path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
32068084/cell_28 | [
"application_vnd.jupyter.stderr_output_7.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn import decomposition
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
train_... | code |
32068084/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import decomposition
import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape
sc = ss()
X_train = sc.fit_transform(X_train)
X_valid = sc.transform(X_va... | code |
32068084/cell_16 | [
"text_plain_output_1.png"
] | from sklearn import decomposition
import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
X_test_final.shape
sc = ss()
X_train = sc.fit_transform(X_train)
X_valid = sc.transform(X_va... | code |
32068084/cell_35 | [
"text_plain_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn import decomposition
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestCl... | code |
32068084/cell_31 | [
"text_plain_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn import decomposition
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_scor... | code |
32068084/cell_27 | [
"text_plain_output_1.png"
] | from sklearn import decomposition
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
train_df_final = pd.read_csv('../input/pump... | code |
32068084/cell_37 | [
"text_plain_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn import decomposition
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestCl... | code |
32068084/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df_final = pd.read_csv('../input/pumpitup-challenge-dataset/train_df_final.csv')
X_test_final = pd.read_csv('../input/pumpitup-challenge-dataset/X_test_final.csv')
train_df_final.shape | code |
104131375/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
genre = pd.DataFrame()
data2 = pd.read_csv('../input/anime-quotes-dataset/lessreal-data.csv', delimiter=';', skiprows=0, low_memory=False)
data2.shape
data2.isnull().sum()
data2.drop(data2.iloc[:, 4:11], axis=1, inplace=Tr... | code |
104131375/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
data.head() | code |
104131375/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
genre = pd.DataFrame()
import matplotlib.pyplot as plt
plt.plot(genre)
plt.xlabel('Anime')
plt.ylabel('Number')
plt.title('Top 10 Animes')
plt.xticks(rotation=70)
plt.grid(True)
plt.show() | code |
104131375/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
genre = pd.DataFrame()
data2 = pd.read_csv('../input/anime-quotes-dataset/lessreal-data.csv', delimiter=';', skiprows=0, low_memory=False)
data2.shape
data2.isnull().sum()
data2.drop(data2.iloc[:, 4:11], axis=1, inplace=Tr... | code |
104131375/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
data.info() | code |
104131375/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
data.Anime.value_counts().head(10) | code |
104131375/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
genre = pd.DataFrame()
data2 = pd.read_csv('../input/anime-quotes-dataset/lessreal-data.csv', delimiter=';', skiprows=0, low_memory=False)
data2.shape | code |
104131375/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
genre = pd.DataFrame()
data2 = pd.read_csv('../input/anime-quotes-dataset/lessreal-data.csv', delimiter=';', skiprows=0, low_memory=False)
data2.shape
data2.info() | code |
104131375/cell_17 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
genre = pd.DataFrame()
data2 = pd.read_csv('../input/anime-quotes-dataset/lessreal-data.csv', delimiter=';', skiprows=0, low_memory=False)
data2.shape
data2.isnull().sum() | code |
104131375/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
genre = pd.DataFrame()
data2 = pd.read_csv('../input/anime-quotes-dataset/lessreal-data.csv', delimiter=';', skiprows=0, low_memory=False)
data2.head() | code |
104131375/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape
genre = pd.DataFrame()
genre | code |
104131375/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/anime-quotes/AnimeQuotes.csv')
data.shape | code |
2022777/cell_4 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
train.isnull().sum()
gender_pivot = train.pivot_table(index='Sex', values='Survived')
class_... | code |
2022777/cell_6 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.models import Sequential
from sklearn import tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import RFECV
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.model_selec... | code |
2022777/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
train.isnull().sum()
train.describe()
holdout.describe() | code |
2022777/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn.feature_selection
from sklearn.feature_selection import RFECV
