path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
18143144/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
np.round(342 * 100 / 891) | code |
18143144/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
train_set[['Sex', 'Survived']].groupby(['Sex']).mean() | code |
18143144/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)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.info() | code |
18143144/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
train_set[['Sex', 'Survived']].groupby(['Sex']).mean().plot(kind='bar') | code |
18143144/cell_35 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
np.... | code |
18143144/cell_31 | [
"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)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
np.round(342 * 100 / 891)
np.round(549 * 100 / 891)
mean ... | code |
18143144/cell_46 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
np.round(34... | code |
18143144/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
np.round(342 * 100 / 891)
np.round(549 * 100 / 891)
mean ... | code |
18143144/cell_22 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
np.round(342 * 100 / 891)
np.round(549 * 100 / 891)
mean ... | code |
18143144/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
np.round(342 * 100 / 891)
np.round(549 * 100 / 891) | code |
18143144/cell_27 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
np.round(342 * 100 / 891)
np.round(549 * 100 / 891)
mean ... | code |
18143144/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
np.round(342 * 100 / 891)
np.round(549 * 100 / 891)
mean ... | code |
18143144/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum()
train_set.Survived.value_counts()
train_set[['Pclass', 'Survived']].groupby(['Pclass']).mean().sort_values(by='Survived', ascendin... | code |
18143144/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().sum() | code |
105178941/cell_4 | [
"text_plain_output_1.png"
] | first_name = ['adnan', 'afnan', 'affan']
last_name = ['k', 's', 'd']
l = len(first_name)
name = []
for i in range(l):
x = first_name[i] + last_name[i]
name.append(x)
first_name = ['adnan', 'afnan']
last_name = ['k', 'd']
l = len(first_name)
name = []
for i in range(l):
x = first_name[i] + ' ' + last_name[i... | code |
105178941/cell_1 | [
"text_plain_output_1.png"
] | first_name = ['adnan', 'afnan', 'affan']
last_name = ['k', 's', 'd']
l = len(first_name)
name = []
for i in range(l):
x = first_name[i] + last_name[i]
name.append(x)
print(name) | code |
105178941/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | b = [1, 2, 3, 4, 5, 6, 7]
num1 = int(input('enter the number to remove in the list'))
for i in b:
if i == num1:
b.remove(i)
print(b) | code |
73087591/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nRowsRead = 1000
df = pd.read_csv('../input/cusersmarildownloadsgermancsv/german.csv', de... | code |
73087591/cell_20 | [
"text_plain_output_1.png"
] | from keras.layers.normalization import BatchNormalization
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, D... | code |
73087591/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nRowsRead = 1000
df = pd.read_csv('../input/cusersmarildownloadsgermancsv/german.csv', delimiter=';', encoding='ISO-8859-2', nrows=nRowsRead)
df.dataframeName = 'german.csv'
nRow, nCol = df.shape
y = df['Creditability']
X = df.iloc[:, :-1]
label ... | code |
73087591/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nRowsRead = 1000
df = pd.read_csv('../input/cusersmarildownloadsgermancsv/german.csv', delimiter=';', encoding='ISO-8859-2', nrows=nRowsRead)
df.dataframeName = 'german.csv'
nRow, nCol = df.shape
print(f'There are {nRow} rows and {nCol} columns')
d... | code |
73087591/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 |
73087591/cell_18 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nRowsRead = 1000
df = pd.read_csv('../input/cusersmarildownloadsgermancsv/german.csv', de... | code |
73087591/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nRowsRead = 1000
df = pd.read_csv('../input/cusersmarildownloadsgermancsv/german.csv', delimiter=';', encoding='ISO-8859-2', nrows=nRowsRead... | code |
73087591/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.r... | code |
73087591/cell_22 | [
"image_output_1.png"
] | from keras.layers.normalization import BatchNormalization
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, D... | code |
73087591/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nRowsRead = 1000
df = pd.read_csv('../input/cusersmarildownloadsgermancsv/german.csv', delimiter=';', encoding='ISO-8859-2', nrows=nRowsRead)
