kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
9,301,232 | train = train.drop(train.loc[(( train['pickup_latitude']==0)&(train['pickup_longitude']==0)) &(( train['dropoff_latitude']!=0)&(train['dropoff_longitude']!=0)) &(train['fare_amount']==0)].index, axis=0 )<filter> | a = df.groupby(['Sex','Pclass','name_title'] ).mean().round(2)
a = a['Age'] | Titanic - Machine Learning from Disaster |
9,301,232 | test.loc[(( test['pickup_latitude']==0)&(test['pickup_longitude']==0)) &(( test['dropoff_latitude']!=0)&(test['dropoff_longitude']!=0)) ]
<filter> | b = df.iloc[:891].groupby(['Sex','Pclass','name_title'])
b_median = b.median()
b = b_median.reset_index() [['Sex', 'Pclass', 'name_title', 'Age']]
b.head() | Titanic - Machine Learning from Disaster |
9,301,232 | train.loc[(( train['pickup_latitude']!=0)&(train['pickup_longitude']!=0)) &(( train['dropoff_latitude']==0)&(train['dropoff_longitude']==0)) &(train['fare_amount']==0)]<drop_column> | for key, value in df['Age'].iteritems() :
if pd.isna(df['Age'][key]):
for key2, value2 in b['Age'].iteritems() :
if df['Sex'][key] == b['Sex'][key2] and df['name_title'][key] == b['name_title'][key2] and df['Pclass'][key] == b['Pclass'][key2]:
df['Age'][key] = b['Age'][key2]
else:
df['Age'][key] = df['Age'][key] | Titanic - Machine Learning from Disaster |
9,301,232 | train = train.drop(train.loc[(( train['pickup_latitude']!=0)&(train['pickup_longitude']!=0)) &(( train['dropoff_latitude']==0)&(train['dropoff_longitude']==0)) &(train['fare_amount']==0)].index, axis=0 )<filter> | df['has_cabin'] = 0
for Key, value in df['Cabin'].iteritems() :
if pd.isna(df['Cabin'][Key]):
df['has_cabin'][Key] = int(0)
else:
df['has_cabin'][Key] = int(1 ) | Titanic - Machine Learning from Disaster |
9,301,232 | test.loc[(( test['pickup_latitude']!=0)&(test['pickup_longitude']!=0)) &(( test['dropoff_latitude']==0)&(test['dropoff_longitude']==0)) ]<filter> | df['Embarked'].fillna("S", inplace=True)
df['Fare'].fillna(df['Fare_limited'].mean() , inplace=True)
df['Age'].fillna(df['Age'].mean() , inplace=True ) | Titanic - Machine Learning from Disaster |
9,301,232 | high_distance = train.loc[(train['H_Distance']>200)&(train['fare_amount']!=0)]<feature_engineering> | le = preprocessing.LabelEncoder()
df['Embarked'] = le.fit_transform(df['Embarked'])
df['Sex'] = le.fit_transform(df['Sex'])
df['name_title'] = le.fit_transform(df['name_title'] ) | Titanic - Machine Learning from Disaster |
9,301,232 | high_distance['H_Distance'] = high_distance.apply(
lambda row:(row['fare_amount'] - 2.50)/1.56,
axis=1
)<train_model> | train_df = df[0:891]
pred_df = df[-418::] | Titanic - Machine Learning from Disaster |
9,301,232 | train.update(high_distance )<filter> | train_df['Survived'] = train_df['Survived'].astype(int ) | Titanic - Machine Learning from Disaster |
9,301,232 | train[train['H_Distance']==0]<filter> | features_X = train_df[['Sex','Age','Pclass','Fare_limited','alone','family_members','name_title']]
target_Y = train_df['Survived']
predict_X = pred_df[['Sex','Age','Pclass','Fare_limited','alone','family_members','name_title']] | Titanic - Machine Learning from Disaster |
9,301,232 | train[(train['H_Distance']==0)&(train['fare_amount']==0)]<drop_column> | train_data, val_data, train_target, val_target = train_test_split(features_X, target_Y, test_size=0.4, random_state=42)
train_data.shape, val_data.shape, len(train_target), len(val_target ) | Titanic - Machine Learning from Disaster |
9,301,232 | train = train.drop(train[(train['H_Distance']==0)&(train['fare_amount']==0)].index, axis = 0 )<define_variables> | model = RandomForestClassifier(n_estimators=1000,min_samples_split=8, min_samples_leaf=4)
model.fit(train_data, train_target)
val_predictions = model.predict(val_data)
