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