To predict something useful from the datasets, we need to implement machine learning algorithms. Since, there are many types of algorithm like SVM, Bayes, Regression, etc. We will be using four algorithms- Dimensionality Reduction It is a very important algorithm as it is unsupervised i.e. it can implement raw data to structured data.
When you have a large dataset then there are the various cases when you are not getting the accurate machine learning models. Their predictions accuracy are not correct as you expected. There can be various reasons for it like Duplicates values e.t.c. One of the other reasons is Outliers. These are the values that don't contribute to the prediction but mainly affect the other descriptive statistic values like mean, median, e.t..c.
How often do you think you're touched by data science in some form or another? Finding your way to this article likely involved a whole bunch of data science (whooaa). To simplify things a bit, I'll explain what data science means to me. "Data Science is the art of applying scientific methods of analysis to any kind of data so that we can unlock important information." If we unpack that, all data science really means is to answer questions by using math and science to go through data that's too much for our brains to process.
Recently, someone from the editorial team for Kite, an AI autocomplete for Python, reached out to see if I would share some of their content. Since I thought the tool looked awesome, I figured I'd help them out. After some chatting, we decided on this data science article by Kirit Thadaka. How often do you think you're touched by data science in some form or another? Finding your way to this article likely involved a whole bunch of data science (whooaa).