path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
320604/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.conn... | code |
320604/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.conn... | code |
320604/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.conn... | code |
320604/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.conn... | code |
320604/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.conn... | code |
320604/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.conn... | code |
320604/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import tree
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL... | code |
320604/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.conn... | code |
320604/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.conn... | code |
320604/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.conn... | code |
320604/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.conn... | code |
1003319/cell_9 | [
"image_output_1.png"
] | import os # for doing directory operations
import dicom
import os
import pandas as pd
data_dir = '../input/sample_images/'
patients = os.listdir(data_dir)
patients
file_list = os.listdir('../input/')
file_list
len(patients) | code |
1003319/cell_2 | [
"text_plain_output_1.png"
] | import os # for doing directory operations
import dicom
import os
import pandas as pd
data_dir = '../input/sample_images/'
patients = os.listdir(data_dir)
patients
file_list = os.listdir('../input/')
file_list | code |
1003319/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
"""
So I want to test some transformations and changes to the modelling data:
** for each of the below get it running on 5% then validate on 20% modelling data)
(1) Try Different combinations of "resizing data"
(2) Try "Resampling approach" in pre-processing
(3) Try all of the tran... | code |
1003319/cell_8 | [
"text_plain_output_1.png"
] | import dicom # for reading dicom files
import os # for doing directory operations
import dicom
import os
import pandas as pd
data_dir = '../input/sample_images/'
patients = os.listdir(data_dir)
patients
file_list = os.listdir('../input/')
file_list
for patient in patients[:1]:
path = data_dir + patient
slice... | code |
1003319/cell_3 | [
"text_plain_output_1.png"
] | import dicom # for reading dicom files
import os # for doing directory operations
import dicom
import os
import pandas as pd
data_dir = '../input/sample_images/'
patients = os.listdir(data_dir)
patients
file_list = os.listdir('../input/')
file_list
for patient in patients[:1]:
path = data_dir + patient
slice... | code |
1003319/cell_10 | [
"text_plain_output_1.png"
] | import dicom # for reading dicom files
import matplotlib.pyplot as plt
import os # for doing directory operations
import dicom
import os
import pandas as pd
data_dir = '../input/sample_images/'
patients = os.listdir(data_dir)
patients
file_list = os.listdir('../input/')
file_list
for patient in patients[:1]:
pa... | code |
128043747/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pygpt4all.models.gpt4all import GPT4All
from pygpt4all.models.gpt4all import GPT4All
model = GPT4All('ggml-gpt4all-l13b-snoozy.bin', n_ctx=2048) | code |
128043747/cell_1 | [
"text_plain_output_1.png"
] | ! pip install pygpt4all
! wget http://gpt4all.io/models/ggml-gpt4all-l13b-snoozy.bin | code |
128043747/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
from pygpt4all.models.gpt4all import GPT4All
import requests
from pygpt4all.models.gpt4all import GPT4All
model = GPT4All('ggml-gpt4all-l13b-snoozy.bin', n_ctx=2048)
def gpt4all(prompt):
model.generate(prompt, n_predict=500, new_text_callback=lambda x: print(x, end=''))
import req... | code |
128043747/cell_5 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
import requests
import requests
from bs4 import BeautifulSoup
repo = '0xk1h0/ChatGPT_DAN'
url = f'https://raw.githubusercontent.com/{repo}/main/README.md'
bsoup = BeautifulSoup(requests.get(url).content.decode('utf8'))
dans = {}
for li in bsoup.find_all('li'):
details = li.get_text('... | code |
128045992/cell_13 | [
"text_plain_output_1.png"
] | from tensorflow.keras import layers
import cv2
import numpy as np
import os
import tensorflow as tf
subdir = ['angry', 'notAngry']
target = {'angry': 0, 'notAngry': 1}
dataset = '../input/fer2013new2class/FER2013NEW2CLASS/train/'
X = []
y = []
for emotions in subdir:
for img_names in os.listdir(dataset + '/' +... | code |
128045992/cell_4 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import os
subdir = ['angry', 'notAngry']
target = {'angry': 0, 'notAngry': 1}
dataset = '../input/fer2013new2class/FER2013NEW2CLASS/train/'
X = []
y = []
for emotions in subdir:
for img_names in os.listdir(dataset + '/' + emotions):
load_images = cv2.imread(dataset + '/' + e... | code |
128045992/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | (X_train.shape, X_test.shape, y_train.shape, y_test.shape) | code |
128045992/cell_3 | [
"text_plain_output_1.png"
] | import cv2
import os
subdir = ['angry', 'notAngry']
target = {'angry': 0, 'notAngry': 1}
dataset = '../input/fer2013new2class/FER2013NEW2CLASS/train/'
X = []
y = []
for emotions in subdir:
for img_names in os.listdir(dataset + '/' + emotions):
load_images = cv2.imread(dataset + '/' + emotions + '/' + img_... | code |
128045992/cell_5 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import os
import tensorflow as tf
subdir = ['angry', 'notAngry']
target = {'angry': 0, 'notAngry': 1}
dataset = '../input/fer2013new2class/FER2013NEW2CLASS/train/'
X = []
y = []
for emotions in subdir:
for img_names in os.listdir(dataset + '/' + emotions):
load_images = cv2... | code |
16142841/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
print(os.listdir('../input')) | code |
