kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
449,069 | test['item_cnt_month'] = 0<concatenate> | def mytree(df):
Model = pd.DataFrame(data = {'Predict':[]})
male_title = ['Master']
for index, row in df.iterrows() :
Model.loc[index, 'Predict'] = 0
if(df.loc[index, 'Sex'] == 'female'):
Model.loc[index, 'Predict'] = 1
if(( df.loc[index, 'Sex'] == 'female')&
(df.loc[index, 'Pclass'] == 3)&
(df.loc[index, 'Embarked'... | Titanic - Machine Learning from Disaster |
449,069 | new_train = new_train.append(test.drop(['ID'], axis=1))<merge> | dtree = tree.DecisionTreeClassifier(random_state = 0)
base_results = model_selection.cross_validate(dtree, data1[data1_x_bin], data1[Target], cv = cv_split)
dtree.fit(data1[data1_x_bin], data1[Target])
print('BEFORE DT Parameters: ', dtree.get_params())
print("BEFORE DT Training w/bin score mean: {:.2f}".format(bas... | Titanic - Machine Learning from Disaster |
449,069 | new_train = pd.merge(new_train, shop, on=['shop_id'], how='left')
new_train.head()<merge> | print('BEFORE DT RFE Training Shape Old: ', data1[data1_x_bin].shape)
print('BEFORE DT RFE Training Columns Old: ', data1[data1_x_bin].columns.values)
print("BEFORE DT RFE Training w/bin score mean: {:.2f}".format(base_results['train_score'].mean() *100))
print("BEFORE DT RFE Test w/bin score mean: {:.2f}".format(bas... | Titanic - Machine Learning from Disaster |
449,069 | new_train = pd.merge(new_train, items.drop('item_name', axis = 1), on=['item_id'], how='left')
new_train.head()<merge> | dot_data = tree.export_graphviz(dtree, out_file=None,
feature_names = data1_x_bin, class_names = True,
filled = True, rounded = True)
graph = graphviz.Source(dot_data)
graph | Titanic - Machine Learning from Disaster |
449,069 | new_train = pd.merge(new_train, item_cat.drop('item_category_name', axis = 1), on=['item_category_id'], how='left')
new_train.head()<merge> | vote_est = [
('ada', ensemble.AdaBoostClassifier()),
('bc', ensemble.BaggingClassifier()),
('etc',ensemble.ExtraTreesClassifier()),
('gbc', ensemble.GradientBoostingClassifier()),
('rfc', ensemble.RandomForestClassifier()),
('gpc', gaussian_process.GaussianProcessClassifier()),
('lr', linear_model.LogisticRegres... | Titanic - Machine Learning from Disaster |
449,069 | def generate_lag(train, months, lag_column):
for month in months:
train_shift = train[['date_block_num', 'shop_id', 'item_id', lag_column]].copy()
train_shift.columns = ['date_block_num', 'shop_id', 'item_id', lag_column+'_lag_'+ str(month)]
train_shift['date_block_num'] += month
train = pd.merge(train, train_shift, on... | grid_n_estimator = [50,100,300]
grid_ratio = [.1,.25,.5,.75,1.0]
grid_learn = [.01,.03,.05,.1,.25]
grid_max_depth = [2,4,6,None]
grid_min_samples = [5,10,.03,.05,.10]
grid_criterion = ['gini', 'entropy']
grid_bool = [True, False]
grid_seed = [0]
vote_param = [{
'ada__n_estimators': grid_n_estimator,
'ada__learning_rate... | Titanic - Machine Learning from Disaster |
449,069 | new_train = downcast_dtypes(new_train )<set_options> | grid_n_estimator = [10, 50, 100, 300]
grid_ratio = [.1,.25,.5,.75, 1.0]
grid_learn = [.01,.03,.05,.1,.25]
grid_max_depth = [2, 4, 6, 8, 10, None]
grid_min_samples = [5, 10,.03,.05,.10]
grid_criterion = ['gini', 'entropy']
grid_bool = [True, False]
grid_seed = [0]
grid_param = [
[{
'n_estimators': grid_n_estimator,
'lea... | Titanic - Machine Learning from Disaster |
449,069 | gc.collect()
<categorify> | grid_hard = ensemble.VotingClassifier(estimators = vote_est , voting = 'hard')
grid_hard_cv = model_selection.cross_validate(grid_hard, data1[data1_x_bin], data1[Target], cv = cv_split)
grid_hard.fit(data1[data1_x_bin], data1[Target])
print("Hard Voting w/Tuned Hyperparameters Training w/bin score mean: {:.2f}".form... | Titanic - Machine Learning from Disaster |
449,069 | %%time
new_train = generate_lag(new_train, [1, 2, 3, 4, 5, 6, 12], 'item_cnt_month' )<merge> | print(data_val.info())
print("-"*10)
data_val['Survived'] = mytree(data_val ).astype(int)
data_val['Survived'] = grid_hard.predict(data_val[data1_x_bin])
submit = data_val[['PassengerId','Survived']]
submit.to_csv(".. /working/submit.csv", index=False)
print('Validation Data Distribution:
', data_val['Survived'].v... | Titanic - Machine Learning from Disaster |
5,206,845 | %%time
group = new_train.groupby(['date_block_num', 'item_id'])['item_cnt_month'].mean().rename('item_month_mean' ).reset_index()
