path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
16124829/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns)
a = list(base.columns)
sns.barplot(y=base['radius_mean'], x=base['diagno... | code |
16124829/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns)
a = list(base.columns)
base['radius_mean'].mean() | code |
16124829/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns) | code |
16124829/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns)
a = list(base.columns)
cor_base = base[['diagnosis', 'radius_mean', 'tex... | code |
16124829/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns)
a = list(base.columns)
cor_base = base[['diagnosis', 'radius_mean', 'tex... | code |
16124829/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns)
a = list(base.columns)
b... | code |
16124829/cell_33 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns)
a = list(base.columns)
base.isnull().sum()
base.diagnosis.std()
base.d... | code |
16124829/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns)
a = list(base.columns)
for i in list(base.columns):
if i != 'diagnos... | code |
16124829/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base.head() | code |
16124829/cell_29 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns)
a = list(base.columns)
base.isnull().sum()
base['diagnosis'].value_coun... | code |
16124829/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns)
a = list(base.columns)
for i in a:
print('-', i) | code |
16124829/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import seaborn as sns
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns)
a = list(base.columns)
sns.boxplot(x='radius_mean', y='diagnosis', data=base) | code |
16124829/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import seaborn as sns
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns)
a = list(base.columns)
sns.distplot(base['texture_mean']) | code |
16124829/cell_32 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns)
a = list(base.columns)
base.isnull().sum()
base.diagnosis.std() | code |
16124829/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns | code |
16124829/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns)
a = list(base.columns)
base.describe() | code |
16124829/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns)
a = list(base.columns)
base.describe(include=['O']) | code |
16124829/cell_3 | [
"text_html_output_1.png"
] | import os
import os
print(os.listdir('../input')) | code |
16124829/cell_31 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns)
a = list(base.columns)
base.isnull().sum()
base['diagnosis'].value_coun... | code |
16124829/cell_24 | [
"image_output_11.png",
"image_output_24.png",
"image_output_25.png",
"image_output_17.png",
"image_output_30.png",
"image_output_14.png",
"image_output_28.png",
"image_output_23.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_21.png",
"image_output_7.p... | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns)
a = list(base.columns)
cor_base = base[['diagnosis', 'radius_mean', 'tex... | code |
16124829/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns)
a = list(base.columns)
sns.scatterplot(x=base['area_mean'], y=base['peri... | code |
16124829/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import seaborn as sns
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns)
a = list(base.columns)
base.isnull().sum() | code |
16124829/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd
base = pd.read_csv('../input/breast-cancer-wisconsin-data/data.csv')
base = base.iloc[:, :32]
base.columns
len(base.columns)
a = list(base.columns)
base['radius_mean'] | code |
88100273/cell_2 | [
"text_plain_output_1.png"
] | !pip install scanpy | code |
88100273/cell_5 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import numpy as np
import scanpy as sc
import scipy
import seaborn as sns
import time
import time
dict_datasets_info = {'krumsiek11': 'Simulated myeloid progenitors [Krumsiek11].', 'moignard15': 'Hematopoiesis in early mouse embryos [Moignard... | code |
32069383/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly_express as px
df = pd.read_csv('../input/2019-world-happiness-report-csv-file/2019.csv')
df.shape
top_10 = df.iloc[0:10, 0:3]
top_10
