Predictive Maintance.ipynb

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      "Installing collected packages: pyjwt, pydotplus, proto-plus, plotly, patsy, networkx, msgpack, Mako, locket, llvmlite, graphviz, google-crc32c, filelock, dataframe-api-compat, colorlog, cloudpickle, xgboost, partd, numba, lightgbm, grpcio-status, google-resumable-media, datashape, alembic, statsmodels, shap, pyOpenSSL, pyod, optuna, modin, mlxtend, imbalanced-learn, google-api-core, db-dtypes, dask, catboost, snowflake-connector-python, ray, google-cloud-core, distributed, category-encoders, snowflake-sqlalchemy, google-cloud-bigquery-storage, google-cloud-bigquery, modin-spreadsheet, anai-opensource\n",
      "Successfully installed Mako-1.3.5 alembic-1.13.2 anai-opensource-0.1.7.2 catboost-1.2.5 category-encoders-2.6.3 cloudpickle-3.0.0 colorlog-6.8.2 dask-2024.7.0 dataframe-api-compat-0.2.7 datashape-0.5.2 db-dtypes-1.2.0 distributed-2024.7.0 filelock-3.15.4 google-api-core-2.19.1 google-cloud-bigquery-3.25.0 google-cloud-bigquery-storage-2.25.0 google-cloud-core-2.4.1 google-crc32c-1.5.0 google-resumable-media-2.7.1 graphviz-0.20.3 grpcio-status-1.62.2 imbalanced-learn-0.12.3 lightgbm-4.4.0 llvmlite-0.43.0 locket-1.0.0 mlxtend-0.20.0 modin-0.31.0 modin-spreadsheet-0.1.2 msgpack-1.0.8 networkx-3.2.1 numba-0.60.0 optuna-3.6.1 partd-1.4.2 patsy-0.5.6 plotly-5.22.0 proto-plus-1.24.0 pyOpenSSL-24.1.0 pydotplus-2.0.2 pyjwt-2.8.0 pyod-2.0.1 ray-2.32.0 shap-0.46.0 snowflake-connector-python-3.11.0 snowflake-sqlalchemy-1.6.1 statsmodels-0.14.2 xgboost-2.1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "torch 2.3.1 requires sympy, which is not installed.\n",
      "torchtext 0.10.0 requires torch==1.9.0, but you have torch 2.3.1 which is incompatible.\n"
     ]
    }
   ],
   "source": [
    "!pip install anai-opensource  https://anai-wheels.s3.ap-south-1.amazonaws.com/TensorFM-1.0.4-py3-none-any.whl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1d9074a1-daf3-433b-bec9-e73ad263d892",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3cddb1fa-045f-460e-8b4c-c2a2647db938",
   "metadata": {},
   "outputs": [],
   "source": [
    "errors= pd.read_csv(r'C:\\Users\\shash\\Deep_Learning\\Predictive-maintenance-using-ML\\maintain_data\\PdM_errors.csv')\n",
    "failures= pd.read_csv(r'C:\\Users\\shash\\Deep_Learning\\Predictive-maintenance-using-ML\\maintain_data\\pdM_failures.csv')\n",
    "machines= pd.read_csv(r'C:\\Users\\shash\\Deep_Learning\\Predictive-maintenance-using-ML\\maintain_data\\pdM_machines.csv')\n",
    "maint= pd.read_csv(r'C:\\Users\\shash\\Deep_Learning\\Predictive-maintenance-using-ML\\maintain_data\\PdM_maint.csv')\n",
    "telemetry= pd.read_csv(r'C:\\Users\\shash\\Deep_Learning\\Predictive-maintenance-using-ML\\maintain_data\\PdM_telemetry.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "815029de-78b0-4a5e-807d-d0df08106b83",
   "metadata": {},
   "source": [
    "# EDA on Errors Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "922d669c-1141-48a5-95a4-5b5542fad9f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>machineID</th>\n",
       "      <th>errorID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-03 07:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>error1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-03 20:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>error3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-04 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>error5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-10 15:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>error4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-22 10:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>error4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  machineID errorID\n",
       "0  2015-01-03 07:00:00          1  error1\n",
       "1  2015-01-03 20:00:00          1  error3\n",
       "2  2015-01-04 06:00:00          1  error5\n",
       "3  2015-01-10 15:00:00          1  error4\n",
       "4  2015-01-22 10:00:00          1  error4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cdec906d-566f-4a31-950b-c1098171202d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error Records count: 3919\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>machineID</th>\n",
       "      <th>errorID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-03 07:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>error1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-03 20:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>error3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-04 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>error5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-10 15:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>error4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-22 10:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>error4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime  machineID errorID\n",
       "0 2015-01-03 07:00:00          1  error1\n",
       "1 2015-01-03 20:00:00          1  error3\n",
       "2 2015-01-04 06:00:00          1  error5\n",
       "3 2015-01-10 15:00:00          1  error4\n",
       "4 2015-01-22 10:00:00          1  error4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errors['datetime']= pd.to_datetime(\n",
    "    errors['datetime'], format=\"%Y-%m-%d %H:%M:%S\")\n",
    "\n",
    "errors['errorID']= errors['errorID'].astype('object')\n",
    "\n",
    "print(\"Error Records count: %d\" %len(errors.index))\n",
    "errors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59a9d0eb-e470-40a2-a2b7-97154ec4b183",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0984340c-3f9b-4f4a-8b5b-f271b6a224e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(errors['errorID'])\n",
    "plt.title(\"Error counts\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dba1eaa-ae67-4059-ad86-e3fd5b0c259c",
   "metadata": {},
   "source": [
    "# EDA on Failure dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "39ef4344-b2da-40d4-9c48-4262a6fd60e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of failures: 761\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>machineID</th>\n",
       "      <th>failure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-05 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>comp4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-03-06 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>comp1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-04-20 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>comp2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-06-19 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>comp4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-09-02 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>comp4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime  machineID failure\n",
       "0 2015-01-05 06:00:00          1   comp4\n",
       "1 2015-03-06 06:00:00          1   comp1\n",
       "2 2015-04-20 06:00:00          1   comp2\n",
       "3 2015-06-19 06:00:00          1   comp4\n",
       "4 2015-09-02 06:00:00          1   comp4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "failures['datetime']= pd.to_datetime(failures['datetime'], format=\"%Y-%m-%d %H:%M:%S\")\n",
    "failures['failure']= failures['failure'].astype('object')\n",
    "\n",
    "print(\"Total number of failures: %d\"%len(failures.index))\n",
    "failures.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9002e1c2-0566-49ad-82be-58c1d9ab0432",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(failures['failure'])\n",
    "plt.title(\"Failure count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f3c18bf-4421-4dcd-a9f0-f12455add730",
   "metadata": {},
   "source": [
    "# EDA On Machine Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "85dbdb82-03bf-49a7-aa1a-b8259a43d2cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machineID</th>\n",
       "      <th>model</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>model3</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>model4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>model3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>model3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>model3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   machineID   model  age\n",
       "0          1  model3   18\n",
       "1          2  model4    7\n",
       "2          3  model3    8\n",
       "3          4  model3    7\n",
       "4          5  model3    2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "machines.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "81e1d38e-0f6d-45b3-a271-622124d90351",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "machines.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ab7467fc-1c96-46d9-98a1-1288a9cf3704",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "models= machines['model'].unique()\n",
    "colors=['blue', 'green', 'orange','red']\n",
    "\n",
    "for i, model in enumerate(models):\n",
    "    plt.hist(machines[machines['model']==model]['age'], bins=10, alpha=0.5, color= colors[i], label=model)\n",
    "\n",
    "\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Machine Age distribution')\n",
    "plt.legend()\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df8bd1bb-102c-40d1-be04-4c71e2ae7993",
   "metadata": {},
   "source": [
    "# EDA on Maint Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a3626cca-17a0-4898-9e5f-97effecfa7cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "maint['datetime']= pd.to_datetime(maint['datetime'], format=\"%Y-%m-%d %H:%M:%S\")\n",
    "maint['comp']= maint['comp'].astype('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b7931a31-efe3-4d12-b8a7-dd06c3134ff7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size: (3286, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>machineID</th>\n",
       "      <th>comp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-06-01 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>comp2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-07-16 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>comp4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-07-31 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>comp3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-12-13 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>comp1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-05 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>comp4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime  machineID   comp\n",
       "0 2014-06-01 06:00:00          1  comp2\n",
       "1 2014-07-16 06:00:00          1  comp4\n",
       "2 2014-07-31 06:00:00          1  comp3\n",
       "3 2014-12-13 06:00:00          1  comp1\n",
       "4 2015-01-05 06:00:00          1  comp4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Size:\",maint.shape)\n",
    "maint.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e6a1ac7c-591a-41d8-9fb8-dca317001c4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(maint['comp'])\n",
    "plt.title(\"Count of components\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed8db066-d4c5-48f0-b205-07abe233b090",
   "metadata": {},
   "source": [
    "# EDA on Telemetry Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e78428f4-8657-4463-9551-1b4bf0340d71",
   "metadata": {},
   "outputs": [],
   "source": [
    "telemetry['datetime']= pd.to_datetime(telemetry['datetime'], format=\"%Y-%m-%d %H:%M:%S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7405c4cf-fb17-4b6b-a452-6703b00d5eca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>machineID</th>\n",
       "      <th>volt</th>\n",
       "      <th>rotate</th>\n",
       "      <th>pressure</th>\n",
       "      <th>vibration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>176.217853</td>\n",
       "      <td>418.504078</td>\n",
       "      <td>113.077935</td>\n",
       "      <td>45.087686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01 07:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>162.879223</td>\n",
       "      <td>402.747490</td>\n",
       "      <td>95.460525</td>\n",
       "      <td>43.413973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01 08:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>170.989902</td>\n",
       "      <td>527.349825</td>\n",
       "      <td>75.237905</td>\n",
       "      <td>34.178847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-01 09:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>162.462833</td>\n",
       "      <td>346.149335</td>\n",
       "      <td>109.248561</td>\n",
       "      <td>41.122144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-01 10:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>157.610021</td>\n",
       "      <td>435.376873</td>\n",
       "      <td>111.886648</td>\n",
       "      <td>25.990511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime  machineID        volt      rotate    pressure  \\\n",
       "0 2015-01-01 06:00:00          1  176.217853  418.504078  113.077935   \n",
       "1 2015-01-01 07:00:00          1  162.879223  402.747490   95.460525   \n",
       "2 2015-01-01 08:00:00          1  170.989902  527.349825   75.237905   \n",
       "3 2015-01-01 09:00:00          1  162.462833  346.149335  109.248561   \n",
       "4 2015-01-01 10:00:00          1  157.610021  435.376873  111.886648   \n",
       "\n",
       "   vibration  \n",
       "0  45.087686  \n",
       "1  43.413973  \n",
       "2  34.178847  \n",
       "3  41.122144  \n",
       "4  25.990511  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "telemetry.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "22b10a9b-4a6a-417a-9fda-d7d1b451a2e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(876100, 6)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "telemetry.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "51e13e11-0745-40e7-81ea-3821b583761a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>machineID</th>\n",
       "      <th>volt</th>\n",
       "      <th>rotate</th>\n",
       "      <th>pressure</th>\n",
       "      <th>vibration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>876100</td>\n",
       "      <td>876100.000000</td>\n",
       "      <td>876100.000000</td>\n",
       "      <td>876100.000000</td>\n",
       "      <td>876100.000000</td>\n",
       "      <td>876100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2015-07-02 18:00:00</td>\n",
       "      <td>50.500000</td>\n",
       "      <td>170.777736</td>\n",
       "      <td>446.605119</td>\n",
       "      <td>100.858668</td>\n",
       "      <td>40.385007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2015-01-01 06:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>97.333604</td>\n",
       "      <td>138.432075</td>\n",
       "      <td>51.237106</td>\n",
       "      <td>14.877054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2015-04-02 12:00:00</td>\n",
       "      <td>25.750000</td>\n",
       "      <td>160.304927</td>\n",
       "      <td>412.305714</td>\n",
       "      <td>93.498181</td>\n",
       "      <td>36.777299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015-07-02 18:00:00</td>\n",
       "      <td>50.500000</td>\n",
       "      <td>170.607338</td>\n",
       "      <td>447.558150</td>\n",
       "      <td>100.425559</td>\n",
       "      <td>40.237247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2015-10-02 00:00:00</td>\n",
       "      <td>75.250000</td>\n",
       "      <td>181.004493</td>\n",
       "      <td>482.176600</td>\n",
       "      <td>107.555231</td>\n",
       "      <td>43.784938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016-01-01 06:00:00</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>255.124717</td>\n",
       "      <td>695.020984</td>\n",
       "      <td>185.951998</td>\n",
       "      <td>76.791072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.866087</td>\n",
       "      <td>15.509114</td>\n",
       "      <td>52.673886</td>\n",
       "      <td>11.048679</td>\n",
       "      <td>5.370361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  datetime      machineID           volt         rotate  \\\n",
       "count               876100  876100.000000  876100.000000  876100.000000   \n",
       "mean   2015-07-02 18:00:00      50.500000     170.777736     446.605119   \n",
       "min    2015-01-01 06:00:00       1.000000      97.333604     138.432075   \n",
       "25%    2015-04-02 12:00:00      25.750000     160.304927     412.305714   \n",
