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stringlengths 13
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|---|---|---|---|
34121580/cell_19 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['Deceased']
df2 = pd.read_csv('../input/covid-19-india/hotspot.csv')
df2.Red | code |
34121580/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
x = df1['Active']
y = target['Deceased']
model = sm.OLS(y, x).fit()
predictions = model.predict(x)
model.summary() | code |
34121580/cell_28 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['Deceased']
df2 = pd.read_csv('../input/covid-19-india/hotspot.csv')
df4 = pd.read_csv('../input/covid-19-india/hotty.csv')
df4.columns | code |
34121580/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['Deceased']
labels = list(df1.State)
decease = list(df1.Deceased)
print(labels) | code |
34121580/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['Deceased']
labels = list(df1.State)
decease = list(df1.Deceased)
explode = []
for i in labels:
explode.append(0.05)
plt.figure(figsize=(15, 10))
plt.pie(decease, labels=labels, autopct='%1.1f%%', startangle=9, explode=explode)
centre_circle = plt.Circle((0, 0), 0.7, fc='white')
fig = plt.gcf()
fig.gca().add_artist(centre_circle)
plt.tight_layout() | code |
34121580/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['Deceased']
labels = list(df1.State)
decease = list(df1.Deceased)
df1['Active'] = df1['Confirmed'] - (df1['Deceased'] + df1['Recovered'])
df1['Deceased Rate (per 100)'] = np.round(100 * df1['Deceased'] / df1['Confirmed'], 2)
df1['Recovered Rate (per 100)'] = np.round(100 * df1['Recovered'] / df1['Confirmed'], 2)
df1.sort_values('Confirmed', ascending=False).fillna(0).style.background_gradient(cmap='Blues', subset=['Confirmed']).background_gradient(cmap='Blues', subset=['Deceased']).background_gradient(cmap='Blues', subset=['Recovered']).background_gradient(cmap='Blues', subset=['Active']).background_gradient(cmap='Blues', subset=['Deceased Rate (per 100)']).background_gradient(cmap='Blues', subset=['Recovered Rate (per 100)']) | code |
34121580/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['Deceased']
labels = list(df1.State)
decease = list(df1.Deceased)
explode = []
for i in labels:
explode.append(0.05)
centre_circle = plt.Circle((0, 0), 0.7, fc='white')
fig = plt.gcf()
fig.gca().add_artist(centre_circle)
plt.tight_layout()
df2 = pd.read_csv('../input/covid-19-india/hotspot.csv')
df2.Red
df2.State
df2.dropna
df3 = df2.drop(37)
plt.scatter(df3.State, df3.Red) | code |
34121580/cell_12 | [
"text_html_output_1.png"
] | from pandas.plotting import andrews_curves
import pandas as pd
df1 = pd.read_csv('../input/indiastate/data state.csv')
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
target = pd.DataFrame(df1.Deceased, columns=['Deceased'])
X = df1[['Confirmed', 'Active', 'Recovered', 'Deceased']]
y = target['Deceased']
from pandas.plotting import andrews_curves
andrews_curves(df1, 'State', ax=None) | code |
128027656/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF_NONE
DF_NONE1 = df.loc[df['ZIP Code'] == 92717]
DF_NONE1['county'].fillna(value='irvine', inplace=True)
DF_NONE1['lat'].fillna(value=33.6462, inplace=True)
DF_NONE1['long'].fillna(value=-117.839, inplace=True)
DF_NONE1
DF_NONE2 = df.loc[df['ZIP Code'] == 92634]
DF_NONE2['county'].fillna(value='Fullerton', inplace=True)
DF_NONE2['lat'].fillna(value=33.8739, inplace=True)
DF_NONE2['long'].fillna(value=-117.9028, inplace=True)
DF_NONE2
DF_NONE3 = df.loc[df['ZIP Code'] == 96651]
DF_NONE3['county'].fillna(value='Rudno and Hronom', inplace=True)
DF_NONE3['lat'].fillna(value=48.4242, inplace=True)
DF_NONE3['long'].fillna(value=18.7071, inplace=True)
DF_NONE3 | code |
128027656/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF_NONE | code |
128027656/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF_NONE
DF_NONE1 = df.loc[df['ZIP Code'] == 92717]
DF_NONE1['county'].fillna(value='irvine', inplace=True)
DF_NONE1['lat'].fillna(value=33.6462, inplace=True)
DF_NONE1['long'].fillna(value=-117.839, inplace=True)
DF_NONE1
DF_NONE2 = df.loc[df['ZIP Code'] == 92634]
DF_NONE2['county'].fillna(value='Fullerton', inplace=True)
DF_NONE2['lat'].fillna(value=33.8739, inplace=True)
DF_NONE2['long'].fillna(value=-117.9028, inplace=True)
DF_NONE2
DF_NONE3 = df.loc[df['ZIP Code'] == 96651]
DF_NONE3['county'].fillna(value='Rudno and Hronom', inplace=True)
DF_NONE3['lat'].fillna(value=48.4242, inplace=True)
DF_NONE3['long'].fillna(value=18.7071, inplace=True)
DF_NONE3
DF_NONE4 = df.loc[df['ZIP Code'] == 9307]
DF_NONE4['county'].fillna(value='Albani', inplace=True)
DF_NONE4['lat'].fillna(value=40.68106, inplace=True)
DF_NONE4['long'].fillna(value=19.63539, inplace=True)
DF_NONE4
DF = df.dropna()
df1 = DF.append(DF_NONE1)
df1 = df1.append(DF_NONE2)
df1 = df1.append(DF_NONE3)
df1 = df1.append(DF_NONE4)
df1.reset_index(drop=True, inplace=True)
df1
df1.describe().T
df1.nunique()
df1 = df1.drop(['ID', 'ZIP Code', 'city', 'states'], axis=1)
nans = df1[df1.isna().any(axis=1)]
print(f'Total rows with NaNs: {nans.shape[0]}\n') | code |
128027656/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df | code |
128027656/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF_NONE
DF_NONE1 = df.loc[df['ZIP Code'] == 92717]
DF_NONE1['county'].fillna(value='irvine', inplace=True)
DF_NONE1['lat'].fillna(value=33.6462, inplace=True)
DF_NONE1['long'].fillna(value=-117.839, inplace=True)
DF_NONE1
DF_NONE2 = df.loc[df['ZIP Code'] == 92634]
DF_NONE2['county'].fillna(value='Fullerton', inplace=True)
DF_NONE2['lat'].fillna(value=33.8739, inplace=True)
DF_NONE2['long'].fillna(value=-117.9028, inplace=True)
DF_NONE2
DF_NONE3 = df.loc[df['ZIP Code'] == 96651]
DF_NONE3['county'].fillna(value='Rudno and Hronom', inplace=True)
DF_NONE3['lat'].fillna(value=48.4242, inplace=True)
DF_NONE3['long'].fillna(value=18.7071, inplace=True)
DF_NONE3
DF_NONE4 = df.loc[df['ZIP Code'] == 9307]
DF_NONE4['county'].fillna(value='Albani', inplace=True)
DF_NONE4['lat'].fillna(value=40.68106, inplace=True)
DF_NONE4['long'].fillna(value=19.63539, inplace=True)
DF_NONE4
DF = df.dropna()
df1 = DF.append(DF_NONE1)
df1 = df1.append(DF_NONE2)
df1 = df1.append(DF_NONE3)
df1 = df1.append(DF_NONE4)
df1.reset_index(drop=True, inplace=True)
df1
df1.describe().T
df1.nunique()
df1 = df1.drop(['ID', 'ZIP Code', 'city', 'states'], axis=1)
df1[df1['Experience'] < 0]['Experience'].value_counts() | code |
128027656/cell_26 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF_NONE
DF_NONE1 = df.loc[df['ZIP Code'] == 92717]
DF_NONE1['county'].fillna(value='irvine', inplace=True)
DF_NONE1['lat'].fillna(value=33.6462, inplace=True)
DF_NONE1['long'].fillna(value=-117.839, inplace=True)
DF_NONE1
DF_NONE2 = df.loc[df['ZIP Code'] == 92634]
DF_NONE2['county'].fillna(value='Fullerton', inplace=True)
DF_NONE2['lat'].fillna(value=33.8739, inplace=True)
DF_NONE2['long'].fillna(value=-117.9028, inplace=True)
DF_NONE2
DF_NONE3 = df.loc[df['ZIP Code'] == 96651]
DF_NONE3['county'].fillna(value='Rudno and Hronom', inplace=True)
DF_NONE3['lat'].fillna(value=48.4242, inplace=True)
DF_NONE3['long'].fillna(value=18.7071, inplace=True)
DF_NONE3
DF_NONE4 = df.loc[df['ZIP Code'] == 9307]
DF_NONE4['county'].fillna(value='Albani', inplace=True)
DF_NONE4['lat'].fillna(value=40.68106, inplace=True)
DF_NONE4['long'].fillna(value=19.63539, inplace=True)
DF_NONE4
DF = df.dropna()
df1 = DF.append(DF_NONE1)
df1 = df1.append(DF_NONE2)
df1 = df1.append(DF_NONE3)
df1 = df1.append(DF_NONE4)
df1.reset_index(drop=True, inplace=True)
df1
df1.describe().T
df1.nunique()
df1 = df1.drop(['ID', 'ZIP Code', 'city', 'states'], axis=1)
nans = df1[df1.isna().any(axis=1)]
numerical = ['Age', 'Experience', 'Income', 'CCAvg', 'Mortgage', 'lat', 'long']
for column in numerical:
plt.figure(figsize=(10, 5))
sns.distplot(df1, x=df1[column])
plt.title(column, backgroundcolor='black', color='white', fontsize=30)
plt.xticks(rotation=90)
plt.xlabel(column, fontsize=20)
plt.grid()
plt.show() | code |
128027656/cell_2 | [
"text_html_output_1.png"
] | pip install basemap | code |
128027656/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF_NONE
DF_NONE1 = df.loc[df['ZIP Code'] == 92717]
DF_NONE1['county'].fillna(value='irvine', inplace=True)
DF_NONE1['lat'].fillna(value=33.6462, inplace=True)
DF_NONE1['long'].fillna(value=-117.839, inplace=True)
DF_NONE1 | code |
128027656/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF_NONE
DF_NONE1 = df.loc[df['ZIP Code'] == 92717]
DF_NONE1['county'].fillna(value='irvine', inplace=True)
DF_NONE1['lat'].fillna(value=33.6462, inplace=True)
DF_NONE1['long'].fillna(value=-117.839, inplace=True)
DF_NONE1
DF_NONE2 = df.loc[df['ZIP Code'] == 92634]
DF_NONE2['county'].fillna(value='Fullerton', inplace=True)
DF_NONE2['lat'].fillna(value=33.8739, inplace=True)
DF_NONE2['long'].fillna(value=-117.9028, inplace=True)
DF_NONE2
DF_NONE3 = df.loc[df['ZIP Code'] == 96651]
DF_NONE3['county'].fillna(value='Rudno and Hronom', inplace=True)
DF_NONE3['lat'].fillna(value=48.4242, inplace=True)
DF_NONE3['long'].fillna(value=18.7071, inplace=True)
DF_NONE3
DF_NONE4 = df.loc[df['ZIP Code'] == 9307]
DF_NONE4['county'].fillna(value='Albani', inplace=True)
DF_NONE4['lat'].fillna(value=40.68106, inplace=True)
DF_NONE4['long'].fillna(value=19.63539, inplace=True)
DF_NONE4
DF = df.dropna()
df1 = DF.append(DF_NONE1)
df1 = df1.append(DF_NONE2)
df1 = df1.append(DF_NONE3)
df1 = df1.append(DF_NONE4)
df1.reset_index(drop=True, inplace=True)
df1
df1.describe().T
df1.nunique() | code |
128027656/cell_1 | [
"text_plain_output_1.png"
] | !pip install zipcodes | code |
128027656/cell_7 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum() | code |
128027656/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF_NONE
DF_NONE1 = df.loc[df['ZIP Code'] == 92717]
DF_NONE1['county'].fillna(value='irvine', inplace=True)
DF_NONE1['lat'].fillna(value=33.6462, inplace=True)
DF_NONE1['long'].fillna(value=-117.839, inplace=True)
DF_NONE1
DF_NONE2 = df.loc[df['ZIP Code'] == 92634]
DF_NONE2['county'].fillna(value='Fullerton', inplace=True)
DF_NONE2['lat'].fillna(value=33.8739, inplace=True)
DF_NONE2['long'].fillna(value=-117.9028, inplace=True)
DF_NONE2
