Kaggle competition: COVID19 Global Forecasting (Week 1)
model.fit(X_train1, Y_train, epochs = 100 )<predict_on_test>
train = pd.read_csv('.. /input/covid19-global-forecasting-week-1/train.csv')
 test = pd.read_csv('.. /input/covid19-global-forecasting-week-1/test.csv')
 submission = pd.read_csv('.. /input/covid19-global-forecasting-week-1/submission.csv')
prediction = model.predict(X_test1 )<define_variables>
test['Date']=test.Date.astype('datetime64[ns]' )
pred = []
for i in prediction:
if i[0] < 0.5:
pred.append(0)
 else:
pred.append(1 )<prepare_output>
train['key']=train['Province/State'].astype('str')+ " " + train['Country/Region'].astype('str')+ " " +train['Lat'].astype('str')+ " " +train['Long'].astype('str')
 test['key']=test['Province/State'].astype('str')+ " " + test['Country/Region'].astype('str')+ " " +test['Lat'].astype('str')+ " " +test['Long'].astype('str')
 train.describe()
pred = np.array(pred )<create_dataframe>
daily_analysis=train.groupby(['Date'] ).sum()
output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': pred})
 len(output )<save_to_csv>
def RMSLE(pred,actual):
return np.sqrt(np.mean(np.power(( np.log(pred+1)-np.log(actual+1)) ,2)) )
output.to_csv('my_submission_nn_1.csv', index = False)
 print("Your submission was successfully saved!" )<import_modules>
train_lag_1=train.groupby(['key'] ).shift(periods=1)
 train_lag_2=train.groupby(['key'] ).shift(periods=2)
 train_lag_3=train.groupby(['key'] ).shift(periods=3)
 train['lag_1_ConfirmedCases']=train_lag_1['ConfirmedCases']
train['lag_1_Fatalities']=train_lag_1['Fatalities']
train['lag_2_ConfirmedCases']=train_lag_2['ConfirmedCases']
train['lag_2_Fatalities']=train_lag_2['Fatalities']
train['lag_3_ConfirmedCases']=train_lag_3['ConfirmedCases']
train['lag_3_Fatalities']=train_lag_3['Fatalities']
train
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns<load_from_csv>
orig_stdout = sys.stdout
Fatalities_all_result_final=pd.DataFrame()
ConfirmedCases_all_result_Final=pd.DataFrame()
for keys in train['key'].unique() :
chk=train[train['key']==keys]
chk.index=chk.Date
fcastperiod=0
fcastperiod1=35
actual=chk[:chk.shape[0]-fcastperiod]
ffcast=chk[chk.shape[0]-fcastperiod-1:]
ffcast
try:
Fatalities_all_result_1=pred_ets(fcastperiod,fcastperiod1,actual,ffcast,'Fatalities' ).reset_index()
except:
Fatalities_all_result_1=pd.DataFrame(pd.date_range(start=chk.Date.min() , periods=60+fcastperiod1+1, freq='D')[1:])
 Fatalities_all_result_1.columns=['index']
Fatalities_all_result_1['best_model']='naive'
Fatalities_all_result_1['best_pred']=0
Fatalities_all_result_1['key']=keys
Fatalities_all_result_final=Fatalities_all_result_final.append(Fatalities_all_result_1,sort=True)
 try:
ConfirmedCases_all_result_1=pred_ets(fcastperiod,fcastperiod1,actual,ffcast,'ConfirmedCases' ).reset_index()
except:
ConfirmedCases_all_result_1=pd.DataFrame(pd.date_range(start=train.Date.min() , periods=60+fcastperiod1+1, freq='D')[1:])
 ConfirmedCases_all_result_1.columns=['index']
ConfirmedCases_all_result_1['best_model']='naive'
ConfirmedCases_all_result_1['best_pred']=1
ConfirmedCases_all_result_1['key']=keys
ConfirmedCases_all_result_Final=ConfirmedCases_all_result_Final.append(ConfirmedCases_all_result_1,sort=True)
 print(' done for %s' % keys)
 sys.stdout = orig_stdout
df_train=pd.read_csv('.. /input/titanic/train.csv')
 df_test=pd.read_csv('.. /input/titanic/test.csv' )<define_variables>
ConfirmedCases_all_result_Final.rename(columns={'index':'Date'},inplace=True)
 Fatalities_all_result_final.rename(columns={'index':'Date'},inplace=True)
 ConfirmedCases_all_result_Final['best_pred']=np.where(ConfirmedCases_all_result_Final['best_pred'] is np.nan , 0,
ConfirmedCases_all_result_Final['best_pred'])
 Fatalities_all_result_final['best_pred']=np.where(Fatalities_all_result_final['best_pred'] is np.nan , 0 ,
Fatalities_all_result_final['best_pred'])
