Exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter notebook:\n",
    "All credits go to: https://www.kaggle.com/sudalairajkumar/simple-exploration-notebook-elo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.io.json import json_normalize\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from plotly import tools\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "from sklearn import model_selection, preprocessing, metrics\n",
    "#import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mData_Dictionary.xlsx\u001b[m\u001b[m          \u001b[31msample_submission.csv\u001b[m\u001b[m\r\n",
      "\u001b[31mhistorical_transactions.csv\u001b[m\u001b[m   \u001b[31mtest.csv\u001b[m\u001b[m\r\n",
      "\u001b[31mmerchants.csv\u001b[m\u001b[m                 \u001b[31mtrain.csv\u001b[m\u001b[m\r\n",
      "\u001b[31mnew_merchant_transactions.csv\u001b[m\u001b[m ~$Data_Dictionary.xlsx\r\n"
     ]
    }
   ],
   "source": [
    "!ls data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('data/train.csv', parse_dates=['first_active_month'])\n",
    "test_df = pd.read_csv('data/test.csv', parse_dates=['first_active_month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows and columns in train:  (201917, 6)\n",
      "Number of rows and columns in test:  (123623, 5)\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of rows and columns in train: \", train_df.shape)\n",
    "print(\"Number of rows and columns in test: \", test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_active_month</th>\n",
       "      <th>card_id</th>\n",
       "      <th>feature_1</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_3</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>C_ID_92a2005557</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.820283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>C_ID_3d0044924f</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.392913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>C_ID_d639edf6cd</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.688056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>C_ID_186d6a6901</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.142495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>C_ID_cdbd2c0db2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.159749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_active_month          card_id  feature_1  feature_2  feature_3  \\\n",
       "0         2017-06-01  C_ID_92a2005557          5          2          1   \n",
       "1         2017-01-01  C_ID_3d0044924f          4          1          0   \n",
       "2         2016-08-01  C_ID_d639edf6cd          2          2          0   \n",
       "3         2017-09-01  C_ID_186d6a6901          4          3          0   \n",
       "4         2017-11-01  C_ID_cdbd2c0db2          1          3          0   \n",
       "\n",
       "     target  \n",
       "0 -0.820283  \n",
       "1  0.392913  \n",
       "2  0.688056  \n",
       "3  0.142495  \n",
       "4 -0.159749  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VM/clSZq9bnfvnw+8APgs8N+BrwG/Bj60swVk5ueAz3WYdxZw1s6+hiRJ6jL0M/MDbdMX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\nOjsOtwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cfe4cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_col = \"target\"\n",
    "\n",
    "plt.figure(figsize={8,6})\n",
    "plt.scatter(range(train_df.shape[0]), np.sort(train_df[target_col].values))\n",
    "plt.xlabel('index', fontsize = 12)\n",
    "plt.ylabel('Loyalty Score', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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xamBDm94AnLZPG7WfqKpbqupTbfqrwHUMd2TdreNnsO8jSV6TZDvwAr7TY5/o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\nYrBLktQRg12SpI4Y7JIkdcRglySpI/8f/mQOHDtEjuMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10edaa0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize={8,6})\n",
    "sns.distplot(train_df[target_col].values, bins=50, color=\"red\", kde=False)\n",
    "plt.title(\"Histogram of Loyalty Score\")\n",
    "plt.xlabel('Loyalty Score', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2207"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(train_df[target_col]<-30).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PtHeSJEmSNAGmLawi4kERcTRwLfBo4KmZ+bLM/PGc9E6SJEmSJsBMn7G6jqr4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\nm7PueEZx7HG+hp3zhTPnozh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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c35bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kSapkASVJkiRJlSygJEmSJKmSBZQkSZIkVbKAkiRJkqRKFlCSJEmSVMkCSpIk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\nmAQKAABgCrFd9qqUYIx2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1018a0160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnt_srs = train_df['first_active_month'].dt.date.value_counts()\n",
    "cnt_srs = cnt_srs.sort_index()\n",
    "plt.figure(figsize=(14,6))\n",
    "sns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8, color='green')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('First active month', fontsize=12)\n",
    "plt.ylabel('Number of cards', fontsize=12)\n",
    "plt.title(\"First active month count in train set\")\n",
    "plt.show()\n",
    "\n",
    "cnt_srs = test_df['first_active_month'].dt.date.value_counts()\n",
    "cnt_srs = cnt_srs.sort_index()\n",
    "plt.figure(figsize=(14,6))\n",
