Market Basket Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instacart - Market Basket Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The project named ‘Instakart Market Basket Analysis’  is about predicting the products which have been purchased previously and the user is most likely to buy same products in their next order. Instacart, a grocery ordering and delivery app, aims to make it easy to fill your refrigerator and pantry with your personal favorites and staples when you need them. After selecting products through the Instacart app, personal shoppers review your order and do the in-store shopping and delivery for you.\n",
    "In this project we are trying to find out all the information we can using the given datasets and apply"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Problem Statement: \n",
    "> We are predicting the products which have been purchased previously and the user is most likely to buy same products in their next order.\n",
    "* Solution Proposed:\n",
    "> Market Basket Analysis is a modelling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items. For example, if you are in an English pub and you buy a pint of beer and don't buy a bar meal, you are more likely to buy crisps (US. chips) at the same time than somebody who didn't buy beer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Importing libraries and reading csv's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "\n",
    "# Make plots larger\n",
    "plt.rcParams['figure.figsize'] = (15, 9)\n",
    "\n",
    "#Loading all the csv files\n",
    "\n",
    "df_aisles = pd.read_csv(\"aisles.csv\")\n",
    "df_departments = pd.read_csv(\"departments.csv\")\n",
    "df_order_products_prior = pd.read_csv(\"order_products__prior.csv\")\n",
    "df_order_products_train = pd.read_csv(\"order_products__train.csv\")\n",
    "df_orders = pd.read_csv(\"orders.csv\")\n",
    "df_products = pd.read_csv(\"products.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### List of files imported and loaded\n",
    "\n",
    "* Aisles.csv – This contains the names of the aisles based on the products in them.\n",
    "* Departments.csv – It has the names of department categorized by products types.\n",
    "* Order_Product_Prior – It has details of all the previous customer orders.\n",
    "* Order_Product_Train.csv – This is the dataset which will be used to train the test dataset explained next.  \n",
    "* Orders.csv – It is the main table containing details about the customer orders and also tells which record belongs to which table, train, prior or test.\n",
    "* Products.csv – This contain detail of all the products sold by Instakart along with their ProductID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>user_id</th>\n",
       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2398795</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>473747</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id eval_set  order_number  order_dow  order_hour_of_day  \\\n",
       "0   2539329        1    prior             1          2                  8   \n",
       "1   2398795        1    prior             2          3                  7   \n",
       "2    473747        1    prior             3          3                 12   \n",
       "3   2254736        1    prior             4          4                  7   \n",
       "4    431534        1    prior             5          4                 15   \n",
       "\n",
       "   days_since_prior_order  \n",
       "0                     NaN  \n",
       "1                    15.0  \n",
       "2                    21.0  \n",
       "3                    29.0  \n",
       "4                    28.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading the orders.csv file\n",
    "df_orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3421083, 7)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Counting the number of rows and columns in orders.csv\n",
    "df_orders.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                       0\n",
       "user_id                        0\n",
       "eval_set                       0\n",
       "order_number                   0\n",
       "order_dow                      0\n",
       "order_hour_of_day              0\n",
       "days_since_prior_order    206209\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding if the dataset has any null values\n",
    "df_orders.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* In the dataset there is a column called eval_set. It bascially tells us that to which csv (train or test) is the column corresponding to. Let us take the count of our test and train values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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LkiRJA2WYlyRJkgbKMC9JkiQNlGFekiRJGijDvCRJkjRQhnlJkiRpoAzzkiRJ0kAZ5iVJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e25ea2b240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior    3214874\n",
      "train     131209\n",
      "test       75000\n",
      "Name: eval_set, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Count no. of rows in each dataset\n",
    "cnt_srs = df_orders.eval_set.value_counts()\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8)\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel('Eval set type', fontsize=12)\n",
    "plt.title('Count of rows in each dataset', fontsize=15)\n",
    "plt.show()\n",
    "print(cnt_srs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* So, in total there have been 3214874 orders and 131209 has been provided for training the model and we need to predict for 75000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "eval_set\n",
       "prior    206209\n",
       "test      75000\n",
       "train    131209\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding number of customers\n",
    "def get_unique_count(x):             ## Defining a function to get unique count for user_id from orders.csv\n",
    "    return len(np.unique(x))\n",
    "\n",
    "cnt_srs = df_orders.groupby(\"eval_set\")[\"user_id\"].aggregate(get_unique_count)\n",
    "cnt_srs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### So, we figured out that there are total 206209 customers in total. We figured that out using the groupby for user_id and getting the unique count function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Validating Dataset: It is told to us that the prior orders for any customer ranging between 4 to 100 is given. Validating it below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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prW3qrj87yftaa0cneV93PUkelOTo7nJqkjOTpAuxTk9yXJJjk5xeVfsNOH4A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\nTilJkiRJkiQNzk4pSZIkSZIkDe5XOZXfX/a2mPsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e200a207f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Validating prior order range\n",
    "cnt_srs = df_orders.groupby(\"user_id\")[\"order_number\"].aggregate(np.max).reset_index()\n",
    "cnt_srs = cnt_srs.order_number.value_counts()\n",
    "\n",
    "#Bar-graph for the order-reorder counts\n",
    "plt.figure(figsize=(20,8))\n",
    "sns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8, color = 'red')\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel('Maximum order number', fontsize=12)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, as per the graph above, it is clear that minimum number of previous orders is 4 and maximum is 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To know more about the customer ordering behaviour pattern, let us first see which day of the week has maximum number of orders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Changing the data labels into name of days of weeks\n",
    "import calendar\n",
    "days=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in df_orders['order_dow']:\n",
    "    days.append(calendar.day_name[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Adding another column for day-name of the week as per the number\n",
    "df_orders['converted_dow']=days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "      <th>converted_dow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <th>3</th>\n",
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       "      <th>6</th>\n",
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       "      <td>7</td>\n",
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       "      <th>7</th>\n",
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       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Tuesday</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2295261</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2550362</td>\n",
       "      <td>1</td>\n",
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       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>30.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
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       "      <td>1</td>\n",
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       "      <th>11</th>\n",
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       "      <td>1</td>\n",
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       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
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       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1901567</td>\n",
       "      <td>2</td>\n",
       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <th>14</th>\n",
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       "      <td>2</td>\n",
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       "      <td>4</td>\n",
       "      <td>2</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1673511</td>\n",
       "      <td>2</td>\n",
       "      <td>prior</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>8.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
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       "      <td>2</td>\n",
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       "      <td>6</td>\n",
       "      <td>2</td>\n",
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       "      <td>13.0</td>\n",
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       "    </tr>\n",
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       "      <th>17</th>\n",
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       "      <td>2</td>\n",
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       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
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       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>788338</td>\n",
       "      <td>2</td>\n",
       "      <td>prior</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1718559</td>\n",
       "      <td>2</td>\n",
       "      <td>prior</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
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       "      <td>Wednesday</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
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       "      <td>2</td>\n",
       "      <td>prior</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Tuesday</td>\n",
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       "      <th>21</th>\n",
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       "      <td>11</td>\n",
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       "      <th>22</th>\n",
       "      <td>3186735</td>\n",
       "      <td>2</td>\n",
       "      <td>prior</td>\n",
       "      <td>12</td>\n",
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       "      <td>28.0</td>\n",
       "      <td>Tuesday</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3268552</td>\n",
       "      <td>2</td>\n",
       "      <td>prior</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>839880</td>\n",
       "      <td>2</td>\n",
       "      <td>prior</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13.0</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1492625</td>\n",
       "      <td>2</td>\n",
       "      <td>train</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1374495</td>\n",
       "      <td>3</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>444309</td>\n",
       "      <td>3</td>\n",
