path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2023611/cell_5 | [
"text_plain_output_1.png"
] | import h5py
f = h5py.File('../input/LetterColorImages.h5', 'r')
keys = list(f.keys())
keys | code |
50224775/cell_9 | [
"text_plain_output_1.png"
] | def binary_search_recursive(array, element, start, end):
if start > end:
return -1
mid = (start + end) // 2
if element == array[mid]:
return mid
if element < array[mid]:
return binary_search_recursive(array, element, start, mid - 1)
else:
return binary_search_recursiv... | code |
50224775/cell_2 | [
"text_plain_output_1.png"
] | A = [1, 25, 35, 250, 500, 750, 1000]
print(A) | code |
50224775/cell_7 | [
"text_plain_output_1.png"
] | def binary_search_recursive(array, element, start, end):
if start > end:
return -1
mid = (start + end) // 2
if element == array[mid]:
return mid
if element < array[mid]:
return binary_search_recursive(array, element, start, mid - 1)
else:
return binary_search_recursiv... | code |
50224775/cell_3 | [
"text_plain_output_1.png"
] | A = [1, 25, 35, 250, 500, 750, 1000]
print(A.index(35)) | code |
50224775/cell_10 | [
"text_plain_output_1.png"
] | def binary_search_recursive(array, element, start, end):
if start > end:
return -1
mid = (start + end) // 2
if element == array[mid]:
return mid
if element < array[mid]:
return binary_search_recursive(array, element, start, mid - 1)
else:
return binary_search_recursiv... | code |
50224775/cell_5 | [
"text_plain_output_1.png"
] | def search(arr, n, x):
for i in range(0, n):
if arr[i] == x:
return i
return -1
arr = [10, 20, 30, 40, 50, 60, 70]
x = 50
n = len(arr)
result = search(arr, n, x)
if result == -1:
print('Elemen tidak ada di array')
else:
print('Elemen ada di indeks', result) | code |
1010259/cell_13 | [
"text_plain_output_1.png"
] | from scipy import sparse
from sklearn import model_selection, preprocessing, ensemble
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
import numpy as np
import pandas as pd
import xgboost as xgb
def runXGB(train_X, train_y, test_X, test_y=None, feature_names=None, seed_val=0, num_roun... | code |
1010259/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
import numpy as np
import pandas as pd
data_path = '../input/'
train_file = data_path + 'train.json'
test_file = data_path + 'test.json'
train_df = pd.read_json(train_file)
test_df = pd.read_json(test_file)
features_to_use = ['bathrooms', ... | code |
1010259/cell_11 | [
"text_plain_output_1.png"
] | from scipy import sparse
from sklearn import model_selection, preprocessing, ensemble
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
import numpy as np
import pandas as pd
data_path = '../input/'
train_file = data_path + 'train.json'
test_file = data_path + 'test.json'
train_df = pd.r... | code |
1010259/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data_path = '../input/'
train_file = data_path + 'train.json'
test_file = data_path + 'test.json'
train_df = pd.read_json(train_file)
test_df = pd.read_json(test_file)
features_to_use = ['bathrooms', 'bedrooms', 'latitude', 'longitude', 'price']
train_df['num_photos'] = train_... | code |
1010259/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | """preds, model = runXGB(train_X, train_y, test_X, num_rounds=400)
out_df = pd.DataFrame(preds)
out_df.columns = ["high", "medium", "low"]
out_df["listing_id"] = test_df.listing_id.values
out_df.to_csv("0315.csv", index=False)""" | code |
1010259/cell_12 | [
"text_plain_output_1.png"
] | from scipy import sparse
from sklearn import model_selection, preprocessing, ensemble
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.metrics import log_loss
import numpy as np
import pandas as pd
import xgboost as xgb
def runXGB(train_X, train_y, test_X, test_y=None, fe... | code |
1010259/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
data_path = '../input/'
train_file = data_path + 'train.json'
test_file = data_path + 'test.json'
train_df = pd.read_json(train_file)
test_df = pd.read_json(test_file)
print(train_df.shape)
print(test_df.shape) | code |
2011002/cell_19 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow as tf
dataset = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
def one_hot(num):
output = np.zeros([1, 10])
output[0, num] = 1
return output.astype(int)
labels_encoded = np.array([np.array([int(i == l) for i in range(10... | code |
2011002/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
from sklearn.preprocessing import OneHotEncoder
import tensorflow as tf | code |
2011002/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
def one_hot(num):
output = np.zeros([1, 10])
output[0, num] = 1
return output.astype(int)
labels_encoded = np.array([np.array([int(i == l) for i in range(10)]) for l in dataset.iloc... | code |
2011002/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
dataset.head() | code |
2011002/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
def one_hot(num):
output = np.zeros([1, 10])
output[0, num] = 1
return output.astype(int)
