Build a machine learning model to predict if a credit card application will get approved

Load the Dataset via pandas

Check the various columns and their datatypes and trends

Using NumPy for dealing with missing values

Dealing with NULLS

Dealing with missing numerics and filling it with values

Using sklearn's preprocessing Label Encoders for converting non-numeric values to numeric for faster calculations

Using sklearn's Model selection for splitting dataset into train and test set

Using sklearn's MinMaxScaler for normalizing the dataset for faster computation

Using sklearn's Logistic Regression for prediction model

Using confusion matrix to view our model's performance

Using GridSearchCV with multiple variable parameter for better model

Displaying the best parameter and its accuracy after gridsearch cv works into our test set