path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
89142701/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test.isnull().sum() | code |
89142701/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.head() | code |
89142701/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.shape
train.isnull().sum() | code |
89142701/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | 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)
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train... | code |
16154547/cell_13 | [
"text_plain_output_1.png"
] | import ase as ase
import dscribe as ds
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
structure = pd.read_csv('../input/structures.csv')
rcut = 10.0
g2_params = [[1, 2], [0.1, 2], [0.01, 2], [1, 6], [0.1, 6], [0.01, 6]]
g4_params = [[1, 4, 1], [0.1, 4, 1], [0.01, 4, 1], ... | code |
16154547/cell_9 | [
"text_plain_output_1.png"
] | import ase as ase
import dscribe as ds
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
structure = pd.read_csv('../input/structures.csv')
rcut = 10.0
g2_params = [[1, 2], [0.1, 2], [0.01, 2], [1, 6], [0.1, 6], [0.01, 6]]
g4_params = [[1, 4, 1], [0.1, 4, 1], [0.01, 4, 1], ... | code |
16154547/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16154547/cell_18 | [
"text_plain_output_1.png"
] | import ase as ase
import dscribe as ds
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)
structure = pd.read_csv('../input/structures.csv')
rcut = 10.0
g2_params = [[1, 2], [0.1, 2], [0.01, 2], [1, 6], [0.1, 6], [0.01... | code |
16154547/cell_8 | [
"text_plain_output_1.png"
] | import ase as ase
import dscribe as ds
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
structure = pd.read_csv('../input/structures.csv')
rcut = 10.0
g2_params = [[1, 2], [0.1, 2], [0.01, 2], [1, 6], [0.1, 6], [0.01, 6]]
g4_params = [[1, 4, 1], [0.1, 4, 1], [0.01, 4, 1], ... | code |
16154547/cell_22 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import ase as ase
import dscribe as ds
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
structure = pd.read_csv('../input/structures.csv')
rcut = 10.0
g2_params = [[1, 2], [0.1, 2], [0.01, 2], [1, 6], [0.1, 6], [0.01, 6]]
g4_params = [[1, 4, 1], [0.1, 4, 1], [0.01, 4, 1], ... | code |
1009798/cell_4 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from glob import glob
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_1')) + 1:-4] for s in type_1_files])
type_2_files = glob(os.pa... | code |
1009798/cell_2 | [
"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 |
1009798/cell_11 | [
"text_plain_output_1.png"
] | from glob import glob
import cv2
import matplotlib.pylab as plt
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_1')) + 1:-4] for s... | code |
1009798/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from glob import glob
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_1')) + 1:-4] for s in type_1_files])
type_2_files = glob(os.pa... | code |
1009798/cell_10 | [
"text_plain_output_1.png"
] | from glob import glob
import cv2
import matplotlib.pylab as plt
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_1')) + 1:-4] for s... | code |
1009798/cell_5 | [
"image_output_2.png",
"image_output_1.png"
] | from glob import glob
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_1')) + 1:-4] for s in type_1_files])
type_2_files = glob(os.pa... | code |
34144954/cell_9 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from tensorflow import keras
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, Callback, ReduceLROnPlateau
from tensorflow.keras.layers import BatchNormalization,Activation,Dropout,Dense
from ... | code |
34144954/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import cv2
import glob
import numpy as np
import numpy as np # linear algebra
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
submission_sumple = pd.... | code |
34144954/cell_1 | [
"text_plain_output_2.png",
"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 |
34144954/cell_7 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import cv2
import glob
import numpy as np
import numpy as np # linear algebra
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
submission_sumple = pd.... | code |
34144954/cell_14 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from tensorflow import keras
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, Callback, ReduceLROnPlateau
from tensorflow.keras.layers import BatchNormalization,Activation,Dropout,Dense
from ... | code |
34144954/cell_5 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission_sumple = pd.read_csv('/kaggle/input/aiacademydeeplearning/sample_submission.csv')
train = pd.read_csv('/kaggle/input/aiacademydeeplearning/train.csv')
num_cols = ['b... | code |
16133160/cell_6 | [
"image_output_1.png"
] | import os
import os
os.listdir('../input') | code |
16133160/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from html.parser import HTMLParser
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
from wordcloud import WordCloud, STOPWORDS
import collections
import gensim
import nltk
import pandas as pd
import pandas as pd
import re