accuracy_score(val_target, val_predictions ) | Titanic - Machine Learning from Disaster |
9,301,232 | rush_hour = train.loc[(((train['Hour']>=6)&(train['Hour']<=20)) &(( train['Day of Week']>=1)&(train['Day of Week']<=5)) &(train['H_Distance']==0)&(train['fare_amount'] < 2.5)) ]
rush_hour<drop_column> | rmse = np.sqrt(mean_squared_error(val_target, val_predictions))
print("RMSE: %f" %(rmse))
print("MAE: " + str(mean_absolute_error(val_predictions, val_target)))
print("R2:" + str(r2_score(val_target, val_predictions)) ) | Titanic - Machine Learning from Disaster |
9,301,232 | train=train.drop(rush_hour.index, axis=0 )<feature_engineering> | test_df = pd.read_csv('.. /input/titanic/test.csv')
predict_Y = model.predict(predict_X)
submissionRF = pd.DataFrame({
"PassengerId": test_df["PassengerId"],
"Survived": predict_Y
})
submissionRF.to_csv('gender_submission.csv', index=False ) | Titanic - Machine Learning from Disaster |
9,301,232 | non_rush_hour = train.loc[(((train['Hour']<6)|(train['Hour']>20)) &(( train['Day of Week']>=1)&(train['Day of Week']<=5)) &(train['H_Distance']==0)&(train['fare_amount'] < 3.0)) ]
non_rush_hour
<filter> | def algorithm_pipeline(X_train_data, X_test_data, y_train_data, y_test_data,
model, param_grid, cv=10, scoring_fit='neg_mean_squared_error',
do_probabilities = False):
gs = GridSearchCV(
estimator=model,
param_grid=param_grid,
cv=cv,
n_jobs=-1,
scoring=scoring_fit,
verbose=2
)
fitted_model = gs.fit(X_train_data, y_t... | Titanic - Machine Learning from Disaster |
9,301,232 | weekends = train.loc[(( train['Day of Week']==0)|(train['Day of Week']==6)) &(train['H_Distance']==0)&(train['fare_amount'] < 3.0)]
weekends
<filter> | model = RandomForestClassifier()
parameters = {
'min_samples_split': [4,8],
'min_samples_leaf': [2,4,8],
'criterion': ['gini', 'entropy'],
'n_estimators': [100]}
model, pred = algorithm_pipeline(train_data, val_data, train_target, val_target, model,
parameters, cv=5)
print(model.best_params_)
rmse = np.sqrt(-model.be... | Titanic - Machine Learning from Disaster |
9,301,232 | train.loc[(train['H_Distance']!=0)&(train['fare_amount']==0)]<feature_engineering> | classifiers = [
KNeighborsClassifier() ,
SVC(probability=True),
DecisionTreeClassifier() ,
RandomForestClassifier() ,
AdaBoostClassifier() ,
GradientBoostingClassifier() ,
GaussianNB() ,
LinearDiscriminantAnalysis() ,
QuadraticDiscriminantAnalysis() ,
LogisticRegression() ]
log_cols = ["Classifier", "Accuracy"]
log =... | Titanic - Machine Learning from Disaster |
9,301,232 | scenario_3 = train.loc[(train['H_Distance']!=0)&(train['fare_amount']==0)]<sort_values> | model = GradientBoostingClassifier(learning_rate=0.005,n_estimators=1000, min_samples_split=2, min_samples_leaf=2)
model.fit(train_data, train_target)
val_predictions = model.predict(val_data)
accuracy_score(val_target, val_predictions ) | Titanic - Machine Learning from Disaster |
9,301,232 | scenario_3.sort_values('H_Distance', ascending=False )<feature_engineering> | rmse = np.sqrt(mean_squared_error(val_target, val_predictions))
print("RMSE: %f" %(rmse))
print("MAE: " + str(mean_absolute_error(val_predictions, val_target)))
print("R2:" + str(r2_score(val_target, val_predictions)) ) | Titanic - Machine Learning from Disaster |
9,301,232 | scenario_3['fare_amount'] = scenario_3.apply(
lambda row:(( row['H_Distance'] * 1.56)+ 2.50), axis=1
)<filter> | Titanic - Machine Learning from Disaster | |
9,301,232 | train.loc[(train['H_Distance']==0)&(train['fare_amount']!=0)]<feature_engineering> | model = Sequential()
model.add(Dense(units = 7, kernel_initializer = 'uniform', activation = 'relu', input_dim = 7))
model.add(Dense(units = 9, kernel_initializer = 'uniform', activation = 'relu'))
model.add(Dense(units = 5, kernel_initializer = 'uniform', activation = 'relu'))
