path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
130007285/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
df.head() | code |
130007285/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean() | code |
130007285/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
Avg_delay_min.head() | code |
130007285/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
Avg_ticket_price.head() | code |
130007285/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
df.head() | code |
130007285/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
sorted_df.head() | code |
130007285/cell_22 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
sorted_df = df.sort_values('Customer ID', ascending=False)
Avg_ticket_price = df.groupby('Ticket Price').mean()
Avg_delay_min = df.groupby('Delay Minutes').mean()
df.dropna()
max_value = np.max(df['Ticket Price'])
max_value | code |
130007285/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
selected_columns = ['Departure City', 'Arrival City', 'Flight Duration', 'Delay Minutes', 'Booking Class']
df_selected = df[selected_columns]
df_selected | code |
130007285/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
filtered_df = df[df['Delay Minutes'] > 60]
filtered_df = df[df['Churned'] == False]
filtered_df.head() | code |
130007285/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/flight-dataset/Flight_data.csv')
df.info() | code |
17143749/cell_13 | [
"text_plain_output_2.png"
] | chunk_iter = _smallstruct.groupby(['molecule_name'])
pool = mp.Pool(4)
funclist = []
for df in tqdm(chunk_iter):
f = pool.apply_async(compute_all_yukawa, [df[1]])
funclist.append(f)
result = []
for f in tqdm(funclist):
result.append(f.get(timeout=120))
smallstruct2 = pd.concat(result) | code |
17143749/cell_11 | [
"text_html_output_1.png"
] | smallstruct1.head(10) | code |
17143749/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
def compute_all_yukawa(x):
return x.apply(compute_yukawa_matrix, axis=1, x2=x)
def compute_yukawa_matrix(x, x2):
notatom = x2[x2.atom_index != x['atom_index']].reset_index(drop=True)
atom = x[['x', 'y', 'z']]
charge = x[['nuclear_charge']]
notatom['dist'] = ((notatom[['x', 'y', 'z']].values - atom.values) ** 2).sum(axis=1)
notatom['dist'] = np.sqrt(notatom['dist'].astype(np.float32))
notatom['dist'] = charge.values * notatom[['nuclear_charge']].values.reshape(-1) * np.exp(-2 * notatom['dist'] / notatom['dist'].max()) / notatom['dist']
s = notatom.groupby('atom')['dist'].transform(lambda x: x.sort_values(ascending=False))
index0, index1 = ([], [])
for i in notatom.atom.unique():
for j in range(notatom[notatom.atom == i].shape[0]):
if j < 5:
index1.append('dist_' + i + '_' + str(j))
index0.append(j)
s.index = index0
s = s[s.index < 5]
s.index = index1
return s
np.allclose(smallstruct2.fillna(0), smallstruct1.fillna(0)) | code |
17143749/cell_14 | [
"text_html_output_1.png"
] | smallstruct2.head(10) | code |
17143749/cell_10 | [
"text_plain_output_1.png"
] | smallstruct1 = _smallstruct.groupby('molecule_name').apply(compute_all_yukawa) | code |
50241935/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data.dropna()
order_wise.shape
order_wise.isnull().sum()
amount = pd.DataFrame(order_wise['Quantity'] * order_wise['UnitPrice'], columns=['Amount'])
order_wise = pd.concat(objs=[order_wise, amount], axis=1, ignore_index=False)
monetary = order_wise.groupby('CustomerID').Amount.sum()
monetary = monetary.reset_index()
frequency = order_wise[['CustomerID', 'InvoiceNo']]
k = frequency.groupby('CustomerID').InvoiceNo.count()
k = pd.DataFrame(k)
k = k.reset_index()
k.columns = ['CustomerID', 'Frequency']
recency = order_wise[['CustomerID', 'InvoiceDate']]
maximum = max(recency.InvoiceDate)
maximum = maximum + pd.DateOffset(days=1)
recency['diff'] = maximum - recency.InvoiceDate
a = recency.groupby('CustomerID')
a['diff'].min() | code |
50241935/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data.dropna()
order_wise.shape
order_wise.isnull().sum()
amount = pd.DataFrame(order_wise['Quantity'] * order_wise['UnitPrice'], columns=['Amount'])
order_wise = pd.concat(objs=[order_wise, amount], axis=1, ignore_index=False)
monetary = order_wise.groupby('CustomerID').Amount.sum()
monetary = monetary.reset_index()
monetary.head(5) | code |
50241935/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.info()
data.describe() | code |
50241935/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data.dropna()
order_wise.shape
order_wise.isnull().sum()
amount = pd.DataFrame(order_wise['Quantity'] * order_wise['UnitPrice'], columns=['Amount'])
order_wise = pd.concat(objs=[order_wise, amount], axis=1, ignore_index=False)
monetary = order_wise.groupby('CustomerID').Amount.sum()
monetary = monetary.reset_index()
frequency = order_wise[['CustomerID', 'InvoiceNo']]
k = frequency.groupby('CustomerID').InvoiceNo.count()
k = pd.DataFrame(k)
k = k.reset_index()
k.columns = ['CustomerID', 'Frequency']
master = monetary.merge(k, on='CustomerID', how='inner')
master.head(5) | code |
50241935/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data.dropna()
order_wise.shape
order_wise.isnull().sum()
amount = pd.DataFrame(order_wise['Quantity'] * order_wise['UnitPrice'], columns=['Amount'])
order_wise = pd.concat(objs=[order_wise, amount], axis=1, ignore_index=False)
monetary = order_wise.groupby('CustomerID').Amount.sum()
monetary = monetary.reset_index()
frequency = order_wise[['CustomerID', 'InvoiceNo']]
k = frequency.groupby('CustomerID').InvoiceNo.count()
k = pd.DataFrame(k)
k = k.reset_index()
k.columns = ['CustomerID', 'Frequency']
master = monetary.merge(k, on='CustomerID', how='inner')
recency = order_wise[['CustomerID', 'InvoiceDate']]
maximum = max(recency.InvoiceDate)
maximum = maximum + pd.DateOffset(days=1)
recency['diff'] = maximum - recency.InvoiceDate
a = recency.groupby('CustomerID')
a['diff'].min()
df = pd.DataFrame(recency.groupby(['CustomerID', 'diff']).min())
df = df.reset_index()
df = df.drop('InvoiceDate', axis=1)
df = df.rename(columns={'diff': 'Recency'})
RFM = k.merge(monetary, on='CustomerID')
RFM = RFM.merge(df, on='CustomerID')
Q1 = RFM.Amount.quantile(0.25)
Q3 = RFM.Amount.quantile(0.75)
IQR = Q3 - Q1
RFM = RFM[(RFM.Amount >= Q1 - 1.5 * IQR) & (RFM.Amount <= Q3 + 1.5 * IQR)]
RFM_norm1 = RFM.drop('CustomerID', axis=1)
RFM_norm1.Recency = RFM_norm1.Recency.dt.days
from sklearn.preprocessing import StandardScaler
standard_scaler = StandardScaler()
RFM_norm1 = standard_scaler.fit_transform(RFM_norm1)
RFM_norm1 = pd.DataFrame(RFM_norm1)
RFM_norm1.columns = ['Frequency', 'Amount', 'Recency']
model_clus5 = KMeans(n_clusters=5, max_iter=50)
model_clus5.fit(RFM_norm1) | code |
50241935/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import scale
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import linkage
from scipy.cluster.hierarchy import dendrogram, cut_tree
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
50241935/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data.dropna()
order_wise.shape
order_wise.isnull().sum() | code |
50241935/cell_18 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data.dropna()
