Real Time First Price Auctions.ipynb

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   "source": [
    "<center><h1>Real Time First Price Auctions</h1></center>"
   ]
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    "<B>Name -</B> Rahul Gaikwad\n",
    "\n",
    "<B>Position -</B> Data Scientist\n",
    "\n",
    "<B>Interview Round -</B> 2 - Modelling Exercise\n",
    "\n",
    "<B>Email Id -</B> rahul.gaikwad2010@gmail.com\n",
    "\n",
    "<B>Mobile No. -</B> +91 9326117192\n",
    "\n",
    "<hr/>\n",
    "\n",
    "### Introduction\n",
    "\n",
    "60% of the digital ad inventory is sold by publishers in **Real-Time first price Auctions.** Once a user lands on a webpage, bidders (advertisers) bid for different ad slots on the page, and the one with the highest winning bid displays their ad in the ad space and pays the amount he bid. This process encourages bid shading – bidding lesser than the perceived value of the ad space to maximize utilization for self while maintaining a particular win rate at the lowest prices. \n",
    "\n",
    "Hence, for publishers, it becomes important to value their inventory (all the users that visit their website * all the ad slots they have on their websites) correctly so that a reserve price or a minimum price can be set up for the auctions. The minimum price\n",
    "\n",
    "<hr/>\n",
    "\n",
    "### Problem Statement\n",
    "\n",
    "In a first-price auction, the highest bidder wins and pays the price they bid if it exceeds the reserve price. The optimal strategy of a bidder is to shade their bids (bid less than their true value of the inventory). However, the bidder needs to win a certain amount to achieve their goals. This \n",
    "suggests they need to shade as much as possible while maintaining a certain win rate.\n",
    "\n",
    "A bidder perceives a certain value out of every impression they win. Each bidder would like to \n",
    "maintain the value they derived out of this set of websites (given in the dataset) in June with a \n",
    "the maximum deviation of 20%. \n",
    "\n",
    "Setting a reserve price induces this by causing bidders to lose at lower bids which encourages higher bidding and more publisher revenue. However, since most of these take place through \n",
    "automated systems, there might be an unknown delay in setting reserve prices & reducing the win rate of bidder & bidder changing their bid shading algorithm & increased publisher revenue.\n",
    "\n",
    "<hr/>\n",
    "\n",
    "### Questions\n",
    "1. What are the reserve prices that he/she can set? \n",
    "2. What is the potential revenue range our publisher can make in July? (Changelog - consider is_weekend column for question 1.)\n",
    "\n",
    "<hr/>"
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    "# 1. Setting the stage\n",
    "\n",
    "1. Import necessary libraries\n",
    "2. Load dataset\n",
    "3. Data Preprocessing\n",
    "<hr/>"
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   "source": [
    "### 1.1 Import necessary libraries"
   ]
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    "# Data representation and computation\n",
    "import pandas as pd  \n",
    "import numpy as np  \n",
    "pd.options.display.float_format = '{:20,.4f}'.format\n",
    "\n",
    "# Graph plotting\n",
    "import matplotlib.pyplot as plt  \n",
    "import seaborn as sns\n",
    "\n",
    "# Data splitting, feature engg., and pipeline to train machine learning models\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import cluster, silhouette_score, v_measure_score, adjusted_rand_score, completeness_score\n",
    "\n",
    "# Machine learning models\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.cluster import DBSCAN\n",
    "\n",
    "# Miscellaneous\n",
    "import warnings\n",
    "from math import log, e, ceil\n",
    "from scipy.stats import entropy\n",
    "from prettytable import PrettyTable\n",
    "\n",
    "# Declaration\n",
    "warnings.filterwarnings('ignore')\n",
    "%precision 2\n",
    "%matplotlib inline\n",
    "sns.set(font_scale=1)"
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   "source": [
    "<br><hr/>"
   ]
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   "source": [
    "### 1.2 Load Data"
   ]
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       "      <th></th>\n",
       "      <th>date</th>\n",
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       "      <td>2019-06-30</td>\n",
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       "      <td>3473</td>\n",
       "      <td>19</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5174</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0000</td>\n",
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       "      <td>1</td>\n",
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       "        date  site_id  ad_type_id  geo_id  device_category_id  advertiser_id  \\\n",
       "0 2019-06-30      351          10     187                   2             84   \n",
       "1 2019-06-30      351          10     187                   2             84   \n",
       "2 2019-06-30      351          10     147                   2             84   \n",
       "3 2019-06-30      351          10     147                   2             84   \n",
       "4 2019-06-30      348          10     147                   3             79   \n",
       "\n",
       "   order_id  line_item_type_id  os_id  integration_type_id  \\\n",
       "0      3473                 19     60                    1   \n",
       "1      3473                 19     58                    1   \n",
       "2      3473                 19     60                    1   \n",
       "3      3473                 19     58                    1   \n",
       "4      3463                 20     59                    1   \n",
       "\n",
       "   monetization_channel_id  ad_unit_id  total_impressions  \\\n",
       "0                        4        5174                 16   \n",
       "1                        4        5174                  6   \n",
       "2                        4        5174                  4   \n",
       "3                        4        5174                  4   \n",
       "4                       19        5138                  4   \n",
       "\n",
       "         total_revenue  viewable_impressions  measurable_impressions  \\\n",
       "0               0.0000                     2                      16   \n",
       "1               0.0000                     0                       6   \n",
       "2               0.0000                     0                       4   \n",
       "3               0.0000                     0                       4   \n",
       "4               0.0000                     0                       4   \n",
       "\n",
       "   revenue_share_percent  \n",
       "0                      1  \n",
       "1                      1  \n",
       "2                      1  \n",
       "3                      1  \n",
       "4                      1  "
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   "source": [
    "# The data is of a particular month and the number of impressions that happened in that month. \n",
    "# To read the data as a time series, we have to pass special arguments to the read_csv command:\n",
    "dateparse = lambda dates: pd.datetime.strptime(dates, '%Y-%m-%d %H:%M:%S')\n",
    "\n",
    "ldf_ad_data = pd.read_csv('../input/real-time-first-price-auctions/Ascendeum_Dataset2.csv', parse_dates=['date'], date_parser=dateparse)\n",
    "ldf_ad_data.head()"
   ]
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    "<br><hr/>"
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   "source": [
    "### 1.3. Data Preprocessing"
   ]
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   "source": [
    "> Here we'll quickly describe a dataset; the number of rows/columns, missing data, data types, preview, etc."
   ]
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   "source": [
    "ldf_ad_data.shape"
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       "      <th>count</th>\n",
       "      <td>567,291.0000</td>\n",
       "      <td>567,291.0000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>347.1078</td>\n",
       "      <td>10.2206</td>\n",
       "      <td>110.2261</td>\n",
       "      <td>1.8997</td>\n",
       "      <td>262.0843</td>\n",
       "      <td>1,951.2737</td>\n",
       "      <td>12.6321</td>\n",
       "      <td>57.2172</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>14.4337</td>\n",
       "      <td>5,136.4366</td>\n",
       "      <td>33.6737</td>\n",
       "      <td>0.0697</td>\n",
       "      <td>12.8383</td>\n",
       "      <td>32.1528</td>\n",
       "      <td>1.0000</td>\n",
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       "      <th>std</th>\n",
       "      <td>2.9304</td>\n",
       "      <td>1.2229</td>\n",
       "      <td>66.1778</td>\n",
       "      <td>0.7920</td>\n",
       "      <td>627.3255</td>\n",
       "      <td>1,652.5457</td>\n",
       "      <td>6.8126</td>\n",
       "      <td>1.9560</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>7.1359</td>\n",
       "      <td>62.5086</td>\n",
       "      <td>220.8654</td>\n",
       "      <td>0.7136</td>\n",
       "      <td>101.9892</td>\n",
       "      <td>207.0965</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>342.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>45.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>5,050.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.1542</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>345.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>79.0000</td>\n",
       "      <td>45.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>56.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>5,099.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>346.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>122.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>88.0000</td>\n",
       "      <td>3,438.0000</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>57.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>5,127.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>350.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>183.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>139.0000</td>\n",
       "      <td>3,460.0000</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>58.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>5,168.0000</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>351.0000</td>\n",
       "      <td>17.0000</td>\n",
       "      <td>308.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>2,646.0000</td>\n",
       "      <td>3,489.0000</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>60.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>5,443.0000</td>\n",
       "      <td>14,452.0000</td>\n",
       "      <td>83.8220</td>\n",
       "      <td>7,392.0000</td>\n",
       "      <td>13,756.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   site_id           ad_type_id               geo_id  \\\n",
       "count         567,291.0000         567,291.0000         567,291.0000   \n",
       "mean              347.1078              10.2206             110.2261   \n",
       "std                 2.9304               1.2229              66.1778   \n",
       "min               342.0000              10.0000               1.0000   \n",
       "25%               345.0000              10.0000              39.0000   \n",
       "50%               346.0000              10.0000             122.0000   \n",
       "75%               350.0000              10.0000             183.0000   \n",
       "max               351.0000              17.0000             308.0000   \n",
       "\n",
       "        device_category_id        advertiser_id             order_id  \\\n",
       "count         567,291.0000         567,291.0000         567,291.0000   \n",
       "mean                1.8997             262.0843           1,951.2737   \n",
