path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
74067865/cell_7 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import plotly.graph_objects as go
healthsysdf = pd.read_csv('../input/world-bank-wdi-212-health-systems/2.12_Health_systems.csv')
healthsysdf = healthsysdf.drop(columns='Province_State')
healthsysdf = healthsysdf.drop(columns='Country_Region')
healthsysdf... | code |
74067865/cell_15 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import pandas as pd
import plotly.graph_objects as go
import plotly.graph_objects as go
healthsysdf = pd.read_csv('../input/world-bank-wdi-212-health-systems/2.12_Health_systems.csv')
healthsysdf = healthsysdf.drop(columns='Province_State')
healthsysdf ... | code |
89136628/cell_21 | [
"text_plain_output_1.png"
] | from kaggle_secrets import UserSecretsClient
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
value_strategy = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/value_strategy_1.csv.csv')
recommended_trades = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/recommende... | code |
89136628/cell_9 | [
"text_html_output_1.png"
] | from kaggle_secrets import UserSecretsClient
import requests
import requests
from kaggle_secrets import UserSecretsClient
user_secrets = UserSecretsClient()
IEX_CLOUD_API_TOKEN = 'Tpk_ddf77a77f6e7464390bb2adc85a2be11'
secret_value_0 = user_secrets.get_secret('IEX_CLOUD_API_TOKEN')
symbol = 'AAPL'
api_url = f'https://... | code |
89136628/cell_25 | [
"text_html_output_1.png"
] | from kaggle_secrets import UserSecretsClient
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
value_strategy = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/value_strategy_1.csv.csv')
recommended_trades = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/recommende... | code |
89136628/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
value_strategy = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/value_strategy_1.csv.csv')
recommended_trades = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/recommended_trades_1.csv.csv')
momentum_srategy = pd.read_csv('/kaggle/in... | code |
89136628/cell_23 | [
"text_plain_output_1.png"
] | from kaggle_secrets import UserSecretsClient
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
value_strategy = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/value_strategy_1.csv.csv')
recommended_trades = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/recommende... | code |
89136628/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
value_strategy = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/value_strategy_1.csv.csv')
recommended_trades = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/recommended_trades_1.csv.csv')
momentum_srategy = pd.read_csv('/kaggle/in... | code |
89136628/cell_2 | [
"text_html_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 |
89136628/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
value_strategy = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/value_strategy_1.csv.csv')
recommended_trades = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/recommended_trades_1.csv.csv')
momentum_srategy = pd.read_csv('/kaggle/in... | code |
89136628/cell_19 | [
"text_html_output_1.png"
] | from kaggle_secrets import UserSecretsClient
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
value_strategy = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/value_strategy_1.csv.csv')
recommended_trades = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/recommende... | code |
89136628/cell_7 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
value_strategy = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/value_strategy_1.csv.csv')
recommended_trades = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/recommended_trades_1.csv.csv')
momentum_srategy = pd.read_csv('/kaggle/in... | code |
89136628/cell_17 | [
"text_html_output_1.png"
] | from kaggle_secrets import UserSecretsClient
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
value_strategy = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/value_strategy_1.csv.csv')
recommended_trades = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/recommende... | code |
89136628/cell_14 | [
"text_html_output_1.png"
] | from kaggle_secrets import UserSecretsClient
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
value_strategy = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/value_strategy_1.csv.csv')
