Fraud_Analysis .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dfa09112",
   "metadata": {},
   "source": [
    "# Data Analysis Task\n",
    "\n",
    "## Basic terminology:\n",
    "-- Fraud - the use of a stolen credit card to purchase online\n",
    "-- A chargeback (CHB) - a charge that is returned to a payment card after a customer\n",
    "successfully disputes an item on their account transactions report\n",
    "COMPANY business model:\n",
    "We collect a small fraction (fee) of the amount of all approved orders and give a chargeback guarantee for the full order amount if an order we approve comes back as a chargeback.\n",
    "\n",
    "## COMPANY decision-engine:\n",
    "All orders are given a score by the model (“classification score”)\n",
    "For each merchant, we define a threshold between 0 to 1 for each model.\n",
    "-- When the classification score is above the threshold, the model’s action will be “approve” and order will be auto-approved\n",
    "-- When the classification score is below the threshold, the model’s action will be “decline” and the order will be auto-declined\n",
    "The following dataset consists of 40,825 orders, classifications for these orders by a single model, and a few of the data points we collect for each order.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## Data Dictionary \n",
    "- order_id: a unique identifier for an order\n",
    "- order_date: the date in which the order was submitted to Riskified\n",
    "- order_status : the actual status of an order (in this dataset we included only approvals\n",
    "and chargebacks)\n",
    "- price: order total amount\n",
    "- digital_product: whether the product is digital or tangible\n",
    "- customer_account_age: the interval between the date the account was created and the\n",
    "order was placed\n",
    "- order_source: the type of device used to place the order (web/mobile device\n",
    "app/phone/etc.)\n",
    "- billing_zip: the zip code filled in the billing details\n",
    "- shipping_name_length: shipping name number of characters\n",
    "- classification_score: the score given by the model\n",
    "\n",
    "## Task \n",
    "1. Rely exclusively on model scores using COMPANY decision-engine, and set a decline threshold that will provide a 90% approval rate\n",
    "2. Plot the model scores distribution\n",
    "3. Assuming we aim at a proportion of 50% between the CHB cost and the total revenue (sum amount of CHBs divided by the total revenue), what would have to be the fee based Riskified’s business model?\n",
    "4. In the dataset, there’s a column indicating whether the purchased goods are digital or tangible. What can you say about the risk level of each segment?\n",
    "5. Free exploration - Provide 2-3 interesting breakdowns or insights.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "11c987cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ea355716",
   "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>order_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>order_status</th>\n",
       "      <th>price</th>\n",
       "      <th>digital_product</th>\n",
       "      <th>customer_account_age</th>\n",
       "      <th>order_source</th>\n",
       "      <th>billing_zip</th>\n",
       "      <th>shipping_name_length</th>\n",
       "      <th>classification_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>906967102</td>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>approved</td>\n",
       "      <td>213.88</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>web</td>\n",
       "      <td>33062</td>\n",
       "      <td>13</td>\n",
       "      <td>0.999219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>906987974</td>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>approved</td>\n",
       "      <td>50.00</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>mobile_app</td>\n",
       "      <td>48317</td>\n",
       "      <td>0</td>\n",
       "      <td>0.995886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>906994260</td>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>approved</td>\n",
       "      <td>100.00</td>\n",
       "      <td>True</td>\n",
       "      <td>328</td>\n",
       "      <td>web</td>\n",
       "      <td>92124</td>\n",
       "      <td>0</td>\n",
       "      <td>0.966901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>906997653</td>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>approved</td>\n",
       "      <td>25.00</td>\n",
       "      <td>True</td>\n",
       "      <td>1371</td>\n",
       "      <td>web</td>\n",
       "      <td>95448</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>907408483</td>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>approved</td>\n",
       "      <td>2035.76</td>\n",
       "      <td>False</td>\n",
       "      <td>139</td>\n",
       "      <td>web</td>\n",
       "      <td>61107</td>\n",
       "      <td>17</td>\n",
       "      <td>0.999965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    order_id  order_date order_status    price  digital_product  \\\n",
       "0  906967102  2019-06-01     approved   213.88            False   \n",
       "1  906987974  2019-06-01     approved    50.00             True   \n",
       "2  906994260  2019-06-01     approved   100.00             True   \n",
       "3  906997653  2019-06-01     approved    25.00             True   \n",
       "4  907408483  2019-06-01     approved  2035.76            False   \n",
       "\n",
       "   customer_account_age order_source billing_zip  shipping_name_length  \\\n",
       "0                     0          web       33062                    13   \n",
       "1                     0   mobile_app       48317                     0   \n",
       "2                   328          web       92124                     0   \n",
       "3                  1371          web       95448                     0   \n",
       "4                   139          web       61107                    17   \n",
       "\n",
       "   classification_score  \n",
       "0              0.999219  \n",
       "1              0.995886  \n",
       "2              0.966901  \n",
       "3              0.999854  \n",
       "4              0.999965  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read orig csv into pandas dataframe and make working copy\n",
    "original = pd.read_csv('fraud_dataset.csv')\n",
    "df = original.copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d752f29",
   "metadata": {},
   "source": [
    "## EDA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0459c7cd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 40825 entries, 0 to 40824\n",
      "Data columns (total 10 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   order_id              40825 non-null  int64  \n",
      " 1   order_date            40825 non-null  object \n",
      " 2   order_status          40825 non-null  object \n",
      " 3   price                 40825 non-null  float64\n",
      " 4   digital_product       40825 non-null  bool   \n",
      " 5   customer_account_age  40825 non-null  int64  \n",
      " 6   order_source          40825 non-null  object \n",
      " 7   billing_zip           40825 non-null  object \n",
      " 8   shipping_name_length  40825 non-null  int64  \n",
      " 9   classification_score  40825 non-null  float64\n",
      "dtypes: bool(1), float64(2), int64(3), object(4)\n",
      "memory usage: 2.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# verify dataset number of rows, col, check for nulls, confirm datatypes\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac912a7",
   "metadata": {},
   "source": [
    "notes\n",
    "- .info confirms size of dataset size (40,825)\n",
    "- no nulls\n",
    "- data types match expected content\n",
    "- order_date not datetime\n",
    "- shipping_name_length does not look relevent for this analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d96eeeee",
   "metadata": {},
   "source": [
    "###### EDA: Object Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "53f46a4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "order_status 2 ['approved' 'chargeback']\n",
      "order_source 2 ['web' 'mobile_app']\n",
      "billing_zip 10328\n"
     ]
    }
   ],
   "source": [
    "# count number of unique items in selected objects and print unique items if less than 20 \n",
    "object_list = ['order_status', 'order_source', 'billing_zip']\n",
    "for item in object_list:\n",
    "    if df[item].nunique() < 20:\n",
    "        print(item, df[item].nunique(),df[item].unique())\n",
    "    else:\n",
    "        print(item, df[item].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4ce2b80",
   "metadata": {},
   "source": [
    "- order status has only 2 unqique values: approved and chargeback\n",
    "- order source has only 2 unique values: web and mobile "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5d24c238",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "order_status             order_status\n",
      "approved           40465\n",
      "chargeback           360\n",
      "order_status             order_status\n",
      "approved        0.991182\n",
      "chargeback      0.008818\n",
      "order_source             order_source\n",
      "web                40151\n",
      "mobile_app           674\n",
      "order_source             order_source\n",
      "web             0.983491\n",
      "mobile_app      0.016509\n"
     ]
    }
   ],
   "source": [
    "# return count and % of unique values in selected objects (order_status and order_source)\n",
    "for item in object_list:\n",
    "    if df[item].nunique() < 20:\n",
    "        print(item, df[item].value_counts().to_frame())\n",
    "        print(item, df[item].value_counts(normalize=True).to_frame())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "553d5c9e",
   "metadata": {},
   "source": [
    "**notes on selected object fields**\n",
    "- billing zips over 10K unique values - not likely to use without further analysis \n",
    "- order status and order source each have 2 unique selections \n",
    "- 99% (40,151) of all order are approved (1% chargebacks)\n",
    "- 98% of all orders: web orders (2% mobile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b26cd9f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total orders: 40,825\n",
      "approval count: 40,465 (99.12%)\n",
      "chargeback count: 360 (0.88%)\n"
     ]
    }
   ],
   "source": [
    "# additional eda around approvals/chargebacks as a proportion of total \n",
    "# count of total orders using rows\n",
    "\n",
    "total_orders = df.shape[0]\n",
    "print ('total orders: {:,d}'.format(total_orders))\n",
    "\n",
    "# count of orders approved /chargebacks\n",
    "approval_count = df['order_status'].value_counts()['approved']\n",
    "chargeback_count = df['order_status'].value_counts()['chargeback']\n",
    "\n",
    "#approved_percent = df['order_status'].value_counts(normalize=True)['approved']\n",
    "#chargeback_percent = df['order_status'].value_counts(normalize=True)['chargeback']\n",
    "approved_percent = approval_count/total_orders *100\n",
    "chargeback_percent = chargeback_count/total_orders *100\n",
    "\n",
    "# print results\n",
    "print(\"approval count: {:,d} ({:.2f}%)\".format(approval_count, approved_percent))\n",
    "print(\"chargeback count: {:,d} ({:.2f}%)\".format(chargeback_count, chargeback_percent))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "233cb573",
   "metadata": {},
   "source": [
    "###### EDA on Integer features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e8ff8ee4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum customer account age: -3 days (-0.01 years)\n",
      "Maximum customer account age: 6298 days (17.25 years)\n",
      "Mean customer account age: 736.80 days (2.02 years)\n"
     ]
    }
   ],
   "source": [
    "# Calculate the minimum, maximum, and mean customer account age in days and years\n",
    "min_customer_age = df.customer_account_age.min()\n",
    "max_customer_age = df.customer_account_age.max()\n",
    "mean_customer_age = df.customer_account_age.mean()\n",
    "\n",
    "# Print the results in both days and years\n",
    "print('Minimum customer account age: {:.0f} days ({:.2f} years)'.format(min_customer_age, min_customer_age/365))\n",
    "print('Maximum customer account age: {:.0f} days ({:.2f} years)'.format(max_customer_age, max_customer_age/365))\n",
    "print('Mean customer account age: {:.2f} days ({:.2f} years)'.format(mean_customer_age, mean_customer_age/365))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ea3e7d6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start order date:  2019-06-01\n",
      "end order date:  2019-07-01\n"
     ]
    }
   ],
   "source": [
    "start_order_date = df.order_date.min()\n",
    "end_order_date = df.order_date.max()\n",
    "print('start order date: ', start_order_date)\n",
    "print('end order date: ', end_order_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a6e0cb0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min_price: $0\n",
      "max_price: $11,033\n",
      "mean_price: $396\n"
     ]
    }
   ],
   "source": [
    "min_price = df.price.min()\n",
    "max_price = df.price.max()\n",
    "mean_price = df.price.mean()\n",
    "print('min_price: ${:.0f}'.format(min_price))\n",
    "print('max_price: ${:,.0f}'.format(max_price))\n",
    "print('mean_price: ${:.0f}'.format(mean_price))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd89fea3",
   "metadata": {},
   "source": [
    "**Notes on integer fields**\n",
    "- customer acount age \n",
    "    - check negative min age - perhaps it is possible to order before \n",
    "     creating account\n",
    "    - metric assumued to be days\n",
    "    - avg customer acount age: 736 days / 2.02 years\n",
    "- date ordered\n",
    "    - can be transformed to datetime\n",
    "    - one month of data\n",
    "- price \n",
    "    - avg order $396"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c33a5aad",
   "metadata": {},
   "source": [
    "**Price EDA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2a9c2ccc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>40825.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>396.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>772.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11033.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         price\n",
       "count  40825.0\n",
       "mean     396.0\n",
       "std      772.0\n",
       "min        0.0\n",
       "25%       35.0\n",
       "50%       82.0\n",
       "75%      280.0\n",
       "max    11033.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create price boxplot \n",
    "fig, ax = plt.subplots(figsize=(20, 2))\n",
    "ax.boxplot(df['price'], vert=False)\n",
    "\n",
    "# label\n",
    "plt.title('Distribution of Price')\n",
    "plt.xlabel('Price')\n",
    "plt.show()\n",
    "\n",
    "#describe (summary stats on price)\n",
    "df.price.describe().round().to_frame()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c6d54e0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82.44\n"
     ]
    }
   ],
   "source": [
    "print(df['price'].median())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae3b91fd",
   "metadata": {},
   "source": [
    "**Price Distribution (Box Plot)**\n",