       "std                 0.7920             627.3255           1,652.5457   \n",
       "min                 1.0000               8.0000              45.0000   \n",
       "25%                 1.0000              79.0000              45.0000   \n",
       "50%                 2.0000              88.0000           3,438.0000   \n",
       "75%                 3.0000             139.0000           3,460.0000   \n",
       "max                 5.0000           2,646.0000           3,489.0000   \n",
       "\n",
       "         line_item_type_id                os_id  integration_type_id  \\\n",
       "count         567,291.0000         567,291.0000         567,291.0000   \n",
       "mean               12.6321              57.2172               1.0000   \n",
       "std                 6.8126               1.9560               0.0000   \n",
       "min                 3.0000              15.0000               1.0000   \n",
       "25%                 4.0000              56.0000               1.0000   \n",
       "50%                11.0000              57.0000               1.0000   \n",
       "75%                19.0000              58.0000               1.0000   \n",
       "max                20.0000              60.0000               1.0000   \n",
       "\n",
       "       monetization_channel_id           ad_unit_id    total_impressions  \\\n",
       "count             567,291.0000         567,291.0000         567,291.0000   \n",
       "mean                   14.4337           5,136.4366              33.6737   \n",
       "std                     7.1359              62.5086             220.8654   \n",
       "min                     1.0000           5,050.0000               0.0000   \n",
       "25%                     4.0000           5,099.0000               0.0000   \n",
       "50%                    19.0000           5,127.0000               2.0000   \n",
       "75%                    19.0000           5,168.0000               6.0000   \n",
       "max                    21.0000           5,443.0000          14,452.0000   \n",
       "\n",
       "             total_revenue  viewable_impressions  measurable_impressions  \\\n",
       "count         567,291.0000          567,291.0000            567,291.0000   \n",
       "mean                0.0697               12.8383                 32.1528   \n",
       "std                 0.7136              101.9892                207.0965   \n",
       "min                -0.1542                0.0000                  0.0000   \n",
       "25%                 0.0000                0.0000                  0.0000   \n",
       "50%                 0.0000                0.0000                  2.0000   \n",
       "75%                 0.0054                2.0000                  6.0000   \n",
       "max                83.8220            7,392.0000             13,756.0000   \n",
       "\n",
       "       revenue_share_percent  \n",
       "count           567,291.0000  \n",
       "mean                  1.0000  \n",
       "std                   0.0000  \n",
       "min                   1.0000  \n",
       "25%                   1.0000  \n",
       "50%                   1.0000  \n",
       "75%                   1.0000  \n",
       "max                   1.0000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldf_ad_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:19:22.388950Z",
     "iopub.status.busy": "2020-11-29T14:19:22.388129Z",
     "iopub.status.idle": "2020-11-29T14:19:22.427418Z",
     "shell.execute_reply": "2020-11-29T14:19:22.426581Z"
    },
    "papermill": {
     "duration": 0.119177,
     "end_time": "2020-11-29T14:19:22.427553",
     "exception": false,
     "start_time": "2020-11-29T14:19:22.308376",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 567291 entries, 0 to 567290\n",
      "Data columns (total 17 columns):\n",
      " #   Column                   Non-Null Count   Dtype         \n",
      "---  ------                   --------------   -----         \n",
      " 0   date                     567291 non-null  datetime64[ns]\n",
      " 1   site_id                  567291 non-null  int64         \n",
      " 2   ad_type_id               567291 non-null  int64         \n",
      " 3   geo_id                   567291 non-null  int64         \n",
      " 4   device_category_id       567291 non-null  int64         \n",
      " 5   advertiser_id            567291 non-null  int64         \n",
      " 6   order_id                 567291 non-null  int64         \n",
      " 7   line_item_type_id        567291 non-null  int64         \n",
      " 8   os_id                    567291 non-null  int64         \n",
      " 9   integration_type_id      567291 non-null  int64         \n",
      " 10  monetization_channel_id  567291 non-null  int64         \n",
      " 11  ad_unit_id               567291 non-null  int64         \n",
      " 12  total_impressions        567291 non-null  int64         \n",
      " 13  total_revenue            567291 non-null  float64       \n",
      " 14  viewable_impressions     567291 non-null  int64         \n",
      " 15  measurable_impressions   567291 non-null  int64         \n",
      " 16  revenue_share_percent    567291 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int64(15)\n",
      "memory usage: 73.6 MB\n"
     ]
    }
   ],
   "source": [
    "ldf_ad_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:19:22.588397Z",
     "iopub.status.busy": "2020-11-29T14:19:22.587509Z",
     "iopub.status.idle": "2020-11-29T14:19:22.614659Z",
     "shell.execute_reply": "2020-11-29T14:19:22.615243Z"
    },
    "papermill": {
     "duration": 0.109061,
     "end_time": "2020-11-29T14:19:22.615412",
     "exception": false,
     "start_time": "2020-11-29T14:19:22.506351",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                       0\n",
       "site_id                    0\n",
       "ad_type_id                 0\n",
       "geo_id                     0\n",
       "device_category_id         0\n",
       "advertiser_id              0\n",
       "order_id                   0\n",
       "line_item_type_id          0\n",
       "os_id                      0\n",
       "integration_type_id        0\n",
       "monetization_channel_id    0\n",
       "ad_unit_id                 0\n",
       "total_impressions          0\n",
       "total_revenue              0\n",
       "viewable_impressions       0\n",
       "measurable_impressions     0\n",
       "revenue_share_percent      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldf_ad_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.074539,
     "end_time": "2020-11-29T14:19:22.763760",
     "exception": false,
     "start_time": "2020-11-29T14:19:22.689221",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Before going further, we will make out the reason behind choosing the measurable impressions over total impressions.\n",
    "\n",
    "By definition:\n",
    "- total_impressions - measurement column measuring the impressions for the particular set of dimensions\n",
    "\n",
    "- viewable_impressions - Number of impressions on the site that were viewable out of all measurable impressions. A display ad is counted as viewable if at least 50% of its area  was displayed on screen for at least one second\n",
    "\n",
    "- measurable_impressions - Impressions that were measurable by Active View out of the total number of eligible impressions. This value should generally be close to 100%. For example, an impression that is rendering in a cross-domain iframe may not be measurable.\n",
    "\n",
    "That means the total impression represents superset. But the measurable impression is successful impressions amongst the total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:19:23.243140Z",
     "iopub.status.busy": "2020-11-29T14:19:23.242117Z",
     "iopub.status.idle": "2020-11-29T14:19:23.245753Z",
     "shell.execute_reply": "2020-11-29T14:19:23.246357Z"
    },
    "papermill": {
     "duration": 0.409659,
     "end_time": "2020-11-29T14:19:23.246517",
     "exception": false,
     "start_time": "2020-11-29T14:19:22.836858",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Impression Report\n",
      "\n",
      "+-------+----------+------------+\n",
      "| Total | Viewable | Measurable |\n",
      "+-------+----------+------------+\n",
      "|  100% |  38.13   |   95.48    |\n",
      "+-------+----------+------------+\n"
     ]
    }
   ],
   "source": [
    "# consider total impression is 100%\n",
    "\n",
    "impression_stats = PrettyTable()\n",
    "impression_stats.field_names = [\"Total\",\"Viewable\",\"Measurable\"]\n",
    "impression_stats.align[\"\"] = \"r\"\n",
    "impression_stats.add_row([\"100%\",\n",
    "                          \"%.2f\"%((sum(ldf_ad_data['viewable_impressions'])/sum(ldf_ad_data['total_impressions']))*100),\n",
    "                          \"%.2f\"%((sum(ldf_ad_data['measurable_impressions'])/sum(ldf_ad_data['total_impressions']))*100)])\n",
    "print(\"Impression Report\\n\")\n",
    "print(impression_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.075607,
     "end_time": "2020-11-29T14:19:23.398719",
     "exception": false,
     "start_time": "2020-11-29T14:19:23.323112",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The above table shows that the rate of eligible impression is **97.25%.** Hence, we will stick to the measurable impression for further calculations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.073343,
     "end_time": "2020-11-29T14:19:23.546233",
     "exception": false,
     "start_time": "2020-11-29T14:19:23.472890",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Dealing with outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:19:23.701878Z",
     "iopub.status.busy": "2020-11-29T14:19:23.701028Z",
     "iopub.status.idle": "2020-11-29T14:19:24.285929Z",
     "shell.execute_reply": "2020-11-29T14:19:24.285148Z"
    },
    "papermill": {
     "duration": 0.666717,
     "end_time": "2020-11-29T14:19:24.286066",
     "exception": false,
     "start_time": "2020-11-29T14:19:23.619349",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize = (10,5))\n",
    "fig.suptitle('Before Outlier Removal - Measurable Impression (left) & Total Revenue (right)')\n",
    "axs[0].boxplot(ldf_ad_data.measurable_impressions);\n",
    "axs[1].boxplot(ldf_ad_data.total_revenue);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:19:24.445391Z",
     "iopub.status.busy": "2020-11-29T14:19:24.444589Z",
     "iopub.status.idle": "2020-11-29T14:19:24.601944Z",
     "shell.execute_reply": "2020-11-29T14:19:24.602673Z"
    },
    "papermill": {
     "duration": 0.240984,
     "end_time": "2020-11-29T14:19:24.602857",
     "exception": false,
     "start_time": "2020-11-29T14:19:24.361873",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#remove the outliers \n",
    "ldf_ad_data = ldf_ad_data[ldf_ad_data['total_revenue'].between(ldf_ad_data['total_revenue'].quantile(.05), ldf_ad_data['total_revenue'].quantile(.95))]\n",
    "ldf_ad_data = ldf_ad_data[ldf_ad_data['measurable_impressions'].between(ldf_ad_data['measurable_impressions'].quantile(.05), ldf_ad_data['measurable_impressions'].quantile(.95))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:19:24.767865Z",
     "iopub.status.busy": "2020-11-29T14:19:24.766957Z",
     "iopub.status.idle": "2020-11-29T14:19:25.243407Z",
     "shell.execute_reply": "2020-11-29T14:19:25.243988Z"
    },
    "papermill": {
     "duration": 0.564724,
     "end_time": "2020-11-29T14:19:25.244169",
     "exception": false,
     "start_time": "2020-11-29T14:19:24.679445",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize = (10,5))\n",
    "fig.suptitle('After Outlier Removal - Measurable Impression (left) & Total Revenue (right)')\n",
    "axs[0].boxplot(ldf_ad_data.measurable_impressions);\n",
    "axs[1].boxplot(ldf_ad_data.total_revenue);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.075651,
     "end_time": "2020-11-29T14:19:25.397423",
     "exception": false,
