Accuracy Analysis of Continuance by using Classification and Regression Algorithms in Python

α
Swayanshu Shanti Pragnya
Swayanshu Shanti Pragnya FCSRC and Data scientist

Send Message

To: Author

Accuracy Analysis of Continuance by using Classification and Regression Algorithms in Python

Article Fingerprint

ReserarchID

CSTSDE33Q9C

Accuracy Analysis of Continuance by using Classification and Regression Algorithms in Python Banner

AI TAKEAWAY

Connecting with the Eternal Ground
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu

Abstract

Reinforcement rate of technics and appositeness towards the convenience of the human being is a perennial mechanism. Mathematics has always been in the root towards the implementation of any algorithm or analysis regarding statistics or language. Extracting more about the data and analyzing them to solve a particular problem is the reason behind any analysis. Scrutiny itself has the different number of outcome which can be predictive or descriptive. Now prediction is how far accurate is tested by using various techniques. The enhancement in problem-solving capability leads to come up with a new aptitude concerning machine learning algorithms. But before prediction of data set collection, exploration, feature extraction, model building, accuracy testing are primarily required to invent. So for explaining all these processes, concept learning is essential. In this paper different algorithms like SVM, Linear and Logistic Regression, Decision tree, and Random forest algorithms will be used to demonstrate the accuracy in titanic data from Kaggle Website with all the required steps by using Python language.

References

14 Cites in Article
  1. Cherifi Mohamed,A Mesloub (2025). An Expectation-Maximization Approach for Logistic Regression with Mixed-Effects Covariates.
  2. K Saranya*,S Rajasekar,C Ramesh,M Alamelu (2020). Performance Model for Media Streaming Bandwidth Allocation using p2p.
  3. Hyeoun-Ae Park (2013). An Introduction to Logistic Regression: From Basic Concepts to Interpretation with Particular Attention to Nursing Domain.
  4. Steven Bagley,Halbert White,Beatrice Golomb (2001). Logistic regression in the medical literature:.
  5. Viv Bewick,Liz Cheek,Jonathan Ball (2004). Unknown Title.
  6. Viv Bewick,Liz Cheek,Jonathan Ball (2004). Statistics review 13: Receiver operating characteristic curves.
  7. J Austin,R Yaffee,D Hinkle (1992). Logistic regression for research in higher education.
  8. Steven Bagley,Halbert White,Beatrice Golomb (2001). Logistic regression in the medical literature:.
  9. Steven Bagley,Halbert White,Beatrice Golomb (2001). Logistic regression in the medical literature:.
  10. Tryambak Chatterjee,Aditi Shukla (2017). Consumer Preferences to Specific Features in Mobile Phones: A Comparative Study.
  11. Ge (2017). Flight Quest Challenge.
  12. (2017). Titanic: Machine Learning from Disaster.
  13. Tara Tharp (2017). “Wiki, Wiki, Wiki—WHAT?” Assessing Online Collaborative Writing.
  14. Kaggle (2017). Table 1: Comparative analysis between available methods on Kaggle dataset..

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Swayanshu Shanti Pragnya. 2018. \u201cAccuracy Analysis of Continuance by using Classification and Regression Algorithms in Python\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 18 (GJCST Volume 18 Issue C2): .

Download Citation

Issue Cover
GJCST Volume 18 Issue C2
Pg. 31- 36
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Classification
GJCST-C Classification: I.1.2
Version of record

v1.2

Issue date

May 25, 2018

Language
en
Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

Article Matrices
Total Views: 5901
Total Downloads: 1584
2026 Trends
Related Research

Published Article

Reinforcement rate of technics and appositeness towards the convenience of the human being is a perennial mechanism. Mathematics has always been in the root towards the implementation of any algorithm or analysis regarding statistics or language. Extracting more about the data and analyzing them to solve a particular problem is the reason behind any analysis. Scrutiny itself has the different number of outcome which can be predictive or descriptive. Now prediction is how far accurate is tested by using various techniques. The enhancement in problem-solving capability leads to come up with a new aptitude concerning machine learning algorithms. But before prediction of data set collection, exploration, feature extraction, model building, accuracy testing are primarily required to invent. So for explaining all these processes, concept learning is essential. In this paper different algorithms like SVM, Linear and Logistic Regression, Decision tree, and Random forest algorithms will be used to demonstrate the accuracy in titanic data from Kaggle Website with all the required steps by using Python language.

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

Quote and Order Details

Contact Person

Invoice Address

Notes or Comments

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

Accuracy Analysis of Continuance by using Classification and Regression Algorithms in Python

Swayanshu Shanti Pragnya
Swayanshu Shanti Pragnya

Research Journals