path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
129000049/cell_45 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
import numpy as np
import pandas as pd
wdf = pd.read_csv('/kaggle/input/historical-weather-data-for-indian-cities/jaipur.csv', parse_dates=['date_time'], index_col... | code |
129000049/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
wdf = pd.read_csv('/kaggle/input/historical-weather-data-for-indian-cities/jaipur.csv', parse_dates=['date_time'], index_col='date_time')
wdf.columns
wdf.shape
wdf.isnull().any()
wdf_num = wdf.loc[:, ['mintempC', 'tempC', 'HeatIndexC', 'pressure']]
wdf_num.shape
wdf_num.columns
weth = wdf_nu... | code |
129000049/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
wdf = pd.read_csv('/kaggle/input/historical-weather-data-for-indian-cities/jaipur.csv', parse_dates=['date_time'], index_col='date_time')
wdf.columns
wdf.shape | code |
129000049/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
wdf = pd.read_csv('/kaggle/input/historical-weather-data-for-indian-cities/jaipur.csv', parse_dates=['date_time'], index_col='date_time')
wdf.columns
wdf.shape
wdf.isnull().any()
wdf_num = wdf.loc[:, ['mintempC', 'tempC', 'HeatIndexC', 'pressure']]
wdf_num.shape | code |
129000049/cell_38 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
import numpy as np
import pandas as pd
wdf = pd.read_csv('/kaggle/input/historical-weather-data-for-indian-cities/jaipur.csv', parse_dates=['date_time'], index_col... | code |
129000049/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
wdf = pd.read_csv('/kaggle/input/historical-weather-data-for-indian-cities/jaipur.csv', parse_dates=['date_time'], index_col='date_time')
wdf.columns
wdf.shape
wdf.isnull().any()
wdf_num = wdf.loc[:, ['mintempC', 'tempC', 'HeatIndexC', 'pressure']]
wdf_num.shape
wdf_num.columns | code |
129000049/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
train_X.shape
train_y.shape
model = LinearRegression()
model.fit(train_X, train_y) | code |
129000049/cell_22 | [
"text_html_output_1.png"
] | train_X.shape | code |
1008454/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
gTemp = pd.read_csv('../input/GlobalTemperatures.csv')
gTempCountry = pd.read_csv('../input/GlobalLandTemperaturesByCountry.csv')
gTempState = pd.read_csv('../input/GlobalLandTemperaturesByState.csv')
gTempMajorCity = pd.read_csv('../input/GlobalLandTemperaturesByMa... | code |
1008454/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
gTemp = pd.read_csv('../input/GlobalTemperatures.csv')
gTempCountry = pd.read_csv('../input/GlobalLandTemperaturesByCountry.csv')
gTempState = pd.read_csv('../input/GlobalLandTemperaturesByState.csv')
gTempMajorCity = pd.read_csv('../input/GlobalLandTemperaturesByMajorCity.csv')
gTempCity = pd.read... | code |
1008454/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
gTemp = pd.read_csv('../input/GlobalTemperatures.csv')
gTempCountry = pd.read_csv('../input/GlobalLandTemperaturesByCountry.csv')
gTempState = pd.read_csv('../input/GlobalLandTemperaturesByState.csv')
gTempMajorCity = pd.read_csv('../input/GlobalLandTemperaturesByMajorCity.csv')
gTempCity = pd.read... | code |
330673/cell_13 | [
"text_plain_output_1.png"
] | from scipy.stats import chisquare
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
from scipy.stats import chisquare
chars = [i for i in df.columns.values if 'char_' in i]
flags = []
for feat in df[c... | code |
330673/cell_9 | [
"text_plain_output_1.png"
] | from scipy.stats import chisquare
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
from scipy.stats import chisquare
chars = [i for i in df.columns.values if 'char_' in i]
flags = []
for feat in df[c... | code |
330673/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import chisquare
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
from scipy.stats import chisquare
chars = [i for i in df.columns.values if 'char_' in i]
flags = []
for feat in df[chars]:
group = df[chars].groupby(feat)
for otherfeat in df[chars].drop(feat, ax... | code |
330673/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import chisquare
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
from scipy.stats import chisquare
chars = [i for i in df.columns.values if 'char_' in i]
flags = []
for feat in df[chars]:
group = df[chars].groupby(feat)
for otherfeat in df[chars].drop(feat, ax... | code |