     "start_time": "2020-11-29T14:19:25.321772",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "From these pieces of information, we can see that some features won't be relevant in our exploratory analysis as mentioned in the documentation (such as order_id, line_item_type_id & Revenue_share_percent). Plus there are so many features to analyze that it may be better to concentrate on the ones which can give us real insights. Let's just remove them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:19:25.587456Z",
     "iopub.status.busy": "2020-11-29T14:19:25.561462Z",
     "iopub.status.idle": "2020-11-29T14:19:25.591250Z",
     "shell.execute_reply": "2020-11-29T14:19:25.590430Z"
    },
    "papermill": {
     "duration": 0.11817,
     "end_time": "2020-11-29T14:19:25.591383",
     "exception": false,
     "start_time": "2020-11-29T14:19:25.473213",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "shortlisted_columns = ['date',\n",
    " 'site_id',\n",
    " 'ad_type_id',\n",
    " 'geo_id',\n",
    " 'device_category_id',\n",
    " 'advertiser_id',\n",
    " 'os_id',\n",
    " 'ad_unit_id',\n",
    " 'total_impressions',\n",
    " 'total_revenue',\n",
    " 'viewable_impressions',\n",
    " 'measurable_impressions']\n",
    "\n",
    "ldf_ad_data = ldf_ad_data[shortlisted_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.076325,
     "end_time": "2020-11-29T14:19:25.745153",
     "exception": false,
     "start_time": "2020-11-29T14:19:25.668828",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<hr/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.076366,
     "end_time": "2020-11-29T14:19:25.898708",
     "exception": false,
     "start_time": "2020-11-29T14:19:25.822342",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 2. Exploratory Data Analysis (EDA)\n",
    "\n",
    "The purpose of this EDA is to find insights that will serve us later for Data cleaning/preparation/transformation which will ultimately be used into a machine learning algorithm.\n",
    "\n",
    "1. EDA of **Date** Variable\n",
    "2. EDA of Remaining Variables\n",
    "\n",
    "<hr/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:19:26.068940Z",
     "iopub.status.busy": "2020-11-29T14:19:26.063746Z",
     "iopub.status.idle": "2020-11-29T14:19:26.076011Z",
     "shell.execute_reply": "2020-11-29T14:19:26.075228Z"
    },
    "papermill": {
     "duration": 0.099705,
     "end_time": "2020-11-29T14:19:26.076157",
     "exception": false,
     "start_time": "2020-11-29T14:19:25.976452",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_dual_graph(x, y1, y2, figsize = (15,7)):\n",
    "    \"\"\"\n",
    "    This method is used to plot dual graph by accepting three params:\n",
    "    :param1 - x = x-axis\n",
    "    :param2 - y1 = y1-axis(left) - Calculate & Plot Mean \n",
    "    :param3 - y2 = y2-axis(right) - Calculate & Plot count\n",
    "    \"\"\"\n",
    "    fig, axes = plt.subplots(nrows=1,ncols=1,figsize=figsize)\n",
    "\n",
    "    ldf_avg_loss_amount_per_x =  ldf_ad_data.groupby(x)[y1].mean().to_frame(name = y1).reset_index()\n",
    "    ldf_avg_claims_per_x =  ldf_ad_data.groupby(x)[y2].count().to_frame(name = y2).reset_index()\n",
    "    ldf_day_wise = ldf_avg_loss_amount_per_x.merge(ldf_avg_claims_per_x)\n",
    "\n",
    "    ldf_day_wise[[y2]].plot(kind = \"bar\", color=[\"c\"], ax=axes, label=\"A\")\n",
    "    axes.set_ylabel(\"# \"+str(y2), fontsize=15)\n",
    "    axes.set_xlabel(x, fontsize=15)\n",
    "\n",
    "    ax2 = axes.twinx()\n",
    "    ax2.set_ylabel(\"Average \"+str(y1), fontsize=15)\n",
    "    ldf_day_wise[[y1]].plot(linestyle='-', marker='o', color=[\"r\"], ax=ax2, label=\"B\")\n",
    "\n",
    "    plt.title(\" & \".join(x)+\" vs # \"+str(y2)+\" (left) & Average \"+str(y1)+\" (right)\", fontsize=15)\n",
    "    plt.grid(which='major', linestyle='-', linewidth='0.2', color='black')\n",
    "    plt.show()\n",
    "\n",
    "    return ldf_day_wise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.078019,
     "end_time": "2020-11-29T14:19:26.232906",
     "exception": false,
     "start_time": "2020-11-29T14:19:26.154887",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 2.1 EDA of Date Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:19:26.398353Z",
     "iopub.status.busy": "2020-11-29T14:19:26.397572Z",
     "iopub.status.idle": "2020-11-29T14:19:26.407775Z",
     "shell.execute_reply": "2020-11-29T14:19:26.407120Z"
    },
    "papermill": {
     "duration": 0.095738,
     "end_time": "2020-11-29T14:19:26.407919",
     "exception": false,
     "start_time": "2020-11-29T14:19:26.312181",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First date in dataset - 2019-06-01 00:00:00\n",
      "Last date in dataset - 2019-06-30 00:00:00\n",
      "No. of Days Present - 29 days 00:00:00\n"
     ]
    }
   ],
   "source": [
    "start_date = ldf_ad_data[\"date\"].min()\n",
    "end_date = ldf_ad_data[\"date\"].max()\n",
    "\n",
    "print(\"First date in dataset - \"+str(start_date))\n",
    "print(\"Last date in dataset - \"+str(end_date))\n",
    "print(\"No. of Days Present - \"+str(end_date-start_date))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.100216,
     "end_time": "2020-11-29T14:19:26.587611",
     "exception": false,
     "start_time": "2020-11-29T14:19:26.487395",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now, if we observe the above statements, the date has only 29 days. So, what we’ll do is, we'll analyze impression & revenue data day-wise & week-wise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.078397,
     "end_time": "2020-11-29T14:19:26.753969",
     "exception": false,
     "start_time": "2020-11-29T14:19:26.675572",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##### Day wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:19:26.926188Z",
     "iopub.status.busy": "2020-11-29T14:19:26.923939Z",
     "iopub.status.idle": "2020-11-29T14:19:28.000917Z",
     "shell.execute_reply": "2020-11-29T14:19:28.000143Z"
    },
    "papermill": {
     "duration": 1.168816,
     "end_time": "2020-11-29T14:19:28.001065",
     "exception": false,
     "start_time": "2020-11-29T14:19:26.832249",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>total_revenue</th>\n",
       "      <th>measurable_impressions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>0.0053</td>\n",
       "      <td>14145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-06-02</td>\n",
       "      <td>0.0053</td>\n",
       "      <td>15550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>17854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-06-04</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>16416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-06-05</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>15607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-06-06</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>16933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-06-07</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>17428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-06-08</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>14697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-06-09</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>16966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-06-10</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>19191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-06-11</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>16541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-06-12</td>\n",
       "      <td>0.0042</td>\n",
       "      <td>15254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-06-13</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>17675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019-06-14</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>18367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019-06-15</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>15113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019-06-16</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>17092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019-06-17</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>19279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019-06-18</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>17081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019-06-19</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>17383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019-06-20</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>19914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>20260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019-06-22</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>17436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019-06-23</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>18756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019-06-24</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>21068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-06-25</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>17260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-06-26</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>14702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-06-27</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>17430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>17540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-06-29</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>14145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>16086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date        total_revenue  measurable_impressions\n",
       "0  2019-06-01               0.0053                   14145\n",
       "1  2019-06-02               0.0053                   15550\n",
       "2  2019-06-03               0.0050                   17854\n",
       "3  2019-06-04               0.0049                   16416\n",
       "4  2019-06-05               0.0049                   15607\n",
       "5  2019-06-06               0.0051                   16933\n",
       "6  2019-06-07               0.0048                   17428\n",
       "7  2019-06-08               0.0049                   14697\n",
       "8  2019-06-09               0.0046                   16966\n",
       "9  2019-06-10               0.0045                   19191\n",
       "10 2019-06-11               0.0046                   16541\n",
       "11 2019-06-12               0.0042                   15254\n",
       "12 2019-06-13               0.0045                   17675\n",
       "13 2019-06-14               0.0045                   18367\n",
       "14 2019-06-15               0.0046                   15113\n",
       "15 2019-06-16               0.0048                   17092\n",
       "16 2019-06-17               0.0047                   19279\n",
       "17 2019-06-18               0.0046                   17081\n",
       "18 2019-06-19               0.0044                   17383\n",
       "19 2019-06-20               0.0046                   19914\n",
       "20 2019-06-21               0.0046                   20260\n",
       "21 2019-06-22               0.0047                   17436\n",
       "22 2019-06-23               0.0047                   18756\n",
       "23 2019-06-24               0.0045                   21068\n",
       "24 2019-06-25               0.0045                   17260\n",
       "25 2019-06-26               0.0047                   14702\n",
       "26 2019-06-27               0.0049                   17430\n",
       "27 2019-06-28               0.0048                   17540\n",
       "28 2019-06-29               0.0049                   14145\n",
       "29 2019-06-30               0.0047                   16086"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_dual_graph(x = ['date'], y1 = 'total_revenue', y2 = 'measurable_impressions', figsize=(15,7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.081595,
     "end_time": "2020-11-29T14:19:28.216142",
     "exception": false,
