Churn rate by cluster (country x industry).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(\"survival\")\n",
    "library(\"survminer\")\n",
    "library(\"ranger\")\n",
    "library(\"ggplot2\")\n",
    "library(\"dplyr\")\n",
    "library(\"ggfortify\")\n",
    "library(\"randomForestSRC\")\n",
    "library(\"ggRandomForests\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_FRANCE_Apparel <- read.csv(\"FRANCE_Apparel.csv\")\n",
    "df_JAPAN_Apparel <- read.csv(\"JAPAN_Apparel.csv\")\n",
    "df_US_Apparel <- read.csv(\"UNITED_STATES_Apparel.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "for(i in 1:nrow(df_FRANCE_Apparel)){\n",
    "    df_FRANCE_Apparel$Churn[i] = 1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "for(i in 1:nrow(df_JAPAN_Apparel)){\n",
    "    df_JAPAN_Apparel$Churn[i] = 1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "for(i in 1:nrow(df_US_Apparel)){\n",
    "    df_US_Apparel$Churn[i] = 1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Selecting only variables used in our model\n",
    "df_FRANCE_Apparel = select(df_FRANCE_Apparel, Churn, churn_date, labels)\n",
    "df_JAPAN_Apparel = select(df_JAPAN_Apparel, Churn, churn_date, labels)\n",
    "df_US_Apparel = select(df_US_Apparel, Churn, churn_date, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
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       "[1] 341"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(df_FRANCE_Apparel$labels[df_FRANCE_Apparel$labels == 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(df_FRANCE_Apparel$labels[df_FRANCE_Apparel$labels == 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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    }
   ],
   "source": [
    "length(df_FRANCE_Apparel$labels[df_FRANCE_Apparel$labels == 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(df_FRANCE_Apparel$labels[df_FRANCE_Apparel$labels == 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(df_JAPAN_Apparel$labels[df_JAPAN_Apparel$labels == 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(df_JAPAN_Apparel$labels[df_JAPAN_Apparel$labels == 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(df_JAPAN_Apparel$labels[df_JAPAN_Apparel$labels == 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(df_JAPAN_Apparel$labels[df_JAPAN_Apparel$labels == 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(df_US_Apparel$labels[df_US_Apparel$labels == 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "length(df_US_Apparel$labels[df_US_Apparel$labels == 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "length(df_US_Apparel$labels[df_US_Apparel$labels == 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "length(df_US_Apparel$labels[df_US_Apparel$labels == 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "length(df_US_Apparel$labels[df_US_Apparel$labels == 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n"
     ]
    },
    {
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714zfqlFw6ObgkpgZDEeiek7VdvyPMn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\nhAQI/B9llpJiXIFyawAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qplot(df_FRANCE_Apparel$labels, geom=\"histogram\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n"
     ]
    },
    {
     "data": {
      "image/png": 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MmEkMQIKUBINoQkRkhphKRFSDFCMiEk\nMUIKEJINIYkRUhohaRFSjJBMCEmMkAKEZENIYoSURkhahBQjJBNCEiOkACHZEJIYIaURkhYh\nxQjJhJDECClASDaEJEZIaYSkRUgxQjIhJDFCChCSDSGJEVIaIWkRUoyQTAhJjJAChGRDSGKE\nlEZIWoQUIyQTQhIjpAAh2RCSGCGlEZIWIcUIyYSQxAgpQEg2hCRGSGmEpEVIMUIyISQxQgoQ\nkg0hiRFSGiFpEVKMkEwISYyQAoRkQ0hihJRGSFqEFCMkE0ISI6QAIdkQkhghpRGSFiHFCMmE\nkMQIKUBINoQkRkhphKRFSDFCMiEkMUIKEJINIYkRUhohaRFSjJBMCEmMkAKEZENIYoSURkha\nhBQjJBNCEiOkACHZEJIYIaURkhYhxQjJhJDECClASDaEJEZIaYSkRUgxQjIhJDFCChCSDSGJ\nEVIaIWkRUoyQTAhJjJAChGRDSGKElEZIWoQUIyQTQhIjpAAh2RCSGCGlEZIWIcUIyYSQxAgp\nQEg2hCRGSGmEpEVIMUIyISQxQgoQkg0hiRFSGiFpEVKMkEwISYyQAoRkQ0hihJRGSFqEFCMk\nE0ISI6QAIdkQkhghpRGSFiHFCMmEkMQIKUBINoQkRkhphKRFSDFCMiEkMUIKEJINIYkRUhoh\naRFSjJBMCEmMkAKEZENIYoSURkhahBQjJBNCEiOkACHZEJIYIaURkhYhxQjJhJDECClASDaE\nJEZIaYSkRUgxQjIhJDFCChCSDSGJEVIaIWkRUoyQTAhJjJAChGRDSGKElEZIWoQUIyQTQhIj\npAAh2RCSGCGlEZIWIcWEIe1qbIqQmjizsOGBMmaZUG3m+yGdsNzpBvJKuRMOlTxfJR9sabwu\npK2NTRFSE2cWNritjFkmVKrlztdTKXe+bflAuRPu6C93vv68r5XhvbqQmnj/49LOhks7Me6R\n0ghJi5BihGRCSGKEFCAkG0ISI6Q0QtIipBghmRCSGCEFCMmGkMQIKY2QtAgpRkgmhCRGSAFC\nsiEkMUJKIyQtQooRkgkhiRFSgJBsCEmMkNIISYuQYoRkQkhihBQgJBtCEiOkNELSIqQYIZkQ\nkhghBQjJhpDECCmNkLQIKUZIJoQkRkgBQrIhJDFCSiMkLUKKEZIJIYkRUoCQbAhJjJDSCEmL\nkGKEZEJIYoQUICQbQhIjpDRC0iKkGCGZEJIYIQUIyYaQxAgpjZC0CClGSCaEJEZIAUKyISQx\nQkojJC1CihGSCSGJEVKAkGwISYyQ0ghJi5BihGRCSGKEFCAkG0ISI6Q0QtIipBghmRCSGCEF\nCMmGkMQIKY2QtAgpRkgmhCRGSAFCsiEkMUJKIyQtQooRkgkhiRFSgJBsCEmMkNIISYuQYoRk\nQkhihBQgJBtCEiOkNELSIqQYIZkQkhghBQjJhpDECCmNkLQIKUZIJoQkRkgBQrIhJDFCSiMk\nLUKKEZIJIYkRUoCQbAhJjJDSCEmLkGKEZEJIYoQUICQbQhIjpDRC0iKkGCGZEJIYIQUIyYaQ\nxAgpjZC0CClGSCaEJEZIAUKyISQxQkojJC1CihGSCSGJEVKAkGwISYyQ0ghJi5BihGRCSGKE\nFCAkG0ISI6Q0QtIipBghmRCSGCEFCMmGkMQIKY2QtAgpRkgmhCRGSAFCsiEkMUJKIyQtQooR\nkgkhiRFSgJBsCEmMkNIISYuQYoRkQkhihBQgJBtCEiOkNELSIqQYIZkQkhghBQjJhpDECCmN\nkLQIKUZIJoQkRkgBQrIhJDFCSiMkLUKKEZIJIYkRUoCQbAhJjJDSCEmLkGKEZEJIYoQUICQb\nQhIjpDRC0iKkGCGZEJIYIQUIyYaQxAgpjZC0CClGSCaEJEZIAUKyISQxQkojJC1CihGSCSGJ\nEVKAkGwISYyQ0ghJi5BiLYW0afG8+qbvqlNPvuSJiS0hJRCSWBuFdM+ZK0dCWnHhuo0rzh8e\n3xJSAiGJtVFIdz/5w3pIXXPW196NTnpgbEtIKYQk1kYh5flISPfNr9YeF908tq09bNm0adPm\n7samCKmJMwsb7C1jlgmVarnzba2UO19vvqvcCXfsLHe+/ryvleE9rYe0+qz67rJVY9vaw9KO\njo4Tmzh/ipCaOBN4Whu/w2khpIX13aWrxra1h5suuuiiT+5qbIqQmjizsOHBMmaZUM3LnW+g\nWvJ8eaXcCYdKnq+St/aKaT2ktSOXdBfcMrYd+2QTF5LcI9lwjyTmv0fqnvtonvfMe2hsS0gp\nhCTWRiF1d62Z19XVn1+xaN2GixdXx7eElEBIYm0U0gfn1N2W77hmwSmX1U4c2xJSAiGJtVFI\nDTQxGyHZEJIYIaURkhYhxQjJhJDECClASDaEJEZIaYSkRUgxQjIhJDFCChCSDSGJEVIaIWkR\nUoyQTAhJjJAChGRDSGKElEZIWoQUIyQTQhIjpAAh2RCSGCGlEZIWIcUIyYSQxAgpQEg2hCRG\nSGmEpEVIMUIyISQxQgoQkg0hiRFSGiFpEVKMkEwISYyQAoRkQ0hihJRGSFqEFCMkE0ISI6QA\nIdkQkhghpRGSFiHFCMmEkMQIKUBINoQkRkhphKRFSDFCMiEkMUIKEJINIYkRUhohaRFSjJBM\nCEmMkAKEZENIYoSURkhahBQjJBNCEiOkACHZEJIYIaURkhYhxQjJhJDECClASDaEJEZIaYSk\nRUgxQjIhJDFCChCSDSGJEVIaIWkRUoyQTAhJjJAChGRDSGKElEZIWoQUIyQTQhIjpAAh2RCS\nGCGlEZIWIcUIyYSQxAgpQEg2hCRGSGmEpEVIMUIyISQxQgoQkg0hiRFSGiFpEVKMkEwISYyQ\nAoRkQ0hihJRGSFqEFCMkE0ISI6QAIdkQkhghpRGSFiHFCMmEkMQIKUBINoQkRkhppYY0A8+T\nkMQIKY2QtAgpRkgOhKRHSAFCsiEkMUJKIyQtQooRkgMh6RFSgJBsCEmMkNIISYuQYoTkQEh6\nhBQgJBtCEiOkNELSIqQYITkQkh4hBQjJhpDECCmNkLQIKUZIDoSkR0gBQrIhJDFCSiMkLUKK\nEZIDIekRUoCQbAhJjJDSCEmLkGKE5EBIeoQUICQbQhIjpDRC0iKkGCE5EJIeIQUIyYaQxAgp\njZC0CClGSA6EpEdIAUKyISQxQkojJC1CihGSAyHpEVKAkGwISYyQ0ghJi5BihORASHqEFCAk\nG0ISI6Q0QtIipBghORCSHiEFCMmGkMQIKY2QtAgpRkgOhKRHSAFCsiEkMUJKIyQtQooRkgMh\n6RFSgJBsCEmMkNIISYuQYoTkQEh6hBQgJBtCEpu5kHY1NsULrIkzCxseKGOW3WbieVatX32S\ngbxS7oRDJc9XyQdbGq8LqaexKV5gTZxZ2FBfGbPsNgPPs3fY+dUn68sHyp1w565y59uVb29l\n+DZdSE28/3FpZ8OlnRj3SGmEpEVIMUJyICQ9QgoQkg0hiRFSGiFpEVKMkBwISY+QAoRkQ0hi\nhJRGSFqEFCMkB0LSI6QAIdkQkhghpRGSFiHFCMmBkPQIKUBINoQkRkhphKRFSDFCciAkPUIK\nEJINIYkRUhohaRFSjJAcCEmPkAKEZENIYoSURkhahBQjJAdC0iOkACHZEJIYIaURkhYhxQjJ\ngZD0CClASDaEJEZIaYSkRUgxQnIgJD1CChCSDSGJEVIaIWkRUoyQHAhJj5AChGRDSGKElEZI\nWoQUIyQHQtIjpAAh2RCSGCGlEZIWIcUIyYGQ9AgpQEg2hCRGSGmEpEVIMUJyICQ9QgoQkg0h\niRFSGiFpEVKMkBwISY+QAoRkQ0hihJRGSFqEFCMkB0LSI6QAIdkQkhghpRGSFiHFCMmBkPQI\nKUBINoQkRkhphKRFSDFCciAkPUIKEJINIYkRUhohaRFSjJAcCEmPkAKEZENIYoSURkhahBQj\nJAdC0iOkACHZeEOa4gmV8l9uN0IKEJINIYkRUhohFUFIjRCSAyHpEVKAkGwISYyQ0gipCEJq\nhJAcCEmPkAKEZENIYoSURkhFEFIjhORASHqEFCAkG0ISI6Q0QiqCkBohJAdC0iOkACHZEJIY\nIaURUhGE1AghORCSHiEFCMmGkMQIKY2QiiCkRgjJgZD0CClASDaEJEZIaYRUBCE1QkgOhKRH\nSAFCsiEkMUJKI6QiCKkRQnIgJD1CChCSDSGJEVIaIRVBSI0QkgMh6RFSgJBsCEmMkNIIqQhC\naoSQHAhJj5AChGRDSGKElEZIRRBSI4TkQEh6hBQgJBtCEiOkNEIqgpAaISQHQtIjpAAh2RCS\nGCGlEVIRhNQIITkQkl45IU37eRKSAyHpEZJimcURUhGE1AghORCSHiEpllkcIRVBSI0QkgMh\n6RGSYpnFEVIRhNQIITkQkh4hKZZZHCEVQUiNEJIDIekRkmKZxRFSEYTUCCE5EJIeISmWWRwh\nFUFIjRCSAyHptXFIfVedevIlTxDSZISk18Yhrbhw3cYV5w8T0iSEpNe+IXXNWV97VzrpAUKa\nhJD02jek++ZXa4+Lbq497Ozt7d32h8amWGYTZxY20FPGLLvNwPPcUnF+9alCcs65h+07y5hl\n2s9z6/RDWn1W/XHZqtrD0o6OjhObOGWKZbY689Ne2z3PqUKa6XXJTft5jt/hTCOkhfXHpfWQ\nvnLuuef+02ArKvlQS+MLGy55vmpe7nyD1XKnG8qHy52wUil3vuHWXqED0w9p7cil3QW3jH3c\n0rVoi79aorhS75FqKtVy5/PeI03WnfeXO2H7/vKT7rmP5nnPvIcIKYWQxNo3pPyKRes2XLy4\nSkgphCTWxiHtuGbBKZdNfAHnMosjJC1CipX7R4SmvcziCEmLkGKEZEJIYoSkWGZxhKRFSDFC\nMiEkMUJSLLM4QtIipBghmRCSGCEpllkcIWkRUoyQTAhJjJAUyyyOkLQIKUZIJoQkRkiKZRZH\nSFqEFCMkE0ISIyTFMosjJC1CihGSCSGJEZJimcURkhYhxQjJhJDECEmxzOIISYuQYoRkQkhi\nhKRYZnGEpEVIMUIyISQxQlIsszhC0iKkGCGZEJIYISmWWRwhaRFSjJBMCEmMkBTLLI6QtAgp\nRkgmhCRGSIplFkdIWoQUIyQTQhIjJMUyiyMkLUKKEZIJIYk9c0JqyddOWFvqfKVb+K6ZXoHX\nb0+4cqaX4HXDCT+e3onlhrSq495S5yvd+9420yvwWt/xyZlegteXOu6b3omEJEVIezlCenog\npL3cXhLS9y7631LnK93VH5/pFXj930XfnOkleK256JHpnVhuSECbIiRAgJAAgTJD6rvq1JMv\neaLECUu3afG8mV6C1ZbPvH/BkmneROwNNiw/bcGSh6d1apkhrbhw3cYV5w+XOGPJ7jlzZXuH\n9JEl6zdf+f7+mV6Gy+CZV2/avHLBzumcW2JIXXPW196VTnqgvBnLdveTP2zrkLZdvinPn5zT\ntm9JPbfWGto05zfTObfEkO6bX609Lrq5vBnL194hjXh4btdML8Fp27XnDk7nvBJDWn1W/XHZ\nqvJmLF/7h7TtvOtmeglGw++Zs2TLtM4sM6SF9celhLQ323jOtdWZXoPThl986sPbp3NiiSGt\nHbm0u+CW8mYsX7uH9ODpd870Etwq03uKJYbUPffR2v3cvIfKm7F8bR7SL0//6UwvwenBs/vz\nvHrGHdM5t8wff1+xaN2Gixe38ZVBd9eaeV1dbfvT4Xzg7G/U/zZb2z7B7WdevuHxVfMfm865\nZYa045oFp1zW3XjcXuuDc+pum+ll2Dw48vzmtO/V3e+Wn77gYz+f1qn8ESFAgJAAAUICBAgJ\nECAkQICQAAFCAgQICRAgJECAkAABQnJacECeV8488Pkbw4O3ZFnWMbLXe0B268jOJ7K62W/9\n5p6H3zL6JxM73r7HFx4f0prO1+Z5z0Wv2W+fl773kdGP4s+lxqMphORUD2l19re37wgP/vqz\nn33BaEjXZge9e2TnE9k/X3fdl5Ydnq3c43D2pZGdSSGND2lNPYx3zl6+6OirX3zoFkJSIiSn\nekhfze6ZdPzloyH92dGLnz3yZvWJ7If1zfbDD9gZHd7/XQc9Wd+ZFNL4kMYGgv1aGBuzz+Q3\nHp9/v9YsIQkRkkd1+aGz3nDLqQfkb69ftP12j8+OhnR/dvlPsxX1vd0h5YuzH8WH1+3/gfrO\nniFNDDnmuO8fs/8Lz+oJ9/Kb/uIFsztuqu0ce