labels_encoded = np.array([np.array([int(i == l) for i in range(10)]) for l in dataset.iloc... | code |
130015070/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk.corpus import stopwords
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.preprocessing.sequence import pad_sequences
import nltk
import pandas as pd
import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tenso... | code |
130015070/cell_9 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
import nltk
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.callbacks ... | code |
130015070/cell_25 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.metrics import classification_report, confusion_matrix
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.preprocessing.sequence import pad_sequences
import nltk
import pandas as pd
import tensorflow as tf
import pandas as pd
import numpy as ... | code |
130015070/cell_23 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.preprocessing.sequence import pad_sequences
import nltk
import pandas as pd
import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tenso... | code |
130015070/cell_26 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.metrics import classification_report, confusion_matrix
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.preprocessing.sequence import pad_sequences
import nltk
import numpy as np
import pandas as pd
import tensorflow as tf
import pandas as... | code |
130015070/cell_11 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
import nltk
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.callbacks ... | code |
130015070/cell_19 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from tensorflow.keras.preprocessing.sequence import pad_sequences
import nltk
import pandas as pd
import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from t... | code |
130015070/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/cyberbullying-classification/cyberbullying_tweets.csv')
df
df.cyberbullying_type.value_counts().plot.barh(xlim=(7800, 8000)) | code |
130015070/cell_3 | [
"text_plain_output_1.png"
] | import nltk
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.callbacks import EarlyStopping
from sklearn.model_selection import... | code |
130015070/cell_24 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.preprocessing.sequence import pad_sequences
import nltk
import pandas as pd
import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tenso... | code |
130015070/cell_27 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.metrics import classification_report, confusion_matrix
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.preprocessing.sequence import pad_sequences
import nltk
import numpy as np
import pandas as pd
import tensorflow as tf
import pandas as... | code |
130015070/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/cyberbullying-classification/cyberbullying_tweets.csv')
df | code |
88102154/cell_9 | [
"text_plain_output_1.png"
] | from tensorflow import keras
from tensorflow.keras.utils import plot_model
def build_model():
inputs = keras.layers.Input(shape=(28, 28, 1))
conv1 = keras.layers.Conv2D(32, (3, 3), strides=1, activation='relu')(inputs)
conv2 = keras.layers.Conv2D(64, (3, 3), strides=1, activation='relu')(conv1)
maxpoo... | code |
88102154/cell_4 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"text_plain_outp... | from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array
import pandas as pd
train = pd.read_csv('../input/digit-recognizer/train.csv')
test = pd.read_csv('../input/digit-recognizer/test.csv')
sample = pd.read_csv('../input/digit-recognizer/sample_submission.csv')
y = train.pop... | code |
88102154/cell_10 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import KFold, train_test_split, StratifiedKFold
from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array
from tqdm.keras import TqdmCallback
import numpy as np
import pandas as pd
train = pd.read_csv('../input/digit-r... | code |
90142889/cell_13 | [
"text_plain_output_1.png"
] | import itertools
import keras
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/demand-forecast-abal2/demand_forecast.csv', parse_dates=['date'])
df
df.groupby(['store', 'item']).size()
input_df = df.set_index(['date', 'store... | code |
90142889/cell_9 | [
"image_output_1.png"
] | train_df.index.levels[1:][0] | code |
90142889/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from statsmodels.graphics.tsaplots import plot_acf
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/demand-forecast-abal2/demand_forecast.csv', parse_dates=['date'])
df
def plot_autocorrelation(store_num, item_num, lag=50):
filtered_df = df[(df['store'] == st... | code |
90142889/cell_2 | [
"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/demand-forecast-abal2/demand_forecast.csv', parse_dates=['date'])
df | code |
90142889/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from statsmodels.graphics.tsaplots import plot_acf
import matplotlib.pyplot as plt