import scipy.cluster.h... | code |
16133160/cell_8 | [
"text_plain_output_1.png"
] | from html.parser import HTMLParser
import pandas as pd
import pandas as pd
import unicodedata
import unicodedata
from html.parser import HTMLParser
class HTMLStripper(HTMLParser):
def __init__(self):
HTMLParser.__init__(self)
self._lines = []
def error(self, message):
pass
@stati... | code |
16133160/cell_15 | [
"text_html_output_1.png"
] | from html.parser import HTMLParser
import pandas as pd
import pandas as pd
import unicodedata
import unicodedata
from html.parser import HTMLParser
class HTMLStripper(HTMLParser):
def __init__(self):
HTMLParser.__init__(self)
self._lines = []
def error(self, message):
pass
@stati... | code |
16133160/cell_16 | [
"text_plain_output_1.png"
] | len(ist) | code |
16133160/cell_24 | [
"text_plain_output_1.png"
] | code | |
16133160/cell_14 | [
"text_plain_output_1.png"
] | from html.parser import HTMLParser
import pandas as pd
import pandas as pd
import unicodedata
import unicodedata
from html.parser import HTMLParser
class HTMLStripper(HTMLParser):
def __init__(self):
HTMLParser.__init__(self)
self._lines = []
def error(self, message):
pass
@stati... | code |
16133160/cell_22 | [
"text_plain_output_1.png"
] | from html.parser import HTMLParser
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
from wordcloud import WordCloud, STOPWORDS
import collections
import gensim
import nltk
import pandas as pd
import pandas as pd
import re
import unicodedata
im... | code |
16133160/cell_10 | [
"text_plain_output_1.png"
] | data_processor = Preprocess() | code |
16133160/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.cluster import AgglomerativeClustering
cluster = AgglomerativeClustering(n_clusters=6, affinity='euclidean', linkage='ward') | code |
16133160/cell_5 | [
"text_plain_output_1.png"
] | !pip install paramiko | code |
34127100/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
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
train_df = train_data.drop('Cabin', axis=True)
categorical_col = train_df.sele... | code |
34127100/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
train_df = train_data.drop('Cabin', axis=T... | code |
34127100/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.info() | code |
34127100/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
train_df = train_data.drop('Cabin', axis=True)
categorical_col = train_df.sele... | code |
34127100/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
print('X_train', len(X_train))
print('X_test', len(X_test))
print('y_train', len(y_train))
print('y_test', len(y_test))
print('te... | code |
34127100/cell_39 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
classifier = LogisticRegression()
classifier.fit(X_train, y_train)
lr_score = classifier.score(X_test, y_test)
predictions = classifier.predict(X_test)
from sklearn.met... | code |
34127100/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
train_df = train_data.drop('Cabin', axis=True)
categorical_col = train_df.sele... | code |
34127100/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
train_df = train_data.drop('Cabin', axis=True)
categorical_col = train_df.sele... | code |
34127100/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 |
34127100/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
sns.heatmap(train_data.isnull()) | code |
34127100/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
train_df = train_data.drop('Cabin', axis=True)
categorical_col = train_df.sele... | code |
34127100/cell_28 | [
"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
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('five... | code |
34127100/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum() | code |
34127100/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
train_df = train_data.drop('Cabin', axis=True)
categorical_col = train_df.sele... | code |
34127100/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.head(3) | code |
34127100/cell_35 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(X_train, y_train) | code |
34127100/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
train_df = train_data.drop('Cabin', axis=True)
categorical_col = train_df.sele... | code |
34127100/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
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
train_df = train_data.drop('Cabin', axis=True)
categorical_col = train_df.sele... | code |
34127100/cell_27 | [
"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
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use('five... | code |
34127100/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
classifier = LogisticRegression()
classifier.fit(X_train, y_train)
lr_score = classifier.score(X_test, y_test)
predictions = classifier.predict(X_test)
from sklearn.metrics import classification_report
print(classi... | code |
34127100/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.isnull().sum()
train_df = train_data.drop('Cabin', axis=True)
categorical_col = train_df.sele... | code |
34127100/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(X_train, y_train)
