path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
88102865/cell_26 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_7.png",
"text_plain_output_8.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/misoimprovedta/ta-misogyny-train (3).csv', header=None, sep='\t')
df_eval = pd.read_csv('../input/misoimprovedta/ta-misogyny-dev (2).csv', header=None, sep='\t')
df_test = pd.read_csv('../input/misoimprovedta/ta-misogyny-test (2).csv', header=None)
def create_labels(sent... | code |
88102865/cell_19 | [
"text_html_output_1.png"
] | for i in range(0,5):
!rm -rf /kaggle/working/outputs
model.train_model(df,eval_data=df_dev,acc=sklearn.metrics.classification_report)
result, model_outputs, preds_list = model.eval_model(df_test_,acc=sklearn.metrics.classification_report)
for j in result.values():
print(j) | code |
88102865/cell_18 | [
"text_html_output_1.png"
] | from simpletransformers.classification import ClassificationModel, ClassificationArgs
model_args = ClassificationArgs()
model_args.overwrite_output_dir = True
model_args.eval_batch_size = 8
model_args.train_batch_size = 8
model_args.learning_rate = 4e-05
model = ClassificationModel('bert', 'google/muril-base-cased',... | code |
88102865/cell_8 | [
"text_html_output_1.png"
] | !pip install simpletransformers | code |
88102865/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from simpletransformers.classification import ClassificationModel, ClassificationArgs
from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('../input/misoimprovedta/ta-misogyny-train (3).csv', header=None, sep='\t')
df_eval = pd.read_csv('../input/misoimprovedta/ta-misogyny-dev (2... | code |
88102865/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('../input/misoimprovedta/ta-misogyny-train (3).csv', header=None, sep='\t')
df_eval = pd.read_csv('../input/misoimprovedta/ta-misogyny-dev (2).csv', header=None, sep='\t')
df_te... | code |
88102865/cell_10 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('../input/misoimprovedta/ta-misogyny-train (3).csv', header=None, sep='\t')
df_eval = pd.read_csv('../input/misoimprovedta/ta-misogyny-dev (2).csv', header=None, sep='\t')
df_test = pd.read_csv('../input/misoimprovedta/ta-misogy... | code |
88102865/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/misoimprovedta/ta-misogyny-train (3).csv', header=None, sep='\t')
df_eval = pd.read_csv('../input/misoimprovedta/ta-misogyny-dev (2).csv', header=None, sep='\t')
df_test = pd.read_csv('../input/misoimprovedta/ta-misogyny-test (2).csv', header=None)
df_test | code |
88099646/cell_9 | [
"text_plain_output_1.png"
] | from matplotlib.pyplot import figure
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV f... | code |
88099646/cell_4 | [
"text_html_output_1.png"
] | from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
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 sn
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.r... | code |
88099646/cell_6 | [
"text_plain_output_1.png"
] | from matplotlib.pyplot import figure
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
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 sn
import pandas as pd
import numpy as np
import random
... | code |
88099646/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')
big_dance.head() | code |
88099646/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 |
88099646/cell_7 | [
"text_plain_output_1.png"
] | from matplotlib.pyplot import figure
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
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 sn
import pandas as pd
import numpy as np
import random
... | code |
88099646/cell_8 | [
"text_plain_output_1.png"
] | from matplotlib.pyplot import figure
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
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 sn
... | code |
88099646/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')
big_dance.columns | code |
88099646/cell_10 | [
"text_html_output_1.png"
] | from matplotlib.pyplot import figure
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV f... | code |
88099646/cell_5 | [
"text_plain_output_1.png"
] | from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
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 sn
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.r... | code |
122260145/cell_42 | [
"text_plain_output_1.png"
] | from sklearn.metrics.pairwise import cosine_similarity
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')... | code |
122260145/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull(... | code |
122260145/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
users.isnull().... | code |
122260145/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
users.head(5) | code |
122260145/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull(... | code |
122260145/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull(... | code |
122260145/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
books.describe(... | code |
122260145/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull(... | code |
122260145/cell_39 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.metrics.pairwise import cosine_similarity
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')... | code |
122260145/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull(... | code |
122260145/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)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.describ... | code |
122260145/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull(... | code |
122260145/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull(... | code |
122260145/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull(... | code |
