<jupyter_start><jupyter_text>Building Predictive Models<jupyter_code>import pandas as pd
import os
import numpy as np<jupyter_output><empty_output><jupyter_text>Import Data<jupyter_code># get the path of processed data
processed_data_path = os.path.join(os.path.pardir, 'data', 'processed')
train_file_path = os.path.join(processed_data_path, 'train.csv')
test_file_path = os.path.join(processed_data_path, 'test.csv')
train_df = pd.read_csv(train_file_path, index_col='PassengerId')
test_df = pd.read_csv(test_file_path, index_col='PassengerId')
train_df.info()
test_df.info()<jupyter_output><class 'pandas.core.frame.DataFrame'>
Int64Index: 418 entries, 892 to 1309
Data columns (total 32 columns):
Age                   418 non-null float64
Fare                  418 non-null float64
FamilySize            418 non-null int64
IsMother              418 non-null int64
IsMale                418 non-null int64
Deck_A                418 non-null int64
Deck_B                418 non-null int64
Deck_C                418 non-null int64
Deck_D                418 non-null int64
Deck_E                418 non-null int64
Deck_F                418 non-null int64
Deck_G                418 non-null int64
Deck_Z                418 non-null int64
Pclass_1              418 non-null int64
Pclass_2              418 non-null int64
Pclass_3              418 non-null int64
Title_Lady            418 non-null int64
Title_Master          418 non-null int64
Title_Miss            418 non-null int64
Title_Mr              418 non-null int64
Title_Mrs             418 non-null int64
Title_Officer         418 n[...]<jupyter_text>Data Preparation<jupyter_code>X = train_df.loc[:,'Age':].as_matrix().astype('float')
Y = train_df['Survived'].ravel() # .ravel() numpy function that creates a flattened 1-D array
print(X.shape, Y.shape)
# train test split
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)
print(X_train.shape, Y_train.shape)
print(X_test.shape, Y_test.shape)
# average survival in train and test
print('mean survival in train : {0:.3f}'.format(np.mean(Y_train)))
print('mean survival in test : {0:.3f}'.format(np.mean(Y_test)))<jupyter_output>mean survival in train : 0.383
mean survival in test : 0.385<jupyter_text>check Scikit-Learn version<jupyter_code>import sklearn
sklearn.__version__<jupyter_output><empty_output><jupyter_text>Baseline Model<jupyter_code># import function
from sklearn.dummy import DummyClassifier

# create model
model_dummy = DummyClassifier(strategy='most_frequent', random_state=0)
# train model
model_dummy.fit(X_train, Y_train)
print('Score for baseline model : {0:.2f}'.format(model_dummy.score(X_test, Y_test)))
# performance metrics
from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score
# accuracy score
print('accuracy for baseline model : {0:.2f}'.format(accuracy_score(Y_test, model_dummy.predict(X_test))))
# Confusion matrix
print('confusion matrix for baseline model : \n {0}'.format(confusion_matrix(Y_test, model_dummy.predict(X_test))))
# precision and recall scores
print('precision for baseline model : {0:.2f}'.format(precision_score(Y_test, model_dummy.predict(X_test))))
print('recall for baseline model : {0:.2f}'.format(recall_score(Y_test, model_dummy.predict(X_test))))<jupyter_output>precision for baseline model : 0.00
recall for baseline model : 0.00<jupyter_text>First Kaggle Submission<jupyter_code># converting to the matrix
test_X = test_df.as_matrix().astype('float')
# get predictions
predictions = model_dummy.predict(test_X)
df_submission = pd.DataFrame({'PassengerId': test_df.index, 'Survived' : predictions})
df_submission.head()
submission_data_path = os.path.join(os.path.pardir, 'data', 'external')
submission_file_path = os.path.join(submission_data_path, '01_dummy.csv')
df_submission.to_csv(submission_file_path, index=False)
def get_submission(model, filename):
    # converting to the matrix
    test_X =test_df.as_matrix().astype('float')
    # make predictions
    predictions = model.predict(test_X)
    # submission dataframe
    df_submission = pd.DataFrame({'PassengerId': test_df.index, 'Survived': predictions})
    # submission file
    submission_data_path = os.path.join(os.path.pardir, 'data', 'external')
    submission_file_path = os.path.join(submission_data_path, filename)
    # write to the file
    df_submission.to_csv(submission_file_path, index=False)
# get submission file
get_submission(model_dummy, '01_dummy.csv')<jupyter_output>C:\Users\Scarllee\Anaconda3\lib\site-packages\ipykernel_launcher.py:3: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  This is separate from the ipykernel package so we can avoid doing imports until<jupyter_text>Logistic Regression Model<jupyter_code># import function
from sklearn.linear_model import LogisticRegression
# create model
model_lr_1 = LogisticRegression(random_state=0)
# train model
model_lr_1.fit(X_train, Y_train)
# Evaluate model
print('Score for logisitic regression -version 1; {0:.2f}'.format(model_lr_1.score(X_test, Y_test)))
# accuracy score
print('accuracy for logistic regression : {0:.2f}'.format(accuracy_score(Y_test, model_lr_1.predict(X_test))))