order_wise.shape
order_wise.isnull().sum()
amount = pd.DataFrame(order_wise['Quantity'] * order_wise['UnitPrice'], columns=['Amount'])
order_wise = pd.concat(objs=[order_wise, amount], axis=1, ignore_index=False)
monetary = order_wise.groupby('CustomerID').Amount.sum()
monetary = monetary.reset_index()
frequency = order_wise[['CustomerID', 'InvoiceNo']]
k = frequency.groupby('CustomerID').InvoiceNo.count()
k = pd.DataFrame(k)
k = k.reset_index()
k.columns = ['CustomerID', 'Frequency']
master = monetary.merge(k, on='CustomerID', how='inner')
recency = order_wise[['CustomerID', 'InvoiceDate']]
maximum = max(recency.InvoiceDate)
maximum = maximum + pd.DateOffset(days=1)
recency['diff'] = maximum - recency.InvoiceDate
a = recency.groupby('CustomerID')
a['diff'].min()
df = pd.DataFrame(recency.groupby(['CustomerID', 'diff']).min())
df = df.reset_index()
df = df.drop('InvoiceDate', axis=1)
df = df.rename(columns={'diff': 'Recency'})
RFM = k.merge(monetary, on='CustomerID')
RFM = RFM.merge(df, on='CustomerID')
Q1 = RFM.Amount.quantile(0.25)
Q3 = RFM.Amount.quantile(0.75)
IQR = Q3 - Q1
RFM = RFM[(RFM.Amount >= Q1 - 1.5 * IQR) & (RFM.Amount <= Q3 + 1.5 * IQR)]
RFM_norm1 = RFM.drop('CustomerID', axis=1)
RFM_norm1.Recency = RFM_norm1.Recency.dt.days
from sklearn.preprocessing import StandardScaler
standard_scaler = StandardScaler()
RFM_norm1 = standard_scaler.fit_transform(RFM_norm1)
RFM_norm1 = pd.DataFrame(RFM_norm1)
RFM_norm1.columns = ['Frequency', 'Amount', 'Recency']
RFM_norm1.head(5) | code |
50241935/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data.dropna()
order_wise.shape
order_wise.isnull().sum()
amount = pd.DataFrame(order_wise['Quantity'] * order_wise['UnitPrice'], columns=['Amount'])
amount.head(5) | code |
50241935/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data.dropna()
order_wise.shape
order_wise.isnull().sum()
amount = pd.DataFrame(order_wise['Quantity'] * order_wise['UnitPrice'], columns=['Amount'])
order_wise = pd.concat(objs=[order_wise, amount], axis=1, ignore_index=False)
monetary = order_wise.groupby('CustomerID').Amount.sum()
monetary = monetary.reset_index()
frequency = order_wise[['CustomerID', 'InvoiceNo']]
k = frequency.groupby('CustomerID').InvoiceNo.count()
k = pd.DataFrame(k)
k = k.reset_index()
k.columns = ['CustomerID', 'Frequency']
master = monetary.merge(k, on='CustomerID', how='inner')
recency = order_wise[['CustomerID', 'InvoiceDate']]
maximum = max(recency.InvoiceDate)
maximum = maximum + pd.DateOffset(days=1)
recency['diff'] = maximum - recency.InvoiceDate
a = recency.groupby('CustomerID')
a['diff'].min()
df = pd.DataFrame(recency.groupby(['CustomerID', 'diff']).min())
df = df.reset_index()
df = df.drop('InvoiceDate', axis=1)
df = df.rename(columns={'diff': 'Recency'})
RFM = k.merge(monetary, on='CustomerID')
RFM = RFM.merge(df, on='CustomerID')
RFM.head(5) | code |
50241935/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data.dropna()
order_wise.shape
order_wise.isnull().sum()
amount = pd.DataFrame(order_wise['Quantity'] * order_wise['UnitPrice'], columns=['Amount'])
order_wise = pd.concat(objs=[order_wise, amount], axis=1, ignore_index=False)
monetary = order_wise.groupby('CustomerID').Amount.sum()
monetary = monetary.reset_index()
frequency = order_wise[['CustomerID', 'InvoiceNo']]
k = frequency.groupby('CustomerID').InvoiceNo.count()
k = pd.DataFrame(k)
k = k.reset_index()
k.columns = ['CustomerID', 'Frequency']
master = monetary.merge(k, on='CustomerID', how='inner')
recency = order_wise[['CustomerID', 'InvoiceDate']]
maximum = max(recency.InvoiceDate)
maximum = maximum + pd.DateOffset(days=1)
recency['diff'] = maximum - recency.InvoiceDate
a = recency.groupby('CustomerID')
a['diff'].min()
df = pd.DataFrame(recency.groupby(['CustomerID', 'diff']).min())
df = df.reset_index()
df = df.drop('InvoiceDate', axis=1)
df = df.rename(columns={'diff': 'Recency'})
RFM = k.merge(monetary, on='CustomerID')
RFM = RFM.merge(df, on='CustomerID')
plt.boxplot(RFM.Amount)
Q1 = RFM.Amount.quantile(0.25)
Q3 = RFM.Amount.quantile(0.75)
IQR = Q3 - Q1
RFM = RFM[(RFM.Amount >= Q1 - 1.5 * IQR) & (RFM.Amount <= Q3 + 1.5 * IQR)] | code |
50241935/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.head(5) | code |
50241935/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data.dropna()
order_wise.shape
order_wise.isnull().sum()
amount = pd.DataFrame(order_wise['Quantity'] * order_wise['UnitPrice'], columns=['Amount'])
order_wise = pd.concat(objs=[order_wise, amount], axis=1, ignore_index=False)
monetary = order_wise.groupby('CustomerID').Amount.sum()
monetary = monetary.reset_index()
frequency = order_wise[['CustomerID', 'InvoiceNo']]
k = frequency.groupby('CustomerID').InvoiceNo.count()
k = pd.DataFrame(k)
k = k.reset_index()
k.columns = ['CustomerID', 'Frequency']
master = monetary.merge(k, on='CustomerID', how='inner')
recency = order_wise[['CustomerID', 'InvoiceDate']]
maximum = max(recency.InvoiceDate)
maximum = maximum + pd.DateOffset(days=1)
recency['diff'] = maximum - recency.InvoiceDate
a = recency.groupby('CustomerID')
a['diff'].min()
df = pd.DataFrame(recency.groupby(['CustomerID', 'diff']).min())
df = df.reset_index()
df = df.drop('InvoiceDate', axis=1)
df = df.rename(columns={'diff': 'Recency'})
RFM = k.merge(monetary, on='CustomerID')
RFM = RFM.merge(df, on='CustomerID')
Q1 = RFM.Amount.quantile(0.25)
Q3 = RFM.Amount.quantile(0.75)
IQR = Q3 - Q1
RFM = RFM[(RFM.Amount >= Q1 - 1.5 * IQR) & (RFM.Amount <= Q3 + 1.5 * IQR)]
RFM.head(5) | code |
50241935/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data.dropna()
order_wise.shape
order_wise.isnull().sum()
amount = pd.DataFrame(order_wise['Quantity'] * order_wise['UnitPrice'], columns=['Amount'])
order_wise = pd.concat(objs=[order_wise, amount], axis=1, ignore_index=False)
monetary = order_wise.groupby('CustomerID').Amount.sum()
monetary = monetary.reset_index()
frequency = order_wise[['CustomerID', 'InvoiceNo']]
k = frequency.groupby('CustomerID').InvoiceNo.count()
k = pd.DataFrame(k)
k = k.reset_index()
k.columns = ['CustomerID', 'Frequency']
recency = order_wise[['CustomerID', 'InvoiceDate']]
maximum = max(recency.InvoiceDate)
maximum = maximum + pd.DateOffset(days=1)
recency['diff'] = maximum - recency.InvoiceDate
a = recency.groupby('CustomerID')
a['diff'].min()
df = pd.DataFrame(recency.groupby(['CustomerID', 'diff']).min())
df = df.reset_index()
df = df.drop('InvoiceDate', axis=1)
df = df.rename(columns={'diff': 'Recency'})
df.head(5) | code |
50241935/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data.dropna()
order_wise.shape
order_wise.isnull().sum()
amount = pd.DataFrame(order_wise['Quantity'] * order_wise['UnitPrice'], columns=['Amount'])
order_wise = pd.concat(objs=[order_wise, amount], axis=1, ignore_index=False)
monetary = order_wise.groupby('CustomerID').Amount.sum()
monetary = monetary.reset_index()
frequency = order_wise[['CustomerID', 'InvoiceNo']]
k = frequency.groupby('CustomerID').InvoiceNo.count()
k = pd.DataFrame(k)
k = k.reset_index()
k.columns = ['CustomerID', 'Frequency']
k.head(5) | code |
