path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128047268/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
GM_df = pd.read_excel('/kaggle/input/juan-anyang/GM.xlsx')
Mining_df = pd.read_excel('/kaggle/input/juan-anyang/Mining.xlsx')
GM_df.fillna(0, inplace=True)
Mining_df.fillna(0, inplace=True)
GM_df['Reading No'] = GM_df['Reading No'].astype(str)
GM_... | code |
128047268/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsRegressor
from sklearn.preproc... | code |
128047268/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
GM_df = pd.read_excel('/kaggle/input/juan-anyang/GM.xlsx')
Mining_df = pd.read_excel('/kaggle/input/juan-anyang/Mining.xlsx')
GM_df.fillna(0, inplace=True)
Mining_df.fillna(0, inplace=True)
GM_df['Reading No'] = GM_df['Reading No'].astype(str)
du... | code |
128047268/cell_2 | [
"text_plain_output_1.png"
] | import seaborn as sns
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
sns.set()
import matplotlib.pyplot as plt
from PIL import Image
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
from sklearn.cluster import KMeans
from sklearn.preprocessing import Sta... | code |
128047268/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 |
128047268/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
GM_df = pd.read_excel('/kaggle/input/juan-anyang/GM.xlsx')
Mining_df = pd.read_excel('/kaggle/input/juan-anyang/Mining.xlsx')
GM_df.fillna(0, inplace=True)
Mining_df.fillna(0, inplace=True)
GM_df['Reading No'] = GM_df['Reading No'].astype(str)
Mi... | code |
128047268/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)
GM_df = pd.read_excel('/kaggle/input/juan-anyang/GM.xlsx')
Mining_df = pd.read_excel('/kaggle/input/juan-anyang/Mining.xlsx')
GM_df.fillna(0, inplace=True)
Mining_df.fillna(0, inplace=True)
GM_df['Reading No'] = GM_df['Reading No'].astype(str)
Mi... | code |
128047268/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
GM_df = pd.read_excel('/kaggle/input/juan-anyang/GM.xlsx')
Mining_df = pd.read_excel('/kaggle/input/juan-anyang/Mining.xlsx')
GM_df.fillna(0, inplace=True)
Mining_df.fillna(0, inplace=True)
GM_df.info() | code |
128047268/cell_10 | [
"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
GM_df = pd.read_excel('/kaggle/input/juan-anyang/GM.xlsx')
Mining_df = pd.read_excel('/kaggle/input/juan-anyang/Mining.xlsx')
GM_df.fillna(0, inplace=True)... | code |
128047268/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
GM_df = pd.read_excel('/kaggle/input/juan-anyang/GM.xlsx')
Mining_df = pd.read_excel('/kaggle/input/juan-anyang/Mining.xlsx')
GM_df.fillna(0, inplace=True)
Mining_df.fillna(0, inplace=True)
Mining_df['Reading No'] = Mining_df['Reading No'].astype(... | code |
130002946/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier | code |
130002946/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 |
130002946/cell_7 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
x = df.iloc[:, :-1]
y = df.iloc[:, 13]
x = x.drop(['Cabin', 'Name', 'PassengerId'], axis='... | code |
130002946/cell_3 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
x = df.iloc[:, :-1]
y = df.iloc[:, 13]
x = x.drop(['Cabin', 'Name', 'PassengerId'], axis='columns')
lvl = LabelEncoder()
x['CryoSleep'] = ... | code |
130000797/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
df.dtypes
df.isnull().sum() | code |
130000797/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import skew, norm
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score, classification_report,... | code |
130000797/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | !pip install dataprep
from dataprep.eda import plot, plot_missing, plot_correlation, plot_diff, create_report | code |
130000797/cell_3 | [
"text_plain_output_1.png"
] | import os
import os
print(os.listdir('/kaggle/input/')) | code |
130000797/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
df.dtypes | code |
50221063/cell_6 | [
"text_plain_output_1.png"
] | def insertionSort(array):
for step in range(1, len(array)):
key = array[step]
j = step - 1
while j >= 0 and key < array[j]:
array[j + 1] = array[j]
j = j - 1
array[j + 1] = key
data = [10, 5, 30, 15, 50, 6, 25]
insertionSort(data)
