path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
90123428/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier
parameters = {'criterion': ['gini', 'entropy'], 'splitter': ['best', 'random'], 'max_depth': [2 * n for n in range(1, 10)], 'max_features': ['auto', 'sqrt'], 'min_samples_leaf': [1, 2, 4], 'min_samples_split': [2, 5, 10]}... | code |
90123428/cell_41 | [
"text_plain_output_1.png"
] | print('Your submission was successfully saved!') | code |
90123428/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
fr... | code |
90123428/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.dtypes | code |
90123428/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.dtypes
train_data.isnull().sum()
test_data.isnull().sum()
... | code |
90123428/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.dtypes
train_data.isnull().sum() | code |
90123428/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.dtypes
train_data.isnull().sum()
test_data.isnull().sum()
... | code |
90123428/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.dtypes
train_data.isnull().sum()
test_data.isnull().sum()
train_data.fillna(train_data.Age.mean(), inplace=True)
t... | code |
90123428/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.head() | code |
90123428/cell_37 | [
"image_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
import numpy as np # linear algebra
Ks = 100
mean_acc = np.zeros(Ks - 1)
std_acc = np.zeros(Ks - 1)
for n in range(1, Ks):
neigh... | code |
90123428/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.dtypes
train_data.isnull().sum()
test_data.isnull().sum()
train_data.fillna(train_data.Age.mean(), inplace=True)
t... | code |
105211208/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
plt.rcParams.update({'figure.max_open_warning': 0})
import seaborn as sns
from sklearn.model_selection i... | code |
105211208/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.columns
pd.set_option('display.max_columns', None)
df.info() | code |
105211208/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
plt.rcParams.update({'figure.max_open_warning': 0})
import seaborn as sns
... | code |
105211208/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.preprocessing import RobustScaler
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import ... | code |
105211208/cell_30 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
plt.rcParams.update({'figure.max_open_wa... | code |
105211208/cell_29 | [
"text_html_output_1.png"
] | from sklearn import linear_model
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
plt.rcParams.update({'figure.max_open_wa... | code |
105211208/cell_26 | [
"image_output_11.png",
"image_output_24.png",
"text_plain_output_5.png",
"text_plain_output_15.png",
"image_output_17.png",
"text_plain_output_9.png",
"image_output_14.png",
"image_output_23.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"image_output_13.png",
"image_output_5.png... | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
plt.rcParams.update({'figure.max_open_warning': 0})
import seaborn as sns
... | code |
105211208/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.columns
pd.set_option('display.max_columns', None)
df.isnull().sum()
df.duplicated().sum() | code |
105211208/cell_19 | [
"image_output_11.png",
"image_output_24.png",
"text_plain_output_5.png",
"text_plain_output_15.png",
"image_output_17.png",
"text_plain_output_9.png",
"image_output_14.png",
"image_output_23.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"image_output_13.png",
"image_output_5.png... | from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
plt.rcParams.update({'figure.max_open_warning': 0})
import seaborn as sns
from sklea... | code |
105211208/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.columns | code |
105211208/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
plt.rcParams.update({'figure.max_open_warning': 0})
import seaborn as sns
from sklea... | code |
105211208/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.columns
pd.set_option('display.max_columns', None)
df.head() | code |
105211208/cell_35 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.preprocessing import RobustScaler
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import ... | code |
105211208/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
plt.rcParams.update({'figure.max_open_warning': 0})
import seaborn as sns
from sklea... | code |
105211208/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
