path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105194699/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique()
df.describe().T.style
df.Potability.value_counts()
df.isnull().sum()
null_columns = pd.DataFrame(df[df.columns[df.isnull().any()]].isnull().sum() * 100 / df.shape[0], columns=['Perc... | code |
105194699/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape | code |
105194699/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique() | code |
90148477/cell_13 | [
"text_html_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas
import seaborn as sns
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
corr = df.corr()
x = df[['clock_speed', 'fc', 'px_height', 'px_width', 'three_g', 'four_g', 'ram']]
y = df['price_range']
regr = linear_model.... | code |
90148477/cell_23 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import numpy as np
import pandas
import seaborn as sns
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
corr = df.corr()
x = df[['clock_speed', 'fc', 'px_height', 'px_width', 'three_g', 'four_g', 'ram']]
y = df['price_range']
... | code |
90148477/cell_20 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas
import seaborn as sns
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
corr = df.corr()
x = df[['clock_speed', 'fc', 'px_height', 'px_width', 'three_g', 'four_g', 'ram']]
y = df['price_range']
regr = linear_model.... | code |
90148477/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
df.describe() | code |
90148477/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import numpy as np
import pandas
import seaborn as sns
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
corr = df.corr()
x = df[['clock_speed', 'fc', 'px_height', 'px_width', 'three_g', 'four_g', 'ram']]
y = df['price_range']
... | code |
90148477/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas
import seaborn as sns
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
corr = df.corr()
plt.figure(figsize=(15, 10))
sns.heatmap(corr, vmax=0.5, annot=True, fmt='.2f')
plt.show() | code |
90148477/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas
import seaborn as sns
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
corr = df.corr()
df.isnull().sum()
df.notnull().sum() | code |
90148477/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
df.hist(figsize=(20, 20))
plt.show() | code |
90148477/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas
import seaborn as sns
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
corr = df.corr()
df.isnull().sum() | code |
90148477/cell_14 | [
"image_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas
import seaborn as sns
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
corr = df.corr()
x = df[['clock_speed', 'fc', 'px_height', 'px_width', 'three_g', 'four_g', 'ram']]
y = df['price_range']
regr = linear_model.... | code |
90148477/cell_22 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas
import seaborn as sns
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
corr = df.corr()
x = df[['clock_speed', 'fc', 'px_height', 'px_width', 'three_g', 'four_g', 'ram']]
y = df['price_range']
regr = linear_model.... | code |
90148477/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
df.head() | code |
128003791/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.head() | code |
128003791/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
rating_across_state = df.groupby('City').mean()
rating_across_state.reset_index(level=0, inplace=True)
plt.xticks(rotati... | code |
128003791/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum() | code |
128003791/cell_2 | [
"text_html_output_1.png"
] | import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
import plotly.express as px | code |
128003791/cell_18 | [
"text_html_output_2.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
rating_across_state = df.groupby('City').mean()
rating_across_state.reset_index(level=0, inplace=True)
plt.xticks(rotati... | code |
128003791/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
rating_across_state = df.groupby('City').mean()
rating_across_state.reset_index(level=0, inplace=True)
plt.figure(figsize=(10, 8))
plt.xlabel('City')... | code |
128003791/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
rating_across_state = df.groupby('City').mean()
rating_across_state.reset_index(level=0, inplace=True)
plt.xticks(rotati... | code |
128003791/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
rating_across_state = df.groupby('City').mean()
rating_across_state.reset_index(level=0, inplace=True)
plt.xticks(rotati... | code |
128003791/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
rating_across_state = df.groupby('City').mean()
rating_across_state.reset_index(level=0, inplace=True)
plt.xticks(rotati... | code |
128003791/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
rating_across_state = df.groupby('City').mean()
rating_across_state.reset_index(level=0, inplace=True)
plt.xticks(rotati... | code |
89135385/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import cv2
import tensorflow as tf
PATH = '../input/rsna-bone-age/boneage-training-dataset/boneage-training-dataset/'
IMGS = os.listdir(PATH)
df = pd.read_csv('../input/rsna-bone-age/boneage-training-dataset.csv')
def _bytes_feature(value):
"""Returns a bytes_list from a string / byte."""