    "* box represents the interquartile range (IQR) - middle 50% of the data\n",
    "* lower edge of box - 25th percentile (Q1)\n",
    "* upper edge of box - 75th percentile (Q3)\n",
    "* horizontal line in box - median (50th percentile) \n",
    "* whiskers - min and max values that are within 1.5 times the IQR from box edge \n",
    "* outliers - data points outside the range \n",
    "* 75% of all items in dataset under $280 price\n",
    "\n",
    "* median price $82\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8598e494",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# price distribution by order status \n",
    "df.hist(column='price', by = 'order_status', color= 'magenta',bins=50)\n",
    "plt.ylabel('count')\n",
    "plt.xlabel('price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dbfc5494",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86.56% of orders are priced under $1000\n"
     ]
    }
   ],
   "source": [
    "# orders with a price under $1000\n",
    "under_1000 = len(df[df['price'] < 1000])/ total_orders *100\n",
    "\n",
    "print('{:.2f}% of orders are priced under $1000'.format(under_1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "17c87a1f",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">describe</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>count</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>std</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>approved</th>\n",
       "      <td>$35.91</td>\n",
       "      <td>$82.44</td>\n",
       "      <td>$284.70</td>\n",
       "      <td>$40,465.00</td>\n",
       "      <td>$11,033.31</td>\n",
       "      <td>$398.44</td>\n",
       "      <td>$0.00</td>\n",
       "      <td>$774.55</td>\n",
       "      <td>$16,122,719.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chargeback</th>\n",
       "      <td>$34.11</td>\n",
       "      <td>$80.95</td>\n",
       "      <td>$200.00</td>\n",
       "      <td>$360.00</td>\n",
       "      <td>$1,033.73</td>\n",
       "      <td>$135.10</td>\n",
       "      <td>$9.80</td>\n",
       "      <td>$147.64</td>\n",
       "      <td>$48,637.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             describe                                                    \\\n",
       "                  25%     50%      75%       count         max     mean   \n",
       "order_status                                                              \n",
       "approved       $35.91  $82.44  $284.70  $40,465.00  $11,033.31  $398.44   \n",
       "chargeback     $34.11  $80.95  $200.00     $360.00   $1,033.73  $135.10   \n",
       "\n",
       "                                         sum  \n",
       "                min      std           price  \n",
       "order_status                                  \n",
       "approved      $0.00  $774.55  $16,122,719.73  \n",
       "chargeback    $9.80  $147.64      $48,637.12  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create price pivot table calculate summary statistics of price for each order_status\n",
    "price_pivot_df = pd.pivot_table(df, index='order_status', values='price', aggfunc=['describe', 'sum'])\n",
    "sum_price_pivot_df = pd.pivot_table(df, index='order_status', values='price', aggfunc='sum')\n",
    "sum_price_pivot_df = price_pivot_df.applymap('${:,.2f}'.format)\n",
    "sum_price_pivot_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a9bb8a3",
   "metadata": {},
   "source": [
    "**Price Distribution**\n",
    "\n",
    "*  86% orders under $1000\n",
    "*  no normal distubtion on price (can drop outliers for additional analysis)\n",
    "\n",
    "*Chargebacks*\n",
    "* chargeback price range $ 9.80 - 1,033\n",
    "\n",
    "* sum of all chargeback: $48,637.12\n",
    "\n",
    "* 75% of all chargebacks price under $200 \n",
    "\n",
    "*Approved* \n",
    "* approved price range $ 0 - 11,033\n",
    "\n",
    "* 75% of approved orders price under $285 \n",
    "\n",
    "* sum of all approved: $16,122,719.73\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "422f92c0",
   "metadata": {},
   "source": [
    "**Classification Score EDA** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6b8d7ba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution of order_status (approved/chargeback) by score (0-1)\n",
    "df.hist(column='classification_score', by='order_status', bins=50, color='magenta')\n",
    "plt.xlabel('Classification Score')\n",
    "plt.ylabel('Order Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b67306b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">classification_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>count</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>approved</th>\n",
       "      <td>0.989386</td>\n",
       "      <td>0.999204</td>\n",
       "      <td>0.999897</td>\n",
       "      <td>40465.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.945900</td>\n",
       "      <td>0.038408</td>\n",
       "      <td>0.163566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chargeback</th>\n",
       "      <td>0.337844</td>\n",
       "      <td>0.785808</td>\n",
       "      <td>0.968291</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.649584</td>\n",
       "      <td>0.048232</td>\n",
       "      <td>0.340546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             classification_score                                         \\\n",
       "                              25%       50%       75%    count       max   \n",
       "order_status                                                               \n",
       "approved                 0.989386  0.999204  0.999897  40465.0  1.000000   \n",
       "chargeback               0.337844  0.785808  0.968291    360.0  0.999997   \n",
       "\n",
       "                                            \n",
       "                  mean       min       std  \n",
       "order_status                                \n",
       "approved      0.945900  0.038408  0.163566  \n",
       "chargeback    0.649584  0.048232  0.340546  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# score box plot by order staus \n",
    "fig, ax = plt.subplots(figsize=(20, 2))\n",
    "df.boxplot(column=['classification_score'], by='order_status', vert=False, color='magenta', ax=ax)\n",
    "plt.xlabel('Classification Score')\n",
    "plt.ylabel('Order Status')\n",
    "plt.title('')\n",
    "plt.show()\n",
    "\n",
    "# pivot table to calculate summary stats on fraud_score on order_status\n",
    "score_pivot_df = df.pivot_table(values=['classification_score'], index='order_status', aggfunc='describe')\n",
    "score_pivot_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "884fb27c",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pw8qVKystKy4upnPnznzwwQfVXp+OHTvyySefANClSxcWL15csW7rrbdm4cKFrFmzBoCSkhJWr15dEVdEIGm98yqJbt268dFHH+U8xqrXIhPbIYccwsMPP1zrucjeXhK9evVi3bp1vP/++/To0YNFixZVxNytWzcWLlyYs87u3btz+eWXM2/ePH7+859XlCkqKqJTp06V4u/UqRMff/wxAIWFhZSXl6/3M5C9ffv27Sudy8w2hYWF1f6cFBYWVmofkujevXvF8VS9xtnH0qJFCwoKCpBU6ZoXFBTQqVMnPvjgAyC5posWLeLzzz9HEltttRWLFy9mzZo17L///jz55JMV5zpzLsvLyyvOZ64YMnV07dq14lx3796dzz77jA8++ICzzz6be++9l/nz51c63sx22eejuLiY7t27I4mPPvqI0aNHc8MNN1Rcx4KCAtatW8dRRx3FfffdV+2569KlC23btmXBggWsWbOGiKgok7kW7du3Z+nSpRXnul27dixbtqzS+Y8IunbtyrJly/j888+BpE19/PHHNX7eZV+boqKiip+Tqtt07tyZFStW8Pnnn1c6hs6dO7NkyZI6f25lZD7/MvEuW7aMdevWVdp/27ZtWbFiBUVFRZx++unccMMNFBUVVbSf4uJi1q5dW7H/oqIiCgoK6Nq1Kx999FFFm8jEmkvmc6uqoqIi1qxZU3GeunTpwrJlyyrqrImkijq23XZbDj30UG644Yb1ylX9/Mred2FhIcOGDePOO++sWF7180YS++23X8XPRFFRER07dqxo44WFhRVtKd/ffZmf0ep+D2TaQPbnMnxxrTJx1Haeqms3VevNHGfmc6lFixYUFhZWtIHMZ1zm9051v3/y2XdVBxxwAE888URe22e35+rkOra6KCoqAsirDW4uWrZsyWeffdYgdRcXF1d8JppZzar+zWI1U3V/YNvGKS0tjZkzZzZ1GHVy/fXX89BDDzF06FDOOuusnGWOOuqoRo7KrPk7/PDDefzxx9dLMFn9yZVUbYxtLX+b83nenGM3M7MtQ12TJOPGjWPd2nW1Pt3mjTffoKSkhDvuuGNjQ2x0kmZFRGnV5Z6TxICkF8k///lPIoJHHnmk4tvqbBMnTmyCyMyavwcffNAJkga2MTewvvltHJvzed6cYzczsy2Dv+zOn4fbGABTp06t6FpaXl7O1KlT1+tN0tjDbMzMzMzMzKx+jBs3Lu+yZWVltGzZsgGj2XS5J4kBMH369IrxzGvXruWxxx5r4ojMzMzMzMzMGpd7khgAgwcP5uGHH66YNG3IkCFNHZKZmZmZmZnVk0svvTTvspk5SbZE7kliAIwYMYKCgqQ5FBQUMGLEiPXKHHHEEY0dlpmZmZmZmVmjcZLEgOTxiQcddBCSOPjgg3M+AvjMM89sgsjMmr/DDz+cNm3aNHUYzVrmka2Nva3lb3M+z5tz7GZmtmXwI4Dz5ySJVRgxYgS77LJLzl4kGU3RmyTTw6WqXH+UtmixaY0ga9++PQDt2rUDqj+W6my//fb1Gk+PHj0oLCystVzV85iJu0OHDgC0atUq53a5bvSLi4vp3r17znozOnbsWPG6S5culdZtvfXWFBUVVbwvKSmpFJeknOdVEl/60pdy7i+zbbZMbIccckjO5VW3y95eEr1792abbbYBkvOcHXO3bt2qrbN79+6MGDGCCy64oFKZoqKi9eLPTl4WFhbWeGNWVFS03rnMbFPTz0nV9iGp0vFUvcbZdbVo0YLi4mJatmxJjx49kERRUREtW7asaAOQXNPi4uKK+rt3715R//77719RrqCggJ49e9KyZctK5zNXDD179qSkpIRtttmGli1b0rJlS3r37l2x37Fjx9KzZ8/1jnerrbZa73wUFxfTq1cvevfuTatWrTjrrLMq2pOkinNUdZb4queuS5cu9O7dm+Li4oprlSmTuRaZn6nMuch8ZmQvA+jatWvFOYOkTdX2eZe9vqioiOLi4pzbdO7cuaLu7GPo3LkzUPfPrYzsY+nWrRstW7Zcb/9t27atiC9znrM/M4qLiyvtP9OeevbsWalNZGLNJfscZ8tsn4mpS5culeqsiaSKWLfffvtqv0So+vmVve+SkhJOOOGESsurnh9JlX4mioqKKn2eFBYWVntdq1NQUFDj74HMuuzPZfjiWmXiyGc/uVStF6j0udSiRYtKnyEZmd871f3+yWffVR1wwAF5b1/1ZzOXXMdWF0VFRXm3wc1FQ076mP2ZaGZWn+TH1jWM0tLSmDlzZlOHYWZmZmZmZlugzNNs6jIXSfa269au42cX/qzaMhePv5g33nyDkpIS7rjjjg2Os6lImhURpVWXb1pfu5uZmZmZmZnZRuvbt2+D76OgoCCvXn6bEydJNoCkFRHRtvaSZmZmZmZmZo3v9NNPb/B9FBcV06NHjwbfT2OqddCmpOGS2qWvL5T0Z0kDGz60jSPJCSAzMzMzMzMzy1s+M1v9LCKWS9oP+DpwC3BDfQci6V5JsyS9ImlMumyFpN9KekHSPyV1S5dPl3SVpBmSXpa0Z7r8Ikk3SvoHcKuk3ul2L6X/95LUQdI8SQXpNq0lvSepSNL2kh5K43hS0k5pmT6Snpb0vKSL6/vYzczMzMzMzKzp5dPbYl36/xHADRHxF0kXNUAsp0XEEkmtgOcl3Q20AV6IiB9I+jnwC+CctHybiNhH0gHATUC/dPkgYL+I+FTSfcCtEXGLpNOAayLiGElzgAOBx4CjgL9HxBpJNwJnRsRbkvYCrge+BlydHvutks5ugGM3MzMzMzMz22S88+47XDy++j4C77zzDqs/W92IETWOfJIk8yX9H3Aw8GtJLWmYRwd/V9Kx6ettgR2AciAzTe4fgT9nlf8TQEQ8Iam9pI7p8r9GxKfp668C30hf3wZcnr6+AziRJEkyArheUltgH2Ba1iPnMs8t2xc4LqueX+c6gLQHzBiAXr165XXQZmZmZmZmZpuSfCZ9bdW6Fa1at2qUCWIbUz5JkhOAw4AJEfGJpB7AD+szCEmDSZIwX42IVZKmAyU5ikY1r7Pfr6xhV5kyfwUuldSZpOfJoyS9Vj6JiAG1bFt95RE3AjdC8gjg2sqbmZmZmZmZbWoaY9LXTVU+PUK6AjOBzyT1AoqA1+s5jg7Ax2mCZCdg76z4jk9fnww8lbXNiQDpXClLI2JpjnpnkPQUARiZ2T4iVgDPkQyjuT8i1kXEMuBtScPTeiWpf7rtv6rUY2ZmZmZmZmbNTD49SR4g6UUhkt4dfYA3gF3rMY6HgDMlvZTW/Uy6fCWwq6RZwFLSxEjqY0kzgPbAadXU+13gJkk/BBYCo7PW3QFMAwZnLRsJ3CDpQpJk0FRgDnAucLukc4G7N/QgzczMzMzMbMu1Zs0a3n//fVavbn5zeWyqSkpK2GabbSgqKsqrvCLqNiokffzvGRFxxgbEV9d9rYiItjmWTwfOj4iZDR3DhiotLY2ZMzfZ8MzMzMzMzKyRvf3227Rr144uXbqQNRemNZCIYPHixSxfvpw+ffpUWidpVkSUVt2mzhOwRsQLwB4bHqaZmZmZmZnZlmf16tVOkDQiSXTp0qVOPXdqHW4j6bystwXAQJKhKw0uVy+SdPngxti/mZmZmZmZWX1ygqRx1fV85zMnSbus12tJ5ijxvBxmZmZmZmZm1qzkM9zm1Yj4ZfrvkoiYAhzV0IGZmZmZmZmZ2ca56KKLmDBhQoPUPW/ePG6//fZ6K7cpyCdJMi7PZWZmZmZmZmbWRCKC8vLyjapj7dq1eZfdopIkkoZK+h3QU9I1Wf9uJhl2Y2ZmZmZmZmaN6IorrqBfv37069ePq666innz5rHzzjszduxYBg4cyHvvvccll1zCjjvuyMEHH8wbb7xRse3cuXM57LDDGDRoEPvvvz+vv/46AKNGjeK8885jyJAhXHDBBTn3+/jjjzNgwAAGDBjAV77yFZYvX86Pf/xjnnzySQYMGMCVV17JvHnz2H///Rk4cCADBw5kxowZAOuVu/nmmznnnHMq6j7yyCOZPn0669atY9SoUfTr14/ddtuNK6+8sgHPZG41zUnyX2AmMAyYlbV8OfD9hgzKzMzMzMzMzCqbNWsWkydP5tlnnyUi2GuvvTjwwAN54403mDx5Mtdffz2zZs1i6tSpvPjii6xdu5aBAwcyaNAgAMaMGcPEiRPZYYcdePbZZxk7diyPPvooAG+++SaPPPIIhYWFOfc9YcIErrvuOvbdd19WrFhBSUkJl112GRMmTOD+++8HYNWqVTz88MOUlJTw1ltvcdJJJzFz5sz1yt1888059zF79mzmz5/Pyy+/DMAnn3xSj2cvP9UmSSJiDjBH0u0RsaYRYzIzMzMzMzOzKp566imOPfZY2rRpA8A3vvENnnzySXr37s3ee+8NwJNPPsmxxx5L69atARg2bBgAK1asYMaMGQwfPryivs8++6zi9fDhw6tNkADsu+++nHfeeYwcOZJvfOMbbLPNNuuVWbNmDeeccw6zZ8+msLCQN998s07H17dvX8rKyvjOd77DEUccwaGHHlqn7etDPk+32U7SpcAuQElmYUT0bbCozMzMzMzMzKySiMi5PJM0ycj12Nvy8nI6duzI7Nmz86qjqh//+MccccQRPPjgg+y999488sgj65W58sor2WqrrZgzZw7l5eWUlJTkqAlatGhRae6U1atXA9CpUyfmzJnD3//+d6677jruvPNObrrpphrjqm/5TNw6GbiBZB6SIcCtwG0NGZSZmZmZmZmZVXbAAQdw7733smrVKlauXMk999zD/vvvv16Ze+65h08//ZTly5dz3333AdC+fXv69OnDtGnTgCThMmfOnLz3PXfuXHbbbTcuuOACSktLef3112nXrh3Lly+vKLN06VJ69OhBQUEBt912G+vWrQNYr9x2223H7NmzKS8v57333uO5554DYNGiRZSXl3Pcccdx8cUX88ILL2zYidoI+fQkaRUR/5SkiHgHuEjSk8AvGjg2MzMzMzMzM0sNHDiQUaNGseeeewLwv//7v3Tq1Gm9MieeeCIDBgygd+/elZIoU6ZM4ayzzmL8+PGsWbOGESNG0L9//7z2fdVVV/HYY49RWFjILrvswtChQykoKKBFixb079+fUaNGMXbsWI477jimTZvGkCFDKnqn7L777pXKfe9736NPnz7stttu9OvXj4EDBwIwf/58Ro8eXdHL5NJLL93oc1ZXqq67TkUB6V/A/sBdwKPAfOCyiNix4cPbfJWWlsbMmTObOgwzMzMzMzPbRLz22mvsvPPOTR3GFifXeZc0KyJKq5bNZ7jN94DWwHeBQcA3gVM3PkwzMzMzMzMzs01HXhO3RsTzwApgNICk4cCzDRmYmZmZmZmZmTWuyZMnc/XVV1datu+++3Ldddc1UUSNK58kyThgWh7LzMzMzMzMzGwzNnr0aEaPHt3UYTSZapMkkoYChwM9JV2Ttao9yZNuzMzMzMzMzMyajZp6kvwXmAkMA2ZlLV8OfL8hgzIzMzMzMzMza2zVJkkiYg4wR9LtEbFGUhHQD5gfER83WoRmZmZmZmZmZo2g2qfbSJooadc0QdIBmAPcCrwo6aRGi9DMzMzMzMzMtjht27Zt9H3WNNxm/4g4M309GngzIo6R1B34G/CnBo/OzMzMzMzMrJkaP348y5cvr7f62rVrx4UXXlhv9eVj7dq1tGiRzzNhNg81HcnnWa8PIX2aTUR8IKlBgzIzMzMzMzNr7pYvX86FP6m/pMb4X42vtcwxxxzDe++9x+rVqzn33HMZM2YMbdu25YwzzuCxxx6jU6dOTJ06lW7dujF48GAGDBjAc889x7Jly7jpppvYc889ueiii/jvf//LvHnz6Nq1K5deeimnnXYaCxcupFu3bkyePJkOHTrQv39/ysrKKCgoYNWqVey4446UlZXx7rvvcvbZZ7Nw4UJat27NpEmT2GmnnXj77bc5+eSTWbt2LYcddli9nZe6qHa4DfCJpCMlfQXYF3gIQFILoFVjBGdmZmZmZmZm9eemm25i1qxZzJw5k2uuuYbFixezcuVKBg4cyAsvvMCBBx7IL3/5y4ryK1euZMaMGVx//fWcdtppFctnzZrFX/7yF26//XbOOeccTj31VF566SVGjhzJd7/73YokyeOPPw7Afffdx9e//nWKiooYM2YMv/vd75g1axYTJkxg7NixAJx77rmcddZZPP/883Tv3r1xT0yqpiTJGcA5wGTgexHxQbr8IOCBhg7MzMzMzMzMzOrXNddcQ//+/dl777157733eOuttygoKODEE08E4Jvf/CZPPfVURfmTTkqmJD3ggANYtmwZn3zyCQDDhg2jVauk/8TTTz/NySefDMApp5xSsf2JJ57IHXfcAcDUqVM58cQTWbFiBTNmzGD48OEMGDCAM844gwULFgDwr3/9q2J/p5xySgOfidxqerrNm8B6/Vsi4u/A3xsyKDMzMzMzMzOrX9OnT+eRRx7h6aefpnXr1gwePJjVq1evVy57io2q021k3rdp06ba/WTKDBs2jHHjxrFkyRJmzZrF1772NVauXEnHjh2ZPXt2jds2lZp6kpiZmZmZmZlZM7F06VI6depE69atef3113nmmWcAKC8v56677gLg9ttvZ7/99qvYJtMT5KmnnqJDhw506NBhvXr32Wcfpk6dCsCUKVMqtm/bti177rkn5557LkceeSSFhYW0b9+ePn36MG3aNAAigjlz5gCw7777VqqnKTSfKWjNzMzMzMzMrFqHHXYYEydOZPfdd2fHHXdk7733BpJeIa+88gqDBg2iQ4cOFYkRgE6dOrHPPvtUTNyayzXXXMNpp53Gb37zm4qJWzNOPPFEhg8fzvTp0yuWTZkyhbPOOovx48ezZs0aRowYQf/+/bn66qs5+eSTufrqqznuuOMa5iTUQhFR/UqpADg+Iu5svJCah9LS0pg5c2ZTh2FmZmZmZmabiNdee42dd9654v2m8gjgtm3bsmLFivWWDx48mAkTJlBaWlof4TWZqucdQNKsiFjvwGrsSRIR5ZLOAZwkMTMzMzMzM6tHG5LQsIaVz3CbhyWdD9wBrMwsjIglDRaVmZmZmZmZmTWKXL1IgEpDZLYU+SRJMg9CPjtrWQB96z8cMzMzMzMzM7OmUWuSJCL6NEYgZmZmZmZmZmZNqdZHAEtqLelCSTem73eQdGTDh2ZmZmZmZmZm1nhqTZIAk4HPgX3S9+8D4xssIjMzMzMzMzOzJpBPkmT7iLgcWAMQEZ8CatCozMzMzMzMzKzBjRo1irvuumuz3OfgwYOZOXNmPUT0hXwmbv1cUiuSyVqRtD3wWb1GYWZmZmZmZraFufj8i1n24bJ6q6/9Vu352YSf1Vt9tYkIIoKCgnz6X2we8kmS/AJ4CNhW0hRgX2BUQwZlZmZmZmZm1twt+3AZZz91du0F83TdftfVWubWW29lwoQJSGL33XensLCQJ554giuuuIIPPviAyy+/nOOPP54VK1Zw9NFH8/HHH7NmzRrGjx/P0Ucfzbx58xg6dChDhgzh6aef5t577+XWW29lypQpbLvttnTt2pVBgwZx/vnnM3fuXM4++2wWLlxI69atmTRpEjvttBMAjzzyCFdffTUffvghV1xxBUceeSTz5s3jlFNOYeXKlQBce+217LNPMvPH5Zdfzm233UZBQQFDhw7lsssuqzim8vJyRo8ezbbbbsv48Rs3O0g+T7d5WNILwN4kw2zOjYhFG7VXMzMzMzMzM2tUr7zyCpdccgn/+te/6Nq1K0uWLOG8885jwYIFPPXUU7z++usMGzaM448/npKSEu655x7at2/PokWL2HvvvRk2bBgAb7zxBpMnT+b6669n5syZ3H333bz44ousXbuWgQMHMmjQIADGjBnDxIkT2WGHHXj22WcZO3Ysjz76KADz5s3j8ccfZ+7cuQwZMoT//Oc/fOlLX+Lhhx+mpKSEt956i5NOOomZM2fyt7/9jXvvvZdnn32W1q1bs2TJkopjWrt2LSNHjqRfv3789Kc/3ehzVG2SRNLAKosWpP/3ktQrIl7Y6L2bmZmZmZmZWaN49NFHOf744+natSsAnTt3BuCYY46hoKCAXXbZhQ8//BBIhtL85Cc/4YknnqCgoID58+dXrOvduzd77703AE899RRHH300rVq1AuCoo44CYMWKFcyYMYPhw4dX7P+zz76YueOEE06goKCAHXbYgb59+/L666/Tp08fzjnnHGbPnk1hYSFvvvkmkPQ6GT16NK1bt64UN8AZZ5zBCSecUC8JEqi5J8lv0/9LgFJgDklPkt2BZ4H96iUCMzMzMzMzM2twEYG0/nNYWrZsWakMwJQpU1i4cCGzZs2iqKiI7bbbjtWrVwPQpk2b9cpXVV5eTseOHZk9e3bO9VXjkMSVV17JVlttxZw5cygvL6ekpKTGuAH22WcfHnvsMX7wgx9UlN8Y1c6uEhFDImII8A4wMCJKI2IQ8BXgPxu9ZzMzMzMzMzNrNAcddBB33nknixcvBqg0bKWqpUuX8qUvfYmioiIee+wx3nnnnZzl9ttvP+677z5Wr17NihUreOCBBwBo3749ffr0Ydq0aUCS6JgzZ07FdtOmTaO8vJy5c+dSVlbGjjvuyNKlS+nRowcFBQXcdtttrFu3DoBDDz2Um266iVWrVq0X97e//W0OP/xwhg8fztq1azfi7CTymbh1p4