Capstone_Project_Final.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "09574b60",
   "metadata": {},
   "source": [
    "# AI200 Capstone Project\n",
    "\n",
    "This Prediction Challenge is modelled after the 2015 Facebook Recruitment Challenge, a prediction competition jointly held by Facebook and Kaggle.\n",
    "\n",
    "In this competition, we are chasing down robots for an online auction site. Human bidders on the site are becoming increasingly frustrated with their inability to win auctions vs. their software-controlled counterparts. As a result, usage from the site's core customer base is plummeting.\n",
    "\n",
    "In order to rebuild customer happiness, the site owners need to eliminate computer generated bidding from their auctions. Their attempt at building a model to identify these bids using behavioral data, including bid frequency over short periods of time, has proven insufficient.\n",
    "\n",
    "The goal of this competition is to identify online auction bids that are placed by \"robots\", helping the site owners easily flag these users for removal from their site to prevent unfair auction activity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "158e74f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "%matplotlib inline\n",
    "\n",
    "pd.options.display.max_columns = None\n",
    "pd.options.display.max_rows = 99"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0312eb16",
   "metadata": {},
   "source": [
    "## Train Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b562c4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0f688505",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of unique bidder_id = 2013\n",
      "No. of unique payment_account = 2013\n",
      "No. of unique address = 2013\n",
      "\n",
      "No. of humans in train data set:  1910\n",
      "No. of bots in train data set:  103\n"
     ]
    }
   ],
   "source": [
    "print('No. of unique bidder_id = {0:}'.format(train['bidder_id'].nunique()))\n",
    "print('No. of unique payment_account = {0:}'.format(train['payment_account'].nunique()))\n",
    "print('No. of unique address = {0:}'.format(train['address'].nunique()))\n",
    "\n",
    "train_human = train[train['outcome'] == 0]\n",
    "print('\\nNo. of humans in train data set: ', len(train_human))\n",
    "\n",
    "train_bot = train[train['outcome'] == 1]\n",
    "print('No. of bots in train data set: ', len(train_bot))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04fdcfc8",
   "metadata": {},
   "source": [
    "There are no missing values in the train dataset. It contains 2013 unique users."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b37a339",
   "metadata": {},
   "source": [
    "# Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "50a1251d",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fc2bdfa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of unique bidder_id = 4700\n",
      "No. of unique payment_account = 4700\n",
      "No. of unique address = 4700\n"
     ]
    }
   ],
   "source": [
    "print('No. of unique bidder_id = {0:}'.format(test['bidder_id'].nunique()))\n",
    "print('No. of unique payment_account = {0:}'.format(test['payment_account'].nunique()))\n",
    "print('No. of unique address = {0:}'.format(test['address'].nunique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "699cb0b2",
   "metadata": {},
   "source": [
    "There are also no missing data in the test dataset. It contains 4700 unique users."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a455622",
   "metadata": {},
   "source": [
    "## Combined Train Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0e1a737d",
   "metadata": {},
   "outputs": [],
   "source": [
    "combine = pd.concat([train, test], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4496b4d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of unique bidder_id = 6713\n",
      "No. of unique payment_account = 6713\n",
      "No. of unique address = 6713\n"
     ]
    }
   ],
   "source": [
    "print('No. of unique bidder_id = {0:}'.format(combine['bidder_id'].nunique()))\n",
    "print('No. of unique payment_account = {0:}'.format(combine['payment_account'].nunique()))\n",
    "print('No. of unique address = {0:}'.format(combine['address'].nunique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63dd2c9c",
   "metadata": {},
   "source": [
    "To access the train dataset, we will use combine[:2013]. To access the test dataset, we will use combine[2013:]."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9ab1761",
   "metadata": {},
   "source": [
    "## Bids Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a55e841b",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids = pd.read_csv('bids.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1b44ccf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bid_id</th>\n",
       "      <th>bidder_id</th>\n",
       "      <th>auction</th>\n",
       "      <th>merchandise</th>\n",
       "      <th>device</th>\n",
       "      <th>time</th>\n",
       "      <th>country</th>\n",
       "      <th>ip</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>c6faf9e526c72bc8383f2bc4417a465d2fa27</td>\n",
       "      <td>ewmzr</td>\n",
       "      <td>jewelry</td>\n",
       "      <td>phone0</td>\n",
       "      <td>9759243157894736</td>\n",
       "      <td>us</td>\n",
       "      <td>69.166.231.58</td>\n",
       "      <td>vasstdc27m7nks3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>d96e93359ed70b7b4eebac163f448afeb50dc</td>\n",
       "      <td>aeqok</td>\n",
       "      <td>furniture</td>\n",
       "      <td>phone1</td>\n",
       "      <td>9759243157894736</td>\n",
       "      <td>in</td>\n",
       "      <td>50.201.125.84</td>\n",
       "      <td>jmqlhflrzwuay9c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>065bdcc596308d1fdd0014261d6e198aa138e</td>\n",
       "      <td>wa00e</td>\n",
       "      <td>home goods</td>\n",
       "      <td>phone2</td>\n",
       "      <td>9759243157894736</td>\n",
       "      <td>py</td>\n",
       "      <td>112.54.208.157</td>\n",
       "      <td>vasstdc27m7nks3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>e97aa310037dd210f52b5b164077a926bb5de</td>\n",
       "      <td>jefix</td>\n",
       "      <td>jewelry</td>\n",
       "      <td>phone4</td>\n",
       "      <td>9759243157894736</td>\n",
       "      <td>in</td>\n",
       "      <td>18.99.175.133</td>\n",
       "      <td>vasstdc27m7nks3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>41a7f56fcc5771d305baa6e989fb2e8cceb21</td>\n",
       "      <td>jefix</td>\n",
       "      <td>jewelry</td>\n",
       "      <td>phone5</td>\n",
       "      <td>9759243157894736</td>\n",
       "      <td>in</td>\n",
       "      <td>145.138.5.37</td>\n",
       "      <td>vasstdc27m7nks3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bid_id                              bidder_id auction merchandise  device  \\\n",
       "0       0  c6faf9e526c72bc8383f2bc4417a465d2fa27   ewmzr     jewelry  phone0   \n",
       "1       1  d96e93359ed70b7b4eebac163f448afeb50dc   aeqok   furniture  phone1   \n",
       "2       2  065bdcc596308d1fdd0014261d6e198aa138e   wa00e  home goods  phone2   \n",
       "3       3  e97aa310037dd210f52b5b164077a926bb5de   jefix     jewelry  phone4   \n",
       "4       4  41a7f56fcc5771d305baa6e989fb2e8cceb21   jefix     jewelry  phone5   \n",
       "\n",
       "               time country              ip              url  \n",
       "0  9759243157894736      us   69.166.231.58  vasstdc27m7nks3  \n",
       "1  9759243157894736      in   50.201.125.84  jmqlhflrzwuay9c  \n",
       "2  9759243157894736      py  112.54.208.157  vasstdc27m7nks3  \n",
       "3  9759243157894736      in   18.99.175.133  vasstdc27m7nks3  \n",
       "4  9759243157894736      in    145.138.5.37  vasstdc27m7nks3  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bids.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bc54072",
   "metadata": {},
   "source": [
    "Recap of the data dictionary for bids dataset\n",
    "\n",
    "* bid_id - unique id for this bid\n",
    "* bidder_id – Unique identifier of a bidder (same as the bidder_id used in train.csv and test.csv)\n",
    "* auction – Unique identifier of an auction\n",
    "* merchandise – The category of the auction site campaign, which means the bidder might come to this site by way of searching for \"home goods\" but ended up bidding for \"sporting goods\" - and that leads to this field being \"home goods\". This categorical field could be a search term, or online advertisement.\n",
    "* device – Phone model of a visitor\n",
    "* time - Time that the bid is made (transformed to protect privacy).\n",
    "* country - The country that the IP belongs to\n",
    "* ip – IP address of a bidder (obfuscated to protect privacy).\n",
    "* url - url where the bidder was referred from (obfuscated to protect privacy)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c94201bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7656334, 9)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bids.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "011ae7ec",
   "metadata": {},
   "source": [
    "The bid dataset contains 7,656,334 unique bids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "11163dd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of unique bids = 7656334\n",
      "No. of unique bidder = 6614\n",
      "No. of unique auction = 15051\n",
      "No. of unique merchandise category = 10\n",
      "No. of unique phone model = 7351\n",
      "No. of unique time bid made = 776529\n",
      "No. of unique country = 199\n",
      "No. of unique IP addresses = 2303991\n",
      "No. of unique url where bidder referred from = 1786351\n"
     ]
    }
   ],
   "source": [
    "print('No. of unique bids = {0:}'.format(bids['bid_id'].nunique()))\n",
    "print('No. of unique bidder = {0:}'.format(bids['bidder_id'].nunique()))\n",
    "print('No. of unique auction = {0:}'.format(bids['auction'].nunique()))\n",
    "print('No. of unique merchandise category = {0:}'.format(bids['merchandise'].nunique()))\n",
    "print('No. of unique phone model = {0:}'.format(bids['device'].nunique()))\n",
    "print('No. of unique time bid made = {0:}'.format(bids['time'].nunique()))\n",
    "print('No. of unique country = {0:}'.format(bids['country'].nunique()))\n",
    "print('No. of unique IP addresses = {0:}'.format(bids['ip'].nunique()))\n",
    "print('No. of unique url where bidder referred from = {0:}'.format(bids['url'].nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cfcbf043",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bid_id            0\n",
       "bidder_id         0\n",
       "auction           0\n",
       "merchandise       0\n",
       "device            0\n",
       "time              0\n",
       "country        8859\n",
       "ip                0\n",
       "url               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bids.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b582e0",
   "metadata": {},
   "source": [
    "Here we see that there are missing data for the `country` column."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97cf5484",
   "metadata": {},
   "source": [
    "## No. of Average Unique IP/Device/URL/Bids per Auction\n",
    "\n",
    "As there are varying number of unique IPs, devices, URLs and bids per auction that each bidder participatd in, we will aggregate them by each unique auction. Due to the large span, we choose to use median instead of mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "13602b1f",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>bidder_id</th>\n",
       "      <th>avg_ip</th>\n",
       "      <th>avg_device</th>\n",
       "      <th>avg_url</th>\n",
       "      <th>avg_bid</th>\n",
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      "text/plain": [
       "                               bidder_id  avg_ip  avg_device  avg_url  avg_bid\n",
       "0  0013f9b8d1f462df4462e1c1216e441ba6e18     1.0         1.0      1.0      1.0\n",
       "1  0014e3b911d1420e43ced1dc4fc18fde0fd0c     1.0         1.0      1.0      1.0\n",
       "2  002828f800c5132e297cfe3d44fbde9aeac51     1.0         1.0      1.0      1.0\n",
       "3  00402f50c4086f320cb6bf94f04462ea32441     1.0         1.0      1.0      1.0\n",
       "4  0040cea6b93afd86768c365d89513ffb7c0ba     1.0         1.0      1.0      1.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_avg_ip = pd.DataFrame(bids.groupby(['bidder_id', 'auction'])['ip'].agg('nunique').groupby(level=0).median()).reset_index()\n",
    "full_avg_ip.rename({'ip': 'avg_ip'}, axis=1, inplace=True)\n",
    "\n",
    "full_avg_device = pd.DataFrame(bids.groupby(['bidder_id', 'auction'])['device'].agg('nunique').groupby(level=0).median()).reset_index()\n",
    "full_avg_device.rename({'device': 'avg_device'}, axis=1, inplace=True)\n",
    "\n",
    "full_avg_url = pd.DataFrame(bids.groupby(['bidder_id', 'auction'])['url'].agg('nunique').groupby(level=0).median()).reset_index()\n",
    "full_avg_url.rename({'url': 'avg_url'}, axis=1, inplace=True)\n",
    "\n",
    "full_avg_bids = pd.DataFrame(bids.groupby(['bidder_id', 'auction']).size().groupby(level=0).median()).reset_index()\n",
    "full_avg_bids.rename({0: 'avg_bid'}, axis=1, inplace=True)\n",
    "\n",
    "full_bids = pd.merge(full_avg_ip, full_avg_device, how = 'left', on = 'bidder_id')\n",
    "full_bids = pd.merge(full_bids, full_avg_url, how = 'left', on = 'bidder_id')\n",
    "full_bids = pd.merge(full_bids, full_avg_bids, how = 'left', on = 'bidder_id')\n",
    "\n",
    "full_bids.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48562083",
   "metadata": {},
   "source": [
    "## Create Dataset based on Unique Bidder\n",
    "\n",
    "We will aggregate the data by each unique bidder, to calculate the sum of each data fields per bidder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b24deafa",
   "metadata": {},
   "outputs": [],
   "source": [
    "bidder = bids.groupby('bidder_id').nunique().reset_index().drop(columns = ['time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7bd4e3c1",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bidder_id</th>\n",
       "      <th>bid_id</th>\n",
       "      <th>auction</th>\n",
       "      <th>merchandise</th>\n",
       "      <th>device</th>\n",
       "      <th>country</th>\n",
       "      <th>ip</th>\n",
       "      <th>url</th>\n",
       "      <th>adevice</th>\n",
       "      <th>aauction</th>\n",
       "      <th>aurl</th>\n",
       "      <th>aip</th>\n",
       "      <th>amerchandise</th>\n",
       "      <th>acountry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0013f9b8d1f462df4462e1c1216e441ba6e18</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0014e3b911d1420e43ced1dc4fc18fde0fd0c</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>002828f800c5132e297cfe3d44fbde9aeac51</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00402f50c4086f320cb6bf94f04462ea32441</td>\n",
       "      <td>249</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>21</td>\n",
       "      <td>151</td>\n",
       "      <td>98</td>\n",
       "      <td>0.397590</td>\n",
       "      <td>0.357430</td>\n",
       "      <td>0.393574</td>\n",
       "      <td>0.606426</td>\n",
       "      <td>0.004016</td>\n",
       "      <td>0.084337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0040cea6b93afd86768c365d89513ffb7c0ba</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>0.677419</td>\n",
       "      <td>0.741935</td>\n",
       "      <td>0.129032</td>\n",
       "      <td>0.903226</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.193548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               bidder_id  bid_id  auction  merchandise  \\\n",
       "0  0013f9b8d1f462df4462e1c1216e441ba6e18       8        6            1   \n",
       "1  0014e3b911d1420e43ced1dc4fc18fde0fd0c       7        7            1   \n",
       "2  002828f800c5132e297cfe3d44fbde9aeac51       2        2            1   \n",
       "3  00402f50c4086f320cb6bf94f04462ea32441     249       89            1   \n",
       "4  0040cea6b93afd86768c365d89513ffb7c0ba      31       23            1   \n",
       "\n",
       "   device  country   ip  url   adevice  aauction      aurl       aip  \\\n",
       "0       7        2    8    4  0.875000  0.750000  0.500000  1.000000   \n",
       "1       7        4    7    4  1.000000  1.000000  0.571429  1.000000   \n",
       "2       1        1    2    1  0.500000  1.000000  0.500000  1.000000   \n",
       "3      99       21  151   98  0.397590  0.357430  0.393574  0.606426   \n",
       "4      21        6   28    4  0.677419  0.741935  0.129032  0.903226   \n",
       "\n",
       "   amerchandise  acountry  \n",
       "0      0.125000  0.250000  \n",
       "1      0.142857  0.571429  \n",
       "2      0.500000  0.500000  \n",
       "3      0.004016  0.084337  \n",
       "4      0.032258  0.193548  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bidder['adevice'] = bidder['device'] / bidder['bid_id']\n",
    "bidder['aauction'] = bidder['auction'] / bidder['bid_id']\n",
    "bidder['aurl'] = bidder['url'] / bidder['bid_id']\n",
