path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
129001471/cell_9 | [
"image_output_1.png"
] | from botsFactoryLib import processData, genExposure, genExposureDown, genPreds, genShiftedSMA, loadModel
import os
import pandas as pd
import plotly.graph_objects as go
import talib
import vectorbt as vbt
import os
import json
import pytz
import talib
import pickle
import numpy as np
import pandas as pd
import da... | code |
129001471/cell_2 | [
"text_html_output_1.png"
] | import os
import pandas as pd
import vectorbt as vbt
import os
import json
import pytz
import talib
import pickle
import numpy as np
import pandas as pd
import datetime as dt
import vectorbt as vbt
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from histDataHandler import loadSuchData
from botsFac... | code |
129001471/cell_8 | [
"text_plain_output_1.png"
] | from botsFactoryLib import processData, genExposure, genExposureDown, genPreds, genShiftedSMA, loadModel
import os
import pandas as pd
import talib
import vectorbt as vbt
import os
import json
import pytz
import talib
import pickle
import numpy as np
import pandas as pd
import datetime as dt
import vectorbt as vbt... | code |
32071289/cell_9 | [
"text_plain_output_1.png"
] | """mae = mean_absolute_error(y_valid_cc, preds_cc['preds'])
msle = mean_squared_log_error(y_valid_cc, preds_cc['preds'])
print("CC MAE: %f MSLE %f" % (mae, msle))
mae = mean_absolute_error(y_valid_ft, preds_ft['preds'])
msle = mean_squared_log_error(y_valid_ft, preds_ft['preds'])
print("FT MAE: %f MSLE %f" % (mae, msle... | code |
32071289/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
covid_train = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv', index_col='Id', parse_dates=['Date'])
covid_test = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv', ... | code |
32071289/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
covid_train = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv', index_col='Id', parse_dates=['Date'])
covid_test = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv', index_col='ForecastId', parse_dates=['Date'])
la... | code |
32071289/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from skl... | code |
32071289/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
covid_train = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv', index_col='Id', parse_dates=['Date'])
covid_test = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv', index_col='ForecastId', parse_dates=['Date'])
la... | code |
32071289/cell_10 | [
"text_plain_output_1.png"
] | """X_train_2, X_valid_2, y_train_cc_2, y_valid_cc_2 = train_test_split(covid_train[X_features_cc], y_cc, random_state=42)
model_cc_2 = RandomForestRegressor(n_estimators=100, random_state=42)
model_cc_2.fit(X_train_2, y_train_cc_2)
predic = model_cc_2.predict(X_valid_2)
mae = mean_absolute_error(y_valid_cc_2, predic)
m... | code |
49116605/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
order = pd.read_csv('/kaggle/input/market-basket-id-ndsc-2020/association_order.csv')
order | code |
49116605/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 |
2036189/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import itertools
import numpy as np # linear algebra
import random
"""
create sample data
user and product are segmented
"""
random.seed(0)
NN_word = 2000
NN_sentence = 10000
NN_SEG = 7
class Seq(object):
def __init__(self, neg_sample=5, batch_size=32, stop=None, common_prods=[50, 100, 200, 500, 1000]):
... | code |
2036189/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import itertools
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import random
"""
create sample data
user and product are segmented
"""
random.seed(0)
NN_word = 2000
NN_sentence = 10000
NN_SEG = 7
class Seq(object):
def __init__(self, neg_sample=5, batch_size=32, stop=None, common_prods=[50... | code |
2036189/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot | code |
90124843/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt
import scipy.stats as st
population = np.random.normal(size=10000000, loc=173, scale=10)
population_mean = np.round(np.mean(population), 2)
print('The population mean is', population_mean, 'and nobody knows this valu... | code |
90124843/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt
import scipy.stats as st
population = np.random.normal(size=10000000, loc=173, scale=10)
population_mean = np.round(np.mean(population), 2)
sample = np.random.choice(population, size=10)
print('\nThe sample mean is'... | code |
90124843/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import scipy.stats as st
import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt
import scipy.stats as st
population = np.random.normal(size=10000000, loc=173, scale=10)
population_mean = np.round(np.mean(population), 2)
sample = np.random.choice(population, size=10)
d... | code |
90124843/cell_3 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt
import scipy.stats as st
population = np.random.normal(size=10000000, loc=173, scale=10)
plt.figure(figsize=(15, 5))
plt.hist(population, bins=100)
plt.grid()
plt.title('Population distribution')
plt.show() | code |
90124843/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import scipy.stats as st
import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt
import scipy.stats as st
population = np.random.normal(size=10000000, loc=173, scale=10)
population_mean = np.round(np.mean(population), 2)
sample = np.random.choice(population, size=10)
d... | code |
73067313/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0, 11)
x = 0.85 ** t
plt.figure(figsize=(12, 8))
plt.subplot(2, 2, 1)
plt.title('Analog Signal', fontsize=20)
plt.plot(t, x, linewidth=3, label='x(t) = (0.85)^t')
plt.xlabel('t', fontsize=15)
plt.ylabel('amplitude', fontsize=15)
plt.legend(loc='upper r... | code |
73067313/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy import signal
import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0, 11)
