Picture this: You are working with time-series data, searching for patterns and investigating the trends over time.
You have done an exploratory data analysis to your time-series data and you have looked for the best methods to detect outliers in your data.
After detection, either you ignored them, removed them or, most likely, you have transformed them.
Now comes the time when you need to evaluate the impact of that treatment: how did the distribution of your data changed? How well is your machine learning model predicting the target variable?
Besides, one could be curious about:
- What metrics will you use to assess the performance of the model?
- How will you visualize the changes in data distribution?
- What factors might have influenced the predictions of your models?
- Is there any bias in the data that might affect evaluation?
We will answer some of these questions using a dataset, so that you can reproducible the results.