path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
106210118/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/marketing-strategy-personalised-offer/sample.csv')
df1 = pd.read_csv('../input/marketing-strategy-personalised-offer/train_data.csv')
feature_list = df1.columns[:-1].values
label = [df1.columns[-1... | code |
106210118/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/marketing-strategy-personalised-offer/sample.csv')
df1 = pd.read_csv('../input/marketing-strategy-personalised-offer/train_data.csv')
feature_list = df1.columns[:-1].values
label = [df1.columns[-1... | code |
106210118/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/marketing-strategy-personalised-offer/sample.csv')
data.head(10) | code |
32071603/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/associativeinference/Associative Inference.csv')
data.columns | code |
32071603/cell_20 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/associativeinference/Associative Inference.csv')
data.columns
age_gr... | code |
32071603/cell_6 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
data = pd.read_csv('/kaggle/input/associativeinference/Associative Inference.csv')
data.columns
age_group = data[['AgeGroup', 'AB1-BC1']]
age_group.head()
grouped = age_group.group... | code |
32071603/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/associativeinference/Associative Inference.csv')
data.columns
data_new = data.copy()
data_new['AB_acc_4_T'] = data['AB_acc_4'] == 1
data_new['AB_acc_4_T'] = data_new['AB_acc_4_T'].apply(lambda x: 'correct' if x e... | code |
32071603/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from scipy import stats
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32071603/cell_8 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/associativeinference/Associative Inference.csv')
data.columns
age_gr... | code |
32071603/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/associativeinference/Associative Inference.csv')
data.columns
data_new = data.copy()
data_new['AB_acc_4_T'] = data['AB_acc_4'] == 1
data_new['AB_acc_4_T'] = data_new['AB_acc_4_T'].apply(lambda x: 'correct' if x e... | code |
32071603/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/associativeinference/Associative Inference.csv')
data.head(5) | code |
32071603/cell_17 | [
"text_html_output_2.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/associativeinference/Associative Inference.csv')
data.columns
data_new = data.copy()
data_new['AB_acc_4_T'] = data['AB_acc_4'] == 1
data_new['AB_acc_4_T'] = data_new['AB_acc_4_T'].apply(lambda x: 'correct' if x e... | code |
32071603/cell_22 | [
"text_plain_output_1.png"
] | from plotly.subplots import make_subplots
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/associativeinference/Associative Inference.csv')
data.columns
age_gr... | code |
32071603/cell_10 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/associativeinference/Associative Inference.csv')
data.columns
age_gr... | code |
32068445/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from datetime import datetime, timedelta, date
fro... | code |
32068445/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from datetime import datetime, timedelta, date
fro... | code |
32068445/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from datetime import datetime, timedelta, date
fro... | code |
32068445/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from datetime import datetime, timedelta, date
import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
fr... | code |
32068445/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from datetime import datetime, timedelta, date
import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
fr... | code |
32068445/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from datetime import datetime, timedelta, date
fro... | code |
32068445/cell_31 | [
"image_output_1.png"
] | from datetime import datetime, timedelta, date
import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
fr... | code |
32068445/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from datetime import datetime, timedelta, date
fro... | code |
73089289/cell_9 | [
"text_html_output_1.png"
] | from keras.applications.xception import Xception, preprocess_input, decode_predictions
from skimage.io import imread_collection, imread
import numpy as np
import numpy as np
import os
import pandas as pd
import pandas as pd
path = '../input/pneumoniamulti/pneumonia-multi2/'
image_names = os.listdir(path)
gray_im... | code |
73089289/cell_4 | [
"text_plain_output_1.png"
] | from keras.applications.xception import Xception, preprocess_input, decode_predictions
pretrained = Xception(weights='imagenet', include_top=False, pooling='avg') | code |
73089289/cell_2 | [
"text_plain_output_1.png"
] | from skimage.io import imread_collection, imread
import os
path = '../input/pneumoniamulti/pneumonia-multi2/'
image_names = os.listdir(path)
gray_images = [imread(path + str(name) + '') for name in image_names]
print('The database has {} segmented images'.format(len(gray_images))) | code |
73089289/cell_7 | [
"text_plain_output_1.png"
] | from keras.applications.xception import Xception, preprocess_input, decode_predictions
from skimage.io import imread_collection, imread
import numpy as np
import numpy as np
import os
path = '../input/pneumoniamulti/pneumonia-multi2/'
image_names = os.listdir(path)
gray_images = [imread(path + str(name) + '') for ... | code |
73089289/cell_5 | [
"text_plain_output_1.png"
] | from keras.applications.xception import Xception, preprocess_input, decode_predictions
pretrained = Xception(weights='imagenet', include_top=False, pooling='avg')
pretrained.summary() | code |
18124360/cell_13 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from efficientnet import EfficientNetB5
from keras.applications import DenseNet121, ResNet50, InceptionV3, Xception
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import... | code |
18124360/cell_2 | [
"text_plain_output_1.png"
] | import tensorflow
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import cv2
import os
import sys
print(os.listdir('../input')) | code |
18124360/cell_11 | [
"image_output_1.png"
] | from efficientnet import EfficientNetB5
from keras.applications import DenseNet121, ResNet50, InceptionV3, Xception
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
import os