if isinstance(valu... | code |
89135385/cell_4 | [
"text_html_output_1.png"
] | df = pd.read_csv('../input/rsna-bone-age/boneage-training-dataset.csv')
df.head() | code |
89135385/cell_3 | [
"text_plain_output_1.png"
] | PATH = '../input/rsna-bone-age/boneage-training-dataset/boneage-training-dataset/'
IMGS = os.listdir(PATH)
print('There are %i train images' % len(IMGS)) | code |
32068481/cell_6 | [
"image_output_1.png"
] | from collections import OrderedDict
from copy import deepcopy
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 plotly.graph_objs as go
import plotly.offline as py
submission = pd.read_csv('/kaggle/input/covid19-global-forecastin... | code |
32068481/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
from datetime import timedelta
from datetime import datetime
import matplotlib.pyplot as plt
from statsmodels.tsa.arima_model import ARIMA
from sklearn.metrics import mean_squared_error
from math import sqrt
from time import time
import math
import seaborn as sns
import warnings
w... | code |
32068481/cell_8 | [
"image_output_11.png",
"text_plain_output_5.png",
"text_plain_output_9.png",
"image_output_14.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"image_output_13.png",
"image_output_5.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"image_outp... | import matplotlib.pyplot as plt
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)
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-... | code |
32068481/cell_3 | [
"image_output_2.png",
"image_output_1.png"
] | import plotly.offline as py
import plotly.tools as tls
import plotly.graph_objs as go
py.init_notebook_mode(connected=True)
import cufflinks as cf
cf.set_config_file(offline=True, world_readable=True, theme='pearl')
import folium
import altair as alt
import missingno as msg
import sys
import warnings
if not sys.warnopt... | code |
121148904/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
val = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.head() | code |
121148904/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
val = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
test_df.isna().sum() | code |
121148904/cell_11 | [
"text_html_output_1.png"
] | parameters_test['Fare'].fillna(value=4, inplace=True) | code |
121148904/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
val = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
test_df.head() | code |
121148904/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
val = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.isna().sum() | code |
17133840/cell_9 | [
"text_plain_output_1.png"
] | from keras.layers import BatchNormalization, Convolution2D , MaxPooling2D
from keras.layers import Dense , Dropout , Lambda, Flatten
from keras.layers.core import Lambda , Dense, Flatten, Dropout
from keras.layers.noise import GaussianDropout
from keras.layers.normalization import BatchNormalization
from keras.mo... | code |
17133840/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, Dropout, Lambda, Flatten
from keras.optimizers import Adam, RMSprop
from sklearn.model_selection import train_test_split
from keras import backe... | code |
17133840/cell_7 | [
"text_plain_output_1.png"
] | from keras.layers import BatchNormalization, Convolution2D , MaxPooling2D
from keras.layers import Dense , Dropout , Lambda, Flatten
from keras.layers.core import Lambda , Dense, Flatten, Dropout
from keras.layers.noise import GaussianDropout
from keras.layers.normalization import BatchNormalization
from keras.mo... | code |
89132099/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-reg... | code |
89132099/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-reg... | code |
89132099/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns | code |
89132099/cell_6 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns
train.shape
train.SalePrice.describe() | code |
89132099/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-reg... | code |
89132099/cell_1 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os... | code |
89132099/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/t... | code |
89132099/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-reg... | code |
89132099/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-reg... | code |
89132099/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-reg... | code |
89132099/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-reg... | code |
89132099/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.head() | code |
89132099/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-reg... | code |
89132099/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-reg... | code |
89132099/cell_5 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns
train.shape | code |
17115900/cell_13 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt
import numpy as np
import xml.etree.ElementTree as ET
ComputeLB = True
DogsOnly = True
import numpy as np, pandas as pd, os
import xml.etree.ElementTree as ET
import matplotlib... | code |
17115900/cell_9 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt
import numpy as np
import xml.etree.ElementTree as ET
ComputeLB = True
DogsOnly = True
import numpy as np, pandas as pd, os
import xml.etree.ElementTree as ET
import matplotlib... | code |
17115900/cell_6 | [
"text_plain_output_1.png"
] | import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device | code |
17115900/cell_8 | [
"text_plain_output_1.png"
] | from PIL import Image
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt
import numpy as np
import xml.etree.ElementTree as ET
ComputeLB = True
DogsOnly = True
import numpy as np, pandas as pd, os
import xml.etree.ElementTree as ET
import matplotlib.pyplot as plt, zipfile
from PIL import Image
from tqd... | code |
17115900/cell_16 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data import TensorDataset, DataLoader
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch
import torch.nn as nn
import xml.etree.ElementTree as ET
device = torch.... | code |
17115900/cell_17 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data import TensorDataset, DataLoader
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch
import torch.nn as nn
import xml.etree.ElementTree as ET
device = torch.... | code |
17115900/cell_10 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt
import numpy as np
import xml.etree.ElementTree as ET
ComputeLB = True
DogsOnly = True
import numpy as np, pandas as pd, os
import xml.etree.ElementTree as ET
import matplotlib... | code |
128032351/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df = df.dropna()
df.describe(include='O').T
df.sample(2) | code |
128032351/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df = df.dropna()
df.describe(include='O').T
df.sample(2)
df = df.drop('Unnamed: 0', axis=1)
df.sample(2) | code |
128032351/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.info() | code |
128032351/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df = df.dropna()
df.describe(include='O').T
df.sample(2)
df = df.drop('Unnamed: 0', axis=1)
df.sample(2)
df.groupby('Hotel_name').sum()['Rating'].sort_values(ascending=False)[:5] | code |
128032351/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df = df.dropna()
df.describe(include='O').T
df.sample(2)
df = df.drop('Unnamed: 0', axis=1)
df.sample(2)
df.groupby('Hotel_name').sum()['Rating'].sort_values(ascending=False)[:5]
df.groupby('Hotel_name').sum()['Price'].sor... | code |
128032351/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df = df.dropna()
df.describe(include='O').T | code |
130017723/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import numpy as np
reg = LinearRegression()
model = reg.fit(x_train, y_train)
y_pred = model.predict(x_test).round()
def diabetes_prediction():
preg = int(input('Enter the prega value:'))
glu = int(input('Enter the glu value:'))
BP = int(input('Enter th... | code |
18149171/cell_13 | [
"text_plain_output_1.png"
] | from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = p... | code |
18149171/cell_9 | [
"image_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import pandas as pd
df = pd.read_csv('../input/house-votes.csv', na_values=['?'])
