path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
16148029/cell_22 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import warnings
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
import pandas as pd
import tensorf... | code |
16148029/cell_10 | [
"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)
train = pd.read_csv('../input/fashion-mnist_train.csv')
train.shape
test = pd.read_csv('../input/fashion-mnist_test.csv')
test.shape
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal'... | code |
16148029/cell_12 | [
"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('../input/fashion-mnist_train.csv')
train.shape
test = pd.read_csv('../input/fashion-mnist_test.csv')
test.shape
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal'... | code |
16148029/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/fashion-mnist_train.csv')
test = pd.read_csv('../input/fashion-mnist_test.csv')
test.head() | code |
72077843/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic... | code |
72077843/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv')
df
df.shape
df.gro... | code |
72077843/cell_4 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv')
df
df.shape
df.des... | code |
72077843/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv... | code |
72077843/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv')
df | code |
72077843/cell_11 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv... | code |
72077843/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 |
72077843/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
cols = ['Sex', 'Pclass', 'Age', 'SibSp']
n_rows = 2
n_cols = 2
fig, axs = plt.subplots(n_rows, n_cols, figsize=(n_cols * 8, n_rows * 8))
for r in range(0, n_rows):
for c in range(0, n_cols):
i = r * n_cols + c
ax = axs[r][c]
sns.countplot(df[cols[i]], hue=df['... | code |
72077843/cell_8 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv')
df
df.shape
df.gro... | code |
72077843/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv')
df
df.shape | code |
72077843/cell_10 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv')
df
df.shape
df.gro... | code |
72077843/cell_12 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv')
df
df.shape
df.gro... | code |
72077843/cell_5 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv')
df
df.shape
df['Su... | code |
122251959/cell_21 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a)
b = 5.3145
math.trunc(b)
x = 3
math.exp(x)
math.log(1000)
math.log(1000, 10)
math.sin(math.pi / 2)
degree = 90
math.sin(math.radians(degree))
math.sqrt(4)
math.factorial(5) | code |
122251959/cell_13 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a)
b = 5.3145
math.trunc(b)
x = 3
math.exp(x) | code |
122251959/cell_9 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a) | code |
122251959/cell_4 | [
"text_plain_output_1.png"
] | import math
math.e | code |
122251959/cell_23 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a)
b = 5.3145
math.trunc(b)
x = 3
math.exp(x)
math.log(1000)
math.log(1000, 10)
math.sin(math.pi / 2)
degree = 90
math.sin(math.radians(degree))
math.sqrt(4)
math.factorial(5)
l = [1.2, 2.3, 3.4, 4.5]
sum(l)
math.fsum(l) | code |
122251959/cell_20 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a)
b = 5.3145
math.trunc(b)
x = 3
math.exp(x)
math.log(1000)
math.log(1000, 10)
math.sin(math.pi / 2)
degree = 90
math.sin(math.radians(degree))
math.sqrt(4) | code |
122251959/cell_18 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a)
b = 5.3145
math.trunc(b)
x = 3
math.exp(x)
math.log(1000)
math.log(1000, 10)
math.sin(math.pi / 2)
degree = 90
math.sin(math.radians(degree)) | code |
122251959/cell_8 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a) | code |
122251959/cell_15 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a)
b = 5.3145
math.trunc(b)
x = 3
math.exp(x)
math.log(1000)
math.log(1000, 10) | code |
122251959/cell_17 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a)
b = 5.3145
math.trunc(b)
x = 3
math.exp(x)
math.log(1000)
math.log(1000, 10)
math.sin(math.pi / 2) | code |
122251959/cell_14 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a)
b = 5.3145
math.trunc(b)
x = 3
math.exp(x)
math.log(1000) | code |
122251959/cell_22 | [
"text_plain_output_1.png"
] | l = [1.2, 2.3, 3.4, 4.5]
sum(l) | code |
122251959/cell_10 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a)
b = 5.3145
math.trunc(b) | code |
122251959/cell_5 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi | code |
33100906/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns
train.head() | code |
33100906/cell_2 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/... | code |
