In the last blog, we discussed the importance of the data cleaning process in a data science project and ways of cleaning the data to convert a raw dataset into a useable form. Here, we are going to talk about how to identify and treat the missing values in the data step by step. Real-world […]
In the last few blogs, we saw how to summarise and analyse the data using statistical methods and visualisations. But the raw data need to be processed to convert it into a usable shape. Data Preparation is the most important and foremost part of Data Science. It involves data pre-processing and data wrangling. Raw data […]
