kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
9,074,840 | train['Complete_Date'] = train['Date'].astype('datetime64[ns]')
test['Complete_Date'] = test['Date'].astype('datetime64[ns]')
month = [int(el[5:7])for el in list(train['Date'].values)]
day = [int(el[8:10])for el in list(train['Date'].values)]
month_test = [int(el[5:7])for el in list(test['Date'].values)]
day_test = [... | transformed_dataset = transform_dataset(dataset)
transformed_dataset.info() | Titanic - Machine Learning from Disaster |
9,074,840 | train['Province_State'].fillna('',inplace=True)
test['Province_State'].fillna('',inplace=True)
train['Province_State']=train['Province_State'].astype(str)
test['Province_State']=test['Province_State'].astype(str)
y= train['Country_Region']+train['Province_State']
y= pd.DataFrame(y, columns= ['Place'])
y_test= test... | X = transformed_dataset.drop(['Survived'], axis=1)
Y = dataset['Survived'] | Titanic - Machine Learning from Disaster |
9,074,840 | Country_df=train["Place"]
ConfirmedCases_df=train["ConfirmedCases"]
Country_df.to_numpy()
ConfirmedCases_df.to_numpy()
Country=Country_df[0]
NbDay = pd.DataFrame(columns=['NbDay'])
day=0
count=0
for x in train["Month"]:
if(ConfirmedCases_df[count]==0):
NbDay = NbDay.append({'NbDay': int(0)}, ignore_index=True)
count=... | X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size = 0.2, random_state = 25 ) | Titanic - Machine Learning from Disaster |
9,074,840 | train=train[['Place','Country_Region','NbDay','ConfirmedCases','Fatalities']]
test=test[['Place','Country_Region','NbDay','ForecastId']]<count_unique_values> | model = tree.DecisionTreeClassifier(max_depth=6,random_state=25 ) | Titanic - Machine Learning from Disaster |
9,074,840 | country_array=train['Place'].to_numpy()
def distinct_values(country_array):
liste=[]
liste.append(country_array[0])
for i in range(1,len(country_array)) :
if country_array[i]!=country_array[i-1]:
liste.append(country_array[i])
return liste
Countries_liste=distinct_values(country_array)
len(Countries_liste )<categori... | model.fit(X_train,Y_train)
print('train score = ', model.score(X_train,Y_train), '
test score = ', model.score(X_test,Y_test)) | Titanic - Machine Learning from Disaster |
9,074,840 | def exponentiate_alpha(column,v):
array=column.to_numpy()
string='NbDay'+str(v)
array=np.power(v,array)
frame=pd.DataFrame(array, columns=[string])
return frame
def product(column1,column2,number):
array=column1.to_numpy()
array2=column2.to_numpy()
string='Product'+str(number)
array=np.multiply(array,array2)
frame... | test = pd.read_csv('.. /input/titanic/test.csv')
test.info() | Titanic - Machine Learning from Disaster |
9,074,840 | df1=exponentiate_alpha(train['NbDay'],1.0001 )<concatenate> | passenger_id = test['PassengerId'] | Titanic - Machine Learning from Disaster |
9,074,840 | df2=product(df1,train['NbDay'],1 )<feature_engineering> | transformed_test = transform_dataset(test)
transformed_test.info() | Titanic - Machine Learning from Disaster |
9,074,840 | train['NbDay_exp']=df1
train['Product']=df12<choose_model_class> | Y_predict = model.predict(transformed_test)
Y_p = pd.DataFrame(Y_predict, columns=['Survived'])
Y_p | Titanic - Machine Learning from Disaster |
9,074,840 | model = xgboost.XGBRegressor(colsample_bytree=0.4,
gamma=0,
learning_rate=0.07,
max_depth=5,
min_child_weight=1.5,
n_estimators=10000,
reg_alpha=0.75,
reg_lambda=0.45,
subsample=0.6,
seed=42,
objective='reg:squarederror',
eval_metric='rmse' )<predict_on_test> | res = pd.concat([passenger_id, Y_p], axis=1)
res | Titanic - Machine Learning from Disaster |
9,074,840 | ConfirmedCasesPredictions=[]
i=1
for country in Countries_liste:
train_=train[train['Place']==country][['NbDay']]
y_=train[train['Place']==country]['ConfirmedCases']
train_=train_.astype(float)
test_=test[test['Place']==country][['NbDay']]
test_=test_.astype(float)
model.fit(train_, y_)
y_pred = model.predict(test_)... | res.to_csv('res.csv', index=None ) | Titanic - Machine Learning from Disaster |
9,074,840 | ConfirmedCases=np.array(ConfirmedCasesPredictions)
ConfirmedCases=pd.DataFrame(ConfirmedCases, columns=['ConfirmedCases'])
test['ConfirmedCases']=ConfirmedCases<predict_on_test> | eli5.explain_weights_sklearn(model, feature_names=X_train.columns.values ) | Titanic - Machine Learning from Disaster |
3,921,809 | ConfirmedFatalities=[]
for country in Countries_liste:
train_=train[train['Place']==country][['NbDay','ConfirmedCases']]
y_=train[train['Place']==country]['Fatalities']
train_=train_.astype(float)
test_=test[test['Place']==country][['NbDay','ConfirmedCases']]
test_=test_.astype(float)
model.fit(train_, y_)
y_pred = ... | raw_data = pd.read_csv('.. /input/train.csv')
print(raw_data.columns)
raw_data.head()
print(raw_data.groupby(['Pclass', 'Sex', 'Embarked'])['Survived'].mean())
print(raw_data.Cabin.nunique())
print(raw_data.isnull().sum())
| Titanic - Machine Learning from Disaster |
3,921,809 | Fatalities=np.array(ConfirmedFatalities)
Fatalities=pd.DataFrame(Fatalities, columns=['Fatalities'])
test['Fatalities']=Fatalities<save_to_csv> | def cabinMap(elem):
d = {'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4, 'F': 5, 'G': 6, 'T': 7, 'N': 8}
elem = d[elem[0]]
return elem
| Titanic - Machine Learning from Disaster |
3,921,809 | sub = pd.DataFrame()