model.add(Dense(units = 1, kernel_initial... | Titanic - Machine Learning from Disaster |
9,301,232 | scenario_4 = train.loc[(train['H_Distance']==0)&(train['fare_amount']!=0)]<filter> | y_pred = model.predict(features_X)
y_pred =(y_pred > 0.5 ).astype(int ).reshape(features_X.shape[0])
accuracy_score(target_Y, y_pred ) | Titanic - Machine Learning from Disaster |
9,301,232 | scenario_4.loc[(scenario_4['fare_amount']<=3.0)&(scenario_4['H_Distance']==0)]<filter> | y_pred = model.predict(predict_X)
y_final =(y_pred > 0.5 ).astype(int ).reshape(predict_X.shape[0] ) | Titanic - Machine Learning from Disaster |
9,301,232 | <filter><EOS> | Titanic - Machine Learning from Disaster | |
8,854,696 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<feature_engineering> | %matplotlib inline
| Titanic - Machine Learning from Disaster |
8,854,696 | scenario_4_sub['H_Distance'] = scenario_4_sub.apply(
lambda row:(( row['fare_amount']-2.50)/1.56), axis=1
)<drop_column> | train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
| Titanic - Machine Learning from Disaster |
8,854,696 | train = train.drop(['key','pickup_datetime'], axis = 1)
test = test.drop(['key','pickup_datetime'], axis = 1 )<prepare_x_and_y> | passenger_id = test.PassengerId | Titanic - Machine Learning from Disaster |
8,854,696 | x_train = train.iloc[:,train.columns!='fare_amount']
y_train = train['fare_amount'].values
x_test = test<import_modules> | train_df['Name_Len'] = train_df['Name'].apply(lambda x: len(x))
train_df['Survived'].groupby(pd.qcut(train_df['Name_Len'],5)).mean() | Titanic - Machine Learning from Disaster |
8,854,696 | import lightgbm as lgbm<init_hyperparams> | def names(train, test):
for i in [train, test]:
i['Name_Len'] = i['Name'].apply(lambda x: len(x))
i['Name_Title'] = i['Name'].apply(lambda x: x.split(',')[1] ).apply(lambda x: x.split() [0])
del i['Name']
return train, test
def age_impute(train, test):
for i in [train, test]:
i['Age_Null_Flag'] = i['Age'].apply(lambda... | Titanic - Machine Learning from Disaster |
8,854,696 | params = {
'boosting_type':'gbdt',
'objective': 'regression',
'nthread': -1,
'verbose': 0,
'num_leaves': 31,
'learning_rate': 0.05,
'max_depth': -1,
'subsample': 0.8,
'subsample_freq': 1,
'colsample_bytree': 0.6,
'reg_aplha': 1,
'reg_lambda': 0.001,
'metric': 'rmse',
'min_split_gain': 0.5,
'min_child_weight': 1,
'min_c... | test['Fare'].fillna(train['Fare'].mean() , inplace = True ) | Titanic - Machine Learning from Disaster |
8,854,696 | train_set = lgbm.Dataset(x_train, y_train, silent=True)
train_set<train_model> | def dummies(train, test, columns = ['Pclass', 'Sex', 'Embarked', 'Ticket_Lett', 'Cabin_Letter', 'Name_Title', 'Fam_Size']):
for column in columns:
train[column] = train[column].apply(lambda x: str(x))
test[column] = test[column].apply(lambda x: str(x))
good_cols = [column+'_'+i for i in train[column].unique() if i in t... | Titanic - Machine Learning from Disaster |
8,854,696 | model = lgbm.train(params, train_set = train_set, num_boost_round=300 )<predict_on_test> | def drop(train, test, bye = ['PassengerId']):
for i in [train, test]:
for z in bye:
del i[z]
return train, test | Titanic - Machine Learning from Disaster |
8,854,696 | pred_test_y = model.predict(x_test, num_iteration = model.best_iteration )<save_to_csv> | train, test = names(train, test)
train, test = age_impute(train, test)
train, test = cabin_num(train, test)
train, test = cabin(train, test)
train, test = embarked_impute(train, test)
train, test = fam_size(train, test)
test['Fare'].fillna(train['Fare'].mean() , inplace = True)
train, test = ticket_grouped(train... | Titanic - Machine Learning from Disaster |
8,854,696 | submission = pd.read_csv('.. /input/new-york-city-taxi-fare-prediction/sample_submission.csv')
submission['fare_amount'] = pred_test_y
