Final Model Posstgres (2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Import libraries \n",
    "import numpy as np\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "from sklearn.preprocessing import QuantileTransformer\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from numpy import int64\n",
    "from sklearn import svm\n",
    "from sklearn.svm import SVC \n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from xgboost import XGBClassifier\n",
    "import xgboost\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.metrics import classification_report, accuracy_score  \n",
    "from sklearn.metrics import precision_score, recall_score \n",
    "from sklearn.metrics import f1_score, matthews_corrcoef \n",
    "from sklearn.metrics import confusion_matrix \n",
    "from pyhive import hive\n",
    "#from impala.dbapi import connect\n",
    "from hdfs import InsecureClient\n",
    "from pyhive import hive\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "from sklearn.utils import resample\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt # plotting\n",
    "import numpy as np # linear algebra\n",
    "import os # accessing directory structure\n",
    "import pandas as pd # data processing\n",
    "from pandas.plotting import scatter_matrix\n",
    "#import library psycopyg2\n",
    "import psycopg2\n",
    "#import library pandas\n",
    "import pandas as pd\n",
    "#import library sqlio\n",
    "import pandas.io.sql as sqlio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pgadmin\n",
    "conn = psycopg2.connect(user=\"data_user\", password=\"kgtopg8932\", host=\"localhost\", database=\"rawData\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pgadmin\n",
    "query = \"select * from schema1.client_google\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>serial_number</th>\n",
       "      <th>model</th>\n",
       "      <th>capacity_bytes</th>\n",
       "      <th>failure</th>\n",
       "      <th>smart_1_normalized</th>\n",
       "      <th>smart_1_raw</th>\n",
       "      <th>smart_2_normalized</th>\n",
       "      <th>smart_2_raw</th>\n",
       "      <th>smart_3_normalized</th>\n",
       "      <th>...</th>\n",
       "      <th>smart_250_normalized</th>\n",
       "      <th>smart_250_raw</th>\n",
       "      <th>smart_251_normalized</th>\n",
       "      <th>smart_251_raw</th>\n",
       "      <th>smart_252_normalized</th>\n",
       "      <th>smart_252_raw</th>\n",
       "      <th>smart_254_normalized</th>\n",
       "      <th>smart_254_raw</th>\n",
       "      <th>smart_255_normalized</th>\n",
       "      <th>smart_255_raw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-12-08</td>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>Hitachi HUA723020ALA640</td>\n",
       "      <td>2.000399e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-12-08</td>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>ST3000NM0033-9ZM178</td>\n",
       "      <td>3.000593e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>126329603.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-12-08</td>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>Hitachi HUA723020ALA640</td>\n",
       "      <td>2.000399e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-12-08</td>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>Hitachi HUA723020ALA640</td>\n",
       "      <td>2.000399e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-12-08</td>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>ST3000NM0033-9ZM178</td>\n",
       "      <td>3.000593e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>126329603.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>29483</th>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>Z302AKTE</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>93890928.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29484</th>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>Z3015LLW</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>241481608.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29485</th>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>W30062HL</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>153500184.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29486</th>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>W300J887</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>101796208.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29487</th>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>WD-WX11DA4DJNCS</td>\n",
       "      <td>WDC WD60EFRX</td>\n",
       "      <td>6.001175e+12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29488 rows × 169 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date    serial_number                    model  capacity_bytes  \\\n",
       "0      2021-12-08   MK0271YGJP8LVA  Hitachi HUA723020ALA640    2.000399e+12   \n",
       "1      2021-12-08         Z1Y45RA5      ST3000NM0033-9ZM178    3.000593e+12   \n",
       "2      2021-12-08   MK0271YGJA4WUA  Hitachi HUA723020ALA640    2.000399e+12   \n",
       "3      2021-12-08   MK0271YGJP8LVA  Hitachi HUA723020ALA640    2.000399e+12   \n",
       "4      2021-12-08         Z1Y45RA5      ST3000NM0033-9ZM178    3.000593e+12   \n",
       "...           ...              ...                      ...             ...   \n",
       "29483  2015-12-29         Z302AKTE              ST4000DM000    4.000787e+12   \n",
       "29484  2015-12-29         Z3015LLW              ST4000DM000    4.000787e+12   \n",
       "29485  2015-12-29         W30062HL              ST4000DM000    4.000787e+12   \n",
       "29486  2015-12-29         W300J887              ST4000DM000    4.000787e+12   \n",
       "29487  2015-12-29  WD-WX11DA4DJNCS             WDC WD60EFRX    6.001175e+12   \n",
       "\n",
       "       failure  smart_1_normalized  smart_1_raw  smart_2_normalized  \\\n",
       "0          0.0               100.0          0.0               135.0   \n",
       "1          0.0                81.0  126329603.0                 NaN   \n",
       "2          0.0               100.0          0.0               135.0   \n",
       "3          0.0               100.0          0.0               135.0   \n",
       "4          0.0                81.0  126329603.0                 NaN   \n",
       "...        ...                 ...          ...                 ...   \n",
       "29483      1.0               115.0   93890928.0                 NaN   \n",
       "29484      1.0               120.0  241481608.0                 NaN   \n",
       "29485      1.0               117.0  153500184.0                 NaN   \n",
       "29486      1.0               116.0  101796208.0                 NaN   \n",
       "29487      1.0               200.0          0.0                 NaN   \n",
       "\n",
       "       smart_2_raw  smart_3_normalized  ...  smart_250_normalized  \\\n",
       "0             84.0               135.0  ...                   NaN   \n",
       "1              NaN                93.0  ...                   NaN   \n",
       "2             84.0               134.0  ...                   NaN   \n",
       "3             84.0               135.0  ...                   NaN   \n",
       "4              NaN                93.0  ...                   NaN   \n",
       "...            ...                 ...  ...                   ...   \n",
       "29483          NaN                93.0  ...                   NaN   \n",
       "29484          NaN                94.0  ...                   NaN   \n",
       "29485          NaN                91.0  ...                   NaN   \n",
       "29486          NaN                91.0  ...                   NaN   \n",
       "29487          NaN               100.0  ...                   NaN   \n",
       "\n",
       "       smart_250_raw  smart_251_normalized  smart_251_raw  \\\n",
       "0                NaN                   NaN            NaN   \n",
       "1                NaN                   NaN            NaN   \n",
       "2                NaN                   NaN            NaN   \n",
       "3                NaN                   NaN            NaN   \n",
       "4                NaN                   NaN            NaN   \n",
       "...              ...                   ...            ...   \n",
       "29483            NaN                   NaN            NaN   \n",
       "29484            NaN                   NaN            NaN   \n",
       "29485            NaN                   NaN            NaN   \n",
       "29486            NaN                   NaN            NaN   \n",
       "29487            NaN                   NaN            NaN   \n",
       "\n",
       "       smart_252_normalized  smart_252_raw  smart_254_normalized  \\\n",
       "0                       NaN            NaN                   NaN   \n",
       "1                       NaN            NaN                   NaN   \n",
       "2                       NaN            NaN                   NaN   \n",
       "3                       NaN            NaN                   NaN   \n",
       "4                       NaN            NaN                   NaN   \n",
       "...                     ...            ...                   ...   \n",
       "29483                   NaN            NaN                   NaN   \n",
       "29484                   NaN            NaN                   NaN   \n",
       "29485                   NaN            NaN                   NaN   \n",
       "29486                   NaN            NaN                   NaN   \n",
       "29487                   NaN            NaN                   NaN   \n",
       "\n",
       "       smart_254_raw  smart_255_normalized  smart_255_raw  \n",
       "0                NaN                   NaN            NaN  \n",
       "1                NaN                   NaN            NaN  \n",
       "2                NaN                   NaN            NaN  \n",
       "3                NaN                   NaN            NaN  \n",
       "4                NaN                   NaN            NaN  \n",
       "...              ...                   ...            ...  \n",
       "29483            NaN                   NaN            NaN  \n",
       "29484            NaN                   NaN            NaN  \n",
       "29485            NaN                   NaN            NaN  \n",
       "29486            NaN                   NaN            NaN  \n",
       "29487            NaN                   NaN            NaN  \n",
       "\n",
       "[29488 rows x 169 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pgadmin\n",
    "dataset = sqlio.read_sql_query(query,conn)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                     object\n",
       "serial_number            object\n",
       "model                    object\n",
       "capacity_bytes          float64\n",
       "failure                 float64\n",
       "                         ...   \n",
       "smart_252_raw           float64\n",
       "smart_254_normalized    float64\n",
       "smart_254_raw           float64\n",
       "smart_255_normalized    float64\n",
       "smart_255_raw           float64\n",
       "Length: 169, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Missing value Imputation \n",
    "dataset['smart_255_raw']=dataset['smart_255_raw'].fillna(dataset['smart_255_raw'].mean())\n",
    "dataset['smart_15_normalized']=dataset['smart_15_normalized'].fillna(dataset['smart_15_normalized'].mean())\n",
    "dataset['smart_255_normalized']=dataset['smart_255_normalized'].fillna(dataset['smart_255_normalized'].mean())\n",
    "dataset['smart_252_raw']=dataset['smart_252_raw'].fillna(dataset['smart_252_raw'].mean())\n",
    "dataset['smart_252_normalized']=dataset['smart_252_normalized'].fillna(dataset['smart_252_normalized'].mean())\n",
    "dataset['smart_251_raw']=dataset['smart_251_raw'].fillna(dataset['smart_251_raw'].mean())\n",
    "dataset['smart_251_normalized']=dataset['smart_251_normalized'].fillna(dataset['smart_251_normalized'].mean())\n",
    "dataset['smart_250_raw']=dataset['smart_250_raw'].fillna(dataset['smart_250_raw'].mean())\n",
    "dataset['smart_250_normalized']=dataset['smart_250_normalized'].fillna(dataset['smart_250_normalized'].mean())\n",
    "dataset['smart_15_raw']=dataset['smart_15_raw'].fillna(dataset['smart_15_raw'].mean())\n",
    "dataset['smart_234_normalized']=dataset['smart_234_normalized'].fillna(dataset['smart_234_normalized'].mean())\n",
    "dataset['smart_234_raw']=dataset['smart_234_raw'].fillna(dataset['smart_234_raw'].mean())\n",
    "dataset['smart_206_normalized']=dataset['smart_206_normalized'].fillna(dataset['smart_206_normalized'].mean())\n",
    "dataset['smart_206_raw']=dataset['smart_206_raw'].fillna(dataset['smart_206_raw'].mean())\n",
    "dataset['smart_210_raw']=dataset['smart_210_raw'].fillna(dataset['smart_210_raw'].mean())\n",
    "dataset['smart_210_normalized']=dataset['smart_210_normalized'].fillna(dataset['smart_210_normalized'].mean())\n",
    "dataset['smart_248_raw']=dataset['smart_248_raw'].fillna(dataset['smart_248_raw'].mean())\n",
    "dataset['smart_248_normalized']=dataset['smart_248_normalized'].fillna(dataset['smart_248_normalized'].mean())\n",
    "dataset['smart_247_normalized']=dataset['smart_247_normalized'].fillna(dataset['smart_247_normalized'].mean())\n",
    "dataset['smart_247_raw']=dataset['smart_247_raw'].fillna(dataset['smart_247_raw'].mean())\n",
    "dataset['smart_166_normalized']=dataset['smart_166_normalized'].fillna(dataset['smart_166_normalized'].mean())\n",
    "dataset['smart_178_raw']=dataset['smart_178_raw'].fillna(dataset['smart_178_raw'].mean())\n",
    "dataset['smart_160_normalized']=dataset['smart_160_normalized'].fillna(dataset['smart_160_normalized'].mean())\n",
    "dataset['smart_161_normalized']=dataset['smart_161_normalized'].fillna(dataset['smart_161_normalized'].mean())\n",
    "dataset['smart_161_raw']=dataset['smart_161_raw'].fillna(dataset['smart_161_raw'].mean())\n",
    "dataset['smart_163_normalized']=dataset['smart_163_normalized'].fillna(dataset['smart_163_normalized'].mean())\n",
    "dataset['smart_163_raw']=dataset['smart_163_raw'].fillna(dataset['smart_163_raw'].mean())\n",
    "dataset['smart_164_normalized']=dataset['smart_164_normalized'].fillna(dataset['smart_164_normalized'].mean())\n",
    "dataset['smart_164_raw']=dataset['smart_164_raw'].fillna(dataset['smart_164_raw'].mean())\n",
    "dataset['smart_165_normalized']=dataset['smart_165_normalized'].fillna(dataset['smart_165_normalized'].mean())\n",
    "dataset['smart_165_raw']=dataset['smart_165_raw'].fillna(dataset['smart_165_raw'].mean())\n",
    "dataset['smart_160_raw']=dataset['smart_160_raw'].fillna(dataset['smart_160_raw'].mean())\n",
    "dataset['smart_176_raw']=dataset['smart_176_raw'].fillna(dataset['smart_176_raw'].mean())\n",
    "dataset['smart_176_normalized']=dataset['smart_176_normalized'].fillna(dataset['smart_176_normalized'].mean())\n",
    "dataset['smart_178_normalized']=dataset['smart_178_normalized'].fillna(dataset['smart_178_normalized'].mean())\n",
    "dataset['smart_167_normalized']=dataset['smart_167_normalized'].fillna(dataset['smart_167_normalized'].mean())\n",
    "dataset['smart_166_raw']=dataset['smart_166_raw'].fillna(dataset['smart_166_raw'].mean())\n",
    "dataset['smart_169_raw']=dataset['smart_169_raw'].fillna(dataset['smart_169_raw'].mean())\n",
    "dataset['smart_169_normalized']=dataset['smart_169_normalized'].fillna(dataset['smart_169_normalized'].mean())\n",
    "dataset['smart_167_raw']=dataset['smart_167_raw'].fillna(dataset['smart_167_raw'].mean())\n",
    "dataset['smart_175_normalized']=dataset['smart_175_normalized'].fillna(dataset['smart_175_normalized'].mean())\n",
    "dataset['smart_175_raw']=dataset['smart_175_raw'].fillna(dataset['smart_175_raw'].mean())\n",
    "dataset['smart_180_raw']=dataset['smart_180_raw'].fillna(dataset['smart_180_raw'].mean())\n",
    "dataset['smart_201_normalized']=dataset['smart_201_normalized'].fillna(dataset['smart_201_normalized'].mean())\n",
    "dataset['smart_201_raw']=dataset['smart_201_raw'].fillna(dataset['smart_201_raw'].mean())\n",
    "dataset['smart_202_normalized']=dataset['smart_202_normalized'].fillna(dataset['smart_202_normalized'].mean())\n",
    "dataset['smart_180_normalized']=dataset['smart_180_normalized'].fillna(dataset['smart_180_normalized'].mean())\n",
    "dataset['smart_179_raw']=dataset['smart_179_raw'].fillna(dataset['smart_179_raw'].mean())\n",
    "dataset['smart_202_raw']=dataset['smart_202_raw'].fillna(dataset['smart_202_raw'].mean())\n",
    "dataset['smart_179_normalized']=dataset['smart_179_normalized'].fillna(dataset['smart_179_normalized'].mean())\n",
    "dataset['smart_13_normalized']=dataset['smart_13_normalized'].fillna(dataset['smart_13_normalized'].mean())\n",
    "dataset['smart_13_raw']=dataset['smart_13_raw'].fillna(dataset['smart_13_raw'].mean())\n",
    "dataset['smart_170_normalized']=dataset['smart_170_normalized'].fillna(dataset['smart_170_normalized'].mean())\n",
    "dataset['smart_17_normalized']=dataset['smart_17_normalized'].fillna(dataset['smart_17_normalized'].mean())\n",
    "dataset['smart_170_raw']=dataset['smart_170_raw'].fillna(dataset['smart_170_raw'].mean())\n",
    "dataset['smart_218_normalized']=dataset['smart_218_normalized'].fillna(dataset['smart_218_normalized'].mean())\n",
    "dataset['smart_231_raw']=dataset['smart_231_raw'].fillna(dataset['smart_231_raw'].mean())\n",
    "dataset['smart_231_normalized']=dataset['smart_231_normalized'].fillna(dataset['smart_231_normalized'].mean())\n",
    "dataset['smart_16_raw']=dataset['smart_16_raw'].fillna(dataset['smart_16_raw'].mean())\n",
    "dataset['smart_245_raw']=dataset['smart_245_raw'].fillna(dataset['smart_245_raw'].mean())\n",
    "dataset['smart_16_normalized']=dataset['smart_16_normalized'].fillna(dataset['smart_16_normalized'].mean())\n",
    "dataset['smart_17_raw']=dataset['smart_17_raw'].fillna(dataset['smart_17_raw'].mean())\n",
    "dataset['smart_245_normalized']=dataset['smart_245_normalized'].fillna(dataset['smart_245_normalized'].mean())\n",
    "dataset['smart_182_normalized']=dataset['smart_182_normalized'].fillna(dataset['smart_182_normalized'].mean())\n",
    "dataset['smart_181_raw']=dataset['smart_181_raw'].fillna(dataset['smart_181_raw'].mean())\n",
    "dataset['smart_181_normalized']=dataset['smart_181_normalized'].fillna(dataset['smart_181_normalized'].mean())\n",
    "dataset['smart_218_raw']=dataset['smart_218_raw'].fillna(dataset['smart_218_raw'].mean())\n",
    "dataset['smart_182_raw']=dataset['smart_182_raw'].fillna(dataset['smart_182_raw'].mean())\n",
    "dataset['smart_174_raw']=dataset['smart_174_raw'].fillna(dataset['smart_174_raw'].mean())\n",
    "dataset['smart_174_normalized']=dataset['smart_174_normalized'].fillna(dataset['smart_174_normalized'].mean())\n",
    "dataset['smart_173_raw']=dataset['smart_173_raw'].fillna(dataset['smart_173_raw'].mean())\n",
    "dataset['smart_173_normalized']=dataset['smart_173_normalized'].fillna(dataset['smart_173_normalized'].mean())\n",
    "dataset['smart_235_normalized']=dataset['smart_235_normalized'].fillna(dataset['smart_235_normalized'].mean())\n",
    "dataset['smart_235_raw']=dataset['smart_235_raw'].fillna(dataset['smart_235_raw'].mean())\n",
    "dataset['smart_232_normalized']=dataset['smart_232_normalized'].fillna(dataset['smart_232_normalized'].mean())\n",
    "dataset['smart_168_raw']=dataset['smart_168_raw'].fillna(dataset['smart_168_raw'].mean())\n",
    "dataset['smart_168_normalized']=dataset['smart_168_normalized'].fillna(dataset['smart_168_normalized'].mean())\n",
    "dataset['smart_177_raw']=dataset['smart_177_raw'].fillna(dataset['smart_177_raw'].mean())\n",
    "dataset['smart_177_normalized']=dataset['smart_177_normalized'].fillna(dataset['smart_177_normalized'].mean())\n",
    "dataset['smart_232_raw']=dataset['smart_232_raw'].fillna(dataset['smart_232_raw'].mean())\n",
    "dataset['smart_254_raw']=dataset['smart_254_raw'].fillna(dataset['smart_254_raw'].mean())\n",
    "dataset['smart_254_normalized']=dataset['smart_254_normalized'].fillna(dataset['smart_254_normalized'].mean())\n",
    "dataset['smart_233_raw']=dataset['smart_233_raw'].fillna(dataset['smart_233_raw'].mean())\n",
    "dataset['smart_233_normalized']=dataset['smart_233_normalized'].fillna(dataset['smart_233_normalized'].mean())\n",
    "dataset['smart_225_raw']=dataset['smart_225_raw'].fillna(dataset['smart_225_raw'].mean())\n",
    "dataset['smart_225_normalized']=dataset['smart_225_normalized'].fillna(dataset['smart_225_normalized'].mean())\n",
    "dataset['smart_11_raw']=dataset['smart_11_raw'].fillna(dataset['smart_11_raw'].mean())\n",
    "dataset['smart_11_normalized']=dataset['smart_11_normalized'].fillna(dataset['smart_11_normalized'].mean())\n",
    "dataset['smart_22_normalized']=dataset['smart_22_normalized'].fillna(dataset['smart_22_normalized'].mean())\n",
    "dataset['smart_22_raw']=dataset['smart_22_raw'].fillna(dataset['smart_22_raw'].mean())\n",
    "dataset['smart_23_normalized']=dataset['smart_23_normalized'].fillna(dataset['smart_23_normalized'].mean())\n",
    "dataset['smart_23_raw']=dataset['smart_23_raw'].fillna(dataset['smart_23_raw'].mean())\n",
    "dataset['smart_24_normalized']=dataset['smart_24_normalized'].fillna(dataset['smart_24_normalized'].mean())\n",
    "dataset['smart_24_raw']=dataset['smart_24_raw'].fillna(dataset['smart_24_raw'].mean())\n",
    "dataset['smart_183_raw']=dataset['smart_183_raw'].fillna(dataset['smart_183_raw'].mean())\n",
    "dataset['smart_183_normalized']=dataset['smart_183_normalized'].fillna(dataset['smart_183_normalized'].mean())\n",
    "dataset['smart_220_normalized']=dataset['smart_220_normalized'].fillna(dataset['smart_220_normalized'].mean())\n",
    "dataset['smart_226_raw']=dataset['smart_226_raw'].fillna(dataset['smart_226_raw'].mean())\n",
    "dataset['smart_226_normalized']=dataset['smart_226_normalized'].fillna(dataset['smart_226_normalized'].mean())\n",
    "dataset['smart_224_raw']=dataset['smart_224_raw'].fillna(dataset['smart_224_raw'].mean())\n",