/y3D31Tnt974uz93/jlfCSMn9Xe5Goh5Q/s\nGs1kfGznkT996/MOOnNrHo/PN3/oT2a99L3T+x1VzyCE5HFFdtqam1732gPyRz6RXX//wB6f\nHQ3pnGc/lh99eP0uaCykZdm98eFdy7P/ySeHNDHkuJe+cc2vP/eceeHezdnJd97xzuzOPD/+\n6NevvD3/732Pv23Nh7N/GQmj78AjN9dDykczmRjb+YpXXnrrBdmcPB6fv+Xg6+++8Q0v2ZHj\nqRCSRfVlr6+9zB/bt3Zpd0P2g0mfHgmpb3btNuea7Lv5REhve3Z3fLh/12teNzgppGBIZ3Z3\n7fGc7PfB3pXvqJ3S+5wz8trbYf1HEh2H76o9zj1w50gY35g164gjflb/MvWPJsZ2ZvUfdZxa\nOz8a35tdVPvgN5dN62+7PYMQksXvs0X1zbFPFdJ19VfuH/Y7Na8X8+3HH9/847/LPrzH4f78\nO9nlk0IKhnQ+t1J7vLEWzMTeqEPfVgvpOUN53pWd21/zxdo6Ri7mHl58eJa9+t/CO6D62M5Z\n9V9D9ZXs1nj80IsO+14b/05PGUKy+NHoHcz8pwrpmAM3d3V1/c2sLWM//s6ec96uPQ735/kp\nz/vdniEFQzpfWT9wV+3GZ2Jv60eO+KNZs7LOWkiH1A49kO32jbF0bvzzz74q+/eRjybGdh6+\n+/w9xv/4ldkL3/f1ivGb1RYIyWLtaEgnPUVID469Xq+pF7PyrrtW/6B+ox8froW0afbc/E1R\nSOGQzlfVj/xndl2w9479Pr32Vw+/rB7SYbVDP8sW/nBE13hIx+dbDj5h5KOJsZ1/svv8PcdX\n/utjR2XH7TJ+t9oBIVmszy6ob944KaTV/1D/1SGz/zrPz8tu+m7dy46auEfK9zxcH3xVdttx\nUUjhkM7n1a+7rs9un9h7LPv72s7QAWMhbck+MHZmLTqbbyYAAAH5SURBVIxvXTwSUj73VfWP\ngrGds+rvOjdkt8fjR30x+5r2G9R2CMli+EVH1l7WDz9rUkifyb5av/D7aL7zwGNHjyzJfhSE\ntMfhekhDRx321jCkaEhntrq2975nPT6x93C2vLbzhewtu0PK33xg/a3ua0uH6mEs2+cn9ZB2\nHnZi/aNgbGctxjw/o/aVovH3L6j/+yHr6z/Dw1MgJI+PZ/Nu+fxhx0wKacsrnvvRFQc//7Ha\n4etHjzyanR2EtMfhkd98de8++4QhRUM6X/7qz635x+y0fGJv8BUvv+2ej554wuy7t4+G9P19\n3/Cv31m278KRd5gnXvnHyy94/apjnn13/aNgbOehr/n8mo/Vv1I0fvOBR395zdePndXm/2Zp\nYYTkUVly8H5H/ceF+066R3r0PS/e//i1eX7cAX27j/zl7O0TIe1xePRXyC3MwpCiIZ1H3P/W\n5x70odqRib37j33eS87ZdseLDnl0NKT8B381e/8jP10ZvVTbeO6f7pcd8u4fj340MfbNx/1k\n9/nx+J/PP3i/Q+b9yPFNaieEtHeb+BF2C3+cZ/f/kIUQIe3dphXSz2+wrOUZjZD2btMKCXqE\nVIK7snFfEH8JQnqaIKQS9D00brq/+lzwJeBESIAAIQEChAQIEBIgQEiAACEBAoQECBASIEBI\ngMD/A527HhONcQKmAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qplot(df_JAPAN_Apparel$labels, geom=\"histogram\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n"
     ]
    },
    {
     "data": {
      "image/png": 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KRuGdJeMaSSIWGGVDOkkiFhhlQzpJIhYYZUM6SSIWGGVDOkkiFhhlQzpJIhYYZU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\nQ5I6YEhSBwxJ6oAhSR0wJKkDhiR14P8BzDbqme3+7TYAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qplot(df_US_Apparel$labels, geom=\"histogram\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Churn</th><th scope=col>churn_date</th><th scope=col>labels</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1       </td><td>2020,077</td><td>0       </td></tr>\n",
