapters, let us now perform the application development using the same data. Setting Up a Project In this chapter, we will understand the process involved in setting up a project to perform logistic regression in Python, in detail. Installing Jupyter We will be using Jupyter – one of the most widely used platforms for machine learning. If you do not have Jupyter installed on your machine, download it from here. For installation, you can follow the instructions on their site to install the platform. As the site suggests, you may prefer to use Anaconda Distribution which comes along with Python and many commonly used Python packages for scientific computing and data science. This will alleviate the need for installing these packages individually. After the successful installation of Jupyter, start a new project, your screen at this stage would look like the following ready to accept your code. Now, change the name of the project from Untitled1 to “Logistic Regression” by clicking the title name and editing it. First, we will be importing several Python packages that we will need in our code. Importing Python Packages For this purpose, type or cut-and-paste the following code in the code editor − In [1]: # import statements import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import preprocessing from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split Your Notebook should look like the following at this stage − Run the code by clicking on the Run button. If no errors are generated, you have successfully installed Jupyter and are now ready for the rest of the development. The first three import statements import pandas, numpy and matplotlib.pyplot packages in our project. The next three statements import the specified modules from sklearn. Our next task is to download the data required for our project. We will learn this in the next chapter. Logistic Regression in Python – Getting Data The steps involved in getting data for performing logistic regression in Python are discussed in detail in this chapter. Downloading Dataset If you have not already downloaded the UCI dataset mentioned earlier, download it now from here. Click on the Data Folder. You will see the following screen − Download the bank.zip file by clicking on the given link. The zip file contains the following files − We will use the bank.csv file for our model development. The bank-names.txt file contains the description of the database that you are going to need later. The bank-full.csv contains a much larger dataset that you may use for more advanced developments. Here we have included the bank.csv file in the