fig = px.pie(top_10, values='Score', names='Country or region', color_discrete_sequence=px.colors... | code |
1007542/cell_6 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fivethirtyeight_ncaa_forecasts (2).csv')
matchups = [[str(x + 1), str(16 - x)] for x in range(8)]
df = df[df.gender == 'mens']
pre = df[df.playin_flag == 1]
data = []
for region in pre.team_region.unique():
for seed in range(2, 17):
res... | code |
1007542/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fivethirtyeight_ncaa_forecasts (2).csv')
matchups = [[str(x + 1), str(16 - x)] for x in range(8)]
df = df[df.gender == 'mens']
pre = df[df.playin_flag == 1]
data = []
for region in pre.team_region.unique():
for seed in range... | code |
1007542/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fivethirtyeight_ncaa_forecasts (2).csv')
df.head() | code |
88101963/cell_23 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
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('../input/scrabble-point-value/turns_train.csv')
tests = pd.read_csv('../input/scrabble-point-value/turns_test.csv')
games =... | code |
88101963/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/scrabble-point-value/turns_train.csv')
tests = pd.read_csv('../input/scrabble-point-value/turns_test.csv')
games = pd.read_csv('../input/scrabble-point-value/games.csv')
sample_submission = pd.read_csv('../input/scrabb... | code |
88101963/cell_19 | [
"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/scrabble-point-value/turns_train.csv')
tests = pd.read_csv('../input/scrabble-point-value/turns_test.csv')
games = pd.read_csv('../input/scrabble-point-value/games.csv')
sample_submission = pd.read_csv('../input/scrabb... | code |
88101963/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from keras.models import Sequential
from keras.layers import Dense, Dropout, BatchNormalization
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.metrics import mean_s... | code |
88101963/cell_7 | [
"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('../input/scrabble-point-value/turns_train.csv')
tests = pd.read_csv('../input/scrabble-point-value/turns_test.csv')
games = pd.read_csv('../input/scrabble-point-value/games.csv')
sample_submission = pd.read_csv('../input/scrabb... | code |
88101963/cell_18 | [
"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/scrabble-point-value/turns_train.csv')
tests = pd.read_csv('../input/scrabble-point-value/turns_test.csv')
games = pd.read_csv('../input/scrabble-point-value/games.csv')
sample_submission = pd.read_csv('../input/scrabb... | code |
88101963/cell_8 | [
"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('../input/scrabble-point-value/turns_train.csv')
tests = pd.read_csv('../input/scrabble-point-value/turns_test.csv')
games = pd.read_csv('../input/scrabble-point-value/games.csv')
sample_submission = pd.read_csv('../input/scrabb... | code |
88101963/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
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('../input/scrabble-point-value/turns_train.csv')
tests = pd.read_csv('../input/scrabble-point-value/turns_test.csv')
games =... | code |
88101963/cell_10 | [
"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/scrabble-point-value/turns_train.csv')
tests = pd.read_csv('../input/scrabble-point-value/turns_test.csv')
games = pd.read_csv('../input/scrabble-point-value/games.csv')
sample_submission = pd.read_csv('../input/scrabb... | code |
88101963/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('../input/scrabble-point-value/turns_train.csv')
tests = pd.read_csv('../input/scrabble-point-value/turns_test.csv')
games = pd.read_csv('../input/scrabble-point-value/games.csv')
sample_submission = pd.read_csv('../input/scrabb... | code |
330925/cell_13 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/NationalNames.csv')
df['Decade'] = df['Year'].apply(lambda x: x - x % 10)
df_pivot = df.pivot_table(values='Count', index=['Decade', 'Name', 'Gender'], aggfunc='sum')
new_df = pd.DataFrame(... | code |
330925/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/NationalNames.csv')
df['Decade'] = df['Year'].apply(lambda x: x - x % 10)
df_pivot = df.pivot_table(values='Count', index=['Decade', 'Name', 'Gender'], aggfunc='sum')
new_df = pd.DataFrame()
new_df['Decade'] = df_pivot.index.get_level_values('Decade')
new_df['Name'] = d... | code |
