Magnetic_Oil_gauge_Fault_indicator.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix, ConfusionMatrixDisplay"
   ]
  },
  {
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       "    DeviceTimeStamp  OTI  WTI  ATI   OLI  OTI_A  OTI_T  MOG_A\n",
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   "source": [
    "tf = pd.read_csv(\"Overview.csv\")\n",
    "tf.head()"
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       "    DeviceTimeStamp  OTI  WTI  ATI   OLI  OTI_A  OTI_T  MOG_A\n",
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    "# Converting to datetime datatype\n",
    "# tf['DeviceTimeStamp'] = pd.to_datetime(tf['DeviceTimeStamp'],format = '%Y-%m-%dT%H:%M:%S')\n",
    "tf.head()\n"
   ]
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       "    DeviceTimeStamp    VL1    VL2    VL3  IL1  IL2  IL3  VL12  VL23  VL31  \\\n",
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       "\n",
       "   INUT  \n",
       "0   0.0  \n",
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   "source": [
    "cv = pd.read_csv(\"CurrentVoltage.csv\")\n",
    "cv.head()\n"
   ]
  },
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       "    DeviceTimeStamp    VL1    VL2    VL3  IL1  IL2  IL3  VL12  VL23  VL31  \\\n",
       "0  2019-06-25T13:06    0.0    0.0    0.0  0.0  0.0  0.0   0.0   0.0   0.0   \n",
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       "4  2019-06-27T10:52  239.9  240.0  240.0  0.0  0.0  0.0   0.0   0.0   0.0   \n",
       "\n",
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    "# Converting to datetime datatype\n",
    "# cv['DeviceTimeStamp'] = pd.to_datetime(cv['DeviceTimeStamp'],format = '%Y-%m-%d %H:%M:%S')\n",
    "cv.head()"
   ]
  },
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    "outputId": "60fea6d6-a13e-454c-a394-feadb864f649"
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   "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>DeviceTimeStamp</th>\n",
       "      <th>OTI</th>\n",
       "      <th>WTI</th>\n",
       "      <th>ATI</th>\n",
       "      <th>OLI</th>\n",
       "      <th>OTI_A</th>\n",
       "      <th>OTI_T</th>\n",
       "      <th>MOG_A</th>\n",
       "      <th>VL1</th>\n",
       "      <th>VL2</th>\n",
       "      <th>VL3</th>\n",
       "      <th>IL1</th>\n",
       "      <th>IL2</th>\n",
       "      <th>IL3</th>\n",
       "      <th>VL12</th>\n",
       "      <th>VL23</th>\n",
       "      <th>VL31</th>\n",
       "      <th>INUT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11050</th>\n",
       "      <td>2019-10-31T23:00</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>246.5</td>\n",
       "      <td>246.0</td>\n",
       "      <td>242.9</td>\n",
       "      <td>67.9</td>\n",
       "      <td>50.5</td>\n",
       "      <td>107.3</td>\n",
       "      <td>426.0</td>\n",
       "      <td>422.5</td>\n",
       "      <td>426.0</td>\n",
       "      <td>50.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4964</th>\n",
       "      <td>2019-08-13T23:45</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>229.7</td>\n",
       "      <td>229.4</td>\n",
       "      <td>231.3</td>\n",
       "      <td>76.2</td>\n",
       "      <td>65.2</td>\n",
       "      <td>107.8</td>\n",
       "      <td>395.6</td>\n",
       "      <td>397.9</td>\n",
       "      <td>400.2</td>\n",
       "      <td>38.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20189</th>\n",
       "      <td>2020-04-11T08:45</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>246.1</td>\n",
       "      <td>245.5</td>\n",
       "      <td>241.8</td>\n",
       "      <td>50.7</td>\n",
       "      <td>26.5</td>\n",
       "      <td>89.7</td>\n",
       "      <td>425.0</td>\n",
       "      <td>421.5</td>\n",
       "      <td>424.9</td>\n",
       "      <td>55.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11832</th>\n",
       "      <td>2019-11-09T19:07</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>243.1</td>\n",
       "      <td>242.4</td>\n",
       "      <td>241.5</td>\n",
       "      <td>123.5</td>\n",
       "      <td>115.6</td>\n",
       "      <td>132.7</td>\n",
       "      <td>420.0</td>\n",
       "      <td>418.4</td>\n",
       "      <td>421.1</td>\n",
       "      <td>14.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19709</th>\n",
       "      <td>2020-04-06T05:30</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>252.1</td>\n",
       "      <td>250.5</td>\n",
       "      <td>46.8</td>\n",