x = 0.85 ** t
n = t
markerline, stemlines, baseline = plt.stem(n, x, label='x(n) = (0.85)^n')
plt.setp(stemlines, 'linewidth', 3)
markerline, stemlines, baseline = plt.stem(n, x)
plt.setp(stemlines, 'linewidth', 3)
xq =... | code |
73067313/cell_10 | [
"image_output_1.png"
] | from scipy import signal
import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0, 11)
x = 0.85 ** t
n = t
markerline, stemlines, baseline = plt.stem(n, x, label='x(n) = (0.85)^n')
plt.setp(stemlines, 'linewidth', 3)
markerline, stemlines, baseline = plt.stem(n, x)
plt.setp(stemlines, 'linewidth', 3)
xq =... | code |
128010001/cell_9 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on... | code |
128010001/cell_25 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on... | code |
128010001/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on_kaggle = False
print(DATA_PATH) | code |
128010001/cell_23 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on... | code |
128010001/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/... | code |
128010001/cell_48 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/... | code |
128010001/cell_2 | [
"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 |
128010001/cell_19 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on... | code |
128010001/cell_18 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on... | code |
128010001/cell_51 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/... | code |
128010001/cell_59 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/... | code |
128010001/cell_58 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/... | code |
128010001/cell_28 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on... | code |
128010001/cell_15 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on... | code |
128010001/cell_43 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/... | code |
128010001/cell_31 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/... | code |
128010001/cell_46 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/... | code |
128010001/cell_14 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on... | code |
128010001/cell_22 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on... | code |
128010001/cell_53 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DA... | code |
128010001/cell_27 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on... | code |
128010001/cell_37 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/... | code |
128010001/cell_12 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
if os.path.exists('/kaggle/input'):
DATA_PATH = '/kaggle/input/citibike-sampled-data-2013-2017/'
on_kaggle = True
else:
DATA_PATH = './'
on... | code |
129024960/cell_42 | [
"image_output_1.png"
] | grid_result_lasso.best_params_ | code |
129024960/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
df.describe() | code |
129024960/cell_56 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
# Setting output to have 5 subplots in a single row
fig, ax = plt.subplots(... | code |
129024960/cell_34 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
# Setting output to have 5 subplots in a single row
fig, ax = plt.subplots(... | code |
129024960/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
fig, ax = plt.subplots(1, 5, figsize=(10, 5))
plt.subplots_adjust(wspace=0.... | code |
129024960/cell_30 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
# Setting output to have 5 subplots in a single row
fig, ax = plt.subplots(... | code |
129024960/cell_44 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
# Setting output to have 5 subplots in a single row
fig, ax = plt.subplots(... | code |
129024960/cell_55 | [
"image_output_1.png"
] | from mlxtend.feature_selection import SequentialFeatureSelector as SFS
sffs = SFS(RandomForestClassifier(), k_features=(1, len(X.columns)), forward=True, floating=True, scoring='accuracy', cv=5)
sffs.fit(X, y)
corr_features = list(sffs.k_feature_names_)
corr_features | code |
129024960/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.head() | code |
129024960/cell_40 | [
"text_html_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.... | code |
129024960/cell_48 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
# Setting output to have 5 subplots in a single row
fig, ax = plt.subplots(... | code |
129024960/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | grid = {'alpha': [0.0001, 0.001, 0.01, 0.1, 1]}
cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=3, random_state=42)
grid_search_lasso = GridSearchCV(estimator=lasso_reg, param_grid=grid, n_jobs=-1, cv=cv, scoring='accuracy', error_score=0)
grid_result_lasso = grid_search_lasso.fit(X_scaled, y) | code |
129024960/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape | code |
129024960/cell_49 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
# Setting output to have 5 subplots in a single row
fig, ax = plt.subplots(... | code |
129024960/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
df.info() | code |
129024960/cell_51 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
# Setting output to have 5 subplots in a single row
fig, ax = plt.subplots(... | code |
129024960/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df['Room_Occupancy_Count'].value_counts().plot(kind='pie') | code |
129024960/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()] | code |
129024960/cell_43 | [
"text_html_output_1.png"
] | grid_result_lasso.best_params_
coef = grid_result_lasso.best_estimator_.coef_
coef | code |
129024960/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()]
df.drop(columns=['Date', 'Time'], axis=1, inplace=True)
df[df.duplicated()]
df.head() | code |
129024960/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('room_occupancy_estimation_dataset.csv')
df.shape
df[df.duplicated()] | code |
2022682/cell_9 | [
"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)
import seaborn as sns