submission.to_csv('s.csv', index=False)
submission.head(20 )<feature_engineering> | rf = RandomForestClassifier(max_features='auto', oob_score=True, random_state=1, n_jobs=-1)
param_grid = { "criterion" : ["entropy"],
"min_samples_leaf" : [6, 4, 2],
"min_samples_split" : [12],
"n_estimators": [825, 800, 775, 750]}
gs = GridSearchCV(estimator=rf, param_grid=param_grid, scoring='accuracy', cv=3, n_jobs... | Titanic - Machine Learning from Disaster |
8,854,696 | warnings.filterwarnings("ignore")
def preparedataset(datasetname):
datasetname['pickup_year']=0
datasetname['pickup_month']=0
datasetname['pickup_day']=0
datasetname['pickup_hour']=0
datasetname['dis'] =0
datasetname['x_dis']=0
datasetname['y_dis']=0
datasetname.head()
for k in range(len(datasetname.index)) :
datetime... | rf = RandomForestClassifier(criterion='entropy',
n_estimators=775,
min_samples_split=12,
min_samples_leaf=2,
max_features='auto',
oob_score=True,
random_state=2,
n_jobs=-1)
rf.fit(train.iloc[:, 1:], train.iloc[:, 0])
Y_pred = rf.predict(test)
print("%.4f" % rf.oob_score_ ) | Titanic - Machine Learning from Disaster |
8,854,696 | <save_to_csv><EOS> | output = pd.DataFrame({'PassengerId': passenger_id, 'Survived': Y_pred})
output.to_csv('my_submission.csv', index=False ) | Titanic - Machine Learning from Disaster |
9,434,906 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<prepare_output> | %matplotlib inline | Titanic - Machine Learning from Disaster |
9,434,906 | test_df=preparedataset(dataset_test)
test_df.head(50 )<save_to_csv> | train = pd.read_csv('.. /input/titanic/train.csv')
test = pd.read_csv('.. /input/titanic/test.csv' ) | Titanic - Machine Learning from Disaster |
9,434,906 | test_df.to_csv('test_preprocessed.csv' )<split> | train['Survived'].value_counts(normalize=True ) | Titanic - Machine Learning from Disaster |
9,434,906 | y = df.fare_amount
X=df.drop('fare_amount',axis=1)
X_train, X_valid, y_train, y_valid = train_test_split(X, y )<find_best_params> | train['Survived'].groupby(train['Pclass'] ).mean() | Titanic - Machine Learning from Disaster |
9,434,906 | warnings.filterwarnings("ignore")
result={}
best_istemator=0
best_learing_rate=0
best_mae=100000000000
for lr in [X / 100 for X in range(10,50, 5)]:
for ns in range(200,701,50):
my_model = XGBRegressor(n_estimators=ns, learning_rate=lr,n_jobs=4)
my_model.fit(X_train, y_train)
predictions = my_model.predict(X_valid)
... | train['Name_Title'] = train['Name'].apply(lambda x: x.split(',')[1] ).apply(lambda x: x.split() [0])
train['Name_Title'].value_counts() | Titanic - Machine Learning from Disaster |
9,434,906 | my_model_2 = XGBRegressor(n_estimators=700, learning_rate=0.2, n_jobs=4)
my_model_2.fit(X_train,y_train)
test_preds = my_model_2.predict(test_df)
output = pd.DataFrame({'key': test_df.index,
'fare_amount': test_preds})
output.to_csv('submission.csv', index=False )<load_from_csv> | train['Survived'].groupby(train['Name_Title'] ).mean() | Titanic - Machine Learning from Disaster |
9,434,906 | train = pd.read_csv(".. /input/train.csv", nrows = 1000000)
test = pd.read_csv(".. /input/test.csv" )<sort_values> | train['Name_Len'] = train['Name'].apply(lambda x: len(x))
train['Survived'].groupby(pd.qcut(train['Name_Len'],5)).mean() | Titanic - Machine Learning from Disaster |
9,434,906 | train.isnull().sum().sort_values(ascending=False )<sort_values> | pd.qcut(train['Name_Len'],5 ).value_counts() | Titanic - Machine Learning from Disaster |
9,434,906 | test.isnull().sum().sort_values(ascending=False )<correct_missing_values> | train['Sex'].value_counts(normalize=True ) | Titanic - Machine Learning from Disaster |
9,434,906 | train = train.dropna()<count_values> | train['Survived'].groupby(train['Sex'] ).mean() | Titanic - Machine Learning from Disaster |