DF_NONE3 = df.loc[df['ZIP Code'] == 96651]
DF_NONE3['county'].fillna(value='Rudno and Hronom', inplace=True)
DF_NONE3['lat'].fillna(value=48.4242, inplace=True)
DF_NONE3['long'].fillna(value=18.7071, inplace=True)
DF_NONE3
DF_NONE4 = df.loc[df['ZIP Code'] == 9307]
DF_NONE4['county'].fillna(value='Albani', inplace=True)
DF_NONE4['lat'].fillna(value=40.68106, inplace=True)
DF_NONE4['long'].fillna(value=19.63539, inplace=True)
DF_NONE4
DF = df.dropna()
df1 = DF.append(DF_NONE1)
df1 = df1.append(DF_NONE2)
df1 = df1.append(DF_NONE3)
df1 = df1.append(DF_NONE4)
df1.reset_index(drop=True, inplace=True)
df1 | code |
128027656/cell_3 | [
"text_plain_output_1.png"
] | from warnings import filterwarnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_classification
from sklearn import metrics
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import MultinomialNB
from sklearn.naive_bayes import ComplementNB
from sklearn.naive_bayes import BernoulliNB
import zipcodes
from mpl_toolkits.basemap import Basemap
from warnings import filterwarnings
filterwarnings('ignore') | code |
128027656/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF_NONE
DF_NONE1 = df.loc[df['ZIP Code'] == 92717]
DF_NONE1['county'].fillna(value='irvine', inplace=True)
DF_NONE1['lat'].fillna(value=33.6462, inplace=True)
DF_NONE1['long'].fillna(value=-117.839, inplace=True)
DF_NONE1
DF_NONE2 = df.loc[df['ZIP Code'] == 92634]
DF_NONE2['county'].fillna(value='Fullerton', inplace=True)
DF_NONE2['lat'].fillna(value=33.8739, inplace=True)
DF_NONE2['long'].fillna(value=-117.9028, inplace=True)
DF_NONE2
DF_NONE3 = df.loc[df['ZIP Code'] == 96651]
DF_NONE3['county'].fillna(value='Rudno and Hronom', inplace=True)
DF_NONE3['lat'].fillna(value=48.4242, inplace=True)
DF_NONE3['long'].fillna(value=18.7071, inplace=True)
DF_NONE3
DF_NONE4 = df.loc[df['ZIP Code'] == 9307]
DF_NONE4['county'].fillna(value='Albani', inplace=True)
DF_NONE4['lat'].fillna(value=40.68106, inplace=True)
DF_NONE4['long'].fillna(value=19.63539, inplace=True)
DF_NONE4
DF = df.dropna()
df1 = DF.append(DF_NONE1)
df1 = df1.append(DF_NONE2)
df1 = df1.append(DF_NONE3)
df1 = df1.append(DF_NONE4)
df1.reset_index(drop=True, inplace=True)
df1
df1.describe().T | code |
128027656/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF_NONE
DF_NONE1 = df.loc[df['ZIP Code'] == 92717]
DF_NONE1['county'].fillna(value='irvine', inplace=True)
DF_NONE1['lat'].fillna(value=33.6462, inplace=True)
DF_NONE1['long'].fillna(value=-117.839, inplace=True)
DF_NONE1
DF_NONE2 = df.loc[df['ZIP Code'] == 92634]
DF_NONE2['county'].fillna(value='Fullerton', inplace=True)
DF_NONE2['lat'].fillna(value=33.8739, inplace=True)
DF_NONE2['long'].fillna(value=-117.9028, inplace=True)
DF_NONE2
DF_NONE3 = df.loc[df['ZIP Code'] == 96651]
DF_NONE3['county'].fillna(value='Rudno and Hronom', inplace=True)
DF_NONE3['lat'].fillna(value=48.4242, inplace=True)
DF_NONE3['long'].fillna(value=18.7071, inplace=True)
DF_NONE3
DF_NONE4 = df.loc[df['ZIP Code'] == 9307]
DF_NONE4['county'].fillna(value='Albani', inplace=True)
DF_NONE4['lat'].fillna(value=40.68106, inplace=True)
DF_NONE4['long'].fillna(value=19.63539, inplace=True)
DF_NONE4 | code |
128027656/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/bank-personal-loan/Bank_Personal_Loan.csv')
df = pd.DataFrame(data)
df
df.isnull().sum()
DF_NONE = df.loc[(df['ZIP Code'] == 92634) | (df['ZIP Code'] == 92717) | (df['ZIP Code'] == 96651) | (df['ZIP Code'] == 9307)]
DF_NONE.reset_index(drop=True, inplace=True)
DF_NONE
DF_NONE1 = df.loc[df['ZIP Code'] == 92717]
DF_NONE1['county'].fillna(value='irvine', inplace=True)
DF_NONE1['lat'].fillna(value=33.6462, inplace=True)
DF_NONE1['long'].fillna(value=-117.839, inplace=True)
DF_NONE1
DF_NONE2 = df.loc[df['ZIP Code'] == 92634]
DF_NONE2['county'].fillna(value='Fullerton', inplace=True)
DF_NONE2['lat'].fillna(value=33.8739, inplace=True)
DF_NONE2['long'].fillna(value=-117.9028, inplace=True)
DF_NONE2 | code |
16118182/cell_21 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().T
bike_corr = bike.corr()
bike_corr
bike_cov = bike.cov()
bike_cov
def outlier_detect(bike):
for i in bike.describe().columns:
Q1=bike.describe().at['25%',i]
Q3=bike.describe().at['75%',i]
IQR=Q3 - Q1
LTV=Q1 - 1.5 * IQR
UTV=Q3 + 1.5 * IQR
x=np.array(bike[i])
p=[]
for j in x:
if j < LTV or j>UTV:
p.append(j)
print('\n Outliers for Column : ', i, ' Outliers count ', len(p))
print(p)
bike.nunique()
bike.duplicated().sum()
bike = bike.drop_duplicates()
bike.duplicated().sum() | code |
16118182/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().T
bike_corr = bike.corr()
bike_corr
bike_cov = bike.cov()
bike_cov | code |
16118182/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum() | code |
16118182/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape | code |
16118182/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().T
bike_corr = bike.corr()
bike_corr
bike_cov = bike.cov()
bike_cov
import matplotlib.pyplot as plt
plt.figure(figsize = (10,5))
ax = sns.boxplot(data = bike, orient = "h", color = "violet", palette = "Set1")
plt.show()
def outlier_detect(bike):
for i in bike.describe().columns:
Q1=bike.describe().at['25%',i]
Q3=bike.describe().at['75%',i]
IQR=Q3 - Q1
LTV=Q1 - 1.5 * IQR
UTV=Q3 + 1.5 * IQR
x=np.array(bike[i])
p=[]
for j in x:
if j < LTV or j>UTV:
p.append(j)
print('\n Outliers for Column : ', i, ' Outliers count ', len(p))
print(p)
bike.nunique()
bike.duplicated().sum()
bike = bike.drop_duplicates()
bike.duplicated().sum()
plt.figure(figsize=(12, 6))
g = sns.distplot(bike['registered'])
g.set_xlabel('registered', fontsize=12)
g.set_ylabel('Frequency', fontsize=12)
g.set_title('Frequency Distribuition- registered bikes', fontsize=20) | code |
16118182/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.info() | code |
16118182/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().T | code |
16118182/cell_19 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().T
bike_corr = bike.corr()
bike_corr
bike_cov = bike.cov()
bike_cov
def outlier_detect(bike):
for i in bike.describe().columns:
Q1=bike.describe().at['25%',i]
Q3=bike.describe().at['75%',i]
IQR=Q3 - Q1
LTV=Q1 - 1.5 * IQR
UTV=Q3 + 1.5 * IQR
x=np.array(bike[i])
p=[]
for j in x:
if j < LTV or j>UTV:
p.append(j)
print('\n Outliers for Column : ', i, ' Outliers count ', len(p))
print(p)
bike.nunique()
bike.duplicated().sum() | code |
16118182/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16118182/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T | code |
16118182/cell_18 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().T
bike_corr = bike.corr()
bike_corr
bike_cov = bike.cov()
bike_cov
def outlier_detect(bike):
for i in bike.describe().columns:
Q1=bike.describe().at['25%',i]
Q3=bike.describe().at['75%',i]
IQR=Q3 - Q1
LTV=Q1 - 1.5 * IQR
UTV=Q3 + 1.5 * IQR
x=np.array(bike[i])
p=[]
for j in x:
if j < LTV or j>UTV:
p.append(j)
print('\n Outliers for Column : ', i, ' Outliers count ', len(p))
print(p)
bike.nunique() | code |
16118182/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T | code |
16118182/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().T
bike_corr = bike.corr()
bike_corr
bike_cov = bike.cov()
bike_cov
all_columns = list(bike)
numeric_columns = ['season', 'holiday', 'workingday', 'weather', 'temp', 'atemp', 'humidity', 'windspeed', 'casual', 'registered', 'count']
categorical_columns = [x for x in all_columns if x not in numeric_columns]
print('\nNumeric columns')
print(numeric_columns)
print('\nCategorical columns')
print(categorical_columns) | code |
16118182/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().T
bike_corr = bike.corr()
bike_corr
bike_cov = bike.cov()
bike_cov
all_columns = list(bike)
numeric_columns = ['season', 'holiday', 'workingday', 'weather', 'temp', 'atemp', 'humidity', 'windspeed', 'casual', 'registered', 'count']
categorical_columns = [x for x in all_columns if x not in numeric_columns]
def outlier_detect(bike):
for i in bike.describe().columns:
Q1=bike.describe().at['25%',i]
Q3=bike.describe().at['75%',i]
IQR=Q3 - Q1
LTV=Q1 - 1.5 * IQR
UTV=Q3 + 1.5 * IQR
x=np.array(bike[i])
p=[]
for j in x:
if j < LTV or j>UTV:
p.append(j)
print('\n Outliers for Column : ', i, ' Outliers count ', len(p))
print(p)
x = bike[numeric_columns]
outlier_detect(x) | code |
16118182/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().T
bike_corr = bike.corr()
bike_corr
bike_cov = bike.cov()
bike_cov
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 5))
ax = sns.boxplot(data=bike, orient='h', color='violet', palette='Set1')
plt.show() | code |
16118182/cell_22 | [
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().T
bike_corr = bike.corr()
bike_corr
bike_cov = bike.cov()
bike_cov
def outlier_detect(bike):
for i in bike.describe().columns:
Q1=bike.describe().at['25%',i]
Q3=bike.describe().at['75%',i]
IQR=Q3 - Q1
LTV=Q1 - 1.5 * IQR
UTV=Q3 + 1.5 * IQR
x=np.array(bike[i])
p=[]
for j in x:
if j < LTV or j>UTV:
p.append(j)
print('\n Outliers for Column : ', i, ' Outliers count ', len(p))
print(p)
bike.nunique()
bike.duplicated().sum()
bike = bike.drop_duplicates()
bike.duplicated().sum()
bike.head(2) | code |
16118182/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]]) | code |
16118182/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns
bike.head(5).T
bike.tail(5).T
bike.isna().sum()
bike.isnull().apply(lambda x: [sum(x), sum(x) * 100 / bike.shape[0]])
bike.describe().T
bike_corr = bike.corr()
bike_corr | code |
16118182/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bike = pd.read_csv('../input/bike_share.csv')
bike.shape
bike.columns | code |
122244202/cell_25 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import BatchNormalization, Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
(X_train.shape, X_val.shape, y_train.shape, y_val.shape)
datagen = ImageDataGenerator(featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, rotation_range=15, zoom_range=0.01, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=False, vertical_flip=False)
datagen.fit(X_train)
