Post 7 | ML | Data Preprocessing – Part 5

Hello, everyone. Thanks for joining me in this 5th tutorial of the Data Preprocessing part of the Machine Learning tutorials. In the last tutorial, we saw how to convert the CATEGORICAL VARIABLES from the STRING format to an INTEGER format. In this tutorial, we are going a step ahead and are going to split the original data … Continue reading Post 7 | ML | Data Preprocessing – Part 5

Post 6 | ML | Data Preprocessing – Part 4

Hello, everyone. Welcome to the Part 4 of the Data Preprocessing of the Machine Learning tutorials. In the last tutorial, we saw how to impute the Missing Data in both Python and R. In this tutorial, we are going to see how to deal with the qualitative entries in the given data. The following infographics show our … Continue reading Post 6 | ML | Data Preprocessing – Part 4

Post 5 | ML | Data Preprocessing – Part 3

Hello, everyone. Thanks for coming back for the third part of the Data Preprocessing section of the Machine Learning tutorial series. In the last tutorial, i.e. Part 2, we saw how to import the downloaded dataset. In this tutorial, we are going to see how to impute the missing data in the input data. The … Continue reading Post 5 | ML | Data Preprocessing – Part 3

Post 4 | ML | Data Preprocessing – Part 2

Hello everyone, thanks for coming back to the next tutorial in Data Preprocessing step of Machine Learning tutorials. Just to refresh your memory, in the last tutorial i.e. Part 1 of Data Preprocessing, we saw how to download the dataset and import the required libraries for performing required operations. In this tutorial, we are going to see how … Continue reading Post 4 | ML | Data Preprocessing – Part 2