MODEL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fb64f9fa",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5e83a4be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.11.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#importing libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.gridspec as gridspec\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from numpy import genfromtxt\n",
    "from scipy.stats import multivariate_normal\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import recall_score , average_precision_score\n",
    "from sklearn.metrics import precision_score, precision_recall_curve\n",
    "%matplotlib inline\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot\n",
    "init_notebook_mode(connected=True)\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6b0b420",
   "metadata": {},
   "source": [
    "## Data Cleaning & Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bcad7524",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/56/8qxbxkps1g73zstry__01qrc0000gn/T/ipykernel_42115/1863539859.py:1: DtypeWarning:\n",
      "\n",
      "Columns (0,2,3,6,7,8,9,10,11,22,27,32,37,42,47,52,57,62,67,72,77,82,87) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#we changes the name of the data set for our convinience\n",
    "train = pd.read_csv(\"/Users/raunakpandey/tensorflow-test/weldright/Submission/NEWDATA.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5355b30a",
   "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>Employee Code</th>\n",
       "      <th>Machine</th>\n",
       "      <th>Production</th>\n",
       "      <th>Order Operation No</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Current</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Flow</th>\n",
       "      <th>...</th>\n",
       "      <th>Unnamed: 78</th>\n",
       "      <th>Unnamed: 79</th>\n",
       "      <th>Unnamed: 80</th>\n",
       "      <th>Unnamed: 81</th>\n",
       "      <th>Unnamed: 82</th>\n",
       "      <th>Unnamed: 83</th>\n",
       "      <th>Unnamed: 84</th>\n",
       "      <th>Unnamed: 85</th>\n",
       "      <th>Unnamed: 86</th>\n",
       "      <th>Unnamed: 87</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(Office id of employee)</td>\n",
       "      <td>(Welding machine name)</td>\n",
       "      <td>(Project order number)</td>\n",
       "      <td>(This captures the activity to be performed by...</td>\n",
       "      <td>(date of activity)</td>\n",
       "      <td>(Timestamp for the activity)</td>\n",
       "      <td>(In Ampere)</td>\n",
       "      <td>(Relative humidity in %)</td>\n",
       "      <td>(in degree celsius)</td>\n",
       "      <td>(in liters per min (LPM))</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>383138</td>\n",
       "      <td>TWLD23</td>\n",
       "      <td>E10002436</td>\n",
       "      <td>40</td>\n",
       "      <td>22-08-2022</td>\n",
       "      <td>10:44:10:129</td>\n",
       "      <td>27.85</td>\n",
       "      <td>84</td>\n",
       "      <td>24</td>\n",
       "      <td>3.12</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>2</th>\n",
       "      <td>383138</td>\n",
       "      <td>TWLD23</td>\n",
       "      <td>E10002436</td>\n",
       "      <td>40</td>\n",
       "      <td>22-08-2022</td>\n",
       "      <td>10:44:10:630</td>\n",
       "      <td>27.38</td>\n",
       "      <td>84</td>\n",
       "      <td>24</td>\n",
       "      <td>3.08</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>383138</td>\n",
       "      <td>TWLD23</td>\n",
       "      <td>E10002436</td>\n",
       "      <td>40</td>\n",
       "      <td>22-08-2022</td>\n",
       "      <td>10:44:11:127</td>\n",
       "      <td>26.89</td>\n",
       "      <td>84</td>\n",
       "      <td>24</td>\n",
       "      <td>3.08</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>383138</td>\n",
       "      <td>TWLD23</td>\n",
       "      <td>E10002436</td>\n",
       "      <td>40</td>\n",
       "      <td>22-08-2022</td>\n",
       "      <td>10:44:11:630</td>\n",
       "      <td>27.86</td>\n",
       "      <td>84</td>\n",
       "      <td>24</td>\n",
       "      <td>3.05</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>5 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Employee Code                 Machine              Production  \\\n",
       "0  (Office id of employee)  (Welding machine name)  (Project order number)   \n",
       "1                   383138                  TWLD23               E10002436   \n",
       "2                   383138                  TWLD23               E10002436   \n",
       "3                   383138                  TWLD23               E10002436   \n",
       "4                   383138                  TWLD23               E10002436   \n",
       "\n",
       "                                  Order Operation No                Date  \\\n",
       "0  (This captures the activity to be performed by...  (date of activity)   \n",
       "1                                                 40          22-08-2022   \n",
       "2                                                 40          22-08-2022   \n",
       "3                                                 40          22-08-2022   \n",
       "4                                                 40          22-08-2022   \n",
       "\n",
       "                           Time      Current                  Humidity  \\\n",
       "0  (Timestamp for the activity)  (In Ampere)  (Relative humidity in %)   \n",
       "1                  10:44:10:129        27.85                        84   \n",
       "2                  10:44:10:630        27.38                        84   \n",
       "3                  10:44:11:127        26.89                        84   \n",
       "4                  10:44:11:630        27.86                        84   \n",
       "\n",
       "           Temperature                       Flow  ... Unnamed: 78  \\\n",
       "0  (in degree celsius)  (in liters per min (LPM))  ...         NaN   \n",
       "1                   24                       3.12  ...         NaN   \n",
       "2                   24                       3.08  ...         NaN   \n",
       "3                   24                       3.08  ...         NaN   \n",
       "4                   24                       3.05  ...         NaN   \n",
       "\n",
       "  Unnamed: 79 Unnamed: 80  Unnamed: 81  Unnamed: 82  Unnamed: 83  Unnamed: 84  \\\n",
       "0         NaN         NaN          NaN          NaN          NaN          NaN   \n",
       "1         NaN         NaN          NaN          NaN          NaN          NaN   \n",
       "2         NaN         NaN          NaN          NaN          NaN          NaN   \n",
       "3         NaN         NaN          NaN          NaN          NaN          NaN   \n",
       "4         NaN         NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "   Unnamed: 85  Unnamed: 86  Unnamed: 87  \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",
       "[5 rows x 88 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e72b61a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train=train.drop([0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a7a91c25",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(train.iloc[:, 13:88], inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6eea0305",
   "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>Employee Code</th>\n",
       "      <th>Machine</th>\n",
       "      <th>Production</th>\n",
       "      <th>Order Operation No</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Current</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Flow</th>\n",
       "      <th>Job Temp</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Defect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>383138</td>\n",
       "      <td>TWLD23</td>\n",
       "      <td>E10002436</td>\n",
       "      <td>40</td>\n",
       "      <td>22-08-2022</td>\n",
       "      <td>10:44:10:129</td>\n",
       "      <td>27.85</td>\n",
       "      <td>84</td>\n",
       "      <td>24</td>\n",
       "      <td>3.12</td>\n",
       "      <td>30.1</td>\n",
       "      <td>9.122</td>\n",
       "      <td>No Defect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>383138</td>\n",
       "      <td>TWLD23</td>\n",
       "      <td>E10002436</td>\n",
       "      <td>40</td>\n",
       "      <td>22-08-2022</td>\n",
       "      <td>10:44:10:630</td>\n",
       "      <td>27.38</td>\n",
       "      <td>84</td>\n",
       "      <td>24</td>\n",
       "      <td>3.08</td>\n",
       "      <td>30.1</td>\n",
       "      <td>9.129</td>\n",
       "      <td>No Defect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>383138</td>\n",
       "      <td>TWLD23</td>\n",
       "      <td>E10002436</td>\n",
       "      <td>40</td>\n",
       "      <td>22-08-2022</td>\n",
       "      <td>10:44:11:127</td>\n",
       "      <td>26.89</td>\n",
       "      <td>84</td>\n",
       "      <td>24</td>\n",
       "      <td>3.08</td>\n",
       "      <td>29.3</td>\n",
       "      <td>9.17</td>\n",
       "      <td>No Defect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>383138</td>\n",
       "      <td>TWLD23</td>\n",
       "      <td>E10002436</td>\n",
       "      <td>40</td>\n",
       "      <td>22-08-2022</td>\n",
       "      <td>10:44:11:630</td>\n",
       "      <td>27.86</td>\n",
       "      <td>84</td>\n",
       "      <td>24</td>\n",
       "      <td>3.05</td>\n",
       "      <td>29.3</td>\n",
       "      <td>9.104</td>\n",
       "      <td>No Defect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>383138</td>\n",
       "      <td>TWLD23</td>\n",
       "      <td>E10002436</td>\n",
       "      <td>40</td>\n",
       "      <td>22-08-2022</td>\n",
       "      <td>10:44:12:132</td>\n",
       "      <td>27.81</td>\n",
       "      <td>84</td>\n",
       "      <td>24</td>\n",
       "      <td>3.05</td>\n",
       "      <td>29.3</td>\n",
       "      <td>9.045</td>\n",
       "      <td>No Defect</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Employee Code Machine Production Order Operation No        Date  \\\n",
       "1        383138  TWLD23  E10002436                 40  22-08-2022   \n",
       "2        383138  TWLD23  E10002436                 40  22-08-2022   \n",
       "3        383138  TWLD23  E10002436                 40  22-08-2022   \n",
       "4        383138  TWLD23  E10002436                 40  22-08-2022   \n",
       "5        383138  TWLD23  E10002436                 40  22-08-2022   \n",
       "\n",
       "           Time Current Humidity Temperature  Flow Job Temp Voltage     Defect  \n",
       "1  10:44:10:129   27.85       84          24  3.12     30.1   9.122  No Defect  \n",
       "2  10:44:10:630   27.38       84          24  3.08     30.1   9.129  No Defect  \n",
       "3  10:44:11:127   26.89       84          24  3.08     29.3    9.17  No Defect  \n",
       "4  10:44:11:630   27.86       84          24  3.05     29.3   9.104  No Defect  \n",
       "5  10:44:12:132   27.81       84          24  3.05     29.3   9.045  No Defect  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d4afaa53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Current               3\n",
       "Employee Code         0\n",
       "Machine               0\n",
       "Production            0\n",
       "Order Operation No    0\n",
       "Date                  0\n",
       "Time                  0\n",
       "Humidity              0\n",
       "Temperature           0\n",
       "Flow                  0\n",
       "Job Temp              0\n",
       "Voltage               0\n",
       "Defect                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dbc7ded6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['22-08-2022', '23-08-2022', '24-08-2022', '25-08-2022',\n",