       "\t<tr><td>1       </td><td>2020,077</td><td>0       </td></tr>\n",
       "\t<tr><td>1       </td><td>2018,385</td><td>0       </td></tr>\n",
       "\t<tr><td>1       </td><td>2017,308</td><td>0       </td></tr>\n",
       "\t<tr><td>1       </td><td>2020,077</td><td>0       </td></tr>\n",
       "\t<tr><td>1       </td><td>2020,077</td><td>0       </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " Churn & churn\\_date & labels\\\\\n",
       "\\hline\n",
       "\t 1        & 2020,077 & 0       \\\\\n",
       "\t 1        & 2020,077 & 0       \\\\\n",
       "\t 1        & 2018,385 & 0       \\\\\n",
       "\t 1        & 2017,308 & 0       \\\\\n",
       "\t 1        & 2020,077 & 0       \\\\\n",
       "\t 1        & 2020,077 & 0       \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Churn | churn_date | labels |\n",
       "|---|---|---|\n",
       "| 1        | 2020,077 | 0        |\n",
       "| 1        | 2020,077 | 0        |\n",
       "| 1        | 2018,385 | 0        |\n",
       "| 1        | 2017,308 | 0        |\n",
       "| 1        | 2020,077 | 0        |\n",
       "| 1        | 2020,077 | 0        |\n",
       "\n"
      ],
      "text/plain": [
       "  Churn churn_date labels\n",
       "1 1     2020,077   0     \n",
       "2 1     2020,077   0     \n",
       "3 1     2018,385   0     \n",
       "4 1     2017,308   0     \n",
       "5 1     2020,077   0     \n",
       "6 1     2020,077   0     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(df_FRANCE_Apparel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the trial and test data sets.\n",
    "# Random sample indexes\n",
    "train_index <- sample(1:nrow(df_FRANCE_Apparel), 0.8 * nrow(df_FRANCE_Apparel))\n",
    "test_index <- setdiff(1:nrow(df_FRANCE_Apparel), train_index)\n",
    "\n",
    "# Build X_train, y_train, X_test, y_test\n",
    "df_train <- df_FRANCE_Apparel[train_index,]\n",
    "df_test <- df_FRANCE_Apparel[test_index,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Object for KM curves\n",
    "gg_dta <- gg_survival(interval = \"churn_date\", censor = \"Churn\", by = \"labels\" ,data=df_train, conf.int = 0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R77Xz8Tmt+4qgDzIdgBANqC5ojTHHFBdIhzGlYLYFqcPAEAAGAS7LEDAEQv9egR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\nDgAgEMIOACAQwg4AIBD/D4DZ4Cx54eVCAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(gg_survival(interval = \"churn_date\", censor = \"Churn\", by = \"labels\",\n",
