Each project comes with 2-5 hours of micro-videos explaining the solution.
Get access to 50+ solved projects with iPython notebooks and datasets.
Add project experience to your Linkedin/Github profiles.
SYL bank is one of Australia’s largest banks. Currently, the loan applications which come in to their various branches are processed manually. The decision whether to grant a loan or not is subjective and due to a lot of applications coming in, it is getting harder for them to decide the loan grant status. Thus, they want to build an automated machine learning solution which will look at different factors and decide whether to grant loan or not to the respective individual.
In this ML problem, we will building a classification model as we have to predict if an applicant should get a loan or not. We will look at various factors of the applicant like credit score, past history and from those we will try to predict the loan granting status. We will also cleanse the data and fill in the missing values so that our ML model performs as expected. Thus we will be giving out a probability score along with Loan Granted or Loan Refused output from the model.
In this data science project, you will contextualize customer data and predict the likelihood a customer will stay at 100 different hotel groups.
In this machine learning project, we will implement Back-propagation Algorithm from scratch for classification problems.
The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.