Kaggle competition: COVID19 Global Forecasting (Week 2)
<SOS> metric: MCRMSLE Kaggle data source: covid19-global-forecasting-(week-2)<statistical_test>
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from statsmodels.tsa.statespace.sarimax import SARIMAX
from statsmodels.tsa.arima_model import ARIMA
from random import random
df_1_over = resample(df_1,
replace=True,
n_samples=len(df_4),
random_state=42)
 df_2_over = resample(df_2,
replace=True,
n_samples=len(df_4),
random_state=42)
 df_3_over = resample(df_3,
replace=True,
n_samples=len(df_4),
random_state=42)
 df_over= pd.concat([df_1_over, df_2_over, df_3_over, df_4])
 df_over['Target'].value_counts()<train_model>
PATH_WEEK2='/kaggle/input/covid19-global-forecasting-week-2'
df_train = pd.read_csv(f'{PATH_WEEK2}/train.csv')
 df_test = pd.read_csv(f'{PATH_WEEK2}/test.csv')
 df_train.head()
df_test.head()
df_train.rename(columns={'Country_Region':'Country'}, inplace=True)
 df_test.rename(columns={'Country_Region':'Country'}, inplace=True)
 df_train.rename(columns={'Province_State':'State'}, inplace=True)
 df_test.rename(columns={'Province_State':'State'}, inplace=True)
 df_train['Date'] = pd.to_datetime(df_train['Date'], infer_datetime_format=True)
 df_test['Date'] = pd.to_datetime(df_test['Date'], infer_datetime_format=True)
 df_train.info()
df_test.info()
y1_Train = df_train.iloc[:, -2]
y1_Train.head()
y2_Train = df_train.iloc[:, -1]
y2_Train.head()
EMPTY_VAL = "EMPTY_VAL"
def fillState(state, country):
if state == EMPTY_VAL: return country
return state
train, test = train_test_split(df_over, test_size=0.2, random_state=42)
 rf.fit(train[feats], train['Target'])
 preds_test = rf.predict(test[feats])
 accuracy_score(test['Target'], preds_test )<randomize_order>
X_Train = df_train.copy()
X_Train['State'].fillna(EMPTY_VAL, inplace=True)
 X_Train['State'] = X_Train.loc[:, ['State', 'Country']].apply(lambda x : fillState(x['State'], x['Country']), axis=1)
 X_Train.loc[:, 'Date'] = X_Train.Date.dt.strftime("%m%d")
 X_Train["Date"] = X_Train["Date"].astype(int)
 X_Train.head()
X_Test = df_test.copy()
X_Test['State'].fillna(EMPTY_VAL, inplace=True)
 X_Test['State'] = X_Test.loc[:, ['State', 'Country']].apply(lambda x : fillState(x['State'], x['Country']), axis=1)
 X_Test.loc[:, 'Date'] = X_Test.Date.dt.strftime("%m%d")
 X_Test["Date"] = X_Test["Date"].astype(int)
 X_Test.head()
df_2_under = resample(df_2,
replace=False,
n_samples=len(df_1),
random_state=42)
 df_3_under = resample(df_3,
replace=False,
n_samples=len(df_1),
random_state=42)
 df_4_under = resample(df_4,
replace=False,
n_samples=len(df_1),
random_state=42)
 df_under= pd.concat([df_1, df_2_under, df_3_under, df_4_under])
 df_under['Target'].value_counts()<predict_on_test>
le = preprocessing.LabelEncoder()
X_Train.Country = le.fit_transform(X_Train.Country)
 X_Train['State'] = le.fit_transform(X_Train['State'])
 X_Train.head()
X_Test.Country = le.fit_transform(X_Test.Country)
 X_Test['State'] = le.fit_transform(X_Test['State'])
 X_Test.head()
df_train.head()
df_train.loc[df_train.Country == 'Afghanistan', :]
df_test.tail()
train, test = train_test_split(df_under, test_size=0.2, random_state=42)
 rf.fit(train[feats], train['Target'])
 preds_test = rf.predict(test[feats])
 accuracy_score(test['Target'], preds_test )<import_modules>
filterwarnings('ignore')
 le = preprocessing.LabelEncoder()
countries = X_Train.Country.unique()
df_out = pd.DataFrame({'ForecastId': [], 'ConfirmedCases': [], 'Fatalities': []})
 for country in countries:
states = X_Train.loc[X_Train.Country == country, :].State.unique()
for state in states:
X_Train_CS = X_Train.loc[(X_Train.Country == country)&(X_Train.State == state), ['State', 'Country', 'Date', 'ConfirmedCases', 'Fatalities']]
y1_Train_CS = X_Train_CS.loc[:, 'ConfirmedCases']
y2_Train_CS = X_Train_CS.loc[:, 'Fatalities']
X_Train_CS = X_Train_CS.loc[:, ['State', 'Country', 'Date']]
X_Train_CS.Country = le.fit_transform(X_Train_CS.Country)
 X_Train_CS['State'] = le.fit_transform(X_Train_CS['State'])
 X_Test_CS = X_Test.loc[(X_Test.Country == country)&(X_Test.State == state), ['State', 'Country', 'Date', 'ForecastId']]
X_Test_CS_Id = X_Test_CS.loc[:, 'ForecastId']
X_Test_CS = X_Test_CS.loc[:, ['State', 'Country', 'Date']]
X_Test_CS.Country = le.fit_transform(X_Test_CS.Country)
 X_Test_CS['State'] = le.fit_transform(X_Test_CS['State'])
 model1 = XGBRegressor(n_estimators=1000)
 model1.fit(X_Train_CS, y1_Train_CS)
 y1_pred = model1.predict(X_Test_CS)
 model2 = XGBRegressor(n_estimators=1000)
 model2.fit(X_Train_CS, y2_Train_CS)
 y2_pred = model2.predict(X_Test_CS)
 df = pd.DataFrame({'ForecastId': X_Test_CS_Id, 'ConfirmedCases': y1_pred, 'Fatalities': y2_pred})
 df_out = pd.concat([df_out, df], axis=0)
import imblearn<prepare_x_and_y>
df_out.ForecastId = df_out.ForecastId.astype('int')
 df_out.tail()
df_out.to_csv('submission.csv', index=False )