kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
10,357,342 | def prepare_features(df, gap):
df["perc_1_ac"] =(df[f"lag_{gap}_cc"] - df[f"lag_{gap}_ft"] - df[f"lag_{gap}_rc"])/ df[f"lag_{gap}_cc"]
df["perc_1_cc"] = df[f"lag_{gap}_cc"] / df.population
df["diff_1_cc"] = df[f"lag_{gap}_cc"] - df[f"lag_{gap + 1}_cc"]
df["diff_2_cc"] = df[f"lag_{gap + 1}_cc"] - df[f"lag_{gap + 2}_cc"]... | scaler = StandardScaler()
train2 = scaler.fit_transform(train)
test2 = scaler.fit_transform(test ) | Titanic - Machine Learning from Disaster |
10,357,342 | def build_predict_lgbm(df_train, df_test, gap):
df_train.dropna(subset = ["target_cc", "target_ft", f"lag_{gap}_cc", f"lag_{gap}_ft"], inplace = True)
target_cc = df_train.target_cc
target_ft = df_train.target_ft
test_lag_cc = df_test[f"lag_{gap}_cc"].values
test_lag_ft = df_test[f"lag_{gap}_ft"].values
df_train.drop(... | KFold_Score = pd.DataFrame()
classifiers = ['Linear SVM', 'Radial SVM', 'LogisticRegression',
'RandomForestClassifier', 'AdaBoostClassifier',
'XGBoostClassifier', 'KNeighborsClassifier','GradientBoostingClassifier']
models = [svm.SVC(kernel='linear'),
svm.SVC(kernel='rbf'),
LogisticRegression(max_iter = 1000),
RandomFo... | Titanic - Machine Learning from Disaster |
10,357,342 | def predict_mad(df_test, gap, val = False):
df_test["avg_diff_cc"] =(df_test[f"lag_{gap}_cc"] - df_test[f"lag_{gap + 3}_cc"])/ 3
df_test["avg_diff_ft"] =(df_test[f"lag_{gap}_ft"] - df_test[f"lag_{gap + 3}_ft"])/ 3
if val:
y_pred_cc = df_test[f"lag_{gap}_cc"] + gap * df_test.avg_diff_cc -(1 - MAD_FACTOR)* df_test.avg_di... | mean = pd.DataFrame(KFold_Score.mean() , index= classifiers)
KFold_Score = pd.concat([KFold_Score,mean.T])
KFold_Score.index=['Fold 1','Fold 2','Fold 3','Fold 4','Fold 5','Mean']
KFold_Score.T.sort_values(by=['Mean'], ascending = False ) | Titanic - Machine Learning from Disaster |
10,357,342 | SEED = 24
LGB_PARAMS = {"objective": "regression",
"num_leaves": 5,
"learning_rate": 0.013,
"bagging_fraction": 0.91,
"feature_fraction": 0.81,
"reg_alpha": 0.13,
"reg_lambda": 0.13,
"metric": "rmse",
"seed": SEED
}
VAL_DAYS = 7
MAD_FACTOR = 0.5<split> | col_name1[0],col_name1[2] = col_name1[2],col_name1[0]
col_name2[0],col_name2[2] = col_name2[2],col_name2[0] | Titanic - Machine Learning from Disaster |
10,357,342 | df_train = df[~df.Id.isna() ]
df_test_full = df[~df.ForecastId.isna() ]<feature_engineering> | train_new = train[col_name1]
test_new = test[col_name2] | Titanic - Machine Learning from Disaster |
10,357,342 | df_preds_val = []
df_preds_test = []
for date in df_test_full.Date.unique() :
print("[INFO] Date:", date)
if date in df_train.Date.values:
df_pred_test = df_test_full.loc[df_test_full.Date == date, ["ForecastId", "ConfirmedCases", "Fatalities"]].rename(columns = {"ConfirmedCases": "ConfirmedCases_test", "Fatalities": ... | train_new = train_new.drop(['Cabin'],axis = 1)
test_new = test_new.drop(['Cabin'],axis = 1 ) | Titanic - Machine Learning from Disaster |
10,357,342 | df = df.merge(pd.concat(df_preds_val, sort = False), on = "Id", how = "left")
df = df.merge(pd.concat(df_preds_test, sort = False), on = "ForecastId", how = "left")
rmsle_cc_lgb = np.sqrt(mean_squared_error(np.log1p(df[~df.ConfirmedCases_val_lgb.isna() ].ConfirmedCases), np.log1p(df[~df.ConfirmedCases_val_lgb.isna() ... | sc = StandardScaler()
train3 = sc.fit_transform(train_new)
test3 = sc.transform(test_new ) | Titanic - Machine Learning from Disaster |
10,357,342 | test = df.loc[~df.ForecastId.isna() , ["ForecastId", "Country_Region", "Province_State", "Date",
"ConfirmedCases_test", "ConfirmedCases_test_lgb", "ConfirmedCases_test_mad",
"Fatalities_test", "Fatalities_test_lgb", "Fatalities_test_mad"]].reset_index()
test["ConfirmedCases"] = 0.3 * test.ConfirmedCases_test_lgb + 0.7 ... | clf = RandomForestClassifier(random_state=0 ) | Titanic - Machine Learning from Disaster |
10,357,342 | test = pd.read_csv(".. /input/covid19-global-forecasting-week-4/test.csv")
train = pd.read_csv(".. /input/covid19-global-forecasting-week-4/train.csv")
train['Province_State'].fillna('', inplace=True)
test['Province_State'].fillna('', inplace=True)
train['Date'] = pd.to_datetime(train['Date'])
test['Date'] = pd.to... | param_grid={
'n_estimators': [200,300],
'max_features': ['auto', 'sqrt'],
'max_depth': [6,7,8],
'criterion':['gini','entropy']
} | Titanic - Machine Learning from Disaster |
10,357,342 | FirstDate = train.groupby('Country_Region' ).min() ['Date'].unique() [0]
train['Last Confirm'] = train['ConfirmedCases'].shift(1)
while train[(train['Last Confirm'] > train['ConfirmedCases'])&(train['Date'] > FirstDate)].shape[0] > 0:
train['Last Confirm'] = train['ConfirmedCases'].shift(1)
train['Last Fatalities'] =... | CV_clf = GridSearchCV(estimator=clf, param_grid=param_grid, cv=5)
CV_clf.fit(train3, pred)
CV_clf.best_params_ | Titanic - Machine Learning from Disaster |
10,357,342 | from statsmodels.tsa.statespace.sarimax import SARIMAX
from statsmodels.tsa.arima_model import ARIMA<feature_engineering> | clf1 = RandomForestClassifier(random_state=0, n_estimators=200, criterion='gini', max_features='auto', max_depth=8)