recommended_trades = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/recommende... | code |
89136628/cell_27 | [
"text_plain_output_1.png"
] | from kaggle_secrets import UserSecretsClient
from scipy import stats
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import requests
value_strategy = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/value_strategy_1.csv.csv')
recommended_trades = pd.read_csv('/kaggle/input/algorithmic-t... | code |
89136628/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
value_strategy = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/value_strategy_1.csv.csv')
recommended_trades = pd.read_csv('/kaggle/input/algorithmic-trading-dataset/recommended_trades_1.csv.csv')
momentum_srategy = pd.read_csv('/kaggle/in... | code |
50244989/cell_9 | [
"text_plain_output_1.png"
] | from autoviml.Auto_NLP import Auto_NLP
train_x, test_x, final, predicted = Auto_NLP(input_feature, train, test, target, score_type='balanced_accuracy', top_num_features=100, modeltype='Classification', verbose=2, build_model=True) | code |
50244989/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | test.head() | code |
50244989/cell_2 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | !pip install autoviml | code |
50244989/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('../input/twitter-sentiment-analysis-analytics-vidya/train_E6oV3lV.csv')
testing = pd.read_csv('../input/twitter-sentiment-analysis-analytics-vidya/test_tweets_anuFYb8.csv')
from sklearn.model_selection import train_test_split
... | code |
50244989/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/twitter-sentiment-analysis-analytics-vidya/train_E6oV3lV.csv')
testing = pd.read_csv('../input/twitter-sentiment-analysis-analytics-vidya/test_tweets_anuFYb8.csv')
final.predict(test_x[input_feature])
testing = pd.read_csv('../input/twitter-sentiment-analysis-analytics-... | code |
50244989/cell_10 | [
"text_plain_output_1.png"
] | final.predict(test_x[input_feature]) | code |
50244989/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/twitter-sentiment-analysis-analytics-vidya/train_E6oV3lV.csv')
testing = pd.read_csv('../input/twitter-sentiment-analysis-analytics-vidya/test_tweets_anuFYb8.csv')
final.predict(test_x[input_feature])
testing = pd.read_csv('../input/twitter-sentiment-analysis-analytics-... | code |
50244989/cell_5 | [
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/twitter-sentiment-analysis-analytics-vidya/train_E6oV3lV.csv')
testing = pd.read_csv('../input/twitter-sentiment-analysis-analytics-vidya/test_tweets_anuFYb8.csv')
df.head() | code |
16168103/cell_4 | [
"image_output_1.png"
] | path = Path('../input/heroes/heroes/')
path.ls() | code |
16168103/cell_20 | [
"image_output_1.png"
] | path = Path('../input/heroes/heroes/')
path.ls()
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.1, ds_tfms=get_transforms(max_warp=0, flip_vert=True, do_flip=True), size=128, bs=16).normalize(imagenet_stats)
learn = create_cnn(data, models.resnet50, metrics=accuracy, model_dir='/tmp/model/')
learn.lr_... | code |
16168103/cell_6 | [
"image_output_1.png"
] | path = Path('../input/heroes/heroes/')
path.ls()
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.1, ds_tfms=get_transforms(max_warp=0, flip_vert=True, do_flip=True), size=128, bs=16).normalize(imagenet_stats)
print(f'Classes: \n {data.classes}')
data.show_batch(rows=8, figsize=(10, 10)) | code |
16168103/cell_26 | [
"text_html_output_1.png"
] | path = Path('../input/heroes/heroes/')
path.ls()
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.1, ds_tfms=get_transforms(max_warp=0, flip_vert=True, do_flip=True), size=128, bs=16).normalize(imagenet_stats)
learn = create_cnn(data, models.resnet50, metrics=accuracy, model_dir='/tmp/model/')
learn.lr_... | code |
16168103/cell_18 | [
"text_html_output_1.png"
] | path = Path('../input/heroes/heroes/')
path.ls()
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.1, ds_tfms=get_transforms(max_warp=0, flip_vert=True, do_flip=True), size=128, bs=16).normalize(imagenet_stats)
learn = create_cnn(data, models.resnet50, metrics=accuracy, model_dir='/tmp/model/')
learn.lr_... | code |
16168103/cell_8 | [
"image_output_1.png"
] | path = Path('../input/heroes/heroes/')
path.ls()
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.1, ds_tfms=get_transforms(max_warp=0, flip_vert=True, do_flip=True), size=128, bs=16).normalize(imagenet_stats)