j4d+ZNRLwsacBG79nMzMzMzMzMGs2uu+7KT3/6Uw488EAKCwv5yle+Um3ZkSNHctRRR1FaWsqAAQMqJlytao899mDYsGH079+f3r17U1paSocOHYCkN8pZZ53F+PHjWbNmDSNGjKB///4A7Ljjjhx44IF8+OGHTJw4kZKSEsaOHctxxx3HtGnTGDJkSEWPlcMOO4zZs2dTWlpKcXExhx9+OL/61a8qYjjvvPNYunQpp5xyClOmTNmop+2ouq4xFQWkqcAK4I8kjwH+JtA2Ik7a4L1uAUpLS6O+n9dsZmZmZmZmm6/XXnuNnXfeueL95v4I4IwVK1bQtm1bVq1axQEHHMCNN97IwIFVpzltOlXPO4CkWRFRWrVsPj1JRgFnAeem758AbtjIGM3MzMzMzMy2aE2R0GgIY8aM4dVXX2X16tV861vf2qQSJHVVY5JEUiFwf0QcDFzZOCGZmZmZmZmZ2ebi9ttvb+oQ6k2NA3UiYh2wSlKHRorHzMzMzMzMzKxJ5DPcZjXwb0kPAyszCyPiuw0WlZmZmZmZmVkzVNPjbK3+1TYPa1X5TPn6APAzkrlIZmX9qzNJN0s6fkO23VD1tU9J0yWtN6mLmZmZmZmZWT5KSkpYvHhxnW/cbcNEBIsXL6akpCTvbfLpSXIH8D8kT7aZGxGrNzC+jaIk1aaIKG+K/ZuZmZmZmZltjG222Yb333+fhQsXNnUoW4ySkhK22WabvMtXmySR1AL4FXAa8A5Jr5NtJE0GfhoRa2qrXNKpwPkkCZaXgHXAAZLOA7oDP4qIuyS1Bf4CdAKKgAsj4i+StgP+BjwGfBU4Jq1zJPAesAiYFRETJG0PXAd0A1YBp0fE62koB0s6F9gKOC8i7k/rvg1ok5Y5JyJmpHH/CDgFKAf+FhE/zjqmAmAy8F5EXFjbOTAzMzMzMzMDKCoqok+fPk0dhtWgpp4kvwHaAX0iYjmApPbAhPTfuTVsi6RdgZ8C+0bEIkmdgSuAHsB+wE7AX4G7SOY9OTYilknqCjwj6a9pVTsCoyNibDrc5TjgK2nsL/DF0J8bgTMj4i1JewHXA19L120HHAhsDzwm6X+Aj4BDImK1pB2APwGlkoYCxwB7RcSqNO7s8zUFeDkiLqnp+M3MrHqTJk2irKysqcMwMzPLacGCBQD06NGjiSMxy1/fvn05/fTTmzqMzV5NSZIjgS9H1mCpNIlxFvA6tSRJSBIUd0XEonTbJenkNPemQ2ZelbRVWlbAryQdQNJ7oydJrw+AdyLimfT1fsBfIuJTAEn3pf+3BfYBpmVNgNMyK5Y7032+JamMJEHzNnCtpAEkPVy+nJY9GJgcEasycWfV839pXTkTJJLGAGMAevXqVcvpMTPbcpWVlTF3zlx6ru7Z1KGYmZmtZ2VJ8ryK1e82yUwDZnU2v2R+U4fQbNSUJInsBEnWwnWS8pllRiTDbKr6rEoZSIbPdAMGRcQaSfOAzMwqK3OUr6oA+CQiBlSzvmocAXwf+BDon26f+QSsLm6AGcAQSb/NNTdLRNxI0qOF0tJSz8RjZlaDnqt7cu7bteXbzczMGt/Vfa4G8O8p22xk2qxtvJqebvNqOv9HJZK+SdKTpDb/BE6Q1CXdrnMNZTsAH6UJkiFA72rKPQUcJakk7T1yBCQ9XIC3JQ1P9yVJ/bO2Gy6pIJ23pC/wRrrPBWkPk1OAwrTsP4DTJLXOEfcfgAdJeqzkM+mtmZmZmZmZmW0marrRPxv4s6TTSOb9CGAPoBVwbG0VR8Qrki4BHpe0DnixhuJTgPskzQRmU00SJiKeT+cqmUMymexMYGm6eiRwg6QLSSZ/nZqWgyQp8jjJEJ4z03lIrgfuThMrj5H2WImIh9IhODMlfU6SFPlJVgxXSOoA3CZppJ+2Y2ZmZmZmZtY8VJskiYj5wF6SvgbsSjIM5W8R8c98K4+IW4BbaljfNv1/EcnTa3LpV+X9hIi4KO3p8QTw27SOt4HDcuxjVDX7fgvYPWvRuKx1lwGXVSk/OOv1L6qJtdmbNGkSgCcEMjMzMzMz28I1x/vDWoeMRMSjwKONEEu+bpS0C8mcJbdExAtNHdCWxE+jMDMzMzMzM2ie94eb3bwaEXFyU8dgZmZmZmZmZs1PTRO3mpmZmZmZmZltMZwkMTMzMzMzMzNjMxxuY01rwYIFfPrpp4wbN672wmZm1SgrK6OouKipwzAzMzNrFhYWL2RN2ZpGv08rKyujVatWjbrPhuaeJGZmZmZmZmZmuCeJ1VGPHj0AuPTSS5s4EjPbnI0bN47Vz65u6jDMzMzMmoVun3ejpG9Jo9+nNccRBu5JYmZmZmZmZmaGkyRmZmZmZmZmZoCTJGZmZmZmZmZmgJMkZmZmZmZmZmaAJ261Ourbt29Th2BmZmZmZmabgOZ4f+gkidXJ6aef3tQhmJmZmZmZ2SagOd4feriNmZmZmZmZmRlOkpiZmZmZmZmZAR5uY2ZmTWR+yXyu7nN1U4dhZma2nvdL3gfw7ynbbMwvmc/2bN/UYTQLTpKYmVmja46TfJmZWfPRZkEbAEp6lDRxJGb52Z7t/fdVPXGSxMzMGl1znOTLzMzMzDZ/npPEzMzMzMzMzAwnSczMzMzMzMzMACdJzMzMzMzMzMwAJ0nMzMzMzMzMzABQRDR1DM2SpIXAO3kU7QosauBwzDYlbvO2JXK7ty2R271tidzubUu0ubb73hHRrepCJ0mamKSZEVHa1HGYNRa3edsSud3blsjt3rZEbve2JWpu7d7DbczMzMzMzMzMcJLEzMzMzMzMzAxwkmRTcGNTB2DWyNzmbUvkdm9bIrd72xK53duWqFm1e89JYmZmZmZmZmaGe5KYmZmZmZmZmQFOkjQKSYdJekPSfyT9OMd6SbomXf+SpIFNEadZfcqj3Y9M2/tLkmZI6t8UcZrVp9rafVa5PSStk3R8Y8Zn1hDyafeSBkuaLekVSY83doxm9S2Pv3M6SLpP0py03Y9uijjN6oukmyR9JOnlatY3m3taJ0kamKRC4DpgKLALcJKkXaoUGwrskP4bA9zQqEGa1bM82/3bwIERsTtwMc1sLKNtefJs95lyvwb+3rgRmtW/fNq9pI7A9cCwiNgVGN7YcZrVpzw/788GXo2I/sBg4LeSihs1ULP6dTNwWA3rm809rZMkDW9P4D8RURYRnwNTgaOrlDkauDUSzwAdJfVo7EDN6lGt7T4iZkTEx+nbZ4BtGjlGs/qWz+c9wHeAu4GPGjM4swaST7s/GfhzRLwLEBFu+7a5y6fdB9BOkoC2wBJgbeOGaVZ/IuIJknZcnWZzT+skScPrCbyX9f79dFldy5htTurapr8N/K1BIzJreLW2e0k9gWOBiY0Yl1lDyufz/stAJ0nTJc2SdGqjRWfWMPJp99cCOwP/Bf4NnBsR5Y0TnlmTaDb3tC2aOoAtgHIsq/pIoXzKmG1O8m7TkoaQJEn2a9CIzBpePu3+KuCCiFiXfLlottnLp923AAYBBwGtgKclPRMRbzZ0cGYNJJ92/3VgNvA1YHvgYUlPRsSyBo7NrKk0m3taJ0ka3vvAtlnvtyHJKNe1jNnmJK82LWl34PfA0IhY3EixmTWUfNp9KTA1TZB0BQ6XtDYi7m2UCM3qX75/5yyKiJXASklPAP0BJ0lsc5VPux8NXBYRAfxH0tvATsBzjROiWaNrNve0Hm7T8J4HdpDUJ52saQTw1ypl/gqcms4IvDewNCIWNHagZvWo1nYvqRfwZ+AUf5tozUSt7T4i+kTEdhGxHXAXMNYJEtvM5fN3zl+A/SW1kNQa2At4rZHjNKtP+bT7d0l6TyFpK2BHoKxRozRrXM3mntY9SRpYRKyVdA7JUwwKgZsi4hVJZ6brJwIPAocD/wFWkWSezTZbebb7nwNdgOvTb9XXRkRpU8VstrHybPdmzUo+7T4iXpP0EPASUA78PiJyPkLSbHOQ5+f9xcDNkv5NMgzhgohY1GRBm20kSX8ieVJTV0nvA78AiqD53dMq6QFmZmZmZmZmZrZl83AbMzMzMzMzMzOcJDEzMzMzMzMzA5wkMTMzMzMzMzMDnCQxMzMzMzMzMwOcJDEzMzMzMzMzA5wkMTMzs0YiqbukqZLmSnpV0oOSvixpO0n19khYSf9P0sHp6/0lvSJptqSeku7awDpHSdo66/3vJe1SD7FuJel+SXMy52Rj6zQzM7MN50cAm5mZWYOTJGAGcEtETEyXDQDaAe8B90dEvwbY70Tg2YiYvJH1TAfOj4iZ9RLYF/X+H/BqRFydvt89Il7ayDpbRMTaegnQzMxsC+OeJGZmZtYYhgBrMgkSgIiYHRFPZhdKe5U8KemF9N8+6fIekp5Ie4S8nPYQKZR0c/r+35K+n5a9WdLxkv4XOAH4uaQp2T1W0m0npNu9JOk76fKfS3o+rfNGJY4HSoEp6f5bSZouqTTd5qS0npcl/TrrWFZIuiTtJfKMpK1ynJcewPtZ5+SlrO1/lNY7R9Jl6bIBaV0vSbpHUqd0+XRJv5L0OHCupEGSHpc0S9LfJfXY8EtnZma25XCSxMzMzBpDP2BWHuU+Ag6JiIHAicA16fKTgb9HxACgPzAbGAD0jIh+EbEbUKm3SET8Hvgr8MOIGFllP2OAPsBXImJ3YEq6/NqI2CPt1dIKODIi7gJmAiMjYkBEfJqpJB2C82vga2k8e0g6Jl3dBngmIvoDTwCn5zje64A/SHpM0k8zQ3okDQWOAfZKt788LX8rcEEa87+BX2TV1TEiDkzP2e+A4yNiEHATcEmOfZuZmVkVLZo6ADMzM7MsRcC16VCcdcCX0+XPAzdJKgLujYjZksqAvpJ+BzwA/KMO+zkYmJgZlhIRS9LlQyT9CGgNdAZeAe6roZ49gOkRsRBA0hTgAOBe4HPg/rTcLOCQqhtHxN8l9QUOA4YCL0rql8Y3OSJWZeKT1IEkEfJ4uvktwLSs6u5I/9+RJCn1cDLKiUJgQW0nxMzMzNyTxMzMzBrHK8CgPMp9H/iQpLdIKVAMEBFPkCQf5gO3STo1Ij5Oy00HzgZ+X4d4BFSamE1SCXA9SQ+M3YBJQEke9VRnTXwx+ds6qvlyKiKWRMTtEXEKSTLogFzx5WFlVkyvpL1eBkTEbhFxaB3rMjMz2yI5SWJmZmaN4VGgpaSKISeS9pB0YJVyHYAFEVEOnELSCwJJvYGPImIS8AdgoKSuQEFE3A38DBhYh3j+AZwpqUVaf2e+SIgsktQWOD6r/HKSSWarehY4UFJXSYXAScDjOcrlJOlrklqnr9sB2wPvpvGdlrWuc0QsBT6WtH+6+SnV7OsNoJukr6bbFknaNd+YzMzMtmQebmNmZmYNLiJC0rHAVZJ+DKwG5gHfq1L0euBuScOBx/iid8Rg4IeS1gArgFOBnsBkSZkvfcbVIaTfkwzleSmtc1JEXCtpEslcH/NIenVk3AxMlPQp8NWs41ogaVwaq4AHI+IvdYhjEMnworUkX179PiKeh4qn/8yU9DnwIPAT4FtpHK2BMmB01Qoj4vN0stlr0iE6LYCrSHrzmJmZWQ38CGAzMzMzMzMzMzzcxszMzMzMzMwMcJLEzMzMzMzMzAxwksTMzMzMzMzMDHCSxMzMzMzMzMwMcJLEzMzMzMzMzAxwksTMzMzMzMzMDHCSxMzMzMzMzMwMcJLEzMzMzMzMzAyA/w+6D3Lxdfa/qgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create classification score boxplot by order status\n",
    "fig, ax = plt.subplots(figsize=(18, 2))\n",
    "sns.boxplot(data=df, x='classification_score', y='order_status', hue='order_status', color='magenta', orient='h', ax=ax)\n",
    "ax.set_xlabel('Classification Score')\n",
    "ax.set_ylabel('Order Status')\n",
    "plt.title('Distribution of Classification Scores by Order Status')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c9db100",
   "metadata": {},
   "source": [
    "** Summary Stats on Classification Score ** \n",
    "\n",
    "*Approved*\n",
    "- Approved scores range from .038 - 1\n",
    "- 50% approved above  .999\n",
    "- 75% approved above .989\n",
    "- low scores that are approved are outliers\n",
    "\n",
    "*Chargebacks*\n",
    "- Chargeback scores range from .048 - .99\n",
    "- 50% chargebacks above .785\n",
    "- 25% chargebacks above .968\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "768a3724",
   "metadata": {},
   "source": [
    "**Notes**\n",
    "- chargebacks: >100 orders with approval scores @/near with high score on the model(1), but ended up being a fraud resulting in chargeback \n",
    "- can investigage chargebacks over thershold and see if common thread\n",
    "- Scale of chargebacks to total count of orders is too small to see chargenback on single histogram "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "62b19e77",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>order_status</th>\n",
       "      <th>price</th>\n",
       "      <th>digital_product</th>\n",
       "      <th>customer_account_age</th>\n",
       "      <th>order_source</th>\n",
       "      <th>billing_zip</th>\n",
       "      <th>shipping_name_length</th>\n",
       "      <th>classification_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21246</th>\n",
       "      <td>941402457</td>\n",
       "      <td>2019-06-25</td>\n",
       "      <td>chargeback</td>\n",
       "      <td>130.95</td>\n",
       "      <td>True</td>\n",
       "      <td>1275</td>\n",
       "      <td>web</td>\n",
       "      <td>94587</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25857</th>\n",
       "      <td>950499862</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>chargeback</td>\n",
       "      <td>50.00</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>mobile_app</td>\n",
       "      <td>14845</td>\n",
       "      <td>0</td>\n",
       "      <td>0.048683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32157</th>\n",
       "      <td>918120660</td>\n",
       "      <td>2019-06-08</td>\n",
       "      <td>chargeback</td>\n",
       "      <td>71.90</td>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>web</td>\n",
       "      <td>37075</td>\n",
       "      <td>0</td>\n",
       "      <td>0.049227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3987</th>\n",
       "      <td>917848677</td>\n",
       "      <td>2019-06-08</td>\n",
       "      <td>chargeback</td>\n",
       "      <td>35.95</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>mobile_app</td>\n",
       "      <td>37075</td>\n",
       "      <td>0</td>\n",
       "      <td>0.050901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18906</th>\n",
       "      <td>929597580</td>\n",
       "      <td>2019-06-16</td>\n",
       "      <td>chargeback</td>\n",
       "      <td>40.95</td>\n",
       "      <td>True</td>\n",
       "      <td>1569</td>\n",
       "      <td>web</td>\n",
       "      <td>92241</td>\n",
       "      <td>0</td>\n",
       "      <td>0.053618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16662</th>\n",
       "      <td>944971017</td>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>chargeback</td>\n",
       "      <td>30.95</td>\n",
       "      <td>True</td>\n",
       "      <td>1135</td>\n",
       "      <td>web</td>\n",
       "      <td>92071</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35265</th>\n",
       "      <td>944521519</td>\n",
       "      <td>2019-06-27</td>\n",
       "      <td>chargeback</td>\n",
       "      <td>114.82</td>\n",
       "      <td>False</td>\n",
       "      <td>447</td>\n",
       "      <td>web</td>\n",
       "      <td>93065</td>\n",
       "      <td>8</td>\n",
       "      <td>0.999648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9326</th>\n",
       "      <td>937489254</td>\n",
       "      <td>2019-06-22</td>\n",
       "      <td>chargeback</td>\n",
       "      <td>15.95</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>web</td>\n",
       "      <td>02118</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38145</th>\n",
       "      <td>939162674</td>\n",
       "      <td>2019-06-23</td>\n",
       "      <td>chargeback</td>\n",
       "      <td>25.00</td>\n",
       "      <td>True</td>\n",
       "      <td>22</td>\n",
       "      <td>web</td>\n",
       "      <td>95208</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26533</th>\n",
       "      <td>928800736</td>\n",
       "      <td>2019-06-16</td>\n",
       "      <td>chargeback</td>\n",
       "      <td>10.00</td>\n",
       "      <td>True</td>\n",
       "      <td>2175</td>\n",
       "      <td>web</td>\n",
       "      <td>83669</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>360 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        order_id  order_date order_status   price  digital_product  \\\n",
       "21246  941402457  2019-06-25   chargeback  130.95             True   \n",
       "25857  950499862  2019-07-01   chargeback   50.00             True   \n",
       "32157  918120660  2019-06-08   chargeback   71.90             True   \n",
       "3987   917848677  2019-06-08   chargeback   35.95             True   \n",
       "18906  929597580  2019-06-16   chargeback   40.95             True   \n",
       "...          ...         ...          ...     ...              ...   \n",
       "16662  944971017  2019-06-28   chargeback   30.95             True   \n",
       "35265  944521519  2019-06-27   chargeback  114.82            False   \n",
       "9326   937489254  2019-06-22   chargeback   15.95             True   \n",
       "38145  939162674  2019-06-23   chargeback   25.00             True   \n",
       "26533  928800736  2019-06-16   chargeback   10.00             True   \n",
       "\n",
       "       customer_account_age order_source billing_zip  shipping_name_length  \\\n",
       "21246                  1275          web       94587                     0   \n",
       "25857                     0   mobile_app       14845                     0   \n",
       "32157                    20          web       37075                     0   \n",
       "3987                      0   mobile_app       37075                     0   \n",
       "18906                  1569          web       92241                     0   \n",
       "...                     ...          ...         ...                   ...   \n",
       "16662                  1135          web       92071                     0   \n",
       "35265                   447          web       93065                     8   \n",
       "9326                      0          web       02118                     0   \n",
       "38145                    22          web       95208                     0   \n",
       "26533                  2175          web       83669                     0   \n",
       "\n",
       "       classification_score  \n",
       "21246              0.048232  \n",
       "25857              0.048683  \n",
       "32157              0.049227  \n",
       "3987               0.050901  \n",
       "18906              0.053618  \n",
       "...                     ...  \n",
       "16662              0.999646  \n",
       "35265              0.999648  \n",
       "9326               0.999649  \n",
       "38145              0.999693  \n",
       "26533              0.999997  \n",
       "\n",
       "[360 rows x 10 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subframes by order_status\n",
    "approved_orders = df[df['order_status']== 'approved']\n",
    "chargeback_orders = df[df['order_status']== 'chargeback']\n",
    "\n",
    "# approved orders subframe\n",
    "approved_orders.sort_values('classification_score')\n",
    "\n",
    "# chargeback orders subframe \n",
    "chargeback_orders.sort_values('classification_score')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e3bab2f",
   "metadata": {},
   "source": [
    "#### Digital Product EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0bb86605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">describe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>count</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_status</th>\n",
       "      <th>digital_product</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">approved</th>\n",
       "      <th>False</th>\n",
       "      <td>107.44</td>\n",
       "      <td>408.94</td>\n",
       "      <td>1893.8875</td>\n",
       "      <td>13498.0</td>\n",
       "      <td>11033.31</td>\n",
       "      <td>915.530484</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1105.069831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>26.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>105.9500</td>\n",
       "      <td>26967.0</td>\n",
       "      <td>2497.74</td>\n",
       "      <td>139.610979</td>\n",
       "      <td>0.00</td>\n",
       "      <td>296.918535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">chargeback</th>\n",
       "      <th>False</th>\n",
       "      <td>202.75</td>\n",
       "      <td>308.69</td>\n",
       "      <td>508.9400</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1033.73</td>\n",