       "      <td>prior</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3002854</td>\n",
       "      <td>3</td>\n",
       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Thursday</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2037211</td>\n",
       "      <td>3</td>\n",
       "      <td>prior</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Wednesday</td>\n",
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       "      <td>...</td>\n",
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       "      <td>prior</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
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       "    <tr>\n",
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       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>3421055</th>\n",
       "      <td>1541132</td>\n",
       "      <td>206208</td>\n",
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       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Wednesday</td>\n",
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       "    <tr>\n",
       "      <th>3421056</th>\n",
       "      <td>2808240</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Monday</td>\n",
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       "    <tr>\n",
       "      <th>3421057</th>\n",
       "      <td>3027766</td>\n",
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       "      <td>prior</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>9.0</td>\n",
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       "      <th>3421058</th>\n",
       "      <td>3356245</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>40</td>\n",
       "      <td>5</td>\n",
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       "      <td>prior</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>11.0</td>\n",
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       "    <tr>\n",
       "      <th>3421060</th>\n",
       "      <td>2675140</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Tuesday</td>\n",
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       "    <tr>\n",
       "      <th>3421061</th>\n",
       "      <td>167903</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Friday</td>\n",
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       "    <tr>\n",
       "      <th>3421062</th>\n",
       "      <td>2393201</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>44</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Sunday</td>\n",
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       "    <tr>\n",
       "      <th>3421063</th>\n",
       "      <td>3292671</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421064</th>\n",
       "      <td>3059777</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421065</th>\n",
       "      <td>2239861</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421066</th>\n",
       "      <td>1285346</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421067</th>\n",
       "      <td>1882108</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Tuesday</td>\n",
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       "    <tr>\n",
       "      <th>3421068</th>\n",
       "      <td>803273</td>\n",
       "      <td>206208</td>\n",
       "      <td>test</td>\n",
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       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>3421069</th>\n",
       "      <td>3154581</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3421070</th>\n",
       "      <td>1889163</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421071</th>\n",
       "      <td>1542354</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421072</th>\n",
       "      <td>688306</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421073</th>\n",
       "      <td>2307371</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421074</th>\n",
       "      <td>3186442</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421075</th>\n",
       "      <td>550836</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421076</th>\n",
       "      <td>2129269</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421077</th>\n",
       "      <td>2558525</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421078</th>\n",
       "      <td>2266710</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421079</th>\n",
       "      <td>1854736</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421080</th>\n",
       "      <td>626363</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421081</th>\n",
       "      <td>2977660</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421082</th>\n",
       "      <td>272231</td>\n",
       "      <td>206209</td>\n",
       "      <td>train</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3421083 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         order_id  user_id eval_set  order_number  order_dow  \\\n",
       "0         2539329        1    prior             1          2   \n",
       "1         2398795        1    prior             2          3   \n",
       "2          473747        1    prior             3          3   \n",
       "3         2254736        1    prior             4          4   \n",
       "4          431534        1    prior             5          4   \n",
       "5         3367565        1    prior             6          2   \n",
       "6          550135        1    prior             7          1   \n",
       "7         3108588        1    prior             8          1   \n",
       "8         2295261        1    prior             9          1   \n",
       "9         2550362        1    prior            10          4   \n",
       "10        1187899        1    train            11          4   \n",
       "11        2168274        2    prior             1          2   \n",
       "12        1501582        2    prior             2          5   \n",
       "13        1901567        2    prior             3          1   \n",
       "14         738281        2    prior             4          2   \n",
       "15        1673511        2    prior             5          3   \n",
       "16        1199898        2    prior             6          2   \n",
       "17        3194192        2    prior             7          2   \n",
       "18         788338        2    prior             8          1   \n",
       "19        1718559        2    prior             9          2   \n",
       "20        1447487        2    prior            10          1   \n",
       "21        1402090        2    prior            11          1   \n",
       "22        3186735        2    prior            12          1   \n",
       "23        3268552        2    prior            13          4   \n",
       "24         839880        2    prior            14          3   \n",
       "25        1492625        2    train            15          1   \n",
       "26        1374495        3    prior             1          1   \n",
       "27         444309        3    prior             2          3   \n",
       "28        3002854        3    prior             3          3   \n",
       "29        2037211        3    prior             4          2   \n",
       "...           ...      ...      ...           ...        ...   \n",
       "3421053   2789700   206208    prior            35          3   \n",
       "3421054    844592   206208    prior            36          6   \n",
       "3421055   1541132   206208    prior            37          2   \n",
       "3421056   2808240   206208    prior            38          0   \n",
       "3421057   3027766   206208    prior            39          2   \n",
       "3421058   3356245   206208    prior            40          5   \n",
       "3421059    442304   206208    prior            41          2   \n",
       "3421060   2675140   206208    prior            42          1   \n",
       "3421061    167903   206208    prior            43          4   \n",
       "3421062   2393201   206208    prior            44          6   \n",
       "3421063   3292671   206208    prior            45          2   \n",
       "3421064   3059777   206208    prior            46          1   \n",
       "3421065   2239861   206208    prior            47          3   \n",
       "3421066   1285346   206208    prior            48          1   \n",
       "3421067   1882108   206208    prior            49          1   \n",
       "3421068    803273   206208     test            50          5   \n",
       "3421069   3154581   206209    prior             1          3   \n",
       "3421070   1889163   206209    prior             2          3   \n",
       "3421071   1542354   206209    prior             3          5   \n",
       "3421072    688306   206209    prior             4          1   \n",
       "3421073   2307371   206209    prior             5          4   \n",
       "3421074   3186442   206209    prior             6          0   \n",
       "3421075    550836   206209    prior             7          2   \n",
       "3421076   2129269   206209    prior             8          3   \n",
       "3421077   2558525   206209    prior             9          4   \n",
       "3421078   2266710   206209    prior            10          5   \n",
       "3421079   1854736   206209    prior            11          4   \n",
       "3421080    626363   206209    prior            12          1   \n",
       "3421081   2977660   206209    prior            13          1   \n",
       "3421082    272231   206209    train            14          6   \n",
       "\n",
       "         order_hour_of_day  days_since_prior_order converted_dow  \n",
       "0                        8                     NaN     Wednesday  \n",
       "1                        7                    15.0      Thursday  \n",
       "2                       12                    21.0      Thursday  \n",
       "3                        7                    29.0        Friday  \n",
       "4                       15                    28.0        Friday  \n",
       "5                        7                    19.0     Wednesday  \n",
       "6                        9                    20.0       Tuesday  \n",
       "7                       14                    14.0       Tuesday  \n",
       "8                       16                     0.0       Tuesday  \n",
       "9                        8                    30.0        Friday  \n",
       "10                       8                    14.0        Friday  \n",
       "11                      11                     NaN     Wednesday  \n",
       "12                      10                    10.0      Saturday  \n",
       "13                      10                     3.0       Tuesday  \n",
       "14                      10                     8.0     Wednesday  \n",
       "15                      11                     8.0      Thursday  \n",
       "16                       9                    13.0     Wednesday  \n",
       "17                      12                    14.0     Wednesday  \n",
       "18                      15                    27.0       Tuesday  \n",
       "19                       9                     8.0     Wednesday  \n",
       "20                      11                     6.0       Tuesday  \n",
       "21                      10                    30.0       Tuesday  \n",
       "22                       9                    28.0       Tuesday  \n",
       "23                      11                    30.0        Friday  \n",
       "24                      10                    13.0      Thursday  \n",
       "25                      11                    30.0       Tuesday  \n",
       "26                      14                     NaN       Tuesday  \n",
       "27                      19                     9.0      Thursday  \n",
       "28                      16                    21.0      Thursday  \n",
       "29                      18                    20.0     Wednesday  \n",
       "...                    ...                     ...           ...  \n",
       "3421053                 22                     4.0      Thursday  \n",
       "3421054                 15                    10.0        Sunday  \n",
       "3421055                 12                     3.0     Wednesday  \n",