learn = create_cnn(data, models.resnet50, metrics=accuracy, model_dir='/tmp/model/')
learn.lr_... | code |
16168103/cell_16 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | path = Path('../input/heroes/heroes/')
path.ls()
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.1, ds_tfms=get_transforms(max_warp=0, flip_vert=True, do_flip=True), size=128, bs=16).normalize(imagenet_stats)
learn = create_cnn(data, models.resnet50, metrics=accuracy, model_dir='/tmp/model/')
learn.lr_... | code |
16168103/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | path = Path('../input/heroes/heroes/')
path.ls()
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.1, ds_tfms=get_transforms(max_warp=0, flip_vert=True, do_flip=True), size=128, bs=16).normalize(imagenet_stats)
learn = create_cnn(data, models.resnet50, metrics=accuracy, model_dir='/tmp/model/')
learn.lr_... | code |
16168103/cell_22 | [
"text_html_output_1.png"
] | path = Path('../input/heroes/heroes/')
path.ls()
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.1, ds_tfms=get_transforms(max_warp=0, flip_vert=True, do_flip=True), size=128, bs=16).normalize(imagenet_stats)
learn = create_cnn(data, models.resnet50, metrics=accuracy, model_dir='/tmp/model/')
learn.lr_... | code |
16168103/cell_10 | [
"text_plain_output_1.png"
] | path = Path('../input/heroes/heroes/')
path.ls()
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.1, ds_tfms=get_transforms(max_warp=0, flip_vert=True, do_flip=True), size=128, bs=16).normalize(imagenet_stats)
learn = create_cnn(data, models.resnet50, metrics=accuracy, model_dir='/tmp/model/')
learn.lr_... | code |
16168103/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | path = Path('../input/heroes/heroes/')
path.ls()
data = ImageDataBunch.from_folder(path, train='.', valid_pct=0.1, ds_tfms=get_transforms(max_warp=0, flip_vert=True, do_flip=True), size=128, bs=16).normalize(imagenet_stats)
learn = create_cnn(data, models.resnet50, metrics=accuracy, model_dir='/tmp/model/')
learn.lr_... | code |
334111/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
test.shape
train = train[train['Semana'] > 8]
ids = test['id']
test = test.drop(['id'], axis=1)
y = train['Demanda_uni_equil']
X = train[test.columns.values]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1729)
del train
pri... | code |
334111/cell_6 | [
"text_plain_output_1.png"
] | test.shape | code |
334111/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import xgboost as xgb
import pandas as pd
import math
import os
import sys
from sklearn.cross_validation import train_test_split
from ml_metrics import rmsle | code |
334111/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | num_rounds = 50
del xg_train | code |
334111/cell_7 | [
"text_plain_output_1.png"
] | dtype = {'Semana': np.uint8, 'Agencia_ID': np.uint16, 'Canal_ID': np.uint8, 'Ruta_SAK': np.uint16, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16, 'Demanda_uni_equil': np.uint16}
filename = '../input/train.csv'
train.head() | code |
334111/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | train = train[train['Semana'] > 8]
print('Training_Shape:', train.shape) | code |
334111/cell_15 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from ml_metrics import rmsle
from sklearn.cross_validation import train_test_split
import math
import numpy as np
import xgboost as xgb
def evalerror(preds, dtrain):
labels = dtrain.get_label()
assert len(preds) == len(labels)
labels = labels.tolist()
preds = preds.tolist()
terms_to_sum = [(mat... | code |
334111/cell_14 | [
"text_plain_output_1.png"
] | from ml_metrics import rmsle
from sklearn.cross_validation import train_test_split
import math
import numpy as np
import xgboost as xgb
def evalerror(preds, dtrain):
labels = dtrain.get_label()
assert len(preds) == len(labels)
labels = labels.tolist()
preds = preds.tolist()
terms_to_sum = [(mat... | code |
334111/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from ml_metrics import rmsle
from sklearn.cross_validation import train_test_split
import math
import numpy as np
import xgboost as xgb
def evalerror(preds, dtrain):
labels = dtrain.get_label()
assert len(preds) == len(labels)
labels = labels.tolist()
preds = preds.tolist()
terms_to_sum = [(mat... | code |
334111/cell_5 | [
"text_plain_output_1.png"
] | print('Loading Test...')
