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Banks often depend on credit score prediction models to approve or deny a loan request. A good prediction model is necessary for a bank so that they can provide maximum credit without exceeding the risk threshold. This data science project uses credit score dataset which has fairly large volume of data (250K). The predictive models will be build following various approaches - random forests, graident boosting and logistic regression. At the end of the project you will build a predictive model that will automatically score each applicant with a credit score which is human readable and easy to interpret.
Given a partial trajectory of a taxi, you will be asked to predict its final destination using the taxi trajectory dataset.
Machine Learning Project in R -Predict which customers will leave an insurance company in the next 12 months.
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.