       "3421056                 15                    19.0        Monday  \n",
       "3421057                 14                     9.0     Wednesday  \n",
       "3421058                  9                    10.0      Saturday  \n",
       "3421059                 14                    11.0     Wednesday  \n",
       "3421060                 19                     6.0       Tuesday  \n",
       "3421061                 14                     3.0        Friday  \n",
       "3421062                 16                     2.0        Sunday  \n",
       "3421063                 11                     3.0     Wednesday  \n",
       "3421064                 10                    13.0       Tuesday  \n",
       "3421065                  4                     9.0      Thursday  \n",
       "3421066                 11                     5.0       Tuesday  \n",
       "3421067                 22                     7.0       Tuesday  \n",
       "3421068                 11                     4.0      Saturday  \n",
       "3421069                 11                     NaN      Thursday  \n",
       "3421070                 17                     7.0      Thursday  \n",
       "3421071                 11                    30.0      Saturday  \n",
       "3421072                 10                    30.0       Tuesday  \n",
       "3421073                 15                     3.0        Friday  \n",
       "3421074                 16                     3.0        Monday  \n",
       "3421075                 13                     9.0     Wednesday  \n",
       "3421076                 17                    22.0      Thursday  \n",
       "3421077                 15                    22.0        Friday  \n",
       "3421078                 18                    29.0      Saturday  \n",
       "3421079                 10                    30.0        Friday  \n",
       "3421080                 12                    18.0       Tuesday  \n",
       "3421081                 12                     7.0       Tuesday  \n",
       "3421082                 14                    30.0        Sunday  \n",
       "\n",
       "[3421083 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YULeAF02znmOAYyaUrwB2nFD+w0ltSJIkSUuFv4gqSZIkdc7QLkmSJHXO0C5JkiR1biFP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\nQ7skSZLUuf8PA+FraqwZto4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e214f0aba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "order_dow\n",
       "0    600905\n",
       "1    587478\n",
       "2    467260\n",
       "3    436972\n",
       "4    426339\n",
       "5    453368\n",
       "6    448761\n",
       "Name: order_id, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding out the busiest day of the week\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.countplot(x=\"converted_dow\", data=df_orders, order=df_orders['converted_dow'].value_counts().index, color='teal')\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "plt.xlabel('Day of week', fontsize=12)\n",
    "plt.title(\"Frequency of order by week day\", fontsize=15)\n",
    "plt.show()\n",
    "cnt_dow = df_orders.groupby('order_dow')['order_id'].aggregate(get_unique_count)\n",
    "cnt_dow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We see that on days Monday and Tuesday maximum number of orders are being placed. So, it's during the weekend that most of the customers tend to order and then ordering tends to go down.\n",
    "* Now, let us try to find out the time of the day when maximum orders are being placed like morning, evening or afternoon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SJEmSVAy7pz0ito+IL2fmk5n5+cx8RWaumZkzgNWBbdpfTUmSJGnlNZLhMZ8B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\nzgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e214f14390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "order_hour_of_day\n",
       "0      22758\n",
       "1      12398\n",
       "2       7539\n",
       "3       5474\n",
       "4       5527\n",
       "5       9569\n",
       "6      30529\n",
       "7      91868\n",
       "8     178201\n",
       "9     257812\n",
       "10    288418\n",
       "11    284728\n",
       "12    272841\n",
       "13    277999\n",
       "14    283042\n",
       "15    283639\n",
       "16    272553\n",
       "17    228795\n",
       "18    182912\n",
       "19    140569\n",
       "20    104292\n",
       "21     78109\n",
       "22     61468\n",
       "23     40043\n",
       "Name: order_id, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Figuring out which time of the day is the busiest\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.countplot(x=\"order_hour_of_day\", data=df_orders, color='teal')\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "plt.xlabel('Hour of day', fontsize=12)\n",
    "plt.title(\"Frequency of order by hour of the day\", fontsize=15)\n",
    "plt.show()\n",
    "cnt_hod = df_orders.groupby('order_hour_of_day')['order_id'].aggregate(get_unique_count)\n",
    "cnt_hod"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the peak hours for ordering are during 10:00 AM to 3:00 PM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, day time of Monday and Tuesday is the peak time for ordering. Let us just plot a heat map to make if more clear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VdUi7fxzw0ao6APhouw9wBHBAWzYCb4QuUQVeBjyMbsL4y6aT1bbNxpHHHb5QZUw8JUmS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\njmNvrtjLOv7miTuW408929b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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e214f1cb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heat-map for the intersection of the day of week and hour of day\n",
    "grouped_df = df_orders.groupby([\"order_dow\", \"order_hour_of_day\"])[\"order_number\"].aggregate(\"count\").reset_index()\n",
    "grouped_df = grouped_df.pivot('order_dow', 'order_hour_of_day', 'order_number')\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(grouped_df)\n",
    "plt.title(\"Frequency of Day of week Vs Hour of day\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now check what is the time period for reordering using the column called days_since_prior order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nwsbV+pVpVclTqkbX5SlZo6t5SPujaI2u2T9Fa3RNnqI1\nuuX3q0iNrslTtEbX5ClWo3u/I5OumFiR7fRt/7ixauXNZ9kWzy1Yvvvg88vkS3XG8//r1j8SViTv\niJK0BumX8NsR8b1ByAQQ6XKy61i+G2eXx+vNwFRJc0ldON8m6aKCeYiIBfnnYuD7LN+Ns8vjNR+Y\nX+n9dgWp8apUniF7A7dGxKKaeV3meTswJyIejIhngO8Bb2rK09H5842I2CEidsvb6f2LWYnjtWjo\nUsP8s64LfJE61KLTPEqDhe4HHBwRdR8wSu2fi6nvAt9VnleRGoNvz3V6C+BWSS8rlIeIWJQ/UC4F\nvk59V/uu6/T38iUEN5F6CfcOcNz1+TwW+HvgsoZFusxzCKk2Q/oDR9HjFRF3R8Q7I2JH0hfF+7rM\n0/CZsFiNXoHPqE06zVOqRq/A/um0RtfkKVqj6/ZPyRrdcLyK1eiW87lIjW7IU6xGN5w/RWt0zjD0\nHXkX0nAdY4fZTv/yxAgMfLU630it1bNJhXJogLNte5b5GMsOcHx5nzO9gubBRvdl2cGNb+pzFgEX\nAGe2LNNZJmAT8gDdwNrAr4H9Sh6vynZ3p36A9c7ykMYeGFeZ/l9gr8Ln0K+BrfP0ScDpJfPkbV4K\nfLhhXpfn887AnaSxqkS6lvyoUudP3sam+efLgbvpGYCyi/3TWwOB01l28N7TatbZiDRY/Yb5NgfY\nqB95Ko9fR/PgvcO+t4zg/tkLuAvYpGWdLvNMrkwfBVwxCMcrz5tL8+C9neQBNqtMH0MaJ6Lk8Toc\nODlPv5p0KYBK5amc09cPyPk8E9g9T+8B3FL4/Bmq0S8ifT47tKv9Q8NnQgrV6KY8lfnX0WGNbtk/\nRWp0S54iNXq445WXmUtHNbpl/xSp0S15itTotuNFgRrdsn+K1OiWPEVqNA3fkUkNeNUB1o+sWXdb\nlh1gfTYjNMD6Sj+BbwFpXJbfk1o+p+XHTgam5ukX5wM9C7gJmNTHLJeQupw+Q2rl/KdcpA7P8wWc\nlbPeQcOb8Ajm2ZXUDXA6cFu+7VMqE/C3wO9ynhnk/0BR6nj1ZNud3FhV8PyZlIvN7aRGkKHzueQ5\ntD1wcz5mPyC9SZTMsw7wEDC+8ljJPJ8jNQrNAC7MbxTFzmfSm9td+Rzao+v901ADXwL8nNTL6+fk\nDxjAG4BzK+semvfTLBoaI0coz7vz9FPAIuCnedkJwNWVdZd7b+lTnlmkD69DNfrswnm+m8/n6cBV\npAF9ix2vnvlzyV+ECp4/F+bfnenAleQvRgWP15rARfmY3Qq8rWSe/Pi3yDWnsmyp/bMrcAupJt4I\n7Fj4/Plkft2/B04lf2ntaP80fSYsUqNb8hSp0S15itToljxFanRTnp5l5tJRjW7ZP0VqdEueIjW6\n7XhRoEa37J8iNbolT5EaTfN35Emk7w+zSN8nhv6r5FRyI2i+Py1nuYf83xVH4jb04s3MzMzMzMzM\nzIrzmFVmZmZmZmZmZjYw3FhlZmZmZmZmZmYDw41VZmZmZmZmZmY2MNxYZWZmZmZmZmZmA8ONVWZm\nZmZmZmZmNjDcWGVmZmbWB5IOlnRN6RzDkfS4pEmlcwyRFJK2Kp3DzMzMylFElM5gZmZm9oJJmgu8\nFHgWeA64C7gAOCcilhaMZitBUgCTI2JW6SxmZmZWhntWmZmZ2arsXRExDtgSOBU4DvhG2UirBklj\nV+X1+/18ZmZmVo4bq8zMzGyVFxGPRMSVwHuBQyRtByBpX0m/k/SopHmSThpaR9KPJB1VfR5J0yUd\noOTLkhZLeiQ/vl3dtiV9SNJsSY9JmiPp4Mrj/11ZLiQdLuleSX+WdJYkVeZ/VNLM/Dx3SdohPz5B\n0nclPZif/xNN+0HStySdLena/DzXS9qyJ8PHJN0L3Ft5bKs8PV7SBXlb90s6QdKLKq/nf/J+WQKc\nVLP9tSSdKWlBvp0paa08b3dJ8yUdJ+mPwDfz45+WtDAvf2jN850h6Q+SFuXXtnbb85mZmdmqz41V\nZmZmNmpExE3AfOAt+aEngA8CGwD7AkdIOiDPOx94/9C6kl4HbA5cDbwT2A14dV73vcBDvduTtC7w\nFWDv3MPrTcBtLRH3A3YCXge8B9gzP8+BpMafDwLrA1OBh3JD0VXA7TnbHsDRkvZs2cbBwCnAxjnL\nt3vmHwDsDGxTs+5XgfHAJOCtOc+HK/N3BmYDmwJfqFl/GrALsH1+jVOAEyrzXwZsROoJd5ikvYBj\ngXcAk4G39zzfF0nHYHtgK9I+OLHp+WrymJmZ2SrIjVVmZmY22iwgNWAQEddFxB0RsTQipgOXkBph\nAP4LmCxpcr7/AeCyiHgaeAYYB7yGNMbnzIhY2LC9pcB2ktaOiIURcWdLtlMj4uGI+APwS1IjDMBH\ngNMi4reRzIqI+0kNW5tExMkR8XREzAa+DhzUso0fRcSvIuIpUuPRGyVNrMz/94hYEhF/ra4kaQyp\nUe4zEfFYRMwFvpT3y5AFEfHViHi2d/3sYODkiFgcEQ8Cn+tZfynw2Yh4Kq//HuCbETEjIp6g0lsr\n9zr7KHBMzvsY8G89r9fZ3GgAAANDSURBVL33+czMzGwUcGOVmZmZjTabA0sAJO0s6Zf5srZHgMNJ\nPY7IjTmXA+/PPZjeB1yY5/0C+E/gLGCRpHMkrd+7odzA8t78vAvzpYWvacn2x8r0X4D18vRE4L6a\n5bcEJkh6eOgG/CtpYPkm8yr5Hs/7YkLd/B4bA2sC91ceu5+0P4dbd8iEmvWr234wIp7sWX5ez/JD\nNgHWAW6pvPaf5Mebns/MzMxGATdWmZmZ2aghaSdS48rQWFEXA1cCEyNiPHA2oMoq55N6A+0B/CUi\nfjM0IyK+EhE7AtuSLkX7dN02I+KnEfEOYDPgblLPpxdqHvCqhsfnRMQGldu4iNin5bme70UlaT1S\nL7MF1cgN6/2J1KNsy8pjLwceWIF1hyyoWb9t2wurefPy1Tx/BbatvPbxEbFeZRn/W2szM7NRyI1V\nZmZmtsqTtL6k/YBLgYsi4o48axywJCKelDQF+MfqerlxainpcrcLK8+3U+6VtQZp3KsngedqtvtS\nSVPz2FVPAY/XLbcCzgWOlbRjHtx9qzww+k3Ao3kQ8bUljZG0XW6Ua7KPpF0lrUkau+rGiBiuRxQR\n8Rypp9kXJI3L2/8UcNELeB2XACdI2kTSxqTxpdrWvxz4kKRtJK0DfLaSZymp4e/LkjYFkLT5MON1\nmZmZ2SjgxiozMzNblV0l6TFSD6RpwH+w7IDgRwIn52VOJDWO9LoA+BuWbVRZn9RQ8mfSpWkPAWfU\nrPsi4J9JvYeWkMbDOvKFvoiI+A5pwPKLgceAHwAb5Qakd5HGtppD6m10LmkQ9CYXkxp9lgA7knqO\nraijSI1zs0m90y4GznsB638euBmYDtwB3JofqxURPwbOBH4BzMo/q47Lj98g6VHgZ8DWLyCPmZmZ\nrYIU4d7TZmZmtvqS9EHgsIjYtXSWlSXpW8D8iDhhuGXNzMzMBpV7VpmZmdlqK196diRwTuksZmZm\nZpa4scrMzMxWS3nsoweBRaTL3czMzMxsAPgyQDMzMzMzMzMzGxjuWWVmZmZmZmZmZgPDjVVmZmZm\nZmZmZjYw3FhlZmZmZmZmZmYDw41VZmZmZmZmZmY2MNxYZWZmZmZmZmZmA8ONVWZmZmZmZmZmNjD+\nD+q11HAC0uD4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e215320cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Finding out after how many days an order is reordered\n",
    "plt.figure(figsize=(20,8))\n",
    "sns.countplot(x=\"days_since_prior_order\", data=df_orders, color='grey')\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "plt.xlabel('Days since prior order', fontsize=12)\n",
    "plt.title(\"Frequency distribution by days since prior order\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Conclusion: Customers tend to reorder after a period of 7 days or 30 days, i.e. weekly or monthly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5896974667922161"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percentage of re-orders in prior set #\n",
    "df_order_products_prior.reordered.sum() / df_order_products_prior.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5985944127509629"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percentage of re-orders in train set #\n",
    "df_order_products_train.reordered.sum() / df_order_products_train.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, on an average, 59% of the ordered products are reordered again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us see the number of products bought in each order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wGc0nAAAAACaj+QQAAADAZPYc6gQAuHz2rq+vtP6+tbWJMgEAADiQmU8AAAAA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\nZCN6qQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e214f14278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#No. of products per order on an average\n",
    "grouped_df = df_order_products_train.groupby(\"order_id\")[\"add_to_cart_order\"].aggregate(\"max\").reset_index()\n",
    "cnt_srs = grouped_df.add_to_cart_order.value_counts()\n",
    "\n",
    "plt.figure(figsize=(20,8))\n",
    "sns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8, color = 'teal')\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel('Number of products in the given order', fontsize=12)\n",
    "#plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, mostly 5 items per order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us merge the tables to get a greater overview of our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>aisle</th>\n",