    "dataset['smart_224_normalized']=dataset['smart_224_normalized'].fillna(dataset['smart_224_normalized'].mean())\n",
    "dataset['smart_222_raw']=dataset['smart_222_raw'].fillna(dataset['smart_222_raw'].mean())\n",
    "dataset['smart_222_normalized']=dataset['smart_222_normalized'].fillna(dataset['smart_222_normalized'].mean())\n",
    "dataset['smart_220_raw']=dataset['smart_220_raw'].fillna(dataset['smart_220_raw'].mean())\n",
    "dataset['smart_18_normalized']=dataset['smart_18_normalized'].fillna(dataset['smart_18_normalized'].mean())\n",
    "dataset['smart_18_raw']=dataset['smart_18_raw'].fillna(dataset['smart_18_raw'].mean())\n",
    "dataset['smart_223_raw']=dataset['smart_223_raw'].fillna(dataset['smart_223_raw'].mean())\n",
    "dataset['smart_223_normalized']=dataset['smart_223_normalized'].fillna(dataset['smart_223_normalized'].mean())\n",
    "dataset['smart_200_raw']=dataset['smart_200_raw'].fillna(dataset['smart_200_raw'].mean())\n",
    "dataset['smart_200_normalized']=dataset['smart_200_normalized'].fillna(dataset['smart_200_normalized'].mean())\n",
    "dataset['smart_242_raw']=dataset['smart_242_raw'].fillna(dataset['smart_242_raw'].mean())\n",
    "dataset['smart_242_normalized']=dataset['smart_242_normalized'].fillna(dataset['smart_242_normalized'].mean())\n",
    "dataset['smart_241_raw']=dataset['smart_241_raw'].fillna(dataset['smart_241_raw'].mean())\n",
    "dataset['smart_241_normalized']=dataset['smart_241_normalized'].fillna(dataset['smart_241_normalized'].mean())\n",
    "dataset['smart_240_raw']=dataset['smart_240_raw'].fillna(dataset['smart_240_raw'].mean())\n",
    "dataset['smart_240_normalized']=dataset['smart_240_normalized'].fillna(dataset['smart_240_normalized'].mean())\n",
    "dataset['smart_189_raw']=dataset['smart_189_raw'].fillna(dataset['smart_189_raw'].mean())\n",
    "dataset['smart_189_normalized']=dataset['smart_189_normalized'].fillna(dataset['smart_189_normalized'].mean())\n",
    "dataset['smart_184_raw']=dataset['smart_184_raw'].fillna(dataset['smart_184_raw'].mean())\n",
    "dataset['smart_184_normalized']=dataset['smart_184_normalized'].fillna(dataset['smart_184_normalized'].mean())\n",
    "dataset['smart_195_raw']=dataset['smart_195_raw'].fillna(dataset['smart_195_raw'].mean())\n",
    "dataset['smart_195_normalized']=dataset['smart_195_normalized'].fillna(dataset['smart_195_normalized'].mean())\n",
    "dataset['smart_191_raw']=dataset['smart_191_raw'].fillna(dataset['smart_191_raw'].mean())\n",
    "dataset['smart_191_normalized']=dataset['smart_191_normalized'].fillna(dataset['smart_191_normalized'].mean())\n",
    "dataset['smart_190_raw']=dataset['smart_190_raw'].fillna(dataset['smart_190_raw'].mean())\n",
    "dataset['smart_190_normalized']=dataset['smart_190_normalized'].fillna(dataset['smart_190_normalized'].mean())\n",
    "dataset['smart_187_normalized']=dataset['smart_187_normalized'].fillna(dataset['smart_187_normalized'].mean())\n",
    "dataset['smart_187_raw']=dataset['smart_187_raw'].fillna(dataset['smart_187_raw'].mean())\n",
    "dataset['smart_188_normalized']=dataset['smart_188_normalized'].fillna(dataset['smart_188_normalized'].mean())\n",
    "dataset['smart_188_raw']=dataset['smart_188_raw'].fillna(dataset['smart_188_raw'].mean())\n",
    "dataset['smart_8_normalized']=dataset['smart_8_normalized'].fillna(dataset['smart_8_normalized'].mean())\n",
    "dataset['smart_2_normalized']=dataset['smart_2_normalized'].fillna(dataset['smart_2_normalized'].mean())\n",
    "dataset['smart_2_raw']=dataset['smart_2_raw'].fillna(dataset['smart_2_raw'].mean())\n",
    "dataset['smart_8_raw']=dataset['smart_8_raw'].fillna(dataset['smart_8_raw'].mean())\n",
    "dataset['smart_196_normalized']=dataset['smart_196_normalized'].fillna(dataset['smart_196_normalized'].mean())\n",
    "dataset['smart_196_raw']=dataset['smart_196_raw'].fillna(dataset['smart_196_raw'].mean())\n",
    "dataset['smart_193_raw']=dataset['smart_193_raw'].fillna(dataset['smart_193_raw'].mean())\n",
    "dataset['smart_193_normalized']=dataset['smart_193_normalized'].fillna(dataset['smart_193_normalized'].mean())\n",
    "dataset['smart_197_normalized']=dataset['smart_197_normalized'].fillna(dataset['smart_197_normalized'].mean())\n",
    "dataset['smart_197_raw']=dataset['smart_197_raw'].fillna(dataset['smart_197_raw'].mean())\n",
    "dataset['smart_3_normalized']=dataset['smart_3_normalized'].fillna(dataset['smart_3_normalized'].mean())\n",
    "dataset['smart_3_raw']=dataset['smart_3_raw'].fillna(dataset['smart_3_raw'].mean())\n",
    "dataset['smart_4_normalized']=dataset['smart_4_normalized'].fillna(dataset['smart_4_normalized'].mean())\n",
    "dataset['smart_4_raw']=dataset['smart_4_raw'].fillna(dataset['smart_4_raw'].mean())\n",
    "dataset['smart_7_normalized']=dataset['smart_7_normalized'].fillna(dataset['smart_7_normalized'].mean())\n",
    "dataset['smart_10_normalized']=dataset['smart_10_normalized'].fillna(dataset['smart_10_normalized'].mean())\n",
    "dataset['smart_7_raw']=dataset['smart_7_raw'].fillna(dataset['smart_7_raw'].mean())\n",
    "dataset['smart_10_raw']=dataset['smart_10_raw'].fillna(dataset['smart_10_raw'].mean())\n",
    "dataset['smart_5_normalized']=dataset['smart_5_normalized'].fillna(dataset['smart_5_normalized'].mean())\n",
    "dataset['smart_5_raw']=dataset['smart_5_raw'].fillna(dataset['smart_5_raw'].mean())\n",
    "dataset['smart_199_raw']=dataset['smart_199_raw'].fillna(dataset['smart_199_raw'].mean())\n",
    "dataset['smart_198_normalized']=dataset['smart_198_normalized'].fillna(dataset['smart_198_normalized'].mean())\n",
    "dataset['smart_199_normalized']=dataset['smart_199_normalized'].fillna(dataset['smart_199_normalized'].mean())\n",
    "dataset['smart_198_raw']=dataset['smart_198_raw'].fillna(dataset['smart_198_raw'].mean())\n",
    "dataset['smart_192_normalized']=dataset['smart_192_normalized'].fillna(dataset['smart_192_normalized'].mean())\n",
    "dataset['smart_192_raw']=dataset['smart_192_raw'].fillna(dataset['smart_192_raw'].mean())\n",
    "dataset['smart_9_raw']=dataset['smart_9_raw'].fillna(dataset['smart_9_raw'].mean())\n",
    "dataset['smart_1_normalized']=dataset['smart_1_normalized'].fillna(dataset['smart_1_normalized'].mean())\n",
    "dataset['smart_1_raw']=dataset['smart_1_raw'].fillna(dataset['smart_1_raw'].mean())\n",
    "dataset['smart_9_normalized']=dataset['smart_9_normalized'].fillna(dataset['smart_9_normalized'].mean())\n",
    "dataset['smart_194_raw']=dataset['smart_194_raw'].fillna(dataset['smart_194_raw'].mean())\n",
    "dataset['smart_194_normalized']=dataset['smart_194_normalized'].fillna(dataset['smart_194_normalized'].mean())\n",
    "dataset['smart_12_raw']=dataset['smart_12_raw'].fillna(dataset['smart_12_raw'].mean())\n",
    "dataset['smart_12_normalized']=dataset['smart_12_normalized'].fillna(dataset['smart_12_normalized'].mean())\n",
    "#dataset['failure']=dataset['failure'].fillna(dataset['failure'].mean())\n",
    "#dataset['capacity_bytes']=dataset['capacity_bytes'].fillna(dataset['capacity_bytes'].mean())\n",
    "#dataset['model']=dataset['model'].fillna(dataset['model'].mean())\n",
    "#dataset['serial_number']=dataset['serial_number'].fillna(dataset['serial_number'].mean())\n",
    "#dataset['date']=dataset['date'].fillna(dataset['date'].mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove CORRILATION VARIYABLE \n",
    "# smart_226_raw - correlated with capacity_bytes\n",
    "# smart_8_normalized - correlated withsmart_2_normalized\n",
    "# smart_254_normalized - correlated with smart_3_normalized\n",
    "# smart_12_raw correlated withsmart_4_raw\n",
    "# smart_192_raw correlated with smart_4_raw\n",
    "# smart_196_normalized correlated with smart_5_normalized\n",
    "# smart_222_normalized correlated with smart_9_normalized\n",
    "# smart_175_raw correlated with smart_9_normalized\n",
    "# smart_190_normalized correlated with smart_11_raw\n",
    "# smart_192_raw correlated with smart_12_raw\n",
    "# smart_13_raw - correlated with capacity_bytes\n",
    "# smart_177_normalized - correlated with smart_22_normalized\n",
    "# smart_164_raw - correlated with smart_165_raw\n",
    "# smart_167_raw - correlated withsmart_165_raw\n",
    "# smart_175_raw - many\n",
    "# smart_190_raw - correlated with smart_194_normalized and smart_196_normalized\n",
    "# smart_202_raw - correlated with smart_180_raw and smart_194_normalized\n",
    "# smart_226_raw - correlated with smart_3_raw\n",
    "# smart_254_normalized - correlated with many\n",
    "\n",
    "\n",
    "dataset = dataset.drop(['smart_226_raw'], 1)\n",
    "dataset = dataset.drop(['smart_8_normalized'], 1)\n",
    "dataset = dataset.drop(['smart_254_normalized'], 1)\n",
    "dataset = dataset.drop(['smart_12_raw'], 1)\n",
    "dataset = dataset.drop(['smart_192_raw'], 1)\n",
    "dataset = dataset.drop(['smart_196_normalized'], 1)\n",
    "dataset = dataset.drop(['smart_222_normalized'], 1)\n",
    "dataset = dataset.drop(['smart_175_raw'], 1)\n",
    "dataset = dataset.drop(['smart_190_normalized'], 1)\n",
    "dataset = dataset.drop(['smart_13_raw'], 1)\n",
    "dataset = dataset.drop(['smart_177_normalized'], 1)\n",
    "dataset = dataset.drop(['smart_164_raw'], 1)\n",
    "dataset = dataset.drop(['smart_167_raw'], 1)\n",
    "dataset = dataset.drop(['smart_190_raw'], 1)\n",
    "dataset = dataset.drop(['smart_202_raw'], 1)\n",
    "\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop date\n",
    "dataset = dataset.drop(['date'], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Univariate analysis & describe analysis\n",
    "#univariate_analysis=dataset.describe()\n",
    "#univariate_analysis.to_csv(r'E:\\work\\Supermicro\\modeling\\data_Q2_2019\\univariate_analysis.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove top 10 missing variables\n",
    "dataset = dataset.drop(['smart_255_raw'], 1)\n",
    "dataset = dataset.drop(['smart_15_normalized'], 1)\n",
    "dataset = dataset.drop(['smart_234_raw'], 1)\n",
    "dataset = dataset.drop(['smart_255_normalized'], 1)\n",
    "dataset = dataset.drop(['smart_15_raw'], 1)\n",
    "dataset = dataset.drop(['smart_234_normalized'], 1)\n",
    "dataset = dataset.drop(['smart_206_normalized'], 1)\n",
    "dataset = dataset.drop(['smart_206_raw'], 1)\n",
    "dataset = dataset.drop(['smart_248_raw'], 1)\n",
    "dataset = dataset.drop(['smart_248_normalized'], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove below variable as it has only one value \n",
    "\n",
    "dataset = dataset.drop(['smart_210_raw'], 1)\n",
    "dataset = dataset.drop(['smart_224_raw'], 1)\n",
    "dataset = dataset.drop(['smart_18_raw'], 1)\n",
    "dataset = dataset.drop(['smart_23_raw'], 1)\n",
    "dataset = dataset.drop(['smart_24_raw'], 1)\n",
    "dataset = dataset.drop(['smart_166_raw'], 1)\n",
    "dataset = dataset.drop(['smart_176_raw'], 1)\n",
    "dataset = dataset.drop(['smart_179_raw'], 1)\n",
    "dataset = dataset.drop(['smart_181_raw'], 1)\n",
    "dataset = dataset.drop(['smart_182_raw'], 1)\n",
    "dataset = dataset.drop(['smart_251_normalized'], 1)\n",
    "dataset = dataset.drop(['smart_250_normalized'], 1)\n",
    "dataset = dataset.drop(['smart_254_raw'], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# normalize the data\n",
    "dataset['normalized_capacity_bytes'] = (dataset['capacity_bytes'] - dataset['capacity_bytes'].min()) / (dataset['capacity_bytes'].max() - dataset['capacity_bytes'].min())\n",
    "dataset['normalized_smart_1_raw'] = (dataset['smart_1_raw'] - dataset['smart_1_raw'].min()) / (dataset['smart_1_raw'].max() - dataset['smart_1_raw'].min())\n",
    "dataset['normalized_smart_241_raw'] = (dataset['smart_241_raw'] - dataset['smart_241_raw'].min()) / (dataset['smart_241_raw'].max() - dataset['smart_241_raw'].min())\n",
    "dataset['normalized_smart_242_raw'] = (dataset['smart_242_raw'] - dataset['smart_242_raw'].min()) / (dataset['smart_242_raw'].max() - dataset['smart_242_raw'].min())\n",
    "dataset['normalized_smart_7_raw'] = (dataset['smart_7_raw'] - dataset['smart_7_raw'].min()) / (dataset['smart_7_raw'].max() - dataset['smart_7_raw'].min())\n",
    "dataset['normalized_smart_9_raw'] = (dataset['smart_9_raw'] - dataset['smart_9_raw'].min()) / (dataset['smart_9_raw'].max() - dataset['smart_9_raw'].min())\n",
    "dataset['normalized_smart_11_raw'] = (dataset['smart_11_raw'] - dataset['smart_11_raw'].min()) / (dataset['smart_11_raw'].max() - dataset['smart_11_raw'].min())\n",
    "dataset['normalized_smart_173_raw'] = (dataset['smart_173_raw'] - dataset['smart_173_raw'].min()) / (dataset['smart_173_raw'].max() - dataset['smart_173_raw'].min())\n",
    "dataset['normalized_smart_188_raw'] = (dataset['smart_188_raw'] - dataset['smart_188_raw'].min()) / (dataset['smart_188_raw'].max() - dataset['smart_188_raw'].min())\n",
    "dataset['normalized_smart_193_raw'] = (dataset['smart_193_raw'] - dataset['smart_193_raw'].min()) / (dataset['smart_193_raw'].max() - dataset['smart_193_raw'].min())\n",
    "dataset['normalized_smart_195_raw'] = (dataset['smart_195_raw'] - dataset['smart_195_raw'].min()) / (dataset['smart_195_raw'].max() - dataset['smart_195_raw'].min())\n",
    "dataset['normalized_smart_225_raw'] = (dataset['smart_225_raw'] - dataset['smart_225_raw'].min()) / (dataset['smart_225_raw'].max() - dataset['smart_225_raw'].min())\n",
    "dataset['normalized_smart_232_raw'] = (dataset['smart_232_raw'] - dataset['smart_232_raw'].min()) / (dataset['smart_232_raw'].max() - dataset['smart_232_raw'].min())\n",
    "dataset['normalized_smart_233_raw'] = (dataset['smart_233_raw'] - dataset['smart_233_raw'].min()) / (dataset['smart_233_raw'].max() - dataset['smart_233_raw'].min())\n",
    "dataset['normalized_smart_235_raw'] = (dataset['smart_235_raw'] - dataset['smart_235_raw'].min()) / (dataset['smart_235_raw'].max() - dataset['smart_235_raw'].min())\n",
    "dataset['normalized_smart_240_raw'] = (dataset['smart_240_raw'] - dataset['smart_240_raw'].min()) / (dataset['smart_240_raw'].max() - dataset['smart_240_raw'].min())\n",
    "dataset['normalized_smart_247_raw'] = (dataset['smart_247_raw'] - dataset['smart_247_raw'].min()) / (dataset['smart_247_raw'].max() - dataset['smart_247_raw'].min())\n",
    "dataset['normalized_smart_251_raw'] = (dataset['smart_251_raw'] - dataset['smart_251_raw'].min()) / (dataset['smart_251_raw'].max() - dataset['smart_251_raw'].min())\n",
    "dataset['normalized_smart_252_raw'] = (dataset['smart_252_raw'] - dataset['smart_252_raw'].min()) / (dataset['smart_252_raw'].max() - dataset['smart_252_raw'].min())\n",
    "dataset['normalized_smart_5_raw'] = (dataset['smart_5_raw'] - dataset['smart_5_raw'].min()) / (dataset['smart_5_raw'].max() - dataset['smart_5_raw'].min())\n",
    "dataset['normalized_smart_197_raw'] = (dataset['smart_197_raw'] - dataset['smart_197_raw'].min()) / (dataset['smart_197_raw'].max() - dataset['smart_197_raw'].min())\n",
    "dataset['normalized_smart_10_raw'] = (dataset['smart_10_raw'] - dataset['smart_10_raw'].min()) / (dataset['smart_10_raw'].max() - dataset['smart_10_raw'].min())\n",
    "dataset['normalized_smart_198_raw'] = (dataset['smart_198_raw'] - dataset['smart_198_raw'].min()) / (dataset['smart_198_raw'].max() - dataset['smart_198_raw'].min())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop after normalization \n",
    "dataset = dataset.drop(['capacity_bytes'], 1)\n",
    "dataset = dataset.drop(['smart_1_raw'], 1)\n",
    "dataset = dataset.drop(['smart_241_raw'], 1)\n",
    "dataset = dataset.drop(['smart_242_raw'], 1)\n",
    "dataset = dataset.drop(['smart_7_raw'], 1)\n",
    "dataset = dataset.drop(['smart_9_raw'], 1)\n",
    "dataset = dataset.drop(['smart_11_raw'], 1)\n",
    "dataset = dataset.drop(['smart_173_raw'], 1)\n",
    "dataset = dataset.drop(['smart_188_raw'], 1)\n",
    "dataset = dataset.drop(['smart_193_raw'], 1)\n",
    "dataset = dataset.drop(['smart_195_raw'], 1)\n",
    "dataset = dataset.drop(['smart_225_raw'], 1)\n",
    "dataset = dataset.drop(['smart_232_raw'], 1)\n",
    "dataset = dataset.drop(['smart_233_raw'], 1)\n",
    "dataset = dataset.drop(['smart_235_raw'], 1)\n",
    "dataset = dataset.drop(['smart_240_raw'], 1)\n",
    "dataset = dataset.drop(['smart_247_raw'], 1)\n",
    "dataset = dataset.drop(['smart_251_raw'], 1)\n",
    "dataset = dataset.drop(['smart_252_raw'], 1)\n",
    "dataset = dataset.drop(['smart_5_raw'], 1)\n",
    "dataset = dataset.drop(['smart_197_raw'], 1)\n",
    "dataset = dataset.drop(['smart_10_raw'], 1)\n",
    "dataset = dataset.drop(['smart_198_raw'], 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one hot encoding or label encoder or creating dummies for all the categorical data\n",
    "# Label Encoding model\n",
    "\n",
    "dataset.model = pd.Categorical(dataset.model)\n",
    "dataset[\"model\"] = dataset[\"model\"].cat.codes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data conversion  ren only if required\n",
    "# dataset.failure = pd.Categorical(dataset.failure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the y variable \n",
    "target = 'failure'   #defining a global variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Machine learning algorithms require that the distribution of our data is uniform or normal\n",
    "qtrans = QuantileTransformer(output_distribution='normal', random_state=0)\n",
    "dataset['smart_1_normalized'] = qtrans.fit_transform(dataset[['smart_1_normalized']])\n",
    "dataset['smart_2_normalized'] = qtrans.fit_transform(dataset[['smart_2_normalized']])\n",
    "dataset['smart_2_raw'] = qtrans.fit_transform(dataset[['smart_2_raw']])\n",
    "dataset['smart_3_normalized'] = qtrans.fit_transform(dataset[['smart_3_normalized']])\n",
    "dataset['smart_3_raw'] = qtrans.fit_transform(dataset[['smart_3_raw']])\n",
    "dataset['smart_4_normalized'] = qtrans.fit_transform(dataset[['smart_4_normalized']])\n",
    "dataset['smart_4_raw'] = qtrans.fit_transform(dataset[['smart_4_raw']])\n",
    "dataset['smart_5_normalized'] = qtrans.fit_transform(dataset[['smart_5_normalized']])\n",
    "dataset['smart_7_normalized'] = qtrans.fit_transform(dataset[['smart_7_normalized']])\n",
    "dataset['smart_8_raw'] = qtrans.fit_transform(dataset[['smart_8_raw']])\n",
    "dataset['smart_9_normalized'] = qtrans.fit_transform(dataset[['smart_9_normalized']])\n",
    "dataset['smart_10_normalized'] = qtrans.fit_transform(dataset[['smart_10_normalized']])\n",
    "dataset['smart_11_normalized'] = qtrans.fit_transform(dataset[['smart_11_normalized']])\n",
    "dataset['smart_12_normalized'] = qtrans.fit_transform(dataset[['smart_12_normalized']])\n",
    "dataset['smart_13_normalized'] = qtrans.fit_transform(dataset[['smart_13_normalized']])\n",
    "dataset['smart_16_normalized'] = qtrans.fit_transform(dataset[['smart_16_normalized']])\n",
    "dataset['smart_16_raw'] = qtrans.fit_transform(dataset[['smart_16_raw']])\n",
    "dataset['smart_17_normalized'] = qtrans.fit_transform(dataset[['smart_17_normalized']])\n",
    "dataset['smart_17_raw'] = qtrans.fit_transform(dataset[['smart_17_raw']])\n",
    "dataset['smart_18_normalized'] = qtrans.fit_transform(dataset[['smart_18_normalized']])\n",
    "dataset['smart_22_normalized'] = qtrans.fit_transform(dataset[['smart_22_normalized']])\n",
    "dataset['smart_22_raw'] = qtrans.fit_transform(dataset[['smart_22_raw']])\n",
    "dataset['smart_23_normalized'] = qtrans.fit_transform(dataset[['smart_23_normalized']])\n",
    "dataset['smart_24_normalized'] = qtrans.fit_transform(dataset[['smart_24_normalized']])\n",
    "dataset['smart_160_normalized'] = qtrans.fit_transform(dataset[['smart_160_normalized']])\n",
    "dataset['smart_160_raw'] = qtrans.fit_transform(dataset[['smart_160_raw']])\n",
    "dataset['smart_161_normalized'] = qtrans.fit_transform(dataset[['smart_161_normalized']])\n",
    "dataset['smart_161_raw'] = qtrans.fit_transform(dataset[['smart_161_raw']])\n",
    "dataset['smart_163_normalized'] = qtrans.fit_transform(dataset[['smart_163_normalized']])\n",
    "dataset['smart_163_raw'] = qtrans.fit_transform(dataset[['smart_163_raw']])\n",
    "dataset['smart_164_normalized'] = qtrans.fit_transform(dataset[['smart_164_normalized']])\n",
    "dataset['smart_165_normalized'] = qtrans.fit_transform(dataset[['smart_165_normalized']])\n",
    "dataset['smart_165_raw'] = qtrans.fit_transform(dataset[['smart_165_raw']])\n",
    "#dataset['smart_166_normalized'] = qtrans.fit_transform(dataset[['smart_166_normalized']])\n",
    "dataset['smart_167_normalized'] = qtrans.fit_transform(dataset[['smart_167_normalized']])\n",
    "dataset['smart_168_normalized'] = qtrans.fit_transform(dataset[['smart_168_normalized']])\n",
    "dataset['smart_168_raw'] = qtrans.fit_transform(dataset[['smart_168_raw']])\n",
    "dataset['smart_169_normalized'] = qtrans.fit_transform(dataset[['smart_169_normalized']])\n",
    "dataset['smart_169_raw'] = qtrans.fit_transform(dataset[['smart_169_raw']])\n",
    "dataset['smart_170_normalized'] = qtrans.fit_transform(dataset[['smart_170_normalized']])\n",
    "dataset['smart_170_raw'] = qtrans.fit_transform(dataset[['smart_170_raw']])\n",
    "dataset['smart_173_normalized'] = qtrans.fit_transform(dataset[['smart_173_normalized']])\n",
    "dataset['smart_174_normalized'] = qtrans.fit_transform(dataset[['smart_174_normalized']])\n",
    "dataset['smart_174_raw'] = qtrans.fit_transform(dataset[['smart_174_raw']])\n",
    "dataset['smart_175_normalized'] = qtrans.fit_transform(dataset[['smart_175_normalized']])\n",
    "dataset['smart_176_normalized'] = qtrans.fit_transform(dataset[['smart_176_normalized']])\n",
    "dataset['smart_177_raw'] = qtrans.fit_transform(dataset[['smart_177_raw']])\n",
    "dataset['smart_178_normalized'] = qtrans.fit_transform(dataset[['smart_178_normalized']])\n",
    "dataset['smart_178_raw'] = qtrans.fit_transform(dataset[['smart_178_raw']])\n",
    "dataset['smart_179_normalized'] = qtrans.fit_transform(dataset[['smart_179_normalized']])\n",
    "dataset['smart_180_normalized'] = qtrans.fit_transform(dataset[['smart_180_normalized']])\n",
    "dataset['smart_180_raw'] = qtrans.fit_transform(dataset[['smart_180_raw']])\n",