9,434,906 | Counter(train['fare_amount']<0 )<drop_column> | train['Survived'].groupby(train['Age'].isnull() ).mean() | Titanic - Machine Learning from Disaster |
9,434,906 | train = train.drop(train[train['fare_amount']<0].index, axis=0)
train.shape<sort_values> | train['Survived'].groupby(pd.qcut(train['Age'],5)).mean() | Titanic - Machine Learning from Disaster |
9,434,906 | train['fare_amount'].sort_values(ascending=False )<filter> | pd.qcut(train['Age'],5 ).value_counts() | Titanic - Machine Learning from Disaster |
9,434,906 | train[train['passenger_count']>6]<drop_column> | train['Survived'].groupby(train['SibSp'] ).mean() | Titanic - Machine Learning from Disaster |
9,434,906 | train = train.drop(train[train['passenger_count']==208].index,axis=0 )<filter> | train['SibSp'].value_counts() | Titanic - Machine Learning from Disaster |
9,434,906 | train[train['pickup_latitude']<-90]<filter> | train['Survived'].groupby(train['Parch'] ).mean() | Titanic - Machine Learning from Disaster |
9,434,906 | train[train['pickup_latitude']>90]<drop_column> | train['Parch'].value_counts() | Titanic - Machine Learning from Disaster |
9,434,906 | train = train.drop(((train[train['pickup_latitude']<-90])|(train[train['pickup_latitude']>90])).index, axis=0 )<filter> | train['Ticket_Len'] = train['Ticket'].apply(lambda x: len(x)) | Titanic - Machine Learning from Disaster |
9,434,906 | train[train['pickup_longitude']<-180]<filter> | train['Ticket_Len'].value_counts() | Titanic - Machine Learning from Disaster |
9,434,906 | train[train['pickup_longitude']>180]<drop_column> | train['Ticket_Lett'] = train['Ticket'].apply(lambda x: str(x)[0] ) | Titanic - Machine Learning from Disaster |
9,434,906 | train = train.drop(((train[train['pickup_longitude']<-180])|(train[train['pickup_longitude']>180])).index, axis=0 )<filter> | train['Ticket_Lett'].value_counts() | Titanic - Machine Learning from Disaster |
9,434,906 | train[train['dropoff_latitude']<-90]<filter> | train.groupby(['Ticket_Lett'])['Survived'].mean() | Titanic - Machine Learning from Disaster |
9,434,906 | train[train['dropoff_latitude']>90]<drop_column> | pd.qcut(train['Fare'], 3 ).value_counts() | Titanic - Machine Learning from Disaster |
9,434,906 | train = train.drop(((train[train['dropoff_latitude']<-90])|(train[train['dropoff_latitude']>90])).index, axis=0 )<filter> | train['Survived'].groupby(pd.qcut(train['Fare'], 3)).mean() | Titanic - Machine Learning from Disaster |
9,434,906 | train[train['dropoff_latitude']<-180]|train[train['dropoff_latitude']>180]<data_type_conversions> | train['Cabin_Letter'] = train['Cabin'].apply(lambda x: str(x)[0] ) | Titanic - Machine Learning from Disaster |
9,434,906 | train['key'] = pd.to_datetime(train['key'])
train['pickup_datetime'] = pd.to_datetime(train['pickup_datetime'] )<data_type_conversions> | train['Cabin_Letter'].value_counts() | Titanic - Machine Learning from Disaster |
9,434,906 | test['key'] = pd.to_datetime(test['key'])
test['pickup_datetime'] = pd.to_datetime(test['pickup_datetime'] )<compute_test_metric> | train['Survived'].groupby(train['Cabin_Letter'] ).mean() | Titanic - Machine Learning from Disaster |
9,434,906 | def haversine_distance(lat1, long1, lat2, long2):
data = [train, test]
for i in data:
R = 6371
phi1 = np.radians(i[lat1])
phi2 = np.radians(i[lat2])
delta_phi = np.radians(i[lat2]-i[lat1])
delta_lambda = np.radians(i[long2]-i[long1])
a = np.sin(delta_phi / 2.0)** 2 + np.cos(phi1)* np.cos(phi2)* np.sin(delta_lambda ... | train['Cabin_num'] = train['Cabin'].apply(lambda x: str(x ).split(' ')[-1][1:])
train['Cabin_num'].replace('an', np.NaN, inplace = True)
train['Cabin_num'] = train['Cabin_num'].apply(lambda x: int(x)if not pd.isnull(x)and x != '' else np.NaN ) | Titanic - Machine Learning from Disaster |
