ML_backorders.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    }
   ],
   "source": [
    "# Predicting Backorders in Inventory Mangement Context. Original dataset from Kaggle's \"Can You Predict Product Backorders?\",\n",
    "# available on https://www.kaggle.com/tiredgeek/predict-bo-trial.\n",
    "\n",
    "# The code for this project is divided into 3 sections as the processign files are bit huge in size.\n",
    "\n",
    "#@author: Sabbella Prasanna\n",
    "\n",
    "print(__doc__)\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.options.mode.chained_assignment = None\n",
    "import matplotlib.pyplot as plt\n",
    "#from sklearn.preprocessing import Imputer\n",
    "#from sklearn.impute import Imputer\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "#Model related library functions\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import svm\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from scipy import stats as st\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "masterData shape: (1929937, 23)\n"
     ]
    }
   ],
   "source": [
    "# Loading the files\n",
    "cols=range(0,23)\n",
    "train = pd.read_csv('Kaggle_Training_Dataset_v2.csv', usecols=cols)\n",
    "test = pd.read_csv('Kaggle_Test_Dataset.csv', usecols=cols)\n",
    "\n",
    "masterData = train.append(test)\n",
    "print(\"masterData shape:\",masterData.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sku</th>\n",
       "      <th>national_inv</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>in_transit_qty</th>\n",
       "      <th>forecast_3_month</th>\n",
       "      <th>forecast_6_month</th>\n",
       "      <th>forecast_9_month</th>\n",
       "      <th>sales_1_month</th>\n",
       "      <th>sales_3_month</th>\n",
       "      <th>sales_6_month</th>\n",
       "      <th>...</th>\n",
       "      <th>pieces_past_due</th>\n",
       "      <th>perf_6_month_avg</th>\n",
       "      <th>perf_12_month_avg</th>\n",
       "      <th>local_bo_qty</th>\n",
       "      <th>deck_risk</th>\n",
       "      <th>oe_constraint</th>\n",
       "      <th>ppap_risk</th>\n",
       "      <th>stop_auto_buy</th>\n",
       "      <th>rev_stop</th>\n",
       "      <th>went_on_backorder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>No</td>\n",
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       "      <td>No</td>\n",
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       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "      <td>1043384</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
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       "      <td>-99.00</td>\n",
       "      <td>-99.00</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "      <th>3</th>\n",
       "      <td>1043852</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1044048</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-99.00</td>\n",
       "      <td>-99.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1044198</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.82</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "      <th>6</th>\n",
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       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1045098</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1045815</td>\n",
       "      <td>140.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>114.0</td>\n",
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       "      <td>-99.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1045867</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>10 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       sku  national_inv  lead_time  in_transit_qty  forecast_3_month  \\\n",
       "0  1026827           0.0        NaN             0.0               0.0   \n",
       "1  1043384           2.0        9.0             0.0               0.0   \n",
       "2  1043696           2.0        NaN             0.0               0.0   \n",
       "3  1043852           7.0        8.0             0.0               0.0   \n",
       "4  1044048           8.0        NaN             0.0               0.0   \n",
       "5  1044198          13.0        8.0             0.0               0.0   \n",
       "6  1044643        1095.0        NaN             0.0               0.0   \n",
       "7  1045098           6.0        2.0             0.0               0.0   \n",
       "8  1045815         140.0        NaN             0.0              15.0   \n",
       "9  1045867           4.0        8.0             0.0               0.0   \n",
       "\n",
       "   forecast_6_month  forecast_9_month  sales_1_month  sales_3_month  \\\n",
       "0               0.0               0.0            0.0            0.0   \n",
       "1               0.0               0.0            0.0            0.0   \n",
       "2               0.0               0.0            0.0            0.0   \n",
       "3               0.0               0.0            0.0            0.0   \n",
       "4               0.0               0.0            0.0            0.0   \n",
       "5               0.0               0.0            0.0            0.0   \n",
       "6               0.0               0.0            0.0            0.0   \n",
       "7               0.0               0.0            0.0            0.0   \n",
       "8             114.0             152.0            0.0            0.0   \n",
       "9               0.0               0.0            0.0            0.0   \n",
       "\n",
       "   sales_6_month  ...  pieces_past_due  perf_6_month_avg perf_12_month_avg  \\\n",
       "0            0.0  ...              0.0            -99.00            -99.00   \n",
       "1            0.0  ...              0.0              0.99              0.99   \n",
       "2            0.0  ...              0.0            -99.00            -99.00   \n",
       "3            0.0  ...              0.0              0.10              0.13   \n",
       "4            0.0  ...              0.0            -99.00            -99.00   \n",
       "5            0.0  ...              0.0              0.82              0.87   \n",
       "6            0.0  ...              0.0            -99.00            -99.00   \n",
       "7            0.0  ...              0.0              0.00              0.00   \n",