model = Sequential([Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(28, 28, 1)), BatchNormalization(), Conv2D(32, (3, 3), padding='same', activation='relu'), BatchNormalization(), MaxPooling2D(2, 2), Dropout(0.25), Conv2D(64, (3, 3), padding='same', activation='relu'), BatchNormalization(), MaxPooling2D(2, 2), Conv2D(128, (3, 3), padding='same', activation='relu'), BatchNormalization(), MaxPooling2D(2, 2), Flatten(), Dense(256, activation='relu'), BatchNormalization(), Dropout(0.25), Dense(10, activation='softmax')])
model.summary()
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
callbacks = MyCallback(monitor='val_accuracy')
history = model.fit(datagen.flow(X_train, y_train, batch_size=100), steps_per_epoch=len(X_train) / 100, epochs=20, validation_data=(X_val, y_val), callbacks=[callbacks]) | code |
122244202/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/digit-recognizer/train.csv')
test_data = pd.read_csv('../input/digit-recognizer/test.csv')
submission = pd.read_csv('../input/digit-recognizer/sample_submission.csv')
train_data.head() | code |
122244202/cell_29 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import BatchNormalization, Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/digit-recognizer/train.csv')
test_data = pd.read_csv('../input/digit-recognizer/test.csv')
submission = pd.read_csv('../input/digit-recognizer/sample_submission.csv')
X = train_data.drop(columns='label')
X = X / 255
X_test = test_data.values.reshape(-1, 28, 28, 1)
X = X.values.reshape(-1, 28, 28, 1)
X_test = X_test / 255
(X_train.shape, X_val.shape, y_train.shape, y_val.shape)
datagen = ImageDataGenerator(featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, rotation_range=15, zoom_range=0.01, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=False, vertical_flip=False)
datagen.fit(X_train)
model = Sequential([Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(28, 28, 1)), BatchNormalization(), Conv2D(32, (3, 3), padding='same', activation='relu'), BatchNormalization(), MaxPooling2D(2, 2), Dropout(0.25), Conv2D(64, (3, 3), padding='same', activation='relu'), BatchNormalization(), MaxPooling2D(2, 2), Conv2D(128, (3, 3), padding='same', activation='relu'), BatchNormalization(), MaxPooling2D(2, 2), Flatten(), Dense(256, activation='relu'), BatchNormalization(), Dropout(0.25), Dense(10, activation='softmax')])
model.summary()
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
callbacks = MyCallback(monitor='val_accuracy')
history = model.fit(datagen.flow(X_train, y_train, batch_size=100), steps_per_epoch=len(X_train) / 100, epochs=20, validation_data=(X_val, y_val), callbacks=[callbacks])
predictions = model.predict(X_test) | code |
122244202/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
122244202/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/digit-recognizer/train.csv')
test_data = pd.read_csv('../input/digit-recognizer/test.csv')
submission = pd.read_csv('../input/digit-recognizer/sample_submission.csv')
train_data.isna().any().describe() | code |
122244202/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/digit-recognizer/train.csv')
test_data = pd.read_csv('../input/digit-recognizer/test.csv')
submission = pd.read_csv('../input/digit-recognizer/sample_submission.csv')
test_data.isna().any().describe() | code |
122244202/cell_16 | [
"text_html_output_1.png"
] | (X_train.shape, X_val.shape, y_train.shape, y_val.shape) | code |
122244202/cell_22 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import BatchNormalization, Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.models import Sequential
model = Sequential([Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(28, 28, 1)), BatchNormalization(), Conv2D(32, (3, 3), padding='same', activation='relu'), BatchNormalization(), MaxPooling2D(2, 2), Dropout(0.25), Conv2D(64, (3, 3), padding='same', activation='relu'), BatchNormalization(), MaxPooling2D(2, 2), Conv2D(128, (3, 3), padding='same', activation='relu'), BatchNormalization(), MaxPooling2D(2, 2), Flatten(), Dense(256, activation='relu'), BatchNormalization(), Dropout(0.25), Dense(10, activation='softmax')])
model.summary() | code |
122244202/cell_27 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import BatchNormalization, Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
(X_train.shape, X_val.shape, y_train.shape, y_val.shape)
datagen = ImageDataGenerator(featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, rotation_range=15, zoom_range=0.01, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=False, vertical_flip=False)
datagen.fit(X_train)
model = Sequential([Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(28, 28, 1)), BatchNormalization(), Conv2D(32, (3, 3), padding='same', activation='relu'), BatchNormalization(), MaxPooling2D(2, 2), Dropout(0.25), Conv2D(64, (3, 3), padding='same', activation='relu'), BatchNormalization(), MaxPooling2D(2, 2), Conv2D(128, (3, 3), padding='same', activation='relu'), BatchNormalization(), MaxPooling2D(2, 2), Flatten(), Dense(256, activation='relu'), BatchNormalization(), Dropout(0.25), Dense(10, activation='softmax')])
model.summary()
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
callbacks = MyCallback(monitor='val_accuracy')
history = model.fit(datagen.flow(X_train, y_train, batch_size=100), steps_per_epoch=len(X_train) / 100, epochs=20, validation_data=(X_val, y_val), callbacks=[callbacks])
fig, ax = plt.subplots(2, 1)
ax[0].plot(history.history['loss'], color='b', label='Training loss')
ax[0].plot(history.history['val_loss'], color='r', label='validation loss', axes=ax[0])
legend = ax[0].legend(loc='best', shadow=True)
ax[1].plot(history.history['accuracy'], color='b', label='Training accuracy')
ax[1].plot(history.history['val_accuracy'], color='r', label='Validation accuracy')
legend = ax[1].legend(loc='best', shadow=True) | code |
18132352/cell_9 | [
"text_plain_output_1.png"
] | from nltk.stem.wordnet import WordNetLemmatizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import pandas as pd
import re
import pandas as pd
df = pd.read_csv('../input/bbc-text.csv')
import re
from nltk.stem.wordnet import WordNetLemmatizer
stop_words = ['in', 'of', 'at', 'a', 'the']
def pre_process(text):
text = str(text).lower()
text = re.sub('((\\d+)[\\.])', '', text)
text = re.sub('</?.*?>', ' <> ', text)
text = text.replace('dont', "don't")
text = re.sub("[^a-zA-Z0-9.']+", ' ', text)
"\n Don't include this in the beginning. \n First check if there are some patterns that may be lost if we remove stopwords.\n "
text = [word for word in text.split(' ') if word not in stop_words]
lmtzr = WordNetLemmatizer()
text = ' '.join((lmtzr.lemmatize(i) for i in text))
return text
for i in range(len(df)):
df.text[i] = pre_process(df.text[i])
# Visualize the distribution of categories
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(10,6))
df.groupby('category').text.count().plot.bar(ylim=0)
plt.show()
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
train_data = df.text[0:int(0.75 * len(df))]
test_data = df.text[int(0.75 * len(df)) + 1:]
train_target = df.category[0:int(0.75 * len(df))]
test_target = df.category[int(0.75 * len(df)) + 1:]
stop_words = ['in', 'of', 'at', 'a', 'the']
ngram_vectorizer = CountVectorizer(binary=True, ngram_range=(1, 3), stop_words=stop_words)
ngram_vectorizer.fit(train_data)
X_train = ngram_vectorizer.transform(train_data)
X_test = ngram_vectorizer.transform(test_data)
model = LogisticRegression()
model.fit(X_train, train_target)
test_acc = accuracy_score(test_target, model.predict(X_test))
print('Test accuracy: {0:.2f}%'.format(100 * test_acc)) | code |
18132352/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.stem.wordnet import WordNetLemmatizer
import pandas as pd
import re
import pandas as pd
df = pd.read_csv('../input/bbc-text.csv')
import re
from nltk.stem.wordnet import WordNetLemmatizer
stop_words = ['in', 'of', 'at', 'a', 'the']
def pre_process(text):
text = str(text).lower()
text = re.sub('((\\d+)[\\.])', '', text)
text = re.sub('</?.*?>', ' <> ', text)
text = text.replace('dont', "don't")
text = re.sub("[^a-zA-Z0-9.']+", ' ', text)
"\n Don't include this in the beginning. \n First check if there are some patterns that may be lost if we remove stopwords.\n "
text = [word for word in text.split(' ') if word not in stop_words]
lmtzr = WordNetLemmatizer()
text = ' '.join((lmtzr.lemmatize(i) for i in text))
return text
for i in range(len(df)):
df.text[i] = pre_process(df.text[i])
df.head(10) | code |
18132352/cell_2 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/bbc-text.csv')
df.head(10) | code |
18132352/cell_11 | [
"text_html_output_1.png"
] | from nltk.stem.wordnet import WordNetLemmatizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/bbc-text.csv')
import re
from nltk.stem.wordnet import WordNetLemmatizer
stop_words = ['in', 'of', 'at', 'a', 'the']
def pre_process(text):
text = str(text).lower()
text = re.sub('((\\d+)[\\.])', '', text)
text = re.sub('</?.*?>', ' <> ', text)
text = text.replace('dont', "don't")
text = re.sub("[^a-zA-Z0-9.']+", ' ', text)
"\n Don't include this in the beginning. \n First check if there are some patterns that may be lost if we remove stopwords.\n "
text = [word for word in text.split(' ') if word not in stop_words]
lmtzr = WordNetLemmatizer()
text = ' '.join((lmtzr.lemmatize(i) for i in text))
return text
for i in range(len(df)):
df.text[i] = pre_process(df.text[i])
# Visualize the distribution of categories
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(10,6))
df.groupby('category').text.count().plot.bar(ylim=0)
plt.show()
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
train_data = df.text[0:int(0.75 * len(df))]
test_data = df.text[int(0.75 * len(df)) + 1:]
train_target = df.category[0:int(0.75 * len(df))]
test_target = df.category[int(0.75 * len(df)) + 1:]
stop_words = ['in', 'of', 'at', 'a', 'the']
ngram_vectorizer = CountVectorizer(binary=True, ngram_range=(1, 3), stop_words=stop_words)
ngram_vectorizer.fit(train_data)
X_train = ngram_vectorizer.transform(train_data)
X_test = ngram_vectorizer.transform(test_data)
model = LogisticRegression()
model.fit(X_train, train_target)
test_acc = accuracy_score(test_target, model.predict(X_test))
import seaborn as sns
from sklearn.metrics import confusion_matrix
conf_mat = confusion_matrix(df.category[int(0.75 * len(df)) + 1:], model.predict(X_test))
