Covid-19 crossed all international, national, regional boundaries so declared as Pandemic across the globe. Since one and half year every country is struggling to deal with this disease either by using preventive measures like lockdown, sanitization or with cure measure like treatment, isolation etc. Most important preventive measure adopted by every country now is Vaccination. But due to confusion, uncertainty and lack of awareness people have different opinion and sentiment about the vaccination drive. There is a drastic requirement to understand the People's sentiments on the vaccine, which may vary for different regions. Especially when these sentiments became vocal on social media, it may create adverse impact on the Vaccination Drive. This paper discusses predictive analytics and visualization methods for analyzing and predicting the summarized sentiments using social media channel: twitter. This approach is useful to provide support the vaccination drive by spreading awareness about the vaccine in the region with extreme negative sentiments. Corona Vaccine related tweets are analyzed and classified using various classification techniques as Bernoulli Naïve Bayes, Linear SVC and Random Forest. Visualization techniques are discussed to showcase summarized sentiments region-wise. This approach is effective and useful for support for vaccination drive for Covid-19 spread prevention. © 2021 IEEE.