path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
32067430/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 |
32067430/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
team_stats = pd.read_csv('/kaggle/input/college-basketball-dataset/cbb.csv')
avg_off = team_stats['ADJOE'].mean()
avg_def = team_stats['ADJDE'].mean()
avg_def - team_stats[team_stats['POSTSEASON'] == 'Champions']['ADJDE'].mean() | code |
32067430/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
team_stats = pd.read_csv('/kaggle/input/college-basketball-dataset/cbb.csv')
avg_off = team_stats['ADJOE'].mean()
avg_def = team_stats['ADJDE'].mean()
print(avg_off, avg_def, sep=',') | code |
32070671/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.read_csv('animes.csv')
for c in df.columns[12:]:
df[c] = df[c].astype('int')
idx = []
for i in df.columns:
idx.append(i.replace('genre_', ''))
df.columns = idx
import seaborn as sns
impo... | code |
32070671/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
df = pd.read_csv('animes.csv')
df.describe() | code |
32070671/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.read_csv('animes.csv')
for c in df.columns[12:]:
df[c] = df[c].astype('int')
idx = []
for i in df.columns:
idx.append(i.replace('genre_', ''))
df.columns = idx
import seaborn as sns
impo... | code |
32070671/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.read_csv('animes.csv')
for c in df.columns[12:]:
df[c] = df[c].astype('int')
idx = []
for i in df.columns:
idx.append(i.replace('genre_', ''))
df.columns = idx
import seaborn as sns
impo... | code |
32070671/cell_26 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.read_csv('animes.csv')
for c in df.columns[12:]:
df[c] = df[c].astype('int')
idx = []
for i in df.columns:
idx.append(i.replace('genre_', ''))
df.columns = idx
import seaborn as sns
impo... | code |
32070671/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
df = pd.read_csv('animes.csv')
for c in df.columns[12:]:
df[c] = df[c].astype('int')
idx = []
for i in df.columns:
idx.append(i.replace('genre_', ''))
df.columns = idx | code |
32070671/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
df = pd.read_csv('animes.csv') | code |
32070671/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.read_csv('animes.csv')
for c in df.columns[12:]:
df[c] = df[c].astype('int')
idx = []
for i in df.columns:
idx.append(i.replace('genre_', ''))
df.columns = idx
import seaborn as sns
impo... | code |
32070671/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.read_csv('animes.csv')
for c in df.columns[12:]:
df[c] = df[c].astype('int')
idx = []
for i in df.columns:
idx.append(i.replace('genre_', ''))
df.columns = idx
import seaborn as sns
impo... | code |
32070671/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.read_csv('animes.csv')
for c in df.columns[12:]:
df[c] = df[c].astype('int')
idx = []
for i in df.columns:
idx.append(i.replace('genre_', ''))
df.columns = idx
import seaborn as sns
impo... | code |
32070671/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.read_csv('animes.csv')
for c in df.columns[12:]:
df[c] = df[c].astype('int')
idx = []
for i in df.columns:
idx.append(i.replace('genre_', ''))
df.columns = idx
import seaborn as sns
impo... | code |
16166543/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data_lokasi = pd.read_csv('../input/catatan_lokasi.csv')
profil_karyawan = pd.read_csv('../input/data_profil.csv')
profil_karyawan.head() | code |
16166543/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data_lokasi = pd.read_csv('../input/catatan_lokasi.csv')
profil_karyawan = pd.read_csv('../input/data_profil.csv')
data_lokasi.head() | code |
50226402/cell_13 | [
"text_plain_output_1.png"
] | def linearSearch(array, n, x):
for i in range(0, n):
if array[i] == x:
return i
return -1
array = [10, 20, 30, 40, 50, 60, 70]
x = 50
n = len(array)
result = linearSearch(array, n, x)
def binary_search_recursive(array, element, start, end):
if start > end:
return -1
mid = (s... | code |
50226402/cell_9 | [
"text_plain_output_1.png"
] | def linearSearch(array, n, x):
for i in range(0, n):
if array[i] == x:
return i
return -1
array = [10, 20, 30, 40, 50, 60, 70]
x = 50
n = len(array)
result = linearSearch(array, n, x)
def binary_search_recursive(array, element, start, end):
if start > end:
return -1
mid = (s... | code |
50226402/cell_4 | [
"text_plain_output_1.png"
] | A = [1, 22, 30, 35, 300, 1000]
A = [1, 22, 30, 35, 300, 1000]
print(A.index(35)) | code |
50226402/cell_6 | [
"text_plain_output_1.png"
