kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
9,390,314 | combine.Functional.value_counts()<count_values> | train_data.drop('Cabin',axis=1,inplace=True)
test_data.drop('Cabin',axis=1,inplace=True ) | Titanic - Machine Learning from Disaster |
9,390,314 | combine['Functional']=combine['Functional'].fillna('Typ')
combine.Functional.value_counts()<count_values> | def impute_age(cols):
Age=cols[0]
Pclass=cols[1]
if pd.isnull(Age):
if Pclass == 1:
return 37
elif Pclass == 2:
return 29
else:
return 24
else:
return Age | Titanic - Machine Learning from Disaster |
9,390,314 | combine.FireplaceQu.value_counts()<count_values> | train_data['Age'] = train_data[['Age','Pclass']].apply(impute_age,axis=1 ) | Titanic - Machine Learning from Disaster |
9,390,314 | combine.Fireplaces.value_counts()<count_missing_values> | test_data['Age'] = test_data[['Age','Pclass']].apply(impute_age,axis=1 ) | Titanic - Machine Learning from Disaster |
9,390,314 | combine.FireplaceQu.isnull().sum()<count_values> | train_data.isnull().sum().sort_values(ascending = False ) | Titanic - Machine Learning from Disaster |
9,390,314 | combine['FireplaceQu']=combine['FireplaceQu'].fillna('None')
combine.FireplaceQu.value_counts()<count_values> | test_data.isnull().sum().sort_values(ascending = False ) | Titanic - Machine Learning from Disaster |
9,390,314 | combine.GarageType.value_counts()<count_values> | list_of_non_numeric_data=list(train_data.select_dtypes(include='object'))
list_of_non_numeric_data | Titanic - Machine Learning from Disaster |
9,390,314 | combine['GarageType']=combine['GarageType'].fillna('None')
combine.GarageType.value_counts()<count_missing_values> | train_data.drop('Ticket',axis=1,inplace=True)
test_data.drop('Ticket',axis=1,inplace=True ) | Titanic - Machine Learning from Disaster |
9,390,314 | combine['GarageYrBlt']=combine['GarageYrBlt'].fillna(0)
combine.GarageYrBlt.isnull().sum()<count_values> | def getTitles(name):
name = str(name)
title = name.split('.')[0]
title = title.split(',')
return title[1] | Titanic - Machine Learning from Disaster |
9,390,314 | combine.GarageFinish.value_counts()<count_values> | train_data['Title'] = train_data['Name'].apply(getTitles)
train_data['Title'] | Titanic - Machine Learning from Disaster |
9,390,314 | combine['GarageFinish']=combine['GarageFinish'].fillna('None')
combine.GarageFinish.value_counts()<count_values> | test_data['Title'] = test_data['Name'].apply(getTitles)
test_data['Title'] | Titanic - Machine Learning from Disaster |
9,390,314 | combine.GarageCars.value_counts()<count_values> | def cleanTitle(title):
if title in [' Mr',' Mrs',' Master',' Miss']:
return title
else:
return "Others" | Titanic - Machine Learning from Disaster |
9,390,314 | combine['GarageCars']=combine['GarageCars'].fillna(0)
combine.GarageCars.value_counts()<feature_engineering> | train_data['Title'] = train_data['Title'].apply(cleanTitle)
test_data['Title'] = test_data['Title'].apply(cleanTitle ) | Titanic - Machine Learning from Disaster |
9,390,314 | combine['GarageArea']=combine['GarageArea'].fillna(0)
combine.GarageArea.isnull().sum()<count_values> | Title_train = pd.get_dummies(train_data['Title'],drop_first=True)
Title_test = pd.get_dummies(test_data['Title'],drop_first=True)
sex_train = pd.get_dummies(train_data['Sex'],drop_first=True)
embark_train = pd.get_dummies(train_data['Embarked'],drop_first=True)
sex_test = pd.get_dummies(test_data['Sex'],drop_first=... | Titanic - Machine Learning from Disaster |
9,390,314 | combine.GarageQual.value_counts()<count_values> | train_data.drop(['Sex','Embarked','Name','Title'],axis=1,inplace=True)
test_data.drop(['Sex','Embarked','Name','Title'],axis=1,inplace=True ) | Titanic - Machine Learning from Disaster |
9,390,314 | combine['GarageQual']=combine['GarageQual'].fillna('None')
