transaction_models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook uses a wide variety of modeling algorithms for a binary classification problem. It reads a file created from a feature selection process that has a reasonably small number of good variables, and we know their order of multivariate importance because we used a proper wrapper method. We can explore # input variables, model algorithms and tune model hyperparameters. At the end we can select our favorite algorithm, run it again and build the final model performace score percentile tables.\n",
    "\n",
    "Here we call the larger fraction population the goods and the smaller fraction the bads. This notebook was originally\n",
    "written for fraud detection but can be used for any binary classification. It uses detection rate as an appropriate measure of goodness.\n",
    "\n",
    "Rather than use a built-in CV, we do a \"manual CV\" by running each model multiple (nitermax) times and average the performance on the training (trn), testing (tst) and out of time (oot) data sets.\n",
    "\n",
    "Some of the ML algorithms are very fast and some are slow. Feel free to comment out any cells/models you want. At the bottom of the notebook you can select your final model/hyperparameters to run one time only and then make the business perfoemance tables for that final model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Things to add: better calculation of FDR@3% when building a model using sampled training data. Right now I just approximate it by using the entire population trntst for a model built with sampled data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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   "source": [
    "from datetime import datetime\n",
    "start_time = datetime.now()\n",
    "\n",
    "import pandas as pd\n",
    "import random\n",
    "import xgboost as xgb\n",
    "from sklearn import svm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.decomposition import PCA\n",
    "import gc\n",
    "import lightgbm as lgb\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(96397, 42)\n",
      "CPU times: user 369 ms, sys: 68.9 ms, total: 438 ms\n",
      "Wall time: 440 ms\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>card_merch_total_14</th>\n",
       "      <th>card_zip3_max_14</th>\n",
       "      <th>card_merch_avg_14</th>\n",
       "      <th>Card_Merchdesc_Zip_med_14</th>\n",
       "      <th>Card_Merchdesc_Zip_max_60</th>\n",
       "      <th>Card_Merchnum_Zip_avg_60</th>\n",
       "      <th>Card_Merchnum_desc_avg_60</th>\n",
       "      <th>Merchdesc_dow_avg_30</th>\n",
       "      <th>Card_Merchnum_desc_med_0</th>\n",
       "      <th>Card_Merchdesc_max_60</th>\n",
       "      <th>...</th>\n",
       "      <th>Card_Merchdesc_Zip_med_30</th>\n",
       "      <th>card_merch_med_0</th>\n",
       "      <th>Card_Merchnum_Zip_med_0</th>\n",
       "      <th>card_zip3_med_0</th>\n",
       "      <th>amount_cat</th>\n",
       "      <th>state_des_avg_3</th>\n",
       "      <th>Merchnum_desc_max_0</th>\n",
       "      <th>card_merch_total_30</th>\n",
       "      <th>Recnum</th>\n",
       "      <th>Fraud</th>\n",
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       "      <td>178.49</td>\n",
       "      <td>178.49</td>\n",
       "      <td>178.49</td>\n",
       "      <td>178.49</td>\n",
       "      <td>178.49</td>\n",
       "      <td>178.49</td>\n",
       "      <td>178.49</td>\n",
       "      <td>...</td>\n",
       "      <td>178.49</td>\n",
       "      <td>178.49</td>\n",
       "      <td>178.49</td>\n",
       "      <td>178.49</td>\n",
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       "      <td>178.49</td>\n",
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       "      <td>3.62</td>\n",
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       "      <td>3.62</td>\n",
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       "      <td>3.62</td>\n",
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       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.24</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
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       "      <td>3.62</td>\n",
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       "      <td>3.62</td>\n",
       "      <td>7.24</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
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      ],
      "text/plain": [
       "   card_merch_total_14  card_zip3_max_14  card_merch_avg_14  \\\n",
       "0                 3.62              3.62               3.62   \n",
       "1                31.42             31.42              31.42   \n",
       "2               178.49            178.49             178.49   \n",
       "3                 3.62              3.62               3.62   \n",
       "4                 7.24              3.62               3.62   \n",
       "\n",
       "   Card_Merchdesc_Zip_med_14  Card_Merchdesc_Zip_max_60  \\\n",
       "0                       3.62                       3.62   \n",
       "1                      31.42                      31.42   \n",
       "2                     178.49                     178.49   \n",
       "3                       3.62                       3.62   \n",
       "4                       3.62                       3.62   \n",
       "\n",
       "   Card_Merchnum_Zip_avg_60  Card_Merchnum_desc_avg_60  Merchdesc_dow_avg_30  \\\n",
       "0                      3.62                       3.62                  3.62   \n",
       "1                     31.42                      31.42                 31.42   \n",
       "2                    178.49                     178.49                178.49   \n",
       "3                      3.62                       3.62                  3.62   \n",
       "4                      3.62                       3.62                  3.62   \n",
       "\n",
       "   Card_Merchnum_desc_med_0  Card_Merchdesc_max_60  ...  \\\n",
       "0                      3.62                   3.62  ...   \n",
       "1                     31.42                  31.42  ...   \n",
       "2                    178.49                 178.49  ...   \n",
       "3                      3.62                   3.62  ...   \n",
       "4                      3.62                   3.62  ...   \n",
       "\n",
       "   Card_Merchdesc_Zip_med_30  card_merch_med_0  Card_Merchnum_Zip_med_0  \\\n",
       "0                       3.62              3.62                     3.62   \n",
       "1                      31.42             31.42                    31.42   \n",
       "2                     178.49            178.49                   178.49   \n",
       "3                       3.62              3.62                     3.62   \n",
       "4                       3.62              3.62                     3.62   \n",
       "\n",
       "   card_zip3_med_0  amount_cat  state_des_avg_3  Merchnum_desc_max_0  \\\n",
       "0             3.62           1             3.62                 3.62   \n",
       "1            31.42           2            31.42                31.42   \n",
       "2           178.49           3           178.49               178.49   \n",
       "3             3.62           1             3.62                 3.62   \n",
       "4             3.62           1             3.62                 3.62   \n",
       "\n",
       "   card_merch_total_30  Recnum  Fraud  \n",
       "0                 3.62       1      0  \n",
       "1                31.42       2      0  \n",
       "2               178.49       3      0  \n",
       "3                 3.62       4      0  \n",
       "4                 7.24       5      0  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "vars = pd.read_csv('vars_final.csv')\n",
    "print(vars.shape)\n",
    "vars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Recnum',\n",
       " 'Fraud',\n",
       " 'card_merch_total_14',\n",
       " 'card_zip3_max_14',\n",
       " 'card_merch_avg_14',\n",
       " 'Card_Merchdesc_Zip_med_14',\n",
       " 'Card_Merchdesc_Zip_max_60',\n",
       " 'Card_Merchnum_Zip_avg_60',\n",
       " 'Card_Merchnum_desc_avg_60',\n",
       " 'Merchdesc_dow_avg_30',\n",
       " 'Card_Merchnum_desc_med_0',\n",
       " 'Card_Merchdesc_max_60']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set the number of variables desired here, and set the names of the y and record number properly\n",
    "NVARS = 10\n",
    "\n",
    "# vars.rename(columns={'record':'Recnum'},inplace=True)\n",
    "# vars.rename(columns={'fraud_label':'Fraud'},inplace=True)\n",
    "numvars = min(NVARS,len(vars)-2)\n",
    "final_vars_list = ['Recnum','Fraud']\n",
    "for i in range(numvars):\n",
    "    final_vars_list.append(vars.columns[i])\n",
    "    \n",
    "final_vars_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Recnum</th>\n",
       "      <th>Fraud</th>\n",
       "      <th>card_merch_total_14</th>\n",
       "      <th>card_zip3_max_14</th>\n",
       "      <th>card_merch_avg_14</th>\n",
       "      <th>Card_Merchdesc_Zip_med_14</th>\n",
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       "      <td>178.49</td>\n",
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       "      <td>178.49</td>\n",
       "      <td>178.49</td>\n",
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       "      <th>3</th>\n",
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       "      <td>3.62</td>\n",
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       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>7.24</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Recnum  Fraud  card_merch_total_14  card_zip3_max_14  card_merch_avg_14  \\\n",
       "0       1      0                 3.62              3.62               3.62   \n",
       "1       2      0                31.42             31.42              31.42   \n",
       "2       3      0               178.49            178.49             178.49   \n",
       "3       4      0                 3.62              3.62               3.62   \n",
       "4       5      0                 7.24              3.62               3.62   \n",
       "\n",
       "   Card_Merchdesc_Zip_med_14  Card_Merchdesc_Zip_max_60  \\\n",
       "0                       3.62                       3.62   \n",
       "1                      31.42                      31.42   \n",
       "2                     178.49                     178.49   \n",
       "3                       3.62                       3.62   \n",
       "4                       3.62                       3.62   \n",
       "\n",
       "   Card_Merchnum_Zip_avg_60  Card_Merchnum_desc_avg_60  Merchdesc_dow_avg_30  \\\n",
       "0                      3.62                       3.62                  3.62   \n",
       "1                     31.42                      31.42                 31.42   \n",
       "2                    178.49                     178.49                178.49   \n",
       "3                      3.62                       3.62                  3.62   \n",
       "4                      3.62                       3.62                  3.62   \n",
       "\n",
       "   Card_Merchnum_desc_med_0  Card_Merchdesc_max_60  \n",
       "0                      3.62                   3.62  \n",
       "1                     31.42                  31.42  \n",
       "2                    178.49                 178.49  \n",
       "3                      3.62                   3.62  \n",
       "4                      3.62                   3.62  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars = vars.filter(final_vars_list,axis=1)\n",
    "vars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96397, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1059"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars['Fraud'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recnum</th>\n",
       "      <th>Fraud</th>\n",
       "      <th>card_merch_total_14</th>\n",
       "      <th>card_zip3_max_14</th>\n",
       "      <th>card_merch_avg_14</th>\n",
       "      <th>Card_Merchdesc_Zip_med_14</th>\n",
       "      <th>Card_Merchdesc_Zip_max_60</th>\n",
       "      <th>Card_Merchnum_Zip_avg_60</th>\n",
       "      <th>Card_Merchnum_desc_avg_60</th>\n",
       "      <th>Merchdesc_dow_avg_30</th>\n",
       "      <th>Card_Merchnum_desc_med_0</th>\n",
       "      <th>Card_Merchdesc_max_60</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
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       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>31.42</td>\n",
       "      <td>31.42</td>\n",
       "      <td>31.42</td>\n",
       "      <td>31.42</td>\n",
       "      <td>31.42</td>\n",
       "      <td>31.42</td>\n",
       "      <td>31.42</td>\n",
       "      <td>31.42</td>\n",
       "      <td>31.42</td>\n",
       "      <td>31.42</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>178.49</td>\n",
       "      <td>178.49</td>\n",
       "      <td>178.49</td>\n",
       "      <td>178.49</td>\n",
       "      <td>178.49</td>\n",
       "      <td>178.49</td>\n",
       "      <td>178.49</td>\n",
       "      <td>178.49</td>\n",
       "      <td>178.49</td>\n",
       "      <td>178.49</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>7.24</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>230.32</td>\n",
       "      <td>230.32</td>\n",
       "      <td>230.32</td>\n",
       "      <td>230.32</td>\n",
       "      <td>230.32</td>\n",
       "      <td>230.32</td>\n",
       "      <td>230.32</td>\n",
       "      <td>230.32</td>\n",
       "      <td>230.32</td>\n",
       "      <td>230.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>62.11</td>\n",
       "      <td>62.11</td>\n",
       "      <td>62.11</td>\n",
       "      <td>62.11</td>\n",
       "      <td>62.11</td>\n",
       "      <td>62.11</td>\n",
       "      <td>62.11</td>\n",
       "      <td>62.11</td>\n",
       "      <td>62.11</td>\n",
       "      <td>62.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>10.86</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Recnum  Fraud  card_merch_total_14  card_zip3_max_14  card_merch_avg_14  \\\n",
       "0       1      0                 3.62              3.62               3.62   \n",
       "1       2      0                31.42             31.42              31.42   \n",
       "2       3      0               178.49            178.49             178.49   \n",
       "3       4      0                 3.62              3.62               3.62   \n",
       "4       5      0                 7.24              3.62               3.62   \n",
       "5       6      0                 3.67              3.67               3.67   \n",
       "6       7      0                 3.62              3.62               3.62   \n",
       "7       8      0               230.32            230.32             230.32   \n",
       "8       9      0                62.11             62.11              62.11   \n",
       "9      10      0                10.86              3.62               3.62   \n",
       "\n",
       "   Card_Merchdesc_Zip_med_14  Card_Merchdesc_Zip_max_60  \\\n",
       "0                       3.62                       3.62   \n",
       "1                      31.42                      31.42   \n",
       "2                     178.49                     178.49   \n",
       "3                       3.62                       3.62   \n",
       "4                       3.62                       3.62   \n",
       "5                       3.67                       3.67   \n",
       "6                       3.62                       3.62   \n",
       "7                     230.32                     230.32   \n",
       "8                      62.11                      62.11   \n",
       "9                       3.62                       3.62   \n",
       "\n",
       "   Card_Merchnum_Zip_avg_60  Card_Merchnum_desc_avg_60  Merchdesc_dow_avg_30  \\\n",
       "0                      3.62                       3.62                  3.62   \n",
       "1                     31.42                      31.42                 31.42   \n",
       "2                    178.49                     178.49                178.49   \n",
       "3                      3.62                       3.62                  3.62   \n",
       "4                      3.62                       3.62                  3.62   \n",
       "5                      3.67                       3.67                  3.67   \n",
       "6                      3.62                       3.62                  3.62   \n",
       "7                    230.32                     230.32                230.32   \n",
       "8                     62.11                      62.11                 62.11   \n",
       "9                      3.62                       3.62                  3.62   \n",
       "\n",
       "   Card_Merchnum_desc_med_0  Card_Merchdesc_max_60  \n",
       "0                      3.62                   3.62  \n",
       "1                     31.42                  31.42  \n",
       "2                    178.49                 178.49  \n",
       "3                      3.62                   3.62  \n",
       "4                      3.62                   3.62  \n",
       "5                      3.67                   3.67  \n",
       "6                      3.62                   3.62  \n",
       "7                    230.32                 230.32  \n",
       "8                     62.11                  62.11  \n",
       "9                      3.62                   3.62  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96397, 12)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recnum</th>\n",
       "      <th>Fraud</th>\n",
       "      <th>card_merch_total_14</th>\n",
       "      <th>card_zip3_max_14</th>\n",
       "      <th>card_merch_avg_14</th>\n",
       "      <th>Card_Merchdesc_Zip_med_14</th>\n",
       "      <th>Card_Merchdesc_Zip_max_60</th>\n",
       "      <th>Card_Merchnum_Zip_avg_60</th>\n",
       "      <th>Card_Merchnum_desc_avg_60</th>\n",
       "      <th>Merchdesc_dow_avg_30</th>\n",
       "      <th>Card_Merchnum_desc_med_0</th>\n",
       "      <th>Card_Merchdesc_max_60</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>96397.000000</td>\n",
       "      <td>96397.000000</td>\n",
       "      <td>96397.000000</td>\n",
       "      <td>96397.000000</td>\n",
       "      <td>96397.000000</td>\n",
       "      <td>96397.000000</td>\n",
       "      <td>96397.000000</td>\n",
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       "      <td>96397.000000</td>\n",
       "      <td>96397.000000</td>\n",
       "      <td>96397.000000</td>\n",
       "      <td>96397.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>48365.481820</td>\n",
       "      <td>0.010986</td>\n",
       "      <td>769.967286</td>\n",
       "      <td>529.353162</td>\n",
       "      <td>401.927811</td>\n",
       "      <td>394.564839</td>\n",
       "      <td>551.295774</td>\n",
       "      <td>404.503353</td>\n",
       "      <td>404.444849</td>\n",
       "      <td>396.465357</td>\n",
       "      <td>393.500463</td>\n",
       "      <td>552.096524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>27945.003883</td>\n",
       "      <td>0.104236</td>\n",
       "      <td>4168.214517</td>\n",
       "      <td>1086.568630</td>\n",
       "      <td>790.425618</td>\n",
       "      <td>783.344833</td>\n",
       "      <td>1104.370845</td>\n",
       "      <td>778.326998</td>\n",
       "      <td>778.414021</td>\n",
       "      <td>696.170708</td>\n",
       "      <td>790.641417</td>\n",
       "      <td>1104.598654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24154.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>57.190000</td>\n",
       "      <td>39.990000</td>\n",
       "      <td>39.180000</td>\n",
       "      <td>52.500000</td>\n",
       "      <td>44.900000</td>\n",
       "      <td>44.970000</td>\n",
       "      <td>53.035000</td>\n",
       "      <td>34.900000</td>\n",
       "      <td>52.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>48365.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>236.920000</td>\n",
       "      <td>212.660000</td>\n",
       "      <td>157.065000</td>\n",
       "      <td>149.970000</td>\n",
       "      <td>223.550000</td>\n",
       "      <td>171.030000</td>\n",
       "      <td>171.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>224.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>72578.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>676.860000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>452.940000</td>\n",
       "      <td>438.450000</td>\n",
       "      <td>658.000000</td>\n",
       "      <td>472.000000</td>\n",
       "      <td>472.000000</td>\n",
       "      <td>478.200000</td>\n",
       "      <td>431.550000</td>\n",
       "      <td>659.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>96753.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>306633.410000</td>\n",
       "      <td>47900.000000</td>\n",
       "      <td>28392.840000</td>\n",
       "      <td>28392.840000</td>\n",
       "      <td>47900.000000</td>\n",
       "      <td>28392.840000</td>\n",
       "      <td>28392.840000</td>\n",
       "      <td>28392.840000</td>\n",
       "      <td>28392.840000</td>\n",
       "      <td>47900.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Recnum         Fraud  card_merch_total_14  card_zip3_max_14  \\\n",
       "count  96397.000000  96397.000000         96397.000000      96397.000000   \n",
       "mean   48365.481820      0.010986           769.967286        529.353162   \n",
       "std    27945.003883      0.104236          4168.214517       1086.568630   \n",
       "min        1.000000      0.000000             0.010000          0.010000   \n",
       "25%    24154.000000      0.000000            77.000000         57.190000   \n",
       "50%    48365.000000      0.000000           236.920000        212.660000   \n",
       "75%    72578.000000      0.000000           676.860000        609.000000   \n",
       "max    96753.000000      1.000000        306633.410000      47900.000000   \n",