    "dataset['smart_181_normalized'] = qtrans.fit_transform(dataset[['smart_181_normalized']])\n",
    "dataset['smart_182_normalized'] = qtrans.fit_transform(dataset[['smart_182_normalized']])\n",
    "dataset['smart_183_normalized'] = qtrans.fit_transform(dataset[['smart_183_normalized']])\n",
    "dataset['smart_183_raw'] = qtrans.fit_transform(dataset[['smart_183_raw']])\n",
    "dataset['smart_184_normalized'] = qtrans.fit_transform(dataset[['smart_184_normalized']])\n",
    "dataset['smart_184_raw'] = qtrans.fit_transform(dataset[['smart_184_raw']])\n",
    "dataset['smart_187_normalized'] = qtrans.fit_transform(dataset[['smart_187_normalized']])\n",
    "dataset['smart_187_raw'] = qtrans.fit_transform(dataset[['smart_187_raw']])\n",
    "dataset['smart_188_normalized'] = qtrans.fit_transform(dataset[['smart_188_normalized']])\n",
    "dataset['smart_189_normalized'] = qtrans.fit_transform(dataset[['smart_189_normalized']])\n",
    "dataset['smart_189_raw'] = qtrans.fit_transform(dataset[['smart_189_raw']])\n",
    "dataset['smart_191_normalized'] = qtrans.fit_transform(dataset[['smart_191_normalized']])\n",
    "dataset['smart_191_raw'] = qtrans.fit_transform(dataset[['smart_191_raw']])\n",
    "dataset['smart_192_normalized'] = qtrans.fit_transform(dataset[['smart_192_normalized']])\n",
    "dataset['smart_193_normalized'] = qtrans.fit_transform(dataset[['smart_193_normalized']])\n",
    "dataset['smart_194_normalized'] = qtrans.fit_transform(dataset[['smart_194_normalized']])\n",
    "dataset['smart_194_raw'] = qtrans.fit_transform(dataset[['smart_194_raw']])\n",
    "dataset['smart_195_normalized'] = qtrans.fit_transform(dataset[['smart_195_normalized']])\n",
    "dataset['smart_196_raw'] = qtrans.fit_transform(dataset[['smart_196_raw']])\n",
    "dataset['smart_197_normalized'] = qtrans.fit_transform(dataset[['smart_197_normalized']])\n",
    "dataset['smart_198_normalized'] = qtrans.fit_transform(dataset[['smart_198_normalized']])\n",
    "dataset['smart_199_normalized'] = qtrans.fit_transform(dataset[['smart_199_normalized']])\n",
    "dataset['smart_199_raw'] = qtrans.fit_transform(dataset[['smart_199_raw']])\n",
    "dataset['smart_200_normalized'] = qtrans.fit_transform(dataset[['smart_200_normalized']])\n",
    "dataset['smart_200_raw'] = qtrans.fit_transform(dataset[['smart_200_raw']])\n",
    "dataset['smart_201_normalized'] = qtrans.fit_transform(dataset[['smart_201_normalized']])\n",
    "dataset['smart_201_raw'] = qtrans.fit_transform(dataset[['smart_201_raw']])\n",
    "dataset['smart_202_normalized'] = qtrans.fit_transform(dataset[['smart_202_normalized']])\n",
    "dataset['smart_210_normalized'] = qtrans.fit_transform(dataset[['smart_210_normalized']])\n",
    "dataset['smart_218_normalized'] = qtrans.fit_transform(dataset[['smart_218_normalized']])\n",
    "dataset['smart_218_raw'] = qtrans.fit_transform(dataset[['smart_218_raw']])\n",
    "dataset['smart_220_normalized'] = qtrans.fit_transform(dataset[['smart_220_normalized']])\n",
    "dataset['smart_220_raw'] = qtrans.fit_transform(dataset[['smart_220_raw']])\n",
    "dataset['smart_222_raw'] = qtrans.fit_transform(dataset[['smart_222_raw']])\n",
    "dataset['smart_223_normalized'] = qtrans.fit_transform(dataset[['smart_223_normalized']])\n",
    "dataset['smart_223_raw'] = qtrans.fit_transform(dataset[['smart_223_raw']])\n",
    "dataset['smart_224_normalized'] = qtrans.fit_transform(dataset[['smart_224_normalized']])\n",
    "dataset['smart_225_normalized'] = qtrans.fit_transform(dataset[['smart_225_normalized']])\n",
    "dataset['smart_226_normalized'] = qtrans.fit_transform(dataset[['smart_226_normalized']])\n",
    "dataset['smart_231_normalized'] = qtrans.fit_transform(dataset[['smart_231_normalized']])\n",
    "dataset['smart_231_raw'] = qtrans.fit_transform(dataset[['smart_231_raw']])\n",
    "dataset['smart_232_normalized'] = qtrans.fit_transform(dataset[['smart_232_normalized']])\n",
    "dataset['smart_233_normalized'] = qtrans.fit_transform(dataset[['smart_233_normalized']])\n",
    "dataset['smart_235_normalized'] = qtrans.fit_transform(dataset[['smart_235_normalized']])\n",
    "dataset['smart_240_normalized'] = qtrans.fit_transform(dataset[['smart_240_normalized']])\n",
    "dataset['smart_241_normalized'] = qtrans.fit_transform(dataset[['smart_241_normalized']])\n",
    "dataset['smart_242_normalized'] = qtrans.fit_transform(dataset[['smart_242_normalized']])\n",
    "dataset['smart_245_normalized'] = qtrans.fit_transform(dataset[['smart_245_normalized']])\n",
    "dataset['smart_245_raw'] = qtrans.fit_transform(dataset[['smart_245_raw']])\n",
    "dataset['smart_247_normalized'] = qtrans.fit_transform(dataset[['smart_247_normalized']])\n",
    "dataset['smart_250_raw'] = qtrans.fit_transform(dataset[['smart_250_raw']])\n",
    "dataset['smart_252_normalized'] = qtrans.fit_transform(dataset[['smart_252_normalized']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove max VIF variables \n",
    "dataset = dataset.drop(['smart_170_raw'], 1)\n",
    "dataset = dataset.drop(['smart_180_normalized'], 1)\n",
    "dataset = dataset.drop(['smart_180_raw'], 1)\n",
    "dataset = dataset.drop(['smart_201_normalized'], 1)\n",
    "dataset = dataset.drop(['smart_201_raw'], 1)\n",
    "dataset = dataset.drop(['smart_218_raw'], 1)\n",
    "dataset = dataset.drop(['smart_16_raw'], 1)\n",
    "dataset = dataset.drop(['smart_17_raw'], 1)\n",
    "dataset = dataset.drop(['smart_245_raw'], 1)\n",
    "dataset = dataset.drop(['smart_169_raw'], 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# validation2\n",
    "validation2=dataset.tail(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>serial_number</th>\n",
       "      <th>model</th>\n",
       "      <th>failure</th>\n",
       "      <th>smart_1_normalized</th>\n",
       "      <th>smart_2_normalized</th>\n",
       "      <th>smart_2_raw</th>\n",
       "      <th>smart_3_normalized</th>\n",
       "      <th>smart_3_raw</th>\n",
       "      <th>smart_4_normalized</th>\n",
       "      <th>smart_4_raw</th>\n",
       "      <th>...</th>\n",
       "      <th>normalized_smart_233_raw</th>\n",
       "      <th>normalized_smart_235_raw</th>\n",
       "      <th>normalized_smart_240_raw</th>\n",
       "      <th>normalized_smart_247_raw</th>\n",
       "      <th>normalized_smart_251_raw</th>\n",
       "      <th>normalized_smart_252_raw</th>\n",
       "      <th>normalized_smart_5_raw</th>\n",
       "      <th>normalized_smart_197_raw</th>\n",
       "      <th>normalized_smart_10_raw</th>\n",
       "      <th>normalized_smart_198_raw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>-1.471555</td>\n",
       "      <td>-0.023839</td>\n",
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       "      <td>-1.805023</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.129866e-12</td>\n",
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       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
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       "      <td>Z300NP33</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.310017</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.439001</td>\n",
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       "      <td>-0.499230</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.133959</td>\n",
       "      <td>4.129254e-01</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000617</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000617</td>\n",
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       "    <tr>\n",
       "      <th>29440</th>\n",
       "      <td>Z304HWPP</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.102440</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>0.027604</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.733883e-12</td>\n",
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       "      <td>0.427661</td>\n",
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       "    <tr>\n",
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       "      <td>S300XB2L</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>-0.421111</td>\n",
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       "      <td>5.199338</td>\n",
       "      <td>-1.301191</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.312493e-11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29442</th>\n",
       "      <td>MK0333YHGNWVYC</td>\n",
       "      <td>14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.976943</td>\n",
       "      <td>-1.705570</td>\n",
       "      <td>1.923204</td>\n",
       "      <td>0.329106</td>\n",
       "      <td>1.660751</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.262686</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29443</th>\n",
       "      <td>Z3011CME</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.963423</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.050924</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.849874</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>3.958950e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29444</th>\n",
       "      <td>WD-WXB1AA3W1886</td>\n",
       "      <td>97</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>0.125788</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-1.513589</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.007737</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29445</th>\n",
       "      <td>Z3016PHJ</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.764710</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-0.076604</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-1.513589</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>5.545228e-11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.093151</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29446</th>\n",
       "      <td>PL2331LAGUKL2J</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.163824</td>\n",
       "      <td>-1.761948</td>\n",
       "      <td>2.806711</td>\n",
       "      <td>2.074445</td>\n",
       "      <td>1.599199</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.673702</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29447</th>\n",
       "      <td>Z300NCXQ</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.773922</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.050924</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.524976</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>9.888448e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29448</th>\n",
       "      <td>WD-WX11A24W2767</td>\n",
       "      <td>101</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>0.125788</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-1.513589</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29449</th>\n",
       "      <td>Z300WFYC</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.791884</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.050924</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.392492</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>6.221727e-11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29450</th>\n",
       "      <td>Z3029Z83</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.310017</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-0.245840</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-1.023003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>2.907452e-11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29451</th>\n",
       "      <td>WD-WCC4N0832588</td>\n",
       "      <td>91</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>2.584227</td>\n",
       "      <td>2.141443</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.301855</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29452</th>\n",
       "      <td>W300C4RZ</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.310017</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.439001</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.673702</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>5.319243e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29453</th>\n",
       "      <td>Z303R49Z</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.529558</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-0.128317</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-1.301191</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>5.461468e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29454</th>\n",
       "      <td>Z3016F9M</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.310017</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.439001</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.338834</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>5.481629e-11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29455</th>\n",
       "      <td>W300JNKT</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.893908</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.439001</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.406079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>7.138411e-11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29456</th>\n",
       "      <td>W300ETDE</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.823172</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.050924</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.499230</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>8.374027e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29457</th>\n",
       "      <td>Z300H202</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.033104</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.439001</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.558392</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>6.405419e-11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.109266</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29458</th>\n",
       "      <td>Z3029FSB</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.773922</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.050924</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.785036</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>2.882581e-11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29459</th>\n",
       "      <td>Z3015WES</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.805692</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.050924</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.378977</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>5.561572e-11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29460</th>\n",
       "      <td>WD-WMC4N0790734</td>\n",
       "      <td>91</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>0.125788</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-1.513589</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29461</th>\n",
       "      <td>Z300H35G</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.023003</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.439001</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.524976</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>6.408261e-11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29462</th>\n",
       "      <td>S2X6CKXD</td>\n",
       "      <td>60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.102440</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>0.027604</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.253606</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29463</th>\n",
       "      <td>S2X67TMD</td>\n",
       "      <td>60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.893908</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>0.027604</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.726606</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29464</th>\n",
       "      <td>Z300WCEW</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.310017</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-0.023839</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.274391</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>7.260034e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29465</th>\n",
       "      <td>Z300E4YV</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.310017</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.439001</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.726606</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>2.956356e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29466</th>\n",
       "      <td>WD-WCC4N0829936</td>\n",
       "      <td>91</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>2.483413</td>\n",
       "      <td>2.190150</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.558392</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29467</th>\n",
       "      <td>S2X6AC6T</td>\n",
       "      <td>60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.023003</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>0.027604</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.558392</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29468</th>\n",
       "      <td>W30065MC</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.023003</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.439001</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.454249</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>6.662445e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29469</th>\n",
       "      <td>S2T0JX0D400726</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.163824</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>-1.439001</td>\n",
       "      <td>2.033148</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-1.023003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29470</th>\n",
       "      <td>WD-WX61E82RX779</td>\n",
       "      <td>85</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>2.175006</td>\n",
       "      <td>1.835591</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.154197</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29471</th>\n",
       "      <td>Z300P8X6</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.310017</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.439001</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.673702</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>8.304746e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.578684</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29472</th>\n",
       "      <td>Z3016R9E</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.310017</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-0.184203</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-1.144896</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>5.448649e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.008058</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29473</th>\n",
       "      <td>WD-WX11D4449211</td>\n",
       "      <td>103</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>2.910107</td>\n",
       "      <td>3.023666</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.673702</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29474</th>\n",
       "      <td>Z300PCJX</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.791884</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.439001</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.726606</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>6.083321e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29475</th>\n",
       "      <td>S3010LAQ</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.862544</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>0.027604</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.036066e-11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29476</th>\n",
       "      <td>WD-WCC4N0295866</td>\n",
       "      <td>91</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>2.901755</td>\n",
       "      <td>2.092381</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.499230</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29477</th>\n",
       "      <td>Z301793E</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.773140</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-0.245840</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-1.023003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.837784e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29478</th>\n",
       "      <td>W3006DDD</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.023003</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.439001</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.419741</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>7.674195e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29479</th>\n",
       "      <td>Z300WCT4</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.844485</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-0.076604</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>-0.931971</td>\n",
       "      <td>-0.153385</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>6.587334e-11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29480</th>\n",
       "      <td>Z300XH85</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.310017</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-0.076604</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>-0.931971</td>\n",
       "      <td>-0.153302</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>6.591243e-11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29481</th>\n",
       "      <td>Z3016L1Q</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.773922</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.050924</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.368215</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>5.589996e-11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29482</th>\n",
       "      <td>Z3020VTC</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.529558</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-0.184203</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-1.144896</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>3.720032e-11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29483</th>\n",