combine.GarageQual.value_counts()<count_values> | train_data=pd.concat([train_data,sex_train,embark_train,Title_train],axis=1)
test_data=pd.concat([test_data,sex_test,embark_test,Title_test],axis=1 ) | Titanic - Machine Learning from Disaster |
9,390,314 | combine.GarageCond.value_counts()<count_values> | from sklearn.preprocessing import MinMaxScaler | Titanic - Machine Learning from Disaster |
9,390,314 | combine['GarageCond']=combine['GarageCond'].fillna('None')
combine.GarageCond.value_counts()<count_values> | X_train=train_data.drop(['Survived','PassengerId'],axis=1)
y_train= train_data['Survived']
X_test=test_data.drop(['PassengerId'],axis=1 ) | Titanic - Machine Learning from Disaster |
9,390,314 | combine.PoolArea.value_counts()<count_values> | Scaler=MinMaxScaler() | Titanic - Machine Learning from Disaster |
9,390,314 | combine.PoolQC.value_counts()<count_values> | X_train = Scaler.fit_transform(X_train)
X_test = Scaler.transform(X_test ) | Titanic - Machine Learning from Disaster |
9,390,314 | combine['PoolQC']=combine['PoolQC'].fillna('None')
combine.PoolQC.value_counts()<count_values> | from sklearn.linear_model import LogisticRegression | Titanic - Machine Learning from Disaster |
9,390,314 | combine.Fence.value_counts()<count_values> | logmodel = LogisticRegression(max_iter=10000)
logmodel.fit(X_train,y_train ) | Titanic - Machine Learning from Disaster |
9,390,314 | combine['Fence']=combine['Fence'].fillna('None')
combine.Fence.value_counts()<count_values> | test_data['Survived']=logmodel.predict(X_test ) | Titanic - Machine Learning from Disaster |
9,390,314 | <count_values><EOS> | test_data[['PassengerId', 'Survived']].to_csv('kaggle_submission.csv', index = False ) | Titanic - Machine Learning from Disaster |
9,059,975 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<count_values> | pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 500)
df_train = pd.read_csv('.. /input/titanic/train.csv')
df_test = pd.read_csv('.. /input/titanic/test.csv')
df_sub = pd.read_csv('.. /input/titanic/gender_submission.csv' ) | Titanic - Machine Learning from Disaster |
9,059,975 | combine.SaleType.value_counts()<count_values> | df = pd.concat([df_train, df_test],sort=False)
df.reset_index(drop=True,inplace=True ) | Titanic - Machine Learning from Disaster |
9,059,975 | combine['SaleType']=combine['SaleType'].fillna('WD')
combine.SaleType.value_counts()<feature_engineering> | df.isnull().sum() | Titanic - Machine Learning from Disaster |
9,059,975 | combine['SalePrice']=combine['SalePrice'].fillna(0)
combine.SalePrice.isnull().sum()<count_missing_values> | df[df['Fare'].isnull() ] | Titanic - Machine Learning from Disaster |
9,059,975 | combine.isnull().sum().sum()<count_values> | df_dropna = df.dropna(subset=['Fare'] ) | Titanic - Machine Learning from Disaster |
9,059,975 | combine.YrSold.value_counts()<count_values> | df['Pclass'].value_counts() | Titanic - Machine Learning from Disaster |
9,059,975 | test_raw.YrSold.value_counts()<feature_engineering> | grouped = df.groupby('Pclass' ) | Titanic - Machine Learning from Disaster |
9,059,975 | combine['Age']=combine['YrSold']-combine['YearBuilt']
combine['PriceFlux']=2011-combine['YrSold']
combine['Renew']=combine['YrSold']-combine['YearRemodAdd']
combine['MSSubClass']=combine['MSSubClass'].apply(str )<categorify> | age60=df[(df['Age']>60)&(df['Age']<70)] | Titanic - Machine Learning from Disaster |
9,059,975 | Label_cols=['MSSubClass','LotShape','LandContour','LandSlope','BldgType','HouseStyle','ExterQual','ExterCond','BsmtQual','BsmtCond','BsmtExposure','BsmtFinType1','BsmtFinType2','HeatingQC','CentralAir','Electrical','KitchenQual','Functional','FireplaceQu','GarageFinish','GarageQual','GarageCond','PavedDrive','PoolQC','... | age60.groupby(['Pclass','Embarked'] ).mean() | Titanic - Machine Learning from Disaster |