new_train = pd.merge(new_train, group, on=['date_block_num', 'item_id'], how='left')
new_train = generate_lag(new_train, [1,2,3,4,5,6,12], 'item_month_mean')
new_train.drop(['item_month_me... | import pandas as pd
import numpy as np
from scipy.stats import mode
from sklearn.svm import SVC
from sklearn import svm
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier | Titanic - Machine Learning from Disaster |
5,206,845 | %%time
group = new_train.groupby(['date_block_num', 'shop_id'])['item_cnt_month'].mean().rename('shop_month_mean' ).reset_index()
new_train = pd.merge(new_train, group, on=['date_block_num', 'shop_id'], how='left')
new_train = generate_lag(new_train, [1,2,3,6,12], 'shop_month_mean')
new_train.drop(['shop_month_mean']... | titanic=pd.read_csv("/kaggle/input/train.csv")
df=titanic.copy()
df.head() | Titanic - Machine Learning from Disaster |
5,206,845 | %%time
group = new_train.groupby(['date_block_num', 'shop_id', 'item_category_id'])['item_cnt_month'].mean().rename('item_category_month_mean' ).reset_index()
new_train = pd.merge(new_train, group, on=['date_block_num', 'shop_id', 'item_category_id'], how='left')
new_train = generate_lag(new_train, [1, 2], 'item_categ... | df.isnull().sum() | Titanic - Machine Learning from Disaster |
5,206,845 | %%time
group = new_train.groupby(['date_block_num', 'main_category_id'])['item_cnt_month'].mean().rename('main_category_month_mean' ).reset_index()
new_train = pd.merge(new_train, group, on=['date_block_num', 'main_category_id'], how='left')
new_train = generate_lag(new_train, [1], 'main_category_month_mean')
new_tra... | test=pd.read_csv("/kaggle/input/test.csv")
test_df=test.copy()
test_df.isnull().sum() | Titanic - Machine Learning from Disaster |
5,206,845 | %%time
group = new_train.groupby(['date_block_num', 'sub_category_id'])['item_cnt_month'].mean().rename('sub_category_month_mean' ).reset_index()
new_train = pd.merge(new_train, group, on=['date_block_num', 'sub_category_id'], how='left')
new_train = generate_lag(new_train, [1], 'sub_category_month_mean')
new_train.d... | df["Initial"]=df["Name"].str.extract('([A-Za-z]+)\.')
print(df["Initial"].unique())
df["Initial"].replace(['Mlle','Mme','Ms','Dr','Major','Lady','Countess','Jonkheer','Col','Rev','Capt','Sir','Don'],
['Miss','Miss','Miss','Mr','Mr','Mrs','Mrs','Other','Other','Other','Mr','Mr','Mr'],inplace=True ) | Titanic - Machine Learning from Disaster |
5,206,845 | new_train = downcast_dtypes(new_train )<import_modules> | df.groupby("Initial")["Age"].mean() | Titanic - Machine Learning from Disaster |
5,206,845 | import xgboost as xgb<filter> | df.loc[(df.Age.isnull())&(df.Initial=='Master'),"Age"]=5
df.loc[(df.Age.isnull())&(df.Initial=='Miss'),"Age"]=22
df.loc[(df.Age.isnull())&(df.Initial=='Mr'),"Age"]=33
df.loc[(df.Age.isnull())&(df.Initial=='Mrs'),"Age"]=36
df.loc[(df.Age.isnull())&(df.Initial=='Other'),"Age"]=46 | Titanic - Machine Learning from Disaster |
5,206,845 | new_train = new_train[new_train.date_block_num > 11]<set_options> | print(df.Embarked.mode())
df.Embarked.fillna("S",inplace=True ) | Titanic - Machine Learning from Disaster |
5,206,845 | gc.collect()<data_type_conversions> | df["Family_size"]=df["SibSp"]+df["Parch"]
df["Alone"]=0
df.loc[df.Family_size==0,"Alone"]=1 | Titanic - Machine Learning from Disaster |
5,206,845 | def fill_na(df):
for col in df.columns:
if('_lag_' in col)&(df[col].isnull().any()):
df[col].fillna(0, inplace=True)
return df<correct_missing_values> | df['Age_band']=0
df.loc[df['Age']<=16,'Age_band']=0
df.loc[(df['Age']>16)&(df['Age']<=32),'Age_band']=1
df.loc[(df['Age']>32)&(df['Age']<=48),'Age_band']=2
df.loc[(df['Age']>48)&(df['Age']<=64),'Age_band']=3
df.loc[df['Age']>64,'Age_band']=4 | Titanic - Machine Learning from Disaster |
5,206,845 | new_train = fill_na(new_train)
<train_model> | df['Fare_cat']=0
df.loc[df['Fare']<=7.91,'Fare_cat']=0
df.loc[(df['Fare']>7.91)&(df['Fare']<=14.454),'Fare_cat']=1
df.loc[(df['Fare']>14.454)&(df['Fare']<=31),'Fare_cat']=2
df.loc[(df['Fare']>31)&(df['Fare']<=513),'Fare_cat']=3 | Titanic - Machine Learning from Disaster |
5,206,845 | def xgtrain() :
regressor = xgb.XGBRegressor(n_estimators = 5000,
learning_rate = 0.01,
max_depth = 10,
subsample = 0.5,
colsample_bytree = 0.5)
regressor_ = regressor.fit(new_train[new_train.date_block_num < 33].drop(['item_cnt_month'], axis=1 ).values,