     "start_time": "2020-11-29T14:19:28.134547",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As seen above, there was an equal distribution of revenue amount as well as measurable impressions throughout the month (day-wise). We cannot completely understand the distribution without magnifying the data. Let's see if we can see any trends week-wise\n",
    "<hr/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.081497,
     "end_time": "2020-11-29T14:19:28.378458",
     "exception": false,
     "start_time": "2020-11-29T14:19:28.296961",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##### Week-wise\n",
    "> Determine Week using iso calender & store them in WeekNo column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:19:28.547321Z",
     "iopub.status.busy": "2020-11-29T14:19:28.546200Z",
     "iopub.status.idle": "2020-11-29T14:19:40.820910Z",
     "shell.execute_reply": "2020-11-29T14:19:40.820156Z"
    },
    "papermill": {
     "duration": 12.361567,
     "end_time": "2020-11-29T14:19:40.821089",
     "exception": false,
     "start_time": "2020-11-29T14:19:28.459522",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "ldf_ad_data[\"WeekNo\"] = ldf_ad_data.apply(lambda x: x[\"date\"].isocalendar()[1], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:19:41.004241Z",
     "iopub.status.busy": "2020-11-29T14:19:41.002454Z",
     "iopub.status.idle": "2020-11-29T14:19:41.507470Z",
     "shell.execute_reply": "2020-11-29T14:19:41.508637Z"
    },
    "papermill": {
     "duration": 0.605966,
     "end_time": "2020-11-29T14:19:41.508889",
     "exception": false,
     "start_time": "2020-11-29T14:19:40.902923",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WeekNo</th>\n",
       "      <th>total_revenue</th>\n",
       "      <th>measurable_impressions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>0.0053</td>\n",
       "      <td>29695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>115901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>119233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>130109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>118231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   WeekNo        total_revenue  measurable_impressions\n",
       "0      22               0.0053                   29695\n",
       "1      23               0.0049                  115901\n",
       "2      24               0.0045                  119233\n",
       "3      25               0.0046                  130109\n",
       "4      26               0.0047                  118231"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_dual_graph(x = ['WeekNo'], y1 = 'total_revenue', y2 = 'measurable_impressions', figsize=(10,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.096144,
     "end_time": "2020-11-29T14:19:41.713098",
     "exception": false,
     "start_time": "2020-11-29T14:19:41.616954",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Again! As seen above, there was an equal distribution of revenue amount as well as measurable impressions throughout the month (week-wise). But there might be chances we see a trade-off between day vs revenue and/or impression on weekdays/weekends.\n",
    "<hr/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.084133,
     "end_time": "2020-11-29T14:19:41.888544",
     "exception": false,
     "start_time": "2020-11-29T14:19:41.804411",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##### Weekend-wise\n",
    "> Let's calculate weekend day using datetime's weekday() method & store them in isWeekend column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:19:42.069245Z",
     "iopub.status.busy": "2020-11-29T14:19:42.068366Z",
     "iopub.status.idle": "2020-11-29T14:19:53.860499Z",
     "shell.execute_reply": "2020-11-29T14:19:53.861162Z"
    },
    "papermill": {
     "duration": 11.887813,
     "end_time": "2020-11-29T14:19:53.861351",
     "exception": false,
     "start_time": "2020-11-29T14:19:41.973538",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "ldf_ad_data[\"is_weekend\"] = ldf_ad_data.apply(lambda x: 1 if x[\"date\"].weekday()>5 else 0, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:19:54.048577Z",
     "iopub.status.busy": "2020-11-29T14:19:54.043661Z",
     "iopub.status.idle": "2020-11-29T14:19:54.426025Z",
     "shell.execute_reply": "2020-11-29T14:19:54.426604Z"
    },
    "papermill": {
     "duration": 0.48256,
     "end_time": "2020-11-29T14:19:54.426753",
     "exception": false,
     "start_time": "2020-11-29T14:19:53.944193",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_weekend</th>\n",
       "      <th>total_revenue</th>\n",
       "      <th>measurable_impressions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>428719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>84450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_weekend        total_revenue  measurable_impressions\n",
       "0           0               0.0047                  428719\n",
       "1           1               0.0048                   84450"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_dual_graph(x = ['is_weekend'], y1 = 'total_revenue', y2 = 'measurable_impressions', figsize=(7,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:19:54.602912Z",
     "iopub.status.busy": "2020-11-29T14:19:54.601892Z",
     "iopub.status.idle": "2020-11-29T14:19:54.614423Z",
     "shell.execute_reply": "2020-11-29T14:19:54.613723Z"
    },
    "papermill": {
     "duration": 0.102684,
     "end_time": "2020-11-29T14:19:54.614567",
     "exception": false,
     "start_time": "2020-11-29T14:19:54.511883",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                 0.8354\n",
       "1                 0.1646\n",
       "Name: is_weekend, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldf_ad_data[\"is_weekend\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.08512,
     "end_time": "2020-11-29T14:19:54.785259",
     "exception": false,
     "start_time": "2020-11-29T14:19:54.700139",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "`is_weekend` seems to share a varied distribution to the one we have with total_revenue i.e. **83%** data is accumulated on `weekdays`!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.085159,
     "end_time": "2020-11-29T14:19:54.956003",
     "exception": false,
     "start_time": "2020-11-29T14:19:54.870844",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<br/><hr/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.084922,
     "end_time": "2020-11-29T14:19:55.127395",
     "exception": false,
     "start_time": "2020-11-29T14:19:55.042473",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 2.2 EDA of Remaining Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.085168,
     "end_time": "2020-11-29T14:19:55.298633",
     "exception": false,
     "start_time": "2020-11-29T14:19:55.213465",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### For this part, let's look at the distribution of all of the features by plotting them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:19:55.484512Z",
     "iopub.status.busy": "2020-11-29T14:19:55.483611Z",
     "iopub.status.idle": "2020-11-29T14:20:01.533610Z",
     "shell.execute_reply": "2020-11-29T14:20:01.532823Z"
    },
    "papermill": {
     "duration": 6.147832,
     "end_time": "2020-11-29T14:20:01.533751",
     "exception": false,
     "start_time": "2020-11-29T14:19:55.385919",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1440 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ldf_ad_data.hist(figsize=(16, 20), bins=50, xlabelsize=8, ylabelsize=8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.09951,
     "end_time": "2020-11-29T14:20:01.724581",
     "exception": false,
     "start_time": "2020-11-29T14:20:01.625071",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Features such as `site_id`, `ad_type_id`, `geo_id`, `device_category_id`, `ad_unit_id`, `week_no` etc. seems to share a similar distribution to the one we have with `total_revenue`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.094309,
     "end_time": "2020-11-29T14:20:01.913421",
     "exception": false,
     "start_time": "2020-11-29T14:20:01.819112",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<hr/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.106115,
     "end_time": "2020-11-29T14:20:02.135640",
     "exception": false,
     "start_time": "2020-11-29T14:20:02.029525",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.100998,
     "end_time": "2020-11-29T14:20:02.328826",
     "exception": false,
     "start_time": "2020-11-29T14:20:02.227828",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now we'll try to find which features are strongly correlated with `total_revenue`. We'll store them in a var called golden_features_list. We'll reuse our `ldf_ad_data` dataset to do so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:02.585544Z",
     "iopub.status.busy": "2020-11-29T14:20:02.583972Z",
     "iopub.status.idle": "2020-11-29T14:20:02.922929Z",
     "shell.execute_reply": "2020-11-29T14:20:02.923878Z"
    },
    "papermill": {
     "duration": 0.470609,
     "end_time": "2020-11-29T14:20:02.924135",
     "exception": false,
     "start_time": "2020-11-29T14:20:02.453526",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is 5 strongly correlated values with total_revenue:\n",
      "total_revenue                          1.0000\n",
      "measurable_impressions                 0.4916\n",
      "total_impressions                      0.4777\n",
      "viewable_impressions                   0.3421\n",
      "geo_id                                 0.1564\n",
      "Name: total_revenue, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "ldf_ad_data_corr = ldf_ad_data.corr()['total_revenue'][:-1] # -1 because the latest row is total_revenue\n",
    "golden_features_list = ldf_ad_data_corr[abs(ldf_ad_data_corr) > 0.1].sort_values(ascending=False)\n",
    "print(\"There is {} strongly correlated values with total_revenue:\\n{}\".format(len(golden_features_list), golden_features_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.111219,
     "end_time": "2020-11-29T14:20:03.151735",
     "exception": false,
     "start_time": "2020-11-29T14:20:03.040516",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Perfect, we now have a list of strongly correlated values.\n",
    "\n",
    "Btw, correlation by itself does not always explain the relationship between data, so plotting them could even lead us to new insights and in the same manner, check that our correlated values have a linear relationship to the total_revenue.\n",
    "\n",
    "For example, relationships such as curvilinear relationships cannot be guessed just by looking at the correlation value so let's take the features we excluded from our correlation table and plot them to see if they show some kind of pattern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:03.411353Z",
     "iopub.status.busy": "2020-11-29T14:20:03.408390Z",
     "iopub.status.idle": "2020-11-29T14:20:26.129061Z",
     "shell.execute_reply": "2020-11-29T14:20:26.128293Z"
    },
    "papermill": {
     "duration": 22.850157,
     "end_time": "2020-11-29T14:20:26.129200",
     "exception": false,
     "start_time": "2020-11-29T14:20:03.279043",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x180 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x180 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0, len(ldf_ad_data.columns), 5):\n",
    "    sns.pairplot(data=ldf_ad_data,\n",
    "                x_vars=ldf_ad_data.columns[i:i+5],\n",
    "                y_vars=['total_revenue'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.096267,
     "end_time": "2020-11-29T14:20:26.322125",
     "exception": false,
     "start_time": "2020-11-29T14:20:26.225858",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.096436,
     "end_time": "2020-11-29T14:20:26.515876",
     "exception": false,
     "start_time": "2020-11-29T14:20:26.419440",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "By looking at correlation, we discovered 11 features that have a strong (or moderate) relationship to revenue.\n",
    "\n",
    "Variables are as follows:\n",
    "\n",
    "- site_id\n",