9,059,975 | combine=combine.drop(['YearBuilt','YearRemodAdd','MoSold','YrSold','GarageYrBlt','GarageArea','TotRmsAbvGrd'],axis=1)
combine.shape<categorify> | age60.groupby(['Pclass','Embarked','SibSp','Parch'] ).mean() | Titanic - Machine Learning from Disaster |
9,059,975 | combine=pd.get_dummies(combine)
combine.shape<set_options> | df.loc[df['PassengerId'] == 1044, 'Fare'] = 7.9 | Titanic - Machine Learning from Disaster |
9,059,975 | pd.options.display.max_columns=None
combine.columns.values<count_values> | df[df['Fare'].isnull() ] | Titanic - Machine Learning from Disaster |
9,059,975 | test_raw.GarageType.value_counts()<drop_column> | df[df['PassengerId'] == 1044] | Titanic - Machine Learning from Disaster |
9,059,975 | combine=combine.drop(['MSZoning_None','Street_Pave','Alley_None','LotConfig_Inside','Neighborhood_NAmes','Condition1_Norm','Condition2_Norm','RoofStyle_Gable','RoofMatl_CompShg','Exterior1st_VinylSd','Exterior2nd_VinylSd','MasVnrType_None','Foundation_PConc','Heating_GasA','GarageType_None'],axis=1)
combine.shape<drop... | [df.Fare]=np.round([df.Fare],1 ) | Titanic - Machine Learning from Disaster |
9,059,975 | train=train.drop(['Id'],axis=1 )<drop_column> | print(df['Ticket'].str.split(expand=True)) | Titanic - Machine Learning from Disaster |
9,059,975 | test=combine[combine.SalePrice==0]
test=test.drop(['Id','SalePrice'],axis=1)
test.shape<compute_test_metric> | Ticket = df['Ticket'].str.extract(' (.*)\s (.*)' ) | Titanic - Machine Learning from Disaster |
9,059,975 | def root_mean_squared_log_error(y_valid, y_preds):
if len(y_preds)!=len(y_valid): return 'error_mismatch'
y_preds_new = [math.log(x)for x in y_preds]
y_valid_new = [math.log(x)for x in y_valid]
return mean_squared_error(y_valid_new, y_preds_new, squared=False )<split> | df['Ticket_head']=Ticket[0]
df['Ticket_num']=Ticket[1] | Titanic - Machine Learning from Disaster |
9,059,975 | y=train['SalePrice']
x=train.drop(['SalePrice'],axis=1)
X_train, X_valid, y_train, y_valid=train_test_split(x,y,random_state=73 )<compute_train_metric> | Ticket_head = df['Ticket_head'].apply(lambda x: x.replace(".", "")if type(x)is str else float(x))
Ticket_head=Ticket_head.fillna('0' ) | Titanic - Machine Learning from Disaster |
9,059,975 | RF_f=RandomForestRegressor(bootstrap=False, max_depth=60, max_features='sqrt',
min_samples_split=4, n_estimators=1700)
RF_f.fit(X_train,y_train)
y_pred_RF_f=RF_f.predict(X_valid)
print('RMSLE:', root_mean_squared_log_error(y_valid, y_pred_RF_f))<compute_train_metric> | df['Ticket_head'] = Ticket_head | Titanic - Machine Learning from Disaster |
9,059,975 | XT=ExtraTreesRegressor(n_estimators=1000,random_state=73)
XT.fit(X_train,y_train)
y_pred_XT=XT.predict(X_valid)
print('RMSLE:', root_mean_squared_log_error(y_valid, y_pred_XT))<compute_train_metric> | df['Ticket_num']=df['Ticket_num'].fillna('0')
df['Ticket_num'].value_counts() | Titanic - Machine Learning from Disaster |
9,059,975 | Ada_f=AdaBoostRegressor(learning_rate=0.03, loss='exponential', n_estimators=2300,
random_state=73)
Ada_f.fit(X_train,y_train)
y_pred_Ada_f=Ada_f.predict(X_valid)
print('RMSLE:', root_mean_squared_log_error(y_valid, y_pred_Ada_f))<compute_train_metric> | df[df['Age'].isnull() ] | Titanic - Machine Learning from Disaster |
9,059,975 | GB_f=GradientBoostingRegressor(max_depth=10, max_features='sqrt',
min_samples_split=12, n_estimators=1500)
GB_f.fit(X_train,y_train)
y_pred_GB_f=GB_f.predict(X_valid)
print('RMSLE:', root_mean_squared_log_error(y_valid, y_pred_GB_f))<compute_train_metric> | gc = df.drop(['PassengerId'], axis=1 ) | Titanic - Machine Learning from Disaster |
9,059,975 | HGB_f=HistGradientBoostingRegressor(learning_rate=0.02,
