path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
49129249/cell_39 | [
"text_plain_output_1.png"
] | from PIL import Image
from imutils import paths
from torch.utils.data import Dataset, random_split, DataLoader
from torchvision.utils import make_grid
from tqdm import tqdm
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
data_dir = '../input/super-hero/Q4-superh... | code |
49129249/cell_2 | [
"image_output_1.png"
] | !pip install imutils
from imutils import paths | code |
49129249/cell_45 | [
"image_output_1.png"
] | from PIL import Image
from imutils import paths
from torch.utils.data import Dataset, random_split, DataLoader
from torchvision.utils import make_grid
from tqdm import tqdm
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
data_dir = '../input/super-hero/Q4-superh... | code |
49129249/cell_32 | [
"text_plain_output_1.png"
] | from PIL import Image
from imutils import paths
from torch.utils.data import Dataset, random_split, DataLoader
from torchvision.utils import make_grid
from tqdm import tqdm
data_dir = '../input/super-hero/Q4-superheroes_image_data/'
train_dir = data_dir + 'CAX_Superhero_Train'
test_dir = data_dir + 'CAX_Superhero_... | code |
49129249/cell_8 | [
"text_plain_output_1.png"
] | from imutils import paths
from tqdm import tqdm
data_dir = '../input/super-hero/Q4-superheroes_image_data/'
train_dir = data_dir + 'CAX_Superhero_Train'
test_dir = data_dir + 'CAX_Superhero_Test'
def create_img_df(dir):
img_list = list(paths.list_images(dir))
data = pd.DataFrame(columns=['File_name', 'Target... | code |
49129249/cell_15 | [
"text_html_output_1.png"
] | label_enc = {'Ant-Man': 0, 'Aquaman': 1, 'Avengers': 2, 'Batman': 3, 'Black Panther': 4, 'Captain America': 5, 'Catwoman': 6, 'Ghost Rider': 7, 'Hulk': 8, 'Iron Man': 9, 'Spiderman': 10, 'Superman': 11}
label_deco = {0: 'Ant-Man', 1: 'Aquaman', 2: 'Avengers', 3: 'Batman', 4: 'Black Panther', 5: 'Captain America', 6: 'C... | code |
49129249/cell_24 | [
"text_plain_output_1.png"
] | from PIL import Image
from imutils import paths
from torch.utils.data import Dataset, random_split, DataLoader
from tqdm import tqdm
import torchvision.transforms as transforms
data_dir = '../input/super-hero/Q4-superheroes_image_data/'
train_dir = data_dir + 'CAX_Superhero_Train'
test_dir = data_dir + 'CAX_Superh... | code |
49129249/cell_10 | [
"text_plain_output_1.png"
] | from imutils import paths
from tqdm import tqdm
data_dir = '../input/super-hero/Q4-superheroes_image_data/'
train_dir = data_dir + 'CAX_Superhero_Train'
test_dir = data_dir + 'CAX_Superhero_Test'
def create_img_df(dir):
img_list = list(paths.list_images(dir))
data = pd.DataFrame(columns=['File_name', 'Target... | code |
49129249/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from imutils import paths
from torch.utils.data import Dataset, random_split, DataLoader
from tqdm import tqdm
import torchvision.transforms as transforms
data_dir = '../input/super-hero/Q4-superheroes_image_data/'
train_dir = data_dir + 'CAX_Superhero_Train'
test_dir = data_dir + 'CAX_Superhero_Test'
label_enc = ... | code |
49129249/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from imutils import paths
from tqdm import tqdm
data_dir = '../input/super-hero/Q4-superheroes_image_data/'
train_dir = data_dir + 'CAX_Superhero_Train'
test_dir = data_dir + 'CAX_Superhero_Test'
def create_img_df(dir):
img_list = list(paths.list_images(dir))
data = pd.DataFrame(columns=['File_name', 'Target... | code |
73082451/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
corruption = pd.read_csv('../input/crime-in-india-2019/Corruption 2019.csv')
corruption.columns
corruption.drop(['Category', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13',... | code |
73082451/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
corruption = pd.read_csv('../input/crime-in-india-2019/Corruption 2019.csv')
corruption.columns
corruption.drop(['Category', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13',... | code |
73082451/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
corruption = pd.read_csv('../input/crime-in-india-2019/Corruption 2019.csv')
corruption.columns | code |
73082451/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
corruption = pd.read_csv('../input/crime-in-india-2019/Corruption 2019.csv')
corruption.columns
corruption.drop(['Category', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13',... | code |
73082451/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
corruption = pd.read_csv('../input/crime-in-india-2019/Corruption 2019.csv')
corruption.columns
corruption.drop(['Category', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13', 'Unnamed: 14'], axis=1, inplace=True)
corruption.head(... | code |
73082451/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
corruption = pd.read_csv('../input/crime-in-india-2019/Corruption 2019.csv')
corruption.columns
corruption.drop(['Category', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13',... | code |
73082451/cell_8 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
corruption = pd.read_csv('../input/crime-in-india-2019/Corruption 2019.csv')