50241935/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0]
order_wise = data.dropna()
order_wise.shape
order_wise.isnull().sum()
amount = pd.DataFrame(order_wise['Quantity'] * order_wise['UnitPrice'], columns=['Amount'])
order_wise = pd.concat(objs=[order_wise, amount], axis=1, ignore_index=False)
monetary = order_wise.groupby('CustomerID').Amount.sum()
monetary = monetary.reset_index()
frequency = order_wise[['CustomerID', 'InvoiceNo']]
k = frequency.groupby('CustomerID').InvoiceNo.count()
k = pd.DataFrame(k)
k = k.reset_index()
k.columns = ['CustomerID', 'Frequency']
recency = order_wise[['CustomerID', 'InvoiceDate']]
maximum = max(recency.InvoiceDate)
maximum = maximum + pd.DateOffset(days=1)
recency['diff'] = maximum - recency.InvoiceDate
recency.head(5) | code |
50241935/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_excel('../input/online-retail-data-set-from-uci-ml-repo/Online Retail.xlsx')
data['InvoiceDate'] = pd.to_datetime(data['InvoiceDate'], format='%d-%m-%Y %H:%M')
data.shape
data.isnull().sum() * 100 / data.shape[0] | code |
105210534/cell_42 | [
"image_output_1.png"
] | l = [('1', 1), ('2', 2), ('3', 3)]
max(l) | code |
105210534/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
np.random.seed(2018)
sample = np.random.choice(train_data.shape[0], 200, replace=False)
subset = train_data.loc[sample]
percentages = [count / subset.shape[0] * 100 for count in subset[187].value_counts()]
percentages[0]
base_color = sb.color_palette()[0]
type_orderion = subset[187].value_counts().index
plt.xticks(rotation=90)
from sklearn.utils import resample
target1 = train_data[train_data[187] == 1]
target2 = train_data[train_data[187] == 2]
target3 = train_data[train_data[187] == 3]
target4 = train_data[train_data[187] == 4]
target0 = train_data[train_data[187] == 0].sample(n=20000, random_state=42)
target1_sample = resample(target1, replace=True, n_samples=20000, random_state=0)
target2_sample = resample(target2, replace=True, n_samples=20000, random_state=0)
target3_sample = resample(target3, replace=True, n_samples=20000, random_state=0)
target4_sample = resample(target4, replace=True, n_samples=20000, random_state=0)
train_data = pd.concat([target0, target1_sample, target2_sample, target3_sample, target4_sample])
train_target = train_data[187].value_counts()
sorted_counts = train_data[187].value_counts()
plt.figure(figsize=[12, 5.01])
plt.pie(sorted_counts, labels=sorted_counts.index, startangle=90, counterclock=False)
plt.axis('square') | code |
105210534/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
np.random.seed(2018)
sample = np.random.choice(train_data.shape[0], 200, replace=False)
subset = train_data.loc[sample]
percentages = [count / subset.shape[0] * 100 for count in subset[187].value_counts()]
percentages[0]
for i in np.arange(len(percentages)):
print(f'the precent of {int(train_target.index[i])} is : {np.round(percentages[i], 2)} %') | code |
105210534/cell_25 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_target = test_data[187].value_counts()
test_target | code |
105210534/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.utils import resample
from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
np.random.seed(2018)
sample = np.random.choice(train_data.shape[0], 200, replace=False)
subset = train_data.loc[sample]
percentages = [count / subset.shape[0] * 100 for count in subset[187].value_counts()]
percentages[0]
base_color = sb.color_palette()[0]
type_orderion = subset[187].value_counts().index
plt.xticks(rotation=90)
from sklearn.utils import resample
target1 = train_data[train_data[187] == 1]
target2 = train_data[train_data[187] == 2]
target3 = train_data[train_data[187] == 3]
target4 = train_data[train_data[187] == 4]
target0 = train_data[train_data[187] == 0].sample(n=20000, random_state=42)
target1_sample = resample(target1, replace=True, n_samples=20000, random_state=0)
target2_sample = resample(target2, replace=True, n_samples=20000, random_state=0)
target3_sample = resample(target3, replace=True, n_samples=20000, random_state=0)
target4_sample = resample(target4, replace=True, n_samples=20000, random_state=0)
train_data = pd.concat([target0, target1_sample, target2_sample, target3_sample, target4_sample])
train_target = train_data[187].value_counts()
sorted_counts = train_data[187].value_counts()
plt.axis('square')
test_target = test_data[187].value_counts()
plt.bar(train_target.index, train_target.values, color='green') | code |
105210534/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.utils import resample
from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
np.random.seed(2018)
sample = np.random.choice(train_data.shape[0], 200, replace=False)
subset = train_data.loc[sample]
percentages = [count / subset.shape[0] * 100 for count in subset[187].value_counts()]
percentages[0]
base_color = sb.color_palette()[0]
type_orderion = subset[187].value_counts().index
plt.xticks(rotation=90)
from sklearn.utils import resample
target1 = train_data[train_data[187] == 1]
target2 = train_data[train_data[187] == 2]
target3 = train_data[train_data[187] == 3]
target4 = train_data[train_data[187] == 4]
target0 = train_data[train_data[187] == 0].sample(n=20000, random_state=42)
target1_sample = resample(target1, replace=True, n_samples=20000, random_state=0)
target2_sample = resample(target2, replace=True, n_samples=20000, random_state=0)
target3_sample = resample(target3, replace=True, n_samples=20000, random_state=0)
target4_sample = resample(target4, replace=True, n_samples=20000, random_state=0)
train_data = pd.concat([target0, target1_sample, target2_sample, target3_sample, target4_sample])
train_target = train_data[187].value_counts()
sorted_counts = train_data[187].value_counts()
plt.axis('square')
test_target = test_data[187].value_counts()
np.random.seed(2020)
sample2 = np.random.choice(test_data.shape[0], 200, replace=False)
subset2 = test_data.loc[sample2]
percentages = [count / subset2.shape[0] * 100 for count in subset2[187].value_counts()]
percentages[0]
from sklearn.utils import resample
target1 = test_data[test_data[187] == 1]
target2 = test_data[test_data[187] == 2]
target3 = test_data[test_data[187] == 3]
target4 = test_data[test_data[187] == 4]
target0 = test_data[test_data[187] == 0].sample(n=2000, random_state=0)
target1_sample = resample(target1, replace=True, n_samples=2000, random_state=0)
target2_sample = resample(target2, replace=True, n_samples=2000, random_state=0)
target3_sample = resample(target3, replace=True, n_samples=2000, random_state=0)
target4_sample = resample(target4, replace=True, n_samples=2000, random_state=0)
test_data = pd.concat([target0, target1_sample, target2_sample, target3_sample, target4_sample])
test_target = test_data[187].value_counts()
test_target | code |
105210534/cell_29 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
np.random.seed(2018)
sample = np.random.choice(train_data.shape[0], 200, replace=False)
subset = train_data.loc[sample]
percentages = [count / subset.shape[0] * 100 for count in subset[187].value_counts()]
percentages[0]