df.columns
pd.set_option('display.max_columns', None)
df.isnull().sum() | code |
105211208/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
plt.rcParams.update({'figure.max_open_warning': 0})
import seaborn as sns
from sklearn.model_selection i... | code |
16118948/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/Mall_Customers.csv')
data.shape
data.isnull().any()
sns.set(style='white', palette='PuBuGn_d', color_codes=True)
size = data['Gender'].value_counts()
plt.figu... | code |
16118948/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/Mall_Customers.csv')
data.shape
data.isnull().any() | code |
16118948/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/Mall_Customers.csv')
data.head() | code |
16118948/cell_2 | [
"text_html_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16118948/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/Mall_Customers.csv')
data.shape
data.describe() | code |
16118948/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/Mall_Customers.csv')
data.shape
data.isnull().any()
sns.set(style='white', palette='PuBuGn_d', color_codes=True)
sns.countplot('Gender', data=data, palette='wi... | code |
16118948/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/Mall_Customers.csv')
data.shape | code |
16118948/cell_24 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/Mall_Customers.csv')
data.shape
data.isnull().any()
sns.set(style='white', palette='PuBuGn_d', color_codes=True)
size = data['Gender'].value_counts()
n = 0
co... | code |
16118948/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/Mall_Customers.csv')
data.shape
data.info() | code |
74056805/cell_21 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from nltk.classify.scikitlearn import SklearnClassifier
from nltk.corpus import stopwords
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.tag import pos_tag
from nltk.tokenize import TweetTokenizer
from sklearn.naive_bayes import MultinomialNB,BernoulliNB
from time import time
import nltk
import panda... | code |
74056805/cell_13 | [
"text_html_output_1.png"
] | from nltk.stem.wordnet import WordNetLemmatizer
from nltk.tag import pos_tag
from nltk.tokenize import TweetTokenizer
from time import time
import pandas as pd
dataset_columns = ['sentiment', 'ids', 'date', 'flag', 'user', 'text']
dataset_encode = 'ISO-8859-1'
tweet_df = pd.read_csv('../input/twitter-sentiment/tra... | code |
74056805/cell_9 | [
"text_plain_output_1.png"
] | from nltk.tokenize import TweetTokenizer
from time import time
import pandas as pd
dataset_columns = ['sentiment', 'ids', 'date', 'flag', 'user', 'text']
dataset_encode = 'ISO-8859-1'
tweet_df = pd.read_csv('../input/twitter-sentiment/training.1600000.processed.noemoticon.csv', encoding=dataset_encode, names=dataset... | code |
74056805/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
dataset_columns = ['sentiment', 'ids', 'date', 'flag', 'user', 'text']
dataset_encode = 'ISO-8859-1'
tweet_df = pd.read_csv('../input/twitter-sentiment/training.1600000.processed.noemoticon.csv', encoding=dataset_encode, names=dataset_columns)
new_df = tweet_df[['sentiment', 'text']]
new_df.head() | code |
74056805/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_columns = ['sentiment', 'ids', 'date', 'flag', 'user', 'text']
dataset_encode = 'ISO-8859-1'
tweet_df = pd.read_csv('../input/twitter-sentiment/training.1600000.processed.noemoticon.csv', encoding=dataset_encode, names=dataset_columns)
new_df = tweet_df[['sentiment', 'text']]
df_pos = twe... | code |
74056805/cell_2 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
dataset_columns = ['sentiment', 'ids', 'date', 'flag', 'user', 'text']
dataset_encode = 'ISO-8859-1'
tweet_df = pd.read_csv('../input/twitter-sentiment/training.1600000.processed.noemoticon.csv', encoding=dataset_encode, names=dataset_columns)
tweet_df.head() | code |
74056805/cell_18 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.tag import pos_tag
from nltk.tokenize import TweetTokenizer
from time import time
import pandas as pd
dataset_columns = ['sentiment', 'ids', 'date', 'flag', 'user', 'text']
dataset_encode = 'ISO-8859-1'
tweet_df = pd.read_... | code |
74056805/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_columns = ['sentiment', 'ids', 'date', 'flag', 'user', 'text']
dataset_encode = 'ISO-8859-1'
tweet_df = pd.read_csv('../input/twitter-sentiment/training.1600000.processed.noemoticon.csv', encoding=dataset_encode, names=dataset_columns)
