path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128010348/cell_33 | [
"image_output_4.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.models import Sequential, Model, load_model
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras import regularizers
from tensorflow.keras.layers impor... | code |
128010348/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
df = df.dropna()
df = df.replace([np.inf, -np.inf], np.nan)
df = df.dropna()
df = df.drop(['Id'], axis=1)
scaler = MinMaxScal... | code |
128010348/cell_2 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
df.head() | code |
128010348/cell_11 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
df = df.dropna()
df = df.replace([np.inf, -np.inf], np.nan)
df = df.dropna()
df = df.drop(['Id'], axis=1)
scaler = MinMaxScaler()
cols_to_scale = df.columns[:-1]
df_norm = pd.DataFr... | code |
128010348/cell_19 | [
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
df = df.dropna()
df = df.replace([np.inf, -np.inf], np.nan)
df = df.dropna()
df = df.drop(['Id'], axis=1)
scaler = MinMaxScal... | code |
128010348/cell_18 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
df = df.dropna()
df = df.replace([np.inf, -np.inf], np.nan)
df = df.dropna()
df = df.drop(['Id'], axis=1)
scaler = MinMaxScal... | code |
128010348/cell_32 | [
"image_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.models import Sequential, Model, load_model
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from tensorflow.keras import regularizers
from tensorflow.keras.layers import Conv2D, MaxPool2D, Activation, Dropout, BatchN... | code |
128010348/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
df = df.dropna()
df = df.replace([np.inf, -np.inf], np.nan)
df = df.dropna()
df = df.drop(['Id'], axis=1)
scaler = MinMaxScaler()
cols_to_scale = df.columns[:-1]
df_norm = pd.DataFr... | code |
128010348/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
df = df.dropna()
df = df.replace([np.inf, -np.inf], np.nan)
df = df.dropna()
df = df.drop(['Id'], axis=1)
scaler = MinMaxScaler()
cols_to_scale = df.columns[:-1]
df_norm = pd.DataFr... | code |
128010348/cell_38 | [
"image_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.models import Sequential, Model, load_model
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras import regularizers
from tensorflow.keras.layers impor... | code |
128010348/cell_35 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.models import Sequential, Model, load_model
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from tensorflow.keras import regularizers
from tensorflow.keras.layers import Conv2D, MaxPool2D, Activation, Dropout, BatchN... | code |
128010348/cell_31 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.models import Sequential, Model, load_model
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from tensorflow.keras import regularizers
from tensorflow.keras.layers import Conv2D, MaxPool2D, Activation, Dropout, BatchN... | code |
128010348/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/iris/Iris.csv')
df = df.dropna()
df = df.replace([np.inf, -np.inf], np.nan)
df = df.dropna()
df = df.drop(['Id'], axis=1)
scaler = MinMaxScaler()
cols_to_scale = df.columns[:-1]
df_norm = pd.DataFr... | code |
128010348/cell_27 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from catboost import CatBoostClassifier, Pool
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from xgbo... | code |
128010348/cell_36 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.models import Sequential, Model, load_model
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from tensorflow.keras import regularizers
from tensorflow.keras.layers import Conv2D, MaxPool2D, Activation, Dropout, BatchN... | code |
17133428/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import numpy as np
import pandas as pd
df = pd.read_csv('../input/raw_lemonade_data.csv')
df['Date'] = pd.to_datetime(df['Date'])
df['Price'] = df.Price.str.replace('$', '').replace(' ', '')
df['Price'] = df.Price.astype(np.float64)
df = df.set_index(df['Date'])
df ... | code |
17133428/cell_10 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import numpy as np
import pandas as pd
df = pd.read_csv('../input/raw_lemonade_data.csv')
df['Date'] = pd.to_datetime(df['Date'])
df['Price'] = df.Price.str.replace('$', '').replace(' ', '')
df['Price'] = df.Price.astype(np.float64)
df = df.set_index(df['Date'])
df ... | code |
17133428/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/raw_lemonade_data.csv')
df['Date'] = pd.to_datetime(df['Date'])
df['Price'] = df.Price.str.replace('$', '').replace(' ', '')
df['Price'] = df.Price.astype(np.float64)
df = df.set_index(df['Date'])
df = df.drop('Date', 1)
df['Revenue'] = df.Price * df.... | code |
73067513/cell_9 | [
"text_plain_output_1.png"
] | s = 'Hi. Welcome to Filoger Bootcamp.'