df.replace('^y$', value=1, regex=True, inplace=True)
df.replace('^n$', value=0, regex=True, inplace=True)
df.fill... | code |
18149171/cell_11 | [
"text_html_output_1.png"
] | from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn import datasets
import matplotlib.pyplot as plt
digits = datasets.load_digits()
print(digits.keys())
print(digits['DESCR'])
print(digits.images.shape)
print(digits.data.shape)
plt.imshow(digits.images[1010], cmap=plt.cm.gray_r, interpolation='n... | code |
18149171/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
df = pd.read_csv('../input/house-votes.csv', na_values=['?'])
df.replace('^y$', value=1, regex=True, inplace=True)
df.replace('^n$', value=0, regex=True, inplace=True)
df.fillna(0, inplace=True)
df.to_csv('house-votes-edited.csv')
from sklearn.n... | code |
18149171/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
X_new = np.array([[0.44764519, 0.95034062, 0.43959532, 0.80122238, 0.26844483, 0.45513802, 0.16595416, 0.56314597, 0.87505639, 0.92836397, 0.80958641, 0.01591928, 0.0294, 0.42548396, 0.65489058, 0.77928102]])
X_new.shape | code |
18149171/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = p... | code |
18149171/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/house-votes.csv', na_values=['?'])
df.replace('^y$', value=1, regex=True, inplace=True)
df.replace('^n$', value=0, regex=True, inplace=True)
df.fillna(0, inplace=True)
df.head() | code |
50222580/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
train_df.describe().T
(train_df.size, train_df.shape)
train_df.isnull().any()
train_df.isnull().sum()
train_df.columns
train_df.isnull().any() | code |
50222580/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
train_df.describe().T | code |
50222580/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
test_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/test.csv')
gender_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/gender_submission.csv')
gender_df.describe().T | code |
50222580/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
train_df.describe().T
(train_df.size, train_df.shape)
train_df.isnull().any()
train_df.isnull().sum()
train_df.columns
train_df.isnull().any()
train_df.isnull().any() | code |
50222580/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
test_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/test.csv')
test_df.describe().T | code |
50222580/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
train_df.describe().T
(train_df.size, train_df.shape)
train_df.isnull().any()
train_df.isnull().sum()
train_df.columns | code |
50222580/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
50222580/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
train_df.describe().T
(train_df.size, train_df.shape)
train_df.isnull().any()
train_df.isnull().sum() | code |
50222580/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
test_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/test.csv')
gender_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/gender_submission.csv')
gender_df.head() | code |
50222580/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
test_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/test.csv')
test_df.describe().T
(test_df.size, test_df.shape) | code |
50222580/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
test_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/test.csv')
gender_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/gender_submission.csv')
gender_df.describe().T
(gender_d... | code |
50222580/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
train_df.describe().T
(train_df.size, train_df.shape)
train_df.isnull().any() | code |
50222580/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
train_df.describe().T
(train_df.size, train_df.shape) | code |
50222580/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
test_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/test.csv')
test_df.head() | code |
50222580/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
train_df.head() | code |
121154415/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
... | code |
121154415/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv'... | code |
121154415/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv'... | code |
121154415/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv'... | code |
121154415/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
... | code |
121154415/cell_30 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
... | code |
121154415/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv'... | code |
121154415/cell_41 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
... | code |
121154415/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.