       "8            0.0  ...              0.0            -99.00            -99.00   \n",
       "9            0.0  ...              0.0              0.82              0.87   \n",
       "\n",
       "   local_bo_qty  deck_risk  oe_constraint  ppap_risk stop_auto_buy rev_stop  \\\n",
       "0           0.0         No             No         No           Yes       No   \n",
       "1           0.0         No             No         No           Yes       No   \n",
       "2           0.0        Yes             No         No           Yes       No   \n",
       "3           0.0         No             No         No           Yes       No   \n",
       "4           0.0        Yes             No         No           Yes       No   \n",
       "5           0.0         No             No         No           Yes       No   \n",
       "6           0.0        Yes             No         No           Yes       No   \n",
       "7           0.0        Yes             No        Yes           Yes       No   \n",
       "8           0.0         No             No         No           Yes       No   \n",
       "9           0.0         No             No         No           Yes       No   \n",
       "\n",
       "  went_on_backorder  \n",
       "0                No  \n",
       "1                No  \n",
       "2                No  \n",
       "3                No  \n",
       "4                No  \n",
       "5                No  \n",
       "6                No  \n",
       "7                No  \n",
       "8                No  \n",
       "9                No  \n",
       "\n",
       "[10 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masterData.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>national_inv</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>in_transit_qty</th>\n",
       "      <th>forecast_3_month</th>\n",
       "      <th>forecast_6_month</th>\n",
       "      <th>forecast_9_month</th>\n",
       "      <th>sales_1_month</th>\n",
       "      <th>sales_3_month</th>\n",
       "      <th>sales_6_month</th>\n",
       "      <th>sales_9_month</th>\n",
       "      <th>min_bank</th>\n",
       "      <th>pieces_past_due</th>\n",
       "      <th>perf_6_month_avg</th>\n",
       "      <th>perf_12_month_avg</th>\n",
       "      <th>local_bo_qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.929935e+06</td>\n",
       "      <td>1.814318e+06</td>\n",
       "      <td>1.929935e+06</td>\n",
       "      <td>1.929935e+06</td>\n",
       "      <td>1.929935e+06</td>\n",
       "      <td>1.929935e+06</td>\n",
       "      <td>1.929935e+06</td>\n",
       "      <td>1.929935e+06</td>\n",
       "      <td>1.929935e+06</td>\n",
       "      <td>1.929935e+06</td>\n",
       "      <td>1.929935e+06</td>\n",
       "      <td>1.929935e+06</td>\n",
       "      <td>1.929935e+06</td>\n",
       "      <td>1.929935e+06</td>\n",
       "      <td>1.929935e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.965683e+02</td>\n",
       "      <td>7.878627e+00</td>\n",
       "      <td>4.306440e+01</td>\n",
       "      <td>1.785399e+02</td>\n",
       "      <td>3.454659e+02</td>\n",
       "      <td>5.066067e+02</td>\n",
       "      <td>5.536816e+01</td>\n",
       "      <td>1.746639e+02</td>\n",
       "      <td>3.415653e+02</td>\n",
       "      <td>5.235771e+02</td>\n",
       "      <td>5.277637e+01</td>\n",
       "      <td>2.016193e+00</td>\n",
       "      <td>-6.899870e+00</td>\n",
       "      <td>-6.462343e+00</td>\n",
       "      <td>6.537039e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.957343e+04</td>\n",
       "      <td>7.054212e+00</td>\n",
       "      <td>1.295420e+03</td>\n",
       "      <td>5.108770e+03</td>\n",
       "      <td>9.831562e+03</td>\n",
       "      <td>1.434543e+04</td>\n",
       "      <td>1.884377e+03</td>\n",
       "      <td>5.188856e+03</td>\n",
       "      <td>9.585030e+03</td>\n",
       "      <td>1.473327e+04</td>\n",
       "      <td>1.257968e+03</td>\n",
       "      <td>2.296112e+02</td>\n",
       "      <td>2.659988e+01</td>\n",
       "      <td>2.588343e+01</td>\n",
       "      <td>3.543230e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.725600e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.900000e+01</td>\n",
       "      <td>-9.900000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.300000e-01</td>\n",
       "      <td>6.600000e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.200000e-01</td>\n",
       "      <td>8.100000e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.000000e+01</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>3.100000e+01</td>\n",
       "      <td>4.700000e+01</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.600000e-01</td>\n",
       "      <td>9.500000e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.233440e+07</td>\n",
       "      <td>5.200000e+01</td>\n",
       "      <td>4.894080e+05</td>\n",
       "      <td>1.510592e+06</td>\n",
       "      <td>2.461360e+06</td>\n",
       "      <td>3.777304e+06</td>\n",
       "      <td>7.417740e+05</td>\n",
       "      <td>1.105478e+06</td>\n",
       "      <td>2.146625e+06</td>\n",
       "      <td>3.205172e+06</td>\n",
       "      <td>3.133190e+05</td>\n",
       "      <td>1.464960e+05</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.253000e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       national_inv     lead_time  in_transit_qty  forecast_3_month  \\\n",
       "count  1.929935e+06  1.814318e+06    1.929935e+06      1.929935e+06   \n",
       "mean   4.965683e+02  7.878627e+00    4.306440e+01      1.785399e+02   \n",
       "std    2.957343e+04  7.054212e+00    1.295420e+03      5.108770e+03   \n",
       "min   -2.725600e+04  0.000000e+00    0.000000e+00      0.000000e+00   \n",
       "25%    4.000000e+00  4.000000e+00    0.000000e+00      0.000000e+00   \n",
       "50%    1.500000e+01  8.000000e+00    0.000000e+00      0.000000e+00   \n",
       "75%    8.000000e+01  9.000000e+00    0.000000e+00      4.000000e+00   \n",
       "max    1.233440e+07  5.200000e+01    4.894080e+05      1.510592e+06   \n",
       "\n",
       "       forecast_6_month  forecast_9_month  sales_1_month  sales_3_month  \\\n",
       "count      1.929935e+06      1.929935e+06   1.929935e+06   1.929935e+06   \n",