16142841/cell_7 | [
"text_plain_output_1.png"
] | print('End') | code |
16142841/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train_data.csv')
test_df = pd.read_csv('../input/test_data.csv') | code |
122244636/cell_4 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MultiLabelBinarizer
import cv2 as cv
import numpy as np
import os
import re
X = []
Y = []
input_shape = (96, 96, 3)
path_to_subset = f'../input/apparel-images-dataset/'
... | code |
122244636/cell_6 | [
"image_output_1.png"
] | from keras.layers import BatchNormalization, Activation, Dropout
from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D
from keras.models import Sequential
from keras.utils import plot_model
from sklearn.preprocessing import MultiLabelBinarizer
import cv2 as cv
import numpy as np
import os
import re
X =... | code |
122244636/cell_7 | [
"text_plain_output_100.png",
"text_plain_output_334.png",
"text_plain_output_673.png",
"text_plain_output_445.png",
"text_plain_output_640.png",
"text_plain_output_201.png",
"text_plain_output_586.png",
"text_plain_output_261.png",
"text_plain_output_565.png",
"text_plain_output_522.png",
"text_... | from keras.callbacks import ModelCheckpoint
from keras.layers import BatchNormalization, Activation, Dropout
from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_spli... | code |
122244636/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import cv2 as cv
import numpy as np
import os
import re
X = []
Y = []
input_shape = (96, 96, 3)
path_to_subset = f'../input/apparel-images-dataset/'
for folder in os.listdir(path_to_subset):
for image in os.listdir(os.path.join(path_to_subset, folder)):
... | code |
122244636/cell_5 | [
"text_plain_output_56.png",
"text_plain_output_35.png",
"text_plain_output_43.png",
"text_plain_output_37.png",
"text_plain_output_5.png",
"text_plain_output_48.png",
"text_plain_output_30.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_44.png",
"text_plain_output... | from keras.layers import BatchNormalization, Activation, Dropout
from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D
from keras.models import Sequential
from sklearn.preprocessing import MultiLabelBinarizer
import cv2 as cv
import numpy as np
import os
import re
X = []
Y = []
input_shape = (96, 96, 3)... | code |
49116351/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.dr... | code |
49116351/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.dr... | code |
49116351/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
subscribers.head() | code |
49116351/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.dr... | code |
49116351/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.dr... | code |
49116351/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 |
49116351/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.dr... | code |
49116351/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.dr... | code |
49116351/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.dr... | code |
49116351/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.dr... | code |
49116351/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.dr... | code |
49116351/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.dr... | code |
49116351/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count()
dataset2 = dataset1.dr... | code |
49116351/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
subscribers = pd.read_csv('../input/daim-hakathon/SUBSCRIBERS_80.csv')
subscribers['subscribed'] = 1
dataset = pd.read_csv('../input/daim-hakathon/COOKIES_80.csv')
dataset1 = pd.merge(dataset, subscribers)
dataset1.count() | code |
325654/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
import csv as csv
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from time import time
train = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
data = np.... | code |
129019305/cell_13 | [
"text_plain_output_1.png"
] | from matplotlib.colors import ListedColormap
from matplotlib.colors import ListedColormap
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as mtp
import numpy as np
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x_train = ... | code |
129019305/cell_11 | [
"text_html_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x_train = sc.fit_transform(x_train)
x_test = sc.transform(x_test)
from sklearn.naive_bayes import GaussianNB... | code |
129019305/cell_8 | [
"image_output_1.png"
] | from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x_train = sc.fit_transform(x_train)
x_test = sc.transform(x_test)
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(x_tr... | code |
129019305/cell_10 | [
"text_html_output_1.png"
] | from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import StandardScaler
import pandas as pd
dataset = pd.read_csv('/kaggle/input/user-data/User_Data.csv')
x = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x_tra... | code |
129019305/cell_12 | [
"text_html_output_1.png"
] | from matplotlib.colors import ListedColormap
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as mtp
import numpy as np
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x_train = sc.fit_transform(x_train)
x_test = sc.transfor... | code |
129019305/cell_5 | [
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/user-data/User_Data.csv')
x = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
dataset.head() | code |
320410/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Y_train = pd.read_csv('../input/genderclassmodel.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
_train = train.copy()
Y_train = _train['Survived'].copy()
_train.drop(['Name', 'Ticket', 'Cabin', 'Survived'],... | code |
320410/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
from IPython.display import display
from sklearn.ensemble import RandomForestClassifier