330673/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
print(df.head()) | code |
330673/cell_11 | [
"text_plain_output_1.png"
] | from scipy.stats import chisquare
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
from scipy.stats import chisquare
chars = [i for i in df.columns.values if 'char_' in i]
flags = []
for feat in df[c... | code |
330673/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import chisquare
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
from scipy.stats import chisquare
chars = [i for i in df.columns.values if 'char_' in i]
flags = []
for feat in df[c... | code |
330673/cell_15 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.stats import chisquare
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
from scipy.stats import chisquare
chars = [i for i in df.columns.values if 'char_' in i]
flags = []
for feat in df[c... | code |
330673/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import chisquare
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
from scipy.stats import chisquare
chars = [i for i in df.columns.values if 'char_' in i]
flags = []
for feat in df[c... | code |
330673/cell_14 | [
"text_plain_output_1.png"
] | from scipy.stats import chisquare
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/people.csv')
from scipy.stats import chisquare
chars = [i for i in df.columns.values if 'char_' in i]
flags = []
for feat in df[c... | code |
325017/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
masterDF = pd.read_csv('../input/emails.csv')
messageList = masterDF['message'].tolist()
bodyList = []
for message in messageList:
firstSplit = message.split('X-FileName: ', 1)[1]
secondSplit = firstSplit.split('.')
if len(secondSplit) > 1:
secondSplit = secondSplit[1]
body... | code |
16135671/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
def test_plot(x):
pass
test_plot(x_train_re) | code |
16135671/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
def test_plot(x):
pass
test_plot(x_test) | code |
16135671/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | !pip install tensorflow-gpu
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
import keras
from keras.models import Model
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, BatchNormalization, Activation
from keras.datasets import cifar10 | code |
16135671/cell_3 | [
"image_output_1.png"
] | from keras.datasets import cifar10
def load_images():
(x_train, _), (x_test, _) = cifar10.load_data()
return (x_train, x_test)
x_train, x_test = load_images() | code |
16135671/cell_14 | [
"image_output_1.png"
] | from keras.datasets import cifar10
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, BatchNormalization, Activation
from keras.models import Model
import keras
import matplotlib.pyplot as plt
def load_images():
(x_train, _), (x_test, _) = cifar10.load_data()
return (x_train, x_test... | code |
16135671/cell_10 | [
"text_plain_output_1.png"
] | import keras
def normalize(x_train, x_test):
x_train = keras.utils.normalize(x_train)
x_test = keras.utils.normalize(x_test)
return (x_train, x_test)
print(x_train_re.shape[1:])
print(x_test_re.shape)
input_shape = x_train.shape[1:]
receptive_field = (3, 3)
pooling_field = (2, 2) | code |
16135671/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
def test_plot(x):
pass
test_plot(x_train) | code |
122264859/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow as tf
mnist = keras.datasets.mnis... | code |
122264859/cell_4 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
import numpy as np
import pandas as pd
mnist = keras.datasets.mnist
(train_images_mnist, train_labels_mnist), (test_images_mnist, test_labels_mnist) = mnist.load_data()
train_images_mnist = np.reshape(train_images_mnist, (train_images... | code |
122264859/cell_19 | [
"image_output_1.png"
] | from imutils.contours import sort_contours
from matplotlib import cm
from sklearn.model_selection import train_test_split
from tensorflow import keras
from tensorflow.keras.models import load_model
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
... | code |
122264859/cell_18 | [
"text_plain_output_1.png"
] | from imutils.contours import sort_contours
from matplotlib import cm
from sklearn.model_selection import train_test_split
from tensorflow import keras
from tensorflow.keras.models import load_model