       "       '26-08-2022', '27-08-2022', '29-08-2022', '30-08-2022',\n",
       "       '01-09-2022', '02-09-2022', '03-09-2022', '05-09-2022',\n",
       "       '06-09-2022', '07-09-2022', '08-09-2022', '09-09-2022',\n",
       "       '10-09-2022', '12-09-2022', '13-09-2022', '14-09-2022',\n",
       "       '15-09-2022', '16-09-2022', '17-09-2022', '19-09-2022'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Date'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eeb51b05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['E10002436', 'Test coopan', '-15003217', -15003217, -150033027,\n",
       "       15003062, '15003062', 'E15003220', 'E15002881', 'E50006711',\n",
       "       'E15003200', 'E15003202', 'E15003056', 'E15002963', 'E10002437',\n",
       "       'E15003219', 'E15003201', 'E15002965', 'E15002966', '#NAME?', '-'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Production'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bff7349e",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Production'], axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a3773413",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f2c3d4f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Machine'], axis=1,inplace=True)\n",
    "\n",
    "\n",
    "\n",
    "#train['Order Operation No'] = train['Order Operation No'].astype(int)\n",
    "train['Current'] = train['Current'].astype(float)\n",
    "train['Humidity'] = train['Humidity'].astype(float)\n",
    "train['Temperature'] = train['Temperature'].astype(float)\n",
    "train['Flow'] = train['Flow'].astype(float)\n",
    "train['Job Temp'] = train['Job Temp'].astype(float)\n",
    "train['Voltage'] = train['Voltage'].astype(float)\n",
    "train['Defect'] = train['Defect'].astype(str)\n",
    "train['Date'] = train['Date'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dff1b4b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['date'] = train['Date'].str[:2]\n",
    "train['month']=train.Date.str.slice(3,5)\n",
    "train.drop(['Date'], axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bfc77ceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Defectinitials'] = train['Defect'].str[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "78b98a20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'Po', 'Tu'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Defectinitials'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9bf65ca7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No    821778\n",
       "Tu      4610\n",
       "Po      1143\n",
       "Name: Defectinitials, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Defectinitials'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "67147e80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "240     471055\n",
       "180     128646\n",
       "1        67310\n",
       "180-     53238\n",
       "40       39068\n",
       "240      20413\n",
       "130      15868\n",
       "30       15473\n",
       "40        8191\n",
       "1         4424\n",
       "130       1790\n",
       "-240      1315\n",
       "-          740\n",
       "Name: Order Operation No, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Order Operation No'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "03d58509",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['porosity']=train['Defectinitials']=='Po'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a65735bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['tungsten']=train['Defectinitials']=='Tu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f09e44c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "old_values = ['Tu', 'Po']\n",
    "new_values = ['Tu', 'Tu']\n",
    "\n",
    "train['Defectinitials']=train.Defectinitials.replace(old_values, new_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ebafa536",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'Tu'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Defectinitials'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f1f05e00",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.rename(columns={'Order Operation No': 'operationnumber',}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1063783c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['40', 40, 1, '1', '180-', '240', 240, 30, 180, '-240', '-', '130',\n",
       "       130], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['operationnumber'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cca75787",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.rename(columns={'Employee Code': 'employeecode',}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d3f7955c",
   "metadata": {},
   "outputs": [],
   "source": [
    "old_values = ['382617']\n",
    "new_values = [382617]\n",
    "\n",
    "train['employeecode']=train.employeecode.replace(old_values, new_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "abf2f68a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['employeecode'] = train['employeecode'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "714124a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([383138.,      0., 394269.,  97346., 394965., 391342., 394365.,\n",
       "       382617., 387268.])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['employeecode'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "11b2eeed",
   "metadata": {},
   "outputs": [],
   "source": [
    "old_values = ['240', '-240','-','130','1','180-']\n",
    "new_values = [240,240,0,130,1,180]\n",
    "\n",
    "train['operationnumber']=train.operationnumber.replace(old_values, new_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "933fc2e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['40', 40, 1, 180, 240, 30, 0, 130], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['operationnumber'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "827c4ec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder = LabelEncoder()\n",
    "train['Defect'] = labelencoder.fit_transform(train['Defectinitials'])\n",
    "train['porosity'] = labelencoder.fit_transform(train['porosity'])\n",
    "train['tungsten'] = labelencoder.fit_transform(train['tungsten'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b24c480c",
   "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>employeecode</th>\n",
       "      <th>operationnumber</th>\n",
       "      <th>Time</th>\n",
       "      <th>Current</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Flow</th>\n",
       "      <th>Job Temp</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Defect</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>Defectinitials</th>\n",
       "      <th>porosity</th>\n",
       "      <th>tungsten</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>10:44:10:129</td>\n",
       "      <td>27.85</td>\n",
       "      <td>84.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.12</td>\n",
       "      <td>30.1</td>\n",
       "      <td>9.122</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>08</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>10:44:10:630</td>\n",
       "      <td>27.38</td>\n",
       "      <td>84.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.08</td>\n",
       "      <td>30.1</td>\n",
       "      <td>9.129</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>08</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>10:44:11:127</td>\n",
       "      <td>26.89</td>\n",
       "      <td>84.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.08</td>\n",
       "      <td>29.3</td>\n",
       "      <td>9.170</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>08</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>10:44:11:630</td>\n",
       "      <td>27.86</td>\n",
       "      <td>84.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.05</td>\n",
       "      <td>29.3</td>\n",
       "      <td>9.104</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>08</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>10:44:12:132</td>\n",
       "      <td>27.81</td>\n",
       "      <td>84.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.05</td>\n",
       "      <td>29.3</td>\n",
       "      <td>9.045</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>08</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employeecode operationnumber          Time  Current  Humidity  Temperature  \\\n",
       "1      383138.0              40  10:44:10:129    27.85      84.0         24.0   \n",
       "2      383138.0              40  10:44:10:630    27.38      84.0         24.0   \n",
       "3      383138.0              40  10:44:11:127    26.89      84.0         24.0   \n",
       "4      383138.0              40  10:44:11:630    27.86      84.0         24.0   \n",
       "5      383138.0              40  10:44:12:132    27.81      84.0         24.0   \n",
       "\n",
       "   Flow  Job Temp  Voltage  Defect date month Defectinitials  porosity  \\\n",
       "1  3.12      30.1    9.122       0   22    08             No         0   \n",
       "2  3.08      30.1    9.129       0   22    08             No         0   \n",
       "3  3.08      29.3    9.170       0   22    08             No         0   \n",
       "4  3.05      29.3    9.104       0   22    08             No         0   \n",
       "5  3.05      29.3    9.045       0   22    08             No         0   \n",
       "\n",
       "   tungsten  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "5         0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 32,
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    "# plot may not be visible when loaded again.\n",
    "##Seeing the data imbalance\n",
    "x = train['Defect'].value_counts().index\n",
    "y = train['Defect'].value_counts().values\n",
    "\n",
    "trace2 = go.Bar(\n",
    "     x=x ,\n",
    "     y=y,\n",
    "     marker=dict(\n",
    "         color=y,\n",
    "         colorscale = 'Viridis',\n",
    "         reversescale = True\n",
    "     ),\n",
    "     name=\"Imbalance\",    \n",
    " )\n",
    "layout = dict(\n",
    "     title=\"Data imbalance\",\n",
    "     #width = 900, height = 500,\n",
    "     xaxis=go.layout.XAxis(\n",
    "     automargin=True),\n",
    "     yaxis=dict(\n",
    "         showgrid=False,\n",
    "         showline=False,\n",
    "         showticklabels=True,\n",
    " #         domain=[0, 0.85],\n",
    "     ), \n",
    ")\n",
    "fig1 = go.Figure(data=[trace2], layout=layout)\n",
    "iplot(fig1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2b8bf8e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Defectinitials'], axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "949f22a0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employeecode</th>\n",
       "      <th>operationnumber</th>\n",
       "      <th>Time</th>\n",
       "      <th>Current</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Flow</th>\n",
       "      <th>Job Temp</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Defect</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>porosity</th>\n",
       "      <th>tungsten</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>10:44:10:129</td>\n",
       "      <td>27.85</td>\n",
       "      <td>84.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.12</td>\n",
       "      <td>30.1</td>\n",
       "      <td>9.122</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>10:44:10:630</td>\n",
       "      <td>27.38</td>\n",
       "      <td>84.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.08</td>\n",
       "      <td>30.1</td>\n",
       "      <td>9.129</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>10:44:11:127</td>\n",
       "      <td>26.89</td>\n",
       "      <td>84.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.08</td>\n",
       "      <td>29.3</td>\n",
       "      <td>9.170</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>10:44:11:630</td>\n",
