The problem CUSTOMER ACQUISITION OF DISH NETWORK solves

The main task here is to forecast customer churn with the help of machine learning and define its cause. If company does this in time, they can decrease churn rate (number of customers who decide to cancel renewal/subscription, stop purchasing or switch to competitors), and increase retention rate (number of customers who continue using services or buying goods). This model could give the average number of customers that uses the dish network regularly. By using the average customer rate, we could gain the user experience from the regular customers as well as invest the amount spent by the customer for the future developments,used to allowing the company to make better decisions.

Challenges we ran into

Formulation of the data in a correct manner.

As the data were scaled it was hard for us to visualise it.

Some data contained zeros in it so we got high error to correct this we can't remove the zeros if we do it the time series will be broke so we added the value 100 to the whole dataset.

Adding extra features to the dataset was very challenging getting the right amount of data for our time series was hard.