path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
50211059/cell_24 | [
"text_html_output_1.png"
] | from sklearn.metrics.pairwise import pairwise_distances
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.metrics.pairwise import pairwise_distances
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sd = pd.read_csv(... | code |
50211059/cell_14 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sd = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample dataset.csv')
df_sd
df_sub = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample submission.csv')
df_sub
df_... | code |
50211059/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.metrics.pairwise import pairwise_distances
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.metrics.pairwise import pairwise_distances
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sd = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample da... | code |
50211059/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sd = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample dataset.csv')
df_sd
df_sub = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample submission.csv')
df_sub
df_data = pd.read_csv('/kaggle/input/hacker-earth-l... | code |
50211059/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_sd = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample dataset.csv')
df_sd
df_sub = pd.read_csv('/kaggle/input/hacker-earth-love-in-the-screens/sample submission.csv')
df_sub
df_data = pd.read_csv('/kaggle/input/hacker-earth-l... | code |
17099534/cell_13 | [
"text_plain_output_1.png"
] | from tensorflow.keras import Model
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, BatchNormalization
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import os
import tensor... | code |
17099534/cell_9 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from tensorflow.keras import Model
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.vgg16 import VGG16
vgg16 = VGG16(input_shape=(image_width, image_height, 3), include_top=False, weights='imagenet')
for layer in vgg16.layers:
layer.trainable = False
vgg16.summary()
from t... | code |
17099534/cell_4 | [
"text_plain_output_1.png"
] | import os
def list_files(startpath):
for root, dirs, files in os.walk(startpath):
level = root.replace(startpath, '').count(os.sep)
indent = ' ' * 4 * level
subindent = ' ' * 4 * (level + 1)
train_dir = '../input/seg_train/seg_train/'
print(os.listdir(train_dir)) | code |
17099534/cell_7 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
def list_files(startpath):
for root, dirs, files in os.walk(startpath):
level = root.replace(startpath, '').count(os.sep)
indent = ' ' * 4 * level
subindent = ' ' * 4 * (level + 1)
train_dir = '../input/seg_trai... | code |
17099534/cell_8 | [
"text_plain_output_1.png"
] | from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.vgg16 import VGG16
vgg16 = VGG16(input_shape=(image_width, image_height, 3), include_top=False, weights='imagenet')
for layer in vgg16.layers:
layer.trainable = False
vgg16.summary() | code |
17099534/cell_3 | [
"text_plain_output_1.png"
] | import os
def list_files(startpath):
for root, dirs, files in os.walk(startpath):
level = root.replace(startpath, '').count(os.sep)
indent = ' ' * 4 * level
subindent = ' ' * 4 * (level + 1)
list_files('../input') | code |
17099534/cell_10 | [
"text_plain_output_1.png"
] | from tensorflow.keras import Model
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, BatchNormalization
import tensorflow as tf
from tensorflow.keras.applications.vgg16 import VGG16
vgg16 = VGG16(input_shape=(image_width, image_h... | code |
17099534/cell_12 | [
"text_plain_output_1.png"
] | from tensorflow.keras import Model
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, BatchNormalization
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import tensorflow as tf
def list_files(startp... | code |
17099534/cell_5 | [
"text_plain_output_1.png"
] | import os
def list_files(startpath):
for root, dirs, files in os.walk(startpath):
level = root.replace(startpath, '').count(os.sep)
indent = ' ' * 4 * level
subindent = ' ' * 4 * (level + 1)
train_dir = '../input/seg_train/seg_train/'
test_dir = '../input/seg_test/seg_test/'
print(os.list... | code |
16120091/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
"""
In this notebook I will see what I can do - EDA-wise without the additional data.
I do that because it is not available for us in the test,
might be we an regenaerate them - but for now, the basic data is what I want to focus on.
"""
test = pd.... | code |
16120091/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
"""
In this notebook I will see what I can do - EDA-wise without the additional data.
I do that because it is not available for us in the test,
might be we an regenaerate them - but for now, the basic data is what I want to focus on.
"""
test = pd.... | code |
16120091/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
"""
In this notebook I will see what I can do - EDA-wise without the additional data.