loss='least_absolute_deviation', max_depth=40,
max_iter=750, min_samples_leaf=2,
random_state=73)
HGB_f.fit(X_train,y_train)
y_pred_HGB_f=HGB_f.predict(X_valid)
print('RMSLE:', root_mean_squared_log_error(y_valid, y_pred_HGB_f))<compute_train_metric> | gc['Fare']=pd.cut(gc['Fare'], 5, labels=False)
column = 'Cabin'
gc[column] = gc[column].fillna(0)
gc[column] = gc[column].str.extract('([A-Za-z]+)', expand = False)
column = 'mrms'
gc[column] = gc['Name'].str.extract('([A-Za-z]+)\.', expand = False)
gc[column] = gc[column].replace(['Col', 'Mlle', 'Major','Countess'... | Titanic - Machine Learning from Disaster |
9,059,975 | XGB=XGBRegressor(n_estimators=1200,learning_rate=0.05,random_state=73)
XGB.fit(X_train,y_train)
y_pred_XGB=XGB.predict(X_valid)
print('RMSLE:', root_mean_squared_log_error(y_valid, y_pred_XGB))<train_on_grid> | list_ce = ['Cabin','Embarked','Sex','mrms','Survived','Fare']
ce_ohe = ce.OneHotEncoder(cols=list_ce,handle_unknown='impute')
gc = ce_ohe.fit_transform(gc ) | Titanic - Machine Learning from Disaster |
9,059,975 |
<choose_model_class> | gc = gc.drop(['Name','Ticket'], axis=1 ) | Titanic - Machine Learning from Disaster |
9,059,975 | XGB_f=XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=0.6, gamma=0.5, gpu_id=-1,
importance_type='gain', interaction_constraints='',
learning_rate=0.05, max_delta_step=0, max_depth=4,
min_child_weight=1, monotone_constraints='() ',
n_estimators=1200, n_jobs=0, nu... | gccollist.remove('Age' ) | Titanic - Machine Learning from Disaster |
9,059,975 | XGB_model_f=XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=0.5, gamma=0.2, gpu_id=-1,
importance_type='gain', interaction_constraints='',
learning_rate=0.05, max_delta_step=0, max_depth=4,
min_child_weight=2, monotone_constraints='() ',
n_estimators=1000, n_jobs... | gc.isnull().sum() | Titanic - Machine Learning from Disaster |
9,059,975 | LGBg_f=LGBMRegressor(objective='regression',num_leaves=5,
learning_rate=0.05, n_estimators=720,
max_bin = 55, bagging_fraction = 0.8,
bagging_freq = 5, feature_fraction = 0.2319,
feature_fraction_seed=9, bagging_seed=9,
min_data_in_leaf =6, min_sum_hessian_in_leaf = 11)
LGBg_f.fit(X_train,y_train)
y_pred_LGBg_f=LGBg_... | gc['Age']=gc['Age'].fillna(gc.groupby(gccollist)['Age'].transform('mean')) | Titanic - Machine Learning from Disaster |
9,059,975 | Vote=VotingRegressor([('gb',GB_f),('hgb',HGB_f),('xgb',XGB_f),('lgb',LGBg_f)])
Vote.fit(X_train,y_train)
y_pred_Vote=Vote.predict(X_valid)
print('RMSLE:', root_mean_squared_log_error(y_valid, y_pred_Vote))<compute_train_metric> | gc.Age.isnull().sum() | Titanic - Machine Learning from Disaster |
9,059,975 | estimators=[('gb',GB_f),('hgb',HGB_f),('xgb',XGB_f),('lgb',LGBg_f)]
Stack=StackingRegressor(estimators=estimators,final_estimator=RandomForestRegressor(n_estimators=800,random_state=42))
Stack.fit(X_train,y_train)
y_pred_Stack=Stack.predict(X_valid)
print('RMSLE:', root_mean_squared_log_error(y_valid, y_pred_Stack))<... | gc[gc['Age'].isnull() ] | Titanic - Machine Learning from Disaster |
9,059,975 | Voting=VotingRegressor([('gb',GB_f),('hgb',HGB_f),('xgb',XGB_f),('lgb',LGBg_f)])
Voting.fit(x,y )<save_to_csv> | gc_corr = gc.corr()
corr_y = pd.DataFrame({"features":gc.columns,"corr_y":gc_corr["Age"]},index=None)
corr_y = corr_y.reset_index(drop=True ) | Titanic - Machine Learning from Disaster |
9,059,975 | preds = Voting.predict(test)
output = pd.DataFrame({'Id': test_raw.Id,
'SalePrice': preds})
output.to_csv('submission.csv', index=False )<compute_test_metric> | corr_y.sort_values('corr_y' ) | Titanic - Machine Learning from Disaster |
9,059,975 | def root_mean_squared_log_error(y_valid, y_preds):
if len(y_preds)!=len(y_valid): return 'error_mismatch'
y_preds_new = [math.log(x)for x in y_preds]
y_valid_new = [math.log(x)for x in y_valid]