sub['ForecastId'] = test['ForecastId']
sub['ConfirmedCases'] = test['ConfirmedCases']
sub['Fatalities'] = test['Fatalities']
sub.to_csv('submission.csv', index=False )<load_from_csv> | y = raw_data.Survived
features = ['Pclass', 'Sex', 'Embarked']
X = raw_data[features].copy()
X.groupby('Sex' ).count() | Titanic - Machine Learning from Disaster |
3,921,809 | train = pd.read_csv('.. /input/covid19-global-forecasting-week-4/train.csv')
train['Date'] = pd.to_datetime(train['Date'])
train['Date'].max()<load_from_csv> | def reformat(x):
d_sex = {'female': 0, 'male': 1}
d_emb = {'C': 0, 'Q': 1, 'S': 2, '3':3}
x['Sex'] = [d_sex[sex] for sex in x['Sex'].values]
x['Embarked'] = x['Embarked'].fillna('3')
x['Embarked'] = [d_emb[elem] for elem in x['Embarked'].values]
try:
x['Cabin'] = x['Cabin'].fillna('N')
x['Cabin'] = x['Cabin'].apply(c... | Titanic - Machine Learning from Disaster |
3,921,809 | test = pd.read_csv('.. /input/covid19-global-forecasting-week-4/test.csv')
test['Date'] = pd.to_datetime(test['Date'])
test['Date'].max()<set_options> | train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1 ) | Titanic - Machine Learning from Disaster |
3,921,809 | def BLEND_WEEK4_1() :
pd.options.display.max_rows = 500
pd.options.display.max_columns = 500
%matplotlib inline
def rmse(yt, yp):
return np.sqrt(np.mean(( yt-yp)**2))
class CovidModel:
def __init__(self):
pass
def predict_first_day(self, date):
return None
def predict_next_day(self, yesterday_pred_df):
return None
clas... | scaler = StandardScaler()
train = scaler.fit_transform(train_X)
valid = scaler.transform(val_X)
print(train.shape)
print(valid.shape ) | Titanic - Machine Learning from Disaster |
3,921,809 | def BLEND_WEEK4_2() :
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
def giba_model() :
def exponential(x, a, k, b):
return a*np.exp(x*k)+ b
def rmse(yt, yp):
return np.sqrt(np.mean(( yt-yp)**2))
train = pd.read_csv('.. /input/covid19-global-forecasting-week-4/train.csv')
train['Date'] = pd.to_datet... | model = RandomForestClassifier(random_state=1)
model.fit(train,train_y)
preds_val = model.predict(valid)
print("Accuracy:")
print(sum(val_y == preds_val)/len(val_y)) | Titanic - Machine Learning from Disaster |
3,921,809 | sub1 = pd.read_csv("submission1.csv")
sub2 = pd.read_csv("submission2.csv")
print(np.corrcoef(sub1['ConfirmedCases'], sub2['ConfirmedCases'])[0][1])
print(np.corrcoef(sub1['Fatalities'] , sub2['Fatalities'])[0][1])
sub1['ConfirmedCases'] = 0.5*sub1['ConfirmedCases'] + 0.5*sub2['ConfirmedCases']
sub1['Fatalities'] =... | cross_val_score(model , train , train_y , cv=5 ) | Titanic - Machine Learning from Disaster |
3,921,809 | import os<set_options> | def applyModel(X_test):
X = X_test[features].copy()
reformat(X)
X = scaler.transform(X)
preds = model.predict(X)
return preds | Titanic - Machine Learning from Disaster |
3,921,809 | %matplotlib inline<import_modules> | X_test = pd.read_csv('.. /input/test.csv')
preds = applyModel(X_test)
output = pd.DataFrame({'PassengerId': X_test.PassengerId,
'Survived': preds})
output.to_csv('submission.csv', index=False ) | Titanic - Machine Learning from Disaster |
3,930,928 | import lightgbm as lgb<import_modules> | raw_data = pd.read_csv('.. /input/train.csv')
raw_data.head() | Titanic - Machine Learning from Disaster |
3,930,928 | from tqdm.notebook import tqdm<import_modules> | print(raw_data.columns)
print(raw_data.isnull().sum() ) | Titanic - Machine Learning from Disaster |
3,930,928 | from sklearn.preprocessing import LabelEncoder<load_from_csv> | raw_data.groupby(['Pclass', 'Sex', 'Embarked'])['Survived'].mean() | Titanic - Machine Learning from Disaster |
3,930,928 | train_df = pd.read_csv(".. /input/covid19-global-forecasting-week-4/train.csv")
test_df = pd.read_csv(".. /input/covid19-global-forecasting-week-4/test.csv")
submission_df = pd.read_csv(".. /input/covid19-global-forecasting-week-4/submission.csv" )<load_from_csv> | y = raw_data.Survived
features = ['Pclass', 'Sex', 'Embarked']
X = raw_data[features].copy() | Titanic - Machine Learning from Disaster |
3,930,928 | region_metadata = pd.read_csv(".. /input/covid19-forecasting-metadata/region_metadata.csv" )<feature_engineering> | def reformat(x):
d_sex = {'female': 0, 'male': 1}
d_emb = {'C': 0, 'Q': 1, 'S': 2, '3':3}
x['Sex'] = [d_sex[sex] for sex in x['Sex'].values]
x['Embarked'] = x['Embarked'].fillna('3')
x['Embarked'] = [d_emb[elem] for elem in x['Embarked'].values]
reformat(X)
X.head() | Titanic - Machine Learning from Disaster |
3,930,928 | def transform_geo_location(df):
lat = df.lat.values
lon = df.lon.values
df["lat_sin"] = np.sin(2 * np.pi * lat / 360)
df["lat_cos"] = np.cos(2 * np.pi * lat / 360)
df["lon_sin"] = np.sin(2 * np.pi * lon / 360)
df["lon_cos"] = np.cos(2 * np.pi * lon / 360)
return df<categorify> | scaler = StandardScaler()
X_scaled = scaler.fit_transform(X ) | Titanic - Machine Learning from Disaster |
3,930,928 | region_metadata = transform_geo_location(region_metadata)
geo_location_columns = ["lat_sin", "lat_cos", "lon_sin", "lon_cos"]<load_from_csv> | model = RandomForestClassifier()
model.fit(X_scaled,y)
cross_val_score(model , X_scaled , y , cv=5 ) | Titanic - Machine Learning from Disaster |
3,930,928 | country_data = pd.read_csv(".. /input/countries-of-the-world/countries of the world.csv")