       "\n",
       "       card_merch_avg_14  Card_Merchdesc_Zip_med_14  \\\n",
       "count       96397.000000               96397.000000   \n",
       "mean          401.927811                 394.564839   \n",
       "std           790.425618                 783.344833   \n",
       "min             0.010000                   0.010000   \n",
       "25%            39.990000                  39.180000   \n",
       "50%           157.065000                 149.970000   \n",
       "75%           452.940000                 438.450000   \n",
       "max         28392.840000               28392.840000   \n",
       "\n",
       "       Card_Merchdesc_Zip_max_60  Card_Merchnum_Zip_avg_60  \\\n",
       "count               96397.000000              96397.000000   \n",
       "mean                  551.295774                404.503353   \n",
       "std                  1104.370845                778.326998   \n",
       "min                     0.010000                  0.010000   \n",
       "25%                    52.500000                 44.900000   \n",
       "50%                   223.550000                171.030000   \n",
       "75%                   658.000000                472.000000   \n",
       "max                 47900.000000              28392.840000   \n",
       "\n",
       "       Card_Merchnum_desc_avg_60  Merchdesc_dow_avg_30  \\\n",
       "count               96397.000000          96397.000000   \n",
       "mean                  404.444849            396.465357   \n",
       "std                   778.414021            696.170708   \n",
       "min                     0.010000              0.010000   \n",
       "25%                    44.970000             53.035000   \n",
       "50%                   171.000000            200.000000   \n",
       "75%                   472.000000            478.200000   \n",
       "max                 28392.840000          28392.840000   \n",
       "\n",
       "       Card_Merchnum_desc_med_0  Card_Merchdesc_max_60  \n",
       "count              96397.000000           96397.000000  \n",
       "mean                 393.500463             552.096524  \n",
       "std                  790.641417            1104.598654  \n",
       "min                    0.010000               0.010000  \n",
       "25%                   34.900000              52.770000  \n",
       "50%                  140.000000             224.300000  \n",
       "75%                  431.550000             659.340000  \n",
       "max                28392.840000           47900.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recnum</th>\n",
       "      <th>Fraud</th>\n",
       "      <th>card_merch_total_14</th>\n",
       "      <th>card_zip3_max_14</th>\n",
       "      <th>card_merch_avg_14</th>\n",
       "      <th>Card_Merchdesc_Zip_med_14</th>\n",
       "      <th>Card_Merchdesc_Zip_max_60</th>\n",
       "      <th>Card_Merchnum_Zip_avg_60</th>\n",
       "      <th>Card_Merchnum_desc_avg_60</th>\n",
       "      <th>Merchdesc_dow_avg_30</th>\n",
       "      <th>Card_Merchnum_desc_med_0</th>\n",
       "      <th>Card_Merchdesc_max_60</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>83966</th>\n",
       "      <td>84296</td>\n",
       "      <td>0</td>\n",
       "      <td>789.32</td>\n",
       "      <td>609.34</td>\n",
       "      <td>394.660</td>\n",
       "      <td>394.660</td>\n",
       "      <td>609.34</td>\n",
       "      <td>394.660000</td>\n",
       "      <td>394.660000</td>\n",
       "      <td>271.69250</td>\n",
       "      <td>394.66</td>\n",
       "      <td>609.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83967</th>\n",
       "      <td>84297</td>\n",
       "      <td>0</td>\n",
       "      <td>235.00</td>\n",
       "      <td>235.00</td>\n",
       "      <td>235.000</td>\n",
       "      <td>235.000</td>\n",
       "      <td>235.00</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>235.00000</td>\n",
       "      <td>235.00</td>\n",
       "      <td>235.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83968</th>\n",
       "      <td>84298</td>\n",
       "      <td>0</td>\n",
       "      <td>600.00</td>\n",
       "      <td>600.00</td>\n",
       "      <td>600.000</td>\n",
       "      <td>600.000</td>\n",
       "      <td>600.00</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>600.00</td>\n",
       "      <td>600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83969</th>\n",
       "      <td>84299</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>365.32</td>\n",
       "      <td>197.660000</td>\n",
       "      <td>197.660000</td>\n",
       "      <td>294.10400</td>\n",
       "      <td>30.00</td>\n",
       "      <td>365.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83970</th>\n",
       "      <td>84300</td>\n",
       "      <td>0</td>\n",
       "      <td>182.00</td>\n",
       "      <td>182.00</td>\n",
       "      <td>182.000</td>\n",
       "      <td>182.000</td>\n",
       "      <td>182.00</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>182.00000</td>\n",
       "      <td>182.00</td>\n",
       "      <td>182.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83971</th>\n",
       "      <td>84301</td>\n",
       "      <td>0</td>\n",
       "      <td>399.61</td>\n",
       "      <td>225.00</td>\n",
       "      <td>199.805</td>\n",
       "      <td>199.805</td>\n",
       "      <td>225.00</td>\n",
       "      <td>199.805000</td>\n",
       "      <td>199.805000</td>\n",
       "      <td>174.61000</td>\n",
       "      <td>174.61</td>\n",
       "      <td>225.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83972</th>\n",
       "      <td>84302</td>\n",
       "      <td>0</td>\n",
       "      <td>53.00</td>\n",
       "      <td>206.90</td>\n",
       "      <td>53.000</td>\n",
       "      <td>75.500</td>\n",
       "      <td>659.76</td>\n",
       "      <td>224.701429</td>\n",
       "      <td>224.701429</td>\n",
       "      <td>128.45875</td>\n",
       "      <td>53.00</td>\n",
       "      <td>659.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83973</th>\n",
       "      <td>84303</td>\n",
       "      <td>0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>531.25</td>\n",
       "      <td>25.000</td>\n",
       "      <td>25.000</td>\n",
       "      <td>28.48</td>\n",
       "      <td>26.740000</td>\n",
       "      <td>26.740000</td>\n",
       "      <td>25.00000</td>\n",
       "      <td>25.00</td>\n",
       "      <td>28.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83974</th>\n",
       "      <td>84304</td>\n",
       "      <td>0</td>\n",
       "      <td>97.17</td>\n",
       "      <td>97.17</td>\n",
       "      <td>97.170</td>\n",
       "      <td>97.170</td>\n",
       "      <td>97.17</td>\n",
       "      <td>97.170000</td>\n",
       "      <td>97.170000</td>\n",
       "      <td>97.17000</td>\n",
       "      <td>97.17</td>\n",
       "      <td>97.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83975</th>\n",
       "      <td>84305</td>\n",
       "      <td>0</td>\n",
       "      <td>6.51</td>\n",
       "      <td>6.51</td>\n",
       "      <td>6.510</td>\n",
       "      <td>6.510</td>\n",
       "      <td>6.51</td>\n",
       "      <td>11.733333</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>6.51000</td>\n",
       "      <td>6.51</td>\n",
       "      <td>6.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Recnum  Fraud  card_merch_total_14  card_zip3_max_14  \\\n",
       "83966   84296      0               789.32            609.34   \n",
       "83967   84297      0               235.00            235.00   \n",
       "83968   84298      0               600.00            600.00   \n",
       "83969   84299      0                30.00             30.00   \n",
       "83970   84300      0               182.00            182.00   \n",
       "83971   84301      0               399.61            225.00   \n",
       "83972   84302      0                53.00            206.90   \n",
       "83973   84303      0                25.00            531.25   \n",
       "83974   84304      0                97.17             97.17   \n",
       "83975   84305      0                 6.51              6.51   \n",
       "\n",
       "       card_merch_avg_14  Card_Merchdesc_Zip_med_14  \\\n",
       "83966            394.660                    394.660   \n",
       "83967            235.000                    235.000   \n",
       "83968            600.000                    600.000   \n",
       "83969             30.000                     30.000   \n",
       "83970            182.000                    182.000   \n",
       "83971            199.805                    199.805   \n",
       "83972             53.000                     75.500   \n",
       "83973             25.000                     25.000   \n",
       "83974             97.170                     97.170   \n",
       "83975              6.510                      6.510   \n",
       "\n",
       "       Card_Merchdesc_Zip_max_60  Card_Merchnum_Zip_avg_60  \\\n",
       "83966                     609.34                394.660000   \n",
       "83967                     235.00                235.000000   \n",
       "83968                     600.00                600.000000   \n",
       "83969                     365.32                197.660000   \n",
       "83970                     182.00                182.000000   \n",
       "83971                     225.00                199.805000   \n",
       "83972                     659.76                224.701429   \n",
       "83973                      28.48                 26.740000   \n",
       "83974                      97.17                 97.170000   \n",
       "83975                       6.51                 11.733333   \n",
       "\n",
       "       Card_Merchnum_desc_avg_60  Merchdesc_dow_avg_30  \\\n",
       "83966                 394.660000             271.69250   \n",
       "83967                 235.000000             235.00000   \n",
       "83968                 600.000000             600.00000   \n",
       "83969                 197.660000             294.10400   \n",
       "83970                 182.000000             182.00000   \n",
       "83971                 199.805000             174.61000   \n",
       "83972                 224.701429             128.45875   \n",
       "83973                  26.740000              25.00000   \n",
       "83974                  97.170000              97.17000   \n",
       "83975                   4.500000               6.51000   \n",
       "\n",
       "       Card_Merchnum_desc_med_0  Card_Merchdesc_max_60  \n",
       "83966                    394.66                 609.34  \n",
       "83967                    235.00                 235.00  \n",
       "83968                    600.00                 600.00  \n",
       "83969                     30.00                 365.32  \n",
       "83970                    182.00                 182.00  \n",
       "83971                    174.61                 225.00  \n",
       "83972                     53.00                 659.76  \n",
       "83973                     25.00                  28.48  \n",
       "83974                     97.17                  97.17  \n",
       "83975                      6.51                   6.51  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the row i vars that corresponds to 11/1 (Recnum=84300)\n",
    "test = vars[vars['Recnum'] > 84295]\n",
    "test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fraud\n",
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "record_save = vars['Recnum']\n",
    "Y_save = pd.DataFrame(vars.loc[:,'Fraud'])\n",
    "Y_save.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scale and truncate field values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>card_merch_total_14</th>\n",
       "      <th>card_zip3_max_14</th>\n",
       "      <th>card_merch_avg_14</th>\n",
       "      <th>Card_Merchdesc_Zip_med_14</th>\n",
       "      <th>Card_Merchdesc_Zip_max_60</th>\n",
       "      <th>Card_Merchnum_Zip_avg_60</th>\n",
       "      <th>Card_Merchnum_desc_avg_60</th>\n",
       "      <th>Merchdesc_dow_avg_30</th>\n",
       "      <th>Card_Merchnum_desc_med_0</th>\n",
       "      <th>Card_Merchdesc_max_60</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>96397.000000</td>\n",
       "      <td>96397.000000</td>\n",
       "      <td>96397.000000</td>\n",
       "      <td>96397.000000</td>\n",
       "      <td>96397.000000</td>\n",
       "      <td>96397.000000</td>\n",
       "      <td>96397.000000</td>\n",
       "      <td>96397.000000</td>\n",
       "      <td>96397.000000</td>\n",
       "      <td>96397.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>769.967286</td>\n",
       "      <td>529.353162</td>\n",
       "      <td>401.927811</td>\n",
       "      <td>394.564839</td>\n",
       "      <td>551.295774</td>\n",
       "      <td>404.503353</td>\n",
       "      <td>404.444849</td>\n",
       "      <td>396.465357</td>\n",
       "      <td>393.500463</td>\n",
       "      <td>552.096524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4168.214517</td>\n",
       "      <td>1086.568630</td>\n",
       "      <td>790.425618</td>\n",
       "      <td>783.344833</td>\n",
       "      <td>1104.370845</td>\n",
       "      <td>778.326998</td>\n",
       "      <td>778.414021</td>\n",
       "      <td>696.170708</td>\n",
       "      <td>790.641417</td>\n",
       "      <td>1104.598654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>57.190000</td>\n",
       "      <td>39.990000</td>\n",
       "      <td>39.180000</td>\n",
       "      <td>52.500000</td>\n",
       "      <td>44.900000</td>\n",
       "      <td>44.970000</td>\n",
       "      <td>53.035000</td>\n",
       "      <td>34.900000</td>\n",
       "      <td>52.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>236.920000</td>\n",
       "      <td>212.660000</td>\n",
       "      <td>157.065000</td>\n",
       "      <td>149.970000</td>\n",
       "      <td>223.550000</td>\n",
       "      <td>171.030000</td>\n",
       "      <td>171.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>224.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>676.860000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>452.940000</td>\n",
       "      <td>438.450000</td>\n",
       "      <td>658.000000</td>\n",
       "      <td>472.000000</td>\n",
       "      <td>472.000000</td>\n",
       "      <td>478.200000</td>\n",
       "      <td>431.550000</td>\n",
       "      <td>659.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>306633.410000</td>\n",
       "      <td>47900.000000</td>\n",
       "      <td>28392.840000</td>\n",
       "      <td>28392.840000</td>\n",
       "      <td>47900.000000</td>\n",
       "      <td>28392.840000</td>\n",
       "      <td>28392.840000</td>\n",
       "      <td>28392.840000</td>\n",
       "      <td>28392.840000</td>\n",
       "      <td>47900.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       card_merch_total_14  card_zip3_max_14  card_merch_avg_14  \\\n",
       "count         96397.000000      96397.000000       96397.000000   \n",
       "mean            769.967286        529.353162         401.927811   \n",
       "std            4168.214517       1086.568630         790.425618   \n",
       "min               0.010000          0.010000           0.010000   \n",
       "25%              77.000000         57.190000          39.990000   \n",
       "50%             236.920000        212.660000         157.065000   \n",
       "75%             676.860000        609.000000         452.940000   \n",
       "max          306633.410000      47900.000000       28392.840000   \n",
       "\n",
       "       Card_Merchdesc_Zip_med_14  Card_Merchdesc_Zip_max_60  \\\n",
       "count               96397.000000               96397.000000   \n",
       "mean                  394.564839                 551.295774   \n",
       "std                   783.344833                1104.370845   \n",
       "min                     0.010000                   0.010000   \n",
       "25%                    39.180000                  52.500000   \n",
       "50%                   149.970000                 223.550000   \n",
       "75%                   438.450000                 658.000000   \n",
       "max                 28392.840000               47900.000000   \n",
       "\n",
       "       Card_Merchnum_Zip_avg_60  Card_Merchnum_desc_avg_60  \\\n",
       "count              96397.000000               96397.000000   \n",
       "mean                 404.503353                 404.444849   \n",
       "std                  778.326998                 778.414021   \n",
       "min                    0.010000                   0.010000   \n",
       "25%                   44.900000                  44.970000   \n",
       "50%                  171.030000                 171.000000   \n",
       "75%                  472.000000                 472.000000   \n",
       "max                28392.840000               28392.840000   \n",
       "\n",
       "       Merchdesc_dow_avg_30  Card_Merchnum_desc_med_0  Card_Merchdesc_max_60  \n",
       "count          96397.000000              96397.000000           96397.000000  \n",
       "mean             396.465357                393.500463             552.096524  \n",
       "std              696.170708                790.641417            1104.598654  \n",
       "min                0.010000                  0.010000               0.010000  \n",
       "25%               53.035000                 34.900000              52.770000  \n",
       "50%              200.000000                140.000000             224.300000  \n",
       "75%              478.200000                431.550000             659.340000  \n",
       "max            28392.840000              28392.840000           47900.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_no_scaling = vars.drop(columns = ['Recnum','Fraud'])\n",
    "X_no_scaling.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X = (X_no_scaling - X_no_scaling.mean()) / X_no_scaling.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use this to cap variables. For some problems it helps\n",
    "Clip = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>card_merch_total_14</th>\n",
       "      <th>card_zip3_max_14</th>\n",
       "      <th>card_merch_avg_14</th>\n",
       "      <th>Card_Merchdesc_Zip_med_14</th>\n",
       "      <th>Card_Merchdesc_Zip_max_60</th>\n",
       "      <th>Card_Merchnum_Zip_avg_60</th>\n",
       "      <th>Card_Merchnum_desc_avg_60</th>\n",
       "      <th>Merchdesc_dow_avg_30</th>\n",
       "      <th>Card_Merchnum_desc_med_0</th>\n",
       "      <th>Card_Merchdesc_max_60</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.639700e+04</td>\n",
       "      <td>9.639700e+04</td>\n",
       "      <td>9.639700e+04</td>\n",
       "      <td>9.639700e+04</td>\n",
       "      <td>9.639700e+04</td>\n",
       "      <td>9.639700e+04</td>\n",
       "      <td>9.639700e+04</td>\n",
       "      <td>9.639700e+04</td>\n",
       "      <td>9.639700e+04</td>\n",
       "      <td>9.639700e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-4.580238e-16</td>\n",
       "      <td>1.642357e-15</td>\n",
       "      <td>9.159462e-15</td>\n",
       "      <td>-1.424971e-15</td>\n",
       "      <td>1.927932e-16</td>\n",
       "      <td>1.083906e-14</td>\n",
       "      <td>1.434370e-15</td>\n",
       "      <td>6.285712e-15</td>\n",
       "      <td>7.526706e-16</td>\n",
       "      <td>3.751579e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.847211e-01</td>\n",
       "      <td>-4.871696e-01</td>\n",
       "      <td>-5.084828e-01</td>\n",
       "      <td>-5.036796e-01</td>\n",
       "      <td>-4.991854e-01</td>\n",
       "      <td>-5.196959e-01</td>\n",
       "      <td>-5.195626e-01</td>\n",
       "      <td>-5.694801e-01</td>\n",
       "      <td>-4.976851e-01</td>\n",
       "      <td>-4.998073e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.662504e-01</td>\n",
       "      <td>-4.345452e-01</td>\n",
       "      <td>-4.579024e-01</td>\n",
       "      <td>-4.536761e-01</td>\n",
       "      <td>-4.516561e-01</td>\n",
       "      <td>-4.620209e-01</td>\n",
       "      <td>-4.618042e-01</td>\n",
       "      <td>-4.933134e-01</td>\n",
       "      <td>-4.535564e-01</td>\n",
       "      <td>-4.520434e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-1.278838e-01</td>\n",
       "      <td>-2.914617e-01</td>\n",
       "      <td>-3.097860e-01</td>\n",
       "      <td>-3.122441e-01</td>\n",
       "      <td>-2.967715e-01</td>\n",
       "      <td>-2.999682e-01</td>\n",
       "      <td>-2.998981e-01</td>\n",
       "      <td>-2.822086e-01</td>\n",
       "      <td>-3.206263e-01</td>\n",
       "      <td>-2.967562e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-2.233745e-02</td>\n",
       "      <td>7.330125e-02</td>\n",
       "      <td>6.453762e-02</td>\n",
       "      <td>5.602279e-02</td>\n",
       "      <td>9.661992e-02</td>\n",
       "      <td>8.672017e-02</td>\n",
       "      <td>8.678563e-02</td>\n",
       "      <td>1.174060e-01</td>\n",
       "      <td>4.812490e-02</td>\n",
       "      <td>9.708818e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.337997e+01</td>\n",
       "      <td>4.359655e+01</td>\n",
       "      <td>3.541246e+01</td>\n",
       "      <td>3.574195e+01</td>\n",
       "      <td>4.287392e+01</td>\n",
       "      <td>3.595961e+01</td>\n",
       "      <td>3.595567e+01</td>\n",
       "      <td>4.021481e+01</td>\n",
       "      <td>3.541345e+01</td>\n",
       "      <td>4.286435e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       card_merch_total_14  card_zip3_max_14  card_merch_avg_14  \\\n",
       "count         9.639700e+04      9.639700e+04       9.639700e+04   \n",
       "mean         -4.580238e-16      1.642357e-15       9.159462e-15   \n",
       "std           1.000000e+00      1.000000e+00       1.000000e+00   \n",
       "min          -1.847211e-01     -4.871696e-01      -5.084828e-01   \n",