       "      <th>department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>33120</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>eggs</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>28985</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Michigan Organic Kale</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>fresh vegetables</td>\n",
       "      <td>produce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9327</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Garlic Powder</td>\n",
       "      <td>104</td>\n",
       "      <td>13</td>\n",
       "      <td>spices seasonings</td>\n",
       "      <td>pantry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>45918</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Coconut Butter</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>oils vinegars</td>\n",
       "      <td>pantry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>30035</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Natural Sweetener</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>baking ingredients</td>\n",
       "      <td>pantry</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  add_to_cart_order  reordered           product_name  \\\n",
       "0         2       33120                  1          1     Organic Egg Whites   \n",
       "1         2       28985                  2          1  Michigan Organic Kale   \n",
       "2         2        9327                  3          0          Garlic Powder   \n",
       "3         2       45918                  4          1         Coconut Butter   \n",
       "4         2       30035                  5          0      Natural Sweetener   \n",
       "\n",
       "   aisle_id  department_id               aisle  department  \n",
       "0        86             16                eggs  dairy eggs  \n",
       "1        83              4    fresh vegetables     produce  \n",
       "2       104             13   spices seasonings      pantry  \n",
       "3        19             13       oils vinegars      pantry  \n",
       "4        17             13  baking ingredients      pantry  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_order_products_prior = pd.merge(df_order_products_prior, df_products, on='product_id', how='left')\n",
    "df_order_products_prior = pd.merge(df_order_products_prior, df_aisles, on='aisle_id', how='left')\n",
    "df_order_products_prior = pd.merge(df_order_products_prior, df_departments, on='department_id', how='left')\n",
    "df_order_products_prior.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the top grossing products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_name</th>\n",
       "      <th>frequency_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Banana</td>\n",
       "      <td>472565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bag of Organic Bananas</td>\n",
       "      <td>379450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Organic Strawberries</td>\n",
       "      <td>264683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Organic Baby Spinach</td>\n",
       "      <td>241921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Organic Hass Avocado</td>\n",
       "      <td>213584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Organic Avocado</td>\n",
       "      <td>176815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Large Lemon</td>\n",
       "      <td>152657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Strawberries</td>\n",
       "      <td>142951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Limes</td>\n",
       "      <td>140627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Organic Whole Milk</td>\n",
       "      <td>137905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Organic Raspberries</td>\n",
       "      <td>137057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Organic Yellow Onion</td>\n",
       "      <td>113426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Organic Garlic</td>\n",
       "      <td>109778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Organic Zucchini</td>\n",
       "      <td>104823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Organic Blueberries</td>\n",
       "      <td>100060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Cucumber Kirby</td>\n",
       "      <td>97315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Organic Fuji Apple</td>\n",
       "      <td>89632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Organic Lemon</td>\n",
       "      <td>87746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Apple Honeycrisp Organic</td>\n",
       "      <td>85020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Organic Grape Tomatoes</td>\n",
       "      <td>84255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                product_name  frequency_count\n",
       "0                     Banana           472565\n",
       "1     Bag of Organic Bananas           379450\n",
       "2       Organic Strawberries           264683\n",
       "3       Organic Baby Spinach           241921\n",
       "4       Organic Hass Avocado           213584\n",
       "5            Organic Avocado           176815\n",
       "6                Large Lemon           152657\n",
       "7               Strawberries           142951\n",
       "8                      Limes           140627\n",
       "9         Organic Whole Milk           137905\n",
       "10       Organic Raspberries           137057\n",
       "11      Organic Yellow Onion           113426\n",
       "12            Organic Garlic           109778\n",
       "13          Organic Zucchini           104823\n",
       "14       Organic Blueberries           100060\n",
       "15            Cucumber Kirby            97315\n",
       "16        Organic Fuji Apple            89632\n",
       "17             Organic Lemon            87746\n",
       "18  Apple Honeycrisp Organic            85020\n",
       "19    Organic Grape Tomatoes            84255"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Counting the total no of products purchased, i.e. the most popular products.\n",
    "cnt_srs = df_order_products_prior['product_name'].value_counts().reset_index().head(20)\n",
    "cnt_srs.columns = ['product_name', 'frequency_count']\n",
    "cnt_srs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, it mostly fruits which customers prefer to buy. So, hopefully the most used aisles should correspond to fruits. Let us look at that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oyH77rEsfkiRJ0pD0Euar6rPA5fOa9wVObPdPBB490v726pwFbJ5ka+ChwBlV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\nQghD1P8D29/7054ZJ/sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e215692c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Count for the sales according to the aisles\n",
    "cnt_srs = df_order_products_prior['aisle'].value_counts().head(20)\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8, color='teal')\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel('Aisle', fontsize=12)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Clearly, fresh fruits and fresh vegetables are the top grossing products and their aisles are the most shopped from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q6f8D/JuZrQJWAqe12v9O4L2El8xwzjUdYf/WksDdZvYYsLPV9puBC83sWeCN\nQAVQ7Zx7ifCy298y+R4ESoEJwMOZS2e/Br7UnRegs1LxxDGEfZoG9MX5+qOp2zl9QSp41ncO6VuX\n1tSO8p1BstK1JGPqbtBPmXNtrzzJ4ZhZEZB2zrVkWph+mukwnTUys0svBWb7zpLrWiK8dtUN0dFN\nhaZlGfKBczXPbSwvLoB+Ob2G9Ih3kay6w3cI6Vm51jKUDSYTdpJeBfwI+LDnPAdJxRNRwpYwFUI9\noCBg8mfvCVYceU/pD0an02tUCMkR/DfJWJ91d5C+oWKoi5xz65xzJznnTnDOneyce8p3pjZ+BGgF\n5h50wga3aFa5S/nOIb3vjPqGGt8ZJOuVEI4wG+s7iPQcFUP9SCqe+ATwUd85+huD6I13pi0SuBbf\nWaR3La6pHeM7g+SEicAfScbUJ7OfUDHUT6TiiYuAH/rO0V8VNxP/8P3B475zSC9yruqkhsZZvmNI\nzlgI/Mx3iP3MLGlmnz3CPtea2fv7KlMuUTHUD6TiiVmEI+eivrP0Z+eucqeO3+U2+c4hvWNcOr02\nqn9D0jUfIBn7lO8QneWc+5lz7jdtt/fX9Ti7Iu9fgFyXiicGAX8AhvrO0t8ZlCRvS++65vqCKb6z\ndNfm/9pM9cpqCoYWMPObMwGovKOSfSv3YQXGgDEDmHj1RKKDDq4NGisaKf9J+eu3m3Y0MeYtYxj1\nhlFU3lVJ9fPVlEwuYeI14cTue57YQ7o2zagLs3+0+ll19fW+M0hO+h7J2Iskqx7s6xOb2Y3A+4Fy\nYAfwTGb7h4FrCKdWWQ+8zzlXZ2ZJoMY59z0ze5hw1PHpwENmdhVwrHOu2cyGAs8DM51zza3ON5qw\nNWxyZtOnnHNPZLb/lnBNt6eAi4B5zrmdZnYT4dJU5YTT1DyTOf8ngWuBFuAl59w7e+VF6iS1DOW+\n/0Ijx/rMsDrmvvXx4Ikj75ndhi8aztTPTD1o26DjBjHzmzOZ+Y2ZFI0rYsd9Ow55XFFpETO+PoMZ\nX5/B9JunExkQYei8oaTr0tStr2PmN2biAkdDeQNBU8Dex/cy8tyRffSsumdxTe043xkkJ0WBO0nG\nZvblSc1sHvBO4CTCSSFPbnX3HzIDfE4gnJ/v6g4Os389zpsJV0m4JLP9ncA9rQuhjP8AfuicOxl4\nKwdWVOhozc75mf32Z5zf6lhfBE7KrDV6bVeee29QMZTDUvHEpwnXXZM+dMVjwexYjTu0Usghg2YN\nOqTVZ8hxQ7BoOFn5wOkDad7d9v/Bg9W8VMOAMQMYMGoAGLgWh3MO1+ywqLHzrzsZecFIrCD7J0A3\n5/Yc39h0rO8ckrOGA3f38Rrvuuy1AAAgAElEQVRmZwB/dM7VOef2Afe2uu84M3sss7bmeziwtmZb\nrdfj/CXwgczPH6D9RcXPB36cmUT4XmComQ2h4zU7FwF/ds7VO+eqgf9tdazngdvN7L2ErUNeqRjK\nUal44nTgO75z5COD4cnb0+t85+hNex7dw5Djhxx2n6onq4gtiAEQLYkydP5QXvnKKxSOKiQyMEL9\nq/UMnZsbV2/Ht6TXmpatke45gb4fxNLRrMm/Bj6eWVvzZg6srdnW6+txZhZBn2pmZwFR59wL7ewf\nARa2WoNzQqbIOdyanR25hHCJk3nAM777LakYykGpeGIUYUWvPl+eTNjNaWeuDrJtjqkesf3e7RCF\n2MJYh/sELQHVz1UTO/nAPqMvHs2Mr8+g9F2lbP/DdsZcPobdj+zmtVtfC4+Zxc6uq2v0nUH6hWtJ\nxt7eR+d6FHiLmZVkWmcWt7pvCFBhZoWELUOd9Rvgd7TfKgTwN+Dj+2+Y2f7VFzpas/NxYLGZFZvZ\nYDKX4cwsAkxyzv0T+DwwDBjchZw9TsVQjknFEwbcRrjmmXh03X3B+OIm168m6dvz+B6qV1Uz6SOT\nMOv4j7qa52sonlJMQezQerx+U9gPuWhcEXuf2Mvkj02mcXMjjZXZW28srqkb7zuD9Bu/JBk7prdP\n4px7lvCP4pXAPcBjre6+CXiScI3MNV047O2EhczvOrj/k8B8M3vezF7iQF+fjtbsfIrwctoqwoE+\nTwNVhP2sbstcxnuOsB/S3i7k7HFqWcg9/wK8wXcIgahjwhfuSj9y83sLzvKdpSdUP1/Nzr/sZNoX\npxEpOvzfSVXLqxi2YFi7923/w3bGXzUe1+IgyGyMQNAUtLu/b+bcztlNTTN855B+Yyhhh+rTSVY1\n9eaJnHPfBL7ZzvafAj9tZ3uy1c9nt3PIRcDvOypMnHM7ab+fahXwhlZrdp7jnNv/18/3nHNJMxtI\n2Jr1/UzH7EWHe259TcVQDknFE4sIK3DJErPLOeO4jcGLL0yNdNRBMSuV/7Sc2jW1tNS0sObTaxjz\n5jHsvG8nQUvAxu9uBKBkegkTrppA855mtvxqC1NvmApA0BhQ82IN4686tDFl3zP7KJlWQuHwwvAY\nM0pY9+V1FE8spmRydq51O6mlZZ1B9o/9l1wyn7BPZ87MQWRmtxC26lx8FA+fDNyVufzVxMFrdv7c\nzGYT9ltakmnRyjpatT5HpOKJIYS976d6jiJtNBaw7qobolPTUSv0nUW67qq9+x79zJ69Z/rOIf3S\nm0lW/dl3CDky9RnKHT9ChVBWKmph5kf/L1jqO4ccnTfV1E70nUH6rV+RjE0+8m7im4qhHJCKJ94C\nXOU7h3Rs0UtuwaTtboPvHNI1Eee2zWpu7vXOrpK3hgO3k4zpszbL6Q3Kcql4Yizwc9855PAMipK3\np/eh6845ZUpzyyu+M0i/twi4wXcIOTwVQ9nvv1DnzpwwpIET3v2wVrbPJefV1aV9Z5C88A2SsZwa\nZJFvVAxlsVQ8cQ0H1oqRHHDZcnf8yH2u0ncO6Zw31dRO8p1B8kIR8BuSMQ2yyFIqhrJUKp6YDvzA\ndw7pGoNY8rb0Jt855MgizlVMb26Z6juH5I25wI2+Q0j7VAxloVQ8EQX+BxjkO4t03dgqTj3/2WC5\n7xxyeNOam1/1nUHyzr+QjJ3gO4QcSsVQdvo4sNB3CDl6V/8tmDqwwVX5ziEdu6C2PjunxJb+rJBw\nuL0mPM4yKoayTCqemAR8w3cO6Z6oY9yNd6ZX+c4hHXtTTe1U3xkkL50EfNF3CDmYiqHs82M8r94r\nPWPGVs44aX2ggigLRZ3bPKWlRZ2nxZebSMaO8x1CDlAxlEUykyte6juH9AwD++wfgqEFLS57l2vP\nUzOamjf6ziB5bQDtLKQq/qgYyhKpeGIocIvvHNKzCtNMu/7P6kydbS6srfMdQWQRydj7fYeQkIqh\n7PFNYILvENLzTnnZnXZMhVvnO4cccEltrZbgkGzwXZKxYb5DiIqhrJCKJ04BPuo7h/QOg8Kbfpdu\nNOc0eikLFDi3aUJLerzvHCLAGDRgJiuoGPIsFU8UEK49pveiHxvUyHFX/j14zHcOgWObml7znUGk\nletIxub6DpHv9AHs3ycBTcKVB974tJs3eq/b6jtHvruotk7/70k2iQC3koyZ7yD5TP8peJSKJ0YB\nX/GdQ/qGweCv3Zbe4jtHvru4pm6G7wwibSwArvYdIp+pGPIrCcR8h5C+M7Kaky9ZESz1nSNfFTq3\nYWw6PdZ3DpF2/CvJ2AjfIfKViiFPUvFEAviI7xzS9973UDBzcJ3b4ztHPko0NpX7ziDSgZHAt3yH\nyFcqhvz5HqD1afJQxDH6K79Lv+g7Rz56Y21doe8MIofxIZKxhO8Q+UjFkAepeOJC4GLfOcSfqdtZ\ndOqa4FnfOfLNRbW16i8k2SwKfNt3iHykYqiPpeKJKPB93znEv+v/HIwc0OzqfefIF0VBsH5UOhjt\nO4fIEVxGMna67xD5RsVQ37sa0AJ9QkHAlM/8IVjhO0e+mNPUpJF8kiu+4ztAvlEx1IdS8cQQ4Ou+\nc0j2OPFVt+jYzW6N7xz54OKauiLfGUQ66TSSsTf7DpFPVAz1rc8RTr8uAoBB9Mt3pF0kcGnfWfo1\n59yFtXXH+o4h0gXfIhmL+g6RL1QM9ZFUPDEC+JTvHJJ9iptJfOj+4HHfOfqzYufWDQ8CzeEiuSQB\nfNB3iHyhYqjvfAYY4juEZKfzVrmTS3c5rZnVS8oamyp8ZxA5CkmSsYG+Q+QDFUN9IBVPjAQ+4TuH\nZC+DgTfflt7pO0d/dXFNbbHvDCJHYTxwve8Q+UDFUN9Qq5Ac0bA65l7+RPCE7xz9jnPBBXV1cd8x\nRI7SZ0jGBvkO0d+pGOplmcVY1SoknXLFo0EiVuvUQtSDSpxbGwuc1gCUXDUSLd3U61QM9b7PAoN9\nh5DcEIERX709/bLvHP3JiY2N231nEOmmz5KMaWqIXqRiqBdlWoU+7juH5JaJuzjtzNXBU75z9BeX\n1NSpA6rkulLgA75D9GcqhnrX5wBd65Uuu+6+YHxxk6vxnSPnOZc+t7Zulu8YIj3gCyRjWty7l6gY\n6iWZeYU+5juH5KaoY8IX7k5rIdduGuTcmiHODfWdQ6QHTAXe7TtEf6ViqPdci1qFpBtmv8ai4zYG\nL/rOkcvmNjTu8J1BpAd9iWRMn9u9QC9qL0jFEwNQXyHpJoPIF+4OBkTTrtl3llz1pppaDV6Q/iQO\nvNV3iP5IxVDveCdhhzeRbilqYeZH79PcQ0fFueaz6+oTvmOI9LB/8R2gP1Ix1Ds+7TuA9B+LXnQL\nJ213G3znyDVDArdmoHO6VC39zYkkY+f6DtHfqBjqYal44lzgRN85pP8wKErent6Hc853llwyv6Fh\nl+8MIr1E3TB6mIqhnneD7wDS/wxp4IR3PaKV7bticU2tRpFJf3Upydhk3yH6ExVDPSgVT8wCLvad\nQ/qnNy9zx4/Y57b5zpETnGs6o75B/YWkv4oC1/kO0Z+oGOpZnwbMdwjpnwxiN9+eVt+hTogFQarY\nuRLfOUR60YdIxop9h+gvVAz1kMwki+/3naO/ubGigkXr13Hphldf33Z/9T4Wb3iVOWvX8EJDfYeP\nXbJ7N4s3vMqlG17ls1u30BgEAHxu61bevGEDP9xxYAqan+7cyT+qq3vvifSQsXtZcN5zwZO+c2S7\nUxoa9/jOINLLRhGOXJYeoGKo57wf0F+iPewtsRg/nzjpoG0zBxTxowkTmF/S8cu9rbmZ2/bu4e4p\nU7l32jGkgb9U72NtQwMAf5o2jWfq66hOp9nR0sLqhnrOGzKkN59Kj/nQA8GUgQ2uyneObHZpTe0w\n3xlE+oA6UvcQFUM952rfAfqj+QMHEose/Gs6vaiIaQOOvIBz2jkanKPFORqCgDEFhRSY0egCAudo\ndo6IGbfs3MHHR43urafQ46KOcTfemX7ed46s5VzDaZpfSPLDPJKxBb5D9AcqhnpAKp44FTjOdw45\nYGxhIR8YMYLzXlnPWa+sZ3AkyumDBjG9qIjSwkLeumkjFw0ZwmtNTThgdnFuXXqfsZVFJ60PVvnO\nkY2GB0FqABy5WhbpHz7hO0B/oGKoZ3zIdwA5WFU6zUM1NTx4zHQenj6Dehdwb1V4ZelLY8byx6nT\n+MCIkdyycwefGDWKn+3ayae3buHuvXs9J+8cA/vsH4IhBS2u0XeWbLOwvkGXECWfvI1kbJTvELlO\nxVA3peKJwagTW9ZZVlfLhMJCRhQUUGjGBYOHsLJNZ+t/VFczp7iEusCxvrGRH46fwL37qqjPdLTO\ndoVpjrn+z8Ey3zmyzZtqakf6ziDShwagz6BuUzHUfe8AtBhkliktKGRVfT31QYBzjuV1tRwzYMDr\n9zc7x2179/DBESNoCAIsMyOCy9yXK0552Z1+TIVb5ztH1nCu7tT6hlm+Y4j0MY1k7iYVQ92nS2S9\n6LNbt/CuTZvY2NTEOa+s5569e/l7dTXnvLKelQ0NXLd5Mx8uLwdge0szH9kc/nxCSQkXDhnC2zZt\n5LKNGwiAK2IHBhj9bs8eLhsaoyQSYVZREQ7HZRs2cFJJCUOjUR9P9agYFN70u3SjOZcbzVm9bGQ6\nSA0I/1KWLiqvCjhnSS2JW2uY85Ma/mN5eAX2pocaOP6nNZz4sxou/J9atla3/6v2+QcbmPOTGhK3\n1vDJvzbgnKOxxXHRbbUc95MafvJU0+v7XvO/9TxXke6T55UnTiYZ06CBbjAtd3T0UvHEHOAF3zlE\n7ptvjy65IHqm7xy+La6uefhbO3ef7TtHLqqoDqioccwtjVLd6Jj381r+9M4SJg6NMLQobDn90ZON\nvLQj4GdvOnhai6XlLXzuwUYevWogAIt+Vce3zytiX6Pjyc1pvn5uEXP/s5aV1w5mVWWaW1Y08ctL\nNRNJD/tXklVf8h0iV6llqHs0nF6ywsVPu7mj97qtvnP4dmlNrTqSHqXSIRHmloatokOKjMToCFv2\nudcLIYDapvan2DegocXRlIbGNDSnHWMHGYURqG+BllaNSTf9s5GvnaPBfr3gvSRj+kw/SnrhjlIq\nnogC7/adQwTAYPDXbktv8Z3DK+dq5jc0xn3H6A827g14riLNqRPD4ujGfzQw6YfV3L66ud1CZuGk\nAs6ZWkDp96sp/X41b5heQGJ0lAumF1BZE3DqL2v5/OlF3Lu2mXmlUcYP0UdPL5gInOs7RK7Sb+TR\nOwsY6zuEyH4jqzn54hXBUt85fBmdTq8pgALfOXJdTZPjrXfV8e8XFb/eKvTN84op//QQ3lNWyI9X\nNB3ymPW7A1I7AzbfMIQtNwzhoY1pHt3UQkHE+O1bB/LcRwbz9tkF/PvyJj5z2gBueKCBt91Vx71r\nm/v66fV36kh9lFQMHb0rfAcQaev9DwUzB9e5vFyX64z6hhrfGXJdczoshN5TVsjlicJD7n93WSH3\npFoO2f7HVDMLJkQZPMAYPMB444wClm8+uIP0T55q4soTCllWnmZAFO58WwnfeFTTZPWwy0nGNLr5\nKKgYOgqZS2SX+84h0lbEMfqmO9Iv+s7hw+Ka2jG+M+Qy5xxX39tAYlSUGxYeuBS2bteBoubetS3E\nRx36sTE5FuGRTS20BI7mtOORTS0kWu23p97xf+taeP8JhdQ1OyIGZtBwaF0l3TMIeKvvELlITcpH\n5xwgdxazkrwybRuLTl0TPPtkPDLXd5Y+41zVSQ2Nml+oG54oT/M/zzdTNibCiT8LG9m+dV4R//Vc\nM2t3BkQMpgyL8LNLwqVrnt6a5mdPh6PC3ja7gIc2tFD201oMuGhGAYtnHWhZ+tojjXz5jCLMjDfM\nKODWp5oo+2kt187TLAi94O3AEt8hco2G1h+FVDzxc+DDvnOIdKQlwqarboiOaSq0vBi/PK6lZcWD\n5VtP8Z1DJAs0AqNIVumycRfoMlkXpeKJAuAtvnOIHE5BwJTP/CFY4TtHXzmrrr7+yHuJ5IUi4I2+\nQ+QaFUNddy6guUwk6534qjv92M1uje8cfWFxTe043xlEsoj+YO8iFUNd93bfAUQ6w6Dgy3ekXSRw\n/XrdA3Nuz/GNTcf6ziGSRS4hGVOHrC5QMdQFukQmuaa4mcSHHgge952jN41vSa+19idGFslXQwkH\n+kgnqRjqmtOAkb5DiHTFeSvdyeN2u3LfOXrL2XV1mqxG5FD6w70LVAx1zSW+A4h0lcHAr92W3u47\nR29ZXFM33ncGkSx0mdYq6zy9UF2jYkhy0rBa5r3lif53ucyc2zm7qWmG7xwiWWgcsMB3iFyhYqiT\nUvHEFGCO7xwiR+sdjwazY7Vup+8cPWlSS8s69RcS6dBlvgPkChVDnbR+2mVn7Y1NTznQLJWSkyIw\n4qu3p9f6ztGTzq2t10qfIh27wHeAXKEZqDvp1msf+iPwZpzbXVK/Y+3Y7c+0lFYun1bSsHOi72wi\nXXHL4sjTjx0Xme87R0/4/eaKV2c1Nx/jO4dIlnLAaJJVu3wHyXYqhjrh1msfigK7gFjb+yxo2Ti0\netNr4yqfLBqz/dl4Ybr+kH1Eskk6wuarPh0d3jjABvnO0h0R57at2lg+1ncOkSx3Bcmqu32HyHZa\nqLVzTqGdQgjARQqmVsWmT62KTWftse9KF6TrVw/fs3bX+IqlI4bvWTM74gK9xpJVogETv3B38OjX\n3hM903eW7pjc3PIKoGJI5PDOA1QMHYE+qDunc9ddzaItBQPLdow+iR2jTwLnqosa96RG71xVP75i\n6aTBtVvVnC9ZYc5rbtGcTcGLL06J5OyggPPr6vr1zNoiPeR83wFygS6TdcKt1z70GLCo2wdy6YrB\nNVtfGbftqci4bSuOHdBcrTXOxJvGAl6+6obotHTUCn1nORp/2rx14/Tmlqm+c4jkgKkkqzb5DpHN\nVAwdwa3XPjQE2E1Pt6I55yJB08vDql6pLK1YPnjUrudnR4Pmkh49h8gRPDrHHv7xpdGzfefoqohz\nFas2lpf6ziGSIz5Esuq/fIfIZrpMdmRn0xuvk5kF0aJZu0fMnrV7xGxwrqGwuebZUbte2FdasXRs\nbN+rcc2fIr3tjBfdgj8tdBs2j7ZpvrN0xbTm5lcBFUMinXMeoGLoMFQMHVnfXG81K24eMGRuRelC\nKkoXggt2ZYbwp0srlx9T0rBrQp/kkLxiUHzzbel9V38q6jDLmeL7gtr6wHcGkRxyHsmYkazSpaAO\nqBg6Mj/TmVtkZP3AsadtnHoxG6dejAUtG4ZWbyovrVxeNGb7s4mCdMNQL7mk3xnSwAnvfDR47I6z\nomf4ztJZb6qpneo7g0gOGQMkgJd8B8lW6jN0GLde+1ABUA0U+85yEOdaClrqU8P3rAmH8O9dqyH8\n0i0Oqq77WLRh91DL+qHqUec2r9xYrslORbrmgySrfuU7RLbSB+jhzSHbCiEAs4KWwoFlO8bMZceY\nueDcvqLGPWvG7FhZX1q5bPLg2q051f9D/DOIJW9Ppz55XUHWF0Mzmpo3AiqGRLrmVEDFUAdUDB1e\nbixZYDa0sXjEKeWTzqV80rng0lsH12x9VUP4pSvG7WXBec8FT/7jpMipvrMczoW1db4jiOSirP53\n7Zsukx3Grdc+9BPgOt85uuXAEP6K0oplQ0bten5ONGjJvtYuyQppo/KDn4oOrC+2rO2Tdn/5lq0T\nWtLjfecQyTEtwFCSVfW+g2QjtQwd3jzfAbqt3SH81fuH8I+L7dswS0P4Zb+oY9yNd6Yf+/KVBVnZ\nmbrAuU0TWtJTfOcQyUEFhJ9pj/sOko1UDHUg03n6eN85epxZcfOAoXMrSk+jovQ0cMHOgfXbXx4T\nDuGfXtKwW39x57mZW1l04ivB8yunR7Lu9//YpqbXABVDIkfnFFQMtUvFUMeys/N0T7PIqLqB40Zt\nnHoJG6deggXNG4bu2/RaaeXykjE7nk0UpBuH+I4ofcvAPntPMOiqG6yxpcCKfOdp7aLauojvDCI5\nTP2GOqBiqGO50Xm6h7lI4bSqYTOmVQ2bwZpZ72kpaKl/fsSe1O7SimUjh+9dm9AQ/vwwIM30T94b\nPPyDy7NrqY6La+pm+M4gksNUDHVAH2wdy/3+Qt0VDuE/fvuYeWwfMy8zhH/3mjE7VmkIfx44da07\nbVqlW79hnGVFAVLo3Iax6bR+50SO3hSSsTEkq7b7DpJtVAx1TMVQW2ZDG4tHvj6E34L0lkG1W18d\nt21FdNy2FbMGNNeM9B1Reo7BgK/8Nl3/wU9HA2fm/fJUorGpHFAxJNI984C/+g6RbVQMtaPfdp7u\nYS4SnVAzZNKE9UMmsX765S4SNK0Ztnd95fjKZUNH7lo9W0P4c9+gRsre94/g0d+cHz3Td5Y31tYV\n+s4g0g/MRsXQIVQMtS8/Ok/3pHAIf3z3yDnx3SPngHP1A5qrnxm5a3X1+IplpUP3bThWQ/hz0yVP\nuZP+crKr2Bkzr6vEX1RbmxWX60Ry3GzfAbKRiqH25WXn6R5lVtI0YOi8itLTqSg9HVywY2Dd9nVj\ntz8TjKtcPr2kcbfXD1bpPIMhX7stnfroxwq8vWdFQbB+VDpQMSTSfSqG2qFiqH3qL9TTLDK6btC4\n0RumXcKGaZdgQfOrsX0by8dVLi8Zs+M5DeHPcqP2ccrFK4JlfzklstDH+ec0NW0BVAyJdF/Cd4Bs\npOU42nHrtQ89STg5lfQF51oKWupeGrFnze7xFUtHDt+zdrbhor5jycECY8eHro8W1pTYsL4+95d3\n7l7+juqaBX19XpF+aiLJqi2+Q2QTtQy17zjfAfKKWUFL4aDWQ/iriht3rx29Y2VDacWySYPrKjSC\nKAtEHKNv+l368S98sGBRn57YOXdhbd2xfXpOkf5tNqBiqBW1DLVx67UPjQY0B0MWsSC9ZXDtllfH\nblsRHbftKQ3h9+x7l0eeWzErclJfna84CF5+atNmFUMiPedTJKv+w3eIbKKWoUNN9h1ADuYi0QnV\nQyZPqB4ymfXT3+qiQVNq2N5120orlg0dteuFORHXklVLRvR3n/pTMOKqG6y+qdBK+uJ8ZY1NFYCK\nIZGeo07UbagYOpSKoWxmZuloUWLXyOMSu0YeFw7hb9r3zKhdL9SUViwdN7R6o4bw97KCgCk3/DF4\n+F+v6JulOi6uqdU0FyI9S8VQGyqGDqUVsXOJWUlTUWze1vGns3X8/iH8214eu/0ZV1q5fHpx4x4N\n4e8FJ73iFs3c4taum2CzevVEzgUX1NXFe/UcIvlHLa1tqM9QG7de+9APgE/7ziE9IxI0vzJ038bN\npZXLS0bveG52QbpxsO9M/UV9IakP3BA9NohYr438KwmC1IpNmzUUWKTnlZCsavAdIluoZehQukzW\njwSRwul7h82cvnfYTFKz3ttc0FK3asTu1J7xlctGDd+zNqEh/EevpJnE1Q8Ej/zijdGzeuscJzY2\nbkfzooj0hknAOt8hsoWKoUPpMll/ZVbYUjjohO1j57N97PxwCH/D7jWjdz7XOL5i2eRBdZVTfUfM\nNeevdCf/76muvHKETeqN419SUzewN44rIiqGWlMxdCi1DOULs1hDychTyyedT/mk87EgvXlw7eYN\n4ypXFIzd/tSsAc21I3xHzHYGA2++LZ36yCcLer4Yci59bm1d7/ZJEslf+qxrRcVQK7de+1AxMNp3\nDvHDRaITq4dMmVg9ZArrZrwtiKYbU8Oq1m0rrVgWG7Xrxdkawt++4bXMe/PS4Ik/nRY5vSePO8i5\nNUOcm9OTxxSR1/VKa26uUjF0sEloWLYAmEXSBcWJXSPLErtGloFzdQOa9j09atfq2vEVS0uHVG+a\nqSH8B7zzkSDxzxNsZ9UgG9VTx5zb0Lijp44lIodQy1ArKoYOpv5C0j6zgU1Fsflbxy9i6/hFrYbw\nP+1KK5fPKG7cO853RJ8iMOKrt6eX3nBNQY8VQ2+qqdXIP5Heo5ahVlQMHUyVsnSORUbXDSodvWHa\nYjZMW0wk3fzK0H0bNpdWLh84eufKRD4O4Z+4i9MWvRg8/ficyPxuH8y55rPr6jWKTKT36POuFRVD\nB9MvhxyVIFo4fe/wY6fvHX4sKfe+5oKWulUjd7+0t7Ri2cjhe1/OmyH8H/u/YNxTM622cYAN6s5x\nhgRuzUDnynoql4gcQi1DragYOpguk0n3ZYbwbxt7MtvGnpwZwr8rNWbHc02llcumDKrb1m9/z6IB\nE7/w++CRr727e3MPzW9o2NVTmUSkXYNJxoaSrNrnO0g2UDF0MLUMSc8zizWUjFrw2uQLeG3yBeEQ\n/prNG8Zte7Jg3Lan44UttcN9R+xJcza5M+ZsCl58cUrkqEeCLa6pHdqTmUSkXcMBFUOoGGpLxZD0\nOheJTqweOmVi9dAprJvx9iCabnxp2N5128dXLo2N3PXinIhLD/CdsTsMIl+8Kyi88jPWEkSs6//H\nONd0Rn2D+guJ9L7hwCbfIbKBiqGDaZI96VvhEP7Zu0aVzd41av8Q/qrnR+1cXTu+cun4odWvzfQd\n8WgUtXDsdfcFj9y6uOuXy2JBkCp27oTeyCUiB+lXrdLdoWLoYJr6X/wyG9hUNGz+1glnsHXCGeCC\n7YPqKteN3fa0G7ftyZwawn/mC+7UPy9wGzePtqldedwpDY17eieRiLShBoAMFUMZt177kAHFvnOI\nHMQiY2oHjR/z6jGX8uoxlxJJN60fum/j1tLK5SVjdjw3Oxo0dWvUVm8yKE7ent77oeujDrNOT1B5\naU3tsN7MJSKvU8tQhoqhA9QqJFkviA6YsXf4sTP2Dj+WVPz1Ifx7SiuWjh6+d10824bwD63nxHc8\nGjx251nRMzr1AOcaTtP8QiJ9RcVQhoqhA1QMSW453BD+imVTBtVnxxD+y5e64/82123fM8TGHGnf\n4UGQGgAn9UUuEVExtJ+KoQNUDEluO3QIf/ngmvKNpdueLBi77elEYUudl8tPBrGbb0sv++R1BUcs\nhhbUN2iYr0jfUTGUodS4nZ8AACAASURBVGLoABVD0q+4SHRS9dCpk6qHTuXlGVcE0XTjS8P3vry9\ntGLZsJG7X5zdl0P4x+1l4bkrgycfOjFy6uH2W1zTv+ZcEsly+veWoWLoABVD0n9lhvDvHHX87J2j\njgfnagc0VT0/eufztaUVS8cPrSnv9SH8H74/mLwsbvvqi639CRWdqzu1viHe2zlE5HUx3wGyhYqh\nA1QMSf4wG9RUNGz+lglnsmXCmeCCbYNqK9eP3f4U4ypXzChu2ju2p08ZdZT+y13pR296f8GZ7d0/\nMh2kBsC8nj6viHQopyd47Ukqhg5QMST5yyJjawePH/vq4Mt49ZjLMkP4N2wprVw+cMyOlT02hP/Y\nLZxxwivB86umR45ve99p9fXVPXEOEem0Qt8BsoWKoQNUDIlkhEP4Z83YO3wWqfj7mwpbaleO2P3S\n3vEVS0cP27s+YbjI0RzXwD53TzDoqs9YU0vUDvqr9NKa2lE9k15EOknFUIaKoQNUDIm0x2xAc+Hg\nE7eNPYVtY08B5/aWNOxcM3rHc03jK5ZNHVi/vUtr+g1IM/2Tfw4e+cHlrZbqcK5mfkOj+guJ9C0V\nQxkqhg5QMSTSGWbD6ktGL3ht8oW8NvlCLGh5bUhN+cZxlSsGjN3+dLwzQ/hPXesWTq10r2wcZ9MB\nRqfTawpgfu+HF5FWVAxlqBg6QMWQyFFwkYLJ+4ZOm7xv6DRennlFEE03vDh878s7wiH8L7U7hN9g\nwFd/m6774KejgTOLnFHfUOMju0ieUzGUoWLoABVDIt1lFkkXlMzZOeoEdo46AZyrLWqqWjVq56q6\n8RXLJgypKZ+xf9dBjZS97x/Bo785P3rm4praI07IKCI9TsVQhoqhA1QMifQ0s0GNRcNO3jLhLLZM\nOAtcUDmotmL9uG1P2bhtK2Ze8lTVSQ/MC14+qaFxlu+oInlIxVCGiqEDtGK9SG+zyLjawRPGvTJ4\nAq9MfzO01D+96IV/lK8b/7ftk2zHyIE0TDHTHyYifUTFUIaKoQPSvgPkqn+uvoelqb/gcJwev4Rz\njn/rQffXN9aw5KFvs6dmO2mX5rzjr2Bh/CK27S3n1//4JkGQ5h1nfIpjxs0hHaT5yV++yEfe8HUG\nFKo+7Y8crnpnxD2/vLilaG0hJzk7b/6vm87bf6+bZpXl8yIvV5xiqbrjIxsKJ9mOESqSRHqFaoAM\nvRAHNPoOkIu27t7A0tRf/r+9Ow+TqyrwPv471UtVApElhCVrhbCFJCQRkgiC6Cg64x1ARXjxxVFx\n0ImgOOiM1uiouMx4XccRgigMyiaivMCAJYMMAYEsQAjZk05I+mZPZ++s3V3Lef+oIumQTnqrrnOr\n7vfzPP2k+nYtv+JJqF+fe+45+ucPT1NNTZ3u+FNKY0ZM0cnHDT1wnxcW/7dOPWGEpv7Nv2n3/p36\n7sOf0qQz36uXljypK6bcoIEDTtV/v3y3Tj91jF5c8oQmnfk+ilCVsbKZ3ca+Pieezc6P5yZmjd7Z\n8T2NabSnDWvMnTbsER288t4on0+aTWsuiC1vmmSW7R0fW1U31GwZ2F+twylJQI+1uQ4QFpShgyhD\nPbBpxxolTxl9oLyccdp5mt/4ki6bcO3BOxmj1sx+WWvVmtmv/vEBisVqVBOrVSbbprZsq2piNdrX\nukeLVs/STR/8gaN3g1KysrbFaMGC+mzzK/HsuJaYJvf8uWKxRjt4eGNu8PA/6N0HjlOSgF7hc6+I\nMnQQfyl6YPCJST356n9pT0uz6mviWrzmZQ0fdOhc2EvHfEi/fPpf9fUHrlFL2z59+n3fUMzE9K4x\nV+q+536gbK5NH3vXLXrqtfv0gYnXyRjj6N2gFDKyKxrqcxtmxrOjmmvs+L58rS6UpE2TzbL958VW\n1Q41W07sr9akMerXl5mACtLiOkBY9LgMGWNulvQ5SXOttdeVLpIzlKEeOPWEEbpswrW6Pf0VxWv7\nacjAUaqJ1Rxyn6XrXtXQgWfo5r/9ibbu2qDb01/RqNPG6cQBp+gfr/ipJGlL83o179umU44fpnun\nf1+5XFbepE/plOOHuXhb6Ka87PrG2vyKmYnMkE219kxJZ7rMc7SSNNJsWnN+bPmmKbFl+8aZVXWU\nJEQYn3tFvRkZulHS31hrG988YIyptdZmex/LCf5S9NBF53xQF53zQUnSEy/freOPHXTIz2c3PK3L\nJlwrY4wGHTdEAwecqqada5U8+eDuC0++co/+dvL1en7RY5p0xnt14oBT9dRr9+tT7/1aWd8Lus7K\n7thYYxfOSmSOX1WbHyejIa4zdcYqFltlBw9flRs8/A+5dx84TklCRDEyVNSjMmSMuVPS6ZKeMMYM\nl/SwpKSkrcaYT0v6hQpL62clfcla+5wx5m4dXG5/iKTbrbXfNsb8s6RrJMUlPWat/ZYxJinpKUkv\nSbpI0npJV1pr9/foXXYNZaiHdu/foQH9TtD23U2aH7ykL3/otkN+fsKxJ6th/es647TztGvfdjXt\nXKuTBpx24OcrNszXcccM1MnHDVUm2ypjYorFYmrL8u80bKzs/h0xO++VeLZmcX1uQt7oXa4zlUIn\nJWn1BbGGpsmxhjdL0sD+ah1BSUIV4HOvqEdlyFo71Rjz15LeI+nzki6XdLG1dr8x5svF+4wzxpwj\n6c/GmLOstTdIkjFmhKSnJf3GGPN+FYbTJ0syKpSrd0laUzz+MWvtZ4wxv5d0laQHevNmO8Ffih66\n+8+3am/LLtXEanXNO29W//gAvbjkSUnSJederr9++8f1wPM/1L/94QbJWl055TM6tt9xkiRrrf5n\n7gP6+8u+KUl652hPv5n+7wcut4d7Vja312je6/Hs/rnx7Pg2owtdZyqXYkkasSo3eMTvc+85cLx9\nSZoSW7Z/rGmsHWa2DOxHSUJl4XOvyFhre/ZAYwIVRno+L8laa79dPP6YpNustdOL378o6SZr7QJj\nTELSC5K+Zq39X2PMjyV9VNLO4tMeK+n7kp6V9Iy19szic3xVUp219ns9e5udmzZ1+uWSnuir5wcq\nTavs4kX1ua0vJzLn7o1pUOePQLEkraUkoUI8rlubP+w6RBiU6mqyve1uH+1SoDslPWqt/d929/2+\ntfaX7e9UPE3WvrHmpD7/H8nuPn5+IPRyssGKulwwM5FNbquxY1znqTRHG0k63WxsPyepfqjZemK/\nwpwkFtWCK4wMFfXFpfUvSLpO0nRjzFmShktqMMbcJGmAtdZvd9+nJX3XGPOgtXaPMWaIpEwfZOoK\nyhAiKS+7eW1tfunMRHbQutr8uSrM/0MJWcViK+2Q4StzQ4Z3UJLeMidp60nFkSRKEvoaEzOL+qIM\n3SHpTmPMQhUmUH/KWttqjPknSRljzLzi/e601t5pjBktaVZxbZk9kj4uN1tj7HLwmoATVnbXlsKW\nGP2X1+XGW9NuuWeUTbEkjViZGzLi4dxfHThOSUKZ7Oz8LtHQ4zlD1Wba1OmnSNrkOgfQV6xs2y5j\n581JZHPz63MTc3yoVpxiSVo7KdawaXJsWctY01hLSUIvfEO3NvfZXNxKwgrUBzEyhKpT3BJj/rz6\n7O45vdwSA+61H0n6XbuRpJjy+dPNhtUXxJZvmhxbtn+caawfYrYOpCShE1tdBwgLRobamTZ1ekYU\nRFSBjGzD0vrcplnx7Jm7auxg13ngRkz53Olmw7qDJWlV3RCzdVA/tQ2nJEHS1bq1+RHXIcKAD/5D\n7ZZ0gusQQE/kZdetqs2/MSORGbq51p4t6exOH4Sqlles5g07dMQbuaFvHUnKjTIbggtiDZsnFeck\nUZIiiZGhIsrQoXaIMoQKYmW3b6jJL5qZyB4fFLbEGOo6E8Ivr1jNCjs0uSI3NPlQ7r0HjrcrSU1v\nzkkaYrae1E9tSWMUdxgZfWOb6wBhQRk61DoVthkBQsvK7tses/NejmfrllbRlhhwr4slaf8401g/\n2GwdSEmqeIwMFVGGDrXadQCgI1Y2u9do3mvxbOvr8ez4jNFFrjMhOo5Wks4w64PzY8spSZWJMlRE\nGToUZQih0iq7aGE8t+3leObcfbEDGx0DoZBXrGa5HZZcnhvWUUlqLM5JevPqtpMSahtBSQqNXbq1\n2dUix6FDGToUZQjOZWVXrajLrZ2ZyCa319ixrvMA3VUsSSOX54aN/G3ufQeOU5JChVGhdihDh1rj\nOgCiKS+7aU1tvmFmInvy+tr8aDF3DVWoCyWpaXJsWetYE9RRkvrcetcBwoQydChGhlA2VrZ5c41d\nMCueOXZFXX68jE51nQlw4Wgl6UyzrvH82PKmKbFlLWNMY/0Qs42SVBqB6wBhQhk6FCND6FNWtrU5\nZue9Gs/ahfW5CTmjS1xnAsIqr1hNgx0+siE3vMOSdEFs+ebJsWX7x5rG+sFm26BEYZ0kSlLXBK4D\nhAkrUL/FtKnTmySd7DoHqoeVze8vbImxZ048e15rTMe5zgRUo2JJWkNJ6pJP69bmX7sOERaMDB1u\ntShDKIE22WVL63NNsxLZs3bH7ETXeYBq134k6cEORpImxRqaJhXnJBVL0ghjVO8wskuB6wBhQhk6\n3GpJk1yHQGXKya5dVZdfNSORGbqlxp4j6RzXmYCoa1+SHshdduB4jXLZMw9O3G4ZY4L44INzkqq9\nJAWuA4QJZehwTKJGt1jZretr8ktmJrInrK7Lj5M0zHUmAJ3LqaZ2mR0+cln0SlJO0lrXIcKEMnQ4\nyhA6ZWX3bovZeS8nsvGldbkJli0xgKrRSUlaNSnWsHlSbFnLWBPUn2a2DkooU2klaZ1ubc66DhEm\nlKHDUYbQISub3WP0+mvxbNu8eHZCxuidrjMBKJ9iSTp9WW746fd3WJKWbZ4UayjOSdo6KB7ekhS4\nDhA2lKHDUYZwiBZjFy6sz+54OZ4dsz/GfDIAhzq0JL3/wPEQl6RGh68dSpShw7HWEJSVXdlQl1s3\nK5E9fUeNHec6D4DKc7SSdJZZt+qCWMPm9nOSyliS3ijDa1QU1hnqwLSp03dJGuA6B8orL7txdW1+\n+YxE5tSNtfZs13kAREuxJK2eFGvYMulgSXpzJKmuhC/1Yd3a/HgJn6/iMTLUseWSzncdAn3PyjY3\n1dgFsxKZAW/U5s+T0WmuMwGIppxqapfaEaOW5kaMuu/wkaSVJSxJi0uXujpQhjo2V5ShqmVlW3bG\n7OuvxrOxRWyJASDkOi9JyzZPjjW0nWuC+i6UpBZJq8oSvIJwmqwD06ZO/6ykX7rOgdKxsvl9RvPm\nxbP7Xotnx7UatsQAUJ1qlc2cZdatKc5Jah1jVsdPO1iSlujW5gmuM4YNI0Mdm+M6AEqjTXbp4vrc\n5tmJzDl7Ynq76zwA0Neyqq1bYpOjluSSo+7LfeDA8VplMyPNphnPOMwWVpShji2U1CqxsV8lysmu\nXlmXD2YkMsO31tjRkka7zgQArmVVW7fCDg1c5wgjylAHbrrzrzLTpk5fKOkC11nQNVZ2y7rClhgn\nranLj5E0wnUmAAih+a4DhBFl6MjmiDIUalZ2z9aYnT87kY031OUmWqNLXWcCgJCjDHWAMnRkzBsK\nISub2W3svNfiucy8eHZCli0xAKCrNgW+1+Q6RBhRho7sNdcBUGBlbavRwvn12Z2vxrNj2RIDAHrk\nFdcBwooydGSLVFiPIeE6SFRlZFc01OXWz0xkz2iusee5zgMAFW626wBhxTpDRzFt6vTZkqa4zhEl\nedkNQW1+xYxE5rRNtfYs13kAoIq8J/C9512HCCNGho5ujihDfc7K7txUYxfOTGTetqqwJcZg15kA\noMrkJL3qOkRYUYaOjnlDfcTK7t8Rs/NeiWdrFtfnJuTZEgMA+tKiwPf2ug4RVpSho+OKshKysrl9\nRvPnxrP75saz49uMLnSdCQAigvlCR0EZOrolkvZJ6u86SCVrlV2yuD635eVEZjRbYgCAE7NcBwgz\nJlB3YtrU6TMkXeQ6R6XJyQYr6nLBzEQ2ua3GJl3nAYCIOyfwvQbXIcKKkaHOvSrKUJfkZbesq80v\nmZnIDlpbmz9XUtJ1JgCAtkpa7jpEmFGGOvespC+6DhFWVnb3lsKWGP2W1+UmsCUGAITOs4HvcRro\nKChDnZsudrA/hJVt22Xs63MS2fyC+tyErNHFrjMBAI7oz64DhB1zhrpg2tTpf5Z0mescLllZ22K0\nYH59dter8ezYlphOcJ0JANAlwwPfW+s6RJgxMtQ1TymiZSgju3xpfW7DrHj2rF01drzrPACAbllG\nEeocZahrnpL0U9chyiUvu66xNr9yRiIzuKmwJQbbYgBAZXrGdYBKwGmyLpo2dXqjqvjqKCu7fWON\nXTQzkTm+sTY/TkbGdSYAQK9dHvjeH12HCDtGhrruKUmfcx2ilKzsvu2FLTFql9TnJuaN3uU6EwCg\nZDKSnncdohJQhrruT6qCMmRlc3uNXp8bz7bOjWfHZwxrKAFAlZoV+N4e1yEqAWWo6yr6EvtW2cWL\n6nNbZycy5+6L6QLXeQAAfY5L6ruIOUPdUGmX2GdlG1fU5VbPTGRHbq+xI1znAQCU1ZTA915xHaIS\nMDLUPaG/xD4v27S2Nr9sRiJ78vra/GhJI11nAgCU3XZJc1yHqBSUoe4J5SX2VnbX5hq7YHY80395\nXX6CjE5xnQkA4NT0wPfyrkNUCk6TddO0qdNXKQSjLVa2tTlmX58Tz2pBfW5CzijhOhMAIDSuD3zv\nN65DVApGhrrvKUk3unhhK5vfX9gSY/er8ex5rTG9w0UOAECotUh61HWISkIZ6r6yl6GMbMOS+tym\n2YnsWbtidkI5XxsAUHH+GPjeLtchKgllqPvKcol9Tnbtqtr8ypmJzLDNtfZsSWf35esBAKrGg64D\nVBrmDPXAtKnTH5P0oVI/r5XdtqEmv3hmIntCUJcfV+rnBwBUvR2STg18r811kErCyFDPPKASlSEr\nu3dbzM57OZGtX1qXm2jZEgMA0HN/oAh1H2WoZ/4oaaek43vyYCub3WP0+mvxbGZeYUuMd5Y2HgAg\nojhF1gOcJuuhaVOn3yXphu48pkV20cJ4dvsr8ey5+2I6qY+iAQCiaY2kZOB7fLB3EyNDPfegulCG\nsrIrl9fl1s5MZEftqLFjy5ALABBND1GEeoYy1HN/kbRW0rC3/iAvu3FNbX75jET2lA21+XMkjSp7\nOgBA1DzgOkCl4jRZL0ybOt2X9FVJsrLNTTV2wax45tg36vLjZRRzHA8AEB0LAt8b7zpEpWJkqHfu\n3xnLX/pKPKtF9bmJOaNLXAcCAEQSE6d7gZGhXkqm0q9JervrHACAyLKSRgS+t9Z1kErFqZzeu8d1\nAABApL1AEeodylDv/VaF7TkAAHCBidO9RBnqpcD3dkh63HUOAEAktUp6xHWISkcZKo1fug4AAIik\nPwW+t9N1iEpHGSqBwPeek7TEdQ4AQOTc7TpANaAMlc401wEAAJGyRNJTrkNUA8pQ6dwnaZfrEACA\nyPgJ22+UBmWoRALf2yPpN65zAAAiYZO4iqxkKEOlNU2Fxa8AAOhLtwW+1+Y6RLWgDJVQ4HvLJT3j\nOgcAoKrtlfQL1yGqCWWo9G53HQAAUNXuKa5xhxKhDJVeWtIbrkMAAKpSTtJ/uA5RbShDJRb4Xl7S\nD1znAABUpUcD32t0HaLaUIb6xr2S2DQPAFBqP3YdoBpRhvpA4HsZST9ynQMAUFVeDHzvFdchqhFl\nqO/cLWmz6xAAgKrBqFAfoQz1kcD39kv6qescAICqsEzSk65DVCvKUN+6QxKXPwIAeuunbL3RdyhD\nfSjwvd2Sfu46BwCgojWpsP8l+ghlqO/9XNIe1yEAABVrWuB7ra5DVDPKUB8LfG+7WDYdANAzu1SY\ncoE+RBkqj59IanEdAgBQcb4f+N421yGqHWWoDALfa1LhUnsAALpqtaSfuQ4RBZSh8vmhGB0CAHTd\n1wLf43OjDChDZRL43loVTpcBANCZVyU95DpEVFCGyuv7kta7DgEACL0vs65Q+VCGyijwvb2SUq5z\nAABC7bHA9150HSJKKEPl96CkWa5DAABCKSPpq65DRA1lqMyKw543S2L4EwDwVr8IfG+F6xBRQxly\nIPC9OZLudZ0DABAqOyV9x3WIKKIMufMvkna7DgEACI1/Y4FFNyhDjgS+t0nS91znAACEQqOk21yH\niCrKkFs/k/SG6xAAAOdSbMbqDmXIocD32iR92XUOAIBTswPf+73rEFFGGXIs8L0nJP3ZdQ4AgDNf\nch0g6ihD4XCLpKzrEACAsnsk8D3WnnOMMhQCge8tkXSH6xwAgLJqVuGXYThGGQqPb0na6joEAKBs\nvhT43jrXIUAZCo3A93ZK+qbrHACAsvhT4Hv3uA6BAspQuPxSEpvzAUB12ynpM65D4CDKUIgEvpeX\n9EmxMjUAVLMvBr63wXUIHEQZCpnA9xol/aPrHACAPvFk4Hv3uQ6BQ1GGQqh4Hvlx1zkAACW1XdI/\nuA6Bw1GGwuuzkppchwAAlMzNge9tdB0Ch6MMhVTge1sk3eA6BwCgJB4LfO9B1yHQMcpQiAW+90dJ\nd7nOAQDola2SproOgSOjDIXfLZJWug4BAOixzwe+t9l1CBwZZSjkAt/bK+nvJOVcZwEAdNsjge89\n7DoEjo4yVAGKm/j5rnMAALpli6QbXYdA5yhDlePbkl5zHQIA0GU3Fi+GQcgZa63rDOiiZCo9WoVC\n1M91FqAUbD6njffeotoBA3XyR7+lXa89qd1znlB250YN/cKDqul/3GGPaVm9QNunH7yuILNtnQZd\n8RX1P+tCbXnyR8psWa1+oybphEs/KUnaOeMh1Z88Uv3PfEfZ3hcg6XeB733MdQh0DSNDFSTwvaWS\nUq5zAKWye84Tqhs47MD3iaHn6pRrv6eat518xMckRpynwdffpsHX36ZTrv13xeriSoycqLbNjZKk\nwZ++Xa3rFivfulfZPdvVtnE5RQjl1iAWV6wolKHKc5ukZ1yHAHoru2ur9q96VceOf/+BY/WnjFLt\ncad0+Tn2NcxQ4vTzFatLyMRqZbNtsjYvm8tKJqbmFx/Q8Zd8vC/iA0eyW9KHAt/b5ToIuo4yVGEC\n37OSrpe0zXUWoDd2PPsrHf/uT8sY0+Pn2Lv0BR0z+lJJUt1Jw1Q7YJA2/uaLOuaci5XdUVjot/6U\nUSXJC3SBlfSJwPeWuQ6C7qEMVaDA99ZLulpS1nUWoCf2vfGKYsccr/ipZ/T4ObJ7tiuzJVC/kW8/\ncOzE931Wg6+/TW+b/BHtfPF+HXfxdWqe+bC2PO5r97z/KUV04Gi+F/ge+0pWIMpQhQp87zlJX3Sd\nA+iJ1vVLtH/Fy1r3i09ryxM/VMvqBdr65I+79Rz7lr2o/mddKFNTe/jPVsxW/alnymZa1LZ1tQZ9\nKKW9i59TPtNSqrcAvFVa0rdch0DPUIYqWOB7d0i603UOoLtOuPRTGnrTvRr6uXs06IqvKDHiPJ10\n+T916zn2Ljl4iqw9m8tq15wn9LYpH5HNtkoqnoazVsoxmIo+sULSdcVpDKhAlKHK9wVJz7sOAZTC\nrjlPaN20Tyq3e6s2/voL2vbUzyVJrRtXHLgtSdnmJuV2b1F8+NjDnmP33LSOHftexeoSqhs0UpLV\nhv+6SfGhoxVLHFuut4Lo2KPChOlm10HQc6wzVAWSqfRASa9IOt11FgCIECvpo4HvPeo6CHqHkaEq\nEPjeNklXqHBJJwCgPL5PEaoOlKEqEfjeYknXScq7zgIAEfCUpG+4DoHSoAxVkcD3npT0ddc5AKDK\nrZT0fwPf45fPKsGcoSqUTKUfUGGUCABQWnskXRj43iLXQVA6jAxVpxtUmFANACit6ylC1YcyVIUC\n32uR9CFJ611nAYAq8oPA9x5xHQKlRxmqUoHvbVShEO13nQUAqsADkv7FdQj0DcpQFQt8b46kv3ed\nAwAq3OMqnB5jkm2VogxVucD3HpL0Hdc5AKBC/a+kawPfYy+XKsbVZBGRTKV/KukW1zkAoILMknRZ\n4Ht7XQdB36IMRUgylb5d0k2ucwBABZgv6d2B7+10HQR9j9Nk0fIFSXe5DgEAIdcg6f0UoehgZChi\nkql0TNKvJX3CdRYACKE1ki4OfG+t6yAoH8pQBCVT6RoVLhO91nUWAAiRJkmXBL63wnUQlBdlKKKS\nqXStpIclfcR1FgAIgR2SLg18b6HrICg/5gxFVPEy0WslPek6CwA4tkfS31CEoosyFGGB72UkXS3p\naddZAMCRFklXBL73susgcIcyFHGB77WqsG3Hs66zAECZZSVdE/jec66DwC3KEN7c2PUKSS+4zgIA\nZZKX9InA95gqAMoQCgLf2yfJU2HFVQCoZlkVitBDroMgHLiaDIdIptLHqbAXzwWuswBAH9gv6erA\n99KugyA8KEM4TDKVPkHSU5KmuM4CACXULOnywPdedB0E4cJpMhwm8L0dkt4j6XHXWQCgRJpUWEeI\nIoTDUIbQocD39ku6StLPXWcBgF4KVNhiY77rIAgnTpOhU8lU+h8l/USUZwCVZ5GkDwS+t8F1EIQX\nZQhdkkylP6LCfmb9XGcBgC56SdKVge9tdx0E4cZv+uiSwPcelfRXkra6zgIAXfB7Se+jCKErKEPo\nssD3Zku6UBI7OgMIsx9Jura4wj7QKU6ToduSqfRASU9Iush1FgBoJyfp5sD37nAdBJWFMoQeSabS\nCUn3S/qo6ywAIGmfCqNBbK+BbuM0GXqkuJ/ZNSpcZQYALjVJejdFCD3FyBB6LZlKf17Sf4pyDaD8\n5kq6KvC9wHUQVC4+vNBrge/dLunDKgxTA0C53CnpIooQeouRIZRMMpU+X9IfJI10nQVAVdsr6bOB\n7/3WdRBUB0aGUDKB770maaIK63sAQF9YLGkSRQilxMgQ+kQylf4HST+TlHCdBUDVuF/S1MD3OCWP\nkqIMoc8kU+lxkh6WNNp1FgAVrUXSFwLfu9t1EFQnyhD6VDKV7i/pdknXu84CoCK9IenqwPfmuQ6C\n6kUZQlkkU+nr43BgHAAACNZJREFUJP1C0gDXWQBUjEclXR/43i7XQVDdKEMom2Qqfaak30l6u+ss\nAEItI+krge/9zHUQRANlCGWVTKXrVdhE8WbXWQCE0lpJ1xQ3hgbKgjIEJ5Kp9JWS7pF0oussAELj\nKUl/F/jeNtdBEC2sMwQnAt/7b0kTJM1wnQWAc/skfUmSRxGCC4wMwalkKl0r6duSUqKcA1H0ZxXW\nDmp0HQTRRRlCKCRT6Usk3SXpbNdZAJTFNkm3BL53v+sgAL+JIxQC33tR0nhJ31PhShIA1eu3kkZT\nhBAWjAwhdJKp9FhJd0ua4joLgJJarcIpsf9xHQRoj5EhhE7ge4skXSTpi5L2OI4DoPfyKuxVOIYi\nhDBiZAihlkylh0u6Q5LnOguAHlko6YbA915xHQQ4EsoQKkIylb5K0n9IGuY6C4AuaZX0XUk/DHyP\neYAINcoQKkZx09dvqLAeSb3jOACO7AVJnwl8b7nrIEBXUIZQcZKp9FmSbpP0ftdZAByiWdJXJN0V\n+B4fLqgYlCFUrGQq/VFJPxWnzgDX2iT9UtL3At/b7DoM0F2UIVS0ZCp9jKSvS7pFUsJxHCBq8pIe\nlPTNwPcCx1mAHqMMoSokU+nBKpSiG8R8IqAcnpT0teJSGEBFowyhqhQvxf9XSddLqnUcB6hGL0pK\nBb4303UQoFQoQ6hKyVT6dEnflPRxSTWO4wDVYL4KI0F/ch0EKDXKEKpaMpU+W9K3JP0fseI60BMr\nVfjF4iGuEEO1ogwhEpKp9BhJ35b0EUnGcRygEmxSYdHEu1g0EdWOMoRISabSEyR9R9LlrrMAIdUs\n6YeSfhb43j7XYYByoAwhkpKp9GQVStEHXGcBQmKHCmsF/Sjwve2uwwDlRBlCpCVT6XeqcCrgPa6z\nAI4skfRzSfczEoSoogwBkpKp9BRJN0q6RizeiOpnJaUl/TzwvWdchwFcowwB7SRT6YEqrFE0VdIo\nx3GAUtst6deSbgt87w3XYYCwoAwBHUim0kaF+UQ3SvLEZfmobG+osLnxrwPf2+06DBA2lCGgE8lU\neoSkf5D095JOdhwH6I5nJP2npD+xRhBwZJQhoIuSqXS9pI9K+pykix3HAY5kn6T7VDgVtsR1GKAS\nUIaAHkim0uNUOIX2cUnHOo4DSIVTYb+SdHfgeztchwEqCWUI6IVkKj1A0idUGC0a4zgOome1pIcl\nPRz43lzXYYBKRRkCSiSZSk+UdJUKW36MdhwH1Wu9pD+oUIBmuw4DVAPKENAHkqn0aBVK0VWSJjqO\ng8rXJOkRFUaBXmIyNFBalCGgjyVT6ZE6WIzeITaKRddsk/T/VChAfwl8L+c4D1C1KENAGSVT6cGS\nPqxCObpUUo3bRAiZnZIeU6EAPRv4XtZxHiASKEOAI8lU+iRJV6pQjN4nqd5tIjiyVtJ0FUaBng58\nr81xHiByKENACCRT6bdJ+tvi1yWShrpNhD60QdJzxa/nA99b6TgPEHmUISCEkql0UoWFHS8pfp0j\n5hpVqiYdWn6WO84D4C0oQ0AFKG4ge7EOFqS3S6pzGgpHskXS88Wv5wLfW+o0DYBOUYaACpRMpftL\nmqJCMbpY0oViJWxXtkv6iw6O/izm0negslCGgCqQTKVrJE3QwXI0UVJSUsxhrGqTkdQgabGkRcU/\nF0taQfkBKhtlCKhSyVQ6LukMFeYbnd3uz7MlHecwWthlVdjn662lZzmXugPViTIERFAylT5VB4tR\n+7KUVHRGk/KSVulg2Xmz+DQEvtfqMhiA8qIMATigg9GkoZJOlDSw+PXm7bDPT8pI2qTCZexH+loZ\n+N5+ZwkBhAZlCEC3JVPpenVcktrfbn/sGElWhdGY/Ftud+dYVtIOFbaq2KbC5OVtHX0xjwdAV1GG\nAABApEVlbgAAAECHKEMAACDSKEMAACDSKEMAACDSKEMAACDSKEMAACDSKEMAACDSKEMAACDSKEMA\nACDSKEMAACDSKEMAACDSKEMAACDSKEMAACDSKEMAACDSKEMAACDSKEMA0EXGmKQxZpHrHABKizIE\nIPKMMTWuMwBwhzIEoKoVR3OWGWPuNcYsMMY8Yozpb4wJjDHfNMa8JOlqY8wEY8zs4n0eM8acUHz8\n+caY+caYWZJuave8nzLG3N7u+z8aY95dvP3Xxpi5xcc9Wzx2jDHmHmPMq8aY140xV5b1PwSAI6IM\nAYiCsyX9ylp7nqRdkm4sHm+x1l5srf2dpPskfbV4n4WSvlW8z68l3WytvbArL2SMGSTpLklXWWvH\nS7q6+KOvS5purZ0k6T2SfmSMOaYE7w1AL1GGAETBWmvtjOLtByRdXLz9sCQZY46TdLy19i/F4/dK\nelcHx+/vwmu9Q9IL1tpGSbLWbi8ef7+klDFmnqTnJSUkDe/5WwJQKrWuAwBAGdgjfL+3k8eZDh77\npqwO/YUy0cljjAqjRQ2dvCaAMmNkCEAUDDfGvHma62OSXmr/Q2tts6QdxphLiof+TtJfrLU7JTUb\nY94cSbqu3cMCSROMMTFjzDBJk4vHZ0m61BgzUpKMMScWjz8t6QvGGFM8PrFk7w5Ar1CGAETBUkmf\nNMYskHSipF90cJ9PqjCPZ4GkCZK+Uzx+vaRpxQnU+9vdf4akRhXmF/1Y0lxJstZukfRZSY8aY+ar\neCpO0ncl1UlaULw8/7ule3sAesNYe6QRYACofMaYpKQ/WmvHOo4CIKQYGQIAAJHGyBAAAIg0RoYA\nAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECk\nUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYA\nAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECk\nUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECkUYYAAECk/X8Q\nyB5SAnEHEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e21595b0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie-chart for department-wise sales.\n",
    "plt.figure(figsize=(10,10))\n",
    "temp_series = df_order_products_prior['department'].value_counts().head(5)\n",
    "labels = (np.array(temp_series.index))\n",
    "sizes = (np.array((temp_series / temp_series.sum())*100))\n",
    "plt.pie(sizes, labels=labels, \n",
    "        autopct='%1.1f%%', startangle=200)\n",
    "plt.title(\"Departments distribution\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* It is clear that the produce department is selling the most. We were hoping it to be frozen because it has meat  but clearly people priorotise fruits and veggies over meat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rUa0aw1q0ADw9bL7eutXlRCL+l5mTww+HDgHQsXZtIiPc/28vOjKS90eMINaZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e2153d8cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finding the reorder ratio with respect to Department.\n",
    "# This means if a certain product is ordered previously, what are the chances that it will be reordered again.\n",
    "grouped_df = df_order_products_prior.groupby([\"department\"])[\"reordered\"].aggregate(\"mean\").reset_index()\n",
    "plt.figure(figsize=(20,9))\n",
    "sns.pointplot(grouped_df['department'].values, grouped_df['reordered'].values, alpha=0.8, color='teal')\n",
    "plt.ylabel('Reorder ratio', fontsize=12)\n",
    "plt.xlabel('Department', fontsize=12)\n",
    "plt.title(\"Department wise reorder ratio\", fontsize=15)\n",
    "plt.xticks(rotation='vertical',fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Dairy eggs have the reorder ratio and personal care has the lowest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Below we are trying to figure out if the reordering of a product depends on the order in which it was added to the cart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Parija\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:179: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    },
    {
     "data": {
      "image/png": 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KbKeiu2RlZGczaflyZq9bR1xMDPExMTyxYAEA1RMTWXjLLbSuVcuXbCIiIiJSskItusvl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\nc6sIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e2610aa978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Add to cart order - reorder ratio\n",
    "# Here we are trying to understand if the order in which a product was added to the cart will affect it's chances of reordering\n",
    "df_order_products_prior[\"add_to_cart_order_mod\"] = df_order_products_prior[\"add_to_cart_order\"].copy() # Making a copy of order_products_prior\n",
    "df_order_products_prior[\"add_to_cart_order_mod\"].loc[df_order_products_prior[\"add_to_cart_order_mod\"]>30] = 30\n",