       "50%    2015-07-02 18:00:00      50.500000     170.607338     447.558150   \n",
       "75%    2015-10-02 00:00:00      75.250000     181.004493     482.176600   \n",
       "max    2016-01-01 06:00:00     100.000000     255.124717     695.020984   \n",
       "std                    NaN      28.866087      15.509114      52.673886   \n",
       "\n",
       "            pressure      vibration  \n",
       "count  876100.000000  876100.000000  \n",
       "mean      100.858668      40.385007  \n",
       "min        51.237106      14.877054  \n",
       "25%        93.498181      36.777299  \n",
       "50%       100.425559      40.237247  \n",
       "75%       107.555231      43.784938  \n",
       "max       185.951998      76.791072  \n",
       "std        11.048679       5.370361  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "telemetry.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cb74132b-684f-409d-bf33-9d0556089f76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df= telemetry.loc[(telemetry['datetime']>pd.to_datetime('2015-01-01')) & (telemetry['datetime'] < pd.to_datetime('2015-02-01'))]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(telemetry['datetime'], telemetry['volt'], marker='o', linestyle='-', color='b')\n",
    "\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Voltage')\n",
    "plt.title('Voltage over time')\n",
    "\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c61fb439-c352-4562-b2f8-99706e52ff86",
   "metadata": {},
   "source": [
    "# Feature Engineering \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf183e86-f2e2-450a-9b4f-c41412f3b85f",
   "metadata": {},
   "source": [
    "# Identifying Lag Features from Telemetry data on a window of 24 hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d7bcec52-da17-4686-b44a-4082a041d252",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machineID</th>\n",
       "      <th>datetime</th>\n",
       "      <th>voltmean_3h</th>\n",
       "      <th>rotatemean_3h</th>\n",
       "      <th>pressuremean_3h</th>\n",
       "      <th>vibrationmean_3h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01 09:00:00</td>\n",
       "      <td>170.028993</td>\n",
       "      <td>449.533798</td>\n",
       "      <td>94.592122</td>\n",
       "      <td>40.893502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01 12:00:00</td>\n",
       "      <td>164.192565</td>\n",
       "      <td>403.949857</td>\n",
       "      <td>105.687417</td>\n",
       "      <td>34.255891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01 15:00:00</td>\n",
       "      <td>168.134445</td>\n",
       "      <td>435.781707</td>\n",
       "      <td>107.793709</td>\n",
       "      <td>41.239405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01 18:00:00</td>\n",
       "      <td>165.514453</td>\n",
       "      <td>430.472823</td>\n",
       "      <td>101.703289</td>\n",
       "      <td>40.373739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01 21:00:00</td>\n",
       "      <td>168.809347</td>\n",
       "      <td>437.111120</td>\n",
       "      <td>90.911060</td>\n",
       "      <td>41.738542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   machineID            datetime  voltmean_3h  rotatemean_3h  pressuremean_3h  \\\n",
       "0          1 2015-01-01 09:00:00   170.028993     449.533798        94.592122   \n",
       "1          1 2015-01-01 12:00:00   164.192565     403.949857       105.687417   \n",
       "2          1 2015-01-01 15:00:00   168.134445     435.781707       107.793709   \n",
       "3          1 2015-01-01 18:00:00   165.514453     430.472823       101.703289   \n",
       "4          1 2015-01-01 21:00:00   168.809347     437.111120        90.911060   \n",
       "\n",
       "   vibrationmean_3h  \n",
       "0         40.893502  \n",
       "1         34.255891  \n",
       "2         41.239405  \n",
       "3         40.373739  \n",
       "4         41.738542  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = []\n",
    "fields = ['volt', 'rotate', 'pressure', 'vibration']\n",
    "for col in fields:\n",
    "    temp.append(pd.pivot_table(telemetry,\n",
    "                               index='datetime',\n",
    "                               columns='machineID',\n",
    "                               values=col).resample('3h', closed='left', label='right').mean().unstack())\n",
    "telemetry_mean_3h = pd.concat(temp, axis=1)\n",
    "telemetry_mean_3h.columns = [i + 'mean_3h' for i in fields]\n",
    "telemetry_mean_3h.reset_index(inplace=True)\n",
    "\n",
    "\n",
    "temp = []\n",
    "\n",
    "for col in fields:\n",
    "    temp.append(pd.pivot_table(telemetry,\n",
    "                               index='datetime',\n",
    "                               columns='machineID',\n",
    "                               values=col).resample('3h', closed='left', label='right').std().unstack())\n",
    "telemetry_sd_3h = pd.concat(temp, axis=1)\n",
    "telemetry_sd_3h.columns = [i + 'sd_3h' for i in fields]\n",
    "telemetry_sd_3h.reset_index(inplace=True)\n",
    "\n",
    "telemetry_mean_3h.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "47e5975a-766f-4869-a7b2-0091b20a0db3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machineID</th>\n",
       "      <th>datetime</th>\n",
       "      <th>voltmean_24h</th>\n",
       "      <th>rotatemean_24h</th>\n",
       "      <th>pressuremean_24h</th>\n",
       "      <th>vibrationmean_24h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 06:00:00</td>\n",
       "      <td>171.536044</td>\n",
       "      <td>456.036706</td>\n",
       "      <td>101.652072</td>\n",
       "      <td>44.017022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 09:00:00</td>\n",
       "      <td>171.069056</td>\n",
       "      <td>457.285237</td>\n",
       "      <td>101.011726</td>\n",
       "      <td>44.148324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 12:00:00</td>\n",
       "      <td>170.859615</td>\n",
       "      <td>461.116153</td>\n",
       "      <td>101.172241</td>\n",
       "      <td>44.672216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 15:00:00</td>\n",
       "      <td>171.566669</td>\n",
       "      <td>457.893518</td>\n",
       "      <td>100.708151</td>\n",
       "      <td>44.993232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 18:00:00</td>\n",
       "      <td>171.536866</td>\n",
       "      <td>457.672110</td>\n",
       "      <td>99.826551</td>\n",
       "      <td>45.160570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 21:00:00</td>\n",
       "      <td>172.800672</td>\n",
       "      <td>454.497453</td>\n",
       "      <td>100.896227</td>\n",
       "      <td>45.690929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-05 00:00:00</td>\n",
       "      <td>171.963248</td>\n",
       "      <td>452.687991</td>\n",
       "      <td>101.312313</td>\n",
       "      <td>45.658369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-05 03:00:00</td>\n",
       "      <td>171.206225</td>\n",
       "      <td>448.104961</td>\n",
       "      <td>101.030466</td>\n",
       "      <td>46.457982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-05 06:00:00</td>\n",
       "      <td>171.999801</td>\n",
       "      <td>449.729553</td>\n",
       "      <td>101.472850</td>\n",
       "      <td>46.879346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-05 09:00:00</td>\n",
       "      <td>171.247302</td>\n",
       "      <td>451.930970</td>\n",
       "      <td>101.368307</td>\n",
       "      <td>47.831655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    machineID            datetime  voltmean_24h  rotatemean_24h  \\\n",
       "23          1 2015-01-04 06:00:00    171.536044      456.036706   \n",
       "24          1 2015-01-04 09:00:00    171.069056      457.285237   \n",
       "25          1 2015-01-04 12:00:00    170.859615      461.116153   \n",
       "26          1 2015-01-04 15:00:00    171.566669      457.893518   \n",
       "27          1 2015-01-04 18:00:00    171.536866      457.672110   \n",
       "28          1 2015-01-04 21:00:00    172.800672      454.497453   \n",
       "29          1 2015-01-05 00:00:00    171.963248      452.687991   \n",
       "30          1 2015-01-05 03:00:00    171.206225      448.104961   \n",
       "31          1 2015-01-05 06:00:00    171.999801      449.729553   \n",
       "32          1 2015-01-05 09:00:00    171.247302      451.930970   \n",
       "\n",
       "    pressuremean_24h  vibrationmean_24h  \n",
       "23        101.652072          44.017022  \n",
       "24        101.011726          44.148324  \n",
       "25        101.172241          44.672216  \n",
       "26        100.708151          44.993232  \n",
       "27         99.826551          45.160570  \n",
       "28        100.896227          45.690929  \n",
       "29        101.312313          45.658369  \n",
       "30        101.030466          46.457982  \n",
       "31        101.472850          46.879346  \n",
       "32        101.368307          47.831655  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = []\n",
    "fields = ['volt', 'rotate', 'pressure', 'vibration']\n",
    "for col in fields:\n",
    "    temp.append(pd.pivot_table(telemetry,\n",
    "                                               index='datetime',\n",
    "                                               columns='machineID',\n",
    "                                               values=col).resample('3h',closed='left',\n",
    "                                                                                label='right',\n",
    "                                                                                ).first().unstack().rolling(window=24, center=False).mean())\n",
    "telemetry_mean_24h = pd.concat(temp, axis=1)\n",
    "telemetry_mean_24h.columns = [i + 'mean_24h' for i in fields]\n",
    "telemetry_mean_24h.reset_index(inplace=True)\n",
    "telemetry_mean_24h = telemetry_mean_24h.loc[-telemetry_mean_24h['voltmean_24h'].isnull()]\n",
    "\n",
    "temp = []\n",
    "fields = ['volt', 'rotate', 'pressure', 'vibration']\n",
    "for col in fields:\n",
    "    temp.append(pd.pivot_table(telemetry,\n",
    "                                              index='datetime',\n",
    "                                              columns='machineID',\n",
    "                                              values=col).resample('3h',\n",
    "                                                                               closed='left',\n",
    "                                                                               label='right',\n",
    "                                                                               ).first().unstack().rolling(window=24, center=False).std())\n",
    "telemetry_sd_24h = pd.concat(temp, axis=1)\n",
    "telemetry_sd_24h.columns = [i + 'sd_24h' for i in fields]\n",
    "telemetry_sd_24h.reset_index(inplace=True)\n",
    "telemetry_sd_24h = telemetry_sd_24h.loc[-telemetry_sd_24h['voltsd_24h'].isnull()]\n",
    "\n",
    "telemetry_mean_24h.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "32e2e690-6cdb-4019-8881-987bec0297cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machineID</th>\n",
       "      <th>datetime</th>\n",
       "      <th>voltmean_3h</th>\n",
       "      <th>rotatemean_3h</th>\n",
       "      <th>pressuremean_3h</th>\n",
       "      <th>vibrationmean_3h</th>\n",
       "      <th>voltsd_3h</th>\n",
       "      <th>rotatesd_3h</th>\n",
       "      <th>pressuresd_3h</th>\n",
       "      <th>vibrationsd_3h</th>\n",
       "      <th>voltmean_24h</th>\n",
       "      <th>rotatemean_24h</th>\n",
       "      <th>pressuremean_24h</th>\n",
       "      <th>vibrationmean_24h</th>\n",
       "      <th>voltsd_24h</th>\n",
       "      <th>rotatesd_24h</th>\n",
       "      <th>pressuresd_24h</th>\n",
       "      <th>vibrationsd_24h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>291977.000000</td>\n",
       "      <td>291977</td>\n",
       "      <td>291977.000000</td>\n",
       "      <td>291977.000000</td>\n",
       "      <td>291977.000000</td>\n",
       "      <td>291977.000000</td>\n",
       "      <td>291977.000000</td>\n",
       "      <td>291977.000000</td>\n",
       "      <td>291977.000000</td>\n",
       "      <td>291977.000000</td>\n",
       "      <td>291977.000000</td>\n",
       "      <td>291977.000000</td>\n",
       "      <td>291977.000000</td>\n",
       "      <td>291977.000000</td>\n",
       "      <td>291977.000000</td>\n",
       "      <td>291977.000000</td>\n",
       "      <td>291977.000000</td>\n",
       "      <td>291977.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50.503899</td>\n",
       "      <td>2015-07-02 19:50:32.314188800</td>\n",
       "      <td>170.777344</td>\n",
       "      <td>446.605536</td>\n",
       "      <td>100.858665</td>\n",
       "      <td>40.384696</td>\n",
       "      <td>13.299177</td>\n",
       "      <td>44.456698</td>\n",
       "      <td>8.885976</td>\n",
       "      <td>4.441105</td>\n",
       "      <td>170.738342</td>\n",
       "      <td>446.622451</td>\n",
       "      <td>100.871860</td>\n",
       "      <td>40.382356</td>\n",
       "      <td>15.055923</td>\n",
       "      <td>50.680485</td>\n",
       "      <td>10.330242</td>\n",
       "      <td>5.103534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2015-01-01 09:00:00</td>\n",
       "      <td>125.532506</td>\n",
       "      <td>211.811184</td>\n",
       "      <td>72.118639</td>\n",
       "      <td>26.569635</td>\n",
       "      <td>0.025509</td>\n",
       "      <td>0.078991</td>\n",
       "      <td>0.027417</td>\n",
       "      <td>0.015278</td>\n",
       "      <td>156.713608</td>\n",
       "      <td>310.118604</td>\n",
       "      <td>91.162625</td>\n",
       "      <td>35.800869</td>\n",
       "      <td>6.178154</td>\n",
       "      <td>18.363177</td>\n",
       "      <td>4.275651</td>\n",
       "      <td>2.108104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>2015-04-02 15:00:00</td>\n",
       "      <td>164.449518</td>\n",
       "      <td>427.559989</td>\n",
       "      <td>96.238713</td>\n",
       "      <td>38.147732</td>\n",
       "      <td>8.027807</td>\n",
       "      <td>26.903727</td>\n",
       "      <td>5.370694</td>\n",
       "      <td>2.684653</td>\n",
       "      <td>168.100594</td>\n",
       "      <td>440.859663</td>\n",
       "      <td>98.730139</td>\n",
       "      <td>39.379127</td>\n",
       "      <td>13.409911</td>\n",
       "      <td>44.992826</td>\n",
       "      <td>8.984156</td>\n",
       "      <td>4.488631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>2015-07-02 21:00:00</td>\n",
       "      <td>170.434250</td>\n",
       "      <td>448.382424</td>\n",
       "      <td>100.234309</td>\n",
       "      <td>40.145805</td>\n",
       "      <td>12.495649</td>\n",
       "      <td>41.794255</td>\n",
       "      <td>8.346061</td>\n",
       "      <td>4.173937</td>\n",
       "      <td>170.285725</td>\n",
       "      <td>448.772454</td>\n",
       "      <td>100.195972</td>\n",
       "      <td>40.107229</td>\n",
       "      <td>14.942428</td>\n",
       "      <td>50.156537</td>\n",
       "      <td>10.009680</td>\n",
       "      <td>5.008386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>2015-10-02 03:00:00</td>\n",
       "      <td>176.612207</td>\n",
       "      <td>468.448273</td>\n",
       "      <td>104.406729</td>\n",
       "      <td>42.227512</td>\n",
       "      <td>17.688547</td>\n",
       "      <td>59.105539</td>\n",
       "      <td>11.790367</td>\n",
       "      <td>5.899868</td>\n",
       "      <td>172.609273</td>\n",
       "      <td>456.129192</td>\n",
       "      <td>101.780484</td>\n",
       "      <td>40.908734</td>\n",
       "      <td>16.556760</td>\n",
       "      <td>55.657662</td>\n",
       "      <td>11.199380</td>\n",
       "      <td>5.589524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>2016-01-01 06:00:00</td>\n",
       "      <td>241.420717</td>\n",
       "      <td>586.682904</td>\n",
       "      <td>162.309656</td>\n",
       "      <td>69.311324</td>\n",
       "      <td>58.444332</td>\n",
       "      <td>179.903039</td>\n",
       "      <td>35.659369</td>\n",
       "      <td>18.305595</td>\n",
       "      <td>206.333895</td>\n",
       "      <td>491.081522</td>\n",
       "      <td>138.291979</td>\n",
       "      <td>55.266429</td>\n",
       "      <td>30.806053</td>\n",
       "      <td>117.198342</td>\n",
       "      <td>30.665847</td>\n",
