kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
8,989,402 | def rmse(actual, predicted):
return sqrt(mean_squared_error(actual, predicted))<load_from_csv> | from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix,classification_report | Titanic - Machine Learning from Disaster |
8,989,402 | print('Train')
train = pd.read_csv(".. /input/train/train.csv")
print(train.shape)
print('Test')
test = pd.read_csv(".. /input/test/test.csv")
print(test.shape)
print('Breeds')
breeds = pd.read_csv(".. /input/breed_labels.csv")
print(breeds.shape)
print('Colors')
colors = pd.read_csv(".. /input/color_labels.c... | xTrain_small,xTest_small,yTrain_small,yTest_small=train_test_split(xTrain,yTrain ) | Titanic - Machine Learning from Disaster |
8,989,402 | target = train['AdoptionSpeed']
train_id = train['PetID']
test_id = test['PetID']
train.drop(['AdoptionSpeed', 'PetID'], axis=1, inplace=True)
test.drop(['PetID'], axis=1, inplace=True )<feature_engineering> | from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV | Titanic - Machine Learning from Disaster |
8,989,402 | doc_sent_mag = []
doc_sent_score = []
nf_count = 0
for pet in train_id:
try:
with open('.. /input/train_sentiment/' + pet + '.json', 'r')as f:
sentiment = json.load(f)
doc_sent_mag.append(sentiment['documentSentiment']['magnitude'])
doc_sent_score.append(sentiment['documentSentiment']['score'])
except FileNotFoundEr... | clf_best_fit=SVC(kernel='linear' ) | Titanic - Machine Learning from Disaster |
8,989,402 | train_desc = train.Description.fillna("none" ).values
test_desc = test.Description.fillna("none" ).values
tfv = TfidfVectorizer(min_df=2, max_features=None,
strip_accents='unicode', analyzer='word', token_pattern=r'(?u)\b\w+\b',
ngram_range=(1, 3), use_idf=1, smooth_idf=1, sublinear_tf=1,
)
tfv.fit(list(train_desc))
... | clf=SVC(kernel='linear' ) | Titanic - Machine Learning from Disaster |
8,989,402 | train_desc = train.Description.fillna("none" ).values
test_desc = test.Description.fillna("none" ).values
tfv = TfidfVectorizer(min_df=3, max_features=10000,
strip_accents='unicode', analyzer='word', token_pattern=r'\w{1,}',
ngram_range=(1, 3), use_idf=1, smooth_idf=1, sublinear_tf=1,
stop_words = 'english')
tfv.fit(l... | clf.fit(xTrain_small,yTrain_small ) | Titanic - Machine Learning from Disaster |
8,989,402 | vertex_xs = []
vertex_ys = []
bounding_confidences = []
bounding_importance_fracs = []
dominant_blues = []
dominant_greens = []
dominant_reds = []
dominant_pixel_fracs = []
dominant_scores = []
label_descriptions = []
label_scores = []
nf_count = 0
nl_count = 0
for pet in train_id:
try:
with open('.. /input/train_metad... | clf.score(xTest_small,yTest_small ) | Titanic - Machine Learning from Disaster |
8,989,402 | train.drop(['Name', 'RescuerID', 'Description'], axis=1, inplace=True)
test.drop(['Name', 'RescuerID', 'Description'], axis=1, inplace=True )<data_type_conversions> | yTest_predicted_small=clf.predict(xTest_small ) | Titanic - Machine Learning from Disaster |
8,989,402 | numeric_cols = ['Age', 'Quantity', 'Fee', 'VideoAmt', 'PhotoAmt', 'AdoptionSpeed', 'doc_sent_mag', 'doc_sent_score', 'dominant_score', 'dominant_pixel_frac', 'dominant_red', 'dominant_green', 'dominant_blue', 'bounding_importance', 'bounding_confidence', 'vertex_x', 'vertex_y', 'label_score'] + ['svd_{}'.format(i)for i... | confusion_matrix(yTest_small,yTest_predicted_small ) | Titanic - Machine Learning from Disaster |
8,989,402 | foo = train.dtypes
cat_feature_names = foo[foo == "category"]
cat_features = [train.columns.get_loc(c)for c in train.columns if c in cat_feature_names]<find_best_model_class> | print(classification_report(yTest_small,yTest_predicted_small)) | Titanic - Machine Learning from Disaster |
8,989,402 | def run_cv_model(train, test, target, model_fn, params={}, eval_fn=None, label='model'):
kf = StratifiedKFold(n_splits=5, random_state=42, shuffle=True)
fold_splits = kf.split(train, target)
cv_scores = []
qwk_scores = []
pred_full_test = 0
pred_train = np.zeros(( train.shape[0], 5))
all_coefficients = np.zeros(( 5, ... | yPredicted=clf.predict(xTest ) | Titanic - Machine Learning from Disaster |
8,989,402 | optR = OptimizedRounder()
coefficients_ = np.mean(results['coefficients'], axis=0)
print(coefficients_)
coefficients_[0] = 1.64
coefficients_[1] = 2.11
coefficients_[3] = 2.85
train_predictions = [r[0] for r in results['train']]
train_predictions = optR.predict(train_predictions, coefficients_ ).astype(int)
Counter(... | clf = RandomForestClassifier(random_state=0)
clf.fit(xTrain_small,yTrain_small ) | Titanic - Machine Learning from Disaster |
8,989,402 | optR = OptimizedRounder()
coefficients_ = np.mean(results['coefficients'], axis=0)
print(coefficients_)
coefficients_[0] = 1.645
coefficients_[1] = 2.115
coefficients_[3] = 2.84
test_predictions = [r[0] for r in results['test']]
test_predictions = optR.predict(test_predictions, coefficients_ ).astype(int)
Counter(te... | clf.score(xTest_small,yTest_small ) | Titanic - Machine Learning from Disaster |
8,989,402 | print("True Distribution:")
print(pd.value_counts(target, normalize=True ).sort_index())
print("Test Predicted Distribution:")
print(pd.value_counts(test_predictions, normalize=True ).sort_index())
print("Train Predicted Distribution:")
print(pd.value_counts(train_predictions, normalize=True ).sort_index())