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
... | code |
122264859/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow as tf
mnist = keras.datasets.mnis... | code |
122264859/cell_15 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from imutils.contours import sort_contours
from matplotlib import cm
import cv2
import imutils
import matplotlib.pyplot as plt
image_path = '/kaggle/input/tester2/pja 4.jpg'
image = cv2.imread(image_path)
edged = cv2.Canny(blurred, 30, 250)
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX... | code |
122264859/cell_17 | [
"text_plain_output_1.png"
] | from imutils.contours import sort_contours
from matplotlib import cm
from sklearn.model_selection import train_test_split
from tensorflow import keras
from tensorflow.keras.optimizers import RMSprop
import cv2
import imutils
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorf... | code |
122264859/cell_14 | [
"text_plain_output_1.png"
] | gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cropped = gray[120:, :]
blurred = cv2.GaussianBlur(cropped, (5, 5), 0)
from matplotlib import cm
fig = plt.figure(figsize=(16, 4))
ax = plt.subplot(1, 4, 1)
ax.imshow(image)
ax.set_title('original image')
ax = plt.subplot(1, 4, 2)
ax.imshow(gray, cmap=cm.binary_r)
ax.set_a... | code |
122264859/cell_10 | [
"text_plain_output_1.png"
] | pip install imutils | code |
122264859/cell_12 | [
"text_plain_output_1.png"
] | from tensorflow.keras.models import load_model
model_path = '/kaggle/working/model_v2'
print('Loading NN model...')
model = load_model(model_path)
print('Done') | code |
122264859/cell_5 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow import keras
from tensorflow.keras.optimizers import RMSprop
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow as tf
mnist = keras.datasets.mnist
(train_images_mnist, train_labels_mnist), (test_images_mnist, test_... | code |
16115621/cell_9 | [
"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')
test = pd.read_csv('../input/test.csv')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
train['SalePrice'].hist(bins=50)
y ... | code |
16115621/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.describe() | code |
16115621/cell_11 | [
"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')
test = pd.read_csv('../input/test.csv')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
y = train['SalePrice'].reset_index(... | code |
16115621/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train['SalePrice'].hist(bins=50) | code |
16115621/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # visualization
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 # visualization
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.reset_index(drop=True, inplace=Tru... | code |
16115621/cell_14 | [
"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')
test = pd.read_csv('../input/test.csv')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
y = train['SalePrice'].reset_index(... | code |
16115621/cell_10 | [
"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')
test = pd.read_csv('../input/test.csv')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
y = train['SalePrice'].reset_index(... | code |
16115621/cell_12 | [
"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')
test = pd.read_csv('../input/test.csv')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
y = train['SalePrice'].reset_index(... | code |
16115621/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.describe() | code |
72097728/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneH... | code |
72097728/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
df = pd.read_csv('../input/30-days-of-ml/train.csv')
test_df = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_df = pd.read_csv('../input/30-days-of-ml/sample_submiss... | code |
2003139/cell_9 | [
"text_plain_output_1.png"
] | from PIL import Image
from keras import optimizers, losses, activations, models
from keras.layers import Convolution2D, Dense, Input, Flatten, Dropout, MaxPooling2D, BatchNormalization, GlobalMaxPool2D, Concatenate
from random import shuffle
import numpy as np # linear algebra
import os
import pandas as pd # data... | code |
2003139/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import os
from PIL import Image
from skimage.transform import resize
from random import shuffle