import tensorflow as tf
import keras
import itertools
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, f... | code |
90142889/cell_15 | [
"text_plain_output_1.png"
] | import itertools
import keras
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
df = pd.read_csv('/kaggle/input/demand-forecast-abal2/demand_forecast.csv', parse_dates=['date'])
df
df.groupby(['store', 'item']).size()
input_df = df.... | code |
90142889/cell_17 | [
"text_html_output_1.png"
] | import itertools
import keras
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 tensorflow as tf
df = pd.read_csv('/kaggle/input/demand-forecast-abal2/demand_forecast.csv', parse_dates=['date'])
df
df.groupby(['store'... | code |
90142889/cell_10 | [
"text_html_output_1.png"
] | train_df.index.levels[1:][0]
train_df.iloc[(train_df.index.get_level_values('store') == 1) & (train_df.index.get_level_values('item') == 1)] | code |
90142889/cell_12 | [
"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"
] | import itertools
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/demand-forecast-abal2/demand_forecast.csv', parse_dates=['date'])
df
df.groupby(['store', 'item']).size()
input_df = df.set_index(['date', 'store', 'item'])
d... | code |
90142889/cell_5 | [
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/demand-forecast-abal2/demand_forecast.csv', parse_dates=['date'])
df
df.groupby(['store', 'item']).size() | code |
121153356/cell_4 | [
"text_plain_output_1.png"
] | dictionary = {'Doğan': 23, 'Efe': 22}
print(dictionary)
print(type(dictionary))
print(dictionary.values())
keys = dictionary.keys()
if 'Doğan' in keys:
print('Yes')
else:
print('No') | code |
121153356/cell_2 | [
"text_plain_output_1.png"
] | var1 = 10
var2 = 20
var3 = 30
list1 = [10, 20, 30]
type(list1)
print(list1[-1])
print(list1[0:2])
list1.append(40)
print(list1)
list1.remove(40)
print(list1)
list1.reverse()
print(list1)
list1.sort()
print(list1) | code |
121153356/cell_1 | [
"text_plain_output_1.png"
] | number = 5
day = 'Monday'
print(number) | code |
121153356/cell_3 | [
"text_plain_output_1.png"
] | def circle_perimeter(r, pi=3.14):
result = 2 * pi * r
return result
print(circle_perimeter(3))
def calculate(x):
result = x * x
return result
result = calculate(3)
print(result)
result2 = lambda x: x * x
print(result2(4)) | code |
2001660/cell_9 | [
"text_plain_output_1.png"
] | from pathlib import Path
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
input_path = Path('../input')
train_path = input_path / 'train'
test_path = input_path / 'test'
cameras = os.listdir(train_path)
train_images = []
for camera in cameras:
for fname in sorted(os.listdir(train... | code |
2001660/cell_23 | [
"text_html_output_1.png"
] | from pathlib import Path
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
input_path = Path('../input')
train_path = input_path / 'train'
test_path = input_path / 'test'
cameras = os.listdir(train_path)
train_images = []
for camera in cameras:
for fname in sorted(os.listdir(train... | code |
2001660/cell_20 | [
"text_plain_output_1.png"
] | from pathlib import Path
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
input_path = Path('../input')
train_path = input_path / 'train'
test_path = input_path / 'test'
cameras = os.listdir(train_path)
train_images = []
for camera in cameras:
for fname in sorted(os.listdir(train... | code |
2001660/cell_6 | [
"text_plain_output_1.png"
] | from pathlib import Path
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
input_path = Path('../input')
train_path = input_path / 'train'
test_path = input_path / 'test'
cameras = os.listdir(train_path)
train_images = []
for camera in cameras:
for fname in sorted(os.listdir(train... | code |
2001660/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from pathlib import Path
from sklearn.decomposition import PCA
import cv2
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
input_path = Path('../input')
train_path = input_path / 'train'
test_path = input_path / 'test'
def get_center_crop(img, d... | code |
2001660/cell_19 | [
"text_plain_output_1.png"
] | from pathlib import Path
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
input_path = Path('../input')
train_path = input_path / 'train'
test_path = input_path / 'test'
cameras = os.listdir(train_path)
train_images = []
for camera in cameras:
for fname in sorted(os.listdir(train... | code |
2001660/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import os
from pathlib import Path
import multiprocessing as mp
import numpy as np
import pandas as pd
from skimage.data import imread
from sklearn.ensemble import RandomForestClassifier
import time
import cv2
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from su... | code |
2001660/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pathlib import Path
from sklearn.ensemble import RandomForestClassifier
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
input_path = Path('../input')
train_path = input_path / 'train'