       "      <td>Z302AKTE</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.102440</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-0.549616</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.928105</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>2.722694e-11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29484</th>\n",
       "      <td>Z3015LLW</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.033104</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-0.245840</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-1.023003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>5.474878e-11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29485</th>\n",
       "      <td>W30062HL</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.310017</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.439001</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.406079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>6.855791e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.006226</td>\n",
       "      <td>0.004768</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29486</th>\n",
       "      <td>W300J887</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.169401</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.439001</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.785036</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>9.906913e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29487</th>\n",
       "      <td>WD-WX11DA4DJNCS</td>\n",
       "      <td>103</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>0.125788</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-1.144896</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50 rows × 120 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         serial_number  model  failure  smart_1_normalized  \\\n",
       "29438         S301PPFL     48      1.0            1.023003   \n",
       "29439         Z300NP33     48      1.0            1.310017   \n",
       "29440         Z304HWPP     48      1.0            1.102440   \n",
       "29441         S300XB2L     48      1.0            1.773922   \n",
       "29442   MK0333YHGNWVYC     14      1.0           -1.976943   \n",
       "29443         Z3011CME     48      1.0            0.963423   \n",
       "29444  WD-WXB1AA3W1886     97      1.0            5.199338   \n",
       "29445         Z3016PHJ     48      1.0            0.764710   \n",
       "29446   PL2331LAGUKL2J      2      1.0            0.163824   \n",
       "29447         Z300NCXQ     48      1.0            1.773922   \n",
       "29448  WD-WX11A24W2767    101      1.0            5.199338   \n",
       "29449         Z300WFYC     48      1.0            0.791884   \n",
       "29450         Z3029Z83     48      1.0            1.310017   \n",
       "29451  WD-WCC4N0832588     91      1.0            5.199338   \n",
       "29452         W300C4RZ     48      1.0            1.310017   \n",
       "29453         Z303R49Z     48      1.0            1.529558   \n",
       "29454         Z3016F9M     48      1.0            1.310017   \n",
       "29455         W300JNKT     48      1.0            0.893908   \n",
       "29456         W300ETDE     48      1.0            0.823172   \n",
       "29457         Z300H202     48      1.0            2.033104   \n",
       "29458         Z3029FSB     48      1.0            1.773922   \n",
       "29459         Z3015WES     48      1.0            0.805692   \n",
       "29460  WD-WMC4N0790734     91      1.0            5.199338   \n",
       "29461         Z300H35G     48      1.0            1.023003   \n",
       "29462         S2X6CKXD     60      1.0            1.102440   \n",
       "29463         S2X67TMD     60      1.0            0.893908   \n",
       "29464         Z300WCEW     48      1.0            1.310017   \n",
       "29465         Z300E4YV     48      1.0            1.310017   \n",
       "29466  WD-WCC4N0829936     91      1.0            5.199338   \n",
       "29467         S2X6AC6T     60      1.0            1.023003   \n",
       "29468         W30065MC     48      1.0            1.023003   \n",
       "29469   S2T0JX0D400726     35      1.0            0.163824   \n",
       "29470  WD-WX61E82RX779     85      1.0            5.199338   \n",
       "29471         Z300P8X6     48      1.0            1.310017   \n",
       "29472         Z3016R9E     48      1.0            1.310017   \n",
       "29473  WD-WX11D4449211    103      1.0            5.199338   \n",
       "29474         Z300PCJX     48      1.0            0.791884   \n",
       "29475         S3010LAQ     48      1.0            0.862544   \n",
       "29476  WD-WCC4N0295866     91      1.0            5.199338   \n",
       "29477         Z301793E     48      1.0            0.773140   \n",
       "29478         W3006DDD     48      1.0            1.023003   \n",
       "29479         Z300WCT4     48      1.0            0.844485   \n",
       "29480         Z300XH85     48      1.0            1.310017   \n",
       "29481         Z3016L1Q     48      1.0            1.773922   \n",
       "29482         Z3020VTC     48      1.0            1.529558   \n",
       "29483         Z302AKTE     48      1.0            1.102440   \n",
       "29484         Z3015LLW     48      1.0            2.033104   \n",
       "29485         W30062HL     48      1.0            1.310017   \n",
       "29486         W300J887     48      1.0            1.169401   \n",
       "29487  WD-WX11DA4DJNCS    103      1.0            5.199338   \n",
       "\n",
       "       smart_2_normalized  smart_2_raw  smart_3_normalized  smart_3_raw  \\\n",
       "29438           -0.421111    -1.471555           -0.023839    -5.199338   \n",
       "29439           -0.421111    -1.471555           -1.439001    -5.199338   \n",
       "29440           -0.421111    -1.471555            0.027604    -5.199338   \n",
       "29441           -0.421111    -1.471555            0.027604    -5.199338   \n",
       "29442           -1.705570     1.923204            0.329106     1.660751   \n",
       "29443           -0.421111    -1.471555           -1.050924    -5.199338   \n",
       "29444           -0.421111    -1.471555            0.125788    -5.199338   \n",
       "29445           -0.421111    -1.471555           -0.076604    -5.199338   \n",
       "29446           -1.761948     2.806711            2.074445     1.599199   \n",
       "29447           -0.421111    -1.471555           -1.050924    -5.199338   \n",
       "29448           -0.421111    -1.471555            0.125788    -5.199338   \n",
       "29449           -0.421111    -1.471555           -1.050924    -5.199338   \n",
       "29450           -0.421111    -1.471555           -0.245840    -5.199338   \n",
       "29451           -0.421111    -1.471555            2.584227     2.141443   \n",
       "29452           -0.421111    -1.471555           -1.439001    -5.199338   \n",
       "29453           -0.421111    -1.471555           -0.128317    -5.199338   \n",
       "29454           -0.421111    -1.471555           -1.439001    -5.199338   \n",
       "29455           -0.421111    -1.471555           -1.439001    -5.199338   \n",
       "29456           -0.421111    -1.471555           -1.050924    -5.199338   \n",
       "29457           -0.421111    -1.471555           -1.439001    -5.199338   \n",
       "29458           -0.421111    -1.471555           -1.050924    -5.199338   \n",
       "29459           -0.421111    -1.471555           -1.050924    -5.199338   \n",
       "29460           -0.421111    -1.471555            0.125788    -5.199338   \n",
       "29461           -0.421111    -1.471555           -1.439001    -5.199338   \n",
       "29462           -0.421111    -1.471555            0.027604    -5.199338   \n",
       "29463           -0.421111    -1.471555            0.027604    -5.199338   \n",
       "29464           -0.421111    -1.471555           -0.023839    -5.199338   \n",
       "29465           -0.421111    -1.471555           -1.439001    -5.199338   \n",
       "29466           -0.421111    -1.471555            2.483413     2.190150   \n",
       "29467           -0.421111    -1.471555            0.027604    -5.199338   \n",
       "29468           -0.421111    -1.471555           -1.439001    -5.199338   \n",
       "29469            5.199338    -5.199338           -1.439001     2.033148   \n",
       "29470           -0.421111    -1.471555            2.175006     1.835591   \n",
       "29471           -0.421111    -1.471555           -1.439001    -5.199338   \n",
       "29472           -0.421111    -1.471555           -0.184203    -5.199338   \n",
       "29473           -0.421111    -1.471555            2.910107     3.023666   \n",
       "29474           -0.421111    -1.471555           -1.439001    -5.199338   \n",
       "29475           -0.421111    -1.471555            0.027604    -5.199338   \n",
       "29476           -0.421111    -1.471555            2.901755     2.092381   \n",
       "29477           -0.421111    -1.471555           -0.245840    -5.199338   \n",
       "29478           -0.421111    -1.471555           -1.439001    -5.199338   \n",
       "29479           -0.421111    -1.471555           -0.076604    -5.199338   \n",
       "29480           -0.421111    -1.471555           -0.076604    -5.199338   \n",
       "29481           -0.421111    -1.471555           -1.050924    -5.199338   \n",
       "29482           -0.421111    -1.471555           -0.184203    -5.199338   \n",
       "29483           -0.421111    -1.471555           -0.549616    -5.199338   \n",
       "29484           -0.421111    -1.471555           -0.245840    -5.199338   \n",
       "29485           -0.421111    -1.471555           -1.439001    -5.199338   \n",
       "29486           -0.421111    -1.471555           -1.439001    -5.199338   \n",
       "29487           -0.421111    -1.471555            0.125788    -5.199338   \n",
       "\n",
       "       smart_4_normalized  smart_4_raw  ...  normalized_smart_233_raw  \\\n",
       "29438            5.199338    -1.805023  ...                  0.270942   \n",
       "29439            5.199338    -0.499230  ...                  0.270942   \n",
       "29440            5.199338    -5.199338  ...                  0.270942   \n",
       "29441            5.199338    -1.301191  ...                  0.270942   \n",
       "29442            5.199338    -0.262686  ...                  0.270942   \n",
       "29443            5.199338    -0.849874  ...                  0.270942   \n",
       "29444            5.199338    -1.513589  ...                  0.270942   \n",
       "29445            5.199338    -1.513589  ...                  0.270942   \n",
       "29446            5.199338    -0.673702  ...                  0.270942   \n",
       "29447            5.199338    -0.524976  ...                  0.270942   \n",
       "29448            5.199338    -1.513589  ...                  0.270942   \n",
       "29449            5.199338    -0.392492  ...                  0.270942   \n",
       "29450            5.199338    -1.023003  ...                  0.270942   \n",
       "29451            5.199338    -0.301855  ...                  0.270942   \n",
       "29452            5.199338    -0.673702  ...                  0.270942   \n",
       "29453            5.199338    -1.301191  ...                  0.270942   \n",
       "29454            5.199338    -0.338834  ...                  0.270942   \n",
       "29455            5.199338    -0.406079  ...                  0.270942   \n",
       "29456            5.199338    -0.499230  ...                  0.270942   \n",
       "29457            5.199338    -0.558392  ...                  0.270942   \n",
       "29458            5.199338    -0.785036  ...                  0.270942   \n",
       "29459            5.199338    -0.378977  ...                  0.270942   \n",
       "29460            5.199338    -1.513589  ...                  0.270942   \n",
       "29461            5.199338    -0.524976  ...                  0.270942   \n",
       "29462            5.199338    -0.253606  ...                  0.270942   \n",
       "29463            5.199338    -0.726606  ...                  0.270942   \n",
       "29464            5.199338    -0.274391  ...                  0.270942   \n",
       "29465            5.199338    -0.726606  ...                  0.270942   \n",
       "29466            5.199338    -0.558392  ...                  0.270942   \n",
       "29467            5.199338    -0.558392  ...                  0.270942   \n",
       "29468            5.199338    -0.454249  ...                  0.270942   \n",
       "29469            5.199338    -1.023003  ...                  0.270942   \n",
       "29470            5.199338    -0.154197  ...                  0.270942   \n",
       "29471            5.199338    -0.673702  ...                  0.270942   \n",
       "29472            5.199338    -1.144896  ...                  0.270942   \n",
       "29473            5.199338    -0.673702  ...                  0.270942   \n",
       "29474            5.199338    -0.726606  ...                  0.270942   \n",
       "29475            5.199338    -5.199338  ...                  0.270942   \n",
       "29476            5.199338    -0.499230  ...                  0.270942   \n",
       "29477            5.199338    -1.023003  ...                  0.270942   \n",
       "29478            5.199338    -0.419741  ...                  0.270942   \n",
       "29479           -0.931971    -0.153385  ...                  0.270942   \n",
       "29480           -0.931971    -0.153302  ...                  0.270942   \n",
       "29481            5.199338    -0.368215  ...                  0.270942   \n",
       "29482            5.199338    -1.144896  ...                  0.270942   \n",
       "29483            5.199338    -0.928105  ...                  0.270942   \n",
       "29484            5.199338    -1.023003  ...                  0.270942   \n",
       "29485            5.199338    -0.406079  ...                  0.270942   \n",
       "29486            5.199338    -0.785036  ...                  0.270942   \n",
       "29487            5.199338    -1.144896  ...                  0.270942   \n",
       "\n",
       "       normalized_smart_235_raw  normalized_smart_240_raw  \\\n",
       "29438                  0.133959              1.129866e-12   \n",
       "29439                  0.133959              4.129254e-01   \n",
       "29440                  0.133959              1.733883e-12   \n",
       "29441                  0.133959              1.312493e-11   \n",
       "29442                  0.133959              1.170476e-01   \n",
       "29443                  0.133959              3.958950e-01   \n",
       "29444                  0.133959              1.170476e-01   \n",
       "29445                  0.133959              5.545228e-11   \n",
       "29446                  0.133959              1.170476e-01   \n",
       "29447                  0.133959              9.888448e-01   \n",
       "29448                  0.133959              1.170476e-01   \n",
       "29449                  0.133959              6.221727e-11   \n",
       "29450                  0.133959              2.907452e-11   \n",
       "29451                  0.133959              1.170476e-01   \n",
       "29452                  0.133959              5.319243e-01   \n",
       "29453                  0.133959              5.461468e-01   \n",
       "29454                  0.133959              5.481629e-11   \n",
       "29455                  0.133959              7.138411e-11   \n",
       "29456                  0.133959              8.374027e-01   \n",
       "29457                  0.133959              6.405419e-11   \n",
       "29458                  0.133959              2.882581e-11   \n",
       "29459                  0.133959              5.561572e-11   \n",
       "29460                  0.133959              1.170476e-01   \n",
       "29461                  0.133959              6.408261e-11   \n",
       "29462                  0.133959              1.170476e-01   \n",
       "29463                  0.133959              1.170476e-01   \n",
       "29464                  0.133959              7.260034e-01   \n",
       "29465                  0.133959              2.956356e-01   \n",
       "29466                  0.133959              1.170476e-01   \n",
       "29467                  0.133959              1.170476e-01   \n",
       "29468                  0.133959              6.662445e-01   \n",
       "29469                  0.133959              1.170476e-01   \n",
       "29470                  0.133959              1.170476e-01   \n",
       "29471                  0.133959              8.304746e-01   \n",
       "29472                  0.133959              5.448649e-01   \n",
       "29473                  0.133959              1.170476e-01   \n",
       "29474                  0.133959              6.083321e-01   \n",
       "29475                  0.133959              1.036066e-11   \n",
       "29476                  0.133959              1.170476e-01   \n",
       "29477                  0.133959              1.837784e-01   \n",
       "29478                  0.133959              7.674195e-01   \n",
       "29479                  0.133959              6.587334e-11   \n",
       "29480                  0.133959              6.591243e-11   \n",
       "29481                  0.133959              5.589996e-11   \n",
       "29482                  0.133959              3.720032e-11   \n",
       "29483                  0.133959              2.722694e-11   \n",
       "29484                  0.133959              5.474878e-11   \n",
       "29485                  0.133959              6.855791e-01   \n",
       "29486                  0.133959              9.906913e-01   \n",
       "29487                  0.133959              1.170476e-01   \n",
       "\n",
       "       normalized_smart_247_raw  normalized_smart_251_raw  \\\n",
       "29438                       0.5                  0.427661   \n",
       "29439                       0.5                  0.427661   \n",
       "29440                       0.5                  0.427661   \n",
       "29441                       0.5                  0.427661   \n",
       "29442                       0.5                  0.427661   \n",
       "29443                       0.5                  0.427661   \n",
       "29444                       0.5                  0.427661   \n",
       "29445                       0.5                  0.427661   \n",
       "29446                       0.5                  0.427661   \n",
       "29447                       0.5                  0.427661   \n",
       "29448                       0.5                  0.427661   \n",
       "29449                       0.5                  0.427661   \n",
       "29450                       0.5                  0.427661   \n",
       "29451                       0.5                  0.427661   \n",
       "29452                       0.5                  0.427661   \n",
       "29453                       0.5                  0.427661   \n",
       "29454                       0.5                  0.427661   \n",
       "29455                       0.5                  0.427661   \n",
       "29456                       0.5                  0.427661   \n",
       "29457                       0.5                  0.427661   \n",
       "29458                       0.5                  0.427661   \n",
       "29459                       0.5                  0.427661   \n",
       "29460                       0.5                  0.427661   \n",
       "29461                       0.5                  0.427661   \n",
       "29462                       0.5                  0.427661   \n",
       "29463                       0.5                  0.427661   \n",
       "29464                       0.5                  0.427661   \n",
       "29465                       0.5                  0.427661   \n",
       "29466                       0.5                  0.427661   \n",
       "29467                       0.5                  0.427661   \n",
       "29468                       0.5                  0.427661   \n",
       "29469                       0.5                  0.427661   \n",
       "29470                       0.5                  0.427661   \n",
       "29471                       0.5                  0.427661   \n",
       "29472                       0.5                  0.427661   \n",
       "29473                       0.5                  0.427661   \n",
       "29474                       0.5                  0.427661   \n",
       "29475                       0.5                  0.427661   \n",
       "29476                       0.5                  0.427661   \n",
       "29477                       0.5                  0.427661   \n",
       "29478                       0.5                  0.427661   \n",
       "29479                       0.5                  0.427661   \n",
       "29480                       0.5                  0.427661   \n",
       "29481                       0.5                  0.427661   \n",
       "29482                       0.5                  0.427661   \n",
       "29483                       0.5                  0.427661   \n",
       "29484                       0.5                  0.427661   \n",
       "29485                       0.5                  0.427661   \n",
       "29486                       0.5                  0.427661   \n",
       "29487                       0.5                  0.427661   \n",
       "\n",
       "       normalized_smart_252_raw  normalized_smart_5_raw  \\\n",
       "29438                  0.427282                0.000000   \n",
       "29439                  0.427282                0.000000   \n",