       "      <td>12.757609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>28.863913</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.501061</td>\n",
       "      <td>33.130486</td>\n",
       "      <td>7.414592</td>\n",
       "      <td>3.478391</td>\n",
       "      <td>6.966005</td>\n",
       "      <td>23.217195</td>\n",
       "      <td>4.656154</td>\n",
       "      <td>2.320281</td>\n",
       "      <td>4.178951</td>\n",
       "      <td>15.686284</td>\n",
       "      <td>3.983127</td>\n",
       "      <td>1.764322</td>\n",
       "      <td>2.383652</td>\n",
       "      <td>8.368899</td>\n",
       "      <td>2.129642</td>\n",
       "      <td>0.921640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           machineID                       datetime    voltmean_3h  \\\n",
       "count  291977.000000                         291977  291977.000000   \n",
       "mean       50.503899  2015-07-02 19:50:32.314188800     170.777344   \n",
       "min         1.000000            2015-01-01 09:00:00     125.532506   \n",
       "25%        26.000000            2015-04-02 15:00:00     164.449518   \n",
       "50%        51.000000            2015-07-02 21:00:00     170.434250   \n",
       "75%        76.000000            2015-10-02 03:00:00     176.612207   \n",
       "max       100.000000            2016-01-01 06:00:00     241.420717   \n",
       "std        28.863913                            NaN       9.501061   \n",
       "\n",
       "       rotatemean_3h  pressuremean_3h  vibrationmean_3h      voltsd_3h  \\\n",
       "count  291977.000000    291977.000000     291977.000000  291977.000000   \n",
       "mean      446.605536       100.858665         40.384696      13.299177   \n",
       "min       211.811184        72.118639         26.569635       0.025509   \n",
       "25%       427.559989        96.238713         38.147732       8.027807   \n",
       "50%       448.382424       100.234309         40.145805      12.495649   \n",
       "75%       468.448273       104.406729         42.227512      17.688547   \n",
       "max       586.682904       162.309656         69.311324      58.444332   \n",
       "std        33.130486         7.414592          3.478391       6.966005   \n",
       "\n",
       "         rotatesd_3h  pressuresd_3h  vibrationsd_3h   voltmean_24h  \\\n",
       "count  291977.000000  291977.000000   291977.000000  291977.000000   \n",
       "mean       44.456698       8.885976        4.441105     170.738342   \n",
       "min         0.078991       0.027417        0.015278     156.713608   \n",
       "25%        26.903727       5.370694        2.684653     168.100594   \n",
       "50%        41.794255       8.346061        4.173937     170.285725   \n",
       "75%        59.105539      11.790367        5.899868     172.609273   \n",
       "max       179.903039      35.659369       18.305595     206.333895   \n",
       "std        23.217195       4.656154        2.320281       4.178951   \n",
       "\n",
       "       rotatemean_24h  pressuremean_24h  vibrationmean_24h     voltsd_24h  \\\n",
       "count   291977.000000     291977.000000      291977.000000  291977.000000   \n",
       "mean       446.622451        100.871860          40.382356      15.055923   \n",
       "min        310.118604         91.162625          35.800869       6.178154   \n",
       "25%        440.859663         98.730139          39.379127      13.409911   \n",
       "50%        448.772454        100.195972          40.107229      14.942428   \n",
       "75%        456.129192        101.780484          40.908734      16.556760   \n",
       "max        491.081522        138.291979          55.266429      30.806053   \n",
       "std         15.686284          3.983127           1.764322       2.383652   \n",
       "\n",
       "        rotatesd_24h  pressuresd_24h  vibrationsd_24h  \n",
       "count  291977.000000   291977.000000    291977.000000  \n",
       "mean       50.680485       10.330242         5.103534  \n",
       "min        18.363177        4.275651         2.108104  \n",
       "25%        44.992826        8.984156         4.488631  \n",
       "50%        50.156537       10.009680         5.008386  \n",
       "75%        55.657662       11.199380         5.589524  \n",
       "max       117.198342       30.665847        12.757609  \n",
       "std         8.368899        2.129642         0.921640  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "telemetry_feat= pd.concat([telemetry_mean_3h,\n",
    "                           telemetry_sd_3h.iloc[:, 2:6],\n",
    "                           telemetry_mean_24h.iloc[:, 2:6],\n",
    "                           telemetry_sd_24h.iloc[:, 2:6]], axis=1).dropna()\n",
    "\n",
    "telemetry_feat.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "30b80862-b144-415e-b942-d4fd4f3a148e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machineID</th>\n",
       "      <th>datetime</th>\n",
       "      <th>voltmean_3h</th>\n",
       "      <th>rotatemean_3h</th>\n",
       "      <th>pressuremean_3h</th>\n",
       "      <th>vibrationmean_3h</th>\n",
       "      <th>voltsd_3h</th>\n",
       "      <th>rotatesd_3h</th>\n",
       "      <th>pressuresd_3h</th>\n",
       "      <th>vibrationsd_3h</th>\n",
       "      <th>voltmean_24h</th>\n",
       "      <th>rotatemean_24h</th>\n",
       "      <th>pressuremean_24h</th>\n",
       "      <th>vibrationmean_24h</th>\n",
       "      <th>voltsd_24h</th>\n",
       "      <th>rotatesd_24h</th>\n",
       "      <th>pressuresd_24h</th>\n",
       "      <th>vibrationsd_24h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 06:00:00</td>\n",
       "      <td>186.092896</td>\n",
       "      <td>451.641253</td>\n",
       "      <td>107.989359</td>\n",
       "      <td>55.308074</td>\n",
       "      <td>13.489090</td>\n",
       "      <td>62.185045</td>\n",
       "      <td>5.118176</td>\n",
       "      <td>4.904365</td>\n",
       "      <td>171.536044</td>\n",
       "      <td>456.036706</td>\n",
       "      <td>101.652072</td>\n",
       "      <td>44.017022</td>\n",
       "      <td>13.716658</td>\n",
       "      <td>41.767447</td>\n",
       "      <td>11.754808</td>\n",
       "      <td>6.878286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 09:00:00</td>\n",
       "      <td>166.281848</td>\n",
       "      <td>453.787824</td>\n",
       "      <td>106.187582</td>\n",
       "      <td>51.990080</td>\n",
       "      <td>24.276228</td>\n",
       "      <td>23.621315</td>\n",
       "      <td>11.176731</td>\n",
       "      <td>3.394073</td>\n",
       "      <td>171.069056</td>\n",
       "      <td>457.285237</td>\n",
       "      <td>101.011726</td>\n",
       "      <td>44.148324</td>\n",
       "      <td>13.741098</td>\n",
       "      <td>41.038218</td>\n",
       "      <td>11.521602</td>\n",
       "      <td>6.929500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 12:00:00</td>\n",
       "      <td>175.412103</td>\n",
       "      <td>445.450581</td>\n",
       "      <td>100.887363</td>\n",
       "      <td>54.251534</td>\n",
       "      <td>34.918687</td>\n",
       "      <td>11.001625</td>\n",
       "      <td>10.580336</td>\n",
       "      <td>2.921501</td>\n",
       "      <td>170.859615</td>\n",
       "      <td>461.116153</td>\n",
       "      <td>101.172241</td>\n",
       "      <td>44.672216</td>\n",
       "      <td>13.915181</td>\n",
       "      <td>33.879652</td>\n",
       "      <td>11.667258</td>\n",
       "      <td>7.162152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 15:00:00</td>\n",
       "      <td>157.347716</td>\n",
       "      <td>451.882075</td>\n",
       "      <td>101.289380</td>\n",
       "      <td>48.602686</td>\n",
       "      <td>24.617739</td>\n",
       "      <td>28.950883</td>\n",
       "      <td>9.966729</td>\n",
       "      <td>2.356486</td>\n",
       "      <td>171.566669</td>\n",
       "      <td>457.893518</td>\n",
       "      <td>100.708151</td>\n",
       "      <td>44.993232</td>\n",
       "      <td>13.583969</td>\n",
       "      <td>33.790685</td>\n",
       "      <td>11.447426</td>\n",
       "      <td>7.244618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 18:00:00</td>\n",
       "      <td>176.450550</td>\n",
       "      <td>446.033068</td>\n",
       "      <td>84.521555</td>\n",
       "      <td>47.638836</td>\n",
       "      <td>8.071400</td>\n",
       "      <td>76.511343</td>\n",
       "      <td>2.636879</td>\n",
       "      <td>4.108621</td>\n",
       "      <td>171.536866</td>\n",
       "      <td>457.672110</td>\n",
       "      <td>99.826551</td>\n",
       "      <td>45.160570</td>\n",
       "      <td>13.590129</td>\n",
       "      <td>33.787875</td>\n",
       "      <td>11.919716</td>\n",
       "      <td>7.167877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    machineID            datetime  voltmean_3h  rotatemean_3h  \\\n",
       "23          1 2015-01-04 06:00:00   186.092896     451.641253   \n",
       "24          1 2015-01-04 09:00:00   166.281848     453.787824   \n",
       "25          1 2015-01-04 12:00:00   175.412103     445.450581   \n",
       "26          1 2015-01-04 15:00:00   157.347716     451.882075   \n",
       "27          1 2015-01-04 18:00:00   176.450550     446.033068   \n",
       "\n",
       "    pressuremean_3h  vibrationmean_3h  voltsd_3h  rotatesd_3h  pressuresd_3h  \\\n",
       "23       107.989359         55.308074  13.489090    62.185045       5.118176   \n",
       "24       106.187582         51.990080  24.276228    23.621315      11.176731   \n",
       "25       100.887363         54.251534  34.918687    11.001625      10.580336   \n",
       "26       101.289380         48.602686  24.617739    28.950883       9.966729   \n",
       "27        84.521555         47.638836   8.071400    76.511343       2.636879   \n",
       "\n",
       "    vibrationsd_3h  voltmean_24h  rotatemean_24h  pressuremean_24h  \\\n",
       "23        4.904365    171.536044      456.036706        101.652072   \n",
       "24        3.394073    171.069056      457.285237        101.011726   \n",
       "25        2.921501    170.859615      461.116153        101.172241   \n",
       "26        2.356486    171.566669      457.893518        100.708151   \n",
       "27        4.108621    171.536866      457.672110         99.826551   \n",
       "\n",
       "    vibrationmean_24h  voltsd_24h  rotatesd_24h  pressuresd_24h  \\\n",
       "23          44.017022   13.716658     41.767447       11.754808   \n",
       "24          44.148324   13.741098     41.038218       11.521602   \n",
       "25          44.672216   13.915181     33.879652       11.667258   \n",
       "26          44.993232   13.583969     33.790685       11.447426   \n",
       "27          45.160570   13.590129     33.787875       11.919716   \n",
       "\n",
       "    vibrationsd_24h  \n",
       "23         6.878286  \n",
       "24         6.929500  \n",
       "25         7.162152  \n",
       "26         7.244618  \n",
       "27         7.167877  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "telemetry_feat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5da05b1-74fd-4d50-a09c-7fe590aec15c",
   "metadata": {},
   "source": [
    "Finding Lag Features from Errors data on windows of 24 hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "06f4447d-afdc-411f-81da-51dc178ffef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "error_count= pd.get_dummies(errors.set_index('datetime')).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3fd428a5-1743-4794-af27-25aa0173b2e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "error_count[['errorID_error1', 'errorID_error2', 'errorID_error3', 'errorID_error4', 'errorID_error5']]= error_count[['errorID_error1', 'errorID_error2', 'errorID_error3', 'errorID_error4', 'errorID_error5']].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4186111d-fc00-41b1-8773-938be8dc861b",
   "metadata": {},
   "outputs": [],
   "source": [
    "error_count.columns = ['datetime', 'machineID',\n",
    "                       'error1', 'error2', 'error3', 'error4', 'error5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ba72be53-853b-42e0-b357-c774cc09f44b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2015-01-03 20:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>2015-01-04 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-10 15:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>2015-01-22 10:00:00</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "             datetime  machineID  error1  error2  error3  error4  error5\n",
       "0 2015-01-03 07:00:00          1       1       0       0       0       0\n",
       "1 2015-01-03 20:00:00          1       0       0       1       0       0\n",
       "2 2015-01-04 06:00:00          1       0       0       0       0       1\n",
       "3 2015-01-10 15:00:00          1       0       0       0       1       0\n",
       "4 2015-01-22 10:00:00          1       0       0       0       1       0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e518beaf-c500-44fd-ab0f-e096f8d6cd7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "error_count= telemetry[['datetime', 'machineID']].merge(error_count, on=['machineID', 'datetime'], how='left').fillna(0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fbf6e791-324d-4cca-9b4e-7abf6975a360",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2015-07-02 18:00:05.931517952</td>\n",
       "      <td>50.499881</td>\n",
       "      <td>0.001152</td>\n",
       "      <td>0.001127</td>\n",
       "      <td>0.000956</td>\n",
       "      <td>0.00083</td>\n",
       "      <td>0.000406</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2015-01-01 06:00:00</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>25%</th>\n",
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       "      <td>0.00000</td>\n",
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       "      <th>50%</th>\n",
       "      <td>2015-07-02 18:00:00</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2015-10-02 00:00:00</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016-01-01 06:00:00</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.866464</td>\n",
       "      <td>0.033928</td>\n",
       "      <td>0.033557</td>\n",
       "      <td>0.030907</td>\n",
       "      <td>0.02879</td>\n",
       "      <td>0.020150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            datetime      machineID         error1  \\\n",
       "count                         876403  876403.000000  876403.000000   \n",
       "mean   2015-07-02 18:00:05.931517952      50.499881       0.001152   \n",
       "min              2015-01-01 06:00:00       1.000000       0.000000   \n",
       "25%              2015-04-02 12:00:00      25.000000       0.000000   \n",
       "50%              2015-07-02 18:00:00      50.000000       0.000000   \n",
       "75%              2015-10-02 00:00:00      76.000000       0.000000   \n",
       "max              2016-01-01 06:00:00     100.000000       1.000000   \n",
       "std                              NaN      28.866464       0.033928   \n",
       "\n",
       "              error2         error3        error4         error5  \n",
       "count  876403.000000  876403.000000  876403.00000  876403.000000  \n",
       "mean        0.001127       0.000956       0.00083       0.000406  \n",
       "min         0.000000       0.000000       0.00000       0.000000  \n",
       "25%         0.000000       0.000000       0.00000       0.000000  \n",
       "50%         0.000000       0.000000       0.00000       0.000000  \n",
       "75%         0.000000       0.000000       0.00000       0.000000  \n",
       "max         1.000000       1.000000       1.00000       1.000000  \n",
       "std         0.033557       0.030907       0.02879       0.020150  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9d2017b0-46ed-470d-bb5e-c5271d4d368a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machineID</th>\n",
       "      <th>datetime</th>\n",
       "      <th>error1count</th>\n",
       "      <th>error2count</th>\n",
       "      <th>error3count</th>\n",
       "      <th>error4count</th>\n",
       "      <th>error5count</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>292077.000000</td>\n",
       "      <td>292077</td>\n",
       "      <td>292077.000000</td>\n",
       "      <td>292077.000000</td>\n",
       "      <td>292077.000000</td>\n",
       "      <td>292077.000000</td>\n",
       "      <td>292077.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50.503898</td>\n",