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection imp... | code |
2022777/cell_3 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
train.isnull().sum()
gender_pivot = train.pivot_table(index='Sex', values='Survived')
gender... | code |
2022777/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
train.isnull().sum()
gender_pivot = train.pivot_table(index='Sex', values='Survived')
class_... | code |
48166874/cell_7 | [
"text_plain_output_1.png"
] | from transformers import AutoModelWithLMHead, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('mrm8488/t5-base-finetuned-wikiSQL')
model = AutoModelWithLMHead.from_pretrained('mrm8488/t5-base-finetuned-wikiSQL') | code |
48166874/cell_14 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png"
] | from datasets import load_dataset
from transformers import AutoModelWithLMHead, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('mrm8488/t5-base-finetuned-wikiSQL')
model = AutoModelWithLMHead.from_pretrained('mrm8488/t5-base-finetuned-wikiSQL')
def get_sql(query):
input_text = 'translate English to SQL:... | code |
48166874/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datasets import load_dataset
valid_dataset = load_dataset('wikisql', split='validation') | code |
48166874/cell_5 | [
"text_plain_output_1.png"
] | from transformers import AutoModelWithLMHead, AutoTokenizer
from datasets import load_dataset
import random, warnings
warnings.filterwarnings('ignore') | code |
2007202/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train['Initial'] = 0
for i in train:
train['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
test['Initial'] = 0
for i in test... | code |
2007202/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
sns.barplot(x='Embarked', y='Survived', hue='Sex', data=train) | code |
2007202/cell_9 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train[['Pclass', 'Survived']].groupby(['Pclass']).mean().sort_values(by='Survived', ascending=False) | code |
2007202/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.describe() | code |
2007202/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train['Initial'] = 0
for i in train:
train['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
test['Init... | code |
2007202/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
print(categorical)
train[categorical].describe() | code |
2007202/cell_29 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train['Initial'] = 0
for i in train:
train['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
test['Initial'] = 0
for i in test... | code |
2007202/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train['Initial'] = 0
for i in train:
train['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
test['Initial'] = 0
for i in test... | code |
2007202/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train['Initial'] = 0
for i in train:
train['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
test['Init... | code |
2007202/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train[['Parch', 'Survived']].groupby(['Parch']).mean().sort_values(by='Survived', ascending=False) | code |
2007202/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train['Initial'] = 0
for i in train:
train['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
test['Initial'] = 0
for i in test... | code |
2007202/cell_7 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum() | code |
2007202/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train['Initial'] = 0
for i in train:
train['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
test['Initial'] = 0
for i in test... | code |
2007202/cell_3 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
2007202/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train['Initial'] = 0
for i in train:
train['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
test['Initial'] = 0
for i in test... | code |
2007202/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
sns.barplot(x='Pclass', y='Survived', hue='Sex', data=train) | code |
2007202/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train[['Sex', 'Survived']].groupby(['Sex']).mean().sort_values(by='Survived', ascending=False) | code |
2007202/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train[['SibSp', 'Survived']].groupby(['SibSp']).mean().sort_values(by='Survived', ascending=False) | code |
2007202/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.info() | code |
2007202/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
categorical = train.dtypes[train.dtypes == 'object'].index
train.isnull().sum()
train['Initial'] = 0
for i in train:
train['Initial'] = train.Name.str.extract('([A-Za-z]+)\\.')