clf1.fit(train3, pred ) | Titanic - Machine Learning from Disaster |
10,357,342 | feature_day = [1,20,50,100,200,500,1000]
def CreateInput(data):
feature = []
for day in feature_day:
data.loc[:,'Number day from ' + str(day)+ ' case'] = 0
if(train[(train['Country_Region'] == country)&(train['Province_State'] == province)&(train['ConfirmedCases'] < day)]['Date'].count() > 0):
fromday = train[(train['C... | pred3 = clf1.predict(test3 ) | Titanic - Machine Learning from Disaster |
10,357,342 | sub1 = df_val_3
submission = sub1[['ForecastId','ConfirmedCases_hat','Fatalities_hat']]
submission.columns = ['ForecastId','ConfirmedCases','Fatalities']<save_to_csv> | pred_test = pred3
output = pd.DataFrame({
'PassengerId': test_data.PassengerId,
'Survived': pred_test
})
output.to_csv('./submission.csv', index=False ) | Titanic - Machine Learning from Disaster |
12,084,336 | TARGETS = ["ConfirmedCases", "Fatalities"]
sub_df = sub0.copy()
for t in TARGETS:
sub_df[t] = np.expm1(np.log1p(submission[t].values)*0.4 + np.log1p(sub0[t].values)*0.6)
sub_df.to_csv("submission.csv", index=False )<count_missing_values> | sns.set(style="darkgrid")
warnings.filterwarnings('ignore')
SEED = 42 | Titanic - Machine Learning from Disaster |
12,084,336 | sub0.isna().sum()<import_modules> | train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.shape, test.shape | Titanic - Machine Learning from Disaster |
12,084,336 | import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
from sklearn.multioutput import MultiOutputRegressor
from sklearn.impute import SimpleImputer<load_from_csv> | def concat(train, test):
return pd.concat([train, test] ).reset_index(drop = True)
def df_divide(df):
return df[:890], df[891:].drop('Survived', axis =1 ) | Titanic - Machine Learning from Disaster |
12,084,336 | train_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission_csv = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv' )<data_type_conversions> | df_all = concat(train, test)
a, b = df_divide(df_all ) | Titanic - Machine Learning from Disaster |
12,084,336 | train_data['Date'] = pd.to_datetime(train_data['Date'], infer_datetime_format=True)
test_data['Date'] = pd.to_datetime(test_data['Date'], infer_datetime_format=True )<data_type_conversions> | dfs = [train, test] | Titanic - Machine Learning from Disaster |
12,084,336 | train_data.loc[:, 'Date'] = train_data.Date.dt.strftime('%y%m%d')
train_data.loc[:, 'Date'] = train_data['Date'].astype(int)
test_data.loc[:, 'Date'] = test_data.Date.dt.strftime('%y%m%d')
test_data.loc[:, 'Date'] = test_data['Date'].astype(int )<feature_engineering> | df_all.isna().sum() | Titanic - Machine Learning from Disaster |
12,084,336 | train_data['Province_State'] = np.where(train_data['Province_State'] == 'nan',train_data['Country_Region'],train_data['Province_State'])
test_data['Province_State'] = np.where(test_data['Province_State'] == 'nan',test_data['Country_Region'],test_data['Province_State'] )<data_type_conversions> | df_all['Age'] = df_all.groupby(['Sex', 'Pclass'])['Age'].apply(lambda x : x.fillna(x.median())) | Titanic - Machine Learning from Disaster |
12,084,336 | convert_dict = {'Province_State': str}
train_data = train_data.astype(convert_dict)
test_data = test_data.astype(convert_dict )<define_variables> | df_all['Embarked'].fillna('S', inplace = True ) | Titanic - Machine Learning from Disaster |
12,084,336 | s =(train_data.dtypes == 'object')
object_cols = list(s[s].index )<import_modules> | df_all[df_all['Embarked'].isna() ] | Titanic - Machine Learning from Disaster |
12,084,336 | from sklearn.preprocessing import LabelEncoder<categorify> | df_all[df_all['Fare'].isna() ] | Titanic - Machine Learning from Disaster |
12,084,336 | label_encoder1 = LabelEncoder()
label_encoder2 = LabelEncoder()
train_data['Province_State'] = label_encoder1.fit_transform(train_data['Province_State'])
test_data['Province_State'] = label_encoder1.transform(test_data['Province_State'])
train_data['Country_Region'] = label_encoder2.fit_transform(train_data['Country_... | g = df_all.groupby(['Pclass','Parch', 'SibSp'])['Fare'].median()
g | Titanic - Machine Learning from Disaster |
12,084,336 | Test_id = test_data.ForecastId<drop_column> | df_all['Cabin'].value_counts() | Titanic - Machine Learning from Disaster |
12,084,336 | train_data.drop(['Id'], axis=1, inplace=True)
test_data.drop('ForecastId', axis=1, inplace=True )<count_missing_values> | df_all['Deck'] = df_all['Cabin'].astype(str ).apply(lambda x : x[0] if x != 'nan' else 'M' ) | Titanic - Machine Learning from Disaster |
12,084,336 | missing_val_count_by_column =(train_data.isnull().sum())
print(missing_val_count_by_column[missing_val_count_by_column>0] )<import_modules> | i = df_all[df_all['Deck'] == 'T'].index
df_all['Deck'].iloc[i] = 'A' | Titanic - Machine Learning from Disaster |
12,084,336 | from xgboost import XGBRegressor<prepare_x_and_y> | df_all['Deck'].replace(['A','B','C'], 'ABC', inplace = True)
df_all['Deck'].replace(['D', 'E'], 'DE', inplace = True)
df_all['Deck'].replace(['F', 'G'], 'FG', inplace = True ) | Titanic - Machine Learning from Disaster |
12,084,336 | X_train = train_data[['Province_State','Country_Region','Date']]
