Background: Novel corona virus (2019-nCoV) has spread in the world since its first human infection in December 2019. India has also witnessed a rising number of infections since March 2020. The Indian government imposed lockdowns in the nation to control the movement of its citizens thereby confining the spread of the virus. Tweeters resorted to usage of social media platform to express their mind. Aim: Through this article, an attempt has been made to understand the mind-set of Indian people using Python and R statistical software, during the recent lockdown 2.0 (15 April 2020 to 3 May 2020) and lockdown 3.0 (4 May 2020 to 17 May 2020) through their tweets on the social media platform Twitter. Also, opinion on e-commerce during this pandemic has been analysed. Method: Analysis has been performed using Python and R statistical software. Also, recent articles related to COVID-19 have been considered and reviewed. Result: Although the country had a positive approach in lockdown 2.0 with only few instances of sadness, disgust and others, the majority of the people had a negative approach in lockdown 3.0. Conclusion: This analysis can help the health specialists to understand people’s mind-set, the authorities to take further corresponding measures in washing out the virus and the e-commerce stakeholders to adapt to the changing attitudes by adjusting demand and supply plans accordingly.

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Summary As it is known that the whole world is battling against the Corona Virus Disease or COVID19 and trying their level best to stop the spread of this pandemic. To avoid the spread, several countries like China, Italy, Spain, America took strict measures like nationwide lockdown or by cordoning off the areas that were suspected of having risks of community spread. Taking cues from the foreign counterparts, the government of India undertook an important decision of nationwide full lockdown on March 25th which was further extended till May 4th, 2020 (47 days‐full lockdown). Looking at the current situation government of India pushed the lockdown further with eased curbs, divided the nation into green, orange and red zones, rapid testing of citizens in containment area, mandatory wearing of masks and following social distancing among others. The outbreak of the pandemic, has led to the large economic shock to the world which was never been experienced since decades. Moreover it brought a great uncertainty over the world wide electricity sector as to slow down the spread of the virus, many countries have issued restrictions, including the closure of malls, educational institutions, halting trains, suspending of flights, implemented partial or full lockdowns, insisted work from home to the employees. In this paper, the impact analysis of electricity consumption of state Haryana (India) is done using machine learning conventional algorithms and artificial neural network and electricity load forecasting is done for a week so as to aid the electricity board to know the consumption of the area pre hand and likewise can restrict the electricity production as per requirement. Thus, it will help power system to secure electricity supply and scheduling and reduce wastes since electricity is difficult to store. For this the dataset from regional electricity boards of Haryana that is, Dakshin Haryana Bijli Vitran Nigam and Uttar Haryana Bijli Vitran Nigam were analysed and electricity loads of state were predicted using python programming and as per result analysis it was observed that artificial neural network out performs conventional machine learning models.

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Web usage mining has assumed importance in learning about web user's behavior and user interactions with the website. It uses data mining techniques to discover non-trivial user behavior patterns. These patterns can then be used to make the predictions of next page to be accessed by the user. Web usage mining consists of the steps like web log preprocessing, pattern discovery and pattern analysis. This paper proposes a novel approach for preprocessing wherein rough set clustering is applied to form the clusters of sessions. These sessions could later on be used to form the knowledge base of rules on the basis of which the next page to be accessed could be prefetched.