330925/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/NationalNames.csv')
df.head() | code |
330925/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
df = pd.read_csv('../input/NationalNames.csv')
df['Decade'] = df['Year'].apply(lambda x: x - x % 10)
df_pivot = df.pivot_table(values='Count', index=['Decade', 'Name', 'Gender'], aggfunc='sum')
new_df = pd.DataFrame()
new_df['Decade'] = df_pivot.ind... | code |
330925/cell_19 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/NationalNames.csv')
df['Decade'] = df['Year'].apply(lambda x: x - x % 10)
df_pivot = df.pivot_table(values='Count', index=['Decade', 'Name', 'Gender'], aggfunc='sum')... | code |
330925/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/NationalNames.csv')
df['Decade'] = df['Year'].apply(lambda x: x - x % 10)
df.tail() | code |
330925/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
df = pd.read_csv('../input/NationalNames.csv')
df['Decade'] = df['Year'].apply(lambda x: x - x % 10)
df_pivot = df.pivot_table(values='Count', index=['Decade', 'Name', 'Gender'], aggfunc='sum')
new_df = pd.DataFrame()
new_df['Decade'] = df_pivot.ind... | code |
330925/cell_17 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
df = pd.read_csv('../input/NationalNames.csv')
df['Decade'] = df['Year'].apply(lambda x: x - x % 10)
df_pivot = df.pivot_table(values='Count', index=['Decade', 'Name', 'Gender'], aggfunc='sum')
new_df = pd.DataFrame()
new_df['Decade'] = df_pivot.ind... | code |
330925/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/NationalNames.csv')
print('Data year ranges from {} to {}'.format(min(df['Year']), max(df['Year']))) | code |
130004949/cell_13 | [
"text_plain_output_1.png"
] | from collections import ChainMap
from esm.model.esm2 import ESM2
from multiprocess import Pool
from tqdm import tqdm
from tqdm import tqdm
from tqdm import tqdm
import esm
import gc
import gc
import numpy as np
import numpy as np
import os
import os
import os
import pandas as pd
import pandas as pd
impo... | code |
130004949/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from multiprocess import Pool
import numpy as np
import numpy as np
import os
import os
import pandas as pd
import pandas as pd
import warnings
import pandas as pd
import numpy as np
import plotly.express as px
from plotly.subplots import make_subplots
import gc
from tqdm import tqdm
import pickle
import os
imp... | code |
130004949/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import numpy as np
import plotly.express as px
from plotly.subplots import make_subplots
import gc
from tqdm import tqdm
import pickle
import os
import warnings
warnings.filterwarnings('ignore')
train_clinical_data = pd.read_csv('../input/amp-parkinsons-disease-... | code |
130004949/cell_11 | [
"text_html_output_1.png"
] | from multiprocess import Pool
import numpy as np
import numpy as np
import os
import os
import pandas as pd
import pandas as pd
import warnings
import pandas as pd
import numpy as np
import plotly.express as px
from plotly.subplots import make_subplots
import gc
from tqdm import tqdm
import pickle
import os
imp... | code |
130004949/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import numpy as np
import plotly.express as px
from plotly.subplots import make_subplots
import gc
from tqdm import tqdm
import pickle
import os
import warnings
warnings.filterwarnings('ignore')
train_clinical_data = pd.read_csv('../input/amp-parkinsons-disease-... | code |
130004949/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import warnings
import pandas as pd
import numpy as np
import plotly.express as px
from plotly.subplots import make_subplots
import gc
from tqdm import tqdm
import pickle
import os
import warnings
warnings.filterwarnings('ignore')
train_clinical_data = pd.read_csv('../input/amp-parkinsons-disease-... | code |
130004949/cell_16 | [
"text_plain_output_1.png"
] | from esm.model.esm2 import ESM2
from multiprocess import Pool
from tqdm import tqdm
from tqdm import tqdm
from tqdm import tqdm
import esm
import gc
import gc
import numpy as np
import numpy as np
import os
import os
import os
import pandas as pd
import pandas as pd
import re
import tensorflow as tf
im... | code |
130004949/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import missingno as msno