] | def linearSearch(array, n, x):
for i in range(0, n):
if array[i] == x:
return i
return -1
array = [10, 20, 30, 40, 50, 60, 70]
x = 50
n = len(array)
result = linearSearch(array, n, x)
if result == -1:
print('Element not found')
else:
print('Element found at index: ', result) | code |
50226402/cell_2 | [
"text_plain_output_1.png"
] | A = [1, 22, 30, 35, 300, 1000]
print(A) | code |
50226402/cell_11 | [
"text_plain_output_1.png"
] | def linearSearch(array, n, x):
for i in range(0, n):
if array[i] == x:
return i
return -1
array = [10, 20, 30, 40, 50, 60, 70]
x = 50
n = len(array)
result = linearSearch(array, n, x)
def binary_search_recursive(array, element, start, end):
if start > end:
return -1
mid = (s... | code |
104115755/cell_21 | [
"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
df = pd.read_csv('../input/adverse-food-events/CAERS_ASCII_2004_2017Q2.csv')
df.shape
df.columns
df.rename(columns={'PRI_Reported Brand/Product Name': 'products_name', 'SYM_One Row Coded Sy... | code |
104115755/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/adverse-food-events/CAERS_ASCII_2004_2017Q2.csv')
df.shape
df.columns
df.rename(columns={'PRI_Reported Brand/Product Name': 'products_name', 'SYM_One Row Coded Symptoms': 'symptoms', 'CI_Gender': 'gender', 'CI_Age at A... | code |
104115755/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/adverse-food-events/CAERS_ASCII_2004_2017Q2.csv')
df.shape
df.columns
df.rename(columns={'PRI_Reported Brand/Product Name': 'products_name', 'SYM_One Row Coded Symptoms': 'symptoms', 'CI_Gender': 'gender', 'CI_Age at A... | code |
104115755/cell_25 | [
"text_plain_output_2.png",
"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
df = pd.read_csv('../input/adverse-food-events/CAERS_ASCII_2004_2017Q2.csv')
df.shape
df.columns
df.rename(columns={'PRI_Reported Brand/Product Name': 'products_name', 'SYM_One Row Coded Sy... | code |
104115755/cell_23 | [
"text_plain_output_1.png",
"image_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
df = pd.read_csv('../input/adverse-food-events/CAERS_ASCII_2004_2017Q2.csv')
df.shape
df.columns
df.rename(columns={'PRI_Reported Brand/Product Name': 'products_name', 'SYM_One Row Coded Sy... | code |
104115755/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/adverse-food-events/CAERS_ASCII_2004_2017Q2.csv')
df.shape | code |
104115755/cell_26 | [
"text_plain_output_2.png",
"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
df = pd.read_csv('../input/adverse-food-events/CAERS_ASCII_2004_2017Q2.csv')
df.shape
df.columns
df.rename(columns={'PRI_Reported Brand/Product Name': 'products_name', 'SYM_One Row Coded Sy... | code |
104115755/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
104115755/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/adverse-food-events/CAERS_ASCII_2004_2017Q2.csv')
df.shape
df.columns | code |
104115755/cell_18 | [
"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
df = pd.read_csv('../input/adverse-food-events/CAERS_ASCII_2004_2017Q2.csv')
df.shape
df.columns
df.rename(columns={'PRI_Reported Brand/Product Name': 'products_name', 'SYM_One Row Coded Sy... | code |
104115755/cell_15 | [
"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
df = pd.read_csv('../input/adverse-food-events/CAERS_ASCII_2004_2017Q2.csv')
df.shape
df.columns
df.rename(columns={'PRI_Reported Brand/Product Name': 'products_name', 'SYM_One Row Coded Sy... | code |
104115755/cell_17 | [
"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
df = pd.read_csv('../input/adverse-food-events/CAERS_ASCII_2004_2017Q2.csv')
df.shape
df.columns
df.rename(columns={'PRI_Reported Brand/Product Name': 'products_name', 'SYM_One Row Coded Sy... | code |
104115755/cell_10 | [
"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/adverse-food-events/CAERS_ASCII_2004_2017Q2.csv')
df.shape
df.columns
df.rename(columns={'PRI_Reported Brand/Product Name': 'products_name', 'SYM_One Row Coded Symptoms': 'symptoms', 'CI_Gender': 'gender', 'CI_Age at A... | code |
104115755/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_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/adverse-food-events/CAERS_ASCII_2004_2017Q2.csv')
df.head() | code |
2045135/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2020652/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
2020652/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd
import numpy as np
import xgboost as xgb
from scipy.optimize import minimize
from sklearn.cross_validation import StratifiedShuffleSplit