    "sns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8, color='green')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('First active month', fontsize=12)\n",
    "plt.ylabel('Number of cards', fontsize=12)\n",
    "plt.title(\"First active month count in test set\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZN28e8+bNY9KkSVHpsB3uNf2PAxcD38SpbXe9SDvbL6JERD4IXKOqnww9vxVY\nrKqf6W37cGr6VVVV3Hb77TTUOwmIlCH4U0cQSBtJMH0EwdQR/ZLgk4v+Srq2cP311/Piiy/SKCm0\nznp/1Pcr7c34mqrwNVXia6wkoaUabXd+VEvPP58ffP/7fb7G98QTT/D4Y7+jrd1JjKNSIX9IG/lD\n/Ewb6mdsWv8k+PvXZnA8kNF1jEfFNfC9JQ3R3zFO34DSunjn70QiBxt9qIJPhPPPP5//+P73I7av\n2tpafv/73/PGm284tXkBRkBwnNNkTxpRyz++FT5S21K7jnFzUjPBq6J4Wui8HHBEiDtysiVg8pTJ\nXH7Z5dx8881R6Un9ve9+l38sXw5Aqs9HbjBIHk6Newzgi3KCfwjlREpK13Ee0tLC3VHe54lQ68IB\noMznozLUijF8WBa/efSRiF4GKC4u5rHfPcbWrVtpbmkGIDN1OCPTchiZMYGRmTmkJLyL607nYPmO\nx2jThq5jnCQZXF14W1T32dRWT2XDIeev8RCNrc5lis5CwJ133kleXt5Z3ydiNf2Q34T+vbX7PnB+\nfq7XF0TkTuBOgAkTJpx1+1/+8pc0NjbRmn8VgYwxEO/OkCHxt3P9+67nnnvuAeDPz7/cL/vVxFQC\niRMIDHOOVZsq0t5I/LGdrFm9mlWrVnHRRRf1aR91dXW0tXfw0fxmFo9uZ1iSO5OyNPuF6284eYzf\neOFP/bbv4cnK0jEdLB3TAbTQ7IfddfH8ZFs6lZWRnZN+5cqV/PnPf0YTFF2g6DiF/vpad8D11588\nxk///eno7k9wbuM7TPEX+qER5JCwd/te9u7Zy9y5c5k1a1ZEd7l3717+sXw584GlwKhgMOpJvqdW\nTj3OK//yl6jvcwjCHJyOhgSVRpx+AX+treGZZ57hzjvvjNi+Nm/ezJq1a8hKG8PsSZczMmM8KYnR\nTfI9dfjbTjkn//35FVHfZ1pSJmlJheSNKAROFgJKKtaxatUqLrjggrCSfrjCTfoTI7bHd+8wziiW\nTjmhZV1U9WHgYXBq+md6s7a2NlasXImqklixDX/DcYKZYwikj+7T9flzofGJvPjiiwC8+OKL6GmG\nkESTtDUSV1+Br6GChEanf8ELL7zQ56T/oQ99iL/+9RmeL0tlZ208k4f4mZIZYGKmn5R+vEqSGq+n\nHONR8f1b+PAH4WBjHHtPxLP3RBx7GhLpCAofueWjEd3PsmXLKC0t5R//+Adaqc4tbrPonyJ5Aqcc\n434rbAAfYMwoAAAgAElEQVS0A/XgO+o0g376059m5syZEd/N5s2bASj2+WgLBskHpqJk9GPiT+bU\n43yWQRUR5Q/V+EuBUp8PDQbZuGEDRDDpf/jDH+bNN//JntI9tPqbaeloJCk+Bd9ZRkBFUkJ80inH\nOCm+fzs/B4J+Wjsaae1oorG9jsWLF3PttddGdB9h994HCE3IMxo4Fu1m/W77jMf5rl2Bk+zfBj6i\nqjt62z6c5v2ioiJWr17N5s1bKNlVQjAQcK6Jpw3Hnz6GQMYYgmkjnfvcR3EoS0Q78oVDg0hLHXGN\nlfgajjpJvtVp6k5LS2fu3DnMmTOHyy67jNGj+z7R4po1a3jllVco3l5E+ZEKwKmkjctQJme0M2WI\nn0mZAXLSAsRFqcdIpDvynYkqVLb42FsfSvINCRyoj6Mj9EvJGjaUgsKZLFy4kGXLlkX8ep2q8uij\nj/L444+jqkicoFnq9MofqTCcqBQCIt6R70zagSqQSsFX6YM6QCExKZGvfPkrXHHFFVHZrd/vZ9Wq\nVaxbt441q1dTU1sLQLYIk1XJwamNZEaxEBDJjnxn045yBOdabhmwT4R2VRITEpg3fz5Llizhkksu\nifhQtOPHj59yr4b4uASGpY5meNo4hqdnMzw9m+SE6PSHgch25AtHS3sj1Y2HqWo8Qk1TBbXNxwgE\nnXsoTJ2azwMP/DeZmZlhvVdEm/dFJBP4GXBz6DUdoQ5196jqibAiOkeq6heRzwD/wDllPXq6hB+u\nWbNmdTX/tba2smPHDrZs2cKWLVvYUVyM/6gzZEMSU+lIyXI68oX+NDEtqgWBiNEg0lLb1Ykvrrma\nuOZqNOB8oTIzhzBv8Xzmzp3LnDlzotJpZOnSpSxd6gxBrK+vdzpMFhc7Y+93bOeNI00AJMYJuRl+\nJmU4Q/ImZfoZMwg69dW2CftOxLOvPo59DfEcaEikITScLzEhgWnT8nl/4UwKCgooKChg1KhRUR0P\nLSJ84hOf4MMf/jDbtm1j8+bNbN6ymT0796DF6vTYHx79QkBEdSb540JclTOhD0B8QjyFBYXMu9Hp\n9FRQUBDVmf3i4+O59NJLufTSS7s6S65du5Y1b73F6pISAl099oVxwWBXISAbSB6gnfk6BVCOE+rA\nBxwWH8dUuzpujR09mutCv+V58+ZFdQ6QUaNG8eSTT3L8+HG2b9/e9bd7zwZKjjqjfDJShpGVOpbh\n6eMYkT6OzJTh+GTgz1Aa1CAnmiupajxMVeNhapsraGx10mdCQgL5+flcMfPCruGp0RrbH25Hvt/h\nNBh+Bafglwt8D2hW1ej2cniX+jpOv3OcbWlpKbt27aJk1y4OlpV1DcORxBQ6Upwx9cEMp1WAuHPr\nNBTJmr60NRFXfwRf4/FQgq9BQyXGpORk8qfmM336NKaFJvFxe4rZzuF7O3fudI7zzmJKS0u7Ov6l\nJAgT0zuYlOlnyhA/BVkdpJ7DZYFI1fT9Qbo65e2rj2NfYxJ1rc5vx+fzMTEvl+kzTo6CcHOYXk+N\njY0UFRWxefNmNm3exO7S3SdbAoYrwRFBdJSe8+WAiNb0O+hK8r5KHzgVahISEiicWcj8eU5BdcaM\nGQNm+t62tjb27NnDzp07nYLtjh0cPnIEcFq2RomPPA0yEad2nnaOhYBI1fT9KIeB/aG/8lAtHiAj\nLY0ZhYUUdBu3PxBmSWxra2PXrl1dc0xs376Dujrny5EYn0RW2slCQFba2HPu0R/Jmn67v5XqpopQ\nTf4wNU1H8QfaARg+fASzZp2cg2LKlCl9nugr0jPyHQUmqWpzt2XpwN5YuOFOa2sre/fuZdeuXZSW\nlrKzZBdlB/Y7BQHxEUwfhT8zm8CQbIJpoyDMGnOfJufxtxFXf4S4E0dIaKyAZqfHZ3JKCtPy85k2\n7WSCHz9+/KCYq9/v91NWVtZtroRi9u3dhz8QIE5gyhA/s4d3MGt4B3kZgbBaAs5lRr5Ox5p9bKtO\noKg6nuK6JFr9zm9lfM64UxL81KlTB9UMiI2NjV0tARs3bWTvnr1dhYDgiCCao2iOQpjnoD4n/San\nI57vcCjJq1OzLiwsZP78+V01+cE0+2F9fX3XcOBtW7eybds22tqdE/6YUCEgD5gCp72DXk/nOjlP\nMJTk9+L0wj8oQkfovD950iTmzptHYWEhM2bMIDs7e1DMzqeqHDlyhO3bt1NUVMS2bUWUlR1wvsci\nDE0dxYj0HHKG5TMiPfxph/uS9IMapLLhEOW1pVQ1HuZEcxWgiPiYPHkSs0NDdmfOnBmRy6c9RTrp\nHwAuUdWybsvygDdV9ezd5ftRf82939bWRlFRERs3buTttzewe3ep84WLT8CfPgb/0An4R0w5ayvA\nu5mGV1rria8sJaH+MNJUBaokJSUzb95cFi5cyIIFC5g4ceKgSPDham9vp7i4mPXr17Nu7Rp273Fu\nHZqZJMwc1sqFY9uZmeU/YwEgnLn3Ox1q9PHa4SS21iRzzLn6wNgxo1m0eAmLFi1i7ty5A3Jmw75o\naGhwxp1v3syaNWsoLy93ZugbHUQnKJqtZ7wQeE7T8LY6U/D6DvogND3GjIIZLDpvEfNCSWig1OQj\nwe/3U1JS4lxy2bSJoqIi2trbSRBhhirzgEmcfdjfu5mGtxplC7DV56M21FI5aeJE5oXuJTF79myG\nDOnP7oDR1dDQQHFxsdMSULSdou3b6ehoJzUpg3FD8xmfNY3haWcu1LzbGfmCGqSqoZxDNSUcPrGb\n1vZmkpKSmT17Vtdl5BkzZkRtXo7uIp30vwZ8DPhvTjbv3ws8oarf7WOsEeXWXfYaGhq6bqKx/u23\nOXL4MBKfSPvwKXSMnoGm9N5EdvYb7ihxJ8pJOFZMXN0hfD4fBYWFnBdK8jNmzHD97k79qaamxjnG\noULAifoGstOVq8a1cNHYNpJ7SU5nS/pBhU2VCSw/lExxbTyJCQksWLiARYsWs2jRInJycgZF7ScS\nVJXdu3ezcuVKlq9YTk11DRIvBPIC6Ax1upD3Iuwb7tSDb7sPOSKgkJuXy3uufg9XXHFF1CfXGUg6\nOjrYsWMHK1eu5NWVK2lsbibT52NOMMiFnH4e/rMl/SBKEbBOhEOhWu/CBQt4zzXXsHjxYk8l+bNp\nbm5m9erVvPrqq6xbt46Ojg7SkjIZnzWD6WMWkRjf+5c5nLn3Wzua2FmxjvLaElram0hKSuKCCy7g\nsssuY8mSJa4UWCOd9AW4A/gITt+UI8AfcTrVuTMA+zQGwq11VZUdO3bw7LPP8uprrxHw+wkMyaYt\nd+k7kv+Zkn5c1V6SD2+E1nqGDhvGshtv5L3vfS8jR47sl88x0HV0dPDaa6/x9J//xK7S3aQmCO/L\nbeK63LZTav5nSvqbKxN4fHc6lc3ODXXe/4EPcsMNN4TdY9bLgsEg27Zt46WXXmL58uVonBLID6D5\n76z5nzXpt4AUC779PpKSknj/Te/n6quvZvLkyVH+FANfW1sba9as4eWXX2btmjUki3BlMMgC3lnz\nP1PSP4LyQijZ544fz7XXX89VV11l5wucGVjfeustXnnlFdauXUtifBIzxixl8qh5xPUYEnimpO8P\ndFB6bAO7jq4nSIALL7yQyy+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WRIZ2W/cVEdkjIrtE5D1uxmmMMcYMdq4nfWAFMFNVZwOl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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10edc8b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FtAMvAJ+OLzsD+NIY1CRjbNGiRfhAL+GDuzn99NNSXY5McuFwmIWL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\nhb5IhjGzOcCDwO3AdOBjwH1mNjO+yR7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ee96278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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m9gMzO+i8+yIicnRQTT//jfkZpu6+y92/6+5nAucAbwWWm9mbZna9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\nSV9ERKRA/H+tpqIn0QJ4wAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eef4b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "sns.violinplot(x=\"feature_1\", y=target_col, data=train_df)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('Feature 1', fontsize=12)\n",
