path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105200129/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/chocolate-bar-ratings/chocolate_bars.csv')
data.isnull().sum()
data['ingredients'].value_counts() | code |
105200129/cell_30 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/chocol... | code |
105200129/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/chocolate-bar-ratings/chocolate_bars.csv')
data.isnull().sum()
data.isnull().sum()
from sklearn.preprocessing import LabelEncoder
En = LabelEncoder... | code |
105200129/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/chocolate-bar-ratings/chocolate_bars.csv')
data.isnull().sum()
data['num_ingredients'].value_counts() | code |
105200129/cell_19 | [
"text_plain_output_1.png"
] | """from sklearn.preprocessing import LabelEncoder
En = LabelEncoder()
Enco_lab = En.fit_transform(data['bar_name'])
data.drop("bar_name", axis=1, inplace=True)
data["bar_name"] = Enco_lab""" | code |
105200129/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 |
105200129/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/chocolate-bar-ratings/chocolate_bars.csv')
data.head() | code |
105200129/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/chocol... | code |
105200129/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/chocolate-bar-ratings/chocolate_bars.csv')
data.isnull().sum() | code |
105200129/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/chocolate-bar-ratings/chocolate_bars.csv')
data.isnull().sum()
data.isnull().sum() | code |
105200129/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/chocolate-bar-ratings/chocolate_bars.csv')
data.isnull().sum()
data.isnull().sum()
data['bean_origin'].value_counts() | code |
105200129/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/chocolate-bar-ratings/chocolate_bars.csv')
data.head() | code |
105200129/cell_17 | [
"text_plain_output_1.png"
] | """from sklearn.preprocessing import LabelEncoder
En = LabelEncoder()
Enco_com = En.fit_transform(data['company_location'])
data.drop("company_location", axis=1, inplace=True)
data["company_location"] = Enco_com""" | code |
105200129/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/chocol... | code |
105200129/cell_24 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/chocolate-bar-ratings/chocolate_bars.csv')
data.isnull().sum()
data.isnull().sum()
from sklearn.pre... | code |
105200129/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/chocolate-bar-ratings/chocolate_bars.csv')
data.isnull().sum()
data.isnull().sum()
from sklearn.preprocessing import LabelEncoder
En = LabelEncoder... | code |
105200129/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/chocol... | code |
34149808/cell_4 | [
"text_plain_output_1.png"
] | from typing import List, Tuple
import numpy as np
def soft_accuracy(y_true: np.ndarray, y_pred: np.ndarray) -> Tuple[List[float], float]:
"""
Args:
y_true (np.ndarray): GT int indices of (N, K). N is number of samples, K is number of possible answers.
y_pred (np.ndarray): Predicted int indices... | code |
34149808/cell_3 | [
"text_plain_output_1.png"
] | from typing import List, Tuple
import numpy as np
def soft_accuracy(y_true: np.ndarray, y_pred: np.ndarray) -> Tuple[List[float], float]:
"""
Args:
y_true (np.ndarray): GT int indices of (N, K). N is number of samples, K is number of possible answers.
y_pred (np.ndarray): Predicted int indices... | code |
34149808/cell_5 | [
"image_output_1.png"
] | from typing import List, Tuple
import matplotlib.pyplot as plt
import numpy as np
def soft_accuracy(y_true: np.ndarray, y_pred: np.ndarray) -> Tuple[List[float], float]:
"""
Args:
y_true (np.ndarray): GT int indices of (N, K). N is number of samples, K is number of possible answers.