y_train = train_data[['ConfirmedCases', 'Fatalities']]<prepare_x_and_y> | df_all.isna().sum() | Titanic - Machine Learning from Disaster |
12,084,336 | y_train_confirm = y_train.ConfirmedCases
y_train_fatality = y_train.Fatalities<split> | df_all.drop('Cabin', axis = 1, inplace = True ) | Titanic - Machine Learning from Disaster |
12,084,336 | x_train = X_train.iloc[:,:].values
x_test = X_train.iloc[:,:].values<train_model> | a[(a>0.1)&(a<1)].dropna().drop_duplicates().rename(columns = {0:"Correlations"} ).style.background_gradient() | Titanic - Machine Learning from Disaster |
12,084,336 | model1 = XGBRegressor(n_estimators=400000)
model1.fit(X_train, y_train_confirm)
y_pred_confirm = model1.predict(test_data )<train_model> | df_all.isnull().sum() | Titanic - Machine Learning from Disaster |
12,084,336 | model2 = XGBRegressor(n_estimators=200000)
model2.fit(X_train,y_train_fatality)
y_pred_fat = model2.predict(test_data )<save_to_csv> | df_all['Fare'] = pd.qcut(df_all['Fare'], 13 ) | Titanic - Machine Learning from Disaster |
12,084,336 | df_sub = pd.DataFrame()
df_sub['ForecastId'] = Test_id
df_sub['ConfirmedCases'] = y_pred_confirm
df_sub['Fatalities'] = y_pred_fat
df_sub.to_csv('submission.csv', index=False )<import_modules> | df_all['Age'] = pd.qcut(df_all['Age'], 10 ) | Titanic - Machine Learning from Disaster |
12,084,336 | %matplotlib inline
<load_from_csv> | df_all['Family Size'] = df_all['Parch'] + df_all['SibSp'] + 1 | Titanic - Machine Learning from Disaster |
12,084,336 | train_df = pd.read_csv(".. /input/covid19-global-forecasting-week-4/train.csv")
test_df = pd.read_csv(".. /input/covid19-global-forecasting-week-4/test.csv")
submission = pd.read_csv(".. /input/covid19-global-forecasting-week-4/submission.csv")
<define_variables> | a = df_all['Ticket'].value_counts()
df_all['TicketC'] = df_all['Ticket'].map(a ) | Titanic - Machine Learning from Disaster |
12,084,336 | columns = ['ln(No_of_Days)', 'Country_Region','ConfirmedCases','Fatalities']
test_set_columns = ['ln(No_of_Days)', 'Country_Region']<feature_engineering> | df_all['Title'] = df_all['Name'].apply(lambda x : x.split(', ')[1].split('.')[0] ) | Titanic - Machine Learning from Disaster |
12,084,336 | def extract_features(df):
df['month'] = df['Date'].apply(lambda x: int(x.split(' ')[0].split('-')[1]))
df['day'] = df['Date'].apply(lambda x: int(x.split(' ')[0].split('-')[2]))
df['is_weekend'] =(( df.Date.astype('datetime64[ns]' ).dt.dayofweek)// 4 == 1 ).astype(float)
df['weekday'] = df.Date.astype('datetime64[ns]'... | df_all['Married'] = 0 | Titanic - Machine Learning from Disaster |
12,084,336 | extract_features(train_df)
extract_features(test_df )<drop_column> | df_all['Married'].loc[(df_all['Title'] == 'Mrs')] = 1 | Titanic - Machine Learning from Disaster |
12,084,336 | for i in range(len(train_df)) :
if train_df["Province_State"][i] != '':
train_df["Country_Region"][i] = train_df["Province_State"][i] + "(" + str(train_df["Country_Region"][i])+ ")"
for i in range(len(test_df)) :
if test_df["Province_State"][i] != '':
test_df["Country_Region"][i] = test_df["Province_State"][i] + "(" + ... | df_all['Title'] = df_all['Title'].replace(['Miss', 'Mrs','Ms', 'Mlle', 'Lady', 'Mme', 'the Countess', 'Dona'], 'Miss/Mrs/Ms')
df_all['Title'] = df_all['Title'].replace(['Dr', 'Col', 'Major', 'Jonkheer', 'Capt', 'Sir', 'Don', 'Rev'], 'Dr/Military/Noble/Clergy' ) | Titanic - Machine Learning from Disaster |
12,084,336 | i = 0
for value in train_df["Country_Region"].unique() :
if i < len(train_df):
j = 1
while(train_df["Country_Region"][i] == value):
train_df["day"][i] = j
j += 1; i += 1
if i == len(train_df):
break
i = 0
for value in test_df["Country_Region"].unique() :
if i < len(test_df):
j = 72
while(test_df["Country_Region"][i] ==... | df_all['Family'] = df_all['Name'].apply(lambda x : x.split(', ')[0] ) | Titanic - Machine Learning from Disaster |
12,084,336 | last_date = train_df.No_of_Days.max()
df_countries = train_df[train_df['No_of_Days']==last_date]
df_countries = df_countries.groupby('Country_Region', as_index=False)['ConfirmedCases','Fatalities'].sum()
df_countries = df_countries.nlargest(10,'ConfirmedCases')
df_trend = train_df.groupby(['No_of_Days','Country_Region... | train = df_all[:891]
test = df_all[891:]
dfs = [train,test] | Titanic - Machine Learning from Disaster |
12,084,336 | train_df['ConfirmedCases'] = np.log1p(train_df['ConfirmedCases'])
train_df['Fatalities'] = np.log1p(train_df['Fatalities'] )<split> | fam_survival_rate = train.groupby('Family')['Survived', 'Family', 'Family Size'].median()
ticket_survival_rate = train.groupby('Ticket')['Survived', 'Ticket','TicketC'].median() | Titanic - Machine Learning from Disaster |
12,084,336 | df_train = train_df[columns]
df_test = test_df[test_set_columns]
<normalization> | family_rates = {}
ticket_rates = {}
for i in range(len(fam_survival_rate)) :
if fam_survival_rate.index[i] in non_unique_fams and fam_survival_rate.iloc[i, 1] > 1:
family_rates[fam_survival_rate.index[i]] = fam_survival_rate.iloc[i,0]
for i in range(len(ticket_survival_rate)) :