       "mean       3.454659e+02      5.066067e+02   5.536816e+01   1.746639e+02   \n",
       "std        9.831562e+03      1.434543e+04   1.884377e+03   5.188856e+03   \n",
       "min        0.000000e+00      0.000000e+00   0.000000e+00   0.000000e+00   \n",
       "25%        0.000000e+00      0.000000e+00   0.000000e+00   0.000000e+00   \n",
       "50%        0.000000e+00      0.000000e+00   0.000000e+00   1.000000e+00   \n",
       "75%        1.200000e+01      2.000000e+01   4.000000e+00   1.500000e+01   \n",
       "max        2.461360e+06      3.777304e+06   7.417740e+05   1.105478e+06   \n",
       "\n",
       "       sales_6_month  sales_9_month      min_bank  pieces_past_due  \\\n",
       "count   1.929935e+06   1.929935e+06  1.929935e+06     1.929935e+06   \n",
       "mean    3.415653e+02   5.235771e+02  5.277637e+01     2.016193e+00   \n",
       "std     9.585030e+03   1.473327e+04  1.257968e+03     2.296112e+02   \n",
       "min     0.000000e+00   0.000000e+00  0.000000e+00     0.000000e+00   \n",
       "25%     0.000000e+00   0.000000e+00  0.000000e+00     0.000000e+00   \n",
       "50%     2.000000e+00   4.000000e+00  0.000000e+00     0.000000e+00   \n",
       "75%     3.100000e+01   4.700000e+01  3.000000e+00     0.000000e+00   \n",
       "max     2.146625e+06   3.205172e+06  3.133190e+05     1.464960e+05   \n",
       "\n",
       "       perf_6_month_avg  perf_12_month_avg  local_bo_qty  \n",
       "count      1.929935e+06       1.929935e+06  1.929935e+06  \n",
       "mean      -6.899870e+00      -6.462343e+00  6.537039e-01  \n",
       "std        2.659988e+01       2.588343e+01  3.543230e+01  \n",
       "min       -9.900000e+01      -9.900000e+01  0.000000e+00  \n",
       "25%        6.300000e-01       6.600000e-01  0.000000e+00  \n",
       "50%        8.200000e-01       8.100000e-01  0.000000e+00  \n",
       "75%        9.600000e-01       9.500000e-01  0.000000e+00  \n",
       "max        1.000000e+00       1.000000e+00  1.253000e+04  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masterData.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sku</th>\n",
       "      <th>national_inv</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>in_transit_qty</th>\n",
       "      <th>forecast_3_month</th>\n",
       "      <th>forecast_6_month</th>\n",
       "      <th>forecast_9_month</th>\n",
       "      <th>sales_1_month</th>\n",
       "      <th>sales_3_month</th>\n",
       "      <th>sales_6_month</th>\n",
       "      <th>...</th>\n",
       "      <th>pieces_past_due</th>\n",
       "      <th>perf_6_month_avg</th>\n",
       "      <th>perf_12_month_avg</th>\n",
       "      <th>local_bo_qty</th>\n",
       "      <th>deck_risk</th>\n",
       "      <th>oe_constraint</th>\n",
       "      <th>ppap_risk</th>\n",
       "      <th>stop_auto_buy</th>\n",
       "      <th>rev_stop</th>\n",
       "      <th>went_on_backorder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1026827</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1043384</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1043696</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1043852</td>\n",
       "      <td>0.989949</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1044048</td>\n",
       "      <td>0.872872</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1044198</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1044643</td>\n",
       "      <td>0.999993</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1045098</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1045815</td>\n",
       "      <td>0.592005</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.063429</td>\n",
       "      <td>0.482061</td>\n",
       "      <td>0.642748</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1045867</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1045918</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1047146</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1047199</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1047661</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1049160</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1049468</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1050390</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1050440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1117808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1050856</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1118063</td>\n",
       "      <td>0.992278</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1127154</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1127410</td>\n",
       "      <td>0.993884</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1128393</td>\n",
       "      <td>0.999541</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1051516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1128591</td>\n",
       "      <td>0.999717</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1130534</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1051715</td>\n",
       "      <td>0.942809</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</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>0.235702</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1053950</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1132770</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1141087</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1054562</td>\n",
       "      <td>0.995037</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1141767</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1156929</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1157130</td>\n",
       "      <td>0.983783</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.103556</td>\n",
       "      <td>0.103556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1055673</td>\n",
       "      <td>0.980581</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1157334</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1055821</td>\n",
       "      <td>0.986394</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1158662</td>\n",
       "      <td>0.999056</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1166209</td>\n",
       "      <td>0.979882</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006583</td>\n",