    "grouped_df = df_order_products_prior.groupby([\"add_to_cart_order_mod\"])[\"reordered\"].aggregate(\"mean\").reset_index()\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.pointplot(grouped_df['add_to_cart_order_mod'].values, grouped_df['reordered'].values, alpha=0.8, color='teal')\n",
    "plt.ylabel('Reorder ratio', fontsize=12)\n",
    "plt.xlabel('Add to cart order', fontsize=12)\n",
    "plt.title(\"Add to cart order - Reorder ratio\", fontsize=15)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* So, the order in which you out items in your cart does matter when you reorder. This makes sense to us as well since we tend to first order all the products we used to buy frequently and then look out for the new products available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3lLvJcH4wcFoNTP1SVauSvI0u1AMcV1Wr+tevp3tS3mbAZ/tFkiRJWm/MuSd1OmRFkiRJ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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e2010d2470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reorder ratio across the day of week\n",
    "df_order_products_train = pd.merge(df_order_products_train, df_orders, on='order_id', how='left')\n",
    "grouped_df = df_order_products_train.groupby([\"order_dow\"])[\"reordered\"].aggregate(\"mean\").reset_index()\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(grouped_df['order_dow'].values, grouped_df['reordered'].values, alpha=0.8, color='teal')\n",
    "plt.ylabel('Reorder ratio', fontsize=12)\n",
    "plt.xlabel('Day of week', fontsize=12)\n",
    "plt.title(\"Reorder ratio across day of week\", fontsize=15)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylim(0.5, 0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q7uzZs4dssiRJkjR+wwby9wH/leSlwEZJ9gNOAd475P4LgTlJdkiyMbAvsGBg\nm9OB3QCSbEkzhGUZcBbwzCSbtRdzPhM4q6quAG5M8pR2dpX9gTOGbI8kSZJ0rzDUkJWqOi7JtTTD\nPi4DDgD+sapOH3L/lUkOpgnXs4DjqmpJkiOARVW1gDuD94U0dwZ9Q1VdA5DkHTShHuCIqrq2ffxq\n4HjgvsAX2kWSJElaZww7hpw2fA8VwGfY/0zgzIGywzuPC3h9uwzuexxw3DTli4DHrW2btO7a7cgj\nx3bscw45ZGzHliRJGjRjIE/ysmEO0IZlSZIkSWthVT3kL+k8DvBU4EqaISvbAg8F/odpeq4lSZI0\nWuP6dthvhvs3YyCvqt2mHif5AHB6Vb2/U/Z3wCPH2zxJkiRp/TbsGPK/ArYcKPsgcDXw2pG2SJIk\nSdqADDvt4ZXAngNlfwlcNc22kiRJkoY0bA/5a4HTkryBZgz5dsBOwAvG1TBJkiRpQzDsPORnJ3kE\n8OfAw4DPA5+fmidckiRJ0tpZbSBPMgv4EbBTVf3H+JskSVoTzrwgSeu21Y4hr6rbae6c+bvjb44k\nSZK0YRl2DPn7gU8neTewHKipFVW1bBwNkyRJkjYEwwbyD7Y/dx8oL2DW6JojSZIkbViGvahz2OkR\nJUmSJK2BYXvIAUiyHbA1sLyqLhtPkyRJ6se4LpCFDfMiWS84loYzVM93kq2SnAcsBT4D/DjJV5M8\nbKytkyRJktZzww5F+TDwPWCzqtoK2Az4LvDv42qYJEmStCEYdsjK04Ctquo2gKr6dZI3Aj8fW8sk\nSZLuxRySo1EZNpBfB+xE00s+5feB60feIkmSJGmM7m3XiwwbyP8Z+HKSjwE/BR4OvBT4xzWuUZKk\nId3b/mhK0jgMO+3hR5L8GHgR8ATgcmC/qvrKOBsnSZIkre+GnvawDd8GcEmS1lGOeZbunYad9nCT\nJO9KsizJDW3ZM5McPN7mSZIkSeu3Yac9PAp4HPBioNqyJcCrx9EoSZIkaUMx7JCV5wCPaqc7vAOg\nqn6eZOvxNU2SdG/jRZaSNHrD9pDfykB4TzIbuGbkLZIkSZI2IMMG8lOBE5LsAJBkK+CDwMnjapgk\nSZK0IRg2kP8D8BPgB8CDgEtopj48YjzNkiRJkjYMw85DfivwOuB17VCVq6uqVrObJEmSpNUYtof8\nt6pqRVVVkickOXUcjZIkSZI2FKvsIU9yP+AwYGeaYSpvA7YEjgR2B04Yc/skSZKG4o2PtK5a3ZCV\no4EnAmcBzwIeDzyaJoi/sqquHm/zJEmSpPXb6gL5HsDOVXVVkg8APwP+pKq+Nv6mSZIkSeu/1QXy\nB1TVVQBVtTzJrwzjkiSNhkMQB0H/AAAZVElEQVQsJMHqA/lGSXYDMlUw+LyqvjKmtkmSJEnrvdUF\n8quA4zrPrxl4XsAjRt0oSZIkaUOxykBeVdtPqB2SJEnSBmmoGwNJqzOucZDgWEhJkrR+W+MbA0mS\nJEkaHQO5JEmS1CMDuSRJktQjx5BP0CTnm3VMtyRJ0rrBHnJJkiSpRwZySZIkqUcGckmSJKlHBnJJ\nkiSpRwZySZIkqUcTC+RJ5ie5OMnSJIdOs/7AJCuSLG6XV7Tlu3XKFie5Ocne7brjk1zaWbfzpM5H\nkiRJGoWJTHuYZBZwNLA7sBxYmGRBVV04sOkpVXVwt6CqzgF2bo+zObAU+FJnkzdU1Wlja7wkSZI0\nRpPqIZ8HLK2qZVV1K3AysNdaHOf5wBeq6jcjbZ0kSZLUk0kF8q2ByzrPl7dlg56X5PtJTkuy7TTr\n9wU+NVD2rnafo5JsMqL2SpIkSRMxqUCeacpq4PnngO2r6gnAl4ET7nKAZCvg8cBZneLDgEcDTwI2\nB940beXJQUkWJVm0YsWKtTsDSZIkaQwmFciXA90e722Ay7sbVNU1VXVL+/QjwC4Dx9gH+GxV3dbZ\n54pq3AJ8nGZozN1U1bFVNbeq5s6ePfsenookSZI0OpMK5AuBOUl2SLIxzdCTBd0N2h7wKXsCFw0c\nYz8GhqtM7ZMkwN7ABSNutyRJkjRWE5llpapWJjmYZrjJLOC4qlqS5AhgUVUtAF6bZE9gJXAtcODU\n/km2p+lhP2/g0CclmU0zJGYx8Koxn4okSZI0UhMJ5ABVdSZw5kDZ4Z3Hh9GMCZ9u358wzUWgVfWM\n0bZSkiRJmizv1ClJkiT1yEAuSZIk9chALkmSJPVoYmPI7412O/LIsRz3nEMOGctxJUmStP6xh1yS\nJEnqkYFckiRJ6pGBXJIkSeqRgVySJEnqkYFckiRJ6pGBXJIkSeqRgVySJEnqkYFckiRJ6pGBXJIk\nSeqRgVySJEnqkYFckiRJ6pGBXJIkSeqRgVySJEnqkYFckiRJ6pGBXJIkSeqRgVySJEnqkYFckiRJ\n6pGBXJIkSeqRgVySJEnqkYFckiRJ6pGBXJIkSeqRgVySJEnqkYFckiRJ6pGBXJIkSeqRgVySJEnq\nkYFckiRJ6pGBXJIkSeqRgVySJEnqkYFckiRJ6pGBXJIkSeqRgVySJEnqkYFckiRJ6pGBXJIkSeqR\ngVySJEnqkYFckiRJ6pGBXJIkSeqRgVySJEnqkYFckiRJ6pGBXJIkSeqRgVySJEnqkYFckiRJ6tHE\nAnmS+UkuTrI0yaHTrD8wyYoki9vlFZ11t3fKF3TKd0jyrSSXJDklycaTOh9JkiRpFCYSyJPMAo4G\nngXsBOyXZKdpNj2lqnZul492ym/qlO/ZKX8vcFRVzQGuA14+rnOQJEmSxmFSPeTzgKVVtayqbgVO\nBva6JwdMEuAZwGlt0QnA3veolZIkSdKETSqQbw1c1nm+vC0b9Lwk309yWpJtO+W/m2RRkvOTTIXu\nLYDrq2rlao4pSZIk3WtNKpBnmrIaeP45YPuqegLwZZoe7ynbVdVc4EXA+5M8cshjNpUnB7WBftGK\nFSvWvPWSJEnSmEwqkC8Huj3e2wCXdzeoqmuq6pb26UeAXTrrLm9/LgPOBZ4IXA08KMlGMx2zs/+x\nVTW3qubOnj37np+NJEmSNCKTCuQLgTntrCgbA/sCC7obJNmq83RP4KK2fLMkm7SPtwSeClxYVQWc\nAzy/3ecA4IyxnoUkSZI0YhutfpN7rqpWJjkYOAuYBRxXVUuSHAEsqqoFwGuT7AmsBK4FDmx3fwxw\nTJI7aD5AvKeqLmzXvQk4Ock7ge8CH5vE+UiSJEmjMpFADlBVZwJnDpQd3nl8GHDYNPt9A3j8DMdc\nRjODiyRJkrRO8k6dkiRJUo8M5JIkSVKPDOSSJElSjwzkkiRJUo8M5JIkSVKPDOSSJElSjwzkkiRJ\nUo8M5JIkSVKPDOSSJElSjwzkkiRJUo8M5JIkSVKPDOSSJElSjwzkkiRJUo8M5JIkSVKPDOSSJElS\njwzkkiRJUo8M5JIkSVKPDOSSJElSjwzkkiRJUo8M5JIkSVKPDOSSJElSjwzkkiRJUo8M5JIkSVKP\nDOSSJElSjwzkkiRJUo8M5JIkSVKPDOSSJElSjwzkkiRJUo8M5JIkSVKPDOSSJElSjwzkkiRJUo8M\n5JIkSVKPDOSSJElSjwzkkiRJUo8M5JIkSVKPDOSSJElSjwzkkiRJUo8M5JIkSVKPDOSSJElSjwzk\nkiRJUo8M5JIkSVKPDOSSJElSjwzkkiRJUo8M5JIkSVKPDOSSJElSjwzkkiRJUo8mFsiTzE9ycZKl\nSQ6dZv2BSVYkWdwur2jLd07yzSRLknw/yQs7+xyf5NLOPjtP6nwkSZKkUdhoEpUkmQUcDewOLAcW\nJllQVRcObHpKVR08UPYbYP+quiTJw4BvJzmrqq5v17+hqk4b6wlIkiRJYzKpHvJ5wNKqWlZVtwIn\nA3sNs2NV/aiqLmkfXw5cBcweW0slSZKkCZpUIN8auKzzfHlbNuh57bCU05JsO7gyyTxgY+DHneJ3\ntfsclWSTkbZakiRJGrNJBfJMU1YDzz8HbF9VTwC+DJxwlwMkWwH/Aby0qu5oiw8DHg08CdgceNO0\nlScHJVmUZNGKFSvW/iwkSZKkEZtUIF8OdHu8twEu725QVddU1S3t048Au0ytS7Ip8HngLVV1fmef\nK6pxC/BxmqExd1NVx1bV3KqaO3u2o10kSZJ07zGpQL4QmJNkhyQbA/sCC7obtD3gU/YELmrLNwY+\nC5xYVadOt0+SAHsDF4ztDCRJkqQxmMgsK1W1MsnBwFnALOC4qlqS5AhgUVUtAF6bZE9gJXAtcGC7\n+z7A04EtkkyVHVhVi4GTksymGRKzGHjVJM5HkiRJGpWJBHKAqjoTOHOg7PDO48NoxoQP7vcJ4BMz\nHPMZI26mJEmSNFHeqVOSJEnqkYFckiRJ6pGBXJIkSeqRgVySJEnqkYFckiRJ6pGBXJIkSeqRgVyS\nJEnqkYFckiRJ6pGBXJIkSeqRgVySJEnqkYFckiRJ6pGBXJIkSeqRgVySJEnqkYFckiRJ6pGBXJIk\nSeqRgVySJEnqkYFckiRJ6pGBXJIkSeqRgVySJEnqkYFckiRJ6pGBXJIkSeqRgVySJEnqkYFckiRJ\n6pGBXJIkSeqRgVySJEnqkYFckiRJ6pGBXJIkSeqRgVySJEnqkYFckiRJ6pGBXJIkSeqRgVySJEnq\nkYFckiRJ6pGBXJIkSeqRgVySJEnqkYFckiRJ6pGBXJIkSeqRgVySJEnqkYFckiRJ6pGBXJIkSeqR\ngVySJEnqkYFckiRJ6pGBXJIkSeqRgVySJEnqkYFckiRJ6pGBXJIkSerRxAJ5kvlJLk6yNMmh06w/\nMMmKJIvb5RWddQckuaRdDuiU75LkB+0x/y1JJnU+kiRJ0ihMJJAnmQUcDTwL2AnYL8lO02x6SlXt\n3C4fbffdHHgr8GRgHvDWJJu1238YOAiY0y7zx3smkiRJ0mhNqod8HrC0qpZV1a3AycBeQ+67B3B2\nVV1bVdcBZwPzk2wFbFpV36yqAk4E9h5H4yVJkqRxmVQg3xq4rPN8eVs26HlJvp/ktCTbrmbfrdvH\nqzumJEmSdK+VpnN5zJUkLwD2qKpXtM9fAsyrqtd0ttkC+FVV3ZLkVcA+VfWMJG8ANqmqd7bb/SPw\nG+CrwD9V1Z+15X8MvLGq/nKa+g+iGdoC8PvAxWtxGlsCV6/Ffmtrfa5vfT4367M+6+uvvvX53KzP\n+qyvv/rWtq6HV9XsYTbcaC0OvjaWA9t2nm8DXN7doKqu6Tz9CPDezr67Dux7blu+zaqO2Tn2scCx\na97sOyVZVFVz78kxrG/ydVmf9VnfhlPf+nxu1md91tdffZOoa1JDVhYCc5LskGRjYF9gQXeDdkz4\nlD2Bi9rHZwHPTLJZezHnM4GzquoK4MYkT2lnV9kfOGPcJyJJkiSN0kR6yKtqZZKDacL1LOC4qlqS\n5AhgUVUtAF6bZE9gJXAtcGC777VJ3kET6gGOqKpr28evBo4H7gt8oV0kSZKkdcakhqxQVWcCZw6U\nHd55fBhw2Az7HgccN035IuBxo23pjO7RkBfr660u67M+69tw6lufz836rM/6+qtv7HVN5KJOSZIk\nSdOb2J06JUmSJN2dgVySJEnq0cTGkK9Lkjya5k6iWwNFM53igqq6aJU7riPa89sa+FZV/apTPr+q\nvjiG+uYBVVULk+wEzAd+2F5XMHZJTqyq/SdU19No7kx7QVV9aQzHfzJwUVX9Msl9gUOBPwQuBN5d\nVTeMuL7XAp+tqstWu/Fo6puahenyqvpykhcB/4dm1qVjq+q2Edf3SOA5NNOyrgQuAT416tdRkqRV\ncQz5gCRvAvYDTubOO4FuQxMSTq6q90y4PS+tqo+P8HivBf6WJuDsDPxdVZ3RrvtOVf3hqOpqj/lW\n4Fk0H/7OBp5MM4/8n9FMX/muEde3YLAI2A34CkBV7Tni+v63qua1j19J89p+lmZ6zs+N+v2SZAnw\nB+3MRcfS3CTrNOBP2/Lnjri+G4BfAz8GPgWcWlUrRlnHQH0n0bxX7gdcDzwA+AzN+aWqDhhhXa8F\n/hI4D/hzYDFwHU1A/5uqOndUdUmStCoG8gFJfgQ8drAnru25W1JVcybcnp9V1XYjPN4PgD+qql8l\n2Z4mzP1HVf1rku9W1RNHVVenvp2BTYArgW06vbvfqqonjLi+79D0Fn+U5tuN0ATJfQGq6rwR1/fb\n1yzJQuDPq2pFkvsD51fV40dc30VV9Zj28V0+QCVZXFU7j7i+7wK70HyAeiHNPQK+TfOafqaqbhxx\nfd+vqick2Qj4OfCwqrq9vdfA90b5fpl6b7bHvx9wZlXtmmQ74IxR/1vQ+ifJg6vqqr7bMS5Jthi4\naZ/upZI8kGamur2BqTtDXkVzf5b3VNX1E2rHF6rqWSM+5qY057YN8IWq+mRn3Yeq6m9GXN9DgbcC\ndwCHA68BnkfTkfl37X1wRs4x5Hd3B/Cwacq3ateNXJLvz7D8AHjIiKubNTVMpap+QnMX1Gcl+X80\n4XXUVlbV7VX1G+DHVfXLtu6bGM/rOZcmML4ZuKHt5bypqs4bdRhv3ae9adUWNB9wVwBU1a9phkCM\n2gVJXto+/l6SuQBJdgRGOpyjVVV1R1V9qapeTvNv40M0w46WjaG++7Qffn+Pppf8gW35JsDvjKG+\nqWF7m7R1UlU/G1NdJHlgkvck+WGSa9rlorbsQeOocxVtGfl9G5JsmuSfkvxHO9you+5DY6jvoUk+\nnOToJFskeVuSHyT59MDN5kZR1+YDyxbA/7b//jcfZV1tffM7jx+Y5GPt34VPJhn13wXa9+CW7eO5\nSZYB30ry0yR/Mob6vpPkLe2wsbFrz+mcJJ9Ism2Ss5PckGRhkpF/+E7ygCRHJFnS1rMiyflJDhx1\nXa1P03zDt2tVbVFVW9B8O3wdcOooK0ryhzMsu9B0wI3ax2nyyX8C+yb5zySbtOueMob6jqfp2LsM\nOAe4CfgL4GvAv4+hPsAx5NN5HfDfSS6h+WUAbAc8Cjh4THU+BNiD5h9OV4BvjLiuK5PsXFWLAdqe\n8mfTzPM+0t7c1q1J7tcG8l2mCttP8yMP5FV1B3BUklPbn79gvO/zB9J8AAhQSR5aVVcmeQDj+YDz\nCuBfk7wFuBr4ZpLLaN6rrxhDfXc5h/abowXAgvZbjlH7GPBDmhuIvRk4tQ0GT6EZRjZKHwUWJjkf\neDrwXoAks2luTjYOn6YZPrVrVV3Z1vdQ4ACaP5q7j7KyJDMNQQvj+8N5Cc0fzpcleR7woqq6hfH9\n4fw8cH+aP5wn0fzh3IvmD+deI6zrauCnA2VbA9+h+TbuESOsC+DdwNQ1PUcCV9AMsXoucAxNT+go\n/UVVHdo+/hfghe11PzsCn6Tp7BilzYAHAeckuZLmW7dTquryEdcz5UM0vZ4Povm7+n+ravckf9qu\n+6MR13cSzfDFPYB9aN6jJwNvSbJjVf3DiOvbvqre2y1o/495b5KXjbiuhTRD/ab7GzeOjoVHVtXz\n2senJ3kz8JU0N5Mch4dU1QcAkvxN53X9QJKXj6lOqCqXgYXmm4On0HxF8fz28awx1vcx4GkzrPvk\niOvaBnjoDOueOoZz22SG8i2Bx0/gd/kXNBc7Tvo9dD9ghzEe//eAP6D5kPOQMdazYw+v3cNohqpA\n85/784F5Y6rrse3xHz2hc7t4bdbdg/pup/kAcM40y01jqG/xwPM3A18HtgC+M4b6vtt5/LNVtWUE\ndf09TUB+fKfs0jG+V77TeTz4uo703Npj/hDYqH18/sC6H4z5/P6YJhRf2b43D5rwe+W7Y6jvewPP\nF7Y/70MzqcGo6/sS8Mbu3wOazr43AV8ecV0XAHNmWHfZGM7tIuA+A2UHAEuAn47zdwe8c2DdyP8t\nTC32kE+jml7W8ydY34yfuKrqRTOtW8u6lq9i3ddHWVd7zFtmKL+apsdprKrq8zQ9aBNVzTcCl47x\n+DcC3xvX8Tv1/GjcdUxT5+Wdx9fTXOcwrrqW0PynPik/TfJG4ISq+gVAO/zgQO78Rm6ULgL+uqou\nGVzRfrMyapskuU/7fyhV9a4ky4Gv0lygO2rdYZcnDqybNcqKqup9SU6m+ebtMpre1nFehPXgJK+n\n6YXcNEmqTQSMZ7jp0cCZSd4DfDHJ+7nzgurFY6jvt6rqa8DXkryG5luiFzL6OyPenOSZNN9qVpK9\nq+r0djjO7SOuC+DXSZ5WVf+T5C9pv3WrqjuSjOPb0xfSzLp1Xvt/SgG/oPlGc58R1/U2Zn4PvmbE\ndQF8DngG8OWpgqo6of0G/ANjqO+MJA+oql9V1VumCpM8Crh4DPUBDlmRpEnq/tF8cFs29UfzBWOo\n7234h3Nk2g6NF7QB62yab8LG5SO01zUAJ9B8q7iiHeI08oBcVR9Ic93Sq4EdafLBjsDpwDtGXR9w\ntw/7VXU7zbcQI59+F3gV8M80QyX3AF6d5Hiai8dfOab6PtoO+bkAeBn8dkjc0aOurKquS/Jxmvfl\n+TUwpTEjfE2r6rQkj26H+9xl+mTg5lHV06nvjdPVV1VfTDNb1qjrO7yt7y7TQ1fV0iQfHXV9U5xl\nRZLuBTLiKU6tb7z1tddQPLKqLljfzs361r36MsEpjSdZV3vM19Bcw7de1vfbeg3kktS/jHiKU+ub\nXH3r87lZ37pRXyY4pfEk69oQ6pvikBVJmpAk359pFaOf4tT61tG6rM/61sJdpjROsitwWpKHM/oZ\nvyZZ14ZQH2Agl6RJmuQUp9a37tZlfda3piY5pfGkp09e3+sDDOSSNEn/BTxg6j/6riTnWt+9ur71\n+dysb92vb38GbkZXVSuB/ZMcsw7XtSHUBziGXJIkSerVOOYylSRJkjQkA7kkSZLUIwO5JGmtJPn9\nJN9NcuMwN+hI8rYkn5hE2yRpXWIgl6R7qf/f3r2FSlVHcRz//giF7IgVFWFoBWVYRE9SRA9d7CHi\nmCUUSVZQEHSBIPAtKoLyIQgqegyyjkU9GEgFGXLS6EIXejEqERRRE9JOF4lzglYPe5+YpunoSDLO\n6fuBgZm9Zq+95mVYe8+a/06yK8nyrm13J/lwUDV1WQuMV9X8qnpu0MVI0rCyIZckkeRYVt06F9j+\nX9ciSf83NuSSNMSSLE0ynmQiyfYkKzpi40nu7Xj9t6vrSSrJA0l2ADv+Jf+KNu9Em29pu30LcA3w\nQpJfkyzpse/5ST5oR1o2A2d0xd9M8n2Sn5JsTXJJu31ZkgOdJwlJViX5xxJykjQb2JBL0pBKMgfY\nBLwHnAU8BIwluaiPNCuBy4GLe+RfArwGPAycCbwDbEoyt6quBbYBD1bVSFV91yP3BuALmkb8SeCu\nrvi7wIVt7V8CYwBV9RlwELi+4713AK/08bkkaWjYkEvSie2t9ur0RJIJ4MWO2BXACLCuqqaqagvN\nDUlu7yP/01V1qKp+6xG7DXi7qjZX1e/AM8DJwJVHSppkMbAMeLSqJqtqK83Jw1+q6qWq+qWqJoHH\ngcuSLGjDL9M04SQ5neauhxv6+FySNDRsyCXpxLayqk6dfgD3d8QWAnuq6o+ObbuBc/rIv2eG2MI2\nHwDtcfYcZf6FwI9VdbirNgCSnJRkXZKdSX4GdrWh6bGWV4HRJCPArcC2qtp/FMeVpKFjQy5Jw2sf\nsChJ53f5YmBv+/wwMK8jdnaPHDPdrnkfzR83AUgSYFFH/pnsB05LckpXbdNWAzcBy4EFwHnThwGo\nqr3Ax8DNwBocV5E0i9mQS9Lw+pSm6V6bZE6Sq4FR4PU2/hVwS5J5SS4A7ukz/xvAjUmua+fVHwEm\ngY+OtGNV7QY+B55IMjfJVW1t0+a3uQ7SnDQ81SPNepqlFS8FNvZZuyQNDRtySRpSVTUFrABuAH6g\nmS+/s6q+ad/yLDAFHKCZyR7rM/+3NHPcz7f5R4HR9rhHYzXNH0YPAY/RNNjT1tOMsOwFvgY+6bH/\nRpor9Bu7Rl8kaVZJ1Uy/VkqSNDhJdgL3VdX7g65Fko4Xr5BLkk5ISVbRzLhvGXQtknQ8Hcud2SRJ\nOq6SjNOsjb6maxUZSZp1HFmRJEmSBsiRFUmSJGmAbMglSZKkAbIhlyRJkgbIhlySJEkaIBtySZIk\naYBsyCVJkqQB+hOCCEYQA1AteQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e2159b2978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Reorder ration across hour od day\n",
    "grouped_df = df_order_products_train.groupby([\"order_hour_of_day\"])[\"reordered\"].aggregate(\"mean\").reset_index()\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(grouped_df['order_hour_of_day'].values, grouped_df['reordered'].values, alpha=0.8, color='teal')\n",
    "plt.ylabel('Reorder ratio', fontsize=12)\n",