33100906/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns
train.describe() | code |
33100906/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns | code |
49119627/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression(C=10)
logreg.fit(X_train, Y_train)
Y_predict1 = logreg.predict(X_test)
score_logreg = logreg.score(X_test, Y_test)
print(score_logreg) | code |
49119627/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/breast-cancer-prediction-dataset/Breast_cancer_data.csv')
print('Dataset :', data.shape)
x = data.iloc[:, [0, 1, 2, 3]].values
data.info()
data[0:10] | code |
49119627/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression(C=10)
logreg.fit(X_train, Y_train)
Y_predict1 = logreg.predict(X_test)
score_logreg = logreg.score(X_test, Y_test)
from sklearn.feature_select... | code |
49119627/cell_5 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns
import seaborn as sns
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression(C=10)
logreg.fit(X_train, Y_train)
Y_predict1 = logreg.predict... | code |
105199751/cell_21 | [
"text_plain_output_1.png"
] | from datetime import datetime
import json
import os
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
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DI... | code |
105199751/cell_13 | [
"text_plain_output_1.png"
] | import json
import os
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
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json... | code |
105199751/cell_4 | [
"text_plain_output_1.png"
] | import json
import os
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
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json... | code |
105199751/cell_6 | [
"text_plain_output_1.png"
] | import json
import os
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
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json... | code |
105199751/cell_2 | [
"text_html_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json'):
LIST_JSON.append(file)
else:
LIST_CSV.append(file)
print(LIST... | code |
105199751/cell_19 | [
"text_plain_output_1.png"
] | from datetime import datetime
import json
import os
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
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DI... | code |
105199751/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 |
105199751/cell_7 | [
"text_plain_output_1.png"
] | import json
import os
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
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json... | code |
105199751/cell_8 | [
"text_html_output_1.png"
] | import json
import os
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
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json... | code |
105199751/cell_3 | [
"text_plain_output_1.png"
] | import json
import os
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
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json... | code |
105199751/cell_14 | [
"text_html_output_1.png"
] | import json
import os
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
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json... | code |
105199751/cell_10 | [
"text_plain_output_1.png"
] | import json
import os
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
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json... | code |
105199751/cell_12 | [
"text_html_output_1.png"
] | import json
import os
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
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json... | code |
129022538/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Parkinson_disease.csv')
df.info() | code |
129022538/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import random
import warnings
warnings.filterwarnings('ignore')
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LinearRegres... | code |
90111237/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pathlib import Path
from pathlib import Path
from pytorch_lightning import LightningModule
from sklearn.manifold import TSNE
from tokenizers import Tokenizer
from torchvision import models
from tqdm import tqdm
import albumentations as A
import cv2
import math
import matplotlib.pyplot as plt
import nump... | code |
90111237/cell_2 | [
"image_output_1.png"
] | from pathlib import Path
import os
import numpy as np
import pandas as pd
from pathlib import Path
import os
for dirname, _, filenames in os.walk('../input/image-text-embeddings'):
for filename in filenames:
print(os.path.join(dirname, filename))
BASE_PATH = Path('/kaggle/input/h-and-m-personalized-fashio... | code |
90111237/cell_11 | [
"text_plain_output_1.png"
] | from pathlib import Path
from pathlib import Path
from pytorch_lightning import LightningModule