return mean_squared_error(y_valid_new, y_preds_new, squared=False )<load_from_csv> | gc['Age'] = np.round(gc['Age'].fillna(gc['Age'].mean())) | Titanic - Machine Learning from Disaster |
9,059,975 | train_data = pd.read_csv('.. /input/house-prices-advanced-regression-techniques/train.csv')
pd.set_option('display.max_columns', None)
train_data.head()<count_unique_values> | gc.Age.isnull().sum() | Titanic - Machine Learning from Disaster |
9,059,975 | print(train_data.columns[train_data.isna().any() ].unique())
len(train_data.columns[train_data.isna().any() ].unique() )<prepare_x_and_y> | df['Age']=gc['Age'] | Titanic - Machine Learning from Disaster |
9,059,975 | features = [x for x in train_data.columns if x not in ['SalePrice']]
X = train_data.drop(['SalePrice'], axis=1)
Y = train_data['SalePrice']<split> | df['Age']=pd.cut(df['Age'], 5, labels=False ) | Titanic - Machine Learning from Disaster |
9,059,975 | X_train, X_valid, y_train, y_valid = train_test_split(X, Y, random_state=42)
numerical_cols = [cname for cname in X_train.columns if
X_train[cname].dtype in ['int64', 'float64']]
categorical_cols = [cname for cname in X_train.columns if
X_train[cname].nunique() < 13 and
X_train[cname].dtype == "object"]
numerical_tran... | df.isnull().sum() | Titanic - Machine Learning from Disaster |
9,059,975 | random_model = RandomForestRegressor(random_state=42, n_estimators=1000)
random_clf = Pipeline(steps=[('preprocessor', preprocessor),
('random_model', random_model)
])
random_clf.fit(X_train, y_train)
random_clf.fit(X_train, y_train)
random_preds = random_clf.predict(X_valid)
print('RMSLE:', root_mean_squared_lo... | pip install --upgrade optuna | Titanic - Machine Learning from Disaster |
9,059,975 | xgb_model = XGBRegressor(n_estimators=1000, learning_rate=0.01, random_state=42)
xgb_clf = Pipeline(steps=[('preprocessor', preprocessor),
('xgb_model', xgb_model)
])
xgb_clf.fit(X_train, y_train, xgb_model__verbose=False)
xgb_clf.fit(X_train, y_train)
xgb_preds = xgb_clf.predict(X_valid)
print('RMSLE:', root_me... | gc=df | Titanic - Machine Learning from Disaster |
9,059,975 | ada_model = AdaBoostRegressor(random_state=42, learning_rate=0.01, n_estimators=1000)
ada_clf = Pipeline(steps=[('preprocessor', preprocessor),
('xgb_model', ada_model)
])
ada_clf.fit(X_train, y_train)
ada_clf.fit(X_train, y_train)
ada_preds = ada_clf.predict(X_valid)
print('RMSLE:', root_mean_squared_log_error(... | gc['Fare']=pd.cut(gc['Fare'], 5, labels=False)
column = 'Cabin'
gc[column] = gc[column].fillna(0)
gc[column] = gc[column].str.extract('([A-Za-z]+)', expand = False)
column = 'mrms'
gc[column] = gc['Name'].str.extract('([A-Za-z]+)\.', expand = False)
gc[column] = gc[column].replace(['Col', 'Mlle', 'Major','Countess'... | Titanic - Machine Learning from Disaster |
9,059,975 | train_data['OverallQual'].isnull().sum()<count_missing_values> | train=gc | Titanic - Machine Learning from Disaster |
9,059,975 | train_data['OverallCond'].isnull().sum()<count_missing_values> | test_x = train[train['Survived'].isnull() ]
test_x = test_x.drop(['Survived'], axis=1)
df_result_dropna = train.dropna(subset=['Survived'])
feature_names = df_result_dropna.drop(['Survived'], axis=1)
feature_names = list(feature_names.columns)
train_y = df_result_dropna['Survived'].astype(int)
train_x = df_result_... | Titanic - Machine Learning from Disaster |
9,059,975 | train_data['YearBuilt'].isnull().sum()<count_missing_values> | tr_x, va_x, tr_y, va_y = train_test_split(train_x, train_y,random_state=42,test_size=0.2 ) | Titanic - Machine Learning from Disaster |
9,059,975 | train_data['YearRemodAdd'].isnull().sum()<count_missing_values> | import optuna.integration.lightgbm as lgb
from sklearn.metrics import accuracy_score,f1_score