def selectionSort(array, siz... | code |
50221063/cell_3 | [
"text_plain_output_1.png"
] | def insertionSort(array):
for step in range(1, len(array)):
key = array[step]
j = step - 1
while j >= 0 and key < array[j]:
array[j + 1] = array[j]
j = j - 1
array[j + 1] = key
data = [10, 5, 30, 15, 50, 6, 25]
insertionSort(data)
print('Sorted Array in Ascend... | code |
18156269/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes.csv')
data.head() | code |
18156269/cell_2 | [
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pandas.plotting import scatter_matrix
import seaborn as sns
import os
print(os.listdir('../input')) | code |
18156269/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes.csv')
data.hist(figsize=(16, 14)) | code |
18156269/cell_18 | [
"image_output_1.png"
] | from pandas.plotting import scatter_matrix
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)
data = pd.read_csv('../input/diabetes.csv')
correlations = data.corr()
correlations = data.corr()
# plot correlation matrix
fig = plt... | code |
18156269/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes.csv')
correlations = data.corr()
correlations = data.corr()
fig = plt.figure(figsize=(16, 14))
ax = fig.add_subplot(111)
cax = ax.matshow(c... | code |
18156269/cell_10 | [
"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)
data = pd.read_csv('../input/diabetes.csv')
data.plot(kind='density', subplots=True, layout=(3, 3), sharex=False, figsize=(16, 14))
plt.show() | code |
18156269/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes.csv')
data.plot(kind='box', subplots=True, layout=(3, 3), sharex=False, sharey=False, figsize=(16, 14))
plt.show() | code |
88101304/cell_42 | [
"text_plain_output_1.png"
] | print(f'Round 1:\n{GRPC_R1_M1}\n{GRPC_R1_M2}\n')
print(f'Round 2:\n{GRPC_R2_M1}\n{GRPC_R2_M2}\n')
print(f'Round 3:\n{GRPC_R3_M1}\n{GRPC_R3_M2}') | code |
88101304/cell_21 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import datetime, timedelta
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder, StandardScaler
import numpy as np
import pandas as pd
import tensorflow as tf
REF_DATE_STR = '2021-10-08 06:00:00+00:00'
RANDOM_SEED = 42069
tf.random.set_seed(RANDOM_SEED... | code |
88101304/cell_32 | [
"text_plain_output_1.png"
] | print(f'Round 1:\n{GRPA_R1_M1}\n{GRPA_R1_M2}\n')
print(f'Round 2:\n{GRPA_R2_M1}\n{GRPA_R2_M2}\n')
print(f'Round 3:\n{GRPA_R3_M1}\n{GRPA_R3_M2}') | code |
88101304/cell_47 | [
"text_plain_output_1.png"
] | print(f'Round 1:\n{GRPD_R1_M1}\n{GRPD_R1_M2}\n')
print(f'Round 2:\n{GRPD_R2_M1}\n{GRPD_R2_M2}\n')
print(f'Round 3:\n{GRPD_R3_M1}\n{GRPD_R3_M2}') | code |
88101304/cell_37 | [
"text_plain_output_1.png"
] | print(f'Round 1:\n{GRPB_R1_M1}\n{GRPB_R1_M2}\n')
print(f'Round 2:\n{GRPB_R2_M1}\n{GRPB_R2_M2}\n')
print(f'Round 3:\n{GRPB_R3_M1}\n{GRPB_R3_M2}') | code |
73073728/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_log_error
model = LinearRegression(iterations=10000, learning_rate=1e-09)
model.fit(X_train, y_train)
mean_squared_log_error(y_test, model.predict(X_test))
scaler = StandardScaler()
scaler.fit(X_train)
X_train, X_test = (scaler.transform(X_train), scaler.transform(X_test))
mod... | code |
73073728/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_log_error
model = LinearRegression(iterations=10000, learning_rate=1e-09)
model.fit(X_train, y_train)
mean_squared_log_error(y_test, model.predict(X_test)) | code |
106195240/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
data = pd.read_csv('../input/time-series-datasets/monthly-beer-production-in-austr.csv', index_col='Month', parse_dates=True)
data.columns = ['Production']
train = data[:'1992-12-31']
test = d... | code |
106195240/cell_13 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/time-series-datasets/monthly-beer-production-in-austr.csv', index_col='Month', parse_dates=True)
data.columns = ['Production']
train = data[:'1992-12-31']
test = data['1993-01-01':]
scale... | code |
106195240/cell_4 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/time-series-datasets/monthly-beer-production-in-austr.csv', index_col='Month', parse_dates=True)
data.columns = ['Production']
data.head() | code |
