We have taken an IPL dataset and analyzed the metrics of different teams in IPL. We have also been using libraries such as pandas, matplotlib, and seaborn to perform exploratory data analysis on top of this IPL data.

And we have been implementing some machine learning algorithms to predict the runs scored by the first batting team with the aid of some explanatory variables. Finally, we have deployed the model using Microsoft Azure.

The IPL has kept us entertained and hooked onto our seats. we are all eager to know who will win the match beforehand and, in the media, there is hype around the winning chances.

I myself being an avid fan of the tournament and the sport, decided to try my hands on a dataset to predict the runs scored with the aid of some explanatory variables.

What if I told you that we could create an app that could anticipate the outcome? These types of wonderful things are possible thanks to the capabilities of Machine Learning and Deep Learning.

We'll look at how to train a model from scratch and incorporate it in a web app using simple and powerful frameworks. There is also some web development.

Model deployment is a great method to show the world what you're capable of while also solving real-world problems.