path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105190429/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/heart-attack-analysis-prediction-dataset/heart.csv')
data.describe().T | code |
90131319/cell_4 | [
"text_html_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd
data_path = '/kaggle/input/covidx9a/'
images_path = '/kaggle/input/covidx-cxr2/train/'
data_file = 'train_COVIDx9A.txt'
train = pd.read_csv(os.path.join(data_path, data_file), header=None, sep=' ')
train.columns = ['patient id', 'filename', 'class', 'data source']
print('Training data sh... | code |
90131319/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
data_path = '/kaggle/input/covidx9a/'
images_path = '/kaggle/input/covidx-cxr2/train/'
data_file = 'train_COVIDx9A.txt'
train = pd.read_csv(os.path.join(data_path, data_file), header=None, sep=' ')
train.columns = ['patient id', 'fi... | code |
90131319/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
data_path = '/kaggle/input/covidx9a/'
images_path = '/kaggle/input/covidx-cxr2/train/'
data_file = 'train_COVIDx9A.txt'
train = pd.read_csv(os.path.join(data_path, data_file), header=None, ... | code |
90131319/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
data_path = '/kaggle/input/covidx9a/'
images_path = '/kaggle/input/covidx-cxr2/train/'
data_file = 'train_COVIDx9A.txt'
train = pd.read_csv(os.path.join(data_path, data_file), header=None, sep=' ')
train.columns = ['patient id', 'fi... | code |
90131319/cell_12 | [
"text_plain_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
data_path = '/kaggle/input/covidx9a/'
images_path = '/kaggle/input/covidx-cxr2/train/'
data_file = 'train_COVIDx9A.txt'
train = pd.read_csv(os.path.join(data_path, data_file), header=None, ... | code |
90131319/cell_5 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import os
import pandas as pd
data_path = '/kaggle/input/covidx9a/'
images_path = '/kaggle/input/covidx-cxr2/train/'
data_file = 'train_COVIDx9A.txt'
train = pd.read_csv(os.path.join(data_path, data_file), header=None, sep=' ')
train.columns = ['patient id', 'filename', 'class', 'data source']
print('Classes:\n', tr... | code |
16154664/cell_9 | [
"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 = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
train['SalePrice'].hist(bins=50)
y ... | code |
16154664/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.describe() | code |
16154664/cell_11 | [
"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 = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
y = train['SalePrice'].reset_index(... | code |
16154664/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
original_y = train['SalePrice'].reset_index(drop=True)
train['SalePrice'].hist(bins=50) | code |
16154664/cell_14 | [
"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 = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
y = train['SalePrice'].reset_index(... | code |
16154664/cell_10 | [
"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 = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
y = train['SalePrice'].reset_index(... | code |
16154664/cell_12 | [
"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 = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
y = train['SalePrice'].reset_index(... | code |
16154664/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.describe() | code |
74045329/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
test_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/test.csv')
test_df
slct_test_df = test_df[['date', 'bathrooms', 'sqft_livin... | code |
74045329/cell_4 | [
"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_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
test_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/test.csv')
test_df | code |
74045329/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df | code |
74045329/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
import matplotlib.pyplot as plt
im... | code |
74045329/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 |
74045329/cell_7 | [
"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
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
import matplotlib.pyplot as plt
import seaborn as sns
cor = train_df.corr()
plt.figure(f... | code |
74045329/cell_8 | [
"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
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
import matplotlib.pyplot as plt
import seaborn as sns
cor = train_df.corr()
slct_train_... | code |
74045329/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
train_df['date'] = train_df['date'].str.replace('T000000', '')
train_df | code |
74045329/cell_12 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', ind... | code |
74045329/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/train.csv', index_col=0)
train_df
test_df = pd.read_csv('/kaggle/input/1056lab-house-price-prediction/test.csv')
test_df
test_df['date'] = test_df['date'].str.replace('T000000',... | code |
18112246/cell_21 | [
"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('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
df.columns
len(df.columns)
dep_var = 'SalePrice'
cat_vars = ['MSSubClass', 'MSZoning', 'Street', 'Alley', 'LotShape', 'LandCo... | code |
18112246/cell_23 | [
"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)
df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
df.columns
len(df.columns)
dep_var = 'SalePrice'
cat_vars = ['MSSubClass', 'MSZoning', 'Street', 'Alley', 'LotShape', 'LandCo... | code |
18112246/cell_20 | [
"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('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
df.columns
len(df.columns)
dep_var = 'SalePrice'
cat_vars = ['MSSubClass', 'MSZoning', 'Street', 'Alley', 'LotShape', 'LandCo... | code |
18112246/cell_6 | [
"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/train.csv')
df.columns
len(df.columns) | code |
18112246/cell_26 | [
"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)
df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