       "      <td>2015-07-02 21:20:32.317505280</td>\n",
       "      <td>0.037483</td>\n",
       "      <td>0.029266</td>\n",
       "      <td>0.025117</td>\n",
       "      <td>0.025541</td>\n",
       "      <td>0.018666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2015-01-01 09:00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>2015-04-02 15:00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>2015-07-02 21:00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>2015-10-02 03:00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>2016-01-01 09:00:00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>28.863914</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.192195</td>\n",
       "      <td>0.154875</td>\n",
       "      <td>0.140627</td>\n",
       "      <td>0.158549</td>\n",
       "      <td>0.134294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           machineID                       datetime    error1count  \\\n",
       "count  292077.000000                         292077  292077.000000   \n",
       "mean       50.503898  2015-07-02 21:20:32.317505280       0.037483   \n",
       "min         1.000000            2015-01-01 09:00:00       0.000000   \n",
       "25%        26.000000            2015-04-02 15:00:00       0.000000   \n",
       "50%        51.000000            2015-07-02 21:00:00       0.000000   \n",
       "75%        76.000000            2015-10-02 03:00:00       0.000000   \n",
       "max       100.000000            2016-01-01 09:00:00       2.000000   \n",
       "std        28.863914                            NaN       0.192195   \n",
       "\n",
       "         error2count    error3count    error4count    error5count  \n",
       "count  292077.000000  292077.000000  292077.000000  292077.000000  \n",
       "mean        0.029266       0.025117       0.025541       0.018666  \n",
       "min         0.000000       0.000000       0.000000       0.000000  \n",
       "25%         0.000000       0.000000       0.000000       0.000000  \n",
       "50%         0.000000       0.000000       0.000000       0.000000  \n",
       "75%         0.000000       0.000000       0.000000       0.000000  \n",
       "max         2.000000       1.500000       3.000000       2.000000  \n",
       "std         0.154875       0.140627       0.158549       0.134294  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = []\n",
    "fields = ['error%d' % i for i in range(1, 6)]\n",
    "for col in fields:\n",
    "    temp.append(pd.pivot_table(error_count,\n",
    "                                              index='datetime',\n",
    "                                              columns='machineID',\n",
    "                                              values=col).resample('3h',\n",
    "                                                                               closed='left',\n",
    "                                                                               label='right',\n",
    "                                                                               ).first().unstack().rolling(window=24, center=False).sum())\n",
    "error_count = pd.concat(temp, axis=1)\n",
    "error_count.columns = [i + 'count' for i in fields]\n",
    "error_count.reset_index(inplace=True)\n",
    "error_count = error_count.dropna()\n",
    "error_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "484996e7-bb19-487b-9236-f27b620e3054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machineID</th>\n",
       "      <th>datetime</th>\n",
       "      <th>error1count</th>\n",
       "      <th>error2count</th>\n",
       "      <th>error3count</th>\n",
       "      <th>error4count</th>\n",
       "      <th>error5count</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 06:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 09:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 12:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 15:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 18:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    machineID            datetime  error1count  error2count  error3count  \\\n",
       "23          1 2015-01-04 06:00:00          0.0          0.0          0.0   \n",
       "24          1 2015-01-04 09:00:00          0.0          0.0          0.0   \n",
       "25          1 2015-01-04 12:00:00          0.0          0.0          0.0   \n",
       "26          1 2015-01-04 15:00:00          0.0          0.0          0.0   \n",
       "27          1 2015-01-04 18:00:00          0.0          0.0          0.0   \n",
       "\n",
       "    error4count  error5count  \n",
       "23          0.0          0.0  \n",
       "24          0.0          1.0  \n",
       "25          0.0          1.0  \n",
       "26          0.0          1.0  \n",
       "27          0.0          1.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_count.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48d63832-3ca0-40d4-b930-886a9c2c61f1",
   "metadata": {},
   "source": [
    "# Identifying Days Since last replacement using maintainence on a window of 24hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1a3bca83-2786-44fb-b4a2-88134bed2966",
   "metadata": {},
   "outputs": [],
   "source": [
    "comp_rep= pd.get_dummies(maint.set_index('datetime')).reset_index()\n",
    "comp_rep.columns= ['datetime', 'machineID', 'comp1', 'comp2', 'comp3', 'comp4']\n",
    "\n",
    "comp_rep= telemetry[['datetime', 'machineID']].merge(comp_rep, on=['datetime', 'machineID'], how='outer').fillna(0).sort_values(by=['machineID', 'datetime'])\n",
    "                                                                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "36882e3a-5cbb-4995-85b3-465b185601bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shash\\AppData\\Local\\Temp\\ipykernel_16436\\2138639516.py:7: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  comp_rep[comp]= comp_rep[comp].fillna(method='ffill')\n",
      "C:\\Users\\shash\\AppData\\Local\\Temp\\ipykernel_16436\\2138639516.py:7: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  comp_rep[comp]= comp_rep[comp].fillna(method='ffill')\n",
      "C:\\Users\\shash\\AppData\\Local\\Temp\\ipykernel_16436\\2138639516.py:7: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  comp_rep[comp]= comp_rep[comp].fillna(method='ffill')\n",
      "C:\\Users\\shash\\AppData\\Local\\Temp\\ipykernel_16436\\2138639516.py:7: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  comp_rep[comp]= comp_rep[comp].fillna(method='ffill')\n",
      "C:\\Users\\shash\\AppData\\Local\\Temp\\ipykernel_16436\\2138639516.py:7: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  comp_rep[comp]= comp_rep[comp].fillna(method='ffill')\n",
      "C:\\Users\\shash\\AppData\\Local\\Temp\\ipykernel_16436\\2138639516.py:7: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  comp_rep[comp]= comp_rep[comp].fillna(method='ffill')\n",
      "C:\\Users\\shash\\AppData\\Local\\Temp\\ipykernel_16436\\2138639516.py:7: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  comp_rep[comp]= comp_rep[comp].fillna(method='ffill')\n",
      "C:\\Users\\shash\\AppData\\Local\\Temp\\ipykernel_16436\\2138639516.py:7: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  comp_rep[comp]= comp_rep[comp].fillna(method='ffill')\n"
     ]
    }
   ],
   "source": [
    "components=['comp1', 'comp2', 'comp3', 'comp4']\n",
    "for comp in components:\n",
    "    comp_rep.loc[comp_rep[comp] < 1, comp]= None\n",
    "    comp_rep.loc[-comp_rep[comp].isnull(),\n",
    "                comp]= comp_rep.loc[-comp_rep[comp].isnull(),\n",
    "                'datetime']\n",
    "    comp_rep[comp]= comp_rep[comp].fillna(method='ffill')\n",
    "\n",
    "\n",
    "comp_rep= comp_rep.loc[comp_rep['datetime'] > pd.to_datetime('2015-01-01')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "30924e2e-70dd-4f4f-a299-db9546fc982d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>machineID</th>\n",
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       "      <th>400</th>\n",
       "      <td>2015-01-01 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-12-13 06:00:00</td>\n",
       "      <td>2014-06-01 06:00:00</td>\n",
       "      <td>2014-07-31 06:00:00</td>\n",
       "      <td>2014-07-16 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>2015-01-01 07:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-12-13 06:00:00</td>\n",
       "      <td>2014-06-01 06:00:00</td>\n",
       "      <td>2014-07-31 06:00:00</td>\n",
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       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>2015-01-01 08:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-12-13 06:00:00</td>\n",
       "      <td>2014-06-01 06:00:00</td>\n",
       "      <td>2014-07-31 06:00:00</td>\n",
       "      <td>2014-07-16 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>2015-01-01 09:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-12-13 06:00:00</td>\n",
       "      <td>2014-06-01 06:00:00</td>\n",
       "      <td>2014-07-31 06:00:00</td>\n",
       "      <td>2014-07-16 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>2015-01-01 10:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-12-13 06:00:00</td>\n",
       "      <td>2014-06-01 06:00:00</td>\n",
       "      <td>2014-07-31 06:00:00</td>\n",
       "      <td>2014-07-16 06:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               datetime  machineID               comp1               comp2  \\\n",
       "400 2015-01-01 06:00:00          1 2014-12-13 06:00:00 2014-06-01 06:00:00   \n",
       "502 2015-01-01 07:00:00          1 2014-12-13 06:00:00 2014-06-01 06:00:00   \n",
       "602 2015-01-01 08:00:00          1 2014-12-13 06:00:00 2014-06-01 06:00:00   \n",
       "702 2015-01-01 09:00:00          1 2014-12-13 06:00:00 2014-06-01 06:00:00   \n",
       "802 2015-01-01 10:00:00          1 2014-12-13 06:00:00 2014-06-01 06:00:00   \n",
       "\n",
       "                  comp3               comp4  \n",
       "400 2014-07-31 06:00:00 2014-07-16 06:00:00  \n",
       "502 2014-07-31 06:00:00 2014-07-16 06:00:00  \n",
       "602 2014-07-31 06:00:00 2014-07-16 06:00:00  \n",
       "702 2014-07-31 06:00:00 2014-07-16 06:00:00  \n",
       "802 2014-07-31 06:00:00 2014-07-16 06:00:00  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp_rep.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8fc9c24e-162b-499f-bda9-98b2caed59ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>machineID</th>\n",
       "      <th>comp1</th>\n",
       "      <th>comp2</th>\n",
       "      <th>comp3</th>\n",
       "      <th>comp4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>876823</td>\n",
       "      <td>876823.000000</td>\n",
       "      <td>876823.000000</td>\n",
       "      <td>876823.000000</td>\n",
       "      <td>876823.000000</td>\n",
       "      <td>876823.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2015-07-02 18:04:36.939815424</td>\n",
       "      <td>50.499106</td>\n",
       "      <td>53.517611</td>\n",
       "      <td>51.537350</td>\n",
       "      <td>52.722043</td>\n",
       "      <td>53.826833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2015-01-01 06:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2015-04-02 12:00:00</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>13.291667</td>\n",
       "      <td>12.125000</td>\n",
       "      <td>13.125000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015-07-02 18:00:00</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>32.791667</td>\n",
       "      <td>29.666667</td>\n",
       "      <td>32.250000</td>\n",
       "      <td>32.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2015-10-02 00:00:00</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>68.708333</td>\n",
       "      <td>66.541667</td>\n",
       "      <td>67.333333</td>\n",
       "      <td>70.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016-01-01 06:00:00</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>491.958333</td>\n",
       "      <td>348.958333</td>\n",
       "      <td>370.958333</td>\n",
       "      <td>394.958333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.866046</td>\n",
       "      <td>62.490219</td>\n",
       "      <td>59.271457</td>\n",
       "      <td>58.875826</td>\n",
       "      <td>59.704062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            datetime      machineID          comp1  \\\n",
       "count                         876823  876823.000000  876823.000000   \n",
       "mean   2015-07-02 18:04:36.939815424      50.499106      53.517611   \n",
       "min              2015-01-01 06:00:00       1.000000       0.000000   \n",
       "25%              2015-04-02 12:00:00      25.000000      13.291667   \n",
       "50%              2015-07-02 18:00:00      50.000000      32.791667   \n",
       "75%              2015-10-02 00:00:00      75.000000      68.708333   \n",
       "max              2016-01-01 06:00:00     100.000000     491.958333   \n",
       "std                              NaN      28.866046      62.490219   \n",
       "\n",
       "               comp2          comp3          comp4  \n",
       "count  876823.000000  876823.000000  876823.000000  \n",
       "mean       51.537350      52.722043      53.826833  \n",
       "min         0.000000       0.000000       0.000000  \n",
       "25%        12.125000      13.125000      13.000000  \n",
       "50%        29.666667      32.250000      32.500000  \n",
       "75%        66.541667      67.333333      70.458333  \n",
       "max       348.958333     370.958333     394.958333  \n",
       "std        59.271457      58.875826      59.704062  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for comp in components:\n",
    "    comp_rep[comp]= (comp_rep[\"datetime\"] - pd.to_datetime(comp_rep[comp])) / np.timedelta64(1, \"D\")\n",
    "comp_rep.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b6182e60-4646-4f0c-a779-4c168eda59b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>machineID</th>\n",
       "      <th>comp1</th>\n",
       "      <th>comp2</th>\n",
       "      <th>comp3</th>\n",
       "      <th>comp4</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>400</th>\n",
       "      <td>2015-01-01 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>169.000000</td>\n",
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       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>2015-01-01 07:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>19.041667</td>\n",
       "      <td>214.041667</td>\n",
       "      <td>154.041667</td>\n",
       "      <td>169.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>2015-01-01 08:00:00</td>\n",
       "      <td>1</td>\n",
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       "      <td>214.083333</td>\n",
       "      <td>154.083333</td>\n",
       "      <td>169.083333</td>\n",
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       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>2015-01-01 09:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>19.125000</td>\n",
       "      <td>214.125000</td>\n",
       "      <td>154.125000</td>\n",
       "      <td>169.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>2015-01-01 10:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>19.166667</td>\n",
       "      <td>214.166667</td>\n",
       "      <td>154.166667</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               datetime  machineID      comp1       comp2       comp3  \\\n",
       "400 2015-01-01 06:00:00          1  19.000000  214.000000  154.000000   \n",
       "502 2015-01-01 07:00:00          1  19.041667  214.041667  154.041667   \n",
       "602 2015-01-01 08:00:00          1  19.083333  214.083333  154.083333   \n",
       "702 2015-01-01 09:00:00          1  19.125000  214.125000  154.125000   \n",
       "802 2015-01-01 10:00:00          1  19.166667  214.166667  154.166667   \n",
       "\n",
       "          comp4  \n",
       "400  169.000000  \n",
       "502  169.041667  \n",
       "602  169.083333  \n",
       "702  169.125000  \n",
       "802  169.166667  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp_rep.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4097ff0-421f-4b4e-8419-f89eebc802b4",
   "metadata": {},
   "source": [
    "# Machine Features: Descriptive stats about Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3cc0e5e3-bb22-42bf-994d-81d39ad1172b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machineID</th>\n",
       "      <th>datetime</th>\n",
       "      <th>voltmean_3h</th>\n",