fig, ax = plt.subplots(figsize=(10, 8))
sns.heatmap(conf_mat, annot=True, fmt='d', xticklabels=df.category.unique(), yticklabels=df.category.unique())
plt.ylabel('Actual')
plt.xlabel('Predicted')
plt.show() | code |
18132352/cell_12 | [
"text_html_output_1.png"
] | from nltk.stem.wordnet import WordNetLemmatizer
from sklearn import metrics
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
import pandas as pd
df = pd.read_csv('../input/bbc-text.csv')
import re
from nltk.stem.wordnet import WordNetLemmatizer
stop_words = ['in', 'of', 'at', 'a', 'the']
def pre_process(text):
text = str(text).lower()
text = re.sub('((\\d+)[\\.])', '', text)
text = re.sub('</?.*?>', ' <> ', text)
text = text.replace('dont', "don't")
text = re.sub("[^a-zA-Z0-9.']+", ' ', text)
"\n Don't include this in the beginning. \n First check if there are some patterns that may be lost if we remove stopwords.\n "
text = [word for word in text.split(' ') if word not in stop_words]
lmtzr = WordNetLemmatizer()
text = ' '.join((lmtzr.lemmatize(i) for i in text))
return text
for i in range(len(df)):
df.text[i] = pre_process(df.text[i])
# Visualize the distribution of categories
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(10,6))
df.groupby('category').text.count().plot.bar(ylim=0)
plt.show()
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
train_data = df.text[0:int(0.75 * len(df))]
test_data = df.text[int(0.75 * len(df)) + 1:]
train_target = df.category[0:int(0.75 * len(df))]
test_target = df.category[int(0.75 * len(df)) + 1:]
stop_words = ['in', 'of', 'at', 'a', 'the']
ngram_vectorizer = CountVectorizer(binary=True, ngram_range=(1, 3), stop_words=stop_words)
ngram_vectorizer.fit(train_data)
X_train = ngram_vectorizer.transform(train_data)
X_test = ngram_vectorizer.transform(test_data)
model = LogisticRegression()
model.fit(X_train, train_target)
test_acc = accuracy_score(test_target, model.predict(X_test))
import seaborn as sns
from sklearn.metrics import confusion_matrix
conf_mat = confusion_matrix(df.category[int(0.75*len(df))+1:], model.predict(X_test))
fig, ax = plt.subplots(figsize=(10,8))
sns.heatmap(conf_mat, annot=True, fmt='d', xticklabels=df.category.unique(), yticklabels=df.category.unique())
plt.ylabel('Actual')
plt.xlabel('Predicted')
plt.show()
from sklearn import metrics
print(metrics.classification_report(test_target, model.predict(X_test), target_names=df.category.unique())) | code |
18132352/cell_5 | [
"image_output_1.png"
] | from nltk.stem.wordnet import WordNetLemmatizer
import matplotlib.pyplot as plt
import pandas as pd
import re
import pandas as pd
df = pd.read_csv('../input/bbc-text.csv')
import re
from nltk.stem.wordnet import WordNetLemmatizer
stop_words = ['in', 'of', 'at', 'a', 'the']
def pre_process(text):
text = str(text).lower()
text = re.sub('((\\d+)[\\.])', '', text)
text = re.sub('</?.*?>', ' <> ', text)
text = text.replace('dont', "don't")
text = re.sub("[^a-zA-Z0-9.']+", ' ', text)
"\n Don't include this in the beginning. \n First check if there are some patterns that may be lost if we remove stopwords.\n "
text = [word for word in text.split(' ') if word not in stop_words]
lmtzr = WordNetLemmatizer()
text = ' '.join((lmtzr.lemmatize(i) for i in text))
return text
for i in range(len(df)):
df.text[i] = pre_process(df.text[i])
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(10, 6))
df.groupby('category').text.count().plot.bar(ylim=0)
plt.show() | code |
73074059/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.groupby('Classe')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.describe() | code |
73074059/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100 | code |
73074059/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
print(f'Número de Linhas e Colunas: {df.shape}')
df.head() | code |
73074059/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.groupby('Classe')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.Classe.value_counts(normalize=True).sort_index() * 100
df.Sexo.value_counts(normalize=True) * 100
df.isnull().sum()
df.Idade.isnull().sum() / df.shape[0] * 100
import seaborn as sns
sns.histplot(data=df.Sobreviveu, x=df.Idade, hue=df.Sobreviveu, bins=14) | code |
73074059/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.groupby('Classe')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.Classe.value_counts(normalize=True).sort_index() * 100
df.Sexo.value_counts(normalize=True) * 100
df.isnull().sum()
df.Idade.isnull().sum() / df.shape[0] * 100
import seaborn as sns
df.groupby('Faixa Etária')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
import seaborn as sns
df.groupby('Renda')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.Cabine.isnull().sum() / df.shape[0] * 100 | code |
73074059/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.groupby('Classe')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.Classe.value_counts(normalize=True).sort_index() * 100
df.Sexo.value_counts(normalize=True) * 100
df.isnull().sum()
df.Idade.isnull().sum() / df.shape[0] * 100
import seaborn as sns
df.groupby('Faixa Etária')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
import seaborn as sns
df.groupby('Renda')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100 | code |
73074059/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.groupby('Classe')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100 | code |
73074059/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100 | code |
73074059/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.groupby('Classe')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.Classe.value_counts(normalize=True).sort_index() * 100
df.Sexo.value_counts(normalize=True) * 100
df.isnull().sum()
df.Idade.isnull().sum() / df.shape[0] * 100 | code |
73074059/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.groupby('Classe')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.Classe.value_counts(normalize=True).sort_index() * 100
df.Sexo.value_counts(normalize=True) * 100 | code |
73074059/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.groupby('Classe')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.Classe.value_counts(normalize=True).sort_index() * 100
df.Sexo.value_counts(normalize=True) * 100
df.isnull().sum() | code |
73074059/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.groupby('Classe')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.Classe.value_counts(normalize=True).sort_index() * 100
df.Sexo.value_counts(normalize=True) * 100
df.isnull().sum()
df.Idade.isnull().sum() / df.shape[0] * 100
import seaborn as sns
df.groupby('Faixa Etária')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
import seaborn as sns
sns.histplot(data=df.Sobreviveu, x=df['Preço da Passagem'], hue=df.Sobreviveu, bins=12) | code |
73074059/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.groupby('Classe')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.Classe.value_counts(normalize=True).sort_index() * 100 | code |
73074059/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.groupby('Classe')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.Classe.value_counts(normalize=True).sort_index() * 100
df.Sexo.value_counts(normalize=True) * 100
df.isnull().sum()
df.Idade.isnull().sum() / df.shape[0] * 100
import seaborn as sns
df.groupby('Faixa Etária')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100 | code |
73074059/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.Sobreviveu.value_counts(normalize=True) * 100
df.groupby('Sexo')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.groupby('Classe')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
df.Classe.value_counts(normalize=True).sort_index() * 100
df.Sexo.value_counts(normalize=True) * 100
df.isnull().sum()
df.Idade.isnull().sum() / df.shape[0] * 100
import seaborn as sns
df.groupby('Faixa Etária')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
import seaborn as sns
df.groupby('Renda')['Sobreviveu'].value_counts(normalize=True).sort_index() * 100
len(df['Preço da Passagem'].unique()) | code |
73074059/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/titanic/train.csv')
df.columns = ['Id', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade', 'Familiares', 'Dependentes', 'Ticket', 'Preço da Passagem', 'Cabine', 'Local de Embarque']
df.head() | code |
32068693/cell_9 | [
"text_html_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df['Province_State'].fillna('state', inplace=True)
df['Country_Region'] = [country_name.replace("'", '') for country_name in df['Country_Region']]
data = []
countries = df.Country_Region.unique()
for country in countries:
provinces = df[df.Country_Region == country].Province_State.unique()
for province in provinces:
temp_df = df[(df['Country_Region'] == country) & (df['Province_State'] == province)]
for i in range(0, 77):
Iday1 = float(temp_df.iloc[i].ConfirmedCases)
Iday2 = float(temp_df.iloc[i + 1].ConfirmedCases)
Iday3 = float(temp_df.iloc[i + 2].ConfirmedCases)
Iday4 = float(temp_df.iloc[i + 3].ConfirmedCases)
Iday5 = float(temp_df.iloc[i + 4].ConfirmedCases)
Iday6 = float(temp_df.iloc[i + 5].ConfirmedCases)
Iday7 = float(temp_df.iloc[i + 6].ConfirmedCases)
Fday1 = float(temp_df.iloc[i].Fatalities)
Fday2 = float(temp_df.iloc[i + 1].Fatalities)
Fday3 = float(temp_df.iloc[i + 2].Fatalities)
Fday4 = float(temp_df.iloc[i + 3].Fatalities)
Fday5 = float(temp_df.iloc[i + 4].Fatalities)
Fday6 = float(temp_df.iloc[i + 5].Fatalities)
Fday7 = float(temp_df.iloc[i + 6].Fatalities)
target_infection = float(temp_df.iloc[i + 7].ConfirmedCases)
target_fatal = float(temp_df.iloc[i + 7].Fatalities)
data.append({'Iday1': Iday1, 'Iday2': Iday2, 'Iday3': Iday3, 'Iday4': Iday4, 'Iday5': Iday5, 'Iday6': Iday6, 'Iday7': Iday7, 'Fday1': Fday1, 'Fday2': Fday2, 'Fday3': Fday3, 'Fday4': Fday4, 'Fday5': Fday5, 'Fday6': Fday6, 'Fday7': Fday7, 'target_infection': target_infection, 'target_fatal': target_fatal})
new_data = pd.DataFrame(data)
X_y = shuffle(new_data)
y_cases = X_y['target_infection']
y_fatal = X_y['target_fatal']
X = X_y.drop(['target_infection', 'target_fatal'], axis=1)
X_train_cases, X_test_cases, y_train_cases, y_test_cases = train_test_split(X, y_cases, test_size=0.33)