test_path = input_path / 'test'
cameras = os.listdir(train_path)
train_images = []
for camera... | code |
2001660/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from pathlib import Path
from sklearn.decomposition import PCA
import cv2
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
input_path = Path('../input')
train_path = input_path / 'train'
test_path = input_path / 'test'
def get_center_crop(img, d... | code |
2001660/cell_12 | [
"text_html_output_1.png"
] | from pathlib import Path
from sklearn.decomposition import PCA
import cv2
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
input_path = Path('../input')
train_path = input_path / 'train'
test_path = input_path / 'test'
def get_center_crop(img, d... | code |
2001660/cell_5 | [
"text_html_output_1.png"
] | from pathlib import Path
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
input_path = Path('../input')
train_path = input_path / 'train'
test_path = input_path / 'test'
cameras = os.listdir(train_path)
train_images = []
for camera in cameras:
for fname in sorted(os.listdir(train... | code |
128043510/cell_21 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train, Y_train)
predictions = lr.predict(X_test)
print('Actual value of the house:- ', Y_test[0])
print('model Predicted values:- ', predictions[0]) | code |
128043510/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn.datasets import load_boston
load_boston = load_boston()
x = load_boston.data
y = load_boston.target
data = pd.DataFrame(x, columns=load_b... | code |
128043510/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn.datasets import load_boston
load_boston = load_boston()
x = load_boston.data
y = load_boston.target
data = pd.DataFrame(x, columns=load_b... | code |
128043510/cell_4 | [
"image_output_1.png"
] | from sklearn.datasets import load_boston
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.datasets import load_boston
load_boston = load_boston()
x = load_boston.data
y = load_boston.target
data = pd.DataFrame(x, columns=load_boston.feature_names)
data['SalePrice'] = y
print(data.s... | code |
128043510/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train, Y_train) | code |
128043510/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.datasets import load_boston
load_boston = load_boston()
x = load_boston.data
y = load_boston.target
data = pd.DataFrame(x, columns=load_boston.feature_names)
data['SalePrice'] = y
data.describ... | code |
128043510/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.datasets import load_boston
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.datasets import load_boston
load_boston = load_boston()
x = load_boston.data
y = load_boston.target
data = pd.DataFrame(x, columns=load_boston.feature_names)
print(data)
data['SalePrice'] = y
d... | code |
128043510/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.datasets import load_boston
load_boston = load_boston()
x = load_boston.data
y = load_boston.target
data = pd.DataFrame(x, columns=load_boston.feature_names)
data['SalePrice'] = y
data.isnull(... | code |
128043510/cell_19 | [
"image_output_1.png"
] | print(X_train.shape)
print(X_test.shape)
print(Y_train.shape)
print(Y_test.shape) | code |
128043510/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.datasets import load_boston
load_boston = load_boston()
x = load_boston.data
y = load_boston.target
data = pd.DataFrame(x, columns=load_boston.feature_names)
data['SalePrice'] = y
data.isnull(... | code |
128043510/cell_16 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy.stats import norm, skew # for some statistics
from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn.datasets import load_boston
load_boston = load_boston(... | code |
128043510/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.datasets import load_boston
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.datasets import load_boston
load_boston = load_boston()
x = load_boston.data
y = load_boston.target
data = pd.DataFrame(x, columns=load_boston.feature_names)
data['SalePrice'] = y
print(load_b... | code |
128043510/cell_14 | [
"text_html_output_1.png"
] | from scipy import stats
from scipy.stats import norm, skew # for some statistics
from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn.datasets import load_boston
load_boston = load_boston(... | code |
128043510/cell_22 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import numpy as np # linear algebra
from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train, Y_train)
predictions = lr.predict(X_test)
from sklearn.metrics import mean_squared_error
mse... | code |
128043510/cell_10 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn.datasets import load_boston
load_boston = load_boston()
x = load_boston.data
y = load_boston.target
data = pd.DataFrame(x, columns=load_b... | code |
128043510/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn.datasets import load_boston
load_boston = load_boston()
x = load_boston.data
y = load_boston.target