I do that because it is not available for us in the test,
might be we an regenaerate them - but for now, the basic data is what I want to focus on.
"""
test = pd.... | code |
16120091/cell_19 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
"""
In this notebook I will see what I can do - EDA-wise without the additional data.
I do that because it is not available for us in the test,
might be we an regenaerate them - but for now, the basic data is what I want to focus on.
"""
test = pd.... | code |
16120091/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
16120091/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
"""
In this notebook I will see what I can do - EDA-wise without the additional data.
I do that because it is not available for us in the test,
might be we an regenaerate them - but for now, the basic data is what I... | code |
16120091/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
"""
In this notebook I will see what I can do - EDA-wise without the additional data.
I do that because it is not available for us in the test,
might be we an regenaerate them - but for now, the basic data is what I want to focus on.
"""
test = pd.... | code |
16120091/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
"""
In this notebook I will see what I can do - EDA-wise without the additional data.
I do that because it is not available for us in the test,
might be we an regenaerate them - but for now, the basic data is what I want to focus on.
"""
test = pd.... | code |
16120091/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
"""
In this notebook I will see what I can do - EDA-wise without the additional data.
I do that because it is not available for us in the test,
might be we an regenaerate them - but for now, the basic data is what I... | code |
16120091/cell_16 | [
"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)
"""
In this notebook I will see what I can do - EDA-wise without the additional data.
I do that because it is not available for us in the test,
might be we an regenaerate them - but for now, the basic data is what I... | code |
16120091/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
"""
In this notebook I will see what I can do - EDA-wise without the additional data.
I do that because it is not available for us in the test,
might be we an regenaerate them - but for now, the basic data is what I want to focus on.
"""
test = pd.... | code |
16120091/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
"""
In this notebook I will see what I can do - EDA-wise without the additional data.
I do that because it is not available for us in the test,
might be we an regenaerate them - but for now, the basic data is what I want to focus on.
"""
test = pd.... | code |
16120091/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
"""
In this notebook I will see what I can do - EDA-wise without the additional data.
I do that because it is not available for us in the test,
might be we an regenaerate them - but for now, the basic data is what I... | code |
16120091/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
"""
In this notebook I will see what I can do - EDA-wise without the additional data.
I do that because it is not available for us in the test,
might be we an regenaerate them - but for now, the basic data is what I want to focus on.
"""
test = pd.... | code |
1007811/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | X_train.info() | code |
1007811/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | y_train = X_train['Survived'].copy()
X_train.drop('Survived', axis=1, inplace=True)
print(y_train.head())
print(X_train.info()) | code |
1007811/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X = pd.read_csv('../input/train.csv', index_col=0)
y = X_train['Survived'].copy()
X.drop('Survived', axis=1, inplace=True)
print(X.head())
print(y.head()) | code |
1007811/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1007811/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.pipeline import Pipeline
pipe = Pipeline([()]) | code |
1007811/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | X_train.head() | code |
2041588/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
cities_crosswalk = pd.read_csv('../in... | code |
2041588/cell_9 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
cities_crosswalk = pd.read_csv('../in... | code |
2041588/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
cities_crosswalk = pd.read_csv('../in... | code |
2041588/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cities_crosswalk = pd.read_csv('../input/cities_crosswalk.csv')
city_time_series = pd.read_csv('../input/City_time_series.csv')
county_time_series = pd.read_csv('../input/County_time_series.csv')
metro_time_series = pd.read_csv('../input/Metro_time... | code |
2041588/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
cities_crosswalk = pd.read_csv('../in... | code |
2041588/cell_15 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
cities_crosswalk = pd.read_csv('../in... | code |
2041588/cell_17 | [
"text_html_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
cities_crosswalk = pd.read_csv('../in... | code |