       "      <td>27.86</td>\n",
       "      <td>84.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.05</td>\n",
       "      <td>29.3</td>\n",
       "      <td>9.104</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>10:44:12:132</td>\n",
       "      <td>27.81</td>\n",
       "      <td>84.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.05</td>\n",
       "      <td>29.3</td>\n",
       "      <td>9.045</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employeecode operationnumber          Time  Current  Humidity  Temperature  \\\n",
       "1      383138.0              40  10:44:10:129    27.85      84.0         24.0   \n",
       "2      383138.0              40  10:44:10:630    27.38      84.0         24.0   \n",
       "3      383138.0              40  10:44:11:127    26.89      84.0         24.0   \n",
       "4      383138.0              40  10:44:11:630    27.86      84.0         24.0   \n",
       "5      383138.0              40  10:44:12:132    27.81      84.0         24.0   \n",
       "\n",
       "   Flow  Job Temp  Voltage  Defect date month  porosity  tungsten  \n",
       "1  3.12      30.1    9.122       0   22    08         0         0  \n",
       "2  3.08      30.1    9.129       0   22    08         0         0  \n",
       "3  3.08      29.3    9.170       0   22    08         0         0  \n",
       "4  3.05      29.3    9.104       0   22    08         0         0  \n",
       "5  3.05      29.3    9.045       0   22    08         0         0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7f3102cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "train22=train.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "446f3e33",
   "metadata": {},
   "source": [
    "# **Feature Engineering**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d61b83d8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employeecode</th>\n",
       "      <th>operationnumber</th>\n",
       "      <th>Time</th>\n",
       "      <th>Current</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Flow</th>\n",
       "      <th>Job Temp</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Defect</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>porosity</th>\n",
       "      <th>tungsten</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>10:44:10:129</td>\n",
       "      <td>27.85</td>\n",
       "      <td>84.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.12</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>10:44:11:127</td>\n",
       "      <td>26.89</td>\n",
       "      <td>84.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.08</td>\n",
       "      <td>29.3</td>\n",
       "      <td>9.170</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>10:44:11:630</td>\n",
       "      <td>27.86</td>\n",
       "      <td>84.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.05</td>\n",
       "      <td>29.3</td>\n",
       "      <td>9.104</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>10:44:12:132</td>\n",
       "      <td>27.81</td>\n",
       "      <td>84.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.05</td>\n",
       "      <td>29.3</td>\n",
       "      <td>9.045</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employeecode operationnumber          Time  Current  Humidity  Temperature  \\\n",
       "1      383138.0              40  10:44:10:129    27.85      84.0         24.0   \n",
       "2      383138.0              40  10:44:10:630    27.38      84.0         24.0   \n",
       "3      383138.0              40  10:44:11:127    26.89      84.0         24.0   \n",
       "4      383138.0              40  10:44:11:630    27.86      84.0         24.0   \n",
       "5      383138.0              40  10:44:12:132    27.81      84.0         24.0   \n",
       "\n",
       "   Flow  Job Temp  Voltage  Defect date month  porosity  tungsten  \n",
       "1  3.12      30.1    9.122       0   22    08         0         0  \n",
       "2  3.08      30.1    9.129       0   22    08         0         0  \n",
       "3  3.08      29.3    9.170       0   22    08         0         0  \n",
       "4  3.05      29.3    9.104       0   22    08         0         0  \n",
       "5  3.05      29.3    9.045       0   22    08         0         0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train22.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f9659e49",
   "metadata": {},
   "outputs": [],
   "source": [
    "# step1\n",
    "train22['processTime']=train22[\"Time\"].apply(\n",
    "    lambda x: \":\".join( \"0\"*(2-len(str(s))) + str(s) for s in x.split(\":\")[:-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3c44a8cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# step2\n",
    "train22['TimeR']=train22['Time'].apply(lambda x: x[::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "03ee5a38",
   "metadata": {},
   "outputs": [],
   "source": [
    "# step3\n",
    "train22['processTime']=train22['processTime'] + '.' + train22['TimeR'].apply(lambda x: x.split(':', 1)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "56255653",
   "metadata": {},
   "outputs": [],
   "source": [
    "# step4\n",
    "train22['timeNEW']=pd.to_datetime(train22['processTime'], format=\"%H:%M:%S.%f\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a02b1cd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Int64Index: 827531 entries, 1 to 827534\n",
      "Series name: timeNEW\n",
      "Non-Null Count   Dtype         \n",
      "--------------   -----         \n",
      "827531 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1)\n",
      "memory usage: 12.6 MB\n"
     ]
    }
   ],
   "source": [
    "train22['timeNEW'].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e505727e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating new features\n",
    "train22['porosityF']=(pow(train22['Flow'],1)*(pow(train22['Job Temp'],1)+273)*train22['Humidity'])\n",
    "train22['HeatInput']=train22['Voltage']*train22['Current']/train22['Flow']\n",
    "train22['power']=train22['Voltage']*train['Current']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bfdf7e2",
   "metadata": {},
   "source": [
    "## Summary of Features\n",
    "**1.porosityF**: Porosity is linked to **gas flow,job temperature and humidity** ,according to the [research papers](https://www.twi-global.com/technical-knowledge/job-knowledge/defects-imperfections-in-welds-porosity-042).So creating a feature porosityF to increase likelihood of detection of porosity defect. <br />\n",
    "\n",
    "**2.HeatInput**: According to the standard formula, heat input is calculated as the product of Voltage and Current divided by the Speed of welding.\n",
    "However, since speed is unknown, a modified version of the formula is used where flow has replaced speed.<br/>\n",
    "\n",
    "**3.power**: It can be calculated as the absolute value of the product of Voltage and Current. <br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1b036f36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop infinities\n",
    "with pd.option_context('mode.use_inf_as_na', True):train22.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e56b0f09",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# scaling for knn and svms\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler() \n",
    "train22[['Current','Humidity','Temperature','Flow','Job Temp','Voltage','HeatInput','porosityF','power']] = scaler.fit_transform(\n",
    "    train22[['Current','Humidity','Temperature','Flow','Job Temp','Voltage','HeatInput','porosityF','power']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4d683b41",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>porosityF</th>\n",
       "      <th>HeatInput</th>\n",
       "      <th>power</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
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       "      <td>0.260563</td>\n",
       "      <td>0.215076</td>\n",
       "      <td>0.009186</td>\n",
       "      <td>0.432404</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>10:44:10.921</td>\n",
       "      <td>921:01:44:01</td>\n",
       "      <td>1900-01-01 10:44:10.921</td>\n",
       "      <td>0.022990</td>\n",
       "      <td>0.026318</td>\n",
       "      <td>0.177555</td>\n",
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       "      <th>2</th>\n",
       "      <td>383138.0</td>\n",
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       "      <td>10:44:10:630</td>\n",
       "      <td>0.182996</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.212310</td>\n",
       "      <td>0.009186</td>\n",
       "      <td>0.432663</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>08</td>\n",
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       "      <td>0</td>\n",
       "      <td>10:44:10.036</td>\n",
       "      <td>036:01:44:01</td>\n",
       "      <td>1900-01-01 10:44:10.036</td>\n",
       "      <td>0.022695</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.175672</td>\n",
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       "      <td>383138.0</td>\n",
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       "      <td>10:44:11:127</td>\n",
       "      <td>0.179944</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.212310</td>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.434178</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10:44:11.721</td>\n",
       "      <td>721:11:44:01</td>\n",
       "      <td>1900-01-01 10:44:11.721</td>\n",
       "      <td>0.022635</td>\n",
       "      <td>0.026311</td>\n",
       "      <td>0.174122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>10:44:11:630</td>\n",
       "      <td>0.185986</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.210235</td>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.431739</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10:44:11.036</td>\n",
       "      <td>036:11:44:01</td>\n",
       "      <td>1900-01-01 10:44:11.036</td>\n",
       "      <td>0.022415</td>\n",
       "      <td>0.026327</td>\n",
       "      <td>0.177367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>10:44:12:132</td>\n",
       "      <td>0.185674</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.210235</td>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.429559</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10:44:12.231</td>\n",
       "      <td>231:21:44:01</td>\n",
       "      <td>1900-01-01 10:44:12.231</td>\n",
       "      <td>0.022415</td>\n",
       "      <td>0.026323</td>\n",
       "      <td>0.176403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employeecode operationnumber          Time   Current  Humidity  \\\n",
       "1      383138.0              40  10:44:10:129  0.185923  0.267516   \n",
       "2      383138.0              40  10:44:10:630  0.182996  0.267516   \n",
       "3      383138.0              40  10:44:11:127  0.179944  0.267516   \n",
       "4      383138.0              40  10:44:11:630  0.185986  0.267516   \n",
       "5      383138.0              40  10:44:12:132  0.185674  0.267516   \n",
       "\n",
       "   Temperature      Flow  Job Temp   Voltage  Defect date month  porosity  \\\n",
       "1     0.260563  0.215076  0.009186  0.432404       0   22    08         0   \n",
       "2     0.260563  0.212310  0.009186  0.432663       0   22    08         0   \n",
       "3     0.260563  0.212310  0.008942  0.434178       0   22    08         0   \n",
       "4     0.260563  0.210235  0.008942  0.431739       0   22    08         0   \n",
       "5     0.260563  0.210235  0.008942  0.429559       0   22    08         0   \n",
       "\n",
       "   tungsten   processTime         TimeR                 timeNEW  porosityF  \\\n",
       "1         0  10:44:10.921  921:01:44:01 1900-01-01 10:44:10.921   0.022990   \n",
       "2         0  10:44:10.036  036:01:44:01 1900-01-01 10:44:10.036   0.022695   \n",
