path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
90156125/cell_16 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Training.csv')
match_2020 = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Matches IPL 2020.csv')
previous_matc... | code |
90156125/cell_17 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Training.csv')
match_2020 = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Matches IPL 2020.csv')
previous_matc... | code |
90156125/cell_24 | [
"text_html_output_1.png"
] | import cudf as pd
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 seaborn as sns
df_train = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Training.csv')
match_2020 = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Mat... | code |
90156125/cell_14 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Training.csv')
match_2020 = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Matches IPL 2020.csv')
previous_match = pd.read_csv('/kaggl... | code |
90156125/cell_22 | [
"text_plain_output_1.png"
] | import cudf as pd
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 seaborn as sns
df_train = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Training.csv')
match_2020 = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Mat... | code |
90156125/cell_10 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Training.csv')
match_2020 = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Matches IPL 2020.csv')
previous_match = pd.read_csv('/kaggl... | code |
90156125/cell_5 | [
"image_output_1.png"
] | import cudf as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Training.csv')
match_2020 = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Matches IPL 2020.csv')
previous_match = pd.read_csv('/kaggl... | code |
32068954/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pyspark.sql import SparkSession
import json
import os
spark = SparkSession.builder.appName('SimpleApp').getOrCreate()
sc = spark.sparkContext
def findArticlePath(article_sha_id):
"""
This function finds the full path given an article_sha_id
"""
ROOT_PATH = '/kaggle/input/CORD-19-research-challe... | code |
32068954/cell_2 | [
"text_plain_output_1.png"
] | !pip install pyspark | code |
104129811/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.graph_objects as go
train = pd.read_csv('../input/standup-targets/train.csv')
y0 = train.score.values
fig = go.Figure()
fig.add_trace(go.Box(y=y0, name='Train', marker_color='#1e90ff'))
fig.update_layout(title_text='Score stats (individual shot)')
fig.update_yaxes()
fig.update_xaxes... | code |
104129811/cell_4 | [
"text_html_output_2.png"
] | import pandas as pd
train = pd.read_csv('../input/standup-targets/train.csv')
train.head() | code |
104129811/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/standup-targets/train.csv')
train | code |
104129811/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/standup-targets/train.csv')
print(f'Number of bullet impacts : {train.shape[0]}')
print(f'Average number of impacts per target : {train.shape[0] / len(train.image_name.unique())}') | code |
104129811/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objects as go
train = pd.read_csv('../input/standup-targets/train.csv')
y0 = train.score.values
fig = go.Figure()
fig.add_trace(go.Box(y=y0, name='Train', marker_color='#1e90ff'))
fig.update_layout(title_text='Score stats (individual shot)')
fig.update_yaxes()
fig.update_xaxes... | code |
104129811/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.graph_objects as go
train = pd.read_csv('../input/standup-targets/train.csv')
y0 = train.score.values
fig = go.Figure()
fig.add_trace(go.Box(y=y0, name='Train', marker_color='#1e90ff'))
fig.update_layout(title_text='Score stats (individual shot)')
fig.update_yaxes()
fig.update_xaxes... | code |
104129811/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.graph_objects as go
train = pd.read_csv('../input/standup-targets/train.csv')
y0 = train.score.values
fig = go.Figure()
fig.add_trace(go.Box(y=y0, name='Train', marker_color='#1e90ff'))
fig.update_layout(title_text='Score stats (individual shot)')
fig.update_yaxes()
fig.update_xaxes... | code |
74052264/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/covidqa/community.csv')
df.head() | code |
74052264/cell_6 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
import pandas as pd
df = pd.read_csv('../input/covidqa/community.csv')
vectorizer = TfidfVectorizer()
vectorizer.fit(np.concatenate((df.question, df.answer))) | code |
74052264/cell_11 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
import pandas as pd
df = pd.read_csv('../input/covidqa/community.csv')
vectorizer = TfidfVectorizer()
vectorizer.fit(np.concatenate((df.question, df.answer)))
Question_vectors = ve... | code |