dtype_test = {'id': np.uint32, 'Semana': np.uint8, 'Agencia_ID': np.uint16, 'Canal_ID': np.uint8, 'Ruta_SAK': np.uint16, 'Cliente_ID': np.uint32, 'Producto_ID': np.uint16}
test.head() | code |
17144046/cell_9 | [
"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
ratings_df = pd.read_csv('../input/u.data', sep='\t', names=['user_id', 'movie_id', 'rating', 'ts'])
ratings_df
movie_df = pd.read_csv('../input/u.item', sep='|', encoding='latin-1', header=None)
movie_df = movie_df[[0, 1]]
... | code |
17144046/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ratings_df = pd.read_csv('../input/u.data', sep='\t', names=['user_id', 'movie_id', 'rating', 'ts'])
ratings_df
movie_df = pd.read_csv('../input/u.item', sep='|', encoding='latin-1', header=None)
movie_df = movie_df[[0, 1]]
movie_df.columns = ['mo... | code |
17144046/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ratings_df = pd.read_csv('../input/u.data', sep='\t', names=['user_id', 'movie_id', 'rating', 'ts'])
ratings_df
mean_ratings = ratings_df.groupby('movie_id').agg({'rating': 'mean'}).reset_index().rename({'rating': 'mean_rating'}, axis=1)
count_rat... | code |
17144046/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ratings_df = pd.read_csv('../input/u.data', sep='\t', names=['user_id', 'movie_id', 'rating', 'ts'])
ratings_df | code |
17144046/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17144046/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)
ratings_df = pd.read_csv('../input/u.data', sep='\t', names=['user_id', 'movie_id', 'rating', 'ts'])
ratings_df
movie_df = pd.read_csv('../input/u.item', sep='|', encoding='latin-1', header=None)
movie_df = movie_df[[0, 1]]
movie_df.columns = ['mo... | code |
17144046/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ratings_df = pd.read_csv('../input/u.data', sep='\t', names=['user_id', 'movie_id', 'rating', 'ts'])
ratings_df
ratings_df['rating'].max() | code |
17144046/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
ratings_df = pd.read_csv('../input/u.data', sep='\t', names=['user_id', 'movie_id', 'rating', 'ts'])
ratings_df
movie_df = pd.read_csv('../input/u.item', sep='|', encoding='latin-1', header=None)
movie_df = movie_df[[0, 1]]
... | code |
17144046/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ratings_df = pd.read_csv('../input/u.data', sep='\t', names=['user_id', 'movie_id', 'rating', 'ts'])
ratings_df
movie_df = pd.read_csv('../input/u.item', sep='|', encoding='latin-1', header=None)
movie_df = movie_df[[0, 1]]
movie_df.columns = ['mo... | code |
89139708/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from pathlib import Path
from torch import nn
from torch.utils.data import Dataset, DataLoader
from torchmetrics import Accuracy
from torchvision import transforms
import os
import pytorch_lightning as pl
import torch
import torchvision
import wandb
img_path = Path('../input/celeba-data... | code |
89139708/cell_9 | [
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_5.png",
"text_html_output_1.png",
"text_html_output_8.png",
"text_html_output_3.png",
"text_html_output_7.png"
] | from pytorch_lightning.loggers import WandbLogger
wandb_logger = WandbLogger(project='gender-detection-vit')
model = LitModel(2, wandb_logger=wandb_logger) | code |
89139708/cell_25 | [
"image_output_1.png"
] | from PIL import Image
from pathlib import Path
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning.trainer import Trainer
from torch import nn
from torch.utils.data import Dataset, DataLoader
from torchmetrics import Accuracy
from torc... | code |
89139708/cell_34 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | from IPython.core.display import display, HTML
from PIL import Image
from io import BytesIO
from pathlib import Path
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning.trainer import Trainer
from torch import nn
from torch.utils.data ... | code |
89139708/cell_30 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from kaggle_secrets import UserSecretsClient
from kaggle_secrets import UserSecretsClient
from torch import nn
from torchmetrics import Accuracy
import pytorch_lightning as pl
import torch
import torchvision
import wandb
class LitModel(pl.LightningModule):
def __init__(self, n_classes, download_pretrained=T... | code |
89139708/cell_29 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from PIL import Image
from pathlib import Path
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning.trainer import Trainer
from torch import nn
from torch.utils.data import Dataset, DataLoader
from torchmetrics import Accuracy
from torc... | code |
89139708/cell_26 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from pathlib import Path
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning.trainer import Trainer
from torch import nn
from torch.utils.data import Dataset, DataLoader
from torchmetrics import Accuracy
from torc... | code |
89139708/cell_16 | [
"text_plain_output_1.png"
] | from PIL import Image
from pathlib import Path
from torch import nn
from torch.utils.data import Dataset, DataLoader
from torchmetrics import Accuracy
from torchvision import transforms
import os
import pytorch_lightning as pl
import torch
import torchvision
import wandb