df.dataframeName = 'german.csv'
nRow, nCol = df.shape
print('df shape is: ', df.shape)
y = df['Creditabil... | code |
1004511/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fivethirtyeight_ncaa_forecasts.csv')
matchups = [[str(x + 1), str(16 - x)] for x in range(8)]
df = df[df.gender == 'mens']
pre = df[df.playin_flag]
data = []
for region in pre.team_region.unique():
data.append([])
for _, row in pre[pre.team_region == region].iter... | code |
1004511/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fivethirtyeight_ncaa_forecasts.csv')
df.head() | code |
1004511/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fivethirtyeight_ncaa_forecasts.csv')
matchups = [[str(x + 1), str(16 - x)] for x in range(8)]
df = df[df.gender == 'mens']
pre = df[df.playin_flag]
data = []
for region in pre.team_region.unique():
data.append([])
for _, row in pre[pre.team_region == region].iter... | code |
2040742/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
images = np.load('../input/olivetti_faces.npy')
images.shape
targets = np.load('../input/olivetti_faces_target.npy')
targets.shape
data = images.reshape(images.shape[0], images.shape[1] * images.shape[2])
data.shape
train = data[targets < 30]
train.shape
n_pixels = data.shape[1]
n_pixels
X_trai... | code |
2040742/cell_9 | [
"image_output_1.png"
] | import numpy as np
images = np.load('../input/olivetti_faces.npy')
images.shape
targets = np.load('../input/olivetti_faces_target.npy')
targets.shape
data = images.reshape(images.shape[0], images.shape[1] * images.shape[2])
data.shape
test = data[targets >= 30]
test.shape
n_faces = test.shape[0] // 10
n_faces | code |
2040742/cell_4 | [
"text_plain_output_1.png"
] | from skimage.io import imshow
import numpy as np
images = np.load('../input/olivetti_faces.npy')
images.shape
firstImage = images[0]
imshow(firstImage) | code |
2040742/cell_6 | [
"image_output_1.png"
] | import numpy as np
images = np.load('../input/olivetti_faces.npy')
images.shape
targets = np.load('../input/olivetti_faces_target.npy')
targets.shape
targets < 30 | code |
2040742/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
images = np.load('../input/olivetti_faces.npy')
images.shape | code |
2040742/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
images = np.load('../input/olivetti_faces.npy')
images.shape
targets = np.load('../input/olivetti_faces_target.npy')
targets.shape
data = images.reshape(images.shape[0], images.shape[1] * images.shape[2])
data.shape
test = data[targets >= 30]
test.shape
n_faces = test.shape[0] // 10
n_faces
fac... | code |
2040742/cell_19 | [
"text_plain_output_1.png"
] | from skimage.io import imshow
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import RidgeCV
from sklearn.neighbors import KNeighborsRegressor
import matplotlib.pyplot as plt
import num... | code |
2040742/cell_7 | [
"image_output_1.png"
] | import numpy as np
images = np.load('../input/olivetti_faces.npy')
images.shape
targets = np.load('../input/olivetti_faces_target.npy')
targets.shape
data = images.reshape(images.shape[0], images.shape[1] * images.shape[2])
data.shape
train = data[targets < 30]
train.shape | code |
2040742/cell_18 | [
"text_plain_output_1.png"
] | from skimage.io import imshow
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import RidgeCV
from sklearn.neighbors import KNeighborsRegressor
import matplotlib.pyplot as plt
import num... | code |
2040742/cell_8 | [
"image_output_1.png"
] | import numpy as np
images = np.load('../input/olivetti_faces.npy')
images.shape
targets = np.load('../input/olivetti_faces_target.npy')
targets.shape
data = images.reshape(images.shape[0], images.shape[1] * images.shape[2])
data.shape
test = data[targets >= 30]
test.shape | code |
2040742/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import RidgeCV
from sklearn.neighbors import KNeighborsRegressor
import numpy as np
images = np.load('../input/olivetti_faces.npy')
images... | code |
2040742/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
images = np.load('../input/olivetti_faces.npy')
images.shape
targets = np.load('../input/olivetti_faces_target.npy')
targets.shape | code |
2040742/cell_17 | [
"text_plain_output_1.png"
] | from skimage.io import imshow
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import RidgeCV
from sklearn.neighbors import KNeighborsRegressor
import matplotlib.pyplot as plt
import num... | code |
2040742/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
images = np.load('../input/olivetti_faces.npy')
images.shape
targets = np.load('../input/olivetti_faces_target.npy')