122260145/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull(... | code |
122260145/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull(... | code |
122260145/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull(... | code |
122260145/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
books.head(5) | code |
122260145/cell_35 | [
"text_html_output_1.png"
] | from sklearn.metrics.pairwise import cosine_similarity
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/... | code |
122260145/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull(... | code |
122260145/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
books.isnull().... | code |
122260145/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull(... | code |
122260145/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.isnull(... | code |
122260145/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('//kaggle/input/book-recommendation-dataset/Books.csv')
ratings = pd.read_csv('//kaggle/input/book-recommendation-dataset/Ratings.csv')
users = pd.read_csv('//kaggle/input/book-recommendation-dataset/Users.csv')
ratings.head(5) | code |
129020570/cell_21 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_absolute_error, mean_squared_error
import math | code |
129020570/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/time-series/JohnsonJohnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
df.time.unique()
df.head() | code |
129020570/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/time-series/JohnsonJohnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
df.time.unique()
train = df[df['time'] < 1980]
test = df[df['time'] >= 1980]
def arithmetic_mean(train, test):
train_mean = train['... | code |
129020570/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/time-series/JohnsonJohnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
df.time.unique()
train = df[df['time'] < 1980]
test = df[df['time'] >= 1980]
def arithmetic_mean(train, test):
train_mean = train['... | code |
129020570/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 |
129020570/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/time-series/JohnsonJohnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
df.time.unique()
train = df[df['time'] < 1980]
test = df[df['time'] >= 1980]
print('train_time', train.time.unique())
print('test_time'... | code |
129020570/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/time-series/JohnsonJohnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
df.time.unique()
train = df[df['time'] < 1980]
test = df[df['time'] >= 1980]
def arithmetic_mean(train, test):
train_mean = train['... | code |
129020570/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/time-series/JohnsonJohnson.csv')
df.head() | code |
129020570/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/time-series/JohnsonJohnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
df.time.unique()
train = df[df['time'] < 1980]
test = df[df['time'] >= 1980]
def arithmetic_mean(train, test):
train_mean = train['... | code |
129020570/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/time-series/JohnsonJohnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
df.time.unique() | code |
1006988/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
Batsman_score = deliveries.groupby('batsman')['batsman_runs'].agg(sum).reset_index().sort_values(by='batsman_runs', ascending=False).reset_index(drop=True)
Top_batsman_... | code |
1006988/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
Batsman_score = deliveries.groupby('batsman')['batsman_runs'].agg(sum).reset_index().sort_values(by='batsman_runs', ascending=False).reset_index(drop=True)
Top_batsman_... | code |
1006988/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt # for plotting Graphs
import numpy as np # for Linear algebra
import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
def autolabel(rects):
for rect in rects:
height = rect.get_h... | code |
1006988/cell_30 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # for plotting Graphs
import numpy as np # for Linear algebra
import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
def autolabel(rects):
for rect in rects:
height = rect.get_h... | code |
1006988/cell_26 | [
"image_output_1.png"
] | import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
Batsman_score = deliveries.groupby('batsman')['batsman_runs'].agg(sum).reset_index().sort_values(by='batsman_runs', ascending=False).reset_index(drop=True)
Top_batsman_... | code |
1006988/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
matches.head(2) | code |
1006988/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # for plotting Graphs
import numpy as np # for Linear algebra
import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
def autolabel(rects):
for rect in rects:
height = rect.get_h... | code |
1006988/cell_28 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # for plotting Graphs
import numpy as np # for Linear algebra
import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
def autolabel(rects):
for rect in rects:
height = rect.get_h... | code |
1006988/cell_8 | [
"image_output_1.png"
] | import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
deliveries.head(2) | code |
1006988/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # for plotting Graphs
import numpy as np # for Linear algebra
import pandas as pd # for data manipulation/CSV I/O
deliveries = pd.read_csv('../input/deliveries.csv')
matches = pd.read_csv('../input/matches.csv')
def autolabel(rects):
for rect in rects:
height = rect.get_h... | code |
1006988/cell_3 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns | code |
128021213/cell_4 | [
"text_plain_output_1.png"
] | ! pip install -q kaggle | code |