lr_score = classifier.score(X_test, y_test)
print(lr_score) | code |
49120184/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.isnull().sum()
test.isnull().sum()
train.replace({'Sex': {'male': 0, 'female': 1}}, inplace=True)
test.replace({'Sex': {'male': 0, ... | code |
49120184/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import GradientBoostingClassifier
clf = GradientBoostingClassifier()
clf.fit(X_train, y_train)
print('学習スコア', clf.score(X_train, y_train))
print('テストスコア', clf.score(X_val, y_val)) | code |
49120184/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.isnull().sum() | code |
49120184/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.isnull().sum()
test.isnull().sum()
train.replace({'Sex': {'male': 0, 'femal... | code |
49120184/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import log_loss
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNei... | code |
49120184/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.isnull().sum()
train.head() | code |
49120184/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import log_loss
from sklearn.metrics import roc_auc_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
import lightgbm as lgb
import sklearn
f... | code |
49120184/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import sklearn
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
print('train score:', model.score(X_train, y_train))
print('test score:', model.score(X_val, y_val)) | code |
49120184/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.isnull().sum()
test.isnull().sum()
train.replace({'Sex': {'male': 0, 'female': 1}}, inplace=True)
test.replace({'Sex': {'male': 0, ... | code |
49120184/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 |
49120184/cell_32 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from xgboost import XGBClassifier
from xgboost import XGBClassifier
xgb = XGBClassifier(objective='binary:logistic')
xgb.fit(X_train, y_train)
pred = xgb.predict(X_val)
from imblearn.over_sampling import SMOTE
method = SMOTE()
X_resampled, y_resampled = method.fit_sample(X_tr... | code |
49120184/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
import sklearn
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
from sklearn.tree import DecisionTreeCla... | code |
49120184/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.isnull().sum()
test.isnull().sum()
train.replace({'Sex': {'male': 0, 'female': 1}}, inplace=True)
test.replace({'Sex': {'male': 0, ... | code |
49120184/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.isnull().sum()
test.isnull().sum()
train.replace({'Sex': {'male': 0, 'female': 1}}, inplace=True)
test.replace({'Sex': {'male': 0, ... | code |
49120184/cell_31 | [
"text_plain_output_1.png"
] | from xgboost import XGBClassifier
from xgboost import XGBClassifier
xgb = XGBClassifier(objective='binary:logistic')
xgb.fit(X_train, y_train)
pred = xgb.predict(X_val)
print(xgb.score(X_train, y_train))
print(xgb.score(X_val, y_val)) | code |
49120184/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.isnull().sum()
test.isnull().sum()
train.replace({'Sex': {'male': 0, 'female': 1}}, inplace=True)
test.replace({'Sex': {'male': 0, ... | code |
49120184/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.isnull().sum()
test.isnull().sum()
train.replace({'Sex': {'male': 0, 'female': 1}}, inplace=True)
... | code |
49120184/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
import sklearn
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(criterion='en... | code |
49120184/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test.isnull().sum() | code |
73061601/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0, 11)
x = 0.85 ** t
plt.figure(figsize=(12, 12))
plt.subplot(2, 2, 1)
plt.title('Analog Signal', fontsize=20)
plt.plot(t, x, linewidth=3, label='x(t) = (0.85)^t')
plt.xlabel('t', fontsize=15)
plt.ylabel('amplitude', fontsize=15)
plt.legend()
plt.subpl... | code |
128024272/cell_12 | [
"text_plain_output_1.png"
] | import tensorflow as tf
import tensorflow as tf
def yolov1(input_shape, num_classes):
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv2D(64, (7, 7), strides=(2, 2), padding='same', input_shape=input_shape))
model.add(tf.keras.layers.LeakyReLU(alpha=0.1))
model.add(tf.keras.layers.Ma... | code |
1005801/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import tensorflow as tf
import random
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1005801/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
df = pd.read_csv('../input/train.csv')
t = pd.DataFrame({'Validation': list(map(lambda x: random.random() < 0.3, range(891)))})
C = pd.concat([df, t], axis=1)
features = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare']
y_train = df... | code |
88098005/cell_4 | [
"text_html_output_1.png"
] | ! pip install -q git+https://github.com/tensorflow/docs | code |
88098005/cell_33 | [
"text_plain_output_1.png"
] | from IPython.display import HTML, display
import cv2
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