       "      <td>362.710500</td>\n",
       "      <td>26.99</td>\n",
       "      <td>216.827451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>30.95</td>\n",
       "      <td>55.95</td>\n",
       "      <td>155.9500</td>\n",
       "      <td>320.0</td>\n",
       "      <td>505.95</td>\n",
       "      <td>106.652188</td>\n",
       "      <td>9.80</td>\n",
       "      <td>107.119672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             describe                                        \\\n",
       "                                price                                         \n",
       "                                  25%     50%        75%    count       max   \n",
       "order_status digital_product                                                  \n",
       "approved     False             107.44  408.94  1893.8875  13498.0  11033.31   \n",
       "             True               26.00   50.00   105.9500  26967.0   2497.74   \n",
       "chargeback   False             202.75  308.69   508.9400     40.0   1033.73   \n",
       "             True               30.95   55.95   155.9500    320.0    505.95   \n",
       "\n",
       "                                                              \n",
       "                                                              \n",
       "                                    mean    min          std  \n",
       "order_status digital_product                                  \n",
       "approved     False            915.530484   0.00  1105.069831  \n",
       "             True             139.610979   0.00   296.918535  \n",
       "chargeback   False            362.710500  26.99   216.827451  \n",
       "             True             106.652188   9.80   107.119672  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a boxplot of price by digital_product\n",
    "sns.boxplot(data=df, x='digital_product', y='price', color='magenta')\n",
    "plt.title('Distribution of Product Type (Digital/Tangible)')\n",
    "plt.show()\n",
    "\n",
    "#pivot \n",
    "digital_pivot_df = df.pivot_table(values=['price'], index=['order_status', 'digital_product'], aggfunc=['describe'])\n",
    "digital_pivot_df\n",
    "\n",
    "#digital_pivot_df = pd.pivot_table(df, index='order_status', values ='price', columns='digital_product', aggfunc = 'sum', margins = True, margins_name='Total')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86351d71",
   "metadata": {},
   "source": [
    "**Digital Product**\n",
    "\n",
    "*Digital Product*\n",
    "\n",
    "* count of 320 chargebacks\n",
    "* price range of digital product chargebacks $ 9.80-505\n",
    "\n",
    "* 25% digital products are more than $ 155\n",
    "\n",
    "*Tangible Product* \n",
    "* count of 40 chargebacks \n",
    "* price range of tangibel product chargebacks $ 26.99 - 1033\n",
    "\n",
    "* 25% tangible products over  $508\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f42828c",
   "metadata": {},
   "source": [
    "## 1. Rely exclusively on model scores using Company decision-engine, and set a decline threshold that will provide a 90% approval rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2ba67d90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In df sorted by score desc, 36742 orders approved, of 40825 total orders, using 0.9 approval threshold.\n",
      "\n",
      "Orders with scores > threshhold score: 0.860766183838456 will be approved.\n",
      "\n",
      "Dataframe with 90% Threshold\n"
     ]
    },
    {
     "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>order_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>order_status</th>\n",
       "      <th>price</th>\n",
       "      <th>digital_product</th>\n",
       "      <th>customer_account_age</th>\n",
       "      <th>order_source</th>\n",
       "      <th>billing_zip</th>\n",
       "      <th>shipping_name_length</th>\n",
       "      <th>classification_score</th>\n",
       "      <th>new_order_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23097</th>\n",
       "      <td>945827823</td>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>approved</td>\n",
       "      <td>16.44</td>\n",
       "      <td>False</td>\n",
       "      <td>759</td>\n",
       "      <td>web</td>\n",
       "      <td>98109</td>\n",
       "      <td>9</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>approved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1094</th>\n",
       "      <td>932303597</td>\n",
       "      <td>2019-06-18</td>\n",
       "      <td>approved</td>\n",
       "      <td>150.00</td>\n",
       "      <td>True</td>\n",
       "      <td>894</td>\n",
       "      <td>web</td>\n",
       "      <td>52761</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>approved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27364</th>\n",
       "      <td>916501223</td>\n",
       "      <td>2019-06-07</td>\n",
       "      <td>approved</td>\n",
       "      <td>105.94</td>\n",
       "      <td>False</td>\n",
       "      <td>5160</td>\n",
       "      <td>web</td>\n",
       "      <td>60047</td>\n",
       "      <td>10</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>approved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22754</th>\n",
       "      <td>916516038</td>\n",
       "      <td>2019-06-07</td>\n",
       "      <td>approved</td>\n",
       "      <td>100.00</td>\n",
       "      <td>True</td>\n",
       "      <td>1267</td>\n",
       "      <td>web</td>\n",
       "      <td>46516</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>approved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22622</th>\n",
       "      <td>925554558</td>\n",
       "      <td>2019-06-13</td>\n",
       "      <td>approved</td>\n",
       "      <td>225.00</td>\n",
       "      <td>True</td>\n",
       "      <td>889</td>\n",
       "      <td>web</td>\n",
       "      <td>52761</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>approved</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        order_id  order_date order_status   price  digital_product  \\\n",
       "23097  945827823  2019-06-28     approved   16.44            False   \n",
       "1094   932303597  2019-06-18     approved  150.00             True   \n",
       "27364  916501223  2019-06-07     approved  105.94            False   \n",
       "22754  916516038  2019-06-07     approved  100.00             True   \n",
       "22622  925554558  2019-06-13     approved  225.00             True   \n",
       "\n",
       "       customer_account_age order_source billing_zip  shipping_name_length  \\\n",
       "23097                   759          web       98109                     9   \n",
       "1094                    894          web       52761                     0   \n",
       "27364                  5160          web       60047                    10   \n",
       "22754                  1267          web       46516                     0   \n",
       "22622                   889          web       52761                     0   \n",
       "\n",
       "       classification_score new_order_status  \n",
       "23097              1.000000         approved  \n",
       "1094               0.999999         approved  \n",
       "27364              0.999999         approved  \n",
       "22754              0.999999         approved  \n",
       "22622              0.999999         approved  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. new df that sorts model scores desc\n",
    "df_sorted = df.sort_values('classification_score', ascending = False)\n",
    "df_sorted\n",
    "\n",
    "# 2. set threshold \n",
    "threshold = 0.90\n",
    "\n",
    "# 3. calculate count of orders that will be approved using threshold \n",
    "num_orders_approved_90 = int(total_orders * threshold)\n",
    "print('In df sorted by score desc,', num_orders_approved_90,'orders approved, '\\\n",
    "      'of', total_orders, 'total orders, using',threshold , 'approval threshold.')\n",
    "\n",
    "# 4. locate 'threshold score' using location of threshold count of sorted_df \n",
    "threshold_score = df_sorted.iloc[num_orders_approved_90]['classification_score']\n",
    "print('\\nOrders with scores > threshhold score:', threshold_score, 'will be approved.')\n",
    "\n",
    "# 5. create new col (new order_status) -populate based approvals based on on >/< threshold score \n",
    "df_sorted[\"new_order_status\"] = \\\n",
    "    np.where(df_sorted[\"classification_score\"]>= threshold_score, \"approved\", \"chargeback\" )\n",
    "\n",
    "print('\\nDataframe with 90% Threshold')\n",
    "df_sorted.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "503c7764",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            new_order_status\n",
      "approved            0.900012\n",
      "chargeback          0.099988\n"
     ]
    }
   ],
   "source": [
    "# confirm new_order_status contains 90% approvals\n",
    "print(df_sorted['new_order_status'].value_counts(normalize=True).to_frame())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1df16c33",
   "metadata": {},
   "source": [
    "Process \n",
    "1. new dataframe sorted by score in descending order\n",
    "2. count orders, identify order number representing approval threshold\n",
    "3. extract the score from the last row of sorted_df represending approval threshold \n",
    "4. create new column and populate setting 'approvals' > threshold score and 'chargebacks' < threshold score\n",
    "5. check work by checking percentage of approvals in new status col "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcd53517",
   "metadata": {},
   "source": [
    "## 2. Plot the model scores distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b1a877a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# new distribution of order status by score using pandas\n",
    "df_sorted.hist(column='classification_score', by='new_order_status', bins=20, color='magenta')\n",
    "plt.xlabel('Classification Score')\n",
    "plt.ylabel('Order Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "de4509e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dist of new model with matplotlib\n",
    "plt.hist(df_sorted[df_sorted['new_order_status'] == 'approved']['classification_score'], color='green', label='approve')\n",
    "plt.hist(df_sorted[df_sorted['new_order_status'] == 'chargeback']['classification_score'], color='red', label='chargeback')\n",
    "\n",
    "# labels and legend\n",
    "plt.xlabel('Classification Score')\n",
    "plt.ylabel('Order Count')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Distribution of Order Status 90% Approval Model')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f561cd",
   "metadata": {},
   "source": [
    "distribution \n",
    "* sanity check to confirm adjusted dataframe  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb45784f",
   "metadata": {},
   "source": [
    "## 3. Assuming we aim at a proportion of 50% between the CHB cost and the total revenue (sum amount of CHBs divided by the total revenue), what would have to be the fee based Company business model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d002f4ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check data to see if chargeback are booked as neg revenue\n",
    "chargeback_neg = df[df['price']< 0].value_counts()\n",
    "chargeback_neg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b34582fc",
   "metadata": {},
   "source": [
    "sanity check \n",
    "- price data all > 0\n",
    "- original eda price min = 0 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "421fee8a",
   "metadata": {},
   "source": [
    "##### Scenario: Calculate Company Fee \n",
    "    * assumption goal fee: cover chargeback cost + 50%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "74a8fee0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total revenue: $16,171,357\n",
      "sum value of approved orders $16,122,720\n",
      "sum value of chargebacks: $48,637\n",
      "\n",
      "chargeback as a percent of total revenue: 0.3%\n",
      "\n",
      "fee amount goal (1.5x sum of chargeback): $72,956\n",
      "\n",
      "fee as percent of revenue: 0.5%\n"
     ]
    }
   ],
   "source": [
    "# total revenue (incl chargebacks)\n",
    "total_rev = df['price'].sum()\n",
    "print('total revenue: ${:,.0f}'.format(total_rev))\n",
    "\n",
    "# calcualate sum approved \n",
    "sum_approved = df[df[\"order_status\"] == \"approved\"][\"price\"].sum()\n",
    "print('sum value of approved orders ${:,.0f}'.format(sum_approved))\n",
    "\n",
    "# calculate sum chargebacks\n",
    "sum_chargeback = df[df[\"order_status\"] == \"chargeback\"][\"price\"].sum()\n",
    "print('sum value of chargebacks: ${:,.0f}'.format(sum_chargeback))\n",
    "\n",
    "# chargeback as a percent of total revenues \n",
    "percent_chargeback_cost_actual = sum_chargeback/total_rev \n",
    "print ('\\nchargeback as a percent of total revenue: {:.1%}'.format(percent_chargeback_cost_actual))\n",
    "\n",
    "#\"proportion of 50% between the CHB cost and the total revenue\"\n",
    "# given the CHB cost .3% of revenue above line was interpreted to \n",
    "# calculate goal  for fee: fee to cover chargeback + 50% of chargeback - adjustments can be made as more clarification is provided\n",
    "fee_amount_50_over_chargeback = 1.5 * sum_chargeback\n",
    "print ('\\nfee amount goal (1.5x sum of chargeback): ${:,.0f}'.format(fee_amount_50_over_chargeback))\n",
    "\n",
    "#fee goal # should rev be total appoved \n",
    "fee_threshild_50_over_chargeback = fee_amount_50_over_chargeback / total_rev \n",
    "print ('\\nfee as percent of revenue: {:.1%}'.format(fee_threshild_50_over_chargeback))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "098d3f38",
   "metadata": {},
   "source": [
    "**Notes**\n",
    "- .45% fee on all transactions is needed to cover all chargebacks + 50% of chargeback fees \n",
    "- seems low should investigate industry standard\n",
    "- given the .3% chargeback costs, this has been interpreted to mean recover all chargeback cost + 50%\n",
    "- adjustments can be made as more information becomes available"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "597c5448",
   "metadata": {},
   "source": [
    "**REQUESTED Clarifiction on original question**\n",
    "- Orig Q: \"a proportion of 50% between the CHB cost and the total revenue\" \n",
    "- Orig definition COMPANY Business Model: \"We collect a small fraction (fee) of the amount of all approved orders and give a chargeback guarantee for the full order amount if an order we approve comes back as a chargeback.\" \n",
    "- Revenue defined as :\"sum amount of CHBs divided by the total revenue\" interpreted as revenue of customer\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c7a12c2",
   "metadata": {},
   "source": [
    "##### Scenario: Calculate COMPANY Fee \n",
    "    * assumption chargeback cost: total revenue = 50%\n",
    "    * COMPANY fee amount assumed using assumed 1.5% fee threshold\n",
    "    * COMPANY fee calculated using assumed goal threshold amount of $100,000  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "de4cda40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total revenue: $16,171,357\n",
      "CHB: Total Revenue (50% ratio): $8,085,678\n",
      "\n",
      "Assumed Company_fee_threshold: 1.5%\n",
