path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
106191525/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/breast-cancer/breast-cancer - breast-cancer.csv')
print(data.shape)
data.head() | code |
106191525/cell_29 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.neighbors import LocalOutlierFactor
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import time
data = pd.read_csv('../i... | code |
106191525/cell_26 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.neighbors import LocalOutlierFactor
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
data = pd.read_csv('../input/breast-cancer/breast-cancer - breast-cancer.csv')
C = data['diagnosis'].value_counts()
corr = data.corr()
top_feature = corr.index[abs(corr... | code |
106191525/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
data = pd.read_csv('../input/breast-cancer/breast-cancer - breast-cancer.csv')
le = LabelEncoder()
data['diagnosis'] = le.fit_transform(data['diagnosis'])
data['diagnosis'].head() | code |
106191525/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/breast-cancer/breast-cancer - breast-cancer.csv')
data.info() | code |
106191525/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
data = pd.read_csv('../input/breast-cancer/breast-cancer - breast-cancer.csv')
C = data['diagnosis'].value_counts()
corr = data.corr()
top_feature = corr.index[abs(corr['diagnosis']) > 0.5]
Important_Data = data[top_fe... | code |
106191525/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
data = pd.read_csv('../input/breast-cancer/breast-cancer - breast-cancer.csv')
C = data['diagnosis'].value_counts()
corr = data.corr()
top_feature = corr.index[abs(corr['diagnosis']) > 0.5]
Important_Data = data[top_fe... | code |
128026526/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
titanic_gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv').head(10)
columns_to_look = ['PassengerId', 'Name', 'Ticket']
for column in columns_to_l... | code |
128026526/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
titanic_gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv').head(10)
titanic_test.head() | code |
128026526/cell_2 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
128026526/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
titanic_gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv').head(10)
titanic_train.info() | code |
128026526/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
titanic_gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv').head(10)
titanic_train.describe() | code |
128026526/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
titanic_gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv').head(10)
columns_to_look = ['PassengerId', 'Name', 'Ticket']
for column in columns_to_l... | code |
128026526/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
titanic_gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv').head(10)
columns_to_look = ['PassengerId', 'Name', 'Ticket']
for column in columns_to_l... | code |
128026526/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
titanic_gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv').head(10)
titanic_train.head() | code |
128026526/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
titanic_gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv').head(10)
columns_to_look = ['PassengerId', 'Name', 'Ticket']
for column in columns_to_l... | code |
128026526/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
titanic_gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv').head(10)
columns_to_look = ['PassengerId', 'Name', 'Ticket']
for column in columns_to_l... | code |
128026526/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
titanic_gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv').head(10)
columns_to_look = ['PassengerId', 'Name', 'Ticket']
for column in columns_to_l... | code |
128026526/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
titanic_gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv').head(10)
columns_to_look = ['PassengerId', 'Name', 'Ticket']
for column in columns_to_l... | code |
128026526/cell_10 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
titanic_gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv').head(10)
print(f'Количество строк-дубликатов в обучающей выборке - {titanic_train.dupli... | code |
128026526/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
titanic_gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv').head(10)
columns_to_look = ['PassengerId', 'Name', 'Ticket']
for column in columns_to_l... | code |
128026526/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
titanic_gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv').head(10)
titanic_gender_submission.head() | code |
322554/cell_21 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
people = pd.read_csv('../input/people.csv')
activity_train = pd.read_csv('../input/act_train.csv')
activity_test = pd.read_csv('../input/act_test.csv')
df = activity_train.merge(people, how='left', on='people_id')
df_test = activity_test.merge(people... | code |
322554/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
people = pd.read_csv('../input/people.csv')
activity_train = pd.read_csv('../input/act_train.csv')
activity_test = pd.read_csv('../input/act_test.csv')
df = activity_train.merge(people, how='left', on='people_id')
df_test = activity_test.merge(people, how='left', on='people_id')
df = df.fillna('0... | code |
322554/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(77, n_jobs=-1, random_state=7)
model.fit(X_train, y_train)
