path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
104130018/cell_40 | [
"application_vnd.jupyter.stderr_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/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape
data.describe().T
data.isnull().sum() / data.shape[0] * 100
data.nunique()
data.Ge... | code |
104130018/cell_29 | [
"application_vnd.jupyter.stderr_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/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape
data.describe().T
data.isnull().sum() / data.shape[0] * 100
data.nunique()
data.Ge... | code |
104130018/cell_2 | [
"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/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data | code |
104130018/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
104130018/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape
data.describe().T | code |
104130018/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape
data.describe().T
data.isnull().sum() / data.shape[0] * 100
data.nunique()
data.Ge... | code |
104130018/cell_28 | [
"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/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape
data.describe().T
data.isnull().sum() / data.shape[0] * 100
data.nunique()
data.Gender.value_counts()
data.groupby('Gender')['Purchase'].... | code |
104130018/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape
data.describe().T
data.isnull().sum() / data.shape[0] * 100
data.nunique()
data.Ge... | code |
104130018/cell_38 | [
"application_vnd.jupyter.stderr_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/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape
data.describe().T
data.isnull().sum() / data.shape[0] * 100
data.nunique()
data.Ge... | code |
104130018/cell_35 | [
"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/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape
data.describe().T
data.isnull().sum() / data.shape[0] * 100
data.nunique()
data.Ge... | code |
104130018/cell_31 | [
"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/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape
data.describe().T
data.isnull().sum() / data.shape[0] * 100
data.nunique()
data.Gender.value_counts()
data.groupby('Gender')['Purchase'].... | code |
104130018/cell_24 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape
data.describe().T
data.isnull().sum() / data.shape[0] * 100
data.nunique()
data.Gender.value_counts()
data.groupby('Gender')['Purchase'].... | code |
104130018/cell_12 | [
"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/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape
data.describe().T
data.isnull().sum() / data.shape[0] * 100
data.nunique() | code |
104130018/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/black-friday-sales-prediction/train_oSwQCTC (1)/train.csv')
data
data.shape
data.info() | code |
2040633/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape
daily_Data.dtypes
daily_Data.columns
daily_Data.isnull().sum()
f, ax = plt.subplots(figsize=(5,5))
plt.hist(x="season", data=daily_Data, color="c");
plt.xlabel("season")
daily_Data.season.value_co... | code |
2040633/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape
daily_Data.dtypes | code |
2040633/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape
daily_Data.dtypes
daily_Data.columns
daily_Data.isnull().sum()
daily_Data.season.value_counts()
daily_Data.holiday.value_counts()
daily_Data.workingday.value_counts()
daily_Data.weather.value_counts()
season = pd.get_dummies(d... | code |
2040633/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape
daily_Data.dtypes
daily_Data.columns
daily_Data.isnull().sum()
f, ax = plt.subplots(figsize=(5,5))
plt.hist(x="season", data=daily_Data, color="c");
plt.xl... | code |
2040633/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape
daily_Data.dtypes
daily_Data.columns
daily_Data.isnull().sum() | code |
2040633/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape
daily_Data.dtypes
daily_Data.columns
daily_Data.isnull().sum()
daily_Data.season.value_counts()
daily_Data.holiday.value_counts()
daily_Data.workingday.value_counts()
daily_Data.weather.value_counts()
season = pd.get_dummies(d... | code |
2040633/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.head() | code |
2040633/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape
daily_Data.dtypes
daily_Data.columns
daily_Data.isnull().sum()
f, ax = plt.subplots(figsize=(5, 5))
plt.hist(x='season', data=daily_Data, color='c')
plt.xlabel('season') | code |
2040633/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape
daily_Data.dtypes
daily_Data.columns
daily_Data.isnull().sum()
f, ax = plt.subplots(figsize=(5,5))
