Karthik Sudershan, Vishnu Soman, Kiran K, Shreenidhi S Deshpande, Prof. Surendra Babu KN


Weather forecasting is mostly a numerical measurerather than a binary conclusion. Precise weather forecasting is one of today’s highlyinteresting tasks which deals with anenormousamount of observations and features. We aimto develop an intellectual weather forecasting module. Asmartforecast based on the freely available data is achievedwith machine learning techniques. This module reflects trails such as maximum temperature, minimum temperature, and rainfall for an experimented period of years, and they are evaluated. The dataset is repossessed from a website named DarkSky.To abridge the recovery of data,a Python API is used to studymeteorological data has been established. The study is grounded on Linear Regression and anArtificial Neural Network model thatforecast next day weather with goodprecision. Anaccurateness of more than 94% is gained based on the dataset. Newexplorations haveshown that ML methods accomplishedbetter presentation than old-style statistical approaches. Machine learning, a subdivision of artificial intelligence has establishedto be a robust method offoreseeing and examining a given data set. This unit plays asignificantpart in the field of agriculture where weather forecasting is a vital aspect.



Machine learning; Weather Predicting; Linear Regression; Artificial Neural Network.

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