Starbucks_Capstone_notebook.ipynb

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   "source": [
    "# Starbucks Capstone Challenge![th.jpg](attachment:th.jpg)\n",
    "\n",
    "### Introduction\n",
    "\n",
    "This data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. Once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free). Some users might not receive any offer during certain weeks. \n",
    "\n",
    "Not all users receive the same offer, and that is the challenge to solve with this data set.\n",
    "\n",
    "Your task is to combine transaction, demographic and offer data to determine which demographic groups respond best to which offer type. This data set is a simplified version of the real Starbucks app because the underlying simulator only has one product whereas Starbucks actually sells dozens of products.\n",
    "\n",
    "Every offer has a validity period before the offer expires. As an example, a BOGO offer might be valid for only 5 days. You'll see in the data set that informational offers have a validity period even though these ads are merely providing information about a product; for example, if an informational offer has 7 days of validity, you can assume the customer is feeling the influence of the offer for 7 days after receiving the advertisement.\n",
    "\n",
    "You'll be given transactional data showing user purchases made on the app including the timestamp of purchase and the amount of money spent on a purchase. This transactional data also has a record for each offer that a user receives as well as a record for when a user actually views the offer. There are also records for when a user completes an offer. \n",
    "\n",
    "Keep in mind as well that someone using the app might make a purchase through the app without having received an offer or seen an offer.\n",
    "\n",
    "# ستاربكس كابستون التحدي\n",
    "### مقدمة\n",
    "\n",
    "تحتوي مجموعة البيانات هذه على بيانات محاكاة تحاكي سلوك العميل على تطبيق ستاربكس لمكافآت الجوّال. مرة كل بضعة أيام، ترسل ستاربكس عرضًا لمستخدمي تطبيق الجوّال. يمكن أن يكون العرض مجرد إعلان عن مشروب أو عرض فعلي مثل الخصم أو BOGO (شراء واحد الحصول على واحد مجانا). قد لا يتلقى بعض المستخدمين أي عرض خلال أسابيع معينة.\n",
    "\n",
    "لا يتلقى جميع المستخدمين نفس العرض ، وهذا هو التحدي الذي يجب حله مع مجموعة البيانات هذه.\n",
    "\n",
    "مهمتك هي الجمع بين البيانات المعاملة والديموغرافية والعرض لتحديد المجموعات الديموغرافية التي تستجيب بشكل أفضل لنوع العرض. مجموعة البيانات هذه هي نسخة مبسطة من تطبيق ستاربكس الحقيقي لأن المحاكية الأساسية لديها منتج واحد فقط في حين تبيع ستاربكس بالفعل عشرات المنتجات.\n",
    "\n",
    "كل عرض له فترة صلاحية قبل انتهاء العرض. على سبيل المثال، قد يكون عرض BOGO صالحًا لمدة 5 أيام فقط. سترى في مجموعة البيانات أن العروض المعلوماتية لها فترة صلاحية على الرغم من أن هذه الإعلانات تقدم فقط معلومات حول منتج ما؛ على سبيل المثال، إذا كان العرض الإعلامي له 7 أيام من الصلاحية، يمكنك أن تفترض أن العميل يشعر بتأثير العرض لمدة 7 أيام بعد تلقي الإعلان.\n",
    "\n",
    "سيتم إعطائك بيانات معاملات توضح عمليات الشراء التي قام بها المستخدم على التطبيق بما في ذلك الطابع الزمني للشراء ومقدار المال الذي يتم إنفاقه على عملية الشراء. تحتوي بيانات المعاملات هذه أيضاً على سجل لكل عرض يتلقاه المستخدم بالإضافة إلى سجل عند عرض العرض بالفعل. هناك أيضا سجلات عندما يكمل المستخدم عرضا.\n",
    "\n",
    "ضع في اعتبارك أيضًا أن شخصًا يستخدم التطبيق قد يقوم بإجراء عملية شراء من خلال التطبيق دون تلقي عرض أو رؤية عرض.\n",
    "\n",
    "### Example\n",
    "\n",
    "To give an example, a user could receive a discount offer buy 10 dollars get 2 off on Monday. The offer is valid for 10 days from receipt. If the customer accumulates at least 10 dollars in purchases during the validity period, the customer completes the offer.\n",
    "\n",
    "However, there are a few things to watch out for in this data set. Customers do not opt into the offers that they receive; in other words, a user can receive an offer, never actually view the offer, and still complete the offer. For example, a user might receive the \"buy 10 dollars get 2 dollars off offer\", but the user never opens the offer during the 10 day validity period. The customer spends 15 dollars during those ten days. There will be an offer completion record in the data set; however, the customer was not influenced by the offer because the customer never viewed the offer.\n",
    "\n",
    "### مثال\n",
    "\n",
    "على سبيل المثال ، يمكن للمستخدم الحصول على عرض خصم شراء 10 دولار الحصول على 2 قبالة يوم الاثنين. يسري العرض لمدة 10 أيام من تاريخ استلامه. إذا كان العميل يجمع ما لا يقل عن 10 دولار في المشتريات خلال فترة الصلاحية، العميل يكمل العرض.\n",
    "\n",
    "ومع ذلك ، هناك بعض الأشياء التي يجب مراقبتها في مجموعة البيانات هذه. الزبائن لا يختارون في العروض التي يتلقونها. وبعبارة أخرى، يمكن للمستخدم تلقي عرض، لا عرض العرض في الواقع، ولا يزال إكمال العرض. على سبيل المثال ، قد يحصل المستخدم على \"شراء 10 دولار الحصول على 2 دولار من العرض\" ، ولكن المستخدم لا يفتح العرض خلال فترة الصلاحية 10 أيام. ينفق العميل 15 دولار خلال تلك الأيام العشرة. وسيكون هناك سجل إنجاز العرض في مجموعة البيانات؛ ومع ذلك، لم يتأثر العميل بالعرض لأن العميل لم ينظر إلى العرض.\n",
    "\n",
    "\n",
    "### Cleaning\n",
    "\n",
    "This makes data cleaning especially important and tricky.\n",
    "\n",
    "You'll also want to take into account that some demographic groups will make purchases even if they don't receive an offer. From a business perspective, if a customer is going to make a 10 dollar purchase without an offer anyway, you wouldn't want to send a buy 10 dollars get 2 dollars off offer. You'll want to try to assess what a certain demographic group will buy when not receiving any offers.\n",
    "\n",
    "\n",
    "### تنظيف\n",
    "\n",
    "وهذا يجعل تنظيف البيانات مهمًا وصعبًا بشكل خاص.\n",
    "\n",
    "كما أنك تريد أن تأخذ في الاعتبار أن بعض المجموعات السكانية ستقوم بعمليات شراء حتى إذا لم تتلق عرضًا. من وجهة نظر تجارية ، إذا كان العميل هو الذهاب الى جعل شراء 10 دولار دون عرض على أي حال ، فإنك لا تريد أن ترسل شراء 10 دولار الحصول على 2 دولار قبالة العرض. ستحتاج إلى محاولة تقييم ما ستشتريه مجموعة سكانية معينة عند عدم تلقي أي عروض.\n",
    "\n",
    "\n",
    "### Final Advice\n",
    "\n",
    "Because this is a capstone project, you are free to analyze the data any way you see fit. For example, you could build a machine learning model that predicts how much someone will spend based on demographics and offer type. Or you could build a model that predicts whether or not someone will respond to an offer. Or, you don't need to build a machine learning model at all. You could develop a set of heuristics that determine what offer you should send to each customer (i.e., 75 percent of women customers who were 35 years old responded to offer A vs 40 percent from the same demographic to offer B, so send offer A).\n",
    "\n",
    "\n",
    "### نصيحة نهائية\n",
    "\n",
    "لأن هذا هو مشروع capstone ، أنت حر في تحليل البيانات بأي طريقة تراها مناسبة. على سبيل المثال، يمكنك إنشاء نموذج تعلم آلي يتنبأ بكمية إنفاق شخص ما استنادًا إلى التركيبة السكانية ونوع العرض. أو يمكنك بناء نموذج يتنبأ بما إذا كان شخص ما سيستجيب لعرض أم لا. أو، لا تحتاج إلى بناء نموذج التعلم الآلي على الإطلاق. يمكنك تطوير مجموعة من الاستدلالات التي تحدد العرض الذي يجب إرساله إلى كل عميل (أي أن 75 في المائة من العملاء من النساء اللواتي كن في الخامسة والثلاثين من العمر استجابن لتقديم A مقابل 40 في المائة من نفس العرض السكاني لتقديم B ، لذا أرسل العرض A)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Sets\n",
    "\n",
    "The data is contained in three files:\n",
    "\n",
    "* portfolio.json - containing offer ids and meta data about each offer (duration, type, etc.)\n",
    "* profile.json - demographic data for each customer\n",
    "* transcript.json - records for transactions, offers received, offers viewed, and offers completed\n",
    "\n",
    "Here is the schema and explanation of each variable in the files:\n",
    "\n",
    "**portfolio.json**\n",
    "* id (string) - offer id\n",
    "* offer_type (string) - type of offer ie BOGO, discount, informational\n",
    "* difficulty (int) - minimum required spend to complete an offer\n",
    "* reward (int) - reward given for completing an offer\n",
    "* duration (int) - time for offer to be open, in days\n",
    "* channels (list of strings)\n",
    "\n",
    "**profile.json**\n",
    "* age (int) - age of the customer \n",
    "* became_member_on (int) - date when customer created an app account\n",
    "* gender (str) - gender of the customer (note some entries contain 'O' for other rather than M or F)\n",
    "* id (str) - customer id\n",
    "* income (float) - customer's income\n",
    "\n",
    "**transcript.json**\n",
    "* event (str) - record description (ie transaction, offer received, offer viewed, etc.)\n",
    "* person (str) - customer id\n",
    "* time (int) - time in hours since start of test. The data begins at time t=0\n",
    "* value - (dict of strings) - either an offer id or transaction amount depending on the record\n",
    "\n",
    "**Note:** If you are using the workspace, you will need to go to the terminal and run the command `conda update pandas` before reading in the files. This is because the version of pandas in the workspace cannot read in the transcript.json file correctly, but the newest version of pandas can. You can access the termnal from the orange icon in the top left of this notebook.  \n",
    "\n",
    "You can see how to access the terminal and how the install works using the two images below.  First you need to access the terminal:\n",
    "\n",
    "<img src=\"pic1.png\"/>\n",
    "\n",
    "Then you will want to run the above command:\n",
    "\n",
    "<img src=\"pic2.png\"/>\n",
    "\n",
    "Finally, when you enter back into the notebook (use the jupyter icon again), you should be able to run the below cell without any errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Library\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import math\n",
    "import json\n",
    "% matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    " \n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the json files\n",
    "portfolio = pd.read_json('data/portfolio.json', orient='records', lines=True)\n",
    "profile = pd.read_json('data/profile.json', orient='records', lines=True)\n",
