path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
48163903/cell_5 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import pandas as pd
import re
df_train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
df_test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
df_sub = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.... | code |
34119268/cell_21 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from pathlib import Path
bs = 16
from pathlib import Path
path = Path('../input/earphones/earphone_dataset')
path.ls()
mi = path / 'redmi_airdots'
galaxy = path / 'galaxy_buds'
airpods = path / 'iphone_airpods'
mi.ls()
fn_paths = []
fn_paths = fn_paths + mi.ls() + galaxy.ls() + airpods.ls()
fn_paths
tfms = get_t... | code |
34119268/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pathlib import Path
from pathlib import Path
path = Path('../input/earphones/earphone_dataset')
path.ls()
mi = path / 'redmi_airdots'
galaxy = path / 'galaxy_buds'
airpods = path / 'iphone_airpods'
mi.ls() | code |
34119268/cell_25 | [
"image_output_1.png"
] | from pathlib import Path
bs = 16
from pathlib import Path
path = Path('../input/earphones/earphone_dataset')
path.ls()
mi = path / 'redmi_airdots'
galaxy = path / 'galaxy_buds'
airpods = path / 'iphone_airpods'
mi.ls()
fn_paths = []
fn_paths = fn_paths + mi.ls() + galaxy.ls() + airpods.ls()
fn_paths
tfms = get_t... | code |
34119268/cell_23 | [
"text_html_output_1.png"
] | from pathlib import Path
bs = 16
from pathlib import Path
path = Path('../input/earphones/earphone_dataset')
path.ls()
mi = path / 'redmi_airdots'
galaxy = path / 'galaxy_buds'
airpods = path / 'iphone_airpods'
mi.ls()
fn_paths = []
fn_paths = fn_paths + mi.ls() + galaxy.ls() + airpods.ls()
fn_paths
tfms = get_t... | code |
34119268/cell_20 | [
"image_output_1.png"
] | from pathlib import Path
bs = 16
from pathlib import Path
path = Path('../input/earphones/earphone_dataset')
path.ls()
mi = path / 'redmi_airdots'
galaxy = path / 'galaxy_buds'
airpods = path / 'iphone_airpods'
mi.ls()
fn_paths = []
fn_paths = fn_paths + mi.ls() + galaxy.ls() + airpods.ls()
fn_paths
tfms = get_t... | code |
34119268/cell_6 | [
"image_output_1.png"
] | from pathlib import Path
from pathlib import Path
path = Path('../input/earphones/earphone_dataset')
path.ls() | code |
34119268/cell_16 | [
"text_plain_output_1.png"
] | from pathlib import Path
bs = 16
from pathlib import Path
path = Path('../input/earphones/earphone_dataset')
path.ls()
mi = path / 'redmi_airdots'
galaxy = path / 'galaxy_buds'
airpods = path / 'iphone_airpods'
mi.ls()
fn_paths = []
fn_paths = fn_paths + mi.ls() + galaxy.ls() + airpods.ls()
fn_paths
tfms = get_t... | code |
34119268/cell_17 | [
"text_plain_output_1.png"
] | from pathlib import Path
bs = 16
from pathlib import Path
path = Path('../input/earphones/earphone_dataset')
path.ls()
mi = path / 'redmi_airdots'
galaxy = path / 'galaxy_buds'
airpods = path / 'iphone_airpods'
mi.ls()
fn_paths = []
fn_paths = fn_paths + mi.ls() + galaxy.ls() + airpods.ls()
fn_paths
tfms = get_t... | code |
34119268/cell_14 | [
"text_plain_output_1.png"
] | from pathlib import Path
bs = 16
from pathlib import Path
path = Path('../input/earphones/earphone_dataset')
path.ls()
mi = path / 'redmi_airdots'
galaxy = path / 'galaxy_buds'
airpods = path / 'iphone_airpods'
mi.ls()
fn_paths = []
fn_paths = fn_paths + mi.ls() + galaxy.ls() + airpods.ls()
fn_paths
tfms = get_t... | code |
34119268/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pathlib import Path
bs = 16
from pathlib import Path
path = Path('../input/earphones/earphone_dataset')
path.ls()
mi = path / 'redmi_airdots'
galaxy = path / 'galaxy_buds'
airpods = path / 'iphone_airpods'
mi.ls()
fn_paths = []
fn_paths = fn_paths + mi.ls() + galaxy.ls() + airpods.ls()
fn_paths
tfms = get_t... | code |
34119268/cell_10 | [
"text_plain_output_1.png"
] | from pathlib import Path
from pathlib import Path
path = Path('../input/earphones/earphone_dataset')
path.ls()
mi = path / 'redmi_airdots'
galaxy = path / 'galaxy_buds'
airpods = path / 'iphone_airpods'
mi.ls()
fn_paths = []
fn_paths = fn_paths + mi.ls() + galaxy.ls() + airpods.ls()
fn_paths | code |
1003611/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | data = pd.read_csv('../input/DSL-StrongPasswordData.csv', header=0)
data = data.reset_index()
hold_cols = [x for x in data.columns if x.startswith('H.')]