l = s.split(' ')
l1 = s.split('.')
print('saize jomalat in text :'.format(s), len(l1) - 1) | code |
73067513/cell_4 | [
"text_plain_output_1.png"
] | s = 'Hi. Welcome to Filoger Bootcamp.'
l = s.split(' ')
print(l) | code |
73067513/cell_6 | [
"text_plain_output_1.png"
] | s = 'Hi. Welcome to Filoger Bootcamp.'
l = s.split(' ')
print('saize klamat in text :'.format(s), len(l)) | code |
73067513/cell_18 | [
"text_plain_output_1.png"
] | def a(email):
pass
def a(speed):
speed > 80
a(100) | code |
73067513/cell_15 | [
"text_plain_output_1.png"
] | def my_func(str):
return 'Iran' in str
my_func('welcome to Iran') | code |
73067513/cell_12 | [
"text_plain_output_1.png"
] | def a(email):
pass
a('ali@domain.com')
a('nazi@gmail.com') | code |
17114755/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
from torch.autograd import Variable
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
train = pd.read_cs... | code |
17114755/cell_4 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train.iloc[:, 1:].values / 255.0
y_train = train.iloc[:, 0].values
x_test = test.values / 255
x_train = np.reshape(x_train, (-1, 1, 28, 28))
x_test = np.reshape(x_test, (-1, 1, 28, 28))... | code |
17114755/cell_20 | [
"text_plain_output_1.png"
] | epochs = 100
for epoch in range(epochs):
running_loss = 0.0
for i, data in enumerate(train_data_loader, 0):
inputs, labels = data
inputs, labels = (inputs.cuda(), labels.cuda())
optimizer.zero_grad()
outputs = classifier(inputs)
loss = criterion(outputs, labels)
l... | code |
17114755/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train.iloc[:, 1:].values / 255.0
y_train = train.iloc[:, 0].values
x_test = test.values / 255
x_tr... | code |
17114755/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
print(train.shape, test.shape) | code |
17114755/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import random
from sklearn.model_selection import train_test_split
import struct
import torch
from PIL import Image
import matplotlib.pyplot as plt
import torchvision
from torch.autograd import Variable
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional a... | code |
17114755/cell_7 | [
"image_output_1.png"
] | import torch
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print(device) | code |
17114755/cell_16 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
from torch.autograd import Variable
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
import torch
import torch.nn as nn
import torch.optim as optim
train = pd.read_csv('../input/train.csv')
test = pd... | code |
17114755/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train.iloc[:, 1:].values / 255.0
y_train = train.iloc[:, 0].values
x_test = test.values / 255
print((x_train.shape, y_train.shape), x_test.shape) | code |
17114755/cell_14 | [
"text_plain_output_1.png"
] | model.train_model() | code |
17114755/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train.iloc[:, 1:].values / 255.0
y_train = train.iloc[:, 0].values
x_test = test.values / 255
x_train = np.reshape(x_train, (-1, 1,... | code |
16166937/cell_21 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/HR_comma_sep.csv')
df.columns
sales_salary = pd.crosstab(d... | code |
16166937/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/HR_comma_sep.csv')
df.columns
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salar... | code |
16166937/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/HR_comma_sep.csv')
df.columns
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_salary['temp'] = sales_salary.index.values
sales_salary.i... | code |
16166937/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/HR_comma_sep.csv')
from sklearn.preprocessing import MinMaxScaler
minmax = MinMaxScaler()
df['average_montly_hours'] = minmax.fit_transform(df[['average_montly_hours']])
df... | code |
16166937/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/HR_comma_... | code |
16166937/cell_6 | [
"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/HR_comma_sep.csv')
df.columns
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_salary['temp'] = sales_salary.index.values
sales_salary.i... | code |
16166937/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/HR_comma_sep.csv')
df.head() | code |
16166937/cell_11 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/HR_comma_sep.csv')
df.columns
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_sal... | code |
16166937/cell_19 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/HR_comma_sep.csv')
df.columns
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_sala... | code |
16166937/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16166937/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
colormap = plt.cm.get_cmap('Greens')
fig, ax = plt.subplots(figsize=(12, 3))
plot = ax.pcolor(sales_salary.T, cmap=colormap, edgecolor='black')
ax.set_xlabel('sales')
ax.set_xticks(np.arange(len(sales_salary.index.values)) + 0.5)