y_pred (n... | code |
16155942/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Shinzo Abe Tweet 20171024 - Tweet.csv')
df.describe() | code |
16155942/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Shinzo Abe Tweet 20171024 - Tweet.csv')
df.head(5) | code |
16155942/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Shinzo Abe Tweet 20171024 - Tweet.csv')
data = df[['Retweets', 'Likes']]
data.corr(method='pearson')
data = df[['Replies', 'Retweets']]
data.corr(method='pearson') | code |
16155942/cell_19 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction import stop_words
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/Shinzo Abe Tweet 20171024 - Tweet.csv')
like_mean = df['Likes'].mean()
df_popular = df.query('Likes > ' + str(like_mean))
df_unpopular = df.query('Likes <... | code |
16155942/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Shinzo Abe Tweet 20171024 - Tweet.csv')
att = ['Replies', 'Retweets', 'Likes']
pd.plotting.scatter_matrix(df[att]) | code |
16155942/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Shinzo Abe Tweet 20171024 - Tweet.csv')
df.head(5) | code |
16155942/cell_17 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction import stop_words
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/Shinzo Abe Tweet 20171024 - Tweet.csv')
like_mean = df['Likes'].mean()
df_popular = df.query('Likes > ' + str(like_mean))
df_unpopular = df.query('Likes <... | code |
16155942/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Shinzo Abe Tweet 20171024 - Tweet.csv')
data = df[['Retweets', 'Likes']]
data.corr(method='pearson') | code |
16155942/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Shinzo Abe Tweet 20171024 - Tweet.csv')
data = df[['Retweets', 'Likes']]
data.corr(method='pearson')
data = df[['Replies', 'Retweets']]
data.corr(method='pearson')
data = df[['Likes', 'Replies']]
data.corr(method='pearson') | code |
89132601/cell_25 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import seaborn as sb
from xgboost import XGBClassifier
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
test_df = pd.read_csv('../input/spaceship-titanic/test.csv')
X_train = train_df.iloc[:, :-1]
Y_train = train_df.iloc[:, -1... | code |
89132601/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import seaborn as sb
from xgboost import XGBClassifier
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
test_df = pd.read_csv('../input/spaceship-titanic/test.csv')
X_train = train_df.iloc[:, :-1]
Y_train = train_df.iloc[:, -1]
X_test = test_df
... | code |
89132601/cell_24 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import seaborn as sb
from xgboost import XGBClassifier
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
test_df = pd.read_csv('../input/spaceship-titanic/test.csv')
X_train = train_df.iloc[:, :-1]
Y_train = train_df.iloc[:, -1... | code |
49127363/cell_9 | [
"text_plain_output_100.png",
"text_plain_output_84.png",
"text_plain_output_56.png",
"text_plain_output_35.png",
"text_plain_output_130.png",
"text_plain_output_117.png",
"text_plain_output_98.png",
"text_plain_output_43.png",
"text_plain_output_78.png",
"text_plain_output_106.png",
"text_plain_... | from collections import Counter
from keras.callbacks import History, EarlyStopping
from keras.layers import Conv1D, BatchNormalization, Dense, Flatten, Activation, Dropout
from keras.models import Sequential
from keras.utils import Sequence
from tensorflow.python.client import device_lib
from time import perf_cou... | code |
49127363/cell_4 | [
"text_plain_output_1.png"
] | from collections import Counter
from keras.callbacks import History, EarlyStopping
from keras.utils import Sequence
from tensorflow.python.client import device_lib
import keras
import numpy as np
import os
import pywt
import soundfile as sf
import tensorflow as tf
import os
from time import perf_counter
impor... | code |
49127363/cell_6 | [
"text_plain_output_1.png"
] | drop_levels = 2
"\n#ss = np.random.random_sample(2**17)\nsig_dwt = pywt.wavedec(sig,WAVELET,mode='per')\nprint('# of levels decomposed {}'.format(dec_lvls))\n" | code |
49127363/cell_1 | [
"text_plain_output_1.png"
] | from keras.callbacks import History, EarlyStopping
from tensorflow.python.client import device_lib
import os
from time import perf_counter
import numpy as np
import soundfile as sf
from collections import Counter
import matplotlib.pyplot as plt
from tensorflow.python.client import device_lib
print(device_lib.list_loc... | code |
72068883/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,f1_score,classification_report
from sklearn.naive_bayes import GaussianNB
model = GaussianNB()
model.fit(X_train, y_train)
predicted = model.predict(X_test)
from sklearn.ensemble impor... | code |
72068883/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