106195240/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
data = pd.read_csv('../input/time-series-datasets/monthly-beer-production-in-austr.csv', index_col='Month', parse_dates=True)
data.columns = ['Production']
train = data[:'1... | code |
106195240/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/time-series-datasets/monthly-beer-production-in-austr.csv', index_col='Month', parse_dates=True)
data.columns = ['Production']
data.plot(figsize=(12, 6))
plt.show() | code |
106195240/cell_19 | [
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
data = pd.read_csv('../input/time-series-datasets/monthly-beer-production-in-austr.csv', index_col='Month', parse_dates=True)
data.columns = ['Production']
train = data[:'1992-12-31']
test = d... | code |
106195240/cell_18 | [
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
data = pd.read_csv('../input/time-series-datasets/monthly-beer-production-in-austr.csv', index_col='Month', parse_dates=True)
data.columns = ['Production']
train = data[:'1992-12-31']
test = d... | code |
106195240/cell_8 | [
"image_output_1.png"
] | from statsmodels.tsa.seasonal import seasonal_decompose
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/time-series-datasets/monthly-beer-production-in-austr.csv', index_col='Month', parse_dates=True)
data.columns = ['Production']
decomposed = seasonal_decompose(data['Production'])
... | code |
106195240/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/time-series-datasets/monthly-beer-production-in-austr.csv', index_col='Month', parse_dates=True)
data.columns = ['Production']
print(data.shape) | code |
106195240/cell_17 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
data = pd.read_csv('../input/time-series-datasets/monthly-beer-production-in-austr.csv', index_col='Month', parse_dates=True)
data.columns = ['Production']
train = data[:'1992-12-31']
test = d... | code |
106195240/cell_24 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
data = pd.read_csv('../input/time-series-datasets/monthly-beer-production-in-austr.csv', index_col='Month', parse_dates=True)
data.columns = ['Production']
train = data[:'1... | code |
106195240/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/time-series-datasets/monthly-beer-production-in-austr.csv', index_col='Month', parse_dates=True)
data.columns = ['Production']
train = data[:'1992-12-31']
test = data['1993-01-01':]
print('Shape of training set: ', train.shape)
print('... | code |
106195240/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/time-series-datasets/monthly-beer-production-in-austr.csv', index_col='Month', parse_dates=True)
data.columns = ['Production']
data.tail() | code |
89135912/cell_9 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
from nltk.corpus import stopwords
import re
stop_words = stopwords.words('english')
def clean(review):
clean_html = BeautifulSoup(review).get_text()
clean_non_letters = re.sub('[^a-zA-Z]', ' ', clean_html)
cleaned_lowecase = clean_non_letters.lower()
words = cleaned_lowe... | code |
89135912/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
traindata = pd.... | code |
89135912/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
traindata = pd.read_csv('/kaggle/input/word2vec-nlp-tutorial/labeledTrainData.tsv.zip', delimiter='\t')
testdata = pd.read_csv('/kaggle/input/word2vec-nlp-tutorial/testData.tsv.zip', header=0, delimiter='\t')
unlabledata = pd.read_csv('/kaggle/inpu... | code |
89135912/cell_23 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
traindata = pd.read_csv('/kaggle/input/word2vec-nlp-tutorial/labeledTrainData.tsv.zip', delimiter='\t')
testdata... | code |
89135912/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
traindata = pd.... | code |
89135912/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
traindata = pd.... | code |
89135912/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
traindata = pd.read_csv('/kaggle/input/word2vec-nlp-tutorial/labeledTrainData.tsv.zip', delimiter='\t')
testdata = pd.read_csv('/kaggle/input/word2vec-nlp-tutorial/testData.tsv.zip', header=0, delimiter='\t')
unlabledata = pd.r... | code |
89135912/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 |