img_path = Path('../input/celeba-data... | code |
89139708/cell_3 | [
"text_plain_output_1.png"
] | !pip install --upgrade wandb | code |
89139708/cell_17 | [
"image_output_1.png"
] | from PIL import Image
from pathlib import Path
from torch import nn
from torch.utils.data import Dataset, DataLoader
from torchmetrics import Accuracy
from torchvision import transforms
import matplotlib.pyplot as plt
import os
import pytorch_lightning as pl
import torch
import torchvision
import wandb
img_... | code |
89139708/cell_24 | [
"text_plain_output_1.png"
] | from kaggle_secrets import UserSecretsClient
from kaggle_secrets import UserSecretsClient
from torch import nn
from torchmetrics import Accuracy
import pytorch_lightning as pl
import torch
import torchvision
import wandb
class LitModel(pl.LightningModule):
def __init__(self, n_classes, download_pretrained=T... | code |
89139708/cell_14 | [
"text_plain_output_1.png"
] | from PIL import Image
from pathlib import Path
from torch import nn
from torch.utils.data import Dataset, DataLoader
from torchmetrics import Accuracy
from torchvision import transforms
import matplotlib.pyplot as plt
import os
import pytorch_lightning as pl
import torch
import torchvision
import wandb
img_... | code |
89139708/cell_27 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_html_output_1.png",
"text_plain_output_1.png"
] | from PIL import Image
from pathlib import Path
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning.trainer import Trainer
from torch import nn
from torch.utils.data import Dataset, DataLoader
from torchmetrics import Accuracy
from torc... | code |
89139708/cell_12 | [
"text_plain_output_1.png"
] | from PIL import Image
from pathlib import Path
from torch import nn
from torch.utils.data import Dataset, DataLoader
from torchmetrics import Accuracy
from torchvision import transforms
import os
import pytorch_lightning as pl
import torch
import torchvision
import wandb
img_path = Path('../input/celeba-data... | code |
18139890/cell_42 | [
"text_plain_output_1.png"
] | from keras.layers import CuDNNLSTM, Activation, Dense, Dropout, Input, Embedding, concatenate, Bidirectional
from keras.models import Sequential, Model
from keras.optimizers import adam
from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer
import numpy as np
import pandas as pd
... | code |
18139890/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
train_data.sample(5)
train_data['target'].value_counts()[0] / train_data.shape[0] * 100
train_data.isna().sum() | code |
18139890/cell_56 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from keras.layers import CuDNNLSTM, Activation, Dense, Dropout, Input, Embedding, concatenate, Bidirectional
from keras.models import Sequential, Model
from keras.optimizers import adam
from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer... | code |
18139890/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/train.csv')
train_data.sample(5)
sns.set_style('darkgrid')
sns.distplot(train_data['target']) | code |
18139890/cell_41 | [
"text_plain_output_1.png"
] | from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer
import numpy as np
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
train_data.sample(5)
train_data['target'].value_counts()[0] / train_data.shape[0] * 100
train_data.isna().sum()
train_data.drop(['id'], axi... | code |
18139890/cell_2 | [
"text_html_output_1.png"
] | import os
import os
print(os.listdir('../input')) | code |
18139890/cell_50 | [
"text_plain_output_1.png"
] | from keras.layers import CuDNNLSTM, Activation, Dense, Dropout, Input, Embedding, concatenate, Bidirectional
from keras.models import Sequential, Model
from keras.optimizers import adam
from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer
from keras.utils import np_utils
import ... | code |
18139890/cell_52 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
train_data = pd.read_csv('../input/train.csv')
train_data.sample(5)
train_data['target'].value_counts()[0] / train_data.shape[0] * 100
train_data.isna().sum()
train_data.drop(['id'], axis=1, inplace=True)
"""Adding additional informative columns, as most toxic tweets contain
excl... | code |
18139890/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import numpy as np
import pandas as pd
import seaborn as sns
from nltk.tokenize import word_tokenize
import re
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
from sklearn.preprocessing import LabelEncoder
from nl... | code |
18139890/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
train_data.sample(5)
train_data['target'].value_counts()[0] / train_data.shape[0] * 100 | code |
18139890/cell_45 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from keras.layers import CuDNNLSTM, Activation, Dense, Dropout, Input, Embedding, concatenate, Bidirectional
from keras.models import Sequential, Model
from keras.optimizers import adam
from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer... | code |
18139890/cell_51 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from keras.layers import CuDNNLSTM, Activation, Dense, Dropout, Input, Embedding, concatenate, Bidirectional
from keras.models import Sequential, Model
from keras.optimizers import adam
from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer... | code |
