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Published Paper Details:
Paper Title
Weather Forecasting System Using Machine Learning and Kotlin-Based Interface
Authors
Prof Amol Mali, Durga Shinde, Saleha Vijapure, Rashmi Pardeshi, Rubina Babude
Keywords
Weather Prediction, LSTM, Kotlin, Machine Learning, Android App, Temperature Forecasting
Abstract
Weather forecasting is crucial in multiple domains such as agriculture, transportation, and disaster management. This paper presents a weather forecasting system that combines machine learning techniques with a Kotlin-based mobile interface. Historical weather data is processed and used to train an LSTM (Long Short-Term Memory) neural network, which predicts weather parameters like temperature, humidity, and rainfall. The frontend interface, developed in Kotlin for Android, allows users to visualize weather trends and receive predictions in real-time. The system is efficient, user-friendly, and can be extended to support live weather feeds.
IJCRT's Publication Details
Unique Identification Number - IJCRT2506970
Paper ID - 289936
Page Number(s) - i251-i253
Pubished in - Volume 13 | Issue 6 | June 2025
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Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Prof Amol Mali, Durga Shinde, Saleha Vijapure, Rashmi Pardeshi, Rubina Babude, "Weather Forecasting System Using Machine Learning and Kotlin-Based Interface", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 6, pp.i251-i253, June 2025, Available at :http://www.ijcrt.org/papers/IJCRT2506970.pdf
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Submit Manuscript Online Impact Factor: 7.97 Review Results : Within 02-03 Days Paper Publication : Within 02-03 Days
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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