    "transcript = pd.read_json('data/transcript.json', orient='records', lines=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###   Data Exploration and Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>channels</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>id</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>reward</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>bogo</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>bogo</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>informational</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>bogo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[web, email]</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>discount</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>discount</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>discount</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[email, mobile, social]</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>informational</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>bogo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>discount</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       channels  difficulty  duration  \\\n",
       "0       [email, mobile, social]          10         7   \n",
       "1  [web, email, mobile, social]          10         5   \n",
       "2          [web, email, mobile]           0         4   \n",
       "3          [web, email, mobile]           5         7   \n",
       "4                  [web, email]          20        10   \n",
       "5  [web, email, mobile, social]           7         7   \n",
       "6  [web, email, mobile, social]          10        10   \n",
       "7       [email, mobile, social]           0         3   \n",
       "8  [web, email, mobile, social]           5         5   \n",
       "9          [web, email, mobile]          10         7   \n",
       "\n",
       "                                 id     offer_type  reward  \n",
       "0  ae264e3637204a6fb9bb56bc8210ddfd           bogo      10  \n",
       "1  4d5c57ea9a6940dd891ad53e9dbe8da0           bogo      10  \n",
       "2  3f207df678b143eea3cee63160fa8bed  informational       0  \n",
       "3  9b98b8c7a33c4b65b9aebfe6a799e6d9           bogo       5  \n",
       "4  0b1e1539f2cc45b7b9fa7c272da2e1d7       discount       5  \n",
       "5  2298d6c36e964ae4a3e7e9706d1fb8c2       discount       3  \n",
       "6  fafdcd668e3743c1bb461111dcafc2a4       discount       2  \n",
       "7  5a8bc65990b245e5a138643cd4eb9837  informational       0  \n",
       "8  f19421c1d4aa40978ebb69ca19b0e20d           bogo       5  \n",
       "9  2906b810c7d4411798c6938adc9daaa5       discount       2  "
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.head(10)\n",
    "#   id         (string) - offer id\n",
    "#   offer_type (string) - type of offer ie BOGO, discount, informational\n",
    "#   difficulty (int)    - minimum required spend to complete an offer\n",
    "#   reward     (int)    - reward given for completing an offer\n",
    "#   duration   (int)    - time for offer to be open, in days\n",
    "#   channels   (list of strings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 6 columns):\n",
      "channels      10 non-null object\n",
      "difficulty    10 non-null int64\n",
      "duration      10 non-null int64\n",
      "id            10 non-null object\n",
      "offer_type    10 non-null object\n",
      "reward        10 non-null int64\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 560.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "portfolio.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bogo             4\n",
       "discount         4\n",
       "informational    2\n",
       "Name: offer_type, dtype: int64"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the count of each offer type\n",
    "portfolio.offer_type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>118</td>\n",
       "      <td>20180712</td>\n",
       "      <td>None</td>\n",
       "      <td>38fe809add3b4fcf9315a9694bb96ff5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75</td>\n",
       "      <td>20170509</td>\n",
       "      <td>F</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>118</td>\n",
       "      <td>20170804</td>\n",
       "      <td>None</td>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  became_member_on gender                                id    income\n",
       "0  118          20170212   None  68be06ca386d4c31939f3a4f0e3dd783       NaN\n",
       "1   55          20170715      F  0610b486422d4921ae7d2bf64640c50b  112000.0\n",
       "2  118          20180712   None  38fe809add3b4fcf9315a9694bb96ff5       NaN\n",
       "3   75          20170509      F  78afa995795e4d85b5d9ceeca43f5fef  100000.0\n",
       "4  118          20170804   None  a03223e636434f42ac4c3df47e8bac43       NaN"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.head()\n",
    "#   age              (int)   - age of the customer\n",
    "#   became_member_on (int)   - date when customer created an app account\n",
    "#   gender           (str)   - gender of the customer (note some entries contain 'O' for other rather than M or F)\n",
    "#   id               (str)   - customer id\n",
    "#   income           (float) - customer's income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17000 entries, 0 to 16999\n",
      "Data columns (total 5 columns):\n",
      "age                 17000 non-null int64\n",
      "became_member_on    17000 non-null int64\n",
      "gender              14825 non-null object\n",
      "id                  17000 non-null object\n",
      "income              14825 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 664.1+ KB\n"
     ]
    }
   ],
   "source": [
    "profile.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17000.000000</td>\n",
       "      <td>1.700000e+04</td>\n",
       "      <td>14825.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>62.531412</td>\n",
       "      <td>2.016703e+07</td>\n",
       "      <td>65404.991568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>26.738580</td>\n",
       "      <td>1.167750e+04</td>\n",
       "      <td>21598.299410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>2.013073e+07</td>\n",
       "      <td>30000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>2.016053e+07</td>\n",
       "      <td>49000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>2.017080e+07</td>\n",
       "      <td>64000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>73.000000</td>\n",
       "      <td>2.017123e+07</td>\n",
       "      <td>80000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>118.000000</td>\n",
       "      <td>2.018073e+07</td>\n",
       "      <td>120000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age  became_member_on         income\n",
       "count  17000.000000      1.700000e+04   14825.000000\n",
       "mean      62.531412      2.016703e+07   65404.991568\n",
       "std       26.738580      1.167750e+04   21598.299410\n",
       "min       18.000000      2.013073e+07   30000.000000\n",
       "25%       45.000000      2.016053e+07   49000.000000\n",
       "50%       58.000000      2.017080e+07   64000.000000\n",
       "75%       73.000000      2.017123e+07   80000.000000\n",
       "max      118.000000      2.018073e+07  120000.000000"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f1b175208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "profile.hist(figsize=(10,10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([None, 'F', 'M', 'O'], dtype=object)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile['gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    8484\n",
       "F    6129\n",
       "O     212\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender=profile.gender.value_counts()\n",
    "gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Tqaqq6vS1RUTk+OhUWCQnJ/OrX/2Kl19+mbVr13LzzTeTnJzcqQs8++yzDB482P56+fLlTJ48mdLSUqKiosjPzwcgLy+PqKgoXn31VSZPnszy5cuB4A2AxcXFFBcXs2rVKu67775DdusTEZHQ6zAsWltb2b9/P3PnzsXv9/Pqq69SUlKCz+fr1D7YgUCA1157zd4S1bIsNm3aRGpqKhDc47usrAyA8vJyMjIyAEhNTWXjxo1YlkVZWRlpaWn06tWL+Ph4EhIS2LJlyzE9aREROTIdrlksX76cQYMGkZWVxfXXX8/1118PBEcLK1ascFyzWLJkCXPnzuXLL78EoL6+nqioKHvtw+v1YpomAKZpEhcXFyzK5SIyMpL6+npM02T48OH2OWNjY+1j2tO/f19crogOHyPh0dG2jXLiUX8F6XVwCIvKykpuv/32w9pvuOEGrr766g7D4h//+AcxMTFcdNFFvPHGG+0+7pv1j7a2AjcMo932jtTX7+vw+xI+PWUv45OF+kt7cH+jw7A45ZRTiIg4/P/QTznlFMc/2O+88w7l5eVUVlZy8OBB9u7dy/33309TUxMtLS24XC4CgQAejwcIjjK++OILvF4vLS0t7NmzB7fbjdfrJRAI2Oc1TdM+RkREukaHaxbNzc3s37//sPYvv/zSfhdTe+bMmUNlZSXl5eU8/PDDjB49mt/97neMGjWKkpISAAoKCvD5fAD4fD4KCgoAKCkpYfTo0RiGgc/no7i4mObmZmpra6mpqeGSSy45qicrIiJHp8OwmDBhAnfeeSd79+612/bs2cOCBQsYN27cUV1w7ty5PPXUU/j9fhoaGsjKygIgMzOThoYG/H4/Tz31lD39NXToUMaPH8+ECRO46aabuPfee9sc7YiISOgYVluLAv+npaWFefPmUVZWxtlnnw1ATU0NPp+PBx54wF6oPtEc6/zi1GXlx6kS+a4n5/mO+znVX6ETiv7qbrRmEdThX3uXy8Xy5cv57LPP2LZtG5ZlMWzYMBISEo57kSIicuLq1NAgISFBASEi0oMd1U55IiLSsygsRETEkcJCREQcKSxERMSRwkJERBwpLERExJHCQkREHCksRETEkcJCREQcKSxERMSRwkJERBwpLERExJHCQkREHIUsLA4ePEhmZiZXX301aWlp/P73vwegtraWrKwsUlJSyM7Otnfca25uJjs7G7/fT1ZWFjt37rTP9cQTT+D3+0lNTaWqqipUJYuISDtCFha9evXimWee4aWXXqKwsJCqqio2b97M8uXLmTx5MqWlpURFRZGfnw9AXl4eUVFRvPrqq0yePJnly5cDsH37doqLiykuLmbVqlXcd999tLa2hqpsERFpQ8jCwjAMTj/9dCC4415LSwuGYbBp0yZSU1MByMjIoKysDIDy8nIyMjIASE1NZePGjViWRVlZGWlpafTq1Yv4+HgSEhLYsmVLqMoWEZE2hHTNorW1lUmTJnHppZdy6aWXEh8fT1RUlL0dq9frxTRNAEzTJC4uDgju0BcZGUl9fT2maeL1eu1zxsbG2seIiEjXCOkm2hERERQVFdHU1MTMmTP59NNPD3uMYRgAtLUVuGEY7bZ3pH//vrhcEUdZtYRSR3v8yolH/RWk1yHEYfGNqKgoRo0axebNm2lqaqKlpQWXy0UgEMDj8QDBUcYXX3yB1+ulpaWFPXv24Ha78Xq9BAIB+1ymadrHtKe+fl9In48cvZ6y8f3JQv0VDIqe8jp0FIohm4bavXs3TU1NABw4cIANGzYwePBgRo0aRUlJCQAFBQX4fD4AfD4fBQUFAJSUlDB69GgMw8Dn81FcXExzczO1tbXU1NRwySWXhKpsERFpQ8hGFnV1dcybN4/W1lYsy2LcuHFcccUVDBkyhFmzZvHII49wwQUXkJWVBUBmZiZz587F7/cTHR3NihUrABg6dCjjx49nwoQJREREcO+99xIRoSkmEZGuZFhtLQp0c8c6ZJy6rPw4VSLf9eQ833E/p/ordELRX92NpqGCdAe3iIg4UliIiIgjhYWIiDhSWIiIiCOFhYiIOFJYiIiII4WFiIg4UliIiIgjhYWIiDhSWIiIiCOFhYiIOFJYiIiII4WFiIg4UliIiIgjhYWIiDhSWIiIiKOQhcUXX3zBjTfeyPjx40lLS+OZZ54BoKGhgSlTppCSksKUKVNobGwEwLIsFi9ejN/vZ+LEiWzdutU+V0FBASkpKaSkpNhbr4qISNcJWVhEREQwb948XnnlFV544QX++te/sn37dnJzc0lMTKS0tJTExERyc3MBqKyspKamhtLSUhYtWsTChQuBYLjk5OSwevVq8vLyyMnJsQNGRES6RsjCwuPxMGzYMAD69evHoEGDME2TsrIy0tPTAUhPT2f9+vUAdrthGIwYMYKmpibq6uqorq4mKSkJt9tNdHQ0SUlJVFVVhapsERFpg6srLrJz507ef/99hg8fzq5du/B4PEAwUHbv3g2AaZp4vV77GK/Xi2mah7XHxsZimmaH1+vfvy8uV0QInokcq472+JUTj/orSK9DF4TFl19+yW233cZdd91Fv3792n2cZVmHtRmG0W57R+rr9x15odIlesrG9ycL9VcwKHrK69BRKIb03VBfffUVt912GxMnTiQlJQWAAQMGUFdXB0BdXR0xMTFAcCQRCATsYwOBAB6P57B20zTtkYmIiHSNkIWFZVncfffdDBo0iClTptjtPp+PwsJCAAoLCxk7duwh7ZZlsXnzZiIjI/F4PCQnJ1NdXU1jYyONjY1UV1eTnJwcqrJFRKQNIZuG+uc//0lRURHnnnsukyZNAmD27NlMmzaN7Oxs8vPziYuLY+XKlQCMGTOGiooK/H4/ffr0YcmSJQC43W5mzJhBZmYmADNnzsTtdoeqbBERaYNhtbUo0M0d6/zi1GXlx6kS+a4n5/mO+znVX6ETiv7qbrRmEaQ7uEVExJHCQkREHCksRETEkcJCREQcKSxERMSRwkJERBwpLERExJHCQkREHCksRETEkcJCREQcKSxERMSRwkJERBwpLERExJHCQkREHCksRETEUcjCYv78+SQmJnLVVVfZbQ0NDUyZMoWUlBSmTJlCY2MjENxVb/Hixfj9fiZOnMjWrVvtYwoKCkhJSSElJYWCgoJQlSsiIh0IWVhcc801rFq16pC23NxcEhMTKS0tJTExkdzcXAAqKyupqamhtLSURYsWsXDhQiAYLjk5OaxevZq8vDxycnLsgBERka4TsrAYOXIk0dHRh7SVlZWRnp4OQHp6OuvXrz+k3TAMRowYQVNTE3V1dVRXV5OUlITb7SY6OpqkpCSqqqpCVbKIiLQjZHtwt2XXrl14PB4APB4Pu3fvBsA0Tbxer/04r9eLaZqHtcfGxmKapuN1+vfvi8sVcZyrl+Oho20b5cSj/grS69DFYdGetrYBNwyj3XYn9fX7jktdcvz1lL2MTxbqL+3B/Y0ufTfUgAEDqKurA6Curo6YmBggOJIIBAL24wKBAB6P57B20