Create train_model.py

Signed-off-by: Fabiana  ⚡️ Campanari <113218619+FabianaCampanari@users.noreply.github.com>

Author: FabianaCampanari

Codes/train_model.py

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+
+import pandas as pd
+import pickle
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+
+dados = {
+    "temperatura": [40, 42, 45, 50, 55, 60, 65, 70, 80, 85, 90, 48, 52, 67, 73, 88],
+    "vibracao":    [0.3, 0.4, 0.5, 0.7, 0.8, 0.9, 1.2, 1.3, 1.5, 1.7, 1.9, 0.6, 0.75, 1.1, 1.4, 1.8],
+    "rpm":         [900, 950, 1000, 1100, 1150, 1200, 1300, 1400, 1500, 1600, 1700, 1050, 1120, 1280, 1450, 1650],
+    "falha":       [0,   0,   0,    0,    0,    1,    1,    1,    1,    1,    1,    0,    0,    1,    1,    1]
+}
+
+df = pd.DataFrame(dados)
+df.to_csv("exemplo_dados.csv", index=False)
+
+X = df[["temperatura", "vibracao", "rpm"]]
+y = df["falha"]
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.25, random_state=42
+)
+
+modelo = RandomForestClassifier(n_estimators=100, random_state=42)
+modelo.fit(X_train, y_train)
+
+preds = modelo.predict(X_test)
+print(classification_report(y_test, preds))
+
+with open("modelo_falha_rf.pkl", "wb") as f:
+    pickle.dump(modelo, f)
+
+print("Modelo salvo como modelo_falha_rf.pkl")
+print("Dataset salvo como exemplo_dados.csv")