       "3         0  10:44:11.721  721:11:44:01 1900-01-01 10:44:11.721   0.022635   \n",
       "4         0  10:44:11.036  036:11:44:01 1900-01-01 10:44:11.036   0.022415   \n",
       "5         0  10:44:12.231  231:21:44:01 1900-01-01 10:44:12.231   0.022415   \n",
       "\n",
       "   HeatInput     power  \n",
       "1   0.026318  0.177555  \n",
       "2   0.026316  0.175672  \n",
       "3   0.026311  0.174122  \n",
       "4   0.026327  0.177367  \n",
       "5   0.026323  0.176403  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train22.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9d2cb40b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train22.drop(['TimeR','processTime','Time','date','month'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c7c4ee0f",
   "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>employeecode</th>\n",
       "      <th>operationnumber</th>\n",
       "      <th>Current</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Flow</th>\n",
       "      <th>Job Temp</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Defect</th>\n",
       "      <th>porosity</th>\n",
       "      <th>tungsten</th>\n",
       "      <th>timeNEW</th>\n",
       "      <th>porosityF</th>\n",
       "      <th>HeatInput</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0.185923</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.215076</td>\n",
       "      <td>0.009186</td>\n",
       "      <td>0.432404</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 10:44:10.921</td>\n",
       "      <td>0.022990</td>\n",
       "      <td>0.026318</td>\n",
       "      <td>0.177555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0.182996</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.212310</td>\n",
       "      <td>0.009186</td>\n",
       "      <td>0.432663</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 10:44:10.036</td>\n",
       "      <td>0.022695</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.175672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0.179944</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.212310</td>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.434178</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 10:44:11.721</td>\n",
       "      <td>0.022635</td>\n",
       "      <td>0.026311</td>\n",
       "      <td>0.174122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0.185986</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.210235</td>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.431739</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 10:44:11.036</td>\n",
       "      <td>0.022415</td>\n",
       "      <td>0.026327</td>\n",
       "      <td>0.177367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>383138.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0.185674</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.210235</td>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.429559</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 10:44:12.231</td>\n",
       "      <td>0.022415</td>\n",
       "      <td>0.026323</td>\n",
       "      <td>0.176403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employeecode operationnumber   Current  Humidity  Temperature      Flow  \\\n",
       "1      383138.0              40  0.185923  0.267516     0.260563  0.215076   \n",
       "2      383138.0              40  0.182996  0.267516     0.260563  0.212310   \n",
       "3      383138.0              40  0.179944  0.267516     0.260563  0.212310   \n",
       "4      383138.0              40  0.185986  0.267516     0.260563  0.210235   \n",
       "5      383138.0              40  0.185674  0.267516     0.260563  0.210235   \n",
       "\n",
       "   Job Temp   Voltage  Defect  porosity  tungsten                 timeNEW  \\\n",
       "1  0.009186  0.432404       0         0         0 1900-01-01 10:44:10.921   \n",
       "2  0.009186  0.432663       0         0         0 1900-01-01 10:44:10.036   \n",
       "3  0.008942  0.434178       0         0         0 1900-01-01 10:44:11.721   \n",
       "4  0.008942  0.431739       0         0         0 1900-01-01 10:44:11.036   \n",
       "5  0.008942  0.429559       0         0         0 1900-01-01 10:44:12.231   \n",
       "\n",
       "   porosityF  HeatInput     power  \n",
       "1   0.022990   0.026318  0.177555  \n",
       "2   0.022695   0.026316  0.175672  \n",
       "3   0.022635   0.026311  0.174122  \n",
       "4   0.022415   0.026327  0.177367  \n",
       "5   0.022415   0.026323  0.176403  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train22.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9fda0db",
   "metadata": {},
   "source": [
    "## Label Encoding "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "991b0f11",
   "metadata": {},
   "source": [
    "Although label encoding for done for all categorical features. It was found to **decrease model** performance so it was not included in final model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "387f8779",
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_labels(df, col, max_col):\n",
    "    top = list(df[col].value_counts().sort_values(ascending=False).head(max_col).index)\n",
    "    print(\"Top Labels: \", top)\n",
    "    for categories in top:\n",
    "        df[col+ \"_\" +str(categories)]=np.where(df[col]==categories,1,0)\n",
    "        print(col + \"_\" + str(categories))\n",
    "    return top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "19bdfe4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "391342.0    162947\n",
       "394965.0    142647\n",
       "97346.0     105249\n",
       "394269.0     78059\n",
       "383138.0     23594\n",
       "394365.0     22993\n",
       "382617.0     15821\n",
       "0.0          11213\n",
       "387268.0      7835\n",
       "Name: employeecode, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train22['employeecode'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0492b640",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top Labels:  [391342.0, 394965.0, 97346.0, 394269.0, 383138.0, 394365.0, 382617.0, 0.0, 387268.0]\n",
      "employeecode_391342.0\n",
      "employeecode_394965.0\n",
      "employeecode_97346.0\n",
      "employeecode_394269.0\n",
      "employeecode_383138.0\n",
      "employeecode_394365.0\n",
      "employeecode_382617.0\n",
      "employeecode_0.0\n",
      "employeecode_387268.0\n"
     ]
    }
   ],
   "source": [
    "top_labels_emp = top_labels(train22, 'employeecode',9 )\n",
    "train22 = train22.drop(['employeecode'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8b611321",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>operationnumber</th>\n",
       "      <th>Current</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Flow</th>\n",
       "      <th>Job Temp</th>\n",
       "      <th>Voltage</th>\n",
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       "      <th>...</th>\n",
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       "      <th>employeecode_391342.0</th>\n",
       "      <th>employeecode_394965.0</th>\n",
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       "      <th>employeecode_394269.0</th>\n",
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       "      <th>employeecode_0.0</th>\n",
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       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
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       "      <td>0</td>\n",
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       "      <td>40</td>\n",
       "      <td>0.182996</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.260563</td>\n",
       "      <td>0.212310</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.174122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>0.185986</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.210235</td>\n",
       "      <td>0.008942</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>40</td>\n",
       "      <td>0.185674</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  operationnumber   Current  Humidity  Temperature      Flow  Job Temp  \\\n",
       "1              40  0.185923  0.267516     0.260563  0.215076  0.009186   \n",
       "2              40  0.182996  0.267516     0.260563  0.212310  0.009186   \n",
       "3              40  0.179944  0.267516     0.260563  0.212310  0.008942   \n",
       "4              40  0.185986  0.267516     0.260563  0.210235  0.008942   \n",
       "5              40  0.185674  0.267516     0.260563  0.210235  0.008942   \n",
       "\n",
       "    Voltage  Defect  porosity  tungsten  ...     power  employeecode_391342.0  \\\n",
       "1  0.432404       0         0         0  ...  0.177555                      0   \n",
       "2  0.432663       0         0         0  ...  0.175672                      0   \n",
       "3  0.434178       0         0         0  ...  0.174122                      0   \n",
       "4  0.431739       0         0         0  ...  0.177367                      0   \n",
       "5  0.429559       0         0         0  ...  0.176403                      0   \n",
       "\n",
       "   employeecode_394965.0  employeecode_97346.0  employeecode_394269.0  \\\n",
       "1                      0                     0                      0   \n",
       "2                      0                     0                      0   \n",
       "3                      0                     0                      0   \n",
       "4                      0                     0                      0   \n",
       "5                      0                     0                      0   \n",
       "\n",
       "   employeecode_383138.0  employeecode_394365.0  employeecode_382617.0  \\\n",
       "1                      1                      0                      0   \n",
       "2                      1                      0                      0   \n",
       "3                      1                      0                      0   \n",
       "4                      1                      0                      0   \n",
       "5                      1                      0                      0   \n",
       "\n",
       "   employeecode_0.0  employeecode_387268.0  \n",
       "1                 0                      0  \n",
       "2                 0                      0  \n",
       "3                 0                      0  \n",
       "4                 0                      0  \n",
       "5                 0                      0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train22.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4e7c957b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "240    315862\n",
       "180    149466\n",
       "1       43255\n",
       "40      26829\n",
       "130     14171\n",
       "30      12500\n",
       "40       8191\n",
       "0          84\n",
       "Name: operationnumber, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train22['operationnumber'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "516348b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top Labels:  [240, 180, 1, 40, 130, 30, '40', 0]\n",
      "operationnumber_240\n",
      "operationnumber_180\n",
      "operationnumber_1\n",
      "operationnumber_40\n",
      "operationnumber_130\n",
      "operationnumber_30\n",
      "operationnumber_40\n",
      "operationnumber_0\n"
     ]
    }
   ],
   "source": [
    "top_labels_emp = top_labels(train22, 'operationnumber', 8 )\n",
    "train22 = train22.drop(['operationnumber'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "047e9c1d",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Voltage</th>\n",
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       "      <th>operationnumber_30</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    Current  Humidity  Temperature      Flow  Job Temp   Voltage  Defect  \\\n",