89135912/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
traindata = pd.read_csv('/kaggle/input/word2vec-nlp-tutorial/labeledTrainData.tsv.zip', delimiter='\t')
testdata = pd.read_csv('/kaggle/input/word2vec-nlp-tutorial/testData.tsv.zip', header=0, delimiter='\t')
unlabledata = pd.r... | code |
89135912/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
traindata = pd.read_csv('/kaggle/input/word2vec-nlp-tutorial/labeledTrainData.tsv.zip', delimiter='\t')
testdata... | code |
89135912/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
traindata = pd.read_csv('/kaggle/input/word2vec-nlp-tutorial/labeledTrainData.tsv.zip', delimiter='\t')
testdata = pd.read_csv('/kaggle/input/word2vec-nlp-tutorial/testData.tsv.zip', header=0, delimiter='\t')
unlabledata = pd.r... | code |
89135912/cell_22 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
model = RandomForestClassifier()
model.fit(x_train, y_train)
pred = model.predict(x_test)
accuracy_score(pred, y_test) | code |
89135912/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
traindata = pd.read_csv('/kaggle/input/word2vec-nlp-tutorial/labeledTrainData.tsv.zip', delimiter='\t')
testdata = pd.read_csv('/kaggle/input/word2vec-nlp-tutorial/testData.tsv.zip', header=0, delimiter='\t')
unlabledata = pd.read_csv('/kaggle/inpu... | code |
16126718/cell_7 | [
"text_plain_output_1.png"
] | from PIL import Image
import numpy as np
import os
import pandas as pd
def get_pixel_data(filepath):
"""
Get the pixel data from an image as a pandas DataFrame.
"""
image = Image.open(filepath)
pixel_data = np.array(image.getdata())
pixel_data = pixel_data.mean(axis=1)
pixel_data = pixel... | code |
16126718/cell_17 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
import numpy as np
import os
import pandas as pd
import random
def get_pixel_data(filepath):
"""
Get the pixel data from an image as a pandas DataFrame.
"""
image = Image.... | code |
16126718/cell_10 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.linear_model import LogisticRegression
import numpy as np
import os
import pandas as pd
import random
def get_pixel_data(filepath):
"""
Get the pixel data from an image as a pandas DataFrame.
"""
image = Image.open(filepath)
pixel_data = np.array(image.getdat... | code |
16126718/cell_12 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
import numpy as np
import os
import pandas as pd
import random
def get_pixel_data(filepath):
"""
Get the pixel data from an image as a pandas DataFrame.
"""
image = Image.... | code |
18146048/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
student_data_mat = pd.read_csv('../input/student_math.csv', delimiter=';')
student_data_por = pd.read_csv('../input/student_language.csv', delimiter=';')
student_data_mat.shape
student_data_por.shape
student_data = pd.merge(student_data_mat, student_data_por, how='outer')
student_data.shape
co... | code |
18146048/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
student_data_mat = pd.read_csv('../input/student_math.csv', delimiter=';')
student_data_por = pd.read_csv('../input/student_language.csv', delimiter=';')
student_data_mat.shape
student_data_por.shape
student_data = pd.merge(student_data_mat, student_data_por, how='outer')
student_data.head() | code |
18146048/cell_25 | [
"text_html_output_1.png"
] | from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
student_data_mat = pd.read_csv('../input/student_math.csv', delimiter=';')
student_data_por = pd.read_csv('../input/student_language.csv', delimiter=';')
student_data_mat.shape
student_data_por.shape
stud... | code |
18146048/cell_23 | [
"text_html_output_1.png"
] | from sklearn.decomposition import PCA
import pandas as pd
student_data_mat = pd.read_csv('../input/student_math.csv', delimiter=';')
student_data_por = pd.read_csv('../input/student_language.csv', delimiter=';')
student_data_mat.shape
student_data_por.shape
student_data = pd.merge(student_data_mat, student_data_po... | code |
18146048/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
student_data_mat = pd.read_csv('../input/student_math.csv', delimiter=';')
student_data_por = pd.read_csv('../input/student_language.csv', delimiter=';')
student_data_mat.shape | code |