data = pd.DataFrame(x, columns=load_b... | code |
128043510/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_boston
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.datasets import load_boston
load_boston = load_boston()
x = load_boston.data
y = load_boston.target
data = pd.DataFrame(x, columns=load_boston.feature_names)
data['SalePrice'] = y
data.info() | code |
17132944/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from gensim.models import KeyedVectors
from gensim.models import Word2Vec
from nltk.tokenize import RegexpTokenizer
import pandas as pd
import gensim
from gensim.models import Word2Vec
from gensim.models import KeyedVectors
import pandas as pd
from nltk.tokenize import RegexpTokenizer
forum_posts = pd.read_csv('../... | code |
17132944/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from gensim.models import KeyedVectors
from gensim.models import Word2Vec
from nltk.tokenize import RegexpTokenizer
import pandas as pd
import gensim
from gensim.models import Word2Vec
from gensim.models import KeyedVectors
import pandas as pd
from nltk.tokenize import RegexpTokenizer
forum_posts = pd.read_csv('../... | code |
17132944/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from gensim.models import KeyedVectors
from gensim.models import Word2Vec
from nltk.tokenize import RegexpTokenizer
import pandas as pd
import gensim
from gensim.models import Word2Vec
from gensim.models import KeyedVectors
import pandas as pd
from nltk.tokenize import RegexpTokenizer
forum_posts = pd.read_csv('../... | code |
2016332/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
feature_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(featur... | code |
2016332/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('../input/mushrooms.csv')
data_df.info() | code |
2016332/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
feature_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(featur... | code |
2016332/cell_18 | [
"text_html_output_1.png"
] | from sklearn.linear_model import RidgeClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import LabelEncoder, PolynomialFeatures
from time ... | code |
2016332/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
feature_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(featur... | code |
2016332/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
feature_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(featur... | code |
2016332/cell_3 | [
"text_html_output_10.png",
"text_html_output_16.png",
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_15.png",
"text_html_output_5.png",
"text_html_output_14.png",
"text_html_output_19.png",
"text_html_output_9.png",
"text_html_output_13.png",
"t... | from subprocess import check_output
np.set_printoptions(suppress=True, linewidth=300)
pd.options.display.float_format = lambda x: '%0.6f' % x
pyo.init_notebook_mode(connected=True)
print(check_output(['ls', '../input']).decode('utf-8')) | code |
2016332/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
feature_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(featur... | code |
2016332/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('../input/mushrooms.csv')
data_df.head() | code |
16116423/cell_6 | [
"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')
sample_sub = pd.read_csv('../input/sample_submission.csv')
from copy import copy
X_train = train.drop(columns=['scalar_coupling_constant']).copy()
y_train = train['s... | code |
16116423/cell_1 | [
"text_plain_output_1.png"
] | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O ... | code |
16116423/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import KFold
import lightgbm as lgbm
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')
sample_sub = pd.read_csv('../input/sample_submission.csv')
from copy import copy
X_train = train.... | code |
16116423/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')
sample_sub = pd.read_csv('../input/sample_submission.csv')
from copy import copy
X_train = train.drop(columns=['scalar_coupling_constant']).copy()
y_train = train['s... | code |
122263646/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import setuptools
from transformers import pipeline
import pandas as pd | code |
122263646/cell_1 | [
"text_plain_output_1.png"
] | #!pip install transformers==2.10.0
#!pip install simpletransformers
!pip install pyopenssl | code |
122263646/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/large-quotes/Quotes_Large.csv')
data.head() | code |
16121779/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df.head() | code |
16121779/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df['Year'] | code |
16121779/cell_2 | [
"text_plain_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16121779/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df.columns
df['Methods'].nunique() | code |
16121779/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
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