We are now going to evaluate the performance of this model on an independent validation set to verify that our model can safely be applied on unknown data.
This model will estimate the risk of churn of telco customers and can be used to set up a retainment policy. Furthermore, we will do it together.
We would like to visualise the variations of the average income in Belgian municipalities. We also want to see clearly which municipality of every region has the highest mean income.
In our previous article, we measured how the market share of a Belgian company evolved between 2016 and 2017. In that analysis we concluded that there was no significant difference between the market share of both years at national level. Now we want to have a closer look at these evolutions with a local approach.
We will analyse two consecutive years (2016 and 2017) and see how the market share has increased. First of all we load the files of the sales by customer and by year. We also have access to the sales potential by postal code and by year.
In this newsletter we want to show you how to use the different join tools in order to analyse your data and obtain great insights. In this case we have data regarding customers and their purchases. In order to make interesting analysis, we have to join both files.
As an analyst you are faced with the reality that not all data is in one system or location. Getting access to that data is one step in the analytics process, blending and combining that data is a key component to create an analytical dataset that companies can use to take informed decisions.
Let’s talk about checking your data quality with Alteryx? So, you have access to the data and can now start your analyses. What to do before you launch yourself into creating workflows with analyses?