KEYPOINT_EDGE_INDS_TO_COLOR = {(0, 1): 'm', (0, 2): 'c', (1, 3): 'm', (2, 4): 'c', (0, 5): 'm', (0, 6): 'c', (5, 7): 'm', (7, 9): 'm', (6, 8): 'c', (8, 10): 'c', (5, 6): 'y', (5, 11): 'm', (... | code |
88098005/cell_28 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
def draw_keypoints(frame, keypoints, threshold=0.11):
width, height, _ = frame.shape
shaped = np.squeeze(np.multiply(keypoints, [width, height, 1]))
for kp in shaped:
ky, kx, kp_conf = kp
if kp_conf > threshold:
cv2.circle(frame, (int(kx), int(ky))... | code |
88098005/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | ! wget -O ngannou.gif https://raw.githubusercontent.com/Justsecret123/Human-pose-estimation/main/Test%20gifs/Ngannou_takedown.gif | code |
88098005/cell_12 | [
"text_plain_output_1.png"
] | import tensorflow_hub as hub
model = hub.load('https://tfhub.dev/google/movenet/multipose/lightning/1')
movenet = model.signatures['serving_default'] | code |
88098005/cell_36 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from IPython.display import HTML, display
from tensorflow_docs.vis import embed
import cv2
import imageio
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
KEYPOINT_EDGE_INDS_TO_COLOR = {(0, 1): 'm', (0, 2): 'c', (1, 3): 'm', (2, 4): 'c', (0, 5): 'm', (0, 6): 'c', (5, 7): 'm', (7, 9): 'm', ... | code |
16116561/cell_13 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import precision_score,recall_score,f1_score,roc_auc_score,roc_curve
from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.preprocessing import StandardScaler
import matplotlib... | code |
16116561/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
file = pd.read_csv('../input/pulsar_stars.csv')
y = file.target_class
X = file[file.columns[:8]]
X.shape
pd.value_counts(y).plot.bar()
plt.title('Data on star detection')
plt.xlabel('Class')
plt.ylabel('Frequency')
y.value_counts() | code |
16116561/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
file = pd.read_csv('../input/pulsar_stars.csv')
y = file.target_class
X = file[file.columns[:8]]
X.shape
y.value_counts()
scal... | code |
16116561/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
file = pd.read_csv('../input/pulsa... | code |
16116561/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import sklearn.metrics as metrics
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import cross_val_score
from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, roc_curve
from sklearn.preprocessing import StandardScaler
from ... | code |
16116561/cell_8 | [
"image_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
file = pd.read_csv('../input/pulsar_stars.csv')
y = file.target_class
X = file[file.co... | code |
16116561/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
file = pd.read_csv('../input/pulsar_stars.csv')
y = file.target_class
X = file[file.columns[:8]]
X.shape | code |
16116561/cell_10 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
file = pd.read_csv('../input/pulsa... | code |
16116561/cell_12 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import precision_score,recall_score,f1_score,roc_auc_score,roc_curve
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.p... | code |
17123947/cell_4 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
my_spark = SparkSession.builder.getOrCreate()
print(my_spark) | code |
17123947/cell_23 | [
"text_plain_output_1.png"
] | from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer, OneHotEncoder, VectorAssembler
from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
my_spark = SparkSession.builder.getOrCreate()
file_path = '../input/flights.csv'
flights = my_spark.read.csv(file_path, header=True... | code |
17123947/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pyspark.ml.classification import LogisticRegression
import numpy as np
import numpy as np # linear algebra
import pyspark.ml.evaluation as evals
import pyspark.ml.tuning as tune
from pyspark.ml.classification import LogisticRegression
lr = LogisticRegression()
import pyspark.ml.evaluation as evals
evaluator ... | code |
17123947/cell_6 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
my_spark = SparkSession.builder.getOrCreate()
file_path = '../input/flights.csv'
flights = my_spark.read.csv(file_path, header=True)
flights.show()
print(my_spark.catalog.listTables())
flights.createOrReplaceTempView('flights')
print(my_spark.c... | code |
17123947/cell_29 | [
"text_plain_output_1.png"
] | from pyspark.ml.classification import LogisticRegression
import numpy as np
import numpy as np # linear algebra
import pyspark.ml.tuning as tune
from pyspark.ml.classification import LogisticRegression
lr = LogisticRegression()
import pyspark.ml.tuning as tune
grid = tune.ParamGridBuilder()
grid = grid.addGrid(lr.... | code |
17123947/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17123947/cell_8 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql import SparkSession
my_spark = SparkSession.builder.getOrCreate()
file_path = '../input/flights.csv'
flights = my_spark.read.csv(file_path, header=True)
flights.createOrReplaceTempView('flights')
flights = flights.withColumn('duration_hrs', flights.air_time / 60)... | code |
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