So, once you have got your business hypothesis ready, it makes sense to spend lot of time and efforts here. Remember the quality of your inputs decide the quality of your output. Steps of Data Exploration and Preparation What is feature variable creation and its benefits ?ġ.What are the common methods of variable transformation ?.
When should we use variable transformation ?.What is the process of Feature Engineering ?.What is the impact of outliers on dataset ?.Techniques of Outlier Detection and Treatment.Which are the methods to treat missing value ?.Why missing value treatment is required ?.Steps of Data Exploration and Preparation.For better understanding, I’ve taken up few examples to demonstrate the complicated concepts. As always, I’ve tried my best to explain these concepts in the simplest manner. I’ve created this tutorial to help you understand the underlying techniques of data exploration.
In my initial days, one of my mentor suggested me to spend significant time on exploration and analyzing data.
I have been a Business Analytics professional for close to three years now. I can confidently say this, because I’ve been through such situations, a lot. In such situation, data exploration techniques will come to your rescue. After some point of time, you’ll realize that you are struggling at improving model’s accuracy. If you are in a state of mind, that machine learning can sail you away from every data storm, trust me, it won’t. There are no shortcuts for data exploration.
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