import pandas as pd
import warnings
import pandas as pd
import numpy as np
import plotly.express as px
from plotly.subplots import make_subplots
import gc
from tqdm import tqdm
import pickle
import os
import warnings
warnings.filterwarnings('ignore')
train_clinical_data = pd.read_csv('../inp... | code |
130004949/cell_17 | [
"text_plain_output_1.png"
] | from collections import ChainMap
from esm.model.esm2 import ESM2
from multiprocess import Pool
from sklearn.preprocessing import MinMaxScaler
from tqdm import tqdm
from tqdm import tqdm
from tqdm import tqdm
import esm
import gc
import gc
import networkx as nx
import numpy as np
import numpy as np
import o... | code |
130004949/cell_14 | [
"text_plain_output_1.png"
] | from esm.model.esm2 import ESM2
from multiprocess import Pool
from tqdm import tqdm
from tqdm import tqdm
from tqdm import tqdm
import esm
import gc
import gc
import numpy as np
import numpy as np
import os
import os
import os
import pandas as pd
import pandas as pd
import re
import tensorflow as tf
im... | code |
130004949/cell_10 | [
"image_output_4.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | !pip install --no-index --no-deps /kaggle/input/fair-esm/fair_esm-2.0.0-py3-none-any.whl | code |
130004949/cell_12 | [
"text_plain_output_1.png"
] | from esm.model.esm2 import ESM2
from multiprocess import Pool
from tqdm import tqdm
from tqdm import tqdm
from tqdm import tqdm
import esm
import gc
import gc
import numpy as np
import numpy as np
import os
import os
import os
import pandas as pd
import pandas as pd
import re
import tensorflow as tf
im... | code |
33112981/cell_6 | [
"text_plain_output_1.png"
] | import json
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
def readTaskFile(filename):
f = open(filename, 'r')
data = json.loads(f.read())
data['id'] = re.sub('(.*/)|(\\.json)', '', filename)
f.close()
return data
filename = testDirector... | code |
33112981/cell_11 | [
"text_plain_output_1.png"
] | f2 = open('submission.csv', 'r')
print(f2.read())
f2.close() | code |
33112981/cell_8 | [
"text_plain_output_1.png"
] | import json
import re # Regular expressions
def flattener(pred):
str_pred = str([row for row in pred])
str_pred = str_pred.replace(', ', '')
str_pred = str_pred.replace('[[', '|')
str_pred = str_pred.replace('][', '|')
str_pred = str_pred.replace(']]', '|')
return str_pred
testDirectory = '/k... | code |
32070001/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
16168087/cell_4 | [
"image_output_1.png"
] | base_dir = '../input/cell_images/cell_images/'
base_path = Path(base_dir)
base_path | code |
16168087/cell_20 | [
"image_output_1.png"
] | base_dir = '../input/cell_images/cell_images/'
base_path = Path(base_dir)
base_path
data = ImageDataBunch.from_folder(base_path, valid_pct=0.1, train='.', ds_tfms=get_transforms(max_warp=0, flip_vert=True), size=128, bs=32, num_workers=0).normalize(imagenet_stats)
learner = create_cnn(data, models.resnet50, metrics=a... | code |
16168087/cell_6 | [
"image_output_1.png"
] | base_dir = '../input/cell_images/cell_images/'
base_path = Path(base_dir)
base_path
data = ImageDataBunch.from_folder(base_path, valid_pct=0.1, train='.', ds_tfms=get_transforms(max_warp=0, flip_vert=True), size=128, bs=32, num_workers=0).normalize(imagenet_stats)
print(f'Classes to classify: \n {data.classes}')
data.... | code |
16168087/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | base_dir = '../input/cell_images/cell_images/'
base_path = Path(base_dir)
base_path
data = ImageDataBunch.from_folder(base_path, valid_pct=0.1, train='.', ds_tfms=get_transforms(max_warp=0, flip_vert=True), size=128, bs=32, num_workers=0).normalize(imagenet_stats)
learner = create_cnn(data, models.resnet50, metrics=a... | code |
16168087/cell_18 | [
"image_output_1.png"
] | base_dir = '../input/cell_images/cell_images/'
base_path = Path(base_dir)
base_path
data = ImageDataBunch.from_folder(base_path, valid_pct=0.1, train='.', ds_tfms=get_transforms(max_warp=0, flip_vert=True), size=128, bs=32, num_workers=0).normalize(imagenet_stats)
learner = create_cnn(data, models.resnet50, metrics=a... | code |
16168087/cell_28 | [
"text_html_output_1.png"
] | base_dir = '../input/cell_images/cell_images/'
base_path = Path(base_dir)
base_path