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import sklearn
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
320410/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Y_train = pd.read_csv('../input/genderclassmodel.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
_train = train.copy()
Y_train = _train['Survived'].copy()... | code |
320410/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Y_train = pd.read_csv('../input/genderclassmodel.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
_train = train.copy()
Y_train = _train['Survived'].copy()... | code |
320410/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Y_train = pd.read_csv('../input/genderclassmodel.csv')
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
print('test set has %s rows.' % test.shape[0])
print('train set has %s rows.' % train.shape[0]) | code |
88105099/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '../input/nuclio10-dsc-1121/sales_train_merged.csv'
df = pd.read_csv(data_path, index_col=0)
df.head() | code |
105197919/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5)
nullValues = choco_data.isnull().sum()
nul... | code |
105197919/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5) | code |
105197919/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5)
nullValues = choco_data.isnull().sum()
nul... | code |
105197919/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5)
nullValues = choco_data.isnull().sum()
nul... | code |
105197919/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 |
105197919/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5)
nullValues = choco_data.isnull().sum()
nul... | code |
105197919/cell_16 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5)
nullVal... | code |
105197919/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data) | code |
105197919/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5)
nullValues = choco_data.isnull().sum()
nul... | code |
105197919/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5)
nullValues = choco_data.isnull().sum()
nul... | code |
105197919/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5)
nullValues = choco_data.isnull().sum()
nul... | code |
105197919/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
choco_data = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
choco_data2 = pd.read_csv('/kaggle/input/chocolate-bar-ratings/chocolate_bars.csv')
len(choco_data)
choco_data.sample(5)
nullValues = choco_data.isnull().sum()
nul... | code |
18116618/cell_21 | [
"image_output_1.png"
] | from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
attributes = corr_matrix['SalePrice'].sort_values(ascending... | code |
18116618/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
co... | code |
18116618/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
housing.plot(kind='scatter', x='GrLivArea', y='SalePrice', alpha=0.1) | code |
18116618/cell_30 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
attributes = corr_matrix['SalePrice'].sort_values(ascending=False)
attributes
top10 = attributes[1:11]... | code |
18116618/cell_44 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import nu... | code |
18116618/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
df.info() | code |
18116618/cell_39 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
impor... | code |
18116618/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
housing = df.drop('SalePrice', axis=1)
housing_labels = df['SalePrice'].copy()
housing.head() | code |
18116618/cell_41 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import numpy as ... | code |
18116618/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
attributes = corr_matrix['SalePrice'].sort_values(ascending=False)
attributes
top10 = attributes[1:11]... | code |
18116618/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
corr_matrix = housing.corr()
attributes = corr_matrix['SalePrice'].sort_values(ascending=False)
attributes | code |
18116618/cell_32 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
co... | code |
18116618/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
df.describe() | code |
18116618/cell_35 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
co... | code |
18116618/cell_43 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as ... | code |
18116618/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
df.hist(bins=50, figsize=(30, 20))
plt.show() | code |
18116618/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
import matplotlib.pyplot as plt
housing = df.copy()
housing = df.drop('SalePrice', axis=1)
housing_labels = df['SalePrice'].copy()
housing_labels.head() | code |
18116618/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
impor... | code |
18116618/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
df.head() | code |
34141752/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
title_list = ['Mrs', 'Mr', 'Master', ... | code |
34141752/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.info()
train.describe() | code |
34141752/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
... | code |
34141752/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 |
34141752/cell_8 | [
"image_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
title_list = ['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev',... | code |
34141752/cell_3 | [
"text_plain_output_1.png"
] | train.head(5) | code |
34141752/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
titl... | code |
105200129/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/chocolate-bar-ratings/chocolate_bars.csv')
data.isnull().sum()
data.isnull().sum()
from sklearn.preprocessing import LabelEncoder
En = LabelEncoder... | code |
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