if ticket_survival_rate.index[i] in non_u... | Titanic - Machine Learning from Disaster |
12,084,336 | submission = []
for country in df_train.Country_Region.unique() :
df_train1 = df_train[df_train["Country_Region"]==country]
cases = np.array(df_train1.ConfirmedCases)
fatalities = np.array(df_train1.Fatalities)
del df_train1['ConfirmedCases']
del df_train1['Fatalities']
lb = LabelEncoder()
df_train1['Country_Region']... | mean_survival_rate = train['Survived'].mean()
train_fam_survival = []
train_fam_survival_NA = []
test_fam_survival = []
test_fam_survival_NA = []
for i in range(len(train)) :
if(train['Family'].iloc[i] in family_rates):
train_fam_survival.append(family_rates[train['Family'].iloc[i]])
train_fam_survival_NA.append(1)
e... | Titanic - Machine Learning from Disaster |
12,084,336 | df_submit = pd.DataFrame(submission)
df_submit.to_csv(r'submission.csv', index=False )<merge> | train_ticket_survival = []
train_ticket_survival_NA = []
test_ticket_survival = []
test_ticket_survival_NA = []
for i in range(len(train)) :
if(train['Ticket'].iloc[i] in ticket_rates):
train_ticket_survival.append(ticket_rates[train['Ticket'].iloc[i]])
train_ticket_survival_NA.append(1)
else:
train_ticket_survival.a... | Titanic - Machine Learning from Disaster |
12,084,336 | output_df = pd.merge(test_df, df_submit, on='ForecastId')
<filter> | train['Family_survival_rate'] = train_fam_survival
test['Family_survival_rate'] = test_fam_survival
train['Family_survival_rate_NA'] = train_fam_survival_NA
test['Family_survival_rate_NA'] = test_fam_survival_NA
train['Ticket_survival_rate'] = train_ticket_survival
test['Ticket_survival_rate'] = test_ticket_survival
tr... | Titanic - Machine Learning from Disaster |
12,084,336 | output_df[output_df['Country_Region'] == 'India']<set_options> | for df in [train, test]:
df['Survival_Rate'] =(df['Family_survival_rate'] + df['Ticket_survival_rate'])/2
df['Survival_Rate_NA'] =(df['Family_survival_rate_NA'] + df['Ticket_survival_rate_NA'])/2 | Titanic - Machine Learning from Disaster |
12,084,336 | warnings.filterwarnings("ignore")
output_notebook(resources=INLINE )<load_from_csv> | non_numerica_features = ['Embarked', 'Sex', 'Deck', 'Title', 'Family Size Grouped', 'Age', 'Fare']
for df in dfs:
for feature in non_numerica_features:
df[feature] = LabelEncoder().fit_transform(df[feature])
| Titanic - Machine Learning from Disaster |
12,084,336 | country_codes = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')
country_codes = country_codes.drop('GDP(BILLIONS)', 1)
country_codes.rename(columns={'COUNTRY': 'Country', 'CODE': 'Code'}, inplace=True )<load_from_csv> | cat_features = ['Sex', 'Pclass', 'Embarked', 'Title', 'Family Size Grouped', 'Deck']
encoded_features =[]
for df in dfs:
for feature in cat_features:
encoded_feature = OneHotEncoder().fit_transform(df[feature].values.reshape(-1,1)).toarray()
n = df[feature].nunique()
cols = ['{}_{}'.format(feature,n)for n in range(1,n+... | Titanic - Machine Learning from Disaster |
12,084,336 | virus_data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv')
prev_index = 0
first_time = False
tmp = 0
for i, row in virus_data.iterrows() :
if(virus_data.loc[i,'SNo'] < 1342 and virus_data.loc[i,'Province/State']=='Hubei'):
if(first_time):
tmp = virus_data.loc[i,'Confirmed']
prev_index ... | train = pd.concat([train, *encoded_features[:6]], axis = 1)
test = pd.concat([test, *encoded_features[6:]], axis = 1)
train.head() | Titanic - Machine Learning from Disaster |
12,084,336 | top_country = virus_data.loc[virus_data['Date'] == virus_data['Date'].iloc[-1]]
top_country = top_country.groupby(['Code','Country'])['Confirmed'].sum().reset_index()
top_country = top_country.sort_values('Confirmed', ascending=False)
top_country = top_country[:30]
top_country_codes = top_country['Country']
top_countr... | df_all = concat(train, test ) | Titanic - Machine Learning from Disaster |
12,084,336 | countries = virus_data[virus_data['Country'].isin(top_country_codes)]
countries_day = countries.groupby(['Date','Code','Country'])['Confirmed','Deaths','Recovered'].sum().reset_index()
exponential_line_x = []
exponential_line_y = []
for i in range(16):
exponential_line_x.append(i)
exponential_line_y.append(i)
india =... | drop_cols = ['Deck', 'Embarked', 'Family', 'Family Size', 'Family Size Grouped', 'Survived',
'Name', 'Parch', 'PassengerId', 'Pclass', 'Sex', 'SibSp', 'Ticket', 'Title',
'Ticket_survival_rate', 'Family_survival_rate', 'Ticket_survival_rate_NA', 'Family_survival_rate_NA']
df_all.drop(columns = drop_cols, inplace = True)... | Titanic - Machine Learning from Disaster |
12,084,336 | init_notebook_mode(connected=False )<load_from_csv> | X_train = train.drop(columns= drop_cols)
Y_train = train['Survived'].values
X_test = test.drop(columns = drop_cols ) | Titanic - Machine Learning from Disaster |
12,084,336 | corona_data=pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv')
choro_map=px.choropleth(corona_data,
locations="Country/Region",
locationmode = "country names",
color="Confirmed",
hover_name="Country/Region",
animation_frame="ObservationDate"
)
choro_map.update_layout(