targets.shape
data = images.reshape(images.shape[0], images.shape[1] * images.shape[2])
data.shape
train = data[targets < 30]
train.shape
n_pixels = data.shape[1]
n_pixels
y_trai... | code |
2040742/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
images = np.load('../input/olivetti_faces.npy')
images.shape
targets = np.load('../input/olivetti_faces_target.npy')
targets.shape
data = images.reshape(images.shape[0], images.shape[1] * images.shape[2])
data.shape
test = data[targets >= 30]
test.shape
n_faces = test.shape[0] // 10
n_faces
fac... | code |
2040742/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
images = np.load('../input/olivetti_faces.npy')
images.shape
data = images.reshape(images.shape[0], images.shape[1] * images.shape[2])
data.shape
n_pixels = data.shape[1]
n_pixels | code |
2040742/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
images = np.load('../input/olivetti_faces.npy')
images.shape
data = images.reshape(images.shape[0], images.shape[1] * images.shape[2])
data.shape | code |
1005671/cell_15 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, pr... | code |
1005671/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd
dfTrain = pd.read_csv('../input/train.csv')
dfTest = pd.read_csv('../input/test.csv')
from sklearn.preprocessing import Lab... | code |
1005671/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
dfTrain = pd.read_csv('../input/train.csv')
dfTest = pd.read_csv('../input/test.csv')
dfTrain.head() | code |
89125510/cell_13 | [
"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
data = pd.read_csv('../input/wine-quality-dataset/WineQT.csv')
data.corr()
data.columns
data = data.drop(['Id'], axis=1)
f,ax=plt.subplots(figsize=(18,9))
sns.heatmap(data.corr(),annot=Tru... | code |
89125510/cell_9 | [
"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
data = pd.read_csv('../input/wine-quality-dataset/WineQT.csv')
data.corr()
data.columns
data = data.drop(['Id'], axis=1)
f, ax = plt.subplots(figsize=(18, 9))
sns.heatmap(data.corr(), anno... | code |
89125510/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/wine-quality-dataset/WineQT.csv')
data.head() | code |
89125510/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/wine-quality-dataset/WineQT.csv')
data.corr()
data.columns | code |
89125510/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
data = pd.read_csv('../input/wine-quality-dataset/WineQT.csv')
data.corr()
data.columns
data = data.drop(['Id'], axis=1)
f,ax=plt.subplots(figsize=(18,9))
sns.heatmap(data.corr(),annot=Tru... | code |
89125510/cell_1 | [
"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 os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89125510/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/wine-quality-dataset/WineQT.csv')
data.corr()
data.columns
data = data.drop(['Id'], axis=1)
data.describe() | code |
89125510/cell_15 | [
"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
data = pd.read_csv('../input/wine-quality-dataset/WineQT.csv')
data.corr()
data.columns
data = data.drop(['Id'], axis=1)
f,ax=plt.subplots(figsize=(18,9))
sns.heatmap(data.corr(),annot=Tru... | code |
89125510/cell_16 | [
"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
data = pd.read_csv('../input/wine-quality-dataset/WineQT.csv')
data.corr()
data.columns
data = data.drop(['Id'], axis=1)
f,ax=plt.subplots(figsize=(18,9))
sns.heatmap(data.corr(),annot=Tru... | code |
89125510/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/wine-quality-dataset/WineQT.csv')
data.info() | code |
89125510/cell_17 | [
"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
data = pd.read_csv('../input/wine-quality-dataset/WineQT.csv')
data.corr()
data.columns
data = data.drop(['Id'], axis=1)
f,ax=plt.subplots(figsize=(18,9))
sns.heatmap(data.corr(),annot=Tru... | code |
89125510/cell_14 | [
"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
data = pd.read_csv('../input/wine-quality-dataset/WineQT.csv')
data.corr()
data.columns
data = data.drop(['Id'], axis=1)
f,ax=plt.subplots(figsize=(18,9))
sns.heatmap(data.corr(),annot=Tru... | code |
89125510/cell_10 | [
"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
data = pd.read_csv('../input/wine-quality-dataset/WineQT.csv')
data.corr()
data.columns
data = data.drop(['Id'], axis=1)
f,ax=plt.subplots(figsize=(18,9))
sns.heatmap(data.corr(),annot=Tru... | code |
89125510/cell_12 | [
"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
data = pd.read_csv('../input/wine-quality-dataset/WineQT.csv')
data.corr()
data.columns
data = data.drop(['Id'], axis=1)