2041588/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
cities_crosswalk = pd.read_csv('../in... | code |
2041588/cell_10 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
cities_crosswalk = pd.read_csv('../in... | code |
2041588/cell_5 | [
"text_html_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight') | code |
32071028/cell_9 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from transformers import BertTokenizer, BertForQuestionAnswering
from wasabi import msg
import json
import os
import pandas as pd
import time
import torch
import pandas as pd
import json
import os... | code |
32071028/cell_8 | [
"text_plain_output_1.png"
] | from transformers import BertTokenizer, BertForQuestionAnswering
TOKENIZER = BertTokenizer.from_pretrained('bert-base-uncased')
MODEL = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad') | code |
32071028/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from transformers import BertTokenizer, BertForQuestionAnswering
from wasabi import msg
import json
import os
import pandas as pd
import time
import torch
import pandas as pd
import json
import os... | code |
32071028/cell_14 | [
"text_plain_output_5.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from transformers import BertTokenizer, BertForQuestionAnswering
from wasabi import msg
import json
import os
import pandas as pd
import time
import torch
import pandas as pd
import json
import os... | code |
122264416/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
Aquifer_Petrignano_input = pd.read_csv('../input/acea-water-prediction/Aquifer_Petrignano.csv')
Lake_Bilancino_input = pd.read_csv('../input/acea-water-prediction/Lake_Bilancino.csv')
Aquifer_Petrignano = Aquifer_Petrignano_input.copy()
Lake_Bilancino = Lake_Bilancino_input.cop... | code |
122264416/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
Aquifer_Petrignano_input = pd.read_csv('../input/acea-water-prediction/Aquifer_Petrignano.csv')
Lake_Bilancino_input = pd.read_csv('../input/acea-water-prediction/Lake_Bilancino.csv')
Aquifer_Petrignano = Aquifer_Petrignano_input.copy()
Lake_Bilancino = Lake_Bilancino_input.copy()
Aquifer_Petrign... | code |
122264416/cell_26 | [
"text_html_output_1.png"
] | from datetime import datetime, date
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
Aquifer_Petrignano_input = pd.read_csv('../input/acea-water-prediction/Aquifer_Petrignano.csv')
Lake_Bilancino_input = pd.read_csv('../input/acea-water-prediction/Lake_Bilancino.csv')
A... | code |
122264416/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
Aquifer_Petrignano_input = pd.read_csv('../input/acea-water-prediction/Aquifer_Petrignano.csv')
Lake_Bilancino_input = pd.read_csv('../input/acea-water-prediction/Lake_Bilancino.csv')
Aquifer_Petrignano = Aquifer_Petrignano_input.copy()
Lake_Bilancino = Lake_Bilancino_input.copy()
Aquifer_Petrign... | code |
122264416/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
Aquifer_Petrignano_input = pd.read_csv('../input/acea-water-prediction/Aquifer_Petrignano.csv')
Lake_Bilancino_input = pd.read_csv('../input/acea-water-prediction/Lake_Bilancino.csv')
Aquifer_Petrignano = Aquifer_Petrignano_input.copy()
Lake_Bilancino = Lake_Bilancino_input.cop... | code |
122264416/cell_8 | [
"image_output_1.png"
] | import pandas as pd
Aquifer_Petrignano_input = pd.read_csv('../input/acea-water-prediction/Aquifer_Petrignano.csv')
Lake_Bilancino_input = pd.read_csv('../input/acea-water-prediction/Lake_Bilancino.csv')
Aquifer_Petrignano = Aquifer_Petrignano_input.copy()
Lake_Bilancino = Lake_Bilancino_input.copy()
Lake_Bilancino.... | code |
122264416/cell_15 | [
"text_html_output_1.png"
] | import missingno as msno
import pandas as pd
Aquifer_Petrignano_input = pd.read_csv('../input/acea-water-prediction/Aquifer_Petrignano.csv')
Lake_Bilancino_input = pd.read_csv('../input/acea-water-prediction/Lake_Bilancino.csv')
Aquifer_Petrignano = Aquifer_Petrignano_input.copy()
Lake_Bilancino = Lake_Bilancino_inp... | code |
122264416/cell_16 | [
"text_html_output_1.png"
] | import missingno as msno
import pandas as pd
Aquifer_Petrignano_input = pd.read_csv('../input/acea-water-prediction/Aquifer_Petrignano.csv')
Lake_Bilancino_input = pd.read_csv('../input/acea-water-prediction/Lake_Bilancino.csv')
Aquifer_Petrignano = Aquifer_Petrignano_input.copy()
Lake_Bilancino = Lake_Bilancino_inp... | code |
122264416/cell_24 | [
"text_html_output_1.png"
] | from datetime import datetime, date
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
Aquifer_Petrignano_input = pd.read_csv('../input/acea-water-prediction/Aquifer_Petrignano.csv')
Lake_Bilancino_input = pd.read_csv('../input/acea-water-prediction/Lake_Bilancino.csv')
A... | code |
122264416/cell_22 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
Aquifer_Petrignano_input = pd.read_csv('../input/acea-water-prediction/Aquifer_Petrignano.csv')
Lake_Bilancino_input = pd.read_csv('../input/acea-water-prediction/Lake_Bilancino.csv')
Aquifer_Petrignano = Aquifer_Petrignano_input.copy()
Lake_Bilancino = Lake_Bilancino_input.cop... | code |
122264416/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
Aquifer_Petrignano_input = pd.read_csv('../input/acea-water-prediction/Aquifer_Petrignano.csv')
