How do you check outliers in your data set and how do you deal with outliers?
5 ways to deal with outliers in data
- Set up a filter in your testing tool. Even though this has a little cost, filtering out outliers is worth it.
- Remove or change outliers during post-test analysis.
- Change the value of outliers.
- Consider the underlying distribution.
- Consider the value of mild outliers.
How do you remove outliers from data analysis?
When you decide to remove outliers, document the excluded data points and explain your reasoning. You must be able to attribute a specific cause for removing outliers. Another approach is to perform the analysis with and without these observations and discuss the differences.
How to prepare a dataset for churn prediction?
To prepare the dataset for modeling churn, we need to encode categorical features to numbers. This means encoding “Yes”, “No” to 0 and 1 so that algorithm can work with the data. This process is called onehot encoding.
Are there any outliers that churn after 70 months?
There are a few outliers that churned after being with Telco for 70 months. The second box plot compares MonthlyCharges against churn. The median MonthlyCharges of churned customers is significantly higher than those who did not churn.
How to train a decision tree for churn prediction?
When working on the churn prediction we usually get a dataset that has one entry per customer session (customer activity in a certain time). Then we could add features like: is a customer only in one country. Sometimes we even have customer event data, which enables us to find patterns of customer behavior in relation to the outcome (churn).
Can a customer have a low churn rate?
Customers with a very high number of additional services do have a low churn rate. For conduction of model training and testing steps, the data set is split into 80% training data and 20% test data. The “Churn” column is defined as the class (the “y”), the remaining columns as the features (the “X”).