       "29440                  0.427282                0.000000   \n",
       "29441                  0.427282                0.000000   \n",
       "29442                  0.427282                0.007752   \n",
       "29443                  0.427282                0.000244   \n",
       "29444                  0.427282                0.007737   \n",
       "29445                  0.427282                0.093151   \n",
       "29446                  0.427282                0.000000   \n",
       "29447                  0.427282                0.000000   \n",
       "29448                  0.427282                0.000000   \n",
       "29449                  0.427282                0.000000   \n",
       "29450                  0.427282                0.000000   \n",
       "29451                  0.427282                0.000000   \n",
       "29452                  0.427282                0.000488   \n",
       "29453                  0.427282                0.000000   \n",
       "29454                  0.427282                0.000000   \n",
       "29455                  0.427282                0.000000   \n",
       "29456                  0.427282                0.000000   \n",
       "29457                  0.427282                0.109266   \n",
       "29458                  0.427282                0.000000   \n",
       "29459                  0.427282                0.000122   \n",
       "29460                  0.427282                0.000000   \n",
       "29461                  0.427282                0.000000   \n",
       "29462                  0.427282                0.000000   \n",
       "29463                  0.427282                0.000000   \n",
       "29464                  0.427282                0.000000   \n",
       "29465                  0.427282                0.000000   \n",
       "29466                  0.427282                0.000000   \n",
       "29467                  0.427282                0.000000   \n",
       "29468                  0.427282                0.000000   \n",
       "29469                  0.427282                0.000000   \n",
       "29470                  0.427282                0.000000   \n",
       "29471                  0.427282                0.578684   \n",
       "29472                  0.427282                0.008058   \n",
       "29473                  0.427282                0.000000   \n",
       "29474                  0.427282                0.000122   \n",
       "29475                  0.427282                0.000000   \n",
       "29476                  0.427282                0.000000   \n",
       "29477                  0.427282                0.000000   \n",
       "29478                  0.427282                0.000000   \n",
       "29479                  0.427282                0.000000   \n",
       "29480                  0.427282                0.000000   \n",
       "29481                  0.427282                0.000000   \n",
       "29482                  0.427282                0.000000   \n",
       "29483                  0.427282                0.000000   \n",
       "29484                  0.427282                0.000000   \n",
       "29485                  0.427282                0.006226   \n",
       "29486                  0.427282                0.000000   \n",
       "29487                  0.427282                0.000000   \n",
       "\n",
       "       normalized_smart_197_raw  normalized_smart_10_raw  \\\n",
       "29438                  0.000000                      0.0   \n",
       "29439                  0.000617                      0.0   \n",
       "29440                  0.000000                      0.0   \n",
       "29441                  0.000056                      0.0   \n",
       "29442                  0.000014                      0.0   \n",
       "29443                  0.000112                      0.0   \n",
       "29444                  0.000000                      0.0   \n",
       "29445                  0.000000                      0.0   \n",
       "29446                  0.000000                      0.0   \n",
       "29447                  0.000000                      0.0   \n",
       "29448                  0.000014                      0.0   \n",
       "29449                  0.000000                      0.0   \n",
       "29450                  0.000000                      0.0   \n",
       "29451                  0.000000                      0.0   \n",
       "29452                  0.000056                      0.0   \n",
       "29453                  0.000000                      0.0   \n",
       "29454                  0.000224                      0.0   \n",
       "29455                  0.000337                      0.0   \n",
       "29456                  0.000000                      0.0   \n",
       "29457                  0.000000                      0.0   \n",
       "29458                  0.000056                      0.0   \n",
       "29459                  0.000393                      0.0   \n",
       "29460                  0.000014                      0.0   \n",
       "29461                  0.000000                      0.0   \n",
       "29462                  0.000000                      0.0   \n",
       "29463                  0.000000                      0.0   \n",
       "29464                  0.000000                      0.0   \n",
       "29465                  0.000000                      0.0   \n",
       "29466                  0.000007                      0.0   \n",
       "29467                  0.000000                      0.0   \n",
       "29468                  0.000000                      0.0   \n",
       "29469                  0.000000                      0.0   \n",
       "29470                  0.000000                      0.0   \n",
       "29471                  0.000224                      0.0   \n",
       "29472                  0.000056                      0.0   \n",
       "29473                  0.000000                      0.0   \n",
       "29474                  0.000056                      0.0   \n",
       "29475                  0.000000                      0.0   \n",
       "29476                  0.000000                      0.0   \n",
       "29477                  0.000000                      0.0   \n",
       "29478                  0.000000                      0.0   \n",
       "29479                  0.000000                      0.0   \n",
       "29480                  0.000000                      0.0   \n",
       "29481                  0.000112                      0.0   \n",
       "29482                  0.000000                      0.0   \n",
       "29483                  0.000000                      0.0   \n",
       "29484                  0.000224                      0.0   \n",
       "29485                  0.004768                      0.0   \n",
       "29486                  0.000000                      0.0   \n",
       "29487                  0.000000                      0.0   \n",
       "\n",
       "       normalized_smart_198_raw  \n",
       "29438                  0.000000  \n",
       "29439                  0.000617  \n",
       "29440                  0.000000  \n",
       "29441                  0.000056  \n",
       "29442                  0.000000  \n",
       "29443                  0.000112  \n",
       "29444                  0.000000  \n",
       "29445                  0.000000  \n",
       "29446                  0.000000  \n",
       "29447                  0.000000  \n",
       "29448                  0.000000  \n",
       "29449                  0.000000  \n",
       "29450                  0.000000  \n",
       "29451                  0.000000  \n",
       "29452                  0.000056  \n",
       "29453                  0.000000  \n",
       "29454                  0.000224  \n",
       "29455                  0.000337  \n",
       "29456                  0.000000  \n",
       "29457                  0.000000  \n",
       "29458                  0.000056  \n",
       "29459                  0.000393  \n",
       "29460                  0.000000  \n",
       "29461                  0.000000  \n",
       "29462                  0.000000  \n",
       "29463                  0.000000  \n",
       "29464                  0.000000  \n",
       "29465                  0.000000  \n",
       "29466                  0.000000  \n",
       "29467                  0.000000  \n",
       "29468                  0.000000  \n",
       "29469                  0.000000  \n",
       "29470                  0.000000  \n",
       "29471                  0.000224  \n",
       "29472                  0.000056  \n",
       "29473                  0.000000  \n",
       "29474                  0.000056  \n",
       "29475                  0.000000  \n",
       "29476                  0.000000  \n",
       "29477                  0.000000  \n",
       "29478                  0.000000  \n",
       "29479                  0.000000  \n",
       "29480                  0.000000  \n",
       "29481                  0.000112  \n",
       "29482                  0.000000  \n",
       "29483                  0.000000  \n",
       "29484                  0.000224  \n",
       "29485                  0.004768  \n",
       "29486                  0.000000  \n",
       "29487                  0.000000  \n",
       "\n",
       "[50 rows x 120 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SPLIT DATA IN TRAIN TEST VALIDAATION\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def train_validate_test_split(dataset, train_percent=.7, validate_percent=.1, seed=None):\n",
    "    np.random.seed(seed)\n",
    "    perm = np.random.permutation(dataset.index)\n",
    "    m = len(dataset.index)\n",
    "    train_end = int(train_percent * m)\n",
    "    validate_end = int(validate_percent * m) + train_end\n",
    "    train = dataset.iloc[perm[:train_end]]\n",
    "    validate = dataset.iloc[perm[train_end:validate_end]]\n",
    "    test = dataset.iloc[perm[validate_end:]]\n",
    "    return train, validate, test\n",
    "\n",
    "df_train, df_validate, df_test = train_validate_test_split(dataset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the y variyable \n",
    "#from sklearn.model_selection import train_test_split\n",
    "#df_train=dataset.sample(frac=0.8,random_state=200) #random state is a seed value\n",
    "#df_test=dataset.drop(df_train.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/smicro/.local/lib/python3.6/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='failure', ylabel='count'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# delete\n",
    "sns.countplot(df_train['failure'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Up-sampling the minority class for train data \n",
    "valid_train = df_train[df_train['failure'] == 0]    #data of HDDs which do not indicate failure\n",
    "failed_train = df_train[df_train['failure'] == 1]   #data of HDDs likely to fail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valid hdds: 10821\n",
      "failing hdds: 9820\n"
     ]
    }
   ],
   "source": [
    "print(\"valid hdds:\",len(valid_train))      #storing the total number of valid HDDs\n",
    "print(\"failing hdds:\",len(failed_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# up-sample # as same length of valid \n",
    "failed_up = resample(failed_train,replace=True,n_samples=len(valid_train),random_state=27)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# resample with train data\n",
    "df_train = pd.concat([valid_train,failed_up])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#features selection\n",
    "features = ['serial_number',\n",
    "'model',\n",
    "'failure',\n",
    "'smart_1_normalized',\n",
    "'smart_2_normalized',\n",
    "'smart_2_raw',\n",
    "'smart_3_normalized',\n",
    "'smart_3_raw',\n",
    "'smart_4_normalized',\n",
    "'smart_4_raw',\n",
    "'smart_5_normalized',\n",
    "'smart_7_normalized',\n",
    "'smart_8_raw',\n",
    "'smart_9_normalized',\n",
    "'smart_10_normalized',\n",
    "'smart_11_normalized',\n",
    "'smart_12_normalized',\n",
    "'smart_13_normalized',\n",
    "'smart_16_normalized',\n",
    "'smart_17_normalized',\n",
    "'smart_18_normalized',\n",
    "'smart_22_normalized',\n",
    "'smart_22_raw',\n",
    "'smart_23_normalized',\n",
    "'smart_24_normalized',\n",
    "'smart_160_normalized',\n",
    "'smart_160_raw',\n",
    "'smart_161_normalized',\n",
    "'smart_161_raw',\n",
    "'smart_163_normalized',\n",
    "'smart_163_raw',\n",
    "'smart_164_normalized',\n",
    "'smart_165_normalized',\n",
    "'smart_165_raw',\n",
    "'smart_166_normalized',\n",
    "'smart_167_normalized',\n",
    "'smart_168_normalized',\n",
    "'smart_168_raw',\n",
    "'smart_169_normalized',\n",
    "'smart_170_normalized',\n",
    "'smart_173_normalized',\n",
    "'smart_174_normalized',\n",
    "'smart_174_raw',\n",
    "'smart_175_normalized',\n",
    "'smart_176_normalized',\n",
    "'smart_177_raw',\n",
    "'smart_178_normalized',\n",
    "'smart_178_raw',\n",
    "'smart_179_normalized',\n",
    "'smart_181_normalized',\n",
    "'smart_182_normalized',\n",
    "'smart_183_normalized',\n",
    "'smart_183_raw',\n",
    "'smart_184_normalized',\n",
    "'smart_184_raw',\n",
    "'smart_187_normalized',\n",
    "'smart_187_raw',\n",
    "'smart_188_normalized',\n",
    "'smart_189_normalized',\n",
    "'smart_189_raw',\n",
    "'smart_191_normalized',\n",
    "'smart_191_raw',\n",
    "'smart_192_normalized',\n",
    "'smart_193_normalized',\n",
    "'smart_194_normalized',\n",
    "'smart_194_raw',\n",
    "'smart_195_normalized',\n",
    "'smart_196_raw',\n",
    "'smart_197_normalized',\n",
    "'smart_198_normalized',\n",
    "'smart_199_normalized',\n",
    "'smart_199_raw',\n",
    "'smart_200_normalized',\n",
    "'smart_200_raw',\n",
    "'smart_202_normalized',\n",
    "'smart_210_normalized',\n",
    "'smart_218_raw',\n",
    "'smart_220_normalized',\n",
    "'smart_220_raw',\n",
    "'smart_222_raw',\n",
    "'smart_223_normalized',\n",
    "'smart_223_raw',\n",
    "'smart_224_normalized',\n",
    "'smart_225_normalized',\n",
    "'smart_226_normalized',\n",
    "'smart_231_normalized',\n",
    "'smart_231_raw',\n",
    "'smart_232_normalized',\n",
    "'smart_233_normalized',\n",
    "'smart_235_normalized',\n",
    "'smart_240_normalized',\n",
    "'smart_241_normalized',\n",
    "'smart_242_normalized',\n",
    "'smart_245_normalized',\n",
    "'smart_247_normalized',\n",
    "'smart_250_raw',\n",
    "'smart_252_normalized',\n",
    "'normalized_capacity_bytes',\n",
    "'normalized_smart_1_raw',\n",
    "'normalized_smart_241_raw',\n",
    "'normalized_smart_242_raw',\n",
    "'normalized_smart_7_raw',\n",
    "'normalized_smart_9_raw',\n",
    "'normalized_smart_11_raw',\n",
    "'normalized_smart_173_raw',\n",
    "'normalized_smart_188_raw',\n",
    "'normalized_smart_193_raw',\n",
    "'normalized_smart_195_raw',\n",
    "'normalized_smart_225_raw',\n",
    "'normalized_smart_232_raw',\n",
    "'normalized_smart_233_raw',\n",
    "'normalized_smart_235_raw',\n",
    "'normalized_smart_240_raw',\n",
    "'normalized_smart_247_raw',\n",
    "'normalized_smart_251_raw',\n",
    "'normalized_smart_252_raw',\n",
    "'normalized_smart_5_raw',\n",
    "'normalized_smart_197_raw',\n",
    "'normalized_smart_10_raw',\n",
    "'normalized_smart_198_raw'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the remaining features\n",
    "misc_feat = [fname for fname in df_train if fname not in features]  #misc features to be dropped "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the remaining features\n",
    "df_train.drop(misc_feat,inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model cannot process string values, we remove the columns # to avoid errors \n",
    "obj = df_train.dtypes[df_train.dtypes == object ].index  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the sting data \n",
    "df_train = df_train.drop(obj,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    10821\n",
       "0.0    10821\n",
       "Name: failure, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['failure'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the values for X_train and Y_train\n",
    "X_train = df_train.drop('failure',axis=1)\n",
    "Y_train = df_train['failure']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Test data # do the all the step above in to Test data\n",
    "valid_test = df_test[df_test['failure'] == 0]\n",
    "failed_test = df_test[df_test['failure'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# upsampling in test data\n",
    "failed_up_test = resample(failed_test,replace=True,n_samples=len(valid_test),random_state=27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    3127\n",
       "0.0    3127\n",
       "Name: failure, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat upsampling with test data\n",
    "df_test = pd.concat([valid_test,failed_up_test])\n",
    "df_test.failure.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>serial_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14552</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14985</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7221</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15211</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21427</th>\n",
       "      <td>S2ZYJ9KFC03608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28872</th>\n",
       "      <td>Z3025KV6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20288</th>\n",
       "      <td>ZCH07NZ5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17712</th>\n",
       "      <td>ZCH08034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20349</th>\n",
       "      <td>S2ZYJ9EF509127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6254 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        serial_number\n",
       "14552  MK0271YGJA4WUA\n",
       "1068   MK0271YGJP8LVA\n",
       "14985  MK0271YGJP8LVA\n",
       "7221   MK0271YGJP8LVA\n",
       "15211        Z1Y45RA5\n",
       "...               ...\n",
       "21427  S2ZYJ9KFC03608\n",
       "28872        Z3025KV6\n",
       "20288        ZCH07NZ5\n",
       "17712        ZCH08034\n",
       "20349  S2ZYJ9EF509127\n",
       "\n",
       "[6254 rows x 1 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get only serial number form test data\n",
    "df_test_serial_number=df_test.serial_number\n",
    "df_test_failure=df_test.failure\n",
    "df_test_serial_number=pd.DataFrame(df_test_serial_number)\n",
    "df_test_serial_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature selection in test data drop missing data in test data \n",
    "df_test.drop(misc_feat,inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the string in test data\n",
    "df_test = df_test.drop(obj,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting values for X_test and Y_test\n",
    "X_test = df_test.drop('failure',axis=1)\n",
    "Y_test = df_test['failure']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    3127\n",
       "0.0    3127\n",
       "Name: failure, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test data count\n",
    "df_test['failure'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    10821\n",
       "0.0    10821\n",
       "Name: failure, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test data count\n",
    "df_train['failure'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>failure</th>\n",
       "      <th>smart_1_normalized</th>\n",
       "      <th>smart_2_normalized</th>\n",
       "      <th>smart_2_raw</th>\n",
       "      <th>smart_3_normalized</th>\n",
       "      <th>smart_3_raw</th>\n",
       "      <th>smart_4_normalized</th>\n",
       "      <th>smart_4_raw</th>\n",
       "      <th>smart_5_normalized</th>\n",
       "      <th>...</th>\n",
       "      <th>normalized_smart_233_raw</th>\n",
       "      <th>normalized_smart_235_raw</th>\n",
       "      <th>normalized_smart_240_raw</th>\n",
       "      <th>normalized_smart_247_raw</th>\n",
       "      <th>normalized_smart_251_raw</th>\n",
       "      <th>normalized_smart_252_raw</th>\n",
       "      <th>normalized_smart_5_raw</th>\n",
       "      <th>normalized_smart_197_raw</th>\n",
       "      <th>normalized_smart_10_raw</th>\n",
       "      <th>normalized_smart_198_raw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11627</th>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.163824</td>\n",
       "      <td>0.892038</td>\n",
       "      <td>0.545244</td>\n",
       "      <td>0.574595</td>\n",
       "      <td>1.027247</td>\n",
       "      <td>-0.931971</td>\n",
       "      <td>1.352623</td>\n",
       "      <td>0.021329</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>0.117048</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</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>8732</th>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.163824</td>\n",
       "      <td>0.892038</td>\n",
       "      <td>0.545244</td>\n",
       "      <td>0.574595</td>\n",
       "      <td>1.027247</td>\n",
       "      <td>-0.931971</td>\n",
       "      <td>1.352623</td>\n",
       "      <td>0.021329</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>0.117048</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</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>1526</th>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.163824</td>\n",
       "      <td>0.892038</td>\n",
       "      <td>0.545244</td>\n",
       "      <td>0.574595</td>\n",
       "      <td>1.027247</td>\n",
       "      <td>-0.931971</td>\n",
       "      <td>1.352623</td>\n",
       "      <td>0.021329</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>0.117048</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</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>533</th>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.163824</td>\n",
       "      <td>0.892038</td>\n",
       "      <td>0.545244</td>\n",