from tokenizers import Tokenizer
from torchvision import models
from tqdm import tqdm
import albumentations as A
import cv2
import math
import numpy as np
import numpy as np # linear algebra
import os
import pand... | code |
90111237/cell_16 | [
"image_output_1.png"
] | from pathlib import Path
from pathlib import Path
from pytorch_lightning import LightningModule
from sklearn.manifold import TSNE
from tokenizers import Tokenizer
from torchvision import models
from tqdm import tqdm
import albumentations as A
import cv2
import math
import matplotlib.pyplot as plt
import nump... | code |
90111237/cell_14 | [
"image_output_1.png"
] | from pathlib import Path
from pathlib import Path
from pytorch_lightning import LightningModule
from sklearn.manifold import TSNE
from tokenizers import Tokenizer
from torchvision import models
from tqdm import tqdm
import albumentations as A
import cv2
import math
import matplotlib.pyplot as plt
import nump... | code |
88083134/cell_4 | [
"image_output_1.png"
] | import pandas as pd
drug = pd.read_csv('../input/drug-classification/drug200.csv')
drug.columns | code |
88083134/cell_6 | [
"image_output_1.png"
] | import pandas as pd
drug = pd.read_csv('../input/drug-classification/drug200.csv')
drug.columns
(drug.size, drug.shape)
drug.info() | code |
88083134/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
drug = pd.read_csv('../input/drug-classification/drug200.csv')
drug.columns
(drug.size, drug.shape)
drug.isnull().sum().sum() | code |
88083134/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import OneHotEncoder, LabelBinarizer, StandardScaler
from sklearn.model_selection import train_test_split
... | code |
88083134/cell_8 | [
"image_output_1.png"
] | import pandas as pd
drug = pd.read_csv('../input/drug-classification/drug200.csv')
drug.columns
(drug.size, drug.shape)
drug.isnull().sum().sum()
drug.describe() | code |
88083134/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import OneHotEncoder, LabelBinarizer, StandardScaler
from sklearn.model_selection import train_test_split
... | code |
88083134/cell_3 | [
"image_output_1.png"
] | import pandas as pd
drug = pd.read_csv('../input/drug-classification/drug200.csv')
drug.head() | code |
88083134/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import OneHotEncoder, LabelBinarizer, StandardScaler
from sklearn.model_selection import train_test_split
... | code |
88083134/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import OneHotEncoder, LabelBinarizer, StandardScaler
from sklearn.model_selection import train_test_split
... | code |
88083134/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import OneHotEncoder, LabelBinarizer, StandardScaler
from sklearn.model_selection import train_test_split
... | code |
88083134/cell_5 | [
"image_output_1.png"
] | import pandas as pd
drug = pd.read_csv('../input/drug-classification/drug200.csv')
drug.columns
(drug.size, drug.shape) | code |
122246772/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_test], axis=0)
df.isnull().sum()
df.drop(['Name... | code |
122246772/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_test], axis=0)
df.isnull().sum()
df.drop(['Name... | code |
122246772/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_test.head() | code |
122246772/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score ,accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
fro... | code |
122246772/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import Normalizer
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_tes... | code |
122246772/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score ,accuracy_score
from xgboost import XGBClassifier
xgb = XGBClassifier()
model1 = xgb.fit(X_train, y_train)
y_hat = model1.predict(X_test)
f1 = f1_score(y_test, y_hat)
acc = accuracy_score(y_test, y_hat)
(f1, acc)
from sklearn.e... | code |
122246772/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score ,accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
fro... | code |
122246772/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_test], axis=0)
df.isnull().sum()
df.drop(['Name... | code |
122246772/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)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_test], axis=0)
df.tail() | code |
122246772/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.metrics import f1_score ,accuracy_score
from sklearn.model_selection import GridSearchCV
from xgboost import XGBClassifier
xgb = XGBClassifier()
model1 = xgb.fit(X_train, y_train)
y_hat = model1.predict(X_test)
f1 = f1_score(y_test, y_hat)
acc = accuracy_score(y_test, y_hat)
(f1, acc)
parameters = {'bo... | code |
