path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
18140562/cell_2 | [
"text_plain_output_1.png"
] | import os
import os
print(os.listdir('../input')) | code |
18140562/cell_19 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.svm import LinearSVC
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
impor... | code |
18140562/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import re
import seaborn as sns
from imblearn.over_sampling import SMOTE
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
stop_words = set(stopwords... | code |
18140562/cell_7 | [
"text_plain_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)
df_train = pd.read_excel('../input/Data_Train.xlsx')
df_test = pd.read_excel('../input/Data_Test.xlsx')
df_train.sample(5)
df_train.isna().sum()
plt.figure(figsize=(8, 5))
lab... | code |
18140562/cell_16 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
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)
... | code |
18140562/cell_22 | [
"image_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.svm import LinearSVC
import pandas a... | code |
89142870/cell_6 | [
"text_plain_output_1.png"
] | from pytorch_lightning.callbacks import Callback
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
from sklearn.model_selection import KFold
from torch.utils.data import DataLoader, TensorDataset, Subset
import gc
import m... | code |
89142870/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_1.png",
"text_html_output_3.png"
] | from pytorch_lightning.callbacks import Callback
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
from sklearn.model_selection import KFold
from torch.utils.data import DataLoader, TensorDataset, Subset
import gc
import m... | code |
89142870/cell_3 | [
"text_plain_output_1.png"
] | from torch.utils.data import DataLoader, TensorDataset, Subset
import gc
import pandas as pd
import torch
train = pd.read_parquet('../input/train-small/train_small.parquet')
float_feature_names = train.drop(['target', 'row_id', 'time_id', 'investment_id'], axis=1).columns
float_input = train[float_feature_names].va... | code |
89142870/cell_5 | [
"text_plain_output_5.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_20.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"image_output_5.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"image_output_7.png",
"t... | from pytorch_lightning.callbacks import Callback
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
from sklearn.model_selection import KFold
from torch.utils.data import DataLoader, TensorDataset, Subset
import gc
import m... | code |
90106156/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yourcabs/YourCabs_training.csv')
df.shape
data = df.drop(['Car_Cancellation', 'Cost_of_error'], axis=1)
target = df[['Car_Cancellation']]
data.isnull().mean()
data = data.drop(['id', 'user_id... | code |
90106156/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yourcabs/YourCabs_training.csv')
df.shape
data = df.drop(['Car_Cancellation', 'Cost_of_error'], axis=1)
target = df[['Car_Cancellation']]
data.info() | code |
90106156/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yourcabs/YourCabs_training.csv')
df.shape
data = df.drop(['Car_Cancellation', 'Cost_of_error'], axis=1)
target = df[['Car_Cancellation']]
data.isnull().mean()
data = data.drop(['id', 'user_id... | code |
90106156/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yourcabs/YourCabs_training.csv')
df.shape
data = df.drop(['Car_Cancellation', 'Cost_of_error'], axis=1)
target = df[['Car_Cancellation']]
data.isnull().mean()
data = data.drop(['id', 'user_id... | code |
90106156/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yourcabs/YourCabs_training.csv')
df.head() | code |
90106156/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yourcabs/YourCabs_training.csv')
df.shape
data = df.drop(['Car_Cancellation', 'Cost_of_error'], axis=1)
target = df[['Car_Cancellation']]
data.isnull().mean()
data = data.drop(['id', 'user_id... | code |
90106156/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yourcabs/YourCabs_training.csv')
df.shape
data = df.drop(['Car_Cancellation', 'Cost_of_error'], axis=1)
target = df[['Car_Cancellation']]
data.isnull().mean() | code |
90106156/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yourcabs/YourCabs_training.csv')
df.shape