tweet_df['sentiment'].value_counts() | code |
74056805/cell_17 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.tag import pos_tag
from nltk.tokenize import TweetTokenizer
from time import time
import pandas as pd
dataset_columns = ['sentiment', 'ids', 'date', 'flag', 'user', 'text']
dataset_encode = 'ISO-8859-1'
tweet_df = pd.read_... | code |
74056805/cell_24 | [
"text_plain_output_1.png"
] | from nltk.classify.scikitlearn import SklearnClassifier
from nltk.corpus import stopwords
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.tag import pos_tag
from nltk.tokenize import TweetTokenizer
from sklearn.naive_bayes import MultinomialNB,BernoulliNB
from time import time
import nltk
import panda... | code |
74056805/cell_10 | [
"text_html_output_1.png"
] | import nltk
import nltk
nltk.download('all') | code |
74056805/cell_12 | [
"text_plain_output_1.png"
] | from nltk.tag import pos_tag
from nltk.tokenize import TweetTokenizer
from time import time
import pandas as pd
dataset_columns = ['sentiment', 'ids', 'date', 'flag', 'user', 'text']
dataset_encode = 'ISO-8859-1'
tweet_df = pd.read_csv('../input/twitter-sentiment/training.1600000.processed.noemoticon.csv', encoding... | code |
74056805/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset_columns = ['sentiment', 'ids', 'date', 'flag', 'user', 'text']
dataset_encode = 'ISO-8859-1'
tweet_df = pd.read_csv('../input/twitter-sentiment/training.1600000.processed.noemoticon.csv', encoding=dataset_encode, names=dataset_columns)
df_pos = tweet_df[tweet_df['sentiment'] == 4]
df_neg =... | code |
73077112/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape
df.columns
rows, columns = df.shape
rows
df.isnull().sum() | code |
73077112/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape
df.columns
df.tail() | code |
73077112/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape
df.columns
rows, columns = df.shape
rows
df.isnull().sum()
df.drop('company', inplace=True, axis=1)
df
df.sort_values(by=['adr'], ascending=False)[['name']][:1] | code |
73077112/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape | code |
73077112/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape
df.columns
rows, columns = df.shape
rows
df.isnull().sum()
df.drop('company', inplace=True, axis=1)
df
df.sort_values(by=['adr'], ascending=False)[['name']][:1]
df.sort_values(by=['total_of_special_requests'], ascendi... | code |
73077112/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape
df.columns
rows, columns = df.shape
rows | code |
73077112/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape
df.columns | code |
73077112/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape
df.columns
rows, columns = df.shape
rows
df.isnull().sum()
df.drop('company', inplace=True, axis=1)
df
df['country'].value_counts(sort=True)[:5] | code |
73077112/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape
df.columns
df.head() | code |
73077112/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape
df.columns
rows, columns = df.shape
rows
df.isnull().sum()
df.drop('company', inplace=True, axis=1)
df | code |
73077112/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape
df.columns
rows, columns = df.shape
rows
df.isnull().sum()
df.drop('company', inplace=True, axis=1)
df
df.sort_values(by=['adr'], ascending=False)[['name']][:1]
Mean_Stay = df['adr'].mean()
round(Mean_Stay, 2) | code |
73077112/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.shape
df.columns
rows, columns = df.shape
rows
df.isnull().sum()
df.drop('company', inplace=True, axis=1)
df
df.sort_values(by=['adr'], ascending=False)[['name']][:1]
Mean_ADR = df['adr'].mean()
round(Mean_ADR, 2) | code |
73077112/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/hotel-booking/hotel_booking.csv')
df.info() | code |
128048982/cell_13 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from transformers import AutoTokenizer
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('gpt2')
example = 'def gcd(a, b):\n """Computes gcd(a,b) via the euclidean algorithm."""\n while b > 0:\n a,b = b, a%b\n return a'
tokens = tokenizer.tokenize(example)
print(tokens)... | code |
128048982/cell_6 | [
"text_plain_output_1.png"
] | from datasets import load_dataset
from datasets import load_dataset
dataset = load_dataset('espejelomar/code_search_net_python_10000_examples')
dataset['train'] | code |
128048982/cell_2 | [
"text_plain_output_1.png"
] | def gcd(a, b):
"""Computes gcd(a,b) via the euclidean algorithm."""
