path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
49118983/cell_44 | [
"text_plain_output_1.png"
] | import tensorflow as tf
import tensorflow.keras.layers as L
import tensorflow.keras.models as M
FE = ['content_emb', 'user_emb', 'duration', 'prior_answer']
TARGET = 'answered_correctly'
x = tr_preprocessed.loc[tr_preprocessed.answered_correctly != -1, FE].values
y = tr_preprocessed.loc[tr_preprocessed.answered_cor... | code |
49118983/cell_40 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu) | code |
49118983/cell_29 | [
"text_plain_output_1.png"
] | 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
import gc
import tensorflow as tf
import tensorflow.keras.models as M
import tensorflow.keras.layers as L
import riiideducation
INPUT_DIR = '/kaggle/input/riiid-test-answer-prediction/'
T... | code |
49118983/cell_26 | [
"text_plain_output_1.png"
] | 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
import gc
import tensorflow as tf
import tensorflow.keras.models as M
import tensorflow.keras.layers as L
import riiideducation
INPUT_DIR = '/kaggle/input/riiid-test-answer-prediction/'
T... | code |
49118983/cell_11 | [
"text_plain_output_1.png"
] | 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
import gc
import tensorflow as tf
import tensorflow.keras.models as M
import tensorflow.keras.layers as L
import riiideducation
INPUT_DIR = '/kaggle/input/riiid-test-answer-prediction/'
T... | code |
49118983/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
import gc
import tensorflow as tf
import tensorflow.keras.models as M
import tensorflow.keras.layers as L
import riiideducation
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
49118983/cell_28 | [
"text_plain_output_1.png"
] | 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
import gc
import tensorflow as tf
import tensorflow.keras.models as M
import tensorflow.keras.layers as L
import riiideducation
INPUT_DIR = '/kaggle/input/riiid-test-answer-prediction/'
T... | code |
49118983/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | 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
import gc
import tensorflow as tf
import tensorflow.keras.models as M
import tensorflow.keras.layers as L
import riiideducation
INPUT_DIR = '/kaggle/input/riiid-test-answer-prediction/'
T... | code |
49118983/cell_3 | [
"text_plain_output_1.png"
] | import gc
gc.collect() | code |
49118983/cell_12 | [
"text_html_output_1.png"
] | 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
import gc
import tensorflow as tf
import tensorflow.keras.models as M
import tensorflow.keras.layers as L
import riiideducation
INPUT_DIR = '/kaggle/input/riiid-test-answer-prediction/'
T... | code |
49118983/cell_36 | [
"text_plain_output_1.png"
] | tr_preprocessed = preprocess(tr) | code |
50243208/cell_13 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
items = pd.read_csv('/kaggle/input/competitive-d... | code |
50243208/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-sc... | code |
50243208/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-sc... | code |
50243208/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
import numpy as np
import os
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import confus... | code |
50243208/cell_15 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
items = pd... | code |
50243208/cell_3 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sales_... | code |
50243208/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
item_categories = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
sales_train = pd.read_csv('/kaggle/input/competitive-data-sc... | code |
72092168/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
p1 = '../input/30dml-30-d-ml-xgb/submission.csv'
p2 = '../input/30dml-catboost/submission.csv'
p3 = '../input/30dml-catboost-xgb-folds/submission.csv'
p4 = '../input/30dml-lightgbm/submission_lgb_5.csv'
all_s = []
for p in [p1, p2, p3, p4]:
all_s.append(pd.read_csv(p))
weig... | code |
90150886/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape
data.dtypes
data['price'] = data['price'].apply(lambda x: str(x).replace('$', '') if '$' in str(x) else str(x))
data['price'] = data['... | code |
90150886/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape
data.dtypes
data['price'] = data['price'].apply(lambda x: str(x).replace('$', '') if '$' in str(x) else str(x))
data['price'] = data['... | code |
90150886/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape | code |
90150886/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape
data.dtypes
data['price'] =... | code |
90150886/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape
data.dtypes
data['price'] = data['price'].apply(lambda x: str(x).replace('$', '') i... | code |
90150886/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape
data.dtypes
data['price'] = data['price'].apply(lambda x: str(x).replace('$', ... | code |
90150886/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape
data.dtypes
data['price'] = data['price'].apply(lambda x: str(x).replace('$', '') i... | code |
90150886/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape
data.dtypes
data['price'] = data['price'].apply(lambda x: str(x).replace('$', '') if '$' in str(x) else str(x))