18146048/cell_26 | [
"text_html_output_1.png"
] | from sklearn.decomposition import PCA
import pandas as pd
student_data_mat = pd.read_csv('../input/student_math.csv', delimiter=';')
student_data_por = pd.read_csv('../input/student_language.csv', delimiter=';')
student_data_mat.shape
student_data_por.shape
student_data = pd.merge(student_data_mat, student_data_po... | code |
18146048/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
student_data_mat = pd.read_csv('../input/student_math.csv', delimiter=';')
student_data_por = pd.read_csv('../input/student_language.csv', delimiter=';')
student_data_mat.shape
student_data_por.shape
student_data = pd.merge(student_data_mat, student_data_por, how='outer')
student_data.shape
st... | code |
18146048/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
student_data_mat = pd.read_csv('../input/student_math.csv', delimiter=';')
student_data_por = pd.read_csv('../input/student_language.csv', delimiter=';')
student_data_mat.shape
student_data_por.shape
student_data = pd.merge(student_data_mat, student_data_por, how='outer')
student_data.shape
co... | code |
18146048/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
student_data_mat = pd.read_csv('../input/student_math.csv', delimiter=';')
student_data_por = pd.read_csv('../input/student_language.csv', delimiter=';')
student_data_por.head() | code |
18146048/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
student_data_mat = pd.read_csv('../input/student_math.csv', delimiter=';')
student_data_por = pd.read_csv('../input/student_language.csv', delimiter=';')
student_data_por.shape | code |
18146048/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
student_data_mat = pd.read_csv('../input/student_math.csv', delimiter=';')
student_data_por = pd.read_csv('../input/student_language.csv', delimiter=';')
student_data_mat.shape
student_data_por.shape
student_data = pd.merge(student_data_mat, student_data_por, how='outer')
student_data.shape
co... | code |
18146048/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
student_data_mat = pd.read_csv('../input/student_math.csv', delimiter=';')
student_data_por = pd.read_csv('../input/student_language.csv', delimiter=';')
student_data_mat.shape
student_data_por.shape
student_data = pd.merge(student_data_mat, student_data_por, how='outer')
student_data.shape
co... | code |
18146048/cell_3 | [
"text_plain_output_1.png"
] | import os
import os
print(os.listdir('../input')) | code |
18146048/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
student_data_mat = pd.read_csv('../input/student_math.csv', delimiter=';')
student_data_por = pd.read_csv('../input/student_language.csv', delimiter=';')
student_data_mat.shape
student_data_por.shape
student_data = pd.merge(student_data_mat, student_data_por, how='outer')
student_data.shape
co... | code |
18146048/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
student_data_mat = pd.read_csv('../input/student_math.csv', delimiter=';')
student_data_por = pd.read_csv('../input/student_language.csv', delimiter=';')
student_data_mat.shape
student_data_por.shape
stud... | code |
18146048/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
student_data_mat = pd.read_csv('../input/student_math.csv', delimiter=';')
student_data_por = pd.read_csv('../input/student_language.csv', delimiter=';')
student_data_mat.shape
student_data_por.shape
student_data = pd.merge(student_data_mat, student_data_por, how='outer')
student_data.shape
co... | code |
18146048/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
import pandas as pd
student_data_mat = pd.read_csv('../input/student_math.csv', delimiter=';')
student_data_por = pd.read_csv('../input/student_language.csv', delimiter=';')
student_data_mat.shape
student_data_por.shape
student_data = pd.merge(student_data_mat, student_data_po... | code |
18146048/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
student_data_mat = pd.read_csv('../input/student_math.csv', delimiter=';')
student_data_por = pd.read_csv('../input/student_language.csv', delimiter=';')
student_data_mat.shape
student_data_por.shape
student_data = pd.merge(student_data_mat, student_data_por, how='outer')