zTtkYmIiHSdLg0Ln89HYWEhAIWFhYwdO/aQdsuy2Lx5M5GRkXg8HpKTk6murqaxsZHGxkaqq6tJTk7uypJFRIQQTkPNnj2bN998k/r6ei6//HJuvfVWpk2bRnZ2Nvn5+cTFxbFy5UoAxowZQ0VFBX6/nz59+rBkyRIA3G43M2bMIDMzE4CZM2fidrtDVbKIiLTDsNpaGOjmjnV+ceqy8uNUiXzXk/N8x/2c6q/QCUV/dTdaswjSHdwiIuJIYSEiIo4UFiIi4khhISIijhQWIiLiSGEhIiKOFBYiIuJIYSEiIo4UFiIi4khhISIijhQWIiLiSGEhIiKOFBYiIuJIYSEiIo5OiG1VRaRn0cfKh06oPlZeIwsREXHUbcKisrKS1NRU/H4/ubm54S5HRKRH6RZh0draym9/+1tWrVpFcXExa9euZfv27eEuS0Skx+gWYbFlyxYSEhKIj4+nV69epKWlUVZWFu6yRER6jG6xwG2aJl6v1/46NjaWLVu2tPv4jvaR7YyXfzfpmI6XrqX+6n7UZ91PtxhZWJZ1WJthGGGoRESkZ+oWYeH1egkEAvbXpmni8XjCWJGISM/SLcLi4osvpqamhtraWpqbmykuLsbnC817iUVE5HDdYs3C5XJx7733ctNNN9Ha2sq1117L0KFDw12WiEiPYVhtLQiIiIh8S7eYhhIRkfBSWIiIiCOFRTd1wQUXMGnSJPvfzp07w12StOO8885j7ty59tctLS2MHj2am2++OYxViZNAIMD06dNJSUnhyiuvZPHixTQ3N4e7rLBRWHRTp512GkVFRfa/s846K9wlSTv69u3Lxx9/zIEDBwB4/fXXiY2NDXNV0hHLsrjlllu48sorKS0tpaSkhH379rFixYpwlxY2CguRLnD55Zfz2muvAVBcXExaWlp4C5IObdq0id69e3PttdcCEBERwV133cWaNWvYv39/mKsLD4VFN3XgwAF7CmrmzJnhLkccTJgwgb///e8cPHiQDz/8kOHDh4e7JOnAxx9/zLBhww5p69evH3FxcXz22Wdhqiq8usV9FnK4b6ahpHs4//zz2blzJ2vXrmXMmDHhLkccWJbV5kcKtdfeE2hkIdJFfD4fDz74oKaguoGhQ4fy73//+5C2vXv3EggE+P73vx+mqsJLYSHSRTIzM5kxYwbnnXdeuEsRB4mJiezfv5/CwkIguKfOsmXLyMjIoE+fPmGuLjwUFiJdxOv18otf/CLcZUgnGIbBH//4R9atW0dKSgqpqan07t2b2bNnh7u0sNHHfYiIiCONLERExJHCQkREHCksRETEkcJCREQcKSxERMSR7uCWHuGrr77i8ccfZ+3atbhcLlwuFwkJCdx2220MGTLkuF1n3rx5XHTRRdxwww1HfY7HHnuMdevWAbBjxw5iYmLo168fAI8//jhxcXHHpVaRI6GwkB5h/vz5HDhwgLy8PKKiorAsi3Xr1vHJJ58c17A4Gi0tLbhc//9Xcfr06UyfPh2AG2+8kalTp3LFFVeEqzwRQGEhPUBNTQ3r16+noqKCqKgoIHjT1fjx4+3HNDc3s2LFCt566y2++uorzj33XBYuXMjpp5/OvHnz6NWrFzU1NQQCAUaMGMEDDzyAYRiYpskdd9xBfX09Z511Fq2trfY59+7dy9KlS/nwww85ePAgo0aNYv78+URERHDjjTfygx/8gHfffZfevXuTm5vbqefy8ssv88orr/Doo48CcPDgQXw+H2vWrKGyspJ169bRp08fe0Ty4IMP4vF4sCyLJ554gvXr19PS0kJcXByLFy9mwIABx/GVlpOZ1izkpLdt2zYSEhKIjo5u9zGrVq0iMjKS/Px8ioqK8Hg8h/wB//jjj/nTn/7E2rVr2bp1Kxs2bABg8eLFjBw5kpdeeon58+fz5ptv2scsXbqUkSNH2ufcvXs3L774ov39jz76iD//+c+dDgqAcePGsXXrVv7zn/8AsHbtWn70ox/Z+2O8/fbbzJ07l5deeokRI0awdOlSAAoKCggEAqxevZrCwkIuvfRSHnzwwU5fV0QjC+lxtm/fzpw5czhw4ACXXXYZCxYsoLy8nL1791JSUgIERxrnn3++fcyVV15J7969AbjwwgvZsWMHSUlJvPHGGyxYsACA+Ph4EhMT7WPKy8vZsmULTz31FBD8WPlvb3o0ceLEQ6afOuPUU08lKyuLF154gVmzZvH8889z55132t8fOXIkCQkJAGRlZZGZmWnX8v7775ORkQEEP+vI7XYf0bWlZ1NYyEnvwgsv5LPPPqOpqYmoqCiGDBlCUVERzz33nP3JopZl8Zvf/OaQP/bf9k1QQHAjnG9PN7XHsiweffRR4uPj2/x+3759j+LZwE9/+lMyMzO57LLL7Omt9q7/zcdpW5bFrbfeSnp6+lFdU0TTUHLSO/vssxk7diwLFixgz549dvu+ffvs//b5fDz99NP21qd79+7lk08+cTz36NGj7aml2tpaNm7ceMg5c3Nz7WDZvXs3tbW1x/x8zjjjDEaOHMmcOXP42c9+dsj33n77bfsaBQUFdpD4fD6ef/55mpqagOBaxwcffHDMtUjPoZGF9AhLly7l0UcfJTMzE5eo1FC/AAAA6UlEQVTLRVRUFB6Ph2nTpgEwbdo0cnJyyMzMxDAMDMPglltuYfDgwR2e9+677+aOO+5g3bp1nHPOOSQlJdnfu+uuu3jooYeYNGkShmFw6qmnctddd7U70jgSWVlZlJWVMWnSpEPaR40axcMPP8wnn3xiL3ADXHvttTQ0NPDzn/8cCI40brjhhkOm2kQ6ok+dFemG/vCHP9DY2GivlwDk5eWxYcMGVqxYEcbK5GSlkYVIN9La2srEiRM59dRTefLJJ8NdjvQgGlmIiIgjLXCLiIgjhYWIiDhSWIiIiCOFhYiIOFJYiIiIo/8H9mPpuY9LofsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f1b16d438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "profile_gender_counts = profile.gender.value_counts()\n",
    "x = ['M','F','O']\n",
    "data = profile_gender_counts\n",
    "plt.bar(x,height = data);\n",
    "xlocs, xlabs = plt.xticks()\n",
    "\n",
    "plt.xlabel('Gender Type');\n",
    "plt.ylabel('Count');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                    0\n",
       "became_member_on       0\n",
       "gender              2175\n",
       "id                     0\n",
       "income              2175\n",
       "dtype: int64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# is any missing values across columns\n",
    "profile.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### transcript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event</th>\n",
       "      <th>person</th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>offer received</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>offer received</td>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>offer received</td>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '2906b810c7d4411798c6938adc9daaa5'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>offer received</td>\n",
       "      <td>8ec6ce2a7e7949b1bf142def7d0e0586</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>offer received</td>\n",
       "      <td>68617ca6246f4fbc85e91a2a49552598</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            event                            person  time  \\\n",
       "0  offer received  78afa995795e4d85b5d9ceeca43f5fef     0   \n",
       "1  offer received  a03223e636434f42ac4c3df47e8bac43     0   \n",
       "2  offer received  e2127556f4f64592b11af22de27a7932     0   \n",
       "3  offer received  8ec6ce2a7e7949b1bf142def7d0e0586     0   \n",
       "4  offer received  68617ca6246f4fbc85e91a2a49552598     0   \n",
       "\n",
       "                                              value  \n",
       "0  {'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}  \n",
       "1  {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}  \n",
       "2  {'offer id': '2906b810c7d4411798c6938adc9daaa5'}  \n",
       "3  {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}  \n",
       "4  {'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}  "
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript.head()\n",
    "#   event  (str)             - record description (ie transaction, offer received, offer viewed, etc.)\n",
    "#   person (str)             - customer id\n",
    "#   time   (int)             - time in hours since start of test. The data begins at time t=0\n",
    "#   value  (dict of strings) - either an offer id or transaction amount depending on the record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 306534 entries, 0 to 306533\n",
      "Data columns (total 4 columns):\n",
      "event     306534 non-null object\n",
      "person    306534 non-null object\n",
      "time      306534 non-null int64\n",
      "value     306534 non-null object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 9.4+ MB\n"
     ]
    }
   ],
   "source": [
    "transcript.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f1b07b400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking the count of each event type\n",
    "x=transcript.event\n",
    "x.hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>306534.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.382940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>200.326314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>186.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>408.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>528.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                time\n",
       "count  306534.000000\n",
       "mean      366.382940\n",
       "std       200.326314\n",
       "min         0.000000\n",
       "25%       186.000000\n",
       "50%       408.000000\n",
       "75%       528.000000\n",
       "max       714.000000"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f1b029be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "transcript.hist(figsize=(10,10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
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       "      <td>{'amount': 19.51}</td>\n",
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       "      <th>12671</th>\n",
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       "                                 person                           value\n",
       "12654  02c083884c7d45b39cc68e1314fec56c  {'amount': 0.8300000000000001}\n",
       "12657  9fa9ae8f57894cc9a3b8a9bbe0fc1b2f               {'amount': 34.56}\n",
       "12659  54890f68699049c2a04d415abc25e717               {'amount': 13.23}\n",
       "12670  b2f1cd155b864803ad8334cdf13c4bd2               {'amount': 19.51}\n",
       "12671  fe97aa22dd3e48c8b143116a8403dd52               {'amount': 18.97}"
      ]
     },
     "execution_count": 190,
     "metadata": {},
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    }
   ],
   "source": [
    "transcript[['person','value']][transcript['event']=='transaction'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}</td>\n",
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       "                                              value\n",
       "0  {'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}\n",
       "1  {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}\n",
       "2  {'offer id': '2906b810c7d4411798c6938adc9daaa5'}\n",
       "3  {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}\n",
       "4  {'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}"
      ]
     },
     "execution_count": 191,
     "metadata": {},
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    }
   ],
   "source": [
    "transcript[['value']][transcript['event']=='offer received'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
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       "      <td>{'offer id': 'f19421c1d4aa40978ebb69ca19b0e20d'}</td>\n",
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       "      <th>12651</th>\n",
       "      <td>{'offer id': '5a8bc65990b245e5a138643cd4eb9837'}</td>\n",
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       "      <th>12652</th>\n",
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       "      <th>12653</th>\n",
       "      <td>{'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}</td>\n",
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       "      <th>12655</th>\n",
       "      <td>{'offer id': '5a8bc65990b245e5a138643cd4eb9837'}</td>\n",
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       "                                                  value\n",
       "12650  {'offer id': 'f19421c1d4aa40978ebb69ca19b0e20d'}\n",
       "12651  {'offer id': '5a8bc65990b245e5a138643cd4eb9837'}\n",
       "12652  {'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}\n",
       "12653  {'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}\n",
       "12655  {'offer id': '5a8bc65990b245e5a138643cd4eb9837'}"
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     "execution_count": 192,
     "metadata": {},
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   ],
   "source": [
    "transcript[['value']][transcript['event']=='offer viewed'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
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       "                                                   value\n",
       "12658  {'offer_id': '2906b810c7d4411798c6938adc9daaa5...\n",
       "12672  {'offer_id': 'fafdcd668e3743c1bb461111dcafc2a4...\n",
       "12679  {'offer_id': '9b98b8c7a33c4b65b9aebfe6a799e6d9...\n",
       "12692  {'offer_id': 'ae264e3637204a6fb9bb56bc8210ddfd...\n",
       "12697  {'offer_id': '4d5c57ea9a6940dd891ad53e9dbe8da0..."