# Confusion matrix
print('confusion matrix for logistic regression : \n {0}'.format(confusion_matrix(Y_test, model_lr_1.predict(X_test))))

# precision and recall scores
print('precision for logistic regression : {0:.2f}'.format(precision_score(Y_test, model_lr_1.predict(X_test))))
print('recall for logistic regression : {0:.2f}'.format(recall_score(Y_test, model_lr_1.predict(X_test))))

# model coefficients
model_lr_1.coef_<jupyter_output><empty_output><jupyter_text>Second Kaggle Submission<jupyter_code># get submission file
get_submission(model_lr_1, '02_lr.csv')<jupyter_output>C:\Users\Scarllee\Anaconda3\lib\site-packages\ipykernel_launcher.py:3: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  This is separate from the ipykernel package so we can avoid doing imports until<jupyter_text>Part 2  Hyperparameter Optimization<jupyter_code># base model
model_lr = LogisticRegression(random_state=0)

from sklearn.model_selection import GridSearchCV
parameters = {'C':[1.0, 10.0, 50.0, 100.0, 1000.0], 'penalty' : ['l1', 'l2']}
clf = GridSearchCV(model_lr, param_grid=parameters, cv=3)
clf.fit(X_train, Y_train)
clf.best_params_
print('best score : {0:.2f}'.format(clf.best_score_))
# evaluate model
print('score for logistic regression - version 2 : {0:.2f}'.format(clf.score(X_test, Y_test)))<jupyter_output>score for logistic regression - version 2 : 0.84<jupyter_text>Making Third Submission<jupyter_code># get submission file
get_submission(clf, '03_lr.csv')<jupyter_output>C:\Users\Scarllee\Anaconda3\lib\site-packages\ipykernel_launcher.py:3: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  This is separate from the ipykernel package so we can avoid doing imports until<jupyter_text>Feature Normalization and Standardization<jupyter_code>from sklearn.preprocessing import MinMaxScaler, StandardScaler<jupyter_output><empty_output><jupyter_text>Feature Normalization<jupyter_code># feature normalization
scaler = MinMaxScaler()
X_train_scaled = scaler.fit_transform(X_train)
# check
X_train_scaled[:,0].min(),X_train_scaled[:,0].max()
# normalize test data
X_test_scaled = scaler.transform(X_test)<jupyter_output><empty_output><jupyter_text>Feature Standardization<jupyter_code># feature standardization
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)<jupyter_output><empty_output><jupyter_text>Create model after standardization<jupyter_code># base model
model_lr = LogisticRegression(random_state=0)
parameters = {'C': [1.0, 10.0, 50.0, 100.0, 1000.0], 'penalty': ['l1','l2']}
clf = GridSearchCV(model_lr, param_grid=parameters, cv=3)
clf.fit(X_train_scaled, Y_train)
clf.best_score_
# evalaute model
print('score for logistic regression - version 2 : {0:.2f}'.format(clf.score(X_test_scaled, Y_test)))<jupyter_output>score for logistic regression - version 2 : 0.84<jupyter_text>Model Persistence<jupyter_code># import pickle library
import pickle
# create the file path
model_file_path = os.path.join(os.path.pardir, 'models','lr_model.pkl')
scaler_file_path = os.path.join(os.path.pardir, 'models','lr_scaler.pkl')
# open the files to write
model_file_pickle = open(model_file_path, 'wb')
scaler_file_pickle = open(scaler_file_path, 'wb')
# persist the model and scaler
pickle.dump(clf, model_file_pickle)
pickle.dump(scaler, scaler_file_pickle)
# close the file
model_file_pickle.close()
scaler_file_pickle.close()<jupyter_output><empty_output><jupyter_text>load the persisted file<jupyter_code># open files in read mode
model_file_pickle = open(model_file_path, 'rb')
scaler_file_pickle = open(scaler_file_path, 'rb')

# load files
clf_loaded = pickle.load(model_file_pickle)
scaler_loaded = pickle.load(scaler_file_pickle)

model_file_pickle.close()
scaler_file_pickle.close()

clf_loaded
scaler_loaded
#transform the test data using loaded scaler object
X_test_scaled = scaler_loaded.transform(X_test)
# calculate the score using loaded model object
print('score for persisted logistic regression : {0:.2f}'.format(clf_loaded.score(X_test_scaled, Y_test)))<jupyter_output>score for persisted logistic regression : 0.84
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