new_train[new_train.date_block_num < 33]['item_cnt_month'].value... | df['Sex'].replace(['male','female'],[0,1],inplace=True)
df['Embarked'].replace(['S','C','Q'],[0,1,2],inplace=True)
df['Initial'].replace(['Mr','Mrs','Miss','Master','Other'],[0,1,2,3,4],inplace=True ) | Titanic - Machine Learning from Disaster |
5,206,845 | %%time
regressor_ = xgtrain()<predict_on_test> | df.drop(['Name','Age','Ticket','Fare','Cabin','Fare_Range','PassengerId'],axis=1,inplace=True ) | Titanic - Machine Learning from Disaster |
5,206,845 | predictions = regressor_.predict(new_train[new_train.date_block_num == 34].drop(['item_cnt_month'], axis = 1 ).values )<load_from_csv> | df.isnull().sum() | Titanic - Machine Learning from Disaster |
5,206,845 | submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv' )<feature_engineering> | test_df.loc[(test_df.Age.isnull())&(test_df.Initial=='Master'),"Age"]=5
test_df.loc[(test_df.Age.isnull())&(test_df.Initial=='Miss'),"Age"]=22
test_df.loc[(test_df.Age.isnull())&(test_df.Initial=='Mr'),"Age"]=33
test_df.loc[(test_df.Age.isnull())&(test_df.Initial=='Mrs'),"Age"]=36
test_df.loc[(test_df.Age.isnull())&(te... | Titanic - Machine Learning from Disaster |
5,206,845 | submission['item_cnt_month'] = predictions<save_to_csv> | test_df["Family_size"]=test_df["SibSp"]+test_df["Parch"]
test_df["Alone"]=0
test_df.loc[df.Family_size==0,"Alone"]=1 | Titanic - Machine Learning from Disaster |
5,206,845 | submission.to_csv('saleslearn.csv', index=False )<set_options> | test_df['Age_band']=0
test_df.loc[test_df['Age']<=16,'Age_band']=0
test_df.loc[(test_df['Age']>16)&(test_df['Age']<=32),'Age_band']=1
test_df.loc[(test_df['Age']>32)&(test_df['Age']<=48),'Age_band']=2
test_df.loc[(test_df['Age']>48)&(test_df['Age']<=64),'Age_band']=3
test_df.loc[test_df['Age']>64,'Age_band']=4 | Titanic - Machine Learning from Disaster |
5,206,845 | %matplotlib inline<load_from_csv> | test_df[test_df.Fare.isnull() ] | Titanic - Machine Learning from Disaster |
5,206,845 | items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
train = pd.read_csv('/k... | test_df['Fare_cat']=0
test_df.loc[test_df['Fare']<=7.91,'Fare_cat']=0
test_df.loc[(test_df['Fare']>7.91)&(test_df['Fare']<=14.454),'Fare_cat']=1
test_df.loc[(test_df['Fare']>14.454)&(test_df['Fare']<=31),'Fare_cat']=2
test_df.loc[(test_df['Fare']>31)&(test_df['Fare']<=513),'Fare_cat']=3 | Titanic - Machine Learning from Disaster |
5,206,845 | train['item_id'].value_counts(ascending = False)[:5]<filter> | test_df['Sex'].replace(['male','female'],[0,1],inplace=True)
test_df['Embarked'].replace(['S','C','Q'],[0,1,2],inplace=True)
test_df['Initial'].replace(['Mr','Mrs','Miss','Master','Other'],[0,1,2,3,4],inplace=True ) | Titanic - Machine Learning from Disaster |
5,206,845 | items.loc[items.item_id == 20949]<filter> | test_df.drop(['Name','Age','Ticket','Fare','Cabin','PassengerId'],axis=1,inplace=True ) | Titanic - Machine Learning from Disaster |
5,206,845 | categories.loc[categories.item_category_id == 71]<sort_values> | test_df.isnull().sum() | Titanic - Machine Learning from Disaster |
5,206,845 | train['item_cnt_day'].sort_values(ascending=False)[:5]<filter> | ytrain=df["Survived"]
del df["Survived"] | Titanic - Machine Learning from Disaster |
5,206,845 | train[train.item_cnt_day == 2169]<filter> | xtrain=df.values
xtest=test_df.values | Titanic - Machine Learning from Disaster |
5,206,845 | items[items.item_id == 11373]<filter> | n_estimators=list(range(100,1100,100))
learn_rate=[0.05,0.1,0.2,0.3,0.25,0.4,0.5,0.6,0.7,0.8,0.9,1]
hyper={'n_estimators':n_estimators,'learning_rate':learn_rate}
gd=GridSearchCV(estimator=AdaBoostClassifier() ,param_grid=hyper,verbose=True)
gd.fit(xtrain,ytrain)
print(gd.best_score_)
print(gd.best_estimator_ ) | Titanic - Machine Learning from Disaster |
5,206,845 | train = train[train.item_cnt_day < 2000]<sort_values> | clf=AdaBoostClassifier(n_estimators=200,random_state=0,learning_rate=0.05)
clf.fit(xtrain,ytrain)
print(clf.score(xtrain,ytrain))
ypred=clf.predict(xtest ) | Titanic - Machine Learning from Disaster |
5,206,845 | train['item_price'].sort_values(ascending = False)[:5]<filter> | submission = pd.DataFrame({
"PassengerId": test["PassengerId"],
"Survived": ypred
})
submission.to_csv('submission.csv', index=False ) | Titanic - Machine Learning from Disaster |
3,999,796 | train[train.item_price == 307980]<filter> | def create_download_link(df, title = "Download CSV file", filename = "data.csv"):