title_text = 'Globa... | X_train = StandardScaler().fit_transform(X_train ) | Titanic - Machine Learning from Disaster |
12,084,336 | zone=pd.read_csv('/kaggle/input/covid-19-india-zone-classification/lockdownindiawarningzones.csv' )<load_from_csv> | X_test = StandardScaler().fit_transform(X_test ) | Titanic - Machine Learning from Disaster |
12,084,336 | covid_India_cases = pd.read_csv('.. /input/covid19-in-india/covid_19_india.csv')
covid_India_cases.rename(columns={'State/UnionTerritory': 'State', 'Cured': 'Recovered', 'Confirmed': 'Confirmed'}, inplace=True)
statewise_cases = pd.DataFrame(covid_India_cases.groupby(['State'])['Confirmed', 'Deaths', 'Recovered'].max... | single_best_model = RandomForestClassifier(criterion='gini',
n_estimators=1100,
max_depth=5,
min_samples_split=4,
min_samples_leaf=5,
max_features='auto',
oob_score=True,
random_state=SEED,
n_jobs=-1,
verbose=1)
leaderboard_model = RandomForestClassifier(criterion='gini',
n_estimators=1750,
max_depth=7,
min_samples_sp... | Titanic - Machine Learning from Disaster |
12,084,336 | covid_India_cases = pd.read_csv('.. /input/covid19-in-india/covid_19_india.csv')
covid_India_cases.rename(columns={'State/UnionTerritory': 'State', 'Cured': 'Recovered', 'Confirmed': 'Confirmed'}, inplace=True)
statewise_cases = pd.DataFrame(covid_India_cases.groupby(['State'])['Confirmed', 'Deaths', 'Recovered'].max... | kfold = StratifiedKFold(n_splits = 10 ) | Titanic - Machine Learning from Disaster |
12,084,336 | ind_map=pd.read_csv('.. /input/covid19-in-india/covid_19_india.csv')
pos=pd.read_csv('.. /input/utm-of-india/UTM ZONES of INDIA.csv')
ind_map1=ind_map.merge(pos , left_on='State/UnionTerritory', right_on='State / Union Territory')
<set_options> | from collections import Counter
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors impo... | Titanic - Machine Learning from Disaster |
12,084,336 | warnings.filterwarnings('ignore')
%matplotlib inline<load_from_csv> | random_state = 2
classifiers = []
classifiers.append(SVC(random_state=random_state, max_iter=1000))
classifiers.append(DecisionTreeClassifier(random_state=random_state))
classifiers.append(AdaBoostClassifier(DecisionTreeClassifier(random_state=random_state),random_state=random_state,learning_rate=0.1))
classifiers.appe... | Titanic - Machine Learning from Disaster |
12,084,336 | age_details = pd.read_csv('.. /input/covid19-in-india/AgeGroupDetails.csv')
india_covid_19 = pd.read_csv('.. /input/covid19-in-india/covid_19_india.csv')
hospital_beds = pd.read_csv('.. /input/covid19-in-india/HospitalBedsIndia.csv')
individual_details = pd.read_csv('.. /input/covid19-in-india/IndividualDetails.csv'... | cv_results = []
for clf in classifiers:
cv_results.append(cross_val_score(clf, X_train, y = Y_train, cv = kfold, scoring = 'accuracy'))
| Titanic - Machine Learning from Disaster |
12,084,336 | confirmed_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')
deaths_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/tim... | Scores = []
Params = []
Algorithm = []
GB = GradientBoostingClassifier()
GB_param = {'loss' : ["deviance"],
'n_estimators' : [100,200,300],
'learning_rate': [0.1, 0.05, 0.01],
'max_depth': [4, 8],
'min_samples_leaf': [100,150],
'max_features': [0.3, 0.1]
}
grid_GB = GridSearchCV(GB, param_grid = GB_param, scoring = 'ac... | Titanic - Machine Learning from Disaster |
12,084,336 | india_covid_19['Date'] = pd.to_datetime(india_covid_19['Date'])
state_testing['Date'] = pd.to_datetime(state_testing['Date'] )<data_type_conversions> | LDA = LinearDiscriminantAnalysis()
LDA_param = {"solver" : ["svd"],
"tol" : [0.0001,0.0002,0.0003]}
grid_LDA = GridSearchCV(LDA, param_grid = LDA_param, cv = kfold, n_jobs = -1, verbose = 1, scoring = 'accuracy')
grid_LDA.fit(X_train, Y_train)
Scores.append(grid_LDA.best_score_)
Params.append(grid_LDA.best_params_)
... | Titanic - Machine Learning from Disaster |
12,084,336 | dates = list(confirmed_df.columns[4:])
dates = list(pd.to_datetime(dates))
dates_india = dates[8:]<feature_engineering> | LR = LogisticRegression(max_iter=1000)
LR_params = {
'penalty':['l1', 'l2'],
'C': np.logspace(0,4,10)
}
grid_LR = GridSearchCV(LR, param_grid = LR_params, cv = kfold, n_jobs = 1, verbose = 1, scoring = 'accuracy')
grid_LR.fit(X_train, Y_train)
Scores.append(grid_LR.best_score_)
Params.append(grid_LR.best_params_)
... | Titanic - Machine Learning from Disaster |
12,084,336 | df = pd.read_csv('.. /input/covid19-in-india/covid_19_india.csv')
data = df.copy()
data['Date'] = data['Date'].apply(pd.to_datetime)
data.drop(['Sno', 'Time'],axis=1,inplace=True)
data_apr = data[data['Date'] > pd.Timestamp(date(2020,4,12)) ]
state_cases = data_apr.groupby('State/UnionTerritory')['Confirmed','Deaths... | RF = RandomForestClassifier()
RF_params = {"max_depth": [None],
"max_features": [1, 3, 10],
"min_samples_split": [2, 3, 10],
"min_samples_leaf": [1, 3, 10],
"bootstrap": [False],
"n_estimators" :[100,300],
"criterion": ["gini"]
}
grid_RF = GridSearchCV(RF, param_grid = RF_params, scoring = 'accuracy', cv = kfold, n_job... | Titanic - Machine Learning from Disaster |
12,084,336 | state_testing = pd.read_csv('.. /input/covid19-in-india/StatewiseTestingDetails.csv')
