Weather Forecast through Data Mining

Authors

  • Swati Pandey  Department of Computer Science and Engineering , Krishna Engineering College, Ghaziabad, Uttar Pradesh, India
  • Shruti Sharma  Department of Computer Science and Engineering , Krishna Engineering College, Ghaziabad, Uttar Pradesh, India
  • Shubham Kumar  Department of Computer Science and Engineering , Krishna Engineering College, Ghaziabad, Uttar Pradesh, India
  • Kanchan Bhatt  Department of Computer Science and Engineering , Krishna Engineering College, Ghaziabad, Uttar Pradesh, India
  • Dr. Rakesh Kumar Arora  Professor, Department of Computer Science and Engineering, Krishna Engineering College, Ghaziabad, Uttar Pradesh, India

DOI:

https://doi.org//10.32628/CSEIT217318

Keywords:

Weather Forecast, Neural Network, Prediction , Parameters, Analysis, Correlation

Abstract

Weather Forecasting is the attempt to predict the weather conditions based on parameters such as temperature, wind, humidity and rainfall. These parameters will be considered for experimental analysis to give the desired results. Data used in this project has been collected from various government institution sites. The algorithm used to predict weather includes Neural Networks(NN), Random Forest, Classification and Regression tree (C &RT), Support Vector Machine, K-nearest neighbor. The correlation analysis of the parameters will help in predicting the future values. This web based application we will have its own chat bot where user can directly communicate about their query related to Weather Forecast and can have experience of two-way communication.

References

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Published

2021-06-30

Issue

Section

Research Articles

How to Cite

[1]
Swati Pandey, Shruti Sharma, Shubham Kumar, Kanchan Bhatt, Dr. Rakesh Kumar Arora, " Weather Forecast through Data Mining, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 3, pp.90-95, May-June-2021. Available at doi : https://doi.org/10.32628/CSEIT217318