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics i... | code |
2020652/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
target = train['target']
train.drop(['id', 'target'], axis=1, inplace=True)
train.shape | code |
34148902/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from torch import nn, optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import torch
import torch.nn.f... | code |
34148902/cell_4 | [
"image_output_2.png",
"image_output_1.png"
] | import torch
use_gpu = torch.cuda.is_available()
use_gpu | code |
34148902/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from torch import nn, optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import torch
import torch.nn.f... | code |
34148902/cell_7 | [
"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
df_train_path = '/kaggle/input/digit-recognizer/train.csv'
df_test_path = '/kaggle/input/digit-recognizer/test.csv'
X_train = pd.read_csv(df_train_path)
X_test = pd.read_csv(df_test_path)
y_tr... | code |
34148902/cell_8 | [
"text_plain_output_1.png",
"image_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
df_train_path = '/kaggle/input/digit-recognizer/train.csv'
df_test_path = '/kaggle/input/digit-recognizer/test.csv'
X_train = pd.read_csv(df_train_path)
X_test = pd.read_csv(df_test_path)
y_tr... | code |
34148902/cell_3 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import torch
from torch import nn, optim
import torch.nn.functional as F
from torch.autograd import Variable
from torch.utils.data import DataLoader
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
... | code |
34148902/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from torch import nn, optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as... | code |
323646/cell_13 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
act_train = pd.read_csv('../input/act_train.csv')
act_test = pd.read_csv('../input/act_test.csv')
act_train_char_10 = act_train[act_train['char_10'].notnull().values]
act_test_char_10 = act_test[a... | code |
323646/cell_3 | [
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
act_train = pd.read_csv('../input/act_train.csv')
act_test = pd.read_csv('../input/act_test.csv')
act_train_char_10 = act_train[act_train['char_10'].notnull().values]
act_test_char_10 = act_test[act_test['char_10'].notnull().values]
drop_list = ['char_1', 'char_2', 'char_3', 'char_4', 'char_5', 'c... | code |
323646/cell_14 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
import pandas as pd
act_train = pd.read_csv('../input/act_train.csv')
act_test = pd.read_csv('../input/act_test.csv')
act_train_char_10 = act_train[act_train['char_10'].notnull().values]
act_test_char_10 = act_te... | code |
323646/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import pandas as pd
act_train = pd.read_csv('../input/act_train.csv')
act_test = pd.read_csv('../input/act_test.csv')
act_train_char_10 = act_train[act_train['char_10'].notnull().values]
act_test_char_10 = act_test[act_test['char_10'].notnull().values]
drop_list = ... | code |
129024559/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_excel('/kaggle/input/arketing-campaign/marketing_campaign.xlsx')
df
df.shape | code |
129024559/cell_25 | [
"text_html_output_1.png"
] | import datetime
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_excel('/kaggle/input/arketing-campaign/marketing_campaign.xlsx')
df
df.shape
df.isnull().sum()
df.isnull().sum()
df.Year_Birth = pd.to_datetime(df['Year_Birth'], format='%Y')
year_now = datetime.date.today().y... | code |
129024559/cell_6 | [
"image_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
import openpyxl
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import f1_score
from sklearn.metrics import recall_score
from sklearn.metrics import cl... | code |
129024559/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_excel('/kaggle/input/arketing-campaign/marketing_campaign.xlsx')
df
df.shape
df.isnull().sum()
df.isnull().sum()
df.describe() | code |
129024559/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_excel('/kaggle/input/arketing-campaign/marketing_campaign.xlsx')
df
df.shape
df.isnull().sum()
df.isnull().sum()
df.head() | code |
129024559/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_excel('/kaggle/input/arketing-campaign/marketing_campaign.xlsx')
df | code |
129024559/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_excel('/kaggle/input/arketing-campaign/marketing_campaign.xlsx')
df
df.shape
df.isnull().sum()
df.isnull().sum()
df['Income'].value_counts() | code |