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2003139/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from keras import optimizers, losses, activations, models
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping, ReduceLROnPlateau, TensorBoard
from keras.layers import Convolution2D, Dense, Input, Flatten, Dropout, MaxPooling2D, BatchNormalization, GlobalMaxPool2D, ... | code |
105185803/cell_1 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.express as px
import os
!pip install kaleido | code |
105185803/cell_5 | [
"text_html_output_2.png"
] | import pandas as pd
import plotly.express as px
stream = open('../input/bbk-kunpeng/perf_test_result.txt', 'r')
stream = stream.read()
ds = pd.DataFrame(columns=['N', 'NRHS', 'data_type', 'gflops', 'uplo'])
datatype = 'single'
N = 0
NRHS = 0
uplo = 'U'
gflops = 0.0
cnt = 0
for line in stream.split('\n'):
if lin... | code |
121149832/cell_4 | [
"application_vnd.jupyter.stderr_output_766.png",
"application_vnd.jupyter.stderr_output_116.png",
"application_vnd.jupyter.stderr_output_74.png",
"application_vnd.jupyter.stderr_output_268.png",
"application_vnd.jupyter.stderr_output_362.png",
"text_plain_output_743.png",
"text_plain_output_673.png",
... | # Install nb_black for autoformatting
!pip install nb_black --quiet | code |
121149832/cell_19 | [
"text_html_output_1.png"
] | from kaggle_secrets import UserSecretsClient
from logging import getLogger, INFO, FileHandler, Formatter, StreamHandler
from sklearn.metrics import accuracy_score
import json
import numpy as np
import os
import os
import pandas as pd
import random
import torch
import wandb
import warnings
import numpy as np... | code |
121149832/cell_3 | [
"text_html_output_4.png",
"text_html_output_2.png",
"text_html_output_5.png",
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png",
"text_html_output_3.png"
] | !pip install onnx_tf
!pip install tflite-runtime
!pip install -q --upgrade wandb | code |
121149832/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import json
import numpy as np
import os
import os
import pandas as pd
import random
import torch
import torch.nn as nn
import warnings
import numpy as np
import pandas as pd
import math
import random
import time
from collections import OrderedDict
import tensorflow ... | code |
121149832/cell_14 | [
"text_plain_output_1.png"
] | from logging import getLogger, INFO, FileHandler, Formatter, StreamHandler
from sklearn.metrics import accuracy_score
import json
import numpy as np
import os
import os
import pandas as pd
import random
import torch
import warnings
import numpy as np
import pandas as pd
import math
import random
import time
f... | code |
48166170/cell_13 | [
"text_html_output_1.png"
] | k_range = range(1, 21)
print('k range', k_range) | code |
48166170/cell_39 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
val_results = []
train_results = []
k_range = range(1, 21)
for k in k_range:
clf_2 = KNeighborsClassifier(n_neighbors=k)
clf_2 = clf_2.fit(X_train, y_train)
pred_train = clf_2... | code |
48166170/cell_26 | [
"image_output_1.png"
] | from sklearn import metrics
from sklearn.model_selection import cross_val_score, learning_curve, validation_curve
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df2 = pd.read_csv('../input/diabetescsv/diabetes.csv')
X = df2.iloc[:, 0:8]
y... | code |
48166170/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df2 = pd.read_csv('../input/diabetescsv/diabetes.csv')
X = df2.iloc[:, 0:8]
y = df2.iloc[:, 8]
print(X) | code |
48166170/cell_17 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
val_results = []
train_results = []
k_range = range(1, 21)
for k in k_range:
clf_2 = KNeighborsClassifier(n_neighbors=k)
clf_2 = clf_2.fit(X_train, y_train)
pred_train = clf_2.predict(X_train)... | code |
48166170/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
clf_3 = KNeighborsClassifier()
param_grid = [{'weights': ['uniform'], 'n_neighbors': list(range(1, 21))}, {'weights': ['distance'], 'n_neighbors': list(range(1, 21))}]
print(param_grid) | code |
48166170/cell_22 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.neighbors import KNeighborsClassifier
val_results = []
train_results = []
k_range = range(1, 21)
for k in k_range:
clf_2 = KNeighborsClassifier(n_neighbors=k)
clf_2 = clf_2.fit(X_train, y_train)
pred_train = clf_2.predict(X_train)