csv = df.to_csv()
b64 = base64.b64encode(csv.encode())
payload = b64.decode()
html = '<a download="{filename}" href="data:text/csv;base64,{payload}" target="_blank">{title}</a>'
html = html.format(payload=payload,title=title,filename=fil... | Titanic - Machine Learning from Disaster |
3,999,796 | items[items.item_id == 6066]<filter> | train_data = pd.read_csv(".. /input/train.csv")
test_data = pd.read_csv(".. /input/test.csv" ) | Titanic - Machine Learning from Disaster |
3,999,796 | train[train.item_id == 6066]<filter> | passenger_id = test_data["PassengerId"] | Titanic - Machine Learning from Disaster |
3,999,796 | train = train[train.item_price < 300000]<sort_values> | train_data[['SibSp','Survived']].groupby(['SibSp'],as_index=False ).count().sort_values(by='Survived',ascending=False)
| Titanic - Machine Learning from Disaster |
3,999,796 | train['item_price'].sort_values() [:5]<filter> | train_data[['Parch','Survived']].groupby(['Parch'],as_index=False ).count().sort_values(by='Survived',ascending=False)
| Titanic - Machine Learning from Disaster |
3,999,796 | train[train.item_price == -1]<feature_engineering> | train_data[['Pclass','Survived']].groupby(['Pclass'],as_index=False ).count().sort_values(by='Survived',ascending=False)
| Titanic - Machine Learning from Disaster |
3,999,796 | price_correction = train[(train.shop_id == 32)&(train.item_id == 2973)&(train.date_block_num ==4)&(train.item_price > 0)].item_price.median()
train.loc[train.item_price<0, 'item_price'] = price_correction<count_unique_values> | train_data[['Embarked','Survived']].groupby(['Embarked'],as_index=False ).count().sort_values(by='Survived',ascending=False)
| Titanic - Machine Learning from Disaster |
3,999,796 | shops_train = train.shop_id.nunique()
shops_test = test.shop_id.nunique()
print("Shops in training set = ", shops_train)
print("Shops in test set = ", shops_test )<feature_engineering> | train_data[['Age','Survived']].groupby(['Age'],as_index=False ).count().sort_values(by='Survived',ascending=False ).head()
| Titanic - Machine Learning from Disaster |
3,999,796 | shops['city'] = shops['shop_name'].str.split(' ' ).map(lambda x: x[0] )<categorify> | target_variable = train_data["Survived"]
train_data.drop(["Survived"], axis = 1, inplace=True ) | Titanic - Machine Learning from Disaster |
3,999,796 | LE = preprocessing.LabelEncoder()
LE.fit_transform(shops['city'] )<categorify> | all_data = pd.concat([train_data, test_data], axis = 0 ) | Titanic - Machine Learning from Disaster |
3,999,796 | shops['city_label'] = LE.fit_transform(shops['city'])
shops.drop(['shop_name', 'city'], axis = 1, inplace = True)
shops.head()<count_values> | all_data.drop(["PassengerId", "Ticket" ], axis=1, inplace = True ) | Titanic - Machine Learning from Disaster |
3,999,796 | len(set(items_test_list)-set(items_train_list))<categorify> | total = all_data.isnull().sum().sort_values(ascending=False)
percent =(all_data.isnull().sum() /all_data.isnull().count() ).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
missing_data[total > 0] | Titanic - Machine Learning from Disaster |
3,999,796 | LE = preprocessing.LabelEncoder()
category_split = categories['item_category_name'].str.split('-')
categories['main_categories_id'] = category_split.map(lambda row: row[0].strip())
categories['main_categories_id'] = LE.fit_transform(categories['main_categories_id'])
categories['sub_category_id'] = category_split.map... | all_data["Cabin"] = all_data["Cabin"].fillna("None" ) | Titanic - Machine Learning from Disaster |
3,999,796 | train['date'] = pd.to_datetime(train['date'], format = '%d.%m.%Y')
train.info()<concatenate> | all_data["Age"] = all_data["Age"].fillna(all_data["Age"].mean())
all_data["Fare"] = all_data["Fare"].fillna(all_data["Fare"].mean() ) | Titanic - Machine Learning from Disaster |
3,999,796 | cartesian_test = []
cartesian_test.append(np.array(jan))
cartesian_test.append(np.array(feb))<prepare_output> | embarked_mode = all_data["Embarked"].mode() [0]
all_data["Embarked"] = all_data["Embarked"].fillna(embarked_mode ) | Titanic - Machine Learning from Disaster |
3,999,796 | cartesian_test = np.vstack(cartesian_test)
cartesian_test_df = pd.DataFrame(cartesian_test, columns = ['shop_id', 'item_id', 'date_block_num'])
cartesian_test_df.head()<data_type_conversions> | simplify_ages(all_data ) | Titanic - Machine Learning from Disaster |