state_testing<load_from_csv> | tuned = pd.DataFrame({
'Algorithm':Algorithm,
'Score':Scores,
'Best Parameters':Params
})
tuned | Titanic - Machine Learning from Disaster |
12,084,336 | labs = pd.read_csv(".. /input/covid19-in-india/ICMRTestingLabs.csv")
fig = px.treemap(labs, path=['state','city'],
color='city', hover_data=['lab','address'],
color_continuous_scale='reds')
fig.show()<load_from_csv> | tuned[tuned['Algorithm'] == 'LinearDiscriminantAnalysis']['Best Parameters'].values | Titanic - Machine Learning from Disaster |
12,084,336 | zone=pd.read_csv('/kaggle/input/covid-19-india-zone-classification/lockdownindiawarningzones.csv')
zone.style.set_properties(**{'background-color': 'black',
'color': 'lawngreen',
'border-color': 'white'} )<load_from_csv> | RF = RandomForestClassifier(bootstrap= False,
criterion='gini',
max_depth=None,
max_features= 3,
min_samples_leaf= 10,
min_samples_split= 10,
n_estimators= 100)
LDA = LinearDiscriminantAnalysis(solver='svd', tol=0.0001)
GB = GradientBoostingClassifier(learning_rate = 0.1, loss = 'deviance', max_depth= 4, max_features... | Titanic - Machine Learning from Disaster |
12,084,336 | country_codes = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')
country_codes = country_codes.drop('GDP(BILLIONS)', 1)
country_codes.rename(columns={'COUNTRY': 'Country', 'CODE': 'Code'}, inplace=True )<load_from_csv> | from sklearn.ensemble import StackingClassifier | Titanic - Machine Learning from Disaster |
12,084,336 | virus_data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv')
prev_index = 0
first_time = False
tmp = 0
for i, row in virus_data.iterrows() :
if(virus_data.loc[i,'SNo'] < 1342 and virus_data.loc[i,'Province/State']=='Hubei'):
if(first_time):
tmp = virus_data.loc[i,'Confirmed']
prev_index ... | def get_stacking() :
base = list()
base.append(( 'rf', RF))
base.append(( 'lda', LDA))
base.append(( 'GB', GB))
meta = LogisticRegression()
model = StackingClassifier(estimators=base,final_estimator=meta, cv = 5)
return model
| Titanic - Machine Learning from Disaster |
12,084,336 | covid_India_cases = pd.read_csv('.. /input/covid19-in-india/covid_19_india.csv')
covid_India_cases=covid_India_cases.dropna()
covid_India_cases.rename(columns={'State/UnionTerritory': 'State', 'Cured': 'Recovered', 'Confirmed': 'Confirmed'}, inplace=True)
covid_India_cases = covid_India_cases.fillna('unknow')
top_co... | from sklearn.model_selection import RepeatedStratifiedKFold | Titanic - Machine Learning from Disaster |
12,084,336 | plt.style.use('fivethirtyeight')
train=pd.read_csv('/kaggle/input/coronavirus-2019ncov/covid-19-all.csv' )<groupby> | def get_models() :
models = dict()
models['rf'] = RF
models['lda'] = LDA
models['GB'] = GB
models['stacking'] = get_stacking()
return models | Titanic - Machine Learning from Disaster |
12,084,336 | in_df = train[train['Country/Region']=='India'].groupby('Date')['Confirmed','Deaths','Recovered'].sum().reset_index(False)
in_df['Active']=in_df['Confirmed']-in_df['Deaths']-in_df['Recovered']
in_df = in_df[in_df.Active>=100]<prepare_x_and_y> | def eval_models(model, X, y):
kfold = RepeatedStratifiedKFold(n_splits=5, random_state= 1)
scores = cross_val_score(model, X, y, cv =kfold, scoring = 'accuracy')
return scores | Titanic - Machine Learning from Disaster |
12,084,336 | in_df['day_count'] = list(range(1,len(in_df)+1))
in_df['increase'] =(in_df.Active-in_df.Active.shift(1))
in_df['rate'] =(in_df.Active-in_df.Active.shift(1)) /in_df.Active
def sigmoid(x,c,a,b):
y = c*1 /(1 + np.exp(-a*(x-b)))
return y
xdata = np.array(list(in_df.day_count)[::2])
ydata = np.array(list(in_df.Active)[::2... | models = get_models()
results = []
for name, model in models.items() :
scores = eval_models(model, X_train, Y_train)
results.append(( name, np.mean(scores)))
| Titanic - Machine Learning from Disaster |
12,084,336 | warnings.filterwarnings("ignore")
output_notebook(resources=INLINE )<load_from_csv> | fit = get_stacking().fit(X_train, Y_train ) | Titanic - Machine Learning from Disaster |
12,084,336 | country_codes = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')
country_codes = country_codes.drop('GDP(BILLIONS)', 1)
country_codes.rename(columns={'COUNTRY': 'Country', 'CODE': 'Code'}, inplace=True )<load_from_csv> | test_sur = pd.Series(fit.predict(X_test), name ='Survived')
test_sur | Titanic - Machine Learning from Disaster |
12,084,336 | virus_data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv')
prev_index = 0
first_time = False
tmp = 0
for i, row in virus_data.iterrows() :
if(virus_data.loc[i,'SNo'] < 1342 and virus_data.loc[i,'Province/State']=='Hubei'):
if(first_time):
tmp = virus_data.loc[i,'Confirmed']
prev_index ... | test1 = test[['PassengerId','Survived']] | Titanic - Machine Learning from Disaster |
12,084,336 | top_country = virus_data.loc[virus_data['Date'] == virus_data['Date'].iloc[-1]]
top_country = top_country.groupby(['Code','Country'])['Confirmed'].sum().reset_index()
top_country = top_country.sort_values('Confirmed', ascending=False)
top_country = top_country[:30]
top_country_codes = top_country['Country']
top_countr... | test1['Survived'] = test_sur.values.astype(int)