2039183/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from gensim.models import Phrases
from gensim.models import word2vec
from gensim.models.phrases import Phraser
from nltk.corpus import stopwords
import logging
import pandas as pd
import pickle
import pickle
import re
import pandas as pd
import re
from nltk.corpus import stopwords
from gensim.models import wor... | code |
2039183/cell_6 | [
"text_plain_output_1.png"
] | from gensim.models import Phrases
from gensim.models.phrases import Phraser
from nltk.corpus import stopwords
import pandas as pd
import re
import pandas as pd
import re
from nltk.corpus import stopwords
from gensim.models import word2vec
import pickle
import nltk.data
import os
tokenizer = nltk.data.load('tokeniz... | code |
2039183/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
path = '../input/'
TRAIN_DATA_FILE = f'{path}train.csv'
TEST_DATA_FILE = f'{path}test.csv'
train = pd.read_csv(TRAIN_DATA_FILE, header=0)
test = pd.read_csv(TEST_DATA_FILE, header=0)
print('Read %d labeled train reviews and %d unlabelled test reviews' % (len(train), len(test)))
all_comm... | code |
2039183/cell_7 | [
"text_plain_output_1.png"
] | from gensim.models import Phrases
from gensim.models.phrases import Phraser
from nltk.corpus import stopwords
import pandas as pd
import pickle
import pickle
import re
import pandas as pd
import re
from nltk.corpus import stopwords
from gensim.models import word2vec
import pickle
import nltk.data
import os
token... | code |
122259592/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
sample_sub = pd.read_csv('sample_submission.csv')
test_data = pd.read_csv('test_dataset.csv', index_col=0)
train_data = pd.read_csv('train_dataset.csv', index_col=0)
sample_sub.head() | code |
48162572/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.read_csv('/kaggle/input/lish-moa/sample_submission.csv')
... | code |
48162572/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.read_csv('/kaggle/input/lish-moa/sample_submission.csv')
... | code |
48162572/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.read_csv('/kaggle/input/lish-moa/sample_submission.csv')
... | code |
48162572/cell_30 | [
"image_output_1.png"
] | import pandas as pd
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.read_csv('/kaggle/input/lish-moa/sample_submission.csv')
... | code |
48162572/cell_44 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
import pandas as pd
import statistics
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.... | code |
48162572/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.read_csv('/kaggle/input/lish-moa/sample_submission.csv')
... | code |
48162572/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd
import statistics
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.read_csv('/kaggle/input/lish-moa/sample... | code |
48162572/cell_41 | [
"image_output_1.png"
] | import pandas as pd
import statistics
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.read_csv('/kaggle/input/lish-moa/sample... | code |
48162572/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.read_csv('/kaggle/input/l... | code |
48162572/cell_52 | [
"text_html_output_1.png"
] | x_train.head() | code |
48162572/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.read_csv('/kaggle/input/lish-moa/sample_submission.csv')
... | code |
48162572/cell_45 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
import pandas as pd
import statistics
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.... | code |
48162572/cell_49 | [
"text_html_output_1.png"
] | from sklearn.decomposition import PCA
import pandas as pd
import statistics
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.... | code |
48162572/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.read_csv('/kaggle/input/l... | code |
48162572/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.read_csv('/kaggle/input/lish-moa/sample_submission.csv')
... | code |
48162572/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.read_csv('/kaggle/input/lish-moa/sample_submission.csv')
... | code |
48162572/cell_31 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.read_csv('/kaggle/input/l... | code |
48162572/cell_46 | [
"text_html_output_1.png"
] | from sklearn.decomposition import PCA
import pandas as pd
import statistics
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.... | code |
48162572/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.read_csv('/kaggle/input/lish-moa/sample_submission.csv')
... | code |
48162572/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.read_csv('/kaggle/input/lish-moa/sample_submission.csv')
... | code |
48162572/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd
import statistics
train_features = pd.read_csv('/kaggle/input/lish-moa/train_features.csv')
train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
