path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
18108547/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
... | code |
18108547/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/data.csv')
list(d... | code |
18108547/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
... | code |
18108547/cell_11 | [
"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/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
... | code |
18108547/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18108547/cell_7 | [
"application_vnd.jupyter.stderr_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/data.csv')
list(df.columns)
print(type(df['Age'][0]))
print(type(df['Nationality'][0]))
print(type(df['Overall'][0]))
print(type(df['Potential'][0]))
print(type(df['Value'][0]))
print(type(df['Wage'][0])) | code |
18108547/cell_18 | [
"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/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
... | code |
18108547/cell_32 | [
"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/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
... | code |
18108547/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
... | code |
18108547/cell_16 | [
"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/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
... | code |
18108547/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = str... | code |
18108547/cell_3 | [
"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/data.csv')
df.head() | code |
18108547/cell_35 | [
"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/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
... | code |
18108547/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
... | code |
18108547/cell_14 | [
"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/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
... | code |
18108547/cell_22 | [
"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/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
num = num * 1000
... | code |
18108547/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
... | code |
18108547/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/data.csv')
list(df.columns) | code |
18108547/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/data.csv')
list(df.columns)
def clean_d(string):
last_char = string[-1]
if last_char == '0':
return 0
string = string[1:-1]
num = float(string)
if last_char == 'K':
... | code |
2000084/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
result = pd.read_csv('../input/catboost1223/catboost1223.csv')
result.head() | code |
2000084/cell_2 | [
"text_plain_output_1.png"
] | !pwd | code |
32065345/cell_9 | [
"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/hmeq-data/hmeq.csv')
df.shape
df.sample(30)
df.describe().T
df['REASON'].astype('category').cat.categories | code |
32065345/cell_4 | [
"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/hmeq-data/hmeq.csv')
df.shape | code |
32065345/cell_6 | [
"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/hmeq-data/hmeq.csv')
df.shape
df.sample(30) | code |
32065345/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 |
32065345/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/hmeq-data/hmeq.csv')
df.shape
df.sample(30)
df.describe().T
df['REASON'].astype('category').cat.codes | code |
32065345/cell_7 | [
"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/hmeq-data/hmeq.csv')
df.shape
df.sample(30)
df.describe().T | code |
32065345/cell_18 | [
"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 sn
df = pd.read_csv('../input/hmeq-data/hmeq.csv')
df.shape
df.sample(30)
df.describe().T
df.dropna(thresh=8, inplace=True)
df.REASON.fillna('DebtCon', inplace=True)
df.JOB.fillna('Other', in... | code |
32065345/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/hmeq-data/hmeq.csv')
df.shape
df.sample(30)
df.describe().T
print('Rows : ', df.shape[0])
print('Columns : ', df.shape[1])
print('\nFeatures : \n', df.columns.tolist())
print('\nMissing values : ', df.isnull().s... | code |
32065345/cell_15 | [
"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/hmeq-data/hmeq.csv')
df.shape
df.sample(30)
df.describe().T
df.dropna(thresh=8, inplace=True)
df.REASON.fillna('DebtCon', inplace=True)
df.JOB.fillna('Other', inplace=True)
print('INFO : ', df.info())
print('Row... | code |
32065345/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/hmeq-data/hmeq.csv')
df.info() | code |
32065345/cell_17 | [
"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/hmeq-data/hmeq.csv')
df.shape
df.sample(30)
df.describe().T
df.dropna(thresh=8, inplace=True)
df.REASON.fillna('DebtCon', inplace=True)
df.JOB.fillna('Other', inplace=True)
for col in df.columns:
if df[col].dtyp... | code |
32065345/cell_10 | [
"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/hmeq-data/hmeq.csv')
df.shape
df.sample(30)
df.describe().T
df['JOB'].astype('category').cat.categories | code |
32065345/cell_12 | [
"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/hmeq-data/hmeq.csv')
df.shape
df.sample(30)
df.describe().T
df['JOB'].astype('category').cat.codes | code |
32065345/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/hmeq-data/hmeq.csv')
df.shape
df.head() | code |
88096283/cell_21 | [
"text_plain_output_1.png"
] | dt = create_model('dt') | code |
88096283/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/train.csv')
test_df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/test.csv')
df.dtypes
pltdf = df.copy()
def detect_NaNs(df_temp):... | code |
88096283/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/train.csv')
test_df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/test.csv')
df.dtypes | code |
88096283/cell_19 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | !pip install pycaret | code |
88096283/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/train.csv')
test_df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/test.csv')
df.dtypes
pltdf = df.copy()
def detect_NaNs(df_temp):... | code |
88096283/cell_24 | [
"text_html_output_1.png"
] | model = dt
plot_model(dt, plot='confusion_matrix') | code |
88096283/cell_22 | [
"text_html_output_1.png"
] | dt = create_model('dt')
dt = tune_model(dt, optimize='Precision') | code |
88096283/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/train.csv')
test_df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/test.csv')
df.dtypes
pltdf = df.copy()
def detect_NaNs(df_temp):
print('NaNs in ... | code |
88096283/cell_12 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/train.csv')
test_df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/test.csv')
df.dtypes
pltdf = df.copy()
def detect_NaNs(df_temp):
return df.isnul... | code |
88096283/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/train.csv')
test_df = pd.read_csv('../input/ml-olympiad-tensorflow-malaysia-user-group/test.csv')
df.dtypes
sns.countplot(df['Class'])
print(len(df.loc[df['Class'] == 0]) / len(df) * 100, '%') | code |
33105697/cell_4 | [
"text_plain_output_1.png"
] | from joblib import Parallel, delayed
import os
img_size = 32
def process_image(img_file):
img = cv2.imread(img_file, cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (img_size, img_size))