switch_cols = [x for x in data.columns if x.startswith('UD.')]
timing_cols = [x for x in data.columns if x.startswith('H.') or x.startswith('UD.')]
def get_subject_d... | code |
1003611/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | data = pd.read_csv('../input/DSL-StrongPasswordData.csv', header=0)
data = data.reset_index()
hold_cols = [x for x in data.columns if x.startswith('H.')]
switch_cols = [x for x in data.columns if x.startswith('UD.')]
timing_cols = [x for x in data.columns if x.startswith('H.') or x.startswith('UD.')]
def get_subject_d... | code |
1003611/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | data = pd.read_csv('../input/DSL-StrongPasswordData.csv', header=0)
data = data.reset_index()
data.head() | code |
1003611/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pandas.tools.plotting import scatter_matrix
data = pd.read_csv('../input/DSL-StrongPasswordData.csv', header=0)
data = data.reset_index()
hold_cols = [x for x in data.columns if x.startswith('H.')]
switch_cols = [x for x in data.columns if x.startswith('UD.')]
timing_cols = [x for x in data.columns if x.startswi... | code |
1003611/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pandas.tools.plotting import scatter_matrix
from pandas.tools.plotting import scatter_matrix
data = pd.read_csv('../input/DSL-StrongPasswordData.csv', header=0)
data = data.reset_index()
hold_cols = [x for x in data.columns if x.startswith('H.')]
switch_cols = [x for x in data.columns if x.startswith('UD.')]
ti... | code |
72098732/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/kk30ml/train.csv', index_col=0)
test = pd.read_csv('../input/kk30ml/test.csv', index_col=0)
train
y = train['target']
X = train.drop(['target'], axis=1)
X.head() | code |
72098732/cell_2 | [
"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/kk30ml/train.csv', index_col=0)
test = pd.read_csv('../input/kk30ml/test.csv', index_col=0)
train | code |
72098732/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 |
72098732/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
model = RandomForestRegressor()
model.fit(X_train, y_train)
pred = model.predict(X_valid)
mse = mean_squared_error(y_valid... | code |
72098732/cell_3 | [
"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/kk30ml/train.csv', index_col=0)
test = pd.read_csv('../input/kk30ml/test.csv', index_col=0)
train
test.head() | code |
72098732/cell_5 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/kk30ml/train.csv', index_col=0)
test = pd.read_csv('../input/kk30ml/test.csv', index_col=0)
train
y = train['target']
X = train.drop(['target'], axis=1)
from sklearn.... | code |
2004802/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Ca... | code |
2004802/cell_34 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Ca... | code |
2004802/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Ca... | code |
2004802/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = f... | code |
2004802/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = f... | code |
2004802/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = f... | code |
2004802/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = f... | code |
2004802/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = f... | code |
2004802/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = f... | code |
2004802/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = f... | code |
2004802/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_full_set = pd.read_csv('../input/train.csv')
full_set_initial = pd.get_dummies(data=train_full_set, columns=['Embarked', 'Sex', 'Survived'], drop_first=True)
full_set_initial = full_set_initial.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
corr = f... | code |
2004802/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train_full_set = pd.read_csv('../input/train.csv')
print('/n/nInformation about Null/ empty data points in each Column of Training set\n\n')
print(train_full_set.info()) | code |
128021214/cell_21 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
import pandas as pd
df_i... | code |
128021214/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
display(df_iris.head(3))
display(df_iris.tail(3))
display(df_iris.describe()) | code |
128021214/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Species').size()
X = df_iris.iloc[:, 1:5]
y = pd.DataFrame(df_iris.iloc[:, 5])
le = LabelEncoder()
y['Species']... | code |
128021214/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Species').size() | code |
128021214/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
from pandas.plotting import andrews_curves
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split... | code |
128021214/cell_11 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | from pandas.plotting import andrews_curves
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Species').si... | code |
128021214/cell_18 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
import pandas as pd
df_i... | code |
128021214/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Species').size()
X = df_iris.iloc[:, 1:5]
y = pd.DataFrame(df_iris.iloc[:, 5])
display(X.head(3), y.head(3))
le... | code |
128021214/cell_15 | [
"text_html_output_4.png",
"text_html_output_2.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/... | code |
128021214/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pandas.plotting import andrews_curves
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Species').size()
X = df_iris.iloc[... | code |