np.random.seed(2020)
sample2 = np.random.choice(test_data.shape[0], 200, replace=False)
subset2 = test_data.loc[sample2]
percentages = [count / subset2.shape[0] * 100 for count in subset2[187].value_counts()]
percentages[0]
for i in np.arange(len(percentages)):
print(f'the precent of {int(test_data.index[i])} is : {np.round(percentages[i], 2)} %') | code |
105210534/cell_26 | [
"image_output_1.png"
] | from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
np.random.seed(2018)
sample = np.random.choice(train_data.shape[0], 200, replace=False)
subset = train_data.loc[sample]
percentages = [count / subset.shape[0] * 100 for count in subset[187].value_counts()]
percentages[0]
base_color = sb.color_palette()[0]
type_orderion = subset[187].value_counts().index
plt.xticks(rotation=90)
from sklearn.utils import resample
target1 = train_data[train_data[187] == 1]
target2 = train_data[train_data[187] == 2]
target3 = train_data[train_data[187] == 3]
target4 = train_data[train_data[187] == 4]
target0 = train_data[train_data[187] == 0].sample(n=20000, random_state=42)
target1_sample = resample(target1, replace=True, n_samples=20000, random_state=0)
target2_sample = resample(target2, replace=True, n_samples=20000, random_state=0)
target3_sample = resample(target3, replace=True, n_samples=20000, random_state=0)
target4_sample = resample(target4, replace=True, n_samples=20000, random_state=0)
train_data = pd.concat([target0, target1_sample, target2_sample, target3_sample, target4_sample])
train_target = train_data[187].value_counts()
sorted_counts = train_data[187].value_counts()
plt.axis('square')
test_target = test_data[187].value_counts()
plt.bar(test_target.index, test_target.values, color=sb.color_palette()[0]) | code |
105210534/cell_41 | [
"image_output_1.png"
] | l = [('1', 1), ('2', 2), ('3', 3)]
if max(l):
print(l) | code |
105210534/cell_2 | [
"image_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
105210534/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
np.random.seed(2018)
sample = np.random.choice(train_data.shape[0], 200, replace=False)
subset = train_data.loc[sample]
subset | code |
105210534/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
np.random.seed(2018)
sample = np.random.choice(train_data.shape[0], 200, replace=False)
subset = train_data.loc[sample]
percentages = [count / subset.shape[0] * 100 for count in subset[187].value_counts()]
percentages[0]
base_color = sb.color_palette()[0]
type_orderion = subset[187].value_counts().index
plt.xticks(rotation=90)
from sklearn.utils import resample
target1 = train_data[train_data[187] == 1]
target2 = train_data[train_data[187] == 2]
target3 = train_data[train_data[187] == 3]
target4 = train_data[train_data[187] == 4]
target0 = train_data[train_data[187] == 0].sample(n=20000, random_state=42)
target1_sample = resample(target1, replace=True, n_samples=20000, random_state=0)
target2_sample = resample(target2, replace=True, n_samples=20000, random_state=0)
target3_sample = resample(target3, replace=True, n_samples=20000, random_state=0)
target4_sample = resample(target4, replace=True, n_samples=20000, random_state=0)
train_data = pd.concat([target0, target1_sample, target2_sample, target3_sample, target4_sample])
train_target = train_data[187].value_counts()
plt.bar(train_target.index, train_target.values, color='green') | code |
105210534/cell_50 | [
"text_plain_output_1.png"
] | from scipy import stats
from sklearn.utils import resample
from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
np.random.seed(2018)
sample = np.random.choice(train_data.shape[0], 200, replace=False)
subset = train_data.loc[sample]
percentages = [count / subset.shape[0] * 100 for count in subset[187].value_counts()]
percentages[0]
base_color = sb.color_palette()[0]
type_orderion = subset[187].value_counts().index
plt.xticks(rotation=90)
from sklearn.utils import resample
target1 = train_data[train_data[187] == 1]
target2 = train_data[train_data[187] == 2]
target3 = train_data[train_data[187] == 3]
target4 = train_data[train_data[187] == 4]
target0 = train_data[train_data[187] == 0].sample(n=20000, random_state=42)
target1_sample = resample(target1, replace=True, n_samples=20000, random_state=0)
target2_sample = resample(target2, replace=True, n_samples=20000, random_state=0)
target3_sample = resample(target3, replace=True, n_samples=20000, random_state=0)
target4_sample = resample(target4, replace=True, n_samples=20000, random_state=0)
train_data = pd.concat([target0, target1_sample, target2_sample, target3_sample, target4_sample])
train_target = train_data[187].value_counts()
sorted_counts = train_data[187].value_counts()
plt.axis('square')
test_target = test_data[187].value_counts()
np.random.seed(2020)
sample2 = np.random.choice(test_data.shape[0], 200, replace=False)
subset2 = test_data.loc[sample2]
percentages = [count / subset2.shape[0] * 100 for count in subset2[187].value_counts()]
percentages[0]
sorted_counts = train_data[187].value_counts()
plt.axis('square')
pearson_coef, p_value = stats.pearsonr(train_data[1], train_data[187])
effictive_list = []
for i in train_data.columns:
if i != 187:
pearson_coef, p_value = stats.pearsonr(train_data[i], train_data[187])
effictive_list.append((f'column number : {i}', f'Pearson Correlation {pearson_coef}', f'P-value : {p_value}'))
else:
break
def add_guassian_noise(signal):
noise = np.random.normal(0, 0.05, 186)
return signal + noise
noise_data = add_guassian_noise(train_data.iloc[0, :186])
plt.figure(figsize=[12, 4.02])
plt.plot(train_data.iloc[0, :186], color='green')
plt.title('data without noise')
plt.xlabel('ECG in mili volts')
plt.ylabel('time in seconds') | code |
105210534/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
train_target | code |
105210534/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
np.random.seed(2018)
sample = np.random.choice(train_data.shape[0], 200, replace=False)
subset = train_data.loc[sample]
percentages = [count / subset.shape[0] * 100 for count in subset[187].value_counts()]
percentages[0]
from sklearn.utils import resample
target1 = train_data[train_data[187] == 1]
target2 = train_data[train_data[187] == 2]
target3 = train_data[train_data[187] == 3]
target4 = train_data[train_data[187] == 4]
target0 = train_data[train_data[187] == 0].sample(n=20000, random_state=42)
target1_sample = resample(target1, replace=True, n_samples=20000, random_state=0)
target2_sample = resample(target2, replace=True, n_samples=20000, random_state=0)
target3_sample = resample(target3, replace=True, n_samples=20000, random_state=0)
target4_sample = resample(target4, replace=True, n_samples=20000, random_state=0)
train_data = pd.concat([target0, target1_sample, target2_sample, target3_sample, target4_sample])
train_target = train_data[187].value_counts()
train_target | code |
105210534/cell_28 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
np.random.seed(2018)
sample = np.random.choice(train_data.shape[0], 200, replace=False)
subset = train_data.loc[sample]
percentages = [count / subset.shape[0] * 100 for count in subset[187].value_counts()]