corruption.columns
corruption.drop(['Category', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13', 'Unnamed: 14'], axis=1, inplace=... | code |
73082451/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
corruption = pd.read_csv('../input/crime-in-india-2019/Corruption 2019.csv')
corruption.columns
corruption.drop(['Category', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13',... | code |
73082451/cell_5 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
corruption = pd.read_csv('../input/crime-in-india-2019/Corruption 2019.csv')
corruption.columns
corruption.info() | code |
16160251/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data frames
points = pd.read_csv('../input/points.csv')
serves = pd.read_csv('../input/serves.csv')
rallies = pd.read_csv('../input/rallies.csv')
events = pd.read_csv('../input/events.csv')
points[['rallyid', 'winner']].groupby('winner').count() | code |
16160251/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data frames
points = pd.read_csv('../input/points.csv')
serves = pd.read_csv('../input/serves.csv')
rallies = pd.read_csv('../input/rallies.csv')
events = pd.read_csv('../input/events.csv')
rallies.head() | code |
16160251/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd # data frames
points = pd.read_csv('../input/points.csv')
serves = pd.read_csv('../input/serves.csv')
rallies = pd.read_csv('../input/rallies.csv')
events = pd.read_csv('../input/events.csv')
df4 = serves.groupby(['server']).count().iloc[:, :1]
df4.columns = ['Serves']
df4 | code |
16160251/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from statistics import mean
import matplotlib.pyplot as plt # visualizations
import pandas as pd # data frames
import seaborn as sns # visualizations
points = pd.read_csv('../input/points.csv')
serves = pd.read_csv('../input/serves.csv')
rallies = pd.read_csv('../input/rallies.csv')
even... | code |
16160251/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # visualizations
import pandas as pd # data frames
import seaborn as sns # visualizations
points = pd.read_csv('../input/points.csv')
serves = pd.read_csv('../input/serves.csv')
rallies = pd.read_csv('../input/rallies.csv')
events = pd.read_csv('../input/ev... | code |
16160251/cell_40 | [
"text_html_output_1.png"
] | from statistics import mean
import matplotlib.pyplot as plt # visualizations
import pandas as pd # data frames
import seaborn as sns # visualizations
points = pd.read_csv('../input/points.csv')
serves = pd.read_csv('../input/serves.csv')
rallies = pd.read_csv('../input/rallies.csv')
even... | code |
16160251/cell_26 | [
"text_html_output_1.png"
] | from statistics import mean
import matplotlib.pyplot as plt # visualizations
import pandas as pd # data frames
import seaborn as sns # visualizations
points = pd.read_csv('../input/points.csv')
serves = pd.read_csv('../input/serves.csv')
rallies = pd.read_csv('../input/rallies.csv')
even... | code |
16160251/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data frames
points = pd.read_csv('../input/points.csv')
serves = pd.read_csv('../input/serves.csv')
rallies = pd.read_csv('../input/rallies.csv')
events = pd.read_csv('../input/events.csv')
df = points.groupby(['winner', 'serve']).count().iloc[:, :1]
df.columns = ['Points Won']
df
... | code |
16160251/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data frames
points = pd.read_csv('../input/points.csv')
serves = pd.read_csv('../input/serves.csv')
rallies = pd.read_csv('../input/rallies.csv')
events = pd.read_csv('../input/events.csv')
points.head() | code |
16160251/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from statistics import mean
import matplotlib.pyplot as plt # visualizations
import pandas as pd # data frames
import seaborn as sns # visualizations
points = pd.read_csv('../input/points.csv')
serves = pd.read_csv('../input/serves.csv')
rallies = pd.read_csv('../input/rallies.csv')
even... | code |
16160251/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data frames
points = pd.read_csv('../input/points.csv')
serves = pd.read_csv('../input/serves.csv')
rallies = pd.read_csv('../input/rallies.csv')
events = pd.read_csv('../input/events.csv')
df = points.groupby(['winner', 'serve']).count().iloc[:, :1]
df.columns = ['Points Won']
df
... | code |
16160251/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data frames
points = pd.read_csv('../input/points.csv')
serves = pd.read_csv('../input/serves.csv')
rallies = pd.read_csv('../input/rallies.csv')
events = pd.read_csv('../input/events.csv')
serves.head() | code |
16160251/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data frames
points = pd.read_csv('../input/points.csv')
serves = pd.read_csv('../input/serves.csv')
rallies = pd.read_csv('../input/rallies.csv')
events = pd.read_csv('../input/events.csv')
df = points.groupby(['winner', 'serve']).count().iloc[:, :1]
df.columns = ['Points Won']
df | code |
16160251/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from statistics import mean
import matplotlib.pyplot as plt # visualizations
import pandas as pd # data frames
import seaborn as sns # visualizations