    "- ad_type_id \n",
    "- geo_id\n",
    "- device_category_id \n",
    "- advertiser_id\n",
    "- os_id\n",
    "- ad_unit_id\n",
    "- is_weekend\n",
    "\n",
    "Also, impression variables are highly correlated (>70%) with total revenue \n",
    "- viewable impressions\n",
    "- measurable impressions\n",
    "- total  impressions "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.094523,
     "end_time": "2020-11-29T14:20:26.705468",
     "exception": false,
     "start_time": "2020-11-29T14:20:26.610945",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<hr/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.095081,
     "end_time": "2020-11-29T14:20:26.895558",
     "exception": false,
     "start_time": "2020-11-29T14:20:26.800477",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 3. Discussion on the potential revenue range our publisher can make in July"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.098628,
     "end_time": "2020-11-29T14:20:27.089912",
     "exception": false,
     "start_time": "2020-11-29T14:20:26.991284",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "A good revenue model is a proven technique used by digital businesses globally, to generate income from traffic on their website, mobile apps, and via digital channels. [Ref](https://www.smartinsights.com/digital-marketing-strategy/online-business-revenue-models/online-revenue-model-options-internet-business/)\n",
    "\n",
    "In the following section, we will set some assumptions to calculate the potential revenue range for july month.\n",
    "1. Background\n",
    "3. Hypothesis\n",
    "4. Calculate Revenue Range for July Month\n",
    "5. Conclusion\n",
    "\n",
    "<hr/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.098065,
     "end_time": "2020-11-29T14:20:27.287056",
     "exception": false,
     "start_time": "2020-11-29T14:20:27.188991",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.1 Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.095699,
     "end_time": "2020-11-29T14:20:27.480251",
     "exception": false,
     "start_time": "2020-11-29T14:20:27.384552",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##### Understand ad revenue\n",
    "\n",
    "Advertising revenue is the monetary income that individuals and businesses earn from displaying paid advertisements on their websites, social media channels, or other platforms surrounding their internet-based content. [Ref](https://en.wikipedia.org/wiki/Advertising_revenue) \n",
    "\n",
    "##### Is revenue equal to cpm ?\n",
    "Your revenue will not be equal to your CPM times your views. This is because CPM reflects what advertisers pay, not what you earn. Also, not all views will have ads. [Ref](https://support.google.com/youtube/answer/9314357?hl=en#:~:text=Your%20revenue%20will%20not%20be,all%20views%20will%20have%20ads.)\n",
    "\n",
    "##### Below are some commonly used parameters:\n",
    "- measurable_impressions - % inventory - the proportion of ad space sold on sites\n",
    "- Number of ad units (ad_unit_id)\n",
    "- CPM - Cost per thousand impressions for ad volume deals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.095376,
     "end_time": "2020-11-29T14:20:27.673614",
     "exception": false,
     "start_time": "2020-11-29T14:20:27.578238",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.2 Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.095115,
     "end_time": "2020-11-29T14:20:27.864724",
     "exception": false,
     "start_time": "2020-11-29T14:20:27.769609",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The assumption is pretty straight forward. We'll consider the sum of revenue & measurable impressions per line item (only those line items whose occurrences are pretty common i.e. at least 5 occurrences). \n",
    "\n",
    "The measurable impression denotes the eligible impressions and it is one of the important parameters in revenue.\n",
    "With that in mind, we will calculate CTR (measurable impression/total impressions). The purpose of calculating CTR that it will change in the future as other categories are pretty static.\n",
    "\n",
    "So, to forecast revenue, change in CTR will play a vital role."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.095541,
     "end_time": "2020-11-29T14:20:28.055789",
     "exception": false,
     "start_time": "2020-11-29T14:20:27.960248",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.3 Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.094494,
     "end_time": "2020-11-29T14:20:28.246190",
     "exception": false,
     "start_time": "2020-11-29T14:20:28.151696",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Step 1. Get sum of total_impressions, measurable_impressions, total_revenue per line item\n",
    "\n",
    "Step 2. Calculate CTR (CTR = measurable impression / total impression)\n",
    "\n",
    "Step 3. Drop unnecessary columns and create dummy variables of categorical column\n",
    "\n",
    "Step 4. Create train-test split\n",
    "\n",
    "Step 5. Fit the model\n",
    "\n",
    "Step 6. Predict on testing data\n",
    "\n",
    "Step 7. Now, using CTR let's create two more columns with boosted & dropped value of CTR by 5%\n",
    "\n",
    "Step 8. Predict on three values on CTR - Original, Boosted(+5%) & Dropped(-5%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:28.456451Z",
     "iopub.status.busy": "2020-11-29T14:20:28.455598Z",
     "iopub.status.idle": "2020-11-29T14:20:28.781655Z",
     "shell.execute_reply": "2020-11-29T14:20:28.781045Z"
    },
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     "duration": 0.440518,
     "end_time": "2020-11-29T14:20:28.781786",
     "exception": false,
     "start_time": "2020-11-29T14:20:28.341268",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Prepare data for modelling.\n",
    "# Group by line item - sum of total_impressions, measurable_impressions, total_revenue\n",
    "ldf_grouped_ad_data = ldf_ad_data.groupby([\"is_weekend\", \"site_id\",\"ad_type_id\", \"geo_id\",\"device_category_id\",\n",
    "                     \"advertiser_id\",\"os_id\",\"ad_unit_id\"])[\"total_impressions\",\"measurable_impressions\",\"total_revenue\"].sum().reset_index()\n",
    "\n",
    "# Below is the line item validator dataframe.\n",
    "# Group by line item - count of any of these -- total_impressions, measurable_impressions, total_revenue\n",
    "ldf_line_item_validator = ldf_ad_data.groupby([\"is_weekend\", \"site_id\",\"ad_type_id\",\n",
    "                     \"geo_id\",\"device_category_id\",\n",
    "                     \"advertiser_id\",\"os_id\",\"ad_unit_id\"])[\"total_revenue\"].count().to_frame(name = 'count').reset_index()\n",
    "\n",
    "ldf_grouped_ad_data = ldf_grouped_ad_data.merge(ldf_line_item_validator)\n",
    "\n",
    "# Select data to be used for modelling i.e. line item combination should be greater than 5\n",
    "# If count is less, treat them as a noise.\n",
    "ldf_grouped_ad_data_to_be_modeled = ldf_grouped_ad_data[(ldf_grouped_ad_data['total_impressions']>0)&\n",
    "                                          (ldf_grouped_ad_data['total_revenue']>0)&\n",
    "                                         (ldf_grouped_ad_data['count']>5)]\n",
    "\n",
    "# Store less frequent data in different data-frame\n",
    "ldf_grouped_ad_data_to_be_ignored_in_modelling = ldf_grouped_ad_data[(ldf_grouped_ad_data['total_impressions']>0)&\n",
    "                                          (ldf_grouped_ad_data['total_revenue']>0)&\n",
    "                                         (ldf_grouped_ad_data['count']<=5)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.094425,
     "end_time": "2020-11-29T14:20:28.970892",
     "exception": false,
     "start_time": "2020-11-29T14:20:28.876467",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Calculate CTR (CTR = measurable impression / total impression)\n",
    "\n",
    "In Internet marketing, CTR stands for click-through rate: a metric that measures the number of clicks advertisers receive on their ads per number of impressions.\n",
    "\n",
    "Achieving a high click-through rate is essential to your PPC success, because it directly affects both your Quality Score and how much you pay every time someone clicks your search ad. [Ref](https://www.wordstream.com/click-through-rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:29.181031Z",
     "iopub.status.busy": "2020-11-29T14:20:29.175792Z",
     "iopub.status.idle": "2020-11-29T14:20:29.455342Z",
     "shell.execute_reply": "2020-11-29T14:20:29.454517Z"
    },
    "papermill": {
     "duration": 0.387615,
     "end_time": "2020-11-29T14:20:29.455478",
     "exception": false,
     "start_time": "2020-11-29T14:20:29.067863",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "ldf_grouped_ad_data_to_be_modeled['CTR'] = ldf_grouped_ad_data_to_be_modeled.apply(lambda x: (x['measurable_impressions']/x['total_impressions'])*100, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.09513,
     "end_time": "2020-11-29T14:20:29.647308",
     "exception": false,
     "start_time": "2020-11-29T14:20:29.552178",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Drop unnecessary columns and create dummy variables of categorical column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:29.847359Z",
     "iopub.status.busy": "2020-11-29T14:20:29.846532Z",
     "iopub.status.idle": "2020-11-29T14:20:29.873699Z",
     "shell.execute_reply": "2020-11-29T14:20:29.873021Z"
    },
    "papermill": {
     "duration": 0.130579,
     "end_time": "2020-11-29T14:20:29.873879",
     "exception": false,
     "start_time": "2020-11-29T14:20:29.743300",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# categorical column\n",
    "cat_col=['is_weekend',\n",
    " 'site_id',\n",
    " 'ad_type_id',\n",
    " 'geo_id',\n",
    " 'device_category_id',\n",
    " 'advertiser_id',\n",
    " 'os_id',\n",
    " 'ad_unit_id']\n",
    "\n",
    "ldf_grouped_ad_data_to_be_modeled = pd.get_dummies(ldf_grouped_ad_data_to_be_modeled, columns=cat_col)\n",
    "ldf_grouped_ad_data_to_be_modeled = ldf_grouped_ad_data_to_be_modeled.drop(['total_impressions','measurable_impressions','count'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.099219,
     "end_time": "2020-11-29T14:20:30.071152",
     "exception": false,
     "start_time": "2020-11-29T14:20:29.971933",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Create train-test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:30.272442Z",
     "iopub.status.busy": "2020-11-29T14:20:30.271577Z",
     "iopub.status.idle": "2020-11-29T14:20:30.286648Z",
     "shell.execute_reply": "2020-11-29T14:20:30.285956Z"
    },
    "papermill": {
     "duration": 0.118548,
     "end_time": "2020-11-29T14:20:30.286775",
     "exception": false,
     "start_time": "2020-11-29T14:20:30.168227",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X=ldf_grouped_ad_data_to_be_modeled.drop(['total_revenue'], axis=1)\n",
    "y=ldf_grouped_ad_data_to_be_modeled['total_revenue']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.25, random_state=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.094987,
     "end_time": "2020-11-29T14:20:30.479685",
     "exception": false,
     "start_time": "2020-11-29T14:20:30.384698",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:30.675721Z",
     "iopub.status.busy": "2020-11-29T14:20:30.674953Z",
     "iopub.status.idle": "2020-11-29T14:20:32.710234Z",
     "shell.execute_reply": "2020-11-29T14:20:32.709486Z"
    },
    "papermill": {
     "duration": 2.135669,
     "end_time": "2020-11-29T14:20:32.710388",
     "exception": false,
     "start_time": "2020-11-29T14:20:30.574719",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=2, random_state=0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regr = RandomForestRegressor(max_depth=2, random_state=0)\n",
    "\n",
    "regr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.097892,
     "end_time": "2020-11-29T14:20:32.906444",
     "exception": false,
     "start_time": "2020-11-29T14:20:32.808552",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Predict on testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:33.123470Z",