country_data.Country = country_data.Country.apply(lambda c: c.rstrip(" "))
country_data.rename(
columns={
"Country": "Country_Region",
},
inplace=True
)<define_variables> | def applyModel(X_test):
X = X_test[features].copy()
reformat(X)
X = scaler.transform(X)
preds = model.predict(X)
return preds | Titanic - Machine Learning from Disaster |
3,930,928 | country_meta_columns = [
'Coastline(coast/area ratio)',
'Net migration',
'Infant mortality(per 1000 births)',
'GDP($ per capita)',
'Literacy(%)',
'Phones(per 1000)',
'Arable(%)',
'Crops(%)',
'Other(%)',
'Climate',
'Birthrate',
'Deathrate',
'Agriculture',
'Industry',
'Service'
]<data_type_conversions> | X_test = pd.read_csv('.. /input/test.csv')
preds = applyModel(X_test)
output = pd.DataFrame({'PassengerId': X_test.PassengerId,
'Survived': preds})
output.to_csv('submission.csv', index=False ) | Titanic - Machine Learning from Disaster |
3,890,342 | for col in country_meta_columns:
country_data[col] =(
country_data[col]
.fillna(-1000)
.apply(lambda v: v.replace(",", ".")if isinstance(v, str)else v)
.astype(np.float32)
)<load_from_csv> | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt | Titanic - Machine Learning from Disaster |
3,890,342 | lockdown_meta_df = pd.read_csv(".. /input/covid19-lockdown-dates-by-country/countryLockdowndates.csv")
lockdown_meta_df.rename(
columns={
"Country/Region": "Country_Region",
"Province": "Province_State",
"Date": "LockdownDate",
"Type": "LockdownType"
},
inplace=True
)
lockdown_meta_df.drop("Reference", axis=1, inpl... | df_train = pd.read_csv(".. /input/train.csv")
df_test = pd.read_csv(".. /input/test.csv" ) | Titanic - Machine Learning from Disaster |
3,890,342 | train_df = train_df.merge(lockdown_meta_df, on=["Province_State", "Country_Region"], how="left")
train_df = train_df.merge(country_data, on=["Country_Region"], how="left" )<categorify> | df_train['Survived'].value_counts() | Titanic - Machine Learning from Disaster |
3,890,342 | train_df["days_since_lockdown"] = np.clip(
(pd.to_datetime(train_df.Date)- pd.to_datetime(train_df.LockdownDate)).dt.days,
a_min=-1,
a_max=None
)
train_df["lockdown_type"] = [
t if d >= 0 else "None" for t, d in zip(train_df.LockdownType, train_df.days_since_lockdown)
]
lockdown_type_encoder = LabelEncoder().fit(tra... | df_train[['Pclass','Survived']].groupby(['Pclass'] ).mean() | Titanic - Machine Learning from Disaster |
3,890,342 | train_df["ConfirmedCases"] = np.log1p(train_df.ConfirmedCases)
train_df["Fatalities"] = np.log1p(train_df.Fatalities )<categorify> | df_test['Survived'] = 0
train_test = df_train.append(df_test ) | Titanic - Machine Learning from Disaster |
3,890,342 | def extract_region(df):
return(
df.Country_Region +
df.Province_State.fillna("" ).apply(lambda s: " + " + s if s else s)
)
region_encoder = LabelEncoder().fit(extract_region(train_df))
train_df["region_id"] = region_encoder.transform(extract_region(train_df))
test_df["region_id"] = region_encoder.transform(extract_re... | train_test = pd.get_dummies(train_test,columns=['Pclass'])
train_test.head() | Titanic - Machine Learning from Disaster |
3,890,342 | stats_df = train_df[["Fatalities", "ConfirmedCases", "region_id"]].groupby("region_id" ).sum()
stats_df["fatalities_to_cases"] = stats_df.Fatalities - stats_df.ConfirmedCases
stats_df.drop(["Fatalities", "ConfirmedCases"], axis=1, inplace=True)
train_df = train_df.merge(stats_df, on="region_id", how="left")
stats_col... | train_test['Sex'] = pd.factorize(train_test['Sex'])[0]
train_test.head() | Titanic - Machine Learning from Disaster |
3,890,342 | def extract_country_from_region(region):
if "+" in region:
country, state = region.split(" + ")
else:
country = region
return country
region_to_country = dict(
zip(
range(len(region_encoder.classes_)) ,
map(extract_country_from_region, region_encoder.classes_)
)
)<merge> | train_test['SibSp_Parch'] = train_test['SibSp'] + train_test['Parch']
train_test = train_test.drop(['SibSp', 'Parch'], axis=1)
train_test.head() | Titanic - Machine Learning from Disaster |
3,890,342 | train_df = train_df.merge(region_metadata, on="region_id", how="left" )<define_variables> | train_test = pd.get_dummies(train_test,columns=["Embarked"])
train_test.head() | Titanic - Machine Learning from Disaster |
3,890,342 | meta_columns = [
"density",
"population",
"area"
]<define_variables> | train_test.loc[train_test["Age"].isnull() ,"age_nan"] = 1
train_test.loc[train_test["Age"].notnull() ,"age_nan"] = 0
train_test = pd.get_dummies(train_test,columns=['age_nan'])
train_test.head() | Titanic - Machine Learning from Disaster |
3,890,342 | categorical_columns = [
"region_id",
"lockdown_type"
]<feature_engineering> | train_test = train_test.drop(['Cabin', 'Ticket', 'Name', 'PassengerId'], axis=1)
train_test.head() | Titanic - Machine Learning from Disaster |
3,890,342 | def compute_historical_features(df, target_column, past_horizon, create_target=True):
features = dict()
for col in meta_columns + country_meta_columns:
features[col] = df[col].unique().item()
for col in geo_location_columns:
features[col] = df[col].unique().item()
for col in stats_columns:
features[col] = df[col].uniqu... | train_test = train_test.fillna(0 ) | Titanic - Machine Learning from Disaster |
3,890,342 | def prepare_datasets(
train_df,
last_train_date,