data['price'] = data['... | code |
90150886/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape
data.dtypes
data['price'] = data['price'].apply(lambda x: str(x).replace('$', ... | code |
90150886/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)
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape
data.dtypes
data['price'] = data['price'].apply(lambda x: str(x).replace('$', '') if '$' in str(x) else str(x))
data['price'] = data['... | code |
90150886/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape
data.dtypes
data['price'] = data['price'].apply(lambda x: str(x).replace('$', '') i... | code |
90150886/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape
data.dtypes
data['price'] = data['price'].apply(lambda x: str(x).replace('$', '') i... | code |
90150886/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape
data.dtypes
data['price'] = data['price'].apply(lambda x: str(x).replace('$', '') if '$' in str(x) else str(x))
data['price'] = data['... | code |
90150886/cell_15 | [
"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)
import seaborn as sns
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape
data.dtypes
data['price'] = data['price'].apply(lambda x: str(x).replace('$', ... | code |
90150886/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.head(10) | code |
90150886/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape
data.dtypes
data['price'] = data['price'].apply(lambda x: str(x).replace('$', '') if '$' in str(x) else str(x))
data['price'] = data['... | code |
90150886/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape
data.dtypes
data['price'] = data['price'].apply(lambda x: str(x).replace('$', '') if '$' in str(x) else str(x))
data['price'] = data['... | code |
90150886/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape
data.dtypes
data['price'] = data['price'].apply(lambda x: str(x).replace('$', '') if '$' in str(x) else str(x))
data['price'] = data['... | code |
90150886/cell_27 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape
data.dtypes
data['price'] = data['price'].apply(lambda x: str(x).replace('$', '') i... | code |
90150886/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape
data.dtypes | code |
90150886/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/mobile-phone-rating/mobile phone rating by dxo.csv', parse_dates=True)
data.shape
data.dtypes
data['price'] =... | code |
73097219/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
def submit(model, test_features, test_ids, filename):
loss_pred = model.predict(test_features)
submission = pd.DataFrame({'id': test_ids, 'loss': loss_pred.reshape(-1)})
submission.to_csv(filename, index=False)
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train... | code |
73097219/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
def submit(model, test_features, test_ids, filename):
loss_pred = model.predict(test_features)
submission = pd.DataFrame({'id': test_ids, 'loss': loss_pred.reshape(-1)})
submission.to_csv(filename, index=False)
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train... | code |
73097219/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
def submit(model, test_features, test_ids, filename):
loss_pred = model.predict(test_features)
submission = pd.DataFrame({'id': test_ids, 'loss': loss_pred.reshape(-1)})
submission.to_csv(filename, index=False)
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train... | code |
73097219/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold
import catboost
import numpy as np
import pandas as pd
import time
def submit(model, test_features, test_ids, filename):
loss_pred = model.predict(test_features)
submission = pd.DataFrame({'id': test_ids, 'loss': loss... | code |
73097219/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
def submit(model, test_features, test_ids, filename):
loss_pred = model.predict(test_features)
submission = pd.DataFrame({'id': test_ids, 'loss': loss_pred.reshape(-1)})
submission.to_csv(filename, index=False)
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train... | code |
73097219/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
def submit(model, test_features, test_ids, filename):
loss_pred = model.predict(test_features)
submission = pd.DataFrame({'id': test_ids, 'loss': loss_pred.reshape(-1)})
submission.to_csv(filename, index=False)
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train... | code |
73097219/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
import catboost
import numpy as np
import pandas as pd
import time
def submit(model, test_features, test_ids, filename):
loss_pred = model.predict(test_features)
submission = pd.DataFrame({'id': test_ids, 'loss': loss_pred.reshape(-1)})
submission.to_csv(f... | code |
74053599/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
test = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
test.head() | code |
74053599/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
test = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
print(len(train))
print(len(test)) | code |
74053599/cell_11 | [
"text_html_output_1.png"