 ConfirmedCases_all_result_Final['best_pred']=np.where(ConfirmedCases_all_result_Final['best_pred'] <0 , 0,
ConfirmedCases_all_result_Final['best_pred'])
 Fatalities_all_result_final['best_pred']=np.where(Fatalities_all_result_final['best_pred'] <0 , 0 ,
Fatalities_all_result_final['best_pred'])
 ConfirmedCases_all_result_Final['best_pred_1']=np.where(ConfirmedCases_all_result_Final['AdditiveDamped'] is np.nan , ConfirmedCases_all_result_Final['best_pred'] ,
ConfirmedCases_all_result_Final['AdditiveDamped'])
 Fatalities_all_result_final['best_pred_1']=np.where(Fatalities_all_result_final['AdditiveDamped'] is np.nan , Fatalities_all_result_final['best_pred'] ,
Fatalities_all_result_final['AdditiveDamped'])
 ConfirmedCases_all_result_Final['best_pred_1']=np.where(ConfirmedCases_all_result_Final['best_pred'] is np.nan , 0,
ConfirmedCases_all_result_Final['best_pred'])
 Fatalities_all_result_final['best_pred_1']=np.where(Fatalities_all_result_final['best_pred'] is np.nan , 0 ,
Fatalities_all_result_final['best_pred'])
 ConfirmedCases_all_result_Final['best_pred_1']=np.where(ConfirmedCases_all_result_Final['best_pred'] <0 , 0,
ConfirmedCases_all_result_Final['best_pred'])
 Fatalities_all_result_final['best_pred_1']=np.where(Fatalities_all_result_final['best_pred'] <0 , 0 ,
Fatalities_all_result_final['best_pred'] )
PassengerId=df_test['PassengerId']<drop_column>
test['Date']=test.Date.astype('datetime64[ns]' )
df_train.drop(['PassengerId','Name','Ticket'],axis=1,inplace=True)
 df_test.drop(['PassengerId','Name','Ticket'],axis=1,inplace=True )<count_missing_values>
eval1 = ConfirmedCases_all_result_Final[['key','Date','best_pred','best_pred_1']].merge(test, how='right', on=['key','Date'])
 eval1.rename(columns={'best_pred':'ConfirmedCases'},inplace=True)
 eval1['ConfirmedCases']=eval1['ConfirmedCases'].fillna(0)
 eval1
df_train.isnull().sum() /len(df_train)*100<drop_column>
eval2 = Fatalities_all_result_final[['key','Date','best_pred','best_pred_1']].merge(test, how='right', on=['key','Date'])
 eval2.rename(columns={'best_pred':'Fatalities'},inplace=True)
 eval2['Fatalities']=eval2['Fatalities'].fillna(0)
 eval2
df_train.drop(['Cabin'],axis=1,inplace=True)
 df_test.drop(['Cabin'],axis=1,inplace=True )<count_missing_values>
sub_prep = eval1[['ForecastId','ConfirmedCases','key']].merge(eval2[['ForecastId','Fatalities']], on=['ForecastId'], how='left')
 sub_prep
df_train.dropna(subset=['Embarked'],inplace=True)
 df_train['Embarked'].isnull().sum()<groupby>
sub = sub_prep.merge(submission['ForecastId'], on=['ForecastId'], how='right')
 sub
age_train_series=df_train.groupby(['Pclass','Sex'])['Age'].transform('median' )<groupby>
sub=sub[['ForecastId','ConfirmedCases','Fatalities']]
sub=sub.sort_values('ForecastId')
 sub
age_test_series=df_test.groupby(['Pclass','Sex'])['Age'].transform('median' )<feature_engineering>
sub.to_csv('submission.csv',header=['ForecastId','ConfirmedCases','Fatalities'],index=False )
df_train['Age']=df_train['Age'].fillna(age_train_series )<feature_engineering>
train['Date']=train.Date.astype('datetime64[ns]')
 verify=train[['key','Date','ConfirmedCases','Fatalities']].merge(test[['key','Date','ForecastId']], how='inner', on=['key','Date'])
 pred=verify[['ForecastId']].merge(sub, how='inner', on=['ForecastId'] )
df_test['Age']=df_test['Age'].fillna(age_test_series )<count_missing_values>
RMSLE(pred['Fatalities'].values,verify['Fatalities'].values )
df_test.isnull().sum()<categorify>
RMSLE(pred['ConfirmedCases'].values,verify['ConfirmedCases'].values )
Fare_test_series=df_test.groupby(['Pclass'])['Fare'].transform('median')
 df_test['Fare']=df_test['Fare'].fillna(Fare_test_series )<categorify>
best_model_key=ConfirmedCases_all_result_Final[['key','best_model']].drop_duplicates()
max_number_current=train.groupby('key' ).max() [['ConfirmedCases','Fatalities']].reset_index()
best_model_key=best_model_key.merge(max_number_current,on='key',how='left')