test['Initial'] = 0
for i in test... | code |
32063079/cell_8 | [
"image_output_1.png"
] | from ipywidgets import interact, interact_manual, fixed
from pathlib import Path
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error
import cufflinks as cf
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
pd.set_option('display.max_row... | code |
32063079/cell_15 | [
"text_plain_output_1.png"
] | from ipywidgets import interact, interact_manual, fixed
from pathlib import Path
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error
import arviz as az
import cufflinks as cf
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pym... | code |
32063079/cell_16 | [
"text_html_output_1.png"
] | from ipywidgets import interact, interact_manual, fixed
from pathlib import Path
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error
import arviz as az
import cufflinks as cf
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pym... | code |
32063079/cell_3 | [
"image_output_1.png"
] | from pathlib import Path
import cufflinks as cf
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('use_inf_as_na', True)
cf.set_config_file(offline=True, theme='solar')
path = Path('../input/novel-corona-virus-2019-dataset/') | code |
32063079/cell_17 | [
"image_output_1.png"
] | from ipywidgets import interact, interact_manual, fixed
from pathlib import Path
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error
import arviz as az
import cufflinks as cf
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pym... | code |
32063079/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from ipywidgets import interact, interact_manual, fixed
from pathlib import Path
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error
import arviz as az
import cufflinks as cf
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pym... | code |
32063079/cell_12 | [
"text_plain_output_1.png"
] | from ipywidgets import interact, interact_manual, fixed
from pathlib import Path
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error
import cufflinks as cf
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc3 as pm
pd.set_opt... | code |
1002861/cell_4 | [
"image_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import tensorflow as tf
import tensorflow as tf
import numpy as np
video_lvl_record = '../input/video_level/train-1.tfrecord'
frame_lvl_record = '../input/fra... | code |
1002861/cell_6 | [
"text_plain_output_1.png"
] | import tensorflow as tf
import tensorflow as tf
import numpy as np
video_lvl_record = '../input/video_level/train-1.tfrecord'
frame_lvl_record = '../input/frame_level/train-1.tfrecord'
vid_ids = []
labels = []
mean_rgb = []
mean_audio = []
for example in tf.python_io.tf_record_iterator(video_lvl_record):
tf_exampl... | code |
1002861/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1002861/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import tensorflow as tf
import tensorflow as tf
import numpy as np
video_lvl_record = '../input/video_level/train-1.tfrecord'
frame_lvl_record = '../input/frame_level/train-1.tfrecord'
vid_ids = []
labels = []
mean_rgb = []
mean_audio = []
for example in tf.python_io.tf_record_iterator(video_lvl_record):
tf_exampl... | code |
17131741/cell_3 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/raw_lemonade_data.csv')
df['Date'] = pd.to_datetime(df['Date'])
df['Price'] = df.Price.str.replace('$', '').replace(' ', '')
df['Price'] = df.Price.astype(np.float64)
df = df.set_index(df['Date'])
df = df.drop('Date', 1)
df['Revenue'] = df.Price * df.... | code |
128033347/cell_21 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train = pd... | code |
128033347/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.dropna(inplace=True)
train.info() | code |
128033347/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.dropna(inplace=True)
sns.scatterplot(train, x='x', y='y') | code |
128033347/cell_19 | [
"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)
import seaborn as sns
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.dropna(inplace=True)
xtr... | code |
128033347/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 |
128033347/cell_7 | [
"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)
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.info() | code |
128033347/cell_18 | [
"text_plain_output_1.png",
"image_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)
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.dropna(inplace=... | code |
128033347/cell_15 | [
"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)
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.dropna(inplace=True)
xtrain = train.x.values.re... | code |
128033347/cell_3 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns | code |
128033347/cell_14 | [
"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)
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
train.dropna(inplace=True)
xtrain = train.x.values.re... | code |
128033347/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test.describe() | code |
73097245/cell_13 | [
"text_html_output_1.png"
] | from numpy.linalg import norm
from scipy.sparse import coo_matrix
from surprise import Dataset,Reader,SVD
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies... | code |
73097245/cell_9 | [
"text_plain_output_1.png"
] | from scipy.sparse import coo_matrix
from surprise import Dataset,Reader,SVD
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies-dataset/ratings_small.csv')
r... | code |
73097245/cell_6 | [
"text_plain_output_1.png"
] | from surprise import Dataset,Reader,SVD
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies-dataset/ratings_small.csv')
rows = ratings.userId.unique()
columns... | code |
73097245/cell_11 | [
