Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

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Predictive Analysis Of Indian GDP Using Machine Learning Algorithms

Authors
C. Siva Kumar1, P. Lakshmi Sagar2, Samala Pavan Kumar3, *, Shaik Mohammad Abrar3, Renati Venkata Sai Susanth3, Sangaraju Sai Yashwanth Varma3
1Assistant Professor, Department of DS, Mohan Babu University, (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, A.P, India
2Assisatnt Professor, Department of CSE, SV College Of Engineering, Tirupati, A.P, India
3UG Scholar, Department of Computer Science and Systems Engineering, Sree Vidyanikethan Engineering College, Tirupati, A.P, India
*Corresponding author. Email: samalpavan999@gmail.com
Corresponding Author
Samala Pavan Kumar
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_56How to use a DOI?
Keywords
Indian GDP Prediction; Economic Indicators,GradientBoosting,Regressor; Linear Regression; Random Forest Regressor,Performance Evaluation
Abstract

In this research endeavor, machine learning algorithms—specifically Linear Regression, Random Forest Regressor, and Gradient Boosting Regressor—are harnessed to anticipate the future trajectory of the Indian Gross Domestic Product. Employing an extensive dataset that incorporates historical GDP, per capita income, imports, exports, and GDP growth rate, the study seeks to evaluate the predictive precision of each model. Post data preprocessing and model training, assessment metrics will be employed to juxtapose the efficacy of these models. The research yields insightful perspectives into the adeptness of these algorithms in predicting Indian GDP, providing policymakers and economists with valuable information to make well-informed decisions. The identification of the most accurate predictive model and critical economic indicators is paramount in this context.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
10.2991/978-94-6463-471-6_56
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_56How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - C. Siva Kumar
AU  - P. Lakshmi Sagar
AU  - Samala Pavan Kumar
AU  - Shaik Mohammad Abrar
AU  - Renati Venkata Sai Susanth
AU  - Sangaraju Sai Yashwanth Varma
PY  - 2024
DA  - 2024/07/30
TI  - Predictive Analysis Of Indian GDP Using Machine Learning Algorithms
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 578
EP  - 586
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_56
DO  - 10.2991/978-94-6463-471-6_56
ID  - Kumar2024
ER  -