f,ax=plt.subplots(figsize=(18,9))
sns.heatmap(data.corr(),annot=Tru... | code |
89125510/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/wine-quality-dataset/WineQT.csv')
data.corr() | code |
2041701/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_train['sex_female'] = df_train['Sex'].apply(lambda x: 1 if x == 'female' else 0)
df_train['age_snr'] = df_train['Age'].apply(lambda x: 1 if x >= 50 else 0)
df_train['age_mid'] = df_train['Age'].apply... | code |
2041701/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_train['sex_female'] = df_train['Sex'].apply(lambda x: 1 if x == 'female' else 0)
df_train['age_snr'] = df_train['Age'].apply(lambda x: 1 if x >= 50 else 0)
df_train['age_mid'] = df_train['Age'].apply... | code |
2041701/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_train['sex_female'] = df_train['Sex'].apply(lambda x... | code |
2041701/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train... | code |
2041701/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
print(df_train.head()) | code |
2041701/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_valid_std = sc.transform(X_valid)
from sklearn.... | code |
2041701/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
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 = pd.read_csv('../input/train.csv')
df_train['sex_female'] = df_train['Sex'].apply(lambda x: 1 if x == 'female' else 0)
df_trai... | code |
2041701/cell_3 | [
"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
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2041701/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(X_train)
X_tra... | code |
2041701/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from... | code |
2041701/cell_22 | [
"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_train = pd.read_csv('../input/train.csv')
df_train['sex_female'] = df_train['Sex'].apply(lambda x: 1 if x == 'female' else 0)
df_train['age_snr'] = df_train['Age'].apply(lambda x: 1 if x >= 50 else 0)
df_train['age_mid'] = df_train['Age'].apply... | code |
2041701/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_train['sex_female'] = df_train['Sex'].apply(lambda x: 1 if x == 'female' else 0)
df_train['age_snr'] = df_train['Age'].apply(lambda x: 1 if x >= 50 else 0)
df_train['age_mid'] = df_train['Age'].apply... | code |
2041701/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_c... | code |
2041701/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
df_train = pd.read_csv('../input/train.csv')
df_train['sex_female'] = df_train['Sex'].apply(lambda x: 1 if x == 'female' else 0)
df_train['age_snr'] = df_train['Age'].apply(lambda x: 1 if x >... | code |
2041701/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
print(df_train.info()) | code |
2041701/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_c... | code |
90124505/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pop = pd.read_csv('../input/vietnam-population-dgp-education-data/pop.csv')
edu = pd.read_csv('../input/vietnam-population-dgp-education-data/Vietnamstudent.csv')
gdp = pd.read_csv('../input/vietnam-population-dgp-education... | code |
90124505/cell_4 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pop = pd.read_csv('../input/vietnam-population-dgp-education-data/pop.csv')
edu = pd.read_csv('../input/vietnam-population-dgp-education-data/Vietnamstudent.csv')
gdp = pd.read_csv('../input/vietnam-population-dgp-education-data/GDPcompare.csv... | code |
90124505/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pop = pd.read_csv('../input/vietnam-population-dgp-education-data/pop.csv')
edu = pd.read_csv('../input/vietnam-population-dgp-education-data/Vietnamstudent.csv')
gdp = pd.read_csv('../input/vietnam-population-dgp-education... | code |
90124505/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
pop = pd.read_csv('../input/vietnam-population-dgp-education-data/pop.csv')
edu = pd.read_csv('../input/vietnam-population-dgp-education-data/Vietnamstudent.csv')
gdp = pd.read_csv('../input/vietnam-population-dgp-education-data/GDPcompare.csv')
pisa = pd.read_csv('../input/viet... | code |
90124505/cell_19 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pop = pd.read_csv('../input/vietnam-population-dgp-education-data/pop.csv')
edu = pd.read_csv('../input/vietnam-population-dgp-education-data/Vietnamstudent.csv')
gdp = pd.read_csv('../input/vietnam-population-dgp-education... | code |
90124505/cell_7 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
pop = pd.read_csv('../input/vietnam-population-dgp-education-data/pop.csv')
