g_series_7.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta,date\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from __future__ import division\n",
    "from sklearn.cluster import KMeans\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.offline as pyoff\n",
    "import plotly.graph_objs as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import KFold, cross_val_score, train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        define('plotly', function(require, exports, module) {\n",
       "            /**\n",
       "* plotly.js v1.47.3\n",
       "* Copyright 2012-2019, Plotly, Inc.\n",
       "* All rights reserved.\n",
       "* Licensed under the MIT license\n",
       "*/\n",

It's strange, I used the same dataset, everything quite identical to the numbers here, but the output is different!? Do we need to set a random_seed for XGBClassifier?
Mine was:
Real Discount Uptick - Order: 608, Revenue: 15200
Predicted Discount Uptick - Order: 558, Revenue: 13950