train_score = metrics.accura... | code |
48166170/cell_37 | [
"image_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
clf_3 = KNeighborsClassifier()
param_grid = [{'weights': ['uniform'], 'n_neighbors': list(range(1, 21))}, {'weights': ['distance'], 'n_neighbors': list(range(1, 21))}]
gs = GridSearchCV(clf_3, param_grid, scoring='acc... | code |
48166170/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df2 = pd.read_csv('../input/diabetescsv/diabetes.csv')
df2.head(10) | code |
16163803/cell_13 | [
"image_output_1.png"
] | """his = model.fit_generator(train_gen,
epochs=10,
steps_per_epoch=len(X_train)/BATCH_SIZE,
validation_data=test_gen,
validation_steps=len(X_test)/BATCH_SIZE)""" | code |
16163803/cell_4 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import os
import os
category = ['cat', 'dog']
EPOCHS = 50
IMGSIZE = 128
BATCH_SIZE = 32
STOPPING_PATIENCE = 15
VERBOSE = 1
MODEL_NAME = 'cnn_50epochs_imgsize128'
OPTIMIZER = 'adam'
TRAINING_DIR = '../input/dogs-vs-cats-redux-kernels-edition/train'
TEST_DIR = '../input/dogs... | code |
16163803/cell_6 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import os
category = ['cat', 'dog']
EPOCHS = 50
IMGSIZE = 128
BATCH_SIZE = 32
STOPPING_PATIENCE = 15
VERBOSE = 1
MODEL_NAME = 'cnn_50epochs_imgsize128'
OPTIMIZER = 'adam'
TRAINING_DIR = '../input/dogs-vs-cats-redux-kernels-edi... | code |
16163803/cell_2 | [
"text_plain_output_1.png"
] | import os
import os
print(os.listdir('../input')) | code |
16163803/cell_11 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, BatchNormalization, Dropout
from keras.layers import Dense, Flatten
from keras.models import Sequential
from keras.utils import to_categorical
import cv2
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import os
category = ['cat', 'do... | code |
16163803/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Conv2D, MaxPool... | code |
16163803/cell_7 | [
"image_output_1.png"
] | from keras.utils import to_categorical
import cv2
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import os
category = ['cat', 'dog']
EPOCHS = 50
IMGSIZE = 128
BATCH_SIZE = 32
STOPPING_PATIENCE = 15
VERBOSE = 1
MODEL_NAME = 'cnn_50epochs_imgsize128'
OPTIMIZER = 'adam'
TRAINING_DIR = ... | code |
16163803/cell_15 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, BatchNormalization, Dropout
from keras.layers import Dense, Flatten
from keras.models import Sequential
from keras.utils import to_categorical
import cv2
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import os
category = ['cat', 'do... | code |
16163803/cell_16 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, BatchNormalization, Dropout
from keras.layers import Dense, Flatten
from keras.models import Sequential
from keras.utils import to_categorical
import cv2
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import os
category = ['cat', 'do... | code |
16163803/cell_14 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, BatchNormalization, Dropout
from keras.layers import Dense, Flatten
from keras.models import Sequential
from keras.utils import to_categorical
import cv2
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import os
category = ['cat', 'do... | code |
105192890/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import torch
from torch import nn
from torch.utils.data import TensorDataset, DataLoader
import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
from sklearn.metrics import r2_score
warnings.simplefilter('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
... | code |
121150886/cell_25 | [
"text_plain_output_1.png"
] | from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from transformers import AutoTokenizer, AutoModel
import re
import torch
import torch
import unicodedata
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
tokenizer = Auto... | code |
121150886/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from ydata_profiling import ProfileReport
import pandas as pd
df_reviews = pd.read_csv('/kaggle/input/arabic-100k-reviews/ar_reviews_100k.tsv', delimiter='\t')
profile = ProfileReport(df_reviews, title='Profiling Report')
profile.to_notebook_iframe() | code |
121150886/cell_6 | [
"text_plain_output_1.png"
] | import torch
if torch.cuda.is_available():
device = torch.device('cuda')
print(f'Using {torch.cuda.device_count()} GPU(s)!')