points = pd.read_csv('../input/points.csv')
serves = pd.read_csv('../input/serves.csv')
rallies = pd.read_csv('../input/rallies.csv')
even... | code |
16160251/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import scipy.stats
from sklearn import preprocessing
from statistics import mean
import os
print(os.listdir('../input')) | code |
16160251/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data frames
points = pd.read_csv('../input/points.csv')
serves = pd.read_csv('../input/serves.csv')
rallies = pd.read_csv('../input/rallies.csv')
events = pd.read_csv('../input/events.csv')
df = points.groupby(['winner', 'serve']).count().iloc[:, :1]
df.columns = ['Points Won']
df
... | code |
16160251/cell_24 | [
"text_html_output_1.png"
] | from statistics import mean
import matplotlib.pyplot as plt # visualizations
import pandas as pd # data frames
import seaborn as sns # visualizations
points = pd.read_csv('../input/points.csv')
serves = pd.read_csv('../input/serves.csv')
rallies = pd.read_csv('../input/rallies.csv')
even... | code |
16160251/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # visualizations
import pandas as pd # data frames
import seaborn as sns # visualizations
points = pd.read_csv('../input/points.csv')
serves = pd.read_csv('../input/serves.csv')
rallies = pd.read_csv('../input/rallies.csv')
events = pd.read_csv('../input/ev... | code |
16160251/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data frames
points = pd.read_csv('../input/points.csv')
serves = pd.read_csv('../input/serves.csv')
rallies = pd.read_csv('../input/rallies.csv')
events = pd.read_csv('../input/events.csv')
events.head() | code |
16160251/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data frames
points = pd.read_csv('../input/points.csv')
serves = pd.read_csv('../input/serves.csv')
rallies = pd.read_csv('../input/rallies.csv')
events = pd.read_csv('../input/events.csv')
rallies1 = rallies.replace({'__undefined__': 'Out/Net (Not Point)'})
df5 = rallies1.groupby(... | code |
299160/cell_3 | [
"text_plain_output_1.png"
] | from time import sleep
for i in range(3):
print(i)
sleep(0.1) | code |
122265078/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
train_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-train.csv'
validation_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-val.csv'
test_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-test.csv'
train_pd = pd.read_csv(train_path)
validati... | code |
122265078/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-train.csv'
validation_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-val.csv'
test_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-test.csv'
train_pd = pd.read_csv(train_path)
validation_pd = pd.... | code |
122265078/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
train_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-train.csv'
validation_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-val.csv'
test_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-test.csv'
train_pd = pd.read_csv(train_path)
validati... | code |
122265078/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
train_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-train.csv'
validation_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-val.csv'
test_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-test.csv'
train_pd = pd.read_csv(train_path)
validati... | code |
122265078/cell_6 | [
"text_plain_output_1.png"
] | import nltk
nltk.download('stopwords')
stop_words_english = nltk.corpus.stopwords.words('english')
print(f'TOTAL LENGHT OF STOP WORDS IN ENGLISH: {len(stop_words_english)}') | code |
122265078/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score,confusion_matrix
from sklearn.naive_bayes import MultinomialNB
clf = MultinomialNB()
clf.fit(xtrain, ytrain)
ypred = clf.predict(xtest)
print(f'ACCURACY SCORE: {accuracy_score(ytest, ypred)}') | code |
122265078/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
train_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-train.csv'
validation_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-val.csv'
test_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-test.csv'
train_pd = pd.read_csv(train_path)
validation_pd = pd.... | code |
122265078/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
train_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-train.csv'
validation_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-val.csv'
test_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-test.csv'
train_pd = pd.read_csv(train_path)
validation_pd = pd.... | code |
122265078/cell_41 | [
"text_plain_output_1.png"
] | from sklearn.naive_bayes import MultinomialNB
clf = MultinomialNB()
clf.fit(xtrain, ytrain)
ypred = clf.predict(xtest)
print(f'MODEL CLASSES:\n\n{clf.classes_}') | code |
122265078/cell_52 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
import nltk
import pandas as pd
nltk.download('stopwords')
stop_words_english = nltk.corpus.stopwords.words('english')
train_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-train.csv'