from sklearn.metrics import confusion_matrix | Titanic - Machine Learning from Disaster |
9,059,975 | train_data['TotalBsmtSF'].isnull().sum()<filter> | best_params = {}
tuning_history = []
params = {'objective': 'binary','metric': 'binary_logloss'}
trn_data= lgb.Dataset(tr_x, label=tr_y)
val_data= lgb.Dataset(va_x, label=va_y)
model = lgb.train(params, trn_data,
valid_sets=[trn_data, val_data],
verbose_eval=0,
best_params=best_params,
tuning_history=tuning_history
) | Titanic - Machine Learning from Disaster |
9,059,975 | Q1 = train_data['TotalBsmtSF'].quantile(0.25)
Q3 = train_data['TotalBsmtSF'].quantile(0.75)
IQR = Q3 - Q1
outliers = train_data.loc[(train_data['TotalBsmtSF'] >(Q3 + 1.75 * IQR)) |(train_data['TotalBsmtSF'] <(Q1 - 1.75 * IQR)) , 'TotalBsmtSF']
print("Percent of Outliers: ", outliers.count() / train_data['TotalBsmtSF'... | prediction = np.rint(model.predict(va_x, num_iteration=model.best_iteration)) | Titanic - Machine Learning from Disaster |
9,059,975 | train_data.drop(train_data.loc[(train_data['TotalBsmtSF'] >(Q3 + 1.75 * IQR)) |(train_data['TotalBsmtSF'] <(Q1 - 1.75 * IQR)) ].index, inplace=True)
train_data.shape<count_missing_values> | accuracy = accuracy_score(va_y, prediction)
best_params = model.params
print("Best params:", best_params)
print("Accuracy = {}".format(accuracy))
print("Params: ")
for key, value in best_params.items() :
print(" {}: {}".format(key, value)) | Titanic - Machine Learning from Disaster |
9,059,975 | train_data['1stFlrSF'].isnull().sum()<filter> | pred_x = np.rint(model.predict(test_x, num_iteration=model.best_iteration)) | Titanic - Machine Learning from Disaster |
9,059,975 | <drop_column><EOS> | df_sub['Survived'] = pred_x.astype(int)
df_sub.to_csv('df_sub_pred_x.csv', index=False ) | Titanic - Machine Learning from Disaster |
9,067,724 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<count_missing_values> | \
%matplotlib inline
| Titanic - Machine Learning from Disaster |
9,067,724 | train_data['GrLivArea'].isnull().sum()<filter> | train_df = pd.read_csv('/kaggle/input/train.csv')
test_df = pd.read_csv('/kaggle/input/test.csv')
combine = [train_df, test_df] | Titanic - Machine Learning from Disaster |
9,067,724 | Q1 = train_data['GrLivArea'].quantile(0.25)
Q3 = train_data['GrLivArea'].quantile(0.75)
IQR = Q3 - Q1
outliers = train_data.loc[(train_data['GrLivArea'] >(Q3 + 1.75 * IQR)) |(train_data['GrLivArea'] <(Q1 - 1.75 * IQR)) , 'GrLivArea']
print("Percent of Outliers: ", outliers.count() / train_data['GrLivArea'].count() * ... | train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False ).mean().sort_values(by='Survived', ascending=False ) | Titanic - Machine Learning from Disaster |
9,067,724 | train_data['FullBath'].isnull().sum()<count_missing_values> | train_df[["Sex", "Survived"]].groupby(['Sex'], as_index=False ).mean().sort_values(by='Survived', ascending=False ) | Titanic - Machine Learning from Disaster |
9,067,724 | train_data['TotRmsAbvGrd'].isnull().sum()<count_missing_values> | train_df[["SibSp", "Survived"]].groupby(['SibSp'], as_index=False ).mean().sort_values(by='Survived', ascending=False ) | Titanic - Machine Learning from Disaster |
9,067,724 | train_data['GarageCars'].isnull().sum()<count_missing_values> | train_df[["Parch", "Survived"]].groupby(['Parch'], as_index=False ).mean().sort_values(by='Survived', ascending=False ) | Titanic - Machine Learning from Disaster |
9,067,724 | train_data['GarageArea'].isnull().sum()<filter> | for dataset in combine:
dataset['Title'] = dataset.Name.str.extract('([A-Za-z]+)\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'] ) | Titanic - Machine Learning from Disaster |
9,067,724 | Q1 = train_data['GarageArea'].quantile(0.25)
Q3 = train_data['GarageArea'].quantile(0.75)
IQR = Q3 - Q1