print('model score ', model.score(X_test, y_test)) | code |
322554/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
people = pd.read_csv('../input/people.csv')
activity_train = pd.read_csv('../input/act_train.csv')
activity_test = pd.read_csv('../input/act_test.csv')
df = activity_train.merge(people, how='left', on='people_id')
df_test = activity_test.merge(people, how='left', on='people_id')
print(df.shape)
p... | code |
322554/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
people = pd.read_csv('../input/people.csv')
activity_train = pd.read_csv('../input/act_train.csv')
activity_test = pd.read_csv('../input/act_test.csv')
df = activity_train.merge(people, how='left', on='people_id')
df_test = activity_test.merge(people, how='left', on='people_id')
df = df.fillna('0... | code |
322554/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
people = pd.read_csv('../input/people.csv')
activity_train = pd.read_csv('../input/act_train.csv')
activity_test = pd.read_csv('../input/act_test.csv')
df = activity_train.merge(people, how='left', on='people_id')
df_test = activity_test.merge(people, how='left', on='people_id')
df = df.fillna('0... | code |
322554/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
people = pd.read_csv('../input/people.csv')
activity_train = pd.read_csv('../input/act_train.csv')
activity_test = pd.read_csv('../input/act_test.csv')
df = activity_train.merge(people, how='left', on='people_id')
df_test = activity_test.merge(people, how='left', on='people_id')
df = df.fillna('0... | code |
322554/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
from IPython.display import display, HTML
from sklearn.preprocessing import LabelEncoder
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier | code |
322554/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
people = pd.read_csv('../input/people.csv')
activity_train = pd.read_csv('../input/act_train.csv')
activity_test = pd.read_csv('../input/act_test.csv')
df = activity_train.merge(people, how='left', on='people_id')
df_test = activity_test.merge(people, how='left', on='people_id')
df = df.fillna('0... | code |
322554/cell_24 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(77, n_jobs=-1, random_state=7)
model.fit(X_train, y_train)
pred = model.predict(X_test)
pred | code |
322554/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
people = pd.read_csv('../input/people.csv')
activity_train = pd.read_csv('../input/act_train.csv')
activity_test = pd.read_csv('../input/act_test.csv')
df = activity_train.merge(people, how='left', on='people_id')
df_test = activity_test.merge(people, how='left', on='people_id')
df = df.fillna('0... | code |
2034706/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.plotly as py
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
foot = pd.read_csv('..... | code |
2034706/cell_3 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.plotly as py
from subprocess import check_output
foot = pd.read_csv('../input/epldata_fin... | code |
2034706/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.plotly as py
from subprocess import check_output
foot = pd.read_csv('../input/epldata_final.c... | code |
90135317/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from bq_helper import BigQueryHelper
from datetime import datetime
from google.cloud import bigquery
import numpy as np
import pandas as pd
from google.cloud import bigquery
from bq_helper import BigQueryHelper
client = bigquery.Client()
query = '\n #standardSQL\n SELECT\n timestamp\n FROM \n ... | code |
90135317/cell_6 | [
"text_plain_output_1.png"
] | from bq_helper import BigQueryHelper
from datetime import datetime
from datetime import datetime, timedelta
from google.cloud import bigquery
from scipy.stats import norm
import numpy as np
import pandas as pd
from google.cloud import bigquery
from bq_helper import BigQueryHelper
client = bigquery.Client()
query =... | code |
90135317/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_1.png"
] | from bq_helper import BigQueryHelper
from google.cloud import bigquery
import numpy as np
import pandas as pd
from google.cloud import bigquery
from bq_helper import BigQueryHelper
client = bigquery.Client()
query = '\n #standardSQL\n SELECT\n timestamp\n FROM \n `bigquery-public-data.bitcoin_... | code |
90135317/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from bq_helper import BigQueryHelper
from datetime import datetime
from google.cloud import bigquery
import numpy as np
import pandas as pd
from google.cloud import bigquery
from bq_helper import BigQueryHelper
client = bigquery.Client()
query = '\n #standardSQL\n SELECT\n timestamp\n FROM \n ... | code |
90135317/cell_5 | [
"text_plain_output_1.png"
] | from bq_helper import BigQueryHelper
from datetime import datetime
from datetime import datetime, timedelta
from google.cloud import bigquery
import numpy as np
import pandas as pd
from google.cloud import bigquery
from bq_helper import BigQueryHelper
client = bigquery.Client()
query = '\n #standardSQL\n SELE... | code |
324023/cell_4 | [
"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/2016-FCC-New-Coders-Survey-Data.csv')
data.columns.values | code |
324023/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 |
324023/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv') | code |
18129560/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | score = model.evaluate(x_test, y_test, batch_size=BATCH_SIZE)
print()
print('ACCURACY:', score[1])
print('LOSS:', score[0]) | code |
18129560/cell_13 | [
"text_html_output_1.png"
] | print('train w2v ....')