       "      <td>0.024307</td>\n",
       "      <td>0.043297</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1056218</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1174215</td>\n",
       "      <td>0.980581</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1057603</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1177491</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1057852</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1058160</td>\n",
       "      <td>0.968364</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>0.121046</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1058265</td>\n",
       "      <td>0.999133</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1180136</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1058368</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1180289</td>\n",
       "      <td>0.948683</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        sku  national_inv  lead_time  in_transit_qty  forecast_3_month  \\\n",
       "0   1026827      0.000000        8.0             0.0          0.000000   \n",
       "1   1043384      1.000000        9.0             0.0          0.000000   \n",
       "2   1043696      1.000000        8.0             0.0          0.000000   \n",
       "3   1043852      0.989949        8.0             0.0          0.000000   \n",
       "4   1044048      0.872872        8.0             0.0          0.000000   \n",
       "5   1044198      1.000000        8.0             0.0          0.000000   \n",
       "6   1044643      0.999993        8.0             0.0          0.000000   \n",
       "7   1045098      1.000000        2.0             0.0          0.000000   \n",
       "8   1045815      0.592005        8.0             0.0          0.063429   \n",
       "9   1045867      1.000000        8.0             0.0          0.000000   \n",
       "10  1045918      0.000000        2.0             0.0          0.000000   \n",
       "11  1047146      1.000000        8.0             0.0          0.000000   \n",
       "12  1047199      1.000000        8.0             0.0          0.000000   \n",
       "13  1047661      1.000000        8.0             0.0          0.000000   \n",
       "14  1049160      1.000000        8.0             0.0          0.000000   \n",
       "15  1049468      1.000000        8.0             0.0          0.000000   \n",
       "16  1050390      1.000000        2.0             0.0          0.000000   \n",
       "17  1050440      1.000000        2.0             0.0          0.000000   \n",
       "18  1117808      1.000000        8.0             0.0          0.000000   \n",
       "19  1050856      1.000000        8.0             0.0          0.000000   \n",
       "20  1118063      0.992278        8.0             0.0          0.000000   \n",
       "21  1127154      1.000000        8.0             0.0          0.000000   \n",
       "22  1127410      0.993884        8.0             0.0          0.000000   \n",
       "23  1128393      0.999541        8.0             0.0          0.000000   \n",
       "24  1051516      1.000000        8.0             0.0          0.000000   \n",
       "25  1128591      0.999717        8.0             0.0          0.000000   \n",
       "26  1130534      1.000000        8.0             0.0          0.000000   \n",
       "27  1051715      0.942809        2.0             0.0          0.000000   \n",
       "28  1053950      0.000000        8.0             0.0          0.000000   \n",
       "29  1132770      1.000000        8.0             0.0          0.000000   \n",
       "30  1141087      1.000000        8.0             0.0          0.000000   \n",
       "31  1054562      0.995037        8.0             0.0          0.000000   \n",
       "32  1141767      1.000000        8.0             0.0          0.000000   \n",
       "33  1156929      1.000000        8.0             0.0          0.000000   \n",
       "34  1157130      0.983783        8.0             0.0          0.000000   \n",
       "35  1055673      0.980581        8.0             0.0          0.000000   \n",
       "36  1157334      0.000000        8.0             0.0          0.577350   \n",
       "37  1055821      0.986394        8.0             0.0          0.000000   \n",
       "38  1158662      0.999056        8.0             0.0          0.000000   \n",
       "39  1166209      0.979882        8.0             0.0          0.000000   \n",
       "40  1056218      1.000000       12.0             0.0          0.000000   \n",
       "41  1174215      0.980581        8.0             0.0          0.000000   \n",
       "42  1057603      1.000000        8.0             0.0          0.000000   \n",
       "43  1177491      1.000000        8.0             0.0          0.000000   \n",
       "44  1057852      1.000000        8.0             0.0          0.000000   \n",
       "45  1058160      0.968364        8.0             0.0          0.000000   \n",
       "46  1058265      0.999133        8.0             0.0          0.000000   \n",
       "47  1180136      0.000000        8.0             0.0          0.000000   \n",
       "48  1058368      1.000000        2.0             0.0          0.000000   \n",
       "49  1180289      0.948683        8.0             0.0          0.000000   \n",
       "\n",
       "    forecast_6_month  forecast_9_month  sales_1_month  sales_3_month  \\\n",
       "0           0.000000          0.000000       0.000000       0.000000   \n",
       "1           0.000000          0.000000       0.000000       0.000000   \n",
       "2           0.000000          0.000000       0.000000       0.000000   \n",
       "3           0.000000          0.000000       0.000000       0.000000   \n",
       "4           0.000000          0.000000       0.000000       0.000000   \n",
       "5           0.000000          0.000000       0.000000       0.000000   \n",
       "6           0.000000          0.000000       0.000000       0.000000   \n",
       "7           0.000000          0.000000       0.000000       0.000000   \n",
       "8           0.482061          0.642748       0.000000       0.000000   \n",
       "9           0.000000          0.000000       0.000000       0.000000   \n",
       "10          0.000000          0.000000       0.000000       0.000000   \n",
