Conclusion
1. Conclusion
Congratulations! You have successfully completed the course. Let's have a small recap of what we learned in this course over the four chapters.2. Chapter 1
We learned what are null values, detecting and replacing them with NaNs and analyzing the amount of missing data in the first chapter.3. Chapter 2
We next went on to analyze the types of missingness in our data which can be either of MCAR, MAR or MNAR. We analyzed the correlations of missingness using heat maps and dendrograms. Likewise, we also visualized and learned how the missingness can vary against another variable. Lastly, in the second chapter we learned when and how to delete data.4. Chapter 3
In the third chapter, we started by working with simple imputations techniques like filling in mean, median, mode or constant. We then explored how to treat missing values in time-series datasets and also graphically evaluated them.5. Chapter 4
Finally in chapter 4, we were introduced to the advanced imputations techniques which use machine learning models like the KNN and MICE models. We also saw how we can impute categorical data. And we finished off by evaluating all the different imputations. With this you have acquainted yourself on how to efficiently deal with missing values!!6. Congratulations!!
Congratulations and all the best!!Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.