      ]
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     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript[['value']][transcript['event']=='offer completed'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Problem Statement and Metrics\n",
    "\n",
    "#### -What is the most gender and age  ?\n",
    "#### -What is the most registration members year ?\n",
    "#### -What is the most event in gender and age ?\n",
    "#### -What is the most viewed offer count in difficulty, duration, offer_type and reward ?\n",
    "#### -What is the most complet offer count in difficulty, duration, offer_type and reward ?\n",
    "#### -What is the most transcript in gender ,age and  registration members for each year ?\n",
    "#### -What is the most offer type each age ?\n",
    "\n",
    "### After Predict\n",
    "#### -What is the most gender and age in 2018 year ?\n",
    "#### -What is the most offer type of gender in 2018 year ?\n",
    "#### -What is the most offer type age in 2018 year ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing and Cleaning "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Portfolio \n",
    "-extract data into new columns from channels,offer_type column with iterable values\n",
    "\n",
    "-add offer name cloumn\n",
    "\n",
    "-rename id to offer_id\n",
    "\n",
    "\n",
    "#### profile\n",
    "-extract age is equal 118\n",
    "\n",
    "-division age 10\n",
    "\n",
    "-remove age up 90 years old \n",
    "\n",
    "-remve gender type 'O'\n",
    "\n",
    "-convert gender F to 0 and M To 1  and convert to int\n",
    "\n",
    "-convert became_member_on to date ,get year from date ,rename to reg_year and convert to int\n",
    "\n",
    "-division income 10000 and convert to int\n",
    "\n",
    "-rename id to person_id\n",
    "\n",
    "#### transcript\n",
    "-extract data into new columns from event\n",
    "\n",
    "-extract data into new columns from value to amount,offer_id and reward  \n",
    "\n",
    "-time is hours division 24 to convert dates\n",
    "\n",
    "-rename person to person_id\n",
    "\n",
    "-rename value_offer_id to offer_id\n",
    "\n",
    "#### general\n",
    "-convert all number to int\n",
    "\n",
    "-join all datasets\n",
    "\n",
    "-rename some columns\n",
    "\n",
    "-drop some columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert columns values to columns zero and one for Analysis and ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create function extract data into new columns from a column with iterable values like lists or dictionaries.\n",
    "def extract_from_iterable_col(df, old_col, drop_old_col=False, unique_values=None, unique_keys=None):\n",
    "    \n",
    "    '''\n",
    "    Extract data into new columns from a column with iterable values like lists or dictionaries.\n",
    "    If extracting from dictionaries, the new column names will be the dictionary keys and the\n",
    "    values will be dictionary values. If extracting from lists, one-hot encode the list values.\n",
    "    \n",
    "    Args:\n",
    "        (1) df (Pandas dataframe) - data containing a column with iterable values\n",
    "        (2) old_col (str) - name of column to extract data from\n",
    "        (3) drop_old_col (bool) - whether or not to drop the old column after extracting data from it\n",
    "        (4) unique_values (list[str]) - pass in unique values if data type of column is list *\n",
    "        (5) unique_values (list[str]) - pass in unique keys if data type of column is dictionary *\n",
    "        * Pass in arg (4) for a list column or arg (5) for a dictionary column\n",
    "        \n",
    "    Returns:\n",
    "        Same data with new columns extracted from the old column (Pandas dataframe).\n",
    "    '''\n",
    "    \n",
    "   \n",
    "    df = df.copy()\n",
    "    \n",
    "    if unique_keys is not None: # for dicts\n",
    "        for k in unique_keys:\n",
    "            if ' ' not in k: # to skip duplicate key with space\n",
    "                df[(old_col+'_'+k).replace(' ', '_')]= df[old_col].apply(lambda d: d[k] if k in d # get dict val\n",
    "                                          else (d[k.replace('_', ' ')] if k.replace('_', ' ') in d # for duplicate key with space\n",
    "                                                else np.NaN)) # val is nan if key is not in dict\n",
    "        \n",
    "    elif unique_values is not None: # for lists\n",
    "        for v in unique_values:\n",
    "            new_col = df[old_col].apply(lambda lst: int(v in lst)) # 1 if val is in list else 0\n",
    "            if np.var(new_col): # if new col is not constant\n",
    "                df[(old_col+'_'+v).replace(' ', '_')] = new_col # add new col\n",
    "\n",
    "    if drop_old_col:\n",
    "        df.drop(old_col, axis=1, inplace=True)\n",
    "        \n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>channels</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>id</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>reward</th>\n",
       "      <th>offer_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>bogo</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>10</td>\n",
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       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>bogo</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>informational</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>bogo</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[web, email]</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>discount</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>discount</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>discount</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[email, mobile, social]</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>informational</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>bogo</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>discount</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       channels  difficulty  duration  \\\n",
       "0       [email, mobile, social]          10         7   \n",
       "1  [web, email, mobile, social]          10         5   \n",
       "2          [web, email, mobile]           0         4   \n",
       "3          [web, email, mobile]           5         7   \n",
       "4                  [web, email]          20        10   \n",
       "5  [web, email, mobile, social]           7         7   \n",
       "6  [web, email, mobile, social]          10        10   \n",
       "7       [email, mobile, social]           0         3   \n",
       "8  [web, email, mobile, social]           5         5   \n",
       "9          [web, email, mobile]          10         7   \n",
       "\n",
       "                                 id     offer_type  reward  offer_name  \n",
       "0  ae264e3637204a6fb9bb56bc8210ddfd           bogo      10           0  \n",
       "1  4d5c57ea9a6940dd891ad53e9dbe8da0           bogo      10           1  \n",
       "2  3f207df678b143eea3cee63160fa8bed  informational       0           2  \n",
       "3  9b98b8c7a33c4b65b9aebfe6a799e6d9           bogo       5           3  \n",
       "4  0b1e1539f2cc45b7b9fa7c272da2e1d7       discount       5           4  \n",
       "5  2298d6c36e964ae4a3e7e9706d1fb8c2       discount       3           5  \n",
       "6  fafdcd668e3743c1bb461111dcafc2a4       discount       2           6  \n",
       "7  5a8bc65990b245e5a138643cd4eb9837  informational       0           7  \n",
       "8  f19421c1d4aa40978ebb69ca19b0e20d           bogo       5           8  \n",
       "9  2906b810c7d4411798c6938adc9daaa5       discount       2           9  "
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add titel name for every offer\n",
    "for row in range(10): \n",
    "    portfolio.at[row, 'offer_name']=row\n",
    "\n",
    "portfolio['offer_name']=portfolio['offer_name'].astype(int)\n",
    "portfolio.head(10)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bogo             4\n",
       "discount         4\n",
       "informational    2\n",
       "Name: offer_type, dtype: int64"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract list values from offer_type data\n",
    "portfolio['offer_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract list values from offer_type data\n",
    "portfolio = extract_from_iterable_col(portfolio, 'offer_type', unique_values=['bogo', 'informational', 'discount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract list values from offer_type data\n",
    "portfolio = extract_from_iterable_col(portfolio, 'channels', unique_values=['web', 'email', 'mobile', 'social'],drop_old_col=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>reward</th>\n",
       "      <th>offer_name</th>\n",
       "      <th>offer_type_bogo</th>\n",
       "      <th>offer_type_informational</th>\n",
       "      <th>offer_type_discount</th>\n",
       "      <th>channels_web</th>\n",
       "      <th>channels_mobile</th>\n",
       "      <th>channels_social</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>bogo</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>bogo</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>informational</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>bogo</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>discount</td>\n",
       "      <td>5</td>\n",
       "      <td>4</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   difficulty  duration                          offer_id     offer_type  \\\n",
       "0          10         7  ae264e3637204a6fb9bb56bc8210ddfd           bogo   \n",
       "1          10         5  4d5c57ea9a6940dd891ad53e9dbe8da0           bogo   \n",
       "2           0         4  3f207df678b143eea3cee63160fa8bed  informational   \n",
       "3           5         7  9b98b8c7a33c4b65b9aebfe6a799e6d9           bogo   \n",
       "4          20        10  0b1e1539f2cc45b7b9fa7c272da2e1d7       discount   \n",
       "\n",
       "   reward  offer_name  offer_type_bogo  offer_type_informational  \\\n",
       "0      10           0                1                         0   \n",
       "1      10           1                1                         0   \n",
       "2       0           2                0                         1   \n",
       "3       5           3                1                         0   \n",
       "4       5           4                0                         0   \n",
       "\n",
       "   offer_type_discount  channels_web  channels_mobile  channels_social  \n",
       "0                    0             0                1                1  \n",
       "1                    0             1                1                1  \n",
       "2                    0             1                1                0  \n",
       "3                    0             1                1                0  \n",
       "4                    1             1                0                0  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename column id to offer_id\n",
    "portfolio = portfolio.rename(columns = {'id':'offer_id'})                                       \n",
    "portfolio.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### profile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Missing data for better analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2175 entries, 0 to 16994\n",
      "Data columns (total 3 columns):\n",
      "gender    0 non-null object\n",
      "income    0 non-null float64\n",
      "age       2175 non-null int64\n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 68.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# creating a dataframe with only the customers with age = 118 \n",
    "# this data frame will include the coressponding gender and income columns to the customers with age = 118\n",
    "df_118 = profile[['gender','income','age']][profile['age']==118]\n",
    "df_118.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We have 2175 customer's without any info for gender,income and age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set age group\n",
    "profile['age']=(((profile['age']/10).astype(int))*10).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove missing data for profile \"age = 118\" or age up 90 years.\n",
    "profile=profile[profile['age']<100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50    3541\n",
       "60    2991\n",
       "40    2309\n",
       "70    1782\n",
       "30    1526\n",
       "20    1369\n",
       "80     831\n",
       "90     254\n",
       "10     205\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile['age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    8482\n",
       "F    6115\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove data for profile \"gender = 'O'\".\n",
    "profile=profile[profile['gender']!='O']\n",
    "profile['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    8482\n",
       "0    6115\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace gender one for man zero for woman \n",
    "profile['gender']=profile['gender'].replace(['M'],'1').replace(['F'],'0').astype(int)\n",
    "profile['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>id</th>\n",
       "      <th>income</th>\n",
       "      <th>reg_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0610b486422d4921ae7d2bf64640c50b</td>\n",
       "      <td>112000.0</td>\n",
       "      <td>2017</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>2017</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60</td>\n",
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       "      <td>70000.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>2018</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>2eeac8d8feae4a8cad5a6af0499a211d</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  gender                                id    income  reg_year\n",
       "1    50       0  0610b486422d4921ae7d2bf64640c50b  112000.0      2017\n",
       "3    70       0  78afa995795e4d85b5d9ceeca43f5fef  100000.0      2017\n",
       "5    60       1  e2127556f4f64592b11af22de27a7932   70000.0      2018\n",
       "8    60       1  389bc3fa690240e798340f5a15918d5c   53000.0      2018\n",