    "plt.ylabel('Loyalty score', fontsize=12)\n",
    "plt.title(\"Feature 1 distribution\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "sns.violinplot(x=\"feature_2\", y=target_col, data=train_df)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('Feature 2', fontsize=12)\n",
    "plt.ylabel('Loyalty score', fontsize=12)\n",
    "plt.title(\"Feature 2 distribution\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "sns.violinplot(x=\"feature_3\", y=target_col, data=train_df)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('Feature 3', fontsize=12)\n",
    "plt.ylabel('Loyalty score', fontsize=12)\n",
    "plt.title(\"Feature 3 distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Historical Transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist_df = pd.read_csv('data/historical_transactions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>authorized_flag</th>\n",
       "      <th>card_id</th>\n",
       "      <th>city_id</th>\n",
       "      <th>category_1</th>\n",
       "      <th>installments</th>\n",
       "      <th>category_3</th>\n",
       "      <th>merchant_category_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>month_lag</th>\n",
       "      <th>purchase_amount</th>\n",
       "      <th>purchase_date</th>\n",
       "      <th>category_2</th>\n",
       "      <th>state_id</th>\n",
       "      <th>subsector_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Y</td>\n",
       "      <td>C_ID_4e6213e9bc</td>\n",
       "      <td>88</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>80</td>\n",
       "      <td>M_ID_e020e9b302</td>\n",
       "      <td>-8</td>\n",
       "      <td>-0.703331</td>\n",
       "      <td>2017-06-25 15:33:07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Y</td>\n",
       "      <td>C_ID_4e6213e9bc</td>\n",
       "      <td>88</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>367</td>\n",
       "      <td>M_ID_86ec983688</td>\n",
       "      <td>-7</td>\n",
       "      <td>-0.733128</td>\n",
       "      <td>2017-07-15 12:10:45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Y</td>\n",
       "      <td>C_ID_4e6213e9bc</td>\n",
       "      <td>88</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>80</td>\n",
       "      <td>M_ID_979ed661fc</td>\n",
       "      <td>-6</td>\n",
       "      <td>-0.720386</td>\n",
       "      <td>2017-08-09 22:04:29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Y</td>\n",
       "      <td>C_ID_4e6213e9bc</td>\n",
       "      <td>88</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>560</td>\n",
       "      <td>M_ID_e6d5ae8ea6</td>\n",
       "      <td>-5</td>\n",
       "      <td>-0.735352</td>\n",
       "      <td>2017-09-02 10:06:26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Y</td>\n",
       "      <td>C_ID_4e6213e9bc</td>\n",
       "      <td>88</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>80</td>\n",
       "      <td>M_ID_e020e9b302</td>\n",
       "      <td>-11</td>\n",
       "      <td>-0.722865</td>\n",
       "      <td>2017-03-10 01:14:19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  authorized_flag          card_id  city_id category_1  installments  \\\n",
       "0               Y  C_ID_4e6213e9bc       88          N             0   \n",
       "1               Y  C_ID_4e6213e9bc       88          N             0   \n",
       "2               Y  C_ID_4e6213e9bc       88          N             0   \n",
       "3               Y  C_ID_4e6213e9bc       88          N             0   \n",
       "4               Y  C_ID_4e6213e9bc       88          N             0   \n",
       "\n",
       "  category_3  merchant_category_id      merchant_id  month_lag  \\\n",
       "0          A                    80  M_ID_e020e9b302         -8   \n",
       "1          A                   367  M_ID_86ec983688         -7   \n",
       "2          A                    80  M_ID_979ed661fc         -6   \n",
       "3          A                   560  M_ID_e6d5ae8ea6         -5   \n",
       "4          A                    80  M_ID_e020e9b302        -11   \n",
       "\n",
       "   purchase_amount        purchase_date  category_2  state_id  subsector_id  \n",
       "0        -0.703331  2017-06-25 15:33:07         1.0        16            37  \n",
       "1        -0.733128  2017-07-15 12:10:45         1.0        16            16  \n",
       "2        -0.720386  2017-08-09 22:04:29         1.0        16            37  \n",
       "3        -0.735352  2017-09-02 10:06:26         1.0        16            34  \n",