data = df.drop(['Car_Cancellation', 'Cost_of_error'], axis=1)
target = df[['Car_Cancellation']]
data.isnull().mean()
data = data.drop(['id', 'user_id... | code |
90106156/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 |
90106156/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yourcabs/YourCabs_training.csv')
df.shape | code |
90106156/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yourcabs/YourCabs_training.csv')
df.shape
data = df.drop(['Car_Cancellation', 'Cost_of_error'], axis=1)
target = df[['Car_Cancellation']]
data.isnull().mean()
data = data.drop(['id', 'user_id... | code |
90106156/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yourcabs/YourCabs_training.csv')
df.shape
data = df.drop(['Car_Cancellation', 'Cost_of_error'], axis=1)
target = df[['Car_Cancellation']]
data.isnull().mean()
data = data.drop(['id', 'user_id... | code |
90106156/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yourcabs/YourCabs_training.csv')
df.shape
data = df.drop(['Car_Cancellation', 'Cost_of_error'], axis=1)
target = df[['Car_Cancellation']]
data.isnull().mean()
data = data.drop(['id', 'user_id... | code |
90106156/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yourcabs/YourCabs_training.csv')
df.shape
data = df.drop(['Car_Cancellation', 'Cost_of_error'], axis=1)
target = df[['Car_Cancellation']]
data.isnull().mean()
data = data.drop(['id', 'user_id... | code |
90106156/cell_46 | [
"text_html_output_1.png"
] | from geopy import distance
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yourcabs/YourCabs_training.csv')
df.shape
data = df.drop(['Car_Cancellation', 'Cost_of_error'], axis=1)
target = df[['Car_Cancellation']]
data.isnull().mean()
data... | code |
90106156/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yourcabs/YourCabs_training.csv')
df.shape
data = df.drop(['Car_Cancellation', 'Cost_of_error'], axis=1)
target = df[['Car_Cancellation']]
data.isnull().mean()
data = data.drop(['id', 'user_id... | code |
90106156/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yourcabs/YourCabs_training.csv')
df.shape
data = df.drop(['Car_Cancellation', 'Cost_of_error'], axis=1)
target = df[['Car_Cancellation']]
data.isnull().mean()
data = data.drop(['id', 'user_id... | code |
32069353/cell_2 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import warnings
import math
import numpy as np
import pandas as pd
from tqdm import tqdm
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import warnings
warnings.filterwarnings('ignore')
PUBLIC_PRIVATE = 1
import os
for dirname, _, filenames in os.walk('/kag... | code |
32069353/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import datetime
from datetime import datetime, timedelta
from scipy.optimize.minpack import curve_fit
from tqdm import tqdm
import math
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import time
import warnings
import math
import numpy as np
import pandas as pd... | code |
32069353/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
sub_example = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
regr_ensemble_sub = pd.read_csv('/kaggle/... | code |
32069353/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import warnings
import math
import numpy as np
import pandas as pd
from tqdm import tqdm
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import warnings
warnings.filterwarnings('ignore')
PUBLIC_PRIVATE = 1
import os
df_train = pd.read_c... | code |
18114340/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import *
from sklearn.metrics import *
regr = LinearRegression()
regr.fit(X_train, y_train)
pred = regr.predict(X_test)
mean_squared_error(pred, y_test) | code |
18114340/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
from keras.optimizers import *
import keras
from keras.layers import *
from keras.models import *
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import * | code |
18114340/cell_8 | [
"text_html_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)
train_df = pd.read_csv('../input/train.csv')
train_df['Test'] = False
test_df = pd.read_csv('../input/test.csv')
test_df['Test'] = True
df = pd.concat([train... | code |
18127692/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
headbrain = pd.read_csv('../input/headbrain.csv')
headbrain = headbrain.values
X = headbrain[:, 2]
Y = h... | code |
18127692/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