    "bidder['aip'] = bidder['ip'] / bidder['bid_id']\n",
    "bidder['amerchandise'] = bidder['merchandise'] / bidder['bid_id']\n",
    "bidder['acountry'] = bidder['country'] / bidder['bid_id']\n",
    "bidder.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72d92c17",
   "metadata": {},
   "source": [
    "# Finding No. of Bids Per Top Data Fields\n",
    "\n",
    "We also created new features by aggregating the sum of top five used IPs, URLs, device, accessing countries, participated auctions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "38a94109",
   "metadata": {},
   "outputs": [],
   "source": [
    "#z = bids.groupby('url').size().reset_index()\n",
    "#z.sort_values(0, ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ff1dea47",
   "metadata": {},
   "outputs": [],
   "source": [
    "auction_1st = bids.groupby(['bidder_id', 'auction']).size().reset_index()\n",
    "auction_1st = auction_1st[auction_1st['auction'] == 'jqx39']\n",
    "auction_1st.rename({0: 'auction_1st'}, axis=1, inplace=True)\n",
    "auction_1st.drop('auction', axis=1, inplace=True)\n",
    "\n",
    "auction_2nd = bids.groupby(['bidder_id', 'auction']).size().reset_index()\n",
    "auction_2nd = auction_2nd[auction_2nd['auction'] == 'jefix']\n",
    "auction_2nd.rename({0: 'auction_2nd'}, axis=1, inplace=True)\n",
    "auction_2nd.drop('auction', axis=1, inplace=True)\n",
    "\n",
    "auction_3rd = bids.groupby(['bidder_id', 'auction']).size().reset_index()\n",
    "auction_3rd = auction_3rd[auction_3rd['auction'] == 'no958']\n",
    "auction_3rd.rename({0: 'auction_3rd'}, axis=1, inplace=True)\n",
    "auction_3rd.drop('auction', axis=1, inplace=True)\n",
    "\n",
    "auction_4th = bids.groupby(['bidder_id', 'auction']).size().reset_index()\n",
    "auction_4th = auction_4th[auction_4th['auction'] == 'opnq4']\n",
    "auction_4th.rename({0: 'auction_4th'}, axis=1, inplace=True)\n",
    "auction_4th.drop('auction', axis=1, inplace=True)\n",
    "\n",
    "auction_5th = bids.groupby(['bidder_id', 'auction']).size().reset_index()\n",
    "auction_5th = auction_5th[auction_5th['auction'] == 'du967']\n",
    "auction_5th.rename({0: 'auction_5th'}, axis=1, inplace=True)\n",
    "auction_5th.drop('auction', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7f6408a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "device_1st = bids.groupby(['bidder_id', 'device']).size().reset_index()\n",
    "device_1st = device_1st[device_1st['device'] == 'phone4']\n",
    "device_1st.rename({0: 'device_1st'}, axis=1, inplace=True)\n",
    "device_1st.drop('device', axis=1, inplace=True)\n",
    "\n",
    "device_2nd = bids.groupby(['bidder_id', 'device']).size().reset_index()\n",
    "device_2nd = device_2nd[device_2nd['device'] == 'phone2']\n",
    "device_2nd.rename({0: 'device_2nd'}, axis=1, inplace=True)\n",
    "device_2nd.drop('device', axis=1, inplace=True)\n",
    "\n",
    "device_3rd = bids.groupby(['bidder_id', 'device']).size().reset_index()\n",
    "device_3rd = device_3rd[device_3rd['device'] == 'phone35']\n",
    "device_3rd.rename({0: 'device_3rd'}, axis=1, inplace=True)\n",
    "device_3rd.drop('device', axis=1, inplace=True)\n",
    "\n",
    "device_4th = bids.groupby(['bidder_id', 'device']).size().reset_index()\n",
    "device_4th = device_4th[device_4th['device'] == 'phone65']\n",
    "device_4th.rename({0: 'device_4th'}, axis=1, inplace=True)\n",
    "device_4th.drop('device', axis=1, inplace=True)\n",
    "\n",
    "device_5th = bids.groupby(['bidder_id', 'device']).size().reset_index()\n",
    "device_5th = device_5th[device_5th['device'] == 'phone3']\n",
    "device_5th.rename({0: 'device_5th'}, axis=1, inplace=True)\n",
    "device_5th.drop('device', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "02529dfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "country_1st = bids.groupby(['bidder_id', 'country']).size().reset_index()\n",
    "country_1st = country_1st[country_1st['country'] == 'in']\n",
    "country_1st.rename({0: 'country_1st'}, axis=1, inplace=True)\n",
    "country_1st.drop('country', axis=1, inplace=True)\n",
    "\n",
    "country_2nd = bids.groupby(['bidder_id', 'country']).size().reset_index()\n",
    "country_2nd = country_2nd[country_2nd['country'] == 'ng']\n",
    "country_2nd.rename({0: 'country_2nd'}, axis=1, inplace=True)\n",
    "country_2nd.drop('country', axis=1, inplace=True)\n",
    "\n",
    "country_3rd = bids.groupby(['bidder_id', 'country']).size().reset_index()\n",
    "country_3rd = country_3rd[country_3rd['country'] == 'id']\n",
    "country_3rd.rename({0: 'country_3rd'}, axis=1, inplace=True)\n",
    "country_3rd.drop('country', axis=1, inplace=True)\n",
    "\n",
    "country_4th = bids.groupby(['bidder_id', 'country']).size().reset_index()\n",
    "country_4th = country_4th[country_4th['country'] == 'tr']\n",
    "country_4th.rename({0: 'country_4th'}, axis=1, inplace=True)\n",
    "country_4th.drop('country', axis=1, inplace=True)\n",
    "\n",
    "country_5th = bids.groupby(['bidder_id', 'country']).size().reset_index()\n",
    "country_5th = country_5th[country_5th['country'] == 'us']\n",
    "country_5th.rename({0: 'country_5th'}, axis=1, inplace=True)\n",
    "country_5th.drop('country', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "91eb0365",
   "metadata": {},
   "outputs": [],
   "source": [
    "ip_1st = bids.groupby(['bidder_id', 'ip']).size().reset_index()\n",
    "ip_1st = ip_1st[ip_1st['ip'] == '37.95.134.218']\n",
    "ip_1st.rename({0: 'ip_1st'}, axis=1, inplace=True)\n",
    "ip_1st.drop('ip', axis=1, inplace=True)\n",
    "\n",
    "ip_2nd = bids.groupby(['bidder_id', 'ip']).size().reset_index()\n",
    "ip_2nd = ip_2nd[ip_2nd['ip'] == '224.130.156.216']\n",
    "ip_2nd.rename({0: 'ip_2nd'}, axis=1, inplace=True)\n",
    "ip_2nd.drop('ip', axis=1, inplace=True)\n",
    "\n",
    "ip_3rd = bids.groupby(['bidder_id', 'ip']).size().reset_index()\n",
    "ip_3rd = ip_3rd[ip_3rd['ip'] == '13.31.35.207']\n",
    "ip_3rd.rename({0: 'ip_3rd'}, axis=1, inplace=True)\n",
    "ip_3rd.drop('ip', axis=1, inplace=True)\n",
    "\n",
    "ip_4th = bids.groupby(['bidder_id', 'ip']).size().reset_index()\n",
    "ip_4th = ip_4th[ip_4th['ip'] == '119.88.125.198']\n",
    "ip_4th.rename({0: 'ip_4th'}, axis=1, inplace=True)\n",
    "ip_4th.drop('ip', axis=1, inplace=True)\n",
    "\n",
    "ip_5th = bids.groupby(['bidder_id', 'ip']).size().reset_index()\n",
    "ip_5th = ip_5th[ip_5th['ip'] == '155.184.236.45']\n",
    "ip_5th.rename({0: 'ip_5th'}, axis=1, inplace=True)\n",
    "ip_5th.drop('ip', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1138abbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "url_1st = bids.groupby(['bidder_id', 'url']).size().reset_index()\n",
    "url_1st = url_1st[url_1st['url'] == 'vasstdc27m7nks3']\n",
    "url_1st.rename({0: 'url_1st'}, axis=1, inplace=True)\n",
    "url_1st.drop('url', axis=1, inplace=True)\n",
    "\n",
    "url_2nd = bids.groupby(['bidder_id', 'url']).size().reset_index()\n",
    "url_2nd = url_2nd[url_2nd['url'] == '7zyltxp0hh36vpp']\n",
    "url_2nd.rename({0: 'url_2nd'}, axis=1, inplace=True)\n",
    "url_2nd.drop('url', axis=1, inplace=True)\n",
    "\n",
    "url_3rd = bids.groupby(['bidder_id', 'url']).size().reset_index()\n",
    "url_3rd = url_3rd[url_3rd['url'] == 'z3qzkki3dy6ndjb']\n",
    "url_3rd.rename({0: 'url_3rd'}, axis=1, inplace=True)\n",
    "url_3rd.drop('url', axis=1, inplace=True)\n",
    "\n",
    "url_4th = bids.groupby(['bidder_id', 'url']).size().reset_index()\n",
    "url_4th = url_4th[url_4th['url'] == 'q0skvht51258k93']\n",
    "url_4th.rename({0: 'url_4th'}, axis=1, inplace=True)\n",
    "url_4th.drop('url', axis=1, inplace=True)\n",
    "\n",
    "url_5th = bids.groupby(['bidder_id', 'url']).size().reset_index()\n",
    "url_5th = url_5th[url_5th['url'] == 'vwjvx8n5d6yjwlj']\n",
    "url_5th.rename({0: 'url_5th'}, axis=1, inplace=True)\n",
    "url_5th.drop('url', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9abf0d19",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_elements = pd.merge(combine, auction_1st, how='left')\n",
    "top_elements = pd.merge(top_elements, auction_2nd, how='left')\n",
    "top_elements = pd.merge(top_elements, auction_3rd, how='left')\n",
    "top_elements = pd.merge(top_elements, auction_4th, how='left')\n",
    "top_elements = pd.merge(top_elements, auction_5th, how='left')\n",
    "\n",
    "top_elements = pd.merge(top_elements, device_1st, how='left')\n",
    "top_elements = pd.merge(top_elements, device_2nd, how='left')\n",
    "top_elements = pd.merge(top_elements, device_3rd, how='left')\n",
    "top_elements = pd.merge(top_elements, device_4th, how='left')\n",
    "top_elements = pd.merge(top_elements, device_5th, how='left')\n",
    "\n",
    "top_elements = pd.merge(top_elements, country_1st, how='left')\n",
    "top_elements = pd.merge(top_elements, country_2nd, how='left')\n",
    "top_elements = pd.merge(top_elements, country_3rd, how='left')\n",
    "top_elements = pd.merge(top_elements, country_4th, how='left')\n",
    "top_elements = pd.merge(top_elements, country_5th, how='left')\n",
    "\n",
    "top_elements = pd.merge(top_elements, ip_1st, how='left')\n",
    "top_elements = pd.merge(top_elements, ip_2nd, how='left')\n",
    "top_elements = pd.merge(top_elements, ip_3rd, how='left')\n",
    "top_elements = pd.merge(top_elements, ip_4th, how='left')\n",
    "top_elements = pd.merge(top_elements, ip_5th, how='left')\n",
    "\n",
    "top_elements = pd.merge(top_elements, url_1st, how='left')\n",
    "top_elements = pd.merge(top_elements, url_2nd, how='left')\n",
    "top_elements = pd.merge(top_elements, url_3rd, how='left')\n",
    "top_elements = pd.merge(top_elements, url_4th, how='left')\n",
    "top_elements = pd.merge(top_elements, url_5th, how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0dca8c33",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_elements = top_elements.drop(columns=['payment_account', 'address', 'outcome'])\n",
    "top_elements = top_elements.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b59f4194",
   "metadata": {},
   "source": [
    "## No. of Last Bid Placed\n",
    "\n",
    "As there is no indication of the end time of each auction, we assume that the last bid placed on each auction would be the winning bid. We then aggregate the data to calculate how many auctions each bidder has won. The hypothesis here is that bots may be more likely to win auctions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5e74996f",
   "metadata": {},
   "outputs": [],
   "source": [
    "last_auction = bids.groupby('auction')['time'].max().reset_index()\n",
    "last_auction['win'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e7d14234",
   "metadata": {},
   "outputs": [],
   "source": [
    "win_df = pd.merge(bids, last_auction, how='left').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "14a7a7e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bid_id</th>\n",
       "      <th>bidder_id</th>\n",
       "      <th>auction</th>\n",
       "      <th>merchandise</th>\n",
       "      <th>device</th>\n",
       "      <th>time</th>\n",
       "      <th>country</th>\n",
       "      <th>ip</th>\n",
       "      <th>url</th>\n",
       "      <th>win</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>c6faf9e526c72bc8383f2bc4417a465d2fa27</td>\n",
       "      <td>ewmzr</td>\n",
       "      <td>jewelry</td>\n",
       "      <td>phone0</td>\n",
       "      <td>9759243157894736</td>\n",
       "      <td>us</td>\n",
       "      <td>69.166.231.58</td>\n",
       "      <td>vasstdc27m7nks3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>d96e93359ed70b7b4eebac163f448afeb50dc</td>\n",
       "      <td>aeqok</td>\n",
       "      <td>furniture</td>\n",
       "      <td>phone1</td>\n",
       "      <td>9759243157894736</td>\n",
       "      <td>in</td>\n",
       "      <td>50.201.125.84</td>\n",
       "      <td>jmqlhflrzwuay9c</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>065bdcc596308d1fdd0014261d6e198aa138e</td>\n",
       "      <td>wa00e</td>\n",
       "      <td>home goods</td>\n",
       "      <td>phone2</td>\n",
       "      <td>9759243157894736</td>\n",
       "      <td>py</td>\n",
       "      <td>112.54.208.157</td>\n",
       "      <td>vasstdc27m7nks3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>e97aa310037dd210f52b5b164077a926bb5de</td>\n",
       "      <td>jefix</td>\n",
       "      <td>jewelry</td>\n",
       "      <td>phone4</td>\n",
       "      <td>9759243157894736</td>\n",
       "      <td>in</td>\n",
       "      <td>18.99.175.133</td>\n",
       "      <td>vasstdc27m7nks3</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>41a7f56fcc5771d305baa6e989fb2e8cceb21</td>\n",
       "      <td>jefix</td>\n",
       "      <td>jewelry</td>\n",
       "      <td>phone5</td>\n",
       "      <td>9759243157894736</td>\n",
       "      <td>in</td>\n",
       "      <td>145.138.5.37</td>\n",
       "      <td>vasstdc27m7nks3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bid_id                              bidder_id auction merchandise  device  \\\n",
       "0       0  c6faf9e526c72bc8383f2bc4417a465d2fa27   ewmzr     jewelry  phone0   \n",
       "1       1  d96e93359ed70b7b4eebac163f448afeb50dc   aeqok   furniture  phone1   \n",
       "2       2  065bdcc596308d1fdd0014261d6e198aa138e   wa00e  home goods  phone2   \n",
       "3       3  e97aa310037dd210f52b5b164077a926bb5de   jefix     jewelry  phone4   \n",
       "4       4  41a7f56fcc5771d305baa6e989fb2e8cceb21   jefix     jewelry  phone5   \n",
       "\n",
       "               time country              ip              url  win  \n",
       "0  9759243157894736      us   69.166.231.58  vasstdc27m7nks3  0.0  \n",
       "1  9759243157894736      in   50.201.125.84  jmqlhflrzwuay9c  0.0  \n",
       "2  9759243157894736      py  112.54.208.157  vasstdc27m7nks3  0.0  \n",
       "3  9759243157894736      in   18.99.175.133  vasstdc27m7nks3  0.0  \n",
       "4  9759243157894736      in    145.138.5.37  vasstdc27m7nks3  0.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "win_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c7cf65fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "winner = win_df.groupby('bidder_id')['win'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8157b5e6",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                                  bidder_id    win\n",
       "3860  94587f4554fd3b271147490c35a27549f89f7  595.0\n",
       "4521  afb0492fabdb7faf1ac0cd9d3ad43d17cd2f0  487.0\n",
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       "2039  4df0f14fc281749facc99234620a8a7c0d803  296.0\n",
       "...                                     ...    ...\n",
       "2479  5f02bdab90416d06ba0153badc147ee31e3c5    0.0\n",
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       "6613  ffef2a34068ab5328795bc9ed2da46aa29626    0.0\n",
       "\n",
       "[6614 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winner.sort_values('win', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40134bae",
   "metadata": {},
   "source": [
    "## No. of First Bid Placed\n",
    "\n",
    "We also aggregated the number of times each bidder placed the starting bids. The hypothesis here is that bots may be more likely to start bidding to drive up interest and price in the auction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "77330d80",
   "metadata": {},
   "outputs": [],
   "source": [
    "first_auction = bids.groupby('auction')['time'].min().reset_index()\n",
    "first_auction['first'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "47e69356",
   "metadata": {},
   "outputs": [],
   "source": [
    "first_df = pd.merge(bids, first_auction, how='left').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d533f998",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   bid_id                              bidder_id auction merchandise  device  \\\n",
       "0       0  c6faf9e526c72bc8383f2bc4417a465d2fa27   ewmzr     jewelry  phone0   \n",
       "1       1  d96e93359ed70b7b4eebac163f448afeb50dc   aeqok   furniture  phone1   \n",
       "2       2  065bdcc596308d1fdd0014261d6e198aa138e   wa00e  home goods  phone2   \n",
       "3       3  e97aa310037dd210f52b5b164077a926bb5de   jefix     jewelry  phone4   \n",
       "4       4  41a7f56fcc5771d305baa6e989fb2e8cceb21   jefix     jewelry  phone5   \n",
       "\n",
       "               time country              ip              url  first  \n",
       "0  9759243157894736      us   69.166.231.58  vasstdc27m7nks3    1.0  \n",
       "1  9759243157894736      in   50.201.125.84  jmqlhflrzwuay9c    1.0  \n",
       "2  9759243157894736      py  112.54.208.157  vasstdc27m7nks3    1.0  \n",
       "3  9759243157894736      in   18.99.175.133  vasstdc27m7nks3    1.0  \n",
       "4  9759243157894736      in    145.138.5.37  vasstdc27m7nks3    1.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "166dab13",
   "metadata": {},
   "outputs": [],
   "source": [
    "first_bid = first_df.groupby('bidder_id')['first'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ff4c1c23",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.0</td>\n",
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       "<p>6614 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "                                  bidder_id  first\n",