submission = pd.read_csv('/kaggle/input/lish-moa/sample... | code |
88092902/cell_9 | [
"text_html_output_4.png",
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_1.png",
"text_html_output_3.png"
] | from cuml.svm import SVR
import matplotlib.pyplot as plt
import pandas as pd
import random
data_types_dict = {'time_id': 'int16', 'investment_id': 'int16', 'target': 'float32'}
features = [f'f_{i}' for i in range(300)]
for f in features:
data_types_dict[f] = 'float32'
train = pd.read_csv('../input/ubiquant-mark... | code |
88092902/cell_6 | [
"text_plain_output_1.png"
] | from cuml.svm import SVR
import pandas as pd
import random
data_types_dict = {'time_id': 'int16', 'investment_id': 'int16', 'target': 'float32'}
features = [f'f_{i}' for i in range(300)]
for f in features:
data_types_dict[f] = 'float32'
train = pd.read_csv('../input/ubiquant-market-prediction/train.csv', usecols... | code |
88092902/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from cuml.svm import SVR
import pandas as pd
import pickle
import random
import ubiquant
data_types_dict = {'time_id': 'int16', 'investment_id': 'int16', 'target': 'float32'}
features = [f'f_{i}' for i in range(300)]
for f in features:
data_types_dict[f] = 'float32'
train = pd.read_csv('../input/ubiquant-marke... | code |
88092902/cell_10 | [
"text_plain_output_1.png"
] | from cuml.svm import SVR
import numpy as np
import pandas as pd
import random
data_types_dict = {'time_id': 'int16', 'investment_id': 'int16', 'target': 'float32'}
features = [f'f_{i}' for i in range(300)]
for f in features:
data_types_dict[f] = 'float32'
train = pd.read_csv('../input/ubiquant-market-prediction... | code |
128005771/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('diabetes_prediction_dataset.csv')
df.head() | code |
32064989/cell_21 | [
"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
pd.options.display.float_format = '{:20,.2f}'.format
import os
Salary = pd.read_csv('../input/salary/Salary.csv')
model = pd.read_csv('../input/salary/Salary.csv')
sum(model['errors0']) | code |
32064989/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt #for visualizing
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
pd.options.display.float_format = '{:20,.2f}'.format
import os
Salary = pd.read_csv('../input/salary/Salary.csv')
plt.scatter(x='YearsExperience',... | code |
32064989/cell_25 | [
"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
pd.options.display.float_format = '{:20,.2f}'.format
import os
Salary = pd.read_csv('../input/salary/Salary.csv')
model = pd.read_csv('../input/salary/Salary.csv')
sum(model['errors2']) | code |
32064989/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
lr_model = LinearRegression()
lr_model.fit(x_train, y_train)
lr_model.intercept_ | code |
32064989/cell_23 | [
"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
pd.options.display.float_format = '{:20,.2f}'.format
import os
Salary = pd.read_csv('../input/salary/Salary.csv')
model = pd.read_csv('../input/salary/Salary.csv')
sum(model['errors1']) | code |
32064989/cell_30 | [
"text_plain_output_1.png"
] | print(x_train.shape, y_train.shape) | code |
32064989/cell_33 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
lr_model = LinearRegression()
lr_model.fit(x_train, y_train) | code |
32064989/cell_20 | [
"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
pd.options.display.float_format = '{:20,.2f}'.format
import os
Salary = pd.read_csv('../input/salary/Salary.csv')
model = pd.read_csv('../input/salary/Salary.csv')
model | code |
32064989/cell_40 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt #for visualizing
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
pd.options.display.float_format = '{:20,.2f}'.format
import os
Salary = pd.read_csv('../input/salary/Salary.csv')
model = pd.read_csv('../input/sa... | code |
32064989/cell_39 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt #for visualizing
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
pd.options.display.float_format = '{:20,.2f}'.format
import os
Salary = pd.read_csv('../input/salary/Salary.csv')
model = pd.read_csv('../input/sa... | code |
32064989/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
pd.options.display.float_format = '{:20,.2f}'.format
import os
Salary = pd.read_csv('../input/salary/Salary.csv')
model = pd.read_csv('../input/salary/Salary.csv')
sum(model['errors2'] ** 2) | code |
32064989/cell_2 | [
"text_plain_output_1.png",
"image_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
pd.options.display.float_format = '{:20,.2f}'.format
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32064989/cell_7 | [
"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
pd.options.display.float_format = '{:20,.2f}'.format
import os
Salary = pd.read_csv('../input/salary/Salary.csv')