target_column,
train_start_offset=50,
step=1,
):
dates = train_df.Date.unique()
train_dates = dates[dates <= last_train_date]
val_dates = dates[dates > last_train_date]
train_features = []
train_subdf = train_df[train_df.Date <= last_train_date]
val_subdf = train_df[tr... | train_data = train_test[:891]
test_data = train_test[891:]
train_data_X = train_data.drop(['Survived'],axis=1)
train_data_Y = train_data['Survived']
test_data_X = test_data.drop(['Survived'],axis=1 ) | Titanic - Machine Learning from Disaster |
3,890,342 | def autoregressive_predict(model, dates, df, target_column, past_horizon):
n_regions = len(region_encoder.classes_)
original_size = len(df)
future_df = pd.DataFrame({
"Date": np.repeat(dates, repeats=n_regions),
"region_id": np.tile(np.arange(n_regions), len(dates))
})
future_df["Country_Region"] = future_df.region_... | clf = RandomForestClassifier(n_estimators=150,min_samples_leaf=2,max_depth=6,oob_score=True)
clf.fit(train_data_X,train_data_Y)
clf.oob_score_
df_test["Survived"] = clf.predict(test_data_X)
result = df_test[['PassengerId','Survived']].set_index('PassengerId')
result.to_csv('result1.csv' ) | Titanic - Machine Learning from Disaster |
1,819,258 | def target_metric(actual, predicted):
return np.sqrt(((actual - predicted)** 2 ).mean() )<create_dataframe> | train = pd.read_csv('.. /input/train.csv')
test = pd.read_csv('.. /input/test.csv' ) | Titanic - Machine Learning from Disaster |
1,819,258 | def train_booster(
train_features_df,
target_column,
categorical_columns,
epochs=20,
train_start_offset=50,
val_df=None
):
train_dataset = lgb.Dataset(
train_features_df.drop([target_column], axis=1),
train_features_df[target_column],
free_raw_data=False,
categorical_feature=categorical_columns
)
params = {
'boost... | train = pd.read_csv('.. /input/train.csv')
test = pd.read_csv('.. /input/test.csv' ) | Titanic - Machine Learning from Disaster |
1,819,258 | train_start_offset = 70
epochs = 10
test_dates = test_df.Date[test_df.Date > train_df.Date.max() ].unique()
merge_columns = ["Date", "region_id"]
for target_column in ["ConfirmedCases", "Fatalities"]:
print()
print("Working on column", target_column)
print()
train_features_df, train_subdf, val_subdf = prepare_datasets... | print("Check NaN values in Test set:")
isnull = test.isnull().sum().reset_index()
isnull.columns = ['Feature', 'Total_null']
total_null = isnull[isnull['Total_null']>0]
total_null | Titanic - Machine Learning from Disaster |
1,819,258 | submission_df.to_csv("submission.csv", index=False )<set_options> | print("Check NaN values in Train set:")
isnull = train.isnull().sum().reset_index()
isnull.columns = ['Feature', 'Total_null']
total_null = isnull[isnull['Total_null']>0]
total_null | Titanic - Machine Learning from Disaster |
1,819,258 | pd.set_option('display.max_columns', 100)
warnings.filterwarnings('ignore')
df_train = pd.read_csv(".. /input/covid19-global-forecasting-week-4/train.csv")
print(df_train.shape)
df_train.head()
df_test = pd.read_csv(".. /input/covid19-global-forecasting-week-4/test.csv")
print(df_test.shape)
df_test.head()
df_tra... | traintest = pd.concat([train, test], axis=0, sort=False ) | Titanic - Machine Learning from Disaster |
1,819,258 | gc.collect()<load_from_csv> | traintest[traintest.duplicated() ].index | Titanic - Machine Learning from Disaster |
1,819,258 | pd.set_option('display.max_columns', 100)
warnings.filterwarnings('ignore')
df_train = pd.read_csv(".. /input/covid19-global-forecasting-week-4/train.csv")
print(df_train.shape)
df_train.head()
df_test = pd.read_csv(".. /input/covid19-global-forecasting-week-4/test.csv")
print(df_test.shape)
df_test.head()
df_tra... | pd.concat([traintest.nunique(dropna=False), traintest.count() , traintest.nunique() /traintest.count() ], axis=1 ) | Titanic - Machine Learning from Disaster |
1,819,258 | gc.collect()<define_variables> | train = train.replace('male', 0)
train = train.replace('female', 1)
test = test.replace('male', 0)
test = test.replace('female', 1 ) | Titanic - Machine Learning from Disaster |
1,819,258 | BAGS = 25
SEED = 1234
SET_FRAC = 0.01
TRUNCATED = False
DROPS = True
PRIVATE = True
USE_PRIORS = False
SUP_DROP = 0.0
ACTIONS_DROP = 0.0
PLACE_FRACTION = 1.0
LT_DECAY_MAX = 0.3
LT_DECAY_MIN = -0.4
SINGLE_MODEL = False
MODEL_Y = 'agg_dff'
pd.options.display.float_format = '{:.8}'.format
plt.rcParams["figure.figsize"] =(... | train['is_train'] = 1
train['origin_index'] = train.index
test['is_train'] = 0
test['origin_index'] = test.index
traintest = pd.concat([train, test], axis=0, sort=False ) | Titanic - Machine Learning from Disaster |
1,819,258 | gc.collect()<set_options> | min_max_scaler = preprocessing.MinMaxScaler(feature_range=(0,1))
num_cols = ['Age', 'Fare', 'SibSp_parch']
cat_cols = ['Sex', 'Cabin', 'Embarked', 'Pclass', 'SibSp', 'Parch']
train['SibSp_parch'] = train.SibSp + train.Parch
test['SibSp_parch'] = test.SibSp + train.Parch
scaled_train = train[num_cols+cat_cols].copy()
sc... | Titanic - Machine Learning from Disaster |
1,819,258 | !pip install tensorflow_addons
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
def swishE(x):
beta = 1.75
return beta * x * tf.keras.backend.sigmoid(x)
def swish(x):
return x * tf.keras.backend.sigmoid(x)
def phrishII(x):
return x*tf.keras.backend.tanh(1.75 * x * tf.keras.backend.sigmoid(x))