percentages[0]
np.random.seed(2020)
sample2 = np.random.choice(test_data.shape[0], 200, replace=False)
subset2 = test_data.loc[sample2]
percentages = [count / subset2.shape[0] * 100 for count in subset2[187].value_counts()]
percentages[0] | code |
105210534/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
plt.bar(train_target.index, train_target.values, color=sb.color_palette()[4]) | code |
105210534/cell_46 | [
"text_plain_output_1.png"
] | from scipy import stats
from sklearn.utils import resample
from sklearn.utils import resample
import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
np.random.seed(2018)
sample = np.random.choice(train_data.shape[0], 200, replace=False)
subset = train_data.loc[sample]
from sklearn.utils import resample
target1 = train_data[train_data[187] == 1]
target2 = train_data[train_data[187] == 2]
target3 = train_data[train_data[187] == 3]
target4 = train_data[train_data[187] == 4]
target0 = train_data[train_data[187] == 0].sample(n=20000, random_state=42)
target1_sample = resample(target1, replace=True, n_samples=20000, random_state=0)
target2_sample = resample(target2, replace=True, n_samples=20000, random_state=0)
target3_sample = resample(target3, replace=True, n_samples=20000, random_state=0)
target4_sample = resample(target4, replace=True, n_samples=20000, random_state=0)
train_data = pd.concat([target0, target1_sample, target2_sample, target3_sample, target4_sample])
pearson_coef, p_value = stats.pearsonr(train_data[1], train_data[187])
effictive_list = []
for i in train_data.columns:
if i != 187:
pearson_coef, p_value = stats.pearsonr(train_data[i], train_data[187])
effictive_list.append((f'column number : {i}', f'Pearson Correlation {pearson_coef}', f'P-value : {p_value}'))
else:
break
print('*' * 75)
print(max(effictive_list))
print('*' * 75) | code |
105210534/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
np.random.seed(2018)
sample = np.random.choice(train_data.shape[0], 200, replace=False)
subset = train_data.loc[sample]
percentages = [count / subset.shape[0] * 100 for count in subset[187].value_counts()]
percentages[0]
base_color = sb.color_palette()[0]
type_orderion = subset[187].value_counts().index
sb.countplot(data=subset, y=187, color=base_color, order=type_orderion)
plt.xticks(rotation=90) | code |
105210534/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
np.random.seed(2018)
sample = np.random.choice(train_data.shape[0], 200, replace=False)
subset = train_data.loc[sample]
percentages = [count / subset.shape[0] * 100 for count in subset[187].value_counts()]
percentages[0] | code |
105210534/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.utils import resample
from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
train_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
test_data = pd.read_csv('/kaggle/input/heartbeat/mitbih_train.csv', header=None)
train_target = train_data[187].value_counts()
np.random.seed(2018)
sample = np.random.choice(train_data.shape[0], 200, replace=False)
subset = train_data.loc[sample]
percentages = [count / subset.shape[0] * 100 for count in subset[187].value_counts()]
percentages[0]
base_color = sb.color_palette()[0]
type_orderion = subset[187].value_counts().index
plt.xticks(rotation=90)
from sklearn.utils import resample
target1 = train_data[train_data[187] == 1]
target2 = train_data[train_data[187] == 2]
target3 = train_data[train_data[187] == 3]
target4 = train_data[train_data[187] == 4]
target0 = train_data[train_data[187] == 0].sample(n=20000, random_state=42)
target1_sample = resample(target1, replace=True, n_samples=20000, random_state=0)
target2_sample = resample(target2, replace=True, n_samples=20000, random_state=0)
target3_sample = resample(target3, replace=True, n_samples=20000, random_state=0)
target4_sample = resample(target4, replace=True, n_samples=20000, random_state=0)
train_data = pd.concat([target0, target1_sample, target2_sample, target3_sample, target4_sample])
train_target = train_data[187].value_counts()
sorted_counts = train_data[187].value_counts()
plt.axis('square')
test_target = test_data[187].value_counts()
sorted_counts = train_data[187].value_counts()
plt.figure(figsize=[12, 5.01])
plt.pie(sorted_counts, labels=sorted_counts.index, startangle=90, counterclock=False)
plt.axis('square') | code |
48165941/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_link = pd.read_csv('../input/dataset/datasets.csv')
df = df_link.copy()
df = df.dropna(axis=0, subset=['Source'])
df.head() | code |
48165941/cell_6 | [
"image_output_1.png"
] | from skimage import io
import matplotlib.pyplot as plt
from skimage import io
image = io.imread('../input/data-image/BurgosPuertaDeLaCoroneria/DPP_0245.JPG')
plt.imshow(image) | code |
48165941/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 |
48165941/cell_7 | [
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
list = os.listdir('../input/data-image/BurgosPuertaDeLaCoroneria')
for i in range(len(list)):
print(list[i]) | code |
48165941/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from skimage import data, color
from skimage import io
from skimage.transform import rescale, resize, downscale_local_mean
import cv2
import cv2
import imageio
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import os
import numpy as np
import pandas as pd
import os
from skimage import io
image = io.imread('../input/data-image/BurgosPuertaDeLaCoroneria/DPP_0245.JPG')
list = os.listdir('../input/data-image/BurgosPuertaDeLaCoroneria')
import matplotlib.image as mpimg
def process(filename):
image = mpimg.imread('../input/data-image/BurgosPuertaDeLaCoroneria/' + filename)
for file in list:
process(file)
import matplotlib.pyplot as plt
import cv2
from skimage import data, color
from skimage.transform import rescale, resize, downscale_local_mean
img = cv2.imread('../input/data-image/BurgosPuertaDeLaCoroneria/DPP_0247.JPG')
image = color.rgb2gray(img)
image_rescaled = rescale(image, 0.25, anti_aliasing=False)
image_resized = resize(image, (image.shape[0] // 4, image.shape[1] // 4),
anti_aliasing=True)
image_downscaled = downscale_local_mean(image, (4, 3))
fig, axes = plt.subplots(nrows=2, ncols=2)
ax = axes.ravel()
ax[0].imshow(image, cmap='gray')
ax[0].set_title("Original image")
ax[1].imshow(image_rescaled, cmap='gray')
ax[1].set_title("Rescaled image (aliasing)")
ax[2].imshow(image_resized, cmap='gray')
ax[2].set_title("Resized image (no aliasing)")
ax[3].imshow(image_downscaled, cmap='gray')
ax[3].set_title("Downscaled image (no aliasing)")
ax[0].set_xlim(0, 512)
ax[0].set_ylim(512, 0)
plt.tight_layout()
plt.show()
import cv2
import numpy as np
import matplotlib.pyplot as plt
import imageio
import imutils
cv2.ocl.setUseOpenCL(False)
# read images and transform them to grayscale
# Make sure that the train image is the image that will be transformed
trainImg = imageio.imread('../input/data-image/BurgosPuertaDeLaCoroneria/DPP_0247.JPG')
trainImg_gray = cv2.cvtColor(trainImg, cv2.COLOR_RGB2GRAY)
queryImg = imageio.imread('../input/data-image/BurgosPuertaDeLaCoroneria/DPP_0246.JPG')