       "25%          -1.662504e-01     -4.345452e-01      -4.579024e-01   \n",
       "50%          -1.278838e-01     -2.914617e-01      -3.097860e-01   \n",
       "75%          -2.233745e-02      7.330125e-02       6.453762e-02   \n",
       "max           7.337997e+01      4.359655e+01       3.541246e+01   \n",
       "\n",
       "       Card_Merchdesc_Zip_med_14  Card_Merchdesc_Zip_max_60  \\\n",
       "count               9.639700e+04               9.639700e+04   \n",
       "mean               -1.424971e-15               1.927932e-16   \n",
       "std                 1.000000e+00               1.000000e+00   \n",
       "min                -5.036796e-01              -4.991854e-01   \n",
       "25%                -4.536761e-01              -4.516561e-01   \n",
       "50%                -3.122441e-01              -2.967715e-01   \n",
       "75%                 5.602279e-02               9.661992e-02   \n",
       "max                 3.574195e+01               4.287392e+01   \n",
       "\n",
       "       Card_Merchnum_Zip_avg_60  Card_Merchnum_desc_avg_60  \\\n",
       "count              9.639700e+04               9.639700e+04   \n",
       "mean               1.083906e-14               1.434370e-15   \n",
       "std                1.000000e+00               1.000000e+00   \n",
       "min               -5.196959e-01              -5.195626e-01   \n",
       "25%               -4.620209e-01              -4.618042e-01   \n",
       "50%               -2.999682e-01              -2.998981e-01   \n",
       "75%                8.672017e-02               8.678563e-02   \n",
       "max                3.595961e+01               3.595567e+01   \n",
       "\n",
       "       Merchdesc_dow_avg_30  Card_Merchnum_desc_med_0  Card_Merchdesc_max_60  \n",
       "count          9.639700e+04              9.639700e+04           9.639700e+04  \n",
       "mean           6.285712e-15              7.526706e-16           3.751579e-15  \n",
       "std            1.000000e+00              1.000000e+00           1.000000e+00  \n",
       "min           -5.694801e-01             -4.976851e-01          -4.998073e-01  \n",
       "25%           -4.933134e-01             -4.535564e-01          -4.520434e-01  \n",
       "50%           -2.822086e-01             -3.206263e-01          -2.967562e-01  \n",
       "75%            1.174060e-01              4.812490e-02           9.708818e-02  \n",
       "max            4.021481e+01              3.541345e+01           4.286435e+01  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# push in any outlier values, then rescale\n",
    "X.clip(-1*Clip,Clip,inplace=True)\n",
    "X = (X_no_scaling - X_no_scaling.mean()) / X_no_scaling.std()\n",
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fraud    179\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# separate data into modeling (traintest) and out of time. Here I'm using the record number to do this separation.\n",
    "# you need to change this oot record number to whatever is appropriate for your data\n",
    "# oot_recnum=84300\n",
    "oot_recnum = 83970\n",
    "X_trntst = X[0:oot_recnum]\n",
    "Y_trntst = Y_save[0:oot_recnum]\n",
    "X_oot = X[oot_recnum:]\n",
    "Y_oot = Y_save[oot_recnum:]\n",
    "Y_oot.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore regularization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Solve a linear regression with ridge and lasso regularization and watch how the variable weights evolve with the regularization strength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge, RidgeCV, Lasso, LassoCV \n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas = 10**np.linspace(2,9,30)*0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 10)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge = Ridge()\n",
    "coefs = []\n",
    "for a in alphas: \n",
    "    ridge.set_params(alpha=a) \n",
    "    ridge.fit(X_trn,Y_trn.values.ravel()) \n",
    "    coefs.append(ridge.coef_) \n",
    "np.shape(coefs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.04 s, sys: 31.3 ms, total: 1.07 s\n",
      "Wall time: 523 ms\n"
     ]
    },
    {
     "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": [
    "%%time\n",
    "%matplotlib inline\n",
    "ax = plt.gca() # Get the current Axes instance\n",
    "ax.plot(alphas, coefs)\n",
    "ax.set_xscale('log')\n",
    "plt.xlabel('alpha') \n",
    "plt.ylabel('standadized coef') \n",
    "plt.title('Ridge')\n",
    "plt.savefig('ridge.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas = 10**np.linspace(-5,0,30)*0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# %%time\n",
    "# # sometimes this cell takes a long time\n",
    "# lasso = Lasso(max_iter=10000) \n",
    "# coefs = [] \n",
    "# for a in alphas: \n",
    "#     lasso.set_params(alpha=a) \n",
    "#     lasso.fit(X_trn,Y_trn.values.ravel()) \n",
    "#     coefs.append(lasso.coef_) \n",
    "# # print('Shape:',np.shape(coefs)\n",
    "# print('Selected Features:', list(vars.columns[np.where(lasso.coef_!=0)[0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "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": [
    "%matplotlib inline\n",
    "ax = plt.gca() # Get the current Axes instance \n",
    "ax.plot(alphas, coefs)\n",
    "ax.set_xscale('log')\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('standerdized coef') \n",
    "plt.title('Lasso')\n",
    "plt.legend()\n",
    "plt.savefig('lasso.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look at PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_trntst.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trntst_save = X_trntst.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZR9kbAlmgmmAKTRliQnWSE6ZPUn8FESlZw4aCu78BnGlm5wInhKt/4+5/GpfKSlRrJs3tj6/SpDsiUpJGuiS11syuBj4IdAM3KRBGl21ppruvnxfXbYu7FBGRAzZSm8JPCE4XLQIuBP5lXCoqcQOT7qhdQURK0UhtCse5+1sAzOxHwNPjU1JpO6yhhqOm1IftCurjJyKlZaQjhcGL7cP5lqVArZk0bas76NekOyJSYkYKhZPMbHt42wGcOPDYzLaPV4GlqDWTZtueHpZv2Bl3KSIiB2Skq4906cxBGhgcb8GqLRxzeGPM1YiIFE6jpEZgZnoCUxtr1F9BREqOQiECZkZrJq0rkESk5CgUIpLNNLNu6x7Wb90TdykiIgVTKERkcNKd1TpaEJHSoVCIyLGHN1JfnVS7goiUFIVCRFLJBKe0NKtdQURKikIhQtmWNC+/vp3tnZp0R0RKg0IhQq2ZZtzhGbUriEiJUChEaN6sJlIJ07zNIlIyFAoRqqtOcfz0STytxmYRKREKhYi1tjTz/NqtdPX2xV2KiMioFAoRy2bSdPX28+I6jSEoIsVPoRCxbCaYdEf9FUSkFCgUIja5oYYjJ9erv4KIlASFwjjIZppZuHqLJt0RkaKnUBgH2Uyajt09rNykSXdEpLgpFMbB3kl3dApJRIpbpKFgZheY2VIzW2Fm1+R5/qNm9kJ4e8LMToqynrhkDqtjckM1C9TYLCJFLrJQMLMkcCNwIXAccImZHTdks1eBs939ROCbwC1R1RMnMyPbklbPZhEpelEeKZwGrHD3le7eDdwFXJS7gbs/4e4DvymfBGZEWE+ssplm1mzZzRvbO+MuRURkWFGGwnRgbc5ye7huOJ8FfhthPbEanHRHRwsiUsSiDAXLsy7vNZlmdi5BKHxlmOcvM7M2M2vbuHHjGJY4fo6fNpG66qTaFUSkqEUZCu3AzJzlGcD6oRuZ2YnArcBF7r45347c/RZ3z7p7dsqUKZEUG7VUMsHJs5oUCiJS1KIMhQXAHDObbWbVwMXAfbkbmNks4B7g4+6+LMJaikK2Jc2S17azQ5PuiEiRiiwU3L0XuAJ4EFgC3O3ui81svpnNDzf7GnAY8H0ze87M2qKqpxi0ZtL0Ozy7ZmvcpYiI5JWKcufufj9w/5B1N+c8vhS4NMoaism8WU0kE0bbqi28fW5pngYTkfKmHs3jqKEmxXFHTFTPZhEpWgqFcZbNNPPs2g56+vrjLkVEZD8KhXHWmknT2dPP4vWadEdEio9CYZxlWzTpjogUL4XCOJs6sZaWw+rUX0FEipJCIQYDg+O5a9IdESkuCoUYnDa7mc27unl10664SxER2YdCIQbZwUl3dApJRIqLQiEGR06uJ11frf4KIlJ0FAoxCCbdadYVSCJSdBQKMWnNpFm1eTcbdmjSHREpHgqFmGQzQX+FhTqFJCJFRKEQk+OnTaK2KqF2BREpKgqFmFSnEsyb2UTbarUriEjxUCjEqDWTZvH67ezq6o27FBERQKEQq2wmTV+/88vn1qt3s4gUBYVCjFozzRw9tYGv3ruIv7npCR5bvlHhICKxUijEqK46xf1ffBvf+sBbeGNbJx//0dN85AdP8uTKzXGXJiIVykrtL9NsNuttbeU3lXNXbx//sWAt3/vTCjbs6OLMow7j7981l1Nb0nGXJiJlwMwWunt21O0UCsWls6ePO59aw00Pr2DTzm7ePncK/+2dc5k3synu0kSkhCkUStzu7l7u+MtqfvDIK3Ts7uH8N0/l6vPncsL0SXGXJiIlSKFQJnZ29XL7469yy6Mr2d7ZywXHH87V75zDsYdPjLs0ESkhCoUys21PD7f9+VVu+/Or7Ozu5T1vOYKrz5/L0VMb4i5NREqAQqFMbd3dzQ8fW8mPH19FZ08fF82bzlXnzSEzuT7u0kSkiCkUytzmnV384NGV3PGXVfT0OR88ZTpXvmMOM9N1cZcmIkVIoVAhNuzo5KaHX+HOp9bQ3+/819aZXHHu0UxrmhB3aSJSRBQKFea1bXv4/kOvcNeCNRjGJafN5PJzj2bqxNq4SxORIqBQqFDtHbu58aEV/KytnWTC+NgZLXz+nKOY3FATd2kiEiOFQoVbs3k3N/xxOfc+205NKsknz8xw2duPJF1fHXdpIhIDhYIAsHLjTm7443Lue349dVVJPnPWbC4960gm1VXFXZqIjCOFguxj2Rs7uOEPy/nNotdorE1x6VlH8pmzMjTWKhxEKoFCQfJ6af12rv/DMn730hs01VXxubcdyafOzFBfk4q7NBGJkEJBRrSofRvf/f1SHlq6kXR9NfPPPpKPn5FhQnUy7tJEJAIKBSnIM2s6+NffL+Ox5ZuY0ljDF845iktOm0VtlcJBpJwoFOSAPP3qFr77+6U8uXILh0+s5fJ3HM1HsjOpTmkeJpFyoFCQg/LEik185/fLWLi6g+lNE7jyHUfzvnnTqE0lSSQs7vJE5CApFOSguTuPLt/Ed3+3lOfbtw2ur04mqEklqKlKUpNKUFuVoCaVHPl+cNvkkNcWdl+TSpBK6mhF5FAVGgq65ET2Y2acPXcKb58zmUeWbeTl13fQ2dNHV2//fvddOcubd/UOu92h/O2RSth+gVGdszyhOkl9TYqG6lRwX5uioSZJQ00V9TVJGmpSNNQEzzXWhtvUpKhJJTDT0Y9ILoWCDMvMOOeYqZxzzNRD2o+7093Xvzckevrp6u2js6d/v2A5mPstu7pZs2U3Ozt72dXVy67uvoLqSiVsMCCC0EjSUFtFQ02S+uqBcNkbKPnCpb4mSWNNFbVVChgpD5GGgpldANwAJIFb3f3aIc9b+Py7gd3Ap9z9mShrkvFnZtSkktSkkkwch85y/f3Oru5ednX1sbOrl51dQVjsGAyNvY9zn9/Z1cu2PT2s69jNrq6+YF13b0FHOQkjCIua3KOVFPXh0cvAKbXq8JTYwH1NVZKaZIKaqkRweq4qQXUyOWQ52G6fZR3lSEQiCwUzSwI3Au8E2oEFZnafu7+Us9mFwJzwdjpwU3gvctASCaOxtmpMemv39zt7eoKA2DEQHp1hkHQPPO7bJ2B2dobPdfXy+rZOdnX1BkdEvf109/bT3dc/Bp9ybxvPvkGzf/DkX793uSaVoCqZIJkwUgkjEd4HywmSCUgmEoPrkoPPDV1O7Pdcvv0lEgT3hoKtCEV5pHAasMLdVwKY2V3ARUBuKFwE3OFBa/eTZtZkZke4+2sR1iVSsER4iqm+JsWhnUTbq79/7+m07t7gVFp3b+7y/uv3fTzSa/dd3tHZO/x7jFE4HYr9QyN/yCQTxi/mn6kxu8ZBlKEwHVibs9zO/kcB+baZDgwbCis37uIjP/jLWNUoUraSCaOuOklddRLI/8u03x33oN3HIbiFywxZHjiL5gOvGXzeB0+x7b88sP+B7YMHucsDz4fP4g79/dDV3xe+X/DMpXcsIKEji8hFGQr5/vWGnp0tZBvM7DLgMoCGI4469MpEBCD4JWuQ/7+iVKIoQ6EdmJmzPANYfxDb4O63ALdA0E/hP/7ur8a2UhGRMnf3/MK2i7JX0AJgjpnNNrNq4GLgviHb3Ad8wgJnANvUniAiEp/IjhTcvdfMrgAeJLgk9TZ3X2xm88PnbwbuJ7gcdQXBJamfjqoeEREZXaT9FNz9foJf/Lnrbs557MDlUdYgIiKF06AyIiIySKEgIiKDFAoiIjJIoSAiIoNKbj4FM9sIrD6Al0wGNkVUTqXQd3jo9B0eGn1/h+4Yd28cbaOSGzrb3accyPZm1lbIxBIyPH2Hh07f4aHR93fozKyg2cl0+khERAYpFEREZFAlhMItcRdQBvQdHjp9h4dG39+hK+g7LLmGZhERiU4lHCmIiEiBFAoiIjKorEPBzC4ws6VmtsLMrom7nlJjZjPN7CEzW2Jmi83sqrhrKkVmljSzZ83s13HXUorCaXp/bmYvhz+LmlDlAJjZl8L/vy+a2b+bWe1I25dtKJhZErgRuBA4DrjEzI6Lt6qS0wv8vbu/GTgDuFzf4UG5ClgSdxEl7AbgAXc/FjgJfZcFM7PpwBeBrLufQDCNwcUjvaZsQwE4DVjh7ivdvRu4C7go5ppKiru/5u7PhI93EPxnnB5vVaXFzGYA7wFujbuWUmRmE4G3Az8CcPdud98aa1GlJwVMMLMUUEee2S1zlXMoTAfW5iy3o19oB83MMsDJwFMxl1Jqrgf+AeiPuY5SdSSwEfhxeAruVjOrj7uoUuHu64B/AdYArxHMbvm7kV5TzqGQbyZyXX97EMysAfgFcLW7b4+7nlJhZu8FNrj7wrhrKWEp4BTgJnc/GdgFqH2wQGbWTHCGZDYwDag3s4+N9JpyDoV2YGbO8gxGOWyS/ZlZFUEg3Onu98RdT4l5K/A+M1tFcPryHWb203hLKjntQLu7Dxyh/pwgJKQw5wOvuvtGd+8B7gHOHOkF5RwKC4A5ZjbbzKoJGlfui7mmkmJmRnAud4m7fzfuekqNu/+ju89w9wzBz9+f3H3Ev9JkX+7+OrDWzI4JV50HvBRjSaVmDXCGmdWF/5/PY5SG+pIbJbVQ7t5rZlcADxK0uN/m7otjLqvUvBX4OLDIzJ4L1301nHtbZLxcCdwZ/nG3Evh0zPWUDHd/ysx+DjxDcDXhs4wy3IWGuRARkUHlfPpIREQOkEJBREQGKRRERGSQQkFERAYpFEREZJBCQfZhZm5m38lZ/rKZfX2M9n27mX1oLPY1yvt8OBxN86Go3ytuZvbViPb7RBT7zdn/bWa2wcxejPJ95MApFGSoLuBvzGxy3IXkCke9LdRngS+4+7lR1VNEIgkFdx+x1+sYuB24IOL3kIOgUJChegk6t3xp6BND/9I3s53h/Tlm9oiZ3W1my8zsWjP7qJk9bWaLzOyonN2cb2aPhdu9N3x90sy+bWYLzOwFM/u7nP0+ZGb/BizKU88l4f5fNLPrwnVfA84Cbjazb+d5zT+Er3nezK4N180zsyfD9743HC8GM3vYzP7VzB4NjzxazeweM1tuZv8cbpMJx/n/Sfj6n5tZXfjceeEgbovCv4xrwvWrzOwbZvZM+Nyx4fr6cLsF4esuCtd/KnzfB8L3/r/h+msJRr98zszuDF//m/CzvWhmH8nz+Uf9THn+bR+2vfMZ3Bn2jB34HJPDx1kzezh8fHZY03Ph52gcWoe7PwpsGbpeioC766bb4A3YCUwEVgGTgC8DXw+fux34UO624f05wFbgCKAGWAd8I3zuKuD6nNc/QPDHyByCcW1qgcuA/xFuUwO0EQzgdQ7BAGiz89Q5jaAL/xSCnvl/At4fPvcwwfjxQ19zIfAEUBcup8P7F4Czw8f/K6feh4Hrcj7H+pzP2A4cBmQIBlp8a7jdbeF3VkswSu/ccP0dBAMKEn63V4aPvwDcGj7+FvCx8HETsAyoBz5F0JN3Urjf1cDM3H+D8PEHgR/mLE/K8x2M+pny/NtuIxg7LAH8BTgr53NMDh9ngYfDx7/K+T4agNQwP2sZ4MW4f+Z12/emIwXZjwcjod5BMDlHoRZ4MP9CF/AKMDA87yKC//wD7nb3fndfTvCL7ljgXcAnLBhK4ymCX7Zzwu2fdvdX87xfK8EvoY3u3gvcSTDu/kjOB37s7rvDz7nFzCYBTe7+SLjNT4bsZ2C8rEXA4pzPuJK9Ay6udffHw8c/JThSOYZgILJlw+x3YHDBhez9ft4FXBN+Dw8TBMCs8Lk/uvs2d+8kGPunJc/nW0RwJHadmb3N3bcN8z0U8plyPe3u7e7eDzzHvv+e+TwOfNfMvkjw3faOsr0UEYWCDOd6gnPzuWPX9xL+zISnEKpznuvKedyfs9zPvmNsDR1XxQmGOb/S3eeFt9m+d8z3XcPUl29o9NFYnvcfTe7nGPoZBz7XcJ+pkP325ezHgA/mfA+z3H3JkO2HvmbvmwYBdCrBL/v/E55KG+m9R/pM+bYf+t6DPw8EATZQx7XApcAE4MmB02NSGhQKkpe7bwHuJgiGAasIfulAMEZ71UHs+sNmlgjbGY4ElhIMWvh5C4bpxszm2ugTqTwFnG1mk8NG6EuAR0Z5ze+Az+Sc80+Hf013mNnbwm0+XsB+hpple+cNvgT4M/AykDGzow9gvw8CV+acsz+5gPfuyfnepgG73f2nBBOrRD3E9Cr2/jx8cGClmR3l7ovc/TqCU4EKhRKiUJCRfAfIvQrphwS/iJ8GTmf4v+JHspTgl+Nvgfnh6ZBbCU6JPGPBJYo/YJQRfN39NeAfgYeA54Fn3P2Xo7zmAYJTJ23hKZovh099Evi2mb0AzCNoVzgQS4BPhq9PE0wI00kwmufPzGwRwV/hN4+yn28SBO0L4ffwzQLe+5Zw+zuBtwBPh5/tvwP/PNILx8A3gBvM7DGCI4gBV4cN3c8Dewj+rfdhZv9O0D5xjJm1m9lnh24j8dAoqSKHwIJpSn/twaToIiVPRwoiIjJIRwoiIjJIRwoiIjJIoSAiIoMUCiIiMkihICIigxQKIiIy6P8Da50mBE4vCc4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca = PCA(n_components = .999, svd_solver = 'full')\n",
    "pca.fit(X_trntst)\n",
    "plt.plot(pca.explained_variance_ratio_)\n",
    "plt.xlabel('Number of components minus 1')\n",
    "plt.ylabel('PC variance')\n",
    "plt.xticks(np.arange(0, len(X_trntst.columns), step=2))\n",
    "plt.axhline(y=0,xmin=0,xmax=len(X_trntst.columns))\n",
    "X_trntst = X_trntst_save.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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",
       "        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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.533139</td>\n",
       "      <td>0.129723</td>\n",
       "      <td>0.014924</td>\n",
       "      <td>0.112895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.436420</td>\n",
       "      <td>0.109263</td>\n",
       "      <td>0.007556</td>\n",
       "      <td>0.109120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.924754</td>\n",
       "      <td>0.001023</td>\n",
       "      <td>-0.031418</td>\n",
       "      <td>0.089151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.533139</td>\n",
       "      <td>0.129723</td>\n",
       "      <td>0.014924</td>\n",
       "      <td>0.112895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.532957</td>\n",
       "      <td>0.130397</td>\n",
       "      <td>0.014525</td>\n",
       "      <td>0.113179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2       PC3       PC4\n",
       "0 -1.533139  0.129723  0.014924  0.112895\n",
       "1 -1.436420  0.109263  0.007556  0.109120\n",
       "2 -0.924754  0.001023 -0.031418  0.089151\n",
       "3 -1.533139  0.129723  0.014924  0.112895\n",
       "4 -1.532957  0.130397  0.014525  0.113179"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We look at the above picture, select how many PCs we want to keep, and then redo the PCA with just this many PCs\n",
    "pca = PCA(n_components = 4, svd_solver = 'full')\n",
    "princ_comps = pca.fit_transform(X_trntst)\n",
    "X_trntst_pca = pd.DataFrame(princ_comps, columns = ['PC' + str(i) for i in range(1, pca.n_components_+1)])\n",
    "X_trntst_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>83970</th>\n",
       "      <td>-0.912542</td>\n",
       "      <td>-0.001561</td>\n",
       "      <td>-0.032348</td>\n",
       "      <td>0.088675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83971</th>\n",
       "      <td>-0.839153</td>\n",
       "      <td>0.052569</td>\n",
       "      <td>-0.009732</td>\n",
       "      <td>0.102327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83972</th>\n",
       "      <td>-0.765169</td>\n",
       "      <td>0.276187</td>\n",
       "      <td>0.410126</td>\n",
       "      <td>-0.256049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83973</th>\n",
       "      <td>-1.304582</td>\n",
       "      <td>0.219003</td>\n",
       "      <td>0.089263</td>\n",
       "      <td>0.097301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83974</th>\n",
       "      <td>-1.207672</td>\n",
       "      <td>0.060872</td>\n",
       "      <td>-0.009868</td>\n",
       "      <td>0.100193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            PC1       PC2       PC3       PC4\n",
       "83970 -0.912542 -0.001561 -0.032348  0.088675\n",
       "83971 -0.839153  0.052569 -0.009732  0.102327\n",
       "83972 -0.765169  0.276187  0.410126 -0.256049\n",
       "83973 -1.304582  0.219003  0.089263  0.097301\n",
       "83974 -1.207672  0.060872 -0.009868  0.100193"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "princ_comps = pca.transform(X_oot)\n",
    "X_oot_orig_pca = pd.DataFrame(princ_comps, columns = ['PC' + str(i) for i in range(1, pca.n_components_+1)],index=X_oot.index)\n",
    "X_oot_orig_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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>card_merch_total_14</th>\n",
       "      <th>card_zip3_max_14</th>\n",
       "      <th>card_merch_avg_14</th>\n",
       "      <th>Card_Merchdesc_Zip_med_14</th>\n",
       "      <th>Card_Merchdesc_Zip_max_60</th>\n",
       "      <th>Card_Merchnum_Zip_avg_60</th>\n",
       "      <th>Card_Merchnum_desc_avg_60</th>\n",
       "      <th>Merchdesc_dow_avg_30</th>\n",
       "      <th>Card_Merchnum_desc_med_0</th>\n",
       "      <th>Card_Merchdesc_max_60</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>83970.000000</td>\n",
       "      <td>83970.000000</td>\n",
       "      <td>83970.000000</td>\n",
       "      <td>83970.000000</td>\n",
       "      <td>83970.000000</td>\n",
       "      <td>83970.000000</td>\n",
       "      <td>83970.000000</td>\n",
       "      <td>83970.000000</td>\n",
       "      <td>83970.000000</td>\n",
       "      <td>83970.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.003015</td>\n",
       "      <td>0.001960</td>\n",
       "      <td>0.002997</td>\n",
       "      <td>0.002809</td>\n",
       "      <td>0.002998</td>\n",
       "      <td>0.002315</td>\n",
       "      <td>0.002318</td>\n",
       "      <td>0.002252</td>\n",
       "      <td>0.003263</td>\n",
       "      <td>0.002961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.058833</td>\n",
       "      <td>1.010735</td>\n",
       "      <td>0.999406</td>\n",
       "      <td>0.998680</td>\n",
       "      <td>1.017370</td>\n",
       "      <td>0.996389</td>\n",
       "      <td>0.996397</td>\n",
       "      <td>0.988724</td>\n",
       "      <td>0.999310</td>\n",
       "      <td>1.017333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.184721</td>\n",
       "      <td>-0.487170</td>\n",
       "      <td>-0.508483</td>\n",
       "      <td>-0.503680</td>\n",
       "      <td>-0.499185</td>\n",
       "      <td>-0.519696</td>\n",
       "      <td>-0.519563</td>\n",
       "      <td>-0.569480</td>\n",
       "      <td>-0.497685</td>\n",
       "      <td>-0.499807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.166286</td>\n",
       "      <td>-0.436791</td>\n",
       "      <td>-0.460066</td>\n",
       "      <td>-0.456017</td>\n",
       "      <td>-0.453920</td>\n",
       "      <td>-0.465389</td>\n",
       "      <td>-0.464977</td>\n",
       "      <td>-0.497610</td>\n",
       "      <td>-0.456238</td>\n",
       "      <td>-0.454551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.127625</td>\n",
       "      <td>-0.293058</td>\n",
       "      <td>-0.309824</td>\n",
       "      <td>-0.312231</td>\n",
       "      <td>-0.299171</td>\n",
       "      <td>-0.300694</td>\n",
       "      <td>-0.300629</td>\n",
       "      <td>-0.282209</td>\n",
       "      <td>-0.320664</td>\n",
       "      <td>-0.298847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.022093</td>\n",
       "      <td>0.074799</td>\n",
       "      <td>0.067220</td>\n",
       "      <td>0.058001</td>\n",
       "      <td>0.098431</td>\n",
       "      <td>0.090575</td>\n",
       "      <td>0.090640</td>\n",
       "      <td>0.119130</td>\n",
       "      <td>0.051436</td>\n",
       "      <td>0.098319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73.379967</td>\n",
       "      <td>43.596553</td>\n",
       "      <td>35.412456</td>\n",
       "      <td>35.741954</td>\n",
       "      <td>42.873917</td>\n",
       "      <td>35.959612</td>\n",
       "      <td>35.955667</td>\n",
       "      <td>40.214813</td>\n",
       "      <td>35.413449</td>\n",
       "      <td>42.864350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       card_merch_total_14  card_zip3_max_14  card_merch_avg_14  \\\n",
       "count         83970.000000      83970.000000       83970.000000   \n",
       "mean              0.003015          0.001960           0.002997   \n",
       "std               1.058833          1.010735           0.999406   \n",
       "min              -0.184721         -0.487170          -0.508483   \n",
       "25%              -0.166286         -0.436791          -0.460066   \n",
       "50%              -0.127625         -0.293058          -0.309824   \n",
       "75%              -0.022093          0.074799           0.067220   \n",
       "max              73.379967         43.596553          35.412456   \n",
       "\n",
       "       Card_Merchdesc_Zip_med_14  Card_Merchdesc_Zip_max_60  \\\n",
       "count               83970.000000               83970.000000   \n",
       "mean                    0.002809                   0.002998   \n",
       "std                     0.998680                   1.017370   \n",
       "min                    -0.503680                  -0.499185   \n",
       "25%                    -0.456017                  -0.453920   \n",
       "50%                    -0.312231                  -0.299171   \n",
       "75%                     0.058001                   0.098431   \n",
       "max                    35.741954                  42.873917   \n",
       "\n",
       "       Card_Merchnum_Zip_avg_60  Card_Merchnum_desc_avg_60  \\\n",
       "count              83970.000000               83970.000000   \n",
       "mean                   0.002315                   0.002318   \n",
       "std                    0.996389                   0.996397   \n",
       "min                   -0.519696                  -0.519563   \n",
       "25%                   -0.465389                  -0.464977   \n",
       "50%                   -0.300694                  -0.300629   \n",
       "75%                    0.090575                   0.090640   \n",
       "max                   35.959612                  35.955667   \n",
       "\n",
       "       Merchdesc_dow_avg_30  Card_Merchnum_desc_med_0  Card_Merchdesc_max_60  \n",
       "count          83970.000000              83970.000000           83970.000000  \n",
       "mean               0.002252                  0.003263               0.002961  \n",
       "std                0.988724                  0.999310               1.017333  \n",
       "min               -0.569480                 -0.497685              -0.499807  \n",
       "25%               -0.497610                 -0.456238              -0.454551  \n",
