Skip to content

Booksellers & Trade Customers: Sign up for online bulk buying at trade.atlanticbooks.com for wholesale discounts

Booksellers: Create Account on our B2B Portal for wholesale discounts

Regression Analysis with Classical and Statistical Learning Methods: An Easy Guide for Data Scientists, Business Analysts and Engineers using Python

by K. C. James
Sold out
₹8,624.00
Original price ₹8,624.00
Original price ₹8,624.00
₹8,624.00
Current price ₹8,624.00

Imported Edition - Ships in 18-21 Days

Free Shipping in India on orders above Rs. 500

Request Bulk Quantity Quote
+91
Book cover type: Paperback
  • ISBN13: 9789348642516
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Academic Enclave
  • Publisher Imprint: Academic Enclave
  • Publication Date:
  • Pages: 502
  • Original Price: USD 80.0
  • Language: English
  • Edition: N/A
  • Item Weight: 862 grams
  • BISAC Subject(s): Software Development & Engineering / General

Regression is a powerful technique in data analysis for modeling relationships between variables, making it crucial for prediction, decision-making, and pattern recognition. This book offers an accessible introduction to regression modeling, tailored for postgraduate students in fields such as data science, engineering, statistics, mathematics, business, and the sciences. It simplifies complex mathematical concepts and emphasizes real-world applications, complemented by coding examples to reinforce key concepts.

The book covers classical regression methods including simple and multiple linear regression, polynomial regression, and logistic regression. It also addresses regression diagnostics, such as model evaluation, outlier detection, and assessment of model assumptions. By integrating classical methods with modern machine learning techniques, it offers a unique perspective. Machine learning techniques like support vector regression, decision trees, and artificial neural networks (ANN) for regression tasks are introduced, demonstrating their complementarity to classical methods through practical examples. The book also explores advanced methods such as Ridge, Lasso, Elastic Net, Principal Component Regression, and Generalized Linear Models (GLMs). These techniques are demonstrated using Python libraries like Statsmodels and Scikit-learn, enabling students to engage in practical learning.

Trusted for over 49 years

Family Owned Company

Secure Payment

All Major Credit Cards/Debit Cards/UPI & More Accepted

New & Authentic Products

India's Largest Distributor

Need Support?

Whatsapp Us