def mis... | from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.linear_model import LinearRegression | Titanic - Machine Learning from Disaster |
1,819,258 | def get_ridgeCV_sub(save_oof=False, save_public_test=False):
train = pd.read_csv('.. /input/covid19-global-forecasting-week-4/train.csv')
train['Province_State'].fillna('', inplace=True)
train['Date'] = pd.to_datetime(train['Date'])
train['day'] = train.Date.dt.dayofyear
train['geo'] = ['_'.join(x)for x in zip(train... | x_train, X_val, Y_train, Y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=0 ) | Titanic - Machine Learning from Disaster |
1,819,258 | gc.collect()<load_from_csv> | model_svm_linear = SVC(kernel='linear', C=1 ).fit(x_train, Y_train)
print("Training score: ", model_svm_linear.score(x_train, Y_train))
print("Validation score: ", model_svm_linear.score(X_val, Y_val)) | Titanic - Machine Learning from Disaster |
1,819,258 | def get_nn_sub() :
df = pd.read_csv(".. /input/covid19-global-forecasting-week-4/train.csv")
sub_df = pd.read_csv(".. /input/covid19-global-forecasting-week-4/test.csv")
coo_df = pd.read_csv(".. /input/covid19week1/train.csv" ).rename(columns={"Country/Region": "Country_Region"})
coo_df = coo_df.groupby("Country_Reg... | model_svm_sigmoid = SVC(kernel='sigmoid' ).fit(x_train, Y_train)
print("Training score: ", model_svm_sigmoid.score(x_train, Y_train))
print("Validation score: ", model_svm_sigmoid.score(X_val, Y_val)) | Titanic - Machine Learning from Disaster |
1,819,258 | gc.collect()<load_from_csv> | model_svm_rbf = SVC(kernel='rbf' ).fit(x_train, Y_train)
print("Training score: ", model_svm_rbf.score(x_train, Y_train))
print("Validation score: ", model_svm_rbf.score(X_val, Y_val)) | Titanic - Machine Learning from Disaster |
1,819,258 | def get_nn_sub() :
df = pd.read_csv(".. /input/covid19-global-forecasting-week-4/train.csv")
sub_df = pd.read_csv(".. /input/covid19-global-forecasting-week-4/test.csv")
coo_df = pd.read_csv(".. /input/covid19week1/train.csv" ).rename(columns={"Country/Region": "Country_Region"})
coo_df = coo_df.groupby("Country_Reg... | model_logistic = LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial' ).fit(x_train, Y_train)
print("Training score: ", model_logistic.score(x_train, Y_train))
print("Validation score: ", model_logistic.score(X_val, Y_val)) | Titanic - Machine Learning from Disaster |
1,819,258 | gc.collect()<set_options> | model = SVC(kernel='linear', C=1 ).fit(X_train, y_train)
print("Training score: ", model_svm_linear.score(X_train, y_train))
class_predict = model.predict(X_test ) | Titanic - Machine Learning from Disaster |
1,819,258 | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
def get_cpmp_sub(save_oof=False, save_public_test=False):
train = pd.read_csv('.. /input/covid19-global-forecasting-week-4/train.csv')
train['Province_State'].fillna('', inplace=True)
train['Date'] = pd.to_datetime(train['Date'])
train['day'] = train.... | test_ID = test['PassengerId'] | Titanic - Machine Learning from Disaster |
1,819,258 | gc.collect()<load_from_csv> | temp = {'PassengerID': test_ID, 'Survived': class_predict}
result = pd.DataFrame(temp ) | Titanic - Machine Learning from Disaster |
1,819,258 | def get_nn_sub3() :
df = pd.read_csv(".. /input/covid19-global-forecasting-week-4/train.csv")
sub_df = pd.read_csv(".. /input/covid19-global-forecasting-week-4/test.csv")
coo_df = pd.read_csv(".. /input/covid19week1/train.csv" ).rename(columns={"Country/Region": "Country_Region"})
coo_df = coo_df.groupby("Country_Re... | result.to_csv('result.csv', index=False ) | Titanic - Machine Learning from Disaster |
1,819,258 | gc.collect()<load_from_csv> | from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.optimizers import Adam
from keras.regularizers import l2
from keras.callbacks import EarlyStopping
from sklearn import preprocessing | Titanic - Machine Learning from Disaster |
1,819,258 | train = pd.read_csv('.. /input/covid19-global-forecasting-week-4/train.csv')
train['Date'] = pd.to_datetime(train['Date'])
def dealing_with_null_values(dataset):
dataset = dataset
for i in dataset.columns:
replace = []
data = dataset[i].isnull()
count = 0
for j,k in zip(data,dataset[i]):
if(j==True):
count = count+1
... | model_neuron = Sequential()
model_neuron.add(Dense(output_dim=256, input_shape=(X_train.shape[1],), activation='relu'))
model_neuron.add(Dropout(0.5))
model_neuron.add(Dense(output_dim=128, input_shape=(X_train.shape[1],), activation='relu'))
model_neuron.add(Dropout(0.5))
model_neuron.add(Dense(output_dim=64, input_sh... | Titanic - Machine Learning from Disaster |
1,819,258 | gc.collect()<prepare_output> | history = model_neuron.fit(X_train, y_train, nb_epoch=10000, validation_split=0.2, callbacks=[EarlyStopping(patience=10)] ) | Titanic - Machine Learning from Disaster |
1,819,258 | buscc = output["ConfirmedCases"]
busf = output["Fatalities"]
sdfcc = sub_df["ConfirmedCases"]
sdff = sub_df["Fatalities"]
sdfcc2 = sub_df20["ConfirmedCases"]
sdff2 = sub_df20["Fatalities"]
sdfcc3 = sub_df40["ConfirmedCases"]
sdff3 = sub_df40["Fatalities"]
sdfcc4 = df_sub1["ConfirmedCases"]
sdff4 = df_sub1["Fatalities"]... | model_neuron = Sequential()
model_neuron.add(Dense(output_dim=256, input_shape=(X_train.shape[1],), activation='relu'))
