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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | WiFi Weather Station Prediction using ML |
| Authors Name: | Somesh Rathod , Romit Aherkar , Akashay Kothule , Vaibhavi Rathod , Shailesh Kulkarni |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_209939
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| Published Paper Id: | IJNRD2311343 |
| Published In: | Volume 8 Issue 11, November-2023 |
| DOI: | |
| Abstract: | Weather plays a crucial role in shaping our daily lives by providing insights into upcoming rain or sunshine. The process employed by meteorologists to anticipate weather conditions is termed weather forecasting. Climatic state parameters are determined by various factors, including temperature, pressure, humidity, dewpoint, precipitation, wind speed, and dataset size. In this experimental analysis we have develop a weather forecast prediction system using machine learning. This model uses Ridge regression algorithm to predict weather events such as temperature, humidity, rain and atmospheric pressure. Weather forecasting is the skill of predicting future weather patterns based on historical parameters such as temperature, humidity, wind direction and speed, precipitation, air haze, solar and terrestrial radiation, among others. The accuracy of weather predictions is contingent on the data collected, and the Linear Regression algorithm serves as the intellectual core of this process. As the number of considered parameters increases, the forecasting accuracy improves, enabling individuals to access tomorrow's weather forecasts. The data must undergo precise classification for effective prediction. To forecast unknowns, a thorough examination of directly or indirectly relevant variables is essential. |
| Keywords: | dewpoint, precipitation, regression, ridge |
| Cite Article: | "WiFi Weather Station Prediction using ML ", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 11, page no.d412-d416, November-2023, Available :http://www.ijnrd.org/papers/IJNRD2311343.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2311343 Registration ID: 209939 Published In: Volume 8 Issue 11, November-2023 DOI (Digital Object Identifier): Page No: d412-d416 Country: Pune, Maharashtra, India Research Area: Electronics & Communication Engg. Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2311343 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2311343 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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