       "12   50       1  2eeac8d8feae4a8cad5a6af0499a211d   51000.0      2017"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get year for became_member_on\n",
    "profile['became_member_on'] = pd.to_datetime(profile['became_member_on'], format='%Y%m%d')\n",
    "# set reg_year from became_member_on column that has the year which customers become members\n",
    "\n",
    "# What is the most complet \n",
    "profile['reg_year'] = profile['became_member_on'].dt.year\n",
    "# drop became_member_on column\n",
    "profile=profile.drop(['became_member_on'], axis=1)\n",
    "\n",
    "profile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017    5501\n",
       "2018    3611\n",
       "2016    2992\n",
       "2015    1564\n",
       "2014     658\n",
       "2013     271\n",
       "Name: reg_year, dtype: int64"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile['reg_year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile['income']=(profile['income']/10000).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>person_id</th>\n",
       "      <th>income</th>\n",
       "      <th>reg_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0610b486422d4921ae7d2bf64640c50b</td>\n",
       "      <td>11</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>10</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>7</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>2eeac8d8feae4a8cad5a6af0499a211d</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  gender                         person_id  income  reg_year\n",
       "1    50       0  0610b486422d4921ae7d2bf64640c50b      11      2017\n",
       "3    70       0  78afa995795e4d85b5d9ceeca43f5fef      10      2017\n",
       "5    60       1  e2127556f4f64592b11af22de27a7932       7      2018\n",
       "8    60       1  389bc3fa690240e798340f5a15918d5c       5      2018\n",
       "12   50       1  2eeac8d8feae4a8cad5a6af0499a211d       5      2017"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename column id to person_id\n",
    "profile = profile.rename(columns = {'id':'person_id'})\n",
    "profile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 14597 entries, 1 to 16999\n",
      "Data columns (total 5 columns):\n",
      "age          14597 non-null int64\n",
      "gender       14597 non-null int64\n",
      "person_id    14597 non-null object\n",
      "income       14597 non-null int64\n",
      "reg_year     14597 non-null int64\n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 684.2+ KB\n"
     ]
    }
   ],
   "source": [
    "profile.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### transcript "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>event_transaction</th>\n",
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       "      <td>{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}</td>\n",
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      ],
      "text/plain": [
       "            event                            person  time  \\\n",
       "0  offer received  78afa995795e4d85b5d9ceeca43f5fef     0   \n",
       "1  offer received  a03223e636434f42ac4c3df47e8bac43     0   \n",
       "2  offer received  e2127556f4f64592b11af22de27a7932     0   \n",
       "3  offer received  8ec6ce2a7e7949b1bf142def7d0e0586     0   \n",
       "4  offer received  68617ca6246f4fbc85e91a2a49552598     0   \n",
       "\n",
       "                                              value  event_transaction  \\\n",
       "0  {'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}                  0   \n",
       "1  {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}                  0   \n",
       "2  {'offer id': '2906b810c7d4411798c6938adc9daaa5'}                  0   \n",
       "3  {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}                  0   \n",
       "4  {'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}                  0   \n",
       "\n",
       "   event_offer_received  event_offer_viewed  event_offer_completed  \n",
       "0                     1                   0                      0  \n",
       "1                     1                   0                      0  \n",
       "2                     1                   0                      0  \n",
       "3                     1                   0                      0  \n",
       "4                     1                   0                      0  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract data into new columns from events\n",
    "transcript = extract_from_iterable_col(transcript, 'event', unique_values=['transaction','offer received','offer viewed','offer completed'])\n",
    "transcript.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "            event                            person  time  event_transaction  \\\n",
       "0  offer received  78afa995795e4d85b5d9ceeca43f5fef     0                  0   \n",
       "1  offer received  a03223e636434f42ac4c3df47e8bac43     0                  0   \n",
       "2  offer received  e2127556f4f64592b11af22de27a7932     0                  0   \n",
       "3  offer received  8ec6ce2a7e7949b1bf142def7d0e0586     0                  0   \n",
       "4  offer received  68617ca6246f4fbc85e91a2a49552598     0                  0   \n",
       "\n",
       "   event_offer_received  event_offer_viewed  event_offer_completed  \\\n",
       "0                     1                   0                      0   \n",
       "1                     1                   0                      0   \n",
       "2                     1                   0                      0   \n",
       "3                     1                   0                      0   \n",
       "4                     1                   0                      0   \n",
       "\n",
       "   value_amount                    value_offer_id  value_reward  \n",
       "0           NaN  9b98b8c7a33c4b65b9aebfe6a799e6d9           NaN  \n",
       "1           NaN  0b1e1539f2cc45b7b9fa7c272da2e1d7           NaN  \n",
       "2           NaN  2906b810c7d4411798c6938adc9daaa5           NaN  \n",
       "3           NaN  fafdcd668e3743c1bb461111dcafc2a4           NaN  \n",
       "4           NaN  4d5c57ea9a6940dd891ad53e9dbe8da0           NaN  "
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract dictionary values from value data\n",
    "transcript = extract_from_iterable_col(transcript, 'value', unique_keys=['amount', 'offer id', 'offer_id', 'reward'],drop_old_col=True)\n",
    "transcript.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                                           total                            \n",
       "event                            offer completed offer received offer viewed\n",
       "value_offer_id                                                              \n",
       "0b1e1539f2cc45b7b9fa7c272da2e1d7          3420.0         7668.0       2663.0\n",
       "2298d6c36e964ae4a3e7e9706d1fb8c2          5156.0         7646.0       7337.0\n",
       "2906b810c7d4411798c6938adc9daaa5          4017.0         7632.0       4118.0\n",
       "3f207df678b143eea3cee63160fa8bed             NaN         7617.0       4144.0\n",
       "4d5c57ea9a6940dd891ad53e9dbe8da0          3331.0         7593.0       7298.0\n",
       "5a8bc65990b245e5a138643cd4eb9837             NaN         7618.0       6687.0\n",
       "9b98b8c7a33c4b65b9aebfe6a799e6d9          4354.0         7677.0       4171.0\n",
       "ae264e3637204a6fb9bb56bc8210ddfd          3688.0         7658.0       6716.0\n",
       "f19421c1d4aa40978ebb69ca19b0e20d          4296.0         7571.0       7264.0\n",
       "fafdcd668e3743c1bb461111dcafc2a4          5317.0         7597.0       7327.0"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=transcript.groupby(['value_offer_id','event'])['time'].count().reset_index(name ='total')\n",
    "x=pd.pivot_table(x, index=[\"value_offer_id\"], columns=[\"event\"], values=[\"total\"], aggfunc=\"sum\")\n",
    "x.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### same offer not completed [3f207df678b143eea3cee63160fa8bed - 5a8bc65990b245e5a138643cd4eb9837]\n",
    "\n",
    "#### same offer completed more than viewed [0b1e1539f2cc45b7b9fa7c272da2e1d7 - 9b98b8c7a33c4b65b9aebfe6a799e6d9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert time from hours to dates by division 24\n",
    "transcript['time']=transcript['time']/24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill null values to 0\n",
    "transcript=transcript.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            event                         person_id  time  event_transaction  \\\n",
       "0  offer received  78afa995795e4d85b5d9ceeca43f5fef   0.0                  0   \n",
       "1  offer received  a03223e636434f42ac4c3df47e8bac43   0.0                  0   \n",
       "2  offer received  e2127556f4f64592b11af22de27a7932   0.0                  0   \n",
       "3  offer received  8ec6ce2a7e7949b1bf142def7d0e0586   0.0                  0   \n",
       "4  offer received  68617ca6246f4fbc85e91a2a49552598   0.0                  0   \n",
       "\n",
       "   event_offer_received  event_offer_viewed  event_offer_completed  \\\n",
       "0                     1                   0                      0   \n",
       "1                     1                   0                      0   \n",
       "2                     1                   0                      0   \n",
       "3                     1                   0                      0   \n",
       "4                     1                   0                      0   \n",
       "\n",
       "   value_amount                          offer_id  value_reward  \n",
       "0           0.0  9b98b8c7a33c4b65b9aebfe6a799e6d9           0.0  \n",
       "1           0.0  0b1e1539f2cc45b7b9fa7c272da2e1d7           0.0  \n",
       "2           0.0  2906b810c7d4411798c6938adc9daaa5           0.0  \n",
       "3           0.0  fafdcd668e3743c1bb461111dcafc2a4           0.0  \n",
       "4           0.0  4d5c57ea9a6940dd891ad53e9dbe8da0           0.0  "
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename columns\n",
    "transcript = transcript.rename(columns = {'person':'person_id'})\n",
    "transcript = transcript.rename(columns = {'value_offer_id':'offer_id'})\n",
    "transcript.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    272955\n",
       "1     33579\n",
       "Name: event_offer_completed, dtype: int64"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript['event_offer_completed'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    248809\n",
       "1     57725\n",
       "Name: event_offer_viewed, dtype: int64"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript['event_offer_viewed'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Merging the three clean datasets (Portfolio, Profile and Transaction ) into ONE Master Clean Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcript_profile_df= pd.merge(transcript, profile, on='person_id', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "inner_join_df= pd.merge(transcript_profile_df,portfolio, on='offer_id', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "inner_join_df=inner_join_df.drop(['person_id'], axis=1)\n",
    "inner_join_df=inner_join_df.drop(['offer_id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event</th>\n",
       "      <th>time</th>\n",
       "      <th>event_transaction</th>\n",
       "      <th>event_offer_received</th>\n",
       "      <th>event_offer_viewed</th>\n",
       "      <th>event_offer_completed</th>\n",
       "      <th>value_amount</th>\n",
       "      <th>value_reward</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>...</th>\n",
       "      <th>duration</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>reward</th>\n",
       "      <th>offer_name</th>\n",
       "      <th>offer_type_bogo</th>\n",
       "      <th>offer_type_informational</th>\n",
       "      <th>offer_type_discount</th>\n",
       "      <th>channels_web</th>\n",
       "      <th>channels_mobile</th>\n",
       "      <th>channels_social</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>offer received</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>bogo</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>offer viewed</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>bogo</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>offer completed</td>\n",
       "      <td>5.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>bogo</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>offer received</td>\n",
       "      <td>17.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>bogo</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>offer viewed</td>\n",
       "      <td>17.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>bogo</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             event   time  event_transaction  event_offer_received  \\\n",
       "0   offer received   0.00                  0                     1   \n",
       "1     offer viewed   0.25                  0                     0   \n",
       "2  offer completed   5.50                  0                     0   \n",
       "3   offer received  17.00                  0                     1   \n",
       "4     offer viewed  17.50                  0                     0   \n",
       "\n",
       "   event_offer_viewed  event_offer_completed  value_amount  value_reward  age  \\\n",
       "0                   0                      0           0.0           0.0   70   \n",
       "1                   1                      0           0.0           0.0   70   \n",
       "2                   0                      1           0.0           5.0   70   \n",
       "3                   0                      0           0.0           0.0   60   \n",
       "4                   1                      0           0.0           0.0   60   \n",
       "\n",
       "   gender       ...         duration  offer_type  reward  offer_name  \\\n",
       "0       0       ...                7        bogo       5           3   \n",
       "1       0       ...                7        bogo       5           3   \n",
       "2       0       ...                7        bogo       5           3   \n",
