# Import pandas import pandas as pd # Load dataset cc_apps = pd.read_csv("/work/datasets/cc_approvals.data",header=None) # Inspect data cc_apps.head()

# Print summary statistics cc_apps_description = cc_apps.describe() print(cc_apps_description) print('\n') # Print DataFrame information cc_apps_info = cc_apps.info() print(cc_apps_info) print('\n') # Inspect missing values in the dataset cc_apps.tail(17)

# Import train_test_split from sklearn.model_selection import train_test_split # Drop the features 11 and 13 cc_apps = cc_apps.drop([11,13], axis=1) # Split into train and test sets cc_apps_train, cc_apps_test = train_test_split(cc_apps, test_size=0.33, random_state=42)

# Import numpy import numpy as np # Replace the '?'s with NaN in the train and test sets cc_apps_train = cc_apps_train.replace("?",np.nan) cc_apps_test = cc_apps_test.replace("?",np.nan)

# Impute the missing values with mean imputation cc_apps_train.fillna(cc_apps.mean(), inplace=True) cc_apps_test.fillna(cc_apps.mean(), inplace=True) # Count the number of NaNs in the datasets and print the counts to verify cc_apps.isna().sum().sort_values()

# Iterate over each column of cc_apps_train for col in cc_apps_train.columns: # Check if the column is of object type if cc_apps_train[col].dtypes == 'object': # Impute with the most frequent value cc_apps_train = cc_apps_train.fillna(cc_apps_train[col].value_counts().index[0]) cc_apps_test = cc_apps_test.fillna(cc_apps_train[col].value_counts().index[0]) # Count the number of NaNs in the dataset and print the counts to verify print(cc_apps_train.isnull().sum()) print(cc_apps_test.isnull().sum())

# Convert the categorical features in the train and test sets independently cc_apps_train = pd.get_dummies(cc_apps_train) cc_apps_test = pd.get_dummies(cc_apps_test) # Reindex the columns of the test set aligning with the train set cc_apps_test = cc_apps_test.reindex(columns=cc_apps_train.columns, fill_value=0)

# Import MinMaxScaler from sklearn.preprocessing import MinMaxScaler # Segregate features and labels into separate variables X_train, y_train = cc_apps_train.iloc[:, :-1].values, cc_apps_train.iloc[:, [-1]].values X_test, y_test = cc_apps_test.iloc[:, :-1].values, cc_apps_test.iloc[:, [-1]].values # Instantiate MinMaxScaler and use it to rescale X_train and X_test scaler = MinMaxScaler(feature_range=(0, 1)) rescaledX_train = scaler.fit_transform(X_train) rescaledX_test = scaler.transform(X_test)

# Import LogisticRegression from sklearn.linear_model import LogisticRegression # Instantiate a LogisticRegression classifier with default parameter values logreg = LogisticRegression() # Fit logreg to the train set logreg.fit(rescaledX_train , y_train.ravel())

# Import confusion_matrix from sklearn.metrics import confusion_matrix # Use logreg to predict instances from the test set and store it y_pred = logreg.predict(rescaledX_test) # Get the accuracy score of logreg model and print it print("Accuracy of logistic regression classifier: ",logreg.score(rescaledX_test,y_test)) # Print the confusion matrix of the logreg model confusion_matrix( y_test,y_pred)

# Import GridSearchCV from sklearn.model_selection import GridSearchCV # Define the grid of values for tol and max_iter tol = [0.01, 0.001, 0.0001] max_iter = [100, 150, 200] # Create a dictionary where tol and max_iter are keys and the lists of their values are corresponding values param_grid = dict(tol=tol, max_iter=max_iter)

# Instantiate GridSearchCV with the required parameters grid_model = GridSearchCV(estimator=logreg, param_grid=param_grid, cv=5) # Fit grid_model to the data grid_model_result = grid_model.fit(rescaledX_train , y_train.ravel()) # Summarize results best_score, best_params = grid_model_result.best_score_, grid_model_result.best_params_ print("Best: %f using %s" % (best_score, best_params)) # Extract the best model and evaluate it on the test set best_model = grid_model_result.best_estimator_ print("Accuracy of logistic regression classifier: ",best_model.score(rescaledX_test,y_test))