       "4        -0.722865  2017-03-10 01:14:19         1.0        16            37  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29112361, 14)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = hist_df.groupby(['card_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = gdf['purchase_amount'].size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>card_id</th>\n",
       "      <th>purchase_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C_ID_00007093c1</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C_ID_0001238066</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C_ID_0001506ef0</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C_ID_0001793786</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C_ID_000183fdda</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           card_id  purchase_amount\n",
       "0  C_ID_00007093c1              149\n",
       "1  C_ID_0001238066              123\n",
       "2  C_ID_0001506ef0               66\n",
       "3  C_ID_0001793786              216\n",
       "4  C_ID_000183fdda              144"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf.columns = [\"card_id\", \"num_hist_transactions\"]\n",
    "train_df = pd.merge(train_df, gdf, on=\"card_id\", how=\"left\")\n",
    "test_df = pd.merge(test_df, gdf, on=\"card_id\", how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt_srs = train_df.groupby(\"num_hist_transactions\")[target_col].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt_srs = cnt_srs.sort_index()"
   ]
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   "cell_type": "code",
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   "outputs": [
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       "31     -0.454701\n",
       "          ...   \n",
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       "Name: target, Length: 1023, dtype: float64"
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     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "cnt_srs"
   ]
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  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt_srs = cnt_srs[:-50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1118, 1113, 1112, 1109, 1107, 1104, 1103, 1101, 1099, 1097,\n",
       "            ...\n",
       "              11,   10,    9,    8,    7,    6,    5,    4,    3,    2],\n",
       "           dtype='int64', name='num_hist_transactions', length=973)"
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     "execution_count": 87,
     "metadata": {},
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       "layout": {
        "title": "Loyalty score by Number of historical transactions"
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-0.3873219315974623, -0.3414264699511132, -0.5362445766485608, -0.5213411421334738, -0.3325787556656019, -0.27876757668212226, -0.3714693623038482, -0.3925499220944956, -0.3495900991964722, -0.4547011316521743, -0.3619403609623428, -0.5050959131643191, -0.2481142595004344, -0.4185033736911231, -0.18926128687974933, -0.29118583031786904, -0.3818457781638043, -0.2593886025734978, -0.2820031055766054, -0.3127647946166076, -0.34630385606907205, -0.23255744457142838, -0.2394020986936415, -0.2644045209304208, -0.22075631395339473, -0.22213860573628902, -0.24599907689408979, -0.2004722893618709, -0.1051667375132708, -0.32118609798076875, -0.0320452188205828, -0.11481482409986256, -0.03740862530645163, 0.02472058041617819, 0.12138051838194433, 0.2259220494961246, 0.1689635809266943, 0.14051706400630914, -0.30301999764705884], \"showlegend\": false, \"marker\": {\"color\": \"orange\"}}], {\"title\": \"Loyalty score by Number of historical transactions\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def scatter_plot(cnt_srs, color):\n",
    "    trace = go.Scatter(\n",
    "        x=cnt_srs.index[::-1],\n",
    "        y=cnt_srs.values[::-1],\n",
    "        showlegend=False,\n",
    "        marker=dict(\n",
    "            color=color,\n",
    "        ),\n",
    "    )\n",
    "    return trace\n",
    "\n",
    "trace = scatter_plot(cnt_srs, \"orange\")\n",
    "layout = dict(\n",
    "    title='Loyalty score by Number of historical transactions',\n",
    "    )\n",
    "data = [trace]\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename=\"Histtranscnt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k0Vtz6p4fzWTbZ9w5K5PtmpmZVVJrcd8G6G5+j5K/o/LiVs6nDszMrFlqLe4PAEcBPy+Z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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eee0e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = [0,10,20,30,40,50,75,100,150,200,500,1000]\n",
    "train_df['binned_num_hist_transactions'] = pd.cut(train_df['num_hist_transactions'], bins)\n",
    "cnt_srs = train_df.groupby(\"binned_num_hist_transactions\")[target_col].mean()\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.boxplot(x=\"binned_num_hist_transactions\", y=target_col, data=train_df, showfliers=False)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('binned_num_hist_transactions', fontsize=12)\n",
    "plt.ylabel('Loyalty Score', fontsize=12)\n",
    "plt.title('binned_num_hist_transactions distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = hist_df.groupby(\"card_id\")\n",
    "gdf = gdf[\"purchase_amount\"].agg(['sum', 'mean', 'std', 'min', 'max']).reset_index()\n",
    "gdf.columns = [\"card_id\", \"sum_hist_trans\", \"mean_hist_trans\", \"std_hist_trans\", \"min_hist_trans\", \"max_hist_trans\"]\n",
    "train_df = pd.merge(train_df, gdf, on=\"card_id\", how=\"left\")\n",