validatio... | code |
122265078/cell_49 | [
"image_output_1.png"
] | import nltk
nltk.download('stopwords')
stop_words_english = nltk.corpus.stopwords.words('english')
test_sentence = "I don't know what's going on in this world, but all you know is that connection will bring freedom. Although we live in an unknown world, the existence of a woman who burns heaven is the cause of all be... | code |
122265078/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
train_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-train.csv'
validation_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-val.csv'
test_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-test.csv'
train_pd = pd.read_csv(train_path)
validation_pd = pd.... | code |
122265078/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-train.csv'
validation_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-val.csv'
test_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-test.csv'
train_pd = pd.read_csv(train_path)
validation_pd = pd.... | code |
122265078/cell_38 | [
"text_html_output_1.png"
] | from sklearn.naive_bayes import MultinomialNB
clf = MultinomialNB()
clf.fit(xtrain, ytrain) | code |
122265078/cell_47 | [
"text_plain_output_1.png"
] | import nltk
nltk.download('stopwords')
stop_words_english = nltk.corpus.stopwords.words('english')
test_sentence = "I don't know what's going on in this world, but all you know is that connection will bring freedom. Although we live in an unknown world, the existence of a woman who burns heaven is the cause of all be... | code |
122265078/cell_35 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import nltk
import pandas as pd
nltk.download('stopwords')
stop_words_english = nltk.corpus.stopwords.words('english')
train_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-train.csv'
validation_path = '/kaggle/input/emotion-classification-... | code |
122265078/cell_43 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score,confusion_matrix
from sklearn.naive_bayes import MultinomialNB
import matplotlib.pyplot as plt
import seaborn as sns
emotions_categories = {'joy': ['joy', 'happy', 'laugh', 'excited', 'surprise'], 'sadness': ['sad', 'disappointed', 'regret', 'depressed', 'lonely'], 'anger'... | code |
122265078/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
train_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-train.csv'
validation_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-val.csv'
test_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-test.csv'
train_pd = pd.read_csv(train_path)
validati... | code |
122265078/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-train.csv'
validation_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-val.csv'
test_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-test.csv'
train_pd = pd.read_csv(train_path)
validation_pd = pd.... | code |
122265078/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
train_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-train.csv'
validation_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-val.csv'
test_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-test.csv'
train_pd = pd.read_csv(train_path)
validati... | code |
122265078/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
train_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-train.csv'
validation_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-val.csv'
test_path = '/kaggle/input/emotion-classification-nlp/emotion-labels-test.csv'
train_pd = pd.read_csv(train_path)
validation_pd = pd.... | code |
122265078/cell_5 | [
"text_plain_output_1.png"
] | import nltk
nltk.download('stopwords')
stop_words_english = nltk.corpus.stopwords.words('english') | code |
16148222/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot a... | code |
16148222/cell_13 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1)
data = pd.read_csv('../input/AAPL.csv')
data['Date'] = pd.to_datetime(data['Date'])
X = np.array(data['Open'])
X = X.reshape(X.shape[0], 1)... | code |
16148222/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/AAPL.csv')
data.head() | code |
16148222/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1)
data = p... | code |
16148222/cell_6 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/AAPL.csv')
data['Date'] = pd.to_datetime(data['Date'])
data.head() | code |
16148222/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1)
data = p... | code |
16148222/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1)
data = pd.read_csv('../input/AAPL.csv')
data['Date'] = pd.to_datetime(data['Date'])
X = np.array(data['Open'])
X = X.reshape(X.shape[0], 1)
X.shape | code |
16148222/cell_10 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1)
data = pd.read_csv('../input/AAPL.csv')
data['Date'] = pd.to_datetime(data['Date'])
X = np.array(data['Open'])
X = X.reshape(X.shape[0], 1)... | code |
16148222/cell_12 | [
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1)
data = pd.read_csv('../input/AAPL.csv')
data['Date'] = pd.to_datetime(data['Date'])
X = np.array(data['Open'])
X = X.reshape(X.shape[0], 1)... | code |