       "      <td>0.574595</td>\n",
       "      <td>1.027247</td>\n",
       "      <td>-0.931971</td>\n",
       "      <td>1.352623</td>\n",
       "      <td>0.021329</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>0.117048</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</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>3101</th>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.163824</td>\n",
       "      <td>0.892038</td>\n",
       "      <td>0.545244</td>\n",
       "      <td>0.574595</td>\n",
       "      <td>1.027247</td>\n",
       "      <td>-0.931971</td>\n",
       "      <td>1.352623</td>\n",
       "      <td>0.021329</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>0.117048</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</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",
       "<p>5 rows × 118 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  failure  smart_1_normalized  smart_2_normalized  smart_2_raw  \\\n",
       "11627     16      0.0            0.163824            0.892038     0.545244   \n",
       "8732      16      0.0            0.163824            0.892038     0.545244   \n",
       "1526      16      0.0            0.163824            0.892038     0.545244   \n",
       "533       16      0.0            0.163824            0.892038     0.545244   \n",
       "3101      16      0.0            0.163824            0.892038     0.545244   \n",
       "\n",
       "       smart_3_normalized  smart_3_raw  smart_4_normalized  smart_4_raw  \\\n",
       "11627            0.574595     1.027247           -0.931971     1.352623   \n",
       "8732             0.574595     1.027247           -0.931971     1.352623   \n",
       "1526             0.574595     1.027247           -0.931971     1.352623   \n",
       "533              0.574595     1.027247           -0.931971     1.352623   \n",
       "3101             0.574595     1.027247           -0.931971     1.352623   \n",
       "\n",
       "       smart_5_normalized  ...  normalized_smart_233_raw  \\\n",
       "11627            0.021329  ...                  0.270942   \n",
       "8732             0.021329  ...                  0.270942   \n",
       "1526             0.021329  ...                  0.270942   \n",
       "533              0.021329  ...                  0.270942   \n",
       "3101             0.021329  ...                  0.270942   \n",
       "\n",
       "       normalized_smart_235_raw  normalized_smart_240_raw  \\\n",
       "11627                  0.133959                  0.117048   \n",
       "8732                   0.133959                  0.117048   \n",
       "1526                   0.133959                  0.117048   \n",
       "533                    0.133959                  0.117048   \n",
       "3101                   0.133959                  0.117048   \n",
       "\n",
       "       normalized_smart_247_raw  normalized_smart_251_raw  \\\n",
       "11627                       0.5                  0.427661   \n",
       "8732                        0.5                  0.427661   \n",
       "1526                        0.5                  0.427661   \n",
       "533                         0.5                  0.427661   \n",
       "3101                        0.5                  0.427661   \n",
       "\n",
       "       normalized_smart_252_raw  normalized_smart_5_raw  \\\n",
       "11627                  0.427282                     0.0   \n",
       "8732                   0.427282                     0.0   \n",
       "1526                   0.427282                     0.0   \n",
       "533                    0.427282                     0.0   \n",
       "3101                   0.427282                     0.0   \n",
       "\n",
       "       normalized_smart_197_raw  normalized_smart_10_raw  \\\n",
       "11627                       0.0                      0.0   \n",
       "8732                        0.0                      0.0   \n",
       "1526                        0.0                      0.0   \n",
       "533                         0.0                      0.0   \n",
       "3101                        0.0                      0.0   \n",
       "\n",
       "       normalized_smart_198_raw  \n",
       "11627                       0.0  \n",
       "8732                        0.0  \n",
       "1526                        0.0  \n",
       "533                         0.0  \n",
       "3101                        0.0  \n",
       "\n",
       "[5 rows x 118 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random Forest Classifier\n",
    "from sklearn.ensemble import RandomForestClassifier \n",
    "\n",
    "#  model creation \n",
    "rfc = RandomForestClassifier() \n",
    "rfc.fit(X_train, Y_train) \n",
    "\n",
    "# Predictions \n",
    "yPred = rfc.predict(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "yPred_df = pd.DataFrame(yPred, columns = ['Model_Prediction'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Results of our predictions\n",
    "\n",
    "\n",
    "  \n",
    "#n_outliers = len(failed) \n",
    "n_errors = (yPred != Y_test).sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model used is: Random Forest classifier\n",
      "The accuracy is 1.0\n",
      "The precision is 1.0\n",
      "The recall is 1.0\n",
      "The F1-Score is 1.0\n",
      "The Matthews correlation coefficient is 1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Model used is: Random Forest classifier\") \n",
    "  \n",
    "acc = accuracy_score(Y_test, yPred) \n",
    "print(\"The accuracy is {}\".format(acc)) \n",
    "  \n",
    "prec = precision_score(Y_test, yPred) \n",
    "print(\"The precision is {}\".format(prec)) \n",
    "  \n",
    "rec = recall_score(Y_test, yPred) \n",
    "print(\"The recall is {}\".format(rec))\n",
    "f1 = f1_score(Y_test, yPred) \n",
    "print(\"The F1-Score is {}\".format(f1)) \n",
    "  \n",
    "MCC = matthews_corrcoef(Y_test, yPred) \n",
    "print(\"The Matthews correlation coefficient is {}\".format(MCC)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modeling approach 1 run the model in live database and run with test data - take 3 min time to load all the data\n",
    "# Modeling approach 2 save the model and run with test data - it does not take time \n",
    "\n",
    "# save the model to disk\n",
    "import pickle\n",
    "# save the model to disk\n",
    "filename = 'finalized_model.sav'\n",
    "pickle.dump(rfc, open(filename, 'wb'))\n",
    "# some time later...######### preprocess test data alone to test the prediction results\n",
    "# load the model from disk\n",
    "\n",
    "# read testdata #########################################################################\n",
    "# read testdata, #Missing value Imputation, # Remove CORRILATION VARIYABLE \n",
    "# drop date # remove top 10 missing variables # Remove below variable as it has only one value # normalize the data \n",
    "# drop after normalization # Label Encoding model\n",
    "# data conversion  # define the y variable #QuantileTransformer \n",
    "# remove max VIF variables #features selection #misc_feat - drop the remaining features \n",
    "# drop the sting data #Splitting the values for X_train and Y_train \n",
    "# get only serial number form test data # pass x test to get the prediction\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    " \n",
    "# Save the trained model as a pickle string.\n",
    "saved_model = pickle.dumps(rfc)\n",
    "# Load the pickled model\n",
    "knn_from_pickle = pickle.loads(saved_model)\n",
    "# loaded_model = pickle.load(open(filename, 'rb'))\n",
    "# Use the loaded pickled model to make predictions\n",
    "#knn_from_pickle.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., ..., 1., 1., 1.])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    " # save the model to disk\n",
    "import pickle\n",
    "# save the model to disk\n",
    "#filename = 'filename.sav'\n",
    "#pickle.dump(rfc, open(filename, 'wb'))\n",
    "\n",
    "\n",
    "# Save the model as a pickle in a file\n",
    "joblib.dump(rfc, 'filename.pkl')\n",
    " \n",
    "# Load the model from the file\n",
    "knn_from_joblib = joblib.load('filename.pkl')\n",
    "# Use the loaded model to make predictions\n",
    "knn_from_joblib.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serial_number</th>\n",
       "      <th>Model_Prediction</th>\n",
       "      <th>Active_Probability</th>\n",
       "      <th>Failure_Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.000008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.000008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.000008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>0.000008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6249</th>\n",
       "      <td>S2ZYJ9KFC03608</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6250</th>\n",
       "      <td>Z3025KV6</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6251</th>\n",
       "      <td>ZCH07NZ5</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6252</th>\n",
       "      <td>ZCH08034</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6253</th>\n",
       "      <td>S2ZYJ9EF509127</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6254 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       serial_number      Model_Prediction  Active_Probability  \\\n",
       "0     MK0271YGJA4WUA              not_fail            0.999992   \n",
       "1     MK0271YGJP8LVA              not_fail            0.999992   \n",
       "2     MK0271YGJP8LVA              not_fail            0.999992   \n",
       "3     MK0271YGJP8LVA              not_fail            0.999992   \n",
       "4           Z1Y45RA5              not_fail            1.000000   \n",
       "...              ...                   ...                 ...   \n",
       "6249  S2ZYJ9KFC03608  Predicted_to_be_fail            0.000000   \n",
       "6250        Z3025KV6  Predicted_to_be_fail            0.000000   \n",
       "6251        ZCH07NZ5  Predicted_to_be_fail            0.000000   \n",
       "6252        ZCH08034  Predicted_to_be_fail            0.000000   \n",
       "6253  S2ZYJ9EF509127  Predicted_to_be_fail            0.000000   \n",
       "\n",
       "      Failure_Probability  \n",
       "0                0.000008  \n",
       "1                0.000008  \n",
       "2                0.000008  \n",
       "3                0.000008  \n",
       "4                0.000000  \n",
       "...                   ...  \n",
       "6249             1.000000  \n",
       "6250             1.000000  \n",
       "6251             1.000000  \n",
       "6252             1.000000  \n",
       "6253             1.000000  \n",
       "\n",
       "[6254 rows x 4 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "loaded_model = pickle.load(open(filename, 'rb'))\n",
    "# accuraccy score with test data\n",
    "accuraccy_result = loaded_model.score(X_test, Y_test)\n",
    "#print(result)\n",
    "# final prediction results \n",
    "prediction_result=loaded_model.predict(X_test)\n",
    "prediction_result\n",
    "#Model_Prediction dataframe\n",
    "Model_Prediction = pd.DataFrame(prediction_result, columns =['Model_Prediction'])\n",
    "#print(\"\\nPandas DataFrame: \")\n",
    "Model_Prediction\n",
    "df_test_serial_number=pd.DataFrame(df_test_serial_number)\n",
    "Model_Prediction['row_num'] = np.arange(len(Model_Prediction))\n",
    "df_test_serial_number['row_num'] = np.arange(len(df_test_serial_number))\n",
    "#result = pd.concat([Model_Prediction, df_test_serial_number], axis=1)\n",
    "# Stack the Data Frames on top of each other\n",
    "Model_Prediction.Model_Prediction.replace((0, 1), ('not_fail', 'Predicted_to_be_fail'), inplace=True)\n",
    "RandomForest_Model_Prediction=df_test_serial_number.merge(Model_Prediction, on='row_num', how='left')\n",
    "RandomForest_Model_Prediction = RandomForest_Model_Prediction.drop(['row_num'], 1)\n",
    "RandomForest_Model_Prediction\n",
    "#3 DELETE\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "y_pred_prob = rfc.predict_proba(X_test)\n",
    "Probability_of_failure = pd.DataFrame(y_pred_prob, columns =['Active_Probability','Failure_Probability'])\n",
    "#e_dataframe = pd.DataFrame(e) \n",
    "#Probability_of_failure = pd.DataFrame(y_pred_prob)\n",
    "Probability_of_failure\n",
    "#3 yPred_df_yPred_validation2\n",
    "# validation2\n",
    "result = pd.concat([RandomForest_Model_Prediction, Probability_of_failure], axis=1)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# AUC ROC \n",
    "\n",
    "logit_roc_auc = roc_auc_score(Y_train, rfc.predict(X_train))\n",
    "fpr, tpr, thresholds = roc_curve(Y_train, rfc.predict_proba(X_train)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Random Forest classifier (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MODEL VALIDATION STEP Start\n",
    "df_validation = df_validate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valid hdds: 1544\n",
      "failing hdds: 1404\n"
     ]
    }
   ],
   "source": [
    "#validation data # do the all the step above in to validation data\n",
    "valid_validation = df_validation[df_validation['failure'] == 0]\n",
    "failed_validation = df_validation[df_validation['failure'] == 1]\n",
    "\n",
    "print(\"valid hdds:\",len(valid_validation))\n",
    "print(\"failing hdds:\",len(failed_validation))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# up-sampling in validation data\n",
    "# get only serial number form validation data\n",
    "df_validation_serial_number=df_validation.serial_number\n",
    "df_validation_failure=df_validation.failure\n",
    "\n",
    "# get only serial number \n",
    "df_validation_serial_number = df_validation_serial_number.to_frame()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# validation2\n",
    "validation2_serial_number=validation2.serial_number\n",
    "validation2_failure=validation2.failure\n",
    "validation2_serial_number = validation2_serial_number.to_frame()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/smicro/.local/lib/python3.6/site-packages/pandas/core/frame.py:4174: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "# validation2\n",
    "validation2.drop(misc_feat,inplace=True,axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# validation2\n",
    "validation2 = validation2.drop(obj,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# validation2\n",
    "X_validation2 = validation2.drop('failure',axis=1)\n",
    "Y_validation2 = validation2['failure']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# validation2\n",
    "yPred_validation2 = rfc.predict(X_validation2) \n",
    "yPred_df_yPred_validation2 = pd.DataFrame(yPred_validation2, columns = ['Model_Prediction'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/smicro/.local/lib/python3.6/site-packages/pandas/core/frame.py:4174: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "# Feature selection in validation data drop missing data in validation data \n",
    "df_validation.drop(misc_feat,inplace=True,axis=1)\n",
    "\n",
    "# drop the string in validation data \n",
    "df_validation = df_validation.drop(obj,axis=1)\n",
    "\n",
    "#Splitting values for X_validation and Y_validation\n",
    "X_validation = df_validation.drop('failure',axis=1)\n",
    "Y_validation = df_validation['failure']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_validation.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Model_Prediction</th>\n",
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       "      <th>0</th>\n",
       "      <td>S301PPFL</td>\n",
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       "      <td>Predicted_to_be_fail</td>\n",
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       "      <td>S300XB2L</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
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       "      <td>Predicted_to_be_fail</td>\n",
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       "    <tr>\n",
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       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>WD-WXB1AA3W1886</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Z3016PHJ</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PL2331LAGUKL2J</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Z300NCXQ</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>WD-WX11A24W2767</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Z300WFYC</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Z3029Z83</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>WD-WCC4N0832588</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>W300C4RZ</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Z303R49Z</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Z3016F9M</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>W300JNKT</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>W300ETDE</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Z300H202</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Z3029FSB</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Z3015WES</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>WD-WMC4N0790734</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Z300H35G</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>S2X6CKXD</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>S2X67TMD</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Z300WCEW</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Z300E4YV</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>WD-WCC4N0829936</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>S2X6AC6T</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>W30065MC</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>S2T0JX0D400726</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>WD-WX61E82RX779</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Z300P8X6</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Z3016R9E</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>WD-WX11D4449211</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Z300PCJX</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>S3010LAQ</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>WD-WCC4N0295866</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Z301793E</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>W3006DDD</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Z300WCT4</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Z300XH85</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Z3016L1Q</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Z3020VTC</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Z302AKTE</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Z3015LLW</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>W30062HL</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>W300J887</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>WD-WX11DA4DJNCS</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      serial_number      Model_Prediction\n",
       "0          S301PPFL  Predicted_to_be_fail\n",
       "1          Z300NP33  Predicted_to_be_fail\n",
       "2          Z304HWPP  Predicted_to_be_fail\n",
       "3          S300XB2L  Predicted_to_be_fail\n",
       "4    MK0333YHGNWVYC  Predicted_to_be_fail\n",
       "5          Z3011CME  Predicted_to_be_fail\n",
       "6   WD-WXB1AA3W1886  Predicted_to_be_fail\n",
       "7          Z3016PHJ  Predicted_to_be_fail\n",
       "8    PL2331LAGUKL2J  Predicted_to_be_fail\n",
       "9          Z300NCXQ  Predicted_to_be_fail\n",
       "10  WD-WX11A24W2767  Predicted_to_be_fail\n",
       "11         Z300WFYC  Predicted_to_be_fail\n",
       "12         Z3029Z83  Predicted_to_be_fail\n",
       "13  WD-WCC4N0832588  Predicted_to_be_fail\n",
       "14         W300C4RZ  Predicted_to_be_fail\n",
       "15         Z303R49Z  Predicted_to_be_fail\n",
       "16         Z3016F9M  Predicted_to_be_fail\n",
       "17         W300JNKT  Predicted_to_be_fail\n",
       "18         W300ETDE  Predicted_to_be_fail\n",
       "19         Z300H202  Predicted_to_be_fail\n",
       "20         Z3029FSB  Predicted_to_be_fail\n",
       "21         Z3015WES  Predicted_to_be_fail\n",
       "22  WD-WMC4N0790734  Predicted_to_be_fail\n",
       "23         Z300H35G  Predicted_to_be_fail\n",
       "24         S2X6CKXD  Predicted_to_be_fail\n",
       "25         S2X67TMD  Predicted_to_be_fail\n",
       "26         Z300WCEW  Predicted_to_be_fail\n",
       "27         Z300E4YV  Predicted_to_be_fail\n",
       "28  WD-WCC4N0829936  Predicted_to_be_fail\n",
       "29         S2X6AC6T  Predicted_to_be_fail\n",
       "30         W30065MC  Predicted_to_be_fail\n",
       "31   S2T0JX0D400726  Predicted_to_be_fail\n",
       "32  WD-WX61E82RX779  Predicted_to_be_fail\n",
       "33         Z300P8X6  Predicted_to_be_fail\n",
       "34         Z3016R9E  Predicted_to_be_fail\n",
       "35  WD-WX11D4449211  Predicted_to_be_fail\n",
       "36         Z300PCJX  Predicted_to_be_fail\n",
       "37         S3010LAQ  Predicted_to_be_fail\n",
       "38  WD-WCC4N0295866  Predicted_to_be_fail\n",
       "39         Z301793E  Predicted_to_be_fail\n",
       "40         W3006DDD  Predicted_to_be_fail\n",
       "41         Z300WCT4  Predicted_to_be_fail\n",
       "42         Z300XH85  Predicted_to_be_fail\n",
       "43         Z3016L1Q  Predicted_to_be_fail\n",
       "44         Z3020VTC  Predicted_to_be_fail\n",
       "45         Z302AKTE  Predicted_to_be_fail\n",
       "46         Z3015LLW  Predicted_to_be_fail\n",
       "47         W30062HL  Predicted_to_be_fail\n",
       "48         W300J887  Predicted_to_be_fail\n",
       "49  WD-WX11DA4DJNCS  Predicted_to_be_fail"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# validation2 \n",
    "validation2_serial_number['row_num'] = np.arange(len(validation2_serial_number))\n",
    "yPred_df_yPred_validation2['row_num'] = np.arange(len(yPred_df_yPred_validation2))\n",
    "result = pd.concat([validation2_serial_number, yPred_df_yPred_validation2], axis=1)\n",
    "# Stack the Data Frames on top of each other\n",