    "test_df = pd.merge(test_df, gdf, on=\"card_id\", how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EszNvXLOi71I6c9GGndljiQFKkiRpFBYN1lW1E0CSr1XVY0ZT0vZZu3YtO91w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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10edaf908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.percentile(train_df[\"sum_hist_trans\"], range(0,101,10))\n",
    "train_df['binned_sum_hist_trans'] = pd.cut(train_df['sum_hist_trans'], bins)\n",
    "#cnt_srs = train_df.groupby(\"binned_sum_hist_trans\")[target_col].mean()\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.boxplot(x=\"binned_sum_hist_trans\", y=target_col, data=train_df, showfliers=False)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('binned_sum_hist_trans', fontsize=12)\n",
    "plt.ylabel('Loyalty score', fontsize=12)\n",
    "plt.title(\"Sum of historical transaction value (Binned) distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MTEywPuvZ8ugtfZfSmd1O3I2JQwwNO2piYoL8/Goe80vP7ruUTp1w3jEcMnHH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\nc6EHgBU0l2R9Y29V9cO2mEdV/WffNfT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P1JvDLrkASatQBrO/d7ocAB4tycXD5TvSSS46\nyUUnuegMay4kHWb7n+vy9sDRwBXA5sA/2f5+y/j6sXLrAKa5YR3xYCxDnwvbS4AM5E9ysQxD/x3p\nkVx0kotOctEZ1ly8oGf5k8DOti+UtBlwHDDwhXWG25uY01sHMECSi4jx5TvSSS46yUUnuegkFzDL\n9oUAtq9kmtSs6QrSp4x40EkuIsaX70gnuegkF53kopNcdCTdA/yB0mK/KTDH9u2SVgJ+a3ublvH1\nY1pU/wPiHEn7S5rTu1LSqpJeWoeC2bNRbFMtuYgYX74jneSik1x0kotOctHZGngtMBfYBri7rl8b\n+HiroFZEWqz7lBEPOslFxPjyHekkF53kopNcdJKL8UmabfvW1nH0K4X1I5ARDzrJRcT48h3pJBed\n5KKTXHSGPReSDgL+w/YtdbbF44AHgVWAt9g+t2mAfUhhHRERERHNSbrI9jPq8jnAB2zPl7QlcLTt\n57WNcPnSxzoiIiIiBsHKkkaGgl7d9nwA25cBq7ULq39psY6IiIiI5iTtT7l48SDgb4EnAicCLwU2\ns71Hw/D6ksI6IiIiIgaCpL8D9qNMZb4ysBA4GfhGnXxsoKWwjoiIiIiYBOljHREREREDQdJWknaQ\nNGPU+h1bxbQiUlhHRERERHOS3gV8D9gfWCDpdT2bP90mqhWz8vJ3iYiIiIh41O0LPNf2YkmbAsdL\n2tT2wZRpzgdeCuuIiIiIGAQr2V4MYPvqeiHj8ZI2YZoU1ukKEhERERGD4EZJzx75oxbZcymzUT6j\nWVQrIKOCRERERERzkjYC7rd9wxjbXmT7Zw3CWiEprCMiIiIiJkG6gkRERETEQJN0WusY+pEW64iI\niIgYaJI2sP2n1nEsTwrriIiIiBgoktYGsH1b61hWRLqCRERERERzkuZIOkbSzcD5wDxJN9V1m7aN\nrj8prCMiIiJiEBwLnASsb3sL25sDGwAnA8c0jaxP6QoSEREREc1Jutz2Fiu6bZBk5sWIiIiIGAQX\nSDoMOBJYWNdtDOwJ/KpZVCsgLdYRERER0ZykVYG9gdcBT66r/wicAvyX7T+3iq1fKawjIiIiIiZB\nLl6MiIiIiIEk6cLWMayIFNYRERERMajUOoAVkcI6IiIiIgbV6a0DWBHpYx0RERERzUmSl1OY9rNP\nS2mxjoiIiIhBcI6k/SXN6V0paVVJL5V0JGXovYGVFuuIiIiIaE7S44G3AW8GngIsAlanNASfCRxm\ne6DHs05hHREREREDRdIqwDrAvbYXtY6nXymsIyIiIiImQfpYR0RERERMghTWERERERGTIIV1RDxq\nJP2npH9t8LxvlfTTqX7eR5Okj0g6fIqe6wxJE7ry/rH4HoxH0r9K+s/WcUREWymsI+IRk3S1pHsl\nLZZ0u6TTJW08st32O2x/smWMo0naVJIl/WrU+nUk/UXS1Q1iOkDSt8ZYb0mbA9j+tO19+nisH0ta\n7n7jsf0q20dO5DGWRdKL6+dlsaS762tc3HObs/xHaUvSy0Z/Tmx/0vY7GoUUEQMihXVETNRrbc8A\nNgBuBL7cOJ5+rSFpm56/dweuahXMIFDxqP6/YPsntmfUz8xf1dVrjayzfe2omFZ6tGOKiJgsOVhF\nxKSwfR9wPPD0kXWSjpB0YF3+O0nXSXqvpJsk/UnSXqP2PbS2et8l6XxJT+3ZvpWksyTdJun3knbp\n2TZb0imS7pQ0D3jofuM4iqUnGngL8M3eHSRtKOkESTdLukrSu3q2bSvpF5IW1ddyiKRVe7Zb0jsk\nXV73OVSS+ohrTL2t2pIeL+lbkm6tjz1f0nqSPgW8GDiktv4eUvd/Yd3njvrvC3se98eSPiXpZ8A9\nwGajW70l7Svpkvq+/E7Sc+r6D0m6omf96x/p6xv1Wn8q6ZOSfgHcDcyRtE9PDFeMiu9l9deTD9T3\n6npJb+nZPrfnvtdJek9dP1vS9+t9bpd0qqQn99xvdv1c/qluP0HSmsCpNaaRVvZ1JR0o6Yie+75e\n0sX1/Tlb0tN6tl0n6X9Juqi+J9+RtFrdtm6NaVH9rP/3ZOQ0IqZGCuuImBSS1gB2Bc4bZ7f1gTWB\nJwN7A4dKemLP9t2ATwBPBP4AfKo+9hOAs4CjgXXrfodJGiniDwXuo7Sav63eludbwG6SHlcfZwZw\nfs/rWYlSQP2mxrsD8G5Jr6y7PAC8hzLO6t/U7f886jnmAs8HngnsArySybEnJY8bA7OBd1DGev0o\n8BPgnbX1952S1gZOB75U9/0CcLqk2T2PtwfwdmAmcE3vE0l6I3AA5cRjFrATcGvdfAWlkF+T8r59\nS9IGk/Qa96C8j7OA6yi/hrym/r0v8GVJz+zZfyPKRBIbUvLxFUmz6rZvAHvbnkl5L86t61cCvgbM\nATYBlgAH9zzm0cCqlJPFdYGDbd8BvBa4tqeV/abewCVtTTlx2x94EvBD4BSVcXlH7AK8HNgMeG59\nvQDvB66s91sf+Fif+YqIAZDCOiIm6mRJi4A7KIXC/x5n3yXAv9teYvv7wGLgaT3bT7I9z/b9wLeB\nZ9f1c4GrbX/D9v115q0TgDdKehzwj8DHbd9tewHQT//g64DfAy+jFI1Hjdr+fOBJtv/d9l9sX0kp\nwnYDsH2B7fNqPFcD/xd4yajHOMj2otq94Zye1zOWXWor5UO3cfZdQimSN7f9QI3lzmXs+xrgcttH\n1Vi/A1xKKQ5HHGH74rp9yaj77wN8zvZ8F3+wfU3NwXdtX2/7QdvHApcD244T94r4uu1L6mflftun\n2r6yxnA28CNKUT/iPuDAuv8pwJ+BLeu2JcDTJc20fZvtC2v8N9s+yfa9NX+fpr6HKtcK7ADsZ/v2\n+rj9th7vBpxi++yaz4MoJx/b9ezzRds32L4VOI3us7GEcnIwp37u0mIdMY2ksI6IidrZ9lrA44F3\nAudKWn8Z+95ai+YR91BaikfcsIxtmwDbjSo630xp0XsSsDKwsOe+S7W6juObwFuBN/HwwnoTYMNR\nz/kRYD0ASVtKOk3SDZJGirJ1Rj3Gsl7PWI6zvVbvbZx9jwJ+ABxTuz18blRraK8NeXg+rqG0wo9Y\nyLJtTGmZfhhJb5H06578bMPDc/BILRVT7c5xfu0esQh4xajnusX2Az1/9+b79ZSW9mtrN5ft6mPO\nkHS4pGvre3h2z2NuXB/zjkcQ+1I5t/0g5USuN+fL+mwcVO/7o9rl5f2P4PkjopEU1hExKWrL6YmU\nLhLbT/LDLwTOHVV4zrC9H3AzcD+lEBrR78gSJ1BadK8cfdFcfc6rRj3nTNuvrtu/Qmn53cL2LErR\n/Yj7UK+I2nr6CdtPB15IadEf6VM8ejrd6yknCb3mAH/sfchxnm4hY/RZl7QJpQX/ncDseiKwgMnL\nwUMxSVqd0n//M8B69bnO7Pe5bJ9veydKd47TgGPqpvcDTwG2re/hS3vuthBYp6c7yZixLcNSOa/d\nijZi6ZwvK9Y7bb/H9qbAzsAHJY3+JSQiBlQK64iYFCpeR+kffckkP/xpwJaS9pC0Sr09X9LWtZXy\nROAASWvU/tJ9jcFs+25KMTXW8HTzgLskfVDS6rUv9jaSnl+3zwTuBBZL2grYb6Ivsl+S/l7SM2o3\nmDsp3QcerJtvpPTbHfF9Su52l7SypF0pfYZP6/PpDgfeJ+m59T3evBbVT6AUmDfXmPaitFg/Glaj\n9HW+GXhA0lxKN43lqu/d7pJm1W4Zd9Hlaialtfj22uf84yP3s72Q0jf6UElr1c/c39bNN1KK7pnL\neNrjgJ1ULthdhVLA30VPH/5x4n2tpKdKEqV71QM98UbEgEthHRETdaqkxZQC71PAnrYvnswnsH0X\n5af/3SitgTcAn6UUXFBaTWfU9UdQLlbr97F/afthXR1qwT6X0vf1KuAWSpG5Zt3lfZQh+u6itNwe\nu4IvayLWp7Tg3kk5iTmXrivLwcAbVEax+FLtwzsXeC/losMPAHNt39LPE9n+LuV9PZryWk8G1rb9\nO+DzwC8oheYzgJ9Nzst7WAyLKBeKngTcBryB/k8MoJxoXVO7e+wN/I+6/guU9/NW4OfAGaPuN7Lf\nZZTXuH+NZwHl146razeYdUfFe3F9zq9QTgZ2BHYao//6WJ5G6ZKymJLPg23/pP+XGhEtyV7eL1oR\nEREREbE8abGOiIiIiJgEKawjIiIiIiZBCuuIiIiIiEmQwjoiIiIiYhKksI6IiIiImAQprCMiIiIi\nJkEK64iIiIiISZDCOiIiIiJiEqSwjoiIiIiYBP8fSH+7XJKNtP8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10edfbdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.percentile(train_df[\"mean_hist_trans\"], range(0,101,10))\n",