       "1  0.185923  0.267516     0.260563  0.215076  0.009186  0.432404       0   \n",
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       "5  0.185674  0.267516     0.260563  0.210235  0.008942  0.429559       0   \n",
       "\n",
       "   porosity  tungsten                 timeNEW  ...  employeecode_382617.0  \\\n",
       "1         0         0 1900-01-01 10:44:10.921  ...                      0   \n",
       "2         0         0 1900-01-01 10:44:10.036  ...                      0   \n",
       "3         0         0 1900-01-01 10:44:11.721  ...                      0   \n",
       "4         0         0 1900-01-01 10:44:11.036  ...                      0   \n",
       "5         0         0 1900-01-01 10:44:12.231  ...                      0   \n",
       "\n",
       "   employeecode_0.0  employeecode_387268.0  operationnumber_240  \\\n",
       "1                 0                      0                    0   \n",
       "2                 0                      0                    0   \n",
       "3                 0                      0                    0   \n",
       "4                 0                      0                    0   \n",
       "5                 0                      0                    0   \n",
       "\n",
       "   operationnumber_180  operationnumber_1  operationnumber_40  \\\n",
       "1                    0                  0                   1   \n",
       "2                    0                  0                   1   \n",
       "3                    0                  0                   1   \n",
       "4                    0                  0                   1   \n",
       "5                    0                  0                   1   \n",
       "\n",
       "   operationnumber_130  operationnumber_30  operationnumber_0  \n",
       "1                    0                   0                  0  \n",
       "2                    0                   0                  0  \n",
       "3                    0                   0                  0  \n",
       "4                    0                   0                  0  \n",
       "5                    0                   0                  0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train22.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "aa6a9991",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping the label encoded columns\n",
    "train22.drop(train22.iloc[:, 13:30], inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f58dc61a",
   "metadata": {},
   "outputs": [
    {
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       "    Current  Humidity  Temperature      Flow  Job Temp   Voltage  Defect  \\\n",
       "1  0.185923  0.267516     0.260563  0.215076  0.009186  0.432404       0   \n",
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       "5  0.185674  0.267516     0.260563  0.210235  0.008942  0.429559       0   \n",
       "\n",
       "   porosity  tungsten                 timeNEW  porosityF  HeatInput     power  \n",
       "1         0         0 1900-01-01 10:44:10.921   0.022990   0.026318  0.177555  \n",
       "2         0         0 1900-01-01 10:44:10.036   0.022695   0.026316  0.175672  \n",
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       "5         0         0 1900-01-01 10:44:12.231   0.022415   0.026323  0.176403  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train22.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "58d69672",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train22.drop(['porosity','tungsten'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9b3334fe",
   "metadata": {},
   "outputs": [
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       "      <td>1900-01-01 10:44:10.036</td>\n",
       "      <td>0.022695</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.175672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.179944</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.212310</td>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.434178</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 10:44:11.721</td>\n",
       "      <td>0.022635</td>\n",
       "      <td>0.026311</td>\n",
       "      <td>0.174122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.185986</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.210235</td>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.431739</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 10:44:11.036</td>\n",
       "      <td>0.022415</td>\n",
       "      <td>0.026327</td>\n",
       "      <td>0.177367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.185674</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.210235</td>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.429559</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 10:44:12.231</td>\n",
       "      <td>0.022415</td>\n",
       "      <td>0.026323</td>\n",
       "      <td>0.176403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Current  Humidity  Temperature      Flow  Job Temp   Voltage  Defect  \\\n",
       "1  0.185923  0.267516     0.260563  0.215076  0.009186  0.432404       0   \n",
       "2  0.182996  0.267516     0.260563  0.212310  0.009186  0.432663       0   \n",
       "3  0.179944  0.267516     0.260563  0.212310  0.008942  0.434178       0   \n",
       "4  0.185986  0.267516     0.260563  0.210235  0.008942  0.431739       0   \n",
       "5  0.185674  0.267516     0.260563  0.210235  0.008942  0.429559       0   \n",
       "\n",
       "                  timeNEW  porosityF  HeatInput     power  \n",
       "1 1900-01-01 10:44:10.921   0.022990   0.026318  0.177555  \n",
       "2 1900-01-01 10:44:10.036   0.022695   0.026316  0.175672  \n",
       "3 1900-01-01 10:44:11.721   0.022635   0.026311  0.174122  \n",
       "4 1900-01-01 10:44:11.036   0.022415   0.026327  0.177367  \n",
       "5 1900-01-01 10:44:12.231   0.022415   0.026323  0.176403  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train22.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b5620c5",
   "metadata": {},
   "source": [
    "## Checking how features relate to two defects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d49fa064",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainy=train22[train22['Defect']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6bd008b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainn=train22[train22['Defect']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "da8fe3d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\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>Current</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Flow</th>\n",
       "      <th>Job Temp</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Defect</th>\n",
       "      <th>timeNEW</th>\n",
       "      <th>porosityF</th>\n",
       "      <th>HeatInput</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>0.010526</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.222683</td>\n",
       "      <td>0.031220</td>\n",
       "      <td>0.095277</td>\n",
       "      <td>1</td>\n",
       "      <td>1900-01-01 10:47:12.779</td>\n",
       "      <td>0.029470</td>\n",
       "      <td>0.025906</td>\n",
       "      <td>0.060745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>0.169293</td>\n",
       "      <td>0.232484</td>\n",
       "      <td>0.253521</td>\n",
       "      <td>0.304979</td>\n",
       "      <td>0.008637</td>\n",
       "      <td>0.414480</td>\n",
       "      <td>1</td>\n",
       "      <td>1900-01-01 16:46:31.803</td>\n",
       "      <td>0.028136</td>\n",
       "      <td>0.026155</td>\n",
       "      <td>0.160741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>0.013641</td>\n",
       "      <td>0.251592</td>\n",
       "      <td>0.246479</td>\n",
       "      <td>0.314661</td>\n",
       "      <td>0.008637</td>\n",
       "      <td>0.095277</td>\n",
       "      <td>1</td>\n",
       "      <td>1900-01-01 17:24:15.755</td>\n",
       "      <td>0.031413</td>\n",
       "      <td>0.025906</td>\n",
       "      <td>0.060745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2254</th>\n",
       "      <td>0.163127</td>\n",
       "      <td>0.251592</td>\n",
       "      <td>0.246479</td>\n",
       "      <td>0.302213</td>\n",
       "      <td>0.008271</td>\n",
       "      <td>0.451770</td>\n",
       "      <td>1</td>\n",
       "      <td>1900-01-01 17:27:12.906</td>\n",
       "      <td>0.030053</td>\n",
       "      <td>0.026176</td>\n",
       "      <td>0.168033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2255</th>\n",
       "      <td>0.165369</td>\n",
       "      <td>0.251592</td>\n",
       "      <td>0.246479</td>\n",
       "      <td>0.302213</td>\n",
       "      <td>0.008271</td>\n",
       "      <td>0.431444</td>\n",
       "      <td>1</td>\n",
       "      <td>1900-01-01 17:27:13.721</td>\n",
       "      <td>0.030053</td>\n",
       "      <td>0.026164</td>\n",
       "      <td>0.163421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Current  Humidity  Temperature      Flow  Job Temp   Voltage  Defect  \\\n",
       "346   0.010526  0.267516     0.260563  0.222683  0.031220  0.095277       1   \n",
       "794   0.169293  0.232484     0.253521  0.304979  0.008637  0.414480       1   \n",
       "1925  0.013641  0.251592     0.246479  0.314661  0.008637  0.095277       1   \n",
       "2254  0.163127  0.251592     0.246479  0.302213  0.008271  0.451770       1   \n",
       "2255  0.165369  0.251592     0.246479  0.302213  0.008271  0.431444       1   \n",
       "\n",
       "                     timeNEW  porosityF  HeatInput     power  \n",
       "346  1900-01-01 10:47:12.779   0.029470   0.025906  0.060745  \n",
       "794  1900-01-01 16:46:31.803   0.028136   0.026155  0.160741  \n",
       "1925 1900-01-01 17:24:15.755   0.031413   0.025906  0.060745  \n",
       "2254 1900-01-01 17:27:12.906   0.030053   0.026176  0.168033  \n",
       "2255 1900-01-01 17:27:13.721   0.030053   0.026164  0.163421  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5bda0b77",
   "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>Current</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Flow</th>\n",
       "      <th>Job Temp</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Defect</th>\n",
       "      <th>timeNEW</th>\n",
       "      <th>porosityF</th>\n",
       "      <th>HeatInput</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.185923</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.215076</td>\n",
       "      <td>0.009186</td>\n",
       "      <td>0.432404</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 10:44:10.921</td>\n",
       "      <td>0.022990</td>\n",
       "      <td>0.026318</td>\n",
       "      <td>0.177555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.182996</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.212310</td>\n",
       "      <td>0.009186</td>\n",
       "      <td>0.432663</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 10:44:10.036</td>\n",
       "      <td>0.022695</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.175672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.179944</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.212310</td>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.434178</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 10:44:11.721</td>\n",
       "      <td>0.022635</td>\n",
       "      <td>0.026311</td>\n",
       "      <td>0.174122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.185986</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.210235</td>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.431739</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 10:44:11.036</td>\n",
       "      <td>0.022415</td>\n",
       "      <td>0.026327</td>\n",