       "      <th>rotatemean_3h</th>\n",
       "      <th>pressuremean_3h</th>\n",
       "      <th>vibrationmean_3h</th>\n",
       "      <th>voltsd_3h</th>\n",
       "      <th>rotatesd_3h</th>\n",
       "      <th>pressuresd_3h</th>\n",
       "      <th>vibrationsd_3h</th>\n",
       "      <th>...</th>\n",
       "      <th>error2count</th>\n",
       "      <th>error3count</th>\n",
       "      <th>error4count</th>\n",
       "      <th>error5count</th>\n",
       "      <th>comp1</th>\n",
       "      <th>comp2</th>\n",
       "      <th>comp3</th>\n",
       "      <th>comp4</th>\n",
       "      <th>model</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 06:00:00</td>\n",
       "      <td>186.092896</td>\n",
       "      <td>451.641253</td>\n",
       "      <td>107.989359</td>\n",
       "      <td>55.308074</td>\n",
       "      <td>13.489090</td>\n",
       "      <td>62.185045</td>\n",
       "      <td>5.118176</td>\n",
       "      <td>4.904365</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.000</td>\n",
       "      <td>217.000</td>\n",
       "      <td>157.000</td>\n",
       "      <td>172.000</td>\n",
       "      <td>model3</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 09:00:00</td>\n",
       "      <td>166.281848</td>\n",
       "      <td>453.787824</td>\n",
       "      <td>106.187582</td>\n",
       "      <td>51.990080</td>\n",
       "      <td>24.276228</td>\n",
       "      <td>23.621315</td>\n",
       "      <td>11.176731</td>\n",
       "      <td>3.394073</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.125</td>\n",
       "      <td>217.125</td>\n",
       "      <td>157.125</td>\n",
       "      <td>172.125</td>\n",
       "      <td>model3</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 12:00:00</td>\n",
       "      <td>175.412103</td>\n",
       "      <td>445.450581</td>\n",
       "      <td>100.887363</td>\n",
       "      <td>54.251534</td>\n",
       "      <td>34.918687</td>\n",
       "      <td>11.001625</td>\n",
       "      <td>10.580336</td>\n",
       "      <td>2.921501</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.250</td>\n",
       "      <td>217.250</td>\n",
       "      <td>157.250</td>\n",
       "      <td>172.250</td>\n",
       "      <td>model3</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 15:00:00</td>\n",
       "      <td>157.347716</td>\n",
       "      <td>451.882075</td>\n",
       "      <td>101.289380</td>\n",
       "      <td>48.602686</td>\n",
       "      <td>24.617739</td>\n",
       "      <td>28.950883</td>\n",
       "      <td>9.966729</td>\n",
       "      <td>2.356486</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.375</td>\n",
       "      <td>217.375</td>\n",
       "      <td>157.375</td>\n",
       "      <td>172.375</td>\n",
       "      <td>model3</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 18:00:00</td>\n",
       "      <td>176.450550</td>\n",
       "      <td>446.033068</td>\n",
       "      <td>84.521555</td>\n",
       "      <td>47.638836</td>\n",
       "      <td>8.071400</td>\n",
       "      <td>76.511343</td>\n",
       "      <td>2.636879</td>\n",
       "      <td>4.108621</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.500</td>\n",
       "      <td>217.500</td>\n",
       "      <td>157.500</td>\n",
       "      <td>172.500</td>\n",
       "      <td>model3</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   machineID            datetime  voltmean_3h  rotatemean_3h  pressuremean_3h  \\\n",
       "0          1 2015-01-04 06:00:00   186.092896     451.641253       107.989359   \n",
       "1          1 2015-01-04 09:00:00   166.281848     453.787824       106.187582   \n",
       "2          1 2015-01-04 12:00:00   175.412103     445.450581       100.887363   \n",
       "3          1 2015-01-04 15:00:00   157.347716     451.882075       101.289380   \n",
       "4          1 2015-01-04 18:00:00   176.450550     446.033068        84.521555   \n",
       "\n",
       "   vibrationmean_3h  voltsd_3h  rotatesd_3h  pressuresd_3h  vibrationsd_3h  \\\n",
       "0         55.308074  13.489090    62.185045       5.118176        4.904365   \n",
       "1         51.990080  24.276228    23.621315      11.176731        3.394073   \n",
       "2         54.251534  34.918687    11.001625      10.580336        2.921501   \n",
       "3         48.602686  24.617739    28.950883       9.966729        2.356486   \n",
       "4         47.638836   8.071400    76.511343       2.636879        4.108621   \n",
       "\n",
       "   ...  error2count  error3count  error4count  error5count   comp1    comp2  \\\n",
       "0  ...          0.0          0.0          0.0          0.0  22.000  217.000   \n",
       "1  ...          0.0          0.0          0.0          1.0  22.125  217.125   \n",
       "2  ...          0.0          0.0          0.0          1.0  22.250  217.250   \n",
       "3  ...          0.0          0.0          0.0          1.0  22.375  217.375   \n",
       "4  ...          0.0          0.0          0.0          1.0  22.500  217.500   \n",
       "\n",
       "     comp3    comp4   model  age  \n",
       "0  157.000  172.000  model3   18  \n",
       "1  157.125  172.125  model3   18  \n",
       "2  157.250  172.250  model3   18  \n",
       "3  157.375  172.375  model3   18  \n",
       "4  157.500  172.500  model3   18  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_feat= telemetry_feat.merge(\n",
    "    error_count, on=['datetime', 'machineID'], how='left')\n",
    "\n",
    "final_feat= final_feat.merge(\n",
    "    comp_rep, on=['datetime', 'machineID'], how='left')\n",
    "\n",
    "final_feat= final_feat.merge(machines, on=['machineID'], how='left')\n",
    "\n",
    "final_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "45fa1e91-bd3f-4da5-b9bb-09fbce1012ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machineID</th>\n",
       "      <th>datetime</th>\n",
       "      <th>voltmean_3h</th>\n",
       "      <th>rotatemean_3h</th>\n",
       "      <th>pressuremean_3h</th>\n",
       "      <th>vibrationmean_3h</th>\n",
       "      <th>voltsd_3h</th>\n",
       "      <th>rotatesd_3h</th>\n",
       "      <th>pressuresd_3h</th>\n",
       "      <th>vibrationsd_3h</th>\n",
       "      <th>...</th>\n",
       "      <th>error1count</th>\n",
       "      <th>error2count</th>\n",
       "      <th>error3count</th>\n",
       "      <th>error4count</th>\n",
       "      <th>error5count</th>\n",
       "      <th>comp1</th>\n",
       "      <th>comp2</th>\n",
       "      <th>comp3</th>\n",
       "      <th>comp4</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>292698.000000</td>\n",
       "      <td>292698</td>\n",
       "      <td>292698.000000</td>\n",
       "      <td>292698.000000</td>\n",
       "      <td>292698.000000</td>\n",
       "      <td>292698.000000</td>\n",
       "      <td>292698.000000</td>\n",
       "      <td>292698.000000</td>\n",
       "      <td>292698.000000</td>\n",
       "      <td>292698.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>292698.000000</td>\n",
       "      <td>292698.000000</td>\n",
       "      <td>292698.000000</td>\n",
       "      <td>292698.000000</td>\n",
       "      <td>292698.000000</td>\n",
       "      <td>292698.000000</td>\n",
       "      <td>292698.000000</td>\n",
       "      <td>292698.000000</td>\n",
       "      <td>292698.000000</td>\n",
       "      <td>292698.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50.501268</td>\n",
       "      <td>2015-07-02 20:05:53.336203520</td>\n",
       "      <td>170.784219</td>\n",
       "      <td>446.575340</td>\n",
       "      <td>100.864117</td>\n",
       "      <td>40.386506</td>\n",
       "      <td>13.300159</td>\n",
       "      <td>44.453806</td>\n",
       "      <td>8.886790</td>\n",
       "      <td>4.441220</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037758</td>\n",
       "      <td>0.029442</td>\n",
       "      <td>0.025289</td>\n",
       "      <td>0.025712</td>\n",
       "      <td>0.018841</td>\n",
       "      <td>53.444887</td>\n",
       "      <td>51.452085</td>\n",
       "      <td>52.640758</td>\n",
       "      <td>53.737431</td>\n",
       "      <td>11.329497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2015-01-01 09:00:00</td>\n",
       "      <td>125.532506</td>\n",
       "      <td>211.811184</td>\n",
       "      <td>72.118639</td>\n",
       "      <td>26.569635</td>\n",
       "      <td>0.025509</td>\n",
       "      <td>0.078991</td>\n",
       "      <td>0.027417</td>\n",
       "      <td>0.015278</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>2015-04-02 15:00:00</td>\n",
       "      <td>164.452628</td>\n",
       "      <td>427.534759</td>\n",
       "      <td>96.239740</td>\n",
       "      <td>38.147926</td>\n",
       "      <td>8.027559</td>\n",
       "      <td>26.903316</td>\n",
       "      <td>5.370347</td>\n",
       "      <td>2.684286</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.250000</td>\n",
       "      <td>12.125000</td>\n",
       "      <td>13.125000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>2015-07-02 21:00:00</td>\n",
       "      <td>170.436600</td>\n",
       "      <td>448.367055</td>\n",
       "      <td>100.236344</td>\n",
       "      <td>40.146085</td>\n",
       "      <td>12.496284</td>\n",
       "      <td>41.792210</td>\n",
       "      <td>8.347255</td>\n",
       "      <td>4.173691</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.750000</td>\n",
       "      <td>29.625000</td>\n",
       "      <td>32.125000</td>\n",
       "      <td>32.375000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>2015-10-02 03:00:00</td>\n",
       "      <td>176.619220</td>\n",
       "      <td>468.434339</td>\n",
       "      <td>104.409335</td>\n",
       "      <td>42.228807</td>\n",
       "      <td>17.690501</td>\n",
       "      <td>59.099500</td>\n",
       "      <td>11.791536</td>\n",
       "      <td>5.900380</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.625000</td>\n",
       "      <td>66.375000</td>\n",
       "      <td>67.250000</td>\n",
       "      <td>70.375000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>2016-01-01 06:00:00</td>\n",
       "      <td>241.420717</td>\n",
       "      <td>586.682904</td>\n",
       "      <td>162.309656</td>\n",
       "      <td>69.311324</td>\n",
       "      <td>58.444332</td>\n",
       "      <td>179.903039</td>\n",
       "      <td>35.659369</td>\n",
       "      <td>18.305595</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>491.875000</td>\n",
       "      <td>348.875000</td>\n",
       "      <td>370.875000</td>\n",
       "      <td>394.875000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>28.863879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.509413</td>\n",
       "      <td>33.161392</td>\n",
       "      <td>7.423015</td>\n",
       "      <td>3.481097</td>\n",
       "      <td>6.967073</td>\n",
       "      <td>23.216722</td>\n",
       "      <td>4.656677</td>\n",
       "      <td>2.320464</td>\n",
       "      <td>...</td>\n",
       "      <td>0.192877</td>\n",
       "      <td>0.155121</td>\n",
       "      <td>0.140891</td>\n",
       "      <td>0.159066</td>\n",
       "      <td>0.134849</td>\n",
       "      <td>62.477055</td>\n",
       "      <td>59.241774</td>\n",
       "      <td>58.860338</td>\n",
       "      <td>59.673951</td>\n",
       "      <td>5.827516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           machineID                       datetime    voltmean_3h  \\\n",
       "count  292698.000000                         292698  292698.000000   \n",
       "mean       50.501268  2015-07-02 20:05:53.336203520     170.784219   \n",
       "min         1.000000            2015-01-01 09:00:00     125.532506   \n",
       "25%        26.000000            2015-04-02 15:00:00     164.452628   \n",
       "50%        50.000000            2015-07-02 21:00:00     170.436600   \n",
       "75%        75.000000            2015-10-02 03:00:00     176.619220   \n",
       "max       100.000000            2016-01-01 06:00:00     241.420717   \n",
       "std        28.863879                            NaN       9.509413   \n",
       "\n",
       "       rotatemean_3h  pressuremean_3h  vibrationmean_3h      voltsd_3h  \\\n",
       "count  292698.000000    292698.000000     292698.000000  292698.000000   \n",
       "mean      446.575340       100.864117         40.386506      13.300159   \n",
       "min       211.811184        72.118639         26.569635       0.025509   \n",
       "25%       427.534759        96.239740         38.147926       8.027559   \n",
       "50%       448.367055       100.236344         40.146085      12.496284   \n",
       "75%       468.434339       104.409335         42.228807      17.690501   \n",
       "max       586.682904       162.309656         69.311324      58.444332   \n",
       "std        33.161392         7.423015          3.481097       6.967073   \n",
       "\n",
       "         rotatesd_3h  pressuresd_3h  vibrationsd_3h  ...    error1count  \\\n",
       "count  292698.000000  292698.000000   292698.000000  ...  292698.000000   \n",
       "mean       44.453806       8.886790        4.441220  ...       0.037758   \n",
       "min         0.078991       0.027417        0.015278  ...       0.000000   \n",
       "25%        26.903316       5.370347        2.684286  ...       0.000000   \n",
       "50%        41.792210       8.347255        4.173691  ...       0.000000   \n",
       "75%        59.099500      11.791536        5.900380  ...       0.000000   \n",
       "max       179.903039      35.659369       18.305595  ...       2.000000   \n",
       "std        23.216722       4.656677        2.320464  ...       0.192877   \n",
       "\n",
       "         error2count    error3count    error4count    error5count  \\\n",
       "count  292698.000000  292698.000000  292698.000000  292698.000000   \n",
       "mean        0.029442       0.025289       0.025712       0.018841   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         2.000000       1.500000       3.000000       2.000000   \n",
       "std         0.155121       0.140891       0.159066       0.134849   \n",
       "\n",
       "               comp1          comp2          comp3          comp4  \\\n",
       "count  292698.000000  292698.000000  292698.000000  292698.000000   \n",
       "mean       53.444887      51.452085      52.640758      53.737431   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%        13.250000      12.125000      13.125000      13.000000   \n",
       "50%        32.750000      29.625000      32.125000      32.375000   \n",
       "75%        68.625000      66.375000      67.250000      70.375000   \n",
       "max       491.875000     348.875000     370.875000     394.875000   \n",
       "std        62.477055      59.241774      58.860338      59.673951   \n",
       "\n",
       "                 age  \n",
       "count  292698.000000  \n",
       "mean       11.329497  \n",
       "min         0.000000  \n",
       "25%         7.000000  \n",
       "50%        12.000000  \n",
       "75%        16.000000  \n",
       "max        20.000000  \n",
       "std         5.827516  \n",
       "\n",
       "[8 rows x 28 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_feat.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0449f823-9442-4bac-8293-ddfdc5938a30",
   "metadata": {},
   "source": [
    "# Construction of Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6466dfdc-7bc4-4e1c-a7c2-a8dba690ab45",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shash\\AppData\\Local\\Temp\\ipykernel_16436\\901816491.py:4: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  labeled_featuers= labeled_features.fillna(\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machineID</th>\n",
       "      <th>datetime</th>\n",
       "      <th>voltmean_3h</th>\n",
       "      <th>rotatemean_3h</th>\n",
       "      <th>pressuremean_3h</th>\n",
       "      <th>vibrationmean_3h</th>\n",
       "      <th>voltsd_3h</th>\n",
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       "      <th>...</th>\n",
       "      <th>error3count</th>\n",
       "      <th>error4count</th>\n",
       "      <th>error5count</th>\n",
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       "      <th>comp2</th>\n",
       "      <th>comp3</th>\n",
       "      <th>comp4</th>\n",
       "      <th>model</th>\n",
       "      <th>age</th>\n",
       "      <th>failure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>186.092896</td>\n",