<creat... | clf_logistic_regression=LogisticRegression(max_iter=1000)
clf_logistic_regression.fit(xTrain_small,yTrain_small ) | Titanic - Machine Learning from Disaster |
8,989,402 | pd.DataFrame(sk_cmatrix(target, train_predictions), index=list(range(5)) , columns=list(range(5)) )<compute_test_metric> | clf.score(xTest_small,yTest_small ) | Titanic - Machine Learning from Disaster |
8,989,402 | quadratic_weighted_kappa(target, train_predictions)
rmse(target, [r[0] for r in results['train']])
submission = pd.DataFrame({'PetID': test_id, 'AdoptionSpeed': test_predictions})
submission.head()<save_to_csv> | final_dataFrame=pd.DataFrame()
final_dataFrame['PassengerId']=testPIds | Titanic - Machine Learning from Disaster |
8,989,402 | submission.to_csv('submission.csv', index=False )<save_to_csv> | from sklearn.neural_network import MLPClassifier | Titanic - Machine Learning from Disaster |
8,989,402 | submission.to_csv('submission.csv', index=False )<set_options> | clf=MLPClassifier(hidden_layer_sizes=(20,10),max_iter=1000,activation='logistic')
clf.fit(xTrain_small,yTrain_small ) | Titanic - Machine Learning from Disaster |
8,989,402 | %matplotlib inline
plt.style.use('ggplot')
py.init_notebook_mode(connected=True)
warnings.filterwarnings("ignore")
pd.set_option('max_colwidth', 500)
pd.set_option('max_columns', 500)
pd.set_option('max_rows', 100)
def kappa(y_true, y_pred):
return cohen_kappa_score(y_true, y_pred, weights='quadratic' )<load_from... | clf.score(xTest_small,yTest_small ) | Titanic - Machine Learning from Disaster |
8,989,402 | breeds = pd.read_csv('.. /input/breed_labels.csv')
colors = pd.read_csv('.. /input/color_labels.csv')
states = pd.read_csv('.. /input/state_labels.csv')
train = pd.read_csv('.. /input/train/train.csv')
test = pd.read_csv('.. /input/test/test.csv')
sub = pd.read_csv('.. /input/test/sample_submission.csv')
train['d... | yPredicted_MLP_Small=clf.predict(xTest_small ) | Titanic - Machine Learning from Disaster |
8,989,402 | ax.patches<count_values> | print(classification_report(yTest_small,yPredicted_MLP_Small)) | Titanic - Machine Learning from Disaster |
8,989,402 | print('Most popular pet names and AdoptionSpeed')
for n in train['Name'].value_counts().index[:5]:
print(n)
print(train.loc[train['Name'] == n, 'AdoptionSpeed'].value_counts().sort_index())
print('' )<feature_engineering> | print(confusion_matrix(yTest_small,yPredicted_MLP_Small)) | Titanic - Machine Learning from Disaster |
8,989,402 | train['Name'] = train['Name'].fillna('Unnamed')
test['Name'] = test['Name'].fillna('Unnamed')
all_data['Name'] = all_data['Name'].fillna('Unnamed')
train['No_name'] = 0
train.loc[train['Name'] == 'Unnamed', 'No_name'] = 1
test['No_name'] = 0
test.loc[test['Name'] == 'Unnamed', 'No_name'] = 1
all_data['No_name'] = 0
... | yPredicted_MLP=clf.predict(xTest ) | Titanic - Machine Learning from Disaster |
8,989,402 | all_data[all_data['Name'].apply(lambda x: len(str(x)))== 3]['Name'].value_counts().tail()<count_values> | final_dataFrame['Survived']=yPredicted_MLP | Titanic - Machine Learning from Disaster |
8,989,402 | train['Age'].value_counts().head(10 )<feature_engineering> | final_dataFrame.to_csv('titanic.csv', index=False)
print("Your submission was successfully saved!" ) | Titanic - Machine Learning from Disaster |
6,587,175 | train['Pure_breed'] = 0
train.loc[train['Breed2'] == 0, 'Pure_breed'] = 1
test['Pure_breed'] = 0
test.loc[test['Breed2'] == 0, 'Pure_breed'] = 1
all_data['Pure_breed'] = 0
all_data.loc[all_data['Breed2'] == 0, 'Pure_breed'] = 1
print(f"Rate of pure breed pets in train data: {train['Pure_breed'].sum() * 100 / train['Pur... | %pylab inline
plt.style.use('seaborn-darkgrid')
sns.set(font_scale=2)
warnings.filterwarnings(action="ignore")
n_arbres = 200
max_depth = 6
noms = [
"Random_Forest",
"Ada_Boost",
"Gradient_Boosting",
"LightGBM",
"XGBoost",
"CatBoost"
]
classifieurs = [
RandomForestClassifier(n_estimators=n_arbres,max_depth=max_depth... | Titanic - Machine Learning from Disaster |
6,587,175 | breeds_dict = {k: v for k, v in zip(breeds['BreedID'], breeds['BreedName'])}<feature_engineering> | train=pd.read_csv(".. /input/titanic/train.csv")
print(train.shape)
train.head() | Titanic - Machine Learning from Disaster |
6,587,175 | train['Breed1_name'] = train['Breed1'].apply(lambda x: '_'.join(breeds_dict[x].split())if x in breeds_dict else 'Unknown')
train['Breed2_name'] = train['Breed2'].apply(lambda x: '_'.join(breeds_dict[x])if x in breeds_dict else '-')
test['Breed1_name'] = test['Breed1'].apply(lambda x: '_'.join(breeds_dict[x].split())i... | test=pd.read_csv(".. /input/titanic/test.csv")
print(test.shape)
test.head() | Titanic - Machine Learning from Disaster |
6,587,175 | ( all_data['Breed1_name'] + '__' + all_data['Breed2_name'] ).value_counts().head(15 )<feature_engineering> | donnees = pd.concat([train,test],sort=False)
donnees.head() | Titanic - Machine Learning from Disaster |
6,587,175 | colors_dict = {k: v for k, v in zip(colors['ColorID'], colors['ColorName'])}
train['Color1_name'] = train['Color1'].apply(lambda x: colors_dict[x] if x in colors_dict else '')
train['Color2_name'] = train['Color2'].apply(lambda x: colors_dict[x] if x in colors_dict else '')
train['Color3_name'] = train['Color3'].appl... | donnees['Title'] = donnees.Name.str.extract('([A-Za-z]+)\.', expand=False)