Salary.info()
Salary.describe() | code |
32064989/cell_32 | [
"text_plain_output_1.png"
] | print(x_test.shape, y_test.shape) | code |
32064989/cell_15 | [
"text_html_output_1.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
pd.options.display.float_format = '{:20,.2f}'.format
import os
Salary = pd.read_csv('../input/salary/Salary.csv')
model = pd.read_csv('../input/salary/Salary.csv')
model | code |
32064989/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
lr_model = LinearRegression()
lr_model.fit(x_train, y_train)
lr_model.intercept_
lr_model.coef_ | code |
32064989/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | x_train | code |
32064989/cell_24 | [
"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
pd.options.display.float_format = '{:20,.2f}'.format
import os
Salary = pd.read_csv('../input/salary/Salary.csv')
model = pd.read_csv('../input/salary/Salary.csv')
sum(model['errors1'] ** 2) | code |
32064989/cell_22 | [
"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
pd.options.display.float_format = '{:20,.2f}'.format
import os
Salary = pd.read_csv('../input/salary/Salary.csv')
model = pd.read_csv('../input/salary/Salary.csv')
sum(model['errors0'] ** 2) | code |
32064989/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt #for visualizing
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
pd.options.display.float_format = '{:20,.2f}'.format
import os
Salary = pd.read_csv('../input/salary/Salary.csv')
model = pd.read_csv('../input/sa... | code |
32064989/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
lr_model = LinearRegression()
lr_model.fit(x_train, y_train)
lr_model.intercept_
lr_model.coef_
lr_model.score(x_train, y_train)
lr_model.score(x_test, y_test) | code |
32064989/cell_12 | [
"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
pd.options.display.float_format = '{:20,.2f}'.format
import os
Salary = pd.read_csv('../input/salary/Salary.csv')
model = pd.read_csv('../input/salary/Salary.csv')
model | code |
32064989/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
lr_model = LinearRegression()
lr_model.fit(x_train, y_train)
lr_model.intercept_
lr_model.coef_
lr_model.score(x_train, y_train) | code |
130025335/cell_25 | [
"image_output_1.png"
] | from tensorflow.keras.utils import load_img
import cv2
import hashlib
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import random
def load_image(filename):
image = load_img('../input/plant-pathology-2021-fgvc8/train_images/' + filename)
def load_random_image(filenames):
... | code |
130025335/cell_30 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
from tensorflow.keras.utils import load_img
import cv2
import hashlib
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import random
def load_image(filename):
image = load_img('../input/plant-pathology-2021-fgvc8/train_im... | code |
130025335/cell_20 | [
"text_plain_output_1.png"
] | from tensorflow.keras.utils import load_img
import cv2
import hashlib
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import random
def load_image(filename):
image = load_img('../input/plant-pathology-2021-fgvc8/train_images/' + filename)
def load_random_image(filenames):
... | code |
130025335/cell_29 | [
"image_output_1.png"
] | from tensorflow.keras.utils import load_img
import cv2
import hashlib
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import random
def load_image(filename):
image = load_img('../input/plant-pathology-2021-fgvc8/train_images/' + filename)
def load_random_image(filenames):
... | code |
130025335/cell_11 | [
"text_plain_output_1.png"
] | from tensorflow.keras.utils import load_img
import cv2
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
def load_image(filename):
image = load_img('../input/plant-pathology-2021-fgvc8/train_images/' + filename)
def load_random_image(filenames):
sample = random.choice(fi... | code |
130025335/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import random
from tensorflow.keras.utils import load_img
import matplotlib.pyplot as plt
import glob as gb
from kaggle_datasets import KaggleDatasets
!pip install -q efficientnet
import efficientnet.tfkeras as efn
import tensorflow as tf
from sklearn.utils import shuffle
from sk... | code |
130025335/cell_18 | [
"text_plain_output_1.png"
] | from tensorflow.keras.utils import load_img
import cv2
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
def load_image(filename):
image = load_img('../input/plant-pathology-2021-fgvc8/train_images/' + filename)
def load_random_image(filenames):
sample = random.choice(fi... | code |
130025335/cell_28 | [
"text_plain_output_1.png"
] | from tensorflow.keras.utils import load_img
import cv2
import hashlib
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import random
def load_image(filename):