128021214/cell_12 | [
"text_plain_output_1.png"
] | from pandas.plotting import andrews_curves
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Species').si... | code |
2010993/cell_42 | [
"text_plain_output_1.png"
] | from sklearn.metrics import auc
from sklearn.metrics import roc_curve
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
gnb.fit(x_train, y_train)
gnb_predict = gnb.predict(x_test)
gnb_predict_prob = gnb.predict_proba(x_test)
fpr, tpr, thresholds = roc_curve(y_t... | code |
2010993/cell_13 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mushrooms.csv')
data.shape
from sklearn.preprocessing import LabelEncoder
lbl = LabelEncoder()
for col in data.columns:
data[col] = lbl.fit_tra... | code |
2010993/cell_25 | [
"image_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mushrooms.csv')
data.shape
from sklearn.preprocessing impor... | code |
2010993/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(x_train, y_train)
lr_predict = lr.predict(x_test)
lr_conf_matrix = confusion_matrix(y_test, lr_predict)
lr_accuracy = a... | code |
2010993/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mushrooms.csv')
data.sha... | code |
2010993/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mushrooms.csv')
data.shape | code |
2010993/cell_39 | [
"text_plain_output_1.png"
] | from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
gnb.fit(x_train, y_train)
gnb_predict = gnb.predict(x_test)
gnb_predict_prob = gnb.predict_proba(x_test)
print(gnb_predict)
print(gnb_predict_prob) | code |
2010993/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split | code |
2010993/cell_48 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier(max_depth=10)
dt.fit(x_train, y_train)
dt_predict = dt.predict(x_test)... | code |
2010993/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
gnb.fit(x_train, y_train)
gnb_predict = gnb.predict(x_test)
gnb_predict_prob = gnb.predict_proba(x_test)
gnb_conf_matrix = confusion_matrix(y_test, gnb... | code |
2010993/cell_54 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import auc
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(max_depth=10)
rf.fit(x_train, y_train)
rf_predict = rf.predict(x_test)
... | code |
2010993/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
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)
... | code |
2010993/cell_50 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.metrics import auc
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import Sta... | code |
2010993/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mushrooms.csv')
data.shape
data.head() | code |
2010993/cell_49 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import auc
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_curve, auc
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier(max_depth=10)
dt.fit(x_train, y_train)
dt_predict = dt.predict(x_test)
dt_pred... | code |
2010993/cell_28 | [
"image_output_1.png"
] | print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape) | code |
2010993/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mushrooms.csv')
data.shape
from sklearn.preprocessing import LabelEncoder
lbl = LabelEncoder()
for col in data.columns:
data[col] = lbl.fit_tra... | code |
2010993/cell_3 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2010993/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mushrooms.csv')
data.shape
from sklearn.preprocessing import LabelEncoder
lbl = LabelEncoder()
fo... | code |
2010993/cell_43 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.metrics import auc
from sklearn.metrics import roc_curve
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt... | code |
2010993/cell_31 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(x_train, y_train)
lr_predict = lr.predict(x_test)
lr_predict_prob = lr.predict_proba(x_test)
print(lr_predict)
print(lr_predict_prob[:, 1]) | code |
2010993/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processin... | code |
2010993/cell_14 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mushrooms.csv')
data.shape
from sklearn.preprocessing import LabelEncoder
lbl = LabelEncoder()
for col in data.columns:
data[col] = lbl.fit_tra... | code |
2010993/cell_53 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(max_depth=10)
rf.fit(x_train, y_train)
rf_predict = rf.predict(... | code |
2010993/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mushrooms.csv')
data.shape
from sklearn.preprocessing import LabelEncoder
lbl = LabelEncoder()
for col in data.columns:
data[col] = lbl.fit_tra... | code |
2010993/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.metrics import auc
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
imp... | code |
2010993/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mushrooms.csv')
data.shape
from sklearn.preprocessing import LabelEncoder
lbl = LabelEncoder()
for col in data.columns:
data[col] = lbl.fit_tra... | code |
2010993/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.metrics import auc
from sklearn.metrics import auc
lr_auc = auc(fpr, tpr)
print(lr_auc) | code |
333589/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
about_data = pd.read_csv('../input/AboutIsis.csv', encoding='ISO-8859-1')
fanboy_data = pd.read_csv('../input/IsisFanboy.csv', encoding='ISO-8859-1')
about_data.keys() | code |
333589/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
about_data = pd.read_csv('../input/AboutIsis.csv', encoding='ISO-8859-1')
fanboy_data = pd.read_csv('../input/IsisFanboy.csv', encoding='ISO-8859-1')