# Opencv defines the color channel in the order BGR.
# Transform it to RGB to be compatible to matplotlib
queryImg_gray = cv2.cvtColor(queryImg, cv2.COLOR_RGB2GRAY)
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, constrained_layout=False, figsize=(16,9))
ax1.imshow(queryImg, cmap="gray")
ax1.set_xlabel("Query image", fontsize=14)
ax2.imshow(trainImg, cmap="gray")
ax2.set_xlabel("Train image (Image to be transformed)", fontsize=14)
plt.show()
def detectAndDescribe(image, method=None):
"""
Compute key points and feature descriptors using an specific method
"""
assert method is not None, "You need to define a feature detection method. Values are: 'sift', 'surf'"
if method == 'sift':
descriptor = cv2.xfeatures2d.SIFT_create()
elif method == 'surf':
descriptor = cv2.xfeatures2d.SURF_create()
elif method == 'brisk':
descriptor = cv2.BRISK_create()
elif method == 'orb':
descriptor = cv2.ORB_create()
kps, features = descriptor.detectAndCompute(image, None)
return (kps, features)
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8), constrained_layout=False)
ax1.imshow(cv2.drawKeypoints(trainImg_gray, kpsA, None, color=(0, 255, 0)))
ax1.set_xlabel('', fontsize=14)
ax2.imshow(cv2.drawKeypoints(queryImg_gray, kpsB, None, color=(0, 255, 0)))
ax2.set_xlabel('(b)', fontsize=14)
plt.show() | code |
48165941/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from skimage import io
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import numpy as np
import pandas as pd
import os
from skimage import io
image = io.imread('../input/data-image/BurgosPuertaDeLaCoroneria/DPP_0245.JPG')
list = os.listdir('../input/data-image/BurgosPuertaDeLaCoroneria')
import matplotlib.image as mpimg
def process(filename):
image = mpimg.imread('../input/data-image/BurgosPuertaDeLaCoroneria/' + filename)
plt.figure()
plt.imshow(image)
for file in list:
process(file) | code |
48165941/cell_15 | [
"text_plain_output_1.png"
] | from skimage import data, color
from skimage import io
from skimage.transform import rescale, resize, downscale_local_mean
import cv2
import cv2
import imageio
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import os
import numpy as np
import pandas as pd
import os
from skimage import io
image = io.imread('../input/data-image/BurgosPuertaDeLaCoroneria/DPP_0245.JPG')
list = os.listdir('../input/data-image/BurgosPuertaDeLaCoroneria')
import matplotlib.image as mpimg
def process(filename):
image = mpimg.imread('../input/data-image/BurgosPuertaDeLaCoroneria/' + filename)
for file in list:
process(file)
import matplotlib.pyplot as plt
import cv2
from skimage import data, color
from skimage.transform import rescale, resize, downscale_local_mean
img = cv2.imread('../input/data-image/BurgosPuertaDeLaCoroneria/DPP_0247.JPG')
image = color.rgb2gray(img)
image_rescaled = rescale(image, 0.25, anti_aliasing=False)
image_resized = resize(image, (image.shape[0] // 4, image.shape[1] // 4),
anti_aliasing=True)
image_downscaled = downscale_local_mean(image, (4, 3))
fig, axes = plt.subplots(nrows=2, ncols=2)
ax = axes.ravel()
ax[0].imshow(image, cmap='gray')
ax[0].set_title("Original image")
ax[1].imshow(image_rescaled, cmap='gray')
ax[1].set_title("Rescaled image (aliasing)")
ax[2].imshow(image_resized, cmap='gray')
ax[2].set_title("Resized image (no aliasing)")
ax[3].imshow(image_downscaled, cmap='gray')
ax[3].set_title("Downscaled image (no aliasing)")
ax[0].set_xlim(0, 512)
ax[0].set_ylim(512, 0)
plt.tight_layout()
plt.show()
import cv2
import numpy as np
import matplotlib.pyplot as plt
import imageio
import imutils
cv2.ocl.setUseOpenCL(False)
trainImg = imageio.imread('../input/data-image/BurgosPuertaDeLaCoroneria/DPP_0247.JPG')
trainImg_gray = cv2.cvtColor(trainImg, cv2.COLOR_RGB2GRAY)
queryImg = imageio.imread('../input/data-image/BurgosPuertaDeLaCoroneria/DPP_0246.JPG')
queryImg_gray = cv2.cvtColor(queryImg, cv2.COLOR_RGB2GRAY)
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, constrained_layout=False, figsize=(16, 9))
ax1.imshow(queryImg, cmap='gray')
ax1.set_xlabel('Query image', fontsize=14)
ax2.imshow(trainImg, cmap='gray')
ax2.set_xlabel('Train image (Image to be transformed)', fontsize=14)
plt.show() | code |
48165941/cell_3 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_link = pd.read_csv('../input/dataset/datasets.csv')
df_link.head() | code |
48165941/cell_10 | [
"text_html_output_1.png"
] | from skimage import data, color
from skimage import io
from skimage.transform import rescale, resize, downscale_local_mean
import cv2
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import os
import numpy as np
import pandas as pd
import os
from skimage import io
image = io.imread('../input/data-image/BurgosPuertaDeLaCoroneria/DPP_0245.JPG')
list = os.listdir('../input/data-image/BurgosPuertaDeLaCoroneria')
import matplotlib.image as mpimg
def process(filename):
image = mpimg.imread('../input/data-image/BurgosPuertaDeLaCoroneria/' + filename)
for file in list:
process(file)
import matplotlib.pyplot as plt
import cv2
from skimage import data, color
from skimage.transform import rescale, resize, downscale_local_mean
img = cv2.imread('../input/data-image/BurgosPuertaDeLaCoroneria/DPP_0247.JPG')
image = color.rgb2gray(img)
image_rescaled = rescale(image, 0.25, anti_aliasing=False)
image_resized = resize(image, (image.shape[0] // 4, image.shape[1] // 4), anti_aliasing=True)
image_downscaled = downscale_local_mean(image, (4, 3))
fig, axes = plt.subplots(nrows=2, ncols=2)
ax = axes.ravel()
ax[0].imshow(image, cmap='gray')
ax[0].set_title('Original image')
ax[1].imshow(image_rescaled, cmap='gray')
ax[1].set_title('Rescaled image (aliasing)')
ax[2].imshow(image_resized, cmap='gray')
ax[2].set_title('Resized image (no aliasing)')
ax[3].imshow(image_downscaled, cmap='gray')
ax[3].set_title('Downscaled image (no aliasing)')
ax[0].set_xlim(0, 512)
ax[0].set_ylim(512, 0)
plt.tight_layout()
plt.show() | code |
48165941/cell_12 | [
"text_html_output_1.png"
] | !pip install imutils | code |
48165941/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
df_link = pd.read_csv('../input/dataset/datasets.csv')
df = df_link.copy()
df = df.dropna(axis=0, subset=['Source'])
import requests
from io import BytesIO
from PIL import Image
for i in range(100):
r = requests.get(df['Source'][i])
print('Status:', r.status_code)
print(r.url) | code |
72103063/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable = df[df['Potability'] == 1]
df_notpotable.isnull().sum()
df_potable.isnull().sum()
df_notpotable.isnull().sum()
df.Potability.value_counts()
df_potable.isnull().sum()
df = pd.concat([df_notpotable, df_potable])
df = df.sample(frac=1)
x = df.drop('Potability', axis=1)
y = df['Potability']