print('Reading the csv file and looking at the first five rows :\n')
headbrain = pd.read_csv('../input/headbrain.csv')
print(headbrain.head()) | code |
18127692/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
headbrain = pd.read_csv('../input/headbrain.csv')
print('HeadBrain Info :\n')
print(headbrain.info()) | code |
18127692/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
import os
print(os.listdir... | code |
18127692/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
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
headbrain = pd.read_csv('../input/headbrain.csv')
headbrain = headbrain.values
X = headbrain[:, 2]
Y... | code |
18127692/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)
headbrain = pd.read_csv('../input/headbrain.csv')
print('Checking for any null values:\n')
print(headbrain.isnull().any()) | code |
18127692/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
headbrain = pd.read_csv('../input/headbrain.csv')
print('Checking for unique values in each column:\n')
print(headbrain.nunique()) | code |
18127692/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)
import seaborn as sns
headbrain = pd.read_csv('../input/headbrain.csv')
plt.figure(figsize=(10, 10))
sns.scatterplot(y='Brain Weight(grams)', x='Head Size(cm^3)', data=headbrain)
plt.show() | code |
18127692/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
headbrain = pd.read_csv('../input/headbrain.csv')
headbrain = headbrain.values
X = headbrain[:, 2]
Y = headbrain[:, 3]
(X.shape, Y.shape) | code |
18127692/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
headbrain = pd.read_csv('../input/headbrain.csv')
print(headbrain.shape) | code |
128012739/cell_4 | [
"text_html_output_1.png"
] | from datetime import datetime
import datetime
import yfinance as yf
ticker = 'SPY'
start_time = datetime(2020, 5, 1)
end_time = datetime(2023, 5, 1)
spy = yf.download(ticker, start=start_time, end=end_time) | code |
128012739/cell_6 | [
"text_plain_output_1.png"
] | from datetime import datetime
import datetime
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas_datareader.data as web
import time
import yfinance as yf
ticker = 'SPY'
start_time = datetime(2020, 5, 1)
end_time = datetime(2023, 5, 1)
spy = yf.download(ticker... | code |
128012739/cell_2 | [
"text_plain_output_1.png"
] | !pip install yfinance --upgrade --no-cache-dir
!pip install empyrical
import yfinance as yf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import pandas_datareader.data as web
import datetime
import matplotlib.dates as mdates
import time
import empyrical as em
i... | code |
128012739/cell_11 | [
"text_plain_output_1.png"
] | from datetime import datetime
import datetime
import numpy as np
import numpy as np
import numpy as np # linear algebra
import yfinance as yf
import yfinance as yf
ticker = 'SPY'
start_time = datetime(2020, 5, 1)
end_time = datetime(2023, 5, 1)
spy = yf.download(ticker, start=start_time, end=end_time)
spy = yf.... | code |
128012739/cell_8 | [
"text_plain_output_1.png"
] | from datetime import datetime
from plotly import offline
import datetime
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas_datareader.data as web
import plotly.graph_objects as go
import time
import yfinance as yf
ticker = 'SPY'
start_time = datetime(2020,... | code |
128012739/cell_10 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from datetime import datetime
import datetime
import yfinance as yf
ticker = 'SPY'
start_time = datetime(2020, 5, 1)
end_time = datetime(2023, 5, 1)
spy = yf.download(ticker, start=start_time, end=end_time)
spy = yf.download(ticker, start=start_time, end=end_time)
start_time = datetime(2020, 5, 1)
end_time = dateti... | code |
128012739/cell_5 | [
"text_html_output_1.png"
] | from datetime import datetime
import datetime
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas_datareader.data as web
import time
import yfinance as yf
ticker = 'SPY'
start_time = datetime(2020, 5, 1)
end_time = datetime(2023, 5, 1)
spy = yf.download(ticker... | code |
74072152/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
df = pd.read_csv('../input/diabetes-prediction-using-logistic-regression/diabetes-dataset.csv')
df.describe
df.is... | code |
74072152/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/diabetes-prediction-using-logistic-regression/diabetes-dataset.csv')