df.columns
len(df.columns)
dep_var = 'SalePrice'
cat_vars = ['MSSubClass', 'MSZoning', 'Street', 'Alley', 'LotShape', 'LandCo... | code |
18112246/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18112246/cell_8 | [
"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/train.csv')
df.columns
len(df.columns)
df['BsmtHalfBath'].unique() | code |
18112246/cell_16 | [
"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)
df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
df.columns
len(df.columns)
dep_var = 'SalePrice'
cat_vars = ['MSSubClass', 'MSZoning', 'Street', 'Alley', 'LotShape', 'LandCo... | code |
18112246/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/train.csv')
df.head() | code |
18112246/cell_17 | [
"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('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
df.columns
len(df.columns)
dep_var = 'SalePrice'
cat_vars = ['MSSubClass', 'MSZoning', 'Street', 'Alley', 'LotShape', 'LandCo... | code |
18112246/cell_24 | [
"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)
df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
df.columns
len(df.columns)
dep_var = 'SalePrice'
cat_vars = ['MSSubClass', 'MSZoning', 'Street', 'Alley', 'LotShape', 'LandCo... | code |
18112246/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)
df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
df.columns
len(df.columns)
dep_var = 'SalePrice'
cat_vars = ['MSSubClass', 'MSZoning', 'Street', 'Alley', 'LotShape', 'LandCo... | code |
18112246/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)
df = pd.read_csv('../input/train.csv')
df.columns | code |
18146508/cell_21 | [
"image_output_1.png"
] | slot = 1
for i in range(2, 102):
slot += 1
slot = 1
for i in range(102, 202):
slot += 1
# Distribution of target within each feature
# Phân bố target theo từng biến
def plot_feat_dist(df1, df2, label1, label2, feat):
i = 0
sns.set_style('whitegrid')
fig, ax = plt.subplots(10, 10, figsize=(30, 30))... | code |
18146508/cell_13 | [
"text_html_output_1.png"
] | slot = 1
for i in range(2, 102):
slot += 1
slot = 1
for i in range(102, 202):
slot += 1
# Distribution of target within each feature
# Phân bố target theo từng biến
def plot_feat_dist(df1, df2, label1, label2, feat):
i = 0
sns.set_style('whitegrid')
fig, ax = plt.subplots(10, 10, figsize=(30, 30))... | code |
18146508/cell_9 | [
"image_output_1.png"
] | slot = 1
plt.figure(figsize=(30, 30))
for i in range(2, 102):
plt.subplot(10, 10, slot)
train.iloc[:, i].hist()
slot += 1 | code |
18146508/cell_4 | [
"image_output_1.png"
] | train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv') | code |
18146508/cell_23 | [
"image_output_1.png"
] | slot = 1
for i in range(2, 102):
slot += 1
slot = 1
for i in range(102, 202):
slot += 1
# Distribution of target within each feature
# Phân bố target theo từng biến
def plot_feat_dist(df1, df2, label1, label2, feat):
i = 0
sns.set_style('whitegrid')
fig, ax = plt.subplots(10, 10, figsize=(30, 30))... | code |
18146508/cell_20 | [
"image_output_1.png"
] | slot = 1
for i in range(2, 102):
slot += 1
slot = 1
for i in range(102, 202):
slot += 1
# Distribution of target within each feature
# Phân bố target theo từng biến
def plot_feat_dist(df1, df2, label1, label2, feat):
i = 0
sns.set_style('whitegrid')
fig, ax = plt.subplots(10, 10, figsize=(30, 30))... | code |
18146508/cell_6 | [
"image_output_1.png"
] | test.head() | code |
18146508/cell_11 | [
"text_html_output_1.png"
] | slot = 1
for i in range(2, 102):
slot += 1
slot = 1
for i in range(102, 202):
slot += 1
sns.countplot(train['target'])
print(train.target.value_counts(normalize=True)) | code |
18146508/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | slot = 1
for i in range(2, 102):
slot += 1
slot = 1
for i in range(102, 202):
slot += 1
# Distribution of target within each feature
# Phân bố target theo từng biến
def plot_feat_dist(df1, df2, label1, label2, feat):
i = 0
sns.set_style('whitegrid')
fig, ax = plt.subplots(10, 10, figsize=(30, 30))... | code |
18146508/cell_7 | [
"image_output_1.png"
] | train.describe() | code |
18146508/cell_18 | [
"image_output_1.png"
] | slot = 1
for i in range(2, 102):
slot += 1
slot = 1
for i in range(102, 202):
slot += 1
# Distribution of target within each feature
# Phân bố target theo từng biến
def plot_feat_dist(df1, df2, label1, label2, feat):
i = 0
sns.set_style('whitegrid')
fig, ax = plt.subplots(10, 10, figsize=(30, 30))... | code |
18146508/cell_8 | [
"image_output_1.png"
] | test.describe() | code |
18146508/cell_16 | [
"text_html_output_1.png"
] | slot = 1
for i in range(2, 102):
slot += 1
slot = 1
for i in range(102, 202):
slot += 1
# Distribution of target within each feature
# Phân bố target theo từng biến
def plot_feat_dist(df1, df2, label1, label2, feat):
i = 0
sns.set_style('whitegrid')
fig, ax = plt.subplots(10, 10, figsize=(30, 30))... | code |
18146508/cell_17 | [
"image_output_1.png"
] | slot = 1
for i in range(2, 102):
slot += 1
slot = 1
for i in range(102, 202):
slot += 1
# Distribution of target within each feature
# Phân bố target theo từng biến
def plot_feat_dist(df1, df2, label1, label2, feat):
i = 0
sns.set_style('whitegrid')
fig, ax = plt.subplots(10, 10, figsize=(30, 30))... | code |
18146508/cell_24 | [
"image_output_1.png"
] | slot = 1
for i in range(2, 102):
slot += 1
slot = 1
for i in range(102, 202):
slot += 1
# Distribution of target within each feature
# Phân bố target theo từng biến
def plot_feat_dist(df1, df2, label1, label2, feat):
i = 0
sns.set_style('whitegrid')
fig, ax = plt.subplots(10, 10, figsize=(30, 30))... | code |
18146508/cell_14 | [
"text_html_output_1.png"
] | slot = 1
for i in range(2, 102):
slot += 1
slot = 1
for i in range(102, 202):
slot += 1
# Distribution of target within each feature
# Phân bố target theo từng biến
def plot_feat_dist(df1, df2, label1, label2, feat):
i = 0
sns.set_style('whitegrid')
fig, ax = plt.subplots(10, 10, figsize=(30, 30))... | code |
18146508/cell_10 | [
"text_plain_output_1.png"
] | slot = 1