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       "      <td>107.989359</td>\n",
       "      <td>55.308074</td>\n",
       "      <td>13.489090</td>\n",
       "      <td>62.185045</td>\n",
       "      <td>5.118176</td>\n",
       "      <td>4.904365</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.000</td>\n",
       "      <td>217.000</td>\n",
       "      <td>157.000</td>\n",
       "      <td>172.000</td>\n",
       "      <td>model3</td>\n",
       "      <td>18</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 09:00:00</td>\n",
       "      <td>166.281848</td>\n",
       "      <td>453.787824</td>\n",
       "      <td>106.187582</td>\n",
       "      <td>51.990080</td>\n",
       "      <td>24.276228</td>\n",
       "      <td>23.621315</td>\n",
       "      <td>11.176731</td>\n",
       "      <td>3.394073</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.125</td>\n",
       "      <td>217.125</td>\n",
       "      <td>157.125</td>\n",
       "      <td>172.125</td>\n",
       "      <td>model3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>2015-01-04 12:00:00</td>\n",
       "      <td>175.412103</td>\n",
       "      <td>445.450581</td>\n",
       "      <td>100.887363</td>\n",
       "      <td>54.251534</td>\n",
       "      <td>34.918687</td>\n",
       "      <td>11.001625</td>\n",
       "      <td>10.580336</td>\n",
       "      <td>2.921501</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.250</td>\n",
       "      <td>217.250</td>\n",
       "      <td>157.250</td>\n",
       "      <td>172.250</td>\n",
       "      <td>model3</td>\n",
       "      <td>18</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 15:00:00</td>\n",
       "      <td>157.347716</td>\n",
       "      <td>451.882075</td>\n",
       "      <td>101.289380</td>\n",
       "      <td>48.602686</td>\n",
       "      <td>24.617739</td>\n",
       "      <td>28.950883</td>\n",
       "      <td>9.966729</td>\n",
       "      <td>2.356486</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>172.375</td>\n",
       "      <td>model3</td>\n",
       "      <td>18</td>\n",
       "      <td>none</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 18:00:00</td>\n",
       "      <td>176.450550</td>\n",
       "      <td>446.033068</td>\n",
       "      <td>84.521555</td>\n",
       "      <td>47.638836</td>\n",
       "      <td>8.071400</td>\n",
       "      <td>76.511343</td>\n",
       "      <td>2.636879</td>\n",
       "      <td>4.108621</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>217.500</td>\n",
       "      <td>157.500</td>\n",
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       "      <td>model3</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   machineID            datetime  voltmean_3h  rotatemean_3h  pressuremean_3h  \\\n",
       "0          1 2015-01-04 06:00:00   186.092896     451.641253       107.989359   \n",
       "1          1 2015-01-04 09:00:00   166.281848     453.787824       106.187582   \n",
       "2          1 2015-01-04 12:00:00   175.412103     445.450581       100.887363   \n",
       "3          1 2015-01-04 15:00:00   157.347716     451.882075       101.289380   \n",
       "4          1 2015-01-04 18:00:00   176.450550     446.033068        84.521555   \n",
       "\n",
       "   vibrationmean_3h  voltsd_3h  rotatesd_3h  pressuresd_3h  vibrationsd_3h  \\\n",
       "0         55.308074  13.489090    62.185045       5.118176        4.904365   \n",
       "1         51.990080  24.276228    23.621315      11.176731        3.394073   \n",
       "2         54.251534  34.918687    11.001625      10.580336        2.921501   \n",
       "3         48.602686  24.617739    28.950883       9.966729        2.356486   \n",
       "4         47.638836   8.071400    76.511343       2.636879        4.108621   \n",
       "\n",
       "   ...  error3count  error4count  error5count   comp1    comp2    comp3  \\\n",
       "0  ...          0.0          0.0          0.0  22.000  217.000  157.000   \n",
       "1  ...          0.0          0.0          1.0  22.125  217.125  157.125   \n",
       "2  ...          0.0          0.0          1.0  22.250  217.250  157.250   \n",
       "3  ...          0.0          0.0          1.0  22.375  217.375  157.375   \n",
       "4  ...          0.0          0.0          1.0  22.500  217.500  157.500   \n",
       "\n",
       "     comp4   model  age  failure  \n",
       "0  172.000  model3   18     none  \n",
       "1  172.125  model3   18     none  \n",
       "2  172.250  model3   18     none  \n",
       "3  172.375  model3   18     none  \n",
       "4  172.500  model3   18     none  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labeled_features= final_feat.merge(\n",
    "    failures, on=['datetime', 'machineID'], how='left')\n",
    "\n",
    "labeled_featuers= labeled_features.fillna(\n",
    "    method='bfill', limit=7)\n",
    "\n",
    "labeled_features= labeled_features.fillna('none')\n",
    "labeled_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7fc3d990-e646-4300-9f86-2f4abc4e1eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machineID</th>\n",
       "      <th>datetime</th>\n",
       "      <th>voltmean_3h</th>\n",
       "      <th>rotatemean_3h</th>\n",
       "      <th>pressuremean_3h</th>\n",
       "      <th>vibrationmean_3h</th>\n",
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       "      <th>pressuresd_3h</th>\n",
       "      <th>vibrationsd_3h</th>\n",
       "      <th>...</th>\n",
       "      <th>error3count</th>\n",
       "      <th>error4count</th>\n",
       "      <th>error5count</th>\n",
       "      <th>comp1</th>\n",
       "      <th>comp2</th>\n",
       "      <th>comp3</th>\n",
       "      <th>comp4</th>\n",
       "      <th>model</th>\n",
       "      <th>age</th>\n",
       "      <th>failure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>8</th>\n",
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       "      <td>model3</td>\n",
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       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-05 06:00:00</td>\n",
       "      <td>185.782709</td>\n",
       "      <td>439.531288</td>\n",
       "      <td>99.413660</td>\n",
       "      <td>51.558082</td>\n",
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       "      <td>model3</td>\n",
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       "    <tr>\n",
       "      <th>1332</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-06-19 06:00:00</td>\n",
       "      <td>172.059069</td>\n",
       "      <td>463.242610</td>\n",
       "      <td>96.905050</td>\n",
       "      <td>53.701413</td>\n",
       "      <td>14.757880</td>\n",
       "      <td>55.874000</td>\n",
       "      <td>3.204981</td>\n",
       "      <td>2.329615</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>model3</td>\n",
       "      <td>18</td>\n",
       "      <td>comp4</td>\n",
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       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-06-19 06:00:00</td>\n",
       "      <td>172.059069</td>\n",
       "      <td>463.242610</td>\n",
       "      <td>96.905050</td>\n",
       "      <td>53.701413</td>\n",
       "      <td>14.757880</td>\n",
       "      <td>55.874000</td>\n",
       "      <td>3.204981</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-09-02 06:00:00</td>\n",
       "      <td>165.530175</td>\n",
       "      <td>413.547130</td>\n",
       "      <td>103.631528</td>\n",
       "      <td>60.142846</td>\n",
       "      <td>9.088618</td>\n",
       "      <td>49.918671</td>\n",
       "      <td>14.438808</td>\n",
       "      <td>5.389723</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>model3</td>\n",
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       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-09-02 06:00:00</td>\n",
       "      <td>165.530175</td>\n",
       "      <td>413.547130</td>\n",
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       "      <td>60.142846</td>\n",
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       "      <td>49.918671</td>\n",
       "      <td>14.438808</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2777</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-12-16 06:00:00</td>\n",
       "      <td>188.941806</td>\n",
       "      <td>421.756494</td>\n",
       "      <td>86.684047</td>\n",
       "      <td>51.410144</td>\n",
       "      <td>35.295874</td>\n",
       "      <td>7.839110</td>\n",
       "      <td>9.139511</td>\n",
       "      <td>3.598700</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>model3</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>17732</th>\n",
       "      <td>7</td>\n",
       "      <td>2015-01-24 06:00:00</td>\n",
       "      <td>177.669082</td>\n",
       "      <td>361.502737</td>\n",
       "      <td>109.447331</td>\n",
       "      <td>46.975392</td>\n",
       "      <td>8.531048</td>\n",
       "      <td>29.773424</td>\n",
       "      <td>4.384169</td>\n",
       "      <td>5.187506</td>\n",
       "      <td>...</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
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       "    <tr>\n",
       "      <th>17734</th>\n",
       "      <td>7</td>\n",
       "      <td>2015-01-24 06:00:00</td>\n",
       "      <td>177.669082</td>\n",
       "      <td>361.502737</td>\n",
       "      <td>109.447331</td>\n",
       "      <td>46.975392</td>\n",
       "      <td>8.531048</td>\n",
       "      <td>29.773424</td>\n",
       "      <td>4.384169</td>\n",
       "      <td>5.187506</td>\n",
       "      <td>...</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>207.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>model3</td>\n",
       "      <td>20</td>\n",
       "      <td>comp4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18577</th>\n",
       "      <td>7</td>\n",
       "      <td>2015-05-09 06:00:00</td>\n",
       "      <td>175.911702</td>\n",
       "      <td>467.458682</td>\n",
       "      <td>103.589910</td>\n",
       "      <td>52.610306</td>\n",
       "      <td>12.400140</td>\n",
       "      <td>69.604586</td>\n",
       "      <td>11.162580</td>\n",
       "      <td>1.658487</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>model3</td>\n",
       "      <td>20</td>\n",
       "      <td>comp4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18578</th>\n",
       "      <td>7</td>\n",
       "      <td>2015-05-09 06:00:00</td>\n",
       "      <td>175.911702</td>\n",
       "      <td>467.458682</td>\n",
       "      <td>103.589910</td>\n",
       "      <td>52.610306</td>\n",
       "      <td>12.400140</td>\n",
       "      <td>69.604586</td>\n",
       "      <td>11.162580</td>\n",
       "      <td>1.658487</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>model3</td>\n",
       "      <td>20</td>\n",
       "      <td>comp4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19661</th>\n",
       "      <td>7</td>\n",
       "      <td>2015-09-21 06:00:00</td>\n",
       "      <td>169.123329</td>\n",
       "      <td>428.763286</td>\n",
       "      <td>99.428189</td>\n",
       "      <td>45.397202</td>\n",
       "      <td>21.723826</td>\n",
       "      <td>100.568119</td>\n",
       "      <td>22.697296</td>\n",
       "      <td>3.416446</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>model3</td>\n",
       "      <td>20</td>\n",
       "      <td>comp4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19662</th>\n",
       "      <td>7</td>\n",
       "      <td>2015-09-21 06:00:00</td>\n",
       "      <td>169.123329</td>\n",
       "      <td>428.763286</td>\n",
       "      <td>99.428189</td>\n",
       "      <td>45.397202</td>\n",
       "      <td>21.723826</td>\n",
       "      <td>100.568119</td>\n",
       "      <td>22.697296</td>\n",
       "      <td>3.416446</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>model3</td>\n",
       "      <td>20</td>\n",
       "      <td>comp4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21120</th>\n",
       "      <td>8</td>\n",
       "      <td>2015-03-22 06:00:00</td>\n",
       "      <td>173.026438</td>\n",
       "      <td>429.852151</td>\n",
       "      <td>102.408383</td>\n",
       "      <td>54.340943</td>\n",
       "      <td>11.452078</td>\n",
       "      <td>42.822464</td>\n",
       "      <td>11.195405</td>\n",
       "      <td>7.115879</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>model3</td>\n",
       "      <td>16</td>\n",
       "      <td>comp4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22201</th>\n",
       "      <td>8</td>\n",
       "      <td>2015-08-04 06:00:00</td>\n",
       "      <td>178.611054</td>\n",
       "      <td>425.076676</td>\n",
       "      <td>102.481539</td>\n",
       "      <td>48.617995</td>\n",
       "      <td>12.652412</td>\n",
       "      <td>22.014775</td>\n",
       "      <td>18.517554</td>\n",
       "      <td>2.918507</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>model3</td>\n",
       "      <td>16</td>\n",
       "      <td>comp4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22801</th>\n",
       "      <td>8</td>\n",
       "      <td>2015-10-18 06:00:00</td>\n",
       "      <td>166.642406</td>\n",
       "      <td>452.842842</td>\n",
       "      <td>104.470047</td>\n",
       "      <td>52.080654</td>\n",
       "      <td>5.920407</td>\n",
       "      <td>46.523492</td>\n",
       "      <td>5.792959</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>model3</td>\n",
       "      <td>16</td>\n",
       "      <td>comp4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       machineID            datetime  voltmean_3h  rotatemean_3h  \\\n",
       "8              1 2015-01-05 06:00:00   185.782709     439.531288   \n",
       "9              1 2015-01-05 06:00:00   185.782709     439.531288   \n",
       "1332           1 2015-06-19 06:00:00   172.059069     463.242610   \n",
       "1333           1 2015-06-19 06:00:00   172.059069     463.242610   \n",
       "1933           1 2015-09-02 06:00:00   165.530175     413.547130   \n",
       "1934           1 2015-09-02 06:00:00   165.530175     413.547130   \n",
       "2777           1 2015-12-16 06:00:00   188.941806     421.756494   \n",
       "17732          7 2015-01-24 06:00:00   177.669082     361.502737   \n",
       "17734          7 2015-01-24 06:00:00   177.669082     361.502737   \n",
       "18577          7 2015-05-09 06:00:00   175.911702     467.458682   \n",
       "18578          7 2015-05-09 06:00:00   175.911702     467.458682   \n",
       "19661          7 2015-09-21 06:00:00   169.123329     428.763286   \n",
       "19662          7 2015-09-21 06:00:00   169.123329     428.763286   \n",
       "21120          8 2015-03-22 06:00:00   173.026438     429.852151   \n",
       "22201          8 2015-08-04 06:00:00   178.611054     425.076676   \n",
       "22801          8 2015-10-18 06:00:00   166.642406     452.842842   \n",
       "\n",
       "       pressuremean_3h  vibrationmean_3h  voltsd_3h  rotatesd_3h  \\\n",
       "8            99.413660         51.558082  14.495664    45.663743   \n",
       "9            99.413660         51.558082  14.495664    45.663743   \n",
       "1332         96.905050         53.701413  14.757880    55.874000   \n",
       "1333         96.905050         53.701413  14.757880    55.874000   \n",
       "1933        103.631528         60.142846   9.088618    49.918671   \n",
       "1934        103.631528         60.142846   9.088618    49.918671   \n",
       "2777         86.684047         51.410144  35.295874     7.839110   \n",
       "17732       109.447331         46.975392   8.531048    29.773424   \n",
       "17734       109.447331         46.975392   8.531048    29.773424   \n",
       "18577       103.589910         52.610306  12.400140    69.604586   \n",
       "18578       103.589910         52.610306  12.400140    69.604586   \n",
       "19661        99.428189         45.397202  21.723826   100.568119   \n",
       "19662        99.428189         45.397202  21.723826   100.568119   \n",
       "21120       102.408383         54.340943  11.452078    42.822464   \n",
       "22201       102.481539         48.617995  12.652412    22.014775   \n",
       "22801       104.470047         52.080654   5.920407    46.523492   \n",
       "\n",
       "       pressuresd_3h  vibrationsd_3h  ...  error3count  error4count  \\\n",
       "8           4.289212        7.330397  ...     0.000000          0.0   \n",
