path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
32071924/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
train_df.isna().sum()
test_df.isna().sum... | code |
32071924/cell_4 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
print(test_df.shape)
test_df.head() | code |
32071924/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
test_df.isna().sum() | code |
32071924/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.... | code |
32071924/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
train_df.isna().sum()
train_df['Province... | code |
32071924/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import plotly.express as px
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-f... | code |
32071924/cell_16 | [
"text_plain_output_1.png"
] | from plotly.subplots import make_subplots
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import plotly.express as px
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = ... | code |
32071924/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
print(train_df.shape)
train_df.tail() | code |
32071924/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import plotly.express as px
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-f... | code |
32071924/cell_5 | [
"text_html_output_2.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
train_df.isna().sum() | code |
88078973/cell_25 | [
"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)
import seaborn as sns
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data =... | code |
88078973/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.head() | code |
88078973/cell_30 | [
"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
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data =... | code |
88078973/cell_33 | [
"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
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data =... | code |
88078973/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes | code |
88078973/cell_39 | [
"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)
import seaborn as sns
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data =... | code |
88078973/cell_41 | [
"text_plain_output_1.png",
"image_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
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data =... | code |
88078973/cell_19 | [
"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)
import seaborn as sns
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data =... | code |
88078973/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 |
88078973/cell_18 | [
"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
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data =... | code |
88078973/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)
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.describe() | code |
88078973/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data['alcohol'] | code |
88078973/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data | code |
88078973/cell_35 | [
"text_plain_output_1.png",
"image_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
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data =... | code |
88078973/cell_31 | [
"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
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data =... | code |
88078973/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum() | code |
88078973/cell_22 | [
"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)
import seaborn as sns
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data =... | code |
88078973/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum() | code |
88078973/cell_27 | [
"image_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
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data =... | code |
88078973/cell_37 | [
"text_plain_output_1.png",
"image_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
wine_data = pd.read_csv('../input/wine-reviews-data/wine.csv')
wine_data
wine_data.dtypes
wine_data.isnull().sum()
wine_data.isnull().sum()
wine_data =... | code |
50222837/cell_13 | [
"image_output_1.png"
] | """import xgboost
xgBoost = xgboost.XGBRegressor(max_depth=3, learning_rate=0.1, n_estimators=100, booster='gbtree')
xgBoost.fit(X_train, Y_train)
print("train score", xgBoost.score(X_train, Y_train))
print("test score", xgBoost.score(X_test, Y_test))
#print("crossVal score", cross_val_score(xgBoost, X, Y, cv=3).mean()... | code |
50222837/cell_9 | [
"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/house-prices-advanced-regression-techniques/train.csv')
def NanColums(df):
percent_nan = 100 * df.isnull().sum() / len(df)
percent_nan = percent_nan[percent... | code |
50222837/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('../input/house-prices-advanced-regression-techniques/train.csv')
df.info() | code |
50222837/cell_20 | [
"text_plain_output_1.png"
] | """import lightgbm
params = [
{
# 'regressor__regressor': [lightgbm.LGBMRegressor()],
'regressor__regressor__boosting_type': ['gbdt'],
'regressor__regressor__n_estimators': [100],
'regressor__regressor__max_depth': [20],
'regressor__regressor__learning_rate' : [0.1],
'r... | code |
50222837/cell_6 | [
"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/house-prices-advanced-regression-techniques/train.csv')
def NanColums(df):
percent_nan = 100 * df.isnull().sum() / len(df)
percent_nan = percent_nan[percent... | code |
50222837/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaled_X_train = scaler.fit_transform(X_train)
scaled_X_test = scaler.transform(X_test)
from sklea... | code |
50222837/cell_11 | [
"image_output_1.png"
] | """import sklearn
sklearn_boost = ensemble.GradientBoostingRegressor(loss='ls', learning_rate=0.1, n_estimators=100)
sklearn_boost.fit(X_train, Y_train)
print("train score", sklearn_boost.score(X_train, Y_train))
print("test score", sklearn_boost.score(X_test, Y_test))
#print("crossVal score", cross_val_score(sklearn_b... | code |
50222837/cell_19 | [
"text_plain_output_1.png"
] | """import xgboost
params = [
{
'learning_rate' : [0.2],
'n_estimators': [250],
'max_depth': [3],
},
]
gsc = GridSearchCV(
estimator=xgboost.XGBRegressor(),
param_grid=params,
cv=3,
scoring='r2',
verbose=0,
n_jobs=-1)
grid_result = gsc.fit... | code |
50222837/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 |
50222837/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"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/house-prices-advanced-regression-techniques/train.csv')
def NanColums(df):
percent_nan = 100 * df.isnull().sum() / len(df)
percent_nan = percent_nan[percent... | code |
50222837/cell_18 | [
"text_plain_output_1.png"
] | """from catboost import CatBoost
params = {
'depth': [7],
'learning_rate' : [0.15],
'l2_leaf_reg': [15,20, 25],
'iterations': [300],
'verbose' : [False], #shut up!!!
