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Basic Logistic Regression Model for Predictive Maintenance Problem
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import pickle | |
from sklearn.linear_model import LogisticRegression | |
import pandas | |
import json | |
from sklearn import preprocessing | |
from sklearn import metrics | |
from sklearn.model_selection import train_test_split | |
with open('training_data.json') as json_data: | |
data = json.load(json_data) | |
df = pandas.DataFrame(data) | |
X = df[['temp', 'vibration', 'current', 'noise']] | |
def normalize_features(X): | |
transformer = preprocessing.Normalizer().fit(X) | |
return transformer.transform(X).tolist() | |
def encode_labels(y): | |
enc = preprocessing.LabelEncoder() | |
enc.fit(y) | |
return [enc.transform(y).tolist(), enc.classes_.tolist()] | |
X = normalize_features(X) | |
y, encoding = encode_labels(df[['state']]) | |
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1) | |
regressor = LogisticRegression(solver='liblinear') | |
regressor.fit(train_X, train_y) | |
y_pred = regressor.predict(val_X) | |
accuracy = metrics.accuracy_score(y_pred, val_y) | |
print(accuracy) | |
file_handler = open('factory_linear_regression.pkl', 'wb') | |
payload = {"model": regressor, "encoding": encoding} | |
pickle.dump(payload, file_handler) | |
file_handler.close() | |
print("pickled model") |
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