129024559/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_excel('/kaggle/input/arketing-campaign/marketing_campaign.xlsx')
df
df.shape
df.isnull().sum()
df.isnull().sum()
df['Education'].unique() | code |
129024559/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_excel('/kaggle/input/arketing-campaign/marketing_campaign.xlsx')
df
df.shape
df.isnull().sum()
df.isnull().sum() | code |
129024559/cell_22 | [
"text_plain_output_1.png"
] | import datetime
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_excel('/kaggle/input/arketing-campaign/marketing_campaign.xlsx')
df
df.shape
df.isnull().sum()
df.isnull().sum()
df.Year_Birth = pd.to_datetime(df['Year_Birth'], format='%Y')
year_now = datetime.date.today().y... | code |
129024559/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_excel('/kaggle/input/arketing-campaign/marketing_campaign.xlsx')
df
df.shape
df.info() | code |
129024559/cell_27 | [
"text_html_output_1.png"
] | import datetime
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_excel('/kaggle/input/arketing-campaign/marketing_campaign.xlsx')
df
df.shape
df.isnull().sum()
df.isnull().sum()
df.Year_Birth = pd.to_datetime(df['Year_Birth'], format='%Y')
year_now = datetime.date.today().y... | code |
129024559/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_excel('/kaggle/input/arketing-campaign/marketing_campaign.xlsx')
df
df.shape
df.isnull().sum() | code |
33115588/cell_13 | [
"text_plain_output_1.png"
] | from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import scale
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
table = pd.read_csv('../input/genre-dataset/genre_dataset.txt')
table = table[table.genre.str.contains('j... | code |
33115588/cell_9 | [
"text_plain_output_1.png"
] | from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import scale
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
table = pd.read_csv('../input/genre-dataset/genre_dataset.txt')
table = table[table.genre.str.contains('jazz and blues') | table... | code |
33115588/cell_4 | [
"image_output_1.png"
] | from sklearn.preprocessing import scale
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
table = pd.read_csv('../input/genre-dataset/genre_dataset.txt')
table = table[table.genre.str.contains('jazz and blues') | table.genre.str.contains('soul and reggae')]
for i in range(4, len(table.columns)):
... | code |
33115588/cell_11 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import scale
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
table = pd.read_csv('../input/genre-dataset/genre_dataset.txt')
table = table[table.genre.str.contains('j... | code |
33115588/cell_16 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import scale
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
table = pd.read_csv('../input/genre-dataset/genre_dataset.txt')
table = table[table.genre.str.conta... | code |
33115588/cell_3 | [
"image_output_1.png"
] | from sklearn.preprocessing import scale
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
table = pd.read_csv('../input/genre-dataset/genre_dataset.txt')
table = table[table.genre.str.contains('jazz and blues') | table.genre.str.contains('soul and reggae')]
for i in range(4, len(table.columns)):
... | code |
33115588/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import scale
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
table = pd.read_csv('../input/genre-dataset/genre_dataset.txt')
table = table[table.genre.str.contains('j... | code |
33115588/cell_10 | [
"text_html_output_1.png"
] | from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import scale
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
table = pd.read_csv('../input/genre-dataset/genre_dataset.txt')
table = table[table.genre.str.contains('jazz and blues') | table... | code |
33115588/cell_12 | [
"image_output_1.png"
] | from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import scale
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
table = pd.read_csv('../input/genre-dataset/genre_dataset.txt')
table = table[table.genre.str.contains('jazz and blues') | table... | code |
17121947/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
from sklearn.svm import LinearSVC
(X_train.shape, y_train.shape)
logRegModel = LogisticRegression()
logRegModel.fit(X_train, y_train)
svc = LinearSVC(random_state=4... | code |
17121947/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
(X_train.shape, y_train.shape)
logRegModel = LogisticRegression()
logRegModel.fit(X_train, y_train) | code |