3,999,796 | def downcast_dtypes(df):
float_cols = [c for c in df if df[c].dtype == "float64"]
int_cols = [c for c in df if df[c].dtype == "int64"]
df[float_cols] = df[float_cols].astype(np.float16)
df[int_cols] = df[int_cols].astype(np.int16)
return df<groupby> | simplify_fares(all_data ) | Titanic - Machine Learning from Disaster |
3,999,796 | x = train.groupby(['shop_id', 'item_id', 'date_block_num'])['item_cnt_day'].sum().rename('item_cnt_month' ).reset_index()
x.head()<merge> | def format_name(df):
df['Lname'] = df.Name.apply(lambda x: x.split(' ')[0])
df['NamePrefix'] = df.Name.apply(lambda x: x.split(' ')[1])
return df | Titanic - Machine Learning from Disaster |
3,999,796 | new_train = pd.merge(cartesian_df, x, on=['shop_id', 'item_id', 'date_block_num'], how='left' ).fillna(0)
new_train['item_cnt_month'] = np.clip(new_train['item_cnt_month'], 0, 20 )<drop_column> | format_name(all_data)
all_data.drop(['Name'], axis=1, inplace=True ) | Titanic - Machine Learning from Disaster |
3,999,796 | del x
del cartesian_df
del cartesian
del cartesian_test
del cartesian_test_df
del feb
del jan
del items_test_list
del items_train_list
del train<sort_values> | all_data["family_members"] = all_data["SibSp"] + all_data["Parch"]
all_data.drop(["SibSp", "Parch" ], axis=1 ) | Titanic - Machine Learning from Disaster |
3,999,796 | new_train.sort_values(['date_block_num','shop_id','item_id'], inplace = True)
new_train.head()<feature_engineering> | all_data.head() | Titanic - Machine Learning from Disaster |
3,999,796 | test.insert(loc=3, column='date_block_num', value=34)
test['item_cnt_month'] = 0
test.head()<concatenate> | def encode_features(df):
features = ['Sex', 'Age', 'Fare', 'Embarked', 'Lname', 'NamePrefix', 'Cabin']
for feature in features:
le = preprocessing.LabelEncoder()
le = le.fit(df[feature])
df[feature] = le.transform(df[feature])
return df
all_data = encode_features(all_data)
all_data.head() | Titanic - Machine Learning from Disaster |
3,999,796 | new_train = new_train.append(test.drop('ID', axis = 1))<merge> | train_data = all_data[:train_data.shape[0]]
test_data = all_data[train_data.shape[0]:]
y = target_variable | Titanic - Machine Learning from Disaster |
3,999,796 | new_train = pd.merge(new_train, shops, on=['shop_id'], how='left')
new_train.head()<merge> | X_train, X_test, y_train, y_test = train_test_split(train_data, y, test_size=0.2, random_state=42 ) | Titanic - Machine Learning from Disaster |
3,999,796 | new_train = pd.merge(new_train, items.drop('item_name', axis = 1), on=['item_id'], how='left')
new_train.head()<merge> | clf = RandomForestClassifier()
parameters = {'n_estimators': [4, 6, 9],
'max_features': ['log2', 'sqrt','auto'],
'criterion': ['entropy', 'gini'],
'max_depth': [2, 3, 5, 10],
'min_samples_split': [2, 3, 5],
'min_samples_leaf': [1,5,8]
}
acc_scorer = make_scorer(accuracy_score)
grid_obj = GridSearchCV(clf, parameters, ... | Titanic - Machine Learning from Disaster |
3,999,796 | new_train = pd.merge(new_train, categories.drop('item_category_name', axis = 1), on=['item_category_id'], how='left')
new_train.head()<merge> | predictions = clf.predict(X_test)
print("Accuracy of Random forest classifier" , accuracy_score(y_test, predictions)) | Titanic - Machine Learning from Disaster |
3,999,796 | def generate_lag(train, months, lag_column):
for month in months:
train_shift = train[['date_block_num', 'shop_id', 'item_id', lag_column]].copy()
train_shift.columns = ['date_block_num', 'shop_id', 'item_id', lag_column+'_lag_'+ str(month)]
train_shift['date_block_num'] += month
train = pd.merge(train, train_shift, on... | lreg = LogisticRegression()
lreg.fit(X_train, y_train)
predictions = lreg.predict(X_test)
print("Accuracy of Logistic Regression" , accuracy_score(y_test, predictions)) | Titanic - Machine Learning from Disaster |
3,999,796 | del items
del categories
del shops
del test<categorify> | predict_survival = lreg.predict(test_data)
my_submission = pd.DataFrame({'PassengerId': passenger_id, 'Survived': predict_survival})
my_submission.to_csv('submission.csv', index=False ) | Titanic - Machine Learning from Disaster |
9,895,226 | new_train = downcast_dtypes(new_train )<set_options> | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import Perceptron
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
fro... | Titanic - Machine Learning from Disaster |