       "3       1       ...                7        bogo       5           3   \n",
       "4       1       ...                7        bogo       5           3   \n",
       "\n",
       "  offer_type_bogo  offer_type_informational  offer_type_discount  \\\n",
       "0               1                         0                    0   \n",
       "1               1                         0                    0   \n",
       "2               1                         0                    0   \n",
       "3               1                         0                    0   \n",
       "4               1                         0                    0   \n",
       "\n",
       "   channels_web  channels_mobile  channels_social  \n",
       "0             1                1                0  \n",
       "1             1                1                0  \n",
       "2             1                1                0  \n",
       "3             1                1                0  \n",
       "4             1                1                0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inner_join_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 146446 entries, 0 to 146445\n",
      "Data columns (total 23 columns):\n",
      "event                       146446 non-null object\n",
      "time                        146446 non-null float64\n",
      "event_transaction           146446 non-null int64\n",
      "event_offer_received        146446 non-null int64\n",
      "event_offer_viewed          146446 non-null int64\n",
      "event_offer_completed       146446 non-null int64\n",
      "value_amount                146446 non-null float64\n",
      "value_reward                146446 non-null float64\n",
      "age                         146446 non-null int64\n",
      "gender                      146446 non-null int64\n",
      "income                      146446 non-null int64\n",
      "reg_year                    146446 non-null int64\n",
      "difficulty                  146446 non-null int64\n",
      "duration                    146446 non-null int64\n",
      "offer_type                  146446 non-null object\n",
      "reward                      146446 non-null int64\n",
      "offer_name                  146446 non-null int64\n",
      "offer_type_bogo             146446 non-null int64\n",
      "offer_type_informational    146446 non-null int64\n",
      "offer_type_discount         146446 non-null int64\n",
      "channels_web                146446 non-null int64\n",
      "channels_mobile             146446 non-null int64\n",
      "channels_social             146446 non-null int64\n",
      "dtypes: float64(3), int64(18), object(2)\n",
      "memory usage: 26.8+ MB\n"
     ]
    }
   ],
   "source": [
    "inner_join_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>event_transaction</th>\n",
       "      <th>event_offer_received</th>\n",
       "      <th>event_offer_viewed</th>\n",
       "      <th>event_offer_completed</th>\n",
       "      <th>value_amount</th>\n",
       "      <th>value_reward</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>income</th>\n",
       "      <th>...</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>reward</th>\n",
       "      <th>offer_name</th>\n",
       "      <th>offer_type_bogo</th>\n",
       "      <th>offer_type_informational</th>\n",
       "      <th>offer_type_discount</th>\n",
       "      <th>channels_web</th>\n",
       "      <th>channels_mobile</th>\n",
       "      <th>channels_social</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>146446.000000</td>\n",
       "      <td>146446.0</td>\n",
       "      <td>146446.000000</td>\n",
       "      <td>146446.000000</td>\n",
       "      <td>146446.000000</td>\n",
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       "      <td>146446.000000</td>\n",
       "      <td>146446.000000</td>\n",
       "      <td>146446.000000</td>\n",
       "      <td>146446.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>146446.000000</td>\n",
       "      <td>146446.000000</td>\n",
       "      <td>146446.000000</td>\n",
       "      <td>146446.000000</td>\n",
       "      <td>146446.000000</td>\n",
       "      <td>146446.000000</td>\n",
       "      <td>146446.000000</td>\n",
       "      <td>146446.000000</td>\n",
       "      <td>146446.000000</td>\n",
       "      <td>146446.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.779045</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.447359</td>\n",
       "      <td>0.334813</td>\n",
       "      <td>0.217828</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.077769</td>\n",
       "      <td>50.225749</td>\n",
       "      <td>0.565915</td>\n",
       "      <td>6.205748</td>\n",
       "      <td>...</td>\n",
       "      <td>7.890048</td>\n",
       "      <td>6.624380</td>\n",
       "      <td>4.445065</td>\n",
       "      <td>4.544938</td>\n",
       "      <td>0.429237</td>\n",
       "      <td>0.152131</td>\n",
       "      <td>0.418632</td>\n",
       "      <td>0.806707</td>\n",
       "      <td>0.917376</td>\n",
       "      <td>0.658987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.262043</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.497223</td>\n",
       "      <td>0.471927</td>\n",
       "      <td>0.412771</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.451707</td>\n",
       "      <td>17.406954</td>\n",
       "      <td>0.495638</td>\n",
       "      <td>2.142927</td>\n",
       "      <td>...</td>\n",
       "      <td>5.037318</td>\n",
       "      <td>2.132563</td>\n",
       "      <td>3.373735</td>\n",
       "      <td>2.888554</td>\n",
       "      <td>0.494969</td>\n",
       "      <td>0.359149</td>\n",
       "      <td>0.493337</td>\n",
       "      <td>0.394882</td>\n",
       "      <td>0.275314</td>\n",
       "      <td>0.474051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>21.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29.750000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                time  event_transaction  event_offer_received  \\\n",
       "count  146446.000000           146446.0         146446.000000   \n",
       "mean       14.779045                0.0              0.447359   \n",
       "std         8.262043                0.0              0.497223   \n",
       "min         0.000000                0.0              0.000000   \n",
       "25%         7.000000                0.0              0.000000   \n",
       "50%        17.000000                0.0              0.000000   \n",
       "75%        21.250000                0.0              1.000000   \n",
       "max        29.750000                0.0              1.000000   \n",
       "\n",
       "       event_offer_viewed  event_offer_completed  value_amount   value_reward  \\\n",
       "count       146446.000000          146446.000000      146446.0  146446.000000   \n",
       "mean             0.334813               0.217828           0.0       1.077769   \n",
       "std              0.471927               0.412771           0.0       2.451707   \n",
       "min              0.000000               0.000000           0.0       0.000000   \n",
       "25%              0.000000               0.000000           0.0       0.000000   \n",
       "50%              0.000000               0.000000           0.0       0.000000   \n",
       "75%              1.000000               0.000000           0.0       0.000000   \n",
       "max              1.000000               1.000000           0.0      10.000000   \n",
       "\n",
       "                 age         gender         income       ...         \\\n",
       "count  146446.000000  146446.000000  146446.000000       ...          \n",
       "mean       50.225749       0.565915       6.205748       ...          \n",
       "std        17.406954       0.495638       2.142927       ...          \n",
       "min        10.000000       0.000000       3.000000       ...          \n",
       "25%        40.000000       0.000000       5.000000       ...          \n",
       "50%        50.000000       1.000000       6.000000       ...          \n",
       "75%        60.000000       1.000000       8.000000       ...          \n",
       "max        90.000000       1.000000      12.000000       ...          \n",
       "\n",
       "          difficulty       duration         reward     offer_name  \\\n",
       "count  146446.000000  146446.000000  146446.000000  146446.000000   \n",
       "mean        7.890048       6.624380       4.445065       4.544938   \n",
       "std         5.037318       2.132563       3.373735       2.888554   \n",
       "min         0.000000       3.000000       0.000000       0.000000   \n",
       "25%         5.000000       5.000000       2.000000       2.000000   \n",
       "50%        10.000000       7.000000       5.000000       5.000000   \n",
       "75%        10.000000       7.000000       5.000000       7.000000   \n",
       "max        20.000000      10.000000      10.000000       9.000000   \n",
       "\n",
       "       offer_type_bogo  offer_type_informational  offer_type_discount  \\\n",
       "count    146446.000000             146446.000000        146446.000000   \n",
       "mean          0.429237                  0.152131             0.418632   \n",
       "std           0.494969                  0.359149             0.493337   \n",
       "min           0.000000                  0.000000             0.000000   \n",
       "25%           0.000000                  0.000000             0.000000   \n",
       "50%           0.000000                  0.000000             0.000000   \n",
       "75%           1.000000                  0.000000             1.000000   \n",
       "max           1.000000                  1.000000             1.000000   \n",
       "\n",
       "        channels_web  channels_mobile  channels_social  \n",
       "count  146446.000000    146446.000000    146446.000000  \n",
       "mean        0.806707         0.917376         0.658987  \n",
       "std         0.394882         0.275314         0.474051  \n",
       "min         0.000000         0.000000         0.000000  \n",
       "25%         1.000000         1.000000         0.000000  \n",
       "50%         1.000000         1.000000         1.000000  \n",
       "75%         1.000000         1.000000         1.000000  \n",
       "max         1.000000         1.000000         1.000000  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inner_join_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the most gender and age  ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f1a07cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "g = sns.countplot(x=\"age\", hue=\"gender\", data=transcript_profile_df)\n",
    "plt.title('Most Popular gender to Each Age Group')\n",
    "plt.ylabel('Total')\n",
    "plt.xlabel('Age Group')  \n",
    "plt.xticks(rotation = 0)\n",
    "plt.legend(title='gender')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### What is the most registration members year ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f1a06b160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 6))\n",
    "sns.countplot(transcript_profile_df['reg_year'])\n",
    "plt.title('Number of Profiles In Each Year')\n",
    "plt.ylabel('Number of Profiles')\n",
    "plt.xlabel('Year')\n",
    "plt.xticks()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### What is the most event in gender ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:703: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f9f1b02d940>"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f1a0d4be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx=transcript_profile_df.groupby(['event','gender'])['time'].count().reset_index(name ='total')\n",
    "g = sns.FacetGrid(xx, col=\"event\")\n",
    "g.map(sns.barplot, \"gender\", \"total\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f1a045dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "g = sns.countplot(x=\"gender\", hue=\"event\", data=transcript_profile_df)\n",
    "plt.title('Most Popular event to Each gender ')\n",
    "plt.ylabel('Total')\n",
    "plt.xlabel('gender ')\n",
    "xlabels = ['female','male']\n",
    "g.set_xticklabels(xlabels)\n",
    "plt.xticks(rotation = 0)\n",
    "plt.legend(title='event')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the most event in age ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:703: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f9f1a071400>"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f1a050ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx=transcript_profile_df.groupby(['event','age'])['time'].count().reset_index(name ='total')\n",
    "g = sns.FacetGrid(xx, col=\"event\")\n",
    "g.map(sns.barplot, \"age\", \"total\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f19e894a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "g = sns.countplot(x=\"age\", hue=\"event\", data=transcript_profile_df)\n",
    "plt.title('Most Popular Offer type to Each Age Group')\n",
    "plt.ylabel('Total')\n",
    "plt.xlabel('Age Group')\n",
    "plt.xticks(rotation = 0)\n",
    "plt.legend(title='Offer Type')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the most viewed offer count in difficulty ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f19e9fdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx=inner_join_df[inner_join_df[\"event\"]==\"offer viewed\"]\n",
    "plt.figure(figsize=(16, 6))\n",
    "sns.countplot(xx['difficulty'])\n",
    "plt.title('Number of Profiles In Each Difficulty')\n",
    "plt.ylabel('Number of Profiles')\n",
    "plt.xlabel('Difficulty')\n",
    "plt.xticks()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the most viewed offer count in duration ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f19e67898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx=inner_join_df[inner_join_df[\"event\"]==\"offer viewed\"]\n",
    "plt.figure(figsize=(16, 6))\n",
    "sns.countplot(xx['duration'])\n",
    "plt.title('Number of Profiles In Each Duration')\n",
    "plt.ylabel('Number of Profiles')\n",
    "plt.xlabel('Duration')\n",