ax.set_xticklabels(sales_salary.index.values)
ax.set_ylabe... | code |
16166937/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import PolynomialFeatures
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/HR_comma_sep.csv')
df.columns
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_sal... | code |
16166937/cell_8 | [
"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/HR_comma_sep.csv')
df.columns
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_salary['temp'] = sales_salary.index.values
sales_salary.i... | code |
16166937/cell_15 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/HR_comma_sep.csv'... | code |
16166937/cell_16 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/HR_comma_sep.csv')
df.columns
sales_salary = pd.crosstab(df['sales... | code |
16166937/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/HR_comma_sep.csv')
df.describe() | code |
16166937/cell_17 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/HR_comma_sep.csv'... | code |
16166937/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/HR_comma_sep.csv')
df.columns
sales_salary = pd.crosstab(df['... | code |
16166937/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/HR_comma_sep.csv')
df.columns
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_sala... | code |
16166937/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/HR_comma_sep.csv')
df.columns
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_salary['temp'] = sales_salary.index.values
sales_salary.i... | code |
16166937/cell_5 | [
"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/HR_comma_sep.csv')
df.columns | code |
129002488/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
accuracy = []
iter = 1
for i in ... | code |
129002488/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import Gaussia... | code |
129002488/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
df.isnull().sum().sum()
df['employment_type'].value_counts() | code |
129002488/cell_25 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.... | code |
129002488/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
df.head() | code |
129002488/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
rfc = RandomForestClassifier(ma... | code |
129002488/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
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('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
df.isnull().sum().sum()
catg = []
for i in df.columns:
... | code |
129002488/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
df.isnull().sum().sum() | code |
129002488/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.... | code |
129002488/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
df.isnull().sum().sum()
df['experience_level'].unique() | code |
129002488/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 |
129002488/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
df.isnull().sum().sum()
df['job_title'].value_counts() | code |
129002488/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
df.isnull().sum().sum()
catg = []
for i in df.columns:
if df[i].dtype == 'O':
catg.append(i)
import seaborn as sns
correl = df.corr()... | code |
129002488/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
df.isnull().sum().sum()
catg = []
for i in df.columns:
if df[i].dtype == 'O':
catg.append(i)
import seaborn as sns
correl = df.corr()... | code |
129002488/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
accuracy = []
iter = 1
for i in ... | code |
129002488/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.... | code |
129002488/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
df.isnull().sum().sum()
catg = []
for i in df.columns:
if df[i].dtype == 'O':
catg.append(i)
for i in catg:
print('UNIQUE VALUES IN {} ARE:'.format(i))
... | code |
129002488/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
df.info() | code |
128024284/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
original = pd.read_csv('/kaggle/input/wild-blueberry-yield-prediction-dataset/WildBlueberryPollinationSimulationData.csv')
orig... | code |
128024284/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
original = pd.read_csv('/kaggle/input/wild-blueberry-yield-prediction-d... | code |
128024284/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
original = pd.read_csv('/kaggle/input/wild-blueberry-yield-prediction-d... | code |
128024284/cell_44 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import RepeatedKFold
from tqdm.notebook import tqdm
import catboost as cb
import lightgbm as lgbm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import pa... | code |
128024284/cell_29 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
original = pd.read_csv('/kaggle/inp... | code |
128024284/cell_39 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import RepeatedKFold
from tqdm.notebook import tqdm
