haseebiba/predicting_creditcard_approvals

predicting_creditcard_approvals

##Prerequisite .pandas .numpy .sklearn .python 3.0 you can dowload these using conda

. first call the dataset cc_appprovals in to Dataframe of pandas

. once you load the dataset now perdform dirrenet funtions of pandas in order to analyse data of the dataset .

.numpy would be used to indetify the missing values then handle missing values

.Convert the non-numeric values to numeric from sklearn.preprocessing

.Using the train_test_split() method, split the data into train and test sets with a split ratio of 33% (test_size argument) and set the random_state argument to 42.

.apply logictic regression model as it can give best result of numeric values

.Call confusion_matrix() with y_test and y_pred to print the confusion matrix

.Define the grid of parameter values for which grid searching is to be performed.

.Find the best score and best parameters for the model using grid search.

.Call the best_score_ and best_params_ attributes on the grid_model_result variable, then print both. it will show you the accuracy of the model