return img
start = time.time()
X_data = []
Y_data = []
for j in range(10):
print('Load folder c{}'.format(j))
pat... | code |
33105697/cell_6 | [
"text_plain_output_1.png"
] | from joblib import Parallel, delayed
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import os
img_size = 32
def process_image(img_file):
img = cv2.imread(img_file, cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (img_size, img_size))
return img
... | code |
33105697/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from joblib import Parallel, delayed
from torch.utils.data import Dataset, DataLoader
import numpy as np
import numpy as np
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
img_size = 32
def process_image(img_file):
img = cv2.... | code |
33105697/cell_14 | [
"text_plain_output_1.png"
] | from joblib import Parallel, delayed
from torch.utils.data import Dataset, DataLoader
import numpy as np
import numpy as np
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
img_size = 32
def process_image(img_file):
img = cv2.... | code |
106193702/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('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_S... | code |
106193702/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_S... | code |
106193702/cell_57 | [
"text_plain_output_1.png",
"image_output_1.png"
] | (X_train.shape, X_test.shape)
X_train.dtypes
X_train.isnull().sum() | code |
106193702/cell_56 | [
"text_plain_output_1.png"
] | (X_train.shape, X_test.shape)
X_train.dtypes | code |
106193702/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_S... | code |
106193702/cell_44 | [
"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('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adh... | code |
106193702/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_S... | code |
106193702/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.head() | code |
106193702/cell_74 | [
"text_html_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thick... | code |
106193702/cell_76 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thick... | code |
106193702/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_S... | code |
106193702/cell_39 | [
"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('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adh... | code |
106193702/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_S... | code |
106193702/cell_65 | [
"text_plain_output_1.png"
] | (X_train.shape, X_test.shape)
X_test.isnull().sum()
X_test.isnull().sum()
X_test.head() | code |
106193702/cell_48 | [
"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('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cel... | code |
106193702/cell_61 | [
"text_plain_output_1.png"
] | (X_train.shape, X_test.shape)
X_train.dtypes
X_train.isnull().sum()
X_test.isnull().sum()
for df1 in [X_train, X_test]:
for col in X_train.columns:
col_median = X_train[col].median()
df1[col].fillna(col_median, inplace=True)
X_train.isnull().sum() | code |
106193702/cell_54 | [
"text_html_output_1.png"
] | (X_train.shape, X_test.shape) | code |
106193702/cell_72 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shap... | code |
106193702/cell_64 | [
"text_plain_output_1.png"
] | (X_train.shape, X_test.shape)
X_train.dtypes
X_train.isnull().sum()
X_test.isnull().sum()
for df1 in [X_train, X_test]:
for col in X_train.columns:
col_median = X_train[col].median()
df1[col].fillna(col_median, inplace=True)
X_train.isnull().sum()
X_train.head() | code |
106193702/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 |
106193702/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape | code |
106193702/cell_32 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal... | code |
106193702/cell_62 | [
"text_plain_output_1.png"
] | (X_train.shape, X_test.shape)
X_test.isnull().sum()
X_test.isnull().sum() | code |
106193702/cell_59 | [
"text_plain_output_1.png"
] | (X_train.shape, X_test.shape)
X_train.dtypes
X_train.isnull().sum()
for col in X_train.columns:
if X_train[col].isnull().mean() > 0:
print(col, round(X_train[col].isnull().mean(), 4)) | code |
106193702/cell_58 | [
"text_plain_output_1.png"
] | (X_train.shape, X_test.shape)
X_test.isnull().sum() | code |
106193702/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_S... | code |
106193702/cell_78 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thick... | code |
106193702/cell_80 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt'... | code |
106193702/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_S... | code |
106193702/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_S... | code |
106193702/cell_77 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thick... | code |
106193702/cell_43 | [
"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('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adh... | code |
106193702/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_S... | code |
106193702/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_S... | code |
106193702/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('/kaggle/input/uci-breast-cancer-wisconsin-original/breast-cancer-wisconsin.data.txt')
df.shape
col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_S... | code |
311188/cell_4 | [
"text_plain_output_1.png"
] | 50 + 100 | code |
311188/cell_6 | [
"text_plain_output_1.png"
] | 100 + 200 | code |
311188/cell_1 | [
"text_plain_output_1.png"
] | 1 + 1 | code |
311188/cell_3 | [
"text_plain_output_1.png"
] | 20 + 30 | code |
311188/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0.0, 2.0, 0.01)
s = np.sin(2 * np.pi * t)
plt.plot(t, s)
plt.xlabel('time (s)')
plt.ylabel('voltage (mV)')
plt.title('About as simple as it gets, folks')
plt.grid(True)
plt.savefig('test.png')
plt.show(... | code |
128005164/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV fil... | code |
128005164/cell_4 | [
"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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.head() | code |
128005164/cell_23 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kagg... | code |
128005164/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/... | code |
128005164/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)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.describe() | code |
128005164/cell_26 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/... | code |
128005164/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
num_columns... | code |
128005164/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 |
128005164/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)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
pd.concat([train, test], axis=0).isnull().sum() | code |
128005164/cell_18 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
num_columns = train.select_dtypes(include=['number']).columns.to... | code |
128005164/cell_8 | [
"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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
num_columns = train.select_dtypes(include=['number']).columns.tolist()
num_columns | code |
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