while b > 0:
a, b = (b, a % b)
return a
print(f'gcd(1337,143) = {gcd(1337, 143)}')
print(f'gcd(1337,7) = {gcd(1337, 7)}') | code |
128048982/cell_1 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from transformers import AutoTokenizer
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('gpt2') | code |
128048982/cell_7 | [
"text_plain_output_1.png"
] | from datasets import load_dataset
from datasets import load_dataset
dataset = load_dataset('espejelomar/code_search_net_python_10000_examples')
dataset['train'][10]['whole_func_string'] | code |
128048982/cell_3 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('gpt2')
example = 'def gcd(a, b):\n """Computes gcd(a,b) via the euclidean algorithm."""\n while b > 0:\n a,b = b, a%b\n return a'
tokens = tokenizer.tokenize(example)
print(tokens)
... | code |
128048982/cell_10 | [
"text_plain_output_1.png"
] | from datasets import load_dataset
from transformers import AutoTokenizer
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('gpt2')
example = 'def gcd(a, b):\n """Computes gcd(a,b) via the euclidean algorithm."""\n while b > 0:\n a,b = b, a%b\n return a'
tokens = tokeniz... | code |
128048982/cell_12 | [
"text_plain_output_1.png"
] | from datasets import load_dataset
from transformers import AutoTokenizer
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('gpt2')
example = 'def gcd(a, b):\n """Computes gcd(a,b) via the euclidean algorithm."""\n while b > 0:\n a,b = b, a%b\n return a'
tokens = tokeniz... | code |
128048982/cell_5 | [
"text_plain_output_1.png"
] | from datasets import load_dataset
from datasets import load_dataset
dataset = load_dataset('espejelomar/code_search_net_python_10000_examples') | code |
128005120/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/breast-cancer-wisconsin-data/data.csv')
df.shape
df.isnull().sum()
df.drop(['Unnamed: 32', 'id'], axis=1, inplace=True)
df.diagnosis.value_counts()
df.diagnosis.replace({'M': 1, 'B': 2}, inp... | code |
128005120/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/breast-cancer-wisconsin-data/data.csv')
df.shape
df.isnull().sum()
df.drop(['Unnamed: 32', 'id'], axis=1, inplace=True)
df | code |
128005120/cell_25 | [
"text_html_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier(n_neighbors=5)
model.fit(x_train, y_train) | code |
128005120/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/breast-cancer-wisconsin-data/data.csv')
df | code |
128005120/cell_34 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier(n_neighbors=5)
model.fit(x_train, y_train)
model.score(x_train, y_train) * 100
model.score(x_test, y_test) * 100
y_pred = model.predict(x_test)
acc = accuracy_score(y_test, y_pred) * 100
ac... | code |
128005120/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/breast-cancer-wisconsin-data/data.csv')
df.shape
df.info() | code |
128005120/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.metrics import classification_report
from sklearn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier(n_neighbors=5)
model.fit(x_train, y_train)
model.score(x_train, y_train) * 100
model.score(x_test, y_test) * 100
y_pred = model.predict(x_test)
report = classification_report(y_test, ... | code |
128005120/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier(n_neighbors=5)
model.fit(x_train, y_train)
model.score(x_train, y_train) * 100
model.score(x_test, y_test) * 100
y_pred = model.predict(x_test)
y_pred | code |
128005120/cell_26 | [
"text_html_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier(n_neighbors=5)
model.fit(x_train, y_train)
model.score(x_train, y_train) * 100 | code |
128005120/cell_2 | [
"text_html_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') | code |
128005120/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/breast-cancer-wisconsin-data/data.csv')
df.shape
df.isnull().sum()
df.drop(['Unnamed: 32', 'id'], axis=1, inplace=True)
