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In this exploratory data analysis, we compare a dataset which consists of various features about renting of houses available on these renting platforms listed by owners of these houses, and try to derive some constructive conclusions by performing Descriptive statistics of the available features.
Machine learning and neural networks used to create a binary classifier capable of predicting whether applicants will be successful if funded by Alphabet Soup.
Multiple linear regression model implementation with automated backward elimination (with p-value and adjusted r-squared) in Python and R for showing the relationship among profit and types of expenditures and the states.