17121947/cell_25 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from xgboost import XGBClassifier,plot_importance
(X_train.shape, y_train.shape)
xgbClf = XGBClassifier(n_estimators=1000)
xgbClf.fit(X_train, y_train)
plot_importance(xgbClf) | code |
17121947/cell_20 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
(X_train.shape, y_train.shape)
gbClf = GradientBoostingClassifier()
gbClf.fit(X_train, y_train)
print('GradientBoost accuracy:', gbClf.score(X_test, y_test) * 100) | code |
17121947/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from xgboost import XGBClassifier,plot_importance
(X_train.shape, y_train.shape)
xgbClf = XGBClassifier(n_estimators=1000)
xgbClf.fit(X_train, y_train)
xgbClf.score(X_test, y_test) * 100 | code |
17121947/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | (X_train.shape, y_train.shape) | code |
17121947/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
(X_train.shape, y_train.shape)
gbClf = GradientBoostingClassifier()
gbClf.fit(X_train, y_train) | code |
17121947/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from matplotlib import pyplot as plt
import os
print(os.listdir('../input')) | code |
17121947/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from matplotlib import pyplot as plt
import os
scaler = Stan... | code |
17121947/cell_16 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.svm import LinearSVC
(X_train.shape, y_train.shape)
svc = LinearSVC(random_state=43)
svc.fit(X_train, y_train) | code |
17121947/cell_17 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.svm import LinearSVC
(X_train.shape, y_train.shape)
logRegModel = LogisticRegression()
logRegModel.fit(X_train, y_train)
svc = LinearSVC(random_state=43)
svc.fit(X_train, y_train)
print('Logistic regression accuracy:', logRegModel.score(X_test, y_te... | code |
17121947/cell_24 | [
"text_plain_output_1.png"
] | from xgboost import XGBClassifier,plot_importance
(X_train.shape, y_train.shape)
xgbClf = XGBClassifier(n_estimators=1000)
xgbClf.fit(X_train, y_train) | code |
121149216/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo.shape
mo.columns
mo.dtypes
mo.isnull().sum() | code |
121149216/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo.shape
mo.columns
mo.head(5) | code |
121149216/cell_9 | [
"text_html_output_2.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo.shape | code |
121149216/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo.shape
mo.columns
mo.dtypes
mo.isnull().sum()
viz_data = mo.copy(True)
viz_data['release_date'].value_counts(normalize=True).sort_values(ascending=False)
mo['b... | code |
121149216/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo.shape
mo.columns
mo.dtypes
mo.isnull().sum()
viz_data = mo.copy(True)
viz_data['release_date'].value_counts(normalize=True).sort_values(ascending=False) | code |
121149216/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo.shape
mo.columns
mo.dtypes
mo.isnull().sum()
viz_data = mo.copy(True)
viz_data['release_date'].value_counts(normalize=True).sort_values(ascending=False)
pvt =... | code |
121149216/cell_40 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo.shape
mo.columns
mo.dtypes
mo.isnull().sum()
viz_data = mo.copy(True)
viz_data['release_date'].value_counts(normalize=True).sort_values(ascending=False)
pvt =... | code |
121149216/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo.shape
mo.columns
mo.dtypes
mo.isnull().sum()
viz_data = mo.copy(True)
viz_data['release_date'].value_counts(normalize=True).sort_values(ascending=False)
mo['b... | code |
121149216/cell_11 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo.shape
mo.columns | code |
121149216/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo.shape
mo.columns
mo.dtypes
mo.info() | code |
121149216/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo | code |
121149216/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo.shape
mo.columns
mo.tail(5) | code |
121149216/cell_3 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt, seaborn as sns, plotly.express as px, plotly.figure_factory as ff
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
121149216/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo.shape
mo.columns
mo.dtypes | code |
121149216/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo.shape
mo.columns
mo.dtypes
mo.isnull().sum()
viz_data = mo.copy(True)
viz_data['release_date'].value_counts(normalize=True).sort_values(ascending=False)
pvt =... | code |
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