<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># Set paths to 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):
 #   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 Preparation<jupyter_code>X = train_df.loc[:, 'Age':].to_numpy().astype('float') # all rows and all columns. Convert to matrix and float
y = train_df['Survived'].ravel() # flattened one dimensional cell
print(X.shape, y.shape) #upper case for matrix X, lower case for one-dimensional y
# 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
# You want to have positive to be evanly distributed among test and train data
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. Provide input data and output data
model_dummy.fit(X_train, y_train)
print('score for baseline model : {0:.2f}'.format(model_dummy.score(X_test, y_test))) # Accuracy
# performance metrics
from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score
# accuracy score
print('accuracy of 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 score
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
/Users/marcin.kokott/opt/miniconda3/envs/minimal_ds/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))<jupyter_text>First Kaggle Submission<jupyter_code># converting to the matrix
test_X = test_df.to_numpy().astype('float')
# get predictions
predictions = model_dummy.predict(test_X)
df_submission = pd.DataFrame({'PassengerId': test_df.index, 'Survived' : predictions})
df_submission.head()
submissions_data_path = os.path.join(os.path.pardir, 'data', 'external')
submissions_file_path = os.path.join(submissions_data_path, '01_dummy.csv')
df_submission.to_csv(submissions_file_path, index=False)
def get_submission_file(model, filename):
    # converting to the matrix
    test_X = test_df.to_numpy().astype('float')
    # get predictions
    predictions = model.predict(test_X)
    # submission data frame
    df_submission = pd.DataFrame({'PassengerId': test_df.index, 'Survived' : predictions})
    # submission file
    submissions_data_path = os.path.join(os.path.pardir, 'data', 'external')
    submissions_file_path = os.path.join(submissions_data_path, filename)
    # write to the file
    df_submission.to_csv(submissions_file_path, index=False)
# get submission file
get_submission_file(model_dummy, '01_dummy.csv')<jupyter_output><empty_output><jupyter_text>Logistic Regression Model<jupyter_code># import function
from sklearn.linear_model import LogisticRegression
# Create model
model_lr_1 = LogisticRegression(max_iter=1000, random_state=0)
# train model
model_lr_1.fit(X_train, y_train)
# evalate the model
print('score for logistics regression - version 1 : {0:.2f}'.format(model_lr_1.score(X_test, y_test)))
# accuracy score
print('accuracy of logistic regression - version 1 : {0:.2f}'.format(accuracy_score(y_test, model_lr_1.predict(X_test))))
# Confusion matrix
print('confusion matrix for logistic regression - version 1 : \n {0}'.format(confusion_matrix(y_test, model_lr_1.predict(X_test))))
# Precision and recall score
print('precision for logistic regression - version 1 : {0:.2f}'.format(precision_score(y_test, model_lr_1.predict(X_test))))
print('recall for logistic regression - version 1 : {0:.2f}'.format(recall_score(y_test, model_lr_1.predict(X_test))))
# model coefficience
# model weights / model parameters
model_lr_1.coef_<jupyter_output><empty_output><jupyter_text>Second Kaggle Submission<jupyter_code># get submission file
get_submission_file(model_lr_1, '02-lr.csv')<jupyter_output><empty_output><jupyter_text>Part 2  Hyperparameter Optimization<jupyter_code># base model
model_lr = LogisticRegression(max_iter=10000, 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 : 0.83<jupyter_text>Making third submission<jupyter_code># get submission file
get_submission_file(clf, '03-lr.csv')<jupyter_output><empty_output><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)
X_train_scaled[:,0].min(), X_train_scaled[:,0].max()<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)
### Create model after standardization
# base model
model_lr = LogisticRegression()
parameters = {'C':[1.0, 10.0, 50.0, 100.0, 1000.0], 'penalty' : ['none','l2']}
clf = GridSearchCV(model_lr, param_grid=parameters, cv=3)
clf.fit(X_train_scaled, y_train)
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 : 0.72<jupyter_text>Model Persistance<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 file 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)
# close files
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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