Contents
How do you handle duplicate data in a dataset?
Practice : Handling Duplicates in R
- DataSet: “./Telecom Data Analysis/Complaints.csv”
- Identify overall duplicates in complaints data.
- Create a new dataset by removing overall duplicates in Complaints data.
- Identify duplicates in complaints data based on cust_id.
Why do we remove duplicate data?
Why is it important to remove duplicate records from my data? You will develop one, complete version of the truth of your customer base allowing you to base strategic decisions on accurate data. Time and money are saved by not sending identical communications multiple times to the same person.
How to duplicate training examples to handle class?
I would like to oversample the training set. Specifically, I would like to duplicating training samples with class 1 so that the training set is balanced (i.e., where the number of samples with class 0 is approximately the same as the number of samples with class 1). How can I do so?
When to split data into training and test sets?
Figure 1. Slicing a single data set into a training set and test set. Make sure that your test set meets the following two conditions: Is large enough to yield statistically meaningful results.
When to remove duplicates from a data-set?
In this case you need to understand how reliable a given outcome is given a set of inputs. Duplicate inputs result in some distribution across your output and thus you need to retain that distribution. In this case removing examples is highly destructive and must be avoided.
Why do you need to know about duplicate data?
Duplicate data has very unintuitive effects on metrics of model efficacy that mean that interpretation of even something as simple as an accuracy metric is impossible without a good understanding of the rates of duplication and contradiction in your dataset. Correspondingly you must be certain to disclose these rates.