       "      <td>0.177367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.185674</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.210235</td>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.429559</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 10:44:12.231</td>\n",
       "      <td>0.022415</td>\n",
       "      <td>0.026323</td>\n",
       "      <td>0.176403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Current  Humidity  Temperature      Flow  Job Temp   Voltage  Defect  \\\n",
       "1  0.185923  0.267516     0.260563  0.215076  0.009186  0.432404       0   \n",
       "2  0.182996  0.267516     0.260563  0.212310  0.009186  0.432663       0   \n",
       "3  0.179944  0.267516     0.260563  0.212310  0.008942  0.434178       0   \n",
       "4  0.185986  0.267516     0.260563  0.210235  0.008942  0.431739       0   \n",
       "5  0.185674  0.267516     0.260563  0.210235  0.008942  0.429559       0   \n",
       "\n",
       "                  timeNEW  porosityF  HeatInput     power  \n",
       "1 1900-01-01 10:44:10.921   0.022990   0.026318  0.177555  \n",
       "2 1900-01-01 10:44:10.036   0.022695   0.026316  0.175672  \n",
       "3 1900-01-01 10:44:11.721   0.022635   0.026311  0.174122  \n",
       "4 1900-01-01 10:44:11.036   0.022415   0.026327  0.177367  \n",
       "5 1900-01-01 10:44:12.231   0.022415   0.026323  0.176403  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bba316b6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4154.000000\n",
       "mean        0.031994\n",
       "std         0.059114\n",
       "min         0.000000\n",
       "25%         0.006885\n",
       "50%         0.018370\n",
       "75%         0.025969\n",
       "max         0.400398\n",
       "Name: porosityF, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainy['porosityF'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "923cbfa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    566204.000000\n",
       "mean          0.026416\n",
       "std           0.050499\n",
       "min           0.000000\n",
       "25%           0.007956\n",
       "50%           0.018504\n",
       "75%           0.023506\n",
       "max           1.000000\n",
       "Name: porosityF, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainn['porosityF'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7461681",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "463400f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2f7c7bf10>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(data=train22, x=\"Current\", y=\"Voltage\", hue=\"Defect\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8d607b9",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "The current and voltages neatly seperate the defective points in a seperate cluster. This information would prove to be crutial for the model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65754145",
   "metadata": {},
   "source": [
    "## Calculating Time Differences and analysing them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b79f9c30",
   "metadata": {},
   "outputs": [],
   "source": [
    "train22=train22.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "33821d83",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=train22.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c3a0b7ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.sort_values(by='timeNEW')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2099d8f9",
   "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>Current</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Flow</th>\n",
       "      <th>Job Temp</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Defect</th>\n",
       "      <th>timeNEW</th>\n",
       "      <th>porosityF</th>\n",
       "      <th>HeatInput</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>597256</th>\n",
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       "         Current  Humidity  Temperature      Flow  Job Temp   Voltage  Defect  \\\n",
       "597256  0.044908  0.248408     0.260563  0.001383  0.008393  0.095277       0   \n",
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       "597220  0.043600  0.248408     0.260563  0.003458  0.008454  0.095277       0   \n",
       "\n",
       "                       timeNEW  porosityF  HeatInput     power  \n",
       "597256 1900-01-01 00:00:03.782   0.000204   0.025906  0.060745  \n",
       "597277 1900-01-01 00:00:04.039   0.000407   0.025906  0.060745  \n",
       "597296 1900-01-01 00:00:05.994   0.000272   0.025906  0.060745  \n",
       "597317 1900-01-01 00:00:07.537   0.000204   0.025906  0.060745  \n",
       "597220 1900-01-01 00:00:10.046   0.000407   0.025906  0.060745  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
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  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "76c93596",
   "metadata": {},
   "outputs": [],
   "source": [
    "train22=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "065c4420",
   "metadata": {},
   "outputs": [
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       "         Current  Humidity  Temperature      Flow  Job Temp   Voltage  Defect  \\\n",
       "597256  0.044908  0.248408     0.260563  0.001383  0.008393  0.095277       0   \n",
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       "597220  0.043600  0.248408     0.260563  0.003458  0.008454  0.095277       0   \n",
       "\n",
       "                       timeNEW  porosityF  HeatInput     power  \n",
       "597256 1900-01-01 00:00:03.782   0.000204   0.025906  0.060745  \n",
       "597277 1900-01-01 00:00:04.039   0.000407   0.025906  0.060745  \n",
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  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "7b5d86c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train22['diff'] = train22['timeNEW'] - train22['timeNEW'].shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3b367762",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "597256                      NaT\n",
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       "597317   0 days 00:00:01.543000\n",
       "597220   0 days 00:00:02.509000\n",
       "                  ...          \n",
       "596916   0 days 00:00:00.816000\n",
       "596923   0 days 00:00:03.293000\n",
       "596934   0 days 00:00:06.306000\n",
       "596948   0 days 00:00:06.407000\n",
       "596949   0 days 00:00:00.105000\n",
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   "id": "2c6859ed",
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   "source": [
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   "execution_count": 75,
   "id": "03d5741c",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 570358 entries, 597256 to 596949\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count   Dtype          \n",
      "---  ------       --------------   -----          \n",
      " 0   Current      570358 non-null  float64        \n",
      " 1   Humidity     570358 non-null  float64        \n",
      " 2   Temperature  570358 non-null  float64        \n",
      " 3   Flow         570358 non-null  float64        \n",
      " 4   Job Temp     570358 non-null  float64        \n",
      " 5   Voltage      570358 non-null  float64        \n",
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      " 8   porosityF    570358 non-null  float64        \n",
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      " 11  diff         570357 non-null  timedelta64[ns]\n",
      "dtypes: datetime64[ns](1), float64(9), int64(1), timedelta64[ns](1)\n",
      "memory usage: 56.6 MB\n"
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   "source": [
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  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "795c233b",
   "metadata": {},
   "outputs": [
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       "         Current  Humidity  Temperature      Flow  Job Temp   Voltage  Defect  \\\n",
       "597256  0.044908  0.248408     0.260563  0.001383  0.008393  0.095277       0   \n",
       "597277  0.050950  0.248408     0.260563  0.003458  0.008362  0.095277       0   \n",
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       "597220  0.043600  0.248408     0.260563  0.003458  0.008454  0.095277       0   \n",
       "\n",
       "                       timeNEW  porosityF  HeatInput     power  \\\n",
       "597256 1900-01-01 00:00:03.782   0.000204   0.025906  0.060745   \n",
       "597277 1900-01-01 00:00:04.039   0.000407   0.025906  0.060745   \n",
       "597296 1900-01-01 00:00:05.994   0.000272   0.025906  0.060745   \n",
       "597317 1900-01-01 00:00:07.537   0.000204   0.025906  0.060745   \n",
       "597220 1900-01-01 00:00:10.046   0.000407   0.025906  0.060745   \n",
       "\n",
       "                         diff  \n",
       "597256                    NaT  \n",
       "597277 0 days 00:00:00.257000  \n",
       "597296 0 days 00:00:01.955000  \n",
       "597317 0 days 00:00:01.543000  \n",
       "597220 0 days 00:00:02.509000  "
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     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
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    "train22.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c33396c2",
   "metadata": {},
   "outputs": [
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       "      <th>597277</th>\n",
       "      <td>0.050950</td>\n",
       "      <td>0.248408</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.003458</td>\n",
       "      <td>0.008362</td>\n",
       "      <td>0.095277</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:04.039</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.025906</td>\n",
       "      <td>0.060745</td>\n",
       "      <td>0 days 00:00:00.257000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597296</th>\n",
       "      <td>0.045531</td>\n",
       "      <td>0.248408</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.002075</td>\n",
       "      <td>0.008484</td>\n",
       "      <td>0.095277</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:05.994</td>\n",
       "      <td>0.000272</td>\n",
       "      <td>0.025906</td>\n",
       "      <td>0.060745</td>\n",
       "      <td>0 days 00:00:01.955000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597317</th>\n",
       "      <td>0.031268</td>\n",
       "      <td>0.248408</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.001383</td>\n",
       "      <td>0.008393</td>\n",
       "      <td>0.095277</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:07.537</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.025906</td>\n",
       "      <td>0.060745</td>\n",
       "      <td>0 days 00:00:01.543000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597220</th>\n",
       "      <td>0.043600</td>\n",
       "      <td>0.248408</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.003458</td>\n",
       "      <td>0.008454</td>\n",
       "      <td>0.095277</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:10.046</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.025906</td>\n",
       "      <td>0.060745</td>\n",
       "      <td>0 days 00:00:02.509000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596916</th>\n",
       "      <td>0.031143</td>\n",
       "      <td>0.248408</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.001383</td>\n",
       "      <td>0.008454</td>\n",
       "      <td>0.095277</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 23:59:17.077</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.025906</td>\n",
       "      <td>0.060745</td>\n",
       "      <td>0 days 00:00:00.816000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596923</th>\n",
       "      <td>0.043912</td>\n",
       "      <td>0.248408</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.003458</td>\n",
       "      <td>0.008454</td>\n",