    "train_df['binned_mean_hist_trans'] = pd.cut(train_df['mean_hist_trans'], bins)\n",
    "#cnt_srs = train_df.groupby(\"binned_mean_hist_trans\")[target_col].mean()\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.boxplot(x=\"binned_mean_hist_trans\", y=target_col, data=train_df, showfliers=False)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('Binned Mean Historical Transactions', fontsize=12)\n",
    "plt.ylabel('Loyalty score', fontsize=12)\n",
    "plt.title(\"Mean of historical transaction value (Binned) distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New Merchant Transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_trans_df = pd.read_csv('data/new_merchant_transactions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>authorized_flag</th>\n",
       "      <th>card_id</th>\n",
       "      <th>city_id</th>\n",
       "      <th>category_1</th>\n",
       "      <th>installments</th>\n",
       "      <th>category_3</th>\n",
       "      <th>merchant_category_id</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>month_lag</th>\n",
       "      <th>purchase_amount</th>\n",
       "      <th>purchase_date</th>\n",
       "      <th>category_2</th>\n",
       "      <th>state_id</th>\n",
       "      <th>subsector_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Y</td>\n",
       "      <td>C_ID_415bb3a509</td>\n",
       "      <td>107</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>307</td>\n",
       "      <td>M_ID_b0c793002c</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.557574</td>\n",
       "      <td>2018-03-11 14:57:36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Y</td>\n",
       "      <td>C_ID_415bb3a509</td>\n",
       "      <td>140</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>307</td>\n",
       "      <td>M_ID_88920c89e8</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.569580</td>\n",
       "      <td>2018-03-19 18:53:37</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Y</td>\n",
       "      <td>C_ID_415bb3a509</td>\n",
       "      <td>330</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>507</td>\n",
       "      <td>M_ID_ad5237ef6b</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.551037</td>\n",
       "      <td>2018-04-26 14:08:44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Y</td>\n",
       "      <td>C_ID_415bb3a509</td>\n",
       "      <td>-1</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>661</td>\n",
       "      <td>M_ID_9e84cda3b1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.671925</td>\n",
       "      <td>2018-03-07 09:43:21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Y</td>\n",
       "      <td>C_ID_ef55cf8d4b</td>\n",
       "      <td>-1</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>166</td>\n",
       "      <td>M_ID_3c86fa3831</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.659904</td>\n",
       "      <td>2018-03-22 21:07:53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  authorized_flag          card_id  city_id category_1  installments  \\\n",
       "0               Y  C_ID_415bb3a509      107          N             1   \n",
       "1               Y  C_ID_415bb3a509      140          N             1   \n",
       "2               Y  C_ID_415bb3a509      330          N             1   \n",
       "3               Y  C_ID_415bb3a509       -1          Y             1   \n",
       "4               Y  C_ID_ef55cf8d4b       -1          Y             1   \n",
       "\n",
       "  category_3  merchant_category_id      merchant_id  month_lag  \\\n",
       "0          B                   307  M_ID_b0c793002c          1   \n",
       "1          B                   307  M_ID_88920c89e8          1   \n",
       "2          B                   507  M_ID_ad5237ef6b          2   \n",
       "3          B                   661  M_ID_9e84cda3b1          1   \n",
       "4          B                   166  M_ID_3c86fa3831          1   \n",
       "\n",
       "   purchase_amount        purchase_date  category_2  state_id  subsector_id  \n",
       "0        -0.557574  2018-03-11 14:57:36         1.0         9            19  \n",
       "1        -0.569580  2018-03-19 18:53:37         1.0         9            19  \n",
       "2        -0.551037  2018-04-26 14:08:44         1.0         9            14  \n",
       "3        -0.671925  2018-03-07 09:43:21         NaN        -1             8  \n",
       "4        -0.659904  2018-03-22 21:07:53         NaN        -1            29  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_trans_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = new_trans_df.groupby(['card_id'])\n",
    "gdf = gdf['purchase_amount'].size().reset_index()\n",
    "gdf.columns = ['card_id', 'num_merch_transactions']\n",
    "train_df = pd.merge(train_df, gdf, on='card_id', how='left')\n",
    "test_df = pd.merge(test_df, gdf, on='card_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkiR1wGItSZIkdcBiLUmSJHXAYi1JkiR1wGItSZIkdcBiLUmSJHVgLIp1kj2TnJLk20nO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\nZmZmZiVwYG1mZmZmVgIH1mZmZmZmJfgPdzhx9Hu2diAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ca06d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = [0,10,20,30,40,50,75,10000]\n",