    "plt.xticks()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the most viewed offer count in offer_type ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f19dd26d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx=inner_join_df[inner_join_df[\"event\"]==\"offer viewed\"]\n",
    "plt.figure(figsize=(16, 6))\n",
    "sns.countplot(xx['offer_type'])\n",
    "plt.title('Number of Profiles In Each offer type')\n",
    "plt.ylabel('Number of Profiles')\n",
    "plt.xlabel('offer type')\n",
    "plt.xticks()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the most viewed offer count in reward ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f1a020da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx=inner_join_df[inner_join_df[\"event\"]==\"offer viewed\"]\n",
    "plt.figure(figsize=(16, 6))\n",
    "sns.countplot(xx['reward'])\n",
    "plt.title('Number of Profiles In Each reward')\n",
    "plt.ylabel('Number of Profiles')\n",
    "plt.xlabel('reward')\n",
    "plt.xticks()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the most completed  offer count in difficulty ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f19d3cd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx=inner_join_df[inner_join_df[\"event\"]==\"offer completed\"]\n",
    "plt.figure(figsize=(16, 6))\n",
    "sns.countplot(xx['difficulty'])\n",
    "plt.title('Number of Profiles In Each Difficulty')\n",
    "plt.ylabel('Number of Profiles')\n",
    "plt.xlabel('Difficulty')\n",
    "plt.xticks()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the most completed  offer count in duration ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f19d6b208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx=inner_join_df[inner_join_df[\"event\"]==\"offer completed\"]\n",
    "plt.figure(figsize=(16, 6))\n",
    "sns.countplot(xx['duration'])\n",
    "plt.title('Number of Profiles In Each Duration')\n",
    "plt.ylabel('Number of Profiles')\n",
    "plt.xlabel('Duration')\n",
    "plt.xticks()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the most completed  offer count in offer_type ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f19d6ff98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx=inner_join_df[inner_join_df[\"event\"]==\"offer completed\"]\n",
    "plt.figure(figsize=(16, 6))\n",
    "sns.countplot(xx['offer_type'])\n",
    "plt.title('Number of Profiles In Each offer type')\n",
    "plt.ylabel('Number of Profiles')\n",
    "plt.xlabel('offer type')\n",
    "plt.xticks()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the most completed  offer count in reward ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f1b15d9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx=inner_join_df[inner_join_df[\"event\"]==\"offer completed\"]\n",
    "plt.figure(figsize=(16, 6))\n",
    "sns.countplot(xx['reward'])\n",
    "plt.title('Number of Profiles In Each reward')\n",
    "plt.ylabel('Number of Profiles')\n",
    "plt.xlabel('reward')\n",
    "plt.xticks()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 146446 entries, 0 to 146445\n",
      "Data columns (total 23 columns):\n",
      "event                       146446 non-null object\n",
      "time                        146446 non-null float64\n",
      "event_transaction           146446 non-null int64\n",
      "event_offer_received        146446 non-null int64\n",
      "event_offer_viewed          146446 non-null int64\n",
      "event_offer_completed       146446 non-null int64\n",
      "value_amount                146446 non-null float64\n",
      "value_reward                146446 non-null float64\n",
      "age                         146446 non-null int64\n",
      "gender                      146446 non-null int64\n",
      "income                      146446 non-null int64\n",
      "reg_year                    146446 non-null int64\n",
      "difficulty                  146446 non-null int64\n",
      "duration                    146446 non-null int64\n",
      "offer_type                  146446 non-null object\n",
      "reward                      146446 non-null int64\n",
      "offer_name                  146446 non-null int64\n",
      "offer_type_bogo             146446 non-null int64\n",
      "offer_type_informational    146446 non-null int64\n",
      "offer_type_discount         146446 non-null int64\n",
      "channels_web                146446 non-null int64\n",
      "channels_mobile             146446 non-null int64\n",
      "channels_social             146446 non-null int64\n",
      "dtypes: float64(3), int64(18), object(2)\n",
      "memory usage: 26.8+ MB\n"
     ]
    }
   ],
   "source": [
    "inner_join_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "inner_join_df=inner_join_df.drop(['event'], axis=1)\n",
    "inner_join_df=inner_join_df.drop(['offer_type'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((117156, 20), (29290, 20), (117156,), (29290,))"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label = inner_join_df['event_offer_completed'].copy()\n",
    "train = inner_join_df.drop(['event_offer_completed'], axis=1)\n",
    "    \n",
    "#Dividing the data into train and test\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(train, label,test_size=0.2, random_state=0)\n",
    "X_train.shape, X_test.shape, Y_train.shape, Y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>event_transaction</th>\n",
       "      <th>event_offer_received</th>\n",
       "      <th>event_offer_viewed</th>\n",
       "      <th>value_amount</th>\n",
       "      <th>value_reward</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>income</th>\n",
       "      <th>reg_year</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>reward</th>\n",
       "      <th>offer_name</th>\n",
       "      <th>offer_type_bogo</th>\n",
       "      <th>offer_type_informational</th>\n",
       "      <th>offer_type_discount</th>\n",
       "      <th>channels_web</th>\n",
       "      <th>channels_mobile</th>\n",
       "      <th>channels_social</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    time  event_transaction  event_offer_received  event_offer_viewed  \\\n",
       "0   0.00                  0                     1                   0   \n",
       "1   0.25                  0                     0                   1   \n",
       "2   5.50                  0                     0                   0   \n",
       "3  17.00                  0                     1                   0   \n",
       "4  17.50                  0                     0                   1   \n",
       "\n",
       "   value_amount  value_reward  age  gender  income  reg_year  difficulty  \\\n",
       "0           0.0           0.0   70       0      10      2017           5   \n",
       "1           0.0           0.0   70       0      10      2017           5   \n",
       "2           0.0           5.0   70       0      10      2017           5   \n",
       "3           0.0           0.0   60       1       7      2018           5   \n",
       "4           0.0           0.0   60       1       7      2018           5   \n",
       "\n",
       "   duration  reward  offer_name  offer_type_bogo  offer_type_informational  \\\n",
       "0         7       5           3                1                         0   \n",
       "1         7       5           3                1                         0   \n",
       "2         7       5           3                1                         0   \n",
       "3         7       5           3                1                         0   \n",
       "4         7       5           3                1                         0   \n",
       "\n",
       "   offer_type_discount  channels_web  channels_mobile  channels_social  \n",
       "0                    0             1                1                0  \n",
       "1                    0             1                1                0  \n",
       "2                    0             1                1                0  \n",
       "3                    0             1                1                0  \n",
       "4                    0             1                1                0  "
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>event_transaction</th>\n",
       "      <th>event_offer_received</th>\n",
       "      <th>event_offer_viewed</th>\n",
       "      <th>value_amount</th>\n",
       "      <th>value_reward</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>income</th>\n",
       "      <th>reg_year</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>reward</th>\n",
       "      <th>offer_name</th>\n",
       "      <th>offer_type_bogo</th>\n",
       "      <th>offer_type_informational</th>\n",
       "      <th>offer_type_discount</th>\n",
       "      <th>channels_web</th>\n",
       "      <th>channels_mobile</th>\n",
       "      <th>channels_social</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70405</th>\n",
       "      <td>24.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28061</th>\n",
       "      <td>7.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2013</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93011</th>\n",
       "      <td>25.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67154</th>\n",
       "      <td>21.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46022</th>\n",
       "      <td>17.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  event_transaction  event_offer_received  event_offer_viewed  \\\n",
       "70405  24.00                  0                     1                   0   \n",
       "28061   7.50                  0                     0                   1   \n",
       "93011  25.75                  0                     0                   1   \n",
       "67154  21.75                  0                     0                   0   \n",
       "46022  17.25                  0                     0                   1   \n",
       "\n",
       "       value_amount  value_reward  age  gender  income  reg_year  difficulty  \\\n",
       "70405           0.0           0.0   60       1       4      2016          10   \n",
       "28061           0.0           0.0   30       0       5      2013          10   \n",
       "93011           0.0           0.0   60       0       9      2016          10   \n",
       "67154           0.0           2.0   60       1       8      2018          10   \n",
       "46022           0.0           0.0   60       1       7      2015           5   \n",
       "\n",
       "       duration  reward  offer_name  offer_type_bogo  \\\n",
       "70405         7       2           9                0   \n",
       "28061         7      10           0                1   \n",
       "93011        10       2           6                0   \n",
       "67154         7       2           9                0   \n",
       "46022         5       5           8                1   \n",
       "\n",
       "       offer_type_informational  offer_type_discount  channels_web  \\\n",
       "70405                         0                    1             1   \n",
       "28061                         0                    0             0   \n",
       "93011                         0                    1             1   \n",
       "67154                         0                    1             1   \n",
       "46022                         0                    0             1   \n",
       "\n",
       "       channels_mobile  channels_social  \n",
       "70405                1                0  \n",
       "28061                1                1  \n",
       "93011                1                1  \n",
       "67154                1                0  \n",
       "46022                1                1  "
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>event_transaction</th>\n",
       "      <th>event_offer_received</th>\n",
       "      <th>event_offer_viewed</th>\n",
       "      <th>value_amount</th>\n",
       "      <th>value_reward</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>income</th>\n",
       "      <th>reg_year</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>reward</th>\n",
       "      <th>offer_name</th>\n",
       "      <th>offer_type_bogo</th>\n",
       "      <th>offer_type_informational</th>\n",
       "      <th>offer_type_discount</th>\n",
       "      <th>channels_web</th>\n",
       "      <th>channels_mobile</th>\n",
       "      <th>channels_social</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57654</th>\n",
       "      <td>21.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73112</th>\n",
       "      <td>24.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36910</th>\n",
       "      <td>21.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108756</th>\n",
       "      <td>7.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127712</th>\n",
       "      <td>17.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2018</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time  event_transaction  event_offer_received  event_offer_viewed  \\\n",
       "57654   21.00                  0                     1                   0   \n",
       "73112   24.00                  0                     1                   0   \n",
       "36910   21.75                  0                     0                   1   \n",
       "108756   7.00                  0                     1                   0   \n",
       "127712  17.00                  0                     1                   0   \n",
       "\n",
       "        value_amount  value_reward  age  gender  income  reg_year  difficulty  \\\n",
       "57654            0.0           0.0   30       1       4      2018           5   \n",
       "73112            0.0           0.0   70       0       9      2017           0   \n",
       "36910            0.0           0.0   20       1       6      2017          10   \n",
       "108756           0.0           0.0   40       1       4      2018           7   \n",
       "127712           0.0           0.0   50       1       6      2018          20   \n",
       "\n",
       "        duration  reward  offer_name  offer_type_bogo  \\\n",
       "57654          5       5           8                1   \n",
       "73112          4       0           2                0   \n",