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape
iris_main.describe
tmp = iris_main.drop('Id', axis=1)
g = sns.pairplot(tmp, hue='Species', markers='+')
plt.show() | code |
2022682/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape | code |
2022682/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape
iris_main.describe
iris_main.info() | code |
2022682/cell_11 | [
"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)
import seaborn as sns
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape
iris_main.describe
tmp = iris_main.drop('Id', axis=1)
g = sns.pairplot(tmp, hue='Species', markers='+')
plt.show()
X = iris_mai... | code |
2022682/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_palette('husl')
from sklearn import metrics
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from subprocess import check_output... | code |
2022682/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape
iris_main.describe
iris_main.describe() | code |
2022682/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape
iris_main.describe
iris_main['Species'].value_counts() | code |
2022682/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.sh... | code |
2022682/cell_16 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape
iris_main.describe
tmp = iris_main.drop('Id', a... | code |
2022682/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
iris_main = pd.read_csv('../input/Iris.csv')
iris_main | code |
2022682/cell_14 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape
iris_main.describe
tmp = iris_main.drop('Id', a... | code |
2022682/cell_12 | [
"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)
import seaborn as sns
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape
iris_main.describe
tmp = iris_main.drop('Id', axis=1)
g = sns.pairplot(tmp, hue='Species', markers='+')
plt.show()
X = iris_mai... | code |
2022682/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
iris_main = pd.read_csv('../input/Iris.csv')
iris_main.shape
iris_main.describe | code |
34150026/cell_13 | [
"text_plain_output_1.png",
"image_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)
import seaborn as sns
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submiss... | code |
34150026/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-cl... | code |
34150026/cell_57 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report,confusion_matrix,accuracy_score
from sklearn.linear_model import LogisticRegression
logit = LogisticRegression(max_iter=1000)
logit.fit(X_train, y_train)
score = lo... | code |
34150026/cell_44 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
from keras.preprocessing import text, sequence
from nltk.corpus import stopwords
from string import punctuation
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../i... | code |
34150026/cell_20 | [
"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)
import seaborn as sns
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submiss... | code |
34150026/cell_55 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report,confusion_matrix,accuracy_score
from sklearn.linear_model import LogisticRegression
logit = LogisticRegression(max_iter=1000)
logit.fit(X_train, y_train)
score = logit.sco... | code |
34150026/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-cl... | code |
34150026/cell_61 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-cl... | code |
34150026/cell_11 | [
"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)
import seaborn as sns
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submiss... | code |
34150026/cell_60 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
from keras.preprocessing import text, sequence
from nltk.corpus import stopwords
from sklearn.linear_model import LogisticRegression
from string import punctuation
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_c... | code |
34150026/cell_52 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
logit = LogisticRegression(max_iter=1000)
logit.fit(X_train, y_train)
score = logit.score(X_train, y_train)
score | code |
34150026/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 |
34150026/cell_49 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
logit = LogisticRegression(max_iter=1000)
logit.fit(X_train, y_train) | code |
34150026/cell_18 | [
"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)
import seaborn as sns
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submiss... | code |
34150026/cell_62 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
from keras.preprocessing import text, sequence
from nltk.corpus import stopwords
from string import punctuation
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../i... | code |
34150026/cell_15 | [
"text_html_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)
import seaborn as sns
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submiss... | code |
34150026/cell_66 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
from keras.preprocessing import text, sequence
from nltk.corpus import stopwords
from sklearn.linear_model import LogisticRegression
from string import punctuation
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_c... | code |
34150026/cell_43 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
from keras.preprocessing import text, sequence
from nltk.corpus import stopwords
from string import punctuation
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../i... | code |
34150026/cell_31 | [
"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)
import seaborn as sns
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submiss... | code |
34150026/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-cl... | code |
34150026/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')
test = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')
submission = pd.read_csv('../input/quora-insincere-questions-cl... | code |
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