] | numerical_cols = [col for col in X_train_full.columns if X_train_full[col].dtype == 'int64' or X_train_full[col].dtype == 'float64']
print(len(numerical_cols)) | code |
74053599/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.preprocessing import OneHotEncoder
from xgboost import XGBRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
test = pd.read_csv('/kaggle/input/home-data-f... | code |
74053599/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 |
74053599/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
test = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
print(len(train.columns))
print(len(train.columns)) | code |
74053599/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
test = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
df_na = train.isna().sum()
df_na = df_na[df_na > 0]
print(len(df_na)) | code |
74053599/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
test = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
numerical_cols = [col for col in X_train_full.columns if X_train... | code |
74053599/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
test = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
train.head() | code |
74053599/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
from xgboost import XGBRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
test = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
numerical_cols = [col f... | code |
74053599/cell_14 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
test = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
numerical_cols = [col for col in X_train_full.columns if X_train... | code |
74053599/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
test = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
numerical_cols = [col for col in X_train_full.columns if X_train_full[col].dtype == 'int64' or X_train_ful... | code |
74053599/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
test = pd.read_csv('/kaggle/input/home-data-for-ml-course/train.csv')
train.describe() | code |
74055991/cell_13 | [
"image_output_1.png"
] | path = '/kaggle/input/cell-images-for-detecting-malaria/cell_images/'
fields = DataBlock(blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, get_y=parent_label, splitter=RandomSplitter(valid_pct=0.2, seed=42), item_tfms=RandomResizedCrop(114, min_scale=0.5), batch_tfms=aug_transforms())
dls = fields.datalo... | code |
74055991/cell_9 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | path = '/kaggle/input/cell-images-for-detecting-malaria/cell_images/'
fields = DataBlock(blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, get_y=parent_label, splitter=RandomSplitter(valid_pct=0.2, seed=42), item_tfms=RandomResizedCrop(114, min_scale=0.5), batch_tfms=aug_transforms())
dls = fields.datalo... | code |
74055991/cell_6 | [
"image_output_1.png"
] | path = '/kaggle/input/cell-images-for-detecting-malaria/cell_images/'
fields = DataBlock(blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, get_y=parent_label, splitter=RandomSplitter(valid_pct=0.2, seed=42), item_tfms=RandomResizedCrop(114, min_scale=0.5), batch_tfms=aug_transforms())
dls = fields.datalo... | code |
74055991/cell_11 | [
"text_plain_output_1.png"
] | path = '/kaggle/input/cell-images-for-detecting-malaria/cell_images/'
fields = DataBlock(blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, get_y=parent_label, splitter=RandomSplitter(valid_pct=0.2, seed=42), item_tfms=RandomResizedCrop(114, min_scale=0.5), batch_tfms=aug_transforms())
dls = fields.datalo... | code |
74055991/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | path = '/kaggle/input/cell-images-for-detecting-malaria/cell_images/'
fields = DataBlock(blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, get_y=parent_label, splitter=RandomSplitter(valid_pct=0.2, seed=42), item_tfms=RandomResizedCrop(114, min_scale=0.5), batch_tfms=aug_transforms())
dls = fields.datalo... | code |
74055991/cell_8 | [
"image_output_1.png"
] | path = '/kaggle/input/cell-images-for-detecting-malaria/cell_images/'
fields = DataBlock(blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, get_y=parent_label, splitter=RandomSplitter(valid_pct=0.2, seed=42), item_tfms=RandomResizedCrop(114, min_scale=0.5), batch_tfms=aug_transforms())
dls = fields.datalo... | code |
74055991/cell_14 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | path = '/kaggle/input/cell-images-for-detecting-malaria/cell_images/'
fields = DataBlock(blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, get_y=parent_label, splitter=RandomSplitter(valid_pct=0.2, seed=42), item_tfms=RandomResizedCrop(114, min_scale=0.5), batch_tfms=aug_transforms())
dls = fields.datalo... | code |
74055991/cell_10 | [
"image_output_1.png"
] | path = '/kaggle/input/cell-images-for-detecting-malaria/cell_images/'
fields = DataBlock(blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, get_y=parent_label, splitter=RandomSplitter(valid_pct=0.2, seed=42), item_tfms=RandomResizedCrop(114, min_scale=0.5), batch_tfms=aug_transforms())
dls = fields.datalo... | code |
74055991/cell_12 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | path = '/kaggle/input/cell-images-for-detecting-malaria/cell_images/'
