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  What is Imputation? Imputation is a technique used for replacing the missing data with some substitute value to retain most of the data/information of the dataset. These techniques are used because removing the data from the dataset every time is not feasible and can lead to a reduction in the size of the dataset to a large extend, which not only raises concerns for biasing the dataset but also leads to incorrect analysis. Fig 1:- Imputation Not Sure What is Missing Data? How it occurs? And its type? Have a look  HERE  to know more about it. Let’s understand the concept of Imputation from the above Fig {Fig 1}. In the above image, I have tried to represent the Missing data on the left table(marked in Red) and by using the Imputation techniques we have filled the missing dataset in the right table(marked in Yellow), without reducing the actual size of the dataset. If we notice here we have increased the column size, which is possible in Imputation(Adding “Missing” category imputation)

  Introduction Machine Learning seems to be a big fascinating term, which attracts a lot of people towards it, and knowing what all we can achieve through it makes the sci-fi imagination of ours jump to another level. No doubt in it, it is a great field and we can achieve everything from an automated reply system to a house cleaning robots, from recommending a movie or a product to help in detecting disease. Most of the things that we see today have already started using ML to better themselves. Though building a model is quite easy, the most challenging task is preprocessing the data and filtering out the Data of Use. So, here I am going to address one of the biggest and common issues that we face at the start of the journey of making a Good ML Model, which is  The   Missing Data . Missing Data can cause many issues and can lead to wrong predictions of our model, which looks like our model failed and started over again. If I have to explain in simple terms, data is like Fuel of our Mo