for i in range(2, 102):
slot += 1
slot = 1
plt.figure(figsize=(30, 30))
for i in range(102, 202):
plt.subplot(10, 10, slot)
train.iloc[:, i].hist()
slot += 1 | code |
18146508/cell_5 | [
"image_output_1.png"
] | train.head() | code |
2026028/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/gender_submissio... | code |
2026028/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/gender_submissio... | code |
2026028/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/gender_submissio... | code |
2026028/cell_56 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Document... | code |
2026028/cell_34 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Document... | code |
2026028/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/gender_submissio... | code |
2026028/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Document... | code |
2026028/cell_44 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Document... | code |
2026028/cell_55 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Document... | code |
2026028/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/gender_submissio... | code |
2026028/cell_39 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Document... | code |
2026028/cell_41 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Document... | code |
2026028/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd
pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_option('display.notebook_repr_html', True)
import seaborn as sns
sns.set(style='whitegrid')
i... | code |
2026028/cell_54 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Document... | code |
2026028/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/gender_submissio... | code |
2026028/cell_60 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:... | code |
2026028/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/gender_submissio... | code |
2026028/cell_50 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Document... | code |
2026028/cell_49 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Document... | code |
2026028/cell_51 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Document... | code |
2026028/cell_58 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Document... | code |
2026028/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/gender_submissio... | code |
2026028/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blo... | code |
2026028/cell_47 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Document... | code |
2026028/cell_43 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Document... | code |
2026028/cell_46 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Document... | code |
2026028/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/gender_submissio... | code |
2026028/cell_53 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Document... | code |
2026028/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/gender_submissio... | code |
2026028/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/gender_submissio... | code |
2026028/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/gender_submissio... | code |
2026028/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/gender_submissio... | code |
2026028/cell_36 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/train.csv')
test = pd.read_csv('C:/Users/Peng/Documents/APS/Data-Analysis-Blog/Kaggle/Titanic/test.csv')
gender_submission = pd.read_csv('C:/Users/Peng/Document... | code |
18112986/cell_13 | [
"text_html_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
IDtest = test['PassengerId']
def detect_outlier(df, n, features):
outlier_indices = []
... | code |
18112986/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
IDtest = test['PassengerId']
def detect_outlier(df, n, features):
outlier_indices = []
... | code |
18112986/cell_20 | [
"image_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
IDtest = test['PassengerId']
def detect_outlier(df, n, features):
outlier_indices = []
... | code |
18112986/cell_11 | [
"text_plain_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
IDtest = test['PassengerId']
def detect_outlier(df, n, features):
outlier_indices = []
... | code |
18112986/cell_19 | [
"image_output_1.png"
] | from collections import Counter
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 = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
IDtest = test['PassengerId']
def detect_outlier(df, n, features):
ou... | code |
18112986/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from collections import Counter
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier
from sklearn.discriminant_analysis import LinearDiscrim... | code |
18112986/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
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 = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
IDtest = test['PassengerId']
def detect_outlier(df, n, features):
ou... | code |
18112986/cell_15 | [
"text_plain_output_1.png"
] | from collections import Counter
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 = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
IDtest = test['PassengerId']
def detect_outlier(df, n, features):
ou... | code |
18112986/cell_16 | [
"text_html_output_1.png"
] | from collections import Counter
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 = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
IDtest = test['PassengerId']
def detect_outlier(df, n, features):
ou... | code |
18112986/cell_17 | [
"image_output_1.png"
] | from collections import Counter
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 = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
IDtest = test['PassengerId']
def detect_outlier(df, n, features):
ou... | code |
18112986/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
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 = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
IDtest = test['PassengerId']
def detect_outlier(df, n, features):
ou... | code |
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