Lake_Bilancino_input = pd.read_csv('../input/acea-water-prediction/Lake_Bilancino.csv')
Aquifer_Petrignano = Aquifer_Petrignano_input.copy()
Lake_Bilancino = Lake_Bilancino_input.copy()
Aquifer_Petrign... | code |
72063152/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = ... | code |
72063152/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
sub | code |
72063152/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
t... | code |
72063152/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.head() | code |
72063152/cell_23 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
t... | code |
72063152/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/... | code |
72063152/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
test.head() | code |
72063152/cell_40 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-... | code |
72063152/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/... | code |
72063152/cell_39 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-... | code |
72063152/cell_41 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-... | code |
72063152/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
test.info() | code |
72063152/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.... | code |
72063152/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.... | code |
72063152/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.... | code |
72063152/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.... | code |
72063152/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-da... | code |
72063152/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/... | code |
72063152/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
print('Training Data Shape: ', train.shape)
print('Testin... | code |
72063152/cell_27 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
t... | code |
72063152/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.info() | code |
72063152/cell_36 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-da... | code |
128012943/cell_42 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance... | code |
128012943/cell_21 | [
"image_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
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
ax = sns.lmplot(x='age', y='expenses', data=df_insure, hue='smoker', palette='Set1') | code |
128012943/cell_13 | [
"application_vnd.jupyter.stderr_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
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
sns.displot(data=df_insure['expenses'])
plt.show() | code |
128012943/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
df_insure['smoker'].value_counts() | code |
128012943/cell_25 | [
"image_output_1.png"
] | x_train.head() | code |
128012943/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
df_insure.head(5) | code |
128012943/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
x_train.shape
x_test.shape
from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder(drop='first')
x_train_ar... | code |
128012943/cell_30 | [
"image_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
x_train.shape
x_test.shape
from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder(drop='first')
x_train_array = ohe.fit_transform(x_train[['sex', 'smoker', 'region']]).toarray()
x_test_array = ohe.transform(x_test[['sex', 'smoker', 'region']]).toarray()
x... | code |
128012943/cell_33 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
x_train.shape
x_test.shape
from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder(drop='first')
x_train_ar... | code |
128012943/cell_20 | [
"image_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
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
df_insure.groupby('children')['expenses'].sum().plot(kind='bar')
plt.ylabel('Insurance charges')
plt.show() | code |
128012943/cell_40 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
x_train.shape
x_test.shape
fro... | code |
128012943/cell_26 | [
"image_output_1.png"
] | x_train.shape | code |
128012943/cell_41 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
x_train.shape
x_test.shape
from sklear... | code |
128012943/cell_2 | [
"image_output_1.png"
] | import seaborn as sns
import matplotlib.pyplot as plt
sns.set()
import warnings
warnings.filterwarnings('ignore') | code |
128012943/cell_19 | [
"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
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
df_insure.groupby('region')['expenses'].sum().plot(kind='bar')
plt.ylabel('Insurance charges')
plt.show() | code |
128012943/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 |
128012943/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
df_insure.describe() | code |
128012943/cell_18 | [
"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
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
df_insure.groupby('region')['smoker'].count().plot(kind='bar')
plt.ylabel('No. of smokers')
plt.show() | code |
128012943/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
x_train.shape
x_test.shape
from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder(drop='first')
x_train_ar... | code |
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