18139890/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
train_data.sample(5) | code |
18139890/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
import seaborn as sns
train_data = pd.read_csv('../input/train.csv')
train_data.sample(5)
sns.set_style('darkgrid')
train_data['target'].value_counts()[0] / train_data.shape[0] * 100
train_data.isna().sum()
train_data.drop(['id'], axis=1, inplace=True)
"""Adding additional informat... | code |
18139890/cell_43 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.callbacks import EarlyStopping
from keras.layers import CuDNNLSTM, Activation, Dense, Dropout, Input, Embedding, concatenate, Bidirectional
from keras.models import Sequential, Model
from keras.optimizers import adam
from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer... | code |
73089284/cell_21 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import plot_confusion_matrix
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/kasco-dataset-russian/Задача 1/Прогнозирование пролонгации/Данные для задачи.txt',... | code |
73089284/cell_20 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import plot_confusion_matrix
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/kasco-dataset-russian/Задача 1/Прогнозирование пролонгации/Данные для задачи.txt',... | code |
73089284/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kasco-dataset-russian/Задача 1/Прогнозирование пролонгации/Данные для задачи.txt', sep=';')
df.shape
df.head() | code |
73089284/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kasco-dataset-russian/Задача 1/Прогнозирование пролонгации/Данные для задачи.txt', sep=';')
df.shape
df = df.copy()
df.reset_index(drop=True)
df.dropna(axis=0, inplace=True)
obj_columns = df.select_dtypes(['object']).columns
df[obj_columns] = df[obj_columns].apply(lamb... | code |
73089284/cell_19 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import pandas as pd
df = pd.read_csv('../input/kasco-dataset-russian/Задача 1/Прогнозирование пролонгации/Данные для задачи.txt', sep=';')
rf_clf = RandomForestClassifier(bootstrap=False)
result_clf = rf_clf.fit(X_train, Y_train)
yrf_predicted = rf_clf.predict(X_te... | code |
73089284/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kasco-dataset-russian/Задача 1/Прогнозирование пролонгации/Данные для задачи.txt', sep=';')
df.shape
df = df.copy()
df.reset_index(drop=True) | code |
73089284/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import pandas as pd
df = pd.read_csv('../input/kasco-dataset-russian/Задача 1/Прогнозирование пролонгации/Данные для задачи.txt', sep=';')
rf_clf = RandomForestClassifier(bootstrap=False)
result_clf = rf_clf.fit(X_train, Y_train)
yrf_predicted = rf_clf.predict(X_te... | code |
73089284/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kasco-dataset-russian/Задача 1/Прогнозирование пролонгации/Данные для задачи.txt', sep=';')
df.shape
df = df.copy()
df.reset_index(drop=True)
df.info() | code |
73089284/cell_16 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import plot_confusion_matrix
import matplotlib.pyplot as plt
logmodel = LogisticRegression(fit_intercept=True)
logmodel.max_iter = 1000
logit_result = logmodel.fit(X_train, Y_train)
ylm_predicted = logit_result.predict(X_test)
plot_confusion_m... | code |
73089284/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import plot_confusion_matrix
import matplotlib.pyplot as plt
logmodel = LogisticRegression(fit_intercept=True)
logmodel.max_iter = 1000
logit_result = logmodel.fit(X_train, Y_train)
ylm_predi... | code |
73089284/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import pandas as pd
df = pd.read_csv('../input/kasco-dataset-russian/Задача 1/Прогнозирование пролонгации/Данные для задачи.txt', sep=';')
rf_clf = RandomForestClassifier(bootstrap=False)
result_clf = rf_clf.fit(X_train, Y_train)
yrf_predicted = rf_clf.predict(X_te... | code |
73089284/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kasco-dataset-russian/Задача 1/Прогнозирование пролонгации/Данные для задачи.txt', sep=';')
df.shape
df = df.copy()
df.reset_index(drop=True)
df.dropna(axis=0, inplace=True)
obj_columns = df.select_dtypes(['object']).columns
df[obj_columns] = df[obj_columns].apply(lamb... | code |
73089284/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kasco-dataset-russian/Задача 1/Прогнозирование пролонгации/Данные для задачи.txt', sep=';')
df.shape | code |
122251329/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
target = ['HeartDisease']
num_attribs = ['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak']
cat_nom_attribs = ['ChestPainType', 'RestingECG', 'ST_Slope']
cat_bin_attribs = ['Sex', '... | code |
122251329/cell_9 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
hrz.head() | code |
122251329/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
target = ['HeartDisease']
num_attribs = ['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak']
cat_nom_attribs = ['ChestPainType', ... | code |
122251329/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
target = ['HeartDisease']
num_attribs = ['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak']
cat_nom_attribs = ['ChestPainType', ... | code |
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