fields = DataBlock(blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, get_y=parent_label, splitter=RandomSplitter(valid_pct=0.2, seed=42), item_tfms=RandomResizedCrop(114, min_scale=0.5), batch_tfms=aug_transforms())
dls = fields.datalo... | code |
90129163/cell_63 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | print(f'Mean accuracy score: {accuracy}') | code |
90129163/cell_21 | [
"text_plain_output_1.png"
] | sub.sample(10) | code |
90129163/cell_81 | [
"text_plain_output_1.png"
] | y_prob = sum(y_probs) / len(y_probs)
y_prob_results = np.argmax(y_prob, axis=1)
y_prob_results = y_prob_results.astype('bool')
sub['Transported'] = y_prob_results
sub.to_csv('submission_twenty_fold_loop_03112022.csv', index=False) | code |
90129163/cell_13 | [
"text_plain_output_1.png"
] | trn_data.head() | code |
90129163/cell_25 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | trn_passenger_ids = set(trn_data['PassengerId'].unique())
tst_passenger_ids = set(tst_data['PassengerId'].unique())
intersection = trn_passenger_ids.intersection(tst_passenger_ids)
print('Overlapped Passengers:', len(intersection)) | code |
90129163/cell_4 | [
"text_plain_output_1.png"
] | 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 |
90129163/cell_56 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
test_size_pct = 0.01
X_train, X_valid, y_train, y_valid = train_test_split(trn_data[features], trn_data[target_feature], test_size=test_size_pct, random_state=42) | code |
90129163/cell_34 | [
"text_plain_output_1.png"
] | trn_relatives = trn_relatives.rename(columns={'PassengerId': 'NumRelatives'})
tst_relatives = tst_relatives.rename(columns={'PassengerId': 'NumRelatives'}) | code |
90129163/cell_23 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | def analyse_categ_target(df, target='Transported'):
transported = df[df[target] == True].shape[0]
not_transported = df[df[target] == False].shape[0]
total = transported + not_transported
print(f'Transported : {transported / total:.2f} %')
print(f'Not Transported : {not_transported / total:.2f} %... | code |
90129163/cell_79 | [
"text_plain_output_1.png"
] | print('Mean accuracy score:', np.array(scores).mean()) | code |
90129163/cell_30 | [
"text_plain_output_1.png"
] | trn_data = total_billed(trn_data)
tst_data = total_billed(tst_data) | code |
90129163/cell_33 | [
"text_plain_output_1.png"
] | trn_relatives = trn_data.groupby('FamilyName')['PassengerId'].count().reset_index()
tst_relatives = tst_data.groupby('FamilyName')['PassengerId'].count().reset_index() | code |
90129163/cell_44 | [
"text_plain_output_1.png"
] | trn_data.head() | code |
90129163/cell_20 | [
"text_plain_output_1.png"
] | tst_data.isnull().sum() | code |
90129163/cell_55 | [
"text_plain_output_1.png"
] | features | code |
90129163/cell_6 | [
"text_plain_output_1.png"
] | import warnings
warnings.filterwarnings('ignore') | code |
90129163/cell_76 | [
"text_plain_output_1.png"
] | N_SPLITS = 20
folds = StratifiedKFold(n_splits=N_SPLITS, shuffle=True) | code |
90129163/cell_29 | [
"text_plain_output_1.png"
] | def total_billed(df):
"""
Calculates total amount billed in the trip to the passenger...
Args:
Returns:
"""
df['Total_Billed'] = df['RoomService'] + df['FoodCourt'] + df['ShoppingMall'] + df['Spa'] + df['VRDeck']
return df | code |
90129163/cell_39 | [
"text_plain_output_1.png"
] | trn_data = route(trn_data)
tst_data = route(tst_data) | code |
90129163/cell_65 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
def feature_importance(clf):
importances = clf.feature_importances_
i = np.argsort(importances)
features = X_train.columns
plt.title('Feature Importance')
plt.barh(range(len(i)), importances[i], align='center')
plt.yticks(range(len(i)), [features[x] for x in i])
... | code |
90129163/cell_48 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | trn_data, tst_data = encode_categorical(trn_data, tst_data, categorical_features) | code |
90129163/cell_73 | [
"text_plain_output_1.png"
] | code | |
90129163/cell_41 | [
"text_plain_output_1.png"
] | trn_data = age_groups(trn_data)
tst_data = age_groups(tst_data) | code |
90129163/cell_61 | [
"text_plain_output_1.png"
] | cls = XGBClassifier(**param)
cls.fit(X_train, y_train, eval_set=[(X_valid, y_valid)], eval_metric=['logloss'], early_stopping_rounds=128, verbose=False) | code |
90129163/cell_54 | [
"text_plain_output_1.png"
] | remove = ['PassengerId', 'Route', 'FirstName_Enc', 'CabinNum_Enc', 'Transported']
features = [feat for feat in trn_data.columns if feat not in remove] | code |
90129163/cell_72 | [
"text_plain_output_1.png"
] | code | |
90129163/cell_67 | [
"text_plain_output_1.png"
] | preds = cls.predict(tst_data[features]) | code |
90129163/cell_60 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | param = {'learning_rate': 0.05, 'n_estimators': 1024, 'n_jobs': -1, 'random_state': 42, 'objective': 'binary:logistic'} | code |
90129163/cell_19 | [
"text_plain_output_1.png"
] | tst_data.head() | code |
90129163/cell_7 | [
"text_plain_output_1.png"
] | DATA_ROWS = None
NROWS = 50
NCOLS = 15
BASE_PATH = '...' | code |
90129163/cell_18 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | trn_data.isnull().sum() | code |
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