outliers = train_data.loc[(train_data['GarageArea'] >(Q3 + 1.75 * IQR)) |(train_data['GarageArea'] <(Q1 - 1.75 * IQR)) , 'GarageArea']
print("Percent of Outliers: ", outliers.count() / train_data['GarageArea'].coun... | for dataset in combine:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\
'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['T... | Titanic - Machine Learning from Disaster |
9,067,724 | train_data.drop(train_data.loc[(train_data['GarageArea'] >(Q3 + 1.75 * IQR)) |(train_data['GarageArea'] <(Q1 - 1.75 * IQR)) ].index, inplace=True)
train_data.shape<train_on_grid> | title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Rare": 5}
for dataset in combine:
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
train_df.head() | Titanic - Machine Learning from Disaster |
9,067,724 |
<choose_model_class> | train_df = train_df.drop(['Name', 'PassengerId'], axis=1)
test_df = test_df.drop(['Name'], axis=1)
combine = [train_df, test_df]
train_df.shape, test_df.shape | Titanic - Machine Learning from Disaster |
9,067,724 | hp_model = XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=0.6, gamma=0.5, gpu_id=-1,
importance_type='gain', interaction_constraints='',
learning_rate=0.02, max_delta_step=0, max_depth=4,
min_child_weight=1, monotone_constraints='() ',
n_estimators=1000, n_jobs=... | for dataset in combine:
dataset['Sex'] = dataset['Sex'].map({'female': 1, 'male': 0} ).astype(int)
train_df.head() | Titanic - Machine Learning from Disaster |
9,067,724 | X = train_data.drop(['SalePrice'], axis=1)
y = train_data.SalePrice<data_type_conversions> | guess_ages = np.zeros(( 2,3))
guess_ages | Titanic - Machine Learning from Disaster |
9,067,724 | X.columns.to_list()<feature_engineering> | for dataset in combine:
for i in range(0, 2):
for j in range(0, 3):
guess_df = dataset[(dataset['Sex'] == i)& \
(dataset['Pclass'] == j+1)]['Age'].dropna()
age_guess = guess_df.median()
guess_ages[i,j] = int(age_guess/0.5 + 0.5)* 0.5
for i in range(0, 2):
for j in range(0, 3):
dataset.loc[(dataset.Age.isnull())&(datas... | Titanic - Machine Learning from Disaster |
9,067,724 | X_feat_eng = X.copy()
X_feat_eng['years_since_update'] = X_feat_eng['YearRemodAdd'] - X_feat_eng['YearBuilt']
X_feat_eng['geometry'] = X_feat_eng['LotArea'] / X_feat_eng['LotFrontage']
X_feat_eng['land_topology'] = X_feat_eng['LandSlope'] + '_' + X_feat_eng['LandContour']
X_feat_eng['value_proposition'] = X_feat_eng['Y... | for dataset in combine:
dataset.loc[ dataset['Age'] <= 24, 'Age'] = 0
dataset.loc[(dataset['Age'] > 24)&(dataset['Age'] <= 32), 'Age'] = 1
dataset.loc[(dataset['Age'] > 32)&(dataset['Age'] <= 80), 'Age'] = 2
train_df.head() | Titanic - Machine Learning from Disaster |
9,067,724 | X_test = pd.read_csv('.. /input/house-prices-advanced-regression-techniques/test.csv' )<feature_engineering> | train_df = train_df.drop(['AgeBand'], axis=1)
combine = [train_df, test_df]
train_df.head() | Titanic - Machine Learning from Disaster |
9,067,724 | X_test['years_since_update'] = X_test['YearRemodAdd'] - X_test['YearBuilt']
X_test['geometry'] = X_test['LotArea'] / X_test['LotFrontage']
X_test['land_topology'] = X_test['LandSlope'] + '_' + X_test['LandContour']
X_test['value_proposition'] = X_test['YearBuilt'] * X_test['OverallQual']
X_test['finished_basement'] = X... | for i in combine:
i['Fam_Size'] = np.where(( i['SibSp']+i['Parch'])== 0 , 0,
np.where(( i['SibSp']+i['Parch'])<= 3,1,2))
del i['SibSp']
del i['Parch']
| Titanic - Machine Learning from Disaster |
9,067,724 | preds = feature_clf.predict(X_test)
output = pd.DataFrame({'Id': X_test.Id,
'SalePrice': preds})
output.to_csv('submission.csv', index=False )<load_from_csv> | Titanic - Machine Learning from Disaster | |
9,067,724 | train = pd.read_csv('.. /input/ames-housing-dataset/AmesHousing.csv')