      "Company_fee_collected using assumed fee threshold: $121,285\n",
      "\n",
      "Company_goal_income: $100,000\n",
      "Company_goal_fee: 1.2%\n"
     ]
    }
   ],
   "source": [
    "# calculate total revenue by adding all enterants in price col\n",
    "total_revenue = df.price.sum()\n",
    "print(\"total revenue: ${:,.0f}\".format(total_revenue))\n",
    "\n",
    "# calculate CHB as 50% of all revenue\n",
    "CHB_to_revenue_50 = total_revenue *.50\n",
    "print(\"CHB: Total Revenue (50% ratio): ${:,.0f}\".format(CHB_to_revenue_50))\n",
    "\n",
    "# set fee (based on CC fees) can be changed\n",
    "Company_fee_threshold = .015\n",
    "print(\"\\nAssumed Company_fee_threshold: {:.1%}\".format(Company_fee_threshold))\n",
    "\n",
    "# calculate amount riskified would collect on accepted orders based on fee threshold\n",
    "Company_fee_collected =  CHB_to_revenue_50 * Company_fee_threshold\n",
    "print(\"Company_fee_collected using assumed fee threshold: ${:,.0f}\".format(Company_fee_collected)) \n",
    "\n",
    "# set goal income (arbitrary ) can be changed\n",
    "Company_goal_income = 100000\n",
    "print(\"\\nCompany_goal_income: ${:,.0f}\".format(Company_goal_income) )\n",
    "\n",
    "Company_goal_fee = Company_goal_income/CHB_to_revenue_50\n",
    "print(\"Company_goal_fee: {:.1%}\".format(Company_goal_fee))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3ac4fea",
   "metadata": {},
   "source": [
    "## 4. In the dataset, there’s a column indicating whether the purchased goods are digital or tangible. What can you say about the risk level of each segment?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4ba6d8a",
   "metadata": {},
   "source": [
    "##### Percent of Orders:  \n",
    "    * Digital Product(True) & Tangible Product(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5c727275",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       digital_product\n",
      "True          0.668389\n",
      "False         0.331611\n"
     ]
    }
   ],
   "source": [
    "print(df.digital_product.value_counts(normalize=True).to_frame())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "37335c0d",
   "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>order_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>approved</th>\n",
       "      <td>0.988273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chargeback</th>\n",
       "      <td>0.011727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            order_status\n",
       "approved        0.988273\n",
       "chargeback      0.011727"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# order_status of digital products\n",
    "df[df['digital_product']==True]['order_status'].value_counts(normalize=True).to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "201f001c",
   "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>order_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>approved</th>\n",
       "      <td>0.997045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chargeback</th>\n",
       "      <td>0.002955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            order_status\n",
       "approved        0.997045\n",
       "chargeback      0.002955"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %approved / chargebacks of tangible products\n",
    "df[df['digital_product']==False]['order_status'].value_counts(normalize=True).to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41cb8a22",
   "metadata": {},
   "source": [
    "##### Sum of Revenue:  \n",
    "    * Digital Product(True) & Tangible Product(False) by Status(approved/chargeback)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d74b5d4c",
   "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>digital_product</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>approved</th>\n",
       "      <td>$12,357,830</td>\n",
       "      <td>$3,764,889</td>\n",
       "      <td>$16,122,720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chargeback</th>\n",
       "      <td>$14,508</td>\n",
       "      <td>$34,129</td>\n",
       "      <td>$48,637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>$12,372,339</td>\n",
       "      <td>$3,799,018</td>\n",
       "      <td>$16,171,357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "digital_product        False        True        Total\n",
       "order_status                                         \n",
       "approved         $12,357,830  $3,764,889  $16,122,720\n",
       "chargeback           $14,508     $34,129      $48,637\n",
       "Total            $12,372,339  $3,799,018  $16,171,357"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create chart to sum price of digital/tangible products sorted by approval status\n",
    "digital_pivot_df = pd.pivot_table(df, index='order_status', values ='price', columns='digital_product', aggfunc = 'sum', margins = True, margins_name='Total')\n",
    "digital_pivot_df = digital_pivot_df.applymap('${:,.0f}'.format)\n",
    "digital_pivot_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a195c78",
   "metadata": {},
   "source": [
    "**Notes**\n",
    "- digital products: 66% of count of orders\n",
    "- digital products: 24% of order totals by price \n",
    "- digital product riskier by count of orders: \n",
    "    - greater number of  chargbacks (1% digital product : .3% of tangible products)\n",
    "- tangible product result >2x $$ lost to chargebacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "241bad3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot digital_product & classifcation_score \n",
    "df[df['digital_product']==True]['classification_score'].hist()\n",
    "plt.title('Digital Product')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d41ec795",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEICAYAAABfz4NwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAX0klEQVR4nO3df7RdZX3n8fdniFJAERC5YoKG1rTKj9KRW8Q607mdtItonYbpkpm0VoLipFK0tIvOGGxX6fxIB9eMFZgWnNQfCa0LSKkOmSJWGntquyRQUBRCpKTiQJpIxJ8EFbn4nT/Oc1mHm5vk5v46uTnv11pn3ec8+3n2fr4knM/de59zkqpCkqR/1u8FSJIODgaCJAkwECRJjYEgSQIMBElSYyBIkgADQSLJrUlWtvYFSf5uH2M7Sd42d6ubnCSV5OX9XofmNwNBB5Uku3seP0jy3Z7nb5qNY1bV66pq/Wzse0ySxe1Fe6yWLydZPZvHPFBJfi/Jn/Z7HeqfBf1egNSrqp431k7yZeBtVfVX/VvRjDumqkaTvAbYlOSeqvpE74AkC6pqtE/r0wDzDEHzQpKzktye5JtJdib5wyTP7dleSd6e5MEk30jyR0nSth2W5L1JHkvyUJJ3tPEL2vbxl4GS5H8l+VaSLyZZuo91vTXJ1nbMv0zyssnUU1W3A1uA05KMJNme5F1JvgJ8OMnhSa5MsqM9rkxyeM9x/2P777AjyVvHrelZ9Yy/DJbk1CS3Jfl6kkeTvDvJMuDdwL9vZzCfn0wdOrQYCJovngZ+EzgeeA2wFPi1cWPeAPwkcAbw74BzWv9/AF4H/ATwKuDc/Rzr1cCX2rEuBz6a5Ljxg5KcS/dF9BeBFwF/C1y/v0LS9VrgVOBzrfvFwHHAy4BVwG8DZ7c1nwGcBfxOm78M+C3g54AlwM/u75g9x34+8FfAJ4CXAC8HNrWzlN8Hbqyq51XVGZPdpw4dBoLmhaq6u6o2V9VoVX0Z+N/Avxo37Iqq+mZVPQz8Nd0XU+iGw1VVtb2qvgFcsZ/D7QKurKqnqupG4AHg5ycY96vAf6+qre0Sz+8DP7Gfs4THgK8DHwBWV9Wm1v8D4PKqerKqvgu8CfgvVbWrqr4K/GfgzT31fLiq7quqJ4Df2089vd4AfKWq3ltV36uqx6vqjgOYr0OY9xA0LyT5UeAPgGHgSLp/d+8eN+wrPe3vAGP3I14CPNKzrbc9kX+qZ3/r4/9r+xjvZcBVSd7bu1RgYZszkeP3cn/gq1X1vZ7nLxm3j941vIRn1763Y03kJOAfD2C8BohnCJovrgW+CCypqqPpXqrJJOfuBBb1PD9pP+MXjt1/aF4K7Jhg3CPAr1bVMT2PI6rqM5NcV6/xXzu8g27gTLSGnTy7hpeOm/sE3dAc8+Jxa/6RSa5BA8ZA0HzxfODbwO4krwAuOoC5G4BLkixMcgzwrv2MPwH49STPSXIe8Erg4xOMez9wWZJTAZK8oI2fCdcDv5PkRUmOB34XGHtL6AbggiSnJDmS7n2OXvcAv5jkyPbZhAt7tv0F8OIkv9FuXD8/yavbtkeBxUl8XRhQ/sFrvvgt4JeBx4E/Bm48gLl/DHwS+ALdm7gfB0bp3qieyB10b9Y+BqwB3lhVXxs/qKo+BrwHuCHJt4H76N68ngn/Dbirrfle4LOtj6q6FbgS+BSwrf3s9T7g+3Rf4NcDH+lZ8+N0b0b/G7qX2B4EfqZt/rP282tJPjtDdWgeif9AjgZNktcB76+qSb1FVBoUniHokJfkiCSvT7IgyUK6l1g+1u91SQcbzxB0yGvX2f8GeAXwXeAW4JKq+nZfFyYdZAwESRLgJSNJUjNvP5h2/PHH1+LFiwF44oknOOqoo/q7oD4Z5NphsOu39sGsHaZX/9133/1YVb1oom3zNhAWL17MXXfdBUCn02FkZKS/C+qTQa4dBrt+ax/p9zL6Zjr1J9nrJ9u9ZCRJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkC5vEnlSWpnxavvqVvx163bHa+tsMzBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgRMIhCSfCjJriT39fT9jyRfTPKFJB9LckzPtsuSbEvyQJJzevrPTHJv23Z1krT+w5Pc2PrvSLJ4ZkuUJE3GZM4Q1gHLxvXdBpxWVT8O/ANwGUCSU4AVwKltzjVJDmtzrgVWAUvaY2yfFwLfqKqXA+8D3jPVYiRJU7ffQKiqTwNfH9f3yaoabU83A4taezlwQ1U9WVUPAduAs5KcCBxdVbdXVQHXAef2zFnf2jcBS8fOHiRJc2cmvv76rcCNrb2QbkCM2d76nmrt8f1jcx4BqKrRJN8CXgg8Nv5ASVbRPctgaGiITqcDwO7du59pD5pBrh0Gu35r7/R1DZeePrr/QbNktuqfViAk+W1gFPjIWNcEw2of/fuas2dn1VpgLcDw8HCNjIwA0Ol0GGsPmkGuHQa7fmsf6esaLujzv4cwG/VP+V1GSVYCbwDe1C4DQfc3/5N6hi0CdrT+RRP0P2tOkgXACxh3iUqSNPumFAhJlgHvAn6hqr7Ts2kjsKK9c+hkujeP76yqncDjSc5u9wfOB27umbOytd8IfKonYCRJc2S/l4ySXA+MAMcn2Q5cTvddRYcDt7X7v5ur6u1VtSXJBuB+upeSLq6qp9uuLqL7jqUjgFvbA+CDwJ8k2Ub3zGDFzJQmSToQ+w2EqvqlCbo/uI/xa4A1E/TfBZw2Qf/3gPP2tw5J0uzyk8qSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkoBJBEKSDyXZleS+nr7jktyW5MH289iebZcl2ZbkgSTn9PSfmeTetu3qJGn9hye5sfXfkWTxDNcoSZqEyZwhrAOWjetbDWyqqiXApvacJKcAK4BT25xrkhzW5lwLrAKWtMfYPi8EvlFVLwfeB7xnqsVIkqZuv4FQVZ8Gvj6uezmwvrXXA+f29N9QVU9W1UPANuCsJCcCR1fV7VVVwHXj5ozt6yZg6djZgyRp7iyY4ryhqtoJUFU7k5zQ+hcCm3vGbW99T7X2+P6xOY+0fY0m+RbwQuCx8QdNsoruWQZDQ0N0Oh0Adu/e/Ux70Axy7TDY9Vt7p69ruPT00b4de7bqn2og7M1Ev9nXPvr3NWfPzqq1wFqA4eHhGhkZAaDT6TDWHjSDXDsMdv3WPtLXNVyw+pa+HXvdsqNmpf6pvsvo0XYZiPZzV+vfDpzUM24RsKP1L5qg/1lzkiwAXsCel6gkSbNsqoGwEVjZ2iuBm3v6V7R3Dp1M9+bxne3y0uNJzm73B84fN2dsX28EPtXuM0iS5tB+LxkluR4YAY5Psh24HLgC2JDkQuBh4DyAqtqSZANwPzAKXFxVT7ddXUT3HUtHALe2B8AHgT9Jso3umcGKGalMknRA9hsIVfVLe9m0dC/j1wBrJui/Czhtgv7v0QJFktQ/flJZkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRIwzUBI8ptJtiS5L8n1SX4oyXFJbkvyYPt5bM/4y5JsS/JAknN6+s9Mcm/bdnWSTGddkqQDN+VASLIQ+HVguKpOAw4DVgCrgU1VtQTY1J6T5JS2/VRgGXBNksPa7q4FVgFL2mPZVNclSZqa6V4yWgAckWQBcCSwA1gOrG/b1wPntvZy4IaqerKqHgK2AWclORE4uqpur6oCruuZI0maIwumOrGq/inJ/wQeBr4LfLKqPplkqKp2tjE7k5zQpiwENvfsYnvre6q1x/fvIckqumcSDA0N0el0ANi9e/cz7UEzyLXDYNdv7Z2+ruHS00f7duzZqn/KgdDuDSwHTga+CfxZkl/Z15QJ+mof/Xt2Vq0F1gIMDw/XyMgIAJ1Oh7H2oBnk2mGw67f2kb6u4YLVt/Tt2OuWHTUr9U/nktHPAg9V1Ver6ingo8BPAY+2y0C0n7va+O3AST3zF9G9xLS9tcf3S5Lm0HQC4WHg7CRHtncFLQW2AhuBlW3MSuDm1t4IrEhyeJKT6d48vrNdXno8ydltP+f3zJEkzZHp3EO4I8lNwGeBUeBzdC/nPA/YkORCuqFxXhu/JckG4P42/uKqerrt7iJgHXAEcGt7SJLm0JQDAaCqLgcuH9f9JN2zhYnGrwHWTNB/F3DadNYiSZoeP6ksSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAmYZiAkOSbJTUm+mGRrktckOS7JbUkebD+P7Rl/WZJtSR5Ick5P/5lJ7m3brk6S6axLknTgpnuGcBXwiap6BXAGsBVYDWyqqiXApvacJKcAK4BTgWXANUkOa/u5FlgFLGmPZdNclyTpAE05EJIcDfw08EGAqvp+VX0TWA6sb8PWA+e29nLghqp6sqoeArYBZyU5ETi6qm6vqgKu65kjSZojC6Yx94eBrwIfTnIGcDdwCTBUVTsBqmpnkhPa+IXA5p7521vfU609vn8PSVbRPZNgaGiITqcDwO7du59pD5pBrh0Gu35r7/R1DZeePtq3Y89W/dMJhAXAq4B3VtUdSa6iXR7ai4nuC9Q++vfsrFoLrAUYHh6ukZERADqdDmPtQTPItcNg12/tI31dwwWrb+nbsdctO2pW6p/OPYTtwPaquqM9v4luQDzaLgPRfu7qGX9Sz/xFwI7Wv2iCfknSHJpyIFTVV4BHkvxY61oK3A9sBFa2vpXAza29EViR5PAkJ9O9eXxnu7z0eJKz27uLzu+ZI0maI9O5ZATwTuAjSZ4LfAl4C92Q2ZDkQuBh4DyAqtqSZAPd0BgFLq6qp9t+LgLWAUcAt7aHJGkOTSsQquoeYHiCTUv3Mn4NsGaC/ruA06azFknS9PhJZUkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJwAwEQpLDknwuyV+058cluS3Jg+3nsT1jL0uyLckDSc7p6T8zyb1t29VJMt11SZIOzEycIVwCbO15vhrYVFVLgE3tOUlOAVYApwLLgGuSHNbmXAusApa0x7IZWJck6QBMKxCSLAJ+HvhAT/dyYH1rrwfO7em/oaqerKqHgG3AWUlOBI6uqturqoDreuZIkubIgmnOvxL4T8Dze/qGqmonQFXtTHJC618IbO4Zt731PdXa4/v3kGQV3TMJhoaG6HQ6AOzevfuZ9qAZ5NphsOu39k5f13Dp6aN9O/Zs1T/lQEjyBmBXVd2dZGQyUyboq33079lZtRZYCzA8PFwjI93DdjodxtqDZpBrh8Gu39pH+rqGC1bf0rdjr1t21KzUP50zhNcCv5Dk9cAPAUcn+VPg0SQntrODE4Fdbfx24KSe+YuAHa1/0QT9kqQ5NOV7CFV1WVUtqqrFdG8Wf6qqfgXYCKxsw1YCN7f2RmBFksOTnEz35vGd7fLS40nObu8uOr9njiRpjkz3HsJErgA2JLkQeBg4D6CqtiTZANwPjAIXV9XTbc5FwDrgCODW9pAkzaEZCYSq6gCd1v4asHQv49YAaybovws4bSbWIkmaGj+pLEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJmEYgJDkpyV8n2ZpkS5JLWv9xSW5L8mD7eWzPnMuSbEvyQJJzevrPTHJv23Z1kkyvLEnSgZrOGcIocGlVvRI4G7g4ySnAamBTVS0BNrXntG0rgFOBZcA1SQ5r+7oWWAUsaY9l01iXJGkKphwIVbWzqj7b2o8DW4GFwHJgfRu2Hji3tZcDN1TVk1X1ELANOCvJicDRVXV7VRVwXc8cSdIcWTATO0myGPjnwB3AUFXthG5oJDmhDVsIbO6Ztr31PdXa4/snOs4qumcSDA0N0el0ANi9e/cz7UEzyLXDYNdv7Z2+ruHS00f7duzZqn/agZDkecCfA79RVd/ex+X/iTbUPvr37KxaC6wFGB4erpGREQA6nQ5j7UEzyLXDYNdv7SN9XcMFq2/p27HXLTtqVuqf1ruMkjyHbhh8pKo+2rofbZeBaD93tf7twEk90xcBO1r/ogn6JUlzaDrvMgrwQWBrVf1Bz6aNwMrWXgnc3NO/IsnhSU6me/P4znZ56fEkZ7d9nt8zR5I0R6Zzyei1wJuBe5Pc0/reDVwBbEhyIfAwcB5AVW1JsgG4n+47lC6uqqfbvIuAdcARwK3tIUmaQ1MOhKr6Oya+/g+wdC9z1gBrJui/CzhtqmuRJE2fn1SWJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgTAgn4vYEySZcBVwGHAB6rqij4vSdI8sHj1Lf1ewiHjoAiEJIcBfwT8HLAd+PskG6vq/v6uTNJkzPWL8qWnj3KBQTDjDopAAM4CtlXVlwCS3AAsB2YlEA6l3ygG/X+MQa5/kGvX7EhV9XsNJHkjsKyq3taevxl4dVW9Y9y4VcCq9vTHgAda+3jgsTla7sFmkGuHwa7f2gfXdOp/WVW9aKINB8sZQibo2yOpqmotsHaPycldVTU8Gws72A1y7TDY9Vv7YNYOs1f/wfIuo+3AST3PFwE7+rQWSRpIB0sg/D2wJMnJSZ4LrAA29nlNkjRQDopLRlU1muQdwF/Sfdvph6pqywHsYo/LSANkkGuHwa7f2gfXrNR/UNxUliT138FyyUiS1GcGgiQJmGeBkGRZkgeSbEuyeoLtSXJ12/6FJK/qxzpnwyRqf1Or+QtJPpPkjH6sczbsr/aecT+Z5On2uZZDxmTqTzKS5J4kW5L8zVyvcbZM4u/9C5L83ySfb7W/pR/rnA1JPpRkV5L79rJ95l/vqmpePOjebP5H4IeB5wKfB04ZN+b1wK10P9dwNnBHv9c9h7X/FHBsa79ukGrvGfcp4OPAG/u97jn+sz+G7qf6X9qen9Dvdc9h7e8G3tPaLwK+Djy332ufofp/GngVcN9ets/46918OkN45ustqur7wNjXW/RaDlxXXZuBY5KcONcLnQX7rb2qPlNV32hPN9P9LMehYDJ/7gDvBP4c2DWXi5sDk6n/l4GPVtXDAFV1qPw3mEztBTw/SYDn0Q2E0bld5uyoqk/TrWdvZvz1bj4FwkLgkZ7n21vfgY6Zjw60rgvp/uZwKNhv7UkWAv8WeP8crmuuTObP/keBY5N0ktyd5Pw5W93smkztfwi8ku4HWe8FLqmqH8zN8vpuxl/vDorPIUzSZL7eYlJfgTEPTbquJD9DNxD+xayuaO5MpvYrgXdV1dPdXxQPKZOpfwFwJrAUOAK4PcnmqvqH2V7cLJtM7ecA9wD/GvgR4LYkf1tV357ltR0MZvz1bj4FwmS+3uJQ/QqMSdWV5MeBDwCvq6qvzdHaZttkah8GbmhhcDzw+iSjVfV/5mSFs2uyf+8fq6ongCeSfBo4A5jvgTCZ2t8CXFHdi+rbkjwEvAK4c26W2Fcz/no3ny4ZTebrLTYC57e772cD36qqnXO90Fmw39qTvBT4KPDmQ+A3w177rb2qTq6qxVW1GLgJ+LVDJAxgcn/vbwb+ZZIFSY4EXg1sneN1zobJ1P4w3TMjkgzR/RbkL83pKvtnxl/v5s0ZQu3l6y2SvL1tfz/dd5i8HtgGfIfubw/z3iRr/13ghcA17Tfl0ToEvg1ykrUfsiZTf1VtTfIJ4AvAD+j+i4MTvlVxPpnkn/1/BdYluZfuJZR3VdUh8bXYSa4HRoDjk2wHLgeeA7P3eudXV0iSgPl1yUiSNIsMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqfn/X0ARwm1sRgEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot digital_product & classifcation_score \n",
    "df[df['digital_product']==False]['classification_score'].hist()\n",
    "plt.title('Tangible Product')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "584ca914",
   "metadata": {},
   "source": [
    "## 5. Free exploration - Provide 2-3 interesting breakdowns or insights."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7460fe9a",
   "metadata": {},
   "source": [
    "#### Order Source Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ea87c7b1",
   "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></th>\n",
       "      <th>order_id</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_source</th>\n",
       "      <th>order_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">mobile_app</th>\n",
       "      <th>approved</th>\n",
       "      <td>663</td>\n",
       "      <td>54,318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chargeback</th>\n",
       "      <td>11</td>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">web</th>\n",
       "      <th>approved</th>\n",
       "      <td>39,802</td>\n",
       "      <td>16,068,402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chargeback</th>\n",
       "      <td>349</td>\n",
       "      <td>47,882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          order_id       price\n",
       "order_source order_status                     \n",
       "mobile_app   approved          663      54,318\n",
       "             chargeback         11         756\n",
       "web          approved       39,802  16,068,402\n",
       "             chargeback        349      47,882"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group order_source and order_status, aggregate count of orders and sum of price for each catagory of grouped cols\n",