print(df.Outcome.value_counts()) | code |
74072152/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model = model.fit(X_train, y_train)
score = model.predict(X_train)
pred = model.predict(X_test)
model.coef_ | code |
74072152/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
df = pd.read_csv('../input/diabetes-prediction-using-logistic-regression/diabetes-dataset.csv')
df.describe
df.is... | code |
74072152/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model = model.fit(X_train, y_train)
score = model.predict(X_train)
pred = model.predict(X_test)
print('Model Accuracy is : ', pred) | code |
74072152/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
df = pd.read_csv('../input/diabetes-prediction-using-logistic-regression/diabetes-dataset.csv')
df.describe
df.is... | code |
74072152/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/diabetes-prediction-using-logistic-regression/diabetes-dataset.csv')
df.head() | code |
74072152/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 |
74072152/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/diabetes-prediction-using-logistic-regression/diabetes-dataset.csv')
df.info() | code |
74072152/cell_32 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model = model.fit(X_train, y_train)
score = model.predict(X_train)
print('Training Score: ', model.score(X_train, y_train))
print('Testing Score: ', model.score(X_test, y_test)) | code |
74072152/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/diabetes-prediction-using-logistic-regression/diabetes-dataset.csv')
df.describe
df.isna().sum()
featureList = ['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']
featureList = ['Gluco... | code |
74072152/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix
model = LogisticRegression()
model = model.fit(X_train, y_train)
score = model.predict(X_train)
pred = model.predict(X_test)
accuracy_score(y_test, pred) | code |
74072152/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/diabetes-prediction-using-logistic-regression/diabetes-dataset.csv')
df.describe
df.isna().sum()
featureList = ['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']
print(df[featureList].... | code |
74072152/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
df = pd.read_csv('../input/diabetes-prediction-using-logistic-regression/diabetes-dataset.csv')
df.describe
df.is... | code |
74072152/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/diabetes-prediction-using-logistic-regression/diabetes-dataset.csv')
df.describe | code |
74072152/cell_27 | [
"image_output_1.png"
] | print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape) | code |
74072152/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/diabetes-prediction-using-logistic-regression/diabetes-dataset.csv')
df.describe
df.isna().sum() | code |
106205967/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
from fastai.tabular.all import *
pd.options.display.float_format = '{:.2f}'.format
set_seed(42)
data = pd.read_csv('../input/medias-cost-prediction-in-foodmart/media prediction and its cost.csv')
Name = sorted(data['class'].unique().tolist())
N = list(range(len(Name)))
normal_mapping = dict(zip(N... | code |
106205967/cell_23 | [
"text_html_output_2.png"
] | import numpy as np
targs = targs.numpy()
preds = np.argmax(preds.numpy(), axis=-1)
print(preds[0:3])
print(targs[0:3]) | code |
106205967/cell_33 | [
"text_html_output_3.png"
] | import numpy as np
import pandas as pd
from fastai.tabular.all import *
pd.options.display.float_format = '{:.2f}'.format
set_seed(42)
data = pd.read_csv('../input/medias-cost-prediction-in-foodmart/media prediction and its cost.csv')
Name = sorted(data['class'].unique().tolist())
N = list(range(len(Name)))
normal_... | code |
106205967/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
from fastai.tabular.all import *
pd.options.display.float_format = '{:.2f}'.format
set_seed(42)
data = pd.read_csv('../input/medias-cost-prediction-in-foodmart/media prediction and its cost.csv')
display(data[0:3].T)
display(data.info())
display(data.select_dtypes(include='object').columns.tolist(... | code |
106205967/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
from fastai.tabular.all import *
pd.options.display.float_format = '{:.2f}'.format
set_seed(42)