plt.hist(x="season", data=daily_Data, color="c");
plt.xlabel("season")
daily_Data.season.value_co... | code |
2040633/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape
daily_Data.dtypes
daily_Data.columns
daily_Data.isnull().sum()
print('season:', daily_Data.season.unique())
print('holiday', daily_Data.holiday.unique())
print('workingday:', daily_Data.workingday.unique())
print('weather:', daily... | code |
2040633/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape
daily_Data.dtypes
daily_Data.columns
daily_Data.isnull().sum()
daily_Data.season.value_counts()
daily_Data.holiday.value_counts()
daily_Data.workingday.value_counts()
daily_Data.weather.value_counts() | code |
2040633/cell_8 | [
"text_plain_output_1.png"
] | from collections import Counter
import pandas as pd
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape
daily_Data.dtypes
daily_Data.columns
daily_Data.isnull().sum()
from collections import Counter
Counter(daily_Data['holiday']) | code |
2040633/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape
daily_Data.dtypes
daily_Data.columns
daily_Data.isnull().sum()
f, ax = plt.subplots(figsize=(5,5))
plt.hist(x="season", data=daily_Data, color="c");
plt.xlabel("season")
daily_Data.season.value_co... | code |
2040633/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape
daily_Data.dtypes
daily_Data.columns
daily_Data.isnull().sum()
daily_Data.season.value_counts()
daily_Data.holiday.value_counts()
daily_Data.workingday.value_counts() | code |
2040633/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape | code |
2040633/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape
daily_Data.dtypes
daily_Data.columns
daily_Data.isnull().sum()
f, ax = plt.subplots(figsize=(5,5))
plt.hist(x="season", data=daily_Data, color="c");
plt.xlabel("season")
daily_Data.season.value_co... | code |
2040633/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape
daily_Data.dtypes
daily_Data.columns
daily_Data.isnull().sum()
daily_Data.season.value_counts()
daily_Data.holiday.value_counts()
daily_Data.workingday.value_counts()
daily_Data.weather.value_counts()
season = pd.get_dummies(d... | code |
2040633/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape
daily_Data.dtypes
daily_Data.columns
daily_Data.isnull().sum()
daily_Data.season.value_counts()
daily_Data.holiday.value_counts() | code |
2040633/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sn
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape
daily_Data.dtypes
daily_Data.columns
daily_Data.isnull().sum()
sn.barplot(x='season', y='count', data=daily_Data) | code |
2040633/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape
daily_Data.dtypes
daily_Data.columns
daily_Data.isnull().sum()
daily_Data.season.value_counts() | code |
2040633/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
daily_Data = pd.read_csv('../input/train.csv')
daily_Data.shape
daily_Data.dtypes
daily_Data.columns | code |
325705/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.cross_validation import KFold
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
gatrain = pd.read_csv('../input/gender_age_train.csv')
gatest = pd.read_csv('../input/gender_age_test.csv')
letarge... | code |
325705/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
gatrain = pd.read_csv('../input/gender_age_train.csv')
gatest = pd.read_csv('../input/gender_age_test.csv')
phone = pd.read_csv('../input/phone_brand_device_model.csv', encoding='utf-8')
phone = phone.drop_duplicates('device_id', keep='first')
lebr... | code |
325705/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
gatrain = pd.read_csv('../input/gender_age_train.csv')
gatest = pd.read_csv('../input/gender_age_test.csv')
phone = pd.read_csv('../input/phone_brand_device_model.csv', encoding='utf-8')
phone.head(3) | code |
325705/cell_19 | [
"text_html_output_1.png"
] | from sklearn.cross_validation import KFold
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
gatrain = pd.read_csv('../input/gender_age_train.csv')
gatest = pd.read_csv('../input/gender_age_test.csv')
letarge... | code |
325705/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import os
from sklearn.preprocessing import LabelEncoder
from sklearn.cross_validation import KFold
from sklearn.metrics import log_loss | code |
325705/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
gatrain = pd.read_csv('../input/gender_age_train.csv')
gatest = pd.read_csv('../input/gender_age_test.csv')
gatrain.head(3) | code |
325705/cell_17 | [
"text_html_output_1.png"
] | from sklearn.cross_validation import KFold
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
gatrain = pd.read_csv('../input/gender_age_train.csv')