       "11          0.000000          0.000000       0.000000       0.000000   \n",
       "12          0.000000          0.000000       0.000000       0.000000   \n",
       "13          0.000000          0.000000       0.000000       0.000000   \n",
       "14          0.000000          0.000000       0.000000       0.000000   \n",
       "15          0.000000          0.000000       0.000000       0.000000   \n",
       "16          0.000000          0.000000       0.000000       0.000000   \n",
       "17          0.000000          0.000000       0.000000       0.000000   \n",
       "18          0.000000          0.000000       0.000000       0.000000   \n",
       "19          0.000000          0.000000       0.000000       0.000000   \n",
       "20          0.000000          0.000000       0.000000       0.000000   \n",
       "21          0.000000          0.000000       0.000000       0.000000   \n",
       "22          0.000000          0.000000       0.000000       0.000000   \n",
       "23          0.000000          0.000000       0.000000       0.000000   \n",
       "24          0.000000          0.000000       0.000000       0.000000   \n",
       "25          0.000000          0.000000       0.000000       0.000000   \n",
       "26          0.000000          0.000000       0.000000       0.000000   \n",
       "27          0.000000          0.000000       0.000000       0.000000   \n",
       "28          0.000000          0.000000       0.288675       0.288675   \n",
       "29          0.000000          0.000000       0.000000       0.000000   \n",
       "30          0.000000          0.000000       0.000000       0.000000   \n",
       "31          0.000000          0.000000       0.000000       0.000000   \n",
       "32          0.000000          0.000000       0.000000       0.000000   \n",
       "33          0.000000          0.000000       0.000000       0.000000   \n",
       "34          0.000000          0.000000       0.000000       0.103556   \n",
       "35          0.000000          0.000000       0.000000       0.000000   \n",
       "36          0.577350          0.577350       0.000000       0.000000   \n",
       "37          0.000000          0.000000       0.000000       0.000000   \n",
       "38          0.000000          0.000000       0.000000       0.000000   \n",
       "39          0.000000          0.000000       0.006583       0.024307   \n",
       "40          0.000000          0.000000       0.000000       0.000000   \n",
       "41          0.000000          0.000000       0.000000       0.000000   \n",
       "42          0.000000          0.000000       0.000000       0.000000   \n",
       "43          0.000000          0.000000       0.000000       0.000000   \n",
       "44          0.000000          0.000000       0.000000       0.000000   \n",
       "45          0.000000          0.000000       0.000000       0.000000   \n",
       "46          0.000000          0.000000       0.000000       0.000000   \n",
       "47          0.000000          0.000000       0.000000       0.000000   \n",
       "48          0.000000          0.000000       0.000000       0.000000   \n",
       "49          0.000000          0.000000       0.000000       0.000000   \n",
       "\n",
       "    sales_6_month  ...  pieces_past_due  perf_6_month_avg  perf_12_month_avg  \\\n",
       "0        0.000000  ...              0.0              0.85               0.83   \n",
       "1        0.000000  ...              0.0              0.99               0.99   \n",
       "2        0.000000  ...              0.0              0.85               0.83   \n",
       "3        0.000000  ...              0.0              0.10               0.13   \n",
       "4        0.000000  ...              0.0              0.85               0.83   \n",
       "5        0.000000  ...              0.0              0.82               0.87   \n",
       "6        0.000000  ...              0.0              0.85               0.83   \n",
       "7        0.000000  ...              0.0              0.00               0.00   \n",
       "8        0.000000  ...              0.0              0.85               0.83   \n",
       "9        0.000000  ...              0.0              0.82               0.87   \n",
       "10       0.000000  ...              0.0              0.91               0.82   \n",
       "11       0.000000  ...              0.0              0.85               0.83   \n",
       "12       0.000000  ...              0.0              0.85               0.83   \n",
       "13       0.000000  ...              0.0              0.85               0.83   \n",
       "14       0.000000  ...              0.0              0.85               0.83   \n",
       "15       0.000000  ...              0.0              0.82               0.78   \n",
       "16       0.000000  ...              0.0              1.00               0.98   \n",
       "17       0.000000  ...              0.0              1.00               1.00   \n",
       "18       0.000000  ...              0.0              0.85               0.83   \n",
       "19       0.000000  ...              0.0              0.85               1.00   \n",
       "20       0.000000  ...              0.0              0.85               0.83   \n",
       "21       0.000000  ...              0.0              0.85               0.83   \n",
       "22       0.000000  ...              0.0              0.85               0.83   \n",
       "23       0.000000  ...              0.0              0.85               0.83   \n",
       "24       0.000000  ...              0.0              0.85               1.00   \n",
       "25       0.000000  ...              0.0              0.85               0.83   \n",
       "26       0.000000  ...              0.0              0.85               0.83   \n",
       "27       0.235702  ...              0.0              0.85               0.83   \n",
       "28       0.288675  ...              0.0              0.85               0.83   \n",
       "29       0.000000  ...              0.0              0.85               0.83   \n",
       "30       0.000000  ...              0.0              0.85               0.83   \n",
       "31       0.000000  ...              0.0              0.85               0.83   \n",