data = pd.read_csv('../input/medias-cost-prediction-in-foodmart/media prediction and its cost.csv')
Name = sorted(data['class'].unique().tolist())
N = list(range(len(Name)))
normal_mapping = dict(zip(N... | code |
106205967/cell_26 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
from fastai.tabular.all import *
pd.options.display.float_format = '{:.2f}'.format
set_seed(42)
data = pd.read_csv('../input/medias-cost-prediction-in-foodmart/media prediction and its cost.csv')
Name = sorted(data['class'].unique().tolist())
N = list(range(len(Name)))
normal_mapping = dict(zip(N... | code |
106205967/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
from fastai.tabular.all import *
pd.options.display.float_format = '{:.2f}'.format
set_seed(42)
data = pd.read_csv('../input/medias-cost-prediction-in-foodmart/media prediction and its cost.csv')
Name = sorted(data['class'].unique().tolist())
N = list(range(len(Name)))
normal_mapping = dict(zip(N... | code |
106205967/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
from fastai.tabular.all import *
pd.options.display.float_format = '{:.2f}'.format
set_seed(42)
data = pd.read_csv('../input/medias-cost-prediction-in-foodmart/media prediction and its cost.csv')
Name = sorted(data['class'].unique().tolist())
N = list(range(len(Name)))
normal_mapping = dict(zip(N... | code |
106205967/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
from fastai.tabular.all import *
pd.options.display.float_format = '{:.2f}'.format
set_seed(42)
data = pd.read_csv('../input/medias-cost-prediction-in-foodmart/media prediction and its cost.csv')
data['class'] = data['cost'].apply(lambda x: ('00' + str(int(x // 10)))[-2:])
display(data['class']) | code |
106205967/cell_18 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
from fastai.tabular.all import *
pd.options.display.float_format = '{:.2f}'.format
set_seed(42)
data = pd.read_csv('../input/medias-cost-prediction-in-foodmart/media prediction and its cost.csv')
Name = sorted(data['class'].unique().tolist())
N = list(range(len(Name)))
normal_mapping = dict(zip(N... | code |
106205967/cell_32 | [
"image_output_1.png"
] | import numpy as np
targs = targs.numpy()
preds = np.argmax(preds.numpy(), axis=-1)
print(tpreds[0:3])
tpreds2 = np.argmax(tpreds, axis=-1)
print(tpreds2[0:3]) | code |
106205967/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
from fastai.tabular.all import *
pd.options.display.float_format = '{:.2f}'.format
set_seed(42)
data = pd.read_csv('../input/medias-cost-prediction-in-foodmart/media prediction and its cost.csv')
Name = sorted(data['class'].unique().tolist())
print(Name)
N = list(range(len(Name)))
normal_mapping ... | code |
106205967/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
from fastai.tabular.all import *
pd.options.display.float_format = '{:.2f}'.format
set_seed(42)
data = pd.read_csv('../input/medias-cost-prediction-in-foodmart/media prediction and its cost.csv')
Name = sorted(data['class'].unique().tolist())
N = list(range(len(Name)))
normal_mapping = dict(zip(N... | code |
106205967/cell_31 | [
"image_output_1.png"
] | import pandas as pd
from fastai.tabular.all import *
pd.options.display.float_format = '{:.2f}'.format
set_seed(42)
data = pd.read_csv('../input/medias-cost-prediction-in-foodmart/media prediction and its cost.csv')
Name = sorted(data['class'].unique().tolist())
N = list(range(len(Name)))
normal_mapping = dict(zip(N... | code |
106205967/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
from fastai.tabular.all import *
pd.options.display.float_format = '{:.2f}'.format
set_seed(42)
data = pd.read_csv('../input/medias-cost-prediction-in-foodmart/media prediction and its cost.csv')
Name = sorted(data['class'].unique().tolist())
N = list(range(len(Name)))
normal_mapping = dict(zip(N... | code |
106205967/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | display(preds)
display(targs) | code |
106205967/cell_10 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
from fastai.tabular.all import *
pd.options.display.float_format = '{:.2f}'.format
set_seed(42)
data = pd.read_csv('../input/medias-cost-prediction-in-foodmart/media prediction and its cost.csv')
data = data.drop('cost', axis=1)
m = len(data)
print(m)
M = list(range(m))
random.seed(2021)
random.... | code |
106205967/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
from fastai.tabular.all import *