     "iopub.status.busy": "2020-11-29T14:20:33.122133Z",
     "iopub.status.idle": "2020-11-29T14:20:33.142031Z",
     "shell.execute_reply": "2020-11-29T14:20:33.141217Z"
    },
    "papermill": {
     "duration": 0.131877,
     "end_time": "2020-11-29T14:20:33.142174",
     "exception": false,
     "start_time": "2020-11-29T14:20:33.010297",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred = regr.predict(X_test)\n",
    "y_pred = np.where(y_pred>0, y_pred, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.100551,
     "end_time": "2020-11-29T14:20:33.344786",
     "exception": false,
     "start_time": "2020-11-29T14:20:33.244235",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Calculate rmse value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:33.548409Z",
     "iopub.status.busy": "2020-11-29T14:20:33.547621Z",
     "iopub.status.idle": "2020-11-29T14:20:33.552942Z",
     "shell.execute_reply": "2020-11-29T14:20:33.553521Z"
    },
    "papermill": {
     "duration": 0.110757,
     "end_time": "2020-11-29T14:20:33.553683",
     "exception": false,
     "start_time": "2020-11-29T14:20:33.442926",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rmse of prediction is: 0.18057\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_log_error\n",
    "print('The rmse of prediction is:', round(mean_squared_log_error(y_pred, y_test) ** 0.5, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.097773,
     "end_time": "2020-11-29T14:20:33.748651",
     "exception": false,
     "start_time": "2020-11-29T14:20:33.650878",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Now, using CTR let's create two more columns with boosted & dropped value of CTR by 5%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:33.967211Z",
     "iopub.status.busy": "2020-11-29T14:20:33.966119Z",
     "iopub.status.idle": "2020-11-29T14:20:34.102099Z",
     "shell.execute_reply": "2020-11-29T14:20:34.100721Z"
    },
    "papermill": {
     "duration": 0.255703,
     "end_time": "2020-11-29T14:20:34.102239",
     "exception": false,
     "start_time": "2020-11-29T14:20:33.846536",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_test_with_added_range = X_test.copy()\n",
    "\n",
    "X_test_with_added_range['CTR_Raised_5%'] = X_test.apply(lambda x: x['CTR']+5 if x['CTR']<=95 else x['CTR'], axis=1)\n",
    "X_test_with_added_range['CTR_Dropped_5%'] = X_test.apply(lambda x: x['CTR']-5, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.100715,
     "end_time": "2020-11-29T14:20:34.313209",
     "exception": false,
     "start_time": "2020-11-29T14:20:34.212494",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Predict on three values on CTR - Original, Boosted(+5%) & Dropped(-5%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:34.524189Z",
     "iopub.status.busy": "2020-11-29T14:20:34.523128Z",
     "iopub.status.idle": "2020-11-29T14:20:34.861146Z",
     "shell.execute_reply": "2020-11-29T14:20:34.860303Z"
    },
    "papermill": {
     "duration": 0.449156,
     "end_time": "2020-11-29T14:20:34.861276",
     "exception": false,
     "start_time": "2020-11-29T14:20:34.412120",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_test_with_added_range = X_test.copy()\n",
    "\n",
    "X_test_with_added_range['CTR'] = X_test.apply(lambda x: x['CTR'] + 5 if x['CTR']<=95 else x['CTR'], axis=1)\n",
    "llst_predicted_revenue_ctr_raised_5 = regr.predict(X_test_with_added_range)\n",
    "\n",
    "X_test_with_added_range['CTR'] = X_test.apply(lambda x: x['CTR'] - 5 if x['CTR']>=5 else x['CTR'], axis=1)\n",
    "llst_predicted_revenue_ctr_dropped_5 = regr.predict(X_test_with_added_range)\n",
    "\n",
    "X_test_with_added_range['CTR'] = X_test['CTR']\n",
    "X_test_with_added_range['CTR_Raised_5%'] = X_test.apply(lambda x: x['CTR']+5 if x['CTR']<=95 else x['CTR'], axis=1)\n",
    "X_test_with_added_range['CTR_Dropped_5%'] = X_test.apply(lambda x: x['CTR']-5, axis=1)\n",
    "X_test_with_added_range['Predicted_Revenue_CTR'] = regr.predict(X_test)\n",
    "X_test_with_added_range['Predicted_Revenue_CTR_Raised_5%'] = llst_predicted_revenue_ctr_raised_5\n",
    "X_test_with_added_range['Predicted_Revenue_CTR_Dropped_5%'] = llst_predicted_revenue_ctr_dropped_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:35.083754Z",
     "iopub.status.busy": "2020-11-29T14:20:35.075248Z",
     "iopub.status.idle": "2020-11-29T14:20:35.090602Z",
     "shell.execute_reply": "2020-11-29T14:20:35.089860Z"
    },
    "papermill": {
     "duration": 0.131534,
     "end_time": "2020-11-29T14:20:35.090722",
     "exception": false,
     "start_time": "2020-11-29T14:20:34.959188",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CTR</th>\n",
       "      <th>is_weekend_0</th>\n",
       "      <th>is_weekend_1</th>\n",
       "      <th>site_id_342</th>\n",
       "      <th>site_id_343</th>\n",
       "      <th>site_id_344</th>\n",
       "      <th>site_id_345</th>\n",
       "      <th>site_id_346</th>\n",
       "      <th>site_id_347</th>\n",
       "      <th>site_id_348</th>\n",
       "      <th>...</th>\n",
       "      <th>ad_unit_id_5178</th>\n",
       "      <th>ad_unit_id_5179</th>\n",
       "      <th>ad_unit_id_5180</th>\n",
       "      <th>ad_unit_id_5181</th>\n",
       "      <th>ad_unit_id_5443</th>\n",
       "      <th>CTR_Raised_5%</th>\n",
       "      <th>CTR_Dropped_5%</th>\n",
       "      <th>Predicted_Revenue_CTR</th>\n",
       "      <th>Predicted_Revenue_CTR_Raised_5%</th>\n",
       "      <th>Predicted_Revenue_CTR_Dropped_5%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42142</th>\n",
       "      <td>100.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>95.0000</td>\n",
       "      <td>0.1848</td>\n",
       "      <td>0.1848</td>\n",
       "      <td>0.1858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9775</th>\n",
       "      <td>90.9091</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>95.9091</td>\n",
       "      <td>85.9091</td>\n",
       "      <td>0.5443</td>\n",
       "      <td>0.5443</td>\n",
       "      <td>0.5443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57422</th>\n",
       "      <td>100.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>95.0000</td>\n",
       "      <td>0.0855</td>\n",
       "      <td>0.0855</td>\n",
       "      <td>0.0865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10424</th>\n",
       "      <td>100.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>95.0000</td>\n",
       "      <td>0.0855</td>\n",
       "      <td>0.0855</td>\n",
       "      <td>0.0865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1827</th>\n",
       "      <td>96.1538</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96.1538</td>\n",
       "      <td>91.1538</td>\n",
       "      <td>0.0865</td>\n",
       "      <td>0.0865</td>\n",
       "      <td>0.0865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 297 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       CTR  is_weekend_0  is_weekend_1  site_id_342  \\\n",
       "42142             100.0000             1             0            0   \n",
       "9775               90.9091             1             0            0   \n",
       "57422             100.0000             1             0            0   \n",
       "10424             100.0000             1             0            0   \n",
       "1827               96.1538             1             0            1   \n",
       "\n",
       "       site_id_343  site_id_344  site_id_345  site_id_346  site_id_347  \\\n",
       "42142            0            0            0            0            0   \n",
       "9775             1            0            0            0            0   \n",
       "57422            0            0            0            0            0   \n",
       "10424            0            1            0            0            0   \n",
       "1827             0            0            0            0            0   \n",
       "\n",
       "       site_id_348  ...  ad_unit_id_5178  ad_unit_id_5179  ad_unit_id_5180  \\\n",
       "42142            0  ...                0                0                0   \n",
       "9775             0  ...                0                0                0   \n",
       "57422            0  ...                0                0                0   \n",
       "10424            0  ...                0                0                0   \n",
       "1827             0  ...                0                0                0   \n",
       "\n",
       "       ad_unit_id_5181  ad_unit_id_5443        CTR_Raised_5%  \\\n",
       "42142                0                0             100.0000   \n",
       "9775                 0                0              95.9091   \n",
       "57422                0                0             100.0000   \n",
       "10424                0                0             100.0000   \n",
       "1827                 0                0              96.1538   \n",
       "\n",
       "            CTR_Dropped_5%  Predicted_Revenue_CTR  \\\n",
       "42142              95.0000                 0.1848   \n",
       "9775               85.9091                 0.5443   \n",
       "57422              95.0000                 0.0855   \n",
       "10424              95.0000                 0.0855   \n",
       "1827               91.1538                 0.0865   \n",
       "\n",
       "       Predicted_Revenue_CTR_Raised_5%  Predicted_Revenue_CTR_Dropped_5%  \n",
       "42142                           0.1848                            0.1858  \n",
       "9775                            0.5443                            0.5443  \n",
       "57422                           0.0855                            0.0865  \n",
       "10424                           0.0855                            0.0865  \n",
       "1827                            0.0865                            0.0865  \n",
       "\n",
       "[5 rows x 297 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_with_added_range.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.099377,
     "end_time": "2020-11-29T14:20:35.290484",
     "exception": false,
     "start_time": "2020-11-29T14:20:35.191107",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.4 Forecasting for July Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:35.499787Z",
     "iopub.status.busy": "2020-11-29T14:20:35.498667Z",
     "iopub.status.idle": "2020-11-29T14:20:35.503914Z",
     "shell.execute_reply": "2020-11-29T14:20:35.502855Z"
    },
    "papermill": {
     "duration": 0.114635,
     "end_time": "2020-11-29T14:20:35.504090",
     "exception": false,
     "start_time": "2020-11-29T14:20:35.389455",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Revenue in Current Month i.e. June (Test Data): 488.62939999999946\n",
      "\n",
      "Predicted Revenue for Next Month i.e. July (Test Data):\n",
      "\n",
      "Revenue on CTR: 547.5412002547148\n",
      "Revenue on Boosted CTR: 547.5351821825286\n",
      "Revenue on Dropped CTR: 549.4178357647615\n"
     ]
    }
   ],
   "source": [
    "print(\"Actual Revenue in Current Month i.e. June (Test Data): \"+str(sum(y_test)))\n",
    "\n",
    "print(\"\\nPredicted Revenue for Next Month i.e. July (Test Data):\")\n",
    "print(\"\\nRevenue on CTR: \"+str(sum(X_test_with_added_range['Predicted_Revenue_CTR'])))\n",
    "print(\"Revenue on Boosted CTR: \"+str(sum(X_test_with_added_range['Predicted_Revenue_CTR_Raised_5%'])))\n",
    "print(\"Revenue on Dropped CTR: \"+str(sum(X_test_with_added_range['Predicted_Revenue_CTR_Dropped_5%'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.098317,
     "end_time": "2020-11-29T14:20:35.702463",
     "exception": false,
     "start_time": "2020-11-29T14:20:35.604146",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Calculating Percent Growth Rate"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.099749,
     "end_time": "2020-11-29T14:20:35.901021",
     "exception": false,