       "32       0.000000  ...              0.0              0.85               0.83   \n",
       "33       0.000000  ...              0.0              0.85               0.83   \n",
       "34       0.103556  ...              0.0              0.85               0.83   \n",
       "35       0.000000  ...              0.0              0.85               0.83   \n",
       "36       0.000000  ...              0.0              0.85               0.83   \n",
       "37       0.000000  ...              0.0              0.85               0.83   \n",
       "38       0.000000  ...              0.0              0.85               0.83   \n",
       "39       0.043297  ...              0.0              0.85               0.83   \n",
       "40       0.000000  ...              0.0              0.99               0.99   \n",
       "41       0.000000  ...              0.0              0.85               0.83   \n",
       "42       0.000000  ...              0.0              0.85               0.83   \n",
       "43       0.000000  ...              0.0              0.85               0.83   \n",
       "44       0.000000  ...              0.0              0.85               0.83   \n",
       "45       0.121046  ...              0.0              0.85               0.83   \n",
       "46       0.000000  ...              0.0              0.85               0.83   \n",
       "47       0.000000  ...              0.0              0.85               0.83   \n",
       "48       0.000000  ...              0.0              0.00               0.00   \n",
       "49       0.000000  ...              0.0              0.85               0.83   \n",
       "\n",
       "    local_bo_qty  deck_risk  oe_constraint  ppap_risk  stop_auto_buy  \\\n",
       "0            0.0          0              0          0              1   \n",
       "1            0.0          0              0          0              1   \n",
       "2            0.0          1              0          0              1   \n",
       "3            0.0          0              0          0              1   \n",
       "4            0.0          1              0          0              1   \n",
       "5            0.0          0              0          0              1   \n",
       "6            0.0          1              0          0              1   \n",
       "7            0.0          1              0          1              1   \n",
       "8            0.0          0              0          0              1   \n",
       "9            0.0          0              0          0              1   \n",
       "10           0.0          0              0          0              1   \n",
       "11           0.0          1              0          0              1   \n",
       "12           0.0          1              0          0              1   \n",
       "13           0.0          1              0          0              0   \n",
       "14           0.0          1              0          0              1   \n",
       "15           0.0          0              0          0              1   \n",
       "16           0.0          0              0          0              1   \n",
       "17           0.0          0              0          0              1   \n",
       "18           0.0          0              0          0              1   \n",
       "19           0.0          0              0          1              1   \n",
       "20           0.0          1              0          0              1   \n",
       "21           0.0          1              0          0              1   \n",
       "22           0.0          1              0          0              1   \n",
       "23           0.0          1              0          1              1   \n",
       "24           0.0          0              0          0              1   \n",
       "25           0.0          1              0          0              1   \n",
       "26           0.0          1              0          1              0   \n",
       "27           0.0          0              0          0              1   \n",
       "28           0.0          1              0          0              1   \n",
       "29           0.0          1              0          0              1   \n",
       "30           0.0          0              0          0              1   \n",
       "31           0.0          0              0          0              1   \n",
       "32           0.0          1              0          0              1   \n",
       "33           0.0          0              0          0              0   \n",
       "34           0.0          1              0          0              1   \n",
       "35           0.0          0              0          0              1   \n",
       "36           0.0          0              0          0              1   \n",
       "37           0.0          1              0          0              1   \n",
       "38           0.0          0              0          0              1   \n",
       "39           0.0          1              0          0              1   \n",
       "40           0.0          0              0          0              1   \n",
       "41           0.0          0              0          0              1   \n",
       "42           0.0          1              0          0              1   \n",
       "43           0.0          0              0          0              1   \n",
       "44           0.0          1              0          0              1   \n",
       "45           0.0          0              0          0              0   \n",
       "46           0.0          1              0          0              1   \n",
       "47           0.0          0              0          0              1   \n",
       "48           0.0          0              0          0              1   \n",
       "49           0.0          1              0          0              1   \n",
       "\n",
       "    rev_stop  went_on_backorder  \n",
       "0          0                  0  \n",
       "1          0                  0  \n",
       "2          0                  0  \n",
       "3          0                  0  \n",
       "4          0                  0  \n",
       "5          0                  0  \n",
       "6          0                  0  \n",
       "7          0                  0  \n",