X_train_fatal, X_test_fatal, y_train_fatal, y_test_fatal = train_test_split(X, y_fatal, test_size=0.33)
best_alpha = 1000
best_itr = 2700
final_reg_case = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_itr)
final_reg_case.fit(X_train_cases, y_train_cases)
pred = final_reg_case.predict(X_test_cases)
print('The RMSE value', mean_squared_error(y_test_cases, pred) ** 0.5) | code |
32068693/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df['Province_State'].fillna('state', inplace=True)
df['Country_Region'] = [country_name.replace("'", '') for country_name in df['Country_Region']]
data = []
countries = df.Country_Region.unique()
for country in countries:
provinces = df[df.Country_Region == country].Province_State.unique()
for province in provinces:
temp_df = df[(df['Country_Region'] == country) & (df['Province_State'] == province)]
for i in range(0, 77):
Iday1 = float(temp_df.iloc[i].ConfirmedCases)
Iday2 = float(temp_df.iloc[i + 1].ConfirmedCases)
Iday3 = float(temp_df.iloc[i + 2].ConfirmedCases)
Iday4 = float(temp_df.iloc[i + 3].ConfirmedCases)
Iday5 = float(temp_df.iloc[i + 4].ConfirmedCases)
Iday6 = float(temp_df.iloc[i + 5].ConfirmedCases)
Iday7 = float(temp_df.iloc[i + 6].ConfirmedCases)
Fday1 = float(temp_df.iloc[i].Fatalities)
Fday2 = float(temp_df.iloc[i + 1].Fatalities)
Fday3 = float(temp_df.iloc[i + 2].Fatalities)
Fday4 = float(temp_df.iloc[i + 3].Fatalities)
Fday5 = float(temp_df.iloc[i + 4].Fatalities)
Fday6 = float(temp_df.iloc[i + 5].Fatalities)
Fday7 = float(temp_df.iloc[i + 6].Fatalities)
target_infection = float(temp_df.iloc[i + 7].ConfirmedCases)
target_fatal = float(temp_df.iloc[i + 7].Fatalities)
data.append({'Iday1': Iday1, 'Iday2': Iday2, 'Iday3': Iday3, 'Iday4': Iday4, 'Iday5': Iday5, 'Iday6': Iday6, 'Iday7': Iday7, 'Fday1': Fday1, 'Fday2': Fday2, 'Fday3': Fday3, 'Fday4': Fday4, 'Fday5': Fday5, 'Fday6': Fday6, 'Fday7': Fday7, 'target_infection': target_infection, 'target_fatal': target_fatal})
new_data = pd.DataFrame(data)
X_y = shuffle(new_data)
y_cases = X_y['target_infection']
y_fatal = X_y['target_fatal']
X = X_y.drop(['target_infection', 'target_fatal'], axis=1)
X_train_cases, X_test_cases, y_train_cases, y_test_cases = train_test_split(X, y_cases, test_size=0.33)
X_train_fatal, X_test_fatal, y_train_fatal, y_test_fatal = train_test_split(X, y_fatal, test_size=0.33)
reg_case = ElasticNet(random_state=42, l1_ratio=0.1, max_iter=2700)
params = [{'alpha': [10 ** (-4), 10 ** (-3), 10 ** (-2), 10 ** (-1), 10 ** 0, 10 ** 1, 10 ** 2, 10 ** 3, 10 ** 4]}]
clf = RandomizedSearchCV(reg_case, params, cv=5, scoring='neg_root_mean_squared_error', return_train_score=True)
search = clf.fit(X_train_cases, y_train_cases)
results = pd.DataFrame.from_dict(clf.cv_results_)
best_alpha = 1000
best_itr = 2700
final_reg_case = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_itr)
final_reg_case.fit(X_train_cases, y_train_cases)
reg_fatal = ElasticNet(random_state=42, l1_ratio=0.1, max_iter=3800)
params = [{'alpha': [10 ** (-4), 10 ** (-3), 10 ** (-2), 10 ** (-1), 10 ** 0, 10 ** 1, 10 ** 2, 10 ** 3, 10 ** 4]}]
clf = RandomizedSearchCV(reg_fatal, params, cv=5, scoring='neg_root_mean_squared_error', return_train_score=True)
search = clf.fit(X_train_fatal, y_train_fatal)
results = pd.DataFrame.from_dict(clf.cv_results_)
best_alpha = 100
best_iter = 3800
final_reg_fatal = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_iter)
final_reg_fatal.fit(X_train_fatal, y_train_fatal)
data = []
countries = df.Country_Region.unique()
for country in countries:
provinces = df[df.Country_Region == country].Province_State.unique()
for province in provinces:
temp_df = df[(df['Country_Region'] == country) & (df['Province_State'] == province)]
for i in range(0, 77):
Iday1 = float(temp_df.iloc[i].ConfirmedCases)
Iday2 = float(temp_df.iloc[i + 1].ConfirmedCases)
Iday3 = float(temp_df.iloc[i + 2].ConfirmedCases)
Iday4 = float(temp_df.iloc[i + 3].ConfirmedCases)
Iday5 = float(temp_df.iloc[i + 4].ConfirmedCases)
Iday6 = float(temp_df.iloc[i + 5].ConfirmedCases)
Iday7 = float(temp_df.iloc[i + 6].ConfirmedCases)
Fday1 = float(temp_df.iloc[i].Fatalities)
Fday2 = float(temp_df.iloc[i + 1].Fatalities)
Fday3 = float(temp_df.iloc[i + 2].Fatalities)
Fday4 = float(temp_df.iloc[i + 3].Fatalities)
Fday5 = float(temp_df.iloc[i + 4].Fatalities)
Fday6 = float(temp_df.iloc[i + 5].Fatalities)
Fday7 = float(temp_df.iloc[i + 6].Fatalities)
if Iday6 == 0:
iavg = 1
else:
iavg = Iday7 / Iday6
if Fday6 == 0:
favg = 1
else:
favg = Fday7 / Fday6
target_infection = float(temp_df.iloc[i + 7].ConfirmedCases)
target_fatal = float(temp_df.iloc[i + 7].Fatalities)
data.append({'Iday1': Iday1, 'Iday2': Iday2, 'Iday3': Iday3, 'Iday4': Iday4, 'Iday5': Iday5, 'Iday6': Iday6, 'Iday7': Iday7, 'Fday1': Fday1, 'Fday2': Fday2, 'Fday3': Fday3, 'Fday4': Fday4, 'Fday5': Fday5, 'Fday6': Fday6, 'Fday7': Fday7, 'iratio': iavg, 'fratio': favg, 'target_infection': target_infection, 'target_fatal': target_fatal})
featured = pd.DataFrame(data)
X_y_f = shuffle(featured)
y_cases_f = X_y_f['target_infection']
y_fatal_f = X_y_f['target_fatal']
X_f = X_y_f.drop(['target_infection', 'target_fatal'], axis=1)
X_train_cases_f, X_test_cases_f, y_train_cases_f, y_test_cases_f = train_test_split(X_f, y_cases_f, test_size=0.33)
X_train_fatal_f, X_test_fatal_f, y_train_fatal_f, y_test_fatal_f = train_test_split(X_f, y_fatal_f, test_size=0.33)
best_alpha = 10000
best_itr = 4500
final_reg_case_f = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_itr)
final_reg_case_f.fit(X_train_cases_f, y_train_cases_f)
pred_f = final_reg_case_f.predict(X_test_cases_f)
best_alpha = 100
best_itr = 2700
final_reg_fatal_f = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_itr)
final_reg_fatal_f.fit(X_train_fatal_f, y_train_fatal_f)
pred_f = final_reg_fatal_f.predict(X_test_fatal_f)
print('RMSE is:', mean_squared_error(y_test_fatal_f, pred_f) ** 0.5) | code |
32068693/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df['Province_State'].fillna('state', inplace=True)
df['Country_Region'] = [country_name.replace("'", '') for country_name in df['Country_Region']]
data = []
countries = df.Country_Region.unique()
for country in countries:
provinces = df[df.Country_Region == country].Province_State.unique()
for province in provinces:
temp_df = df[(df['Country_Region'] == country) & (df['Province_State'] == province)]
for i in range(0, 77):
Iday1 = float(temp_df.iloc[i].ConfirmedCases)
Iday2 = float(temp_df.iloc[i + 1].ConfirmedCases)
Iday3 = float(temp_df.iloc[i + 2].ConfirmedCases)
Iday4 = float(temp_df.iloc[i + 3].ConfirmedCases)
Iday5 = float(temp_df.iloc[i + 4].ConfirmedCases)
Iday6 = float(temp_df.iloc[i + 5].ConfirmedCases)
Iday7 = float(temp_df.iloc[i + 6].ConfirmedCases)
Fday1 = float(temp_df.iloc[i].Fatalities)
Fday2 = float(temp_df.iloc[i + 1].Fatalities)
Fday3 = float(temp_df.iloc[i + 2].Fatalities)
Fday4 = float(temp_df.iloc[i + 3].Fatalities)
Fday5 = float(temp_df.iloc[i + 4].Fatalities)
Fday6 = float(temp_df.iloc[i + 5].Fatalities)
Fday7 = float(temp_df.iloc[i + 6].Fatalities)
target_infection = float(temp_df.iloc[i + 7].ConfirmedCases)
target_fatal = float(temp_df.iloc[i + 7].Fatalities)
data.append({'Iday1': Iday1, 'Iday2': Iday2, 'Iday3': Iday3, 'Iday4': Iday4, 'Iday5': Iday5, 'Iday6': Iday6, 'Iday7': Iday7, 'Fday1': Fday1, 'Fday2': Fday2, 'Fday3': Fday3, 'Fday4': Fday4, 'Fday5': Fday5, 'Fday6': Fday6, 'Fday7': Fday7, 'target_infection': target_infection, 'target_fatal': target_fatal})
new_data = pd.DataFrame(data)
X_y = shuffle(new_data)
y_cases = X_y['target_infection']
y_fatal = X_y['target_fatal']
X = X_y.drop(['target_infection', 'target_fatal'], axis=1)
X_train_cases, X_test_cases, y_train_cases, y_test_cases = train_test_split(X, y_cases, test_size=0.33)
X_train_fatal, X_test_fatal, y_train_fatal, y_test_fatal = train_test_split(X, y_fatal, test_size=0.33)
print('Shape of infection train dataset:', (X_train_cases.shape, y_train_cases.shape))
print('Shape of infection test dataset:', (X_test_cases.shape, y_test_cases.shape))
print('Shape of fatal train dataset:', (X_train_fatal.shape, y_train_fatal.shape))
print('Shape of fatal test dataset:', (X_test_fatal.shape, y_test_fatal.shape)) | code |
32068693/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
print(df.shape, '\n', df.head()) | code |
32068693/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df['Province_State'].fillna('state', inplace=True)
df['Country_Region'] = [country_name.replace("'", '') for country_name in df['Country_Region']]
data = []
countries = df.Country_Region.unique()
for country in countries:
provinces = df[df.Country_Region == country].Province_State.unique()
for province in provinces:
temp_df = df[(df['Country_Region'] == country) & (df['Province_State'] == province)]
for i in range(0, 77):
Iday1 = float(temp_df.iloc[i].ConfirmedCases)
Iday2 = float(temp_df.iloc[i + 1].ConfirmedCases)
Iday3 = float(temp_df.iloc[i + 2].ConfirmedCases)
Iday4 = float(temp_df.iloc[i + 3].ConfirmedCases)
Iday5 = float(temp_df.iloc[i + 4].ConfirmedCases)
Iday6 = float(temp_df.iloc[i + 5].ConfirmedCases)
Iday7 = float(temp_df.iloc[i + 6].ConfirmedCases)
Fday1 = float(temp_df.iloc[i].Fatalities)
Fday2 = float(temp_df.iloc[i + 1].Fatalities)
Fday3 = float(temp_df.iloc[i + 2].Fatalities)
Fday4 = float(temp_df.iloc[i + 3].Fatalities)
Fday5 = float(temp_df.iloc[i + 4].Fatalities)
Fday6 = float(temp_df.iloc[i + 5].Fatalities)
Fday7 = float(temp_df.iloc[i + 6].Fatalities)
target_infection = float(temp_df.iloc[i + 7].ConfirmedCases)
target_fatal = float(temp_df.iloc[i + 7].Fatalities)
data.append({'Iday1': Iday1, 'Iday2': Iday2, 'Iday3': Iday3, 'Iday4': Iday4, 'Iday5': Iday5, 'Iday6': Iday6, 'Iday7': Iday7, 'Fday1': Fday1, 'Fday2': Fday2, 'Fday3': Fday3, 'Fday4': Fday4, 'Fday5': Fday5, 'Fday6': Fday6, 'Fday7': Fday7, 'target_infection': target_infection, 'target_fatal': target_fatal})
new_data = pd.DataFrame(data)
X_y = shuffle(new_data)
y_cases = X_y['target_infection']
y_fatal = X_y['target_fatal']
X = X_y.drop(['target_infection', 'target_fatal'], axis=1)
X_train_cases, X_test_cases, y_train_cases, y_test_cases = train_test_split(X, y_cases, test_size=0.33)
X_train_fatal, X_test_fatal, y_train_fatal, y_test_fatal = train_test_split(X, y_fatal, test_size=0.33)