    "data = df_train), error = \"none\") +\n",
    "labs(y = \"Survival Probability\", x = \"churn_date\",\n",
    "color = \"labels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the trial and test data sets.\n",
    "# Random sample indexes\n",
    "train_index <- sample(1:nrow(df_JAPAN_Apparel), 0.8 * nrow(df_JAPAN_Apparel))\n",
    "test_index <- setdiff(1:nrow(df_JAPAN_Apparel), train_index)\n",
    "\n",
    "# Build X_train, y_train, X_test, y_test\n",
    "df_train <- df_JAPAN_Apparel[train_index,]\n",
    "df_test <- df_JAPAN_Apparel[test_index,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Object for KM curves\n",
    "gg_dta <- gg_survival(interval = \"churn_date\", censor = \"Churn\", by = \"labels\" ,data=df_train, conf.int = 0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAA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\nQCCEHQBAIIQdAEAg/j+29u/nysa87gAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(gg_survival(interval = \"churn_date\", censor = \"Churn\", by = \"labels\",\n",
    "data = df_train), error = \"none\") +\n",
    "labs(y = \"Survival Probability\", x = \"churn_date\",\n",
    "color = \"labels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the trial and test data sets.\n",
    "# Random sample indexes\n",
    "train_index <- sample(1:nrow(df_US_Apparel), 0.8 * nrow(df_US_Apparel))\n",
    "test_index <- setdiff(1:nrow(df_US_Apparel), train_index)\n",
    "\n",
    "# Build X_train, y_train, X_test, y_test\n",
    "df_train <- df_US_Apparel[train_index,]\n",
    "df_test <- df_US_Apparel[test_index,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Object for KM curves\n",
    "gg_dta <- gg_survival(interval = \"churn_date\", censor = \"Churn\", by = \"labels\" ,data=df_train, conf.int = 0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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58+fXpLy507d4bD4b59+7b2zc3N/eSTT0Kh0IwZM8Lh8IsvvpiVlXXKKadYXTMA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\nQBIEOwAAAEkQ7AAAACTxf4/w9cZMkIk6AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(gg_survival(interval = \"churn_date\", censor = \"Churn\", by = \"labels\",\n",
    "data = df_train), error = \"none\") +\n",
    "labs(y = \"Churn rate\", x = \"churn_date\",\n",
    "color = \"labels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}