gatest = pd.read_csv('../input/gender_age_test.csv')
letarge... | code |
325705/cell_14 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
gatrain = pd.read_csv('../input/gender_age_train.csv')
gatest = pd.read_csv('../input/gender_age_test.csv')
letarget = LabelEncoder().fit(gatrain.group.values)
y = letarget.transform(gatrain.group.values)
n_classes = len(letarget.classes_)
phone = p... | code |
129019808/cell_21 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy import stats
from scipy.stats import t
from scipy.stats import ttest_1samp
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import panda... | code |
129019808/cell_9 | [
"image_output_1.png"
] | from scipy.stats import t
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('/kaggle/input/iedata/iedatason.xlsx', sheet_name='Form Yanıtları 1')
mean = np.mean(df['Result'])
std = np.std(df['Result'])
import numpy as n... | code |
129019808/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('/kaggle/input/iedata/iedatason.xlsx', sheet_name='Form Yanıtları 1')
df.info() | code |
129019808/cell_2 | [
"text_html_output_1.png"
] | import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
sns.set() | code |
129019808/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.stats import t
from scipy.stats import ttest_1samp
import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('/kaggle/input/iedata/iedatason.xlsx', sheet_name='Form Yanıtları 1... | code |
129019808/cell_19 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy import stats
from scipy.stats import t
from scipy.stats import ttest_1samp
import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
imp... | code |
129019808/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 |
129019808/cell_7 | [
"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_excel('/kaggle/input/iedata/iedatason.xlsx', sheet_name='Form Yanıtları 1')
mean = np.mean(df['Result'])
std = np.std(df['Result'])
print('Mean: ', mean)
print('Standard Deviation: ', std) | code |
129019808/cell_18 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy.stats import t
from scipy.stats import ttest_1samp
import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I... | code |
129019808/cell_16 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy.stats import t
from scipy.stats import ttest_1samp
import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)... | code |
129019808/cell_14 | [
"text_html_output_1.png"
] | from scipy.stats import t
from scipy.stats import ttest_1samp
import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import statsmodels.api as sm
df = pd.read_e... | code |
129019808/cell_12 | [
"text_plain_output_1.png"
] | from scipy.stats import t
from scipy.stats import ttest_1samp
import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('/kaggle/input/iedata/iedatason.xlsx', sheet_n... | code |
129019808/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_excel('/kaggle/input/iedata/iedatason.xlsx', sheet_name='Form Yanıtları 1')
df.head() | code |
105186835/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | DEPT_name = input('please enter your department name')
DEPT_revenue = input('please enter your department revenue') | code |
129039496/cell_42 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.neural_network import MLPClassifier
from sklearn.pipeline im... | code |
129039496/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
from sklearn.metrics.pairwise import cosine_similarity
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uc... | code |
129039496/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
from sklearn.metrics.pairwise import cosine_similarity
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uc... | code |
129039496/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
from sklearn.metrics.pairwise import cosine_similarity
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uc... | code |
129039496/cell_44 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_curve, auc, confusion_matrix
from sklearn.model_selection import Gri... | code |
129039496/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-of-words/docword.nips.txt', '/kaggle/input/uci-b... | code |
129039496/cell_18 | [
"text_html_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-... | code |
129039496/cell_8 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-of-words/docword.nips.txt', '/kaggle/input/uci-b... | code |
129039496/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-... | code |
129039496/cell_16 | [
"text_html_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-... | code |
129039496/cell_43 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_curve, auc, confusion_matrix