import pandas as pd
import sys
test_df = pd.read_csv('../input/aptos2... | code |
18124360/cell_7 | [
"text_html_output_1.png"
] | import os
import sys
print(os.listdir('../input/efficientnet/efficientnet-master/efficientnet-master/efficientnet'))
sys.path.append(os.path.abspath('../input/efficientnet/efficientnet-master/efficientnet-master/'))
from efficientnet import EfficientNetB5 | code |
18124360/cell_15 | [
"text_plain_output_1.png"
] | from efficientnet import EfficientNetB5
from keras.applications import DenseNet121, ResNet50, InceptionV3, Xception
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import... | code |
18124360/cell_10 | [
"text_plain_output_1.png"
] | from efficientnet import EfficientNetB5
from keras.applications import DenseNet121, ResNet50, InceptionV3, Xception
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
import os
import pandas as pd
import sys
test_df = pd.read_csv('../input/aptos2... | code |
105174125/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import cv2
import numpy as np
import pandas as pd
import tensorflow as tf
import layoutparser as lp
import matplotlib.pyplot as plt
from PIL import Image
from pdf2image import convert_from_path
from paddleocr import PaddleOCR, draw_ocr | code |
104122172/cell_13 | [
"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
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum()
df.notnull()
df.dropna()
df.fillna
sns.scatterplot(x='Rank', y='Country', data=df, h... | code |
104122172/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum()
df.notnull()
df.dropna() | code |
104122172/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.info() | code |
104122172/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.describe() | code |
104122172/cell_11 | [
"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 = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum()
df.notnull()
df.dropna()
df.fillna
sns.histplot(x='World Population Percentage', dat... | code |
104122172/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
104122172/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum() | code |
104122172/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum()
df.notnull() | code |
104122172/cell_15 | [
"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
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum()
df.notnull()
df.dropna()
df.fillna
sns.pairplot(df, hue='Country', height=2) | code |
104122172/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum()
df.notnull()
df.dropna()
df.fillna
df = np.rand... | code |
104122172/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.head() | code |
104122172/cell_14 | [
"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 = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum()
df.notnull()
df.dropna()
df.fillna
sns.pairplot(df, hue='Continent', height=2) | code |
104122172/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum()
df.notnull()
df.dropna()
df.fillna | code |
104122172/cell_12 | [
"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 = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum()
df.notnull()
df.dropna()
df.fillna
sns.boxplot(x='Rank', y='Country', data=df)
plt.s... | code |
104122172/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape | code |
328803/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import math
import pandas as pd
names_data = pd.read_csv('../input/NationalNames.csv')
frequent_names = names_data[names_data['Count'] > 500]
indexed_names = frequent_names.set_index(['Year', 'Name'])['Count']
def ambiguity_measure(grouped_frame):
return 2 * (1 - grouped_frame.max() / grouped_f... | code |
328803/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import math
import pandas as pd
names_data = pd.read_csv('../input/NationalNames.csv')
frequent_names = names_data[names_data['Count'] > 500]
indexed_names = frequent_names.set_index(['Year', 'Name'])['Count']
def ambiguity_measure(grouped_frame):
return 2 * (1 - grouped_frame.max() / grouped_f... | code |
106203709/cell_13 | [
"text_plain_output_1.png"
] | import nltk
import pandas as pd
df = pd.read_csv('/kaggle/input/online-retails-sale-dataset/Online Retail.csv')
import nltk
sc = df.copy()[['StockCode', 'Description']]
sc.dropna(inplace=True)
sc.Description = sc.Description.str.lower()
items = sc.groupby('StockCode').Description.unique()
items = list(zip(items.inde... | code |
106203709/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/online-retails-sale-dataset/Online Retail.csv')
for x in df.columns:
print(x)
print('\n')
print(df.size) | code |
106203709/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/online-retails-sale-dataset/Online Retail.csv')
print('Number of Unique Items {}'.format(df.StockCode.nunique()))
print('\n')
print(df.Description) | code |
106203709/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/online-retails-sale-dataset/Online Retail.csv')
print('Number of duplicates: {}'.format(df.duplicated().sum()))
print('\n')
print(df.isnull().sum()) | code |
106203709/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/online-retails-sale-dataset/Online Retail.csv')
print(df.CustomerID.nunique())
print('\n')
print(df.Country.value_counts()) | code |
104121398/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
sns.scatterplot(x=df['age'], y=df['sex'], hue=df['target']) | code |
104121398/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=41)
classifier.fit(X_train, y_train) | code |
104121398/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.info() | code |
104121398/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
df[df['age'] >= 50]['target'].value_counts() * 100 / df.shape[0]
df[df['age'] < 50]['target'].value_counts() * 100 / df.shape[0] | code |
104121398/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.describe() | code |
104121398/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import pandas as pd
import seabor... | code |
104121398/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
classifier = LogisticRegression(random_state=41)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
from sklearn.metrics import confusion_matrix
cm = confus... | code |
104121398/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
df[df['sex'] == 1]['target'].value_counts() | code |
104121398/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