    "order_source_group = df.groupby(['order_source', 'order_status']).agg({'order_id': 'count', 'price': 'sum'}, margins = True)\n",
    "\n",
    "# format for large numbers\n",
    "order_source_group =order_source_group.applymap('{:,.0f}'.format)\n",
    "\n",
    "#display grouping \n",
    "order_source_group"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e15450b",
   "metadata": {},
   "source": [
    "### observation \n",
    "- 98% orders via web\n",
    "- 99.7% of sales via web\n",
    "- 98.4% of chargeback **costs** are from web\n",
    "- higher proportion of mobile sales result in chargebacks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49e87fce",
   "metadata": {},
   "source": [
    "#### Time Series "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b0621ce0",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2019-06-01',\n",
       " '2019-06-02',\n",
       " '2019-06-03',\n",
       " '2019-06-04',\n",
       " '2019-06-05',\n",
       " '2019-06-06',\n",
       " '2019-06-07',\n",
       " '2019-06-08',\n",
       " '2019-06-09',\n",
       " '2019-06-10',\n",
       " '2019-06-11',\n",
       " '2019-06-12',\n",
       " '2019-06-13',\n",
       " '2019-06-14',\n",
       " '2019-06-15',\n",
       " '2019-06-16',\n",
       " '2019-06-17',\n",
       " '2019-06-18',\n",
       " '2019-06-19',\n",
       " '2019-06-20',\n",
       " '2019-06-21',\n",
       " '2019-06-22',\n",
       " '2019-06-23',\n",
       " '2019-06-24',\n",
       " '2019-06-25',\n",
       " '2019-06-26',\n",
       " '2019-06-27',\n",
       " '2019-06-28',\n",
       " '2019-06-29',\n",
       " '2019-06-30',\n",
       " '2019-07-01']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.order_date.unique().tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "285e10cc",
   "metadata": {},
   "source": [
    "* order_date: 6/1/19 - 7/1/19"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cb950cd",
   "metadata": {},
   "source": [
    "##### Count Events in Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39f13913",
   "metadata": {},
   "source": [
    "###### Chargeback Count Analysis for the Whole Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2663e022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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G/gT5lWd67CHuaFHL7uUp5SoSUzIYPH4NF7NzuSuqNgvjk5m7/Rjl3N3oGOpPr4ga9AwLxK9COUeH6rSWJqTQLKgy/j5ejg6lQEUOOWeM+Q34rZRiUUopdZV+3XKUVYkneadfBAG+9v+gcXMT+reqzcd/7ubw6XPUrlLB7mUq5ex2J6czaPxawDB1eAxNalTinb4RbDp0mj+2HWNu3DH+3Hmc/3MT2jeoZk2ga5TKa9ZVpGVmsfHgGR6zU5+MkqBtk5VSykWlnsvi7d/iaRHsx+A2dUqt3P6tagPw04YjpVamUs5qR1Ia945bg5vAD9aEGSxfMFvVrcrfbw9jxUvd+HVkB4Z3rs/h0+d5dWYcbf65kIFfrubrlftISj3v4GfheKv2nCAn19ClUXVHh1IoW6bRVkop5YT+M38np85e5JthbUq1p3lw1Qp0aODPjA2HeOqmhk7Zy12p0hB3JJX7JqylvKc73z8aQz3/god6FBGa1/ajeW0//nZLY3YlpzPHWgP95ux43pwdT8s6fvSOqEHviJoEV73xfsFZmpCCr5cHLev4OTqUQhWbNIuIlzHmwhXLqhpjTtkvLKWUUkXZfOgMU9YeZFj7ekQEVS718gdE1+aZHzazeu9JOti586FSzmjTwdM8MHEdlbw9+WF4jM2JrojQpEYlmtSoxHM9GpGYksHcuGPMiUvin3/s5J9/7CS8ViV6R9SgV0RNGlb3sfMzcTxjDEt3pdC+YTU83Z23EYQtkf0sIp55/4hITWCB/UJSSilVlOycXF6duY3qvl6M6tnIITHcEl6DSt4eTFuvYzarG0/s/lPcP2EdVSqUY9oI2xPmgjQI8OHJbg357alOLP9bN169tSleHm58MD+B7mOW0mPMUr5Zua8Eo3c+iSkZHE3NdOqmGWBb84xZwAwRuRsIBn4FXrBnUEoppQr37eoDbD+axmdDovDxckwrO29Pd/q1DOKH9YdIPZdF5QqexW+kVBmwOvEkD09aT41K3nz/aAw1KnuX2L6Dq1bg0c71ebRzfY6lZjJv+zFmbT7CG7PjCQ+qTOuQqiVWljNZsss6dbaTjs+cp9iaZmPMeCw1y7OA2cBjxpj5do5LKaVUAY6lZjJ6/i66Ng6gd0QNh8YyMDqYi9m5/LJFOwSqG8OK3ScY9s06gvzK88OIkk2Yr1SjsjcPtg/h+0diCPD14oN5uzDG2K08R1qakEKDgIpOPxpPoUmziIzKuwHeWGqZNwMx1mVKKaVK2du/xZOda3irTwQiju2AFxFUmbCalXRabXVDWLzzOA9NWk9ItYr8MDyG6r72S5jzK1/OnSe7NmDtvlOsSjxZKmWWpsysHNbtO+X0TTOg6Jpm33w3H2AmsCffMqWUUqVo8a7j/L4tiaduakidas5RI3NP62DijqSx/Wiqo0NRym7mbz/G8MmxNAr0YeqjMVQr5ck3BrWtQ63K3oyeX/Zqm9fsPcmF7Fy6NHbOWQDzK2pGwDdLMxCllFKFy8zK4fVfttMgoCKPdnaewf/7Rtbi3T92MH39Id7sW/qjeChlb39sS+LpqZuICKrMpIfaULl86bff9/JwZ+RNobwycxtLdqXQrYnz18raamlCCl4ebrSt5/zttYtt0ywiC0TEL9//VURknl2jUkopdZlPF+/h4KlzvN0vAi8Pd0eHc4lfhXLcEl6DWZuPkpmV4+hwlCpRv2w+wlNTNxEZ7Mfkhx2TMOcZEF2b4KrlGb2gbNU2L0tIoW39anh7Os/7WmFsGXIuwBhzJu8fY8xpoOx8xVFKKSe353gGXyxN5K6WQbRv4Hy9y++JDib1fBbz45MdHYpSJebHDYd5dtpmWodUYdJDbfD1duwIMZ7ubjxzcyPijqQxb3vZeK0dOnWOxJSzdGnk/E0zwLakOUdELs3PKiJ1gbLzFUcppZyYMYbXZm2jvKc7r9zW1NHhFKh9g2oE+ZVnhnYIVGXE1HUHefHHLXRs6M/XQ9tQ0UFDO16pX2Qt6vtXZOyCBHJzXT8VW7bbMtRcFycfai6PLUnzq8AKEZksIpOBZcD/2TcspZRSALM2H2HN3lO81LsJ/qXc+chWbm7CgOjarNhzgsOnzzk6HKWuy7er9/N/P2+ja6MAxj8QTflyztNswMPdjWd7NGJXcjq/b0tydDjXbVlCCkF+5WkQ4BqzHtoyTvNcIAqYBkwHWhljtE2zUkrZWeq5LN75bQct6/gxqHWd4jdwoP6tagOWn7SVclVfLd/LP37ZTo+wQL64v5VTtrO9vVlNGgf6MnZhAtk5uY4O55pl5eSycs9JOjcKcPjwmbaydYLvHOA4kAqEiUjn4jYQkYkiclxE4vIte0NEjojIZuvt1msLWymlyr5/z9vJmfNZvNuvGW5uzv2hUrtKBTo29GdG7OEy8bOxuvF8tmQP7/y+g1ub1eCzIVFO1eE2Pzc34bkeoexNOcsvm486OpxrtvHAaTIuZLtM0wywbfSMR7A0yZgHvGn9+4YN+/4G6FXA8rHGmEjr7Q/bQ1VKqRvHxoOnmbruIEPbhxBWq5Kjw7HJgOhgjpw5XyYnYFBllzGGjxbu5v25u+gbWYuP722Jp7utdYqOcUt4DcJrVeKjRbvJctHa5mW7U3B3E9o3LENJM/AM0Bo4YIzpBrQEUorbyBizDDh1feEpZV9Hz5xn48HTjg5Dqctk5+Ty6sw4An29ea5HI0eHY7OeYYFULu/JNO0QqFzI6PkJjF2YQP9WtRkzMBIPJ0+YAUSE53s24uCpcy7bJGppQgqt6lShkoNHJbkatlwZmcaYTAAR8TLG7AQaX0eZI0Vkq7X5RpXCVhKR4SISKyKxKSnF5uhKXbWE5HT6fLKSuz5bxajpmzlz7qKjQ1IKgG9W7WdHUhpv9AnDx0l67dvC29OdfpG1mLf9mL6elEtYnXiSTxbv4Z7oYN6/uznuTt4MKr9ujasTGezHfxft5kK2a42RnpJ+gbgjaXR2oaYZYFvSfNg6ucksYIGI/AJcayOaz4EGQCSQBIwubEVjzDhjTLQxJjogwDXG71OuI/5oGveOW4ObwCMd6/Hr5qN0H7OUP7YllalB45XrSUo9z9gFCXRrHMAt4TUcHc5VG9g6mIvZuS7d1lLdGIwxjFmwi8BKXrzZN9zp+w1cSUR4oWdjjqZm8sM61/p1Z8WevKHmXGvaD1tGz7jTGHPGGPMG8HdgAtDvWgozxiQbY3KMMbnAeKDNtexHqeux7XAqg8avwcvDjWkj2vHa7WH8OrIjNSuX54kpG3nsuw0cT8t0dJjqBvXW7Hiycw1v9Y1wmR7l+YXXqkxEUCWmrXetD3F141m2+wTr959m5E2hTjlKhi06NKxGm3pV+WTxHs5fdJ3a5qW7UqhWsRzhLtJfI49NDXdEJEpEngaaA4eNMdf0u5uI1Mz3751AXGHrKmUPGw+eZvBXa/D19mD6iHbU868IQFitSsx8oj3/17sJS3al0H3MUqbHHtJaZ1WqFu88zpy4Yzx9cyjBVSs4OpxrNjA6mPikNOKOpDo6FKUKZIxhzPxdBPmV557oYEeHc81EhOd7NCIl/QLfrTng6HBskptrWLb7BJ1C/V2udt+W0TP+AUwCqgH+wNci8poN200FVgONReSwiDwMvC8i20RkK9ANeO66olfqKqzff4r7v1pL1YrlmDai3V+SEg93N0Z0acCcZzrRpGYl/vbjVu6fsI5Dp3SyBmV/5y/m8I9f42hY3YdHO9V3dDjXpW+LIMp5uDFdOwQqJ7Vox3G2HE7lmZtDKefh/B3/itK2fjU6hfrz+dJEzl7IdnQ4xdp+NI1TZy/SpbHrNb215UoZBLQ2xrxujHkdiAGGFLeRMWaQMaamMcbTGFPbGDPBGHO/MaaZMaa5MaaPMcb1p7NRLmFV4gkemLCOwMreTBvejiC/8oWuWz/Ahx8ejeGdfhFsPnSGnmOXMXHFPnJ07FllR58s3s2hU+d5p1+Ey3+IV67gSa/wGszadITMLNf5yVjdGHJzDaMXJBBSrQJ3RQU5OpwSMapHI06dvcg3q/Y7OpRiLU04DkCn0LKZNO8HvPP97wUk2iUaJ7JoRzI9xy4lPTPL0aGo67QsIYVhX6+ndpXy/DA8hhqVvYvdxs1NuC+mLvOf60xM/aq89Vs8A75Yxe7k9FKIWN1o5m8/xrhle7k7qjYx9as5OpwScU/rYNIys5m3/ZijQ1HqMnO3H2NHUhrPdA91ieHlbNGyThVublKdccv2kubkecvShBQigirh7+Pl6FCuWqFXi4j8V0Q+Bi4A20XkGxH5Gks75IzSCtBRfL09SUjOYGmCDnfnyhbvPM4j38Zaao+Hx1Ddt/iEOb9afuWZOLQ1H94Tyb4TZ7nt4xX814UHk1fO5UTGBUZ+v5HhkzfQsLovr9zaxNEhlZh29atRu0p5ZsS65hiyqmzKyTWMWZBAw+o+9GlRNmqZ8zzXoxGp57OYsHyfo0MpVFpmFhsPnqFLI9erZYaia5pjgQ3ATOAVYDGwBHgVmGP3yBysVd0qVK1YjgXxyY4ORV2jeduPMXxyLI0DfZn6aFuqXeO3WhGhX8sgFozqwi0RNRi9IIE7/ruCrYfPlGzA6oZhjGHmpsN0H7OU+duTeb5HI34d2eGar1Fn5OYmDGgVzIo9J7RfgHIas7ccZc/xDJ7r3silxmS2RURQZXqF12DCin2cPuuc46Sv2nOCnFzjckPN5Sk0aTbGTMq7AdOBNVcsK9Pc3YSbmlRn8c7jWqvogn7fmsSTUzYSXqsy3z3SFr8K5a57n/4+Xvx3UEvGPxDN6XMX6ffpSt77Y4e22VRX5ciZ8wz7Zj3PTdtCff+K/P50R566OdTpp+29Fv2jayOCy85YpsqW7JxcPlyYQNOalegd4XpjoNviuR6NOHsxm3HL9zo6lAItTUjBx8uDlnX8HB3KNbFl9Iw7gM3AXOv/kSLyq53jcgo9wgJJy8xm/T6dDdyVzNp0hKembqRlHT8mP9yGyuVLdorOHmGBzH+uC/e0DubLZXvp9eEy1uw9WaJlqLInN9cwec0Beo5Zytq9p3j9jjBmPNae0EBfR4dmN0F+5enY0J8fNxzWjrTK4X7eeIT9J88xqkcjlxvqzFaNa/hyR/NafLNyPynpFxwdzmWMMSxLOEGHhtVctpLAlqjfwDIJyRkAY8xmoJ7dInIinUL98fJwY7420XAZM2IP8dz0zbStV41vhrXB105z2lcu78l7dzXn+0fakmvg3nFreHXmNu04qgq0NyWDe8et4e+z4mhZpwrzn+vMsA71ytzPwwUZGB3MkTPnWZV4wtGhqBvYxexcPlq0mxa1K9O9qWs2DbDVs91DuZCdwxdLnWvMhsSUDI6cOe+yTTPAtqQ52xhz5Qj1N0SVQYVyHnRs6M+C+GSd5MIFfL/2IC/+uJWODf2ZOLQ1Fb087F5m+4b+zHu2M490rMfUdQfpOXYZi3cet3u5yjVk5+TyxdJEen+0nJ3H0ni/f3MmP9zGpScuuVo9wwPxq+CpMwS6iOPpmWWySeK02EMcOXOeUT0bu+RMm1ejfoAPd0XVZvKaAxxLdZ7ZbZfssgys0LmRv4MjuXa2JM1xIjIYcBeRUBH5L7DKznE5jR5hgRw5c54dSTrUmDObtGo/r8zcRrfGAYx/IJry5UpvStTy5dx57fYwfnq8Pb7eHgz7Zj2bDp4utfKVc4o/mka/z1byrzk76do4gIWjujAwOrjMf2BfycvDnX6RQczfnuy0nZMUXMjOYcz8XbR/70+GfLWWDBeYJMNWmVk5fPrnHqLrVqFzqOsmbFfjmZtDyc01fLp4j6NDuWTZ7hM0CKhI7SquW2lgS9L8FBCOZei5qUAa8KwdY3IqNzcNRAQdRcOJjV+2l9d/3U6PsEC+uL8V3p6llzDn17JOFX5+ogO+3h5MWOG8Q/4o+8rMyuGDebvo88kKjqVm8tmQKL64rxXVK13dcIdlycDoYC7m5PLL5iOODkUVYOPB09z+8Qo+/nMPHRr6s+HAaR6YsNbpx/u11fdrD3IsLZNRPRvdMF9ag6tWYGDrYH5Yf5DDpx0/ek1mVg5r95506aYZYEPSbIw5Z4x51RjT2hgTbb3vPPX9dhbg60XLYD8W7tCk2Rl9ungP7/6xg9ua1eSzIVF4eTgmYc7j4+XBoDZ1mBN3jCNnzjs0FlX6Nhw4xW0fL+eTxXvoGxnEwlFduLVZzRvmg7owYbUq0SyoMtNiD2tTNydy7mI2b82O5+7PV3H2QjZfD2vNpIfa8Onglmw7ksr9X60l9ZxrJ87nLmbz2ZI9tG9QjfYNboxa5jwjuzVEEP67yPG1zWv2nuRCdq5LN80A20bPmC0iv15xmywiz4jIDVF10iOsBtuOpJKUqkmQszDGMHZBAv+Zt4t+kbX46N5Ip+mN+2D7EAC+Xb3foXGo0nP2QjZv/Lqd/l+sJjMrl0kPtWH0wBYlMtRhWTEwujY7ktLYfjTN0aEoYOWeE9zy4TImrtzHfW3rMu+5znRrbKkF7BVRk8+HtGJHUjqDxq/hlAs3q/l29QFOZFzk+Z6NHB1KqavlV57Bbevw48bD7D9x1qGxLEs4gZeHm8vPeGpLlrEXywyA4623NCAZaGT9v8zrEWZ5I1moTTScgjGG/8zbxUeLdtO/VW1GD4x0qqlQg/zK0yu8BlPXHuRsGWoXqAq2LCGFnmOX8c2q/TwQY0k+XHW2K3vqExmEl4ebdgh0sNTzWbz041aGfLUWDzc3pg2P4e1+EX8Zaah7WCDjHmhFYkoGg8at4USGcw1fZov0zCy+XJpI18YBtKpb1dHhOMQTXRvg6S58tGi3Q+NYmnCctvWrOaz5ZEmxJdNoaYwZbIyZbb3dB7QxxjwJRNk5PqfQIMCHev4Vdeg5J2CM4d3fd/DZkkQGtanD+3c3d8phux7qWI+0zGx+2qiTOpRVqeeyeGHGFh6YuA4vTzdmPNaON/tG4FMKo7a4osrlPekVUYNfNh/RCYEcZN72Y/QYs5QfNx7m8a4NmPNMJ9oWUfPXtXF1Jg5tzYFTZ7l33BqOp7lWy8yvV+7n9LksRvW48WqZ81Sv5M0D7UKYtfkIu5MdM6DB4dPnSEw5WyY6YdqSNAeISJ28f6z385656/5mcxVEhB5hgazZe7LMdIxwNcYYNh08zajpW/hqxT4ebFeXf94Z4bQD1LeqW4XIYD++XrmfXJ3UoUw5euY8E1fso/vYpczcdIQnujbgj6c70TrkxqzJuhr3RAeTlpnNvO3HSnS/F7NzWbLrOO/N2eFUQ2w5i5T0Czw5ZSMjJm+gmo8Xs57owEu9mthU69ehoT+ThrUh6cx57hm3xmWaKaaey2L88r30CAukeW0/R4fjUCM616eCpzsfLnRMbfOyBMsY7V0bu/4vcLZUiYwCVohIIiBYJjZ5QkQqAmV+Ou08PcICGbdsL0t3pXBHi1qODueGkJNr2HDgNH9sS2Le9mMkpWbi4SY83rUBf7vF+cfafKhjPZ6euonFu45zc9NAR4ejrsPBk+eYE5fEnLhjbD50BoAWwX58PbQ1EUGVHRucC4mpX43gquWZHnuIvpFB17WvzKwcliWkMDfuGAt3JJOWaWkKdSw1k4/ubVkS4bo8YwwzNx3hrd/iOXchhxdvaczwzvWvuv9H2/rV+PbhtgyduI6BX67m+0dinH6s8fHL95KemX1D1zLnqebjxbAO9fhk8R6ePJpGWK1KpVr+0oTjBPmVp0GAT6mWaw9FJs0i4gb4AqFAEyxJ8858o2d8aNfonEhUnSpUq1iOBfHJmjTbUVZOLmv3nmJOXBLztidzIuMC5Tzc6Bzqz/M9G9OjaSCVK9hnlr+S1juiBjUrezNx5T5Nml3QnuPpzNl2jDlxx4hPsnReiwiqxIu3NKZXRI0y8QFQ2tzchAGtghmzIIFDp85ddeJ19kI2S3al8EdcEot3HufcxRwql/ekZ3gNekfUYHXiSSas3McTXRvSuEbZnZ7cFkfOnOfVmdtYsiuFqDp+vN+/OQ2rX/sxaVW3Ct890pb7J6zl3nFr+P7RttStVrEEIy45p85e5OuV+7iteU2a1izdBNFZPdqpPpNW72fswgTGPxBdauVm5eSycs9J7mhRNkYRKjJpNsbkishIY8x0YEspxeSU3N2Em5pUZ+72Y2Tl5DrNSA1lwYXsHFbuOcGcbcdYsCOZM+eyKO/pTrcmAfSKqMlNTaq7ZDtRT3c3HmgXwr/n7mRHUpq+eTs5Yww7ktKZa61R3n08A4CoOn68emtTekXUcPraNVdwd6vajF2YwIwNh22qBUzLzGLRjmTmbDvG0oQULmTn4u9Tjr6RQdzarAYx9atdej+OqlOFH9Yf4sOFCXx+Xyt7PxWnlJtrmLL2AP+asxMDvHFHGPe3CymRvh8tgv34/tEY7p+w1lLj/GiMU355/HJpIuezcniue6ijQ3EalSt48min+oxZkMCWQ2doEexXKuVuOniGjAvZZaZztC2ZyAIReQGYBlwas8QYc8puUTmp7mGBzNhwmHX7TtGhoes3aHek8xdzWJqQwty4JBbtOE76hWx8vTy4uWl1ekXUpEujgFKd1c9eBrepw8eLdjNxxT7+M6CFo8NRVzDGsOVwKnPikpgbd4wDJ8/hJtA6pCpv9gnnlvAa1Kh8Q4ysWWqC/MrTKTSAH2MP8czNoQUmc6fOXmRBvKWWf+WeE2TlGGpU8mZQmzr0iqhB65CqBW5XpWI5HupYj48X7SbuSOoN13QmMSWD//tpG+v2n6JTqD//vLNZiX/RiwiqzNThMdz31Vru+XINUx9tS2ig89TqH0/PZNLq/fSLDLqumvWyaFiHECau3MeYBQlMeqhNqZS5NOE47m5C+zKSM9mSND9k/ftkvmUGqF/y4Ti3TqH+eHm4sSA+WZPma5BxIZs/dx5nblwSi3emcD4rB78Klh71vZvVoENDf4dPTlLSKlfwpH+r2kxbf4iXejfB38fL0SHd8PLays+JS2Je3DGOWtvKt2tQjRGdG9AzPFDPk50NjK7NyO83sWLPiUs1UMfTM5m3PZk525JYu+8UObmG2lXKM7R9CL2b1SSytp9NHX8f7liPSav2M3ZBAhOGtrb3U3EK2Tm5jFu+lw8X7sbbw43/9G9O/1a17fZzeJMalfhheAyDx1uaanz3SFun+SXts8WJZOUYnr5Za5mv5OvtyYjODfj33J3E7j9FdCl0Xl6aYGkeVMnbNZpVFqfYpNkYU680AnEFFcp50CnUnwXxybx+R1iZaJ9jb6nnsli4I5k5cUks232Ci9m5+Pt4cVdUEL0jatK2ftUy39RlaIcQJq85wHdrDvBsd+folGKMuaGu3+ycXNbu+19b+ZT0/7WVH9WzMd2bVteJSEpRj7BAqlTw5JuV+9hzPIO5cUnEHjiNMVA/oCKPdalP74iahNeqdNXXaeXyngzvXJ//zNvFpoOnaVmnip2ehXPYfjSVl37aStyRNHqF1+CtfuFU97X/ryMNq/sybUQ7Bo9fw6Dxa/ju4bYOr9k/euY83689yIBWtQnxd8721o72YPu6TFixl9HzE5g6PMauZZ3IuEDckTReKEMTy9jUUFREIoAw4NIr0Rjzrb2CcmY9wgJZuOM48UlphNe6sX76uxpZObl8uTSRj//cw8XsXGpW9mZI2zr0jqhJq7pVnHJsZXtpEODDTU2q892aAzzWpYFDB3c3xjBy6ibcRfh40I0xwkBS6nnun7COPcczKO/pTtfGAfSKqMFNTar/ZUIHVTq8PNzp1zKIr1fuZ/GuFJrU8OWZm0O5tVlNQqv7XPcXuqHtQ5iwwvIz9OSH25ZQ1M4lMyuHT/7cwxdLE/GrUI7Ph0TRu1nNUo2hnn9Fpo9ox73jLInztw+1ceiXlE8W78FgGHlTQ4fF4OwqlPPg8a4Nefu3eH5Yd5B729QpfqNrtHx3CgBdGlW3WxmlrdikWUReB7piSZr/AHoDK4AbMmm+qUkgIttYEJ+sSXMhth1O5cUft7DzWDq3NavJI53q0cLGn1bLqoc61OO+CWuZveUoA6KDHRbHr1uO8vvWJACGtK1T5MQGZcHh0+cYPH4tp89e5ONBLenRNLBMtJUvC56+KZT6AT50bOhPvRKuFazo5cHjXRrw7h87WLfvFG3qla0xtDccOMXfftxKYspZ7o6qzd9vb+qwX0qCq1Zg+mOWGuf7J6zj62GtHTJm+aFT55i+/hCD29ahdhXtsFuU+2LqsGTXcV7+eRs5xjCkbV27lLN0VwrVKpYjvJSHuLMnW34X7w/cDBwzxgwDWgA3bIO/AF8voupUYeEOnR3wSplZOfxrzk76fbaSU2cv8uX9rfh0SBQt61S5oRNmgA4Nq9Gkhi8TVuzDGMdMdpJ6Pou3f9tBs6DKVPf1YvSCBIfFUhoOnDzLPV+u4cy5i3z3SFv6tKilCbMTqVKxHPfH1C3xhDnPfTF1CfD14oP5u8rMdX72QjZv/Lqd/l+sJjMrl0kPtWH0wBYOb1oU5FeeacPbUd3XiwcnrmN14slSj+GjRbtxdxOe7Ka1zMXx8nBn/APRdGscwKsz4/hm5b4SLyM317B89wk6hfqXqc9/W5Lm88aYXCBbRCoBx7kBOwHm171pIHFH0jh6xjVmRioNa/eepPdHy/liaSL9o2qzYFQXbgmv4eiwnIaI8FCHeuw8ls7qvaX/gQLwwbxdnDp7gffuasaT3Rqybt8pVu5xTCz2tjclg3u+XMO5i9l8/2hMqQ2vpJxH+XLujCxD1/myhBR6jl3GpNX7ebBdCPOf6+xUw3jVqOzNDyNiCPIrz7Bv1l36ab407E3J4OeNh7kvpi6BlXS0G1t4e7rzxf2t6BEWyBuz4xm3LLFE97/9aBonz16ksxNdoyXBlqQ5VkT8gPHABmAjsK64jURkoogcF5G4fMuqisgCEdlt/euSPTR6hFkmqtDaZkjPzOK1Wdu4Z9wasnNzmfJIW/7dvzmVy2tb0Sv1iaxFtYrlmLii5L/VF2fzoTN8t/YAD7QLISKoMve2CaZWZe8yVQuXZ3dy+qXrcerwGId3TlKOk3edj17gutd56rksXpixhQcmrsPL040ZI9rxRp9wKjrh2PXVfb35YXgMIdUq8vCkWBbvPF4q5X64cDdeHu483rVBqZRXVnh5uPPZkChua1aTf/6xk08X7ymxfS9NsJz7TqE3WNJsjHnCGHPGGPMF0AN40NpMozjfAL2uWPYysMgYEwossv7vchpW96G+f0UWxN/YSfPince5Zewypqw9yEMd6jHv2c46FF8RvD3dGRJTl0U7j7PvxNniNygh2Tm5vDpzGwE+Xjxv7cXs5eHOUzeHsvnQGRbvKp0PttKwIymNe8etQYAfhsfQpEbZaUunrl7edb7poGte53Pjkug+dikzNx3hyW4N+OPpTqUyTNj1qObjxdRHY2gU6MPwybHM337MruXtOpbO7K1HGdohRIeKvAae7m58dG8k/SJr8Z95uxhbQs32liWcICKoEgG+Zeuc2DTWl4gEiUh7oA7gJyKdi9vGGLMMuHIClL7AJOv9SUA/20N1Lj3CAlmz9yRpmVmODqXUnTp7keembWbYN+up6OXBT4+35x93hFGhnPPVfDib+2Lq4OnmZpc2ZIWZvOYA24+m8fod4ZeNFtG/VW3qVK3A6Pllo21z3JFUBo1fQzkPN6aNaKcTGyjgf9f5GBdqw388PZPHv9vAY99tpLqvF7+O7MCLtzRx6Mg7V6NKxXJMeSSG8FqVeWLKRr5bc4BzF7PtUtaHCxOoWM6D4Z1u6Faj18XD3Y3RAyPp36o2Hy3azfvzru+XmbTMLDYcPE3nMlbLDDYkzSLyb2Al8BrwovX2wjWWF2iMSQKw/i10HBIRGS4isSISm5JSem2jbNUjLJCsHMOSXc4Xm70YY/ht61F6jFnK7C1HefrmUH57uiNRZXwc1JJU3debO1rUYsaGw6Set/8XruS0TEbPT6BzowBubXZ5G3NPdzeevjmU7UfTmGfn2iB723zoDIPHr6FiOQ+mDW9nt85lyvXkXedxR9KYt925fx00xjAj9hA9xixj0c7j/K1XY2Y92cElR2qqXN6TyQ+3IapOFV6bFUfLtxYwYnIsszYdKbHKprgjqcyJO8bDHetRpaKOs3493N2E9+9uzqA2dfh8SSLv/L7jmhPnVXtOkJNrnKrNfUmxpWqwH9DYGHPBzrFcxhgzDhgHEB0d7XTVAy3rVKFaxXIsjE+mT4tajg7H7pLTMnltVhwL4pNpXruyU80A5Woe6hjCTxsPM239QYZ3tm8bvLd+i+diTi5v9w0vcOzbfpG1+GzJHsYsSKBHWA2XHD87dv8phn69nqoVy/H9o211uCn1F3nX+dgFCfQMC3TK3vyHTp3jlZnbWL77BK1DqvCvu5vTIMDH0WFdF19vT6YOj2HdvlPMjUti7vZjzNueTDl3Nzo0rEbviJqWiW6uMeEduyCByuU9ebiTzsFWEtzchH/eGUE5d2HCin1k5eTyxh3hV/16WZpwAh8vD6Lqlr0KNVuS5r2AJ1ASSXOyiNQ0xiSJSE0sI3G4JHc34eam1ZkTd4ysnNwyO6udMYbpsYd45/cdXMzO5ZVbm/BQh3p4lNHnWxrCa1Umpn5VJq06YNdjuWTXcX7fmsTzPRpRt1rBNa8e7m48270RT0/dxG9bj9I3MsgusdjLmr0neeib9dSo5M33j8ZQo7L2nFd/ddl1vi3JqSo6cnMN367ez/vzdiHA233DGdK2rlMm9tfC3TpFfbsG1Xj9jnA2HTrD3Lgk5sQdY/FPW3GfKbSrX41eETXoGR5o82yGmw6eZtHO47x4S+MyM0WzMxAR3ugTTjkPN8YvtyTO7/ZrZvP1aIxhWUIK7RtUK5N5UaHPSET+KyIfA+eAzSLypYh8nHe7xvJ+BR603n8Q+OUa9+MUujcNJD0zm7V7r2y6XTYcPHmOIV+t5aWfthFWsxLznu3M8M4NNGEuAQ93rM+RM+eZa6dmEZlZOfzjl+3UD6jI8C5Ft/W7vVlNGgf68tHC3WTn5NolHntYsfsEQ79eR5BfeX4YoQmzKlredf7hggSnuc73HM9g4JereWN2PK1DqjLvuc7c3y6kzCTMV3JzE1rVrcKrt4Wx/G/dmD2yIyM61+fomfO8NiuOtv9cxMAvVjNxxb5ih3QdsyCBahXLMbR9SOkEfwMREV65tSlPdmvA1HWHePHHreTk2vaDf2JKBkfOnKdL47LXNAOKrmmOtf7dgCXZvSoiMhXLTIL+InIYeB34FzBdRB4GDgIDrna/zqRTaADenm4siD9Gx9CyM2pETq7h65X7GD