       "9           4.289212        7.330397  ...     0.000000          0.0   \n",
       "1332        3.204981        2.329615  ...     0.000000          0.0   \n",
       "1333        3.204981        2.329615  ...     0.000000          0.0   \n",
       "1933       14.438808        5.389723  ...     0.000000          0.0   \n",
       "1934       14.438808        5.389723  ...     0.000000          0.0   \n",
       "2777        9.139511        3.598700  ...     0.000000          0.0   \n",
       "17732       4.384169        5.187506  ...     0.333333          0.0   \n",
       "17734       4.384169        5.187506  ...     0.333333          0.0   \n",
       "18577      11.162580        1.658487  ...     0.000000          0.0   \n",
       "18578      11.162580        1.658487  ...     0.000000          0.0   \n",
       "19661      22.697296        3.416446  ...     0.000000          0.0   \n",
       "19662      22.697296        3.416446  ...     0.000000          0.0   \n",
       "21120      11.195405        7.115879  ...     0.000000          0.0   \n",
       "22201      18.517554        2.918507  ...     0.000000          0.0   \n",
       "22801       5.792959        4.005665  ...     0.000000          0.0   \n",
       "\n",
       "       error5count  comp1  comp2  comp3  comp4   model  age  failure  \n",
       "8         1.000000   23.0  218.0  158.0    0.0  model3   18    comp4  \n",
       "9         1.000000    0.0  218.0  158.0    0.0  model3   18    comp4  \n",
       "1332      1.000000    0.0   30.0   15.0  135.0  model3   18    comp4  \n",
       "1333      1.000000    0.0   30.0   15.0    0.0  model3   18    comp4  \n",
       "1933      1.000000    0.0   60.0   90.0   75.0  model3   18    comp4  \n",
       "1934      1.000000    0.0   60.0   90.0    0.0  model3   18    comp4  \n",
       "2777      1.000000   75.0   30.0   15.0    0.0  model3   18    comp4  \n",
       "17732     0.333333  207.0    0.0  207.0   57.0  model3   20    comp4  \n",
       "17734     0.333333  207.0    0.0  207.0    0.0  model3   20    comp4  \n",
       "18577     1.000000    0.0   30.0   45.0  105.0  model3   20    comp4  \n",
       "18578     1.000000    0.0   30.0   45.0    0.0  model3   20    comp4  \n",
       "19661     1.000000    0.0   30.0   60.0  135.0  model3   20    comp4  \n",
       "19662     1.000000    0.0   30.0   60.0    0.0  model3   20    comp4  \n",
       "21120     1.000000   60.0   15.0  204.0    0.0  model3   16    comp4  \n",
       "22201     1.000000   30.0   45.0   15.0    0.0  model3   16    comp4  \n",
       "22801     1.000000   15.0   60.0   90.0    0.0  model3   16    comp4  \n",
       "\n",
       "[16 rows x 30 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labeled_features.loc[labeled_features['failure']== 'comp4'][:16]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d102c2a-73de-41fe-b893-3a4f9be6b389",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "638265a0-2541-4cc9-b2dc-9267c8b85e1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training on 2015-07-31 01:00:00 to 2015-08-01 01:00:00\n",
      "Training on 2015-08-31 01:00:00 to 2015-09-01 01:00:00\n",
      "Training on 2015-09-30 01:00:00 to 2015-10-01 01:00:00\n"
     ]
    }
   ],
   "source": [
    "threshold_dates = [[pd.to_datetime('2015-07-31 01:00:00'), pd.to_datetime('2015-08-01 01:00:00')],\n",
    "                   [pd.to_datetime('2015-08-31 01:00:00'),\n",
    "                    pd.to_datetime('2015-09-01 01:00:00')],\n",
    "                   [pd.to_datetime('2015-09-30 01:00:00'), pd.to_datetime('2015-10-01 01:00:00')]]\n",
    "\n",
    "test_results = []\n",
    "anai_models = []\n",
    "train_dfs = []\n",
    "for last_train_date, first_test_date in threshold_dates:\n",
    "    print('Training on %s to %s' % (last_train_date, first_test_date))\n",
    "    train_y = labeled_features.loc[labeled_features['datetime']\n",
    "                                   < last_train_date, 'failure']\n",
    "    train_X = pd.get_dummies(labeled_features.loc[labeled_features['datetime'] < last_train_date].drop(['datetime',\n",
    "                                                                                                        'machineID',\n",
    "                                                                                                        'failure'], axis= 1))\n",
    "    df = pd.concat([train_X, train_y], axis=1)\n",
    "    train_dfs.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a502b56f-2e09-4583-9964-762e684ad87d",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machineID</th>\n",
       "      <th>datetime</th>\n",
       "      <th>voltmean_3h</th>\n",
       "      <th>rotatemean_3h</th>\n",
       "      <th>pressuremean_3h</th>\n",
       "      <th>vibrationmean_3h</th>\n",
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       "      <th>error4count</th>\n",
       "      <th>error5count</th>\n",
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       "      <th>comp2</th>\n",
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       "      <td>34.918687</td>\n",
       "      <td>11.001625</td>\n",
       "      <td>10.580336</td>\n",
       "      <td>2.921501</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>22.250</td>\n",
       "      <td>217.250</td>\n",
       "      <td>157.250</td>\n",
       "      <td>172.250</td>\n",
       "      <td>model3</td>\n",
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       "    <tr>\n",
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       "      <td>451.882075</td>\n",
       "      <td>101.289380</td>\n",
       "      <td>48.602686</td>\n",
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       "      <td>157.375</td>\n",
       "      <td>172.375</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-04 18:00:00</td>\n",
       "      <td>176.450550</td>\n",
       "      <td>446.033068</td>\n",
       "      <td>84.521555</td>\n",
       "      <td>47.638836</td>\n",
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       "      <td>4.108621</td>\n",
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       "   machineID            datetime  voltmean_3h  rotatemean_3h  pressuremean_3h  \\\n",
       "0          1 2015-01-04 06:00:00   186.092896     451.641253       107.989359   \n",
       "1          1 2015-01-04 09:00:00   166.281848     453.787824       106.187582   \n",
       "2          1 2015-01-04 12:00:00   175.412103     445.450581       100.887363   \n",
       "3          1 2015-01-04 15:00:00   157.347716     451.882075       101.289380   \n",
       "4          1 2015-01-04 18:00:00   176.450550     446.033068        84.521555   \n",
       "\n",
       "   vibrationmean_3h  voltsd_3h  rotatesd_3h  pressuresd_3h  vibrationsd_3h  \\\n",
       "0         55.308074  13.489090    62.185045       5.118176        4.904365   \n",
       "1         51.990080  24.276228    23.621315      11.176731        3.394073   \n",
       "2         54.251534  34.918687    11.001625      10.580336        2.921501   \n",
       "3         48.602686  24.617739    28.950883       9.966729        2.356486   \n",
       "4         47.638836   8.071400    76.511343       2.636879        4.108621   \n",
       "\n",
       "   ...  error3count  error4count  error5count   comp1    comp2    comp3  \\\n",
       "0  ...          0.0          0.0          0.0  22.000  217.000  157.000   \n",
       "1  ...          0.0          0.0          1.0  22.125  217.125  157.125   \n",
       "2  ...          0.0          0.0          1.0  22.250  217.250  157.250   \n",
       "3  ...          0.0          0.0          1.0  22.375  217.375  157.375   \n",
       "4  ...          0.0          0.0          1.0  22.500  217.500  157.500   \n",
       "\n",
       "     comp4   model  age  failure  \n",
       "0  172.000  model3   18     none  \n",
       "1  172.125  model3   18     none  \n",
       "2  172.250  model3   18     none  \n",
       "3  172.375  model3   18     none  \n",
       "4  172.500  model3   18     none  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labeled_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5262a4b-0d39-4db1-9dab-e418856e3fd8",
   "metadata": {},
   "source": [
    "# Modelling on 1st Threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b03934a6-775d-4025-8daf-348c01b0ccec",
   "metadata": {},
   "outputs": [
    {
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       "      <td>3.394073</td>\n",
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       "      <td>2.921501</td>\n",
       "      <td>170.859615</td>\n",
       "      <td>461.116153</td>\n",
       "      <td>...</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>157.347716</td>\n",
       "      <td>451.882075</td>\n",
       "      <td>101.289380</td>\n",
       "      <td>48.602686</td>\n",
       "      <td>24.617739</td>\n",
       "      <td>28.950883</td>\n",
       "      <td>9.966729</td>\n",
       "      <td>2.356486</td>\n",
       "      <td>171.566669</td>\n",
       "      <td>457.893518</td>\n",
       "      <td>...</td>\n",
       "      <td>22.375</td>\n",
       "      <td>217.375</td>\n",
       "      <td>157.375</td>\n",
       "      <td>172.375</td>\n",
       "      <td>18</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176.450550</td>\n",
       "      <td>446.033068</td>\n",
       "      <td>84.521555</td>\n",
       "      <td>47.638836</td>\n",
       "      <td>8.071400</td>\n",
       "      <td>76.511343</td>\n",
       "      <td>2.636879</td>\n",
       "      <td>4.108621</td>\n",
       "      <td>171.536866</td>\n",
       "      <td>457.672110</td>\n",
       "      <td>...</td>\n",
       "      <td>22.500</td>\n",
       "      <td>217.500</td>\n",
       "      <td>157.500</td>\n",
       "      <td>172.500</td>\n",
       "      <td>18</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   voltmean_3h  rotatemean_3h  pressuremean_3h  vibrationmean_3h  voltsd_3h  \\\n",
       "0   186.092896     451.641253       107.989359         55.308074  13.489090   \n",
       "1   166.281848     453.787824       106.187582         51.990080  24.276228   \n",
       "2   175.412103     445.450581       100.887363         54.251534  34.918687   \n",
       "3   157.347716     451.882075       101.289380         48.602686  24.617739   \n",
       "4   176.450550     446.033068        84.521555         47.638836   8.071400   \n",
       "\n",
       "   rotatesd_3h  pressuresd_3h  vibrationsd_3h  voltmean_24h  rotatemean_24h  \\\n",
       "0    62.185045       5.118176        4.904365    171.536044      456.036706   \n",
       "1    23.621315      11.176731        3.394073    171.069056      457.285237   \n",
       "2    11.001625      10.580336        2.921501    170.859615      461.116153   \n",
       "3    28.950883       9.966729        2.356486    171.566669      457.893518   \n",
       "4    76.511343       2.636879        4.108621    171.536866      457.672110   \n",
       "\n",
       "   ...   comp1    comp2    comp3    comp4  age  model_model1  model_model2  \\\n",
       "0  ...  22.000  217.000  157.000  172.000   18         False         False   \n",
       "1  ...  22.125  217.125  157.125  172.125   18         False         False   \n",
       "2  ...  22.250  217.250  157.250  172.250   18         False         False   \n",
       "3  ...  22.375  217.375  157.375  172.375   18         False         False   \n",
       "4  ...  22.500  217.500  157.500  172.500   18         False         False   \n",
       "\n",
       "   model_model3  model_model4  failure  \n",
       "0          True         False     none  \n",
       "1          True         False     none  \n",
       "2          True         False     none  \n",
       "3          True         False     none  \n",
       "4          True         False     none  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dfs[1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bd1588df-65e0-40df-995f-7aada76588d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "failure\n",
       "none     168373\n",
       "comp2       215\n",
       "comp1       190\n",
       "comp4       149\n",
       "comp3       111\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dfs[0]['failure'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "85800229-0669-48e1-9ad4-35478188f83c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, df in enumerate(train_dfs):\n",
    "    df[['model_model1', 'model_model2', 'model_model3', 'model_model4']] = df[['model_model1', 'model_model2', 'model_model3', 'model_model4']].astype(int)\n",
    "    train_dfs[i] = df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5c824709-6265-45ea-a92d-83aa8db1a8e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>voltmean_3h</th>\n",
       "      <th>rotatemean_3h</th>\n",
       "      <th>pressuremean_3h</th>\n",
       "      <th>vibrationmean_3h</th>\n",
       "      <th>voltsd_3h</th>\n",
       "      <th>rotatesd_3h</th>\n",
       "      <th>pressuresd_3h</th>\n",
       "      <th>vibrationsd_3h</th>\n",
       "      <th>voltmean_24h</th>\n",
       "      <th>rotatemean_24h</th>\n",
       "      <th>...</th>\n",
       "      <th>comp1</th>\n",
       "      <th>comp2</th>\n",
       "      <th>comp3</th>\n",
       "      <th>comp4</th>\n",
       "      <th>age</th>\n",
       "      <th>model_model1</th>\n",
       "      <th>model_model2</th>\n",
       "      <th>model_model3</th>\n",
       "      <th>model_model4</th>\n",
       "      <th>failure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>186.092896</td>\n",
       "      <td>451.641253</td>\n",
       "      <td>107.989359</td>\n",
       "      <td>55.308074</td>\n",
       "      <td>13.489090</td>\n",
       "      <td>62.185045</td>\n",
       "      <td>5.118176</td>\n",
       "      <td>4.904365</td>\n",
       "      <td>171.536044</td>\n",
       "      <td>456.036706</td>\n",
       "      <td>...</td>\n",
       "      <td>22.000</td>\n",
       "      <td>217.000</td>\n",
       "      <td>157.000</td>\n",
       "      <td>172.000</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>166.281848</td>\n",
       "      <td>453.787824</td>\n",
       "      <td>106.187582</td>\n",
       "      <td>51.990080</td>\n",
       "      <td>24.276228</td>\n",
       "      <td>23.621315</td>\n",
       "      <td>11.176731</td>\n",
       "      <td>3.394073</td>\n",
       "      <td>171.069056</td>\n",
       "      <td>457.285237</td>\n",
       "      <td>...</td>\n",
       "      <td>22.125</td>\n",
       "      <td>217.125</td>\n",
       "      <td>157.125</td>\n",
       "      <td>172.125</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>175.412103</td>\n",
       "      <td>445.450581</td>\n",
       "      <td>100.887363</td>\n",
       "      <td>54.251534</td>\n",
       "      <td>34.918687</td>\n",
       "      <td>11.001625</td>\n",
       "      <td>10.580336</td>\n",
       "      <td>2.921501</td>\n",
       "      <td>170.859615</td>\n",
       "      <td>461.116153</td>\n",
       "      <td>...</td>\n",
       "      <td>22.250</td>\n",
       "      <td>217.250</td>\n",
       "      <td>157.250</td>\n",
       "      <td>172.250</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>157.347716</td>\n",
       "      <td>451.882075</td>\n",
       "      <td>101.289380</td>\n",
       "      <td>48.602686</td>\n",
       "      <td>24.617739</td>\n",
       "      <td>28.950883</td>\n",
       "      <td>9.966729</td>\n",
       "      <td>2.356486</td>\n",
       "      <td>171.566669</td>\n",
       "      <td>457.893518</td>\n",
       "      <td>...</td>\n",
       "      <td>22.375</td>\n",
       "      <td>217.375</td>\n",
       "      <td>157.375</td>\n",
       "      <td>172.375</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176.450550</td>\n",
       "      <td>446.033068</td>\n",
       "      <td>84.521555</td>\n",
       "      <td>47.638836</td>\n",
       "      <td>8.071400</td>\n",
       "      <td>76.511343</td>\n",
       "      <td>2.636879</td>\n",
       "      <td>4.108621</td>\n",
       "      <td>171.536866</td>\n",
       "      <td>457.672110</td>\n",
       "      <td>...</td>\n",
       "      <td>22.500</td>\n",
       "      <td>217.500</td>\n",
       "      <td>157.500</td>\n",
       "      <td>172.500</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291541</th>\n",
       "      <td>181.789600</td>\n",
       "      <td>443.816655</td>\n",
       "      <td>95.819894</td>\n",
       "      <td>39.563678</td>\n",
       "      <td>4.020958</td>\n",
       "      <td>16.224728</td>\n",
