<jupyter_start><jupyter_text>Import Data<jupyter_code>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):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   Age                 418 non-null    float64
 1   Fare                418 non-null    float64
 2   FamilySize          418 non-null    int64  
 3   IsMother            418 non-null    int64  
 4   IsMale              418 non-null    int64  
 5   Deck_A              418 non-null    int64  
 6   Deck_B              418 non-null    int64  
 7   Deck_C              418 non-null    int64  
 8   Deck_D              418 non-null    int64  
 9   Deck_E              418 non-null    int64  
 10  Deck_F              418 non-null    int64  
 11  Deck_G              418 non-null    int64  
 12  Deck_Z              418 non-null    int64  
 13  Pclass_1            418 non-null    int64  
 14  Pclass_2            418 non-null    int64  
 15  Pclass_3            418 non-null    int64  
 16  Title[...]<jupyter_text>Data Preperation<jupyter_code>X = train_df.loc[:,'Age':].values.astype('float')
y = train_df['Survived'].ravel()
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>from sklearn.dummy import DummyClassifier
model_dummy = DummyClassifier(strategy='most_frequent', random_state=0)
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
print('accuracy for baseline model : {0:.2f}'.format(accuracy_score(y_test, model_dummy.predict(X_test))))
print('confusion matrix for baseline model: \n {}'.format(confusion_matrix(y_test,model_dummy.predict(X_test))))
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 using Baseline Model<jupyter_code>test_X = test_df.values.astype('float')
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)
# create function of above cels
def get_submission_file(model, filename):
    test_X = test_df.values.astype('float')
    predictions = model.predict(test_X)
    df_submission = pd.DataFrame({'PassengerId': test_df.index, 'Survived': predictions})
    submission_data_path = os.path.join(os.path.pardir, 'data', 'external')
    submission_file_path = os.path.join(submission_data_path, filename)
    df_submission.to_csv(submission_file_path, index=False)
get_submission_file(model_dummy, '01_dummy.csv')<jupyter_output><empty_output><jupyter_text>Logistic Regression Model<jupyter_code>from sklearn.linear_model import LogisticRegression
# create model
model_lr_1 = LogisticRegression(random_state=0, max_iter=800)
# train model
model_lr_1.fit(X_train,y_train)
# evaluate model
print('score for logistic regression - version 1 : {0:.2f}'.format(model_lr_1.score(X_test, y_test)))<jupyter_output>score for logistic regression - version 1 : 0.83<jupyter_text>Second Kaggle Submission<jupyter_code>get_submission_file(model_lr_1, '02_lr.csv')<jupyter_output><empty_output>
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