edu = pd.read_csv('../input/vietnam-population-dgp-education-data/Vietnamstudent.csv')
gdp = pd.read_csv('../input/vietnam-population-dgp-education-data/GDPcompare.csv')
pisa = pd.read_csv('../input/viet... | code |
90124505/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
pop = pd.read_csv('../input/vietnam-population-dgp-education-data/pop.csv')
edu = pd.read_csv('../input/vietnam-population-dgp-education-data/Vietnamstudent.csv')
gdp = pd.read_csv('../input/vietnam-population-dgp-education-data/GDPcompare.csv')
pisa = pd.read_csv('../input/viet... | code |
90124505/cell_8 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
pop = pd.read_csv('../input/vietnam-population-dgp-education-data/pop.csv')
edu = pd.read_csv('../input/vietnam-population-dgp-education-data/Vietnamstudent.csv')
gdp = pd.read_csv('../input/vietnam-population-dgp-education-data/GDPcompare.csv')
pisa = pd.read_csv('../input/viet... | code |
90124505/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
pop = pd.read_csv('../input/vietnam-population-dgp-education-data/pop.csv')
edu = pd.read_csv('../input/vietnam-population-dgp-education-data/Vietnamstudent.csv')
gdp = pd.read_csv('../input/vietnam-population-dgp-education-data/GDPcompare.csv')
pisa = pd.read_csv('../input/vietnam-population-dgp-e... | code |
90124505/cell_3 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
pop = pd.read_csv('../input/vietnam-population-dgp-education-data/pop.csv')
edu = pd.read_csv('../input/vietnam-population-dgp-education-data/Vietnamstudent.csv')
gdp = pd.read_csv('../input/vietnam-population-dgp-education-data/GDPcompare.csv... | code |
90124505/cell_17 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
pop = pd.read_csv('../input/vietnam-population-dgp-education-data/pop.csv')
edu = pd.read_csv('../input/vietnam-population-dgp-education-data/Vietnamstudent.csv')
gdp = pd.read_csv('../input/vietnam-population-dgp-education-data/GDPcompare.csv')
pisa = pd.read_csv('../input/viet... | code |
90124505/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
pop = pd.read_csv('../input/vietnam-population-dgp-education-data/pop.csv')
edu = pd.read_csv('../input/vietnam-population-dgp-education-data/Vietnamstudent.csv')
gdp = pd.read_csv('../input/vietnam-population-dgp-education-data/GDPcompare.csv')
pisa = pd.read_csv('../input/viet... | code |
90124505/cell_12 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
pop = pd.read_csv('../input/vietnam-population-dgp-education-data/pop.csv')
edu = pd.read_csv('../input/vietnam-population-dgp-education-data/Vietnamstudent.csv')
gdp = pd.read_csv('../input/vietnam-population-dgp-education... | code |
90124505/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
pop = pd.read_csv('../input/vietnam-population-dgp-education-data/pop.csv')
edu = pd.read_csv('../input/vietnam-population-dgp-education-data/Vietnamstudent.csv')
gdp = pd.read_csv('../input/vietnam-population-dgp-education-data/GDPcompare.csv')
pisa = pd.read_csv('../input/vietnam-population-dgp-e... | code |
16150255/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # For loading and processing the dataset
df_train = pd.read_csv('../input/train.csv')
df_train = df_train.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
df_train = pd.concat([df_train, pd.get_dummies(df_train['Embarked'], pref... | code |
16150255/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # For loading and processing the dataset
df_train = pd.read_csv('../input/train.csv')
df_train.head(5) | code |
16150255/cell_11 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # For loading and processing the dataset
df_train = pd.read_csv('../input/train.csv')
df_train = df_train.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
df_train = pd.concat([df_train, pd.get_dummies(df_train['Embarked'], pref... | code |
16150255/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # For loading and processing the dataset
df_train = pd.read_csv('../input/train.csv')
df_train = df_train.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
df_train = pd.concat([df_train, pd.get_dummies(df_train['Embarked'], prefix='Embarked')], axis=1)
df_train = df_train.drop('Emb... | code |
16150255/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # For loading and processing the dataset
df_train = pd.read_csv('../input/train.csv')
df_train = df_train.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
df_train = pd.concat([df_train, pd.get_dummies(df_train['Embarked'], prefix='Embarked')], axis=1)
df_train = df_train.drop('Emb... | code |
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