       "3860  94587f4554fd3b271147490c35a27549f89f7  563.0\n",
       "2090  502bb0fb8297abbf699d995d24796ca2c87fc  511.0\n",
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       "5861  e41a3e528811e5c6ff9a6035cd70b2058a416  324.0\n",
       "2039  4df0f14fc281749facc99234620a8a7c0d803  320.0\n",
       "...                                     ...    ...\n",
       "2509  6023bb71a11b5199e56fb5151c1e9e55d6175    0.0\n",
       "2508  6018c2f4765e8be40da5dc999e7f88977c9ed    0.0\n",
       "2507  60094c45da7817e4eecbd1cf6f54c55f1662a    0.0\n",
       "2506  600723513fdc753051d8c4da5aa4dee584b32    0.0\n",
       "6613  ffef2a34068ab5328795bc9ed2da46aa29626    0.0\n",
       "\n",
       "[6614 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_bid.sort_values('first', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6927026f",
   "metadata": {},
   "source": [
    "## Mean Time between Bids\n",
    "\n",
    "We also aggregated the mean time and median time between bids. The hypothesis here is that the mean/median time between bids would be shorter for bids placed by bots as they are automated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fec44974",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>0013f9b8d1f462df4462e1c1216e441ba6e18</td>\n",
       "      <td>9.680602e+11</td>\n",
       "      <td>0.012720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0014e3b911d1420e43ced1dc4fc18fde0fd0c</td>\n",
       "      <td>2.120026e+12</td>\n",
       "      <td>0.027857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>002828f800c5132e297cfe3d44fbde9aeac51</td>\n",
       "      <td>6.236816e+13</td>\n",
       "      <td>0.819524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00402f50c4086f320cb6bf94f04462ea32441</td>\n",
       "      <td>5.466914e+10</td>\n",
       "      <td>0.000718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0040cea6b93afd86768c365d89513ffb7c0ba</td>\n",
       "      <td>2.548244e+12</td>\n",
       "      <td>0.033484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               bidder_id     mean_time  mean_time_scaled\n",
       "0  0013f9b8d1f462df4462e1c1216e441ba6e18  9.680602e+11          0.012720\n",
       "1  0014e3b911d1420e43ced1dc4fc18fde0fd0c  2.120026e+12          0.027857\n",
       "2  002828f800c5132e297cfe3d44fbde9aeac51  6.236816e+13          0.819524\n",
       "3  00402f50c4086f320cb6bf94f04462ea32441  5.466914e+10          0.000718\n",
       "4  0040cea6b93afd86768c365d89513ffb7c0ba  2.548244e+12          0.033484"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_diff1 = bids.groupby('bidder_id')['time'].apply(lambda x: x.diff().mean()).reset_index(name='mean_time')\n",
    "time_diff1['mean_time_scaled'] = (time_diff1['mean_time'] - time_diff1['mean_time'].min()) / (time_diff1['mean_time'].max() - time_diff1['mean_time'].min())\n",
    "time_diff1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57e6a6d3",
   "metadata": {},
   "source": [
    "## Median Time between Bids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6aeade2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bidder_id</th>\n",
       "      <th>med_time</th>\n",
       "      <th>med_time_scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0013f9b8d1f462df4462e1c1216e441ba6e18</td>\n",
       "      <td>7.747368e+11</td>\n",
       "      <td>0.010180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0014e3b911d1420e43ced1dc4fc18fde0fd0c</td>\n",
       "      <td>1.989842e+12</td>\n",
       "      <td>0.026147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>002828f800c5132e297cfe3d44fbde9aeac51</td>\n",
       "      <td>6.236816e+13</td>\n",
       "      <td>0.819524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00402f50c4086f320cb6bf94f04462ea32441</td>\n",
       "      <td>2.442105e+10</td>\n",
       "      <td>0.000321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0040cea6b93afd86768c365d89513ffb7c0ba</td>\n",
       "      <td>6.717368e+11</td>\n",
       "      <td>0.008827</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               bidder_id      med_time  med_time_scaled\n",
       "0  0013f9b8d1f462df4462e1c1216e441ba6e18  7.747368e+11         0.010180\n",
       "1  0014e3b911d1420e43ced1dc4fc18fde0fd0c  1.989842e+12         0.026147\n",
       "2  002828f800c5132e297cfe3d44fbde9aeac51  6.236816e+13         0.819524\n",
       "3  00402f50c4086f320cb6bf94f04462ea32441  2.442105e+10         0.000321\n",
       "4  0040cea6b93afd86768c365d89513ffb7c0ba  6.717368e+11         0.008827"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_diff2 = bids.groupby('bidder_id')['time'].apply(lambda x: x.diff().median()).reset_index(name='med_time')\n",
    "time_diff2['med_time_scaled'] = (time_diff2['med_time'] - time_diff2['med_time'].min()) / (time_diff2['med_time'].max() - time_diff2['med_time'].min())\n",
    "time_diff2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96ac6ffd",
   "metadata": {},
   "source": [
    "## Ratio of Multiple Bids at Same Time over Total No. of Bids\n",
    "\n",
    "We also notice that there are some bidders who placed several bids across different auctions at the same time stamp. The hypothesis is that this may be an indication of bot as humans less likely to be able to place bids for different auctions at the same time stamp (even if he opens multiple browser tabs, he cannot do it all at the same time)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "902a3f5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dup_time = bids.groupby('bidder_id')['time'].nunique().reset_index(name = 'dup_time')\n",
    "num_bids = bids.groupby('bidder_id').size().reset_index(name = 'num_bids')\n",
    "dup_time['multi_bids'] = num_bids['num_bids'] / dup_time['dup_time']\n",
    "dup_time['num_multi_bids'] = num_bids['num_bids'] - dup_time['dup_time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "10dbaf0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dup_time.drop(columns=['dup_time'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "468af89e",
   "metadata": {},
   "source": [
    "## Simply Taking Mean and Median of Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "db06e97a",
   "metadata": {},
   "outputs": [],
   "source": [
    "med_raw_time = bids.groupby('bidder_id')['time'].median().reset_index().rename(columns = {'time': 'med_raw_time'})\n",
    "mean_raw_time = bids.groupby('bidder_id')['time'].mean().reset_index().rename(columns = {'time': 'mean_raw_time'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "887578de",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_final = pd.merge(winner, time_diff1, how='left')\n",
    "time_final = pd.merge(time_final, time_diff2, how='left')\n",
    "time_final = pd.merge(time_final, dup_time, how='left')\n",
    "time_final = pd.merge(time_final, med_raw_time, how='left')\n",
    "time_final = pd.merge(time_final, mean_raw_time, how='left')\n",
    "time_final = pd.merge(time_final, first_bid, how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c615ed",
   "metadata": {},
   "source": [
    "## Finding Mean Time Between Switching IP/URL/Device\n",
    "\n",
    "We also tried to find how often a bidder switches between different IPs, URLs and devices. The hypothesis is that a human would more likely use the same IPs, URLs and devices. The mean time should be large when switching between different IPs (e.g. when a the bidder freshly connects to the internet and gets reassigned with a new IP address)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7f191e56",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_time_switchip = bids.groupby('bidder_id').apply(pd.DataFrame.sort_values, 'time').reset_index(drop=True)\n",
    "mean_time_switchip = mean_time_switchip.drop_duplicates(subset=['bidder_id', 'ip'], keep='first')\n",
    "mean_time_switchip = mean_time_switchip.groupby('bidder_id')['time'].apply(lambda x: x.diff().mean()).reset_index()\n",
    "mean_time_switchip = mean_time_switchip.fillna(0)\n",
    "mean_time_switchip = mean_time_switchip.rename({'time': 'switchip'}, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "16041529",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_time_switchurl = bids.groupby('bidder_id').apply(pd.DataFrame.sort_values, 'time').reset_index(drop=True)\n",
    "mean_time_switchurl = mean_time_switchurl.drop_duplicates(subset=['bidder_id', 'url'], keep='first')\n",
    "mean_time_switchurl = mean_time_switchurl.groupby('bidder_id')['time'].apply(lambda x: x.diff().mean()).reset_index()\n",
    "mean_time_switchurl = mean_time_switchurl.fillna(0)\n",
    "mean_time_switchurl = mean_time_switchurl.rename({'time': 'switchurl'}, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3f4b71b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_time_switchdevice = bids.groupby('bidder_id').apply(pd.DataFrame.sort_values, 'time').reset_index(drop=True)\n",
    "mean_time_switchdevice = mean_time_switchdevice.drop_duplicates(subset=['bidder_id', 'device'], keep='first')\n",
    "mean_time_switchdevice = mean_time_switchdevice.groupby('bidder_id')['time'].apply(lambda x: x.diff().mean()).reset_index()\n",
    "mean_time_switchdevice = mean_time_switchdevice.fillna(0)\n",
    "mean_time_switchdevice = mean_time_switchdevice.rename({'time': 'switchdevice'}, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5fc4d900",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_final = pd.merge(time_final, mean_time_switchip, how='left')\n",
    "time_final = pd.merge(time_final, mean_time_switchurl, how='left')\n",
    "time_final = pd.merge(time_final, mean_time_switchdevice, how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "76ddbf45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>bidder_id</th>\n",
       "      <th>win</th>\n",
       "      <th>mean_time</th>\n",
       "      <th>mean_time_scaled</th>\n",
       "      <th>med_time</th>\n",
       "      <th>med_time_scaled</th>\n",
       "      <th>multi_bids</th>\n",
       "      <th>num_multi_bids</th>\n",
       "      <th>med_raw_time</th>\n",
       "      <th>mean_raw_time</th>\n",
       "      <th>first</th>\n",
       "      <th>switchip</th>\n",
       "      <th>switchurl</th>\n",
       "      <th>switchdevice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0013f9b8d1f462df4462e1c1216e441ba6e18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.680602e+11</td>\n",
       "      <td>0.012720</td>\n",
       "      <td>7.747368e+11</td>\n",
       "      <td>0.010180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9765059868421052</td>\n",
       "      <td>9765504381578946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.680602e+11</td>\n",
       "      <td>1.126842e+12</td>\n",
       "      <td>1.119561e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0014e3b911d1420e43ced1dc4fc18fde0fd0c</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.120026e+12</td>\n",
       "      <td>0.027857</td>\n",
       "      <td>1.989842e+12</td>\n",
       "      <td>0.026147</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9767265368421052</td>\n",
       "      <td>9766911496240602</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.120026e+12</td>\n",
       "      <td>4.240053e+12</td>\n",
       "      <td>2.120026e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>002828f800c5132e297cfe3d44fbde9aeac51</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.236816e+13</td>\n",
       "      <td>0.819524</td>\n",
       "      <td>6.236816e+13</td>\n",
       "      <td>0.819524</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9675085342105264</td>\n",
       "      <td>9675085342105264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.236816e+13</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00402f50c4086f320cb6bf94f04462ea32441</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.466914e+10</td>\n",
       "      <td>0.000718</td>\n",
       "      <td>2.442105e+10</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9766228842105264</td>\n",
       "      <td>9766256764954558</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.038632e+10</td>\n",
       "      <td>1.397727e+11</td>\n",
       "      <td>1.368937e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0040cea6b93afd86768c365d89513ffb7c0ba</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.548244e+12</td>\n",
       "      <td>0.033484</td>\n",
       "      <td>6.717368e+11</td>\n",
       "      <td>0.008827</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9696703421052632</td>\n",
       "      <td>9676575140916806</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.831382e+12</td>\n",
       "      <td>2.513018e+13</td>\n",
       "      <td>3.769526e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               bidder_id  win     mean_time  mean_time_scaled  \\\n",
       "0  0013f9b8d1f462df4462e1c1216e441ba6e18  0.0  9.680602e+11          0.012720   \n",
       "1  0014e3b911d1420e43ced1dc4fc18fde0fd0c  1.0  2.120026e+12          0.027857   \n",
       "2  002828f800c5132e297cfe3d44fbde9aeac51  0.0  6.236816e+13          0.819524   \n",
       "3  00402f50c4086f320cb6bf94f04462ea32441  0.0  5.466914e+10          0.000718   \n",
       "4  0040cea6b93afd86768c365d89513ffb7c0ba  0.0  2.548244e+12          0.033484   \n",
       "\n",
       "       med_time  med_time_scaled  multi_bids  num_multi_bids  \\\n",
       "0  7.747368e+11         0.010180         1.0               0   \n",
       "1  1.989842e+12         0.026147         1.0               0   \n",
       "2  6.236816e+13         0.819524         1.0               0   \n",
       "3  2.442105e+10         0.000321         1.0               0   \n",
       "4  6.717368e+11         0.008827         1.0               0   \n",
       "\n",
       "       med_raw_time     mean_raw_time  first      switchip     switchurl  \\\n",
       "0  9765059868421052  9765504381578946    0.0  9.680602e+11  1.126842e+12   \n",
       "1  9767265368421052  9766911496240602    0.0  2.120026e+12  4.240053e+12   \n",
       "2  9675085342105264  9675085342105264    0.0  6.236816e+13  0.000000e+00   \n",
       "3  9766228842105264  9766256764954558    0.0  9.038632e+10  1.397727e+11   \n",
       "4  9696703421052632  9676575140916806    0.0  2.831382e+12  2.513018e+13   \n",
       "\n",
       "   switchdevice  \n",
       "0  1.119561e+12  \n",
       "1  2.120026e+12  \n",
       "2  0.000000e+00  \n",
       "3  1.368937e+11  \n",
       "4  3.769526e+12  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f99ed8db",
   "metadata": {},
   "source": [
    "## Time Analysis\n",
    "\n",
    "From the plot, we can see that across the bids dataset, there are gaps where no bids were placed at all. Significantly, there are three time blocks where biddings took place."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "62c4e9cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time', ylabel='Density'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data = bids, x ='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6cb67dd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_block = (bids['time'].max() - bids['time'].min()) / 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "df9cc1a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_start_1 = bids['time'].min()\n",
    "time_end_1 = time_start_1 + time_block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2fbd104e",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_start_2 = time_end_1\n",
    "time_end_2 = time_start_2 + time_block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "71fe4e67",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_start_3 = time_end_2\n",
    "time_end_3 = time_start_3 + time_block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "13247259",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_start_4 = time_end_3\n",
    "time_end_4 = time_start_4 + time_block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a7e7ed3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_start_5 = time_end_4\n",
    "time_end_5 =  time_start_5 + time_block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f38c5d36",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_first_block = bids[(bids['time'] >= time_start_1) & (bids['time'] < time_end_1)]\n",
    "bids_second_block = bids[(bids['time'] >= time_start_2) & (bids['time'] < time_end_2)]\n",
    "bids_third_block = bids[(bids['time'] >= time_start_3) & (bids['time'] < time_end_3)]\n",
    "bids_fourth_block = bids[(bids['time'] >= time_start_4) & (bids['time'] < time_end_4)]\n",
    "bids_fifth_block = bids[(bids['time'] >= time_start_5) & (bids['time'] < time_end_5)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcb44782",
   "metadata": {},
   "source": [
    "By zooming in to one of the time blocks, we also see a \"wave\" pattern, which indicates that there are period of time where bidding activity is high or low. From this, we assume that there would be less bidding activity during the silent hours (e.g. from midnight to dawn when humans would be asleep). This could help in identifying whether a bidder is bot or human."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "79b11502",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time', ylabel='Density'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data = bids_first_block, x = 'time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e3da4c50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import find_peaks\n",