train_df.isnull().sum() | code |
72068883/cell_11 | [
"text_plain_output_1.png"
] | from sklearn import tree
from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,f1_score,classification_report
classifier = tree.DecisionTreeClassifier(max_depth=2, random_state=0)
classifier.fit(X_train, y_train)
predictions = classifier.predict(X_test)
print(accuracy_score(y_test, predictions))... | code |
72068883/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,f1_score,classification_report
from sklearn.svm import SVC
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score
svclassifier = SVC(C=1.0, kernel='linear')
svclassifier.fit(X_train, y_train)
y_pred = svclassifier.pre... | code |
72068883/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,f1_score,classification_report
from sklearn.ensemble import RandomForestClassifier
random_forest = RandomForestClassifier(n_estimators=180, max_depth=4, random_state=0)
random_forest.fit(X_t... | code |
72068883/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,f1_score,classification_report
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
print('The accuracy of the Knn classifier... | code |
72068883/cell_22 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
train_df.isnull().sum()
test_df.isnull().sum()
def train_preprocess(train_df):
train_df = train_df.fillna(train_df.groupby('Survived').transform('mean'))
train_df['... | code |
72068883/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,f1_score,classification_report
from sklearn.naive_bayes import GaussianNB
model = GaussianNB()
model.fit(X_train, y_train)
predicted = model.predict(X_test)
print(accuracy_score(y_test, predicted))
print(precision_score(y_test, predicted, ave... | code |
72068883/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/train.csv')
test_df = pd.read_csv('../input/titanic/test.csv')
test_df.isnull().sum() | code |
128040649/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import os
import pandas as pd
covid_path = '/kaggle/input/covid19/COVID-19_Radiography_Dataset/COVID/images/'
normal_path = '/kaggle/input/covid19/COVID-19_Radiography_Dataset/Normal/images/'
base_dir = 'base_dir'
os.mkdir(base_... | code |
128040649/cell_2 | [
"text_html_output_1.png"
] | import os
import cv2
import imageio
import pandas as pd
import numpy as np
from sklearn.utils import shuffle
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
import tensorflow
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropo... | code |
128040649/cell_8 | [
"text_html_output_1.png"
] | from sklearn.utils import shuffle
import os
import pandas as pd
covid_path = '/kaggle/input/covid19/COVID-19_Radiography_Dataset/COVID/images/'
normal_path = '/kaggle/input/covid19/COVID-19_Radiography_Dataset/Normal/images/'
base_dir = 'base_dir'
os.mkdir(base_dir)
train_dir = os.path.join(base_dir, 'train_dir')
o... | code |
128040649/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.utils import shuffle
import os
import pandas as pd
covid_path = '/kaggle/input/covid19/COVID-19_Radiography_Dataset/COVID/images/'
normal_path = '/kaggle/input/covid19/COVID-19_Radiography_Dataset/Normal/images/'
base_dir = 'base_dir'
os.mkdir(base_dir)
train_dir = os.path.join(base_dir, 'train_dir')
o... | code |
128040649/cell_5 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np
covid_path = '/kaggle/input/covid19/COVID-19_Radiography_Dataset/COVID/images/'
normal_path = '/kaggle/input/covid19/COVID-19_Radiography_Dataset/Normal/images/'
def img_preprocessing(image_path):
img = cv2.imread(image_path, 0)
org_img = img.cop... | code |
128006817/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.head() | code |
128006817/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum() | code |
128006817/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
import plotly.express as px | code |
128006817/cell_18 | [
"text_html_output_2.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
df_rating = df.groupby('City')['Rating'].mean().reset_index()
df_rating = df_rating.sort_values('Rating')
average_cost = df.groupby(['City'])[... | code |
128006817/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
df_rating = df.groupby('City')['Rating'].mean().reset_index()
df_rating = df_rating.sort_values('Rating')
plt.figure(figsize=(16, 10))
plt.pie(df_rating['Rating'], labels=d... | code |
128006817/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
df_rating = df.groupby('City')['Rating'].mean().reset_index()
df_rating = df_rating.sort_values('Rating')
average_cost = df.groupby(['City'])[... | code |
128006817/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
df_rating = df.groupby('City')['Rating'].mean().reset_index()
df_rating = df_rating.sort_values('Rating')