9,895,226 | gc.collect()<define_search_space> | data = pd.read_csv("/kaggle/input/titanic/train.csv")
test_data = pd.read_csv("/kaggle/input/titanic/test.csv")
data.shape | Titanic - Machine Learning from Disaster |
9,895,226 | %%time
new_train = generate_lag(new_train, [1,2,3,4,5,6,12], 'item_cnt_month' )<merge> | data.nunique().sort_values(ascending=False ).head(5 ) | Titanic - Machine Learning from Disaster |
9,895,226 | %%time
group = new_train.groupby(['date_block_num', 'item_id'])['item_cnt_month'].mean().rename('item_month_mean' ).reset_index()
new_train = pd.merge(new_train, group, on=['date_block_num', 'item_id'], how='left')
new_train = generate_lag(new_train, [1,2,3,6,12], 'item_month_mean')
new_train.drop(['item_month_mean']... | data.drop(["Name","Ticket","Cabin"],axis=1,inplace=True)
data.head() ,data.shape | Titanic - Machine Learning from Disaster |
9,895,226 | %%time
group = new_train.groupby(['date_block_num', 'shop_id'])['item_cnt_month'].mean().rename('shop_month_mean' ).reset_index()
new_train = pd.merge(new_train, group, on=['date_block_num', 'shop_id'], how='left')
new_train = generate_lag(new_train, [1,2,3,6,12], 'shop_month_mean')
new_train.drop(['shop_month_mean']... | imp = SimpleImputer(missing_values=np.nan, strategy='mean')
data.Age = imp.fit_transform(data[['Age']] ).ravel()
imp2 = SimpleImputer(missing_values=np.nan,strategy='most_frequent')
data.Embarked = imp2.fit_transform(data[['Embarked']] ).ravel()
data.Age.mean() ,data.Age.std() ,data.Age.isnull().sum() | Titanic - Machine Learning from Disaster |
9,895,226 | %%time
group = new_train.groupby(['date_block_num', 'shop_id', 'item_category_id'])['item_cnt_month'].mean().rename('shop_category_month_mean' ).reset_index()
new_train = pd.merge(new_train, group, on=['date_block_num', 'shop_id', 'item_category_id'], how='left')
new_train = generate_lag(new_train, [1, 2], 'shop_categ... | data.groupby('Sex' ).Survived.mean() | Titanic - Machine Learning from Disaster |
9,895,226 | %%time
group = new_train.groupby(['date_block_num', 'main_category_id'])['item_cnt_month'].mean().rename('main_category_month_mean' ).reset_index()
new_train = pd.merge(new_train, group, on=['date_block_num', 'main_category_id'], how='left')
new_train = generate_lag(new_train, [1], 'main_category_month_mean')
new_tra... | data['relatives'] = data['SibSp'] + data['Parch']
data.loc[data['relatives'] > 0, 'not_alone'] = 0
data.loc[data['relatives'] == 0, 'not_alone'] = 1
data['not_alone'] = data['not_alone'].astype(int ) | Titanic - Machine Learning from Disaster |
9,895,226 | %%time
group = new_train.groupby(['date_block_num', 'sub_category_id'])['item_cnt_month'].mean().rename('sub_category_month_mean' ).reset_index()
new_train = pd.merge(new_train, group, on=['date_block_num', 'sub_category_id'], how='left')
new_train = generate_lag(new_train, [1], 'sub_category_month_mean')
new_train.d... | data = data.join(pd.get_dummies(data['Sex']))
data = data.join(pd.get_dummies(data['Embarked']))
data.tail() | Titanic - Machine Learning from Disaster |
9,895,226 | new_train['month'] = new_train['date_block_num'] % 12<categorify> | test_data.drop(["Name","Ticket","Cabin"],axis=1,inplace=True)
test_data['relatives'] = test_data['SibSp'] + test_data['Parch']
test_data.loc[test_data['relatives'] > 0, 'not_alone'] = 0
test_data.loc[test_data['relatives'] == 0, 'not_alone'] = 1
test_data['not_alone'] = test_data['not_alone'].astype(int)
test_data.Ag... | Titanic - Machine Learning from Disaster |
9,895,226 | holiday_dict = {
0: 6,
1: 3,
2: 2,
3: 8,
4: 3,
5: 3,
6: 2,
7: 8,
8: 4,
9: 8,
10: 5,
11: 4,
}
new_train['holidays_in_month'] = new_train['month'].map(holiday_dict )<categorify> |
f = ['Pclass','Age','SibSp','Parch','Fare','relatives','not_alone','female','C','Q']
len(f ) | Titanic - Machine Learning from Disaster |
9,895,226 | moex = {
12: 659, 13: 640, 14: 1231,
15: 881, 16: 764, 17: 663,
18: 743, 19: 627, 20: 692,
21: 736, 22: 680, 23: 1092,
24: 657, 25: 863, 26: 720,
27: 819, 28: 574, 29: 568,
30: 633, 31: 658, 32: 611,
33: 770, 34: 723,
}
new_train['moex_value'] = new_train.date_block_num.map(moex)
new_train = downcast_dtypes(new_train ... | test_data.Fare.fillna(method = 'ffill',inplace=True ) | Titanic - Machine Learning from Disaster |
9,895,226 | gc.collect()<filter> | X = data[f]
X_test = test_data[f]
y = data['Survived'] | Titanic - Machine Learning from Disaster |