122246772/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.metrics import f1_score ,accuracy_score
from xgboost import XGBClassifier
xgb = XGBClassifier()
model1 = xgb.fit(X_train, y_train)
y_hat = model1.predict(X_test)
f1 = f1_score(y_test, y_hat)
acc = accuracy_score(y_test, y_hat)
(f1, acc) | code |
122246772/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 |
122246772/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_test], axis=0)
df.isnull().sum() | code |
122246772/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score ,accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
from xgboost import XGBClassifier
xgb = XGBClassifier()
mod... | code |
122246772/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_test], axis=0)
df.isnull().sum()
df.info() | code |
122246772/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_train.head() | code |
122246772/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_test], axis=0)
df.isnull().sum()
df.drop(['Name... | code |
122246772/cell_31 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score ,accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
from xgboost import XGBClassifier
xgb = XGBClassifier()
model1 = xgb.fit(X_train, y_train)
y_hat = model1.predic... | code |
122246772/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_test], axis=0)
df.isnull().sum()
df.drop(['Name... | code |
122246772/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_test], axis=0)
df.isnull().sum()
df.drop(['Name... | code |
122246772/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score ,accuracy_score
from xgboost import XGBClassifier
xgb = XGBClassifier()
model1 = xgb.fit(X_train, y_train)
y_hat = model1.predict(X_test)
f1 = f1_score(y_test, y_hat)
acc = accuracy_score(y_test, y_hat)
(f1, acc)
from sklearn.e... | code |
122246772/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape) | code |
128030195/cell_13 | [
"text_plain_output_1.png"
] | from keras.models import Model, load_model
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D, Dropout
from tensorflow.keras.layers import Dense, Input, Conv2D, MaxPool2D, Flatten ,... | code |
128030195/cell_4 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_8.png",
"text_plain_output_2.pn... | import tensorflow as tf
data_dir = '/kaggle/input/rgb-arabic-alphabets-sign-language-dataset-jpg/images_after'
images = tf.keras.utils.image_dataset_from_directory(data_dir, labels='inferred', label_mode='int', class_names=None, color_mode='rgb', batch_size=32, image_size=(256, 256), shuffle=True, seed=123, validation... | code |
128030195/cell_2 | [
"text_html_output_2.png"
] | import matplotlib.pyplot as plt
import os
import cv2
import pickle
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tqdm import tqdm
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import confusion_matrix
from keras.models import Model, load_mod... | code |
128030195/cell_1 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_8.png",
"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 |
128030195/cell_7 | [
"image_output_1.png"
] | import plotly.express as px
import tensorflow as tf
data_dir = '/kaggle/input/rgb-arabic-alphabets-sign-language-dataset-jpg/images_after'
images = tf.keras.utils.image_dataset_from_directory(data_dir, labels='inferred', label_mode='int', class_names=None, color_mode='rgb', batch_size=32, image_size=(256, 256), shuff... | code |
128030195/cell_8 | [
"text_plain_output_100.png",
"text_plain_output_334.png",
"text_plain_output_445.png",
"text_plain_output_201.png",
"text_plain_output_261.png",
"text_plain_output_565.png",
"text_plain_output_522.png",
"text_plain_output_84.png",
"text_plain_output_521.png",
"text_plain_output_322.png",
"text_p... | import matplotlib.pyplot as plt
import tensorflow as tf
import os
import cv2
import pickle
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tqdm import tqdm
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import confusion_matrix
from keras.mode... | code |
128030195/cell_16 | [
"text_plain_output_1.png"
] | from keras.models import Model, load_model
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D, Dropout
from tensorflow.keras.layers import Dense, Input, Conv2D, MaxPool2D, Flatten ,... | code |
128030195/cell_3 | [
"image_output_1.png"
] | import tensorflow as tf
data_dir = '/kaggle/input/rgb-arabic-alphabets-sign-language-dataset-jpg/images_after'
images = tf.keras.utils.image_dataset_from_directory(data_dir, labels='inferred', label_mode='int', class_names=None, color_mode='rgb', batch_size=32, image_size=(256, 256), shuffle=True, seed=123, validation... | code |
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