       "50%               -0.282209                 -0.320664              -0.298847  \n",
       "75%                0.119130                  0.051436               0.098319  \n",
       "max               40.214813                 35.413449              42.864350  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_trntst.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12427.000000</td>\n",
       "      <td>12427.000000</td>\n",
       "      <td>12427.000000</td>\n",
       "      <td>12427.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.065318</td>\n",
       "      <td>-0.007258</td>\n",
       "      <td>0.004024</td>\n",
       "      <td>-0.004344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.792001</td>\n",
       "      <td>0.646520</td>\n",
       "      <td>0.468131</td>\n",
       "      <td>0.387631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.545698</td>\n",
       "      <td>-19.899470</td>\n",
       "      <td>-9.303463</td>\n",
       "      <td>-14.058395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.293270</td>\n",
       "      <td>-0.103707</td>\n",
       "      <td>-0.097399</td>\n",
       "      <td>-0.052476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.821140</td>\n",
       "      <td>0.063737</td>\n",
       "      <td>-0.007941</td>\n",
       "      <td>0.071681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.193802</td>\n",
       "      <td>0.132513</td>\n",
       "      <td>0.045297</td>\n",
       "      <td>0.107818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>93.147610</td>\n",
       "      <td>11.290805</td>\n",
       "      <td>15.513500</td>\n",
       "      <td>8.803248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                PC1           PC2           PC3           PC4\n",
       "count  12427.000000  12427.000000  12427.000000  12427.000000\n",
       "mean      -0.065318     -0.007258      0.004024     -0.004344\n",
       "std        2.792001      0.646520      0.468131      0.387631\n",
       "min       -1.545698    -19.899470     -9.303463    -14.058395\n",
       "25%       -1.293270     -0.103707     -0.097399     -0.052476\n",
       "50%       -0.821140      0.063737     -0.007941      0.071681\n",
       "75%        0.193802      0.132513      0.045297      0.107818\n",
       "max       93.147610     11.290805     15.513500      8.803248"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_oot_orig_pca.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# zscale the PCs\n",
    "mean = X_trntst_pca.mean()\n",
    "stdev = X_trntst_pca.std()\n",
    "X_trntst_pca = (X_trntst_pca - mean)/stdev\n",
    "X_oot_orig_pca = (X_oot_orig_pca - mean)/stdev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.397000e+04</td>\n",
       "      <td>8.397000e+04</td>\n",
       "      <td>8.397000e+04</td>\n",
       "      <td>8.397000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.839709e-16</td>\n",
       "      <td>7.573468e-17</td>\n",
       "      <td>-2.471312e-16</td>\n",
       "      <td>5.637188e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-5.324738e-01</td>\n",
       "      <td>-1.960193e+01</td>\n",
       "      <td>-3.067537e+01</td>\n",
       "      <td>-3.690457e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-4.668320e-01</td>\n",
       "      <td>-9.418230e-02</td>\n",
       "      <td>-1.954183e-01</td>\n",
       "      <td>-1.200531e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-2.927865e-01</td>\n",
       "      <td>6.671806e-02</td>\n",
       "      <td>-1.164569e-02</td>\n",
       "      <td>1.825460e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.204663e-02</td>\n",
       "      <td>1.316668e-01</td>\n",
       "      <td>6.775808e-02</td>\n",
       "      <td>2.554804e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.736512e+01</td>\n",
       "      <td>6.119428e+01</td>\n",
       "      <td>3.728152e+01</td>\n",
       "      <td>3.968844e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                PC1           PC2           PC3           PC4\n",
       "count  8.397000e+04  8.397000e+04  8.397000e+04  8.397000e+04\n",
       "mean   2.839709e-16  7.573468e-17 -2.471312e-16  5.637188e-17\n",
       "std    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00\n",
       "min   -5.324738e-01 -1.960193e+01 -3.067537e+01 -3.690457e+01\n",
       "25%   -4.668320e-01 -9.418230e-02 -1.954183e-01 -1.200531e-01\n",
       "50%   -2.927865e-01  6.671806e-02 -1.164569e-02  1.825460e-01\n",
       "75%    9.204663e-02  1.316668e-01  6.775808e-02  2.554804e-01\n",
       "max    3.736512e+01  6.119428e+01  3.728152e+01  3.968844e+01"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_trntst_pca.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12427.000000</td>\n",
       "      <td>12427.000000</td>\n",
       "      <td>12427.000000</td>\n",
       "      <td>12427.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.022501</td>\n",
       "      <td>-0.006851</td>\n",
       "      <td>0.008091</td>\n",
       "      <td>-0.009948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.961810</td>\n",
       "      <td>0.610333</td>\n",
       "      <td>0.941264</td>\n",
       "      <td>0.887663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.532474</td>\n",
       "      <td>-18.785671</td>\n",
       "      <td>-18.706326</td>\n",
       "      <td>-32.193259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.445516</td>\n",
       "      <td>-0.097902</td>\n",
       "      <td>-0.195838</td>\n",
       "      <td>-0.120168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.282872</td>\n",
       "      <td>0.060170</td>\n",
       "      <td>-0.015967</td>\n",
       "      <td>0.164146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.066763</td>\n",
       "      <td>0.125096</td>\n",
       "      <td>0.091078</td>\n",
       "      <td>0.246899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>32.088195</td>\n",
       "      <td>10.658845</td>\n",
       "      <td>31.192750</td>\n",
       "      <td>20.159147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                PC1           PC2           PC3           PC4\n",
       "count  12427.000000  12427.000000  12427.000000  12427.000000\n",
       "mean      -0.022501     -0.006851      0.008091     -0.009948\n",
       "std        0.961810      0.610333      0.941264      0.887663\n",
       "min       -0.532474    -18.785671    -18.706326    -32.193259\n",
       "25%       -0.445516     -0.097902     -0.195838     -0.120168\n",
       "50%       -0.282872      0.060170     -0.015967      0.164146\n",
       "75%        0.066763      0.125096      0.091078      0.246899\n",
       "max       32.088195     10.658845     31.192750     20.159147"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_oot_orig_pca.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(83970, 4)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_trntst_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12427, 4)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_oot_orig_pca.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsample the larger class if desired"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.010479933309515303\n",
      "(1751, 10) 1751\n"
     ]
    }
   ],
   "source": [
    "# set the ratio of goods to bads that you would like. This next line is the ratio of goods to bads that you want for modeling\n",
    "sample_ratio_desired = 1\n",
    "\n",
    "temp = X_trntst.copy()\n",
    "temp['Fraud'] = Y_trntst['Fraud']\n",
    "temp.head()\n",
    "goods = temp[temp['Fraud']==0]\n",
    "bads = temp[temp['Fraud']==1]\n",
    "actual_bad_fraction = len(bads)/len(temp)\n",
    "actual_good_fraction = 1 - actual_bad_fraction\n",
    "print(actual_bad_fraction)\n",
    "fraction = sample_ratio_desired * actual_bad_fraction\n",
    "goods_sampled = goods.sample(frac = fraction)\n",
    "all_sampled = pd.concat([goods_sampled,bads])\n",
    "all_sampled.sort_index(inplace=True)\n",
    "Y_trntst_sampled = pd.DataFrame(all_sampled['Fraud'])\n",
    "X_trntst_sampled = all_sampled.drop(columns=['Fraud'])\n",
    "del [temp,goods,bads,all_sampled]\n",
    "gc.collect()\n",
    "print(X_trntst_sampled.shape,len(Y_trntst_sampled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "niter = 0\n",
    "nitermax = 10\n",
    "jittersize = .1\n",
    "X_oot_orig = X_oot.copy()\n",
    "pd.options.mode.chained_assignment = None  # default='warn'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can comment in/out any of these model cells and just explore one model type. You can also just rerun that single cell multiple times (hit shift-enter on that cell) as you manually explore different model hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "Modeling_output = pd.DataFrame(columns=['Model','Trn','Tst','OOT'],index=range(1000))\n",
    "counter = 0\n",
    "model_counter = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.6329113924050633 0.6411290322580645 0.5418994413407822\n",
      "1 0.6197411003236246 0.6068702290076335 0.44692737430167595\n",
      "2 0.635 0.6178571428571429 0.5586592178770949\n",
      "3 0.6477272727272727 0.6022727272727273 0.49162011173184356\n",
      "4 0.6220839813374806 0.6582278481012658 0.553072625698324\n",
      "5 0.6247960848287113 0.6441947565543071 0.5027932960893855\n",
      "6 0.6472545757071547 0.5734767025089605 0.5307262569832403\n",
      "7 0.6395348837209303 0.6007194244604317 0.547486033519553\n",
      "8 0.6270096463022508 0.6201550387596899 0.5083798882681564\n",
      "9 0.6116504854368932 0.6526717557251909 0.5083798882681564\n",
      "trn    0.630771\n",
      "tst    0.621757\n",
      "oot    0.518994\n",
      "dtype: float64\n",
      "CPU times: user 6.8 s, sys: 137 ms, total: 6.94 s\n",
      "Wall time: 1.77 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Logistic regression\n",
    "\n",
    "FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n",
    "for niter in range(nitermax):    \n",
    "    X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n",
    "\n",
    "    model = LogisticRegression()\n",
    " \n",
    "    X_oot = X_oot_orig.copy()\n",
    "    X_trn_save = X_trn.copy()\n",
    "    Y_trn_save = Y_trn.copy()\n",
    "\n",
    "    model.fit(X_trn, Y_trn.values.ravel())   \n",
    "\n",
    "    predictions = model.predict_proba(X_trn_save)[:,1]\n",
    "    X_trn['predicted'] = predictions\n",
    "    X_trn['Fraud'] = Y_trn_save['Fraud']\n",
    "    topRows = int(round(X_trn.shape[0]*0.03))\n",
    "    temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n",
    "\n",
    "    predictions = model.predict_proba(X_tst)[:,1]\n",
    "    X_tst['predicted']=predictions\n",
    "    X_tst['Fraud'] = Y_tst['Fraud']\n",
    "    topRows = int(round(X_tst.shape[0]*0.03))\n",
    "    temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n",
    "\n",
    "    predictions = model.predict_proba(X_oot)[:,1]\n",
    "    X_oot['predicted']=predictions\n",
    "    X_oot['Fraud'] = Y_oot['Fraud']\n",
    "    topRows = int(round(X_oot.shape[0]*0.03))\n",
    "    temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n",
    "    print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n",
    "    Modeling_output.iloc[counter] = ['log reg',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n",
    "    counter = counter + 1\n",
    "    \n",
    "print(FDR3.mean())\n",
    "model_counter = model_counter + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1.0 0.5864661654135338 0.29608938547486036\n",
      "1 1.0 0.6174242424242424 0.31843575418994413\n",
      "2 1.0 0.5795053003533569 0.21787709497206703\n",
      "3 1.0 0.5793650793650794 0.33519553072625696\n",
      "4 1.0 0.551094890510949 0.2737430167597765\n",
      "5 1.0 0.5605536332179931 0.2737430167597765\n",
      "6 1.0 0.6190476190476191 0.3407821229050279\n",
      "7 1.0 0.5902255639097744 0.3128491620111732\n",
      "8 1.0 0.536 0.35195530726256985\n",
      "9 1.0 0.6245353159851301 0.2737430167597765\n",
      "trn    1.000000\n",
      "tst    0.584422\n",
      "oot    0.299441\n",
      "dtype: float64\n",
      "CPU times: user 7.42 s, sys: 45.6 ms, total: 7.46 s\n",
      "Wall time: 6.92 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Single DT\n",
    "\n",
    "FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n",
    "for niter in range(nitermax):    \n",
    "    X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n",
    "\n",
    "    model = DecisionTreeClassifier()\n",
    " \n",
    "    X_oot = X_oot_orig.copy()\n",
    "    X_trn_save = X_trn.copy()\n",
    "    Y_trn_save = Y_trn.copy()\n",
    "\n",
    "    model.fit(X_trn, Y_trn.values.ravel())   \n",
    "\n",
    "    predictions = model.predict_proba(X_trn_save)[:,1]\n",
    "    X_trn['predicted'] = predictions\n",
    "    X_trn['Fraud'] = Y_trn_save['Fraud']\n",
    "    topRows = int(round(X_trn.shape[0]*0.03))\n",
    "    temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n",
    "\n",
    "    predictions = model.predict_proba(X_tst)[:,1]\n",
    "    X_tst['predicted']=predictions\n",
    "    X_tst['Fraud'] = Y_tst['Fraud']\n",
    "    topRows = int(round(X_tst.shape[0]*0.03))\n",
    "    temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n",
    "\n",
    "    predictions = model.predict_proba(X_oot)[:,1]\n",
    "    X_oot['predicted']=predictions\n",
    "    X_oot['Fraud'] = Y_oot['Fraud']\n",
    "    topRows = int(round(X_oot.shape[0]*0.03))\n",
    "    temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n",
    "    print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n",
    "    Modeling_output.iloc[counter] = ['DT',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n",
    "    counter = counter + 1\n",
    "\n",
    "print(FDR3.mean())\n",
    "model_counter = model_counter + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1.0 0.803030303030303 0.3687150837988827\n",
      "1 1.0 0.8142292490118577 0.4245810055865922\n",
      "2 1.0 0.8577405857740585 0.3687150837988827\n",
      "3 1.0 0.8482142857142857 0.4301675977653631\n",
      "4 1.0 0.8269896193771626 0.36312849162011174\n",
      "5 1.0 0.8201438848920863 0.35195530726256985\n",
      "6 1.0 0.8206106870229007 0.5195530726256983\n",
      "7 1.0 0.8127659574468085 0.5307262569832403\n",
      "8 1.0 0.8611111111111112 0.35195530726256985\n",
      "9 1.0 0.8414634146341463 0.41899441340782123\n",
      "trn    1.000000\n",
      "tst    0.830630\n",
      "oot    0.412849\n",
      "dtype: float64\n",
      "CPU times: user 2min 10s, sys: 617 ms, total: 2min 10s\n",
      "Wall time: 2min 11s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# RF\n",
    "\n",
    "FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n",
    "for niter in range(nitermax):    \n",
    "    X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n",
    "\n",
    "    model = RandomForestClassifier()\n",
    "    X_oot = X_oot_orig.copy()\n",
    "    X_trn_save = X_trn.copy()\n",
    "    Y_trn_save = Y_trn.copy()\n",
    "\n",
    "    model.fit(X_trn, Y_trn.values.ravel())   \n",
    "\n",
    "    predictions = model.predict_proba(X_trn_save)[:,1]\n",
    "    X_trn['predicted'] = predictions\n",
    "    X_trn['Fraud'] = Y_trn_save['Fraud']\n",
    "    topRows = int(round(X_trn.shape[0]*0.03))\n",
    "    temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n",
    "\n",
    "    predictions = model.predict_proba(X_tst)[:,1]\n",
    "    X_tst['predicted']=predictions\n",
    "    X_tst['Fraud'] = Y_tst['Fraud']\n",
    "    topRows = int(round(X_tst.shape[0]*0.03))\n",
    "    temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n",
    "\n",
    "    predictions = model.predict_proba(X_oot)[:,1]\n",
    "    X_oot['predicted']=predictions\n",
    "    X_oot['Fraud'] = Y_oot['Fraud']\n",
    "    topRows = int(round(X_oot.shape[0]*0.03))\n",
    "    temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n",
    "    print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n",
    "    Modeling_output.iloc[counter] = ['RF',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n",
    "    counter = counter + 1\n",
    "    \n",
    "print(FDR3.mean())\n",
    "model_counter = model_counter + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.9888712241653418 0.7649402390438247 0.3128491620111732\n",
      "1 0.9804241435562806 0.7865168539325843 0.3463687150837989\n",
      "2 0.9867768595041322 0.8 0.3128491620111732\n",
      "3 0.9841269841269841 0.808 0.3463687150837989\n",
      "4 0.9834437086092715 0.8043478260869565 0.3407821229050279\n",
      "5 0.9851485148514851 0.791970802919708 0.37988826815642457\n",
      "6 0.9886914378029079 0.7854406130268199 0.35195530726256985\n",
      "7 0.9839486356340289 0.7937743190661478 0.3463687150837989\n",
      "8 0.9826771653543307 0.7877551020408163 0.3463687150837989\n",
      "9 0.9773095623987034 0.7870722433460076 0.35195530726256985\n",
      "trn    0.984142\n",
      "tst    0.790982\n",
      "oot    0.343575\n",
      "dtype: float64\n",
      "CPU times: user 28.9 s, sys: 880 ms, total: 29.8 s\n",
      "Wall time: 4.09 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# LGBM\n",
    "\n",
    "FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n",
    "for niter in range(nitermax):    \n",
    "    X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n",
    "\n",
    "    model = lgb.LGBMClassifier()\n",
    "\n",
    "    X_oot = X_oot_orig.copy()\n",
    "    X_trn_save = X_trn.copy()\n",
    "    Y_trn_save = Y_trn.copy()\n",
    "\n",
    "    model.fit(X_trn, Y_trn.values.ravel())   \n",
    "\n",
    "    predictions = model.predict_proba(X_trn_save)[:,1]\n",
    "    X_trn['predicted'] = predictions\n",
    "    X_trn['Fraud'] = Y_trn_save['Fraud']\n",
    "    topRows = int(round(X_trn.shape[0]*0.03))\n",
    "    temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter,'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n",
    "\n",
    "    predictions = model.predict_proba(X_tst)[:,1]\n",
    "    X_tst['predicted']=predictions\n",
    "    X_tst['Fraud'] = Y_tst['Fraud']\n",
    "    topRows = int(round(X_tst.shape[0]*0.03))\n",
    "    temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter,'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n",
    "\n",
    "    predictions = model.predict_proba(X_oot)[:,1]\n",
    "    X_oot['predicted']=predictions\n",
    "    X_oot['Fraud'] = Y_oot['Fraud']\n",
    "    topRows = int(round(X_oot.shape[0]*0.03))\n",
    "    temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter,'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n",
    "    print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n",
    "    Modeling_output.iloc[counter] = ['LGBM',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n",
    "    counter = counter + 1\n",
    "    \n",
    "print(FDR3.mean())\n",
    "model_counter = model_counter + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 (58779, 10) (58779, 1)\n",
      "0 (116300, 10) (116300, 1)\n",
      "Fraud    629\n",
      "dtype: int64\n",
      "Fraud    58150\n",
      "dtype: int64\n",
      "0 0.9109697933227345 0.8207171314741036 0.31843575418994413\n",
      "1 (58779, 10) (58779, 1)\n",
      "1 (116280, 10) (116280, 1)\n",
      "Fraud    639\n",
      "dtype: int64\n",
      "Fraud    58140\n",
      "dtype: int64\n",
      "1 0.9280125195618153 0.7966804979253111 0.33519553072625696\n",
      "2 (58779, 10) (58779, 1)\n",
      "2 (116306, 10) (116306, 1)\n",
      "Fraud    626\n",
      "dtype: int64\n",
      "Fraud    58153\n",
      "dtype: int64\n",
      "2 0.9217252396166135 0.7992125984251969 0.3016759776536313\n",
      "3 (58779, 10) (58779, 1)\n",
      "3 (116312, 10) (116312, 1)\n",
      "Fraud    623\n",
      "dtype: int64\n",
      "Fraud    58156\n",
      "dtype: int64\n",
      "3 0.9293739967897271 0.8132295719844358 0.31843575418994413\n",
      "4 (58779, 10) (58779, 1)\n",
      "4 (116318, 10) (116318, 1)\n",
      "Fraud    620\n",
      "dtype: int64\n",
      "Fraud    58159\n",
      "dtype: int64\n",
      "4 0.9016129032258065 0.823076923076923 0.3407821229050279\n",
      "5 (58779, 10) (58779, 1)\n",
      "5 (116372, 10) (116372, 1)\n",
      "Fraud    593\n",
      "dtype: int64\n",
      "Fraud    58186\n",
      "dtype: int64\n",
      "5 0.924114671163575 0.7979094076655052 0.3128491620111732\n",
      "6 (58779, 10) (58779, 1)\n",
      "6 (116326, 10) (116326, 1)\n",
      "Fraud    616\n",
      "dtype: int64\n",
      "Fraud    58163\n",
      "dtype: int64\n",
      "6 0.9188311688311688 0.7954545454545454 0.3240223463687151\n",
      "7 (58779, 10) (58779, 1)\n",
      "7 (116296, 10) (116296, 1)\n",
      "Fraud    631\n",
      "dtype: int64\n",
      "Fraud    58148\n",
      "dtype: int64\n",
      "7 0.9239302694136292 0.8232931726907631 0.3240223463687151\n",
      "8 (58779, 10) (58779, 1)\n",
      "8 (116338, 10) (116338, 1)\n",
      "Fraud    610\n",
      "dtype: int64\n",
      "Fraud    58169\n",
      "dtype: int64\n",
      "8 0.9180327868852459 0.7666666666666667 0.30726256983240224\n",
      "9 (58779, 10) (58779, 1)\n",
      "9 (116334, 10) (116334, 1)\n",
      "Fraud    612\n",
      "dtype: int64\n",
      "Fraud    58167\n",
      "dtype: int64\n",
      "9 0.9035947712418301 0.8171641791044776 0.33519553072625696\n",
      "trn    0.918020\n",
      "tst    0.805340\n",
      "oot    0.321788\n",
      "dtype: float64\n",
      "CPU times: user 52.2 s, sys: 2.63 s, total: 54.8 s\n",
      "Wall time: 8.25 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# LGBM with SMOTE\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n",
    "for niter in range(nitermax):    \n",
    "    X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n",
    "    \n",
    "    sm = SMOTE()\n",
    "    \n",
    "    X_trn_sm, Y_trn_sm = sm.fit_resample(X_trn,Y_trn)\n",
    "    \n",
    "    print(niter, X_trn.shape,Y_trn.shape)\n",
    "    print(niter, X_trn_sm.shape,Y_trn_sm.shape)\n",
    "    print(Y_trn.sum())\n",
    "    print(Y_trn_sm.sum())\n",
    "\n",
    "    model = lgb.LGBMClassifier()\n",
    "    \n",
    "    X_oot = X_oot_orig.copy()\n",
    "    X_trn_save = X_trn.copy()\n",
    "    Y_trn_save = Y_trn.copy()\n",