pd.crosstab(donnees['Title'], donnees['Sex'] ) | Titanic - Machine Learning from Disaster |
6,587,175 | gender_dict = {1: 'Male', 2: 'Female', 3: 'Mixed'}
for i in all_data['Type'].unique() :
for j in all_data['Gender'].unique() :
df = all_data.loc[(all_data['Type'] == i)&(all_data['Gender'] == j)]
top_colors = list(df['full_color'].value_counts().index)[:5]
j = gender_dict[j]
print(f"Most popular colors of {j} {i}s: {' ... | donnees['Title'] = donnees['Title'].replace(['Capt','Col','Major','Dr','Rev'], 'Autres')
donnees['Title'] = donnees['Title'].replace(['Lady', 'Countess', 'Don', 'Sir', 'Jonkheer', 'Dona'], 'Noblesse')
donnees['Title'] = donnees['Title'].replace('Mlle', 'Miss')
donnees['Title'] = donnees['Title'].replace('Ms', 'Miss'... | Titanic - Machine Learning from Disaster |
6,587,175 | images = [i.split('-')[0] for i in os.listdir('.. /input/train_images/')]
size_dict = {1: 'Small', 2: 'Medium', 3: 'Large', 4: 'Extra Large'}
for t in all_data['Type'].unique() :
for m in all_data['MaturitySize'].unique() :
df = all_data.loc[(all_data['Type'] == t)&(all_data['MaturitySize'] == m)]
top_breeds = list(df[... | donnees.Name = donnees.Name.str.extract('([A-Za-z]+)\,', expand=False)
donnees.head() | Titanic - Machine Learning from Disaster |
6,587,175 | c = 0
strange_pets = []
for i, row in all_data[all_data['Breed1_name'].str.contains('air')].iterrows() :
if 'Short' in row['Breed1_name'] and row['FurLength'] == 1:
pass
elif 'Medium' in row['Breed1_name'] and row['FurLength'] == 2:
pass
elif 'Long' in row['Breed1_name'] and row['FurLength'] == 3:
pass
else:
c += 1
str... | donnees['TailleFamille'] = donnees['Parch'] + donnees['SibSp'] + 1
donnees.TailleFamille = donnees.TailleFamille.astype('int8' ) | Titanic - Machine Learning from Disaster |
6,587,175 | train['health'] = train['Vaccinated'].astype(str)+ '_' + train['Dewormed'].astype(str)+ '_' + train['Sterilized'].astype(str)+ '_' + train['Health'].astype(str)
test['health'] = test['Vaccinated'].astype(str)+ '_' + test['Dewormed'].astype(str)+ '_' + test['Sterilized'].astype(str)+ '_' + test['Health'].astype(str)
m... | donnees['Pont'] = donnees.Cabin.str.extract('([A-Za-z])', expand=False)
donnees.Pont = donnees.Pont.fillna('Na')
pd.crosstab(donnees.Pont, np.ones(donnees.shape[0])) | Titanic - Machine Learning from Disaster |
6,587,175 | train['Quantity'].value_counts().head(10 )<sort_values> | donnees['TicketNum'] = donnees.Ticket.replace(regex=r'([^0-9]+)',value='')
donnees.Ticket = donnees.Ticket.replace(regex=r'([^a-zA-Z]+)',value='')
donnees.Ticket = donnees.Ticket.replace({r'^(CASOTON|SOTONO|STONO|STONOQ)$':'SOTONOQ',
r'^(SC|SCParis)$':'SCPARIS',
r'^FCC$':'FC',
r'^$':'Vide'}, regex=True ) | Titanic - Machine Learning from Disaster |
6,587,175 | all_data.sort_values('Fee', ascending=False)[['Name', 'Description', 'Fee', 'AdoptionSpeed', 'dataset_type']].head(10 )<feature_engineering> | donnees.rename(columns={"Pclass": "Classe",
"Embarked": "Port",
"Cabin": "Cabine",
"SibSp": "ConjointsOuFratrie",
"Parch": "EnfantsOuParents",
},
inplace=True ) | Titanic - Machine Learning from Disaster |
6,587,175 | states_dict = {k: v for k, v in zip(states['StateID'], states['StateName'])}
train['State_name'] = train['State'].apply(lambda x: '_'.join(states_dict[x].split())if x in states_dict else 'Unknown')
test['State_name'] = test['State'].apply(lambda x: '_'.join(states_dict[x].split())if x in states_dict else 'Unknown')
a... | donnees.Port = donnees.Port.fillna('Pas')
donnees.Cabine = donnees.Cabine.apply(lambda x: 0 if type(x)== float else 1)
donnees.head() | Titanic - Machine Learning from Disaster |
6,587,175 | all_data['State_name'].value_counts(normalize=True ).head()<count_values> | donnees.isnull().sum() | Titanic - Machine Learning from Disaster |
6,587,175 | all_data['RescuerID'].value_counts().head()<count_values> | donnees[donnees['Fare'].isnull() ] | Titanic - Machine Learning from Disaster |
6,587,175 | train['VideoAmt'].value_counts()<count_values> | donnees[(donnees['Port'] == 'S')&(donnees['Classe'] == 3)].Fare.median() | Titanic - Machine Learning from Disaster |
6,587,175 | print(F'Maximum amount of photos in {train["PhotoAmt"].max() }')
train['PhotoAmt'].value_counts().head()<train_on_grid> | coefficient = 1.2
ageCalc = donnees[~donnees.Age.isna() ].groupby(['Sex','Classe'] ).agg({'Age':['mean','std']})
ageCalc.columns = ['_'.join(col ).rstrip('_')for col in ageCalc.columns]
ageCalc.reset_index(inplace=True)
ageCalc['borneMin'] = ageCalc.Age_mean - ageCalc.Age_std*coefficient
ageCalc['borneMax'] = ageCalc... | Titanic - Machine Learning from Disaster |
6,587,175 | tokenizer = TweetTokenizer()
vectorizer = TfidfVectorizer(ngram_range=(1, 2), tokenizer=tokenizer.tokenize)
vectorizer.fit(all_data['Description'].fillna('' ).values)
X_train = vectorizer.transform(train['Description'].fillna(''))
rf = RandomForestClassifier(n_estimators=20)
rf.fit(X_train, train['AdoptionSpeed'] )<... | ageRand = pd.DataFrame(columns=['Sex','Classe','Age'])
for i in [(row.Sex,row.Classe,np.random.randint(round(row.borneMin),round(row.borneMax),size=row.nb))
for indx, row in ageCalc.iterrows() ]:
calc = pd.DataFrame(columns=['Sex','Classe','Age'])
calc.Age = i[2]
calc.Sex = i[0]
calc.Classe = i[1]
ageRand = pd.concat... | Titanic - Machine Learning from Disaster |