 best_model_key=best_model_key.sort_values('ConfirmedCases',ascending=False ).reset_index(drop=True )
df_train=pd.get_dummies(df_train,drop_first=True)
<categorify>
train_ck=train.groupby(['Country/Region','Date'] ).sum().reset_index()
train_ck['key']=train_ck['Country/Region']
df_test=pd.get_dummies(df_test,drop_first=True )<prepare_x_and_y>
Fatalities_all_result_final=pd.DataFrame()
ConfirmedCases_all_result_Final=pd.DataFrame()
for keys in train_ck['key'].unique() :
chk=train_ck[train_ck['key']==keys]
chk.index=chk.Date
fcastperiod=0
fcastperiod1=35
actual=chk[:chk.shape[0]-fcastperiod]
ffcast=chk[chk.shape[0]-fcastperiod-1:]
ffcast
try:
Fatalities_all_result_1=pred_ets(fcastperiod,fcastperiod1,actual,ffcast,'Fatalities' ).reset_index()
except:
Fatalities_all_result_1=pd.DataFrame(pd.date_range(start=train.Date.min() , periods=60+fcastperiod1+1, freq='D')[1:])
 Fatalities_all_result_1.columns=['index']
Fatalities_all_result_1['best_model']='naive'
Fatalities_all_result_1['best_pred']=0
Fatalities_all_result_1['key']=keys
Fatalities_all_result_final=Fatalities_all_result_final.append(Fatalities_all_result_1,sort=True)
 try:
ConfirmedCases_all_result_1=pred_ets(fcastperiod,fcastperiod1,actual,ffcast,'ConfirmedCases' ).reset_index()
except:
ConfirmedCases_all_result_1=pd.DataFrame(pd.date_range(start=train.Date.min() , periods=60+fcastperiod1+1, freq='D')[1:])
 ConfirmedCases_all_result_1.columns=['index']
ConfirmedCases_all_result_1['best_model']='naive'
ConfirmedCases_all_result_1['best_pred']=1
ConfirmedCases_all_result_1['key']=keys
ConfirmedCases_all_result_Final=ConfirmedCases_all_result_Final.append(ConfirmedCases_all_result_1,sort=True)
 print(' done for %s' % keys )
X=df_train.drop('Survived',axis=1)
 y=df_train['Survived']<split>
ConfirmedCases_all_result_Final.rename(columns={'index':'Date'},inplace=True)
 Fatalities_all_result_final.rename(columns={'index':'Date'},inplace=True)
 ConfirmedCases_all_result_Final['best_pred']=np.where(ConfirmedCases_all_result_Final['best_pred'] is np.nan , 0,
ConfirmedCases_all_result_Final['best_pred'])
 Fatalities_all_result_final['best_pred']=np.where(Fatalities_all_result_final['best_pred'] is np.nan , 0 ,
Fatalities_all_result_final['best_pred'])
 ConfirmedCases_all_result_Final['best_pred']=np.where(ConfirmedCases_all_result_Final['best_pred'] <0 , 0,
ConfirmedCases_all_result_Final['best_pred'])
 Fatalities_all_result_final['best_pred']=np.where(Fatalities_all_result_final['best_pred'] <0 , 0 ,
Fatalities_all_result_final['best_pred'])
 ConfirmedCases_all_result_Final['best_pred_1']=np.where(ConfirmedCases_all_result_Final['AdditiveDamped'] is np.nan , ConfirmedCases_all_result_Final['best_pred'] ,
ConfirmedCases_all_result_Final['AdditiveDamped'])
 Fatalities_all_result_final['best_pred_1']=np.where(Fatalities_all_result_final['AdditiveDamped'] is np.nan , Fatalities_all_result_final['best_pred'] ,
Fatalities_all_result_final['AdditiveDamped'])
 ConfirmedCases_all_result_Final['best_pred_1']=np.where(ConfirmedCases_all_result_Final['best_pred'] is np.nan , 0,
ConfirmedCases_all_result_Final['best_pred'])
 Fatalities_all_result_final['best_pred_1']=np.where(Fatalities_all_result_final['best_pred'] is np.nan , 0 ,
Fatalities_all_result_final['best_pred'])
 ConfirmedCases_all_result_Final['best_pred_1']=np.where(ConfirmedCases_all_result_Final['best_pred'] <0 , 0,
ConfirmedCases_all_result_Final['best_pred'])
 Fatalities_all_result_final['best_pred_1']=np.where(Fatalities_all_result_final['best_pred'] <0 , 0 ,
Fatalities_all_result_final['best_pred'] )
<import_modules><EOS> 
best_model_key=ConfirmedCases_all_result_Final[['key','best_model']].drop_duplicates()
max_number_current=train_ck.groupby('key' ).max() [['ConfirmedCases','Fatalities']].reset_index()
best_model_key=best_model_key.merge(max_number_current,on='key',how='left')
 best_model_key=best_model_key.sort_values('ConfirmedCases',ascending=False ).reset_index(drop=True )