Machine learning is a rapidly growing field that has revolutionized the way we approach problem solving. Logistic Regression is one of the fundamental algorithms in this field, used for binary classification problems. In this article, we will explore the use of Logistic Regression for machine learning in Python, including the theory behind it, how to implement it and some practical examples.
What is Logistic Regression?
Logistic Regression is a statistical method that is used to analyze a dataset and make predictions based on the data. In simple terms, it can be used to determine the relationship between a dependent variable and one or more independent variables. The dependent variable in logistic regression should be categorical, that is it should only have two categories (binary). Either 0 or 1, Yes/No,True/False
Understanding the Problem Statement
For the purpose of this article, we will consider a problem statement of classifying the loan applicants as either Good or Bad. The data will contain the information about the loan applicants like their age, salary, and credit score, etc. The problem statement is to predict if a loan application will be approved or not based on the data available.
Theory Behind Logistic Regression
The Logistic Regression model is based on the logistic function, which is a sigmoid function that maps any real-valued number to a value between 0 and 1. The logistic function is defined as:
f(x) = 1 / (1 + e^-x)
The logistic regression model uses this function to model the probability of a binary outcome based on a set of independent variables. The model is represented as:
p(y = 1|x) = f(b0 + b1x1 + b2x2 + ... + bnxn)
where p(y = 1|x) is the probability of the positive class (y = 1) given the set of independent variables (x), b0, b1, b2, …, bn are the model coefficients, and x1, x2, …, xn are the independent variables.
Importing Required Libraries
In Python, we will start by importing the necessary libraries for building the model. The following libraries will be imported:
import pandas as pd import numpy as np 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
Loading the Data
The data for this article can be found on Kaggle. The data contains information about the loan applicants like their age, salary, credit score, etc. The first step is to load the data into a data frame using the following code:
data = pd.read_csv("loan_data.csv")
Exploratory Data Analysis (EDA)
Before building the model, it is always a good practice to perform Exploratory Data Analysis (EDA) on the data. This will give us a good understanding of the data and help us identify any trends or patterns in the data. We will start by checking for any missing values in the data:
sns.heatmap(data.isnull(), yticklabels=False, cbar=False, cmap='viridis')
As we can see from the heat map above, there are no missing values in the data. Next, we will check for any outliers in the data using a box plot:
sns.boxplot(x='Loan_Status', y='Credit_History', data=data)
From the boxplot above, we can see that there are no outliers in the data.
The next step is to preprocess the data so that it is ready to be used in the model. We will start by splitting the data into the independent and dependent variables:
X = data.iloc[:, :-1].values y = data.iloc[:, -1].values
Next, we will split the data into training and testing sets:
Advantages of Logistic Regression
- Simple and easy to implement: Logistic Regression is a relatively simple and straightforward algorithm to implement, making it a popular choice for solving classification problems.
- Fast: Logistic Regression is computationally fast, making it suitable for solving