    "plt.xlabel('Hour of day', fontsize=12)\n",
    "plt.title(\"Reorder ratio across hour of day\", fontsize=15)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylim(0.5, 0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Parija\\Anaconda3\\lib\\site-packages\\gensim\\utils.py:1197: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
      "  warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import gensim\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Reading csv's\n",
    "train_orders = pd.read_csv(\"order_products__train.csv\")\n",
    "prior_orders = pd.read_csv(\"order_products__prior.csv\")\n",
    "products = pd.read_csv(\"products.csv\").set_index('product_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Coverting product_id's to string\n",
    "train_orders[\"product_id\"] = train_orders[\"product_id\"].astype(str)\n",
    "prior_orders[\"product_id\"] = prior_orders[\"product_id\"].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-39-297661cca409>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Grouping by order_id\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mtrain_products\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_orders\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"order_id\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'product_id'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mprior_products\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprior_orders\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"order_id\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'product_id'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m    714\u001b[0m         \u001b[1;31m# ignore SettingWithCopy here in case the user mutates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    715\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0moption_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'mode.chained_assignment'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 716\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_python_apply_general\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    717\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    718\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_python_apply_general\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby.py\u001b[0m in \u001b[0;36m_python_apply_general\u001b[1;34m(self, f)\u001b[0m\n\u001b[0;32m    718\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_python_apply_general\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    719\u001b[0m         keys, values, mutated = self.grouper.apply(f, self._selected_obj,\n\u001b[1;32m--> 720\u001b[1;33m                                                    self.axis)\n\u001b[0m\u001b[0;32m    721\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    722\u001b[0m         return self._wrap_applied_output(\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, f, data, axis)\u001b[0m\n\u001b[0;32m   1795\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1796\u001b[0m         \u001b[0mresult_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1797\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgroup_keys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msplitter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1798\u001b[0m             \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'name'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1799\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby.py\u001b[0m in \u001b[0;36m__iter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   4404\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4405\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4406\u001b[1;33m         \u001b[0msdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_sorted_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4407\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4408\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mngroups\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby.py\u001b[0m in \u001b[0;36m_get_sorted_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   4423\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4424\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_sorted_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4425\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_idx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4426\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4427\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_chop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice_obj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mtake\u001b[1;34m(self, indices, axis, convert, is_copy, **kwargs)\u001b[0m\n\u001b[0;32m   1926\u001b[0m         new_data = self._data.take(indices,\n\u001b[0;32m   1927\u001b[0m                                    \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_block_manager_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1928\u001b[1;33m                                    convert=True, verify=True)\n\u001b[0m\u001b[0;32m   1929\u001b[0m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1930\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mtake\u001b[1;34m(self, indexer, axis, verify, convert)\u001b[0m\n\u001b[0;32m   4009\u001b[0m         \u001b[0mnew_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4010\u001b[0m         return self.reindex_indexer(new_axis=new_labels, indexer=indexer,\n\u001b[1;32m-> 4011\u001b[1;33m                                     axis=axis, allow_dups=True)\n\u001b[0m\u001b[0;32m   4012\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4013\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlsuffix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrsuffix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mreindex_indexer\u001b[1;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy)\u001b[0m\n\u001b[0;32m   3895\u001b[0m             new_blocks = [blk.take_nd(indexer, axis=axis, fill_tuple=(\n\u001b[0;32m   3896\u001b[0m                 fill_value if fill_value is not None else blk.fill_value,))\n\u001b[1;32m-> 3897\u001b[1;33m                 for blk in self.blocks]\n\u001b[0m\u001b[0;32m   3898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3899\u001b[0m         \u001b[0mnew_axes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m   3895\u001b[0m             new_blocks = [blk.take_nd(indexer, axis=axis, fill_tuple=(\n\u001b[0;32m   3896\u001b[0m                 fill_value if fill_value is not None else blk.fill_value,))\n\u001b[1;32m-> 3897\u001b[1;33m                 for blk in self.blocks]\n\u001b[0m\u001b[0;32m   3898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3899\u001b[0m         \u001b[0mnew_axes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mtake_nd\u001b[1;34m(self, indexer, axis, new_mgr_locs, fill_tuple)\u001b[0m\n\u001b[0;32m   1044\u001b[0m             \u001b[0mfill_value\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfill_tuple\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1045\u001b[0m             new_values = algos.take_nd(values, indexer, axis=axis,\n\u001b[1;32m-> 1046\u001b[1;33m                                        allow_fill=True, fill_value=fill_value)\n\u001b[0m\u001b[0;32m   1047\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1048\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnew_mgr_locs\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\algorithms.py\u001b[0m in \u001b[0;36mtake_nd\u001b[1;34m(arr, indexer, axis, out, fill_value, mask_info, allow_fill)\u001b[0m\n\u001b[0;32m   1465\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_shape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'F'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1466\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1467\u001b[1;33m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_shape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1468\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1469\u001b[0m     func = _get_take_nd_function(arr.ndim, arr.dtype, out.dtype, axis=axis,\n",
      "\u001b[1;31mMemoryError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Grouping by order_id\n",
    "train_products = train_orders.groupby(\"order_id\").apply(lambda order: order['product_id'].tolist())\n",
    "prior_products = prior_orders.groupby(\"order_id\").apply(lambda order: order['product_id'].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Joining \n",
    "sentences = prior_products.append(train_products)\n",
    "longest = np.max(sentences.apply(len))\n",
    "sentences = sentences.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Using the Word2Vec package from Gensim model\n",
    "model = gensim.models.Word2Vec(sentences, size=100, window=longest, min_count=2, workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Creating a list\n",
    "vocab = list(model.wv.vocab.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing PCS from Ski-kit learn\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(model.wv.syn0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_batch(vocab, model, n_batches=3):\n",
    "    output = list()\n",
    "    for i in range(0, n_batches):\n",
    "        rand_int = np.random.randint(len(vocab), size=1)[0]\n",
    "        suggestions = model.most_similar(positive=[vocab[rand_int]], topn=5)\n",
    "        suggest = list()\n",
    "        for i in suggestions:\n",
    "            suggest.append(i[0])\n",
    "        output += suggest\n",
    "        output.append(vocab[rand_int])\n",
    "    return output\n",
    "def plot_with_labels(low_dim_embs, labels, filename='tsne.png'):\n",
    "    \"\"\"From Tensorflow's tutorial.\"\"\"\n",
    "    assert low_dim_embs.shape[0] >= len(labels), \"More labels than embeddings\"\n",
    "    plt.figure(figsize=(21, 21))  #in inches\n",
    "    for i, label in enumerate(labels):\n",
    "        x, y = low_dim_embs[i,:]\n",
    "        plt.scatter(x, y)\n",
    "        plt.annotate(label,\n",
    "                     xy=(x, y),\n",
    "                     xytext=(5, 2),\n",
    "                     textcoords='offset points',\n",
    "                     ha='right',\n",
    "                     va='bottom')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "embeds = []\n",
    "labels = []\n",
    "for item in get_batch(vocab, model, n_batches=4):\n",
    "    embeds.append(model[item])\n",
    "    labels.append(products.loc[int(item)]['product_name'])\n",
    "embeds = np.array(embeds)\n",
    "embeds = pca.fit_transform(embeds)\n",
    "plot_with_labels(embeds, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Created a bag of words to find out which cultures product are selling the most.\n",
    "df_bow = pd.read_csv(\"bow.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Bag of Words with Name of Cultures\n",
    "df_bow.Culture_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for x in df_bow.Culture_names :\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_products.product_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Dictionary\n",
    "cultname=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Splitting each Product in sepaarte Words\n",
    "for i in df_products.product_name:\n",
    "    a=i.split()\n",
    "    for j in df_bow.Culture_names:\n",
    "        if j in a:\n",
    "            cultname.append(j)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Combining the pair in dictionary\n",
    "counts = dict()\n",
    "for i in cultname:\n",
    "    counts[i] = counts.get(i,0) + 1\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "counts.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Bar plot of Dictionary\n",
    "plt.figure(figsize=(18,10))\n",
    "plt.bar(counts.keys(),counts.values(),color='b')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Output: \n",
    "Greek and Italian are the most sold products."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The text in the document is from Instacart Kaggle Competition is licensed under CC BY 3.0 https://creativecommons.org/licenses/by/3.0/us/\n",
    "The code in the document by J. Hamilton,TowardsDataScience\n",
    "is licensed under the MIT License https://opensource.org/licenses/MIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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