       "      <td>24.3</td>\n",
       "      <td>50.5</td>\n",
       "      <td>437.3</td>\n",
       "      <td>434.4</td>\n",
       "      <td>437.8</td>\n",
       "      <td>24.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DeviceTimeStamp   OTI  WTI   ATI    OLI  OTI_A  OTI_T  MOG_A    VL1  \\\n",
       "11050  2019-10-31T23:00  27.0  0.0  25.0   87.0    0.0    0.0    0.0  246.5   \n",
       "4964   2019-08-13T23:45  32.0  0.0  28.0   41.0    0.0    0.0    0.0  229.7   \n",
       "20189  2020-04-11T08:45  32.0  1.0  31.0  100.0    0.0    0.0    0.0  246.1   \n",
       "11832  2019-11-09T19:07  24.0  0.0  25.0   86.0    0.0    0.0    0.0  243.1   \n",
       "19709  2020-04-06T05:30  26.0  1.0  25.0  100.0    0.0    0.0    0.0  253.0   \n",
       "\n",
       "         VL2    VL3    IL1    IL2    IL3   VL12   VL23   VL31  INUT  \n",
       "11050  246.0  242.9   67.9   50.5  107.3  426.0  422.5  426.0  50.3  \n",
       "4964   229.4  231.3   76.2   65.2  107.8  395.6  397.9  400.2  38.4  \n",
       "20189  245.5  241.8   50.7   26.5   89.7  425.0  421.5  424.9  55.7  \n",
       "11832  242.4  241.5  123.5  115.6  132.7  420.0  418.4  421.1  14.4  \n",
       "19709  252.1  250.5   46.8   24.3   50.5  437.3  434.4  437.8  24.7  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge both dataframes along DeviceTimeStamp\n",
    "transformer = pd.merge(tf, cv, on='DeviceTimeStamp')\n",
    "transformer.sample(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 378
    },
    "id": "s79PisX5fVSt",
    "outputId": "0199e5e7-6bc3-42ed-ee52-a1ed5ffa6818"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Oil Temperature\n",
    "plt.figure(figsize=(18,4))\n",
    "\n",
    "plt.plot(transformer.DeviceTimeStamp, transformer.OTI, label=\"Oil Temperature Indicator\",color='red')\n",
    "plt.title(\"Long-term transformer Oil Temperature\",fontsize = 30,color='Red')\n",
    "plt.legend()\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Temperature (C°)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "BE3KMH0hftDu",
    "outputId": "fdccac3a-631f-4fdf-98bc-7d3f864c5f93"
   },
   "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>OTI</th>\n",
       "      <th>WTI</th>\n",
       "      <th>ATI</th>\n",
       "      <th>OLI</th>\n",
       "      <th>OTI_A</th>\n",
       "      <th>OTI_T</th>\n",
       "      <th>VL1</th>\n",
       "      <th>VL2</th>\n",
       "      <th>VL3</th>\n",
       "      <th>IL1</th>\n",
       "      <th>IL2</th>\n",
       "      <th>IL3</th>\n",
       "      <th>VL12</th>\n",
       "      <th>VL23</th>\n",
       "      <th>VL31</th>\n",
       "      <th>INUT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</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",
       "      <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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>238.7</td>\n",
       "      <td>238.7</td>\n",
       "      <td>238.8</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>238.4</td>\n",
       "      <td>238.5</td>\n",
       "      <td>238.5</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>239.9</td>\n",
       "      <td>240.0</td>\n",
       "      <td>240.0</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>239.9</td>\n",
       "      <td>240.0</td>\n",
       "      <td>240.0</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OTI  WTI  ATI   OLI  OTI_A  OTI_T    VL1    VL2    VL3  IL1  IL2  IL3  \\\n",
       "0  0.0  0.0  0.0  37.0    0.0    0.0    0.0    0.0    0.0  0.0  0.0  0.0   \n",
       "1  0.0  0.0  0.0  38.0    0.0    0.0  238.7  238.7  238.8  0.0  0.0  0.0   \n",
       "2  0.0  0.0  0.0  38.0    0.0    0.0  238.4  238.5  238.5  0.0  0.0  0.0   \n",
       "3  0.0  0.0  0.0  39.0    0.0    0.0  239.9  240.0  240.0  0.0  0.0  0.0   \n",
       "4  0.0  0.0  0.0  39.0    0.0    0.0  239.9  240.0  240.0  0.0  0.0  0.0   \n",
       "\n",
       "   VL12  VL23  VL31  INUT  \n",
       "0   0.0   0.0   0.0   0.0  \n",
       "1   0.0   0.0   0.0   0.0  \n",
       "2   0.0   0.0   0.0   0.0  \n",
       "3   0.0   0.0   0.0   0.0  \n",
       "4   0.0   0.0   0.0   0.0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train test split\n",
    "X = transformer.drop(['DeviceTimeStamp','MOG_A'],axis=1)\n",
    "y = transformer['MOG_A']\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=.20,random_state=21)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "gBQ8RvT2ftFz"
   },
   "outputs": [],
   "source": [
