Hello, all, if you want to become a data scientist or engineer, then you have to showcase your practical
skills and the best way to showcase your practical skills by solving some real world use cases, by
solving some real life challenges, because understanding machine learning, understanding data science
is never going to help out until unless you don't have your technical skills under Ullas, you don't
have your own projects on your resume.
That's why in the schools we all have come up with these three real world challenges, three real world
projects for you that will definitely help you and that will increase your chance of being recruited
in some interviews of top notch based companies like Amazon, Google, Netflix, Facebook and in some
top MNC as well.
The very first project of machine learning that you guys are going to consider in this practical course
is all about hotel booking prediction.
So here we have to protect rather the particular booking is going to cancel or not.
So in this entire project, this is exactly our main strategy, that if we are going to collect some
data after it, what we are going to do, we are going to pre-process that data.
We are going to perform some data cleaning on the data because in real world, what our data we will
get, we will always get our raw data and we have to make that data ready for our machine learning purposes
after we have to perform something known as exploratory data analysis to understand what data, what
exactly is going inside.
In my data, after what we have to do, we have to apply various techniques of feature engineering and
see if we have to encode our categorical data, which is exactly known as future encoding operate.
We have to do something known as our glide detection operator.
We have to select some important features for our model that contribute most to the dependent features
by doing some statistical techniques.
And after doing all these systems that we are going to do prediction using Savrin machine learning algorithm,
depending upon what use case we have, that we have classification, whether we have regression or whether
we have a cluster in use.
So this is going to be your entire life cycle of the Machine Learning Project.
So a second use case is exactly my healthcare use case in which we are going to consider data of those
patient that have kidney disease or not.
So we have to predict whether a particular patient or a particular person having all these different
different properties can have a chronic kidney disease or not using some classification algorithms.
And what we have to do, we have to understand our data as well.
And from this huge chunk of data, we have to analyze it and extract some meaningful insights from it.
And the third, you will see this is exactly most demanding use case, which is exactly my airline industry
use case.
So in this case, we have to consider data of flights.
So we have to understand this data, how we can understand by analyzing this data, by extracting some
amazing insights and add that we have to build such a machine learning model that can predict what exactly
it can with a fear of the takers of airlines on different different routes, having all these different
different features.
So we have to build such a cool and fancy machine learning model for this use case as well.
So for what you guys are waiting for, just with me and have a phone for all the upcoming sessions.