0/AXc34d07IxjUuk6ZfSN3hJubVKdutQpMXLGP25uXfK3XZ4v3cPDUOb5/pC1eHkV3HnJzE57r0YjHvtvArM1H6d+qdonHU9IW7zrOiMkbqO9fkSmPtKWa9ppXxXCm6zwrJ5dxy/by0cLdVPByZ8zAFtzZMui6pw93JSJCs9qVaVa7Mi/e0piE5AzmxCUxN+4Yb/0Wz1u/xRMZ7EfviBr0jqhJnWr/a3a1du9Jlu8+wau3NnXKoffKAhHhhZ6NKefuztiFCWTn5jJ6QItiK82WJpwAKJOdAKHopPkPIMAYE59/oYhEAMX2pjDGDCrkoZttD8+5lS/nTseGASyIT+aNPgW3GXU1Ccnp/O3HrWw+dIabmlTn3TsjqFm5vKPDKnPc3IRh7UN4Y3Y8mw6epmUJdqbcczyDz5cmcmfLINrbOATgLeGBRARV4qNFCfSNrOXUP6stiE/mySkbaVTDh8kPtdUOQMpmznCdxx1J5W8/biU+KY3bmtXkjT7hZW5YrqslIjSu4UvjGr48270Re1MymLv9GHO2HeO9OTt5b85OwmpWsiTQzWowekEC1X29uC/GPtM/KwsR4ZnuoXh6CO/P3UVWTi4f3duyyNfN0oQU6gdUJLhq2exbUlTS/F/g8wKWBwGvAIPtEpGL6RkWyMIdyWw/muaykyhkZuWwLCGFuXHHmL31KD5eHnx0byR9WtQqE18EnNWA6GBGL0hgwop9fDK4ZJJmYwx/nxVHeU93Xrm1qc3biQjP92jMsG/WMyP2MIPb1imReEraH9uSeHrqJsKDKvPtQ21KZRKdrKwsDh8+TGZmpt3LUhbe3t7Url0bT8+SPb+Ovs6/Wr6X9+bspGrFcnxxXyt6ReisqQWpH+DDE10b8kTXhhw6dY55248xJ+4YoxckMHpBAgBv9gmnfDnXGILP1T3RtSHl3N145/cdZOVs5JPBLQv8BTMzK4e1e0867edHSSgqaW5mjFl65UJjzDwRGW3HmFzKTU2rI2Kp/XKlpPnshWyW7Erhj7gkFu88zrmLOVQu78mA6GCe79FIf+4uBRW9PLi3dTATV+7n6Jnz1PK7/hr9XzYfZfXek7zTL+Kqa6+6Ng6gZR0//vvnbu6KCnK6MWF/2XyEUdO30DLYj6+Htb5szGl7Onz4ML6+voSEhOiXyFJgjOHkyZMcPnyYevVKflQER13nn/y5mw/mJ9ArvAb/vrs5lSto5zVbBFetwCOd6vNIp/okp2Uyb/sxDpw8x71tgh0d2g3lkU718XR34/Vft/PY5A18fl+rv7x21u47xYXs3DI51Fyeon6bKuoVra92K38fL1rVqeISU2qnZWYxc9Nhhn8bS9TbC3jy+42s3XuSvpFBTH64DbGvdeefdzbThLkUPdg+BGMMk1bvv+59pZ7L4p3fLe0AB7e5+m/6eW3YklIz+WHdweuOpyT9uOEwz03bTHTdKkx6qE2pJcwAmZmZVKtWTRPmUiIiVKtWzW41+3m1zaV1nRtjGLMggQ/mJ3BXyyA+GdxSE+ZrFFjJmwfahfD328OK7auhSt6D7UP4553NWJKQwiOTYjl/Meeyx5fuSqGchxtt61VzUIT2V1RN824RudUY80f+hSLSG8swdMqqe1gg/5qzkyNnzhNUArWFJenU2YssiLf8tLVyzwmycgw1KnkzqE0dekXUoHVIVZccm7esqF2lAr0jajJ17UGeuTn0umZVfH/eTk6dvcg3w9pcc6fN9g2q0bZeVT5dksg9res4xc+fP6w7yP/N3EaHBv6MfyDaITFpwly67H28OzQsnevcGMO/5+7ii6WJDIyuzXt3Ndf3W+XSBretg6e78LeftjLsm3VMeLD1pc6YSxOO07ZeVaf43LCXomqanwM+FJFvROQp620S8BHwTOmE5xp6hAUCsDDeOWqbj6dnMnnNAYZ8tYbW7y7kpZ+2sed4BkPbh/DzE+1Z9fJNvNEnnJj61fQN3Ak81DGEtMxsftpw+Jr3sengab5fd5Ch7etdVzMhEeH5no1JSb/A5DX7r3k/JeXb1ft5+edtdGkUwFcPOiZhVmVP/uv8uzUH7FKGMYa3f9vBF0sTGdK2Dv/ShFmVEQOig/nwnkjW7z/NgxPXkZ6ZxeHT50hMOVumm2ZAETXNxpgEEWmGpcNfhHXxUmCEMUZ7xOTTIMCH+gEVWRCfzIMOGjPyyJnzzI07xty4JGIPnMYYqB9Qkce61Kd3RE3Ca1XS2jInFVWnCi2C/Zi4cv81TWqQnZPLqzPjCPT1ZlTPRtcdT5t6VekU6s8XS/cyuG1dfBw0pNNXy/fyzu876BEWWGjHE6WuVd51/vnSRAa1rVOi13luruH1X7czec0BhrYP4fU7wvT9V5UpfSODLFPUT93E/RPWXerUesMmzQDWqbO/LqVYXFqPsEAmLN9H6vmsUunRD5aprWduOsKcuGNsOXQGgCY1fHnm5lBubVaT0Oo++kbtAkSEhzqE8MwPm1mScJybmgRe1faTVh8gPimNz4ZEldgH//M9G9Pv05V8s3IfI28KLZF9Xo3PlyTy77k7ubVZjWKHOCqL3njjDXx8fHjhhRdKfN/79+9n1apVDB5c9ABItq7nyvKu80mr9vNkt4Ylss/cXMMrM7fxw/pDjOhcn5d7N9H3YVUm3dqsJh5uwpPfb2TzoTPUquxNw+quPfV7cW6sTyI76hkWSHauYcmu0pkZ/ETGBfp8soJ/zdmJMYa/9WrM4he6MvfZzjzbvRGNAn31jdqF3NqsJjUrezNhxb6r2u5YaiZj5u+ia+MAepfg8FWRwX50b1qdccv2kno+q8T2WxxjDB8t3M2/5+6kT4tafHwDJMzGGHJzr2+GuuzsbJvX3b9/P99//32JrefKIoP9uLlJdb5cmlgi13lOruHFH7fyw/pDjOzWUBNmVeb1DK/BuPujKefhRs/wGmX+ei/bn0alKDK4Cv4+5Vi4w/5Jc06u4empmzhzLotZT3bg15EdeaJrQ+r5V7R72co+PN3deKBdCCv3nGTnsTSbt3vrt+1k5xre6hNR4m9Wz/VoRFpm9lUn8tfKGMPo+QmMXZjA3VG1GXtPZJmZsn3MmDFEREQQERHBhx9+yP79+2natClPPPEEUVFRHDp0iHfffZfGjRvTvXt3du3adWnbxMREevXqRatWrejUqRM7d+4EYOjQoYwaNYpu3brx0ksvFVju0qVLiYyMJDIykpYtW5Kens7LL7/M8uXLiYyMZOzYsezfv59OnToRFRVFVFQUq1atAvjLet988w0jR468tO/bb7+dJUuWkJOTw9ChQ4mIiKBZs2aMHTvWjkey5JXUdZ6dk8tz0zbz08bDjOrRiBduaVzmEwilALo1qc6Kl7rxcu8mjg7F/owxBd6ARda//y5sndK6tWrVyriCF2dsNhH/mGsuZOXYtZz35+4wdV/6zUxbf9Cu5ajSdfrsBdPktTnmxRmbbVr/z53Jpu5Lv5n/LkqwW0yPTY414f+Ya05lXLBbGcYYk5uba979Pd7Ufek38/JPW0xOTq5dy7sa8fHx17V9bGysiYiIMBkZGSY9Pd2EhYWZjRs3GhExq1evvmyds2fPmtTUVNOgQQPzn//8xxhjzE033WQSEizneM2aNaZbt27GGGMefPBBc9ttt5ns7OxCy7799tvNihUrjDHGpKenm6ysLLN48WJz2223XVrn7Nmz5vz588YYYxISEkze++2V63399dfmySefvPT/bbfdZhYvXmxiY2NN9+7dLy0/ffr0NR+r/K73uF+N673OL2TlmMcmx5q6L/1mPlu8p4SjU0qVNiDWFJCPFlWNU1NEugB9RKSliETlv5VCPu9yeoTVIP1CNmv3nbRbGYt2JPPp4kTuiQ5mYLQO7l6W+FUox92tgpi1+SgnMi4UuW5mVg7/+CWOBgEVebRzfbvF9FyPRpy9mM2Xy+w3yqQxhjdnxzNu2V4eaFeXd/s1u+Yh85zRihUruPPOO6lYsSI+Pj7cddddLF++nLp16xITEwPA8uXLufPOO6lQoQKVKlWiT58+AGRkZLBq1SoGDBhAZGQkI0aMICkp6dK+BwwYgLt74R0kO3TowKhRo/j44485c+YMHh5/bfOelZXFo48+SrNmzRgwYADx8fFX9fzq16/P3r17eeqpp5g7dy6VKlW6qu2dwfVc5xeyc3hiykbmxB3jtdua8njXBnaIUCnlDIpKmv8BvAzUBsYAo/PdPrB/aK6nY0N/vD3dWGCnoecOnTrHc9M2E1azEm/2DbdLGcqxhnWox8XsXKasKXrShU/+3MOhU+d5p18zu44q0SjQlz4tajFp1X5S0otO5K9Fbq7h1VlxfLNqPw93rMebfcLLVMIM5P1y9xcVK17enKqgn/Jzc3Px8/Nj8+bNl247duwodB9Xevnll/nqq684f/48MTExl5p25Dd27FgCAwPZsmULsbGxXLx4scB9eXh4XNb2Om/ykSpVqrBlyxa6du3Kp59+yiOPPFJkTM7oWq/zzKwcRkzewMIdybzVN5xHOtnvC6xSyvEKTZqNMT8aY3oD7xtjul1xu6kUY3QZ5cu50yk0gIXxyYV+UF6rzKwcHp+yAQN8UcD0lapsaBDgQ7fGAUxec4AL2TkFrrPneDpfLkvkrpZBtGtg/5mXnrk5lAvZOXy+JLFE95uTa3jpp618v/Ygj3dtwGu3NS2TbUA7d+7MrFmzOHfuHGfPnmXmzJl06tTpL+vMnDmT8+fPk56ezuzZswGoVKkS9erVY8aMGYAlAd+yZYvNZScmJtKsWTNeeukloqOj2blzJ76+vqSnp19aJzU1lZo1a+Lm5sbkyZPJybFcd1euFxISwubNm8nNzeXQoUOsW7cOgBMnTpCbm8vdd9/N22+/zcaNG6/tQDnY1V7n5y/m8MikWJYmpPDeXc14oF2IfQNUSjlcsb1sjDFvi0gfEfnAeru9NAJzVT3CAjmamsn2o7Z35rLFm7O3E3ckjTEDI6lTrUKJ7ls5l4c71udExgV+3Xz0L48ZY3htVhzlPd155bampRJP/QAf7o6qzXdrD3AstWSGaM/OyeWFGVuYseEwz9wcyt/KcKepqKgohg4dSps2bWjbti2PPPIIVapU+cs699xzD5GRkdx9992XJdVTpkxhwoQJtGjRgvDwcH755Reby/7www+JiIigRYsWlC9fnt69e9O8eXM8PDxo0aIFY8eO5YknnmDSpEnExMSQkJBwqfb6yvU6dOhAvXr1aNasGS+88AJRUZZWekeOHKFr165ERkYydOhQ3nvvvRI4aqXvaq7zsxeyGfr1OlYlnuA//Vsw6BqmrVdKuR4prkZURN4D2gBTrIsGYWkg/X92ju2S6OhoExsbW1rFXZeTGReIfnchT98UynM9rn+iCYAfNxzmhRlbeLxrA17qdQP0Tr3BGWPo9eFy3NyEP57ueFky+fPGw4yavoV/3tmMwW1L74P60KlzdPtgCfe2Ceadfs2ua19ZObk8O20zv29N4sVbGpfY+Lj2smPHDpo2LZ0vKOp/HHHcbbnO0zOzGPr1ejYfOsOYgS3oGxlUqjEqpexPRDYYY6KvXG7LeE63AT2MMRONMROBXtZlqgDVfLxoVadKibVr3pGUxqszt9GufjWeL6EkXDk3EeGhjiHsSEpj9d7/dSo9c+4i7/6+g5Z1/Li3del2Ag2uWoF7Wgczbf0hDp06d837uZidy8jvN/L71iReubWJ0yfM6sYSXLUCA63X+eHTf73OU89lcd+EdWw5dIb/DmqpCbNSNxhbB0H1y3e/sh3iKFN6hAUSn5RW4Jvu1UjLzOLx7zZQubwnHw9qWWbGrFXF6xsZRNWK5Zi4Yv+lZe/P28WZ81kOG11i5E0NERH+++fua9o+MyuHx77bwLztybx+RxjDO+soAyXl66+/vjQec97tySefdHRYLumpvOt80Z7Llp8+e5HBX60h/mgqnw2J4tZmNR0UoVLKUWzJwt4DNonINyIyCdgA/NO+Ybm2HmGWaZAXXkdtszGGv83YyqHT5/lkcBQBvl4lFZ5yAd6e7tzXtg6Ldiaz/8RZNhw4zfdrDzKsfQhhtRwzpFfNyuUZ3KYOP208wv4TZ69q28ysHB79NpY/dx7n3TsjGNahnp2ivDENGzbsshE2Nm/ezKeffurosFxS3nX+48bDl67zExkXGDR+DbuPZzDugWh6hpfc7JtKKddhS0fAqUAM8LP11s4Y84O9A3Nl9QN8aBBQkQU7rj1p/mr5PuZuP8bLvZrQpl7VEoxOuYr72tXFw00Yv3wvr82Ko0Ylb551cBOdJ7o1wNNd+GiR7bXN5y5mM+zr9azYc4L3+zdnSNu6doxQqeuX/zo/npbJvePWsP/kWSY+2Jpujas7OjyllIPY9Hu/MSbJGPOrMeYXY8wxewdVFvQIq8HavadIPZ911duu23eKf83dSa/wGjzSSWvkblTVfb25o0Utpqw9yI6kNN7oE4aP118npyjtmB5sF8KszUfYnZxe7PoZF7IZOnE9a/edZMzAFjohj3IJ+a/zu79YxdEz5/lmWBs6hvo7OjSllANpI1k76REWSHauYcmu41e13fH0TEZ+v5HgKuV5f0DzMjsMl7LNwx0tX5pualKdW5zkJ+ERXRpQwdOdDxcWXduclpnF/RPWsuHgaT4e1JI7W9YupQiVun551/nps1l8+1AbYurbf0x0pZRz06TZTiKD/fD3KXdVo2hk5+Ty9NRNpGVm8fl9rajk7WnHCJUrCK9VmYlDo/lgQAun+QJVtWI5HupYj9+3JRFfyHjkZ85d5L6v1hJ3JJVPB0dxe/NapRxl2TZ06FB+/PFHlyyza9euuMIQolUrlmPq8Bh+GdmB6BBtIqeUKiZpFhE3EYkrrWDKEnc34eYmgSzdlcLF7NziNwBGL0hgzd5TvNOvGU1rOqazl3I+NzUJpGrFco4O4zKPdKyPr7cHYxYk/OWxU2cvMnj8WnYmpfPFfa3oFeEcNeTKwhhz2XTYqnDNa/vRIMDH0WEopZxEkQ0kjTG5IrJFROoYYw6WVlBlRY+wQKbFHmLN3pN0bhRQ5LoL4pP5fEkig9oE07+V/oytnFvlCp4M71Sf0QsS2HLoDC2C/QBISb/AkK/WcODkOb56MLrY697VvDl7e6G169cqrFYlXr8jvMh1vv32Wz744ANEhObNm+Pu7s6yZcsYM2YMx44d4/3336d///5kZGTQt29fTp8+TVZWFu+88w59+/Zl//799O7dm27durF69WpmzZrFt99+y5QpUwgODsbf359WrVrxwgsvkJiYyJNPPklKSgoVKlRg/PjxNGlimVRp4cKFfPTRRyQnJzNmzBhuv/129u/fz/3338/Zs5aRJj755BPat28PwPvvv8/kyZNxc3Ojd+/e/Otf/7r0nHJzcxk2bBjBwcG88847JXpMlVLKHmzpVVQT2C4i64BL40wZY/pca6Eish9IB3KA7IJmXSkLOob6U97TnQXxyUUmDwdOnmXU9M1EBBX/4amUsxjWsR4TV+5j9IIEvn2oDclpmQwev4ajZzL5emhr2jfUTlMlYfv27bz77rusXLkSf39/Tp06xahRo0hKSmLFihXs3LmTPn360L9/f7y9vZk5cyaVKlXixIkTxMTE0KeP5a16165dfP3113z22WfExsby008/sWnTJrKzs4mKiqJVq1YADB8+nC+++ILQ0FDWrl3LE088wZ9//gnA/v37Wbp0KYmJiXTr1o09e/ZQvXp1FixYgLe3N7t372bQoEHExsYyZ84cZs2axdq1a6lQoQKnTp269Jyys7MZMmQIERERvPrqq6V/UJVS6hrYkjS/aaeyuxljTthp307B29OdTqH+LNyRzFt9wwtsk5qZlcPj321EgM+HtMLb0730A1XqGvh4efBYlwa8N2cnv245ypj5u0hJv8Ckh9qU2WESHfGl9s8//6R///74+1u+hFStajm2/fr1w83NjbCwMJKTLX0njDG88sorLFu2DDc3N44cOXLpsbp16xITEwPAihUr6Nu3L+XLlwfgjjvuACAjI4NVq1YxYMCAS+VfuHDh0v2BAwfi5uZGaGgo9evXZ+fOndSrV4+RI0eyefNm3N3dSUiwNNlZuHAhw4YNo0KFCpfFDTBixAgGDhyoCbNSyqUUmzQbY5aKSF0g1BizUEQqAJrZ2ahHWCDz45OJO5JGs9p/nUzx9V+2E5+UxoQHowmuWsEBESp17R5oF8L45ft4euomfL08+PbhtrSqW8XRYZUpxpgCv3B7eXldtg7AlClTSElJYcOGDXh6ehISEkJmZiYAFStW/Mv6V8rNzcXPz4/NmzcX+PiVcYgIY8eOJTAwkC1btpCbm4u3t3eRcQO0b9+exYsX8/zzz19aXymlnF2xo2eIyKPAj8CX1kVBwKzrLNcA80Vkg4gML6Tc4SISKyKxKSkp11mc49zUpDpuQoETnUyPPcS02EM82a0BNzcNdEB0Sl2f8uXcealXY4L8yjPlUU2Y7eHmm29m+vTpnDx5EuCyZg5XSk1NpXr16nh6erJ48WIOHDhQ4HodO3Zk9uzZZGZmkpGRwe+//w5ApUqVqFevHjNmzAAsie+WLVsubTdjxgxyc3NJTExk7969NG7cmNTUVGrWrImbmxuTJ08mJycHgJ49ezJx4kTOnTv3l7gffvhhbr31VgYMGEB2dvZ1HB2llCo9tgw59yTQAUgDMMbsBq53SqQOxpgooDfwpIh0vnIFY8w4Y0y0MSY6IMB1OxNV8/GiVd0qfxl6bvvRVP4+K472DaoxqkdjB0Wn1PUbEB3Mipe60by2n6NDKZPCw8N59dVX6dKlCy1atGDUqFGFrjtkyBBiY2OJjo5mypQplzrwXal169b06dOHFi1acNdddxEdHU3lypZfwqZMmcKECRNo0aIF4eHh/PLLL5e2a9y4MV26dKF379588cUXeHt788QTTzBp0iRiYmJISEi4VKPdq1cv+vTpQ3R0NJGRkXzwwQeXxTBq1CiioqK4//77dTQPpZRLkMJ+pru0gshaY0xbEdlkjGkpIh7ARmNM8xIJQOQNIMMY80Fh60RHRxtXGNezMOOWJfLPP3ay/G/dCK5agdTzWfT5ZAWZWTn8/nQn/H28it+JUsohduzYQdOmTR0dRonLyMjAx8eHc+fO0blzZ8aNG0dUVJSjw7qkrB53pZTzE5ENBQ1SYUtN81IReQUoLyI9gBnA7OsIpKKI+ObdB3oCZXos6B5hlnFqF+5IxhjDizO2cOT0eT4dHKUJs1LKIYYPH05kZCRRUVHcfffdTpUwK6WUM7Jl9IyXgYeBbcAI4A/gq+soMxCYae0g4gF8b4yZex37c3r1/CvSsLoPC+KTuZidy/z4ZF67ranOMqWUcpjvv//e0SEopZRLsWX0jFwRmQSsxdKBb5cprk1H0fvbC7S41u1dVY+wQMYt28vafae4tVkNHu5Yz9EhKaVsVNRIEKrkXcdHjFJK2Y0to2fcBiQCHwOfAHtEpLe9AytreoQFkpNrqFu1Av++u7l+ACvlIry9vTl58qQmcqXEGMPJkyd1KDqllNOxpXnGaCwTkewBEJEGwO/AHHsGVtZE1vbjhZ6NuLVZTXy9PR0djlLKRrVr1+bw4cO48tCXrsbb25vatWs7OgyllLqMLUnz8byE2WovcNxO8ZRZbm7CyJtCHR2GUuoqeXp6Uq+eNqdSSqkbXaFJs4jcZb27XUT+AKZjadM8AFhfCrEppZRSSinlFIqqab4j3/1koIv1fgqg034ppZRSSqkbRqFJszFmWGkGopRSSimllLOyZUbAesBTQAj5kmxjTB+7RnZ5DCnAgdIqLx9/4IQDylVF0/PifPScOCc9L85Hz4lz0vPifBx5TuoaYwKuXGhL0rwFmIBlcpPcvOXGmKUlHaGzEZHYgqZRVI6l58X56DlxTnpenI+eE+ek58X5OOM5sWX0jExjzMd2j0QppZRSSiknZUvS/JGIvA7MBy7kLTTGbLRbVEoppZRSSjkRW5LmZsD9wE38r3mGsf5f1o1zdACqQHpenI+eE+ek58X56DlxTnpenI/TnRNb2jTvBJobYy6WTkhKKaWUUko5Fzcb1tkC+Nk5DqWUUkoppZyWLc0zAoGdIrKey9s0l9qQc0oppZRSSjmSLTXNrwN3Av8ERue7lToRCRaRxSKyQ0S2i8gz1uVVRWSBiOy2/q1iXV7Nun6GiHxyxb7uEZGt1v28X0SZrURkm4jsEZGPRUTyPTZQROKt+/i+kO29RGSadfu1IhKS77G5InJGRH67zkPjMGXwnNQRkfnW5xOf/zFX4qLnpbOIbBSRbBHpf8Vj+lq5fF/OcE70teK48zLKus5WEVkkInXzPaavlcv35QznRF8rJXxeRGSsiGy23hJE5Ewh25dsDmaMcZkbUBOIst73BRKAMOB94GXr8peBf1vvVwQ6Ao8Bn+TbTzXgIBBg/X8ScHMhZa4D2gECzAF6W5eHApuAKtb/qxey/RPAF9b79wLT8j12M5bpyn9z9LHVc3LpsSVAD+t9H6CCo4/xDXReQoDmwLdA/yse09eK850Tfa047rx0yzvewOPo54qznxN9rZTweblinaeAiYVsX6I5WLE1zSKSLiJp1lumiOSISFpx29mDMSbJWIe6M8akAzuAIKAvloOO9W8/6zpnjTErgMwrdlUfSDDGpFj/XwjcfWV5IlITqGSMWW0sR/jbvH0DjwKfGmNOW8s6XkjY+WP7Ebg575uSMWYRkG7Tk3dSZemciEgY4GGMWWDdPsMYc86mA+FkXPG8GGP2G2O2km8SpXyP6Wvlfxx+TvS14vDzsjjf8V4D1M73mL5W/sfh50RfK3Y7L/kNAqYWEnaJ5mDFJs3GGF9jTCXrzRvLE/ukuO3szVrF3hJYCwQaY5LAclKB6sVsvgdoIiIhIuKB5SQEF7BeEHA43/+HrcsAGgGNRGSliKwRkV6FlBUEHLLGlg2kYvmWVeaUgXPSCDgjIj+LyCYR+Y+IuBcTt9NzofNywygD50RfK3/lqPPyMJYauDKpDJwTfa38VUmcl7w46gL1gD8LKatEczBbOgJexhgzS0RevtYCS4KI+AA/Ac8aY9Lkf02PbGKMOS0ijwPTsNSgrMLyzecvRRW0ufWvB5afbbpi+Ua5XEQijDFnrmIfZUYZOSceQCcsbwQHrbEMxTKNvEtysfNyQygj50RfK1dwxHkRkfuAaKDLVQXrIsrIOdHXyhVK6LzkuRf40RiTU1i4NuzDZsUmzSJyV75/3bBcDA5L+kTEE8vJmmKM+dm6OFlEahpjkqzV+YX9LH+JMWY2MNu6z+FAjvXb3wbrKr8Cn5PvZy/r/aPW+4eBNcaYLGCfiOwCQkWkH3CbtYxI63rBwGHrN6rKwKlree7Oqgydk8PAJmPMXmsMs4AYXPTNzQXPS5lXhs6JvlYKUJrnRUS6A68CXYwxFyhjytA50ddKAUrgvOS5F3gyX3zvYscczJbRM+7Id7sFS/uPvtda4PUQy9eZCcAOY8yYfA/9Cjxovf8g8IsN+6pu/VsFS0Pxr4wxOcaYSOvtH9afGdJFJMZa9gP59j0LS8N/RMQfy08we40xr+bto4DY+gN/WtvmlAll7JysB6qISID1sZuA+Ks4HE7DRc9LmVbGzom+VgreV6mcFxFpCXwJ9DGF991wWWXsnOhrpeB9Xe95QUQaA1WA1XnL7J6DGSfokWnrDUsvTANsBTZbb7diaZ+yCNht/Vs13zb7sXyryMDyjSPMunwqlgs3Hri3iDKjgTggEUtb7rxZFAUYY91+W2H7ALyBGVja8KwD6ud7bDmQApy3xnaLo4+xnhN6WJ/LNuAboJyjj/ENdF5aW8s9C5wEtutrxanPib5WHHdeFgLJ+eL9VV8rTn1O9LVSwufF+tgbwL+KiblEc7BCp9EWkX8U+ICFMca8XcTjSimllFJKlRlFJc3PF7C4IpZeodWMMT72DEwppZRSSilnUWjSfNlKIr7AM1gS5unAaFMG21EppZRSSilVkCJHzxCRqsAoYAiWwaGjjHWAb6WUUkoppW4UhSbNIvIf4C5gHNDMGJNRalEppZRSSinlRIpq05wLXACyuXxcZsHSEbCS/cNTSimllFLK8Qodp9kY42aMKW8un0a7Ut7/pRmkUkq5GhHJEZHNIrJdRLaIyCgRKXJsfLFMKzv4GsqqLSK/iMhuEUkUkY9EpJyN2y4RkeirLdOG/XYVkd9Ker9KKeUotkxuopRS6uqdN5ZB9sOxjNN6K/B6MduEAFeVNFsH/f8ZmGWMCcUy+YIP8G4B6xY7C6wN5blf7z6UUsoVadKslFJ2Zh1taDgwUixCRGS5iGy03tpbV/0X0MlaQ/2ciLiLyH9EZL2IbBWREQXs/iYg0xjztbWsHOA54CERqSAiQ0VkhojMBuaLSHkR+cG6v2lA+bwdiUhPEVltjWmGiPhYl+8XkX+IyApgQBHr9RKRndb17rLP0VRKKce47loHpZRSxTPG7LU2z6gOHAd6GGMyRSQUy+xY0cDLwAvGmNsBRGQ4kGqMaS0iXsBKEZlvjNmXb9fhwIYrykoTkYNAQ+uidkBzY8wpERkFnDPGNBeR5sBGa1n+wGtAd2PMWRF5CcvoSW9Z95FpjOloXe/nK9cTkfeB8ViS+D3AtJI6dkop5Qw0aVZKqdIj1r+ewCciEgnkYGlSUZCeQHMR6W/9vzIQCuRPmoXLO2sXtHyBMeaU9X5n4GMAY8xWEdlqXR4DhGFJzAHKAavz7W9aMes1AfYZY3YDiMh3WGrXlVKqTNCkWSmlSoGI1MeSIB/H0rY5GWiBpZlcZmGbAU8ZY+YVsevtwN1XlFUJCAYSgVbA2Su2KSzJXmCMGVRIOWeLWs/6BaD42bKUUspFaZtmpZSyMxEJAL4APjGWcT4rA0nGmFzgfiCvc1064Jtv03nA4yLiad1PIxGpeMXuFwEVROQB6zruwGjgG2PMuQLCWYZlwipEJAJobl2+BuggIg2tj1UQkYJqwAtbbydQT0QaWNcrLPlWSimXpEmzUkrZR/m8IeeAhcB84E3rY58BD4rIGixNM/JqcbcC2dYh6p4DvgLigY0iEgd8yRW/EFqT8DuxdNDbDSRgqbl+pZC4Pgd8rM0y/gass+4nBRgKTLU+tgZLk4vLFLaeMSYTS3OM360dAQ/YeqCUUsoVFDq5iVJKKaWUUspCa5qVUkoppZQqhibNSimllFJKFUOTZqWUUkoppYqhSbNSSimllFLF0KRZKaWUUkqpYmjSrJRSSimlVDE0aVZKKaWUUqoY/w9kLJzfUD6KuQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create df with only chargeback orders\n",
    "chargebacks_df = df[df['order_status'] == 'chargeback']\n",
    "\n",
    "# group by order date and status, count orders\n",
    "CHB_count_by_date = chargebacks_df.groupby(['order_date', 'order_status']).size().reset_index(name='count')\n",
    "\n",
    "# reset index, pivot table on status to create columns chargeback counts\n",
    "CHB_count_pivot = CHB_count_by_date.pivot(index='order_date', columns='order_status', values='count')\n",
    "\n",
    "# create line plot\n",
    "fig, ax = plt.subplots(figsize=(12, 2))\n",
    "CHB_count_pivot.plot(ax=ax)\n",
    "\n",
    "# add title and labels\n",
    "plt.title('Count Chargeback Orders - By Date')\n",
    "plt.xlabel('Date Ordered')\n",
    "plt.ylabel('Number of Chargeback Orders')\n",
    "\n",
    "# show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "343829c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max: order_date      2019-07-01\n",
      "order_status    chargeback\n",
      "count                   19\n",
      "dtype: object\n",
      "min: order_date      2019-06-01\n",
      "order_status    chargeback\n",
      "count                    4\n",
      "dtype: object\n",
      "\n",
      "mean count    11.612903\n",
      "dtype: float64\n",
      "\n",
      "median count    11.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('max:',CHB_count_by_date.max())\n",
    "print('min:',CHB_count_by_date.min())\n",
    "print('\\nmean', CHB_count_by_date.mean())\n",
    "print('\\nmedian', CHB_count_by_date.median())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "decfd005",
   "metadata": {},
   "source": [
    "**Notes: Time Series - Count of Events**\n",
    "- count of chargeback events flucates between 4 and 19 per day\n",
    "- mean count of chargeback event of 11.6\n",
    "- median count of chargeback events of 11\n",
    "- no room for interpretation of seasonality in this one month time series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "195e7bf7",
   "metadata": {},
   "source": [
    "##### Sum Price of Chargebacks  By Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2ac43f01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group by date_ordered and sum the price of orders\n",