from sklearn.model_selection import Gri... | code |
129039496/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-... | code |
129039496/cell_27 | [
"text_html_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
from sklearn.metrics.pairwise import cosine_similarity
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uc... | code |
129039496/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-of-words/docword.nips.txt', '/kaggle/input/uci-b... | code |
129039496/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-of-words/docword.nips.txt', '/kaggle/input/uci-b... | code |
34134596/cell_30 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import re
import string
review_text_train = pd.read_csv('../input/review_text_train.csv', index_col=False, delimiter=',', header=0)
review_text_test = pd.read_csv('../input/review_text_test.csv', index_col=False, delimiter=',', header=0... | code |
34134596/cell_44 | [
"text_html_output_1.png"
] | from collections import Counter
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import re
import string
review_text_train = pd.read_csv('../input/review_text_train.csv', index_col=False, delimiter=',', header=0)
review_text_test = pd.read_csv('../input/review_text_test.csv', index_c... | code |
34134596/cell_55 | [
"text_html_output_1.png"
] | from collections import Counter
from sklearn.feature_extraction import text
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
... | code |
34134596/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
review_text_train = pd.read_csv('../input/review_text_train.csv', index_col=False, delimiter=',', header=0)
review_text_test = pd.read_csv('../input/review_text_test.csv', index_col=False, delimiter=',', header=0)
review_meta_train = pd.read_csv('../input/review_meta_train.csv', index_col=False, de... | code |
34134596/cell_54 | [
"text_html_output_1.png"
] | from collections import Counter
from sklearn.feature_extraction import text
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
... | code |
34134596/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
review_text_train = pd.read_csv('../input/review_text_train.csv', index_col=False, delimiter=',', header=0)
review_text_test = pd.read_csv('../input/review_text_test.csv', index_col=False, delimiter=',', header=0)
review_meta_train = pd.read_csv('../input/review_meta_train.csv', index_col=False, de... | code |
34134596/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
review_text_train = pd.read_csv('../input/review_text_train.csv', index_col=False, delimiter=',', header=0)
review_text_test = pd.read_csv('../input/review_text_test.csv', index_col=False, delimiter=',', header=0)
review_meta_train = pd.read_csv('../input/review_meta_train.csv', index_col=False, de... | code |
34134596/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import re
import string
review_text_train = pd.read_csv('../input/review_text_train.csv', index_col=False, delimiter=',', header=0)
review_text_test = pd.read_csv('../input/review_text_test.csv', index_col=False, delimiter=',', header=0... | code |
34134596/cell_58 | [
"image_output_1.png"
] | from sklearn.feature_extraction import text
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import re
import string
review_text_train = pd.read_csv('../input/review_text_train.csv', index_col=False, delimiter=',', header=0... | code |
34134596/cell_8 | [
"image_output_1.png"
] | import pandas as pd
review_text_train = pd.read_csv('../input/review_text_train.csv', index_col=False, delimiter=',', header=0)
review_text_test = pd.read_csv('../input/review_text_test.csv', index_col=False, delimiter=',', header=0)
review_meta_train = pd.read_csv('../input/review_meta_train.csv', index_col=False, de... | code |
34134596/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
review_text_train = pd.read_csv('../input/review_text_train.csv', index_col=False, delimiter=',', header=0)
review_text_test = pd.read_csv('../input/review_text_test.csv', index_col=False, delimiter=',', header=0)
review_meta_train = pd.read_csv('../input/review_meta_train.csv', index_col=False, de... | code |
34134596/cell_38 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import re
import string
review_text_train = pd.read_csv('../input/review_text_train.csv', index_col=False, delimiter=',', header=0)
review_text_test = pd.read_csv('../input/review_text_test.csv', index_col=False, delimiter=',', header=0... | code |
34134596/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
review_text_train = pd.read_csv('../input/review_text_train.csv', index_col=False, delimiter=',', header=0)
review_text_test = pd.read_csv('../input/review_text_test.csv', index_col=False, delimiter=',', header=0)