w2v_model.train(documents, total_examples=len(documents), epochs=W2V_EPOCH)
print('done') | code |
18129560/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | documents = [_text.split() for _text in df_train.text]
print('training tweets count', len(documents)) | code |
18129560/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Activation, Dense, Dropout, Embedding, Flatten, Conv1D, MaxPooling1D, LSTM
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from sklearn.preprocessing import LabelEncoder
import numpy as np
import numpy as np # linear algebra
import time
DATASET_... | code |
18129560/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Activation, Dense, Dropout, Embedding, Flatten, Conv1D, MaxPooling1D, LSTM
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from sklearn.preprocessing import LabelEncoder
import numpy as np
import numpy as np # linear algebra
import pickle
import... | code |
18129560/cell_20 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | callbacks = [ReduceLROnPlateau(monitor='val_loss', patience=5, cooldown=0), EarlyStopping(monitor='val_acc', min_delta=0.0001, patience=5)]
history = model.fit(x_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, validation_split=0.1, verbose=1, callbacks=callbacks) | code |
18129560/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
DATASET_COLUMNS = ['target', 'ids', 'date', 'flag', 'user', 'text']
DATASET_ENCODING = 'ISO-8859-1'
TRAIN_SIZE = 0.8
TEXT_CLEANING_RE = '@\\S+|https?:\\S+|http?:\\S|[^A-Za-z0-9]+'
W2V_SIZE = 300
W2V_WINDOW = 7
W2V_EPOCH = 32
W2V_MIN_COUN... | code |
18129560/cell_29 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import confusion_matrix, classification_report, accuracy_score
accuracy_score(y_test_1d, y_pred_1d) | code |
18129560/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Activation, Dense, Dropout, Embedding, Flatten, Conv1D, MaxPooling1D, LSTM
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from sklearn.preprocessing import LabelEncoder
import numpy as np
import numpy as np # linear algebra
import time
DATASET_... | code |
18129560/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score
from sklearn.manifold import TSNE
from sklearn.feature_extraction... | code |
18129560/cell_11 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | w2v_model.build_vocab(documents) | code |
18129560/cell_19 | [
"text_plain_output_1.png"
] | from keras.layers import Activation, Dense, Dropout, Embedding, Flatten, Conv1D, MaxPooling1D, LSTM
from keras.models import Sequential
import numpy as np
import numpy as np # linear algebra
DATASET_COLUMNS = ['target', 'ids', 'date', 'flag', 'user', 'text']
DATASET_ENCODING = 'ISO-8859-1'
TRAIN_SIZE = 0.8
TEXT_CLE... | code |
18129560/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | decode_map = {0: 'NEGATIVE', 2: 'NEUTRAL', 4: 'POSITIVE'}
def decode_sentiment(label):
return decode_map[int(label)]
df.target = df.target.apply(lambda x: decode_sentiment(x)) | code |
18129560/cell_18 | [
"text_plain_output_1.png"
] | from keras.layers import Activation, Dense, Dropout, Embedding, Flatten, Conv1D, MaxPooling1D, LSTM
import numpy as np
import numpy as np # linear algebra
DATASET_COLUMNS = ['target', 'ids', 'date', 'flag', 'user', 'text']
DATASET_ENCODING = 'ISO-8859-1'
TRAIN_SIZE = 0.8
TEXT_CLEANING_RE = '@\\S+|https?:\\S+|http?:\... | code |
18129560/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import confusion_matrix, classification_report, accuracy_score
print(classification_report(y_test_1d, y_pred_1d)) | code |
18129560/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
DATASET_COLUMNS = ['target', 'ids', 'date', 'flag', 'user', 'text']
DATASET_ENCODING = 'ISO-8859-1'
TRAIN_SIZE = 0.8
TEXT_CLEANING_RE = '@\\S+|https?:\\S+|http?:\\S|[^A-Za-z0-9]+'
W2V... | code |
18129560/cell_15 | [
"text_plain_output_1.png"
] | tokenizer = Tokenizer()
tokenizer.fit_on_texts(df_train.text)
vocab_size = len(tokenizer.word_index) + 1
print('Total words', vocab_size) | code |
18129560/cell_16 | [
"text_plain_output_1.png"
] | x_train = pad_sequences(tokenizer.texts_to_sequences(df_train.text), maxlen=SEQUENCE_LENGTH)
x_test = pad_sequences(tokenizer.texts_to_sequences(df_test.text), maxlen=SEQUENCE_LENGTH) | code |
18129560/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import nltk
nltk.download('stopwords') | code |