test1 | Titanic - Machine Learning from Disaster |
12,084,336 | <set_options><EOS> | test1.to_csv('Stacked.csv', index = False ) | Titanic - Machine Learning from Disaster |
13,735,522 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<load_from_csv> | style.use('https://raw.githubusercontent.com/JoseGuzman/minibrain/master/minibrain/paper.mplstyle')
train = pd.read_csv(".. /input/titanic/train.csv", index_col='PassengerId')
test = pd.read_csv('.. /input/titanic/test.csv', index_col='PassengerId' ) | Titanic - Machine Learning from Disaster |
13,735,522 | corona_data=pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv')
choro_map=px.choropleth(corona_data,
locations="Country/Region",
locationmode = "country names",
color="Confirmed",
hover_name="Country/Region",
animation_frame="ObservationDate"
)
choro_map.update_layout(
title_text = 'Globa... | mydf = train.groupby('Survived' ).mean()
mydf | Titanic - Machine Learning from Disaster |
13,735,522 | zone=pd.read_csv('/kaggle/input/covid-19-india-zone-classification/lockdownindiawarningzones.csv' )<load_from_csv> | train.groupby('Sex')['Survived'].mean() | Titanic - Machine Learning from Disaster |
13,735,522 | covid_India_cases = pd.read_csv('.. /input/covid19-in-india/covid_19_india.csv')
covid_India_cases.rename(columns={'State/UnionTerritory': 'State', 'Cured': 'Recovered', 'Confirmed': 'Confirmed'}, inplace=True)
statewise_cases = pd.DataFrame(covid_India_cases.groupby(['State'])['Confirmed', 'Deaths', 'Recovered'].max... | from sklearn.pipeline import Pipeline | Titanic - Machine Learning from Disaster |
13,735,522 | covid_India_cases = pd.read_csv('.. /input/covid19-in-india/covid_19_india.csv')
covid_India_cases.rename(columns={'State/UnionTerritory': 'State', 'Cured': 'Recovered', 'Confirmed': 'Confirmed'}, inplace=True)
statewise_cases = pd.DataFrame(covid_India_cases.groupby(['State'])['Confirmed', 'Deaths', 'Recovered'].max... | class FeaturesTransformer() :
def __init__(self, create_family=True, dissect_cabin = True):
self.create_family = create_family
self.dissect_cabin = dissect_cabin
def fit(self, X, y=None, **fit_params):
return self
def transform(self, X, **transform_params):
mydf = X.copy()
mydf['Title'] = X.Name.str.extract('([... | Titanic - Machine Learning from Disaster |
13,735,522 | ind_map=pd.read_csv('.. /input/covid19-in-india/covid_19_india.csv')
pos=pd.read_csv('.. /input/utm-of-india/UTM ZONES of INDIA.csv')
ind_map1=ind_map.merge(pos , left_on='State/UnionTerritory', right_on='State / Union Territory')
<set_options> | preprocess = Pipeline([
('Preprocessing', FeaturesTransformer(create_family=True))
])
mytrain = preprocess.fit_transform(train ) | Titanic - Machine Learning from Disaster |
13,735,522 | warnings.filterwarnings('ignore')
%matplotlib inline<load_from_csv> | mytrain.groupby('Survived')['Title'].value_counts() | Titanic - Machine Learning from Disaster |
13,735,522 | age_details = pd.read_csv('.. /input/covid19-in-india/AgeGroupDetails.csv')
india_covid_19 = pd.read_csv('.. /input/covid19-in-india/covid_19_india.csv')
hospital_beds = pd.read_csv('.. /input/covid19-in-india/HospitalBedsIndia.csv')
individual_details = pd.read_csv('.. /input/covid19-in-india/IndividualDetails.csv'... | class FillerTransformer() :
def __init__(self, mean=True):
self.mean = mean
def fit(self, X, y=None, **fit_params):
return self
def transform(self, X, **transform_params):
mydf = X.copy()
mydf['Embarked'] = mydf['Embarked'].fillna('C')
mydf['Fare'].fillna(mydf['Fare'].median() , inplace = True)
if self.mean:
... | Titanic - Machine Learning from Disaster |
13,735,522 | confirmed_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')
deaths_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/tim... | preprocess = Pipeline([
('Preprocessing', FeaturesTransformer(create_family=True)) ,
('FillingValues', FillerTransformer(mean=True))
])
mytrain = preprocess.fit_transform(train)
mytrain.Age.isnull().sum() | Titanic - Machine Learning from Disaster |
13,735,522 | india_covid_19['Date'] = pd.to_datetime(india_covid_19['Date'])
state_testing['Date'] = pd.to_datetime(state_testing['Date'] )<data_type_conversions> | class ColumnsDelete() :
def __init__(self, columns=None):
self.columns = columns
def fit(self, X, y=None, **fit_params):
return self
def transform(self, X, **transform_params):
mydf = X.copy()
if self.columns:
mydf.drop(self.columns, axis=1, inplace=True)
return mydf | Titanic - Machine Learning from Disaster |
13,735,522 | dates = list(confirmed_df.columns[4:])
dates = list(pd.to_datetime(dates))
dates_india = dates[8:]<feature_engineering> | preprocess = Pipeline([
('Preprocessing', FeaturesTransformer(create_family=True)) ,
('FillingValues', FillerTransformer(mean=True)) ,
('DeleteColumns', ColumnsDelete(['Room', 'Ticket']))
])
mytrain = preprocess.fit_transform(train)
mytest = preprocess.fit_transform(test)
mytrain.info() , mytest.info() | Titanic - Machine Learning from Disaster |
13,735,522 | df = pd.read_csv('.. /input/covid19-in-india/covid_19_india.csv')
data = df.copy()
data['Date'] = data['Date'].apply(pd.to_datetime)
