Rain Prediction Using Machine Learning

Registration ID: IJNRD_180981

Published ID: IJNRD2204082

DOI: Click Here to Get

Vijithra Nair , Megha Mathew , Sweta Bhattacharjee , Arashdip Singh , Prof. Payel Thakur

ARIMA, CatBoost, Random Forest, Rainfall prediction, XgBoost

As agriculture being the key point of survival, Rainfall is the important source for its cultivation. Rainfall prediction has always been a major problem as prediction of rainfall gives awareness to people and to know in advance about rain so as to take necessary precautions to protect their crops from rain. A particular dataset is taken from Kaggle community and this project predicts whether it will rain tomorrow or not by using the rainfall in dataset. CatBoost model is implemented in this project as it is an open sourced machine learning algorithm, and features great quality without the parameter tuning, categorical feature support, improved accuracy and fast prediction. CatBoost model is a gradient boosting toolkit and two critical algorithms classical and innovative are introduced to create a fight in prediction shift present in currently existing implementations of gradient boosting algorithms. CatBoost performed very well giving an AUC (Area under curve) score 0.8 and ROC ( Receiver operating characteristic curve) score as 89. ROC is called as an evaluating curve whereas AUC presents a degree or measure of separability as the model is skilled enough to distinguish between classes. An Exploratory data analysis is done to examine data distribution, outliers and provides tools for visualizing and understanding the data through graphical representation. A dashboard is implemented to showcase the information that is represented in datasets i.e. any changes in the data will result in different types of graphs. A linear SVC (Support vector classifier) provides a best fit hyperplane that divides the data and feeds some features to the classifier to detect what the predicted class is and results in desired output.

Published Paper   E-Certificate

Vijithra Nair, Megha Mathew, Sweta Bhattacharjee, Arashdip Singh, & Prof. Payel Thakur (April-2022). Rain Prediction Using Machine Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 7(4), 687-693. https://ijnrd.org/papers/IJNRD2204082.pdf

Volume 7 Issue 4, April-2022

Pages : 687-693

Paper Reg. ID: IJNRD_180981

Published Paper Id: IJNRD2204082

Downloads: 000121981

Research Area: Computer Engineering 

Country: Navi Mumbai/Raigarh, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2204082.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2204082

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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