       "36910          7      10           0                1   \n",
       "108756         7       3           5                0   \n",
       "127712        10       5           4                0   \n",
       "\n",
       "        offer_type_informational  offer_type_discount  channels_web  \\\n",
       "57654                          0                    0             1   \n",
       "73112                          1                    0             1   \n",
       "36910                          0                    0             0   \n",
       "108756                         0                    1             1   \n",
       "127712                         0                    1             1   \n",
       "\n",
       "        channels_mobile  channels_social  \n",
       "57654                 1                1  \n",
       "73112                 1                0  \n",
       "36910                 1                1  \n",
       "108756                1                1  \n",
       "127712                0                0  "
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    91589\n",
       "1    25567\n",
       "Name: event_offer_completed, dtype: int64"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(Y_train)['event_offer_completed'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22957\n",
       "1     6333\n",
       "Name: event_offer_completed, dtype: int64"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(Y_test)['event_offer_completed'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    114546\n",
       "1     31900\n",
       "Name: event_offer_completed, dtype: int64"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining a function to calculate the accuracy for the models we will try below \n",
    "def predict_score(model):\n",
    "    pred = model.predict(X_test)\n",
    "\n",
    "    # Calculate the absolute errors\n",
    "    errors = abs(pred - Y_test)\n",
    "    \n",
    "    # Calculate mean absolute percentage error\n",
    "    mean_APE = 100 * (errors / Y_test)\n",
    "    accuracy = 100 - np.mean(mean_APE)\n",
    "    \n",
    "    return round(accuracy, 4),pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of DTR classifier on training set: 100.0%.\n",
      "Prediction Accuracy: 100.0%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dtr = DecisionTreeRegressor().fit(X_train,Y_train)\n",
    "print(f'Accuracy of DTR classifier on training set: {round(dtr.score(X_train, Y_train)*100,2)}%.')\n",
    "accuracy,pred=predict_score(dtr)\n",
    "print(f'Prediction Accuracy: {accuracy}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVM classifier on training set: 100.0%.\n",
      "Prediction Accuracy: 99.9684%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svm = SVC(gamma = 'auto').fit(X_train, Y_train)\n",
    "print(f'Accuracy of SVM classifier on training set: {round(svm.score(X_train, Y_train)*100,2)}%.')\n",
    "accuracy,pred=predict_score(svm)\n",
    "print(f'Prediction Accuracy: {accuracy}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22959\n",
       "1     6331\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(pred)[0].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above table, shows the accuracy score related with using different models of supervised learning. As presented on the table, we had 100% accuracy in both training and testing ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Review Questions:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### - What is the predict for event when have gender, age group, income group and offer type will complet offer or not in 2018 year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>event_transaction</th>\n",
       "      <th>event_offer_received</th>\n",
       "      <th>event_offer_viewed</th>\n",
       "      <th>value_amount</th>\n",
       "      <th>value_reward</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>income</th>\n",
       "      <th>reg_year</th>\n",
       "      <th>...</th>\n",
       "      <th>duration</th>\n",
       "      <th>reward</th>\n",
       "      <th>offer_name</th>\n",
       "      <th>offer_type_bogo</th>\n",
       "      <th>offer_type_informational</th>\n",
       "      <th>offer_type_discount</th>\n",
       "      <th>channels_web</th>\n",
       "      <th>channels_mobile</th>\n",
       "      <th>channels_social</th>\n",
       "      <th>event_offer_completed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57654</th>\n",
       "      <td>21.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2018</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108756</th>\n",
       "      <td>7.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2018</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127712</th>\n",
       "      <td>17.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2018</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110065</th>\n",
       "      <td>22.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2018</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78865</th>\n",
       "      <td>24.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2018</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         time  event_transaction  event_offer_received  event_offer_viewed  \\\n",
       "57654   21.00                  0                     1                   0   \n",
       "108756   7.00                  0                     1                   0   \n",
       "127712  17.00                  0                     1                   0   \n",
       "110065  22.75                  0                     0                   0   \n",
       "78865   24.00                  0                     1                   0   \n",
       "\n",
       "        value_amount  value_reward  age  gender  income  reg_year  \\\n",
       "57654            0.0           0.0   30       1       4      2018   \n",
       "108756           0.0           0.0   40       1       4      2018   \n",
       "127712           0.0           0.0   50       1       6      2018   \n",
       "110065           0.0           3.0   50       0       7      2018   \n",
       "78865            0.0           0.0   30       1       3      2018   \n",
       "\n",
       "                ...            duration  reward  offer_name  offer_type_bogo  \\\n",
       "57654           ...                   5       5           8                1   \n",
       "108756          ...                   7       3           5                0   \n",
       "127712          ...                  10       5           4                0   \n",
       "110065          ...                   7       3           5                0   \n",
       "78865           ...                   4       0           2                0   \n",
       "\n",
       "        offer_type_informational  offer_type_discount  channels_web  \\\n",
       "57654                          0                    0             1   \n",
       "108756                         0                    1             1   \n",
       "127712                         0                    1             1   \n",
       "110065                         0                    1             1   \n",
       "78865                          1                    0             1   \n",
       "\n",
       "        channels_mobile  channels_social  event_offer_completed  \n",
       "57654                 1                1                      0  \n",
       "108756                1                1                      0  \n",
       "127712                0                0                      0  \n",
       "110065                1                1                      1  \n",
       "78865                 1                0                      0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_2018=X_test.copy()\n",
    "X_test_2018['event_offer_completed']=pred\n",
    "\n",
    "X_test_2018=X_test_2018[X_test_2018['reg_year']==2018]\n",
    "\n",
    "X_test_2018.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6659, 1056)"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_2018.shape[0],X_test_2018['event_offer_completed'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The predict for event will complet offer or not in 2018 year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### - What is the predict for most offer type in 20018 year ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6659 entries, 0 to 6658\n",
      "Data columns (total 32 columns):\n",
      "time                          6659 non-null float64\n",
      "event_transaction             6659 non-null int64\n",
      "event_offer_received          6659 non-null int64\n",
      "event_offer_viewed            6659 non-null int64\n",
      "value_amount                  6659 non-null float64\n",
      "value_reward                  6659 non-null float64\n",
      "age                           6659 non-null int64\n",
      "gender                        6659 non-null int64\n",
      "income                        6659 non-null int64\n",
      "reg_year                      6659 non-null int64\n",
      "difficulty_x                  6659 non-null int64\n",
      "duration_x                    6659 non-null int64\n",
      "reward_x                      6659 non-null int64\n",
      "offer_name                    6659 non-null int64\n",
      "offer_type_bogo_x             6659 non-null int64\n",
      "offer_type_informational_x    6659 non-null int64\n",
      "offer_type_discount_x         6659 non-null int64\n",
      "channels_web_x                6659 non-null int64\n",
      "channels_mobile_x             6659 non-null int64\n",
      "channels_social_x             6659 non-null int64\n",
      "event_offer_completed         6659 non-null int64\n",
      "difficulty_y                  6659 non-null int64\n",
      "duration_y                    6659 non-null int64\n",
      "offer_id                      6659 non-null object\n",
      "offer_type                    6659 non-null object\n",
      "reward_y                      6659 non-null int64\n",
      "offer_type_bogo_y             6659 non-null int64\n",
      "offer_type_informational_y    6659 non-null int64\n",
      "offer_type_discount_y         6659 non-null int64\n",
      "channels_web_y                6659 non-null int64\n",
      "channels_mobile_y             6659 non-null int64\n",
      "channels_social_y             6659 non-null int64\n",
      "dtypes: float64(3), int64(27), object(2)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "X_test_2018= pd.merge(X_test_2018, portfolio, on='offer_name', how='inner')\n",
    "X_test_2018.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the most gender and age in 2018 year ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f1a05b160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "g = sns.countplot(x=\"age\", hue=\"gender\", data=X_test_2018)\n",
    "plt.title('Most Popular gender to Each Age Group')\n",
    "plt.ylabel('Total')\n",
    "plt.xlabel('Age Group')  \n",
    "plt.xticks(rotation = 0)\n",
    "plt.legend(title='gender')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the most offer type of gender in 2018 year ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f19dc68d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "g = sns.countplot(x=\"gender\", hue=\"offer_type\", data=X_test_2018)\n",
    "plt.title('Most Popular Offers to Each gender ')\n",
    "plt.ylabel('Total')\n",
    "plt.xlabel('gender ')\n",
    "xlabels = ['female','male']\n",
    "g.set_xticklabels(xlabels)\n",
    "plt.xticks(rotation = 0)\n",
    "plt.legend(title='Offer Type')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the most offer type age  in 2018 year ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f19cfd0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "g = sns.countplot(x=\"age\", hue=\"offer_type\", data=X_test_2018)\n",
    "plt.title('Most Popular Offers to Each Age Group')\n",
    "plt.ylabel('Total')\n",
    "plt.xlabel('Age Group')\n",
    "plt.xticks(rotation = 0)\n",
    "plt.legend(title='Offer Type')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the most gender and age ?\n",
    "#### The male with age 50 years old is the most .\n",
    "\n",
    "### What is the most registration members year ?\n",
    "#### 2017 is the most year registration members .\n",
    "\n",
    "### What is the most event in gender ?\n",
    "#### The male with transation event is the most .\n",
    "\n",
    "### What is the most event in age ?\n",
    "####  The transation event with age 50 years old is the most .\n",
    "\n",
    "### What is the most viewed and completed offer count in difficulty ?\n",
    "#### difficulty 10 is the most but mast not use 20 difficulty .\n",
    "\n",
    "### What is the most viewed and completed offer count in duration ?\n",
    "#### duration 7 is the most but mast not use 4 and 5 duration .\n",
    "\n",
    "### What is the most viewed and completed offer count in offer type ?\n",
    "#### bogo and discount offer type is the most but mast not use info offer type .\n",
    "\n",
    "### What is the most viewed and completed offer count in reward ?\n",
    "#### 5 is the most ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the most gender and age in 2018 year ?\n",
    "##### The male with age 50 years old is the most .\n",
    "\n",
    "### What is the most offer type of gender in 2018 year ?\n",
    "##### The male is the most than female .\n",
    "\n",
    "### What is the most offer type age in 2018 year ?\n",
    "##### The age 50 years old is the most age with bogo and discount offer type ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Challenges \n",
    "#### In table portfolio have column channels with array values how split it and convert to columns.\n",
    "#### In table transcript have column value with 3 values how split it and convert to columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Github  link\n",
    "https://github.com/karimsoft/Starbucks_Capstone\n",
    "\n",
    "#### Medium link\n",
    "https://karimsoft.medium.com/karimsoft-starbucks-capstone-58821de875ac"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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