6,587,175 | train['Description'] = train['Description'].fillna('')
test['Description'] = test['Description'].fillna('')
all_data['Description'] = all_data['Description'].fillna('')
train['desc_length'] = train['Description'].apply(lambda x: len(x))
train['desc_words'] = train['Description'].apply(lambda x: len(x.split()))
test[... | donnees.Age01.isna().sum() | Titanic - Machine Learning from Disaster |
6,587,175 | sentiment_dict = {}
for filename in os.listdir('.. /input/train_sentiment/'):
with open('.. /input/train_sentiment/' + filename, 'r')as f:
sentiment = json.load(f)
pet_id = filename.split('.')[0]
sentiment_dict[pet_id] = {}
sentiment_dict[pet_id]['magnitude'] = sentiment['documentSentiment']['magnitude']
sentiment_dic... | donnees.Age = donnees.Age01.values
donnees.drop(columns=['AgeOld','Age01'],inplace=True ) | Titanic - Machine Learning from Disaster |
6,587,175 | train['lang'] = train['PetID'].apply(lambda x: sentiment_dict[x]['language'] if x in sentiment_dict else 'no')
train['magnitude'] = train['PetID'].apply(lambda x: sentiment_dict[x]['magnitude'] if x in sentiment_dict else 0)
train['score'] = train['PetID'].apply(lambda x: sentiment_dict[x]['score'] if x in sentiment_... | listeVariblesInitiales = donnees.drop(columns=['Name'] ).columns
donnees = donnees.set_index('PassengerId' ).sort_index() | Titanic - Machine Learning from Disaster |
6,587,175 | cols_to_use = ['Type', 'Age', 'Breed1', 'Breed2', 'Gender', 'Color1', 'Color2',
'Color3', 'MaturitySize', 'FurLength', 'Vaccinated', 'Dewormed',
'Sterilized', 'Health', 'Quantity', 'Fee', 'State', 'RescuerID', 'health', 'Free', 'score',
'VideoAmt', 'PhotoAmt', 'AdoptionSpeed', 'No_name', 'Pure_breed', 'desc_length', 'd... | donnees['TitreFamille'] = donnees.apply(lambda ligne : 'Homme' if ligne['Title'] == 'Mr'
else 'Femme' if ligne['Sex'] == 'Femme'
else 'Garçon' if ligne['Title'] == 'Master'
else 'Homme' , axis=1)
plt.figure(figsize=(14,12))
plt.title('La distribution du titre calculé pour le groupe famille',size=20)
sns.countplot(x='... | Titanic - Machine Learning from Disaster |
6,587,175 | cat_cols = ['Type', 'Breed1', 'Breed2', 'Gender', 'Color1', 'Color2',
'Color3', 'MaturitySize', 'FurLength', 'Vaccinated', 'Dewormed',
'Sterilized', 'Health', 'State', 'RescuerID',
'No_name', 'Pure_breed', 'health', 'Free']<data_type_conversions> | donnees['GroupFamille'] = donnees.Name+'-'+\
donnees.Classe.apply(lambda x: '%1d' % x)+'-'+\
donnees.Fare.apply(lambda x: '%.3f' % x)+'-'+\
donnees.Port+'-'+donnees.TicketNum
donnees.GroupFamille.unique() [:6] | Titanic - Machine Learning from Disaster |
6,587,175 | more_cols = []
for col1 in cat_cols:
for col2 in cat_cols:
if col1 != col2 and col1 not in ['RescuerID', 'State'] and col2 not in ['RescuerID', 'State']:
train[col1 + '_' + col2] = train[col1].astype(str)+ '_' + train[col2].astype(str)
test[col1 + '_' + col2] = test[col1].astype(str)+ '_' + test[col2].astype(str)
mor... | donnees['GroupTicket'] = donnees.Classe.apply(lambda x: '%1d' % x)+'-'+\
donnees.Fare.apply(lambda x: '%.3f' % x)+'-'+\
donnees.Port+'-'+donnees.TicketNum
donnees.GroupTicket.unique() [:6] | Titanic - Machine Learning from Disaster |
6,587,175 | %%time
indexer = {}
for col in cat_cols:
_, indexer[col] = pd.factorize(train[col].astype(str))
for col in tqdm_notebook(cat_cols):
train[col] = indexer[col].get_indexer(train[col].astype(str))
test[col] = indexer[col].get_indexer(test[col].astype(str))
<prepare_x_and_y> | donnees.drop(columns=['Name','TitreFamille','GroupFamille'], inplace=True)
listeVariblesAvecGroups = donnees.columns | Titanic - Machine Learning from Disaster |
6,587,175 | y = train['AdoptionSpeed']
train = train.drop(['AdoptionSpeed'], axis=1 )<choose_model_class> | def conversionVariableCategorielle(donnees,variable):
valeurs = list(donnees[variable].sort_values().unique())
dicoVar = {nom:indx for indx,nom in enumerate(valeurs)}
dicoVarRev = {indx:nom for indx,nom in enumerate(valeurs)}
donnees[variable] = donnees[variable].apply(lambda x : dicoVar[x])
return dicoVar,dicoVarRev... | Titanic - Machine Learning from Disaster |
6,587,175 | n_fold = 5
folds = StratifiedKFold(n_splits=n_fold, shuffle=True, random_state=15 )<split> | apprentissage = donnees[~donnees.Survived.isnull() ]
apprentissage.Survived = apprentissage.Survived.astype('int8')
X = apprentissage.drop(columns='Survived')
y = apprentissage.Survived
apprentissage.head() | Titanic - Machine Learning from Disaster |
6,587,175 | def train_model(X=train, X_test=test, y=y, params=None, folds=folds, model_type='lgb', plot_feature_importance=False, averaging='usual', make_oof=False):
result_dict = {}
if make_oof:
oof = np.zeros(( len(X), 5))
prediction = np.zeros(( len(X_test), 5))
scores = []
feature_importance = pd.DataFrame()
for fold_n,(train_... | test = donnees[donnees.Survived.isnull() ]
test.reset_index().head() | Titanic - Machine Learning from Disaster |
6,587,175 | params = {'num_leaves': 512,
'objective': 'multiclass',
'max_depth': -1,
'learning_rate': 0.01,
"boosting": "gbdt",
"feature_fraction": 0.9,
"bagging_freq": 3,
"bagging_fraction": 0.9,
"bagging_seed": 11,
"random_state": 42,
"verbosity": -1,
"num_class": 5}<train_model> | X = apprentissage.drop(columns='Survived')
y = apprentissage.Survived
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.112, stratify = y, random_state = 101)