     "start_time": "2020-11-29T14:20:35.801272",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The percent change from one period to another is calculated from the formula:\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "Where:\n",
    "\n",
    "- PR = Percent Rate\n",
    "- VPresent = Present or Future Value\n",
    "- VPast = Past or Present Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:36.110674Z",
     "iopub.status.busy": "2020-11-29T14:20:36.109695Z",
     "iopub.status.idle": "2020-11-29T14:20:36.114423Z",
     "shell.execute_reply": "2020-11-29T14:20:36.113331Z"
    },
    "papermill": {
     "duration": 0.112373,
     "end_time": "2020-11-29T14:20:36.114564",
     "exception": false,
     "start_time": "2020-11-29T14:20:36.002191",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Growth Rate:  12.06\n"
     ]
    }
   ],
   "source": [
    "july_growth_rate = (sum(X_test_with_added_range['Predicted_Revenue_CTR_Raised_5%']) - sum(y_test))/sum(y_test)*100\n",
    "print(\"Growth Rate: \",\"%.2f\"%july_growth_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.099944,
     "end_time": "2020-11-29T14:20:36.316851",
     "exception": false,
     "start_time": "2020-11-29T14:20:36.216907",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.5 Result Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.101524,
     "end_time": "2020-11-29T14:20:36.520139",
     "exception": false,
     "start_time": "2020-11-29T14:20:36.418615",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### What is the potential revenue range our publisher can make in July? \n",
    "\n",
    "As we can see in the above statements, we have predicted a **12.06%** growth in revenue for July month if we boost CTR by **5%.** Now, on that term, our revenue range for July month will be **488.62 to 547.53** dollars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.099236,
     "end_time": "2020-11-29T14:20:36.719277",
     "exception": false,
     "start_time": "2020-11-29T14:20:36.620041",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<hr/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.099266,
     "end_time": "2020-11-29T14:20:36.918862",
     "exception": false,
     "start_time": "2020-11-29T14:20:36.819596",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 4. Discussion on the reserve prices that he/she can set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.103184,
     "end_time": "2020-11-29T14:20:37.122670",
     "exception": false,
     "start_time": "2020-11-29T14:20:37.019486",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "A reserve price is a **minimum price** a publisher is willing to accept from an advertiser. In an auction, the publisher is not required to disclose the reserve price to a potential advertiser. If the reserve price is not met, the publisher is not required to sell the item, even to the highest bidder(advertiser). [Ref](https://www.investopedia.com/terms/r/reserve-price.asp#:~:text=A%20reserve%20price%20is%20a,even%20to%20the%20highest%20bidder.)\n",
    "\n",
    "In the following section, we will set some assumptions to determine the optimal reserve price.\n",
    "1. Background\n",
    "3. Hypothesis\n",
    "4. Determine Optimal Reserve Price\n",
    "5. Conclusion\n",
    "\n",
    "<hr/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.102206,
     "end_time": "2020-11-29T14:20:37.326900",
     "exception": false,
     "start_time": "2020-11-29T14:20:37.224694",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4.1 Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.102267,
     "end_time": "2020-11-29T14:20:37.531754",
     "exception": false,
     "start_time": "2020-11-29T14:20:37.429487",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Setting a reserve price involves finding the perfect line between ensuring the reserve is high enough to guarantee you a comfortable amount if reached and low enough that it will not be prohibitive to encouraging the bidding on the day. There is a common fear of setting the reserve price too low. [Ref](https://realestateview.com.au/advice/selling/the-ins-and-outs-of-setting-a-reserve-price/)\n",
    "\t\n",
    "What is a line item in advertising?\n",
    "\n",
    "Line items contain information about how an ad should show on your website or app, such as when and where specifically to show the ad. Line items also contain details that help an ad reach a specific demographic or audience.\n",
    "\n",
    "In our context, I believe 1 line items represent as follows: [See 2.2 (Conclusion)]\n",
    "\n",
    "| Feature | Description |\n",
    "| -: | :- |\n",
    "| site_id | denotes a different website\n",
    "| ad_type_id | can be display ads , video ads, text ads etc\n",
    "| geo_id | denotes a different country.\n",
    "| device_category_id | desktop , mobile, tablet\n",
    "| advertiser_id |  denotes a different bidder in the auction\n",
    "| os_id | operating system (android, ios etc)\n",
    "| ad_unit_id | one page can have more than one ad units\n",
    "| is_weekend | 1 if weekend otherwise; 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.100861,
     "end_time": "2020-11-29T14:20:37.733518",
     "exception": false,
     "start_time": "2020-11-29T14:20:37.632657",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4.2 Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.106987,
     "end_time": "2020-11-29T14:20:37.943072",
     "exception": false,
     "start_time": "2020-11-29T14:20:37.836085",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The assumptions that I have kept in mind to determine optimal reserve price is based on CPM. CPM shares a linear relationship with 'bids' or 'price'. It is calculated as revenue/impressions * 1000.\n",
    "In simple terms, CPM represents  'bids' or 'price'. In that way, we can determine, what is the price for winning a bid of a particular line item by looking at the CPM. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.10033,
     "end_time": "2020-11-29T14:20:38.145447",
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     "start_time": "2020-11-29T14:20:38.045117",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4.3 Determine Optimal Reserve Price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.101164,
     "end_time": "2020-11-29T14:20:38.348772",
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     "start_time": "2020-11-29T14:20:38.247608",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Step 1. Get total measusable_impressions & total_revenue per line item i.e. group by above features and get sum(measusable_impressions) & sum(total_revenue)\n",
    "\n",
    "Step 2. Now, we will apply the CPM formula on newly created columns and create a new column which is CPM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:38.564454Z",
     "iopub.status.busy": "2020-11-29T14:20:38.563374Z",
     "iopub.status.idle": "2020-11-29T14:20:38.694521Z",
     "shell.execute_reply": "2020-11-29T14:20:38.693796Z"
    },
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     "end_time": "2020-11-29T14:20:38.694644",
     "exception": false,
     "start_time": "2020-11-29T14:20:38.452873",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "ldf_grouped_ad_data = ldf_ad_data.groupby([\"is_weekend\", \"site_id\",\"ad_type_id\",\n",
    "                     \"geo_id\",\"device_category_id\",\n",
    "                     \"advertiser_id\",\"os_id\",\"ad_unit_id\"])[\"measurable_impressions\",\"total_revenue\"].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:38.900516Z",
     "iopub.status.busy": "2020-11-29T14:20:38.899590Z",
     "iopub.status.idle": "2020-11-29T14:20:38.904187Z",
     "shell.execute_reply": "2020-11-29T14:20:38.903553Z"
    },
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     "duration": 0.109798,
     "end_time": "2020-11-29T14:20:38.904317",
     "exception": false,
     "start_time": "2020-11-29T14:20:38.794519",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91574, 10)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldf_grouped_ad_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:39.116540Z",
     "iopub.status.busy": "2020-11-29T14:20:39.115719Z",
     "iopub.status.idle": "2020-11-29T14:20:39.185361Z",
     "shell.execute_reply": "2020-11-29T14:20:39.184735Z"
    },
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     "duration": 0.180462,
     "end_time": "2020-11-29T14:20:39.185489",
     "exception": false,
     "start_time": "2020-11-29T14:20:39.005027",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_weekend</th>\n",
       "      <th>site_id</th>\n",
       "      <th>ad_type_id</th>\n",
       "      <th>geo_id</th>\n",
       "      <th>device_category_id</th>\n",
       "      <th>advertiser_id</th>\n",
       "      <th>os_id</th>\n",
       "      <th>ad_unit_id</th>\n",
       "      <th>measurable_impressions</th>\n",
       "      <th>total_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>91,574.0000</td>\n",
       "      <td>91,574.0000</td>\n",
       "      <td>91,574.0000</td>\n",
       "      <td>91,574.0000</td>\n",
       "      <td>91,574.0000</td>\n",
       "      <td>91,574.0000</td>\n",
       "      <td>91,574.0000</td>\n",
       "      <td>91,574.0000</td>\n",
       "      <td>91,574.0000</td>\n",
       "      <td>91,574.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.3378</td>\n",
       "      <td>346.7380</td>\n",
       "      <td>10.2279</td>\n",
       "      <td>101.8062</td>\n",
       "      <td>1.9437</td>\n",
       "      <td>275.6568</td>\n",
       "      <td>57.1691</td>\n",
       "      <td>5,132.2748</td>\n",
       "      <td>20.5490</td>\n",
       "      <td>0.0264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.4729</td>\n",
       "      <td>2.8976</td>\n",
       "      <td>1.2425</td>\n",
       "      <td>60.4044</td>\n",
       "      <td>0.8028</td>\n",
       "      <td>643.7672</td>\n",
       "      <td>2.2161</td>\n",
       "      <td>64.6270</td>\n",
       "      <td>140.6948</td>\n",
       "      <td>0.1904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>342.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>5,050.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>345.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>46.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>79.0000</td>\n",
       "      <td>56.0000</td>\n",
       "      <td>5,097.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>346.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>99.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>57.0000</td>\n",
       "      <td>5,119.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>349.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>160.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>139.0000</td>\n",
       "      <td>58.0000</td>\n",
       "      <td>5,168.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>0.0036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>351.0000</td>\n",
       "      <td>17.0000</td>\n",
       "      <td>308.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>2,646.0000</td>\n",
       "      <td>60.0000</td>\n",
       "      <td>5,443.0000</td>\n",
       "      <td>17,820.0000</td>\n",
       "      <td>23.0186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                is_weekend              site_id           ad_type_id  \\\n",
       "count          91,574.0000          91,574.0000          91,574.0000   \n",
       "mean                0.3378             346.7380              10.2279   \n",
       "std                 0.4729               2.8976               1.2425   \n",
       "min                 0.0000             342.0000              10.0000   \n",
       "25%                 0.0000             345.0000              10.0000   \n",
       "50%                 0.0000             346.0000              10.0000   \n",
       "75%                 1.0000             349.0000              10.0000   \n",
       "max                 1.0000             351.0000              17.0000   \n",
       "\n",
       "                    geo_id   device_category_id        advertiser_id  \\\n",
