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Weather forecasting is an essential task in any region of the world for proper planning of various sectors that are affected by climate change. In Warangal, most sectors, such as agriculture and electricity, are mainly influenced by climate conditions. In this study, weather (WX) in the Warangal region was forecast in terms of temperature and humidity. A radial basis function neural network was used in this study to forecast humidity and temperature. Humidity and temperature data were collected for the period of January 2021 to December 2021. Based on the simulation results, it is observed that the radial basis function neural network model performs better than other machine learning models when forecasting temperature and humidity.

Recommender Systems (RS) are software applications which aim to support users in their decision making while interacting with large information spaces. Most recommender systems are designed for recommending items to individuals. In this paper we provide experimental results related to developing a content-based group recommender system. To this end we make two important contributions. (1) Implementation of a group recommender system based on decision-lists as proposed recently in (Padmanabhan et al., 2011) using MovieLens dataset which is a relatively huge data-set (100,000 ratings from 943 users on 1682 movies) as compared to the data-set size of 150 used in (Padmanabhan et al., 2011) (2) We use seven variants of decision-tree measures and built an empirical comparison table to check for precision rate in group recommendation based on different social-choice theory strategies.