about_data.keys()
fanboy_space_split = [str(i).split() for i in fanboy_data['tweets']]
fanboy_ha... | code |
333589/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
from matplotlib import *
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
333589/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
about_data = pd.read_csv('../input/AboutIsis.csv', encoding='ISO-8859-1')
fanboy_data = pd.read_csv('../input/IsisFanboy.csv', encoding='ISO-8859-1')
about_data.keys()
print(len(set(fanboy_data['username'])), len(set(about_data['username']))) | code |
333589/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib
import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
about_data = pd.read_csv('../input/AboutIsis.csv', encoding='ISO-8859-1')
fanboy_data = pd.read_csv('../input/IsisFanboy.csv', encoding='ISO-8859-1')
about_data.keys()
fanboy_space_split = [str(i).split()... | code |
333589/cell_12 | [
"text_plain_output_1.png"
] | import networkx as nx
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
about_data = pd.read_csv('../input/AboutIsis.csv', encoding='ISO-8859-1')
fanboy_data = pd.read_csv('../input/IsisFanboy.csv', encoding='ISO-8859-1')
about_data.keys()
fanboy_space_split = [str(i).split() for i in fanboy_da... | code |
327075/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df = pd.read_csv('../input/Indicators.csv')
Indicator_array = df[['IndicatorName', 'IndicatorCode']].drop_duplicates().values
modified_indicators = []
unique_indicator_codes = []
for ele in Indicator_array:
indicator = ele[0]
i... | code |
327075/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df = pd.read_csv('../input/Indicators.csv')
Indicator_array = df[['IndicatorName', 'IndicatorCode']].drop_duplicates().values
modified_indicators = []
unique_indicator_codes = []
for ele in Indicator_array:
indicator = ele[0]
i... | code |
104115416/cell_9 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, BatchNormalization, Dropout
from keras.layers import Dense, Dropout
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
import os
DATA_DIR = '../input/catsvsdogstest/cats-vs-dogs-1000/dogs_cats_sample_1000/dogs_... | code |
104115416/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import os
print(os.listdir('../input/catsvsdogstest')) | code |
104115416/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import os
DATA_DIR = '../input/catsvsdogstest/cats-vs-dogs-1000/dogs_cats_sample_1000/dogs_cats_sample_1000/'
train_dir = os.path.join(DATA_DIR, 'train')
valid_dir = os.path.join(DATA_DIR, 'valid')
test_dir = os.path.join(DATA_DIR, 'test')
train_cats_dir = os.path.join(train_dir, 'cats')
train_dogs_dir = os.path.joi... | code |
104115416/cell_8 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, BatchNormalization, Dropout
from keras.layers import Dense, Dropout
from keras.models import Sequential
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(C... | code |
104115416/cell_10 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, BatchNormalization, Dropout
from keras.layers import Dense, Dropout
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import os
DATA_DIR = '../input/catsvsdogstest/cats-vs-dogs... | code |
104115416/cell_12 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, BatchNormalization, Dropout
from keras.layers import Dense, Dropout
from keras.models import Sequential
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
import os
DATA_DIR = '../input/c... | code |
16144426/cell_13 | [
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Seri... | code |
16144426/cell_9 | [
"text_plain_output_1.png"
] | import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Series(embeddings['modi'][:5])
embedding... | code |
16144426/cell_25 | [
"text_html_output_1.png"
] | from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Seri... | code |
16144426/cell_4 | [
"text_plain_output_1.png"
] | import gensim
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Series(embeddings['modi'][:5]) | code |
16144426/cell_34 | [
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
from nltk.stem import PorterStemmer
from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import accuracy_score
from sklearn.metrics import accuracy_score
import gensim
import nlt... | code |
16144426/cell_23 | [
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Seri... | code |
16144426/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.metrics import accuracy_score
model = RandomForestClassifier(n_estimators=800)
model.fit(xtrain, ytrain)
test_pred = mo... | code |
16144426/cell_6 | [
"text_plain_output_1.png"
] | import gensim
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Series(embeddings['modi'][:5])
url = 'https://bit.ly/... | code |
16144426/cell_29 | [
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2v... | code |
16144426/cell_26 | [
"text_plain_output_1.png"
] | from nltk.stem import PorterStemmer
import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Seri... | code |
16144426/cell_2 | [
"text_html_output_1.png"
] | import gensim
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True) | code |
16144426/cell_11 | [
"text_plain_output_1.png"
] | import gensim
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '../input/embeddings/GoogleNews-vectors-negative300/GoogleNews-vectors-negative300.bin'
embeddings = gensim.models.KeyedVectors.load_word2vec_format(path, binary=True)
pd.Series(embeddings['modi'][:5])
url = 'ht... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.