data = ImageDataBunch.from_folder(base_path, valid_pct=0.1, train='.', ds_tfms=get_transforms(max_warp=0, flip_vert=True), size=128, bs=32, num_workers=0).normalize(imagenet_stats)
learner = create_cnn(data, models.resnet50, metrics=a... | code |
16168087/cell_8 | [
"image_output_1.png"
] | base_dir = '../input/cell_images/cell_images/'
base_path = Path(base_dir)
base_path
data = ImageDataBunch.from_folder(base_path, valid_pct=0.1, train='.', ds_tfms=get_transforms(max_warp=0, flip_vert=True), size=128, bs=32, num_workers=0).normalize(imagenet_stats)
learner = create_cnn(data, models.resnet50, metrics=a... | code |
16168087/cell_16 | [
"text_html_output_1.png"
] | base_dir = '../input/cell_images/cell_images/'
base_path = Path(base_dir)
base_path
data = ImageDataBunch.from_folder(base_path, valid_pct=0.1, train='.', ds_tfms=get_transforms(max_warp=0, flip_vert=True), size=128, bs=32, num_workers=0).normalize(imagenet_stats)
learner = create_cnn(data, models.resnet50, metrics=a... | code |
16168087/cell_24 | [
"text_html_output_1.png"
] | base_dir = '../input/cell_images/cell_images/'
base_path = Path(base_dir)
base_path
data = ImageDataBunch.from_folder(base_path, valid_pct=0.1, train='.', ds_tfms=get_transforms(max_warp=0, flip_vert=True), size=128, bs=32, num_workers=0).normalize(imagenet_stats)
learner = create_cnn(data, models.resnet50, metrics=a... | code |
16168087/cell_14 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | base_dir = '../input/cell_images/cell_images/'
base_path = Path(base_dir)
base_path
data = ImageDataBunch.from_folder(base_path, valid_pct=0.1, train='.', ds_tfms=get_transforms(max_warp=0, flip_vert=True), size=128, bs=32, num_workers=0).normalize(imagenet_stats)
learner = create_cnn(data, models.resnet50, metrics=a... | code |
16168087/cell_22 | [
"image_output_1.png"
] | base_dir = '../input/cell_images/cell_images/'
base_path = Path(base_dir)
base_path
data = ImageDataBunch.from_folder(base_path, valid_pct=0.1, train='.', ds_tfms=get_transforms(max_warp=0, flip_vert=True), size=128, bs=32, num_workers=0).normalize(imagenet_stats)
learner = create_cnn(data, models.resnet50, metrics=a... | code |
16168087/cell_10 | [
"text_plain_output_1.png"
] | base_dir = '../input/cell_images/cell_images/'
base_path = Path(base_dir)
base_path
data = ImageDataBunch.from_folder(base_path, valid_pct=0.1, train='.', ds_tfms=get_transforms(max_warp=0, flip_vert=True), size=128, bs=32, num_workers=0).normalize(imagenet_stats)
learner = create_cnn(data, models.resnet50, metrics=a... | code |
16168087/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | base_dir = '../input/cell_images/cell_images/'
base_path = Path(base_dir)
base_path
data = ImageDataBunch.from_folder(base_path, valid_pct=0.1, train='.', ds_tfms=get_transforms(max_warp=0, flip_vert=True), size=128, bs=32, num_workers=0).normalize(imagenet_stats)
learner = create_cnn(data, models.resnet50, metrics=a... | code |
88105225/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
tweets = pd.read_csv('/kaggle/input/twitter-sentiment-analysis-10/Sentiment140.tenPercent.sample.tweets.tsv', delimiter='\t')
tweets.isnull().values.any().sum()
print('Shape before removing duplicate rows:', tweets.shape)
twe... | code |
88105225/cell_33 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
import emoji
import re # regular expression operations
import wordninja
s... | code |
88105225/cell_20 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from wordcloud import WordCloud
import emoji
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re # regular expression operations
import wordninja
tweets = p... | code |
88105225/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | !pip install wordninja
!pip install emoji
!pip install catboost
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from wordcloud import WordCloud
import nltk # used commonly for NLP tasks
nltk.download('stopwords')
from nltk.corpus import stopwords
nltk.download('wordnet')
fro... | code |
88105225/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import emoji
import re # regular expression operations
import wordninja
stop_words = stopwords.words('english')
abbreviation... | code |
88105225/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | # The gloVe is a pretrained word embedding model
!wget http://nlp.stanford.edu/data/glove.6B.zip