9,895,226 | new_train = new_train[new_train.date_block_num > 11]<data_type_conversions> | level1 = LogisticRegression() | Titanic - Machine Learning from Disaster |
9,895,226 | def fill_na(df):
for col in df.columns:
if('_lag_' in col)&(df[col].isnull().any()):
df[col].fillna(0, inplace=True)
return df
new_train = fill_na(new_train )<train_model> | model = StackingClassifier(estimators=level0, final_estimator=level1 ) | Titanic - Machine Learning from Disaster |
9,895,226 | def xgtrain() :
regressor = xgb.XGBRegressor(n_estimators = 5000,
learning_rate = 0.01,
max_depth = 10,
subsample = 0.5,
colsample_bytree = 0.5)
regressor_ = regressor.fit(new_train[new_train.date_block_num < 33].drop(['item_cnt_month'], axis=1 ).values,
new_train[new_train.date_block_num < 33]['item_cnt_month'].value... | model.fit(X,y ) | Titanic - Machine Learning from Disaster |
9,895,226 | %%time
regressor_ = xgtrain()<predict_on_test> | pred_y = model.predict(X_test ) | Titanic - Machine Learning from Disaster |
9,895,226 | <save_to_csv><EOS> | output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': pred_y})
output.to_csv('my_submission.csv', index=False)
print("Submitted successfully!" ) | Titanic - Machine Learning from Disaster |
8,370,213 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<set_options> | warnings.filterwarnings("ignore", category=DeprecationWarning)
sns.set() | Titanic - Machine Learning from Disaster |
8,370,213 | %matplotlib inline
pd.set_option('display.float_format', lambda x: '%.3f' % x)
pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', 100)
print('Done' )<load_from_csv> | train_data = pd.read_csv(".. /input/titanic/train.csv" ) | Titanic - Machine Learning from Disaster |
8,370,213 | path = '.. /input/competitive-data-science-predict-future-sales/'
DF_sales = pd.read_csv(path + 'sales_train.csv')
DF_items = pd.read_csv(path + 'items.csv')
DF_item_cat = pd.read_csv(path + 'item_categories.csv')
DF_shops = pd.read_csv(path + 'shops.csv')
DF_test = pd.read_csv(path + 'test.csv')
DF_sample_subs = ... | train_data.drop(columns=['Name', 'Ticket', 'Cabin','PassengerId'], axis=1, inplace=True ) | Titanic - Machine Learning from Disaster |
8,370,213 | print('---' * 10)
print('sales_train check
')
print(DF_sales.isna().sum())
print('---' * 10)
print('item_cat check
')
print(DF_items.isna().sum())
print('---' * 10)
print('item check
')
print(DF_item_cat.isna().sum())
print('---' * 10)
print('shops check
')
print(DF_shops.isna().sum() )<feature_engineering> | train_data['Sex'] = train_data['Sex'].map({'male':0,'female':1} ) | Titanic - Machine Learning from Disaster |
8,370,213 | DF_sales = DF_sales[DF_sales.item_price<100000]
DF_sales = DF_sales[DF_sales.item_cnt_day<1000]
DF_sales = DF_sales[DF_sales.item_price > 0].reset_index(drop=True)
DF_sales.loc[DF_sales.item_cnt_day < 0, 'item_cnt_day'] = 0<feature_engineering> | train_data['Age'].isnull().sum() | Titanic - Machine Learning from Disaster |
8,370,213 | DF_sales.loc[DF_sales.shop_id == 0, 'shop_id'] = 57
DF_sales.loc[DF_sales.shop_id == 1, 'shop_id'] = 58
DF_sales.loc[DF_sales.shop_id == 11, 'shop_id'] = 10<feature_engineering> | train_data['Embarked'].value_counts() | Titanic - Machine Learning from Disaster |
8,370,213 | DF_shops.loc[DF_shops.shop_name == 'Сергиев Посад ТЦ "7Я"', 'shop_name'] = 'СергиевПосад ТЦ "7Я"'
DF_shops['shop_city'] = DF_shops['shop_name'].str.split(' ' ).map(lambda x: x[0])
DF_shops['shop_cat'] = DF_shops['shop_name'].str.split(' ' ).map(lambda x: x[1])
DF_shops.head(5 )<categorify> | train_data['Embarked'].fillna(value='S',axis=0, inplace=True ) | Titanic - Machine Learning from Disaster |
8,370,213 | DF_shops['shop_city'] = LabelEncoder().fit_transform(DF_shops['shop_city'])
DF_shops['shop_cat'] = LabelEncoder().fit_transform(DF_shops['shop_cat'])
DF_shops.drop(['shop_name'], axis=1, inplace= True)
DF_shops.head(5 )<merge> | train_data['Embarked'] = train_data['Embarked'].map({'S':0,'C':1,'Q':2} ) | Titanic - Machine Learning from Disaster |
8,370,213 | DF_items = pd.merge(DF_items, DF_item_cat, on = 'item_category_id')
DF_items<feature_engineering> | train_data.fillna(value=train_data['Age'].mean() , axis=0, inplace=True ) | Titanic - Machine Learning from Disaster |
8,370,213 | DF_items['item_sub_cat_1'] = np.select(
[DF_items.item_category_id.isin(range(0,8)) ,
DF_items.item_category_id.isin(range(10,18)) ,