    "\n",
    "    model.fit(X_trn_sm, Y_trn_sm.values.ravel())   \n",
    "\n",
    "    predictions = model.predict_proba(X_trn_save)[:,1]\n",
    "    X_trn['predicted'] = predictions\n",
    "    X_trn['Fraud'] = Y_trn_save['Fraud']\n",
    "    topRows = int(round(X_trn.shape[0]*0.03))\n",
    "    temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter,'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n",
    "\n",
    "    predictions = model.predict_proba(X_tst)[:,1]\n",
    "    X_tst['predicted']=predictions\n",
    "    X_tst['Fraud'] = Y_tst['Fraud']\n",
    "    topRows = int(round(X_tst.shape[0]*0.03))\n",
    "    temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter,'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n",
    "\n",
    "    predictions = model.predict_proba(X_oot)[:,1]\n",
    "    X_oot['predicted']=predictions\n",
    "    X_oot['Fraud'] = Y_oot['Fraud']\n",
    "    topRows = int(round(X_oot.shape[0]*0.03))\n",
    "    temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter,'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n",
    "    print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n",
    "    Modeling_output.iloc[counter] = ['LGBM with SMOTE',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n",
    "    counter = counter + 1\n",
    "    \n",
    "print(FDR3.mean())\n",
    "model_counter = model_counter + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# %%time\n",
    "# # LGBM with jitter\n",
    "\n",
    "# jittersize = .1\n",
    "# FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n",
    "# for niter in range(nitermax):    \n",
    "#     X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n",
    "    \n",
    "#     print(niter, X_trn.shape,Y_trn.shape)\n",
    "    \n",
    "#     X_trn_bads = X_trn[Y_trn == 1]\n",
    "#     Y_trn_bads = Y_trn[Y_trn == 1]\n",
    "    \n",
    "#     print(X_trn_bads.head())\n",
    "#     for i in range(2):\n",
    "#         X_trn_more = X_trn_bads*(1+jittersize*random.uniform(-1,1))\n",
    "#         X_trn = X_trn.append(X_trn_more,ignore_index=True)\n",
    "#         Y_trn = Y_trn.append(Y_trn_bads,ignore_index=True)\n",
    "        \n",
    "#     print(niter, X_trn.shape,Y_trn.shape)\n",
    "\n",
    "#     model = lgb.LGBMClassifier()\n",
    "\n",
    "#     X_oot = X_oot_orig.copy()\n",
    "#     X_trn_save = X_trn.copy()\n",
    "#     Y_trn_save = Y_trn.copy()\n",
    "\n",
    "#     model.fit(X_trn, Y_trn.values.ravel())   \n",
    "\n",
    "#     predictions = model.predict_proba(X_trn_save)[:,1]\n",
    "#     X_trn['predicted'] = predictions\n",
    "#     X_trn['Fraud'] = Y_trn_save['Fraud']\n",
    "#     topRows = int(round(X_trn.shape[0]*0.03))\n",
    "#     temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter,'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n",
    "\n",
    "#     predictions = model.predict_proba(X_tst)[:,1]\n",
    "#     X_tst['predicted']=predictions\n",
    "#     X_tst['Fraud'] = Y_tst['Fraud']\n",
    "#     topRows = int(round(X_tst.shape[0]*0.03))\n",
    "#     temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter,'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n",
    "\n",
    "#     predictions = model.predict_proba(X_oot)[:,1]\n",
    "#     X_oot['predicted']=predictions\n",
    "#     X_oot['Fraud'] = Y_oot['Fraud']\n",
    "#     topRows = int(round(X_oot.shape[0]*0.03))\n",
    "#     temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter,'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n",
    "#     print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n",
    "#     Modeling_output.iloc[counter] = ['LGBM with jitter',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n",
    "#     counter = counter + 1\n",
    "    \n",
    "# print(FDR3.mean())\n",
    "# model_counter = model_counter + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.7328990228013029 0.7255639097744361 0.44692737430167595\n",
      "1 0.7178683385579937 0.7396694214876033 0.5307262569832403\n",
      "2 0.7615262321144675 0.7091633466135459 0.40782122905027934\n",
      "3 0.7288961038961039 0.678030303030303 0.547486033519553\n",
      "4 0.7166392092257001 0.7106227106227107 0.39664804469273746\n",
      "5 0.7138047138047138 0.7027972027972028 0.39664804469273746\n",
      "6 0.7515527950310559 0.6949152542372882 0.3687150837988827\n",
      "7 0.7433774834437086 0.6956521739130435 0.39106145251396646\n",
      "8 0.7096247960848288 0.7153558052434457 0.43575418994413406\n",
      "9 0.7335526315789473 0.7169117647058824 0.4692737430167598\n",
      "trn    0.730974\n",
      "tst    0.708868\n",
      "oot    0.439106\n",
      "dtype: float64\n",
      "CPU times: user 9min 15s, sys: 8.27 s, total: 9min 23s\n",
      "Wall time: 2min 23s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# NN\n",
    "\n",
    "FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n",
    "for niter in range(nitermax):  \n",
    "    X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n",
    "\n",
    "    model = MLPClassifier()\n",
    "                          \n",
    "    X_oot = X_oot_orig.copy()\n",
    "    X_trn_save = X_trn.copy()\n",
    "    Y_trn_save = Y_trn.copy()\n",
    "\n",
    "    model.fit(X_trn, Y_trn.values.ravel())   \n",
    "\n",
    "    predictions = model.predict_proba(X_trn_save)[:,1]\n",
    "    X_trn['predicted'] = predictions\n",
    "    X_trn['Fraud'] = Y_trn_save['Fraud']\n",
    "    topRows = int(round(X_trn.shape[0]*0.03))\n",
    "    temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n",
    "\n",
    "    predictions = model.predict_proba(X_tst)[:,1]\n",
    "    X_tst['predicted']=predictions\n",
    "    X_tst['Fraud'] = Y_tst['Fraud']\n",
    "    topRows = int(round(X_tst.shape[0]*0.03))\n",
    "    temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n",
    "\n",
    "    predictions = model.predict_proba(X_oot)[:,1]\n",
    "    X_oot['predicted']=predictions\n",
    "    X_oot['Fraud'] = Y_oot['Fraud']\n",
    "    topRows = int(round(X_oot.shape[0]*0.03))\n",
    "    temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n",
    "    print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n",
    "    Modeling_output.iloc[counter] = ['NN',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n",
    "    counter = counter + 1\n",
    "    \n",
    "print(FDR3.mean())\n",
    "model_counter = model_counter + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# %%time\n",
    "# # NN on pc's\n",
    "\n",
    "# FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n",
    "# for niter in range(nitermax):  \n",
    "#     X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst_pca, Y_trntst, test_size = .3)\n",
    "\n",
    "#     model = MLPClassifier()\n",
    "\n",
    "#     X_oot = X_oot_orig_pca.copy()\n",
    "#     X_trn_save = X_trn.copy()\n",
    "#     Y_trn_save = Y_trn.copy()\n",
    "\n",
    "#     model.fit(X_trn, Y_trn.values.ravel())   \n",
    "\n",
    "#     predictions = model.predict_proba(X_trn_save)[:,1]\n",
    "#     X_trn['predicted'] = predictions\n",
    "#     X_trn['Fraud'] = Y_trn_save['Fraud']\n",
    "#     topRows = int(round(X_trn.shape[0]*0.03))\n",
    "#     temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n",
    "\n",
    "#     predictions = model.predict_proba(X_tst)[:,1]\n",
    "#     X_tst['predicted']=predictions\n",
    "#     X_tst['Fraud'] = Y_tst['Fraud']\n",
    "#     topRows = int(round(X_tst.shape[0]*0.03))\n",
    "#     temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n",
    "\n",
    "#     predictions = model.predict_proba(X_oot)[:,1]\n",
    "#     X_oot['predicted']=predictions\n",
    "#     X_oot['Fraud'] = Y_oot['Fraud']\n",
    "#     topRows = int(round(X_oot.shape[0]*0.03))\n",
    "#     temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n",
    "#     print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n",
    "#     Modeling_output.iloc[counter] = ['NN_PCs',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n",
    "#     counter = counter + 1\n",
    "    \n",
    "# print(FDR3.mean())\n",
    "# model_counter = model_counter + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# # GBC\n",
    "\n",
    "# FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n",
    "# for niter in range(nitermax):  \n",
    "#     X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n",
    "\n",
    "#     model = GradientBoostingClassifier()\n",
    "\n",
    "#     X_oot = X_oot_orig.copy()\n",
    "#     X_trn_save = X_trn.copy()\n",
    "#     Y_trn_save = Y_trn.copy()\n",
    "\n",
    "#     model.fit(X_trn, Y_trn.values.ravel())   \n",
    "\n",
    "#     predictions = model.predict_proba(X_trn_save)[:,1]\n",
    "#     X_trn['predicted'] = predictions\n",
    "#     X_trn['Fraud'] = Y_trn_save['Fraud']\n",
    "#     topRows = int(round(X_trn.shape[0]*0.03))\n",
    "#     temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n",
    "\n",
    "#     predictions = model.predict_proba(X_tst)[:,1]\n",
    "#     X_tst['predicted']=predictions\n",
    "#     X_tst['Fraud'] = Y_tst['Fraud']\n",
    "#     topRows = int(round(X_tst.shape[0]*0.03))\n",
    "#     temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n",
    "\n",
    "#     predictions = model.predict_proba(X_oot)[:,1]\n",
    "#     X_oot['predicted']=predictions\n",
    "#     X_oot['Fraud'] = Y_oot['Fraud']\n",
    "#     topRows = int(round(X_oot.shape[0]*0.03))\n",
    "#     temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n",
    "#     print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n",
    "#     Modeling_output.iloc[counter] = ['GBC',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n",
    "#     counter = counter + 1\n",
    "    \n",
    "# print(FDR3.mean())\n",
    "# model_counter = model_counter + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.9292763157894737 0.8088235294117647 0.3743016759776536\n",
      "1 0.9392 0.8352941176470589 0.3687150837988827\n",
      "2 0.9185303514376997 0.8464566929133859 0.3407821229050279\n",
      "3 0.9336650082918739 0.8050541516245487 0.3575418994413408\n",
      "4 0.9297385620915033 0.8059701492537313 0.4301675977653631\n",
      "5 0.9408866995073891 0.7749077490774908 0.3575418994413408\n",
      "6 0.9247135842880524 0.7955390334572491 0.3687150837988827\n",
      "7 0.9320388349514563 0.8015267175572519 0.3743016759776536\n",
      "8 0.9298531810766721 0.8089887640449438 0.4748603351955307\n",
      "9 0.927536231884058 0.8262548262548263 0.3743016759776536\n",
      "trn    0.930544\n",
      "tst    0.810882\n",
      "oot    0.382123\n",
      "dtype: float64\n",
      "CPU times: user 5min 53s, sys: 9.71 s, total: 6min 3s\n",
      "Wall time: 58.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Catboost\n",
    "\n",
    "FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n",
    "for niter in range(nitermax):  \n",
    "    X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n",
    "\n",
    "    model = CatBoostClassifier(verbose=0)\n",
    "\n",
    "    X_oot = X_oot_orig.copy()\n",
    "    X_trn_save = X_trn.copy()\n",
    "    Y_trn_save = Y_trn.copy()\n",
    "\n",
    "    model.fit(X_trn, Y_trn.values.ravel())   \n",
    "\n",
    "    predictions = model.predict_proba(X_trn_save)[:,1]\n",
    "    X_trn['predicted'] = predictions\n",
    "    X_trn['Fraud'] = Y_trn_save['Fraud']\n",
    "    topRows = int(round(X_trn.shape[0]*0.03))\n",
    "    temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n",
    "\n",
    "    predictions = model.predict_proba(X_tst)[:,1]\n",
    "    X_tst['predicted']=predictions\n",
    "    X_tst['Fraud'] = Y_tst['Fraud']\n",
    "    topRows = int(round(X_tst.shape[0]*0.03))\n",
    "    temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n",
    "\n",
    "    predictions = model.predict_proba(X_oot)[:,1]\n",
    "    X_oot['predicted']=predictions\n",
    "    X_oot['Fraud'] = Y_oot['Fraud']\n",
    "    topRows = int(round(X_oot.shape[0]*0.03))\n",
    "    temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n",
    "    print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n",
    "    Modeling_output.iloc[counter] = ['cat boost',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n",
    "    counter = counter + 1\n",
    "    \n",
    "print(FDR3.mean())\n",
    "model_counter = model_counter + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94.42045454545455\n",
      "0 0.8227272727272728 0.8227272727272728 0.4581005586592179\n",
      "1 0.8284090909090909 0.8284090909090909 0.5027932960893855\n",
      "2 0.8 0.8 0.5139664804469274\n",
      "3 0.8420454545454545 0.8420454545454545 0.3743016759776536\n",
      "4 0.8227272727272728 0.8227272727272728 0.4022346368715084\n",
      "5 0.8284090909090909 0.8284090909090909 0.39106145251396646\n",
      "6 0.8022727272727272 0.8022727272727272 0.4860335195530726\n",
      "7 0.8227272727272728 0.8227272727272728 0.36312849162011174\n",
      "8 0.8159090909090909 0.8159090909090909 0.3743016759776536\n",
      "9 0.8125 0.8125 0.4748603351955307\n",
      "trn    0.819773\n",
      "tst    0.819773\n",
      "oot    0.434078\n",
      "dtype: float64\n",
      "CPU times: user 40.5 s, sys: 901 ms, total: 41.4 s\n",
      "Wall time: 5.87 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# NOTE this cell has been substantially modified to evaluate a sampled trn/tst data set. \n",
    "# Only use this cell if you do downsampling of the goods.\n",
    "# each good needs to have a weight of (1-actual_ratio)/sample_ratio_desired\n",
    "# it's hard to get the correct FDR@3% for the actual train and test, so I just use the original trntst after the model is built for evaluation\n",
    "\n",
    "xmult = actual_good_fraction / (actual_bad_fraction * sample_ratio_desired)\n",
    "print(xmult)\n",
    "FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n",
    "for niter in range(nitermax):  \n",
    "    X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst_sampled, Y_trntst_sampled, test_size = .3)\n",
    "\n",
    "    model = lgb.LGBMClassifier()\n",
    "\n",
    "    model.fit(X_trn, Y_trn.values.ravel())  \n",
    "    \n",
    "    X_oot = X_oot_orig.copy()\n",
    "    X_trn = X_trntst.copy()\n",
    "    Y_trn = Y_trntst.copy()\n",
    "    X_tst = X_trntst.copy()\n",
    "    Y_tst = Y_trntst.copy()\n",
    "\n",
    "\n",
    "    predictions = model.predict_proba(X_trntst)[:,1]\n",
    "    X_trn['predicted'] = predictions\n",
    "    X_trn['Fraud'] = Y_trntst['Fraud']\n",
    "    topRows = int(round(X_trn.shape[0]*0.03))\n",
    "    temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n",
    "\n",
    "    predictions = model.predict_proba(X_trntst)[:,1]\n",
    "    X_tst['predicted']=predictions\n",
    "    X_tst['Fraud'] = Y_trntst['Fraud']\n",
    "    topRows = int(round(X_tst.shape[0]*0.03))\n",
    "    temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n",
    "\n",
    "    predictions = model.predict_proba(X_oot)[:,1]\n",
    "    X_oot['predicted']=predictions\n",
    "    X_oot['Fraud'] = Y_oot['Fraud']\n",
    "    topRows = int(round(X_oot.shape[0]*0.03))\n",
    "    temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n",
    "    print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n",
    "    Modeling_output.iloc[counter] = ['LGBM sampled',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n",
    "    counter = counter + 1\n",
    "    \n",
    "print(FDR3.mean())\n",
    "model_counter = model_counter + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# %%time\n",
    "# # Catboost on pc's\n",
    "\n",
    "# FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n",
    "# for niter in range(nitermax):  \n",
    "#     X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst_pca, Y_trntst, test_size = .3)\n",
    "\n",
    "#     model = CatBoostClassifier()\n",
    "\n",
    "#     X_oot = X_oot_orig_pca.copy()\n",
    "#     X_trn_save = X_trn.copy()\n",
    "#     Y_trn_save = Y_trn.copy()\n",
    "\n",
    "#     model.fit(X_trn, Y_trn.values.ravel())   \n",
    "\n",
    "#     predictions = model.predict_proba(X_trn_save)[:,1]\n",
    "#     X_trn['predicted'] = predictions\n",
    "#     X_trn['Fraud'] = Y_trn_save['Fraud']\n",
    "#     topRows = int(round(X_trn.shape[0]*0.03))\n",
    "#     temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n",
    "\n",
    "#     predictions = model.predict_proba(X_tst)[:,1]\n",
    "#     X_tst['predicted']=predictions\n",
    "#     X_tst['Fraud'] = Y_tst['Fraud']\n",
    "#     topRows = int(round(X_tst.shape[0]*0.03))\n",
    "#     temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n",
    "\n",
    "#     predictions = model.predict_proba(X_oot)[:,1]\n",
    "#     X_oot['predicted']=predictions\n",
    "#     X_oot['Fraud'] = Y_oot['Fraud']\n",
    "#     topRows = int(round(X_oot.shape[0]*0.03))\n",
    "#     temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n",
    "#     print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n",
    "#     Modeling_output.iloc[counter] = ['cat boost_PCs',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n",
    "#     counter = counter + 1\n",
    "    \n",
    "# print(FDR3.mean())\n",
    "# model_counter = model_counter + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# %%time\n",
    "# # unsupervised model using pc's. \n",
    "\n",
    "# FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n",
    "# for niter in range(nitermax):  \n",
    "#     X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst_pca, Y_trntst, test_size = .3)\n",
    "\n",
    "#     X_oot = X_oot_orig_pca.copy()\n",
    "#     X_trn_save = X_trn.copy()\n",
    "#     Y_trn_save = Y_trn.copy()\n",
    "\n",
    "#     pow = 2\n",
    "#     oop = 1/pow\n",
    "#     predictions = ((X_trn.abs()**pow).sum(axis=1))**oop\n",
    "#     X_trn['predicted'] = predictions\n",
    "#     X_trn['Fraud'] = Y_trn_save['Fraud']\n",
    "#     topRows = int(round(X_trn.shape[0]*0.03))\n",
    "#     temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n",
    "\n",
    "#     predictions = ((X_tst.abs()**pow).sum(axis=1))**oop\n",
    "#     X_tst['predicted']=predictions\n",
    "#     X_tst['Fraud'] = Y_tst['Fraud']\n",
    "#     topRows = int(round(X_tst.shape[0]*0.03))\n",
    "#     temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n",
    "\n",
    "#     predictions = ((X_oot.abs()**pow).sum(axis=1))**oop\n",
    "#     X_oot['predicted']=predictions\n",
    "#     X_oot['Fraud'] = Y_oot['Fraud']\n",
    "#     topRows = int(round(X_oot.shape[0]*0.03))\n",
    "#     temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n",
    "#     print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n",
    "#     Modeling_output.iloc[counter] = ['unsupervised outliers',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n",
    "#     counter = counter + 1\n",
    "    \n",
    "# print(FDR3.mean())\n",
    "# model_counter = model_counter + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# # XGB\n",
    "\n",
    "# FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n",
    "# for niter in range(nitermax):  \n",
    "#     X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n",
    "\n",
    "#     model = xgb.XGBClassifier()\n",
    "\n",
    "#     X_oot = X_oot_orig.copy()\n",
    "#     X_trn_save = X_trn.copy()\n",
    "#     Y_trn_save = Y_trn.copy()\n",
    "\n",
    "#     model.fit(X_trn, Y_trn.values.ravel())   \n",
    "\n",
    "#     predictions = model.predict_proba(X_trn_save)[:,1]\n",
    "#     X_trn['predicted'] = predictions\n",
    "#     X_trn['Fraud'] = Y_trn_save['Fraud']\n",
    "#     topRows = int(round(X_trn.shape[0]*0.03))\n",
    "#     temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n",
    "\n",
    "#     predictions = model.predict_proba(X_tst)[:,1]\n",
    "#     X_tst['predicted']=predictions\n",
    "#     X_tst['Fraud'] = Y_tst['Fraud']\n",
    "#     topRows = int(round(X_tst.shape[0]*0.03))\n",
    "#     temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n",
    "\n",
    "#     predictions = model.predict_proba(X_oot)[:,1]\n",
    "#     X_oot['predicted']=predictions\n",
    "#     X_oot['Fraud'] = Y_oot['Fraud']\n",
    "#     topRows = int(round(X_oot.shape[0]*0.03))\n",
    "#     temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n",
    "#     print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n",
    "#     Modeling_output.iloc[counter] = ['XGB',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n",
    "#     counter = counter + 1\n",
    "    \n",
    "# print(FDR3.mean())\n",
    "# model_counter = model_counter + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# # Knn\n",
    "# # Knn can be very slow with a lot of records.\n",
    "\n",
    "# FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n",
    "# for niter in range(nitermax):  \n",
    "#     X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n",
    "\n",
    "#     model = KNeighborsClassifier()\n",
    "    \n",
    "#     X_oot = X_oot_orig.copy()\n",
    "#     X_trn_save = X_trn.copy()\n",
    "#     Y_trn_save = Y_trn.copy()\n",
    "\n",
    "#     model.fit(X_trn, Y_trn.values.ravel())   \n",
    "\n",
    "#     predictions = model.predict_proba(X_trn_save)[:,1]\n",
    "#     X_trn['predicted'] = predictions\n",
    "#     X_trn['Fraud'] = Y_trn_save['Fraud']\n",
    "#     topRows = int(round(X_trn.shape[0]*0.03))\n",
    "#     temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n",
    "\n",
    "#     predictions = model.predict_proba(X_tst)[:,1]\n",
    "#     X_tst['predicted']=predictions\n",
    "#     X_tst['Fraud'] = Y_tst['Fraud']\n",
    "#     topRows = int(round(X_tst.shape[0]*0.03))\n",
    "#     temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n",
    "\n",
    "#     predictions = model.predict_proba(X_oot)[:,1]\n",
    "#     X_oot['predicted']=predictions\n",
    "#     X_oot['Fraud'] = Y_oot['Fraud']\n",
    "#     topRows = int(round(X_oot.shape[0]*0.03))\n",
    "#     temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n",
    "#     print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n",
