Pre-Processing and Normalization of the Historical Weather Data Collected from Secondary Data Source for Rainfall Prediction
Deepak Sharma1, Priti Sharma2
1Deepak Sharma, Research Scholar, Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak (Haryana), India.
2Dr. Priti Sharma, Assistant professor, Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak (Haryana), India.
Manuscript received on 26 May 2023 | Revised Manuscript received on 13 June 2023 | Manuscript Accepted on 15 November 2023 | Manuscript published on 30 November 2023 | PP: 11-15 | Volume-3 Issue-2 November 2023 | Retrieval Number: 100.1/ijdm.B1629113223 | DOI: 10.54105/ijdm.B1629.113223
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Abstract: In the twenty first century, data analysis has become the talk of the town. Almost every company or organization depends on data analysis for taking future decision. The most important step in data analysis after data collection is the preprocessing of the collected data. The main aim of data analysis is to find meaningful pattern by processing large amount of data. In data preprocessing, the inconsistency of collected data has been removed. After storing data for a relatively longer period, it becomes noisy and inconsistent. While measuring various parameter due to error in the instrument or human error, the value become incorrect or invalid. It is necessary to remove the invalid data otherwise it will deflect the results and produce error in the prediction. In this work preprocessing of the weather data has been analyzed for rainfall prediction using data mining.
Keywords: Data Mining, Data Collection, Data Preprocessing, Secondary Data Sources, Weather Data, Rainfall Prediction, Machine Learning.
Article of the Scope: Data Mining