df.hist(bins=10, figsize=(20, 15), color='teal') | code |
72103063/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable = df[df['Potability'] == 1]
df_potable.isnull().sum()
df_potable.isnull().sum() | code |
72103063/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)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable = df[df['Potability'] == 1]
df_potable.isnull().sum() | code |
72103063/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable = df[df['Potability'] == 1]
df_notpotable.isnull().sum()
df_potable.isnull().sum()
df_notpotable.isnull().sum()
df.Potability.value_counts()
df_potable.isnull().sum()
df = pd.concat([df_notpotable, df_potable])
df = df.sample(frac=1)
x = df.drop('Potability', axis=1)
y = df['Potability']
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(x)
x = scaler.transform(x)
x = pd.DataFrame(x)
x | code |
72103063/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
print('number of rows: ', df.shape[0])
print('number of column: ', df.shape[1])
df.Potability.value_counts() | code |
72103063/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable = df[df['Potability'] == 1]
df_notpotable.isnull().sum()
df_notpotable.isnull().sum() | code |
72103063/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 |
72103063/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable = df[df['Potability'] == 1]
df_notpotable.isnull().sum()
df_potable.isnull().sum()
df_notpotable.isnull().sum()
df.Potability.value_counts()
df_potable.isnull().sum()
df = pd.concat([df_notpotable, df_potable])
df = df.sample(frac=1)
df.head() | code |
72103063/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable = df[df['Potability'] == 1]
df_notpotable.isnull().sum() | code |
72103063/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable = df[df['Potability'] == 1]
df_notpotable.isnull().sum()
df_potable.isnull().sum()
df_notpotable.isnull().sum()
df.Potability.value_counts()
df_potable.isnull().sum()
df = pd.concat([df_notpotable, df_potable])
df.head() | code |
72103063/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.head() | code |
72103063/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable = df[df['Potability'] == 1]
df_notpotable.isnull().sum()
df_potable.isnull().sum()
df_notpotable.isnull().sum()
df.Potability.value_counts()
df_potable.isnull().sum()
df = pd.concat([df_notpotable, df_potable])
df = df.sample(frac=1)
x = df.drop('Potability', axis=1)
y = df['Potability']
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(25, 10))
p1 = fig.add_subplot(2, 2, 1)
p1.hist(df.ph[df.Potability == 0], bins=20, alpha=0.4)
p1.hist(df.ph[df.Potability == 1], bins=20, alpha=0.4)
plt.title('pH')
plt.xlabel('pH')
plt.ylabel('Count')
labels = ['0', '1']
plt.legend(labels)
p1 = fig.add_subplot(2, 2, 2)
p1.hist(df.Hardness[df.Potability == 0], bins=20, alpha=0.4)
p1.hist(df.Hardness[df.Potability == 1], bins=20, alpha=0.4)
plt.title('Hardness')
plt.xlabel('Hardness')
plt.ylabel('Count')
labels = ['0', '1']
plt.legend(labels)
p1 = fig.add_subplot(2, 2, 3)
p1.hist(df.Solids[df.Potability == 0], bins=20, alpha=0.4)
p1.hist(df.Solids[df.Potability == 1], bins=20, alpha=0.4)
plt.title('Solids')
plt.xlabel('Solids')
plt.ylabel('Count')
labels = ['0', '1']
plt.legend(labels)
p1 = fig.add_subplot(2, 2, 4)
p1.hist(df.Chloramines[df.Potability == 0], bins=20, alpha=0.4)
p1.hist(df.Chloramines[df.Potability == 1], bins=20, alpha=0.4)
plt.title('Chloramines')
plt.xlabel('Chloramines')
plt.ylabel('Count')
labels = ['0', '1']
plt.legend(labels)
plt.subplots_adjust(wspace=0.1, hspace=0.3)
plt.show() | code |
72103063/cell_10 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df_notpotable = df[df['Potability'] == 0]
df_potable = df[df['Potability'] == 1]
df_notpotable.isnull().sum()
df_potable.isnull().sum()
from sklearn.impute import SimpleImputer
impute = SimpleImputer(missing_values=np.nan, strategy='mean')
impute.fit(df_notpotable[['ph']])
impute.fit(df_notpotable[['Sulfate']])
impute.fit(df_notpotable[['Trihalomethanes']])
df_notpotable['ph'] = impute.transform(df_notpotable[['ph']])
df_notpotable['Sulfate'] = impute.transform(df_notpotable[['Sulfate']])
df_notpotable['Trihalomethanes'] = impute.transform(df_notpotable[['Trihalomethanes']])
impute.fit(df_potable[['ph']])
impute.fit(df_potable[['Sulfate']])
impute.fit(df_potable[['Trihalomethanes']])
df_potable['ph'] = impute.transform(df_potable[['ph']])
df_potable['Sulfate'] = impute.transform(df_potable[['Sulfate']])
df_potable['Trihalomethanes'] = impute.transform(df_potable[['Trihalomethanes']]) | code |
72103063/cell_12 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum()
df.Potability.value_counts()
df.Potability.value_counts() | code |
72103063/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.isnull().sum() | code |
122261656/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
import numpy as np
data = data.values[:, 1:]
np.random.seed(0)
n = 30
m = data.shape[0]
length = data.shape[1]
signMatrix = np.zeros((n, m))
print(data[44505, :] == data[46157, :]) | code |
122261656/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
data.head() | code |
122261656/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
import numpy as np
data = data.values[:, 1:]
np.random.seed(0)
n = 30
m = data.shape[0]
length = data.shape[1]
signMatrix = np.zeros((n, m))
def Jaccard(i, j):
iData = data[i, :]
jData = data[j, :]
count1 = 0
count2 = 0
for k in range(length):
if max(iData[k], jData[k]):
count1 += 1
if min(iData[k], jData[k]):
count2 += 1
return count2 / count1
Jaccard(1, 2) | code |
122261656/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
import numpy as np
data = data.values[:, 1:]
print(data)
np.random.seed(0)
n = 30
m = data.shape[0]
print(m)
length = data.shape[1]
print(length)
signMatrix = np.zeros((n, m)) | code |
122261656/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.metrics import jaccard_score
from tqdm import tqdm
import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
import numpy as np
data = data.values[:, 1:]
np.random.seed(0)
n = 30
m = data.shape[0]
length = data.shape[1]
signMatrix = np.zeros((n, m))
from sklearn.metrics import jaccard_score
jaccardDict = {}
for i in tqdm(range(1, m)):
jaccardDict[i] = jaccard_score(data[0, :], data[i, :])
sorted(jaccardDict.items(), key=lambda x: x[1], reverse=True)[:30] | code |
122261656/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
import numpy as np
data = data.values[:, 1:]
np.random.seed(0)
n = 30
m = data.shape[0]
length = data.shape[1]
signMatrix = np.zeros((n, m))
for i in range(n):
seq = np.arange(0, length)
np.random.shuffle(seq)
for j in range(m):
for k in seq:
if data[j][k] == 1:
signMatrix[i][j] = k + 1
break
signMatrix | code |
122261656/cell_16 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import itertools
import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
import numpy as np
data = data.values[:, 1:]
np.random.seed(0)
n = 30
m = data.shape[0]
length = data.shape[1]
signMatrix = np.zeros((n, m))
def bucketAllocate(r, b):
Bucket = {}
for i in range(b):
tmp = signMatrix[b:b + r]
for j in range(m):
signBand = list(tmp[:, j])
signBand.append(b)
hashValue = hash(str(signBand))
if hashValue not in Bucket:
Bucket[hashValue] = [j]
else:
Bucket[hashValue].append(j)
return Bucket
r = 30
b = 1
bucket = bucketAllocate(r, b)
def Jaccard(i, j):
iData = data[i, :]
jData = data[j, :]
count1 = 0
count2 = 0
for k in range(length):
if max(iData[k], jData[k]):
count1 += 1
if min(iData[k], jData[k]):
count2 += 1
return count2 / count1
Jaccard(1, 2)
import itertools
from tqdm import tqdm
MaxSimilarity = 0
SimilarPair = None
for obj in tqdm(bucket.values()):
for p in itertools.combinations(obj, 2):
jaccardSim = Jaccard(p[0], p[1])
if jaccardSim > MaxSimilarity:
MaxSimilarity = jaccardSim
SimilarPair = p
def bucketFind(r, b):
Bucket = {}
hash0 = 0
for i in range(b):
tmp = signMatrix[b:b + r]
for j in range(m):
signBand = list(tmp[:, j])
signBand.append(b)
hashValue = hash(str(signBand))
if j == 0:
hash0 = hashValue
elif hashValue == hash0:
Bucket[j] = Jaccard(0, j)
return Bucket
r = 10
b = 3
bucket = bucketFind(r, b)
print(bucket) | code |
122261656/cell_17 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import tqdm
import itertools
import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
import numpy as np
data = data.values[:, 1:]
np.random.seed(0)
n = 30
m = data.shape[0]
length = data.shape[1]
signMatrix = np.zeros((n, m))
def bucketAllocate(r, b):
Bucket = {}
for i in range(b):
tmp = signMatrix[b:b + r]
for j in range(m):
signBand = list(tmp[:, j])
signBand.append(b)
hashValue = hash(str(signBand))
if hashValue not in Bucket:
Bucket[hashValue] = [j]
else:
Bucket[hashValue].append(j)
return Bucket
r = 30
b = 1
bucket = bucketAllocate(r, b)
def Jaccard(i, j):
iData = data[i, :]
jData = data[j, :]
count1 = 0
count2 = 0
for k in range(length):
if max(iData[k], jData[k]):
count1 += 1
if min(iData[k], jData[k]):
count2 += 1
return count2 / count1
Jaccard(1, 2)
import itertools
from tqdm import tqdm
MaxSimilarity = 0
SimilarPair = None
for obj in tqdm(bucket.values()):
for p in itertools.combinations(obj, 2):
jaccardSim = Jaccard(p[0], p[1])
if jaccardSim > MaxSimilarity:
MaxSimilarity = jaccardSim
SimilarPair = p
def bucketFind(r, b):
Bucket = {}
hash0 = 0
for i in range(b):
tmp = signMatrix[b:b + r]
for j in range(m):
signBand = list(tmp[:, j])
signBand.append(b)
hashValue = hash(str(signBand))
if j == 0:
hash0 = hashValue
elif hashValue == hash0:
Bucket[j] = Jaccard(0, j)
return Bucket
r = 10
b = 3
bucket = bucketFind(r, b)
sorted(bucket.items(), key=lambda x: x[1], reverse=True)[:30] | code |
122261656/cell_10 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
import numpy as np
data = data.values[:, 1:]
np.random.seed(0)
n = 30
m = data.shape[0]
length = data.shape[1]
signMatrix = np.zeros((n, m))
def bucketAllocate(r, b):
Bucket = {}
for i in range(b):
tmp = signMatrix[b:b + r]
for j in range(m):
signBand = list(tmp[:, j])
signBand.append(b)
hashValue = hash(str(signBand))
if hashValue not in Bucket:
Bucket[hashValue] = [j]
else:
Bucket[hashValue].append(j)
return Bucket
r = 30
b = 1
bucket = bucketAllocate(r, b)
print(len(bucket)) | code |
122261656/cell_12 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import itertools
import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/docs-for-lsh/docs_for_lsh.csv')
import numpy as np
data = data.values[:, 1:]
np.random.seed(0)
n = 30
m = data.shape[0]
length = data.shape[1]
signMatrix = np.zeros((n, m))
def bucketAllocate(r, b):
Bucket = {}
for i in range(b):
tmp = signMatrix[b:b + r]
for j in range(m):
signBand = list(tmp[:, j])
signBand.append(b)
hashValue = hash(str(signBand))
if hashValue not in Bucket:
Bucket[hashValue] = [j]
else:
Bucket[hashValue].append(j)
return Bucket
r = 30
b = 1
bucket = bucketAllocate(r, b)
def Jaccard(i, j):
iData = data[i, :]
jData = data[j, :]
count1 = 0
count2 = 0
for k in range(length):
if max(iData[k], jData[k]):
count1 += 1
if min(iData[k], jData[k]):
count2 += 1
return count2 / count1
Jaccard(1, 2)
import itertools
from tqdm import tqdm
MaxSimilarity = 0
SimilarPair = None
for obj in tqdm(bucket.values()):
for p in itertools.combinations(obj, 2):
jaccardSim = Jaccard(p[0], p[1])
if jaccardSim > MaxSimilarity:
MaxSimilarity = jaccardSim
SimilarPair = p
print(SimilarPair)
print(MaxSimilarity) | code |
88094115/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
x = df[['YearsExperience']]
x
y = df.iloc[:, 1].values
y
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(x, y)
y_pred = model.predict(x)
y_pred
model.coef_ | code |
88094115/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
df.head() | code |
88094115/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
x = df[['YearsExperience']]
x | code |
88094115/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
x = df[['YearsExperience']]
x
y = df.iloc[:, 1].values
y
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(x, y)
y_pred = model.predict(x)
y_pred | code |
88094115/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 |
88094115/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
y = df.iloc[:, 1].values
y | code |
88094115/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
x = df[['YearsExperience']]
x
y = df.iloc[:, 1].values
y
plt.scatter(x, y)
plt.show() | code |
88094115/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
x = df[['YearsExperience']]
x
y = df.iloc[:, 1].values
y
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(x, y)
y_pred = model.predict(x)
y_pred
model.coef_
model.intercept_
model.predict([[4]]) | code |
88094115/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
x = df[['YearsExperience']]
x
y = df.iloc[:, 1].values
y
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(x, y)
y_pred = model.predict(x)
y_pred
r2_score(y, y_pred) * 100 | code |
88094115/cell_14 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
x = df[['YearsExperience']]
x
y = df.iloc[:, 1].values
y
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(x, y)
y_pred = model.predict(x)
y_pred
model.coef_
model.intercept_ | code |
88094115/cell_10 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
x = df[['YearsExperience']]
x
y = df.iloc[:, 1].values
y
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(x, y) | code |
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