model_neuron.add(Dense(output_dim=128, input_shape=(X_train.shape[1],), activation='relu'))
model_neuron.add(Dense(output_dim=64, input_shape=(X_train.shape[1],), activation='relu'))
model_neuron.add(... | Titanic - Machine Learning from Disaster |
1,819,258 | pd.set_option('display.max_rows',500)
pd.set_option('display.max_columns',900)
warnings.filterwarnings('ignore')
<load_from_csv> | class_predict = model_neuron.predict_classes(X_test)
class_predict = class_predict.reshape(( class_predict.shape[0],))
test_ID = test['PassengerId']
temp = {'PassengerID': test_ID, 'Survived': class_predict}
result = pd.DataFrame(temp)
result.to_csv('result_neuron.csv', index=False ) | Titanic - Machine Learning from Disaster |
1,819,258 | <feature_engineering><EOS> | cabins_list = traintest.Cabin.fillna('NaN')
cabins = []
for value in cabins_list:
if value != 'NaN':
cabins += value.split(' ')
unique_cabins = set(cabins)
print("Number unique in cabins: ", len(unique_cabins))
print("Number of Cabins: ", len(cabins))
print("Number of nan in Cabin: ", traintest.Cabin.isnull().sum())... | Titanic - Machine Learning from Disaster |
12,485,520 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<feature_engineering> | !pip install seaborn==0.11.0
%matplotlib inline
py.init_notebook_mode(connected=True)
warnings.filterwarnings('ignore')
random.seed(1455)
np.random.seed(1455)
sns.set_theme()
clear_output() | Titanic - Machine Learning from Disaster |
12,485,520 | df['Mortalidade']=np.where(df['ConfirmedCases']==0,0,df['Fatalities']/df['ConfirmedCases'])
df[(df['Country_Region']=='Brazil')&(df['Id']>0)].tail()<feature_engineering> | train = pd.read_csv('.. /input/titanic/train.csv')
test = pd.read_csv('.. /input/titanic/test.csv')
PassengerId = test['PassengerId']
test.head(5 ) | Titanic - Machine Learning from Disaster |
12,485,520 | df['Province_State'].fillna('Vazio',inplace=True)
df['Local']=np.where(df['Province_State']== 'Vazio',df['Country_Region'],df['Country_Region']+'/'+df['Province_State'] )<data_type_conversions> | train['Ticket_type'] = train['Ticket'].apply(lambda x: x[0:4])
train['Ticket_type'] = train['Ticket_type'].astype('category')
train['Ticket_type'] = train['Ticket_type'].cat.codes
test['Ticket_type'] = test['Ticket'].apply(lambda x: x[0:4])
test['Ticket_type'] = test['Ticket_type'].astype('category')
test['Ticket_t... | Titanic - Machine Learning from Disaster |
12,485,520 | df_test=df[df['ForecastId']>0]
df['Date']=df['Date'].astype('str')
df=df[df['Id']>0]
df['ConfirmedCases'].fillna(0,inplace=True)
print(df.dtypes)
df[(df['Local']=='Brazil')].tail()<feature_engineering> | train['IsAlone'] = 0
train.loc[train['FamilySize'] == 1, 'IsAlone'] = 1
test['IsAlone'] = 0
test.loc[test['FamilySize'] == 1, 'IsAlone'] = 1 | Titanic - Machine Learning from Disaster |
12,485,520 | df_f=df[df['Month']>2]
df0=df[(df['Day_num'].between(0,14)) ]
df1=df[(df['Day_num'].between(1,15)) ]
df2=df[(df['Day_num'].between(2,16)) ]
df3=df[(df['Day_num'].between(3,17)) ]
df4=df[(df['Day_num'].between(4,18)) ]
df5=df[(df['Day_num'].between(5,19)) ]
df6=df[(df['Day_num'].between(6,20)) ]
df7=df[(df['Day_num'].be... | train['Fare'] = train['Fare'].fillna(train['Fare'].median())
test['Fare'] = test['Fare'].fillna(train['Fare'].median())
train['CategoricalFare'] = pd.qcut(train['Fare'], 4 ) | Titanic - Machine Learning from Disaster |
12,485,520 | def make_decay(df):
dft=df.pivot_table(index='Local',columns='Date',values='ConfirmedCases' ).reset_index()
Lista_colunas=['Local','dia_01','dia_02','dia_03','dia_04','dia_05','dia_06','dia_07',
'dia_08','dia_09','dia_10','dia_11','dia_12','dia_13','dia_14','dia_15']
dft_copy=dft.copy()
dft.columns=Lista_colunas
C1=np.... | def get_title(name):
title_search = re.search('([A-Za-z]+)\.', name)
if title_search:
return title_search.group(1)
return ""
for dataset in whole_data:
dataset['Title'] = dataset['Name'].apply(get_title)
for dataset in whole_data:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', '... | Titanic - Machine Learning from Disaster |
12,485,520 | dfmodel.columns = ["".join(c if c.isalnum() else "_" for c in str(x)) for x in dfmodel.columns]
dftr.columns = ["".join(c if c.isalnum() else "_" for c in str(x)) for x in dftr.columns]
dfmodel.fillna(-99,inplace=True)
dftr.fillna(-99,inplace=True)
resposta=dftr['Decay']
dfteste_cat= dftr.drop(columns=['Crescimento_1... | for dataset in whole_data:
dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0
dataset.loc[(dataset['Fare'] > 7.91)&(dataset['Fare'] <= 14.454), 'Fare'] = 1
dataset.loc[(dataset['Fare'] > 14.454)&(dataset['Fare'] <= 31), 'Fare'] = 2
dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3
dataset['Fare'] = dataset['... | Titanic - Machine Learning from Disaster |
12,485,520 | import catboost
from catboost import CatBoostRegressor, Pool<define_variables> | train['Embarked'] = train['Embarked'].fillna('S')
test['Embarked'] = test['Embarked'].fillna('S')
train['Embarked'] = train['Embarked'].map({'S': 0, 'C': 1, 'Q': 2} ).astype(int)
test['Embarked'] = test['Embarked'].map({'S': 0, 'C': 1, 'Q': 2} ).astype(int)
fig = sns.displot(data=train, x="Embarked", hue="Survived"... | Titanic - Machine Learning from Disaster |
12,485,520 | train_pool = Pool(X_train,
label=y_train
)
val_pool = Pool(X_test,
label=y_test
)
test_pool = Pool(dfteste,