    "#     Modeling_output.iloc[counter] = ['Knn',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n",
    "#     counter = counter + 1\n",
    "    \n",
    "# print(FDR3.mean())\n",
    "# model_counter = model_counter + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# # SVM\n",
    "# # SVM can be very slow. It scales like the # training records cubed\n",
    "\n",
    "# FDR3 = pd.DataFrame(np.zeros((nitermax,3)), columns=('trn', 'tst', 'oot'))\n",
    "# for niter in range(nitermax):  \n",
    "#     X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n",
    "\n",
    "#     model = svm.SVC()\n",
    "    \n",
    "#     X_oot = X_oot_orig.copy()\n",
    "#     X_trn_save = X_trn.copy()\n",
    "#     Y_trn_save = Y_trn.copy()\n",
    "\n",
    "#     model.fit(X_trn, Y_trn.values.ravel())   \n",
    "\n",
    "#     predictions = model.predict_proba(X_trn_save)[:,1]\n",
    "#     X_trn['predicted'] = predictions\n",
    "#     X_trn['Fraud'] = Y_trn_save['Fraud']\n",
    "#     topRows = int(round(X_trn.shape[0]*0.03))\n",
    "#     temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n",
    "\n",
    "#     predictions = model.predict_proba(X_tst)[:,1]\n",
    "#     X_tst['predicted']=predictions\n",
    "#     X_tst['Fraud'] = Y_tst['Fraud']\n",
    "#     topRows = int(round(X_tst.shape[0]*0.03))\n",
    "#     temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n",
    "\n",
    "#     predictions = model.predict_proba(X_oot)[:,1]\n",
    "#     X_oot['predicted']=predictions\n",
    "#     X_oot['Fraud'] = Y_oot['Fraud']\n",
    "#     topRows = int(round(X_oot.shape[0]*0.03))\n",
    "#     temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n",
    "#     needed = temp.loc[:,'Fraud']\n",
    "#     FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n",
    "#     print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n",
    "#     Modeling_output.iloc[counter] = ['SVM',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n",
    "#     counter = counter + 1\n",
    "    \n",
    "# print(FDR3.mean())\n",
    "# model_counter = model_counter + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model comparison plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "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>Model</th>\n",
       "      <th>Trn</th>\n",
       "      <th>Tst</th>\n",
       "      <th>OOT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>log reg</td>\n",
       "      <td>0.632911</td>\n",
       "      <td>0.641129</td>\n",
       "      <td>0.541899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>log reg</td>\n",
       "      <td>0.619741</td>\n",
       "      <td>0.60687</td>\n",
       "      <td>0.446927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>log reg</td>\n",
       "      <td>0.635</td>\n",
       "      <td>0.617857</td>\n",
       "      <td>0.558659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>log reg</td>\n",
       "      <td>0.647727</td>\n",
       "      <td>0.602273</td>\n",
       "      <td>0.49162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>log reg</td>\n",
       "      <td>0.622084</td>\n",
       "      <td>0.658228</td>\n",
       "      <td>0.553073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>log reg</td>\n",
       "      <td>0.624796</td>\n",
       "      <td>0.644195</td>\n",
       "      <td>0.502793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>log reg</td>\n",
       "      <td>0.647255</td>\n",
       "      <td>0.573477</td>\n",
       "      <td>0.530726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>log reg</td>\n",
       "      <td>0.639535</td>\n",
       "      <td>0.600719</td>\n",
       "      <td>0.547486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>log reg</td>\n",
       "      <td>0.62701</td>\n",
       "      <td>0.620155</td>\n",
       "      <td>0.50838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>log reg</td>\n",
       "      <td>0.61165</td>\n",
       "      <td>0.652672</td>\n",
       "      <td>0.50838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.586466</td>\n",
       "      <td>0.296089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.617424</td>\n",
       "      <td>0.318436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>DT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.579505</td>\n",
       "      <td>0.217877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.579365</td>\n",
       "      <td>0.335196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>DT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.551095</td>\n",
       "      <td>0.273743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>DT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.560554</td>\n",
       "      <td>0.273743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.340782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>DT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.590226</td>\n",
       "      <td>0.312849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>DT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.351955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>DT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.624535</td>\n",
       "      <td>0.273743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Model       Trn       Tst       OOT\n",
       "0   log reg  0.632911  0.641129  0.541899\n",
       "1   log reg  0.619741   0.60687  0.446927\n",
       "2   log reg     0.635  0.617857  0.558659\n",
       "3   log reg  0.647727  0.602273   0.49162\n",
       "4   log reg  0.622084  0.658228  0.553073\n",
       "5   log reg  0.624796  0.644195  0.502793\n",
       "6   log reg  0.647255  0.573477  0.530726\n",
       "7   log reg  0.639535  0.600719  0.547486\n",
       "8   log reg   0.62701  0.620155   0.50838\n",
       "9   log reg   0.61165  0.652672   0.50838\n",
       "10       DT       1.0  0.586466  0.296089\n",
       "11       DT       1.0  0.617424  0.318436\n",
       "12       DT       1.0  0.579505  0.217877\n",
       "13       DT       1.0  0.579365  0.335196\n",
       "14       DT       1.0  0.551095  0.273743\n",
       "15       DT       1.0  0.560554  0.273743\n",
       "16       DT       1.0  0.619048  0.340782\n",
       "17       DT       1.0  0.590226  0.312849\n",
       "18       DT       1.0     0.536  0.351955\n",
       "19       DT       1.0  0.624535  0.273743"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = Modeling_output.dropna()\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80, 4)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Type</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>log reg</td>\n",
       "      <td>Trn</td>\n",
       "      <td>0.632911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>log reg</td>\n",
       "      <td>Trn</td>\n",
       "      <td>0.619741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>log reg</td>\n",
       "      <td>Trn</td>\n",
       "      <td>0.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>log reg</td>\n",
       "      <td>Trn</td>\n",
       "      <td>0.647727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>log reg</td>\n",
       "      <td>Trn</td>\n",
       "      <td>0.622084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Model Type     Value\n",
       "0  log reg  Trn  0.632911\n",
       "1  log reg  Trn  0.619741\n",
       "2  log reg  Trn     0.635\n",
       "3  log reg  Trn  0.647727\n",
       "4  log reg  Trn  0.622084"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unpivot = df.melt( id_vars='Model', value_vars=['Trn','Tst','OOT'], var_name=['Type'], value_name='Value')\n",
    "df_unpivot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Type</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>log reg</td>\n",
       "      <td>Trn</td>\n",
       "      <td>0.632911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>log reg</td>\n",
       "      <td>Trn</td>\n",
       "      <td>0.619741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>log reg</td>\n",
       "      <td>Trn</td>\n",
       "      <td>0.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>log reg</td>\n",
       "      <td>Trn</td>\n",
       "      <td>0.647727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>log reg</td>\n",
       "      <td>Trn</td>\n",
       "      <td>0.622084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Model Type     Value\n",
       "0  log reg  Trn  0.632911\n",
       "1  log reg  Trn  0.619741\n",
       "2  log reg  Trn     0.635\n",
       "3  log reg  Trn  0.647727\n",
       "4  log reg  Trn  0.622084"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_compare = df_unpivot[(df_unpivot['Type']=='Trn') | (df_unpivot['Type']=='Tst') | (df_unpivot['Type']=='OOT')]\n",
    "df_compare.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Trn</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Tst</th>\n",
       "      <th colspan=\"2\" halign=\"left\">OOT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DT</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.584422</td>\n",
       "      <td>0.029833</td>\n",
       "      <td>0.299441</td>\n",
       "      <td>0.040731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGBM</th>\n",
       "      <td>0.984142</td>\n",
       "      <td>0.003562</td>\n",
       "      <td>0.790982</td>\n",
       "      <td>0.012078</td>\n",
       "      <td>0.343575</td>\n",
       "      <td>0.019397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGBM sampled</th>\n",
       "      <td>0.819773</td>\n",
       "      <td>0.012634</td>\n",
       "      <td>0.819773</td>\n",
       "      <td>0.012634</td>\n",
       "      <td>0.434078</td>\n",
       "      <td>0.058773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGBM with SMOTE</th>\n",
       "      <td>0.918020</td>\n",
       "      <td>0.009674</td>\n",
       "      <td>0.805340</td>\n",
       "      <td>0.017753</td>\n",
       "      <td>0.321788</td>\n",
       "      <td>0.012685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NN</th>\n",
       "      <td>0.730974</td>\n",
       "      <td>0.017170</td>\n",
       "      <td>0.708868</td>\n",
       "      <td>0.017303</td>\n",
       "      <td>0.439106</td>\n",
       "      <td>0.060526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.830630</td>\n",
       "      <td>0.020177</td>\n",
       "      <td>0.412849</td>\n",
       "      <td>0.066282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cat boost</th>\n",
       "      <td>0.930544</td>\n",
       "      <td>0.006532</td>\n",
       "      <td>0.810882</td>\n",
       "      <td>0.020496</td>\n",
       "      <td>0.382123</td>\n",
       "      <td>0.039957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log reg</th>\n",
       "      <td>0.630771</td>\n",
       "      <td>0.011884</td>\n",
       "      <td>0.621757</td>\n",
       "      <td>0.026992</td>\n",
       "      <td>0.518994</td>\n",
       "      <td>0.034383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Trn                 Tst                 OOT          \n",
       "                     mean       std      mean       std      mean       std\n",
       "Model                                                                      \n",
       "DT               1.000000  0.000000  0.584422  0.029833  0.299441  0.040731\n",
       "LGBM             0.984142  0.003562  0.790982  0.012078  0.343575  0.019397\n",
       "LGBM sampled     0.819773  0.012634  0.819773  0.012634  0.434078  0.058773\n",
       "LGBM with SMOTE  0.918020  0.009674  0.805340  0.017753  0.321788  0.012685\n",
       "NN               0.730974  0.017170  0.708868  0.017303  0.439106  0.060526\n",
       "RF               1.000000  0.000000  0.830630  0.020177  0.412849  0.066282\n",
       "cat boost        0.930544  0.006532  0.810882  0.020496  0.382123  0.039957\n",
       "log reg          0.630771  0.011884  0.621757  0.026992  0.518994  0.034383"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = df.groupby('Model').agg({'Trn':['mean','std'],'Tst':['mean','std'],'OOT':['mean','std']})\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(15,10))\n",
    "ax = sns.boxplot(x='Model',y='Value',hue='Type', data=df_compare, palette=['navy','r','g'])\n",
    "\n",
    "plt.ylim(.3,.9)\n",
    "plt.ylabel('Score (FDR3%)')\n",
    "plt.grid(axis='y')\n",
    "plt.savefig('modeling.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duration:  0:06:08.764642\n"
     ]
    }
   ],
   "source": [
    "print('duration: ', datetime.now() - start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.6261829652996845 0.6097560975609756 0.4301675977653631\n",
      "1 0.6266233766233766 0.6325757575757576 0.441340782122905\n",
      "2 0.022764227642276424 0.033962264150943396 0.055865921787709494\n",
      "3 0.6218905472636815 0.6064981949458483 0.5027932960893855\n",
      "4 0.04019292604501608 0.015503875968992248 0.055865921787709494\n",
      "5 0.0416 0.03137254901960784 0.061452513966480445\n",
      "6 0.038523274478330656 0.019455252918287938 0.061452513966480445\n",
      "7 0.6330128205128205 0.6015625 0.43575418994413406\n",
      "8 0.6377049180327868 0.5925925925925926 0.4581005586592179\n",
      "9 0.6261980830670927 0.5984251968503937 0.4245810055865922\n",
      "loop trn tst oot 1 0.39146931389650663 0.3741704281583399 0.2927374301675978\n",
      "0 0.7330097087378641 0.7519083969465649 0.4245810055865922\n",
      "1 0.7634069400630915 0.6666666666666666 0.3854748603351955\n",
      "2 0.7728026533996684 0.7184115523465704 0.3743016759776536\n",
      "3 0.7712 0.7450980392156863 0.5307262569832403\n",
      "4 0.750814332247557 0.7255639097744361 0.547486033519553\n",
      "5 0.7666666666666667 0.7214285714285714 0.35195530726256985\n",
      "6 0.7193548387096774 0.6923076923076923 0.5251396648044693\n",
      "7 0.7088815789473685 0.7058823529411765 0.5363128491620112\n",
      "8 0.7483974358974359 0.71875 0.5251396648044693\n",
      "9 0.7775919732441472 0.7163120567375887 0.3854748603351955\n",
      "loop trn tst oot 21 0.7512126127913478 0.7162329238364953 0.45865921787709485\n",
      "0 0.7852459016393443 0.774074074074074 0.37988826815642457\n",
      "1 0.7710437710437711 0.7342657342657343 0.3743016759776536\n",
      "2 0.774247491638796 0.7163120567375887 0.3575418994413408\n",
      "3 0.8019323671497585 0.7258687258687259 0.37988826815642457\n",
      "4 0.785234899328859 0.7394366197183099 0.3687150837988827\n",
      "5 0.8183361629881154 0.718213058419244 0.3463687150837989\n",
      "6 0.7882736156351792 0.7180451127819549 0.3463687150837989\n",
      "7 0.7916666666666666 0.7535714285714286 0.3575418994413408\n",
      "8 0.815359477124183 0.7126865671641791 0.3407821229050279\n",
      "9 0.7632450331125827 0.7681159420289855 0.4245810055865922\n",
      "loop trn tst oot 41 0.7894585386327255 0.7360589319630224 0.3675977653631285\n",
      "0 0.8279742765273312 0.7674418604651163 0.31843575418994413\n",
      "1 0.8357963875205254 0.7859778597785978 0.36312849162011174\n",
      "2 0.7903494176372712 0.7777777777777778 0.35195530726256985\n",
      "3 0.8327974276527331 0.7364341085271318 0.33519553072625696\n",
      "4 0.8564437194127243 0.7528089887640449 0.3463687150837989\n",
      "5 0.8243021346469622 0.7416974169741697 0.3463687150837989\n",
      "6 0.8294314381270903 0.74822695035461 0.329608938547486\n",
      "7 0.8258706467661692 0.7653429602888087 0.35195530726256985\n",
      "8 0.8363047001620746 0.7566539923954373 0.3240223463687151\n",
      "9 0.819935691318328 0.7868217054263565 0.33519553072625696\n",
      "loop trn tst oot 61 0.827920583977121 0.761918362075205 0.3402234636871508\n",
      "0 0.7987421383647799 0.7827868852459017 0.36312849162011174\n",
      "1 0.8246205733558178 0.7700348432055749 0.33519553072625696\n",
      "2 0.8801996672212978 0.7383512544802867 0.29608938547486036\n",
      "3 0.8164251207729468 0.749034749034749 0.3743016759776536\n",
      "4 0.8680781758957655 0.7443609022556391 0.22346368715083798\n",
      "5 0.8070175438596491 0.7351778656126482 0.3743016759776536\n",
      "6 0.8811410459587956 0.7269076305220884 0.33519553072625696\n",
      "7 0.815831987075929 0.7931034482758621 0.3407821229050279\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stevecoggeshall/opt/anaconda3/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 0.8879598662207357 0.7553191489361702 0.30726256983240224\n",
      "9 0.8022328548644339 0.766798418972332 0.3687150837988827\n",
      "loop trn tst oot 81 0.8382248973590152 0.7561875146541253 0.33184357541899445\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1h 18min 28s, sys: 1min 1s, total: 1h 19min 30s\n",
      "Wall time: 20min 33s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "training = []\n",
    "testing = []\n",
    "oot = []\n",
    "results = pd.DataFrame(np.zeros((niter,3)),columns=['trn','tst','oot'])\n",
    "for i in range(1,101,20):\n",
    "    for niter in range(nitermax):\n",
    "        X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n",
    "\n",
    "        model = MLPClassifier(hidden_layer_sizes=(i,i))\n",
    "\n",
    "        model.fit(X_trn, Y_trn.values.ravel()) \n",
    "\n",
    "        X_oot = X_oot_orig.copy()\n",
    "        X_trn_save = X_trn.copy()\n",
    "        Y_trn_save = Y_trn.copy()\n",
    "    \n",
    "        predictions = model.predict_proba(X_trn_save)[:,1]\n",
    "        X_trn['predicted'] = predictions\n",
    "        X_trn['Fraud'] = Y_trn_save['Fraud']\n",
    "        topRows = int(round(X_trn.shape[0]*0.03))\n",
    "        temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n",
    "        needed = temp.loc[:,'Fraud']\n",
    "        results.loc[niter,'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n",
    "\n",
    "        predictions = model.predict_proba(X_tst)[:,1]\n",
    "        X_tst['predicted']=predictions\n",
    "        X_tst['Fraud'] = Y_tst['Fraud']\n",
    "        topRows = int(round(X_tst.shape[0]*0.03))\n",
    "        temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n",
    "        needed = temp.loc[:,'Fraud']\n",
    "        results.loc[niter,'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n",
    "\n",
    "        predictions = model.predict_proba(X_oot)[:,1]\n",
    "        X_oot['predicted']=predictions\n",
    "        X_oot['Fraud'] = Y_oot['Fraud']\n",
    "        topRows = int(round(X_oot.shape[0]*0.03))\n",
    "        temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n",
    "        needed = temp.loc[:,'Fraud']\n",
    "        results.loc[niter,'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n",
    "        print(niter, results.loc[niter,'trn'],results.loc[niter,'tst'],results.loc[niter,'oot'],)\n",
    "\n",
    "    results_mean_trn = results['trn'].mean()\n",
    "    results_mean_tst = results['tst'].mean()\n",
    "    results_mean_oot = results['oot'].mean()\n",
    "    print('loop', 'trn', 'tst', 'oot', i, results_mean_trn, results_mean_tst, results_mean_oot)\n",
    "    training.append(results_mean_trn)\n",
    "    testing.append(results_mean_tst)\n",
    "    oot.append(results_mean_oot)\n",
    "\n",
    "table=pd.DataFrame({'n': range(1,len(training)+1),'training':training,'testing':testing,'oot':oot})\n",
    "table.set_index('n',inplace=True) \n",
    "table.plot()\n",
    "plt.savefig('complexity_NN.pdf', format='pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rest of the notebook makes the tables for your final model of choice. You need to run that final model only once (no CV). If you want you can run the below cell over and over by itself until it gives you a model you like (due to the stochastic nature of some ML algorithms, but you can't change from your best hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.7129629629629629 0.7025862068965517 0.39664804469273746\n",
      "1 0.7255216693418941 0.7198443579766537 0.5586592178770949\n",
      "CPU times: user 1min 27s, sys: 1.57 s, total: 1min 29s\n",
      "Wall time: 23.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "for niter in range(100):    \n",
    "    X_trn, X_tst, Y_trn, Y_tst = train_test_split(X_trntst, Y_trntst, test_size = .3)\n",
    "\n",
    "# here's where you put your final model of choice\n",
    "# I run this loop a large number of times with an unreasonably high stopping criterion (the break condition)\n",
    "# I then look at all these runs and select a value of the oot performance where I want to break out this loop\n",
    "# and that will be my final model run of choice\n",
    "\n",
    "    model = MLPClassifier()\n",
    "\n",
    "    X_oot = X_oot_orig.copy()\n",
    "    X_trn_save = X_trn.copy()\n",
    "    Y_trn_save = Y_trn.copy()\n",
    "\n",
    "    model.fit(X_trn, Y_trn.values.ravel())   \n",
    "\n",
    "    predictions = model.predict_proba(X_trn_save)[:,1]\n",
    "    X_trn['predicted'] = predictions\n",
    "    X_trn['Fraud'] = Y_trn_save['Fraud']\n",
    "    topRows = int(round(X_trn.shape[0]*0.03))\n",
    "    temp = X_trn.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'trn'] = sum(needed)/sum(X_trn.loc[:,'Fraud'])\n",
    "\n",
    "    predictions = model.predict_proba(X_tst)[:,1]\n",
    "    X_tst['predicted']=predictions\n",
    "    X_tst['Fraud'] = Y_tst['Fraud']\n",
    "    topRows = int(round(X_tst.shape[0]*0.03))\n",
    "    temp = X_tst.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'tst'] = sum(needed)/sum(X_tst.loc[:,'Fraud'])\n",
    "\n",
    "    predictions = model.predict_proba(X_oot)[:,1]\n",
    "    X_oot['predicted']=predictions\n",
    "    X_oot['Fraud'] = Y_oot['Fraud']\n",
    "    topRows = int(round(X_oot.shape[0]*0.03))\n",
    "    temp = X_oot.sort_values('predicted',ascending=False).head(topRows)\n",
    "    needed = temp.loc[:,'Fraud']\n",
    "    FDR3.loc[niter, 'oot'] = sum(needed)/sum(X_oot.loc[:,'Fraud'])\n",
    "    print(niter, FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot'])\n",
    "    Modeling_output.iloc[counter] = ['LGBM',FDR3.loc[niter, 'trn'],FDR3.loc[niter, 'tst'],FDR3.loc[niter, 'oot']]\n",
    "    counter = counter + 1\n",
    "# choose a good break point\n",
    "    if(FDR3.loc[niter, 'oot'] > .55): break\n",
    "    \n",