pd.options.display.float_format = '{:.2f}'.format
set_seed(42)
data = pd.read_csv('../input/medias-cost-prediction-in-foodmart/media prediction and its cost.csv')
Name = sorted(data['class'].unique().tolist())
N = list(range(len(Name)))
normal_mapping = dict(zip(N... | code |
106205967/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
from fastai.tabular.all import *
pd.options.display.float_format = '{:.2f}'.format
set_seed(42)
data = pd.read_csv('../input/medias-cost-prediction-in-foodmart/media prediction and its cost.csv')
Name = sorted(data['class'].unique().tolist())
N = list(range(len(Name)))
normal_mapping = dict(zip(N... | code |
33104580/cell_4 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import warnings
import numpy as np
import pandas as pd
import os
from urllib.request import urlopen
import json
import plotly.express as px
import plotly.offline as py
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import seaborn as sns
import ma... | code |
33104580/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from datetime import datetime, timedelta
import numpy as np
import pandas as pd
latest_date = datetime.today() - timedelta(days=1)
latest_date = latest_date.strftime('%m/%d/%y')[1:]
df_cases = pd.read_csv('https://usafactsstatic.blob.core.windows.net/public/data/covid-19/covid_confirmed_usafacts.csv')[['countyFIPS',... | code |
33104580/cell_11 | [
"text_plain_output_1.png"
] | from datetime import datetime, timedelta
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import os
from urllib.request import urlopen
import json
import plotly.express as px
import plotly.offline as py
i... | code |
33104580/cell_19 | [
"text_plain_output_1.png"
] | from datetime import datetime, timedelta
from urllib.request import urlopen
import json
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import plotly.express as px
import plotly.express as px
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import ... | code |
33104580/cell_7 | [
"image_output_1.png"
] | from datetime import datetime, timedelta
import numpy as np
import pandas as pd
latest_date = datetime.today() - timedelta(days=1)
latest_date = latest_date.strftime('%m/%d/%y')[1:]
df_cases = pd.read_csv('https://usafactsstatic.blob.core.windows.net/public/data/covid-19/covid_confirmed_usafacts.csv')[['countyFIPS',... | code |
33104580/cell_8 | [
"text_html_output_1.png"
] | !pip install chart_studio
!pip install plotly-geo | code |
33104580/cell_17 | [
"text_html_output_1.png"
] | from datetime import datetime, timedelta
from urllib.request import urlopen
import json
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import plotly.express as px
import plotly.express as px
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import ... | code |
33104580/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from datetime import datetime, timedelta
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import os
from urllib.request import urlopen
import json
import plotly.express as px
import plotly.offline as py
i... | code |
33104580/cell_14 | [
"text_plain_output_1.png"
] | from datetime import datetime, timedelta
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import os
from urllib.request import urlopen
import json
import plotly.express as px
import plotly.offline as py
i... | code |
33104580/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from datetime import datetime, timedelta
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import os
from urllib.request import urlopen
import json
import plotly.express as px
import plotly.offline as py
i... | code |
130007697/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 |
130007697/cell_18 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import cv2 as cv
import matplotlib.pyplot as plt
import torch
import cv2 as cv
import matplotlib.pyplot as plt
img = cv.imread('/kaggle/input/car-plate-detection/images/Cars0.png')
img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
rec = cv.rectangle(img, (226, 125), (419, 173), (0, 250, 0), 2)
rec = cv.circle(rec, ((226 + 41... | code |
130007697/cell_16 | [
"text_plain_output_1.png"
] | import torch
import torch
yolo = torch.hub.load('ultralytics/yolov5', 'custom', path='/kaggle/working/yolov5/runs/train/exp/weights/best.pt') | code |
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