    "import seaborn as sns\n",
    "\n",
    "ax = sns.kdeplot(data = bids_third_block, x = 'time')\n",
    "x = ax.lines[0].get_xdata() # Get the x data of the distribution\n",
    "y = ax.lines[0].get_ydata() # Get the y data of the distribution\n",
    "xy = [[x[i], y[i]] for i in range(len(x))]\n",
    "\n",
    "# find peak\n",
    "peak_coord = [xy[i] for i in find_peaks(y)[0]]\n",
    "#sorted_peak = sorted(peak_coord, key=lambda x: x[1])\n",
    "\n",
    "# sort peak based on its `y` coord\n",
    "#sorted_peak.reverse()\n",
    "# second peak\n",
    "#second_peak = sorted_peak[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "239c7db7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[9695991069379348.0, 1.175369960412207e-13],\n",
       " [9700339344260208.0, 1.1438110423682936e-13],\n",
       " [9703713005805704.0, 7.711486305935637e-14],\n",
       " [9704912529910768.0, 1.1898782255522677e-13],\n",
       " [9708810983252230.0, 8.1665306723761e-14]]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peak_coord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5439ab82",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_between_peak = peak_coord[3][0] - peak_coord[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "41035ed5",
   "metadata": {},
   "outputs": [],
   "source": [
    "sixhr_time_block = time_between_peak / 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5002ee83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1143296412640.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sixhr_time_block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9a0dd856",
   "metadata": {},
   "outputs": [],
   "source": [
    "evening0 = time_start_1 + sixhr_time_block\n",
    "night0 = evening0 + sixhr_time_block\n",
    "morning1 = night0 + sixhr_time_block\n",
    "afternoon1 = morning1 + sixhr_time_block\n",
    "evening1 = afternoon1 + sixhr_time_block\n",
    "night1 = evening1 + sixhr_time_block\n",
    "morning2 = night1 + sixhr_time_block\n",
    "afternoon2 = morning2 + sixhr_time_block\n",
    "evening2 = afternoon2 + sixhr_time_block\n",
    "night2 = evening2 + sixhr_time_block\n",
    "morning3 = night2 + sixhr_time_block\n",
    "afternoon3 = morning3 + sixhr_time_block\n",
    "evening3 = afternoon3 + sixhr_time_block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3b8bf5e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_hour(x):\n",
    "    if (x >= 9631916842105263) and (x<= 9645558894736842):\n",
    "        start_time = 9631916842105263\n",
    "        evening0 = start_time + sixhr_time_block\n",
    "        night0 = evening0 + sixhr_time_block\n",
    "        morning1 = night0 + sixhr_time_block\n",
    "        afternoon1 = morning1 + sixhr_time_block\n",
    "        evening1 = afternoon1 + sixhr_time_block\n",
    "        night1 = evening1 + sixhr_time_block\n",
    "        morning2 = night1 + sixhr_time_block\n",
    "        afternoon2 = morning2 + sixhr_time_block\n",
    "        evening2 = afternoon2 + sixhr_time_block\n",
    "        night2 = evening2 + sixhr_time_block\n",
    "        morning3 = night2 + sixhr_time_block\n",
    "        afternoon3 = morning3 + sixhr_time_block\n",
    "        evening3 = afternoon3 + sixhr_time_block\n",
    "        \n",
    "        if (x >= start_time) and (x < evening0):\n",
    "            return 3\n",
    "        elif (x >= evening0) and (x < night0):\n",
    "            return 4\n",
    "        elif (x >= night0) and (x < morning1):\n",
    "            return 1\n",
    "        elif (x >= morning1) and (x < afternoon1):\n",
    "            return 2\n",
    "        elif (x >= afternoon1) and (x < evening1):\n",
    "            return 3\n",
    "        elif (x >= evening1) and (x < night1):\n",
    "            return 4\n",
    "        elif (x >= night1) and (x < morning2):\n",
    "            return 1\n",
    "        elif (x >= morning2) and (x < afternoon2):\n",
    "            return 2\n",
    "        elif (x >= afternoon2) and (x < evening2):\n",
    "            return 3\n",
    "        elif (x >= evening2) and (x < night2):\n",
    "            return 4\n",
    "        elif (x >= night2) and (x < morning3):\n",
    "            return 1\n",
    "        elif (x >= morning3) and (x < afternoon3):\n",
    "            return 2\n",
    "        elif (x >= afternoon3) and (x < evening3):\n",
    "            return 3\n",
    "        \n",
    "    elif (x >= 9695580000000000) and (x<= 9709222052631578):\n",
    "        start_time = 9695580000000000\n",
    "        evening0 = start_time + sixhr_time_block\n",
    "        night0 = evening0 + sixhr_time_block\n",
    "        morning1 = night0 + sixhr_time_block\n",
    "        afternoon1 = morning1 + sixhr_time_block\n",
    "        evening1 = afternoon1 + sixhr_time_block\n",
    "        night1 = evening1 + sixhr_time_block\n",
    "        morning2 = night1 + sixhr_time_block\n",
    "        afternoon2 = morning2 + sixhr_time_block\n",
    "        evening2 = afternoon2 + sixhr_time_block\n",
    "        night2 = evening2 + sixhr_time_block\n",
    "        morning3 = night2 + sixhr_time_block\n",
    "        afternoon3 = morning3 + sixhr_time_block\n",
    "        evening3 = afternoon3 + sixhr_time_block\n",
    "        \n",
    "        if (x >= start_time) and (x < evening0):\n",
    "            return 3\n",
    "        elif (x >= evening0) and (x < night0):\n",
    "            return 4\n",
    "        elif (x >= night0) and (x < morning1):\n",
    "            return 1\n",
    "        elif (x >= morning1) and (x < afternoon1):\n",
    "            return 2\n",
    "        elif (x >= afternoon1) and (x < evening1):\n",
    "            return 3\n",
    "        elif (x >= evening1) and (x < night1):\n",
    "            return 4\n",
    "        elif (x >= night1) and (x < morning2):\n",
    "            return 1\n",
    "        elif (x >= morning2) and (x < afternoon2):\n",
    "            return 2\n",
    "        elif (x >= afternoon2) and (x < evening2):\n",
    "            return 3\n",
    "        elif (x >= evening2) and (x < night2):\n",
    "            return 4\n",
    "        elif (x >= night2) and (x < morning3):\n",
    "            return 1\n",
    "        elif (x >= morning3) and (x < afternoon3):\n",
    "            return 2\n",
    "        elif (x >= afternoon3) and (x < evening3):\n",
    "            return 3\n",
    "        \n",
    "    elif (x >= 9759243157894736) and (x<= 9772885210526315):\n",
    "        start_time = 9759243157894736\n",
    "        evening0 = start_time + sixhr_time_block\n",
    "        night0 = evening0 + sixhr_time_block\n",
    "        morning1 = night0 + sixhr_time_block\n",
    "        afternoon1 = morning1 + sixhr_time_block\n",
    "        evening1 = afternoon1 + sixhr_time_block\n",
    "        night1 = evening1 + sixhr_time_block\n",
    "        morning2 = night1 + sixhr_time_block\n",
    "        afternoon2 = morning2 + sixhr_time_block\n",
    "        evening2 = afternoon2 + sixhr_time_block\n",
    "        night2 = evening2 + sixhr_time_block\n",
    "        morning3 = night2 + sixhr_time_block\n",
    "        afternoon3 = morning3 + sixhr_time_block\n",
    "        evening3 = afternoon3 + sixhr_time_block\n",
    "        \n",
    "        if (x >= start_time) and (x < evening0):\n",
    "            return 3\n",
    "        elif (x >= evening0) and (x < night0):\n",
    "            return 4\n",
    "        elif (x >= night0) and (x < morning1):\n",
    "            return 1\n",
    "        elif (x >= morning1) and (x < afternoon1):\n",
    "            return 2\n",
    "        elif (x >= afternoon1) and (x < evening1):\n",
    "            return 3\n",
    "        elif (x >= evening1) and (x < night1):\n",
    "            return 4\n",
    "        elif (x >= night1) and (x < morning2):\n",
    "            return 1\n",
    "        elif (x >= morning2) and (x < afternoon2):\n",
    "            return 2\n",
    "        elif (x >= afternoon2) and (x < evening2):\n",
    "            return 3\n",
    "        elif (x >= evening2) and (x < night2):\n",
    "            return 4\n",
    "        elif (x >= night2) and (x < morning3):\n",
    "            return 1\n",
    "        elif (x >= morning3) and (x < afternoon3):\n",
    "            return 2\n",
    "        elif (x >= afternoon3) and (x < evening3):\n",
    "            return 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "780356ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids['period'] = bids['time'].apply(set_hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c2c9d8f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_period = bids.groupby('bidder_id')['period'].apply(lambda x: x.value_counts().head(1)).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e89676ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_period.rename({'level_1': 'tod'}, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b98a0201",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_period_all = bids.groupby(['bidder_id','period']).size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "fe10e14d",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_period_all = bids_period_all.rename({0: 'count_period'}, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b9fccf9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_period_all_unmelt = bids_period_all.pivot_table(values = 'count_period', index = 'bidder_id', columns = 'period').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b85f655a",
   "metadata": {},
   "outputs": [],
   "source": [
    "rename_dict = {1: 'morning', 2:'afternoon', 3:'evening', 4:'night'}\n",
    "bids_period_all_unmelt = bids_period_all_unmelt.rename(rename_dict, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a138f203",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_period_all_unmelt = bids_period_all_unmelt.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f7618f8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_period_count = bids_period_all_unmelt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e283ca",
   "metadata": {},
   "source": [
    "## Bids Per Time Block\n",
    "\n",
    "Since we have identified three time blocks where bidding took place, we tried to see if aggregate the bids placed in each time block. The hypothesis is that a bot would be more active in placing bids and hence more likely to be bidding across all three time blocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6e7742b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_block1 = bids[(bids['time'] >= 9631916842105263) & (bids['time'] <= 9645558894736842)]\n",
    "bids_block1 = bids_block1.groupby('bidder_id').size().reset_index().rename({0: 'bids_block1'}, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "699d152d",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_block2 = bids[(bids['time'] >= 9695580000000000) & (bids['time'] <= 9709222052631578)]\n",
    "bids_block2 = bids_block2.groupby('bidder_id').size().reset_index().rename({0: 'bids_block2'}, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1acacd7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_block3 = bids[(bids['time'] >= 9759243157894736) & (bids['time'] <= 9772885210526315)]\n",
    "bids_block3 = bids_block3.groupby('bidder_id').size().reset_index().rename({0: 'bids_block3'}, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d0767f9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_blocks = pd.merge(bids_block1, bids_block2, how = 'outer')\n",
    "bids_blocks = pd.merge(bids_blocks, bids_block3, how = 'outer')\n",
    "bids_blocks = bids_blocks.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "efe2e404",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_blocks['mean_bids_blocks'] = (bids_blocks['bids_block1'] + bids_blocks['bids_block2'] + bids_blocks['bids_block3'])/3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5893abf",
   "metadata": {},
   "source": [
    "## Grouping Country to Continent\n",
    "\n",
    "We also tried to see if there is any pattern for country. As there are too many unique countries, we tried to aggregate them by continent and created a new categorical feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "dc8bb25b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_dict = {'af': 'Asia', 'al': 'Europe', 'aq': 'Antarctica', 'dz': 'Africa', 'as': 'Oceania', 'ad': 'Europe', 'ao': 'Africa', 'ag': 'North America', 'az': 'Asia', 'ar': 'South America', 'au': 'Oceania', 'at': 'Europe', 'bs': 'North America', 'bh': 'Asia', 'bd': 'Asia', 'am': 'Asia', 'bb': 'North America', 'be': 'Europe', 'bm': 'North America', 'bt': 'Asia', 'bo': 'South America', 'ba': 'Europe', 'bw': 'Africa', 'bv': 'Antarctica', 'br': 'South America', 'bz': 'North America', 'io': 'Asia', 'sb': 'Oceania', 'vg': 'North America', 'bn': 'Asia', 'bg': 'Europe', 'mm': 'Asia', 'bi': 'Africa', 'by': 'Europe', 'kh': 'Asia', 'cm': 'Africa', 'ca': 'North America', 'cv': 'Africa', 'ky': 'North America', 'cf': 'Africa', 'lk': 'Asia', 'td': 'Africa', 'cl': 'South America', 'cn': 'Asia', 'tw': 'Asia', 'cx': 'Asia', 'cc': 'Asia', 'co': 'South America', 'km': 'Africa', 'yt': 'Africa', 'cg': 'Africa', 'cd': 'Africa', 'ck': 'Oceania', 'cr': 'North America', 'hr': 'Europe', 'cu': 'North America', 'cy': 'Asia', 'cz': 'Europe', 'bj': 'Africa', 'dk': 'Europe', 'dm': 'North America', 'do': 'North America', 'ec': 'South America', 'sv': 'North America', 'gq': 'Africa', 'et': 'Africa', 'er': 'Africa', 'ee': 'Europe', 'fo': 'Europe', 'fk': 'South America', 'gs': 'Antarctica', 'fj': 'Oceania', 'fi': 'Europe', 'ax': 'Europe', 'fr': 'Europe', 'gf': 'South America', 'pf': 'Oceania', 'tf': 'Antarctica', 'dj': 'Africa', 'ga': 'Africa', 'ge': 'Asia', 'gm': 'Africa', 'ps': 'Asia', 'de': 'Europe', 'gh': 'Africa', 'gi': 'Europe', 'ki': 'Oceania', 'gr': 'Europe', 'gl': 'North America', 'gd': 'North America', 'gp': 'North America', 'gu': 'Oceania', 'gt': 'North America', 'gn': 'Africa', 'gy': 'South America', 'ht': 'North America', 'hm': 'Antarctica', 'va': 'Europe', 'hn': 'North America', 'hk': 'Asia', 'hu': 'Europe', 'is': 'Europe', 'in': 'Asia', 'id': 'Asia', 'ir': 'Asia', 'iq': 'Asia', 'ie': 'Europe', 'il': 'Asia', 'it': 'Europe', 'ci': 'Africa', 'jm': 'North America', 'jp': 'Asia', 'kz': 'Asia', 'jo': 'Asia', 'ke': 'Africa', 'kp': 'Asia', 'kr': 'Asia', 'kw': 'Asia', 'kg': 'Asia', 'la': 'Asia', 'lb': 'Asia', 'ls': 'Africa', 'lv': 'Europe', 'lr': 'Africa', 'ly': 'Africa', 'li': 'Europe', 'lt': 'Europe', 'lu': 'Europe', 'mo': 'Asia', 'mg': 'Africa', 'mw': 'Africa', 'my': 'Asia', 'mv': 'Asia', 'ml': 'Africa', 'mt': 'Europe', 'mq': 'North America', 'mr': 'Africa', 'mu': 'Africa', 'mx': 'North America', 'mc': 'Europe', 'mn': 'Asia', 'md': 'Europe', 'me': 'Europe', 'ms': 'North America', 'ma': 'Africa', 'mz': 'Africa', 'om': 'Asia', 'na': 'Africa', 'nr': 'Oceania', 'np': 'Asia', 'nl': 'Europe', 'an': 'North America', 'cw': 'North America', 'aw': 'North America', 'sx': 'North America', 'bq': 'North America', 'nc': 'Oceania', 'vu': 'Oceania', 'nz': 'Oceania', 'ni': 'North America', 'ne': 'Africa', 'ng': 'Africa', 'nu': 'Oceania', 'nf': 'Oceania', 'no': 'Europe', 'mp': 'Oceania', 'um': 'North America', 'fm': 'Oceania', 'mh': 'Oceania', 'pw': 'Oceania', 'pk': 'Asia', 'pa': 'North America', 'pg': 'Oceania', 'py': 'South America', 'pe': 'South America', 'ph': 'Asia', 'pn': 'Oceania', 'pl': 'Europe', 'pt': 'Europe', 'gw': 'Africa', 'tl': 'Asia', 'pr': 'North America', 'qa': 'Asia', 're': 'Africa', 'ro': 'Europe', 'ru': 'Asia', 'rw': 'Africa', 'bl': 'North America', 'sh': 'Africa', 'kn': 'North America', 'ai': 'North America', 'lc': 'North America', 'mf': 'North America', 'pm': 'North America', 'vc': 'North America', 'sm': 'Europe', 'st': 'Africa', 'sa': 'Asia', 'sn': 'Africa', 'rs': 'Europe', 'sc': 'Africa', 'sl': 'Africa', 'sg': 'Asia', 'sk': 'Europe', 'vn': 'Asia', 'si': 'Europe', 'so': 'Africa', 'za': 'Africa', 'zw': 'Africa', 'es': 'Europe', 'ss': 'Africa', 'eh': 'Africa', 'sd': 'Africa', 'sr': 'South America', 'sj': 'Europe', 'sz': 'Africa', 'se': 'Europe', 'ch': 'Europe', 'sy': 'Asia', 'tj': 'Asia', 'th': 'Asia', 'tg': 'Africa', 'tk': 'Oceania', 'to': 'Oceania', 'tt': 'North America', 'ae': 'Asia', 'tn': 'Africa', 'tr': 'Asia', 'tm': 'Asia', 'tc': 'North America', 'tv': 'Oceania', 'ug': 'Africa', 'ua': 'Europe', 'mk': 'Europe', 'eg': 'Africa', 'gb': 'Europe', 'uk': 'Europe', 'gg': 'Europe', 'je': 'Europe', 'im': 'Europe', 'tz': 'Africa', 'us': 'North America', 'vi': 'North America', 'bf': 'Africa', 'uy': 'South America', 'uz': 'Asia', 've': 'South America', 'wf': 'Oceania', 'ws': 'Oceania', 'ye': 'Asia', 'zm': 'Africa', 'xx': 'Oceania', 'xe': 'Asia', 'xd': 'Asia', 'xs': 'Asia', 'zz': 'Unknown', 'eu': 'Europe'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5071a46f",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_country = pd.DataFrame(bids[['bidder_id','country', 'ip']].copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "2d34e49a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bidder_id</th>\n",