average_cost = df.groupby(['City'])[... | code |
128006817/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
df_rating = df.groupby('City')['Rating'].mean().reset_index()
df_rating = df_rating.sort_values('Rating')
average_cost = df.groupby(['City'])[... | code |
128006817/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
df_rating = df.groupby('City')['Rating'].mean().reset_index()
df_rating = df_rating.sort_values('Rating')
average_cost ... | code |
90139661/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 |
90139661/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import gc
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/challenges-in-representation-learning-facial-expression-recognition-challenge/icml_face_data.csv')
dataset.columns = ['emotion', 'Usage', 'pixels']
test_dataset = datas... | code |
128030655/cell_21 | [
"text_html_output_1.png"
] | import os , string , sys , numpy, spacy , nltk, re, random, timeit
import pandas as pd
working_dir = '../input/nyt-comments/'
headlines = []
for filename in os.listdir(working_dir):
if 'Articles' in filename:
article_df = pd.read_csv(working_dir + filename)
headlines.extend(list(article_df.headlin... | code |
128030655/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os , string , sys , numpy, spacy , nltk, re, random, timeit
import pandas as pd
working_dir = '../input/nyt-comments/'
headlines = []
for filename in os.listdir(working_dir):
if 'Articles' in filename:
article_df = pd.read_csv(working_dir + filename)
headlines.extend(list(article_df.headlin... | code |
128030655/cell_9 | [
"text_plain_output_1.png"
] | import os , string , sys , numpy, spacy , nltk, re, random, timeit
import pandas as pd
working_dir = '../input/nyt-comments/'
headlines = []
for filename in os.listdir(working_dir):
if 'Articles' in filename:
article_df = pd.read_csv(working_dir + filename)
headlines.extend(list(article_df.headlin... | code |
128030655/cell_4 | [
"image_output_1.png"
] | import tensorflow as tf
import pandas as pd
import os, string, sys, numpy, spacy, nltk, re, random, timeit
import numpy as np
import matplotlib.pyplot as plt
from spacy import displacy
import plotly.express as px
from tensorflow import keras
from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional, Dro... | code |
128030655/cell_33 | [
"text_plain_output_1.png"
] | from tensorflow import keras
from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import numpy as np
import os , ... | code |
128030655/cell_29 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import numpy as np
import os , string , sys , numpy, spacy , nltk, re, random, timeit
import pandas as pd
import tensorflow as tf
working_dir = '../input/nyt-comments/'
headlines = []
for fi... | code |
128030655/cell_7 | [
"text_plain_output_1.png"
] | import os , string , sys , numpy, spacy , nltk, re, random, timeit
import pandas as pd
working_dir = '../input/nyt-comments/'
headlines = []
for filename in os.listdir(working_dir):
if 'Articles' in filename:
article_df = pd.read_csv(working_dir + filename)
headlines.extend(list(article_df.headlin... | code |
128030655/cell_38 | [
"text_plain_output_1.png"
] | from tensorflow import keras
from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import matplotlib.pyplot as plt
... | code |
128030655/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os , string , sys , numpy, spacy , nltk, re, random, timeit
import pandas as pd
working_dir = '../input/nyt-comments/'
headlines = []
for filename in os.listdir(working_dir):
if 'Articles' in filename:
article_df = pd.read_csv(working_dir + filename)
headlin... | code |
128030655/cell_35 | [
"text_plain_output_1.png"
] | from tensorflow import keras
from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import numpy as np
import os , ... | code |
128030655/cell_43 | [
"text_plain_output_1.png"
] | from tensorflow import keras
from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import numpy as np
import os , ... | code |
128030655/cell_31 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import numpy as np
import os , string , sys , numpy, spacy , nltk, re, random, timeit
import pandas as pd
import tensorflow as tf
working_dir = '../input/nyt-comments/'
headlines = []
for fi... | code |
128030655/cell_46 | [
"image_output_1.png"
] | from tensorflow import keras
from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import numpy as np
import os , ... | code |
128030655/cell_24 | [
"text_html_output_2.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import os , string , sys , numpy, spacy , nltk, re, random, timeit
import pandas as pd
working_dir = '../input/nyt-comments/'
headlines = []
for filename in os.listdir(working_dir):
if 'Articles' in filename:
article_df = pd.read_csv(working_dir +... | code |