       "8          0                  0  \n",
       "9          0                  0  \n",
       "10         0                  0  \n",
       "11         0                  0  \n",
       "12         0                  0  \n",
       "13         0                  0  \n",
       "14         0                  0  \n",
       "15         0                  0  \n",
       "16         0                  0  \n",
       "17         0                  0  \n",
       "18         0                  0  \n",
       "19         0                  0  \n",
       "20         0                  0  \n",
       "21         0                  0  \n",
       "22         0                  0  \n",
       "23         0                  0  \n",
       "24         0                  0  \n",
       "25         0                  0  \n",
       "26         0                  0  \n",
       "27         0                  0  \n",
       "28         0                  0  \n",
       "29         0                  0  \n",
       "30         0                  0  \n",
       "31         0                  0  \n",
       "32         0                  0  \n",
       "33         0                  0  \n",
       "34         0                  0  \n",
       "35         0                  0  \n",
       "36         0                  0  \n",
       "37         0                  0  \n",
       "38         0                  0  \n",
       "39         0                  0  \n",
       "40         0                  0  \n",
       "41         0                  0  \n",
       "42         0                  0  \n",
       "43         0                  0  \n",
       "44         0                  0  \n",
       "45         0                  0  \n",
       "46         0                  0  \n",
       "47         0                  0  \n",
       "48         0                  0  \n",
       "49         0                  0  \n",
       "\n",
       "[50 rows x 23 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def process(raw_file):\n",
    "    # The following are strategies adopted to sanitize the raw data:\n",
    "    #1.0 Binaries are converted from strings ('Yes' and 'No') to 1 and 0.\n",
    "    #2.0 The attributes related to quantities are normalized(std dev equal to 1) per row. Thereby, with the normalization,\n",
    "    # the orders with different magnitudes are approximated.\n",
    "    #Missing values for lead tims and perf month average are replaced using the series median and mean by leverage the usage of\n",
    "    #Impute library function in sklearn.\n",
    "    \n",
    "    #Impute the missing values with the median and rest with NA drop them\n",
    "    imputer = SimpleImputer(missing_values= np.nan, strategy='median')\n",
    "    raw_file['lead_time'] = imputer.fit_transform(raw_file['lead_time'].values.reshape(-1, 1))\n",
    "    \n",
    "    raw_file = raw_file.dropna()\n",
    "    imp = SimpleImputer(missing_values = -99, strategy = 'median')\n",
    "    for col in ['perf_6_month_avg', 'perf_12_month_avg']:\n",
    "        raw_file[col] = imp.fit_transform(raw_file[col].values.reshape(-1, 1))\n",
    "        \n",
    "    #converting to binaries\n",
    "    for col in ['potential_issue', 'deck_risk', 'oe_constraint', 'ppap_risk',\n",
    "               'stop_auto_buy', 'rev_stop', 'went_on_backorder']:\n",
    "        raw_file[col] = (raw_file[col] == 'Yes').astype(int)\n",
    "        \n",
    "    #Normalization\n",
    "    from sklearn.preprocessing import normalize\n",
    "    quantifiable_fields = ['national_inv', 'in_transit_qty', 'forecast_3_month',\n",
    "                          'forecast_6_month', 'forecast_9_month', 'min_bank',\n",
    "                          'local_bo_qty', 'pieces_past_due', 'sales_1_month', \n",
    "                          'sales_3_month', 'sales_6_month', 'sales_9_month']\n",
    "    raw_file[quantifiable_fields] = normalize(raw_file[quantifiable_fields], axis = 1)\n",
    "    return raw_file\n",
    "\n",
    "processed_master_data = process(masterData)\n",
    "\n",
    "processed_master_data.head(50)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained variance ratio: 34.32%\n",
      "[0.2342628  0.10890335]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Giving the general interpretation of the dataset through 2d plots using PCA.\n",
    "\n",
    "def plot_2d(X, y, title=''):\n",
    "    \"\"\"\n",
    "    Plot the two major components using PCA, giving a general interpretation of\n",
    "    the dataset.\n",
    "    \"\"\"\n",
    "    from sklearn.preprocessing import StandardScaler\n",
    "    X_std = StandardScaler().fit_transform(X)\n",
    "\n",
    "    from sklearn.decomposition import PCA\n",
    "    dec = PCA(n_components=2)\n",
    "    X_reduced = dec.fit_transform(X_std)\n",
    "    \n",
    "    f, ax = plt.subplots(figsize=(6,6))\n",
    "    ax.scatter(X_reduced[y==0,0], X_reduced[y==0,1], \n",
    "               facecolors='none', edgecolors='0.75', label=\"Negative\")\n",
    "    ax.scatter(X_reduced[y==1,0], X_reduced[y==1,1], c='0.25', marker='*', \n",
    "               label='Positive')\n",
    "    ax.legend(loc='lower left')\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.set_xlabel('PC1')\n",
    "    ax.set_ylabel('PC2')\n",
    "    print (\"Explained variance ratio: %.2f%%\" % \n",
    "           (100*dec.explained_variance_ratio_.sum()))\n",
    "    print (dec.explained_variance_ratio_)\n",
    "    #plt.savefig('img/plot2d.jpeg',format='jpeg',bbox_inches='tight',dpi=450)\n",
    "    plt.show()\n",
    "    \n",
    "sample = processed_master_data.sample(5000, random_state = 36)\n",
    "x_sample = sample.drop('went_on_backorder', axis = 1).values\n",
    "y_sample = sample['went_on_backorder'].values\n",
    "\n",
    "plot_2d(x_sample, y_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1929935, 23), (1929935,))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Prepare the data for the modeling\n",
    "X = pd.DataFrame(processed_master_data.columns)\n",
    "X = processed_master_data[processed_master_data.columns]\n",
    "y = processed_master_data['went_on_backorder']\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((19299, 23), (19299,), (1910636, 23), (1910636,))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the 30% sample data for model explorations\n",