best_alpha = 1000
best_itr = 2700
final_reg_case = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_itr)
final_reg_case.fit(X_train_cases, y_train_cases)
best_alpha = 100
best_iter = 3800
final_reg_fatal = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_iter)
final_reg_fatal.fit(X_train_fatal, y_train_fatal) | code |
32068693/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df['Province_State'].fillna('state', inplace=True)
df['Country_Region'] = [country_name.replace("'", '') for country_name in df['Country_Region']]
data = []
countries = df.Country_Region.unique()
for country in countries:
provinces = df[df.Country_Region == country].Province_State.unique()
for province in provinces:
temp_df = df[(df['Country_Region'] == country) & (df['Province_State'] == province)]
for i in range(0, 77):
Iday1 = float(temp_df.iloc[i].ConfirmedCases)
Iday2 = float(temp_df.iloc[i + 1].ConfirmedCases)
Iday3 = float(temp_df.iloc[i + 2].ConfirmedCases)
Iday4 = float(temp_df.iloc[i + 3].ConfirmedCases)
Iday5 = float(temp_df.iloc[i + 4].ConfirmedCases)
Iday6 = float(temp_df.iloc[i + 5].ConfirmedCases)
Iday7 = float(temp_df.iloc[i + 6].ConfirmedCases)
Fday1 = float(temp_df.iloc[i].Fatalities)
Fday2 = float(temp_df.iloc[i + 1].Fatalities)
Fday3 = float(temp_df.iloc[i + 2].Fatalities)
Fday4 = float(temp_df.iloc[i + 3].Fatalities)
Fday5 = float(temp_df.iloc[i + 4].Fatalities)
Fday6 = float(temp_df.iloc[i + 5].Fatalities)
Fday7 = float(temp_df.iloc[i + 6].Fatalities)
target_infection = float(temp_df.iloc[i + 7].ConfirmedCases)
target_fatal = float(temp_df.iloc[i + 7].Fatalities)
data.append({'Iday1': Iday1, 'Iday2': Iday2, 'Iday3': Iday3, 'Iday4': Iday4, 'Iday5': Iday5, 'Iday6': Iday6, 'Iday7': Iday7, 'Fday1': Fday1, 'Fday2': Fday2, 'Fday3': Fday3, 'Fday4': Fday4, 'Fday5': Fday5, 'Fday6': Fday6, 'Fday7': Fday7, 'target_infection': target_infection, 'target_fatal': target_fatal})
new_data = pd.DataFrame(data)
X_y = shuffle(new_data)
y_cases = X_y['target_infection']
y_fatal = X_y['target_fatal']
X = X_y.drop(['target_infection', 'target_fatal'], axis=1)
X_train_cases, X_test_cases, y_train_cases, y_test_cases = train_test_split(X, y_cases, test_size=0.33)
X_train_fatal, X_test_fatal, y_train_fatal, y_test_fatal = train_test_split(X, y_fatal, test_size=0.33)
reg_case = ElasticNet(random_state=42, l1_ratio=0.1, max_iter=2700)
params = [{'alpha': [10 ** (-4), 10 ** (-3), 10 ** (-2), 10 ** (-1), 10 ** 0, 10 ** 1, 10 ** 2, 10 ** 3, 10 ** 4]}]
clf = RandomizedSearchCV(reg_case, params, cv=5, scoring='neg_root_mean_squared_error', return_train_score=True)
search = clf.fit(X_train_cases, y_train_cases)
results = pd.DataFrame.from_dict(clf.cv_results_)
best_alpha = 1000
best_itr = 2700
final_reg_case = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_itr)
final_reg_case.fit(X_train_cases, y_train_cases)
reg_fatal = ElasticNet(random_state=42, l1_ratio=0.1, max_iter=3800)
params = [{'alpha': [10 ** (-4), 10 ** (-3), 10 ** (-2), 10 ** (-1), 10 ** 0, 10 ** 1, 10 ** 2, 10 ** 3, 10 ** 4]}]
clf = RandomizedSearchCV(reg_fatal, params, cv=5, scoring='neg_root_mean_squared_error', return_train_score=True)
search = clf.fit(X_train_fatal, y_train_fatal)
results = pd.DataFrame.from_dict(clf.cv_results_)
best_alpha = 100
best_iter = 3800
final_reg_fatal = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_iter)
final_reg_fatal.fit(X_train_fatal, y_train_fatal)
data = []
countries = df.Country_Region.unique()
for country in countries:
provinces = df[df.Country_Region == country].Province_State.unique()
for province in provinces:
temp_df = df[(df['Country_Region'] == country) & (df['Province_State'] == province)]
for i in range(0, 77):
Iday1 = float(temp_df.iloc[i].ConfirmedCases)
Iday2 = float(temp_df.iloc[i + 1].ConfirmedCases)
Iday3 = float(temp_df.iloc[i + 2].ConfirmedCases)
Iday4 = float(temp_df.iloc[i + 3].ConfirmedCases)
Iday5 = float(temp_df.iloc[i + 4].ConfirmedCases)
Iday6 = float(temp_df.iloc[i + 5].ConfirmedCases)
Iday7 = float(temp_df.iloc[i + 6].ConfirmedCases)
Fday1 = float(temp_df.iloc[i].Fatalities)
Fday2 = float(temp_df.iloc[i + 1].Fatalities)
Fday3 = float(temp_df.iloc[i + 2].Fatalities)
Fday4 = float(temp_df.iloc[i + 3].Fatalities)
Fday5 = float(temp_df.iloc[i + 4].Fatalities)
Fday6 = float(temp_df.iloc[i + 5].Fatalities)
Fday7 = float(temp_df.iloc[i + 6].Fatalities)
if Iday6 == 0:
iavg = 1
else:
iavg = Iday7 / Iday6
if Fday6 == 0:
favg = 1
else:
favg = Fday7 / Fday6
target_infection = float(temp_df.iloc[i + 7].ConfirmedCases)
target_fatal = float(temp_df.iloc[i + 7].Fatalities)
data.append({'Iday1': Iday1, 'Iday2': Iday2, 'Iday3': Iday3, 'Iday4': Iday4, 'Iday5': Iday5, 'Iday6': Iday6, 'Iday7': Iday7, 'Fday1': Fday1, 'Fday2': Fday2, 'Fday3': Fday3, 'Fday4': Fday4, 'Fday5': Fday5, 'Fday6': Fday6, 'Fday7': Fday7, 'iratio': iavg, 'fratio': favg, 'target_infection': target_infection, 'target_fatal': target_fatal})
featured = pd.DataFrame(data)
X_y_f = shuffle(featured)
y_cases_f = X_y_f['target_infection']
y_fatal_f = X_y_f['target_fatal']
X_f = X_y_f.drop(['target_infection', 'target_fatal'], axis=1)
X_train_cases_f, X_test_cases_f, y_train_cases_f, y_test_cases_f = train_test_split(X_f, y_cases_f, test_size=0.33)
X_train_fatal_f, X_test_fatal_f, y_train_fatal_f, y_test_fatal_f = train_test_split(X_f, y_fatal_f, test_size=0.33)
best_alpha = 10000
best_itr = 4500
final_reg_case_f = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_itr)
final_reg_case_f.fit(X_train_cases_f, y_train_cases_f)
best_alpha = 100
best_itr = 2700
final_reg_fatal_f = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_itr)
final_reg_fatal_f.fit(X_train_fatal_f, y_train_fatal_f) | code |
32068693/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import math
import random
from sklearn.metrics import mean_squared_error
from sklearn import metrics
from sklearn.linear_model import ElasticNet
from sklearn.model_selection import RandomizedSearchCV
import pickle
from sklearn.model_selection import train_test_split
from tqdm import tqdm
from sklearn.utils import shuffle
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32068693/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df['Province_State'].fillna('state', inplace=True)
df['Country_Region'] = [country_name.replace("'", '') for country_name in df['Country_Region']]
data = []
countries = df.Country_Region.unique()
for country in countries:
provinces = df[df.Country_Region == country].Province_State.unique()
for province in provinces:
temp_df = df[(df['Country_Region'] == country) & (df['Province_State'] == province)]
for i in range(0, 77):
Iday1 = float(temp_df.iloc[i].ConfirmedCases)
Iday2 = float(temp_df.iloc[i + 1].ConfirmedCases)
Iday3 = float(temp_df.iloc[i + 2].ConfirmedCases)
Iday4 = float(temp_df.iloc[i + 3].ConfirmedCases)
Iday5 = float(temp_df.iloc[i + 4].ConfirmedCases)
Iday6 = float(temp_df.iloc[i + 5].ConfirmedCases)
Iday7 = float(temp_df.iloc[i + 6].ConfirmedCases)
Fday1 = float(temp_df.iloc[i].Fatalities)
Fday2 = float(temp_df.iloc[i + 1].Fatalities)
Fday3 = float(temp_df.iloc[i + 2].Fatalities)
Fday4 = float(temp_df.iloc[i + 3].Fatalities)
Fday5 = float(temp_df.iloc[i + 4].Fatalities)
Fday6 = float(temp_df.iloc[i + 5].Fatalities)
Fday7 = float(temp_df.iloc[i + 6].Fatalities)
target_infection = float(temp_df.iloc[i + 7].ConfirmedCases)
target_fatal = float(temp_df.iloc[i + 7].Fatalities)
data.append({'Iday1': Iday1, 'Iday2': Iday2, 'Iday3': Iday3, 'Iday4': Iday4, 'Iday5': Iday5, 'Iday6': Iday6, 'Iday7': Iday7, 'Fday1': Fday1, 'Fday2': Fday2, 'Fday3': Fday3, 'Fday4': Fday4, 'Fday5': Fday5, 'Fday6': Fday6, 'Fday7': Fday7, 'target_infection': target_infection, 'target_fatal': target_fatal})
new_data = pd.DataFrame(data)
X_y = shuffle(new_data)
y_cases = X_y['target_infection']
y_fatal = X_y['target_fatal']
X = X_y.drop(['target_infection', 'target_fatal'], axis=1)
X_train_cases, X_test_cases, y_train_cases, y_test_cases = train_test_split(X, y_cases, test_size=0.33)
X_train_fatal, X_test_fatal, y_train_fatal, y_test_fatal = train_test_split(X, y_fatal, test_size=0.33)
best_alpha = 1000
best_itr = 2700
final_reg_case = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_itr)
final_reg_case.fit(X_train_cases, y_train_cases) | code |
32068693/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df['Province_State'].fillna('state', inplace=True)
df['Country_Region'] = [country_name.replace("'", '') for country_name in df['Country_Region']]
data = []
countries = df.Country_Region.unique()
for country in countries:
provinces = df[df.Country_Region == country].Province_State.unique()
for province in provinces:
temp_df = df[(df['Country_Region'] == country) & (df['Province_State'] == province)]
for i in range(0, 77):
Iday1 = float(temp_df.iloc[i].ConfirmedCases)
Iday2 = float(temp_df.iloc[i + 1].ConfirmedCases)
Iday3 = float(temp_df.iloc[i + 2].ConfirmedCases)
Iday4 = float(temp_df.iloc[i + 3].ConfirmedCases)
Iday5 = float(temp_df.iloc[i + 4].ConfirmedCases)
Iday6 = float(temp_df.iloc[i + 5].ConfirmedCases)
Iday7 = float(temp_df.iloc[i + 6].ConfirmedCases)
Fday1 = float(temp_df.iloc[i].Fatalities)
Fday2 = float(temp_df.iloc[i + 1].Fatalities)
Fday3 = float(temp_df.iloc[i + 2].Fatalities)
Fday4 = float(temp_df.iloc[i + 3].Fatalities)
Fday5 = float(temp_df.iloc[i + 4].Fatalities)
Fday6 = float(temp_df.iloc[i + 5].Fatalities)
Fday7 = float(temp_df.iloc[i + 6].Fatalities)
target_infection = float(temp_df.iloc[i + 7].ConfirmedCases)
target_fatal = float(temp_df.iloc[i + 7].Fatalities)
data.append({'Iday1': Iday1, 'Iday2': Iday2, 'Iday3': Iday3, 'Iday4': Iday4, 'Iday5': Iday5, 'Iday6': Iday6, 'Iday7': Iday7, 'Fday1': Fday1, 'Fday2': Fday2, 'Fday3': Fday3, 'Fday4': Fday4, 'Fday5': Fday5, 'Fday6': Fday6, 'Fday7': Fday7, 'target_infection': target_infection, 'target_fatal': target_fatal})
new_data = pd.DataFrame(data)
X_y = shuffle(new_data)
y_cases = X_y['target_infection']
y_fatal = X_y['target_fatal']
X = X_y.drop(['target_infection', 'target_fatal'], axis=1)
X_train_cases, X_test_cases, y_train_cases, y_test_cases = train_test_split(X, y_cases, test_size=0.33)
X_train_fatal, X_test_fatal, y_train_fatal, y_test_fatal = train_test_split(X, y_fatal, test_size=0.33)
