Review on weather prediction using machine learning

  Rubhi gupta

In this paper, we have evaluated the machine learning techniques to predict weather with much accuracy. During this research process we have used following parameters to predict weather: temperature, rainfall, evaporation, sunshine, wind speed, wind direction, cloud, humidity and size of dataset. This research aims to compare the performance of some machine learning algorithms for predicting weather using weather data. From the collected weather data which contains some weather attributes, which are most relevant to weather prediction. In this paper, various Machine Learning Techniques have explored which includes Naive Bayes Bernoulli, Logistic Regression, Naive Bayes Gaussian and KNN. The experimental results show that Naive Bayes Bernoulli algorithm has good level of accuracy than other algorithms.

Keywords- Weather Forecast, Machine Learning Techniques: Naive Bayes Bernoulli, Logistic Regression, Naive Bayes Gaussian, KNN classification, Data pre-processing.

Unique Identification Number - IJEDR2001052
Page Number(s) - 270-273
Pubished in - Volume 8 | Issue 1 | January 2020
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Rubhi gupta,   "Review on weather prediction using machine learning", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.8, Issue 1, pp.270-273, January 2020, Available at :http://www.ijedr.org/papers/IJEDR2001052.pdf