    "yPred_df_yPred_validation2.Model_Prediction.replace((0, 1), ('not_fail', 'Predicted_to_be_fail'), inplace=True)\n",
    "RandomForest_Model_Prediction=validation2_serial_number.merge(yPred_df_yPred_validation2, on='row_num', how='left')\n",
    "RandomForest_Model_Prediction = RandomForest_Model_Prediction.drop(['row_num'], 1)\n",
    "RandomForest_Model_Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# for normal validation data\n",
    "yPred = rfc.predict(X_validation) \n",
    "yPred_df = pd.DataFrame(yPred, columns = ['Model_Prediction'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assign the row_num before merge\n",
    "df_validation_serial_number['row_num'] = np.arange(len(df_validation_serial_number))\n",
    "yPred_df['row_num'] = np.arange(len(yPred_df))\n",
    "result = pd.concat([df_validation_serial_number, yPred_df], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>serial_number</th>\n",
       "      <th>Model_Prediction</th>\n",
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       "      <td>MK0271YGJP8LVA</td>\n",
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       "      <th>1</th>\n",
       "      <td>ZA11LZ76</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>not_fail</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Z300GZ6V</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>not_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2943</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>not_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2944</th>\n",
       "      <td>S300SQ3J</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2945</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>not_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2946</th>\n",
       "      <td>W300H3GP</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2947</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>not_fail</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2948 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       serial_number      Model_Prediction\n",
       "0     MK0271YGJP8LVA              not_fail\n",
       "1           ZA11LZ76  Predicted_to_be_fail\n",
       "2     MK0271YGJA4WUA              not_fail\n",
       "3           Z300GZ6V  Predicted_to_be_fail\n",
       "4           Z1Y45RA5              not_fail\n",
       "...              ...                   ...\n",
       "2943  MK0271YGJP8LVA              not_fail\n",
       "2944        S300SQ3J  Predicted_to_be_fail\n",
       "2945        Z1Y45RA5              not_fail\n",
       "2946        W300H3GP  Predicted_to_be_fail\n",
       "2947        Z1Y45RA5              not_fail\n",
       "\n",
       "[2948 rows x 2 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stack the Data Frames on top of each other\n",
    "yPred_df.Model_Prediction.replace((0, 1), ('not_fail', 'Predicted_to_be_fail'), inplace=True)\n",
    "RandomForest_Model_Prediction=df_validation_serial_number.merge(yPred_df, on='row_num', how='left')\n",
    "RandomForest_Model_Prediction = RandomForest_Model_Prediction.drop(['row_num'], 1)\n",
    "RandomForest_Model_Prediction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/smicro/.local/lib/python3.6/site-packages/pandas/core/series.py:4582: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  method=method,\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>serial_number</th>\n",
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       "      <th>Model_Prediction</th>\n",
       "      <th>row_num</th>\n",
       "      <th>Active_Probability</th>\n",
       "      <th>Failure_Probability</th>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Z1Y45RA5</td>\n",
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       "      <td>MK0271YGJP8LVA</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Z1Y45RA5</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>5</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>5</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
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       "      <td>0.999912</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Z1Y45RA5</td>\n",
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       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>8</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>8</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>9</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>9</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>10</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>11</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>11</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>12</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>12</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>13</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>13</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>14</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>14</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>15</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>15</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>16</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>17</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>17</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>18</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>18</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>19</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>19</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>20</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>20</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>21</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>21</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>22</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>22</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>23</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>23</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>24</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>24</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>25</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>25</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>26</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>26</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>27</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>27</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>28</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>28</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>29</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>29</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>30</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>30</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>31</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>31</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>32</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>32</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>33</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>33</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>34</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>34</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>35</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>35</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>36</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>36</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>37</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>37</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>38</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>38</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>39</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>39</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>40</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>40</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>41</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>41</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>42</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>42</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>43</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>43</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>44</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>44</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>45</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>45</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>46</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>46</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>47</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>47</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>48</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>48</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>49</td>\n",
       "      <td>not_fail</td>\n",
       "      <td>49</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     serial_number  row_num Model_Prediction  row_num  Active_Probability  \\\n",
       "0   MK0271YGJP8LVA        0         not_fail        0            0.999912   \n",
       "1         Z1Y45RA5        1         not_fail        1            1.000000   \n",
       "2   MK0271YGJA4WUA        2         not_fail        2            0.999912   \n",
       "3   MK0271YGJP8LVA        3         not_fail        3            0.999912   \n",
       "4         Z1Y45RA5        4         not_fail        4            1.000000   \n",
       "5   MK0271YGJA4WUA        5         not_fail        5            0.999912   \n",
       "6   MK0271YGJP8LVA        6         not_fail        6            0.999912   \n",
       "7         Z1Y45RA5        7         not_fail        7            1.000000   \n",
       "8   MK0271YGJA4WUA        8         not_fail        8            0.999912   \n",
       "9   MK0271YGJP8LVA        9         not_fail        9            0.999912   \n",
       "10        Z1Y45RA5       10         not_fail       10            1.000000   \n",
       "11  MK0271YGJA4WUA       11         not_fail       11            0.999912   \n",
       "12  MK0271YGJP8LVA       12         not_fail       12            0.999912   \n",
       "13        Z1Y45RA5       13         not_fail       13            1.000000   \n",
       "14  MK0271YGJA4WUA       14         not_fail       14            0.999912   \n",
       "15  MK0271YGJP8LVA       15         not_fail       15            0.999912   \n",
       "16        Z1Y45RA5       16         not_fail       16            1.000000   \n",
       "17  MK0271YGJA4WUA       17         not_fail       17            0.999912   \n",
       "18  MK0271YGJP8LVA       18         not_fail       18            0.999912   \n",
       "19        Z1Y45RA5       19         not_fail       19            1.000000   \n",
       "20  MK0271YGJA4WUA       20         not_fail       20            0.999912   \n",
       "21  MK0271YGJP8LVA       21         not_fail       21            0.999912   \n",
       "22        Z1Y45RA5       22         not_fail       22            1.000000   \n",
       "23  MK0271YGJA4WUA       23         not_fail       23            0.999912   \n",
       "24  MK0271YGJP8LVA       24         not_fail       24            0.999912   \n",
       "25        Z1Y45RA5       25         not_fail       25            1.000000   \n",
       "26  MK0271YGJA4WUA       26         not_fail       26            0.999912   \n",
       "27  MK0271YGJP8LVA       27         not_fail       27            0.999912   \n",
       "28        Z1Y45RA5       28         not_fail       28            1.000000   \n",
       "29  MK0271YGJA4WUA       29         not_fail       29            0.999912   \n",
       "30  MK0271YGJP8LVA       30         not_fail       30            0.999912   \n",
       "31        Z1Y45RA5       31         not_fail       31            1.000000   \n",
       "32  MK0271YGJA4WUA       32         not_fail       32            0.999912   \n",
       "33  MK0271YGJP8LVA       33         not_fail       33            0.999912   \n",
       "34        Z1Y45RA5       34         not_fail       34            1.000000   \n",
       "35  MK0271YGJA4WUA       35         not_fail       35            0.999912   \n",
       "36  MK0271YGJP8LVA       36         not_fail       36            0.999912   \n",
       "37        Z1Y45RA5       37         not_fail       37            1.000000   \n",
       "38  MK0271YGJA4WUA       38         not_fail       38            0.999912   \n",
       "39  MK0271YGJP8LVA       39         not_fail       39            0.999912   \n",
       "40        Z1Y45RA5       40         not_fail       40            1.000000   \n",
       "41  MK0271YGJA4WUA       41         not_fail       41            0.999912   \n",
       "42  MK0271YGJP8LVA       42         not_fail       42            0.999912   \n",
       "43        Z1Y45RA5       43         not_fail       43            1.000000   \n",
       "44  MK0271YGJA4WUA       44         not_fail       44            0.999912   \n",
       "45  MK0271YGJP8LVA       45         not_fail       45            0.999912   \n",
       "46        Z1Y45RA5       46         not_fail       46            1.000000   \n",
       "47  MK0271YGJA4WUA       47         not_fail       47            0.999912   \n",
       "48  MK0271YGJP8LVA       48         not_fail       48            0.999912   \n",
       "49        Z1Y45RA5       49         not_fail       49            1.000000   \n",
       "\n",
       "    Failure_Probability  \n",
       "0              0.000088  \n",
       "1              0.000000  \n",
       "2              0.000088  \n",
       "3              0.000088  \n",
       "4              0.000000  \n",
       "5              0.000088  \n",
       "6              0.000088  \n",
       "7              0.000000  \n",
       "8              0.000088  \n",
       "9              0.000088  \n",
       "10             0.000000  \n",
       "11             0.000088  \n",
       "12             0.000088  \n",
       "13             0.000000  \n",
       "14             0.000088  \n",
       "15             0.000088  \n",
       "16             0.000000  \n",
       "17             0.000088  \n",
       "18             0.000088  \n",
       "19             0.000000  \n",
       "20             0.000088  \n",
       "21             0.000088  \n",
       "22             0.000000  \n",
       "23             0.000088  \n",
       "24             0.000088  \n",
       "25             0.000000  \n",
       "26             0.000088  \n",
       "27             0.000088  \n",
       "28             0.000000  \n",
       "29             0.000088  \n",
       "30             0.000088  \n",
       "31             0.000000  \n",
       "32             0.000088  \n",
       "33             0.000088  \n",
       "34             0.000000  \n",
       "35             0.000088  \n",
       "36             0.000088  \n",
       "37             0.000000  \n",
       "38             0.000088  \n",
       "39             0.000088  \n",
       "40             0.000000  \n",
       "41             0.000088  \n",
       "42             0.000088  \n",
       "43             0.000000  \n",
       "44             0.000088  \n",
       "45             0.000088  \n",
       "46             0.000000  \n",
       "47             0.000088  \n",
       "48             0.000088  \n",
       "49             0.000000  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3 DELETE\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "y_pred_prob = rfc.predict_proba(X_validation2)\n",
    "Probability_of_failure = pd.DataFrame(y_pred_prob, columns =['Active_Probability','Failure_Probability'])\n",
    "#e_dataframe = pd.DataFrame(e) \n",
    "#Probability_of_failure = pd.DataFrame(y_pred_prob)\n",
    "Probability_of_failure\n",
    "#3\n",
    "result = pd.concat([validation2_serial_number, yPred_df_yPred_validation2, Probability_of_failure], axis=1)\n",
    "result.Model_Prediction.replace((0, 1), ('not_fail', 'Predicted_to_be_fail'), inplace=True)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    1537\n",
       "1.0    1411\n",
       "Name: failure, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_validation['failure'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "not_fail                1537\n",
       "Predicted_to_be_fail    1411\n",
       "Name: Model_Prediction, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RandomForest_Model_Prediction['Model_Prediction'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this step is not required for validation data\n",
    "#Splitting values for X_test and Y_test\n",
    "X_test_val = df_validation.drop('failure',axis=1)\n",
    "Y_test_val = df_validation['failure']\n",
    "#confusion matrix test data\n",
    "LABELS = ['Healthy', 'Failed'] \n",
    "conf_matrix = confusion_matrix(Y_test_val, yPred) \n",
    "plt.figure(figsize =(12, 12)) \n",
    "sns.heatmap(conf_matrix, xticklabels = LABELS,  \n",
    "            yticklabels = LABELS, annot = True, fmt =\"d\"); \n",
    "plt.title(\"Confusion matrix\") \n",
    "plt.ylabel('True class') \n",
    "plt.xlabel('Predicted class') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print output of the model\n",
    "RandomForest_Model_Prediction.to_csv(r'E:\\work\\Supermicro\\modeling\\data_Q2_2019\\RandomForest_Model_Prediction.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#HTML\n",
    "html = RandomForest_Model_Prediction.to_html()\n",
    "# write html to file\n",
    "text_file = open(\"RandomForest_Model_Prediction.html\", \"w\")\n",
    "text_file.write(html)\n",
    "text_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "######### end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model used is: LogisticRegression\n",
      "The accuracy is 1.0\n",
      "The precision is 1.0\n",
      "The recall is 1.0\n",
      "The F1-Score is 1.0\n",
      "The Matthews correlation coefficient is 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/smicro/.local/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    }
   ],
   "source": [
    "### lets try GLM model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "glm_model = LogisticRegression()\n",
    "#  model creation \n",
    "glm_model = LogisticRegression() \n",
    "glm_model.fit(X_train, Y_train) \n",
    "\n",
    "# predictions(Notice the caps'P' of yPred to differentiate between model 1 and 2) \n",
    "yPred = glm_model.predict(X_test) \n",
    "\n",
    "yPred_df = pd.DataFrame(yPred, columns = ['Model_Prediction'])\n",
    "\n",
    "#Results of our predictions\n",
    "\n",
    "  \n",
    "#n_outliers = len(failed) \n",
    "n_errors = (yPred != Y_test).sum() \n",
    "\n",
    "print(\"Model used is: LogisticRegression\") \n",
    "  \n",
    "acc = accuracy_score(Y_test, yPred) \n",
    "print(\"The accuracy is {}\".format(acc)) \n",
    "  \n",
    "prec = precision_score(Y_test, yPred) \n",
    "print(\"The precision is {}\".format(prec)) \n",
    "  \n",
    "rec = recall_score(Y_test, yPred) \n",
    "print(\"The recall is {}\".format(rec))\n",
    "f1 = f1_score(Y_test, yPred) \n",
    "print(\"The F1-Score is {}\".format(f1)) \n",
    "  \n",
    "MCC = matthews_corrcoef(Y_test, yPred) \n",
    "print(\"The Matthews correlation coefficient is {}\".format(MCC)) \n",
    "# YOU NEED TO FOLLOW THE VALIDATION STEP "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., ..., 1., 1., 1.])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import pickle\n",
    " \n",
    "# Save the trained model as a pickle string.\n",
    "saved_model = pickle.dumps(glm_model)\n",
    " \n",
    "# Load the pickled model\n",
    "knn_from_pickle = pickle.loads(saved_model)\n",
    " \n",
    "# Use the loaded pickled model to make predictions\n",
    "knn_from_pickle.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>model</th>\n",
       "      <th>smart_1_normalized</th>\n",
       "      <th>smart_2_normalized</th>\n",
       "      <th>smart_2_raw</th>\n",
       "      <th>smart_3_normalized</th>\n",
       "      <th>smart_3_raw</th>\n",
       "      <th>smart_4_normalized</th>\n",
       "      <th>smart_4_raw</th>\n",
       "      <th>smart_5_normalized</th>\n",
       "      <th>smart_7_normalized</th>\n",
       "      <th>...</th>\n",
       "      <th>normalized_smart_233_raw</th>\n",
       "      <th>normalized_smart_235_raw</th>\n",
       "      <th>normalized_smart_240_raw</th>\n",
       "      <th>normalized_smart_247_raw</th>\n",
       "      <th>normalized_smart_251_raw</th>\n",
       "      <th>normalized_smart_252_raw</th>\n",
       "      <th>normalized_smart_5_raw</th>\n",
       "      <th>normalized_smart_197_raw</th>\n",
       "      <th>normalized_smart_10_raw</th>\n",
       "      <th>normalized_smart_198_raw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3096</th>\n",
       "      <td>16</td>\n",
       "      <td>0.163824</td>\n",
       "      <td>0.892038</td>\n",
       "      <td>1.186981</td>\n",
       "      <td>1.242061</td>\n",
       "      <td>0.444534</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>0.633317</td>\n",
       "      <td>0.021329</td>\n",
       "      <td>0.748009</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7856</th>\n",
       "      <td>16</td>\n",
       "      <td>0.163824</td>\n",
       "      <td>0.892038</td>\n",
       "      <td>0.545244</td>\n",
       "      <td>0.574595</td>\n",
       "      <td>1.027247</td>\n",
       "      <td>-0.931971</td>\n",
       "      <td>1.352623</td>\n",
       "      <td>0.021329</td>\n",
       "      <td>0.748009</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1829</th>\n",
       "      <td>16</td>\n",
       "      <td>0.163824</td>\n",
       "      <td>0.892038</td>\n",
       "      <td>0.545244</td>\n",
       "      <td>0.574595</td>\n",
       "      <td>1.027247</td>\n",
       "      <td>-0.931971</td>\n",
       "      <td>1.352623</td>\n",
       "      <td>0.021329</td>\n",
       "      <td>0.748009</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12149</th>\n",