print(X_train.shape, y_train.shape)
print(X_test.shape, y_test.shape)
plt.figure(figsize=(10,6))
plt.hist(y_train,label='apprentissage');... | Titanic - Machine Learning from Disaster |
6,587,175 | result_dict_lgb = train_model(X=train, X_test=test, y=y, params=params, model_type='lgb', plot_feature_importance=True, make_oof=True )<train_model> | resultats = comparaisonsClassifieurs(classifieursArbresDict, X_train, X_test, y_train, y_test ) | Titanic - Machine Learning from Disaster |
6,587,175 | xgb_params = {'eta': 0.01, 'max_depth': 9, 'subsample': 0.9, 'colsample_bytree': 0.9,
'objective': 'multi:softprob', 'eval_metric': 'merror', 'silent': True, 'nthread': 4, 'num_class': 5}
result_dict_xgb = train_model(params=xgb_params, model_type='xgb', make_oof=True )<prepare_output> | classifieur = CatBoostClassifier(iterations=250, depth=3, silent=True ) | Titanic - Machine Learning from Disaster |
6,587,175 | prediction =(result_dict_lgb['prediction'] + result_dict_xgb['prediction'] ).argmax(1)
submission = pd.DataFrame({'PetID': sub.PetID, 'AdoptionSpeed': [int(i)for i in prediction]})
submission.head()<save_to_csv> | resultats = controleClassifieur(classifieur, X_train, X_test, y_train, y_test ) | Titanic - Machine Learning from Disaster |
6,587,175 | submission.to_csv('submission.csv', index=False )<save_to_csv> | resultatsFinaux,classifieursCV = effectueValidationCroisee(classifieur,
X,
y,
n_splits = 15 ) | Titanic - Machine Learning from Disaster |
6,587,175 | submission.to_csv('submission.csv', index=False )<set_options> | output = pd.DataFrame({'PassengerId':test.index, 'Survived':np.zeros(test.shape[0])})
for i in range(len(classifieursCV)) :
output.Survived += classifieursCV[i].predict_proba(test)[:,1]
output.Survived /= len(classifieursCV)
output.Survived = output.Survived.round().astype('int8')
output.head() | Titanic - Machine Learning from Disaster |
6,587,175 | <compute_test_metric><EOS> | output.to_csv('submission008.csv',index=False ) | Titanic - Machine Learning from Disaster |
1,780,981 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<compute_test_metric> | warnings.filterwarnings('ignore', category = DeprecationWarning)
warnings.filterwarnings('ignore', category = FutureWarning)
sns.set(style='white', context='notebook', palette='deep')
plt.style.use('bmh')
train=pd.read_csv('.. /input/train.csv')
test=pd.read_csv('.. /input/test.csv')
IDtest = test["PassengerId"]
... | Titanic - Machine Learning from Disaster |
1,780,981 | class OptimizedRounder(object):
def __init__(self):
self.coef_ = 0
def _kappa_loss(self, coef, X, y):
X_p = np.copy(X)
for i, pred in enumerate(X_p):
if pred < coef[0]:
X_p[i] = 0
elif pred >= coef[0] and pred < coef[1]:
X_p[i] = 1
elif pred >= coef[1] and pred < coef[2]:
X_p[i] = 2
elif pred >= coef[2] and pred < coe... | def IQR_outlier(df,n,features):
outlier_indices=[]
for col in features:
Q1=np.percentile(df[col],25)
Q3=np.percentile(df[col],75)
IQR=Q3-Q1
outlier_step=1.5*IQR
outlier_list_col=df[(df[col]<Q1-outlier_step)|(df[col]>Q3+outlier_step)].index
print('Total Number Outliers of',col,' : ',len(outlier_list_col))
print('Perce... | Titanic - Machine Learning from Disaster |
1,780,981 | def rmse(actual, predicted):
return sqrt(mean_squared_error(actual, predicted))<load_from_csv> | train.loc[Outliers_to_drop,num_features] | Titanic - Machine Learning from Disaster |
1,780,981 | %%time
print('Train')
train = pd.read_csv(".. /input/train/train.csv")
print(train.shape)
print('Test')
test = pd.read_csv(".. /input/test/test.csv")
print(test.shape)
print('Breeds')
breeds = pd.read_csv(".. /input/breed_labels.csv")
print(breeds.shape)
print('Colors')
colors = pd.read_csv(".. /input/color_l... | dataset["Fare"].isnull().sum() | Titanic - Machine Learning from Disaster |
1,780,981 | target = train['AdoptionSpeed']
train_id = train['PetID']
test_id = test['PetID']
train.drop(['AdoptionSpeed', 'PetID'], axis=1, inplace=True)
test.drop(['PetID'], axis=1, inplace=True )<feature_engineering> | dataset["Fare"]=dataset["Fare"].fillna(dataset["Fare"].median() ) | Titanic - Machine Learning from Disaster |
1,780,981 | doc_sent_mag = []
doc_sent_score = []
nf_count = 0
for pet in train_id:
try:
with open('.. /input/train_sentiment/' + pet + '.json', 'r')as f:
sentiment = json.load(f)
doc_sent_mag.append(sentiment['documentSentiment']['magnitude'])
doc_sent_score.append(sentiment['documentSentiment']['score'])
except FileNotFoundEr... | dataset["Fare"] = dataset["Fare"].map(lambda i: np.log(i)if i > 0 else 0 ) | Titanic - Machine Learning from Disaster |
1,780,981 | train_desc = train.Description.fillna("none" ).values
test_desc = test.Description.fillna("none" ).values
tfv = TfidfVectorizer(min_df=2, max_features=None,
strip_accents='unicode', analyzer='word', token_pattern=r'(?u)\b\w+\b',
ngram_range=(1, 3), use_idf=1, smooth_idf=1, sublinear_tf=1,
)
tfv.fit(list(train_desc))
... | dataset['Sex']=dataset['Sex'].map({'male':0,'female':1} ) | Titanic - Machine Learning from Disaster |
1,780,981 | train_desc = train.Description.fillna("none" ).values
test_desc = test.Description.fillna("none" ).values
tfv = TfidfVectorizer(min_df=3, max_features=10000,
strip_accents='unicode', analyzer='word', token_pattern=r'\w{1,}',
ngram_range=(1, 3), use_idf=1, smooth_idf=1, sublinear_tf=1,
