path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
88094115/cell_12 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
x = df[['YearsExperience']]
x
y = df.iloc[:, 1].valu... | code |
88094115/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/years-of-experience-and-salary-dataset/Salary_Data.csv')
df.info() | code |
2019997/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Input
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import panda... | code |
2019997/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Input
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import panda... | code |
2019997/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Input
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import panda... | code |
2019997/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
from keras.layers import Input
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Model... | code |
2019997/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Input
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import panda... | code |
2019997/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Input
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import panda... | code |
106198216/cell_21 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.functions import format_number
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.... | code |
106198216/cell_13 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.columns
df_wmt.describe().show() | code |
106198216/cell_9 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.columns | code |
106198216/cell_25 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.functions import format_number
from pyspark.sql.functions import mean
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv',... | code |
106198216/cell_23 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.functions import format_number
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.... | code |
106198216/cell_2 | [
"text_plain_output_1.png"
] | pip install pyspark | code |
106198216/cell_28 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.functions import format_number
from pyspark.sql.functions import max,min
from pyspark.sql.functions import mean
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart... | code |
106198216/cell_8 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.show(1, vertical=True) | code |
106198216/cell_15 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.columns
df_wmt.describe().printSchema() | code |
106198216/cell_3 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark | code |
106198216/cell_17 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.functions import format_number
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.... | code |
106198216/cell_10 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.columns
df_wmt.printSchema() | code |
106198216/cell_5 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
walmart_spark = SparkSession.builder.appName('Walmart_Stock_Price').getOrCreate()
walmart_spark
df_wmt = walmart_spark.read.csv('../input/wmtdata/WMT.csv', header=True, inferSchema=True)
df_wmt.show() | code |
16152737/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
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/training.csv')
test = pd.read_csv('../input/test.csv')
uncommon_features = []
for i in trai... | code |
16152737/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/training.csv')
test = pd.read_csv('../input/test.csv')
uncommon_features = []
for i in train.columns:
if i not in test.columns:
uncommon_features.append(i)... | code |
16152737/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | print('Eliminate features')
filter_out = ['id', 'min_ANNmuon', 'production', 'mass', 'signal', 'SPDhits', 'CDF1', 'CDF2', 'CDF3', 'isolationb', 'isolationc', 'p0_pt', 'p1_pt', 'p2_pt', 'p0_p', 'p1_p', 'p2_p', 'p0_eta', 'p1_eta', 'p2_eta', 'isolationa', 'isolationb', 'isolationc', 'isolationd', 'isolatione', 'isolationf... | code |
16152737/cell_4 | [
"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/training.csv')
test = pd.read_csv('../input/test.csv')
print('Missing values in train: ', train.isnull().sum().sum()) | code |
16152737/cell_23 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout
from keras.models import Sequential
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
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/training.csv')... | code |
16152737/cell_20 | [
"text_plain_output_1.png"
] | from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.utils import to_categorical
from keras.datasets import mnist
from keras.utils.vis_utils import model_to_dot
from IPython.display import SVG
from keras.utils import np_utils | code |
16152737/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/training.csv')
test = pd.read_csv('../input/test.csv')
uncommon_features = []
for i in train.columns:
if i not in test.columns:
uncommon_features.append(i)
print('Extra features in train: ', uncommon_featu... | code |
16152737/cell_2 | [
"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/training.csv')
test = pd.read_csv('../input/test.csv')
print('train.shape:{} test.shape:{}'.format(train.shape, test.shape)) | code |
16152737/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
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/training.csv')
test = pd.read_csv('../input/test.csv')
uncommon_features = []
for i in trai... | code |
16152737/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import roc_curve, auc
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
print(os.listdir('..... | code |
16152737/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
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/training.csv')
test = pd.read_csv('../input/test.csv')
uncommon_features = []
for i in trai... | code |
16152737/cell_8 | [
"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/training.csv')
test = pd.read_csv('../input/test.csv')
uncommon_features = []
for i in train.columns:
if i not in test.columns:
uncommon_features.append(i)
def add_features(data):
df = data.copy()
... | code |
16152737/cell_16 | [
"text_html_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
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/training.csv')
test = pd.read_csv('../input/test.csv')
uncommon_features = []
for i in trai... | code |
16152737/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/training.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
16152737/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
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)
train = pd.read_csv('../input/training.csv')
test = pd.read_csv('../input/test.csv')
unc... | code |
16152737/cell_5 | [
"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/training.csv')
test = pd.read_csv('../input/test.csv')
print('Missing values in test: ', train.isnull().sum().sum()) | code |
17111990/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from IPython.display import Image
import os
!ls ../input/
Image("../input/images/twitter.png")
Image("../input/images/Trump_New_York_Times_tweet_.jpg") | code |
17111990/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from IPython.display import Image
import os
!ls ../input/
Image("../input/images/history-bigdata.jpg") | code |
17111990/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from IPython.display import Image
import os
!ls ../input/
Image("../input/images/threev.png") | code |
17111990/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from IPython.display import Image
import os
!ls ../input/
Image("../input/images/bda-696x394.jpg") | code |
17111990/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from IPython.display import Image
import os
!ls ../input/
Image("../input/images/company.jpg") | code |
17111990/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input'))
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
17111990/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from IPython.display import Image
import os
!ls ../input/
Image("../input/images/Management.png") | code |
104116119/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
print((train_df.isna().sum() / train_df.shape[0])[train_df.isna().sum() / train_df.shape[0] > 0.4]) | code |
104116119/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_df.drop(['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature'], axis=1, inplace=True)
test_df.drop(['Alley', 'Fi... | code |