18129560/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
DATASET_COLUMNS = ['target', 'ids', 'date', 'flag', 'user', 'text']
DATASET_ENCODING = 'ISO-8859-1'
TRAIN_SIZE = 0.8
TEXT_CLEANING_RE = '@\\S+|https?:\\S+|http?:\\S|[^A-Za-z0-9]+'
W2V_SIZE = 300
W2V_WINDOW = 7
W2V_EPOCH = 32
W2V_MIN_COUNT = 10
SEQUENCE_LENGTH = 300
EPOCHS... | code |
18129560/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Activation, Dense, Dropout, Embedding, Flatten, Conv1D, MaxPooling1D, LSTM
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from sklearn.preprocessing import LabelEncoder
import numpy as np
import numpy as np # linear algebra
import time
DATASET_... | code |
18129560/cell_14 | [
"text_plain_output_1.png"
] | words = w2v_model.wv.vocab.keys()
vocab_size = len(words)
w2v_model.most_similar('love') | code |
18129560/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'b', label='Training acc')
plt.plot(epochs, val_acc, 'r', label='Validation acc')
plt.title('Training an... | code |
18129560/cell_10 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | w2v_model = gensim.models.word2vec.Word2Vec(size=W2V_SIZE, window=W2V_WINDOW, min_count=W2V_MIN_COUNT, workers=8) | code |
18129560/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | y_pred_1d = []
y_test_1d = list(df_test.target)
scores = model.predict(x_test, verbose=1, batch_size=8000)
y_pred_1d = [decode_sentiment(score, include_neutral=False) for score in scores]
def plot_confusion_matrix(cm, classes, title='Confusion matrix', cmap=plt.cm.Blues):
"""
This function prints and plots the ... | code |
18129560/cell_12 | [
"text_plain_output_1.png"
] | words = w2v_model.wv.vocab.keys()
vocab_size = len(words)
print('Vocab size', vocab_size) | code |
18129560/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
DATASET_COLUMNS = ['target', 'ids', 'date', 'flag', 'user', 'text']
DATASET_ENCODING = 'ISO-8859-1'
TRAIN_SIZE = 0.8
TEXT_CLEANING_RE = '@\\S+|https?:\\S+|http?:\\S|[^A-Za-z0-9]+'
W2V_SIZE = 300
W2V_WINDOW = 7
W2V_EPOCH = 32
W2V_MIN_COUN... | code |
106211916/cell_21 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv').copy()
test = pd.read_csv('../input/titanic/test.csv').copy()
train.drop('Name', axis=1, inplace=True)
test.drop('Name', axis=1, inplace=True... | code |
106211916/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)
train = pd.read_csv('../input/titanic/train.csv').copy()
test = pd.read_csv('../input/titanic/test.csv').copy()
train.drop('Name', axis=1, inplace=True)
test.drop('Name', axis=1, inplace=True)
train.drop('Ticket', axis=1, inp... | code |
106211916/cell_25 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.linear_model import LogisticRegression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv').copy()
test = pd.read_csv('../input/titanic/test.csv').copy()
train.drop('Name', axis=1,... | code |
106211916/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)
train = pd.read_csv('../input/titanic/train.csv').copy()
test = pd.read_csv('../input/titanic/test.csv').copy()
train.drop('Name', axis=1, inplace=True)
test.drop('Name', axis=1, inplace=True)
train.drop('Ticket', axis=1, inp... | code |
106211916/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 |
106211916/cell_18 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv').copy()
test = pd.read_csv('../input/titanic/test.csv').copy()
train.drop('Name', axis=1, inplace=True)
test.drop('Name', axis=1, inplace=True... | code |
106211916/cell_28 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.linear_model import LogisticRegression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv').copy()
test = pd.read_csv('../input/titanic/test.csv').copy()
train.drop('Name', axis=1,... | code |
106211916/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)
train = pd.read_csv('../input/titanic/train.csv').copy()
test = pd.read_csv('../input/titanic/test.csv').copy()
train.drop('Name', axis=1, inplace=True)
test.drop('Name', axis=1, inplace=True)
train.drop('Ticket', axis=1, inp... | code |
106211916/cell_24 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.linear_model import LogisticRegression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv').copy()
test = pd.read_csv('../input/titanic/test.csv').copy()
train.drop('Name', axis=1,... | code |
106211916/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)