}
gsc = GridSearchCV(
estimator=catboost.CatBoostRegressor(),
param_grid=params,
... | code |
50222837/cell_15 | [
"text_plain_output_1.png"
] | """#стакнем-ка ридж регерссию и метод опорных векторов
from sklearn.linear_model import RidgeCV
from sklearn.svm import LinearSVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import StackingRegressor
import warnings
warnings.filterwarnings('ignore') #нужна линеризация данных, а мне лень
esti... | code |
50222837/cell_16 | [
"text_plain_output_1.png"
] | """#среднее по рандомным деревьям показывает неплохой результат
from sklearn.ensemble import RandomForestRegressor
Begging = RandomForestRegressor(max_depth=30, n_estimators=300)
Begging.fit(X_train, Y_train)
print("train score", Begging.score(X_train, Y_train))
print("test score", Begging.score(X_test, Y_test))
#print... | code |
50222837/cell_17 | [
"text_plain_output_1.png"
] | """import sklearn
params = {
'learning_rate': [0.05],
'n_estimators' : [200],
'max_depth' : [6]
}
gsc = GridSearchCV(
estimator=ensemble.GradientBoostingRegressor(),
param_grid=params,
cv=3)
grid_result = gsc.fit(X_train, Y_train)
print('Best params:', grid_result... | code |
50222837/cell_14 | [
"image_output_1.png"
] | """import lightgbm
lgbreg = lightgbm.LGBMRegressor(boosting_type='gbdt', num_leaves=31, learning_rate=0.1, n_estimators=100)
lgbreg.fit(X_train, Y_train)
print("train score", lgbreg.score(X_train, Y_train))
print("test score", lgbreg.score(X_test, Y_test))
#print("crossVal score", cross_val_score(lgbreg, X, Y, cv=3).me... | code |
50222837/cell_10 | [
"text_plain_output_1.png"
] | """Y = df["SalePrice"] #value for prediction
X = df.drop("SalePrice", axis=1) #data
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, random_state=98987)""" | code |
50222837/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import ElasticNet
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaled_X_train = scaler.fit_transform(X_train)
scaled_X_test = scaler.transform(X_test)
from sklea... | code |
50222837/cell_12 | [
"text_plain_output_1.png"
] | """import catboost
cboost = catboost.CatBoostRegressor(loss_function='RMSE', verbose=False)
cboost.fit(X_train, Y_train)
print("train score", cboost.score(X_train, Y_train))
print("test score", cboost.score(X_test, Y_test))
#print("crossVal score", cross_val_score(cboost, X, Y, cv=3).mean())""" | code |
50222837/cell_5 | [
"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/house-prices-advanced-regression-techniques/train.csv')
def NanColums(df):
percent_nan = 100 * df.isnull().sum() / len(df)
percent_nan = percent_nan[percent... | code |
73075118/cell_9 | [
"text_html_output_1.png"
] | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
glass = pd.read_csv('/kaggle/input/glass/glass.csv')
X = glass.copy().drop(['Type'], axis=1)
y = glass['Type'].copy()
from sklearn.discriminant_analysis import LinearDiscrimin... | code |
73075118/cell_19 | [
"text_plain_output_1.png"
] | from plotly.subplots import make_subplots
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
glass = pd.read_csv('/kaggle/input/glass/glass.csv')
X = glass.copy()... | code |
73075118/cell_7 | [
"text_html_output_1.png"
] | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
import pandas as pd
import plotly.express as px
glass = pd.read_csv('/kaggle/input/glass/glass.csv')
X = glass.copy().drop(['Type'], axis=1)
y = glass['Type'].copy()
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
lda_model =... | code |
73075118/cell_16 | [
"text_html_output_1.png"
] | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
import pandas as pd
import plotly.express as px
glass = pd.read_csv('/kaggle/input/glass/glass.csv')
X = glass.copy().drop(['Type'], axis=1)
y = glass['Type'].copy()
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
lda_model =... | code |
73075118/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
glass = pd.read_csv('/kaggle/input/glass/glass.csv')
glass | code |
73075118/cell_17 | [
"text_html_output_2.png"
] | from plotly.subplots import make_subplots
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
glass = pd.read_csv('/kaggle/input/glass/glass.csv')
X = glass.copy().drop(['Type'], axis=1)
y = glass['Type'].copy()
fr... | code |
33108543/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_Befor... | code |
33108543/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_Before", title="Mem_Score_Before over Age",
... | code |
33108543/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
plt.figure(figsize=(16, 6))
sns.barplot(x='Drug', y='Mem_Score_Before', data=cleaned_data, order=cleaned_data.Drug.unique().tol... | code |
33108543/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
data.head() | code |
33108543/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_Before", title="Mem_Score_Before over Age",
... | code |
33108543/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
data.describe() | code |
33108543/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
cleaned_data.Drug.unique() | code |
33108543/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_Before", title="Mem_Score_Before over Age",