label=resposta
)<train_model> | drop_elements = ['PassengerId', 'Name', 'Ticket', 'Cabin', 'SibSp']
train = train.drop(drop_elements, axis = 1)
train = train.drop(['CategoricalAge', 'CategoricalFare'], axis = 1)
test = test.drop(drop_elements, axis = 1)
train.head(5 ) | Titanic - Machine Learning from Disaster |
12,485,520 | model = CatBoostRegressor(objective='RMSE')
model.fit(train_pool, plot=True, eval_set=val_pool, verbose=500 )<train_model> | y_train = train['Survived'].ravel()
train = train.drop(['Survived'], axis=1)
x_train = train.values
x_test = test.values | Titanic - Machine Learning from Disaster |
12,485,520 | params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 2,
'learning_rate': 0.01, 'loss': 'ls'}
clfGB = GradientBoostingRegressor(**params)
clfGB.fit(X_train, y_train)
rGB=clfGB.predict(dfteste)
clfRF = RandomForestRegressor()
clfRF.fit(X_train, y_train)
rRF=clfRF.predict(dfteste)
<init_hyperparams> | test_data_with_labels = pd.read_csv("https://github.com/thisisjasonjafari/my-datascientise-handcode/raw/master/005-datavisualization/titanic.csv")
test_data = pd.read_csv('.. /input/titanic/test.csv')
for i, name in enumerate(test_data_with_labels['name']):
if '"' in name:
test_data_with_labels['name'][i] = re.sub('"... | Titanic - Machine Learning from Disaster |
12,485,520 | params= {'boosting_type' : 'dart',
'max_depth':-1,
'objective':'regression',
'nthread': 5,
'num_leaves':64,
'learning_rate':0.01,
'max_bin':256,
'subsample_for_bin':200,
'subsample':1,
'subsample_freq':1,
'colsample_bytree':0.8,
'reg_alpha':1.2,
'reg_lambda':1.2,
'min_split_gain':0.5,
'min_child_weight':1,
'min_child_s... | gbm = xgb.XGBClassifier(n_estimators= 100,
max_depth = 4,
gamma = 0.9,
nthread = -1,
scale_pos_weight=1,
random_state = 3101)
gbm.fit(x_train, y_train)
xgb_predictions = gbm.predict(x_test)
score_gbm = gbm.score(x_train, y_train)
print(f'Random Forest Classifier score(Train Accuracy): {score_gbm}')
test_acc_gbm = ... | Titanic - Machine Learning from Disaster |
12,485,520 | resp=model.predict(test_pool)
respLGB=lgbm_cases.predict(dfteste)
print("MSE CatBoost: %.4f" %mean_squared_error(resp,resposta))
print("MSE GradientBoosting: %.4f" %mean_squared_error(rGB,resposta))
print("MSE RandomForest: %.4f" %mean_squared_error(rRF,resposta))
print("MSE LightGBM: %.4f" %mean_squared_error(respLG... | rfc = RandomForestClassifier(n_estimators = 4,
max_features = 5,
random_state = 216)
rfc.fit(x_train, y_train)
score_rfc = rfc.score(x_train, y_train)
out_rfc = rfc.predict(x_test)
print(f'Random Forest Classifier score(Train Accuracy): {score_rfc}')
test_acc_rfc = accuracy_score(y_true, out_rfc)
print(f'Random F... | Titanic - Machine Learning from Disaster |
12,485,520 | dftr['Modelado']=np.where(dftr['Erro']>0.33,dftr['Decay'],dftr['Previsto'])
dftr[dftr['Local'].isin(['Brazil','US/New York','US/New Jersey','US/Florida','Italy','Spain','France','Germany'])]<feature_engineering> | knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(x_train, y_train)
score_knn = knn.score(x_train, y_train)
out_knn = knn.predict(x_test)
print(f'K- Nearest Neighbour ClassifierClassifier score(Train Accuracy): {score_knn}')
test_acc_knn = accuracy_score(y_true, out_knn)
print(f'K- Nearest Neighbour Classifier Cl... | Titanic - Machine Learning from Disaster |
12,485,520 | dft=df_f.pivot_table(index='Local',columns='Date',values='ConfirmedCases' ).reset_index()
dft_copy=dft.copy()
C1=np.where(
(dft.iloc[: , -15].values)==0,
(np.power(dft.iloc[: , -8].values/(( dft.iloc[: , -15].values)+1),1/7)) -(1)
,(np.power(dft.iloc[: , -8].values/(( dft.iloc[: , -15].values)) ,1/7)) -(1)
)
C1=np.w... | svc = SVC(C = 5, kernel = 'linear', random_state = 8)
svc.fit(x_train, y_train)
score_svc = svc.score(x_train, y_train)
out_svc = svc.predict(x_test)
print(f'Support Vector Machine Classifier score(Train Accuracy): {score_svc}')
test_acc_svc = accuracy_score(y_true, out_svc)
print(f'K- Support Vector Machine Clas... | Titanic - Machine Learning from Disaster |
12,485,520 | dt[dt['Local'].isin(['Brazil','US/New York','US/New Jersey','US/Illinois','US/California','Italy','Spain','France','Germany'])]<feature_engineering> | vclf = VotingClassifier(estimators=[('gb',gbm),('rf',rfc),('knn',knn),('svm',svc)], voting='hard', weights=[2,3,1,2])
vclf.fit(x_train, y_train)
out_vclf = vclf.predict(x_test)
score_voting = vclf.score(x_train, y_train)
print(f'Voting Classifier score(Train Accuracy): {score_voting}')
test_acc_voting = accuracy_s... | Titanic - Machine Learning from Disaster |
12,485,520 | <feature_engineering><EOS> | classifier = ['XGBoost', 'RandomForest', 'KNN', 'SVC', 'VotingEnsemble']
train_acc = [score_gbm, score_rfc, score_knn, score_svc, score_voting]
test_acc = [test_acc_gbm, test_acc_rfc, test_acc_knn, test_acc_svc, test_acc_voting]
score_df = pd.DataFrame({'classifier': classifier, 'train_acc': train_acc, 'test_acc': t... | Titanic - Machine Learning from Disaster |
4,026,243 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<train_model> | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.layers import Input, Dense, BatchNormalization, Add, GaussianNoise, Dropout
from keras.models import Model
from keras.layers import Wrapper
from keras.callbacks import ReduceLROnPlateau
from keras.utils import to_categorical