       "      <th>country</th>\n",
       "      <th>ip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c6faf9e526c72bc8383f2bc4417a465d2fa27</td>\n",
       "      <td>us</td>\n",
       "      <td>69.166.231.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d96e93359ed70b7b4eebac163f448afeb50dc</td>\n",
       "      <td>in</td>\n",
       "      <td>50.201.125.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>065bdcc596308d1fdd0014261d6e198aa138e</td>\n",
       "      <td>py</td>\n",
       "      <td>112.54.208.157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e97aa310037dd210f52b5b164077a926bb5de</td>\n",
       "      <td>in</td>\n",
       "      <td>18.99.175.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41a7f56fcc5771d305baa6e989fb2e8cceb21</td>\n",
       "      <td>in</td>\n",
       "      <td>145.138.5.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               bidder_id country              ip\n",
       "0  c6faf9e526c72bc8383f2bc4417a465d2fa27      us   69.166.231.58\n",
       "1  d96e93359ed70b7b4eebac163f448afeb50dc      in   50.201.125.84\n",
       "2  065bdcc596308d1fdd0014261d6e198aa138e      py  112.54.208.157\n",
       "3  e97aa310037dd210f52b5b164077a926bb5de      in   18.99.175.133\n",
       "4  41a7f56fcc5771d305baa6e989fb2e8cceb21      in    145.138.5.37"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bids_country.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "42dce42a",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_country.iloc[1038739, 1] = 'th'\n",
    "bids_country.iloc[2040266, 1] = 'th'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9bf03f1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_country.iloc[5566304, 1] = 'my'\n",
    "bids_country.iloc[6614612, 1] = 'my'\n",
    "bids_country.iloc[6726177, 1] = 'my'\n",
    "bids_country.iloc[7357215, 1] = 'my'\n",
    "bids_country.iloc[7453716, 1] = 'my'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2ed9d19a",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_country = bids_country.fillna('missing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "ffef81ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "def country_to_continent(x):\n",
    "    if x == 'missing':\n",
    "        return 'Unknown'\n",
    "    else:\n",
    "        return cc_dict[x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "53dbadbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_country['continent'] = bids_country['country'].apply(country_to_continent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2f4eefb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['North America', 'Asia', 'South America', 'Africa', 'Oceania',\n",
       "       'Europe', 'Unknown'], dtype=object)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bids_country['continent'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1eb51518",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_country_all = bids_country.groupby(['bidder_id','continent']).size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "23324938",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_country_all = bids_country_all.rename({0: 'count_continent'}, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "429ec660",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_country_all_unmelt = bids_country_all.pivot_table(values = 'count_continent', index = 'bidder_id', columns = 'continent').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "0c9d7e1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_country_all_unmelt = bids_country_all_unmelt.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5df163b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "bids_country_count = bids_country_all_unmelt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b08c561b",
   "metadata": {},
   "source": [
    "## Creating Type of Payment Account\n",
    "\n",
    "We notice that for the payment account, the unique values are the last 5 characters. For the first 32 characters, there are some sort of pattern and they may be representative of the type of acocunt (for example like credit card, Visa cards begin with 4 and have 13/16 digits, while Mastercards begin with 5 and have 16 digits). The hypothesis is that bots may be more inclined to use certain payment account that is more obscured."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "57014f26",
   "metadata": {},
   "outputs": [],
   "source": [
    "combine_pay = pd.concat([train, test], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "3b089c62",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_32(x):\n",
    "    return x[:32]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "2f388bdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "combine_pay['first32'] = combine_pay['payment_account'].apply(extract_32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "08cefe06",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "combine_pay['first32_encode'] = le.fit_transform(combine_pay['first32'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "853f9a37",
   "metadata": {},
   "outputs": [],
   "source": [
    "combine_pay = combine_pay.drop(['payment_account', 'address', 'outcome', 'first32'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "b22c53f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bidder_id</th>\n",
       "      <th>first32_encode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4a791121f1d9c9c192d13051301984388c491</td>\n",
       "      <td>1631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6feeeab451fcc546e1c43867e04bd5d2294fc</td>\n",
       "      <td>1631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0bdfae6168380fa2e00853fd3d6199ba8ed7d</td>\n",
       "      <td>1631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3b4586382b3ff164e8bad37b15a268a540996</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10c7f4bdc67a50bab856b9ca76a83ce980f96</td>\n",
       "      <td>1631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               bidder_id  first32_encode\n",
       "0  4a791121f1d9c9c192d13051301984388c491            1631\n",
       "1  6feeeab451fcc546e1c43867e04bd5d2294fc            1631\n",
       "2  0bdfae6168380fa2e00853fd3d6199ba8ed7d            1631\n",
       "3  3b4586382b3ff164e8bad37b15a268a540996             801\n",
       "4  10c7f4bdc67a50bab856b9ca76a83ce980f96            1631"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine_pay.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "898f0571",
   "metadata": {},
   "source": [
    "## Combine all datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "cb6cd3f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#bids_period = pd.read_csv('bids_period.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "3a835bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#bids_period_count = pd.read_csv('bids_period_count.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "384edde8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#bids_country_count = pd.read_csv('bids_country_count.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "43d0190b",
   "metadata": {},
   "outputs": [],
   "source": [
    "first = pd.merge(combine, bidder, how = 'left', on = 'bidder_id')\n",
    "second = pd.merge(first, time_final, how = 'left', on = 'bidder_id')\n",
    "final = pd.merge(second, full_bids, how = 'left', on = 'bidder_id')\n",
    "final = pd.merge(final, bids_period, how = 'left', on = 'bidder_id')\n",
    "final = pd.merge(final, top_elements, how = 'left', on = 'bidder_id')\n",
    "final = pd.merge(final, bids_period_count, how = 'left', on = 'bidder_id')\n",
    "final = pd.merge(final, bids_country_count, how = 'left', on = 'bidder_id')\n",
    "final = pd.merge(final, combine_pay, how = 'left', on = 'bidder_id')\n",
    "final = pd.merge(final, bids_blocks, how = 'left', on = 'bidder_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "0520d7a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "final.drop(columns = ['payment_account', 'address'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "6f8af755",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6713, 75)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "f44c82a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "final['merauction'] = final['merchandise'] / final['auction']\n",
    "final['ipauction'] = final['ip'] / final['auction']\n",
    "final['devauction'] = final['device'] / final['auction']\n",
    "final['urlauction'] = final['url'] / final['auction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "da1d4954",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_final = final[:2013]\n",
    "test_final = final[2013:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "6dd27b05",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bidder_id</th>\n",
       "      <th>outcome</th>\n",
       "      <th>bid_id</th>\n",
       "      <th>auction</th>\n",
       "      <th>merchandise</th>\n",
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       "      <th>country</th>\n",
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       "      <th>aauction</th>\n",
       "      <th>aurl</th>\n",
       "      <th>aip</th>\n",
       "      <th>amerchandise</th>\n",
       "      <th>acountry</th>\n",
       "      <th>win</th>\n",
       "      <th>mean_time</th>\n",
       "      <th>mean_time_scaled</th>\n",
       "      <th>med_time</th>\n",
       "      <th>med_time_scaled</th>\n",
       "      <th>multi_bids</th>\n",
       "      <th>num_multi_bids</th>\n",
       "      <th>med_raw_time</th>\n",
       "      <th>mean_raw_time</th>\n",
       "      <th>first</th>\n",
       "      <th>switchip</th>\n",
       "      <th>switchurl</th>\n",
       "      <th>switchdevice</th>\n",
       "      <th>avg_ip</th>\n",
       "      <th>avg_device</th>\n",
       "      <th>avg_url</th>\n",
       "      <th>avg_bid</th>\n",
       "      <th>tod</th>\n",
       "      <th>period</th>\n",
       "      <th>auction_1st</th>\n",
       "      <th>auction_2nd</th>\n",
       "      <th>auction_3rd</th>\n",
       "      <th>auction_4th</th>\n",
       "      <th>auction_5th</th>\n",
       "      <th>device_1st</th>\n",
       "      <th>device_2nd</th>\n",
       "      <th>device_3rd</th>\n",
       "      <th>device_4th</th>\n",
       "      <th>device_5th</th>\n",
       "      <th>country_1st</th>\n",
       "      <th>country_2nd</th>\n",
       "      <th>country_3rd</th>\n",
       "      <th>country_4th</th>\n",
       "      <th>country_5th</th>\n",
       "      <th>ip_1st</th>\n",
       "      <th>ip_2nd</th>\n",
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       "      <th>ip_4th</th>\n",
       "      <th>ip_5th</th>\n",
       "      <th>url_1st</th>\n",
       "      <th>url_2nd</th>\n",
       "      <th>url_3rd</th>\n",
       "      <th>url_4th</th>\n",
       "      <th>url_5th</th>\n",
       "      <th>morning</th>\n",
       "      <th>afternoon</th>\n",
       "      <th>evening</th>\n",
       "      <th>night</th>\n",
       "      <th>Africa</th>\n",
       "      <th>Asia</th>\n",
       "      <th>Europe</th>\n",
       "      <th>North America</th>\n",
       "      <th>Oceania</th>\n",
       "      <th>South America</th>\n",
       "      <th>Unknown</th>\n",
       "      <th>first32_encode</th>\n",
       "      <th>bids_block1</th>\n",
       "      <th>bids_block2</th>\n",
       "      <th>bids_block3</th>\n",
       "      <th>mean_bids_blocks</th>\n",
       "      <th>merauction</th>\n",
       "      <th>ipauction</th>\n",
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       "      <th>urlauction</th>\n",
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       "  </thead>\n",
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       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.711121e+11</td>\n",
       "      <td>0.007504</td>\n",
       "      <td>3.458421e+11</td>\n",
       "      <td>0.004544</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.764806e+15</td>\n",
       "      <td>9.766127e+15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.913463e+11</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.010429e+12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
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       "      <td>0.055556</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.055556</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.233579e+12</td>\n",
       "      <td>0.042490</td>\n",
       "      <td>3.233579e+12</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>9.766036e+15</td>\n",
       "      <td>9.766866e+15</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>6.467158e+12</td>\n",
       "      <td>6.467158e+12</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1631</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0bdfae6168380fa2e00853fd3d6199ba8ed7d</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.379000e+12</td>\n",
       "      <td>0.031260</td>\n",
       "      <td>2.532053e+12</td>\n",
       "      <td>0.033271</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.769820e+15</td>\n",
       "      <td>9.769395e+15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.379000e+12</td>\n",
       "      <td>3.154105e+12</td>\n",
       "      <td>3.154105e+12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1631</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.706442e+15</td>\n",
       "      <td>9.706442e+15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10c7f4bdc67a50bab856b9ca76a83ce980f96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.341935</td>\n",
       "      <td>0.148387</td>\n",
       "      <td>0.587097</td>\n",
       "      <td>0.793548</td>\n",
       "      <td>0.006452</td>\n",
       "      <td>0.012903</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.777888e+10</td>\n",
       "      <td>0.001022</td>\n",
       "      <td>1.368421e+10</td>\n",
       "      <td>0.000180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.761839e+15</td>\n",
       "      <td>9.762118e+15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.817990e+10</td>\n",
       "      <td>1.330883e+11</td>\n",
       "      <td>1.820516e+11</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1631</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>51.666667</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>5.347826</td>\n",
       "      <td>2.304348</td>\n",
       "      <td>3.956522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>c3904e52ba5c4ce978a9f451a1665c8bd51d4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.761130e+15</td>\n",
       "      <td>9.761130e+15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1631</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2e421b3e55923090bc2809d35c6046beb356d</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.598902e+12</td>\n",
       "      <td>0.021010</td>\n",
       "      <td>5.842105e+10</td>\n",
       "      <td>0.000768</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.761140e+15</td>\n",
       "      <td>9.763141e+15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.131289e+12</td>\n",
       "      <td>1.598902e+12</td>\n",
       "      <td>4.262579e+12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1278</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>c96eaf2aee105bceb4c5c239f3d9e91fa1846</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.088235</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.088235</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.696077e+11</td>\n",
       "      <td>0.002229</td>\n",
       "      <td>3.300000e+10</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.764426e+15</td>\n",
       "      <td>9.764104e+15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.805501e+11</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.708211e+12</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1631</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>11.333333</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>c3e0e8c134ad9dbc5dd4d59acf10fd1139e60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.821989e+13</td>\n",
       "      <td>0.896416</td>\n",
       "      <td>6.821989e+13</td>\n",
       "      <td>0.896416</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.670665e+15</td>\n",
       "      <td>9.670665e+15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.821989e+13</td>\n",
       "      <td>6.821989e+13</td>\n",
       "      <td>6.821989e+13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1905</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>164f6ffdff7755d51aec64048f3b8fe15bac3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.313915e+12</td>\n",
       "      <td>0.069825</td>\n",
       "      <td>7.534211e+11</td>\n",
       "      <td>0.009900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.702238e+15</td>\n",
       "      <td>9.697818e+15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.280081e+12</td>\n",
       "      <td>1.145300e+13</td>\n",
       "      <td>6.908089e+12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2420</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               bidder_id  outcome  bid_id  auction  \\\n",