       "      <td>16</td>\n",
       "      <td>0.163824</td>\n",
       "      <td>0.892038</td>\n",
       "      <td>1.186981</td>\n",
       "      <td>0.574595</td>\n",
       "      <td>1.027247</td>\n",
       "      <td>-0.931971</td>\n",
       "      <td>1.352623</td>\n",
       "      <td>0.021329</td>\n",
       "      <td>0.748009</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10047</th>\n",
       "      <td>16</td>\n",
       "      <td>0.163824</td>\n",
       "      <td>0.892038</td>\n",
       "      <td>0.545244</td>\n",
       "      <td>1.242061</td>\n",
       "      <td>0.444534</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>0.633317</td>\n",
       "      <td>0.021329</td>\n",
       "      <td>0.748009</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.170476e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>20723</th>\n",
       "      <td>48</td>\n",
       "      <td>1.310017</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.050924</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.558392</td>\n",
       "      <td>0.021329</td>\n",
       "      <td>-0.243255</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.246868e-10</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16613</th>\n",
       "      <td>24</td>\n",
       "      <td>-0.593940</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-0.076604</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-1.513589</td>\n",
       "      <td>0.021329</td>\n",
       "      <td>-0.341493</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>3.659275e-11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.003540</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27483</th>\n",
       "      <td>37</td>\n",
       "      <td>0.963423</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.050924</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.301855</td>\n",
       "      <td>0.021329</td>\n",
       "      <td>-1.639539</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>4.268884e-01</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000056</td>\n",
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       "    <tr>\n",
       "      <th>20486</th>\n",
       "      <td>22</td>\n",
       "      <td>-1.684464</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.994971</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.849874</td>\n",
       "      <td>0.021329</td>\n",
       "      <td>-0.480839</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>2.034825e-11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.004151</td>\n",
       "      <td>0.003253</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18408</th>\n",
       "      <td>48</td>\n",
       "      <td>1.773922</td>\n",
       "      <td>-0.421111</td>\n",
       "      <td>-1.471555</td>\n",
       "      <td>-1.050924</td>\n",
       "      <td>-5.199338</td>\n",
       "      <td>5.199338</td>\n",
       "      <td>-0.785036</td>\n",
       "      <td>-1.606755</td>\n",
       "      <td>-0.480839</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270942</td>\n",
       "      <td>0.133959</td>\n",
       "      <td>1.492561e-10</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.427661</td>\n",
       "      <td>0.427282</td>\n",
       "      <td>0.035649</td>\n",
       "      <td>0.001739</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001739</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6138 rows × 117 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  smart_1_normalized  smart_2_normalized  smart_2_raw  \\\n",
       "3096      16            0.163824            0.892038     1.186981   \n",
       "7856      16            0.163824            0.892038     0.545244   \n",
       "1829      16            0.163824            0.892038     0.545244   \n",
       "12149     16            0.163824            0.892038     1.186981   \n",
       "10047     16            0.163824            0.892038     0.545244   \n",
       "...      ...                 ...                 ...          ...   \n",
       "20723     48            1.310017           -0.421111    -1.471555   \n",
       "16613     24           -0.593940           -0.421111    -1.471555   \n",
       "27483     37            0.963423           -0.421111    -1.471555   \n",
       "20486     22           -1.684464           -0.421111    -1.471555   \n",
       "18408     48            1.773922           -0.421111    -1.471555   \n",
       "\n",
       "       smart_3_normalized  smart_3_raw  smart_4_normalized  smart_4_raw  \\\n",
       "3096             1.242061     0.444534            5.199338     0.633317   \n",
       "7856             0.574595     1.027247           -0.931971     1.352623   \n",
       "1829             0.574595     1.027247           -0.931971     1.352623   \n",
       "12149            0.574595     1.027247           -0.931971     1.352623   \n",
       "10047            1.242061     0.444534            5.199338     0.633317   \n",
       "...                   ...          ...                 ...          ...   \n",
       "20723           -1.050924    -5.199338            5.199338    -0.558392   \n",
       "16613           -0.076604    -5.199338            5.199338    -1.513589   \n",
       "27483           -1.050924    -5.199338            5.199338    -0.301855   \n",
       "20486           -1.994971    -5.199338            5.199338    -0.849874   \n",
       "18408           -1.050924    -5.199338            5.199338    -0.785036   \n",
       "\n",
       "       smart_5_normalized  smart_7_normalized  ...  normalized_smart_233_raw  \\\n",
       "3096             0.021329            0.748009  ...                  0.270942   \n",
       "7856             0.021329            0.748009  ...                  0.270942   \n",
       "1829             0.021329            0.748009  ...                  0.270942   \n",
       "12149            0.021329            0.748009  ...                  0.270942   \n",
       "10047            0.021329            0.748009  ...                  0.270942   \n",
       "...                   ...                 ...  ...                       ...   \n",
       "20723            0.021329           -0.243255  ...                  0.270942   \n",
       "16613            0.021329           -0.341493  ...                  0.270942   \n",
       "27483            0.021329           -1.639539  ...                  0.270942   \n",
       "20486            0.021329           -0.480839  ...                  0.270942   \n",
       "18408           -1.606755           -0.480839  ...                  0.270942   \n",
       "\n",
       "       normalized_smart_235_raw  normalized_smart_240_raw  \\\n",
       "3096                   0.133959              1.170476e-01   \n",
       "7856                   0.133959              1.170476e-01   \n",
       "1829                   0.133959              1.170476e-01   \n",
       "12149                  0.133959              1.170476e-01   \n",
       "10047                  0.133959              1.170476e-01   \n",
       "...                         ...                       ...   \n",
       "20723                  0.133959              1.246868e-10   \n",
       "16613                  0.133959              3.659275e-11   \n",
       "27483                  0.133959              4.268884e-01   \n",
       "20486                  0.133959              2.034825e-11   \n",
       "18408                  0.133959              1.492561e-10   \n",
       "\n",
       "       normalized_smart_247_raw  normalized_smart_251_raw  \\\n",
       "3096                        0.5                  0.427661   \n",
       "7856                        0.5                  0.427661   \n",
       "1829                        0.5                  0.427661   \n",
       "12149                       0.5                  0.427661   \n",
       "10047                       0.5                  0.427661   \n",
       "...                         ...                       ...   \n",
       "20723                       0.5                  0.427661   \n",
       "16613                       0.5                  0.427661   \n",
       "27483                       0.5                  0.427661   \n",
       "20486                       0.5                  0.427661   \n",
       "18408                       0.5                  0.427661   \n",
       "\n",
       "       normalized_smart_252_raw  normalized_smart_5_raw  \\\n",
       "3096                   0.427282                0.000000   \n",
       "7856                   0.427282                0.000000   \n",
       "1829                   0.427282                0.000000   \n",
       "12149                  0.427282                0.000000   \n",
       "10047                  0.427282                0.000000   \n",
       "...                         ...                     ...   \n",
       "20723                  0.427282                0.000000   \n",
       "16613                  0.427282                0.003540   \n",
       "27483                  0.427282                0.000000   \n",
       "20486                  0.427282                0.004151   \n",
       "18408                  0.427282                0.035649   \n",
       "\n",
       "       normalized_smart_197_raw  normalized_smart_10_raw  \\\n",
       "3096                   0.000000                      0.0   \n",
       "7856                   0.000000                      0.0   \n",
       "1829                   0.000000                      0.0   \n",
       "12149                  0.000000                      0.0   \n",
       "10047                  0.000000                      0.0   \n",
       "...                         ...                      ...   \n",
       "20723                  0.000056                      0.0   \n",
       "16613                  0.000337                      0.0   \n",
       "27483                  0.000056                      0.0   \n",
       "20486                  0.003253                      0.0   \n",
       "18408                  0.001739                      0.0   \n",
       "\n",
       "       normalized_smart_198_raw  \n",
       "3096                   0.000000  \n",
       "7856                   0.000000  \n",
       "1829                   0.000000  \n",
       "12149                  0.000000  \n",
       "10047                  0.000000  \n",
       "...                         ...  \n",
       "20723                  0.000056  \n",
       "16613                  0.000337  \n",
       "27483                  0.000056  \n",
       "20486                  0.003253  \n",
       "18408                  0.001739  \n",
       "\n",
       "[6138 rows x 117 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/smicro/.local/lib/python3.6/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[03:32:33] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Model used is: XGBClassifier\n",
      "The accuracy is 1.0\n",
      "The precision is 1.0\n",
      "The recall is 1.0\n",
      "The F1-Score is 1.0\n",
      "The Matthews correlation coefficient is 1.0\n"
     ]
    }
   ],
   "source": [
    "### lets try XGB model 3rd model\n",
    "\n",
    "XGB_model = XGBClassifier()\n",
    "#  model creation \n",
    "XGB_model = XGBClassifier() \n",
    "XGB_model.fit(X_train, Y_train) \n",
    "\n",
    "# predictions(Notice the caps'P' of yPred to differentiate between model 1 and 2) \n",
    "yPred = XGB_model.predict(X_test) \n",
    "yPred_df = pd.DataFrame(yPred, columns = ['Model_Prediction'])\n",
    "\n",
    "#Results of our predictions\n",
    "\n",
    "  \n",
    "#n_outliers = len(failed) \n",
    "n_errors = (yPred != Y_test).sum() \n",
    "\n",
    "print(\"Model used is: XGBClassifier\") \n",
    "  \n",
    "acc = accuracy_score(Y_test, yPred) \n",
    "print(\"The accuracy is {}\".format(acc)) \n",
    "  \n",
    "prec = precision_score(Y_test, yPred) \n",
    "print(\"The precision is {}\".format(prec)) \n",
    "  \n",
    "rec = recall_score(Y_test, yPred) \n",
    "print(\"The recall is {}\".format(rec))\n",
    "f1 = f1_score(Y_test, yPred) \n",
    "print(\"The F1-Score is {}\".format(f1)) \n",
    "  \n",
    "MCC = matthews_corrcoef(Y_test, yPred) \n",
    "print(\"The Matthews correlation coefficient is {}\".format(MCC)) \n",
    "# YOU NEED TO FOLLOW THE VALIDATION STEP "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model used is: KNeighborsClassifier\n",
      "The accuracy is 1.0\n",
      "The precision is 1.0\n",
      "The recall is 1.0\n",
      "The F1-Score is 1.0\n",
      "The Matthews correlation coefficient is 1.0\n"
     ]
    }
   ],
   "source": [
    "# 4th model KNeighborsClassifier\n",
    "KNeighborsClassifier_model = KNeighborsClassifier()\n",
    "#  model creation \n",
    "KNeighborsClassifier_model = KNeighborsClassifier() \n",
    "KNeighborsClassifier_model.fit(X_train, Y_train) \n",
    "\n",
    "# predictions(Notice the caps'P' of yPred to differentiate between model 1 and 2) \n",
    "yPred = KNeighborsClassifier_model.predict(X_test) \n",
    "\n",
    "yPred_df = pd.DataFrame(yPred, columns = ['Model_Prediction'])\n",
    "\n",
    "#Results of our predictions\n",
    "  \n",
    "#n_outliers = len(failed) \n",
    "n_errors = (yPred != Y_test).sum() \n",
    "\n",
    "print(\"Model used is: KNeighborsClassifier\") \n",
    "  \n",
    "acc = accuracy_score(Y_test, yPred) \n",
    "print(\"The accuracy is {}\".format(acc)) \n",
    "  \n",
    "prec = precision_score(Y_test, yPred) \n",
    "print(\"The precision is {}\".format(prec)) \n",
    "  \n",
    "rec = recall_score(Y_test, yPred) \n",
    "print(\"The recall is {}\".format(rec))\n",
    "f1 = f1_score(Y_test, yPred) \n",
    "print(\"The F1-Score is {}\".format(f1)) \n",
    "  \n",
    "MCC = matthews_corrcoef(Y_test, yPred) \n",
    "print(\"The Matthews correlation coefficient is {}\".format(MCC)) \n",
    "# YOU NEED TO FOLLOW THE VALIDATION STEP "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model used is: GaussianNB\n",
      "The accuracy is 0.9750733137829912\n",
      "The precision is 1.0\n",
      "The recall is 0.9501466275659824\n",
      "The F1-Score is 0.9744360902255639\n",
      "The Matthews correlation coefficient is 0.951329560647042\n"
     ]
    }
   ],
   "source": [
    "# 5th model GaussianNB_model\n",
    "\n",
    "GaussianNB_model = GaussianNB()\n",
    "#  model creation \n",
    "GaussianNB_model = GaussianNB() \n",
    "GaussianNB_model.fit(X_train, Y_train) \n",
    "# predictions(Notice the caps'P' of yPred to differentiate between model 1 and 2) \n",
    "yPred = GaussianNB_model.predict(X_test) \n",
    "yPred_df = pd.DataFrame(yPred, columns = ['Model_Prediction'])\n",
    "#Results of our predictions\n",
    "#n_outliers = len(failed) \n",
    "n_errors = (yPred != Y_test).sum() \n",
    "print(\"Model used is: GaussianNB\") \n",
    "acc = accuracy_score(Y_test, yPred) \n",
    "print(\"The accuracy is {}\".format(acc)) \n",
    "prec = precision_score(Y_test, yPred) \n",
    "print(\"The precision is {}\".format(prec)) \n",
    "rec = recall_score(Y_test, yPred) \n",
    "print(\"The recall is {}\".format(rec))\n",
    "f1 = f1_score(Y_test, yPred) \n",
    "print(\"The F1-Score is {}\".format(f1)) \n",
    "MCC = matthews_corrcoef(Y_test, yPred) \n",
    "print(\"The Matthews correlation coefficient is {}\".format(MCC)) \n",
    "# YOU NEED TO FOLLOW THE VALIDATION STEP "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(gamma='auto')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 6th model svm model\n",
    "#Import svm model \n",
    "svm_model = SVC(gamma='auto')\n",
    "svm_model.fit(X_train, Y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model used is: svm\n",
      "The accuracy is 1.0\n",
      "The precision is 1.0\n",
      "The recall is 1.0\n",
      "The F1-Score is 1.0\n",
      "The Matthews correlation coefficient is 1.0\n"
     ]
    }
   ],
   "source": [
    "# predictions(Notice the caps'P' of yPred to differentiate between model 1 and 2) \n",
    "yPred = svm_model.predict(X_test) \n",
    "yPred_df = pd.DataFrame(yPred, columns = ['Model_Prediction'])\n",
    "#Results of our predictions\n",
    "#n_outliers = len(failed) \n",
    "n_errors = (yPred != Y_test).sum() \n",
    "print(\"Model used is: svm\") \n",
    "acc = accuracy_score(Y_test, yPred) \n",
    "print(\"The accuracy is {}\".format(acc)) \n",
    "prec = precision_score(Y_test, yPred) \n",
    "print(\"The precision is {}\".format(prec)) \n",
    "rec = recall_score(Y_test, yPred) \n",
    "print(\"The recall is {}\".format(rec))\n",
    "f1 = f1_score(Y_test, yPred) \n",
    "print(\"The F1-Score is {}\".format(f1)) \n",
    "MCC = matthews_corrcoef(Y_test, yPred) \n",
    "print(\"The Matthews correlation coefficient is {}\".format(MCC)) \n",
    "# YOU NEED TO FOLLOW THE VALIDATION STEP \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>serial_number</th>\n",
       "      <th>Model_Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>not_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>not_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>not_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ZHZ38M6K</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>not_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2943</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>not_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2944</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>not_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2945</th>\n",
       "      <td>ZA180RAD</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2946</th>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>not_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2947</th>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>not_fail</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2948 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       serial_number      Model_Prediction\n",
       "0     MK0271YGJP8LVA              not_fail\n",
       "1     MK0271YGJA4WUA              not_fail\n",
       "2     MK0271YGJP8LVA              not_fail\n",
       "3           ZHZ38M6K  Predicted_to_be_fail\n",
       "4           Z1Y45RA5              not_fail\n",
       "...              ...                   ...\n",
       "2943  MK0271YGJP8LVA              not_fail\n",
       "2944  MK0271YGJP8LVA              not_fail\n",
       "2945        ZA180RAD  Predicted_to_be_fail\n",
       "2946  MK0271YGJA4WUA              not_fail\n",
       "2947  MK0271YGJP8LVA              not_fail\n",
       "\n",
       "[2948 rows x 2 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the final results\n",
    "RandomForest_Model_Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete the existing table in SQL DB\n",
    "import psycopg2\n",
    "from psycopg2.extensions import ISOLATION_LEVEL_AUTOCOMMIT\n",
    "psqlCon         = psycopg2.connect(\"dbname=rawData user=data_user password=kgtopg8932\");\n",
    "psqlCon.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT);\n",
    "psqlCursor      = psqlCon.cursor();\n",
    "tableName       = \"ml_prediction\";\n",
    "dropTableStmt   = \"DROP TABLE %s;\"%tableName;\n",
    "psqlCursor.execute(dropTableStmt);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#upload new table in to SQL DB\n",
    "from sqlalchemy import create_engine\n",
    "engine = create_engine('postgresql://data_user:kgtopg8932@localhost:5432/rawData')\n",
    "RandomForest_Model_Prediction.to_sql('ml_prediction', engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CHECK THE TABLE \n",
    "query1 = \"select * from ml_prediction\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "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>index</th>\n",
       "      <th>serial_number</th>\n",
       "      <th>Model_Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>not_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>not_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Z304LVV7</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>not_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>ZA12624J</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2943</th>\n",
       "      <td>2943</td>\n",
       "      <td>Z1Y45RA5</td>\n",
       "      <td>not_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2944</th>\n",
       "      <td>2944</td>\n",
       "      <td>6XW1NWQL</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2945</th>\n",
       "      <td>2945</td>\n",
       "      <td>Z304KCGN</td>\n",
       "      <td>Predicted_to_be_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2946</th>\n",
       "      <td>2946</td>\n",
       "      <td>MK0271YGJP8LVA</td>\n",
       "      <td>not_fail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2947</th>\n",
       "      <td>2947</td>\n",
       "      <td>MK0271YGJA4WUA</td>\n",
       "      <td>not_fail</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2948 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index   serial_number      Model_Prediction\n",
       "0         0        Z1Y45RA5              not_fail\n",
       "1         1        Z1Y45RA5              not_fail\n",
       "2         2        Z304LVV7  Predicted_to_be_fail\n",
       "3         3  MK0271YGJP8LVA              not_fail\n",
       "4         4        ZA12624J  Predicted_to_be_fail\n",
       "...     ...             ...                   ...\n",
       "2943   2943        Z1Y45RA5              not_fail\n",
       "2944   2944        6XW1NWQL  Predicted_to_be_fail\n",
       "2945   2945        Z304KCGN  Predicted_to_be_fail\n",
       "2946   2946  MK0271YGJP8LVA              not_fail\n",
       "2947   2947  MK0271YGJA4WUA              not_fail\n",
       "\n",
       "[2948 rows x 3 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = sqlio.read_sql_query(query1,conn)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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