reg_case = ElasticNet(random_state=42, l1_ratio=0.1, max_iter=2700)
params = [{'alpha': [10 ** (-4), 10 ** (-3), 10 ** (-2), 10 ** (-1), 10 ** 0, 10 ** 1, 10 ** 2, 10 ** 3, 10 ** 4]}]
clf = RandomizedSearchCV(reg_case, params, cv=5, scoring='neg_root_mean_squared_error', return_train_score=True)
search = clf.fit(X_train_cases, y_train_cases)
results = pd.DataFrame.from_dict(clf.cv_results_)
best_alpha = 1000
best_itr = 2700
final_reg_case = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_itr)
final_reg_case.fit(X_train_cases, y_train_cases)
reg_fatal = ElasticNet(random_state=42, l1_ratio=0.1, max_iter=3800)
params = [{'alpha': [10 ** (-4), 10 ** (-3), 10 ** (-2), 10 ** (-1), 10 ** 0, 10 ** 1, 10 ** 2, 10 ** 3, 10 ** 4]}]
clf = RandomizedSearchCV(reg_fatal, params, cv=5, scoring='neg_root_mean_squared_error', return_train_score=True)
search = clf.fit(X_train_fatal, y_train_fatal)
results = pd.DataFrame.from_dict(clf.cv_results_)
best_alpha = 100
best_iter = 3800
final_reg_fatal = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_iter)
final_reg_fatal.fit(X_train_fatal, y_train_fatal)
data = []
countries = df.Country_Region.unique()
for country in countries:
provinces = df[df.Country_Region == country].Province_State.unique()
for province in provinces:
temp_df = df[(df['Country_Region'] == country) & (df['Province_State'] == province)]
for i in range(0, 77):
Iday1 = float(temp_df.iloc[i].ConfirmedCases)
Iday2 = float(temp_df.iloc[i + 1].ConfirmedCases)
Iday3 = float(temp_df.iloc[i + 2].ConfirmedCases)
Iday4 = float(temp_df.iloc[i + 3].ConfirmedCases)
Iday5 = float(temp_df.iloc[i + 4].ConfirmedCases)
Iday6 = float(temp_df.iloc[i + 5].ConfirmedCases)
Iday7 = float(temp_df.iloc[i + 6].ConfirmedCases)
Fday1 = float(temp_df.iloc[i].Fatalities)
Fday2 = float(temp_df.iloc[i + 1].Fatalities)
Fday3 = float(temp_df.iloc[i + 2].Fatalities)
Fday4 = float(temp_df.iloc[i + 3].Fatalities)
Fday5 = float(temp_df.iloc[i + 4].Fatalities)
Fday6 = float(temp_df.iloc[i + 5].Fatalities)
Fday7 = float(temp_df.iloc[i + 6].Fatalities)
if Iday6 == 0:
iavg = 1
else:
iavg = Iday7 / Iday6
if Fday6 == 0:
favg = 1
else:
favg = Fday7 / Fday6
target_infection = float(temp_df.iloc[i + 7].ConfirmedCases)
target_fatal = float(temp_df.iloc[i + 7].Fatalities)
data.append({'Iday1': Iday1, 'Iday2': Iday2, 'Iday3': Iday3, 'Iday4': Iday4, 'Iday5': Iday5, 'Iday6': Iday6, 'Iday7': Iday7, 'Fday1': Fday1, 'Fday2': Fday2, 'Fday3': Fday3, 'Fday4': Fday4, 'Fday5': Fday5, 'Fday6': Fday6, 'Fday7': Fday7, 'iratio': iavg, 'fratio': favg, 'target_infection': target_infection, 'target_fatal': target_fatal})
featured = pd.DataFrame(data)
X_y_f = shuffle(featured)
y_cases_f = X_y_f['target_infection']
y_fatal_f = X_y_f['target_fatal']
X_f = X_y_f.drop(['target_infection', 'target_fatal'], axis=1)
X_train_cases_f, X_test_cases_f, y_train_cases_f, y_test_cases_f = train_test_split(X_f, y_cases_f, test_size=0.33)
X_train_fatal_f, X_test_fatal_f, y_train_fatal_f, y_test_fatal_f = train_test_split(X_f, y_fatal_f, test_size=0.33)
best_alpha = 10000
best_itr = 4500
final_reg_case_f = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_itr)
final_reg_case_f.fit(X_train_cases_f, y_train_cases_f) | code |
32068693/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df['Province_State'].fillna('state', inplace=True)
df['Country_Region'] = [country_name.replace("'", '') for country_name in df['Country_Region']]
print(df.shape, '\n', df.head()) | code |
32068693/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df['Province_State'].fillna('state', inplace=True)
df['Country_Region'] = [country_name.replace("'", '') for country_name in df['Country_Region']]
data = []
countries = df.Country_Region.unique()
for country in countries:
provinces = df[df.Country_Region == country].Province_State.unique()
for province in provinces:
temp_df = df[(df['Country_Region'] == country) & (df['Province_State'] == province)]
for i in range(0, 77):
Iday1 = float(temp_df.iloc[i].ConfirmedCases)
Iday2 = float(temp_df.iloc[i + 1].ConfirmedCases)
Iday3 = float(temp_df.iloc[i + 2].ConfirmedCases)
Iday4 = float(temp_df.iloc[i + 3].ConfirmedCases)
Iday5 = float(temp_df.iloc[i + 4].ConfirmedCases)
Iday6 = float(temp_df.iloc[i + 5].ConfirmedCases)
Iday7 = float(temp_df.iloc[i + 6].ConfirmedCases)
Fday1 = float(temp_df.iloc[i].Fatalities)
Fday2 = float(temp_df.iloc[i + 1].Fatalities)
Fday3 = float(temp_df.iloc[i + 2].Fatalities)
Fday4 = float(temp_df.iloc[i + 3].Fatalities)
Fday5 = float(temp_df.iloc[i + 4].Fatalities)
Fday6 = float(temp_df.iloc[i + 5].Fatalities)
Fday7 = float(temp_df.iloc[i + 6].Fatalities)
target_infection = float(temp_df.iloc[i + 7].ConfirmedCases)
target_fatal = float(temp_df.iloc[i + 7].Fatalities)
data.append({'Iday1': Iday1, 'Iday2': Iday2, 'Iday3': Iday3, 'Iday4': Iday4, 'Iday5': Iday5, 'Iday6': Iday6, 'Iday7': Iday7, 'Fday1': Fday1, 'Fday2': Fday2, 'Fday3': Fday3, 'Fday4': Fday4, 'Fday5': Fday5, 'Fday6': Fday6, 'Fday7': Fday7, 'target_infection': target_infection, 'target_fatal': target_fatal})
new_data = pd.DataFrame(data)
X_y = shuffle(new_data)
y_cases = X_y['target_infection']
y_fatal = X_y['target_fatal']
X = X_y.drop(['target_infection', 'target_fatal'], axis=1)
X_train_cases, X_test_cases, y_train_cases, y_test_cases = train_test_split(X, y_cases, test_size=0.33)
X_train_fatal, X_test_fatal, y_train_fatal, y_test_fatal = train_test_split(X, y_fatal, test_size=0.33)
reg_case = ElasticNet(random_state=42, l1_ratio=0.1, max_iter=2700)
params = [{'alpha': [10 ** (-4), 10 ** (-3), 10 ** (-2), 10 ** (-1), 10 ** 0, 10 ** 1, 10 ** 2, 10 ** 3, 10 ** 4]}]
clf = RandomizedSearchCV(reg_case, params, cv=5, scoring='neg_root_mean_squared_error', return_train_score=True)
search = clf.fit(X_train_cases, y_train_cases)
results = pd.DataFrame.from_dict(clf.cv_results_)
best_alpha = 1000
best_itr = 2700
final_reg_case = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_itr)
final_reg_case.fit(X_train_cases, y_train_cases)
reg_fatal = ElasticNet(random_state=42, l1_ratio=0.1, max_iter=3800)
params = [{'alpha': [10 ** (-4), 10 ** (-3), 10 ** (-2), 10 ** (-1), 10 ** 0, 10 ** 1, 10 ** 2, 10 ** 3, 10 ** 4]}]
clf = RandomizedSearchCV(reg_fatal, params, cv=5, scoring='neg_root_mean_squared_error', return_train_score=True)
search = clf.fit(X_train_fatal, y_train_fatal)
results = pd.DataFrame.from_dict(clf.cv_results_)
best_alpha = 100
best_iter = 3800
final_reg_fatal = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_iter)
final_reg_fatal.fit(X_train_fatal, y_train_fatal)
data = []
countries = df.Country_Region.unique()
for country in countries:
provinces = df[df.Country_Region == country].Province_State.unique()
for province in provinces:
temp_df = df[(df['Country_Region'] == country) & (df['Province_State'] == province)]
for i in range(0, 77):
Iday1 = float(temp_df.iloc[i].ConfirmedCases)
Iday2 = float(temp_df.iloc[i + 1].ConfirmedCases)
Iday3 = float(temp_df.iloc[i + 2].ConfirmedCases)
Iday4 = float(temp_df.iloc[i + 3].ConfirmedCases)
Iday5 = float(temp_df.iloc[i + 4].ConfirmedCases)
Iday6 = float(temp_df.iloc[i + 5].ConfirmedCases)
Iday7 = float(temp_df.iloc[i + 6].ConfirmedCases)
Fday1 = float(temp_df.iloc[i].Fatalities)
Fday2 = float(temp_df.iloc[i + 1].Fatalities)
Fday3 = float(temp_df.iloc[i + 2].Fatalities)
Fday4 = float(temp_df.iloc[i + 3].Fatalities)
Fday5 = float(temp_df.iloc[i + 4].Fatalities)
Fday6 = float(temp_df.iloc[i + 5].Fatalities)
Fday7 = float(temp_df.iloc[i + 6].Fatalities)
if Iday6 == 0:
iavg = 1
else:
iavg = Iday7 / Iday6
if Fday6 == 0:
favg = 1
else:
favg = Fday7 / Fday6
target_infection = float(temp_df.iloc[i + 7].ConfirmedCases)
target_fatal = float(temp_df.iloc[i + 7].Fatalities)
data.append({'Iday1': Iday1, 'Iday2': Iday2, 'Iday3': Iday3, 'Iday4': Iday4, 'Iday5': Iday5, 'Iday6': Iday6, 'Iday7': Iday7, 'Fday1': Fday1, 'Fday2': Fday2, 'Fday3': Fday3, 'Fday4': Fday4, 'Fday5': Fday5, 'Fday6': Fday6, 'Fday7': Fday7, 'iratio': iavg, 'fratio': favg, 'target_infection': target_infection, 'target_fatal': target_fatal})
featured = pd.DataFrame(data)
X_y_f = shuffle(featured)
y_cases_f = X_y_f['target_infection']
y_fatal_f = X_y_f['target_fatal']
X_f = X_y_f.drop(['target_infection', 'target_fatal'], axis=1)
X_train_cases_f, X_test_cases_f, y_train_cases_f, y_test_cases_f = train_test_split(X_f, y_cases_f, test_size=0.33)
X_train_fatal_f, X_test_fatal_f, y_train_fatal_f, y_test_fatal_f = train_test_split(X_f, y_fatal_f, test_size=0.33)
best_alpha = 10000
best_itr = 4500
final_reg_case_f = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_itr)
final_reg_case_f.fit(X_train_cases_f, y_train_cases_f)
pred_f = final_reg_case_f.predict(X_test_cases_f)
print('RMSE is:', mean_squared_error(y_test_cases_f, pred_f) ** 0.5) | code |
32068693/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
test.head() | code |
32068693/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df['Province_State'].fillna('state', inplace=True)
df['Country_Region'] = [country_name.replace("'", '') for country_name in df['Country_Region']]
data = []
countries = df.Country_Region.unique()
for country in countries:
provinces = df[df.Country_Region == country].Province_State.unique()
for province in provinces:
temp_df = df[(df['Country_Region'] == country) & (df['Province_State'] == province)]
for i in range(0, 77):
Iday1 = float(temp_df.iloc[i].ConfirmedCases)
Iday2 = float(temp_df.iloc[i + 1].ConfirmedCases)
Iday3 = float(temp_df.iloc[i + 2].ConfirmedCases)
Iday4 = float(temp_df.iloc[i + 3].ConfirmedCases)
Iday5 = float(temp_df.iloc[i + 4].ConfirmedCases)
Iday6 = float(temp_df.iloc[i + 5].ConfirmedCases)
Iday7 = float(temp_df.iloc[i + 6].ConfirmedCases)
Fday1 = float(temp_df.iloc[i].Fatalities)
Fday2 = float(temp_df.iloc[i + 1].Fatalities)
Fday3 = float(temp_df.iloc[i + 2].Fatalities)
Fday4 = float(temp_df.iloc[i + 3].Fatalities)
Fday5 = float(temp_df.iloc[i + 4].Fatalities)
Fday6 = float(temp_df.iloc[i + 5].Fatalities)
Fday7 = float(temp_df.iloc[i + 6].Fatalities)
target_infection = float(temp_df.iloc[i + 7].ConfirmedCases)
target_fatal = float(temp_df.iloc[i + 7].Fatalities)
