Python Logistic Regression: Examples & Steps

Madhusudhan Anand

Embarking on a Journey: Unveiling My Passions and Pursuits Greetings from Bangalore, India! My name is Madhusudhan Anand, and life has been a beautiful ride of experiences and challenges. Growing up, my family's nomadic nature led us to traverse various cities in Karnataka, immersing me in the rich tapestry of diverse cultures. These encounters have left an indelible mark on my journey, shaping my passions across four distinctive realms: product development, teaching, problem-solving, and writing. As the co-founder of Ambee, a vibrant climate tech startup, my forte is transforming promising ideas into tangible, revenue-generating products. I channel my creative energy, technical expertise, and entrepreneurial spirit with every project to make a meaningful impact. Teaching has become more than just a hobby—it has become a way for me to ignite a spark of knowledge and inspiration in others. Over the years, I've had the privilege of mentoring and training over 2000 programmers worldwide. Sharing my insights and empowering aspiring talents in the world of data science and programming has been a profitable endeavor. Problem-solving is the fuel that drives my passion. With an optimistic and multidimensional perspective, I approach every challenge as an opportunity for growth. From my roots in data science and remote sensing to exploring the realms of climate change, IoT, and AI, I've harnessed my problem-solving prowess to create innovative products at Ambee. Writing has always been my sanctuary—an avenue to channel my thoughts, emotions, and ideas. I am captivated by the power of the written word to inspire, educate, and connect. Through my blog, I promise to deliver authentic, informative, and infused content with my personal touch. I'll share insights from my journey, staying true to my values and unwavering commitment to honesty. As I embark on this blogging adventure, I dedicate this platform to my late father, a constant source of inspiration and strength. His memory will forever reside in my heart, guiding me to be true to myself and positively impact the world. Join me on this exhilarating journey of exploration, learning, and growth. Let's delve into the fascinating realms of technology, data science, and personal reflections. Welcome to my world!

My name is Madhusudhan Anand; I am a data scientist, CTO, and co-founder at Ambee. I write about climate, ESG, research, tech, coding, my experiences running a startup, product mgmt & data science.

Introduction: Logistic Regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. The outcome is measured with a dichotomous variable (0 or 1). In this tutorial, we will walk through the basics of logistic regression, implement it using Python, and apply it to a real-world dataset.

Prerequisites:

Python 3.x
Numpy
Pandas
Matplotlib
Scikit-learn

Steps: (Skipped EDA and others to keep context concise)

Import necessary libraries
Load and preprocess the dataset
Split the dataset into training and test sets
Implement logistic regression
Train and evaluate the model
Visualize the results
Import necessary libraries:

pythonCopy codeimport numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

Load and preprocess the dataset: For this tutorial, we'll use the Titanic dataset which can be found at https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv

pythonCopy codeurl = "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv"
data = pd.read_csv(url)

# Drop irrelevant columns
data = data.drop(['Name', 'Ticket', 'Cabin'], axis=1)

# Encode categorical variables
data['Sex'] = data['Sex'].map({'male': 0, 'female': 1})
data['Embarked'] = data['Embarked'].map({'C': 0, 'Q': 1, 'S': 2})

# Fill missing values
data['Age'].fillna(data['Age'].median(), inplace=True)
data['Embarked'].fillna(data['Embarked'].mode()[0], inplace=True)

# Display the processed dataset
print(data.head())

Split the dataset into training and test sets:

pythonCopy codeX = data.drop('Survived', axis=1)
y = data['Survived']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Implement logistic regression:

pythonCopy codelog_reg = LogisticRegression(solver='liblinear')

Train and evaluate the model:

pythonCopy codelog_reg.fit(X_train, y_train)

y_pred = log_reg.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
confusion = confusion_matrix(y_test, y_pred)
report = classification_report(y_test, y_pred)

print("Accuracy: ", accuracy)
print("Confusion Matrix: \n", confusion)
print("Classification Report: \n", report)

Visualize the results:

pythonCopy codeplt.figure(figsize=(8, 6))
plt.scatter(X_test['Age'], y_test, color='blue', label='Actual')
plt.scatter(X_test['Age'], y_pred, color='red', label='Predicted', marker='x')
plt.xlabel('Age')
plt.ylabel('Survived')
plt.legend()
plt.show()

In this tutorial, we have gone through the basics of logistic regression, implemented it using Python, and applied it to the Titanic dataset. You can experiment with different datasets and explore various options provided by the LogisticRegression class in scikit-learn for tuning the model.

Master Logistic Regression with Python: Step-by-Step Guide and Code Examples

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