       "0  4a791121f1d9c9c192d13051301984388c491      0.0    24.0     18.0   \n",
       "1  6feeeab451fcc546e1c43867e04bd5d2294fc      0.0     3.0      1.0   \n",
       "2  0bdfae6168380fa2e00853fd3d6199ba8ed7d      0.0     4.0      4.0   \n",
       "3  3b4586382b3ff164e8bad37b15a268a540996      0.0     1.0      1.0   \n",
       "4  10c7f4bdc67a50bab856b9ca76a83ce980f96      0.0   155.0     23.0   \n",
       "5  c3904e52ba5c4ce978a9f451a1665c8bd51d4      0.0     1.0      1.0   \n",
       "6  2e421b3e55923090bc2809d35c6046beb356d      0.0     8.0      8.0   \n",
       "7  c96eaf2aee105bceb4c5c239f3d9e91fa1846      0.0    34.0     10.0   \n",
       "8  c3e0e8c134ad9dbc5dd4d59acf10fd1139e60      0.0     2.0      2.0   \n",
       "9  164f6ffdff7755d51aec64048f3b8fe15bac3      0.0    14.0     10.0   \n",
       "\n",
       "   merchandise  device  country     ip   url   adevice  aauction      aurl  \\\n",
       "0          1.0    14.0      6.0   20.0   1.0  0.583333  0.750000  0.041667   \n",
       "1          1.0     2.0      1.0    3.0   2.0  0.666667  0.333333  0.666667   \n",
       "2          1.0     2.0      1.0    4.0   2.0  0.500000  1.000000  0.500000   \n",
       "3          1.0     1.0      1.0    1.0   1.0  1.000000  1.000000  1.000000   \n",
       "4          1.0    53.0      2.0  123.0  91.0  0.341935  0.148387  0.587097   \n",
       "5          1.0     1.0      1.0    1.0   1.0  1.000000  1.000000  1.000000   \n",
       "6          1.0     2.0      1.0    3.0   8.0  0.250000  1.000000  1.000000   \n",
       "7          1.0     3.0      3.0   32.0   1.0  0.088235  0.294118  0.029412   \n",
       "8          1.0     2.0      1.0    2.0   2.0  1.000000  1.000000  1.000000   \n",
       "9          1.0    11.0      5.0   12.0   7.0  0.785714  0.714286  0.500000   \n",
       "\n",
       "        aip  amerchandise  acountry  win     mean_time  mean_time_scaled  \\\n",
       "0  0.833333      0.041667  0.250000  0.0  5.711121e+11          0.007504   \n",
       "1  1.000000      0.333333  0.333333  0.0  3.233579e+12          0.042490   \n",
       "2  1.000000      0.250000  0.250000  0.0  2.379000e+12          0.031260   \n",
       "3  1.000000      1.000000  1.000000  0.0           NaN               NaN   \n",
       "4  0.793548      0.006452  0.012903  0.0  7.777888e+10          0.001022   \n",
       "5  1.000000      1.000000  1.000000  0.0           NaN               NaN   \n",
       "6  0.375000      0.125000  0.125000  0.0  1.598902e+12          0.021010   \n",
       "7  0.941176      0.029412  0.088235  0.0  1.696077e+11          0.002229   \n",
       "8  1.000000      0.500000  0.500000  0.0  6.821989e+13          0.896416   \n",
       "9  0.857143      0.071429  0.357143  0.0  5.313915e+12          0.069825   \n",
       "\n",
       "       med_time  med_time_scaled  multi_bids  num_multi_bids  med_raw_time  \\\n",
       "0  3.458421e+11         0.004544         1.0             0.0  9.764806e+15   \n",
       "1  3.233579e+12         0.042490         1.0             0.0  9.766036e+15   \n",
       "2  2.532053e+12         0.033271         1.0             0.0  9.769820e+15   \n",
       "3           NaN              NaN         1.0             0.0  9.706442e+15   \n",
       "4  1.368421e+10         0.000180         1.0             0.0  9.761839e+15   \n",
       "5           NaN              NaN         1.0             0.0  9.761130e+15   \n",
       "6  5.842105e+10         0.000768         1.0             0.0  9.761140e+15   \n",
       "7  3.300000e+10         0.000434         1.0             0.0  9.764426e+15   \n",
       "8  6.821989e+13         0.896416         1.0             0.0  9.670665e+15   \n",
       "9  7.534211e+11         0.009900         1.0             0.0  9.702238e+15   \n",
       "\n",
       "   mean_raw_time  first      switchip     switchurl  switchdevice  avg_ip  \\\n",
       "0   9.766127e+15    0.0  6.913463e+11  0.000000e+00  1.010429e+12     1.0   \n",
       "1   9.766866e+15    0.0  3.233579e+12  6.467158e+12  6.467158e+12     3.0   \n",
       "2   9.769395e+15    0.0  2.379000e+12  3.154105e+12  3.154105e+12     1.0   \n",
       "3   9.706442e+15    0.0  0.000000e+00  0.000000e+00  0.000000e+00     1.0   \n",
       "4   9.762118e+15    0.0  9.817990e+10  1.330883e+11  1.820516e+11     2.0   \n",
       "5   9.761130e+15    0.0  0.000000e+00  0.000000e+00  0.000000e+00     1.0   \n",
       "6   9.763141e+15    0.0  2.131289e+12  1.598902e+12  4.262579e+12     1.0   \n",
       "7   9.764104e+15    0.0  1.805501e+11  0.000000e+00  2.708211e+12     3.5   \n",
       "8   9.670665e+15    0.0  6.821989e+13  6.821989e+13  6.821989e+13     1.0   \n",
       "9   9.697818e+15    0.0  6.280081e+12  1.145300e+13  6.908089e+12     1.0   \n",
       "\n",
       "   avg_device  avg_url  avg_bid  tod  period  auction_1st  auction_2nd  \\\n",
       "0         1.0      1.0      1.0  2.0    11.0          0.0          0.0   \n",
       "1         2.0      2.0      3.0  4.0     2.0          0.0          3.0   \n",
       "2         1.0      1.0      1.0  1.0     1.0          0.0          0.0   \n",
       "3         1.0      1.0      1.0  4.0     1.0          0.0          0.0   \n",
       "4         2.0      2.0      2.0  3.0    76.0          0.0          0.0   \n",
       "5         1.0      1.0      1.0  4.0     1.0          0.0          1.0   \n",
       "6         1.0      1.0      1.0  3.0     4.0          0.0          0.0   \n",
       "7         1.0      1.0      3.5  3.0    27.0          0.0          0.0   \n",
       "8         1.0      1.0      1.0  3.0     2.0          0.0          0.0   \n",
       "9         1.0      1.0      1.0  3.0     5.0          0.0          0.0   \n",
       "\n",
       "   auction_3rd  auction_4th  auction_5th  device_1st  device_2nd  device_3rd  \\\n",
       "0          0.0          0.0          0.0         6.0         0.0         3.0   \n",
       "1          0.0          0.0          0.0         0.0         0.0         0.0   \n",
       "2          0.0          0.0          0.0         3.0         0.0         0.0   \n",
       "3          0.0          0.0          0.0         0.0         0.0         0.0   \n",
       "4          0.0          0.0          0.0        19.0         1.0         1.0   \n",
       "5          0.0          0.0          0.0         0.0         0.0         0.0   \n",
       "6          0.0          0.0          0.0         0.0         0.0         0.0   \n",
       "7          0.0          0.0          0.0         0.0         0.0         0.0   \n",
       "8          0.0          0.0          0.0         0.0         0.0         0.0   \n",
       "9          3.0          0.0          0.0         0.0         0.0         0.0   \n",
       "\n",
       "   device_4th  device_5th  country_1st  country_2nd  country_3rd  country_4th  \\\n",
       "0         0.0         0.0          0.0          5.0          0.0          0.0   \n",
       "1         0.0         0.0          0.0          0.0          3.0          0.0   \n",
       "2         0.0         0.0          0.0          0.0          4.0          0.0   \n",
       "3         0.0         0.0          0.0          0.0          0.0          0.0   \n",
       "4         0.0        11.0        154.0          0.0          0.0          0.0   \n",
       "5         0.0         0.0          0.0          0.0          0.0          0.0   \n",
       "6         0.0         0.0          0.0          0.0          8.0          0.0   \n",
       "7         0.0         0.0          0.0          0.0          0.0          0.0   \n",
       "8         0.0         0.0          2.0          0.0          0.0          0.0   \n",
       "9         1.0         2.0          8.0          3.0          1.0          0.0   \n",
       "\n",
       "   country_5th  ip_1st  ip_2nd  ip_3rd  ip_4th  ip_5th  url_1st  url_2nd  \\\n",
       "0          0.0     0.0     0.0     0.0     0.0     0.0     24.0      0.0   \n",
       "1          0.0     0.0     0.0     0.0     0.0     0.0      1.0      0.0   \n",
       "2          0.0     0.0     0.0     0.0     0.0     0.0      3.0      0.0   \n",
       "3          0.0     0.0     0.0     0.0     0.0     0.0      1.0      0.0   \n",
       "4          1.0     0.0     0.0     0.0     0.0     0.0     54.0      0.0   \n",
       "5          0.0     0.0     0.0     0.0     0.0     0.0      1.0      0.0   \n",
       "6          0.0     0.0     0.0     0.0     0.0     0.0      0.0      0.0   \n",
       "7          0.0     0.0     0.0     0.0     0.0     0.0     34.0      0.0   \n",
       "8          0.0     0.0     0.0     0.0     0.0     0.0      1.0      0.0   \n",
       "9          0.0     0.0     0.0     0.0     0.0     0.0      5.0      0.0   \n",
       "\n",
       "   url_3rd  url_4th  url_5th  morning  afternoon  evening  night  Africa  \\\n",
       "0      0.0      0.0      0.0      5.0       11.0      8.0    0.0    23.0   \n",
       "1      0.0      0.0      0.0      0.0        0.0      1.0    2.0     0.0   \n",
       "2      0.0      0.0      0.0      1.0        1.0      1.0    1.0     0.0   \n",
       "3      0.0      0.0      0.0      0.0        0.0      0.0    1.0     0.0   \n",
       "4      0.0      0.0      0.0     53.0        6.0     76.0   20.0     0.0   \n",
       "5      0.0      0.0      0.0      0.0        0.0      0.0    1.0     0.0   \n",
       "6      0.0      0.0      0.0      0.0        2.0      4.0    2.0     0.0   \n",
       "7      0.0      0.0      0.0      2.0        1.0     27.0    4.0     1.0   \n",
       "8      0.0      0.0      0.0      0.0        0.0      2.0    0.0     0.0   \n",
       "9      0.0      0.0      0.0      2.0        2.0      5.0    5.0     3.0   \n",
       "\n",
       "    Asia  Europe  North America  Oceania  South America  Unknown  \\\n",
       "0    1.0     0.0            0.0      0.0            0.0      0.0   \n",
       "1    3.0     0.0            0.0      0.0            0.0      0.0   \n",
       "2    4.0     0.0            0.0      0.0            0.0      0.0   \n",
       "3    1.0     0.0            0.0      0.0            0.0      0.0   \n",
       "4  154.0     0.0            1.0      0.0            0.0      0.0   \n",
       "5    1.0     0.0            0.0      0.0            0.0      0.0   \n",
       "6    8.0     0.0            0.0      0.0            0.0      0.0   \n",
       "7    0.0    33.0            0.0      0.0            0.0      0.0   \n",
       "8    2.0     0.0            0.0      0.0            0.0      0.0   \n",
       "9   11.0     0.0            0.0      0.0            0.0      0.0   \n",
       "\n",
       "   first32_encode  bids_block1  bids_block2  bids_block3  mean_bids_blocks  \\\n",
       "0            1631          0.0          0.0         24.0          8.000000   \n",
       "1            1631          0.0          0.0          3.0          1.000000   \n",
       "2            1631          0.0          0.0          4.0          1.333333   \n",
       "3             801          0.0          1.0          0.0          0.333333   \n",
       "4            1631          0.0          0.0        155.0         51.666667   \n",
       "5            1631          0.0          0.0          1.0          0.333333   \n",
       "6            1278          0.0          0.0          8.0          2.666667   \n",
       "7            1631          0.0          0.0         34.0         11.333333   \n",
       "8            1905          1.0          1.0          0.0          0.666667   \n",
       "9            2420          1.0         13.0          0.0          4.666667   \n",
       "\n",
       "   merauction  ipauction  devauction  urlauction  \n",
       "0    0.055556   1.111111    0.777778    0.055556  \n",
       "1    1.000000   3.000000    2.000000    2.000000  \n",
       "2    0.250000   1.000000    0.500000    0.500000  \n",
       "3    1.000000   1.000000    1.000000    1.000000  \n",
       "4    0.043478   5.347826    2.304348    3.956522  \n",
       "5    1.000000   1.000000    1.000000    1.000000  \n",
       "6    0.125000   0.375000    0.250000    1.000000  \n",
       "7    0.100000   3.200000    0.300000    0.100000  \n",
       "8    0.500000   1.000000    1.000000    1.000000  \n",
       "9    0.100000   1.200000    1.100000    0.700000  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "9718a1c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = train_final[train_final['outcome'] == 1]\n",
    "h = train_final[train_final['outcome'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "99f6bd18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='mean_bids_blocks', ylabel='Density'>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data = h, x = 'mean_bids_blocks') #blue\n",
    "sns.kdeplot(data = b, x = 'mean_bids_blocks') #orange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "40e88cf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2013 entries, 0 to 2012\n",
      "Data columns (total 79 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   bidder_id         2013 non-null   object \n",
      " 1   outcome           2013 non-null   float64\n",
      " 2   bid_id            1984 non-null   float64\n",
      " 3   auction           1984 non-null   float64\n",
      " 4   merchandise       1984 non-null   float64\n",
      " 5   device            1984 non-null   float64\n",
      " 6   country           1984 non-null   float64\n",
      " 7   ip                1984 non-null   float64\n",
      " 8   url               1984 non-null   float64\n",
      " 9   adevice           1984 non-null   float64\n",
      " 10  aauction          1984 non-null   float64\n",
      " 11  aurl              1984 non-null   float64\n",
      " 12  aip               1984 non-null   float64\n",
      " 13  amerchandise      1984 non-null   float64\n",
      " 14  acountry          1984 non-null   float64\n",
      " 15  win               1984 non-null   float64\n",
      " 16  mean_time         1682 non-null   float64\n",
      " 17  mean_time_scaled  1682 non-null   float64\n",
      " 18  med_time          1682 non-null   float64\n",
      " 19  med_time_scaled   1682 non-null   float64\n",
      " 20  multi_bids        1984 non-null   float64\n",
      " 21  num_multi_bids    1984 non-null   float64\n",
      " 22  med_raw_time      1984 non-null   float64\n",
      " 23  mean_raw_time     1984 non-null   float64\n",
      " 24  first             1984 non-null   float64\n",
      " 25  switchip          1984 non-null   float64\n",
      " 26  switchurl         1984 non-null   float64\n",
      " 27  switchdevice      1984 non-null   float64\n",
      " 28  avg_ip            1984 non-null   float64\n",
      " 29  avg_device        1984 non-null   float64\n",
      " 30  avg_url           1984 non-null   float64\n",
      " 31  avg_bid           1984 non-null   float64\n",
      " 32  tod               1984 non-null   float64\n",
      " 33  period            1984 non-null   float64\n",
      " 34  auction_1st       2013 non-null   float64\n",
      " 35  auction_2nd       2013 non-null   float64\n",
      " 36  auction_3rd       2013 non-null   float64\n",
      " 37  auction_4th       2013 non-null   float64\n",
      " 38  auction_5th       2013 non-null   float64\n",
      " 39  device_1st        2013 non-null   float64\n",
      " 40  device_2nd        2013 non-null   float64\n",
      " 41  device_3rd        2013 non-null   float64\n",
      " 42  device_4th        2013 non-null   float64\n",
      " 43  device_5th        2013 non-null   float64\n",
      " 44  country_1st       2013 non-null   float64\n",
      " 45  country_2nd       2013 non-null   float64\n",
      " 46  country_3rd       2013 non-null   float64\n",
      " 47  country_4th       2013 non-null   float64\n",
      " 48  country_5th       2013 non-null   float64\n",
      " 49  ip_1st            2013 non-null   float64\n",
      " 50  ip_2nd            2013 non-null   float64\n",
      " 51  ip_3rd            2013 non-null   float64\n",
      " 52  ip_4th            2013 non-null   float64\n",
      " 53  ip_5th            2013 non-null   float64\n",
      " 54  url_1st           2013 non-null   float64\n",
      " 55  url_2nd           2013 non-null   float64\n",
      " 56  url_3rd           2013 non-null   float64\n",
      " 57  url_4th           2013 non-null   float64\n",
      " 58  url_5th           2013 non-null   float64\n",
      " 59  morning           1984 non-null   float64\n",
      " 60  afternoon         1984 non-null   float64\n",
      " 61  evening           1984 non-null   float64\n",
      " 62  night             1984 non-null   float64\n",
      " 63  Africa            1984 non-null   float64\n",
      " 64  Asia              1984 non-null   float64\n",
      " 65  Europe            1984 non-null   float64\n",
      " 66  North America     1984 non-null   float64\n",
      " 67  Oceania           1984 non-null   float64\n",
      " 68  South America     1984 non-null   float64\n",
      " 69  Unknown           1984 non-null   float64\n",
      " 70  first32_encode    2013 non-null   int32  \n",
      " 71  bids_block1       1984 non-null   float64\n",
      " 72  bids_block2       1984 non-null   float64\n",