df[df['age'] >= 50]['target'].value_counts() * 100 / df.shape[0] | code |
104121398/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df['sex'].value_counts() | code |
104121398/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
sns.countplot(x='sex', data=df) | code |
104121398/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
females = df[df['sex'] == 1]['age'].value_counts()
females
plt.figure(figsize=(15, 15))
sns.barplot(x=females.index, y=females.values) | code |
104121398/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
males = df[df['sex'] == 0]['age'].value_counts()
males | code |
104121398/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.head() | code |
104121398/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
females = df[df['sex'] == 1]['age'].value_counts()
females
males = df[df['sex'] == 0]['age'].value_counts()
males
plt.figure(figsize=(15, ... | code |
104121398/cell_31 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.tree import DecisionTreeClassifier
classifier = LogisticRegression(random_state=41)
classifier.fit(X_train, y_train)
y_pred = class... | code |
104121398/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
females = df[df['sex'] == 1]['age'].value_counts()
females | code |
104121398/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
plt.figure(figsize=(20, 10))
sns.heatmap(df.corr(), annot=True) | code |
104121398/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
classifier = LogisticRegression(random_state=41)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
from ... | code |
104121398/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
df[df['sex'] == 0]['target'].value_counts() | code |
104121398/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
print(df.isnull().sum())
print(df.isnull().values.any())
print(df.isnull().values.sum()) | code |
106204307/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sentence_transformers import SentenceTransformer, util
from typing import List, Union
import PIL
import clip
import os
import requests
import torch
from typing import List, Union
import torch
import clip
import PIL
from PIL import Image
import requests
import numpy as np
import os
from sentence_transformers... | code |
129024099/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
dfx = df.copy()
cat = []
num = []
for n,... | code |
129024099/cell_2 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
dfx = df.copy()
cat = []
num = []
for n,... | code |
129024099/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
df.head() | code |
129024099/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error, r2_score, mean_squared_error
from sklearn.model_selection import train_test_split, cross_validate
from sklearn.preprocessing import LabelEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pprint
import seaborn as ... | code |
129024099/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
dfx = df.copy()
cat = []
num = []
for n,... | code |
129024099/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import mean_absolute_error, r2_score, mean_squared_error
from sklearn.model_selection import train_test_split, cross_validate
from sklearn.preprocessing import LabelEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pprint
import seaborn as ... | code |
129024099/cell_5 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
dfx = df.copy()
cat = []
num = []
for n,... | code |
34126027/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Flatten, Dense, Input, Dropout, Conv2D, MaxPool2D, BatchNormalization
from keras.models import Model
from keras.optimizers import Adam
from keras.utils import to_categorical, plot_model
DROPOUT_RATE = 0.3
CONV_ACTIVATION = 'relu'
img_in = Input(shape=(48, 48, 1))
X = Conv2D(64, (3, 3), padd... | code |
34126027/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.models import Model
from keras.layers import Flatten, Dense, Input, Dropout, Conv2D, MaxPool2D, BatchNormalization
from keras.utils import to_categorical, plot_model
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.optimizers import Adam
from keras.preprocessing.image ... | code |
34126027/cell_19 | [
"image_output_2.png",
"image_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Flatten, Dense, Input, Dropout, Conv2D, MaxPool2D, BatchNormalization
from keras.models import Model
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.utils imp... | code |
34126027/cell_15 | [
"image_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Flatten, Dense, Input, Dropout, Conv2D, MaxPool2D, BatchNormalization
from keras.models import Model
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.utils imp... | code |
34126027/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)
data_fer = pd.read_csv('../input/fer2013/fer2013.csv')
data_fer.head() | code |
34126027/cell_17 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Flatten, Dense, Input, Dropout, Conv2D, MaxPool2D, BatchNormalization
from keras.models import Model
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.utils imp... | code |
34126027/cell_14 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Flatten, Dense, Input, Dropout, Conv2D, MaxPool2D, BatchNormalization
from keras.models import Model
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.utils imp... | code |
34126027/cell_12 | [
"text_html_output_1.png"
] | from keras.layers import Flatten, Dense, Input, Dropout, Conv2D, MaxPool2D, BatchNormalization
from keras.models import Model
from keras.optimizers import Adam
DROPOUT_RATE = 0.3
CONV_ACTIVATION = 'relu'
img_in = Input(shape=(48, 48, 1))
X = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', activati... | code |
2025290/cell_42 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = housepric... | code |
2025290/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = hous... | code |
2025290/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat = houseprice.select_dtypes(include=[object])
le =... | code |
2025290/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
le = LabelEncoder()
housedfnum['MSSubClass'] = le.fit_transf... | code |
2025290/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat... | code |
2025290/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number]) | code |
2025290/cell_34 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat... | code |
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