16148222/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/AAPL.csv')
plt.figure(figsize=(15, 10))
plt.plot(data['Open'], color='blue', label='Apple Open Stock Price')
plt.title('Apple Stock Market Open Price vs Time')
plt.xlabel('Date')
plt.ylabel('Apple Stock Price')
plt.legend()
plt.show() | code |
122263833/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.describe().style.background_gradient(cmap='Blues')
df.isna().mean() / len(df)
df.n... | code |
122263833/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)
df = pd.read_csv('/kaggle/input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.describe().style.background_gradient(cmap='Blues')
df.isna().mean() / len(df)
df.nunique()
for i in range(len(df.Branch.value_counts())):... | code |
122263833/cell_9 | [
"image_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/supermarket-sales/supermarket_sales - Sheet1.csv')
df.describe().style.background_gradient(cmap='Blues')
df.isna().mean() / len(df)
df.nunique() | code |
122263833/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.describe().style.background_gradient(cmap='Blues')
df.isna().mean() / len(df)
df.n... | code |
122263833/cell_4 | [
"image_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/supermarket-sales/supermarket_sales - Sheet1.csv')
df.head() | code |
122263833/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.describe().style.background_gradient(cmap='Blues')
df.isna().mean() / len(df)
df.n... | code |
122263833/cell_6 | [
"image_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/supermarket-sales/supermarket_sales - Sheet1.csv')
df.describe().style.background_gradient(cmap='Blues') | code |
122263833/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.describe().style.background_gradient(cmap='Blues')
df.isna().mean() / len(df)
df.n... | code |
122263833/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.describe().style.background_gradient(cmap='Blues')
df.isna().mean() / len(df)
df.n... | code |
122263833/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 |
122263833/cell_7 | [
"image_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/supermarket-sales/supermarket_sales - Sheet1.csv')
df.describe().style.background_gradient(cmap='Blues')
df.isna().mean() / len(df) | code |
122263833/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.describe().style.background_gradient(cmap='Blues')
df.isna().mean() / len(df)
df.n... | code |
122263833/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.describe().style.background_gradient(cmap='Blues')
df.isna().mean() / len(df)
df.n... | code |
122263833/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/supermarket-sales/supermarket_sales - Sheet1.csv')
df.describe().style.background_gradient(cmap='Blues')
df.isna().mean() / len(df)
df.n... | code |
122263833/cell_5 | [
"image_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/supermarket-sales/supermarket_sales - Sheet1.csv')
df.info() | code |
1007503/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style='white', color_codes=True)
iris = pd.read_csv('../input/Iris.csv')
sns.jointplot(x='SepalLengthCm', y='SepalWidthCm',... | code |
1007503/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style='white', color_codes=True)
iris = pd.read_csv('../input/Iris.csv')
iris['Species'].value_counts() | code |
1007503/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style='white', color_codes=True)
iris = pd.read_csv('../input/Iris.csv')
iris.head() | code |
1007503/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style='white', color_codes=True)
iris = pd.read_csv('../input/Iris.csv')
iris.plot(kind='scatter', x='PetalLengthCm', y='Pe... | code |
1007503/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | s | code |
130003964/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
df.columns
years_exp = df.YearsExperience.values
years_exp
salary = df.Salary.values
salary
x = years_exp
y = salary
plt.plot
x = x.reshape(-1, 1)
x
... | code |
130003964/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
df.columns
years_exp = df.YearsExperience.values
years_exp
salary = df.Salary.values
salary
x = years_exp
y = salary
x = x.reshape(-1, 1)
x | code |
130003964/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
df.columns
df.describe() | code |
130003964/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
df.columns
years_exp = df.YearsExperience.values
years_exp
salary = df.Salary.values
salary | code |
130003964/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
df.head(5) | code |
130003964/cell_11 | [
"text_html_output_1.png"
] | (x_train, len(x_train)) | code |
130003964/cell_19 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
import numpy as np # linear algebra
lr = LinearRegression()
lr.fit(x_train, y_train)
y_predict = lr.predict([[1.2], [3.3]])
y_predict
lr.score(x_test, y_test) * 100
y_predict = lr.predict(x_test)
y_predict
print(metrics.mean_absolute_e... | code |
130003964/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrics
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
... | code |
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