train.drop(['PID'], axis=1, inplace=True)
origin = pd.read_csv('.. /input/house-prices-advanced-regression-techniques/train.csv')
train.columns = origin.columns
test = pd.read_csv('.. /input/house-prices-advanced-regression-techniques/test.csv')
... | train_df.head() | Titanic - Machine Learning from Disaster |
9,067,724 | missing = test.isnull().sum()
missing = missing[missing>0]
train.drop(missing.index, axis=1, inplace=True)
train.drop(['Electrical'], axis=1, inplace=True)
test.dropna(axis=1, inplace=True)
test.drop(['Electrical'], axis=1, inplace=True )<feature_engineering> | Titanic - Machine Learning from Disaster | |
9,067,724 | l_test = tqdm(range(0, len(test)) , desc='Matching')
for i in l_test:
for j in range(0, len(train)) :
for k in range(1, len(test.columns)) :
if test.iloc[i,k] == train.iloc[j,k]:
continue
else:
break
else:
submission.iloc[i, 1] = train.iloc[j, -1]
break
l_test.close()<save_to_csv> | freq_port = train_df.Embarked.dropna().mode() [0]
freq_port | Titanic - Machine Learning from Disaster |
9,067,724 | submission.to_csv('result-with-best.csv', index=False )<import_modules> | for dataset in combine:
dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)
train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False ).mean().sort_values(by='Survived', ascending=False ) | Titanic - Machine Learning from Disaster |
9,067,724 | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import OneHotEncoder, LabelEncoder, StandardScaler, MinMaxScaler, RobustScaler
from sklearn.neighbors import KNeighborsRegressor
from sklearn.feature_selection import RFE, SelectPercentile, f_regressi... | for dataset in combine:
dataset['Embarked'] = dataset['Embarked'].map({'S': 0, 'C': 1, 'Q': 2} ).astype(int)
train_df.head() | Titanic - Machine Learning from Disaster |
9,067,724 | random_state = 831<load_from_csv> | test_df['Fare'].fillna(test_df['Fare'].dropna().median() , inplace=True)
test_df.head() | Titanic - Machine Learning from Disaster |
9,067,724 | data_train = pd.read_csv(".. /input/house-prices-advanced-regression-techniques/train.csv" ).set_index("Id")
data_test = pd.read_csv(".. /input/house-prices-advanced-regression-techniques/test.csv" ).set_index("Id")
data = pd.concat([data_train, data_test] )<feature_engineering> | for dataset in combine:
dataset.loc[ dataset['Fare'] <= 128, 'Fare'] = 0
dataset.loc[(dataset['Fare'] > 128)&(dataset['Fare'] <= 256.2), 'Fare'] = 1
dataset.loc[(dataset['Fare'] > 265.2)&(dataset['Fare'] <= 384.3), 'Fare'] = 2
dataset.loc[ dataset['Fare'] > 384.3, 'Fare'] = 3
dataset['Fare'] = dataset['Fare'].astype(in... | Titanic - Machine Learning from Disaster |
9,067,724 | def expand_categorical(data, features):
d = data[features]
values = np.unique(d.values.flatten() ).tolist()
result = pd.DataFrame(index=data.index, columns=values)
for i, row in d.iterrows() :
v = np.unique(row.values.flatten() ).tolist()
result.loc[i,v] = 1
result.fillna(0, inplace=True)
result.columns = [features[0... | train_df = train_df.drop(['Embarked', 'Title'], axis=1)
test_df = test_df.drop(['Embarked', 'Title'], axis=1)
combine = [train_df, test_df]
train_df.head() | Titanic - Machine Learning from Disaster |
9,067,724 | analyse_numeric("SalePrice" )<feature_engineering> | train_df = train_df.drop(['Pclass'], axis=1)
test_df = test_df.drop(['Pclass'], axis=1)
combine = [train_df, test_df]
train_df.head() | Titanic - Machine Learning from Disaster |
9,067,724 | data["SalePrice"] = np.log(data["SalePrice"] )<data_type_conversions> | X_train = train_df.drop("Survived", axis=1)
Y_train = train_df["Survived"]
X_test = test_df.drop("PassengerId", axis=1 ).copy()
X_train.shape, Y_train.shape, X_test.shape | Titanic - Machine Learning from Disaster |
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