    "model_counter = model_counter + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trn_eval = X_trn.copy()\n",
    "X_tst_eval = X_tst.copy()\n",
    "X_oot_eval = X_oot.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
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       "        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>card_merch_total_14</th>\n",
       "      <th>card_zip3_max_14</th>\n",
       "      <th>card_merch_avg_14</th>\n",
       "      <th>Card_Merchdesc_Zip_med_14</th>\n",
       "      <th>Card_Merchdesc_Zip_max_60</th>\n",
       "      <th>Card_Merchnum_Zip_avg_60</th>\n",
       "      <th>Card_Merchnum_desc_avg_60</th>\n",
       "      <th>Merchdesc_dow_avg_30</th>\n",
       "      <th>Card_Merchnum_desc_med_0</th>\n",
       "      <th>Card_Merchdesc_max_60</th>\n",
       "      <th>predicted</th>\n",
       "      <th>Fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>89206</th>\n",
       "      <td>-0.119305</td>\n",
       "      <td>23.262218</td>\n",
       "      <td>-0.163517</td>\n",
       "      <td>-0.155595</td>\n",
       "      <td>-0.252285</td>\n",
       "      <td>-0.169368</td>\n",
       "      <td>-0.169273</td>\n",
       "      <td>-0.142688</td>\n",
       "      <td>-0.152813</td>\n",
       "      <td>-0.252958</td>\n",
       "      <td>0.999926</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90369</th>\n",
       "      <td>1.283442</td>\n",
       "      <td>23.262218</td>\n",
       "      <td>7.233701</td>\n",
       "      <td>7.308487</td>\n",
       "      <td>5.042087</td>\n",
       "      <td>7.342835</td>\n",
       "      <td>7.342089</td>\n",
       "      <td>8.220921</td>\n",
       "      <td>7.242385</td>\n",
       "      <td>5.040323</td>\n",
       "      <td>0.999913</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89186</th>\n",
       "      <td>9.768838</td>\n",
       "      <td>7.148446</td>\n",
       "      <td>1.131783</td>\n",
       "      <td>0.742253</td>\n",
       "      <td>7.013345</td>\n",
       "      <td>1.146067</td>\n",
       "      <td>1.382985</td>\n",
       "      <td>1.557548</td>\n",
       "      <td>0.095302</td>\n",
       "      <td>7.011174</td>\n",
       "      <td>0.999303</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89183</th>\n",
       "      <td>9.722223</td>\n",
       "      <td>7.148446</td>\n",
       "      <td>1.176766</td>\n",
       "      <td>0.793514</td>\n",
       "      <td>7.013345</td>\n",
       "      <td>1.191749</td>\n",
       "      <td>1.454852</td>\n",
       "      <td>1.742342</td>\n",
       "      <td>0.144831</td>\n",
       "      <td>7.011174</td>\n",
       "      <td>0.999245</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89174</th>\n",
       "      <td>9.315610</td>\n",
       "      <td>7.148446</td>\n",
       "      <td>1.161467</td>\n",
       "      <td>0.742253</td>\n",
       "      <td>7.013345</td>\n",
       "      <td>1.176212</td>\n",
       "      <td>1.445630</td>\n",
       "      <td>1.728709</td>\n",
       "      <td>0.095302</td>\n",
       "      <td>7.011174</td>\n",
       "      <td>0.998893</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89130</th>\n",
       "      <td>9.112811</td>\n",
       "      <td>7.148446</td>\n",
       "      <td>1.242556</td>\n",
       "      <td>0.793514</td>\n",
       "      <td>7.013345</td>\n",
       "      <td>1.258561</td>\n",
       "      <td>1.505739</td>\n",
       "      <td>1.917739</td>\n",
       "      <td>0.060318</td>\n",
       "      <td>7.011174</td>\n",
       "      <td>0.998456</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89121</th>\n",
       "      <td>6.919649</td>\n",
       "      <td>2.844300</td>\n",
       "      <td>0.990067</td>\n",
       "      <td>0.793514</td>\n",
       "      <td>2.778581</td>\n",
       "      <td>1.002148</td>\n",
       "      <td>1.126262</td>\n",
       "      <td>0.648317</td>\n",
       "      <td>0.005451</td>\n",
       "      <td>2.777283</td>\n",
       "      <td>0.983969</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89128</th>\n",
       "      <td>7.032132</td>\n",
       "      <td>2.844300</td>\n",
       "      <td>0.955244</td>\n",
       "      <td>0.742253</td>\n",
       "      <td>2.778581</td>\n",
       "      <td>0.966783</td>\n",
       "      <td>1.074085</td>\n",
       "      <td>0.570554</td>\n",
       "      <td>0.025333</td>\n",
       "      <td>2.777283</td>\n",
       "      <td>0.983460</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89120</th>\n",
       "      <td>6.526073</td>\n",
       "      <td>2.844300</td>\n",
       "      <td>0.966029</td>\n",
       "      <td>0.742253</td>\n",
       "      <td>2.778581</td>\n",
       "      <td>0.977736</td>\n",
       "      <td>1.100613</td>\n",
       "      <td>0.420585</td>\n",
       "      <td>-0.014432</td>\n",
       "      <td>2.777283</td>\n",
       "      <td>0.982486</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89117</th>\n",
       "      <td>6.440982</td>\n",
       "      <td>2.844300</td>\n",
       "      <td>1.010629</td>\n",
       "      <td>0.793514</td>\n",
       "      <td>2.778581</td>\n",
       "      <td>1.023029</td>\n",
       "      <td>1.169116</td>\n",
       "      <td>0.540736</td>\n",
       "      <td>0.005451</td>\n",
       "      <td>2.777283</td>\n",
       "      <td>0.982083</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87173</th>\n",
       "      <td>0.559000</td>\n",
       "      <td>10.016530</td>\n",
       "      <td>3.413442</td>\n",
       "      <td>3.453696</td>\n",
       "      <td>2.307834</td>\n",
       "      <td>3.463193</td>\n",
       "      <td>3.462881</td>\n",
       "      <td>3.883436</td>\n",
       "      <td>3.423169</td>\n",
       "      <td>2.306633</td>\n",
       "      <td>0.979089</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89114</th>\n",
       "      <td>6.341771</td>\n",
       "      <td>2.844300</td>\n",
       "      <td>1.055899</td>\n",
       "      <td>0.893042</td>\n",
       "      <td>2.778581</td>\n",
       "      <td>1.069004</td>\n",
       "      <td>1.241456</td>\n",
       "      <td>0.712811</td>\n",
       "      <td>-0.014432</td>\n",
       "      <td>2.777283</td>\n",
       "      <td>0.978847</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89083</th>\n",
       "      <td>5.388373</td>\n",
       "      <td>2.844300</td>\n",
       "      <td>1.038297</td>\n",
       "      <td>0.793514</td>\n",
       "      <td>2.778581</td>\n",
       "      <td>1.051127</td>\n",
       "      <td>1.179062</td>\n",
       "      <td>-0.153813</td>\n",
       "      <td>-0.131685</td>\n",
       "      <td>2.777283</td>\n",
       "      <td>0.954761</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89077</th>\n",
       "      <td>5.249519</td>\n",
       "      <td>2.844300</td>\n",
       "      <td>1.177200</td>\n",
       "      <td>0.992571</td>\n",
       "      <td>2.778581</td>\n",
       "      <td>1.192190</td>\n",
       "      <td>1.383197</td>\n",
       "      <td>1.558065</td>\n",
       "      <td>0.984757</td>\n",
       "      <td>2.777283</td>\n",
       "      <td>0.941451</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89082</th>\n",
       "      <td>5.296705</td>\n",
       "      <td>2.844300</td>\n",
       "      <td>1.097374</td>\n",
       "      <td>0.893042</td>\n",
       "      <td>2.778581</td>\n",
       "      <td>1.111123</td>\n",
       "      <td>1.265332</td>\n",
       "      <td>-0.286978</td>\n",
       "      <td>-0.248938</td>\n",
       "      <td>2.777283</td>\n",
       "      <td>0.925749</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89074</th>\n",
       "      <td>5.006737</td>\n",
       "      <td>2.844300</td>\n",
       "      <td>1.316607</td>\n",
       "      <td>1.020113</td>\n",
       "      <td>2.778581</td>\n",
       "      <td>1.333764</td>\n",
       "      <td>1.485234</td>\n",
       "      <td>1.672156</td>\n",
       "      <td>1.012045</td>\n",
       "      <td>2.777283</td>\n",
       "      <td>0.924850</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89068</th>\n",
       "      <td>4.294732</td>\n",
       "      <td>2.844300</td>\n",
       "      <td>1.178781</td>\n",
       "      <td>0.992571</td>\n",
       "      <td>2.778581</td>\n",
       "      <td>1.193795</td>\n",
       "      <td>1.320888</td>\n",
       "      <td>1.488396</td>\n",
       "      <td>0.984757</td>\n",
       "      <td>2.777283</td>\n",
       "      <td>0.873528</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89060</th>\n",
       "      <td>3.537990</td>\n",
       "      <td>2.844300</td>\n",
       "      <td>1.001603</td>\n",
       "      <td>0.893042</td>\n",
       "      <td>2.778581</td>\n",
       "      <td>1.013864</td>\n",
       "      <td>1.099718</td>\n",
       "      <td>1.241097</td>\n",
       "      <td>0.886147</td>\n",
       "      <td>2.777283</td>\n",
       "      <td>0.795388</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89057</th>\n",
       "      <td>3.313470</td>\n",
       "      <td>2.844300</td>\n",
       "      <td>1.028780</td>\n",
       "      <td>0.992571</td>\n",
       "      <td>2.778581</td>\n",
       "      <td>1.041462</td>\n",
       "      <td>1.146056</td>\n",
       "      <td>1.292910</td>\n",
       "      <td>0.984757</td>\n",
       "      <td>2.777283</td>\n",
       "      <td>0.721146</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86673</th>\n",
       "      <td>1.019346</td>\n",
       "      <td>4.131784</td>\n",
       "      <td>5.841020</td>\n",
       "      <td>5.903218</td>\n",
       "      <td>4.045312</td>\n",
       "      <td>5.928506</td>\n",
       "      <td>5.927919</td>\n",
       "      <td>1.219521</td>\n",
       "      <td>5.850085</td>\n",
       "      <td>4.043752</td>\n",
       "      <td>0.688786</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       card_merch_total_14  card_zip3_max_14  card_merch_avg_14  \\\n",
       "89206            -0.119305         23.262218          -0.163517   \n",
       "90369             1.283442         23.262218           7.233701   \n",
       "89186             9.768838          7.148446           1.131783   \n",
       "89183             9.722223          7.148446           1.176766   \n",
       "89174             9.315610          7.148446           1.161467   \n",
       "89130             9.112811          7.148446           1.242556   \n",
       "89121             6.919649          2.844300           0.990067   \n",
       "89128             7.032132          2.844300           0.955244   \n",
       "89120             6.526073          2.844300           0.966029   \n",
       "89117             6.440982          2.844300           1.010629   \n",
       "87173             0.559000         10.016530           3.413442   \n",
       "89114             6.341771          2.844300           1.055899   \n",
       "89083             5.388373          2.844300           1.038297   \n",
       "89077             5.249519          2.844300           1.177200   \n",
       "89082             5.296705          2.844300           1.097374   \n",
       "89074             5.006737          2.844300           1.316607   \n",
       "89068             4.294732          2.844300           1.178781   \n",
       "89060             3.537990          2.844300           1.001603   \n",
       "89057             3.313470          2.844300           1.028780   \n",
       "86673             1.019346          4.131784           5.841020   \n",
       "\n",
       "       Card_Merchdesc_Zip_med_14  Card_Merchdesc_Zip_max_60  \\\n",
       "89206                  -0.155595                  -0.252285   \n",
       "90369                   7.308487                   5.042087   \n",
       "89186                   0.742253                   7.013345   \n",
       "89183                   0.793514                   7.013345   \n",
       "89174                   0.742253                   7.013345   \n",
       "89130                   0.793514                   7.013345   \n",
       "89121                   0.793514                   2.778581   \n",
       "89128                   0.742253                   2.778581   \n",
       "89120                   0.742253                   2.778581   \n",
       "89117                   0.793514                   2.778581   \n",
       "87173                   3.453696                   2.307834   \n",
       "89114                   0.893042                   2.778581   \n",
       "89083                   0.793514                   2.778581   \n",
       "89077                   0.992571                   2.778581   \n",
       "89082                   0.893042                   2.778581   \n",
       "89074                   1.020113                   2.778581   \n",
       "89068                   0.992571                   2.778581   \n",
       "89060                   0.893042                   2.778581   \n",
       "89057                   0.992571                   2.778581   \n",
       "86673                   5.903218                   4.045312   \n",
       "\n",
       "       Card_Merchnum_Zip_avg_60  Card_Merchnum_desc_avg_60  \\\n",
       "89206                 -0.169368                  -0.169273   \n",
       "90369                  7.342835                   7.342089   \n",
       "89186                  1.146067                   1.382985   \n",
       "89183                  1.191749                   1.454852   \n",
       "89174                  1.176212                   1.445630   \n",
       "89130                  1.258561                   1.505739   \n",
       "89121                  1.002148                   1.126262   \n",
       "89128                  0.966783                   1.074085   \n",
       "89120                  0.977736                   1.100613   \n",
       "89117                  1.023029                   1.169116   \n",
       "87173                  3.463193                   3.462881   \n",
       "89114                  1.069004                   1.241456   \n",
       "89083                  1.051127                   1.179062   \n",
       "89077                  1.192190                   1.383197   \n",
       "89082                  1.111123                   1.265332   \n",
       "89074                  1.333764                   1.485234   \n",
       "89068                  1.193795                   1.320888   \n",
       "89060                  1.013864                   1.099718   \n",
       "89057                  1.041462                   1.146056   \n",
       "86673                  5.928506                   5.927919   \n",
       "\n",
       "       Merchdesc_dow_avg_30  Card_Merchnum_desc_med_0  Card_Merchdesc_max_60  \\\n",
       "89206             -0.142688                 -0.152813              -0.252958   \n",
       "90369              8.220921                  7.242385               5.040323   \n",
       "89186              1.557548                  0.095302               7.011174   \n",
       "89183              1.742342                  0.144831               7.011174   \n",
       "89174              1.728709                  0.095302               7.011174   \n",
       "89130              1.917739                  0.060318               7.011174   \n",
       "89121              0.648317                  0.005451               2.777283   \n",
       "89128              0.570554                  0.025333               2.777283   \n",
       "89120              0.420585                 -0.014432               2.777283   \n",
       "89117              0.540736                  0.005451               2.777283   \n",
       "87173              3.883436                  3.423169               2.306633   \n",
       "89114              0.712811                 -0.014432               2.777283   \n",
       "89083             -0.153813                 -0.131685               2.777283   \n",
       "89077              1.558065                  0.984757               2.777283   \n",
       "89082             -0.286978                 -0.248938               2.777283   \n",
       "89074              1.672156                  1.012045               2.777283   \n",
       "89068              1.488396                  0.984757               2.777283   \n",
       "89060              1.241097                  0.886147               2.777283   \n",
       "89057              1.292910                  0.984757               2.777283   \n",
       "86673              1.219521                  5.850085               4.043752   \n",
       "\n",
       "       predicted  Fraud  \n",
       "89206   0.999926      1  \n",
       "90369   0.999913      1  \n",
       "89186   0.999303      1  \n",
       "89183   0.999245      1  \n",
       "89174   0.998893      1  \n",
       "89130   0.998456      1  \n",
       "89121   0.983969      1  \n",
       "89128   0.983460      1  \n",
       "89120   0.982486      1  \n",
       "89117   0.982083      1  \n",
       "87173   0.979089      0  \n",
       "89114   0.978847      1  \n",
       "89083   0.954761      1  \n",
       "89077   0.941451      1  \n",
       "89082   0.925749      1  \n",
       "89074   0.924850      1  \n",
       "89068   0.873528      1  \n",
       "89060   0.795388      1  \n",
       "89057   0.721146      1  \n",
       "86673   0.688786      0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['bin','#recs','#g','#b','%g','%b','tot','cg','cb','%cg','FDR','KS','FPR']\n",
    "FDR_trn = pd.DataFrame(np.zeros((101, 13)), columns = cols)\n",
    "FDR_tst = pd.DataFrame(np.zeros((101, 13)), columns = cols)\n",
    "FDR_oot = pd.DataFrame(np.zeros((101, 13)), columns = cols)\n",
    "trn_sorted = X_trn_eval.sort_values('predicted',ascending=False)\n",
    "tst_sorted = X_tst_eval.sort_values('predicted',ascending=False)\n",
    "oot_sorted = X_oot_eval.sort_values('predicted',ascending=False)\n",
    "bad_tot_trn = sum(X_trn_eval.loc[:, 'Fraud'])\n",
    "bad_tot_tst = sum(X_tst_eval.loc[:, 'Fraud'])\n",
    "bad_tot_oot = sum(X_oot_eval.loc[:, 'Fraud'])\n",
    "num_tot_trn = len(X_trn_eval)\n",
    "num_tot_tst = len(X_tst_eval)\n",
    "num_tot_oot = len(X_oot_eval)\n",
    "good_tot_trn = num_tot_trn - bad_tot_trn\n",
    "good_tot_tst = num_tot_tst - bad_tot_tst\n",
    "good_tot_oot = num_tot_oot - bad_tot_oot\n",
    "oot_sorted.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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",
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       "        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>bin</th>\n",
       "      <th>#recs</th>\n",
       "      <th>#g</th>\n",
       "      <th>#b</th>\n",
       "      <th>%g</th>\n",
       "      <th>%b</th>\n",
       "      <th>tot</th>\n",
       "      <th>cg</th>\n",
       "      <th>cb</th>\n",
       "      <th>%cg</th>\n",
       "      <th>FDR</th>\n",
       "      <th>KS</th>\n",
       "      <th>FPR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>51.612903</td>\n",
       "      <td>48.387097</td>\n",
       "      <td>124.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.522534</td>\n",
       "      <td>33.519553</td>\n",
       "      <td>32.997019</td>\n",
       "      <td>1.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>85.600000</td>\n",
       "      <td>14.400000</td>\n",
       "      <td>249.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.396146</td>\n",
       "      <td>43.575419</td>\n",
       "      <td>42.179273</td>\n",
       "      <td>2.192308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>82.258065</td>\n",
       "      <td>17.741935</td>\n",
       "      <td>373.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.228935</td>\n",
       "      <td>55.865922</td>\n",
       "      <td>53.636986</td>\n",
       "      <td>2.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.193548</td>\n",
       "      <td>0.806452</td>\n",
       "      <td>497.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>3.233181</td>\n",
       "      <td>56.424581</td>\n",
       "      <td>53.191400</td>\n",
       "      <td>3.920792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>96.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>97.580645</td>\n",
       "      <td>2.419355</td>\n",
       "      <td>11930.0</td>\n",
       "      <td>11752.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>95.950359</td>\n",
       "      <td>99.441341</td>\n",
       "      <td>3.490982</td>\n",
       "      <td>66.022472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>97.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.193548</td>\n",
       "      <td>0.806452</td>\n",
       "      <td>12054.0</td>\n",
       "      <td>11875.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>96.954605</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3.045395</td>\n",
       "      <td>66.340782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>98.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12178.0</td>\n",
       "      <td>11999.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>97.967015</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>2.032985</td>\n",
       "      <td>67.033520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>99.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12303.0</td>\n",
       "      <td>12124.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>98.987590</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.012410</td>\n",
       "      <td>67.731844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>100.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12427.0</td>\n",
       "      <td>12248.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.424581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bin  #recs     #g    #b          %g         %b      tot       cg  \\\n",
       "0      0.0    0.0    0.0   0.0    0.000000   0.000000      0.0      0.0   \n",
       "1      1.0  124.0   64.0  60.0   51.612903  48.387097    124.0     64.0   \n",
       "2      2.0  125.0  107.0  18.0   85.600000  14.400000    249.0    171.0   \n",
       "3      3.0  124.0  102.0  22.0   82.258065  17.741935    373.0    273.0   \n",