student_data.shape | code |
18146048/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
student_data_mat = pd.read_csv('../input/student_math.csv', delimiter=';')
student_data_por = pd.read_csv('../input/student_language.csv', delimiter=';')
student_data_mat.shape
student_data_por.shape
student_data = pd.merge(student_data_mat, student_data_por, how='outer')
student_data.shape
co... | code |
18146048/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
student_data_mat = pd.read_csv('../input/student_math.csv', delimiter=';')
student_data_por = pd.read_csv('../input/student_language.csv', delimiter=';')
student_data_mat.head() | code |
73095391/cell_20 | [
"text_html_output_1.png"
] | import cv2
import glob
import numpy as np
import pandas as pd
RESIZED_WIDTH, RESIZED_HEIGHT = (224, 224)
EACH_WIDTH, EACH_HEIGHT = (RESIZED_WIDTH // 2, RESIZED_HEIGHT // 2)
OUTPUT_FORMAT = 'jpg'
OUTPUT_DIR = 'data_argument_224_224'
train_dir = 'train_images'
train_paths = glob.glob(f'{data_dir}/{train_dir}/*.jpg')... | code |
73095391/cell_1 | [
"text_plain_output_1.png"
] | !apt install zip | code |
73095391/cell_7 | [
"text_plain_output_1.png"
] | data_dir = "../input/plant-pathology-2021-fgvc8"
!ls {data_dir} | code |
73095391/cell_22 | [
"text_html_output_1.png"
] | !zip -r {OUTPUT_DIR}_resized.zip ./{OUTPUT_DIR}/* | code |
73095391/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
TRAIN_DF = pd.read_csv('../input/pp-csv/clearned_train.csv')
TRAIN_DF | code |
1007093/cell_9 | [
"image_output_1.png"
] | from keras.layers import Dense, Dropout, Lambda, Flatten
from keras.models import Sequential
from keras.optimizers import Adam ,RMSprop
from keras.utils import np_utils
import numpy as np
import pandas as pd
train_file = pd.read_csv('../input/train.csv')
test_images = pd.read_csv('../input/test.csv')
train_image... | code |
1007093/cell_4 | [
"image_output_1.png"
] | import pandas as pd
train_file = pd.read_csv('../input/train.csv')
test_images = pd.read_csv('../input/test.csv')
train_images = train_file.ix[:, 1:].values.astype('float32')
print(train_images.shape)
train_labels = train_file.ix[:, 0].values.astype('int32')
print(train_labels.shape) | code |
1007093/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Dropout, Lambda, Flatten
from keras.optimizers import Adam, RMSprop
from sklearn.model_selection import train_test_split | code |
1007093/cell_15 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout, Lambda, Flatten
from keras.models import Sequential
from keras.optimizers import Adam ,RMSprop
from keras.utils import np_utils
import numpy as np
import pandas as pd
train_file = pd.read_csv('../input/train.csv')
test_images = pd.read_csv('../input/test.csv')
train_image... | code |
1007093/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']
epochs = range(1, len(loss_values) + 1)
plt.plot(epochs, loss_values, 'bo')
plt.plot(epochs, val_loss_values, 'b+')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.show() | code |
1007093/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train_file = pd.read_csv('../input/train.csv')
print(train_file.shape)
test_images = pd.read_csv('../input/test.csv')
print(test_images.shape) | code |
1007093/cell_17 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout, Lambda, Flatten
from keras.models import Sequential
from keras.optimizers import Adam ,RMSprop
from keras.utils import np_utils
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_file = pd.read_csv('../input/train... | code |
1007093/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense, Dropout, Lambda, Flatten
from keras.models import Sequential
from keras.optimizers import Adam ,RMSprop
from keras.utils import np_utils
import numpy as np
import pandas as pd
train_file = pd.read_csv('../input/train.csv')
test_images = pd.read_csv('../input/test.csv')
train_image... | code |
1008693/cell_25 | [
"text_html_output_1.png"
] | from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
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
employees.shape
employees.mean()... | code |
1008693/cell_4 | [
"text_plain_output_1.png"
] | employees.shape | code |
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