data.drop(['Sno', 'Time'],axis=1,inplace=True)
data_apr = data[data['Date'] > pd.Timestamp(date(2020,4,12)) ]
state_cases = data_apr.groupby('State/UnionTerritory')['Confirmed','Deaths... | class CategoryEncoder() :
def __init__(self, columns=None):
self.columns = columns
def fit(self, X, y=None, **fit_params):
return self
def transform(self, X, **transform_params):
mydf = X.copy()
mydf['Sex'] = mydf['Sex'].map({'male': 1, 'female': 0})
if self.columns:
mydf = pd.get_dummies(mydf, columns=self.colu... | Titanic - Machine Learning from Disaster |
13,735,522 | state_testing = pd.read_csv('.. /input/covid19-in-india/StatewiseTestingDetails.csv')
state_testing<load_from_csv> | preprocess = Pipeline([
('Preprocessing', FeaturesTransformer(create_family=True)) ,
('FillingValues', FillerTransformer(mean=True)) ,
('DeleteColumns', ColumnsDelete(['Room', 'Ticket'])) ,
('CategoEncoder', CategoryEncoder(['Embarked', 'CabinDeck', 'Title'])) ,
])
mytrain = preprocess.fit_transform(train)
mytrai... | Titanic - Machine Learning from Disaster |
13,735,522 | labs = pd.read_csv(".. /input/covid19-in-india/ICMRTestingLabs.csv")
fig = px.treemap(labs, path=['state','city'],
color='city', hover_data=['lab','address'],
color_continuous_scale='reds')
fig.show()<load_from_csv> | class myZScaler(BaseEstimator, TransformerMixin):
def __init__(self, columns = all, **init_params):
self.columns = columns
self.scaler = StandardScaler(**init_params)
def fit(self, X, y=None):
self.scaler.fit(X[self.columns], y)
return self
def transform(self, X: pd):
if self.columns is all:
self.columns = X.co... | Titanic - Machine Learning from Disaster |
13,735,522 | zone=pd.read_csv('/kaggle/input/covid-19-india-zone-classification/lockdownindiawarningzones.csv')
zone.style.set_properties(**{'background-color': 'black',
'color': 'lawngreen',
'border-color': 'white'} )<load_from_csv> | preprocess = Pipeline([
('Preprocessing', FeaturesTransformer(create_family=True)) ,
('FillingValues', FillerTransformer(mean=False)) ,
('DeleteColumns', ColumnsDelete(['Room', 'Ticket'])) ,
('CategoEncoder', CategoryEncoder(['Embarked', 'CabinDeck', 'Title'])) ,
('z-score' , myZScaler(['Age', 'Fare', 'FamilySize'... | Titanic - Machine Learning from Disaster |
13,735,522 | country_codes = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')
country_codes = country_codes.drop('GDP(BILLIONS)', 1)
country_codes.rename(columns={'COUNTRY': 'Country', 'CODE': 'Code'}, inplace=True )<load_from_csv> | mytrain = preprocess.fit_transform(train)
mytest = preprocess.fit_transform(test ) | Titanic - Machine Learning from Disaster |
13,735,522 | virus_data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv')
prev_index = 0
first_time = False
tmp = 0
for i, row in virus_data.iterrows() :
if(virus_data.loc[i,'SNo'] < 1342 and virus_data.loc[i,'Province/State']=='Hubei'):
if(first_time):
tmp = virus_data.loc[i,'Confirmed']
prev_index ... | target = mytrain['Survived']
mytrain.drop(['Survived'], axis=1, inplace=True ) | Titanic - Machine Learning from Disaster |
13,735,522 | covid_India_cases = pd.read_csv('.. /input/covid19-in-india/covid_19_india.csv')
covid_India_cases=covid_India_cases.dropna()
covid_India_cases.rename(columns={'State/UnionTerritory': 'State', 'Cured': 'Recovered', 'Confirmed': 'Confirmed'}, inplace=True)
covid_India_cases = covid_India_cases.fillna('unknow')
top_co... | RFCmodel = RandomForestClassifier(random_state=42)
RFCmodel.fit(mytrain, target)
print(f'Accuracy: {RFCmodel.score(mytrain,target)*100:.2f}%' ) | Titanic - Machine Learning from Disaster |
13,735,522 | plt.style.use('fivethirtyeight')
train=pd.read_csv('/kaggle/input/coronavirus-2019ncov/covid-19-all.csv' )<groupby> | myprediction = RFCmodel.predict(mytrain)
print(f'Accuracy : {accuracy_score(myprediction,target)*100:.2f}%')
print(f'Precission: {precision_score(myprediction, target)*100:.2f}%')
print(f'Recall : {recall_score(myprediction, target)*100:.2f}%')
print(f'F1-score : {f1_score(myprediction, target)*100:.2f}%' ) | Titanic - Machine Learning from Disaster |
13,735,522 | in_df = train[train['Country/Region']=='India'].groupby('Date')['Confirmed','Deaths','Recovered'].sum().reset_index(False)
in_df['Active']=in_df['Confirmed']-in_df['Deaths']-in_df['Recovered']
in_df = in_df[in_df.Active>=100]<prepare_x_and_y> | def evaluate_model(model):
accuracy = cross_val_score(model, mytrain, target, cv=5, scoring='accuracy' ).mean()
return accuracy
models = {
'Logistic regression':LogisticRegression(random_state = 42),
'Decision tree':DecisionTreeClassifier(random_state = 42),
'Random forest':RandomForestClassifier(random_state = 42)
... | Titanic - Machine Learning from Disaster |
13,735,522 | in_df['day_count'] = list(range(1,len(in_df)+1))
in_df['increase'] =(in_df.Active-in_df.Active.shift(1))
in_df['rate'] =(in_df.Active-in_df.Active.shift(1)) /in_df.Active
def sigmoid(x,c,a,b):
y = c*1 /(1 + np.exp(-a*(x-b)))
return y
xdata = np.array(list(in_df.day_count)[::2])
ydata = np.array(list(in_df.Active)[::2... | from sklearn.model_selection import GridSearchCV | Titanic - Machine Learning from Disaster |
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