104116119/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_folds_df = pd.read_csv('./train_folds.csv')
train_folds_df = train_folds_df.drop(['Id'], axis=1)
train_folds_df.he... | code |
104116119/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_df.drop(['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature'], axis=1, inplace=True)
test_df.drop(['Alley', 'Fi... | code |
104116119/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_df.head(2) | code |
104116119/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_folds_df = pd.read_csv('./train_folds.csv')
train_folds_df.head(1) | code |
33096184/cell_21 | [
"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/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarke... | code |
33096184/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0] | code |
33096184/cell_44 | [
"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/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
df1_pc... | code |
33096184/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/titanic/train.csv')
df.head() | code |
33096184/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df... | code |
33096184/cell_39 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df... | code |
33096184/cell_19 | [
"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/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape | code |
33096184/cell_50 | [
"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/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
df1_pc... | code |
33096184/cell_52 | [
"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/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
df1_pc... | code |
33096184/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/titanic/train.csv')
df.tail() | code |
33096184/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df... | code |
33096184/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)
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
df1_pc... | code |
33096184/cell_8 | [
"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/titanic/train.csv')
df.shape | code |
33096184/cell_38 | [
"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/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
df1_pc... | code |
33096184/cell_17 | [
"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/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum() | code |
33096184/cell_43 | [
"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/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
df1_pc... | code |
33096184/cell_31 | [
"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/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
df1_pc... | code |
33096184/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/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarke... | code |
33096184/cell_22 | [
"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/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarke... | code |
33096184/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df.shape
df.info() | code |
33096184/cell_37 | [
"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/titanic/train.csv')
df.shape
df.isnull().sum() * 100 / df.shape[0]
df1 = df.copy()
df1.drop('Cabin', axis=1, inplace=True)
df1.Embarked.isnull().sum()
df1 = df1.dropna(axis=0, subset=['Embarked'])
df1.shape
df1_pc... | code |
33096184/cell_12 | [
"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/titanic/train.csv')
df.shape
df.describe() | code |
1004561/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
os.listdir('../input') | code |
1004561/cell_33 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test_images = test.values.astype('float32')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_images_reshaped = train_images.resh... | code |
1004561/cell_55 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_images_reshaped = train_images.reshape(train_images.shape[0], 28, 2... | code |
1004561/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_labels.shape
train_labels[0:10] | code |
1004561/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_images_reshaped = train_images.reshape(train_images.shape[0], 28, 2... | code |
1004561/cell_48 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_images_reshaped = train_images.reshape(train_images.shape[0], 28, 2... | code |
1004561/cell_45 | [
"text_plain_output_1.png"
] | history = model.fit(train_images, train_labels, validation_split=0.05, nb_epoch=25, batch_size=64) | code |
1004561/cell_28 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_labels.shape | code |
1004561/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.models import Sequential
from keras.layers import Dense, Dropout, Lambda, Flatten | code |
1004561/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
print(train.shape)
train.head() | code |
1004561/cell_38 | [
"text_plain_output_1.png"
] | from keras.utils.np_utils import to_categorical
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_labels.shape
from keras.utils.np_utils import to_c... | code |
1004561/cell_47 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_images_reshaped = train_images.reshape(train_images.shape[0], 28, 2... | code |
1004561/cell_17 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
print(test.shape)
test.head() | code |
1004561/cell_46 | [
"text_plain_output_1.png"
] | history_dict = history.history
history_dict.keys() | code |
1004561/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_images_reshaped = train_images.reshape(train_images.shape[0], 28, 28)
train_images_reshaped.shape | code |
1004561/cell_22 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_labels[0:10] | code |
1004561/cell_53 | [
"image_output_1.png"
] | model = Sequential()
model.add(Dense(64, activation='relu', input_dim=28 * 28))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.15))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.15))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer=RMSprop(lr=0.0001), loss='categorical_cros... | code |
1004561/cell_37 | [
"text_plain_output_1.png"
] | from keras.utils.np_utils import to_categorical
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_images = train.ix[:, 1:].values.astype('float32')
train_labels = train.ix[:, 0].values.astype('int32')
train_labels.shape
from keras.utils.np_utils import to_c... | code |
90140081/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('E:\\Dockship\\Credict card\\TRAIN.csv')
df.head() | code |
90125749/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
directories = ['../input/csc4851-homework4/birds_400/test', '../input/csc4851-homework4/birds_400/train', '../in... | code |
90125749/cell_2 | [
"text_plain_output_1.png"
] | !ls | code |
90125749/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 |
90125749/cell_5 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
directories = ['../input/csc4851-homework4/birds_400/test', '../input/csc4851-homework4/birds_400/train', '../input//csc4851-homework4/birds_400/... | code |
16144712/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import decomposition
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/train.csv')
label = data['label']
pixels = data.drop('label', axis=1)
from sklearn import decomposition
pca = decomposition.PCA()
pca.n_components = 2
pca_d... | code |
16144712/cell_10 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn import decomposition
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/train.csv')
label = data['label']
pixels = data.drop('label', axis=1)
from sklearn import decomposition
pca = decompositio... | code |
104120795/cell_42 | [
"image_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt # visualization
import numpy as np # linear algebra
import pandas as pd # read and wrangle dataframes
import seaborn as sns # statistical visualizations and aesthetics
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
... | code |
104120795/cell_9 | [
"image_output_1.png"
] | import pandas as pd # read and wrangle dataframes
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.dtypes
df['Type'].value_counts() | code |
104120795/cell_25 | [
"text_plain_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # read and wrangle dataframes
df = pd.read_csv('../input/glass/glass.csv')
features = df.columns[:-1].tolist()
df.dtypes
def outlier_hunt(df):
"""
Takes a dataframe df of features and returns a list of the indices
... | code |
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