English | Portuguese (BR) |
---|---|
Provide | Provê |
Enhancement | Aprimoramento |
Enforce | Força |
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# Alias: | |
alias cpython="cd Workspace/cpython/" | |
alias kali="sudo docker container start kali-container && sudo docker exec -it kali-container /bin/bash" | |
alias stopkali="sudo docker container stop kali-container" | |
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def ApplyesKFold(x_axis, y_axis): | |
# Linear Models. | |
from sklearn.linear_model import LinearRegression | |
from sklearn.linear_model import ElasticNet | |
from sklearn.linear_model import Ridge | |
from sklearn.linear_model import Lasso | |
# Cross-Validation models. | |
from sklearn.model_selection import cross_val_score | |
from sklearn.model_selection import KFold |
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import matplotlib.pyplot as plt | |
import pandas as pd | |
from sklearn.model_selection import cross_val_score # Cross Validation Function. | |
from sklearn.model_selection import KFold # KFold Class. | |
from sklearn.linear_model import LinearRegression # Linear Regression class. | |
df = pd.read_csv("../datasets/Admission_Predict.csv") | |
df.drop('Serial No.', axis = 1, inplace = True) |
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import pandas as pd | |
pd.set_option('display.max_columns', 42) | |
data = pd.read_csv('../datasets/2015-building-energy-benchmarking.csv') | |
# Exibe a média de cada coluna. | |
print((data.isnull().sum() / len(data['OSEBuildingID'])) * 100, '\n') | |
data['ENERGYSTARScore'] = data['ENERGYSTARScore'].fillna(data['ENERGYSTARScore'].median()) |
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import pandas as pd | |
pd.set_option('display.max_columns', 18) | |
data = pd.read_csv('../datasets/athlete_events.csv') | |
data['Height'] = data['Height'].fillna(data['Height'].mean()) | |
data['Weight'] = data['Weight'].fillna(data['Weight'].mean()) | |
print(data[['Height', 'Weight']].head(20)) |
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import pandas as pd | |
pd.set_option('display.max_columns', 18) | |
data = pd.read_csv('../datasets/athlete_events.csv') | |
data['Medal'] = data['Medal'].fillna('Nenhuma') | |
print(data['Medal'].head(10)) |
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