    "#Normalizing the data\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "# [31, 0, 28, 45, 0, 0]\n",
    "# [242, 243.2, 240.1, 71.2, 35, 80.4, 426.5, 423.24, 427.1, 42.9 ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Kk14RI-fdyU_",
    "outputId": "56b96c3a-18f8-46cf-e35b-103d31255955"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy    : 100.0 %\n",
      "Model Accuracy Score : 98.83 %\n",
      "\u001b[1m--------------------------------------------------------\u001b[0m\n",
      "Classification_Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.99      0.99      3684\n",
      "         1.0       0.94      0.95      0.94       409\n",
      "\n",
      "    accuracy                           0.99      4093\n",
      "   macro avg       0.97      0.97      0.97      4093\n",
      "weighted avg       0.99      0.99      0.99      4093\n",
      "\n",
      "\u001b[1m--------------------------------------------------------\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[3658,   26],\n",
       "       [  22,  387]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest trained model performance analysis\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, y_train)\n",
    "y_pred_rf = random_forest.predict(X_test)\n",
    "random_forest.score(X_train, y_train)\n",
    "\n",
    "random_forest_train = round(random_forest.score(X_train, y_train) * 100, 2)\n",
    "random_forest_accuracy = round(accuracy_score(y_pred_rf, y_test) * 100, 2)\n",
    "\n",
    "print(\"Training Accuracy    :\",random_forest_train ,\"%\")\n",
    "print(\"Model Accuracy Score :\",random_forest_accuracy ,\"%\")\n",
    "print(\"\\033[1m--------------------------------------------------------\\033[0m\")\n",
    "print(\"Classification_Report: \\n\",classification_report(y_test,y_pred_rf))\n",
    "print(\"\\033[1m--------------------------------------------------------\\033[0m\")\n",
    "confusion_matrix(y_test, y_pred_rf)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m0i2VjZbo8q-"
   },
   "source": [
    "**Question:** Find if there is fault in Magnetic Oil Gauge of the transformer for the given values from sensor.\n",
    "[31, 0, 28, 45, 0, 0, 242, 243.2, 240.1, 71.2, 35, 80.4, 426.5, 423.24, 427.1, 42.9 ]\n",
    "Need to normalize these value before passing into the model for prediction.\n",
    "\n",
    "The meaning of the above data in order is:\n",
    "OTI- Oil Temperature Indicator\n",
    "WTI- Winding Temperature Indicator\n",
    "ATI- Ambient Temperature Indicator\n",
    "OLI- Oil Level Indicator\n",
    "OTIA- Oil Temperature Indicator Alarm\n",
    "OIIT- Oil Temperature Indicator Trip\n",
    "MOG_A- Magnetic oil gauge indicator\n",
    "\n",
    "VL1- Phase Line 1\n",
    "VL2- Phase Line 2\n",
    "VL3- Phase Line 3\n",
    "\n",
    "IL1- Current Line 1\n",
    "IL2- Current Line 2\n",
    "IL3- Current Line 3\n",
    "\n",
    "VL12- Voltage Line 12\n",
    "VL23- Voltage Line 23\n",
    "VL31- Voltage Line 31\n",
    "INUT- Neutral Current\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KQBpUwY4gCnU",
    "outputId": "4fd0d500-b332-43ca-bce5-9d14bb1ea561"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# random_forest.predict([[31, 0, 28, 45, 0, 0, 242, 243.2, 240.1, 71.2, 35, 80.4, 426.5, 423.24, 427.1, 42.9]]\n",
    "# Model Prediction using random normalized data\n",
    "random_forest.predict([[0.14,\n",
    " 0.0,\n",
    " 0.68,\n",
    " 0.06,\n",
    " 0.0,\n",
    " 0.0,\n",
    " 0.86,\n",
    " 0.85,\n",
    " 0.86,\n",
    " 0.76,\n",
    " 0.61,\n",
    " 0.67,\n",
    " 0.86,\n",
    " 0.87,\n",
    " 0.87,\n",
    " 0.08]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nTkK_sAzyjvu",
    "outputId": "21bee247-2701-4926-8850-16b68fefa253"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Testing prediction value on random test data\n",
    "random_forest.predict([X_test[3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KbHWmIuSyxZY",
    "outputId": "579367f0-5c5b-4189-bb12-dd6a0b465be0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest.predict([X_test[3990]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tCgJz3Wa0E5c",
    "outputId": "5db22c4b-c3e7-47c2-9a88-a2479f8f976f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['random_forest.joblib']"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model dump as joblib file to use it in web-app\n",
    "import joblib\n",
    "joblib.dump(random_forest, 'random_forest.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16,)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_columns = X.columns\n",