    "X_sample, X_data, y_sample, y_data = train_test_split(X, y, test_size = 0.99, random_state = 42)\n",
    "X_sample.shape, y_sample.shape, X_data.shape, y_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17369, 23), (1930, 23), (17369,), (1930,))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_sample, y_sample, test_size = 0.10, random_state = 42)\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train KNN model with default parameters on 30% sample data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1915\n",
      "           1       1.00      1.00      1.00        15\n",
      "\n",
      "    accuracy                           1.00      1930\n",
      "   macro avg       1.00      1.00      1.00      1930\n",
      "weighted avg       1.00      1.00      1.00      1930\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#KNN\n",
    "steps = [('scaler', StandardScaler()),\n",
    "        ('knn', KNeighborsClassifier(n_jobs = -1))]\n",
    "\n",
    "#Create the pipeline:pipeline\n",
    "pipeline_knn = Pipeline(steps)\n",
    "\n",
    "#Fit to the training set\n",
    "pipeline_knn.fit(X_train, y_train)\n",
    "knn_prediction = pipeline_knn.predict(X_test)\n",
    "#getting the classification report\n",
    "knn_report = classification_report(y_test, knn_prediction)\n",
    "#print the report\n",
    "print(knn_report)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train SVM model with default parameters on 30% sample data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1915\n",
      "           1       1.00      1.00      1.00        15\n",
      "\n",
      "    accuracy                           1.00      1930\n",
      "   macro avg       1.00      1.00      1.00      1930\n",
      "weighted avg       1.00      1.00      1.00      1930\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Train default SVC on the data and getting the basic results\n",
    "steps = [('scaler', StandardScaler()),\n",
    "        ('svc', svm.SVC(class_weight = 'balanced'))]\n",
    "\n",
    "#Create the pipeline : pipeline\n",
    "pipeline_svc = Pipeline(steps)\n",
    "\n",
    "#Fit to the training set\n",
    "pipeline_svc.fit(X_train, y_train)\n",
    "svc_prediction = pipeline_svc.predict(X_test)\n",
    "\n",
    "#get the classification report\n",
    "svc_report = classification_report(y_test, svc_prediction)\n",
    "\n",
    "#print the report\n",
    "print(svc_report)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train LogisticRegression model with default parameters on 30% data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train the deault LogisticRegression model\n",
    "steps = [('scaler', StandardScaler()),\n",
    "        ('logreg', LogisticRegression(class_weight = 'balanced'))]\n",
    "\n",
    "#Create the pipeline : pipeline\n",
    "pipeline_logreg = Pipeline(steps)\n",
    "\n",
    "#Fit to the training set\n",
    "pipeline_logreg.fit(X_train, y_train)\n",
    "logreg_prediction = pipeline_logreg.predict(X_test)\n",
    "\n",
    "#get the classification report\n",
    "logreg_report = classification_report(y_test, logreg_prediction)\n",
    "\n",
    "#print the report\n",
    "print(logreg_report)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train RandomForest classifier model with default parameters on 30% data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train the default RandomForest classifier model\n",
    "rfmodel = RandomForestClassifier(random_state = 32)\n",
    "#Fit to the training set\n",
    "rfmodel.fit(X_train, y_train)\n",
    "rfmodel_prediction = rfmodel.predict(X_test)\n",
    "#get the classification report\n",
    "rfmodel_report = classification_report(y_test, rfmodel_prediction)\n",
    "#print the report\n",
    "print(rfmodel_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train GradientBoostingClassifier model with default paramatars on 10% data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train the dataset with Gradient Boosting\n",
    "gbmodel = GradientBoostingClassifier(random_state = 32)\n",
    "#Fit to the training set\n",
    "gbmodel.fit(X_train, y_train)\n",
    "gbmodel_prediction = gbmodel.predict(X_test)\n",
    "#get the classification report\n",
    "gbmodel_report = classification_report(y_test, gbmodel_prediction)\n",
    "#print the report\n",
    "print(gbmodel_report)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train AdaBoostClassifier with randomforest estimator\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train default on 10% data set with Adaboost\n",
    "abmodel = AdaboostClassifier(random_state =32)\n",
    "#Fit the training set\n",
    "abmodel.fit(X_train, y_train)\n",
    "abmodel_prediction = abmodel.predict(X_test)\n",
    "#get the classification report\n",
    "abmodel_report = classfication_report(y_test,abmodel_prediction)\n",
    "#print the report\n",
    "print(abmodel_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "abmodel_rf = AdaBoostClassifier(base_estimator  = rfmodel, random_state = 32)\n",
    "#Fit the training model\n",
    "abmodel_rf.fit(X_train, y_train)\n",
    "abmodel_prediction_rf = abmodel_rf.predict(X_test)\n",
    "#get the classification report\n",
    "abmodel_report_rf = classification_report(y_test, abmodel_prediction_rf)\n",
    "#print the report\n",
    "print(abmodel_report_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot the ROC Curves\n",
    "fig, ax1 = plt.subplot(figsize = (9,9))\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "estimators = [pipeline_knn, pipeline_logreg, rfmodel, gbmodel, abmodel]\n",
    "classifiers = ['KNN', 'LogisticRegression', 'RandomForestClassifier', 'GradientBoostingClassifier',\n",
    "              'AdaBoostClassifier']\n",
    "colors = ['b', 'g', 'r', 'c', 'm']\n",
    "for i, estimator in enumerate(estimators):\n",
    "    y_pred_prob = estimator.predict_proba(X_test)[:,1]\n",
    "    fpr,tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
    "    plt.plot(fpr, tpr, label = classifiers[i], colors = colors[i])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Negative Rate')\n",
    "plt.legend(loc = 4)\n",
    "plt.title('ROC Curve')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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