       "      <td>0.095277</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 23:59:20.370</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.025906</td>\n",
       "      <td>0.060745</td>\n",
       "      <td>0 days 00:00:03.293000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596934</th>\n",
       "      <td>0.044597</td>\n",
       "      <td>0.248408</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.003458</td>\n",
       "      <td>0.008423</td>\n",
       "      <td>0.095277</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 23:59:26.676</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.025906</td>\n",
       "      <td>0.060745</td>\n",
       "      <td>0 days 00:00:06.306000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596948</th>\n",
       "      <td>0.041732</td>\n",
       "      <td>0.248408</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008576</td>\n",
       "      <td>0.095277</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 23:59:33.083</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>0.025906</td>\n",
       "      <td>0.060745</td>\n",
       "      <td>0 days 00:00:06.407000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596949</th>\n",
       "      <td>0.042604</td>\n",
       "      <td>0.248408</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.001383</td>\n",
       "      <td>0.008576</td>\n",
       "      <td>0.095277</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 23:59:33.188</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.025906</td>\n",
       "      <td>0.060745</td>\n",
       "      <td>0 days 00:00:00.105000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>570357 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Current  Humidity  Temperature      Flow  Job Temp   Voltage  Defect  \\\n",
       "597256  0.044908  0.248408     0.260563  0.001383  0.008393  0.095277       0   \n",
       "597277  0.050950  0.248408     0.260563  0.003458  0.008362  0.095277       0   \n",
       "597296  0.045531  0.248408     0.260563  0.002075  0.008484  0.095277       0   \n",
       "597317  0.031268  0.248408     0.260563  0.001383  0.008393  0.095277       0   \n",
       "597220  0.043600  0.248408     0.260563  0.003458  0.008454  0.095277       0   \n",
       "...          ...       ...          ...       ...       ...       ...     ...   \n",
       "596916  0.031143  0.248408     0.260563  0.001383  0.008454  0.095277       0   \n",
       "596923  0.043912  0.248408     0.260563  0.003458  0.008454  0.095277       0   \n",
       "596934  0.044597  0.248408     0.260563  0.003458  0.008423  0.095277       0   \n",
       "596948  0.041732  0.248408     0.260563  0.000000  0.008576  0.095277       0   \n",
       "596949  0.042604  0.248408     0.260563  0.001383  0.008576  0.095277       0   \n",
       "\n",
       "                       timeNEW  porosityF  HeatInput     power  \\\n",
       "597256 1900-01-01 00:00:03.782   0.000204   0.025906  0.060745   \n",
       "597277 1900-01-01 00:00:04.039   0.000407   0.025906  0.060745   \n",
       "597296 1900-01-01 00:00:05.994   0.000272   0.025906  0.060745   \n",
       "597317 1900-01-01 00:00:07.537   0.000204   0.025906  0.060745   \n",
       "597220 1900-01-01 00:00:10.046   0.000407   0.025906  0.060745   \n",
       "...                        ...        ...        ...       ...   \n",
       "596916 1900-01-01 23:59:17.077   0.000204   0.025906  0.060745   \n",
       "596923 1900-01-01 23:59:20.370   0.000407   0.025906  0.060745   \n",
       "596934 1900-01-01 23:59:26.676   0.000407   0.025906  0.060745   \n",
       "596948 1900-01-01 23:59:33.083   0.000068   0.025906  0.060745   \n",
       "596949 1900-01-01 23:59:33.188   0.000204   0.025906  0.060745   \n",
       "\n",
       "                         diff  \n",
       "597256                    NaT  \n",
       "597277 0 days 00:00:00.257000  \n",
       "597296 0 days 00:00:01.955000  \n",
       "597317 0 days 00:00:01.543000  \n",
       "597220 0 days 00:00:02.509000  \n",
       "...                       ...  \n",
       "596916 0 days 00:00:00.816000  \n",
       "596923 0 days 00:00:03.293000  \n",
       "596934 0 days 00:00:06.306000  \n",
       "596948 0 days 00:00:06.407000  \n",
       "596949 0 days 00:00:00.105000  \n",
       "\n",
       "[570357 rows x 12 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping unnecessary value\n",
    "train22.drop([491453])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "41504f39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data type conversion\n",
    "train22['diffFloat']=train22[('diff')].values.astype(\"float64\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "688bf7e4",
   "metadata": {},
   "source": [
    "### Feature Creation\n",
    "Technically defined as the speed with which the welder performs the welds, here information about the length of each weld was not given so it was assumed that each weld length is the same.\n",
    "So here speed was just calculated as inverse of time difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "7a767103",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train22['speed']=1e9/(train22['diffFloat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "0916e41e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop infinities\n",
    "with pd.option_context('mode.use_inf_as_na', True):train22.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6657e9f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "train22.drop(['timeNEW'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "eb81f47a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train22.drop(['diff'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "4b8fdbad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    567136.000000\n",
       "mean         54.480500\n",
       "std         125.097087\n",
       "min           0.000424\n",
       "25%           8.264463\n",
       "50%          16.393443\n",
       "75%          41.666667\n",
       "max        1000.000000\n",
       "Name: speed, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train22['speed'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e047b1be",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Current</th>\n",
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       "      <th>Job Temp</th>\n",
       "      <th>Voltage</th>\n",
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       "      <td>0.248408</td>\n",
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       "      <td>0.031268</td>\n",
       "      <td>0.248408</td>\n",
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       "      <td>0.060745</td>\n",
       "      <td>1.543000e+09</td>\n",
       "      <td>0.648088</td>\n",
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       "      <td>2.509000e+09</td>\n",
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       "      <td>0.248408</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.003458</td>\n",
       "      <td>0.008332</td>\n",
       "      <td>0.095277</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.025906</td>\n",
       "      <td>0.060745</td>\n",
       "      <td>6.950000e+08</td>\n",
       "      <td>1.438849</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "         Current  Humidity  Temperature      Flow  Job Temp   Voltage  Defect  \\\n",
       "597277  0.050950  0.248408     0.260563  0.003458  0.008362  0.095277       0   \n",
       "597296  0.045531  0.248408     0.260563  0.002075  0.008484  0.095277       0   \n",
       "597317  0.031268  0.248408     0.260563  0.001383  0.008393  0.095277       0   \n",
       "597220  0.043600  0.248408     0.260563  0.003458  0.008454  0.095277       0   \n",
       "597219  0.036001  0.248408     0.260563  0.003458  0.008332  0.095277       0   \n",
       "\n",
       "        porosityF  HeatInput     power     diffFloat     speed  \n",
       "597277   0.000407   0.025906  0.060745  2.570000e+08  3.891051  \n",
       "597296   0.000272   0.025906  0.060745  1.955000e+09  0.511509  \n",
       "597317   0.000204   0.025906  0.060745  1.543000e+09  0.648088  \n",
       "597220   0.000407   0.025906  0.060745  2.509000e+09  0.398565  \n",
       "597219   0.000407   0.025906  0.060745  6.950000e+08  1.438849  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train22.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a262d91c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train22.drop(['diffFloat'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e39dbfac",
   "metadata": {},
   "outputs": [],
   "source": [
    "train22[['speed']] = scaler.fit_transform(\n",
    "    train22[['speed']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "66b2cd33",
   "metadata": {},
   "outputs": [
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       "         Current  Humidity  Temperature      Flow  Job Temp   Voltage  Defect  \\\n",
       "597277  0.050950  0.248408     0.260563  0.003458  0.008362  0.095277       0   \n",
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       "597219  0.036001  0.248408     0.260563  0.003458  0.008332  0.095277       0   \n",
       "\n",
       "        porosityF  HeatInput     power     speed  \n",
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     "execution_count": 87,
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   "source": [
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  },
  {
   "cell_type": "code",
   "execution_count": 88,
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       "         Current  Humidity  Temperature      Flow  Job Temp   Voltage  Defect  \\\n",
       "597277  0.050950  0.248408     0.260563  0.003458  0.008362  0.095277       0   \n",
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       "597219  0.036001  0.248408     0.260563  0.003458  0.008332  0.095277       0   \n",
       "\n",
       "        porosityF  HeatInput     power     speed  \n",
       "597277   0.000407   0.025906  0.060745  0.003891  \n",
       "597296   0.000272   0.025906  0.060745  0.000511  \n",
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  {
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   "id": "4e0ebaeb",
   "metadata": {},
   "source": [
    "## Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "de3b2e96",
   "metadata": {},
   "outputs": [],
   "source": [
    "train=train22.copy()\n",
    "train.drop(['Defect'],axis=1,inplace=True)\n",
    "train['Defect']=train22['Defect']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "44f88d8e",
   "metadata": {},
   "outputs": [
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       "         Current  Humidity  Temperature      Flow  Job Temp   Voltage  \\\n",
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       "597219  0.036001  0.248408     0.260563  0.003458  0.008332  0.095277   \n",
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "a4a3b3b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "v_features = train.iloc[:,1:10].columns"
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  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "48f585b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "rnd_clf = RandomForestClassifier(n_estimators = 100 , criterion = 'entropy',random_state = 0)\n",
    "rnd_clf.fit(train.iloc[:,1:10],train.iloc[:,10]);"
   ]
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  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "18f2d0c0",
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":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"title\":{\"text\":\"Barplot of Feature importances\"},\"width\":600,\"yaxis\":{\"showgrid\":false,\"showline\":false,\"showticklabels\":true}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('fcbf39d0-5c4c-4705-8722-42ff0aa56705');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot may not be visible when loaded again.\n",