train = pd.read_csv('../input/titanic/train.csv').copy()
test = pd.read_csv('../input/titanic/test.csv').copy()
train.drop('Name', axis=1, inplace=True)
test.drop('Name', axis=1, inplace=True)
train.drop('Ticket', axis=1, inp... | code |
106211916/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv').copy()
test = pd.read_csv('../input/titanic/test.csv').copy()
train.drop('Name', axis=1, inplace=True)
test.drop('Name', axis=1, inplace=True)
train.drop('Ticket', axis=1, inp... | code |
129035406/cell_4 | [
"text_plain_output_1.png"
] | !python3 /content/OIDv4_ToolKit/main.py downloader --classes Car --type_csv train --limit 100 --multiclasses 1 -y
!python3 /content/OIDv4_ToolKit/main.py downloader --classes Car --type_csv validation --limit 30 --multiclasses 1 -y
!python3 /content/OIDv4_ToolKit/main.py downloader --classes Car --type_csv test --limit... | code |
129035406/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | file_path = '/content/OIDv4_ToolKit/classes.txt'
with open(file_path, mode='w') as f:
f.write('Car')
print(f'File {file_path} has been updated.') | code |
129035406/cell_2 | [
"text_plain_output_1.png"
] | !git clone https://github.com/EscVM/OIDv4_ToolKit.git | code |
129035406/cell_3 | [
"text_plain_output_1.png"
] | !pip3 install -r /content/OIDv4_ToolKit/requirements.txt | code |
2019859/cell_25 | [
"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)
import seaborn as sns
train = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
train.isnull().sum... | code |
2019859/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | 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', dtype={'Age': np.float64})
test = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
train.isnull().sum(axis=0) | code |
2019859/cell_6 | [
"text_html_output_1.png"
] | 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', dtype={'Age': np.float64})
test = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
test['Sex'].value_counts() | code |
2019859/cell_26 | [
"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)
import seaborn as sns
train = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
train.isnull().sum... | code |
2019859/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from sklearn.cross_validation import train_test_split
from sklearn.naive_bayes import MultinomialNB, BernoulliNB, GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC, SVC
from sklearn.ensemble import ... | code |
2019859/cell_7 | [
"text_plain_output_1.png"
] | 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', dtype={'Age': np.float64})
test = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
train.isnull().sum(axis=0)
train['Survived'].plot(kind='hist') | code |
2019859/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | 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', dtype={'Age': np.float64})
test = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
train.head() | code |
2019859/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import cross_val_score
from sklearn.cross_validation import cross_val_score
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier, ExtraTreesClassifier, AdaBoostClassifier, BaggingClassifier
from sklearn.gaussian_process import GaussianProcess... | code |
2019859/cell_5 | [
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
train.isnull().sum(axis=0)
sns.distplot(train['Far... | code |
32068245/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/sinan-dataset/multiple_linear_regression_dataset.csv', sep=';')
data
data.info() | code |
32068245/cell_20 | [
"text_plain_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)
data = pd.read_csv('/kaggle/input/sinan-dataset/multiple_linear_regression_dataset.csv', sep=';')
data
from sklearn.linear_model import LinearRegression
linear_reg... | code |
32068245/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32068245/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/sinan-dataset/multiple_linear_regression_dataset.csv', sep=';')
data | code |
32068245/cell_18 | [
"text_plain_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)
data = pd.read_csv('/kaggle/input/sinan-dataset/multiple_linear_regression_dataset.csv', sep=';')
data
from sklearn.linear_model import LinearRegression
linear_reg... | code |
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