9,434,906 | haversine_distance('pickup_latitude', 'pickup_longitude', 'dropoff_latitude', 'dropoff_longitude' )<feature_engineering> | pd.qcut(train['Cabin_num'],3 ).value_counts() | Titanic - Machine Learning from Disaster |
9,434,906 | data = [train,test]
for i in data:
i['Year'] = i['pickup_datetime'].dt.year
i['Month'] = i['pickup_datetime'].dt.month
i['Date'] = i['pickup_datetime'].dt.day
i['Day of Week'] = i['pickup_datetime'].dt.dayofweek
i['Hour'] = i['pickup_datetime'].dt.hour<sort_values> | train['Survived'].groupby(pd.qcut(train['Cabin_num'], 3)).mean() | Titanic - Machine Learning from Disaster |
9,434,906 | train.sort_values(['H_Distance','fare_amount'], ascending=False )<filter> | train['Embarked'].value_counts() | Titanic - Machine Learning from Disaster |
9,434,906 | train.loc[(( train['pickup_latitude']==0)&(train['pickup_longitude']==0)) &(( train['dropoff_latitude']!=0)&(train['dropoff_longitude']!=0)) &(train['fare_amount']==0)]<drop_column> | train['Embarked'].value_counts(normalize=True ) | Titanic - Machine Learning from Disaster |
9,434,906 | train = train.drop(train.loc[(( train['pickup_latitude']==0)&(train['pickup_longitude']==0)) &(( train['dropoff_latitude']!=0)&(train['dropoff_longitude']!=0)) &(train['fare_amount']==0)].index, axis=0 )<filter> | train['Survived'].groupby(train['Embarked'] ).mean() | Titanic - Machine Learning from Disaster |
9,434,906 | test.loc[(( test['pickup_latitude']==0)&(test['pickup_longitude']==0)) &(( test['dropoff_latitude']!=0)&(test['dropoff_longitude']!=0)) ]
<filter> | def names(train, test):
for i in [train, test]:
i['Name_Len'] = i['Name'].apply(lambda x: len(x))
i['Name_Title'] = i['Name'].apply(lambda x: x.split(',')[1] ).apply(lambda x: x.split() [0])
del i['Name']
return train, test | Titanic - Machine Learning from Disaster |
9,434,906 | train.loc[(( train['pickup_latitude']!=0)&(train['pickup_longitude']!=0)) &(( train['dropoff_latitude']==0)&(train['dropoff_longitude']==0)) &(train['fare_amount']==0)]<drop_column> | def age_impute(train, test):
for i in [train, test]:
i['Age_Null_Flag'] = i['Age'].apply(lambda x: 1 if pd.isnull(x)else 0)
data = train.groupby(['Name_Title', 'Pclass'])['Age']
i['Age'] = data.transform(lambda x: x.fillna(x.mean()))
return train, test | Titanic - Machine Learning from Disaster |
9,434,906 | train = train.drop(train.loc[(( train['pickup_latitude']!=0)&(train['pickup_longitude']!=0)) &(( train['dropoff_latitude']==0)&(train['dropoff_longitude']==0)) &(train['fare_amount']==0)].index, axis=0 )<filter> | def fam_size(train, test):
for i in [train, test]:
i['Fam_Size'] = np.where(( i['SibSp']+i['Parch'])== 0 , 'Solo',
np.where(( i['SibSp']+i['Parch'])<= 3,'Nuclear', 'Big'))
del i['SibSp']
del i['Parch']
return train, test | Titanic - Machine Learning from Disaster |
9,434,906 | test.loc[(( test['pickup_latitude']!=0)&(test['pickup_longitude']!=0)) &(( test['dropoff_latitude']==0)&(test['dropoff_longitude']==0)) ]<filter> | def cabin(train, test):
for i in [train, test]:
i['Cabin_Letter'] = i['Cabin'].apply(lambda x: str(x)[0])
del i['Cabin']
return train, test | Titanic - Machine Learning from Disaster |
9,434,906 | high_distance = train.loc[(train['H_Distance']>200)&(train['fare_amount']!=0)]<feature_engineering> | def cabin_num(train, test):
for i in [train, test]:
i['Cabin_num1'] = i['Cabin'].apply(lambda x: str(x ).split(' ')[-1][1:])
i['Cabin_num1'].replace('an', np.NaN, inplace = True)
i['Cabin_num1'] = i['Cabin_num1'].apply(lambda x: int(x)if not pd.isnull(x)and x != '' else np.NaN)
i['Cabin_num'] = pd.qcut(train['Cabin_... | Titanic - Machine Learning from Disaster |
9,434,906 | high_distance['H_Distance'] = high_distance.apply(
lambda row:(row['fare_amount'] - 2.50)/1.56,
axis=1
)<train_model> | def embarked_impute(train, test):
for i in [train, test]:
i['Embarked'] = i['Embarked'].fillna('S')
return train, test | Titanic - Machine Learning from Disaster |
9,434,906 | train.update(high_distance )<filter> | test['Fare'].fillna(train['Fare'].mean() , inplace = True ) | Titanic - Machine Learning from Disaster |
9,434,906 | train[train['H_Distance']==0]<filter> | def dummies(train, test, columns = ['Pclass', 'Sex', 'Embarked', 'Ticket_Lett', 'Cabin_Letter', 'Name_Title', 'Fam_Size']):
for column in columns:
train[column] = train[column].apply(lambda x: str(x))
test[column] = test[column].apply(lambda x: str(x))
good_cols = [column+'_'+i for i in train[column].unique() if i in t... | Titanic - Machine Learning from Disaster |
9,434,906 | train[(train['H_Distance']==0)&(train['fare_amount']==0)]<drop_column> | def drop(train, test, bye = ['PassengerId']):
for i in [train, test]:
for z in bye:
del i[z]
return train, test | Titanic - Machine Learning from Disaster |
9,434,906 | train = train.drop(train[(train['H_Distance']==0)&(train['fare_amount']==0)].index, axis = 0 )<define_variables> | train = pd.read_csv('.. /input/titanic/train.csv')
test = pd.read_csv('.. /input/titanic/test.csv')
train, test = names(train, test)
train, test = age_impute(train, test)
train, test = cabin_num(train, test)
train, test = cabin(train, test)
train, test = embarked_impute(train, test)
train, test = fam_size(train,... | Titanic - Machine Learning from Disaster |
9,434,906 | rush_hour = train.loc[(((train['Hour']>=6)&(train['Hour']<=20)) &(( train['Day of Week']>=1)&(train['Day of Week']<=5)) &(train['H_Distance']==0)&(train['fare_amount'] < 2.5)) ]
rush_hour<drop_column> | rf = RandomForestClassifier(criterion='gini',
n_estimators=700,
min_samples_split=10,
min_samples_leaf=1,
max_features='auto',
oob_score=True,
random_state=1,
n_jobs=-1)
rf.fit(train.iloc[:, 1:], train.iloc[:, 0])
print("%.4f" % rf.oob_score_ ) | Titanic - Machine Learning from Disaster |
9,434,906 | train=train.drop(rush_hour.index, axis=0 )<feature_engineering> | pd.concat(( pd.DataFrame(train.iloc[:, 1:].columns, columns = ['variable']),
pd.DataFrame(rf.feature_importances_, columns = ['importance'])) ,
axis = 1 ).sort_values(by='importance', ascending = False)[:20] | Titanic - Machine Learning from Disaster |
9,434,906 | <filter><EOS> | predictions = rf.predict(test)
predictions = pd.DataFrame(predictions, columns=['Survived'])
test = pd.read_csv('.. /input/titanic/test.csv')
predictions = pd.concat(( test.iloc[:, 0], predictions), axis = 1)
predictions.to_csv('titanicUI1.csv', sep=",", index = False ) | Titanic - Machine Learning from Disaster |
9,401,023 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<filter> | import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib.pyplot as plt | Titanic - Machine Learning from Disaster |
9,401,023 | train.loc[(train['H_Distance']!=0)&(train['fare_amount']==0)]<feature_engineering> | train = pd.read_csv('.. /input/titanic/train.csv')
test = pd.read_csv('.. /input/titanic/test.csv' ) | Titanic - Machine Learning from Disaster |
9,401,023 | scenario_3 = train.loc[(train['H_Distance']!=0)&(train['fare_amount']==0)]<sort_values> | def percent_null_value(df):
total = df.isnull().sum()
percent = df.isnull().sum() / len(df)
df = pd.DataFrame([total, percent] ).T
df.columns = ['Total', 'Percent']
return df | Titanic - Machine Learning from Disaster |
9,401,023 | scenario_3.sort_values('H_Distance', ascending=False )<feature_engineering> | def percent_unique_value(df, col):
total = df[col].value_counts()
percent = df[col].value_counts() / len(df)
df = pd.DataFrame([total, percent] ).T
df.columns = ['Total', 'Percent']
return df | Titanic - Machine Learning from Disaster |
9,401,023 | scenario_3['fare_amount'] = scenario_3.apply(
lambda row:(( row['H_Distance'] * 1.56)+ 2.50), axis=1
