India’s GDP Growth Prediction System

Abstract

This paper presents a deep learning approach that is integrated so as to forecast India’s GDP growth; it combines the structured economic indicators from the Reserve Bank of India (RBI) with the unstructured financial news data. Customary models depend only on numerical data. This study leverages natural language processing techniques, in particular tokenization, lemmatization, and TF-IDF vectorization, to extract meaningful understandings from news articles, capturing real-time sentiment, with context. The TensorFlow neural network model is trained on the combined dataset, then assessed via Mean Absolute Error (MAE) and R-squared score (R²), reaching satisfactory predictive accuracy. A Python pipeline that is automated streamlines all of the workflow, which goes from data scraping and preprocessing to prediction and visualization. The results show integrating unstructured news data improves the reliability for GDP forecasting in an important way, offering a more dynamic and thorough comprehension of economic trends

Introduction

1. Overview

Gross Domestic Product (GDP) is a critical metric for assessing a country's economic health. However, traditional models like ARIMA, linear regression, and seasonal forecasting struggle to adapt to economic shocks (e.g., policy changes, global crises). This study addresses these limitations by combining structured data (e.g., RBI economic indicators) with unstructured news data (e.g., financial articles) using Natural Language Processing (NLP) and deep learning.

2. Motivation

• India’s complex economy involves diverse and dynamic factors, making GDP prediction especially difficult.

• Traditional models are slow to respond to real-time developments.

• Financial news articles capture economic sentiment and timely updates, offering valuable forecasting potential when combined with numerical data.

3. Approach & Methodology

A. Data Sources

• Structured Data: RBI indicators such as inflation, interest rates, industrial production, and GDP history.

• Unstructured Data: Economic news articles collected using web scraping tools (e.g., BeautifulSoup, Selenium).

B. Preprocessing

• Structured Data: Cleaned and normalized for modeling.

• Text Data: Processed using NLP (stopword removal, tokenization, lemmatization), then vectorized using TF-IDF to quantify word importance.

C. Feature Fusion & Model Design

• Structured and unstructured data are combined into a single feature matrix.

• A deep learning regression model built with TensorFlow includes:

  • Dense layers with ReLU activation

  • Dropout layers for regularization

  • Adam optimizer for training

D. Prediction & Evaluation

• The model predicts quarterly GDP growth.

• Evaluation metrics include:

  • Mean Absolute Error (MAE)

  • Root Mean Squared Error (RMSE)

  • R² Score

E. Workflow Automation

An automated Python pipeline handles:

• Data scraping

• Preprocessing

• Model retraining (if needed)

• Predictions

• Dashboard visualizations

F. Visualization

Results are presented using Matplotlib and Seaborn to display:

• Predicted vs. actual GDP trends

• Influence of economic sentiment over time

4. Implementation & Results

• Developed in Python using libraries such as TensorFlow, Keras, BeautifulSoup, and SQLite.

• Demonstrated higher accuracy and responsiveness compared to models using only structured or only unstructured data.

• The hybrid model adapts better to real-world dynamics and economic sentiment shifts.

5. Background & Related Work

• Existing GDP prediction studies mostly rely on structured data or global economies.

• This study fills a gap by focusing on India and integrating real-time sentiment from news sources.

• Inspired by similar successes in stock market forecasting using sentiment analysis.

6. Proposed Algorithm

The hybrid model:

• Integrates real-time economic sentiment and historical trends.

• Bridges the gap between traditional econometric models and adaptive machine learning methods.

• Offers scalable, automated, and context-aware GDP forecasting.

7. Future Scope

• Enhancements with advanced NLP models, LSTM networks, or transformers.

• Use of AI-driven sentiment analysis, molecular modeling, or nanotech-based delivery methods for better prediction and interpretability.

• Integration into web-based platforms for real-time public or institutional access.

Conclusion

This project aimed to bridge the gap between traditional economic forecasting methods and modern data-driven approaches by creating a hybrid system that predicts India’s GDP growth using both structured data (like official RBI indicators) and unstructured data (news articles from reliable financial sources). The system effectively combines numerical economic variables with textual sentiment extracted from current events to provide a more holistic and real-time view of the country’s economic health. The development process included building a robust data pipeline to collect and preprocess diverse data types, applying TF-IDF and sentiment analysis techniques to extract relevant insights from news, and feeding this data into machine learning models such as Linear Regression and Random Forest for GDP prediction. The results were further visualized using dynamic graphs and a lightweight web interface, making the entire prediction workflow both accessible and interpretable to end users. This integration of technical depth with user experience highlights the system’s practicality and real-world value. However, the journey does not end here. There is significant scope to enhance the system’s performance and reach. For instance, incorporating deep learning models like LSTM (for time series analysis) or transformer-based models like BERT (for better understanding of news semantics) could substantially improve prediction accuracy. The system can also be expanded to support multilingual news sources, covering a wider range of sentiments from diverse regions of India. Additionally, integrating real-time streaming data (e.g., live economic feeds or news APIs) would make the predictions more up-to-date and relevant for policymakers or investors. From a deployment perspective, converting this prototype into a fully functional, cloud-hosted application with role-based access and data security features would enable institutions such as research bodies, government departments, or financial firms to utilize it in decision-making processes. Furthermore, incorporating predictive confidence scores and “what-if” scenario testing could make the tool more interactive and powerful for economic planning. In conclusion, this project not only showcases the potential of hybrid machine learning models in economic forecasting but also opens doors to intelligent, scalable, and real-time GDP prediction systems that adapt to changing economic landscapes.

References

[1] Reserve Bank of India, “Handbook of Statistics on Indian Economy,” [Online]. Available: https://www.rbi.org.in [2] Ministry of Statistics and Programme Implementation, Government of India, “National Accounts Statistics,” [Online]. Available: https://mospi.gov.in [3] A. S. Aslam, S. J. Khan, and R. M. Noor, “GDP prediction using machine learning and sentiment analysis of economic news,” Procedia Computer Science, vol. 199, pp. 345-352, 2022. [4] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. [5] T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794, 2016. [6] M. Vaswani et al., “Attention is all you need,” in Advances in Neural Information Processing Systems, vol. 30, 2017. [7] Google Developers, “TF-IDF Text Feature Extraction,” [Online]. Available: https://developers.google.com/machine-learning/guides/text-classification/step-3 [8] N. Reimers and I. Gurevych, “Sentence-BERT: Sentence embeddings using Siamese BERT-networks,” arXiv preprint arXiv:1908.10084, 2019. [9] K. Clark, M. Luong, Q. V. Le, and C. D. Manning, “ELECT

Copyright

Copyright © 2025 Sudhansh Rasam, Vrinda Patil, Rahul Akula, Debarathi Goshal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.