    "x, y = (list(x) for x in zip(*sorted(zip(rnd_clf.feature_importances_, train.iloc[:,1:10].columns), \n",
    "                                                            reverse = False)))\n",
    "trace2 = go.Bar(\n",
    "    x=x ,\n",
    "    y=y,\n",
    "    marker=dict(\n",
    "        color=x,\n",
    "        colorscale = 'Viridis',\n",
    "        reversescale = True\n",
    "    ),\n",
    "    name='Random Forest Feature importance',\n",
    "    orientation='h',\n",
    ")\n",
    "\n",
    "layout = dict(\n",
    "    title='Barplot of Feature importances',\n",
    "     width = 600, height = 1000,\n",
    "    yaxis=dict(\n",
    "        showgrid=False,\n",
    "        showline=False,\n",
    "        showticklabels=True,\n",
    "#         domain=[0, 0.85],\n",
    "    ),\n",
    "    margin=dict(\n",
    "    l=300,\n",
    "),\n",
    ")\n",
    "\n",
    "fig1 = go.Figure(data=[trace2], layout=layout)\n",
    "iplot(fig1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "4413d447",
   "metadata": {},
   "outputs": [],
   "source": [
    "#However it was found during various runs that removing speed gave the best model performance\n",
    "train22.drop(['speed'],inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "121613da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>Current</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Flow</th>\n",
       "      <th>Job Temp</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Defect</th>\n",
       "      <th>porosityF</th>\n",
       "      <th>HeatInput</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>567136.000000</td>\n",
       "      <td>567136.000000</td>\n",
       "      <td>567136.000000</td>\n",
       "      <td>567136.000000</td>\n",
       "      <td>567136.000000</td>\n",
       "      <td>567136.000000</td>\n",
       "      <td>567136.000000</td>\n",
       "      <td>567136.000000</td>\n",
       "      <td>567136.000000</td>\n",
       "      <td>567136.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.071760</td>\n",
       "      <td>0.238188</td>\n",
       "      <td>0.259644</td>\n",
       "      <td>0.176225</td>\n",
       "      <td>0.051207</td>\n",
       "      <td>0.219171</td>\n",
       "      <td>0.007279</td>\n",
       "      <td>0.026454</td>\n",
       "      <td>0.026923</td>\n",
       "      <td>0.098142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.085077</td>\n",
       "      <td>0.028689</td>\n",
       "      <td>0.021833</td>\n",
       "      <td>0.106359</td>\n",
       "      <td>0.193493</td>\n",
       "      <td>0.176755</td>\n",
       "      <td>0.085004</td>\n",
       "      <td>0.050561</td>\n",
       "      <td>0.009587</td>\n",
       "      <td>0.063085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.014077</td>\n",
       "      <td>0.229299</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.088520</td>\n",
       "      <td>0.009186</td>\n",
       "      <td>0.095277</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007932</td>\n",
       "      <td>0.025906</td>\n",
       "      <td>0.060745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.026783</td>\n",
       "      <td>0.242038</td>\n",
       "      <td>0.260563</td>\n",
       "      <td>0.190180</td>\n",
       "      <td>0.009644</td>\n",
       "      <td>0.095277</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018504</td>\n",
       "      <td>0.025906</td>\n",
       "      <td>0.060745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.127998</td>\n",
       "      <td>0.251592</td>\n",
       "      <td>0.267606</td>\n",
       "      <td>0.233057</td>\n",
       "      <td>0.011414</td>\n",
       "      <td>0.427489</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023518</td>\n",
       "      <td>0.026179</td>\n",
       "      <td>0.136002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Current       Humidity    Temperature           Flow  \\\n",
       "count  567136.000000  567136.000000  567136.000000  567136.000000   \n",
       "mean        0.071760       0.238188       0.259644       0.176225   \n",
       "std         0.085077       0.028689       0.021833       0.106359   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.014077       0.229299       0.260563       0.088520   \n",
       "50%         0.026783       0.242038       0.260563       0.190180   \n",
       "75%         0.127998       0.251592       0.267606       0.233057   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "            Job Temp        Voltage         Defect      porosityF  \\\n",
       "count  567136.000000  567136.000000  567136.000000  567136.000000   \n",
       "mean        0.051207       0.219171       0.007279       0.026454   \n",
       "std         0.193493       0.176755       0.085004       0.050561   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.009186       0.095277       0.000000       0.007932   \n",
       "50%         0.009644       0.095277       0.000000       0.018504   \n",
       "75%         0.011414       0.427489       0.000000       0.023518   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "           HeatInput          power  \n",
       "count  567136.000000  567136.000000  \n",
       "mean        0.026923       0.098142  \n",
       "std         0.009587       0.063085  \n",
       "min         0.000000       0.000000  \n",
       "25%         0.025906       0.060745  \n",
       "50%         0.025906       0.060745  \n",
       "75%         0.026179       0.136002  \n",
       "max         1.000000       1.000000  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train22.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d493664",
   "metadata": {},
   "source": [
    "## Model Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "bd9400a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "y= train22['Defect']\n",
    "X=train22.drop(['Defect'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "deda623f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "skf=StratifiedKFold(n_splits=5, random_state=None, shuffle=True)\n",
    "\n",
    "for train_index, test_index in skf.split(X, y):\n",
    "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "dc5555b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "sm = SMOTE(random_state = 2)\n",
    "X_train, y_train = sm.fit_sample(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "54a56673",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/raunakpandey/tensorflow-test/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3723: RuntimeWarning:\n",
      "\n",
      "Degrees of freedom <= 0 for slice\n",
      "\n",
      "/Users/raunakpandey/tensorflow-test/env/lib/python3.8/site-packages/numpy/core/_methods.py:222: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in true_divide\n",
      "\n",
      "/Users/raunakpandey/tensorflow-test/env/lib/python3.8/site-packages/numpy/core/_methods.py:254: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in double_scalars\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ABOD(contamination=0.1, method='fast', n_neighbors=5)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyod.models.abod import ABOD\n",
    "clf = ABOD()\n",
    "clf.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "9a4dc45b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/raunakpandey/tensorflow-test/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3723: RuntimeWarning:\n",
      "\n",
      "Degrees of freedom <= 0 for slice\n",
      "\n",
      "/Users/raunakpandey/tensorflow-test/env/lib/python3.8/site-packages/numpy/core/_methods.py:222: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in true_divide\n",
      "\n",
      "/Users/raunakpandey/tensorflow-test/env/lib/python3.8/site-packages/numpy/core/_methods.py:254: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in double_scalars\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ea076b47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.989089980985016"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, pred, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "f69ddc33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9927177832438485"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, pred, average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "375ec391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4981727927513406"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, pred, average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "41e662a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "\n",
    "cm = confusion_matrix(y_test, pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
    "                              )\n",
    "disp.plot()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "4d67939a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clfFinal = RandomForestClassifier(max_depth=2, random_state=0)\n",
    "clfFinal.fit(X_train, y_train)\n",
    "pred=clfFinal.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "39b822f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7897426326151074"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_score(y_test, pred, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ef627d85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6621703827131107"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, pred, average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "f5724b71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4146831036218226"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, pred, average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "1fb8d04e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "\n",
    "cm = confusion_matrix(y_test, pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
    "                              )\n",
    "disp.plot()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b3be72f",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "The problem statement has asked us to provide with the weighted F1 score of the model.\n",
    "However it doesn't tell the full story.\n",
    "It is heavily biased due to severity of class imbalance that is present even after successive data cleaning.\n",
    "Although the model ABOD \"performs\" better according to the F1 score(~0.99) but it performs poorly during the actual detection of defects. Hence the model Random Forest Classifier is much better and should be considered.\n",
    "This model is able to **succesfully detect 676 defects** even before they have happened."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19afdafa",
   "metadata": {},
   "source": [
    "### Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "421fe4b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ABOD.sav']"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "# save the model to disk\n",
    "filename = 'ABOD.sav'\n",
    "joblib.dump(clf, filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "2341f4c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Random_Forest.sav']"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "# save the model to disk\n",
    "filename = 'Random_Forest.sav'\n",
    "joblib.dump(clfFinal, filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "65ec0dd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment this to loead the model\n",
    "# # load the model from disk\n",
    "# loaded_model = joblib.load(filename)\n",
    "# result = loaded_model.score(X_test, y_test)\n",
    "# print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1828127",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "**The model is able to predict defects about 60% of the time even before they have occured.<br />\n",
    "the f1 score of model is 0.13. The model when applied to practical uses will help in significant reduction of errors and help save companies resources.**"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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