Example_Predictive_Maintenance_for_Manufacturing_Equipment.ipynb

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        "from sklearn.ensemble import RandomForestRegressor\n",
        "import numpy as np\n",
        "\n",
        "def get_equipment_sensor_data():\n",
        "    # Simulated function to fetch real-time equipment sensor data\n",
        "    # In a real implementation, this would involve capturing actual sensor readings\n",
        "    sensor_data = np.array([\n",
        "        [15.0, 30.0, 0.98, 400],\n",
        "        [14.8, 29.5, 0.95, 420],\n",
        "        # More simulated sensor data...\n",
        "    ])\n",
        "    return sensor_data\n",
        "\n",
        "# Sample target maintenance schedule in hours (to be replaced with actual target data)\n",
        "maintenance_schedule_target = np.array([120, 150])\n",
        "\n",
        "# Predictive maintenance for manufacturing machinery\n",
        "model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
        "sensor_data = get_equipment_sensor_data()\n",
        "\n",
        "# Fit the model (in a real scenario, you would split your data into training and test sets)\n",
        "model.fit(sensor_data, maintenance_schedule_target)\n",
        "\n",
        "# Predict maintenance schedule based on new sensor data\n",
        "predicted_maintenance_schedule = model.predict(sensor_data)\n",
        "print(\"Recommended Maintenance Schedule:\", predicted_maintenance_schedule)"
      ]
    }
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