data.append({'Iday1': Iday1, 'Iday2': Iday2, 'Iday3': Iday3, 'Iday4': Iday4, 'Iday5': Iday5, 'Iday6': Iday6, 'Iday7': Iday7, 'Fday1': Fday1, 'Fday2': Fday2, 'Fday3': Fday3, 'Fday4': Fday4, 'Fday5': Fday5, 'Fday6': Fday6, 'Fday7': Fday7, 'target_infection': target_infection, 'target_fatal': target_fatal})
new_data = pd.DataFrame(data)
X_y = shuffle(new_data)
y_cases = X_y['target_infection']
y_fatal = X_y['target_fatal']
X = X_y.drop(['target_infection', 'target_fatal'], axis=1)
X_train_cases, X_test_cases, y_train_cases, y_test_cases = train_test_split(X, y_cases, test_size=0.33)
X_train_fatal, X_test_fatal, y_train_fatal, y_test_fatal = train_test_split(X, y_fatal, test_size=0.33)
reg_case = ElasticNet(random_state=42, l1_ratio=0.1, max_iter=2700)
params = [{'alpha': [10 ** (-4), 10 ** (-3), 10 ** (-2), 10 ** (-1), 10 ** 0, 10 ** 1, 10 ** 2, 10 ** 3, 10 ** 4]}]
clf = RandomizedSearchCV(reg_case, params, cv=5, scoring='neg_root_mean_squared_error', return_train_score=True)
search = clf.fit(X_train_cases, y_train_cases)
results = pd.DataFrame.from_dict(clf.cv_results_)
reg_fatal = ElasticNet(random_state=42, l1_ratio=0.1, max_iter=3800)
params = [{'alpha': [10 ** (-4), 10 ** (-3), 10 ** (-2), 10 ** (-1), 10 ** 0, 10 ** 1, 10 ** 2, 10 ** 3, 10 ** 4]}]
clf = RandomizedSearchCV(reg_fatal, params, cv=5, scoring='neg_root_mean_squared_error', return_train_score=True)
search = clf.fit(X_train_fatal, y_train_fatal)
results = pd.DataFrame.from_dict(clf.cv_results_)
data = []
countries = df.Country_Region.unique()
for country in countries:
provinces = df[df.Country_Region == country].Province_State.unique()
for province in provinces:
temp_df = df[(df['Country_Region'] == country) & (df['Province_State'] == province)]
for i in range(0, 77):
Iday1 = float(temp_df.iloc[i].ConfirmedCases)
Iday2 = float(temp_df.iloc[i + 1].ConfirmedCases)
Iday3 = float(temp_df.iloc[i + 2].ConfirmedCases)
Iday4 = float(temp_df.iloc[i + 3].ConfirmedCases)
Iday5 = float(temp_df.iloc[i + 4].ConfirmedCases)
Iday6 = float(temp_df.iloc[i + 5].ConfirmedCases)
Iday7 = float(temp_df.iloc[i + 6].ConfirmedCases)
Fday1 = float(temp_df.iloc[i].Fatalities)
Fday2 = float(temp_df.iloc[i + 1].Fatalities)
Fday3 = float(temp_df.iloc[i + 2].Fatalities)
Fday4 = float(temp_df.iloc[i + 3].Fatalities)
Fday5 = float(temp_df.iloc[i + 4].Fatalities)
Fday6 = float(temp_df.iloc[i + 5].Fatalities)
Fday7 = float(temp_df.iloc[i + 6].Fatalities)
if Iday6 == 0:
iavg = 1
else:
iavg = Iday7 / Iday6
if Fday6 == 0:
favg = 1
else:
favg = Fday7 / Fday6
target_infection = float(temp_df.iloc[i + 7].ConfirmedCases)
target_fatal = float(temp_df.iloc[i + 7].Fatalities)
data.append({'Iday1': Iday1, 'Iday2': Iday2, 'Iday3': Iday3, 'Iday4': Iday4, 'Iday5': Iday5, 'Iday6': Iday6, 'Iday7': Iday7, 'Fday1': Fday1, 'Fday2': Fday2, 'Fday3': Fday3, 'Fday4': Fday4, 'Fday5': Fday5, 'Fday6': Fday6, 'Fday7': Fday7, 'iratio': iavg, 'fratio': favg, 'target_infection': target_infection, 'target_fatal': target_fatal})
featured = pd.DataFrame(data)
X_y_f = shuffle(featured)
y_cases_f = X_y_f['target_infection']
y_fatal_f = X_y_f['target_fatal']
X_f = X_y_f.drop(['target_infection', 'target_fatal'], axis=1)
X_train_cases_f, X_test_cases_f, y_train_cases_f, y_test_cases_f = train_test_split(X_f, y_cases_f, test_size=0.33)
X_train_fatal_f, X_test_fatal_f, y_train_fatal_f, y_test_fatal_f = train_test_split(X_f, y_fatal_f, test_size=0.33)
print('Shape of featurized infection train dataset:', (X_train_cases_f.shape, y_train_cases_f.shape))
print('Shape of featurized infection test dataset:', (X_test_cases_f.shape, y_test_cases_f.shape))
print('Shape of featurized fatal train dataset:', (X_train_fatal_f.shape, y_train_fatal_f.shape))
print('Shape of featurized fatal test dataset:', (X_test_fatal_f.shape, y_test_fatal_f.shape)) | code |
32068693/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df['Province_State'].fillna('state', inplace=True)
df['Country_Region'] = [country_name.replace("'", '') for country_name in df['Country_Region']]
data = []
countries = df.Country_Region.unique()
for country in countries:
provinces = df[df.Country_Region == country].Province_State.unique()
for province in provinces:
temp_df = df[(df['Country_Region'] == country) & (df['Province_State'] == province)]
for i in range(0, 77):
Iday1 = float(temp_df.iloc[i].ConfirmedCases)
Iday2 = float(temp_df.iloc[i + 1].ConfirmedCases)
Iday3 = float(temp_df.iloc[i + 2].ConfirmedCases)
Iday4 = float(temp_df.iloc[i + 3].ConfirmedCases)
Iday5 = float(temp_df.iloc[i + 4].ConfirmedCases)
Iday6 = float(temp_df.iloc[i + 5].ConfirmedCases)
Iday7 = float(temp_df.iloc[i + 6].ConfirmedCases)
Fday1 = float(temp_df.iloc[i].Fatalities)
Fday2 = float(temp_df.iloc[i + 1].Fatalities)
Fday3 = float(temp_df.iloc[i + 2].Fatalities)
Fday4 = float(temp_df.iloc[i + 3].Fatalities)
Fday5 = float(temp_df.iloc[i + 4].Fatalities)
Fday6 = float(temp_df.iloc[i + 5].Fatalities)
Fday7 = float(temp_df.iloc[i + 6].Fatalities)
target_infection = float(temp_df.iloc[i + 7].ConfirmedCases)
target_fatal = float(temp_df.iloc[i + 7].Fatalities)
data.append({'Iday1': Iday1, 'Iday2': Iday2, 'Iday3': Iday3, 'Iday4': Iday4, 'Iday5': Iday5, 'Iday6': Iday6, 'Iday7': Iday7, 'Fday1': Fday1, 'Fday2': Fday2, 'Fday3': Fday3, 'Fday4': Fday4, 'Fday5': Fday5, 'Fday6': Fday6, 'Fday7': Fday7, 'target_infection': target_infection, 'target_fatal': target_fatal})
new_data = pd.DataFrame(data)
X_y = shuffle(new_data)
y_cases = X_y['target_infection']
y_fatal = X_y['target_fatal']
X = X_y.drop(['target_infection', 'target_fatal'], axis=1)
X_train_cases, X_test_cases, y_train_cases, y_test_cases = train_test_split(X, y_cases, test_size=0.33)
X_train_fatal, X_test_fatal, y_train_fatal, y_test_fatal = train_test_split(X, y_fatal, test_size=0.33)
best_alpha = 1000
best_itr = 2700
final_reg_case = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_itr)
final_reg_case.fit(X_train_cases, y_train_cases)
pred = final_reg_case.predict(X_test_cases)
best_alpha = 100
best_iter = 3800
final_reg_fatal = ElasticNet(random_state=42, alpha=best_alpha, l1_ratio=0.1, max_iter=best_iter)
final_reg_fatal.fit(X_train_fatal, y_train_fatal)
pred = final_reg_fatal.predict(X_test_fatal)
print('The RMSE value', mean_squared_error(y_test_fatal, pred) ** 0.5) | code |
32068693/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df['Province_State'].fillna('state', inplace=True)
df['Country_Region'] = [country_name.replace("'", '') for country_name in df['Country_Region']]
data = []
countries = df.Country_Region.unique()
for country in countries:
provinces = df[df.Country_Region == country].Province_State.unique()
for province in provinces:
temp_df = df[(df['Country_Region'] == country) & (df['Province_State'] == province)]
for i in range(0, 77):
Iday1 = float(temp_df.iloc[i].ConfirmedCases)
Iday2 = float(temp_df.iloc[i + 1].ConfirmedCases)
Iday3 = float(temp_df.iloc[i + 2].ConfirmedCases)
Iday4 = float(temp_df.iloc[i + 3].ConfirmedCases)
Iday5 = float(temp_df.iloc[i + 4].ConfirmedCases)
Iday6 = float(temp_df.iloc[i + 5].ConfirmedCases)
Iday7 = float(temp_df.iloc[i + 6].ConfirmedCases)
Fday1 = float(temp_df.iloc[i].Fatalities)
Fday2 = float(temp_df.iloc[i + 1].Fatalities)
Fday3 = float(temp_df.iloc[i + 2].Fatalities)
Fday4 = float(temp_df.iloc[i + 3].Fatalities)
Fday5 = float(temp_df.iloc[i + 4].Fatalities)
Fday6 = float(temp_df.iloc[i + 5].Fatalities)
Fday7 = float(temp_df.iloc[i + 6].Fatalities)
target_infection = float(temp_df.iloc[i + 7].ConfirmedCases)
target_fatal = float(temp_df.iloc[i + 7].Fatalities)
data.append({'Iday1': Iday1, 'Iday2': Iday2, 'Iday3': Iday3, 'Iday4': Iday4, 'Iday5': Iday5, 'Iday6': Iday6, 'Iday7': Iday7, 'Fday1': Fday1, 'Fday2': Fday2, 'Fday3': Fday3, 'Fday4': Fday4, 'Fday5': Fday5, 'Fday6': Fday6, 'Fday7': Fday7, 'target_infection': target_infection, 'target_fatal': target_fatal})
new_data = pd.DataFrame(data)
print('The shape of new dataFrame:', new_data.shape, '\nThe columns are:', new_data.columns)
print(new_data.head(-5)) | code |
128018474/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/btcusd/data1.csv')
df['Datetime'] = [i for i in range(len(df['Datetime']))]
new_df = df[['Open', 'Volume']]
x = np.array(new_df['Open']).reshape(-1, 1)
y = np.array(new_df['Volume']).reshape(-1, 1)
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25)
regr = linear_model.LinearRegression()
regr.fit(X_train, y_train)
print(f'Regr-Test: {regr.score(X_test, y_test)}')
y_pred = regr.predict(X_test)
plt.scatter(X_test, y_test, color='b')
plt.plot(X_test, y_pred, color='r')
plt.show() | code |
128018474/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/btcusd/data1.csv')
print(df.head(10))
df['Datetime'] = [i for i in range(len(df['Datetime']))]
new_df = df[['Open', 'Volume']]
sns.lmplot(data=new_df, x='Open', y='Volume', order=2, ci=None)
plt.show() | code |
128022704/cell_6 | [
"text_plain_output_1.png"
] | array_list = array('B')
for i in range(ELEMENTS_LIMIT):
array_list.append(i) | code |
128022704/cell_11 | [
"text_plain_output_1.png"
] | data = array_list[:]
for i in range(ELEMENTS_LIMIT - 1):
_ = data.pop() | code |
128022704/cell_1 | [
"text_plain_output_1.png"
] | !python --version | code |
128022704/cell_7 | [
"text_plain_output_1.png"
] | deque_list = deque()
for i in range(ELEMENTS_LIMIT):
deque_list.append(i) | code |
128022704/cell_8 | [
"text_plain_output_1.png"
] | from array import array
from collections import deque
from sys import getsizeof
ELEMENTS_LIMIT = 2 ** 8 - 1
def fill_and_print_details(x):
for i in range(ELEMENTS_LIMIT):
x.append(i)
usual_list = []
array_list = array('B')
deque_list = deque()
fill_and_print_details(usual_list)
fill_and_print_details(array_list)
fill_and_print_details(deque_list) | code |
128022704/cell_15 | [
"text_plain_output_1.png"
] | data = array_list[:]
for i in range(ELEMENTS_LIMIT - 1):
_ = data.pop(0) | code |
128022704/cell_16 | [
"text_plain_output_1.png"
] | data = deque_list.copy()
for i in range(ELEMENTS_LIMIT - 1):
_ = data.popleft() | code |
128022704/cell_14 | [
"text_plain_output_1.png"
] | data = usual_list.copy()
for i in range(ELEMENTS_LIMIT - 1):
_ = data.pop(0) | code |
128022704/cell_10 | [
"text_plain_output_1.png"
] | data = usual_list.copy()
for i in range(ELEMENTS_LIMIT - 1):
_ = data.pop() | code |
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