"text_plain_output_1.png"
] | from scipy.sparse import coo_matrix
from surprise import Dataset,Reader,SVD
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies-dataset/ratings_small.csv')
r... | code |
73097245/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 |
73097245/cell_7 | [
"text_plain_output_1.png"
] | from surprise import Dataset,Reader,SVD
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies-dataset/ratings_small.csv')
rows = ratings.userId.unique()
columns... | code |
73097245/cell_3 | [
"text_html_output_1.png"
] | from surprise import Dataset,Reader,SVD
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies-dataset/ratings_small.csv')
rows = ratings.userId.unique()
columns... | code |
73097245/cell_14 | [
"text_html_output_1.png"
] | from numpy.linalg import norm
from scipy.sparse import coo_matrix
from surprise import Dataset,Reader,SVD
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies... | code |
73097245/cell_5 | [
"text_plain_output_1.png"
] | from surprise import Dataset,Reader,SVD
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from surprise import Dataset, Reader, SVD
reader = Reader()
ratings = pd.read_csv('../input/the-movies-dataset/ratings_small.csv')
rows = ratings.userId.unique()
columns... | code |
17118943/cell_23 | [
"text_plain_output_1.png"
] | import keras as K
import numpy as np
import pandas as pd
import tensorflow as tf
np.random.seed(4)
tf.set_random_seed(13)
dfBoston = pd.read_csv('../input/boston_mm_tab.csv', header=None)
dfBoston
X = dfBoston[np.arange(0, 13)]
y = dfBoston[13]
tf.logging.set_verbosity(tf.logging.ERROR)
init = K.initializers.Rand... | code |
17118943/cell_19 | [
"text_plain_output_1.png"
] | import keras as K
import numpy as np
import pandas as pd
import tensorflow as tf
np.random.seed(4)
tf.set_random_seed(13)
dfBoston = pd.read_csv('../input/boston_mm_tab.csv', header=None)
dfBoston
X = dfBoston[np.arange(0, 13)]
y = dfBoston[13]
tf.logging.set_verbosity(tf.logging.ERROR)
init = K.initializers.Rand... | code |
17118943/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import keras as K
import numpy as np
import tensorflow as tf
np.random.seed(4)
tf.set_random_seed(13)
tf.logging.set_verbosity(tf.logging.ERROR)
init = K.initializers.RandomUniform(seed=1)
simple_sgd = K.optimizers.SGD(lr=0.01)
model = K.models.Sequential()
model.add(K.layers.Dense(units=10, input_dim=13, kernel_in... | code |
17118943/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import keras as K
import tensorflow as tf
import pandas as pd
import seaborn as sns
import os
from matplotlib import pyplot as plt
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' | code |
17118943/cell_17 | [
"text_plain_output_1.png"
] | import keras as K
import numpy as np
import pandas as pd
import tensorflow as tf
np.random.seed(4)
tf.set_random_seed(13)
dfBoston = pd.read_csv('../input/boston_mm_tab.csv', header=None)
dfBoston
X = dfBoston[np.arange(0, 13)]
y = dfBoston[13]
tf.logging.set_verbosity(tf.logging.ERROR)
init = K.initializers.Rand... | code |
72086533/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabulardata-kfolds-created/train_folds.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_submission = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
useful_features = [c for c in df_train.co... | code |
72086533/cell_10 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor
import pandas as pd
df_train = pd.read_csv('../input/tabulardata-kfolds-created/train_folds.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_submission = pd.read_csv('../input/tabular-playground-... | code |
72086533/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabulardata-kfolds-created/train_folds.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_submission = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
sample_submission.head() | code |
2011179/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
movies.isnull().values.any()
movies.isnull().values.any()
movies = movies.dropna()
ind_animation = 'Animation'
ind_children = 'Children'
animation1 = movies['genres'].s... | code |
2011179/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
movies.isnull().values.any()
movies.head() | code |
2011179/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
movies.isnull().values.any()
movies.isnull().values.any()
movies = movies.dropna()
ind_animation = 'Animation'
ind_children = 'Children'
animation1 = movies['genres'].s... | code |
2011179/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
tags.head() | code |
2011179/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
movies.isnull().values.any()
movies.isnull().values.any()
movies = movies.dropna()
ind_animation = 'Animation'
ind_children = 'Children'
animation1 = movies['genres'].s... | code |
2011179/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
ratings.head() | code |
2011179/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
movies.isnull().values.any()
movies.isnull().values.any()
movies = movies.dropna()
ind_animation = 'Animation'
ind_children = 'Children'
animation1 = movies['genres'].s... | code |
2011179/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
movies.isnull().values.any() | code |
2011179/cell_3 | [
"text_html_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2011179/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
movies = pd.read_csv('../input/movie.csv')
tags = pd.read_csv('../input/tag.csv')
ratings = pd.read_csv('../input/rating.csv')
movies.isnull().values.any()
movies.isnull().values.any()
movies = movies.dropna()
ind_animation = 'Animation'
ind_children = 'Children'
animation1 = movies['genres'].s... | code |
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