    "train_df['binned_num_merch_transactions'] = pd.cut(train_df['num_merch_transactions'], bins)\n",
    "cnt_srs = train_df.groupby('binned_num_merch_transactions')[target_col].mean()\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.boxplot(x=\"binned_num_merch_transactions\", y=target_col, data=train_df, showfliers=False)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('binned_num_merch_transactions', fontsize=12)\n",
    "plt.ylabel(\"Loyalty Score\", fontsize=12)\n",
    "plt.title(\"Number of new merchant transactions (Binned) distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = new_trans_df.groupby(\"card_id\")\n",
    "gdf = gdf['purchase_amount'].agg(['sum', 'mean', 'std', 'min', 'max']).reset_index()\n",
    "gdf.columns =['card_id', 'sum_merch_trans', 'mean_merch_trans','std_merch_trans', 'min_merch_trans', 'max_merch_trans']\n",
    "train_df = pd.merge(train_df, gdf, on='card_id', how='left')\n",
    "test_df = pd.merge(test_df, gdf, on='card_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FP6crBmtJkiQtOXvvvXffJSyawVqSJEkj1VcL8rjZx1qSJEnqgMFakiRJ6oDB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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ca01438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.nanpercentile(train_df['sum_merch_trans'], range(0,101,10))\n",
    "train_df['binned_sum_merch_trans'] = pd.cut(train_df['sum_merch_trans'], bins)\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.boxplot(x=\"binned_sum_merch_trans\", y=target_col, data=train_df, showfliers=False)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel(\"binned_sum_merch_trans\", fontsize=12)\n",
    "plt.ylabel(\"Loyalty Score\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xPVfSqyUdLqlH0pmSbpH0Tds/ioh/nGwhtg+W9HlJMyWdEhEnTPbPQqGnp0cPbLhdxy1c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\n4+Lv6zErAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110101828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.nanpercentile(train_df['mean_merch_trans'], range(0,101,10))\n",
    "train_df['binned_mean_merch_trans'] = pd.cut(train_df['mean_merch_trans'], bins)\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.boxplot(x=\"binned_mean_merch_trans\", y=target_col, data=train_df, showfliers=False)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel(\"binned_mean_merch_trans\", fontsize=12)\n",
    "plt.ylabel(\"Loyalty Score\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Baseline Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['year'] = train_df['first_active_month'].dt.year\n",
    "train_df['month'] = train_df['first_active_month'].dt.month\n",
    "test_df['year'] = test_df['first_active_month'].dt.year\n",
    "test_df['month'] = test_df['first_active_month'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_active_month</th>\n",
       "      <th>card_id</th>\n",
       "      <th>feature_1</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_3</th>\n",
       "      <th>target</th>\n",
       "      <th>num_hist_transactions</th>\n",
       "      <th>binned_num_hist_transactions</th>\n",
       "      <th>sum_hist_trans</th>\n",
       "      <th>mean_hist_trans</th>\n",
       "      <th>...</th>\n",
       "      <th>num_merch_transactions</th>\n",
       "      <th>sum_merch_trans</th>\n",
       "      <th>mean_merch_trans</th>\n",
       "      <th>std_merch_trans</th>\n",
       "      <th>min_merch_trans</th>\n",
       "      <th>max_merch_trans</th>\n",
       "      <th>binned_sum_merch_trans</th>\n",
       "      <th>binned_mean_merch_trans</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>C_ID_92a2005557</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.820283</td>\n",
       "      <td>260</td>\n",
       "      <td>(200, 500]</td>\n",
       "      <td>-165.968739</td>\n",
       "      <td>-0.638341</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>-13.244202</td>\n",
       "      <td>-0.575835</td>\n",
       "      <td>0.135812</td>\n",
       "      <td>-0.724368</td>\n",
       "      <td>-0.296112</td>\n",
       "      <td>(-70.813, -8.882]</td>\n",
       "      <td>(-0.61, -0.565]</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>C_ID_3d0044924f</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.392913</td>\n",
       "      <td>350</td>\n",
       "      <td>(200, 500]</td>\n",
       "      <td>-210.006336</td>\n",
       "      <td>-0.600018</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-4.355735</td>\n",
       "      <td>-0.725956</td>\n",
       "      <td>0.014326</td>\n",
       "      <td>-0.739410</td>\n",
       "      <td>-0.701858</td>\n",
       "      <td>(-4.574, -3.483]</td>\n",
       "      <td>(-0.747, -0.717]</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>C_ID_d639edf6cd</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.688056</td>\n",
       "      <td>43</td>\n",
       "      <td>(40, 50]</td>\n",
       "      <td>-29.167391</td>\n",
       "      <td>-0.678311</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.700326</td>\n",
       "      <td>-0.700326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.700326</td>\n",
       "      <td>-0.700326</td>\n",
       "      <td>(-0.739, -0.578]</td>\n",
       "      <td>(-0.701, -0.684]</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>C_ID_186d6a6901</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.142495</td>\n",