fro... | Titanic - Machine Learning from Disaster |
4,026,243 | params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 2,
'learning_rate': 0.01, 'loss': 'ls'}
clfGB = GradientBoostingRegressor(**params)
clfGB.fit(X_trainD, y_trainD)
rGB=clfGB.predict(dfteste_d)
clfRF = RandomForestRegressor()
clfRF.fit(X_trainD, y_trainD)
rRF=clfRF.predict(dfteste_d)
params= {'boo... | train_set = pd.read_csv(".. /input/train.csv")
train_dfY = train_set['Survived']
test_set = pd.read_csv(".. /input/test.csv")
submission = test_set['PassengerId'].copy()
print(train_set.shape)
print(test_set.shape)
print(train_dfY.shape)
| Titanic - Machine Learning from Disaster |
4,026,243 | resp=model.predict(test_pool_d)
respLGB=lgbm_cases.predict(dfteste_d)
print("MSE CatBoost: %.4f" %mean_squared_error(resp,resposta))
print("MSE GradientBoosting: %.4f" %mean_squared_error(rGB,resposta))
print("MSE RandomForest: %.4f" %mean_squared_error(rRF,resposta))
print("MSE LightGBM: %.4f" %mean_squared_error(re... | train_set['Cabin'].value_counts()
| Titanic - Machine Learning from Disaster |
4,026,243 | dftr_d['Previsto']=np.where(( dftr_d['Crescimento_2'])>(dftr_d['Crescimento_1']),dftr_d['Previsto'],dftr_d['Previsto']/7)
copy_dftr=dftr_d.copy()
dftr_d=df_f.pivot_table(index='Local',columns='Date',values='Mortalidade' ).reset_index()
dftr_copy=dftr_d.copy()
C1=np.where(
(dftr_d.iloc[: , -15].values)==0,
(np.power(d... | datasets = [train_set, test_set]
originalData = train_set
for dataset in datasets:
dataset['Age'].fillna(dataset["Age"].median() , inplace=True)
dataset['Embarked'].fillna(dataset["Embarked"].mode() [0], inplace=True)
dataset['Fare'].fillna(dataset['Fare'].median() , inplace = True)
dataset.drop(['PassengerId','Cabi... | Titanic - Machine Learning from Disaster |
4,026,243 | dftr_d['2020-04-15']=(1+dftr_d['Cres_2020-04-15'])*dftr_d['2020-04-14']
dftr_d['2020-04-16']=(1+dftr_d['Cres_2020-04-16'])*dftr_d['2020-04-15']
dftr_d['2020-04-17']=(1+dftr_d['Cres_2020-04-17'])*dftr_d['2020-04-16']
dftr_d['2020-04-18']=(1+dftr_d['Cres_2020-04-18'])*dftr_d['2020-04-17']
dftr_d['2020-04-19']=(1+dftr_d['... | for dataset in datasets:
StringArray = dataset['Name'].str.split(", ", expand=True)
StringArray = StringArray[1].str.split(".", expand=True)
dataset['Title'] = StringArray[0]
title_names = dataset['Title'].value_counts()
print('===Prior to grouping===')
print(title_names)
title_names =(dataset['Title'].value_counts... | Titanic - Machine Learning from Disaster |
4,026,243 | dftr_d[dftr_d['Local'].isin(['Brazil','US/New York','US/New Jersey','US/Florida','Italy','Spain','France','Germany'])]<feature_engineering> | label = LabelEncoder()
enc = OneHotEncoder(handle_unknown='ignore')
train_set = datasets[0]
test_set = datasets[1]
train_set = train_set.drop(['Survived'], axis=1)
EncodedDataFrames = []
print(train_set.shape, test_set.shape)
wholeData = pd.concat([train_set, test_set], ignore_index=True)
wholeData['Sex_Code'] = la... | Titanic - Machine Learning from Disaster |
4,026,243 | dfm=df_f.pivot_table(index='Local',columns='Date',values='Fatalities' ).reset_index()
mortes_adj=dfm.iloc[: , -1].values.sum() / dft_copy.iloc[: , -1].values.sum()
dft['mortes']=dfm.iloc[: , -1].values / dft_copy.iloc[: , -1].values
print(mortes_adj)
dft.head()<filter> | wholeData = wholeData.drop(['Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Title', 'Pclass'], axis= 1)
[train_set, test_set] = np.split(wholeData, [891], axis= 0)
train_dfX = train_set
print(train_dfX.shape, train_dfY.shape)
train_dfX,val_dfX,train_dfY, val_dfY = train_test_split(train_dfX,train_dfY , ... | Titanic - Machine Learning from Disaster |
4,026,243 | dft[dft['Local'].isin(['Brazil','US/New York','US/New Jersey','US/Florida','Italy','Spain','France','Germany'])]<feature_engineering> |
model = Sequential()
model.add(Dense(40, kernel_initializer = 'glorot_normal', bias_initializer='zeros', activation = 'relu', kernel_regularizer=regularizers.l2(0.01), input_dim = 19))
model.add(Dropout(0.2))
model.add(Dense(20, kernel_initializer = 'glorot_normal', bias_initializer='zeros', activation = 'relu', kern... | Titanic - Machine Learning from Disaster |
4,026,243 | dft['mortes']=np.where(dft['mortes']>(2*mortes_adj),(2*mortes_adj),np.where(dft['mortes']<(mortes_adj/2),(mortes_adj/2),dft['mortes']))<merge> | train_history = model.fit(train_dfX, train_dfY, batch_size=24, epochs= 20, validation_data=(val_dfX, val_dfY)) | Titanic - Machine Learning from Disaster |
4,026,243 | <merge><EOS> | y_test = model.predict(test_set)
y_formatted = np.where(y_test > 0.5, 1, 0)
y_dataFrame = pd.DataFrame(np.ravel(y_formatted), columns=['Survived'])
submission = pd.concat([submission, y_dataFrame], axis=1)
submission.to_csv('submission.csv', index=False ) | Titanic - Machine Learning from Disaster |
4,293,763 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<merge> | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings | Titanic - Machine Learning from Disaster |
4,293,763 | dftestefinal=pd.merge(df_test,dffat,on=['Local','Date'],how='left')
dftestefinal['Fatalities']=dftestefinal['ConfirmedCases']*dftestefinal['Mortalidade']
dftestefinal.tail()<sort_values> | warnings.filterwarnings("ignore" ) | Titanic - Machine Learning from Disaster |
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