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@article{152715,
        author = {Sirisha D and P. Srijani   and  R. Dharani and K. Durga Vinusha and K. Pavan Kalyan},
        title = {Predicting Rainfall using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {4},
        pages = {145-150},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=152715},
        abstract = {Rainfall prediction is paramount for predicting the climatic conditions in any country. It is demanding responsibility of meteorological department to predict the frequency of rainfall with uncertainty. Moreover, it is complicated to predict the rainfall accurately with changing climatic conditions. It is challenging to forecast the rainfall for both summer and rainy seasons. In the current work, a rainfall prediction model is proposed using Multiple Linear Regression (MLR) using the dataset of India. The data taken is from 1901 to 2015 monthly wise. The input data comprises of numerous meteorological parameters in order to predict the rainfall precisely. The proposed machine learning model devised is evaluated using the parameters viz., Mean Square Error (MSE), accuracy, and correlation. The experimental results, it can be observed that the proposed machine learning model provides better results compared to the existing algorithms.},
        keywords = {Machine learning, Regression Technique, Classification Technique, rainfall prediction, accuracy},
        month = {},
        }