review_meta_train = pd.read_csv('../input/review_meta_train.csv', index_col=False, de... | code |
34134596/cell_46 | [
"text_html_output_1.png"
] | from collections import Counter
from sklearn.feature_extraction.text import CountVectorizer
import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
import string
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import preprocessing, feature_extractio... | code |
34134596/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
import string
review_text_train = pd.read_csv('../input/review_text_train.csv', index_col=False, delimiter=',', header=0)
review_text_test = pd.read_csv('../input/review_text_test.csv', index_col=False, delimiter=',', header=0)
review_meta_train = pd.read_csv('../input/review_meta_train... | code |
34134596/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
review_text_train = pd.read_csv('../input/review_text_train.csv', index_col=False, delimiter=',', header=0)
review_text_test = pd.read_csv('../input/review_text_test.csv', index_col=False, delimiter=',', header=0)
review_meta_train = pd.read_csv('../input/review_meta_train.csv', index_col=False, de... | code |
34134596/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
review_text_train = pd.read_csv('../input/review_text_train.csv', index_col=False, delimiter=',', header=0)
review_text_test = pd.read_csv('../input/review_text_test.csv', index_col=False, delimiter=',', header=0)
review_meta_train = pd.read_csv('../input/review_meta_train.csv', index_col=False, de... | code |
73099078/cell_13 | [
"text_plain_output_1.png"
] | from transformers import DistilBertTokenizerFast
from transformers import TFDistilBertForSequenceClassification, TFTrainer, TFTrainingArguments
import tensorflow as tf
from transformers import DistilBertTokenizerFast
tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
train_encodings = to... | code |
73099078/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
messages = pd.read_csv('../input/spam-or-ham/spam.csv', usecols=['v1', 'v2'], encoding='ISO-8859-1')
messages = messages.rename(columns={'v1': 'label', 'v2': 'message'})
y = list(messages['label'])
y[:5] | code |
73099078/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
messages = pd.read_csv('../input/spam-or-ham/spam.csv', usecols=['v1', 'v2'], encoding='ISO-8859-1')
messages = messages.rename(columns={'v1': 'label', 'v2': 'message'})
X = list(messages['message'])
X[:5]
y = list(message... | code |
73099078/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
messages = pd.read_csv('../input/spam-or-ham/spam.csv', usecols=['v1', 'v2'], encoding='ISO-8859-1')
messages = messages.rename(columns={'v1': 'label', 'v2': 'message'})
messages.head() | code |
73099078/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
messages = pd.read_csv('../input/spam-or-ham/spam.csv', usecols=['v1', 'v2'], encoding='ISO-8859-1')
messages.head() | code |
73099078/cell_7 | [
"text_plain_output_1.png"
] | !pip install transformers | code |
73099078/cell_8 | [
"text_plain_output_1.png"
] | from transformers import DistilBertTokenizerFast
from transformers import DistilBertTokenizerFast
tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased') | code |
73099078/cell_15 | [
"text_plain_output_1.png"
] | from transformers import DistilBertTokenizerFast
from transformers import TFDistilBertForSequenceClassification, TFTrainer, TFTrainingArguments
import tensorflow as tf
from transformers import DistilBertTokenizerFast
tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
train_encodings = to... | code |
73099078/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.metrics import classification_report
from transformers import DistilBertTokenizerFast
from transformers import TFDistilBertForSequenceClassification, TFTrainer, TFTrainingArguments
import tensorflow as tf
from transformers import DistilBertTokenizerFast
tokenizer = DistilBertTokenizerFast.from_pretrain... | code |
73099078/cell_3 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
messages = pd.read_csv('../input/spam-or-ham/spam.csv', usecols=['v1', 'v2'], encoding='ISO-8859-1')
messages = messages.rename(columns={'v1': 'label', 'v2': 'message'})
X = list(messages['message'])
X[:5] | code |
73099078/cell_14 | [
"text_plain_output_1.png"
] | from transformers import DistilBertTokenizerFast
from transformers import TFDistilBertForSequenceClassification, TFTrainer, TFTrainingArguments
import tensorflow as tf
from transformers import DistilBertTokenizerFast
tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
train_encodings = to... | code |
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