Analizzare i dati IoT in Python
Matthias Voppichler
IT Developer
logreg = LogisticRegression() logreg.fit(X_train, y_train) print(logreg.score(X_test, y_test))
logreg = LogisticRegression() logreg.fit(X_train, y_train)
print(logreg.score(X_test, y_test))
0.78145113
StandardScaler
print(data.head())
humidity temperature pressure timestamp 2018-10-01 00:00:00 81.0 11.8 1013.4 2018-10-01 00:15:00 79.7 11.9 1013.1 2018-10-01 00:30:00 81.0 12.1 1013.0 2018-10-01 00:45:00 79.7 11.7 1012.7 2018-10-01 01:00:00 84.3 11.2 1012.6
from sklearn.preprocessing import StandardScaler sc = StandardScaler() sc.fit(data) print(sc.mean_) print(sc.var_)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(data)
print(sc.mean_) print(sc.var_)
[ 71.8826716 14.17002019 1018.17042396] [372.78261022 20.37926608 53.67519188]
data_scaled = sc.transform(data)
df_scaled = pd.DataFrame(data_scaled, columns=data.columns, index=data.index) print(data_scaled.head())
humidity temperature pressure timestamp 2018-10-01 00:00:00 0.472215 -0.524998 -0.651134 2018-10-01 00:15:00 0.404884 -0.502847 -0.692082 2018-10-01 00:30:00 0.472215 -0.458543 -0.705731 2018-10-01 00:45:00 0.404884 -0.547150 -0.746679 2018-10-01 01:00:00 0.643132 -0.657908 -0.760329
logreg = LogisticRegression() logreg.fit(X_train_scaled, y_train_scaled) print(logreg.score(X_test_scaled, y_test_scaled))
0.88145113
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