    "feature_columns.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16,)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature Importances\n",
    "feature_importances = random_forest.feature_importances_\n",
    "feature_importances.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_columns</th>\n",
       "      <th>feature_importances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OTI</td>\n",
       "      <td>0.047787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WTI</td>\n",
       "      <td>0.000114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ATI</td>\n",
       "      <td>0.039377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OLI</td>\n",
       "      <td>0.387429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OTI_A</td>\n",
       "      <td>0.002571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OTI_T</td>\n",
       "      <td>0.001388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>VL1</td>\n",
       "      <td>0.044424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>VL2</td>\n",
       "      <td>0.061851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>VL3</td>\n",
       "      <td>0.042237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>IL1</td>\n",
       "      <td>0.048575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>IL2</td>\n",
       "      <td>0.086774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>IL3</td>\n",
       "      <td>0.045219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>VL12</td>\n",
       "      <td>0.041449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>VL23</td>\n",
       "      <td>0.046053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>VL31</td>\n",
       "      <td>0.062263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>INUT</td>\n",
       "      <td>0.042489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feature_columns  feature_importances\n",
       "0              OTI             0.047787\n",
       "1              WTI             0.000114\n",
       "2              ATI             0.039377\n",
       "3              OLI             0.387429\n",
       "4            OTI_A             0.002571\n",
       "5            OTI_T             0.001388\n",
       "6              VL1             0.044424\n",
       "7              VL2             0.061851\n",
       "8              VL3             0.042237\n",
       "9              IL1             0.048575\n",
       "10             IL2             0.086774\n",
       "11             IL3             0.045219\n",
       "12            VL12             0.041449\n",
       "13            VL23             0.046053\n",
       "14            VL31             0.062263\n",
       "15            INUT             0.042489"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'feature_columns':feature_columns, 'feature_importances':feature_importances})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "xXldOKkH2Ydu"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.1 , 0.  , 0.5 , ..., 0.97, 0.97, 0.17],\n",
       "       [0.12, 0.  , 0.64, ..., 0.93, 0.92, 0.38],\n",
       "       [0.1 , 0.  , 0.5 , ..., 0.95, 0.95, 0.28],\n",
       "       ...,\n",
       "       [0.14, 0.  , 0.66, ..., 0.9 , 0.89, 0.31],\n",
       "       [0.14, 0.  , 0.66, ..., 0.89, 0.89, 0.17],\n",
       "       [0.06, 0.  , 0.3 , ..., 0.99, 0.99, 0.15]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a sensor_data.csv file for simulating real-time generation of sensor data.\n",
    "# pd.to_csv(X_train,'input_values.csv')\n",
    "X_train\n",
    "x = np.round(X_train,2)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('sensor_data.csv', x, delimiter=\",\", fmt=\"%.2f\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>10219</th>\n",
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       "      <td>0.41</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.1</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0    1     2    3    4    5     6     7     8     9     10    11  \\\n",
       "10219  0.08  1.0  0.41  1.0  0.0  0.0  0.98  0.97  0.97  0.13  0.07  0.14   \n",
       "\n",
       "         12    13    14   15  \n",
       "10219  0.99  0.99  0.99  0.1  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_dataframe = pd.DataFrame(x)\n",
    "x_dataframe.sample(1)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "ml",
   "language": "python",
   "name": "ml"
  },
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    "version": 3
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