stop_words = 'english')
tfv.fit(l... | print('Number of Null entries: ',dataset['Embarked'].isnull().sum())
print('Most common dock: ',dataset.Embarked.mode() [0] ) | Titanic - Machine Learning from Disaster |
1,780,981 | vertex_xs = []
vertex_ys = []
bounding_confidences = []
bounding_importance_fracs = []
dominant_blues = []
dominant_greens = []
dominant_reds = []
dominant_pixel_fracs = []
dominant_scores = []
label_descriptions = []
label_scores = []
nf_count = 0
nl_count = 0
for pet in train_id:
try:
with open('.. /input/train_metad... | display(dataset.Cabin.shape)
print('Number of null values : ', dataset.Cabin.isnull().sum())
print('Percentage of null values : ', round(dataset.Cabin.isnull().sum() /dataset.Cabin.shape[0]*100),'%' ) | Titanic - Machine Learning from Disaster |
1,780,981 | %%time
train.drop(['Name', 'RescuerID', 'Description'], axis=1, inplace=True)
test.drop(['Name', 'RescuerID', 'Description'], axis=1, inplace=True )<data_type_conversions> | dataset.isnull().sum() | Titanic - Machine Learning from Disaster |
1,780,981 | numeric_cols = ['Age', 'Quantity', 'Fee', 'VideoAmt', 'PhotoAmt', 'AdoptionSpeed', 'doc_sent_mag', 'doc_sent_score', 'dominant_score', 'dominant_pixel_frac', 'dominant_red', 'dominant_green', 'dominant_blue', 'bounding_importance', 'bounding_confidence', 'vertex_x', 'vertex_y', 'label_score'] + ['svd_{}'.format(i)for i... | index_NaN_age = dataset["Age"][dataset.Age.isnull() ].index
for i in index_NaN_age :
age_med = dataset["Age"].median()
age_pred = dataset["Age"][(( dataset['SibSp'] == dataset['SibSp'][i])&(dataset['Parch'] == dataset['Parch'][i])&(dataset['Pclass'] == dataset['Pclass'][i])) ].median()
if not np.isnan(age_pred):
datase... | Titanic - Machine Learning from Disaster |
1,780,981 | n_repeats = 2
n_splits = 5
def run_cv_model(train, test, target, model_fn, params={}, eval_fn=None, label='model'):
kf = RepeatedStratifiedKFold(n_splits=n_splits, random_state=42, n_repeats = n_repeats)
fold_splits = kf.split(train, target)
cv_scores = []
qwk_scores = []
pred_full_test = 0
pred_train = np.zeros(( tr... | dataset.isnull().sum() | Titanic - Machine Learning from Disaster |
1,780,981 | optR = OptimizedRounder()
coefficients_ = np.mean(results['coefficients'], axis=0)
print(coefficients_)
train_predictions = [r[0] for r in results['train']]
train_predictions = optR.predict(train_predictions, coefficients_ ).astype(int)
Counter(train_predictions )<predict_on_test> | print('Mean : ', dataset[['Cabin','Survived']].groupby('Cabin' ).mean())
print('Count : ', dataset[['Cabin','Survived']].groupby('Cabin' ).count() ) | Titanic - Machine Learning from Disaster |
1,780,981 | optR = OptimizedRounder()
test_predictions = [r[0] for r in results['test']]
test_predictions = optR.predict(test_predictions, coefficients_ ).astype(int)
Counter(test_predictions )<create_dataframe> | dataset['Title']=pd.Series([i.split(',')[1].split('.')[0].strip() for i in dataset.Name] ) | Titanic - Machine Learning from Disaster |
1,780,981 | pd.DataFrame(sk_cmatrix(target, train_predictions), index=list(range(5)) , columns=list(range(5)) )<compute_test_metric> | dataset["Title"] = dataset["Title"].replace(['Lady', 'the Countess','Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
dataset["Title"] = dataset["Title"].replace(['Ms', 'Mlle'], 'Miss')
dataset["Title"] = dataset["Title"].replace(['Mme'], 'Mrs' ) | Titanic - Machine Learning from Disaster |
1,780,981 | quadratic_weighted_kappa(target, train_predictions )<compute_test_metric> | Ticket=[]
for i in list(dataset['Ticket']):
if i.isdigit() :
Ticket.append('X')
else:
Ticket.append(i.split(' ')[0])
dataset['Ticket']=Ticket | Titanic - Machine Learning from Disaster |
1,780,981 | rmse(target, [r[0] for r in results['train']] )<prepare_output> | Ticket = []
for i in list(dataset.Ticket):
if not i.isdigit() :
Ticket.append(i.replace(".","" ).replace("/","" ).strip())
else:
Ticket.append(i)
dataset['Ticket']=Ticket | Titanic - Machine Learning from Disaster |
1,780,981 | submission = pd.DataFrame({'PetID': test_id, 'AdoptionSpeed': test_predictions})
submission.head()<save_to_csv> | dataset = pd.get_dummies(dataset, columns = ["Cabin"], prefix="Cab")
dataset = pd.get_dummies(dataset, columns = ["Embarked"], prefix="Em")
dataset = pd.get_dummies(dataset, columns = ["Fsize"], prefix="Fam")
dataset = pd.get_dummies(dataset, columns = ["Pclass"], prefix="Pc")
dataset = pd.get_dummies(dataset, colu... | Titanic - Machine Learning from Disaster |
1,780,981 | submission.to_csv('submission.csv', index=False )<save_to_csv> | dataset.drop(labels = ['Name','PassengerId'], axis = 1, inplace = True ) | Titanic - Machine Learning from Disaster |
1,780,981 | submission.to_csv('submission.csv', index=False )<set_options> | Y_train=dataset[:train_len]['Survived']
X_train=data[:train_len]
test=data[train_len:]
| Titanic - Machine Learning from Disaster |
1,780,981 | warnings.filterwarnings("ignore")
%matplotlib inline<define_variables> | Titanic - Machine Learning from Disaster | |
1,780,981 | LABELS = ["isFraud"]
all_files = glob.glob(".. /input/lgmodels/*.csv")
all_files<load_from_csv> | from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV, cross_val_score, Stra... | Titanic - Machine Learning from Disaster |
1,780,981 | outs = [pd.read_csv(f, index_col=0)for f in all_files]