DF_items.item_category_id.isin(range(18,32)) ,
DF_items.item_category_id.isin(range(32,37)) ,
DF_items.item_category_id.isin(range(37,42)) ,
DF_items.item_category_id.isin(range(42,55)) ... | train_data[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False ).mean() | Titanic - Machine Learning from Disaster |
8,370,213 | DF_items['item_sub_cat_1'] = LabelEncoder().fit_transform(DF_items['item_sub_cat_1'])
DF_items.drop(['item_name','item_category_name'], axis=1, inplace= True)
DF_items<data_type_conversions> | train_data[['Sex', 'Survived']].groupby(['Sex'], as_index=False ).mean() | Titanic - Machine Learning from Disaster |
8,370,213 | DF_all = []
cols = ['date_block_num','shop_id','item_id']
for i in range(34):
sales = DF_sales[DF_sales.date_block_num==i]
DF_all.append(np.array(list(product([i], sales.shop_id.unique() , sales.item_id.unique())) , dtype='int16'))
DF_all = pd.DataFrame(np.vstack(DF_all), columns=cols)
DF_all['date_block_num'] = DF_al... | train_data[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False ).mean() | Titanic - Machine Learning from Disaster |
8,370,213 | DF_test.drop(['ID'], axis=1, inplace = True)
DF_test['date_block_num'] = 34
DF_test['date_block_num'] = DF_test['date_block_num']
DF_test['shop_id'] = DF_test['shop_id']
DF_test['item_id'] = DF_test['item_id']
DF_test.head()<merge> | train_data[['SibSp', 'Survived']].groupby('SibSp', as_index=False ).mean() | Titanic - Machine Learning from Disaster |
8,370,213 | DF_all = pd.concat([DF_all, DF_test], ignore_index=True, sort=False, keys=cols)
DF_all = pd.merge(DF_all, DF_shops, on=['shop_id'], how='left')
DF_all = pd.merge(DF_all, DF_items, on=['item_id'], how='left')
DF_all.fillna(0, inplace=True)
DF_all<data_type_conversions> | train_data[['Parch', 'Survived']].groupby('Parch', as_index=False ).mean() | Titanic - Machine Learning from Disaster |
8,370,213 | DF_all.date_block_num = DF_all.date_block_num.astype(np.int8)
DF_all.shop_id = DF_all.shop_id.astype(np.int8)
DF_all.item_id = DF_all.item_id.astype(np.int16)
DF_all.shop_city = DF_all.shop_city.astype(np.int8)
DF_all.shop_cat = DF_all.shop_cat.astype(np.int8)
DF_all.item_category_id = DF_all.item_category_id.asty... | train_data['AgeBand'] = pd.cut(train_data['Age'], 9 ) | Titanic - Machine Learning from Disaster |
8,370,213 | def lag_feature(df, lags, col):
tmp = df[['date_block_num','shop_id','item_id',col]]
for i in lags:
shifted = tmp.copy()
shifted.columns = ['date_block_num','shop_id','item_id', col+'_lag_'+str(i)]
shifted['date_block_num'] += i
df = pd.merge(df, shifted, on=['date_block_num','shop_id','item_id'], how='left')
return d... | train_data[['AgeBand','Survived']].groupby('AgeBand', as_index=False ).mean() | Titanic - Machine Learning from Disaster |
8,370,213 | temp = DF_sales.groupby(['shop_id','item_id','date_block_num'] ).agg(item_cnt_month=('item_cnt_day',sum))
temp.columns = ['item_cnt_month']
temp.reset_index(inplace=True)
DF_all = pd.merge(DF_all, temp, on=cols, how='left')
DF_all['item_cnt_month'] =(DF_all['item_cnt_month']
.fillna(0)
.clip(0,20)
.astype(np.float16... | train_data.loc[ train_data['Age'] <= 18, 'Age'] = 0
train_data.loc[(train_data['Age'] > 18)&(train_data['Age'] <= 44), 'Age'] = 1
train_data.loc[(train_data['Age'] > 44)&(train_data['Age'] <= 53), 'Age'] = 2
train_data.loc[(train_data['Age'] > 53)&(train_data['Age'] <= 62), 'Age'] = 3
train_data.loc[ train_data['Age'] ... | Titanic - Machine Learning from Disaster |
8,370,213 | ts = time.time()
DF_all = lag_feature(DF_all, [1, 2, 3], 'item_cnt_month')
temp = DF_all.groupby(['date_block_num'] ).agg({'item_cnt_month' : ['mean']})
temp.columns = ['avg_month']
temp.reset_index(inplace=True)
DF_all = pd.merge(DF_all, temp, on=['date_block_num'], how='left')
DF_all = lag_feature(DF_all, [1, 2, ... | train_data.drop(labels='AgeBand', axis=1, inplace=True ) | Titanic - Machine Learning from Disaster |
8,370,213 | DF_all['month'] = DF_all['date_block_num'] % 12
days = pd.Series([31,28,31,30,31,30,31,31,30,31,30,31])
DF_all['days'] = DF_all['month'].map(days)
DF_all['years'] = np.select(
[DF_all.date_block_num.isin(range(0,12)) ,
DF_all.date_block_num.isin(range(12,25)) ,
DF_all.date_block_num.isin(range(25,35)) ],
['13','14',... | train_data['FareBand'] = pd.qcut(train_data['Fare'], 5 ) | Titanic - Machine Learning from Disaster |
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