128030655/cell_14 | [
"text_plain_output_1.png"
] | import os , string , sys , numpy, spacy , nltk, re, random, timeit
import pandas as pd
import plotly.express as px
working_dir = '../input/nyt-comments/'
headlines = []
for filename in os.listdir(working_dir):
if 'Articles' in filename:
article_df = pd.read_csv(working_dir + filename)
headlines.e... | code |
128030655/cell_27 | [
"image_output_1.png"
] | from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import numpy as np
import os , string , sys , numpy, spacy , nltk, re, random, timeit
import pandas as pd
import tensorflow as tf
working_dir = '../input/nyt-comments/'
headlines = []
for fi... | code |
122257913/cell_21 | [
"text_html_output_1.png"
] | from sklearn import linear_model
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('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
(train.shape, test.shape)
train.... | code |
122257913/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
(train.shape, test.shape)
train.sample(5)
import matplotlib.pyplot as... | code |
122257913/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
(train.shape, test.shape)
train.head() | code |
122257913/cell_34 | [
"image_output_1.png"
] | from sklearn import linear_model
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
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('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kagg... | code |
122257913/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
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('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
(train.shape, test.shape)
train.... | code |
122257913/cell_30 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
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('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
... | code |
122257913/cell_33 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
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('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kagg... | code |
122257913/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
(train.shape, test.shape) | code |
122257913/cell_29 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
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('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
... | code |
122257913/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
(train.shape, test.shape)
train.sample(5) | code |
122257913/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 |
122257913/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
(train.shape, test.shape)
train.info() | code |
122257913/cell_32 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
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('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kagg... | code |
122257913/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
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('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
(train.shape, test.shape)
train.... | code |
122257913/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
(train.shape, test.shape)
test.info() | code |
122257913/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
(train.shape, test.shape)
train.sample(5)
import matplotlib.pyplot as... | code |
122257913/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
(train.shape, test.shape)
train.sample(5)
import matplotlib.pyplot as... | code |
122257913/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
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('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
(train.shape, test.shape)
train.... | code |
122257913/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
(train.shape, test.shape)
train.tail() | code |
32065703/cell_21 | [
"image_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import string
root_path = '/kaggle/input/CORD-19-research-challenge/'
metadata_path = root_path + 'metadata.csv'
metadata_df = pd.... | code |
32065703/cell_13 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import string
root_path = '/kaggle/input/CORD-19-research-challenge/'
metadata_path = root_path + 'metadata.csv'
metadata_df = pd.... | code |
32065703/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/CORD-19-research-challenge/'
metadata_path = root_path + 'metadata.csv'
metadata_df = pd.read_csv(metadata_path, dtype={'pubmed_id': str, 'Microsoft Academic Paper ID': str, 'doi': str})
metadata_df = metadata_df.fillna(0... | code |
32065703/cell_23 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import string
root_path = '/kaggle/input/CORD-19-research-challenge/'
metadata_path = root_path + 'metadata.csv'
metadata_df = pd.... | code |
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