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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.
Forecast the business for the upcoming years by Exploring Hidden Trends, Calculating Machine Productivity , Extrapolation and Assumptions and Summarizing Answers through Visualizations.
The goal of this data science project is to take an image of a handwritten single digit, and determine what that digit is.
There are different time series forecasting methods to forecast stock price, demand etc. In this machine learning project, you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example.