concat_sub = pd.concat(outs, axis=1)
cols = list(map(lambda x: "m" + str(x), range(len(concat_sub.columns))))
concat_sub.columns = cols
concat_sub.reset_index(inplace=True )<feature_engineering> | kfold = StratifiedKFold(n_splits=10 ) | Titanic - Machine Learning from Disaster |
1,780,981 | rank = np.tril(concat_sub.iloc[:,1:].corr().values,-1)
m =(rank>0 ).sum()
m_gmean, s = 0, 0
for n in range(min(rank.shape[0],m)) :
mx = np.unravel_index(rank.argmin() , rank.shape)
w =(m-n)/(m+n)
print(w)
m_gmean += w*(np.log(concat_sub.iloc[:,mx[0]+1])+np.log(concat_sub.iloc[:,mx[1]+1])) /2
s += w
rank[mx] = 1
m_g... | random_state = 42
classifiers = []
classifiers.append(SVC(random_state=random_state))
classifiers.append(DecisionTreeClassifier(random_state=random_state))
classifiers.append(RandomForestClassifier(random_state=random_state))
classifiers.append(KNeighborsClassifier())
classifiers.append(LogisticRegression(random_state... | Titanic - Machine Learning from Disaster |
1,780,981 | concat_sub['isFraud'] = m_gmean
concat_sub[['TransactionID','isFraud']].to_csv('stack_gmean.csv',
index=False, float_format='%.4g' )<set_options> | SVMC = SVC(probability=True)
svc_param_grid = {'kernel': ['rbf'], 'gamma': [ 0.001, 0.01, 0.1, 1],'C': [1, 10, 50, 100,200,300, 1000]}
gsSVMC = GridSearchCV(SVMC,param_grid = svc_param_grid, cv=kfold, scoring="accuracy", n_jobs= 4, verbose = 1)
gsSVMC.fit(X_train,Y_train)
SVMC_best = gsSVMC.best_estimator_
gsSVMC.be... | Titanic - Machine Learning from Disaster |
1,780,981 | warnings.filterwarnings('ignore' )<define_variables> | DTC = DecisionTreeClassifier()
dt_param_grid = {'max_features': ['auto', 'sqrt', 'log2'],'min_samples_split': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
'min_samples_leaf':[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],'random_state':[42]}
gsDTC = GridSearchCV(DTC,param_grid = dt_param_grid, cv=kfold, scoring="accuracy", n... | Titanic - Machine Learning from Disaster |
1,780,981 | def seed_everything(seed=0):
random.seed(seed)
np.random.seed(seed )<define_variables> | RFC = RandomForestClassifier()
rf_param_grid = {"max_depth": [None],"max_features": [1, 3, 10],"min_samples_split": [2, 3, 10],"min_samples_leaf": [1, 3, 10],
"bootstrap": [False],"n_estimators" :[100,300],"criterion": ["gini"]}
gsRFC = GridSearchCV(RFC,param_grid = rf_param_grid, cv=kfold, scoring="accuracy", n_jobs= ... | Titanic - Machine Learning from Disaster |
1,780,981 | SEED = 42
seed_everything(SEED)
TARGET = 'isFraud'
START_DATE = datetime.datetime.strptime('2017-11-30', '%Y-%m-%d' )<init_hyperparams> | LRC = LogisticRegression()
lr_param_grid = {'penalty':['l1', 'l2'],'C': np.logspace(0, 4, 10)}
gsLRC=GridSearchCV(LRC,param_grid=lr_param_grid,cv=kfold,scoring='accuracy', n_jobs= 4, verbose = 1)
gsLRC.fit(X_train,Y_train)
LRC_best = gsLRC.best_estimator_
gsLRC.best_score_ | Titanic - Machine Learning from Disaster |
1,780,981 | lgb_params = {
'objective':'binary',
'boosting_type':'gbdt',
'metric':'auc',
'n_jobs':-1,
'learning_rate':0.01,
'num_leaves': 2**8,
'max_depth':-1,
'tree_learner':'serial',
'colsample_bytree': 0.7,
'subsample_freq':1,
'subsample':0.7,
'n_estimators':20000,
'max_bin':255,
'verbose':-1,
'seed': SEED,
'early_stopping_roun... | KNNC = KNeighborsClassifier()
knn_param_grid = {'n_neighbors':[3, 4, 5, 6, 7, 8],'leaf_size':[1, 2, 3, 5],
'weights':['uniform', 'distance'],'algorithm':['auto', 'ball_tree','kd_tree','brute']}
gsKNNC=GridSearchCV(KNNC,param_grid=knn_param_grid,cv=kfold,scoring='accuracy',n_jobs=4,verbose=1)
gsKNNC.fit(X_train,Y_train... | Titanic - Machine Learning from Disaster |
1,780,981 | print('Load Data')
train_df = pd.read_pickle('.. /input/ieee-data-minification/train_transaction.pkl')
train_df['DT_M'] = train_df['TransactionDT'].apply(lambda x:(START_DATE + datetime.timedelta(seconds = x)))
train_df['DT_M'] =(train_df['DT_M'].dt.year-2017)*12 + train_df['DT_M'].dt.month
test_df = train_df[train_... | votingC = VotingClassifier(estimators=[('svc', SVMC_best),('rfc', RFC_best),('lrc', LRC_best)], voting='soft', n_jobs=4)
votingC = votingC.fit(X_train, Y_train ) | Titanic - Machine Learning from Disaster |
1,780,981 | for col in list(train_df):
if train_df[col].dtype=='O':
print(col)
train_df[col] = train_df[col].fillna('unseen_before_label')
test_df[col] = test_df[col].fillna('unseen_before_label')
train_df[col] = train_df[col].astype(str)
test_df[col] = test_df[col].astype(str)
le = LabelEncoder()
le.fit(list(train_df[col])+l... | Titanic - Machine Learning from Disaster | |
1,780,981 | rm_cols = [
'TransactionID','TransactionDT',
TARGET,
'DT_M'
]
rm_cols += ['V'+str(i)for i in range(1,340)]
features_columns = [col for col in list(train_df)if col not in rm_cols]
<define_variables> | test_Survived = votingC.predict(test ).astype(int)
submission = pd.DataFrame({
"PassengerId": IDtest,
"Survived": test_Survived
})
submission.to_csv('Titanic_test_prediction_V9.csv', index=False ) | Titanic - Machine Learning from Disaster |
1,780,981 | <train_model><EOS> | accuracy_score(Y_train,votingC.predict(X_train)) | Titanic - Machine Learning from Disaster |
11,108,910 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<prepare_x_and_y> | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt | Titanic - Machine Learning from Disaster |
11,108,910 | print('
print('KFold training...')
