we can predict feedback given by customer as positive or negative.

As we know insurance becomes a important part of life after covid surge. Due to which insurance market grows exponentially. Also there are new market players entering in sector makes it is very competitive for old market players. As a role of service industry it is primary goal of every insurance company to look after customers reviews and feedbacks. As those positive feedbacks may result into further recommendation of insurance and ultimately result in growth of company. So it is very important to analyze feedbacks from customers.

We are provided with a dataset which contains feedback submitted by customers after settlement of claims. A record contains both categorical and continuous features. The target feature is promoter or detractor.

This is clearly a classification problem where target is numerical value and we have to predict class label by using given features. Also we already have target value in train data set so we can do supervised learning and train model on given data set.

As we can afford to take time to predict loss, there is no requirement of very low latency.