    "sum_by_date = chargebacks_df.groupby('order_date')['price'].sum().reset_index(name='sum_price')\n",
    "\n",
    "# set date_ordered as the index\n",
    "sum_by_date.set_index('order_date', inplace=True)\n",
    "\n",
    "# create a line plot\n",
    "fig, ax = plt.subplots(figsize=(12, 2))\n",
    "sum_by_date.plot(ax=ax)\n",
    "\n",
    "# add title and labels\n",
    "plt.title('Sum Chargeback Orders by Date')\n",
    "plt.xlabel('Date Ordered')\n",
    "plt.ylabel('Sum of Prices')\n",
    "\n",
    "# show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bb7d494e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum sum of price by date: $3,175\n",
      "Minimum sum of price by date: $527\n",
      "Mean sum of price by date: $1,569\n",
      "Median sum of price by date: $1,451\n"
     ]
    }
   ],
   "source": [
    "maximum_price_by_date = sum_by_date['sum_price'].max()\n",
    "minimum_price_by_date = sum_by_date['sum_price'].min()\n",
    "mean_price_by_date = sum_by_date['sum_price'].mean()\n",
    "median_price_by_date = sum_by_date['sum_price'].median()\n",
    "\n",
    "print('Maximum sum of price by date: ${:,.0f}'.format(maximum_price_by_date))\n",
    "print('Minimum sum of price by date: ${:,.0f}'.format(minimum_price_by_date))\n",
    "print('Mean sum of price by date: ${:,.0f}'.format(mean_price_by_date))\n",
    "print('Median sum of price by date: ${:,.0f}'.format(median_price_by_date))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2f73bc94",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>order_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_status</th>\n",
       "      <th>chargeback</th>\n",
       "      <th>chargeback</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-06-01</th>\n",
       "      <td>1425.76</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-02</th>\n",
       "      <td>526.90</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-03</th>\n",
       "      <td>805.84</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-04</th>\n",
       "      <td>1591.49</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-05</th>\n",
       "      <td>1470.56</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-06</th>\n",
       "      <td>2618.37</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07</th>\n",
       "      <td>2438.15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-08</th>\n",
       "      <td>1320.39</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-09</th>\n",
       "      <td>1450.62</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10</th>\n",
       "      <td>1581.70</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-11</th>\n",
       "      <td>1000.12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-12</th>\n",
       "      <td>1761.85</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-13</th>\n",
       "      <td>2195.68</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-14</th>\n",
       "      <td>1304.64</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-15</th>\n",
       "      <td>2665.42</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-16</th>\n",
       "      <td>2152.52</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-17</th>\n",
       "      <td>2470.37</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-18</th>\n",
       "      <td>1365.78</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-19</th>\n",
       "      <td>1153.15</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20</th>\n",
       "      <td>606.66</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-21</th>\n",
       "      <td>762.68</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-22</th>\n",
       "      <td>585.55</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-23</th>\n",
       "      <td>767.47</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-24</th>\n",
       "      <td>1229.56</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-25</th>\n",
       "      <td>1036.69</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-26</th>\n",
       "      <td>2027.76</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-27</th>\n",
       "      <td>3175.35</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-28</th>\n",
       "      <td>2061.90</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-29</th>\n",
       "      <td>1882.09</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>908.80</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>2293.30</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  price   order_id\n",
       "order_status chargeback chargeback\n",
       "order_date                        \n",
       "2019-06-01      1425.76         14\n",
       "2019-06-02       526.90          4\n",
       "2019-06-03       805.84          8\n",
       "2019-06-04      1591.49          9\n",
       "2019-06-05      1470.56         11\n",
       "2019-06-06      2618.37         12\n",
       "2019-06-07      2438.15         15\n",
       "2019-06-08      1320.39         13\n",
       "2019-06-09      1450.62          6\n",
       "2019-06-10      1581.70         11\n",
       "2019-06-11      1000.12          5\n",
       "2019-06-12      1761.85          8\n",
       "2019-06-13      2195.68         11\n",
       "2019-06-14      1304.64         12\n",
       "2019-06-15      2665.42         14\n",
       "2019-06-16      2152.52         19\n",
       "2019-06-17      2470.37         19\n",
       "2019-06-18      1365.78         11\n",
       "2019-06-19      1153.15         12\n",
       "2019-06-20       606.66          6\n",
       "2019-06-21       762.68          9\n",
       "2019-06-22       585.55         11\n",
       "2019-06-23       767.47         13\n",
       "2019-06-24      1229.56         10\n",
       "2019-06-25      1036.69          9\n",
       "2019-06-26      2027.76         15\n",
       "2019-06-27      3175.35         18\n",
       "2019-06-28      2061.90         17\n",
       "2019-06-29      1882.09         11\n",
       "2019-06-30       908.80          8\n",
       "2019-07-01      2293.30         19"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by date and status, sum of price, count number of orders\n",
    "agg_by_date = chargebacks_df.groupby(['order_date', 'order_status']).agg({'price': 'sum', 'order_id': 'count'}).reset_index()\n",
    "\n",
    "# pivot table to set columns for price sum and oder count and reset index\n",
    "time_price_count = agg_by_date.pivot(index='order_date', columns='order_status')\n",
    "\n",
    "time_price_count "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0ccc82f",
   "metadata": {},
   "source": [
    "##### Analysis of Chargebacks by Day of Week "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d54cc587",
   "metadata": {},
   "source": [
    "###### Count of Chargeback Events by Day of Week "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2e0df3ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# copy \n",
    "chargebacks_df_datetime = chargebacks_df.copy()\n",
    "\n",
    "# convert order_date to datetime type \n",
    "chargebacks_df_datetime['order_date'] = pd.to_datetime(chargebacks_df_datetime['order_date'])\n",
    "\n",
    "# group by weekday of order date and status, count orders\n",
    "CHB_count_by_weekday = chargebacks_df_datetime.groupby([chargebacks_df_datetime['order_date'].dt.weekday, 'order_status']).size().reset_index(name='count')\n",
    "\n",
    "# reset index, pivot table on status to create columns for approved/chargeback counts\n",
    "CHB_count_pivot_weekday = CHB_count_by_weekday.pivot(index='order_date', columns='order_status', values='count')\n",
    "\n",
    "# create a bar chart, set x axis to days of week \n",
    "ax = CHB_count_pivot_weekday.plot(kind='bar', figsize=(12, 2), color='magenta').set_xticklabels(['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'])\n",
    "\n",
    "# labels\n",
    "plt.title('Chargeback Orders - Count by Day of Week')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Count Chargeback Orders')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1e7b1d3a",
   "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>order_status</th>\n",
       "      <th>chargeback</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "order_status  chargeback\n",
       "order_date              \n",
       "0                     67\n",
       "1                     34\n",
       "2                     46\n",
       "3                     47\n",
       "4                     53\n",
       "5                     63\n",
       "6                     50"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CHB_count_pivot_weekday"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc3e5e46",
   "metadata": {},
   "source": [
    "###### Count of Events by Day of Week "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6087dc9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group by weekday of order date and sum prices\n",
    "sum_by_weekday = chargebacks_df_datetime.groupby(chargebacks_df_datetime['order_date'].dt.weekday)['price'].sum()\n",
    "\n",
    "\n",
    "# create a magenta bar chart\n",
    "fig, ax = plt.subplots(figsize=(12, 2))\n",
    "sum_by_weekday.plot(kind='bar', color='magenta', ax=ax).set_xticklabels(['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'])\n",
    "\n",
    "# labels\n",
    "plt.title('Sum (Price) of Chargeback Orders - by Day of Week')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Sum (Price)')\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ccb065cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group by weekday of order date and sum prices\n",
    "sum_by_weekday = chargebacks_df_datetime.groupby(chargebacks_df_datetime['order_date'].dt.weekday)['price'].sum()\n",
    "\n",
    "# Create a magenta bar chart\n",
    "fig, ax = plt.subplots(figsize=(12, 2))\n",
    "sum_by_weekday.plot(kind='bar', color='magenta', ax=ax).set_xticklabels(['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'])\n",
    "\n",
    "# Add labels to the chart\n",
    "ax.set_title('Sum (Price) of Chargeback Orders - by Day of Week')\n",
    "ax.set_xlabel('Day of Week')\n",
    "ax.set_ylabel('Sum (Price)')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "934e31b9",
   "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>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8380.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4994.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6413.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8596.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6567.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7879.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5806.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price\n",
       "order_date         \n",
       "0           8380.77\n",
       "1           4994.08\n",
       "2           6413.32\n",
       "3           8596.06\n",
       "4           6567.37\n",
       "5           7879.21\n",
       "6           5806.31"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_by_weekday.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9b298df0",
   "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>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6948.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1365.534379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4994.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6109.815000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6567.370000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8129.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8596.060000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             price\n",
       "count     7.000000\n",
       "mean   6948.160000\n",
       "std    1365.534379\n",
       "min    4994.080000\n",
       "25%    6109.815000\n",
       "50%    6567.370000\n",
       "75%    8129.990000\n",
       "max    8596.060000"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_summary = sum_by_weekday.describe().to_frame()\n",
    "price_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2346ca98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum sum of price by day of week: $8,596\n",
      "Minimum sum of price by day of week: $4,994\n",
      "Mean sum of price by day of week: $6,948\n",
      "Median sum of price by day of week: $6,567\n"
     ]
    }
   ],
   "source": [
    "maximum_price_by_weekday = sum_by_weekday.max()\n",
    "minimum_price_by_weekday = sum_by_weekday.min()\n",
    "mean_price_by_weekday = sum_by_weekday.mean()\n",
    "median_price_by_weekday = sum_by_weekday.median()\n",
    "\n",
    "print('Maximum sum of price by day of week: ${:,.0f}'.format(maximum_price_by_weekday))\n",
    "print('Minimum sum of price by day of week: ${:,.0f}'.format(minimum_price_by_weekday))\n",
    "print('Mean sum of price by day of week: ${:,.0f}'.format(mean_price_by_weekday))\n",
    "print('Median sum of price by day of week: ${:,.0f}'.format(median_price_by_weekday))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "037fb2cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\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>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6948.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1365.534379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4994.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6109.815000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6567.370000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8129.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8596.060000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             price\n",
       "count     7.000000\n",
       "mean   6948.160000\n",
       "std    1365.534379\n",
       "min    4994.080000\n",
       "25%    6109.815000\n",
       "50%    6567.370000\n",
       "75%    8129.990000\n",
       "max    8596.060000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_summary = sum_by_weekday.describe().to_frame()\n",
    "price_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "243931d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4053"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.customer_account_age.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "814b6396",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# explore relationship bet classifcation score and account age \n",
    "df.plot.scatter(x='customer_account_age', y='classification_score')\n",
    "plt.title('Customer age and Classification Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ef7092b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a scatterplot of price by customer_age_years\n",
    "sns.scatterplot(data=df, x='customer_account_age', y='price')\n",
    "plt.title('customer account age v price')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59ffb2a3",
   "metadata": {},
   "source": [
    "### NEXT STEPS\n",
    "- learn more about the model \n",
    "- one hot encode and run correlations against score to identify which features are more closely correlated with score.\n",
    "- create groupings for account_age and price and explore risk\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ec9ddbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad1f6726",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}