      " 73  bids_block3       1984 non-null   float64\n",
      " 74  mean_bids_blocks  1984 non-null   float64\n",
      " 75  merauction        1984 non-null   float64\n",
      " 76  ipauction         1984 non-null   float64\n",
      " 77  devauction        1984 non-null   float64\n",
      " 78  urlauction        1984 non-null   float64\n",
      "dtypes: float64(77), int32(1), object(1)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "train_final.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "3c507a47",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_final = test_final.drop(columns = ['outcome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "977adf30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4700 entries, 2013 to 6712\n",
      "Data columns (total 78 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   bidder_id         4700 non-null   object \n",
      " 1   bid_id            4630 non-null   float64\n",
      " 2   auction           4630 non-null   float64\n",
      " 3   merchandise       4630 non-null   float64\n",
      " 4   device            4630 non-null   float64\n",
      " 5   country           4630 non-null   float64\n",
      " 6   ip                4630 non-null   float64\n",
      " 7   url               4630 non-null   float64\n",
      " 8   adevice           4630 non-null   float64\n",
      " 9   aauction          4630 non-null   float64\n",
      " 10  aurl              4630 non-null   float64\n",
      " 11  aip               4630 non-null   float64\n",
      " 12  amerchandise      4630 non-null   float64\n",
      " 13  acountry          4630 non-null   float64\n",
      " 14  win               4630 non-null   float64\n",
      " 15  mean_time         3875 non-null   float64\n",
      " 16  mean_time_scaled  3875 non-null   float64\n",
      " 17  med_time          3875 non-null   float64\n",
      " 18  med_time_scaled   3875 non-null   float64\n",
      " 19  multi_bids        4630 non-null   float64\n",
      " 20  num_multi_bids    4630 non-null   float64\n",
      " 21  med_raw_time      4630 non-null   float64\n",
      " 22  mean_raw_time     4630 non-null   float64\n",
      " 23  first             4630 non-null   float64\n",
      " 24  switchip          4630 non-null   float64\n",
      " 25  switchurl         4630 non-null   float64\n",
      " 26  switchdevice      4630 non-null   float64\n",
      " 27  avg_ip            4630 non-null   float64\n",
      " 28  avg_device        4630 non-null   float64\n",
      " 29  avg_url           4630 non-null   float64\n",
      " 30  avg_bid           4630 non-null   float64\n",
      " 31  tod               4630 non-null   float64\n",
      " 32  period            4630 non-null   float64\n",
      " 33  auction_1st       4700 non-null   float64\n",
      " 34  auction_2nd       4700 non-null   float64\n",
      " 35  auction_3rd       4700 non-null   float64\n",
      " 36  auction_4th       4700 non-null   float64\n",
      " 37  auction_5th       4700 non-null   float64\n",
      " 38  device_1st        4700 non-null   float64\n",
      " 39  device_2nd        4700 non-null   float64\n",
      " 40  device_3rd        4700 non-null   float64\n",
      " 41  device_4th        4700 non-null   float64\n",
      " 42  device_5th        4700 non-null   float64\n",
      " 43  country_1st       4700 non-null   float64\n",
      " 44  country_2nd       4700 non-null   float64\n",
      " 45  country_3rd       4700 non-null   float64\n",
      " 46  country_4th       4700 non-null   float64\n",
      " 47  country_5th       4700 non-null   float64\n",
      " 48  ip_1st            4700 non-null   float64\n",
      " 49  ip_2nd            4700 non-null   float64\n",
      " 50  ip_3rd            4700 non-null   float64\n",
      " 51  ip_4th            4700 non-null   float64\n",
      " 52  ip_5th            4700 non-null   float64\n",
      " 53  url_1st           4700 non-null   float64\n",
      " 54  url_2nd           4700 non-null   float64\n",
      " 55  url_3rd           4700 non-null   float64\n",
      " 56  url_4th           4700 non-null   float64\n",
      " 57  url_5th           4700 non-null   float64\n",
      " 58  morning           4630 non-null   float64\n",
      " 59  afternoon         4630 non-null   float64\n",
      " 60  evening           4630 non-null   float64\n",
      " 61  night             4630 non-null   float64\n",
      " 62  Africa            4630 non-null   float64\n",
      " 63  Asia              4630 non-null   float64\n",
      " 64  Europe            4630 non-null   float64\n",
      " 65  North America     4630 non-null   float64\n",
      " 66  Oceania           4630 non-null   float64\n",
      " 67  South America     4630 non-null   float64\n",
      " 68  Unknown           4630 non-null   float64\n",
      " 69  first32_encode    4700 non-null   int32  \n",
      " 70  bids_block1       4630 non-null   float64\n",
      " 71  bids_block2       4630 non-null   float64\n",
      " 72  bids_block3       4630 non-null   float64\n",
      " 73  mean_bids_blocks  4630 non-null   float64\n",
      " 74  merauction        4630 non-null   float64\n",
      " 75  ipauction         4630 non-null   float64\n",
      " 76  devauction        4630 non-null   float64\n",
      " 77  urlauction        4630 non-null   float64\n",
      "dtypes: float64(76), int32(1), object(1)\n",
      "memory usage: 2.8+ MB\n"
     ]
    }
   ],
   "source": [
    "test_final.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "a30bf5a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_final = train_final.fillna(0)\n",
    "#train_final.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "1ab94f21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2013 entries, 0 to 2012\n",
      "Data columns (total 79 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   bidder_id         2013 non-null   object \n",
      " 1   outcome           2013 non-null   float64\n",
      " 2   bid_id            2013 non-null   float64\n",
      " 3   auction           2013 non-null   float64\n",
      " 4   merchandise       2013 non-null   float64\n",
      " 5   device            2013 non-null   float64\n",
      " 6   country           2013 non-null   float64\n",
      " 7   ip                2013 non-null   float64\n",
      " 8   url               2013 non-null   float64\n",
      " 9   adevice           2013 non-null   float64\n",
      " 10  aauction          2013 non-null   float64\n",
      " 11  aurl              2013 non-null   float64\n",
      " 12  aip               2013 non-null   float64\n",
      " 13  amerchandise      2013 non-null   float64\n",
      " 14  acountry          2013 non-null   float64\n",
      " 15  win               2013 non-null   float64\n",
      " 16  mean_time         2013 non-null   float64\n",
      " 17  mean_time_scaled  2013 non-null   float64\n",
      " 18  med_time          2013 non-null   float64\n",
      " 19  med_time_scaled   2013 non-null   float64\n",
      " 20  multi_bids        2013 non-null   float64\n",
      " 21  num_multi_bids    2013 non-null   float64\n",
      " 22  med_raw_time      2013 non-null   float64\n",
      " 23  mean_raw_time     2013 non-null   float64\n",
      " 24  first             2013 non-null   float64\n",
      " 25  switchip          2013 non-null   float64\n",
      " 26  switchurl         2013 non-null   float64\n",
      " 27  switchdevice      2013 non-null   float64\n",
      " 28  avg_ip            2013 non-null   float64\n",
      " 29  avg_device        2013 non-null   float64\n",
      " 30  avg_url           2013 non-null   float64\n",
      " 31  avg_bid           2013 non-null   float64\n",
      " 32  tod               2013 non-null   float64\n",
      " 33  period            2013 non-null   float64\n",
      " 34  auction_1st       2013 non-null   float64\n",
      " 35  auction_2nd       2013 non-null   float64\n",
      " 36  auction_3rd       2013 non-null   float64\n",
      " 37  auction_4th       2013 non-null   float64\n",
      " 38  auction_5th       2013 non-null   float64\n",
      " 39  device_1st        2013 non-null   float64\n",
      " 40  device_2nd        2013 non-null   float64\n",
      " 41  device_3rd        2013 non-null   float64\n",
      " 42  device_4th        2013 non-null   float64\n",
      " 43  device_5th        2013 non-null   float64\n",
      " 44  country_1st       2013 non-null   float64\n",
      " 45  country_2nd       2013 non-null   float64\n",
      " 46  country_3rd       2013 non-null   float64\n",
      " 47  country_4th       2013 non-null   float64\n",
      " 48  country_5th       2013 non-null   float64\n",
      " 49  ip_1st            2013 non-null   float64\n",
      " 50  ip_2nd            2013 non-null   float64\n",
      " 51  ip_3rd            2013 non-null   float64\n",
      " 52  ip_4th            2013 non-null   float64\n",
      " 53  ip_5th            2013 non-null   float64\n",
      " 54  url_1st           2013 non-null   float64\n",
      " 55  url_2nd           2013 non-null   float64\n",
      " 56  url_3rd           2013 non-null   float64\n",
      " 57  url_4th           2013 non-null   float64\n",
      " 58  url_5th           2013 non-null   float64\n",
      " 59  morning           2013 non-null   float64\n",
      " 60  afternoon         2013 non-null   float64\n",
      " 61  evening           2013 non-null   float64\n",
      " 62  night             2013 non-null   float64\n",
      " 63  Africa            2013 non-null   float64\n",
      " 64  Asia              2013 non-null   float64\n",
      " 65  Europe            2013 non-null   float64\n",
      " 66  North America     2013 non-null   float64\n",
      " 67  Oceania           2013 non-null   float64\n",
      " 68  South America     2013 non-null   float64\n",
      " 69  Unknown           2013 non-null   float64\n",
      " 70  first32_encode    2013 non-null   int32  \n",
      " 71  bids_block1       2013 non-null   float64\n",
      " 72  bids_block2       2013 non-null   float64\n",
      " 73  bids_block3       2013 non-null   float64\n",
      " 74  mean_bids_blocks  2013 non-null   float64\n",
      " 75  merauction        2013 non-null   float64\n",
      " 76  ipauction         2013 non-null   float64\n",
      " 77  devauction        2013 non-null   float64\n",
      " 78  urlauction        2013 non-null   float64\n",
      "dtypes: float64(77), int32(1), object(1)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "train_final.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "b56e1d45",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_final = test_final.fillna(0)\n",
    "#test_final.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "7a805f01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4700 entries, 2013 to 6712\n",
      "Data columns (total 78 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   bidder_id         4700 non-null   object \n",
      " 1   bid_id            4700 non-null   float64\n",
      " 2   auction           4700 non-null   float64\n",
      " 3   merchandise       4700 non-null   float64\n",
      " 4   device            4700 non-null   float64\n",
      " 5   country           4700 non-null   float64\n",
      " 6   ip                4700 non-null   float64\n",
      " 7   url               4700 non-null   float64\n",
      " 8   adevice           4700 non-null   float64\n",
      " 9   aauction          4700 non-null   float64\n",
      " 10  aurl              4700 non-null   float64\n",
      " 11  aip               4700 non-null   float64\n",
      " 12  amerchandise      4700 non-null   float64\n",
      " 13  acountry          4700 non-null   float64\n",
      " 14  win               4700 non-null   float64\n",
      " 15  mean_time         4700 non-null   float64\n",
      " 16  mean_time_scaled  4700 non-null   float64\n",
      " 17  med_time          4700 non-null   float64\n",
      " 18  med_time_scaled   4700 non-null   float64\n",
      " 19  multi_bids        4700 non-null   float64\n",
      " 20  num_multi_bids    4700 non-null   float64\n",
      " 21  med_raw_time      4700 non-null   float64\n",
      " 22  mean_raw_time     4700 non-null   float64\n",
      " 23  first             4700 non-null   float64\n",
      " 24  switchip          4700 non-null   float64\n",
      " 25  switchurl         4700 non-null   float64\n",
      " 26  switchdevice      4700 non-null   float64\n",
      " 27  avg_ip            4700 non-null   float64\n",
      " 28  avg_device        4700 non-null   float64\n",
      " 29  avg_url           4700 non-null   float64\n",
      " 30  avg_bid           4700 non-null   float64\n",
      " 31  tod               4700 non-null   float64\n",
      " 32  period            4700 non-null   float64\n",
      " 33  auction_1st       4700 non-null   float64\n",
      " 34  auction_2nd       4700 non-null   float64\n",
      " 35  auction_3rd       4700 non-null   float64\n",
      " 36  auction_4th       4700 non-null   float64\n",
      " 37  auction_5th       4700 non-null   float64\n",
      " 38  device_1st        4700 non-null   float64\n",
      " 39  device_2nd        4700 non-null   float64\n",
      " 40  device_3rd        4700 non-null   float64\n",
      " 41  device_4th        4700 non-null   float64\n",
      " 42  device_5th        4700 non-null   float64\n",
      " 43  country_1st       4700 non-null   float64\n",
      " 44  country_2nd       4700 non-null   float64\n",
      " 45  country_3rd       4700 non-null   float64\n",
      " 46  country_4th       4700 non-null   float64\n",
      " 47  country_5th       4700 non-null   float64\n",
      " 48  ip_1st            4700 non-null   float64\n",
      " 49  ip_2nd            4700 non-null   float64\n",
      " 50  ip_3rd            4700 non-null   float64\n",
      " 51  ip_4th            4700 non-null   float64\n",
      " 52  ip_5th            4700 non-null   float64\n",
      " 53  url_1st           4700 non-null   float64\n",
      " 54  url_2nd           4700 non-null   float64\n",
      " 55  url_3rd           4700 non-null   float64\n",
      " 56  url_4th           4700 non-null   float64\n",
      " 57  url_5th           4700 non-null   float64\n",
      " 58  morning           4700 non-null   float64\n",
      " 59  afternoon         4700 non-null   float64\n",
      " 60  evening           4700 non-null   float64\n",
      " 61  night             4700 non-null   float64\n",
      " 62  Africa            4700 non-null   float64\n",
      " 63  Asia              4700 non-null   float64\n",
      " 64  Europe            4700 non-null   float64\n",
      " 65  North America     4700 non-null   float64\n",
      " 66  Oceania           4700 non-null   float64\n",
      " 67  South America     4700 non-null   float64\n",
      " 68  Unknown           4700 non-null   float64\n",
      " 69  first32_encode    4700 non-null   int32  \n",
      " 70  bids_block1       4700 non-null   float64\n",
      " 71  bids_block2       4700 non-null   float64\n",
      " 72  bids_block3       4700 non-null   float64\n",
      " 73  mean_bids_blocks  4700 non-null   float64\n",
      " 74  merauction        4700 non-null   float64\n",
      " 75  ipauction         4700 non-null   float64\n",
      " 76  devauction        4700 non-null   float64\n",
      " 77  urlauction        4700 non-null   float64\n",
      "dtypes: float64(76), int32(1), object(1)\n",
      "memory usage: 2.8+ MB\n"
     ]
    }
   ],
   "source": [
    "test_final.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22ea5db1",
   "metadata": {},
   "source": [
    "## Building Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "bc3251b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bidder_id</th>\n",
       "      <th>outcome</th>\n",
       "      <th>bid_id</th>\n",
       "      <th>auction</th>\n",
       "      <th>merchandise</th>\n",
       "      <th>device</th>\n",
       "      <th>country</th>\n",
       "      <th>ip</th>\n",
       "      <th>url</th>\n",
       "      <th>adevice</th>\n",
       "      <th>aauction</th>\n",
       "      <th>aurl</th>\n",
       "      <th>aip</th>\n",
       "      <th>amerchandise</th>\n",
       "      <th>acountry</th>\n",
       "      <th>win</th>\n",
       "      <th>mean_time</th>\n",
       "      <th>mean_time_scaled</th>\n",
       "      <th>med_time</th>\n",
       "      <th>med_time_scaled</th>\n",
       "      <th>multi_bids</th>\n",
       "      <th>num_multi_bids</th>\n",
       "      <th>med_raw_time</th>\n",
       "      <th>mean_raw_time</th>\n",
       "      <th>first</th>\n",
       "      <th>switchip</th>\n",
       "      <th>switchurl</th>\n",
       "      <th>switchdevice</th>\n",
       "      <th>avg_ip</th>\n",
       "      <th>avg_device</th>\n",
       "      <th>avg_url</th>\n",
       "      <th>avg_bid</th>\n",
       "      <th>tod</th>\n",
       "      <th>period</th>\n",
       "      <th>auction_1st</th>\n",
       "      <th>auction_2nd</th>\n",
       "      <th>auction_3rd</th>\n",
       "      <th>auction_4th</th>\n",
       "      <th>auction_5th</th>\n",
       "      <th>device_1st</th>\n",
       "      <th>device_2nd</th>\n",
       "      <th>device_3rd</th>\n",
       "      <th>device_4th</th>\n",
       "      <th>device_5th</th>\n",