df.diagnosis.value_counts()
df.diagnosis.replace({'M': 1, 'B': 2}, inp... | code |
128005120/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 |
128005120/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/breast-cancer-wisconsin-data/data.csv')
df.shape
df.isnull().sum() | code |
128005120/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)
df = pd.read_csv('/kaggle/input/breast-cancer-wisconsin-data/data.csv')
df.shape
df.isnull().sum()
df.drop(['Unnamed: 32', 'id'], axis=1, inplace=True)
df.diagnosis.value_counts()
df.diagnosis.replace({'M': 1, 'B': 2}, inp... | code |
128005120/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/breast-cancer-wisconsin-data/data.csv')
df.shape
df.isnull().sum()
df.drop(['Unnamed: 32', 'id'], axis=1, inplace=True)
df.diagnosis.value_counts()
df.diagnosis.replace({'M': 1, 'B': 2}, inp... | code |
128005120/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/breast-cancer-wisconsin-data/data.csv')
model = KNeighborsClassifier(n_neighbors=5)
model.fit(x_train, y_train)
model.score(x_train, y_train... | code |
128005120/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/breast-cancer-wisconsin-data/data.csv')
df.shape
df.isnull().sum()
df.drop(['Unnamed: 32', 'id'], axis=1, inplace=True)
df.diagnosis.value_counts()
df.diagnosis.replace({'M': 1, 'B': 2}, inp... | code |
128005120/cell_22 | [
"text_html_output_1.png"
] | print(x_train.shape)
print(x_test.shape)
print(y_train.shape)
print(y_test.shape) | code |
128005120/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/breast-cancer-wisconsin-data/data.csv')
df.shape
df.isnull().sum()
df.drop(['Unnamed: 32', 'id'], axis=1, inplace=True)
df.diagnosis.value_counts() | code |
128005120/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier(n_neighbors=5)
model.fit(x_train, y_train)
model.score(x_train, y_train) * 100
model.score(x_test, y_test) * 100 | code |
128005120/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier(n_neighbors=5)
model.fit(x_train, y_train)
model.score(x_train, y_train) * 100
model.score(x_test, y_test) * 100
y_pred = model.predict(x_test)
performance = confusion_matrix(y_test, y_pre... | code |
128005120/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/breast-cancer-wisconsin-data/data.csv')
df.shape
df.isnull().sum()
df.drop(['Unnamed: 32', 'id'], axis=1, inplace=True)
df.diagnosis.value_counts()
df.diagnosis.replace({'M': 1, 'B': 2}, inp... | code |
128005120/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/breast-cancer-wisconsin-data/data.csv')
df.shape | code |
90150781/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/flight-price-prediction/Clean_Dataset.csv')
df.shape
df.drop('Unnamed: 0', axis=1, inplace=True)
df.isnull().sum()
df.columns
df.airline.value_counts()
plt.figure(figsize=(10, 8))
plt1 = df.airline.value_c... | code |
90150781/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/flight-price-prediction/Clean_Dataset.csv')
df.shape
df.drop('Unnamed: 0', axis=1, inplace=True)
df.isnull().sum()
df.columns
df.airline.value_counts() | code |
90150781/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/flight-price-prediction/Clean_Dataset.csv')
df.shape
df.drop('Unnamed: 0', axis=1, inplace=True)
df.isnull().sum() | code |
90150781/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/flight-price-prediction/Clean_Dataset.csv')
df.shape | code |
90150781/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/flight-price-prediction/Clean_Dataset.csv')
df.shape
df.drop('Unnamed: 0', axis=1, inplace=True)
df.isnull().sum()
df.columns
df.airline.value_counts()
plt.figure(figsize=(10, 8))
plt1 = df.airline.value_counts().plot(kind='bar'... | code |
90150781/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/flight-price-prediction/Clean_Dataset.csv')
df.shape
df.head() | code |
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