       "      <td>7.755167</td>\n",
       "      <td>2.474557</td>\n",
       "      <td>168.711921</td>\n",
       "      <td>455.964639</td>\n",
       "      <td>...</td>\n",
       "      <td>3.250</td>\n",
       "      <td>3.250</td>\n",
       "      <td>18.250</td>\n",
       "      <td>63.250</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291542</th>\n",
       "      <td>173.083263</td>\n",
       "      <td>438.704284</td>\n",
       "      <td>94.424390</td>\n",
       "      <td>39.083103</td>\n",
       "      <td>11.783864</td>\n",
       "      <td>66.494982</td>\n",
       "      <td>9.682734</td>\n",
       "      <td>6.214271</td>\n",
       "      <td>169.123090</td>\n",
       "      <td>454.870037</td>\n",
       "      <td>...</td>\n",
       "      <td>3.375</td>\n",
       "      <td>3.375</td>\n",
       "      <td>18.375</td>\n",
       "      <td>63.375</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291543</th>\n",
       "      <td>174.048390</td>\n",
       "      <td>433.016353</td>\n",
       "      <td>99.925802</td>\n",
       "      <td>40.676765</td>\n",
       "      <td>4.574859</td>\n",
       "      <td>25.076437</td>\n",
       "      <td>11.605534</td>\n",
       "      <td>5.577813</td>\n",
       "      <td>169.428252</td>\n",
       "      <td>451.809900</td>\n",
       "      <td>...</td>\n",
       "      <td>3.500</td>\n",
       "      <td>3.500</td>\n",
       "      <td>18.500</td>\n",
       "      <td>63.500</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291544</th>\n",
       "      <td>167.883990</td>\n",
       "      <td>435.511531</td>\n",
       "      <td>96.602542</td>\n",
       "      <td>38.877619</td>\n",
       "      <td>10.131349</td>\n",
       "      <td>55.710554</td>\n",
       "      <td>1.266057</td>\n",
       "      <td>7.099589</td>\n",
       "      <td>169.641578</td>\n",
       "      <td>448.168972</td>\n",
       "      <td>...</td>\n",
       "      <td>3.625</td>\n",
       "      <td>3.625</td>\n",
       "      <td>18.625</td>\n",
       "      <td>63.625</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291545</th>\n",
       "      <td>176.781659</td>\n",
       "      <td>442.259006</td>\n",
       "      <td>97.540743</td>\n",
       "      <td>37.500466</td>\n",
       "      <td>27.590127</td>\n",
       "      <td>46.843177</td>\n",
       "      <td>14.603949</td>\n",
       "      <td>2.434314</td>\n",
       "      <td>171.652207</td>\n",
       "      <td>446.435975</td>\n",
       "      <td>...</td>\n",
       "      <td>3.750</td>\n",
       "      <td>3.750</td>\n",
       "      <td>18.750</td>\n",
       "      <td>63.750</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>169038 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        voltmean_3h  rotatemean_3h  pressuremean_3h  vibrationmean_3h  \\\n",
       "0        186.092896     451.641253       107.989359         55.308074   \n",
       "1        166.281848     453.787824       106.187582         51.990080   \n",
       "2        175.412103     445.450581       100.887363         54.251534   \n",
       "3        157.347716     451.882075       101.289380         48.602686   \n",
       "4        176.450550     446.033068        84.521555         47.638836   \n",
       "...             ...            ...              ...               ...   \n",
       "291541   181.789600     443.816655        95.819894         39.563678   \n",
       "291542   173.083263     438.704284        94.424390         39.083103   \n",
       "291543   174.048390     433.016353        99.925802         40.676765   \n",
       "291544   167.883990     435.511531        96.602542         38.877619   \n",
       "291545   176.781659     442.259006        97.540743         37.500466   \n",
       "\n",
       "        voltsd_3h  rotatesd_3h  pressuresd_3h  vibrationsd_3h  voltmean_24h  \\\n",
       "0       13.489090    62.185045       5.118176        4.904365    171.536044   \n",
       "1       24.276228    23.621315      11.176731        3.394073    171.069056   \n",
       "2       34.918687    11.001625      10.580336        2.921501    170.859615   \n",
       "3       24.617739    28.950883       9.966729        2.356486    171.566669   \n",
       "4        8.071400    76.511343       2.636879        4.108621    171.536866   \n",
       "...           ...          ...            ...             ...           ...   \n",
       "291541   4.020958    16.224728       7.755167        2.474557    168.711921   \n",
       "291542  11.783864    66.494982       9.682734        6.214271    169.123090   \n",
       "291543   4.574859    25.076437      11.605534        5.577813    169.428252   \n",
       "291544  10.131349    55.710554       1.266057        7.099589    169.641578   \n",
       "291545  27.590127    46.843177      14.603949        2.434314    171.652207   \n",
       "\n",
       "        rotatemean_24h  ...   comp1    comp2    comp3    comp4  age  \\\n",
       "0           456.036706  ...  22.000  217.000  157.000  172.000   18   \n",
       "1           457.285237  ...  22.125  217.125  157.125  172.125   18   \n",
       "2           461.116153  ...  22.250  217.250  157.250  172.250   18   \n",
       "3           457.893518  ...  22.375  217.375  157.375  172.375   18   \n",
       "4           457.672110  ...  22.500  217.500  157.500  172.500   18   \n",
       "...                ...  ...     ...      ...      ...      ...  ...   \n",
       "291541      455.964639  ...   3.250    3.250   18.250   63.250    5   \n",
       "291542      454.870037  ...   3.375    3.375   18.375   63.375    5   \n",
       "291543      451.809900  ...   3.500    3.500   18.500   63.500    5   \n",
       "291544      448.168972  ...   3.625    3.625   18.625   63.625    5   \n",
       "291545      446.435975  ...   3.750    3.750   18.750   63.750    5   \n",
       "\n",
       "        model_model1  model_model2  model_model3  model_model4  failure  \n",
       "0                  0             0             1             0     none  \n",
       "1                  0             0             1             0     none  \n",
       "2                  0             0             1             0     none  \n",
       "3                  0             0             1             0     none  \n",
       "4                  0             0             1             0     none  \n",
       "...              ...           ...           ...           ...      ...  \n",
       "291541             0             0             0             1     none  \n",
       "291542             0             0             0             1     none  \n",
       "291543             0             0             0             1     none  \n",
       "291544             0             0             0             1     none  \n",
       "291545             0             0             0             1     none  \n",
       "\n",
       "[169038 rows x 31 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dfs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99322242-1768-482e-8542-c018bd2d5606",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "import anai\n",
    "from anai.preprocessing import Preprocessor\n",
    "\n",
    "ai1= anai.run(target= 'failure', df= train_dfs[0], predictor= ['xgb', 'cat', 'lgbm', 'gbc', 'rfc'], ensemble= False)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5cd5fa7a-a3ec-4446-a155-d8a665f98117",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "from catboost import CatBoostClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9690bde2-0506-4ca5-a590-62287bb16a0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(169038, 31)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dfs[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b6c0b885-d888-4387-b255-77a9bca7aba2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1= train_dfs[0].drop('failure', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ec1b9f9d-2718-42e6-9a86-9e140eedc649",
   "metadata": {},
   "outputs": [],
   "source": [
    "y1= train_dfs[0]['failure']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "06248ea6-6c12-476e-9fe1-85c265339e04",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "lb= LabelEncoder()\n",
    "y1= lb.fit_transform(y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a89665a7-a706-4a82-b53f-91c723c63ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, ..., 4, 4, 4], dtype=int64)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "75ce840b-949b-4a7e-9452-d8d43116ad83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>voltmean_3h</th>\n",
       "      <th>rotatemean_3h</th>\n",
       "      <th>pressuremean_3h</th>\n",
       "      <th>vibrationmean_3h</th>\n",
       "      <th>voltsd_3h</th>\n",
       "      <th>rotatesd_3h</th>\n",
       "      <th>pressuresd_3h</th>\n",
       "      <th>vibrationsd_3h</th>\n",
       "      <th>voltmean_24h</th>\n",
       "      <th>rotatemean_24h</th>\n",
       "      <th>...</th>\n",
       "      <th>error5count</th>\n",
       "      <th>comp1</th>\n",
       "      <th>comp2</th>\n",
       "      <th>comp3</th>\n",
       "      <th>comp4</th>\n",
       "      <th>age</th>\n",
       "      <th>model_model1</th>\n",
       "      <th>model_model2</th>\n",
       "      <th>model_model3</th>\n",
       "      <th>model_model4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>186.092896</td>\n",
       "      <td>451.641253</td>\n",
       "      <td>107.989359</td>\n",
       "      <td>55.308074</td>\n",
       "      <td>13.489090</td>\n",
       "      <td>62.185045</td>\n",
       "      <td>5.118176</td>\n",
       "      <td>4.904365</td>\n",
       "      <td>171.536044</td>\n",
       "      <td>456.036706</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.000</td>\n",
       "      <td>217.000</td>\n",
       "      <td>157.000</td>\n",
       "      <td>172.000</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>166.281848</td>\n",
       "      <td>453.787824</td>\n",
       "      <td>106.187582</td>\n",
       "      <td>51.990080</td>\n",
       "      <td>24.276228</td>\n",
       "      <td>23.621315</td>\n",
       "      <td>11.176731</td>\n",
       "      <td>3.394073</td>\n",
       "      <td>171.069056</td>\n",
       "      <td>457.285237</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.125</td>\n",
       "      <td>217.125</td>\n",
       "      <td>157.125</td>\n",
       "      <td>172.125</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>175.412103</td>\n",
       "      <td>445.450581</td>\n",
       "      <td>100.887363</td>\n",
       "      <td>54.251534</td>\n",
       "      <td>34.918687</td>\n",
       "      <td>11.001625</td>\n",
       "      <td>10.580336</td>\n",
       "      <td>2.921501</td>\n",
       "      <td>170.859615</td>\n",
       "      <td>461.116153</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.250</td>\n",
       "      <td>217.250</td>\n",
       "      <td>157.250</td>\n",
       "      <td>172.250</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>157.347716</td>\n",
       "      <td>451.882075</td>\n",
       "      <td>101.289380</td>\n",
       "      <td>48.602686</td>\n",
       "      <td>24.617739</td>\n",
       "      <td>28.950883</td>\n",
       "      <td>9.966729</td>\n",
       "      <td>2.356486</td>\n",
       "      <td>171.566669</td>\n",
       "      <td>457.893518</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.375</td>\n",
       "      <td>217.375</td>\n",
       "      <td>157.375</td>\n",
       "      <td>172.375</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176.450550</td>\n",
       "      <td>446.033068</td>\n",
       "      <td>84.521555</td>\n",
       "      <td>47.638836</td>\n",
       "      <td>8.071400</td>\n",
       "      <td>76.511343</td>\n",
       "      <td>2.636879</td>\n",
       "      <td>4.108621</td>\n",
       "      <td>171.536866</td>\n",
       "      <td>457.672110</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.500</td>\n",
       "      <td>217.500</td>\n",
       "      <td>157.500</td>\n",
       "      <td>172.500</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   voltmean_3h  rotatemean_3h  pressuremean_3h  vibrationmean_3h  voltsd_3h  \\\n",
       "0   186.092896     451.641253       107.989359         55.308074  13.489090   \n",
       "1   166.281848     453.787824       106.187582         51.990080  24.276228   \n",
       "2   175.412103     445.450581       100.887363         54.251534  34.918687   \n",
       "3   157.347716     451.882075       101.289380         48.602686  24.617739   \n",
       "4   176.450550     446.033068        84.521555         47.638836   8.071400   \n",
       "\n",
       "   rotatesd_3h  pressuresd_3h  vibrationsd_3h  voltmean_24h  rotatemean_24h  \\\n",
       "0    62.185045       5.118176        4.904365    171.536044      456.036706   \n",
       "1    23.621315      11.176731        3.394073    171.069056      457.285237   \n",
       "2    11.001625      10.580336        2.921501    170.859615      461.116153   \n",
       "3    28.950883       9.966729        2.356486    171.566669      457.893518   \n",
       "4    76.511343       2.636879        4.108621    171.536866      457.672110   \n",
       "\n",
       "   ...  error5count   comp1    comp2    comp3    comp4  age  model_model1  \\\n",
       "0  ...          0.0  22.000  217.000  157.000  172.000   18             0   \n",
       "1  ...          1.0  22.125  217.125  157.125  172.125   18             0   \n",
       "2  ...          1.0  22.250  217.250  157.250  172.250   18             0   \n",
       "3  ...          1.0  22.375  217.375  157.375  172.375   18             0   \n",
       "4  ...          1.0  22.500  217.500  157.500  172.500   18             0   \n",
       "\n",
       "   model_model2  model_model3  model_model4  \n",
       "0             0             1             0  \n",
       "1             0             1             0  \n",
       "2             0             1             0  \n",
       "3             0             1             0  \n",
       "4             0             1             0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "9a8a2925-fbec-4a19-9a0f-1ee2d71f2030",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test= train_test_split(x1, y1, test_size=0.2, random_state= 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "5b3085f3-d497-4362-a324-54e86309170c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.019612 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 5155\n",
      "[LightGBM] [Info] Number of data points in the train set: 135230, number of used features: 30\n",
      "[LightGBM] [Info] Start training from score -6.810786\n",
      "[LightGBM] [Info] Start training from score -6.644248\n",
      "[LightGBM] [Info] Start training from score -7.372081\n",
      "[LightGBM] [Info] Start training from score -7.002548\n",
      "[LightGBM] [Info] Start training from score -0.003949\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n",
      "LightGBM Accuracy: 0.9953857075248462\n",
      "CatBoost Accuracy: 0.9994675816374823\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shash\\.conda\\envs\\deeplearning\\lib\\site-packages\\xgboost\\core.py:158: UserWarning: [19:30:47] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-06abd128ca6c1688d-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Accuracy: 0.9987576904874585\n",
      "GBC Accuracy: 0.9978111689540937\n"
     ]
    }
   ],
   "source": [
    "models= {\n",
    "    \"LightGBM\":lgb.LGBMClassifier(),\n",
    "    \"CatBoost\":CatBoostClassifier(verbose= 0),\n",
    "    \"XGBoost\" :XGBClassifier(use_label_encoder= False, eval_metric='logloss'),\n",
    "    \"GBC\": GradientBoostingClassifier()\n",
    "}\n",
    "\n",
    "for name, model in models.items():\n",
    "    model.fit(x_train, y_train)\n",
    "    preds= model.predict(x_test)\n",
    "    accuracy= accuracy_score(y_test, preds)\n",
    "    print(f\"{name} Accuracy: {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "28e3a73f-0d35-4a01-adf5-6631ad3ebe81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model.pkl']"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "joblib.dump(model, \"model.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "57406ce1-5610-4d30-b5b1-aa3b9effbd39",
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model= joblib.load(\"model.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f94ec7a1-090a-45ae-a324-dcd7218332f9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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