image = load_img('../input/plant-pathology-2021-fgvc8/train_images/' + filename)
def load_random_image(filenames):
... | code |
130025335/cell_3 | [
"text_plain_output_1.png"
] | import tensorflow as tf
gpus = tf.config.list_physical_devices('GPU')
print(gpus)
if len(gpus) == 1:
strategy = tf.distribute.OneDeviceStrategy(device='/gpu:0')
else:
strategy = tf.distribute.MirroredStrategy() | code |
130025335/cell_17 | [
"text_plain_output_1.png"
] | from tensorflow.keras.utils import load_img
import cv2
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
def load_image(filename):
image = load_img('../input/plant-pathology-2021-fgvc8/train_images/' + filename)
def load_random_image(filenames):
sample = random.choice(fi... | code |
130025335/cell_31 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
from tensorflow.keras.utils import load_img
import cv2
import hashlib
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import random
def load_image(filename):
image = load_img('../input/plant-pathology-2021-fgvc8/train_im... | code |
130025335/cell_24 | [
"text_plain_output_1.png"
] | from tensorflow.keras.utils import load_img
import cv2
import hashlib
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import random
def load_image(filename):
image = load_img('../input/plant-pathology-2021-fgvc8/train_images/' + filename)
def load_random_image(filenames):
... | code |
130025335/cell_22 | [
"image_output_1.png"
] | from tensorflow.keras.utils import load_img
import cv2
import hashlib
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import random
def load_image(filename):
image = load_img('../input/plant-pathology-2021-fgvc8/train_images/' + filename)
def load_random_image(filenames):
... | code |
130025335/cell_5 | [
"image_output_1.png"
] | import tensorflow as tf
gpus = tf.config.list_physical_devices('GPU')
if len(gpus) == 1:
strategy = tf.distribute.OneDeviceStrategy(device='/gpu:0')
else:
strategy = tf.distribute.MirroredStrategy()
tf.config.optimizer.set_experimental_options({'auto_mixed_precision': True})
print('Mixed precision enabled') | code |
18159957/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/bank-additional-full.csv', sep=';', decimal='.', header=0, names=['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'p... | code |
18159957/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bank-additional-full.csv', sep=';', decimal='.', header=0, names=['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx', 'cons.conf.i... | code |
18159957/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bank-additional-full.csv', sep=';', decimal='.', header=0, names=['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx', 'cons.conf.i... | code |
18159957/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bank-additional-full.csv', sep=';', decimal='.', header=0, names=['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx', 'cons.conf.i... | code |
18159957/cell_19 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/bank-additional-full.csv', sep=';', decimal='.', header=0, names=['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'p... | code |
18159957/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bank-additional-full.csv', sep=';', decimal='.', header=0, names=['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx', 'cons.conf.i... | code |
18159957/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/bank-additional-full.csv', sep=';', decimal='.', header=0, names=['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'p... | code |
18159957/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bank-additional-full.csv', sep=';', decimal='.', header=0, names=['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx', 'cons.conf.i... | code |
18159957/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/bank-additional-full.csv', sep=';', decimal='.', header=0, names=['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'p... | code |
18159957/cell_16 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bank-additional-full.csv', sep=';', decimal='.', header=0, names=['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx', 'cons.conf.i... | code |
18159957/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/bank-additional-full.csv', sep=';', decimal='.', header=0, names=['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'p... | code |
18159957/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/bank-additional-full.csv', sep=';', decimal='.', header=0, names=['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'p... | code |
18159957/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bank-additional-full.csv', sep=';', decimal='.', header=0, names=['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx', 'cons.conf.i... | code |
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