!unzip glove.6B.zip | code |
88105225/cell_48 | [
"text_plain_output_1.png"
] | from keras.layers import LSTM
from keras.layers.core import Activation, Dropout, Dense
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from nltk.corpus import stopwords
from ten... | code |
88105225/cell_41 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
import emoji
import numpy as np
import numpy as np # linear algebra
import re # regular expression operations
import wordninja
stop_words = stopwords.words('english')
abbreviations = {'a.m.': 'before midday', 'acct': 'account', 'afaik': 'as far as i know', 'afk': 'away from keybo... | code |
88105225/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
tweets = pd.read_csv('/kaggle/input/twitter-sentiment-analysis-10/Sentiment140.tenPercent.sample.tweets.tsv', delimiter='\t')
tweets.isnull().values.any().sum()
tweets = tweets.drop_duplicates()
tweets['tweet_text'][0] | code |
88105225/cell_19 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from wordcloud import WordCloud
import emoji
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re # regular expression operations
import wordninja
tweets = p... | code |
88105225/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 |
88105225/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
tweets = pd.read_csv('/kaggle/input/twitter-sentiment-analysis-10/Sentiment140.tenPercent.sample.tweets.tsv', delimiter='\t')
tweets.head() | code |
88105225/cell_45 | [
"text_plain_output_1.png"
] | from keras.layers import LSTM
from keras.layers.core import Activation, Dropout, Dense
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from nltk.corpus import stopwords
import e... | code |
88105225/cell_49 | [
"text_plain_output_1.png"
] | from keras.layers import LSTM
from keras.layers.core import Activation, Dropout, Dense
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from nltk.corpus import stopwords
from skl... | code |
88105225/cell_18 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import emoji
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re # regular expression operations
import wordninja
tweets = pd.read_csv('/kaggle/input/twitter-sentiment-analysis-10/Sentiment1... | code |
88105225/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from wordcloud import WordCloud
import ... | code |
88105225/cell_51 | [
"text_plain_output_1.png"
] | from keras.layers import LSTM
from keras.layers.core import Activation, Dropout, Dense
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from nltk.corpus import stopwords
from nlt... | code |
88105225/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)
tweets = pd.read_csv('/kaggle/input/twitter-sentiment-analysis-10/Sentiment140.tenPercent.sample.tweets.tsv', delimiter='\t')
tweets.isnull().values.any().sum() | code |
88105225/cell_38 | [
"text_plain_output_1.png"
] | from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(X_train)
word_index = tokenizer.word_index
X_train_tok = tokenizer.texts_to_sequences(X_train)
X_test_tok = tokenizer.texts_to_sequences(X_test)
vocab_size = len(tokeni... | code |
88105225/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.model_selection import train_test_split
from wordcloud import WordCloud
import emoji
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re # regul... | code |
88105225/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)
import seaborn as sns
tweets = pd.read_csv('/kaggle/input/twitter-sentiment-analysis-10/Sentiment140.tenPercent.sample.tweets.tsv', delimiter='\t')
tweets.isnull().values.any().sum()
tweets = tweets.drop_duplicates()
sns.co... | code |
88105225/cell_27 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
import emoji
import re # regular expression operations
import wordninja
stop_words = stopwords.words('english')
abbreviations = {'a.m.': 'before midday', 'acct': 'account', 'afaik': 'as far as i know', 'afk': 'away from k... | code |
88105225/cell_37 | [
"text_plain_output_1.png"
] | from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
tokenizer = Tokenizer()
tokenizer.fit_on_texts(X_train)
word_index = tokenizer.word_index
X_train_tok = tokenizer.texts_to_sequences(X_train)
X_test_tok = tokenizer.texts_to_sequences(X_test)
vocab_size = len(tokeni... | code |
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