128021213/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | !pip install scikit-optimize
import numpy as np
import pandas as pd
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score,classification_report
from sklearn.model_selection import train_test_split
import math
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassi... | code |
128021213/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from google.colab import files
from google.colab import files
files.upload() | code |
323429/cell_4 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import sqlite3
import numpy as np
import pandas as pd
import sqlite3
import nltk
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
import scipy
from subprocess import check_output
con = sqlite3.connect('../input/database.sqlite')
cur = con.cursor()
sqlS... | code |
323429/cell_2 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import sqlite3
import numpy as np
import pandas as pd
import sqlite3
import nltk
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
import scipy
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
con = sqlite3.conn... | code |
17112386/cell_13 | [
"image_output_1.png"
] | from PIL import Image
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import os
import torch
batch_size = 32
latent_dim = 256
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class DogDataset(Dataset):
def __in... | code |
17112386/cell_20 | [
"text_plain_output_5.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_13.png",
"image_output_5.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_6.png",
"text_plain_output_1.png",
"image_output_3... | from PIL import Image
from torch import nn, optim
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import os
import torch
import torch.nn.functional as F
batch_size = 32
latent_dim = 256
device = to... | code |
17112386/cell_18 | [
"image_output_1.png"
] | from PIL import Image
from torch import nn, optim
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import os
import torch
import torch.nn.functional as F
batch_size = 32
latent_dim = 256
device = to... | code |
17112386/cell_24 | [
"image_output_1.png"
] | from PIL import Image
from torch import nn, optim
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import os
import torch
import torch.nn.functional as F
batch_size = 32
latent_dim = 256
device = to... | code |
17112386/cell_22 | [
"image_output_1.png"
] | from PIL import Image
from torch import nn, optim
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import os
import torch
import torch.nn.functional as F
batch_size = 32
latent_dim = 256
device = to... | code |
128024816/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
idsUnique = len(set(df_train.Id))
idsTotal = df_train.shape[0]
idsDupli = idsTotal - idsUnique
df_train.drop('Id', axis=1, inplace=True)
df_train = df_train[df_train.GrLivArea < 4000]
def... | code |
128024816/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
df_train.describe() | code |
128024816/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
print(set(df_train.Id))
idsUnique = len(set(df_train.Id))
idsTotal = df_train.shape[0]
idsDupli = idsTotal - idsUnique
df_train.drop('Id', axis=1, inplace=True)
df_train.head() | code |
128024816/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_train.shape | code |
128024816/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
idsUnique = len(set(df_train.Id))
idsTotal = df_train.shape[0]
idsDupli = idsTotal - idsUnique
df_train.drop('Id', axis=1, inplace=True)
df_train = df_train[df_train... | code |
128024816/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
idsUnique = len(set(df_train.Id))
idsTotal = df_train.shape[0]
idsDupli = idsTotal - idsUnique
df_train.drop('Id', axis=1, inplace=True)
plt.scatter(df_train.GrLivArea, df_train.SalePrice)... | code |
128024816/cell_5 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_train.shape
df_train.hist(bins=200, figsize=(20, 15)) | code |
34127932/cell_42 | [
"text_plain_output_1.png"
] | import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passeng... | code |
34127932/cell_63 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = tr... | code |
34127932/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passeng... | code |
34127932/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passeng... | code |
34127932/cell_57 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
d... | code |
34127932/cell_56 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
d... | code |
34127932/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passeng... | code |
34127932/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passeng... | code |
34127932/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passeng... | code |
34127932/cell_44 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passeng... | code |
34127932/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1... | code |
34127932/cell_55 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
d... | code |
34127932/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1... | code |
34127932/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passeng... | code |
34127932/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1... | code |
34127932/cell_65 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler,... | code |
34127932/cell_48 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1... | code |
34127932/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1... | code |
34127932/cell_61 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = tr... | code |
34127932/cell_2 | [
"text_html_output_1.png"
] | import os
import os
os.getcwd() | code |
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