)<filter> | df = percent_null_value(train)
df.head(len(df)) | Titanic - Machine Learning from Disaster |
9,401,023 | train.loc[(train['H_Distance']==0)&(train['fare_amount']!=0)]<feature_engineering> | df = percent_null_value(test)
df.head(len(df)) | Titanic - Machine Learning from Disaster |
9,401,023 | scenario_4 = train.loc[(train['H_Distance']==0)&(train['fare_amount']!=0)]<filter> | train[train.Embarked.isnull() ] | Titanic - Machine Learning from Disaster |
9,401,023 | scenario_4.loc[(scenario_4['fare_amount']<=3.0)&(scenario_4['H_Distance']==0)]<filter> | train[(train.Pclass == 1)&(train.Sex == 'female')]['Embarked'].value_counts() | Titanic - Machine Learning from Disaster |
9,401,023 | scenario_4.loc[(scenario_4['fare_amount']>3.0)&(scenario_4['H_Distance']==0)]<filter> | train.Embarked.fillna('S', inplace = True ) | Titanic - Machine Learning from Disaster |
9,401,023 | scenario_4_sub = scenario_4.loc[(scenario_4['fare_amount']>3.0)&(scenario_4['H_Distance']==0)]<feature_engineering> | test[test.Fare.isnull() ] | Titanic - Machine Learning from Disaster |
9,401,023 | scenario_4_sub['H_Distance'] = scenario_4_sub.apply(
lambda row:(( row['fare_amount']-2.50)/1.56), axis=1
)<drop_column> | missing_value = test[(test.Pclass == 3)&(test.Sex == 'male')]['Fare'].mean()
test.Fare.fillna(missing_value, inplace = True ) | Titanic - Machine Learning from Disaster |
9,401,023 | train = train.drop(['key','pickup_datetime'], axis = 1)
test = test.drop(['key','pickup_datetime'], axis = 1 )<prepare_x_and_y> | data = [train, test]
for dataset in data:
dataset.Cabin.fillna('N', inplace = True)
dataset['Cabin'] = dataset['Cabin'].apply(lambda x : x[0] ) | Titanic - Machine Learning from Disaster |
9,401,023 | x_train = train.iloc[:,train.columns!='fare_amount']
y_train = train['fare_amount'].values
x_test = test<choose_model_class> | survived = train.Survived
all_data = pd.concat([train, test] ) | Titanic - Machine Learning from Disaster |
9,401,023 | rf = RandomForestRegressor()
rf.fit(x_train, y_train)
rf_predict = rf.predict(x_test)
<save_to_csv> | mapping = {'N': 0,
'C': 1,
'B': 2,
'D': 3,
'E': 4,
'A': 5,
'F': 6,
'G': 6,
'T': 6}
all_data['Cabin'] = all_data['Cabin'].map(mapping ) | Titanic - Machine Learning from Disaster |
9,401,023 | submission = pd.read_csv('.. /input/sample_submission.csv')
submission['fare_amount'] = rf_predict
submission.to_csv('submission_1.csv', index=False)
submission.head(20 )<load_from_csv> | all_data['title'] = all_data['Name'].apply(lambda x : x.split('.')[0])
all_data['title'] = all_data['title'].apply(lambda x : x.split(' ')[-1] ) | Titanic - Machine Learning from Disaster |
9,401,023 | train_df=pd.read_csv(".. /input/train.csv",nrows=1000000)
test_df=pd.read_csv(".. /input/test.csv" )<count_missing_values> | mapping = {'Mr': 0,
'Miss': 1,
'Mrs': 2,
'Master': 3,
'Dr': 4,
'Rev': 4,
'Col': 4,
'Mlle': 4,
'Major': 4,
'Ms': 4,
'Mme': 4,
'Lady': 4,
'Countess':4,
'Don': 4,
'Sir': 4,
'Jonkheer': 4,
'Capt': 4,
'Dona': 4}
all_data['title'] = all_data['title'].map(mapping ) | Titanic - Machine Learning from Disaster |
9,401,023 | train_df.isnull().sum()<drop_column> | all_data['FamilySize'] = all_data['SibSp'] + all_data['Parch'] + 1 | Titanic - Machine Learning from Disaster |
9,401,023 | train_df= train_df.drop(train_df[train_df.isnull().any(1)].index, axis = 0 )<count_values> | mapping = {1: 1, 2: 2, 3: 3, 4: 0, 6: 0, 5: 0, 7: 0, 11: 0, 8: 0}
all_data['FamilySize'] = all_data['FamilySize'].map(mapping ) | Titanic - Machine Learning from Disaster |
9,401,023 | Counter(train_df['fare_amount']<0 )<drop_column> | all_data['is_alone'] = all_data['FamilySize'].apply(lambda x : 1 if x == 1 else 0 ) | Titanic - Machine Learning from Disaster |
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