folds = KFold(n_splits=N_SPLITS, shuffle=True, random_state=SEED)
X,y = train_df[features_columns], train_df[TARGET]
P = test_df[features_columns]
RESULTS['kfold'] = 0
for fold_,(trn_idx, val_idx)in enumerate(folds.split(X, y)) :
print('Fold:',fold_+1)
tr_x, tr_y = X.iloc[trn_idx,:... | ds = pd.read_csv("/kaggle/input/titanic/train.csv" ) | Titanic - Machine Learning from Disaster |
11,108,910 | print('
print('StratifiedKFold training...')
folds = StratifiedKFold(n_splits=N_SPLITS, shuffle=True, random_state=SEED)
X,y = train_df[features_columns], train_df[TARGET]
P = test_df[features_columns]
RESULTS['stratifiedkfold'] = 0
for fold_,(trn_idx, val_idx)in enumerate(folds.split(X, y, groups=y)) :
print('Fold:'... | Y = ds.Survived
X = ds.drop(['PassengerId','Name','Survived'],axis = 1 ) | Titanic - Machine Learning from Disaster |
11,108,910 | print('
print('LBO training...')
train_df['DT_M'] = train_df['TransactionDT'].apply(lambda x:(START_DATE + datetime.timedelta(seconds = x)))
train_df['DT_M'] =(train_df['DT_M'].dt.year-2017)*12 + train_df['DT_M'].dt.month
main_train_set = train_df[train_df['DT_M']<(train_df['DT_M'].max())].reset_index(drop=True)
val... | print(X.isnull().sum() ) | Titanic - Machine Learning from Disaster |
11,108,910 | print('
print('GroupKFold timeblocks split training...')
folds = GroupKFold(n_splits=N_SPLITS)
train_df['groups'] = train_df['TransactionDT'].apply(lambda x:(START_DATE + datetime.timedelta(seconds = x)))
train_df['groups'] =(train_df['groups'].dt.year-2017)*12 + train_df['groups'].dt.month
X,y = train_df[features_c... | X_temp = X
Sex_Embarked = {"Sex":{"male": 1.,"female": 0.},
"Embarked":{"S": 0.,"C": 1.,"Q": 2.}}
X_temp = X_temp.replace(Sex_Embarked, inplace=False)
CT = ColumnTransformer(transformers = [('encoder',OrdinalEncoder() ,['Ticket'])],remainder = 'passthrough')
X_temp = pd.DataFrame(CT.fit_transform(X_temp.drop(['Cabin'... | Titanic - Machine Learning from Disaster |
11,108,910 | print('
print('GroupKFold uID split training...')
folds = GroupKFold(n_splits=N_SPLITS)
train_df['groups'] = ''
for col in ['card1','card2','card3','card5','addr1','addr2',]:
train_df['groups'] = '_' + train_df[col].astype(str)
X,y = train_df[features_columns], train_df[TARGET]
split_groups = train_df['groups']
P = ... | imputer = SimpleImputer(missing_values=np.nan, strategy = 'mean')
ds.Age = imputer.fit_transform(ds.Age.values.reshape(-1,1))
X.Age = imputer.fit_transform(X.Age.values.reshape(-1,1)) | Titanic - Machine Learning from Disaster |
11,108,910 | print('
print('Intermediate results...')
final_df = []
for current_strategy in list(RESULTS.iloc[:,2:]):
auc_score = metrics.roc_auc_score(RESULTS[TARGET], RESULTS[current_strategy])
final_df.append([current_strategy, auc_score])
final_df = pd.DataFrame(final_df, columns=['Stategy', 'Result'])
final_df.sort_values(... | ds.Embarked = SimpleImputer(missing_values=np.nan, strategy = 'most_frequent' ).fit_transform(ds.Embarked.values.reshape(-1,1))
X.Embarked = SimpleImputer(missing_values=np.nan, strategy = 'most_frequent' ).fit_transform(X.Embarked.values.reshape(-1,1)) | Titanic - Machine Learning from Disaster |
11,108,910 | print('
print('LBO full set training...')
train_df['DT_M'] = train_df['TransactionDT'].apply(lambda x:(START_DATE + datetime.timedelta(seconds = x)))
train_df['DT_M'] =(train_df['DT_M'].dt.year-2017)*12 + train_df['DT_M'].dt.month
main_train_set = train_df[train_df['DT_M']<(train_df['DT_M'].max())].reset_index(drop=T... | X_Cabins = X.Cabin.dropna().str.contains('C',regex = False)
X_Cabins = X.Cabin[X_Cabins.loc[X_Cabins == True].index]
print(X_Cabins ) | Titanic - Machine Learning from Disaster |
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