       "      <th>country_1st</th>\n",
       "      <th>country_2nd</th>\n",
       "      <th>country_3rd</th>\n",
       "      <th>country_4th</th>\n",
       "      <th>country_5th</th>\n",
       "      <th>ip_1st</th>\n",
       "      <th>ip_2nd</th>\n",
       "      <th>ip_3rd</th>\n",
       "      <th>ip_4th</th>\n",
       "      <th>ip_5th</th>\n",
       "      <th>url_1st</th>\n",
       "      <th>url_2nd</th>\n",
       "      <th>url_3rd</th>\n",
       "      <th>url_4th</th>\n",
       "      <th>url_5th</th>\n",
       "      <th>morning</th>\n",
       "      <th>afternoon</th>\n",
       "      <th>evening</th>\n",
       "      <th>night</th>\n",
       "      <th>Africa</th>\n",
       "      <th>Asia</th>\n",
       "      <th>Europe</th>\n",
       "      <th>North America</th>\n",
       "      <th>Oceania</th>\n",
       "      <th>South America</th>\n",
       "      <th>Unknown</th>\n",
       "      <th>first32_encode</th>\n",
       "      <th>bids_block1</th>\n",
       "      <th>bids_block2</th>\n",
       "      <th>bids_block3</th>\n",
       "      <th>mean_bids_blocks</th>\n",
       "      <th>merauction</th>\n",
       "      <th>ipauction</th>\n",
       "      <th>devauction</th>\n",
       "      <th>urlauction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4a791121f1d9c9c192d13051301984388c491</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.711121e+11</td>\n",
       "      <td>0.007504</td>\n",
       "      <td>3.458421e+11</td>\n",
       "      <td>0.004544</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.764806e+15</td>\n",
       "      <td>9.766127e+15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.913463e+11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.010429e+12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1631</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.055556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               bidder_id  outcome  bid_id  auction  \\\n",
       "0  4a791121f1d9c9c192d13051301984388c491      0.0    24.0     18.0   \n",
       "\n",
       "   merchandise  device  country    ip  url   adevice  aauction      aurl  \\\n",
       "0          1.0    14.0      6.0  20.0  1.0  0.583333      0.75  0.041667   \n",
       "\n",
       "        aip  amerchandise  acountry  win     mean_time  mean_time_scaled  \\\n",
       "0  0.833333      0.041667      0.25  0.0  5.711121e+11          0.007504   \n",
       "\n",
       "       med_time  med_time_scaled  multi_bids  num_multi_bids  med_raw_time  \\\n",
       "0  3.458421e+11         0.004544         1.0             0.0  9.764806e+15   \n",
       "\n",
       "   mean_raw_time  first      switchip  switchurl  switchdevice  avg_ip  \\\n",
       "0   9.766127e+15    0.0  6.913463e+11        0.0  1.010429e+12     1.0   \n",
       "\n",
       "   avg_device  avg_url  avg_bid  tod  period  auction_1st  auction_2nd  \\\n",
       "0         1.0      1.0      1.0  2.0    11.0          0.0          0.0   \n",
       "\n",
       "   auction_3rd  auction_4th  auction_5th  device_1st  device_2nd  device_3rd  \\\n",
       "0          0.0          0.0          0.0         6.0         0.0         3.0   \n",
       "\n",
       "   device_4th  device_5th  country_1st  country_2nd  country_3rd  country_4th  \\\n",
       "0         0.0         0.0          0.0          5.0          0.0          0.0   \n",
       "\n",
       "   country_5th  ip_1st  ip_2nd  ip_3rd  ip_4th  ip_5th  url_1st  url_2nd  \\\n",
       "0          0.0     0.0     0.0     0.0     0.0     0.0     24.0      0.0   \n",
       "\n",
       "   url_3rd  url_4th  url_5th  morning  afternoon  evening  night  Africa  \\\n",
       "0      0.0      0.0      0.0      5.0       11.0      8.0    0.0    23.0   \n",
       "\n",
       "   Asia  Europe  North America  Oceania  South America  Unknown  \\\n",
       "0   1.0     0.0            0.0      0.0            0.0      0.0   \n",
       "\n",
       "   first32_encode  bids_block1  bids_block2  bids_block3  mean_bids_blocks  \\\n",
       "0            1631          0.0          0.0         24.0               8.0   \n",
       "\n",
       "   merauction  ipauction  devauction  urlauction  \n",
       "0    0.055556   1.111111    0.777778    0.055556  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "a7f55bbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = final.corr()\n",
    "#corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "0def8918",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bidder_id', 'outcome', 'bid_id', 'auction', 'merchandise', 'device',\n",
       "       'country', 'ip', 'url', 'adevice', 'aauction', 'aurl', 'aip',\n",
       "       'amerchandise', 'acountry', 'win', 'mean_time', 'mean_time_scaled',\n",
       "       'med_time', 'med_time_scaled', 'multi_bids', 'num_multi_bids',\n",
       "       'med_raw_time', 'mean_raw_time', 'first', 'switchip', 'switchurl',\n",
       "       'switchdevice', 'avg_ip', 'avg_device', 'avg_url', 'avg_bid', 'tod',\n",
       "       'period', 'auction_1st', 'auction_2nd', 'auction_3rd', 'auction_4th',\n",
       "       'auction_5th', 'device_1st', 'device_2nd', 'device_3rd', 'device_4th',\n",
       "       'device_5th', 'country_1st', 'country_2nd', 'country_3rd',\n",
       "       'country_4th', 'country_5th', 'ip_1st', 'ip_2nd', 'ip_3rd', 'ip_4th',\n",
       "       'ip_5th', 'url_1st', 'url_2nd', 'url_3rd', 'url_4th', 'url_5th',\n",
       "       'morning', 'afternoon', 'evening', 'night', 'Africa', 'Asia', 'Europe',\n",
       "       'North America', 'Oceania', 'South America', 'Unknown',\n",
       "       'first32_encode', 'bids_block1', 'bids_block2', 'bids_block3',\n",
       "       'mean_bids_blocks', 'merauction', 'ipauction', 'devauction',\n",
       "       'urlauction'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "3a41a74b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9842132505175982\n",
      "0.9155094829506866\n",
      "0.6340100670090041\n",
      "0.4359256117109846\n",
      "0.2660794991699327\n",
      "0.37967551868401545\n",
      "Accuracy: 0.9582799487508008\n",
      "Error: 0.041720051249199225\n",
      "Recall: 0.041720051249199225\n",
      "Specificity: 0.9952928612907352\n",
      "Precision: 0.7579365079365079\n",
      "\n",
      "F1: 0.39582684925822176\n",
      "\n",
      "F1 weighted: 0.9485379101646365\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from imblearn.ensemble import BalancedRandomForestClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "\n",
    "# trying cost sensitive learning\n",
    "from sklearn.utils import class_weight\n",
    "\n",
    "\n",
    "features = ['bid_id', 'auction', 'merchandise', 'device', 'country', \n",
    "            'ip', 'url', 'adevice', 'aauction', 'aurl', \n",
    "            'aip','amerchandise', 'acountry', 'win', 'mean_time', \n",
    "            'mean_time_scaled','med_time', 'med_time_scaled', 'multi_bids', 'num_multi_bids',\n",
    "            'med_raw_time', 'mean_raw_time', 'first', 'avg_ip', 'avg_device',\n",
    "            'avg_url', 'avg_bid', 'tod', 'period', 'auction_1st', \n",
    "            'auction_2nd','auction_3rd', 'device_1st', 'device_2nd', 'device_3rd', \n",
    "            'country_1st','country_2nd', 'country_3rd', 'ip_1st', 'ip_2nd', \n",
    "            'ip_3rd', 'url_1st','url_2nd', 'url_3rd', 'auction_4th', \n",
    "            'auction_5th', 'device_4th', 'device_5th', 'country_4th', 'country_5th',\n",
    "            'ip_4th', 'ip_5th', 'url_4th', 'url_5th', 'morning', \n",
    "            'afternoon', 'evening', 'night','Africa', 'Asia', \n",
    "            'Europe', 'North America', 'Oceania', 'South America','Unknown', \n",
    "            'first32_encode','merauction', 'ipauction', 'devauction', 'urlauction'] #70f\n",
    "\n",
    "X = train_final[features]\n",
    "y = train_final['outcome']\n",
    "\n",
    "#kf = KFold(n_splits = 10, shuffle =True, random_state = 5)\n",
    "\n",
    "# use stratified kfold\n",
    "kf = StratifiedKFold(n_splits = 9, shuffle =True, random_state = 5)\n",
    "\n",
    "k_fold_auc = []\n",
    "k_fold_auc_proba = []\n",
    "k_fold_auc_train = []\n",
    "k_fold_mc = []\n",
    "k_fold_acc = []\n",
    "k_fold_err = []\n",
    "k_fold_rec = []\n",
    "k_fold_spec = []\n",
    "k_fold_pre = []\n",
    "k_fold_f1 = []\n",
    "k_fold_f1_weight = []\n",
    "k_fold_aps = []\n",
    "k_fold_cks = []\n",
    "\n",
    "\n",
    "from sklearn.metrics.pairwise import euclidean_distances\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.combine import SMOTEENN\n",
    "from imblearn.over_sampling import ADASYN\n",
    "from imblearn.combine import SMOTETomek\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "\n",
    "for train_index, test_index in kf.split(X, y):\n",
    "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index] \n",
    "\n",
    "    CV_rf = RandomForestClassifier(n_estimators = 100, max_depth = 20, min_samples_split = 3, min_samples_leaf = 1, \n",
    "                                   random_state = 5)\n",
    "\n",
    "        \n",
    "    CV_rf.fit(X_train, y_train)\n",
    "    \n",
    "    rf_pred = CV_rf.predict(X_train)\n",
    "    auc = roc_auc_score(y_train, rf_pred)\n",
    "    k_fold_auc_train.append(auc)\n",
    "    \n",
    "    rf_pred = CV_rf.predict(X_test)\n",
    "    auc = roc_auc_score(y_test, rf_pred)\n",
    "    k_fold_auc.append(auc)\n",
    "    \n",
    "    rf_pred = CV_rf.predict(X_test)\n",
    "    mc = matthews_corrcoef(y_test, rf_pred)\n",
    "    k_fold_mc.append(mc)\n",
    "    \n",
    "    rf_pred = CV_rf.predict(X_test)\n",
    "    aps = average_precision_score(y_test, rf_pred)\n",
    "    k_fold_aps.append(aps)\n",
    "    \n",
    "    rf_pred = CV_rf.predict(X_test)\n",
    "    cks = cohen_kappa_score(y_test, rf_pred)\n",
    "    k_fold_cks.append(cks)\n",
    "    \n",
    "    rf_pred = CV_rf.predict(X_test)\n",
    "    rf_pred_proba = CV_rf.predict_proba(X_test)\n",
    "    auc = roc_auc_score(y_test, rf_pred_proba[:,1])\n",
    "    k_fold_auc_proba.append(auc)\n",
    "    \n",
    "    cm = confusion_matrix(y_test, rf_pred)\n",
    "    TN, FP, FN, TP = cm.ravel()\n",
    "    k_fold_acc.append(((TN+TP) / (TN+FP+FN+TP)))\n",
    "    k_fold_err.append((FN+FP) / (TN+FP+FN+TP))\n",
    "    k_fold_rec.append((FN+FP) / (TN+FP+FN+TP))\n",
    "    k_fold_spec.append(TN / (TN+FP))\n",
    "    k_fold_pre.append(TP / (TP+FP))\n",
    "    k_fold_f1_weight.append(f1_score(y_test, rf_pred,average='weighted'))\n",
    "    k_fold_f1.append(f1_score(y_test, rf_pred))\n",
    "    \n",
    "\n",
    "mean_auc_proba = np.mean(k_fold_auc_proba)\n",
    "mean_auc = np.mean(k_fold_auc)\n",
    "mean_auc_train = np.mean(k_fold_auc_train)\n",
    "mean_mc = np.mean(k_fold_mc)\n",
    "mean_aps = np.mean(k_fold_aps)\n",
    "mean_cks = np.mean(k_fold_cks)\n",
    "print(mean_auc_train)\n",
    "print(mean_auc_proba)\n",
    "print(mean_auc)\n",
    "print(mean_mc)\n",
    "print(mean_aps)\n",
    "print(mean_cks)\n",
    "\n",
    "print('Accuracy: {0:}'.format(np.mean(k_fold_acc)))\n",
    "print('Error: {0:}'.format(np.mean(k_fold_err)))\n",
    "print('Recall: {0:}'.format(np.mean(k_fold_rec)))\n",
    "print('Specificity: {0:}'.format(np.mean(k_fold_spec)))\n",
    "print('Precision: {0:}'.format(np.mean(k_fold_pre)))\n",
    "\n",
    "print('\\nF1: {0:}'.format(np.mean(k_fold_f1)))\n",
    "print('\\nF1 weighted: {0:}'.format(np.mean(k_fold_f1_weight)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "74dc63ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6747759282970551,\n",
       " 0.5885616730687154,\n",
       " 0.5809748427672956,\n",
       " 0.6666666666666666,\n",
       " 0.7083333333333334,\n",
       " 0.5345911949685533,\n",
       " 0.5885506003430532,\n",
       " 0.6818181818181819,\n",
       " 0.6818181818181819]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_fold_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "40e13c5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05739962765806246"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(k_fold_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "addd9432",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.74649368e-02, 1.46404374e-02, 6.86297629e-04, 1.47008407e-02,\n",
       "       8.47513273e-03, 1.42253783e-02, 1.71789226e-02, 2.63893040e-02,\n",
       "       4.21468720e-02, 2.75195187e-02, 1.95868754e-02, 1.89992993e-02,\n",
       "       2.87183171e-02, 9.63339559e-03, 1.73177763e-02, 2.10162938e-02,\n",
       "       2.11235881e-02, 2.52709899e-02, 1.93616595e-02, 1.63601333e-02,\n",
       "       3.22721075e-02, 3.12522932e-02, 8.01909484e-03, 4.53204202e-03,\n",
       "       2.41443014e-03, 3.93431624e-03, 1.67262612e-02, 1.03958705e-02,\n",
       "       4.12689213e-02, 5.65802131e-03, 5.33937438e-03, 2.87899231e-03,\n",
       "       2.19614268e-02, 1.59286672e-02, 2.12550000e-02, 7.48057713e-03,\n",
       "       9.53282071e-03, 1.18160005e-02, 2.60033609e-03, 2.76179466e-03,\n",
       "       2.66883491e-03, 2.21816748e-02, 0.00000000e+00, 0.00000000e+00,\n",
       "       4.30444559e-03, 1.43846110e-03, 1.25546083e-02, 1.33631070e-02,\n",
       "       4.58577606e-03, 1.18003539e-02, 1.67823205e-03, 2.15613081e-03,\n",
       "       0.00000000e+00, 8.71183458e-05, 1.80524805e-02, 2.68030805e-02,\n",
       "       2.58337575e-02, 2.58126462e-02, 1.08180881e-02, 1.20927864e-02,\n",
       "       1.80617140e-02, 1.13161009e-02, 2.57030465e-02, 6.83418647e-03,\n",
       "       1.56213292e-03, 2.07472362e-02, 1.25634587e-02, 1.72641929e-02,\n",
       "       2.44264200e-02, 1.64456120e-02])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CV_rf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "ecf5a812",
   "metadata": {},
   "outputs": [],
   "source": [
    "fi = CV_rf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "b82dff55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = features, y = fi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd3748f7",
   "metadata": {},
   "source": [
    "from sklearn.model_selection import GridSearchCV, StratifiedKFold\n",
    "rf = RandomForestClassifier(random_state = 5)\n",
    "\n",
    "X = train_final[features]\n",
    "y = train_final['outcome']\n",
    "\n",
    "#Creating a dictionary grid for grid search\n",
    "param_grid = {'n_estimators': [150, 200, 250],\n",
    "              'max_depth': [10, 20, 30],\n",
    "              'min_samples_split': [2, 3, 4, 5],\n",
    "              'min_samples_leaf': [1, 2, 3, 4, 5]}\n",
    "\n",
    "#Fitting grid search to the train data with 5 folds\n",
    "gridsearch = GridSearchCV(estimator= rf, \n",
    "                          param_grid= param_grid,\n",
    "                          cv=StratifiedKFold(n_splits = 9, shuffle =True, random_state = 5), \n",
    "                          n_jobs=-1, \n",
    "                          scoring='f1', \n",
    "                          verbose=2).fit(X, y)\n",
    "                          \n",
    "gridsearch.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "15877f0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=20, min_samples_split=3, random_state=102)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf1 = RandomForestClassifier(n_estimators = 100, max_depth = 20, min_samples_split = 3, min_samples_leaf = 1, \n",
    "                               random_state = 5)\n",
    "rf2 = RandomForestClassifier(n_estimators = 100, max_depth = 20, min_samples_split = 3, min_samples_leaf = 1, \n",
    "                               random_state = 10)\n",
    "rf3 = RandomForestClassifier(n_estimators = 100, max_depth = 20, min_samples_split = 3, min_samples_leaf = 1, \n",
    "                               random_state = 23)\n",
    "rf4 = RandomForestClassifier(n_estimators = 100, max_depth = 20, min_samples_split = 3, min_samples_leaf = 1, \n",
    "                               random_state = 47)\n",
    "rf5 = RandomForestClassifier(n_estimators = 100, max_depth = 20, min_samples_split = 3, min_samples_leaf = 1, \n",
    "                               random_state = 102)\n",
    "\n",
    "X = train_final[features]\n",
    "y = train_final['outcome']\n",
    "\n",
    "rf1.fit(X, y)\n",
    "rf2.fit(X, y)\n",
    "rf3.fit(X, y)\n",
    "rf4.fit(X, y)\n",
    "rf5.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "6436de83",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1 = rf1.predict_proba(test_final[features])\n",
    "y_pred2 = rf2.predict_proba(test_final[features])\n",
    "y_pred3 = rf3.predict_proba(test_final[features])\n",
    "y_pred4 = rf4.predict_proba(test_final[features])\n",
    "y_pred5 = rf5.predict_proba(test_final[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "524d678b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_final = np.mean([y_pred1[:,1], y_pred2[:,1], y_pred3[:,1], y_pred4[:,1], y_pred5[:,1]], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "c44f3e9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\"Id\": test_final[\"bidder_id\"], \"Predicted\": y_pred_final})\n",
    "submission.to_csv('final_submission2.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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