The data was first pre-processed and analysis libraries like Numpy and Pandas were used to make it ready to be utilized by a machine learning algorithm.Problems like standard scaling, categorical data and missing values were handled with appropriate techniques. Then, we used LogisticRegression model.
The data was first pre-processed and analysis libraries like Numpy and Pandas were used to make it ready to be utilized by a machine learning algorithm.Problems like standard scaling, categorical data and missing values were handled with appropriate techniques. Then, we used LogisticRegression model.