... | code |
33108543/cell_18 | [
"text_html_output_1.png"
] | from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_Befor... | code |
33108543/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_Before", title="Mem_Score_Before over Age",
... | code |
33108543/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_Before", title="Mem_Score_Before over Age",
... | code |
33108543/cell_14 | [
"text_html_output_2.png",
"text_html_output_3.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_Before", title="Mem_Score_Before over Age",
... | code |
33108543/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x='age', y='Mem_Score_Before', title='Mem_Score_Before over Age', color... | code |
33108543/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
cleaned_data = data.copy()
fig = px.bar(cleaned_data, x="age", y="Mem_Score_Before", title="Mem_Score_Before over Age",
... | code |
33108543/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/memory-test-on-drugged-islanders-data/Islander_data.csv')
data.info() | code |
130025642/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_cs... | code |
130025642/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/playground-series-s3e15/data.csv')
df.columns
corr_matrix = df.corr()
df.isnull().sum()
df.isnull().sum()
df_missing = df[df['x_e_out [-]'].isnull()]
df_non_missing = df[~df['x_e_out [-]'].i... | code |
130025642/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/playground-series-s3e15/data.csv')
df.info() | code |
130025642/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/playground-series-s3e15/data.csv')
df.columns
corr_matrix = df.corr()
df.isnull().sum() | code |
130025642/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 |
130025642/cell_8 | [
"application_vnd.jupyter.stderr_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/playground-series-s3e15/data.csv')
df.columns | code |
130025642/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt | code |
130025642/cell_17 | [
"image_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/playground-series-s3e15/data.csv')
df.columns
corr_matrix = df.corr()
df.isnull().sum()
df.isnull().sum()
df_missing = df[df['x_e_out [-]'].isnull()]
df_non_missing = df[~df['x_e_out [-]'].i... | code |
130025642/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/playground-series-s3e15/data.csv')
df.columns
corr_matrix = df.corr()
df.isnull().sum()
df.isnull().sum() | code |
130025642/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
df.columns
corr_matrix = df.corr()
df.isnull().sum()
df.isnull().sum()
df_missing = df[df['x_e_out [-]'].isnull()]
df_non_m... | code |
130025642/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | 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
df = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
df.columns
corr_matrix = df.corr()
plt.figure(figsize=(10, 8))
sns.heatmap(corr_matrix, cmap='coolwar... | code |
130025642/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('/kaggle/input/playground-series-s3e15/data.csv')
df.columns
corr_matrix = df.corr()
df.isnull().sum()
df.describe() | code |
130025642/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)
df = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
df.head() | code |
106212685/cell_21 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb['last review'] = pd.to_datetime(abnb['last review'])
abnb['Construction year'] = pd.to_datetime(abnb['Construction year'])
abnb.dtypes
abnb... | code |
106212685/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape | code |
106212685/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum() | code |
106212685/cell_25 | [
"image_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = ... | code |
106212685/cell_34 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = ... | code |
106212685/cell_23 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = ... | code |
106212685/cell_33 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = ... | code |
106212685/cell_44 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = ... | code |
106212685/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns | code |
106212685/cell_40 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = ... | code |
106212685/cell_29 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = ... | code |
106212685/cell_48 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = ... | code |
106212685/cell_41 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = ... | code |
106212685/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull() | code |
106212685/cell_19 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = ... | code |
106212685/cell_18 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes
abnb.memory_usage().sum()
abnb.isnull()
abnb.columns = [col.lower().replace(' ', '_') for col in abnb.columns]
abnb.shape
text = ... | code |
106212685/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
abnb = pd.read_csv('/kaggle/input/airbnbopendata/Airbnb_Open_Data.csv')
abnb.columns
abnb.dtypes | code |
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