       "count          91,574.0000          91,574.0000          91,574.0000   \n",
       "mean              101.8062               1.9437             275.6568   \n",
       "std                60.4044               0.8028             643.7672   \n",
       "min                 1.0000               1.0000               8.0000   \n",
       "25%                46.0000               1.0000              79.0000   \n",
       "50%                99.0000               2.0000              90.0000   \n",
       "75%               160.0000               3.0000             139.0000   \n",
       "max               308.0000               5.0000           2,646.0000   \n",
       "\n",
       "                     os_id           ad_unit_id  measurable_impressions  \\\n",
       "count          91,574.0000          91,574.0000             91,574.0000   \n",
       "mean               57.1691           5,132.2748                 20.5490   \n",
       "std                 2.2161              64.6270                140.6948   \n",
       "min                15.0000           5,050.0000                  0.0000   \n",
       "25%                56.0000           5,097.0000                  0.0000   \n",
       "50%                57.0000           5,119.0000                  2.0000   \n",
       "75%                58.0000           5,168.0000                  8.0000   \n",
       "max                60.0000           5,443.0000             17,820.0000   \n",
       "\n",
       "             total_revenue  \n",
       "count          91,574.0000  \n",
       "mean                0.0264  \n",
       "std                 0.1904  \n",
       "min                 0.0000  \n",
       "25%                 0.0000  \n",
       "50%                 0.0000  \n",
       "75%                 0.0036  \n",
       "max                23.0186  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldf_grouped_ad_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.102301,
     "end_time": "2020-11-29T14:20:39.389915",
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     "start_time": "2020-11-29T14:20:39.287614",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Calculate CPM - Cost Per Mille"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:39.601028Z",
     "iopub.status.busy": "2020-11-29T14:20:39.600061Z",
     "iopub.status.idle": "2020-11-29T14:20:39.603507Z",
     "shell.execute_reply": "2020-11-29T14:20:39.602893Z"
    },
    "papermill": {
     "duration": 0.110526,
     "end_time": "2020-11-29T14:20:39.603679",
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     "start_time": "2020-11-29T14:20:39.493153",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def calculate_cpm(n, d):\n",
    "    \"\"\"\n",
    "    CPM – cost per Mille. This is one of the most important ways to measure \n",
    "    performance. It is. Calculated as revenue/impressions * 1000. 'bids' and 'price' are \n",
    "    measured in terms of CPM\n",
    "    \"\"\"\n",
    "    return (n / d)*1000 if d else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:39.813586Z",
     "iopub.status.busy": "2020-11-29T14:20:39.812821Z",
     "iopub.status.idle": "2020-11-29T14:20:41.920699Z",
     "shell.execute_reply": "2020-11-29T14:20:41.920037Z"
    },
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     "duration": 2.215568,
     "end_time": "2020-11-29T14:20:41.920845",
     "exception": false,
     "start_time": "2020-11-29T14:20:39.705277",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "ldf_grouped_ad_data['cpm/reserve_price'] = ldf_grouped_ad_data.apply(lambda x: calculate_cpm(((x['total_revenue'])),x['measurable_impressions']) , axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-29T14:20:42.143121Z",
     "iopub.status.busy": "2020-11-29T14:20:42.142275Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_weekend</th>\n",
       "      <th>site_id</th>\n",
       "      <th>ad_type_id</th>\n",
       "      <th>geo_id</th>\n",
       "      <th>device_category_id</th>\n",
       "      <th>advertiser_id</th>\n",
       "      <th>os_id</th>\n",
       "      <th>ad_unit_id</th>\n",
       "      <th>measurable_impressions</th>\n",
       "      <th>total_revenue</th>\n",
       "      <th>cpm/reserve_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>91,574.0000</td>\n",
       "      <td>91,574.0000</td>\n",
       "      <td>91,574.0000</td>\n",
       "      <td>91,574.0000</td>\n",
       "      <td>91,574.0000</td>\n",
       "      <td>91,574.0000</td>\n",
       "      <td>91,574.0000</td>\n",
       "      <td>91,574.0000</td>\n",
       "      <td>91,574.0000</td>\n",
       "      <td>91,574.0000</td>\n",
       "      <td>91,574.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.3378</td>\n",
       "      <td>346.7380</td>\n",
       "      <td>10.2279</td>\n",
       "      <td>101.8062</td>\n",
       "      <td>1.9437</td>\n",
       "      <td>275.6568</td>\n",
       "      <td>57.1691</td>\n",
       "      <td>5,132.2748</td>\n",
       "      <td>20.5490</td>\n",
       "      <td>0.0264</td>\n",
       "      <td>0.5687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.4729</td>\n",
       "      <td>2.8976</td>\n",
       "      <td>1.2425</td>\n",
       "      <td>60.4044</td>\n",
       "      <td>0.8028</td>\n",
       "      <td>643.7672</td>\n",
       "      <td>2.2161</td>\n",
       "      <td>64.6270</td>\n",
       "      <td>140.6948</td>\n",
       "      <td>0.1904</td>\n",
       "      <td>1.4160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>342.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>5,050.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>345.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>46.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>79.0000</td>\n",
       "      <td>56.0000</td>\n",
       "      <td>5,097.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>346.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>99.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>57.0000</td>\n",
       "      <td>5,119.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>349.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>160.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>139.0000</td>\n",
       "      <td>58.0000</td>\n",
       "      <td>5,168.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>351.0000</td>\n",
       "      <td>17.0000</td>\n",
       "      <td>308.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>2,646.0000</td>\n",
       "      <td>60.0000</td>\n",
       "      <td>5,443.0000</td>\n",
       "      <td>17,820.0000</td>\n",
       "      <td>23.0186</td>\n",
       "      <td>52.2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                is_weekend              site_id           ad_type_id  \\\n",
       "count          91,574.0000          91,574.0000          91,574.0000   \n",
       "mean                0.3378             346.7380              10.2279   \n",
       "std                 0.4729               2.8976               1.2425   \n",
       "min                 0.0000             342.0000              10.0000   \n",
       "25%                 0.0000             345.0000              10.0000   \n",
       "50%                 0.0000             346.0000              10.0000   \n",
       "75%                 1.0000             349.0000              10.0000   \n",
       "max                 1.0000             351.0000              17.0000   \n",
       "\n",
       "                    geo_id   device_category_id        advertiser_id  \\\n",
       "count          91,574.0000          91,574.0000          91,574.0000   \n",
       "mean              101.8062               1.9437             275.6568   \n",
       "std                60.4044               0.8028             643.7672   \n",
       "min                 1.0000               1.0000               8.0000   \n",
       "25%                46.0000               1.0000              79.0000   \n",
       "50%                99.0000               2.0000              90.0000   \n",
       "75%               160.0000               3.0000             139.0000   \n",
       "max               308.0000               5.0000           2,646.0000   \n",
       "\n",
       "                     os_id           ad_unit_id  measurable_impressions  \\\n",
       "count          91,574.0000          91,574.0000             91,574.0000   \n",
       "mean               57.1691           5,132.2748                 20.5490   \n",
       "std                 2.2161              64.6270                140.6948   \n",
       "min                15.0000           5,050.0000                  0.0000   \n",
       "25%                56.0000           5,097.0000                  0.0000   \n",
       "50%                57.0000           5,119.0000                  2.0000   \n",
       "75%                58.0000           5,168.0000                  8.0000   \n",
       "max                60.0000           5,443.0000             17,820.0000   \n",
       "\n",
       "             total_revenue    cpm/reserve_price  \n",
       "count          91,574.0000          91,574.0000  \n",
       "mean                0.0264               0.5687  \n",
       "std                 0.1904               1.4160  \n",
       "min                 0.0000               0.0000  \n",
       "25%                 0.0000               0.0000  \n",
       "50%                 0.0000               0.0000  \n",
       "75%                 0.0036               0.5000  \n",
       "max                23.0186              52.2000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldf_grouped_ad_data.describe()"
   ]
  },
  {
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   "source": [
    "### 4.4 Result Summary"
   ]
  },
  {
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    "As compared to the bid value in real life, here the derived reserve prices justify the minimum amount publisher should set to lose at lower bids, which encourage higher bidding and more publisher revenue.\n",
    "\n",
    "As we can see in the above table, most of the time, bidding prices are reasonably close to **30-50 cents**. This means the assumptions we have kept in mind could be correct."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr/>"
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "# 5. Conclusion"
   ]
  },
  {
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   "source": [
    "Digital markets are playing catch-up compared to some other industries when it comes to AI. There is a lot to gain from an AI-first world. The proposed solution has the potential to provide benefits to publishers.\n",
    "\n",
    "Here are some points which may provide the concrete direction to the research we held:\n",
    "- line_item_type_id could provide a basis for selecting the combination of features. But as stated in the problem statement, we have ignored it.\n",
    "- Also, boosting or dropping in CTR value resulted in an almost 12% growth rate, makes me think what if we could calibrate the impressions itself and see the trade-off in growth rate. Calibration in terms of actual value could make a big difference in CTR and later on in revenue.\n",
    "\n",
    "Due to time constraints, I couldn't try all possible combinations but this study may, later on, provide a good basis for forecasting research.\n",
    "\n",
    "Here I would like to highlight some points to align my thoughts:\n",
    "- Quantum of data- There was only one month of data was given to forecasting the solution, which I believe is less to support the pieces of evidence I present.\n",
    "- Lack of Time- The solution is built in 2 straight days. Having said this, expertise in analytics & tools/libraries such as python, pandas, Jupyter Notebook paced the progress. Also, if we improve the base for the domain knowledge that would save some time.\n"
   ]
  },
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     "start_time": "2020-11-29T14:20:43.174344",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "<center><h1>End Here</h1></center>"
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