import catboost as cb
import lightgbm as lgbm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sh... | code |
128024284/cell_41 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import RepeatedKFold
from tqdm.notebook import tqdm
import catboost as cb
import lightgbm as lgbm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sh... | code |
128024284/cell_2 | [
"image_output_1.png"
] | import pandas as pd
import numpy as np
from tqdm.notebook import tqdm
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import shap
from sklearn.model_selection import RepeatedKFold
from sklearn.metrics import mean_absolute_error
from sklearn import linear_model
import xgboost as ... | code |
128024284/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
original = pd.read_csv('/kaggle/input/wild-blueberry-yield-prediction-d... | code |
128024284/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
original = pd.read_csv('/kaggle/input/wild-blueberry-yield-prediction-d... | code |
128024284/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
original = pd.read_csv('/kaggle/input/wild-blueberry-yield-prediction-d... | code |
128024284/cell_35 | [
"image_output_1.png"
] | from sklearn import linear_model
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import RepeatedKFold
from tqdm.notebook import tqdm
import catboost as cb
import lightgbm as lgbm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import pa... | code |
128024284/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
original = pd.read_csv('/kaggle/input/wild-blueberry-yield-prediction-d... | code |
128024284/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
original = pd.read_csv('/kaggle/input/wild-blueberry-yield-prediction-d... | code |
90129110/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import metrics
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as ... | code |
90129110/cell_27 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import numpy as np
import p... | code |
2040512/cell_13 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
daily_Data = pd.read_csv('../input/KaggleV2-May-2016.csv')
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(daily_Data['Gender'])
daily_Data['Gender'] = le.transform(daily_Data['Gender'])
le.fit(daily_Data['No-show'])
daily_Data['No-show... | code |
2040512/cell_9 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
daily_Data = pd.read_csv('../input/KaggleV2-May-2016.csv')
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(daily_Data['Gender'])
daily_Data['Gender'] = le.transform(daily_Data['Gender'])
le.fit(daily_Data['No-show'])
daily_Data['No-show... | code |
2040512/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
daily_Data = pd.read_csv('../input/KaggleV2-May-2016.csv')
(daily_Data['Gender'] == 'F').value_counts() | code |
2040512/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
daily_Data = pd.read_csv('../input/KaggleV2-May-2016.csv')
f, ax = plt.subplots(figsize=(15, 10))
sn.countplot(y='Handcap', data=daily_Data, color='c') | code |
2040512/cell_11 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
daily_Data = pd.read_csv('../input/KaggleV2-May-2016.csv')
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(daily_Data['Gender'])
daily_Data['Gender'] = le.transform(daily_Data['Gender'])
le.fit(daily_Data['No-show'])
daily_Data['No-show... | code |
2040512/cell_7 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
daily_Data = pd.read_csv('../input/KaggleV2-May-2016.csv')
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(daily_Data['Gender'])
daily_Data['Gender'] = le.transform(daily_Data['Gender'])
le.fit(daily_Data['No-show'])
daily_Data['No-show... | code |
2040512/cell_8 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
daily_Data = pd.read_csv('../input/KaggleV2-May-2016.csv')
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(daily_Data['Gender'])
daily_Data['Gender'] = le.transform(daily_Data['Gender'])
le.fit(daily_Data['No-show'])
daily_Data['No-show... | code |
2040512/cell_16 | [
"image_output_1.png"
] | from datetime import datetime
from sklearn import preprocessing
import calendar
import pandas as pd
daily_Data = pd.read_csv('../input/KaggleV2-May-2016.csv')
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(daily_Data['Gender'])
daily_Data['Gender'] = le.transform(daily_Data['Gender'])
l... | code |
2040512/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
daily_Data = pd.read_csv('../input/KaggleV2-May-2016.csv')
daily_Data.head() | code |
2040512/cell_17 | [
"text_html_output_1.png"
] | from datetime import datetime
from sklearn import preprocessing
import calendar
import pandas as pd
daily_Data = pd.read_csv('../input/KaggleV2-May-2016.csv')
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(daily_Data['Gender'])
daily_Data['Gender'] = le.transform(daily_Data['Gender'])
l... | code |
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