print(f'Device name: {torch.cuda.get_device_name(0)}')
else:
device = torch.device('cpu')
print('No GPU available.') | code |
121150886/cell_19 | [
"text_plain_output_1.png"
] | from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from transformers import AdamW, get_linear_schedule_with_warmup
from transformers import AutoTokenizer, AutoModel
import numpy as np
import random
import re
import time
import torch
import torch
import torch.nn as nn
impo... | code |
121150886/cell_1 | [
"text_plain_output_1.png"
] | !pip install Arabic-Stopwords
!pip install emoji
# !pip install Tashaphyne
# !pip install qalsadi
# !pip install np_utils
!pip install ydata-profiling | code |
121150886/cell_8 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('asafaya/bert-mini-arabic') | code |
121150886/cell_15 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import torch
import torch.nn as nn
from transformers import BertModel
class BertClassifier(nn.Module):
def __init__(self, freeze_bert=False, version='mini'):
super(BertClassifier, self).__init__()
D_in = 256 if version == 'mini' else 758
H = 50
D_out = 2
self.bert = AutoModel... | code |
121150886/cell_12 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from transformers import AutoTokenizer, AutoModel
import pandas as pd
import re
import torch
import unicodedata
df_reviews = pd.read_csv('/kaggle/input/arabic-100k-reviews/ar_reviews_100k.tsv', delimiter='\t')
label_mapping = {'Positive': 1, 'Negative': 0}
df_... | code |
121150886/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df_reviews = pd.read_csv('/kaggle/input/arabic-100k-reviews/ar_reviews_100k.tsv', delimiter='\t')
label_mapping = {'Positive': 1, 'Negative': 0}
df_reviews = df_reviews[df_reviews.label != 'Mixed']
print(df_reviews.shape)
df_reviews.label = df_reviews.label.map(label_mapping)
df_reviews.label.valu... | code |
128034284/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
def get_nan_dummy(series):
"""Given a Series containing NaN and several classes return a dummy Series
indicating 0 for NaN and 1 for non-NaN data
Parameters
----------
- series : pd.Series, input series or col to dummify
Return
------
- s : pd.Series the dummy Series... | code |
128034284/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import norm, zscore
from sklearn.compose import make_column_transformer
from sklearn.compose import make_column_selector
from sklearn.ensemble import GradientBoostingRegressor, HistGradie... | code |
2019512/cell_9 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from sklearn.cluster import KMeans, SpectralClustering, AgglomerativeClustering
from sklearn.feature_selection import VarianceThreshold
from sklearn.manifold import MDS
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read... | code |
2019512/cell_6 | [
"image_output_1.png"
] | from sklearn.feature_selection import VarianceThreshold
from sklearn.manifold import MDS
from sklearn.preprocessing import StandardScaler
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
from subprocess import check_output... | code |
2019512/cell_2 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
frame = pd.read_csv('../input/HR_comma_sep.csv').rename(columns={'sales': 'position'})
frame.iloc[:, 5:].head() | code |
2019512/cell_1 | [
"text_html_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
from subprocess import check_output
frame = pd.read_csv('../input/HR_comma_sep.csv').rename(columns={'sales': 'position'})
frame.iloc[:, :5].head() | code |
2019512/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.cluster import KMeans, SpectralClustering, AgglomerativeClustering
from sklearn.feature_selection import VarianceThreshold
from sklearn.manifold import MDS
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read... | code |
2019512/cell_8 | [
"image_output_1.png"
] | from sklearn.cluster import KMeans, SpectralClustering, AgglomerativeClustering
from sklearn.feature_selection import VarianceThreshold
from sklearn.manifold import MDS
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read... | code |
2019512/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 numpy as np
import pandas as pd
from subprocess import check_output
frame = pd.read_csv('../input/HR_comma_sep.csv').rename(columns={'sales': 'position'})
pd.DataFrame({'dtypes': frame.dtypes, 'isnull': pd.isnull(frame).any(), 'count distin... | code |
2019512/cell_10 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans, SpectralClustering, AgglomerativeClustering
from sklearn.feature_selection import VarianceThreshold
from sklearn.manifold import MDS
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read... | code |
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