       "      <td>77</td>\n",
       "      <td>(75, 100]</td>\n",
       "      <td>-49.491364</td>\n",
       "      <td>-0.642745</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-4.654372</td>\n",
       "      <td>-0.664910</td>\n",
       "      <td>0.065882</td>\n",
       "      <td>-0.734135</td>\n",
       "      <td>-0.566740</td>\n",
       "      <td>(-6.139, -4.574]</td>\n",
       "      <td>(-0.665, -0.642]</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>C_ID_cdbd2c0db2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.159749</td>\n",
       "      <td>133</td>\n",
       "      <td>(100, 150]</td>\n",
       "      <td>-48.687656</td>\n",
       "      <td>-0.366073</td>\n",
       "      <td>...</td>\n",
       "      <td>36.0</td>\n",
       "      <td>-19.926237</td>\n",
       "      <td>-0.553507</td>\n",
       "      <td>0.223821</td>\n",
       "      <td>-0.739395</td>\n",
       "      <td>0.450886</td>\n",
       "      <td>(-70.813, -8.882]</td>\n",
       "      <td>(-0.565, -0.487]</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_active_month          card_id  feature_1  feature_2  feature_3  \\\n",
       "0         2017-06-01  C_ID_92a2005557          5          2          1   \n",
       "1         2017-01-01  C_ID_3d0044924f          4          1          0   \n",
       "2         2016-08-01  C_ID_d639edf6cd          2          2          0   \n",
       "3         2017-09-01  C_ID_186d6a6901          4          3          0   \n",
       "4         2017-11-01  C_ID_cdbd2c0db2          1          3          0   \n",
       "\n",
       "     target  num_hist_transactions binned_num_hist_transactions  \\\n",
       "0 -0.820283                    260                   (200, 500]   \n",
       "1  0.392913                    350                   (200, 500]   \n",
       "2  0.688056                     43                     (40, 50]   \n",
       "3  0.142495                     77                    (75, 100]   \n",
       "4 -0.159749                    133                   (100, 150]   \n",
       "\n",
       "   sum_hist_trans  mean_hist_trans  ...    num_merch_transactions  \\\n",
       "0     -165.968739        -0.638341  ...                      23.0   \n",
       "1     -210.006336        -0.600018  ...                       6.0   \n",
       "2      -29.167391        -0.678311  ...                       1.0   \n",
       "3      -49.491364        -0.642745  ...                       7.0   \n",
       "4      -48.687656        -0.366073  ...                      36.0   \n",
       "\n",
       "   sum_merch_trans  mean_merch_trans std_merch_trans min_merch_trans  \\\n",
       "0       -13.244202         -0.575835        0.135812       -0.724368   \n",
       "1        -4.355735         -0.725956        0.014326       -0.739410   \n",
       "2        -0.700326         -0.700326             NaN       -0.700326   \n",
       "3        -4.654372         -0.664910        0.065882       -0.734135   \n",
       "4       -19.926237         -0.553507        0.223821       -0.739395   \n",
       "\n",
       "   max_merch_trans binned_sum_merch_trans  binned_mean_merch_trans  year  \\\n",
       "0        -0.296112      (-70.813, -8.882]          (-0.61, -0.565]  2017   \n",
       "1        -0.701858       (-4.574, -3.483]         (-0.747, -0.717]  2017   \n",
       "2        -0.700326       (-0.739, -0.578]         (-0.701, -0.684]  2016   \n",
       "3        -0.566740       (-6.139, -4.574]         (-0.665, -0.642]  2017   \n",
       "4         0.450886      (-70.813, -8.882]         (-0.565, -0.487]  2017   \n",
       "\n",
       "   month  \n",
       "0      6  \n",
       "1      1  \n",
       "2      8  \n",
       "3      9  \n",
       "4     11  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['first_active_month', 'card_id', 'feature_1', 'feature_2', 'feature_3',\n",
       "       'target', 'num_hist_transactions', 'binned_num_hist_transactions',\n",
       "       'sum_hist_trans', 'mean_hist_trans', 'std_hist_trans', 'min_hist_trans',\n",
       "       'max_hist_trans', 'binned_sum_hist_trans', 'binned_mean_hist_trans',\n",
       "       'num_merch_transactions_x', 'binned_num_merch_transactions',\n",
       "       'num_merch_transactions_y', 'num_merch_transactions', 'sum_merch_trans',\n",
       "       'mean_merch_trans', 'std_merch_trans', 'min_merch_trans',\n",
       "       'max_merch_trans', 'binned_sum_merch_trans', 'binned_mean_merch_trans',\n",
       "       'year', 'month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['first_active_month', 'card_id', 'feature_1', 'feature_2', 'feature_3',\n",
       "       'num_hist_transactions', 'sum_hist_trans', 'mean_hist_trans',\n",
       "       'std_hist_trans', 'min_hist_trans', 'max_hist_trans',\n",
       "       'num_merch_transactions_x', 'num_merch_transactions_y',\n",
       "       'num_merch_transactions', 'sum_merch_trans', 'mean_merch_trans',\n",
       "       'std_merch_trans', 'min_merch_trans', 'max_merch_trans', 'year',\n",
       "       'month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.to_csv('data/processed_train.csv', index = False, index_label=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df.to_csv('data/processed_test.csv', index=False, index_label=False)"
   ]
  }
 ],
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