       "4      4.0  124.0  123.0   1.0   99.193548   0.806452    497.0    396.0   \n",
       "..     ...    ...    ...   ...         ...        ...      ...      ...   \n",
       "96    96.0  124.0  121.0   3.0   97.580645   2.419355  11930.0  11752.0   \n",
       "97    97.0  124.0  123.0   1.0   99.193548   0.806452  12054.0  11875.0   \n",
       "98    98.0  124.0  124.0   0.0  100.000000   0.000000  12178.0  11999.0   \n",
       "99    99.0  125.0  125.0   0.0  100.000000   0.000000  12303.0  12124.0   \n",
       "100  100.0  124.0  124.0   0.0  100.000000   0.000000  12427.0  12248.0   \n",
       "\n",
       "        cb         %cg         FDR         KS        FPR  \n",
       "0      0.0    0.000000    0.000000   0.000000   0.000000  \n",
       "1     60.0    0.522534   33.519553  32.997019   1.066667  \n",
       "2     78.0    1.396146   43.575419  42.179273   2.192308  \n",
       "3    100.0    2.228935   55.865922  53.636986   2.730000  \n",
       "4    101.0    3.233181   56.424581  53.191400   3.920792  \n",
       "..     ...         ...         ...        ...        ...  \n",
       "96   178.0   95.950359   99.441341   3.490982  66.022472  \n",
       "97   179.0   96.954605  100.000000   3.045395  66.340782  \n",
       "98   179.0   97.967015  100.000000   2.032985  67.033520  \n",
       "99   179.0   98.987590  100.000000   1.012410  67.731844  \n",
       "100  179.0  100.000000  100.000000   0.000000  68.424581  \n",
       "\n",
       "[101 rows x 13 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost_fraud = 1000\n",
    "cost_fp = 30\n",
    "for i in range(101):\n",
    "    percent_rows_trn = int(round(X_trn_eval.shape[0]*0.01*i))\n",
    "    percent_rows_tst = int(round(X_tst_eval.shape[0]*0.01*i))\n",
    "    percent_rows_oot = int(round(X_oot_eval.shape[0]*0.01*i))\n",
    "    temp_trn = trn_sorted.head(percent_rows_trn)\n",
    "    temp_tst = tst_sorted.head(percent_rows_tst)\n",
    "    temp_oot = oot_sorted.head(percent_rows_oot)\n",
    "    num_bad_trn = sum(temp_trn.loc[:,'Fraud'])\n",
    "    num_bad_tst = sum(temp_tst.loc[:,'Fraud'])\n",
    "    num_bad_oot = sum(temp_oot.loc[:,'Fraud'])\n",
    "    num_tot_trn = len(temp_trn)\n",
    "    num_tot_tst = len(temp_tst)\n",
    "    num_tot_oot = len(temp_oot)\n",
    "    num_good_trn = num_tot_trn - num_bad_trn\n",
    "    num_good_tst = num_tot_tst - num_bad_tst\n",
    "    num_good_oot = num_tot_oot - num_bad_oot\n",
    "    \n",
    "    FDR_trn.loc[i, 'bin'] = i\n",
    "    FDR_trn.loc[i,'#recs'] = 0\n",
    "    FDR_trn.loc[i, 'tot'] = num_tot_trn\n",
    "    FDR_trn.loc[i, 'cg'] = num_good_trn\n",
    "    FDR_trn.loc[i, 'cb'] = num_bad_trn\n",
    "    FDR_trn.loc[i, 'Fraud Savings'] = FDR_trn.loc[i, 'cb'] * cost_fraud\n",
    "    FDR_trn.loc[i, 'FP Loss'] = FDR_trn.loc[i, 'cg'] * cost_fp\n",
    "    FDR_trn.loc[i, 'Overall Savings'] = FDR_trn.loc[i, 'Fraud Savings'] - FDR_trn.loc[i, 'FP Loss']\n",
    "    FDR_tst.loc[i, 'bin'] = i\n",
    "    FDR_tst.loc[i, 'tot'] = num_tot_tst\n",
    "    FDR_tst.loc[i, 'cg'] = num_good_tst\n",
    "    FDR_tst.loc[i, 'cb'] = num_bad_tst\n",
    "    FDR_tst.loc[i, 'Fraud Savings'] = FDR_tst.loc[i, 'cb'] * cost_fraud\n",
    "    FDR_tst.loc[i, 'FP Loss'] = FDR_tst.loc[i, 'cg'] * cost_fp\n",
    "    FDR_tst.loc[i, 'Overall Savings'] = FDR_tst.loc[i, 'Fraud Savings'] - FDR_tst.loc[i, 'FP Loss']\n",
    "    FDR_oot.loc[i, 'bin'] = i\n",
    "    FDR_oot.loc[i, 'tot'] = num_tot_oot\n",
    "    FDR_oot.loc[i, 'cg'] = num_good_oot\n",
    "    FDR_oot.loc[i, 'cb'] = num_bad_oot\n",
    "\n",
    "    if i != 0:\n",
    "        FDR_trn.loc[i, '#g'] = num_good_trn - FDR_trn.loc[i-1, 'cg']\n",
    "        FDR_trn.loc[i, '#b'] = num_bad_trn - FDR_trn.loc[i-1, 'cb']\n",
    "        FDR_trn.loc[i,'#recs'] = FDR_trn.loc[i, '#g'] + FDR_trn.loc[i, '#b']\n",
    "        FDR_trn.loc[i, '%g'] = 100* (num_good_trn - FDR_trn.loc[i-1, 'cg']) / (num_tot_trn - FDR_trn.loc[i-1, 'tot'])\n",
    "        FDR_trn.loc[i, '%b'] = 100 - FDR_trn.loc[i, '%g']\n",
    "        FDR_trn.loc[i, '%cg'] = 100 * num_good_trn / good_tot_trn\n",
    "        FDR_trn.loc[i, 'FDR'] = 100 * num_bad_trn / bad_tot_trn\n",
    "        FDR_trn.loc[i, 'KS'] = FDR_trn.loc[i, 'FDR'] - FDR_trn.loc[i, '%cg']\n",
    "        FDR_trn.loc[i, 'FPR'] = num_good_trn / num_bad_trn\n",
    "        FDR_tst.loc[i, '#g'] = num_good_tst - FDR_tst.loc[i-1, 'cg']\n",
    "        FDR_tst.loc[i, '#b'] = num_bad_tst - FDR_tst.loc[i-1, 'cb']\n",
    "        FDR_tst.loc[i,'#recs'] = FDR_tst.loc[i, '#g'] + FDR_tst.loc[i, '#b']\n",
    "        FDR_tst.loc[i, '%g'] = 100* (num_good_tst - FDR_tst.loc[i-1, 'cg']) / (num_tot_tst - FDR_tst.loc[i-1, 'tot'])\n",
    "        FDR_tst.loc[i, '%b'] = 100 - FDR_tst.loc[i, '%g']\n",
    "        FDR_tst.loc[i, '%cg'] = 100 * num_good_tst / good_tot_tst\n",
    "        FDR_tst.loc[i, 'FDR'] = 100 * num_bad_tst / bad_tot_tst\n",
    "        FDR_tst.loc[i, 'KS'] = FDR_tst.loc[i, 'FDR'] - FDR_tst.loc[i, '%cg']\n",
    "        FDR_tst.loc[i, 'FPR'] = num_good_tst / num_bad_tst\n",
    "        FDR_oot.loc[i, '#g'] = num_good_oot - FDR_oot.loc[i-1, 'cg']\n",
    "        FDR_oot.loc[i, '#b'] = num_bad_oot - FDR_oot.loc[i-1, 'cb']\n",
    "        FDR_oot.loc[i,'#recs'] = FDR_oot.loc[i, '#g'] + FDR_oot.loc[i, '#b']\n",
    "        FDR_oot.loc[i, '%g'] = 100* (num_good_oot - FDR_oot.loc[i-1, 'cg']) / (num_tot_oot - FDR_oot.loc[i-1, 'tot'])\n",
    "        FDR_oot.loc[i, '%b'] = 100 - FDR_oot.loc[i, '%g']\n",
    "        FDR_oot.loc[i, '%cg'] = 100 * num_good_oot / good_tot_oot\n",
    "        FDR_oot.loc[i, 'FDR'] = 100 * num_bad_oot / bad_tot_oot\n",
    "        FDR_oot.loc[i, 'KS'] = FDR_oot.loc[i, 'FDR'] - FDR_oot.loc[i, '%cg']\n",
    "        FDR_oot.loc[i, 'FPR'] = num_good_oot / num_bad_oot\n",
    "\n",
    "FDR_oot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max possible savings: 20,724,000.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cost_fraud = 400\n",
    "cost_fp = 20\n",
    "# xmult: oot is only 2 out of 12 months. 100,000 sample transactions out of 10 million/year\n",
    "xoot = 12/2 * 10000000/100000\n",
    "Financials_trn = pd.DataFrame(np.zeros((101, 3)), columns = ['Fraud Savings','FP Loss','Overall Savings'])\n",
    "Financials_tst = pd.DataFrame(np.zeros((101, 3)), columns = ['Fraud Savings','FP Loss','Overall Savings'])\n",
    "Financials_oot = pd.DataFrame(np.zeros((101, 3)), columns = ['Fraud Savings','FP Loss','Overall Savings'])\n",
    "for i in range(101):\n",
    "    Financials_trn.loc[i, 'Fraud Savings'] = FDR_trn.loc[i, 'cb'] * cost_fraud * xoot\n",
    "    Financials_trn.loc[i, 'FP Loss'] = FDR_trn.loc[i, 'cg'] * cost_fp * xoot\n",
    "    Financials_trn.loc[i, 'Overall Savings'] = Financials_trn.loc[i, 'Fraud Savings'] - Financials_trn.loc[i, 'FP Loss']\n",
    "    Financials_tst.loc[i, 'Fraud Savings'] = FDR_tst.loc[i, 'cb'] * cost_fraud * xoot\n",
    "    Financials_tst.loc[i, 'FP Loss'] = FDR_tst.loc[i, 'cg'] * cost_fp * xoot\n",
    "    Financials_tst.loc[i, 'Overall Savings'] = Financials_tst.loc[i, 'Fraud Savings'] - Financials_tst.loc[i, 'FP Loss']\n",
    "    Financials_oot.loc[i, 'Fraud Savings'] = FDR_oot.loc[i, 'cb'] * cost_fraud * xoot\n",
    "    Financials_oot.loc[i, 'FP Loss'] = FDR_oot.loc[i, 'cg'] * cost_fp * xoot\n",
    "    Financials_oot.loc[i, 'Overall Savings'] = Financials_oot.loc[i, 'Fraud Savings'] - Financials_oot.loc[i, 'FP Loss']\n",
    "\n",
    "max_savings = Financials_oot['Overall Savings'].max(0)\n",
    "print('Max possible savings: '+'{:,}'.format(max_savings))\n",
    "yupper = max_savings * 1.5\n",
    "plt.rcParams.update({'font.size':20})\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(Financials_oot['Fraud Savings'], color='green')\n",
    "plt.plot(Financials_oot['FP Loss'], color='red')\n",
    "plt.plot(Financials_oot['Overall Savings'], color='blue')\n",
    "plt.xlim(0,30)\n",
    "plt.ylim(0,yupper)\n",
    "plt.ticklabel_format(style='plain')\n",
    "plt.savefig('savings.png', format='png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "FDR3.to_csv('FDR3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "FDR_trn.to_csv('FDR_trn.csv', index=False)\n",
    "FDR_tst.to_csv('FDR_tst.csv', index=False)\n",
    "FDR_oot.to_csv('FDR_oot.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duration:  0:27:07.295743\n"
     ]
    }
   ],
   "source": [
    "print(\"duration: \", datetime.now() - start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/stevecoggeshall/Documents/Teaching/Data sets/done/transactions/transactions 2023 new/give to students'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 191 ms, sys: 46.5 ms, total: 237 ms\n",
      "Wall time: 239 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(96397, 10)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df = pd.read_csv('card transactions.csv')\n",
    "df.dropna(how='all', axis=1, inplace=True)\n",
    "df = df[df['Transtype'] == 'P']\n",
    "df = df[df['Amount'] <= 3000000]\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['prediction'] = model.predict_proba(X)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_scale = 1/df['prediction'].max()\n",
    "df['prediction'] = df['prediction']*pred_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>Recnum</th>\n",
       "      <th>Cardnum</th>\n",
       "      <th>Date</th>\n",
       "      <th>Merchnum</th>\n",
       "      <th>Merch description</th>\n",
       "      <th>Merch state</th>\n",
       "      <th>Merch zip</th>\n",
       "      <th>Transtype</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Fraud</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5142190439</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEX SHP 12/23/09 AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5142183973</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>61003026333</td>\n",
       "      <td>SERVICE MERCHANDISE #81</td>\n",
       "      <td>MA</td>\n",
       "      <td>1803.0</td>\n",
       "      <td>P</td>\n",
       "      <td>31.42</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5142131721</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>4503082993600</td>\n",
       "      <td>OFFICE DEPOT #191</td>\n",
       "      <td>MD</td>\n",
       "      <td>20706.0</td>\n",
       "      <td>P</td>\n",
       "      <td>178.49</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5142148452</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEX SHP 12/28/09 AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5142190439</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5509006296254</td>\n",
       "      <td>FEDEX SHP 12/23/09 AB#</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Recnum     Cardnum       Date       Merchnum        Merch description  \\\n",
       "0       1  5142190439 2010-01-01  5509006296254   FEDEX SHP 12/23/09 AB#   \n",
       "1       2  5142183973 2010-01-01    61003026333  SERVICE MERCHANDISE #81   \n",
       "2       3  5142131721 2010-01-01  4503082993600        OFFICE DEPOT #191   \n",
       "3       4  5142148452 2010-01-01  5509006296254   FEDEX SHP 12/28/09 AB#   \n",
       "4       5  5142190439 2010-01-01  5509006296254   FEDEX SHP 12/23/09 AB#   \n",
       "\n",
       "  Merch state  Merch zip Transtype  Amount  Fraud  prediction  \n",
       "0          TN    38118.0         P    3.62      0    0.000873  \n",
       "1          MA     1803.0         P   31.42      0    0.000876  \n",
       "2          MD    20706.0         P  178.49      0    0.000897  \n",
       "3          TN    38118.0         P    3.62      0    0.000873  \n",
       "4          TN    38118.0         P    3.62      0    0.000886  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 96397 entries, 0 to 96752\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   Recnum             96397 non-null  int64         \n",
      " 1   Cardnum            96397 non-null  int64         \n",
      " 2   Date               96397 non-null  datetime64[ns]\n",
      " 3   Merchnum           93199 non-null  object        \n",
      " 4   Merch description  96397 non-null  object        \n",
      " 5   Merch state        95377 non-null  object        \n",
      " 6   Merch zip          92097 non-null  float64       \n",
      " 7   Transtype          96397 non-null  object        \n",
      " 8   Amount             96397 non-null  float64       \n",
      " 9   Fraud              96397 non-null  int64         \n",
      " 10  prediction         96397 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(3), int64(3), object(4)\n",
      "memory usage: 8.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recnum</th>\n",
       "      <th>Cardnum</th>\n",
       "      <th>Date</th>\n",
       "      <th>Merchnum</th>\n",
       "      <th>Merch description</th>\n",
       "      <th>Merch state</th>\n",
       "      <th>Merch zip</th>\n",
       "      <th>Transtype</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Fraud</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60047</th>\n",
       "      <td>60048</td>\n",
       "      <td>5142138135</td>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>900009045549</td>\n",
       "      <td>KRACKELER SCIENTIFIC</td>\n",
       "      <td>NY</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>P</td>\n",
       "      <td>1664.87</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60031</th>\n",
       "      <td>60032</td>\n",
       "      <td>5142138135</td>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>900009045549</td>\n",
       "      <td>KRACKELER SCIENTIFIC</td>\n",
       "      <td>NY</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>P</td>\n",
       "      <td>24778.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60027</th>\n",
       "      <td>60028</td>\n",
       "      <td>5142138135</td>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>900009045549</td>\n",
       "      <td>KRACKELER SCIENTIFIC</td>\n",
       "      <td>NY</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3058.56</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60004</th>\n",
       "      <td>60005</td>\n",
       "      <td>5142138135</td>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>900009045549</td>\n",
       "      <td>KRACKELER SCIENTIFIC</td>\n",
       "      <td>NY</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>P</td>\n",
       "      <td>173.19</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59998</th>\n",
       "      <td>59999</td>\n",
       "      <td>5142138135</td>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>900009045549</td>\n",
       "      <td>KRACKELER SCIENTIFIC</td>\n",
       "      <td>NY</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>P</td>\n",
       "      <td>9117.34</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59988</th>\n",
       "      <td>59989</td>\n",
       "      <td>5142138135</td>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>900009045549</td>\n",
       "      <td>KRACKELER SCIENTIFIC</td>\n",
       "      <td>NY</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3437.71</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59933</th>\n",
       "      <td>59934</td>\n",
       "      <td>5142138135</td>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>900009045549</td>\n",
       "      <td>KRACKELER SCIENTIFIC</td>\n",
       "      <td>NY</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>P</td>\n",
       "      <td>1941.07</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59917</th>\n",
       "      <td>59918</td>\n",
       "      <td>5142138135</td>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>900009045549</td>\n",
       "      <td>KRACKELER SCIENTIFIC</td>\n",
       "      <td>NY</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>P</td>\n",
       "      <td>5454.92</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59914</th>\n",
       "      <td>59915</td>\n",
       "      <td>5142138135</td>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>900009045549</td>\n",
       "      <td>KRACKELER SCIENTIFIC</td>\n",
       "      <td>NY</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>P</td>\n",
       "      <td>324.50</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59911</th>\n",
       "      <td>59912</td>\n",
       "      <td>5142138135</td>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>900009045549</td>\n",
       "      <td>KRACKELER SCIENTIFIC</td>\n",
       "      <td>NY</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>P</td>\n",
       "      <td>10598.09</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59910</th>\n",
       "      <td>59911</td>\n",
       "      <td>5142138135</td>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>900009045549</td>\n",
       "      <td>KRACKELER SCIENTIFIC</td>\n",
       "      <td>NY</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>P</td>\n",
       "      <td>2096.08</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59902</th>\n",
       "      <td>59903</td>\n",
       "      <td>5142138135</td>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>900009045549</td>\n",
       "      <td>KRACKELER SCIENTIFIC</td>\n",
       "      <td>NY</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>P</td>\n",
       "      <td>11368.59</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59892</th>\n",
       "      <td>59893</td>\n",
       "      <td>5142138135</td>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>900009045549</td>\n",
       "      <td>KRACKELER SCIENTIFIC</td>\n",
       "      <td>NY</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>P</td>\n",
       "      <td>4764.70</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59878</th>\n",
       "      <td>59879</td>\n",
       "      <td>5142138135</td>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>900009045549</td>\n",
       "      <td>KRACKELER SCIENTIFIC</td>\n",
       "      <td>NY</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>P</td>\n",
       "      <td>24366.78</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59843</th>\n",
       "      <td>59844</td>\n",
       "      <td>5142138135</td>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>900009045549</td>\n",
       "      <td>KRACKELER SCIENTIFIC</td>\n",
       "      <td>NY</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>P</td>\n",
       "      <td>23382.48</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59817</th>\n",
       "      <td>59818</td>\n",
       "      <td>5142138135</td>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>900009045549</td>\n",
       "      <td>KRACKELER SCIENTIFIC</td>\n",
       "      <td>NY</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>P</td>\n",
       "      <td>7360.42</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59791</th>\n",
       "      <td>59792</td>\n",
       "      <td>5142138135</td>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>900009045549</td>\n",
       "      <td>KRACKELER SCIENTIFIC</td>\n",
       "      <td>NY</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>P</td>\n",
       "      <td>5376.42</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59749</th>\n",
       "      <td>59750</td>\n",
       "      <td>5142138135</td>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>900009045549</td>\n",
       "      <td>KRACKELER SCIENTIFIC</td>\n",
       "      <td>NY</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>P</td>\n",
       "      <td>6927.27</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59744</th>\n",
       "      <td>59745</td>\n",
       "      <td>5142138135</td>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>900009045549</td>\n",
       "      <td>KRACKELER SCIENTIFIC</td>\n",
       "      <td>NY</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>P</td>\n",
       "      <td>12193.36</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82562</th>\n",
       "      <td>82563</td>\n",
       "      <td>5142152857</td>\n",
       "      <td>2010-10-21</td>\n",
       "      <td>9662801069973</td>\n",
       "      <td>KIESUB CORPORATION</td>\n",
       "      <td>NV</td>\n",
       "      <td>89109.0</td>\n",
       "      <td>P</td>\n",
       "      <td>1976.33</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Recnum     Cardnum       Date       Merchnum     Merch description  \\\n",
       "60047   60048  5142138135 2010-08-04   900009045549  KRACKELER SCIENTIFIC   \n",
       "60031   60032  5142138135 2010-08-04   900009045549  KRACKELER SCIENTIFIC   \n",
       "60027   60028  5142138135 2010-08-04   900009045549  KRACKELER SCIENTIFIC   \n",
       "60004   60005  5142138135 2010-08-04   900009045549  KRACKELER SCIENTIFIC   \n",
       "59998   59999  5142138135 2010-08-04   900009045549  KRACKELER SCIENTIFIC   \n",
       "59988   59989  5142138135 2010-08-04   900009045549  KRACKELER SCIENTIFIC   \n",
       "59933   59934  5142138135 2010-08-04   900009045549  KRACKELER SCIENTIFIC   \n",
       "59917   59918  5142138135 2010-08-04   900009045549  KRACKELER SCIENTIFIC   \n",
       "59914   59915  5142138135 2010-08-04   900009045549  KRACKELER SCIENTIFIC   \n",
       "59911   59912  5142138135 2010-08-04   900009045549  KRACKELER SCIENTIFIC   \n",
       "59910   59911  5142138135 2010-08-04   900009045549  KRACKELER SCIENTIFIC   \n",
       "59902   59903  5142138135 2010-08-04   900009045549  KRACKELER SCIENTIFIC   \n",
       "59892   59893  5142138135 2010-08-04   900009045549  KRACKELER SCIENTIFIC   \n",
       "59878   59879  5142138135 2010-08-04   900009045549  KRACKELER SCIENTIFIC   \n",
       "59843   59844  5142138135 2010-08-04   900009045549  KRACKELER SCIENTIFIC   \n",
       "59817   59818  5142138135 2010-08-04   900009045549  KRACKELER SCIENTIFIC   \n",
       "59791   59792  5142138135 2010-08-04   900009045549  KRACKELER SCIENTIFIC   \n",
       "59749   59750  5142138135 2010-08-04   900009045549  KRACKELER SCIENTIFIC   \n",
       "59744   59745  5142138135 2010-08-04   900009045549  KRACKELER SCIENTIFIC   \n",
       "82562   82563  5142152857 2010-10-21  9662801069973    KIESUB CORPORATION   \n",
       "\n",
       "      Merch state  Merch zip Transtype    Amount  Fraud  prediction  \n",
       "60047          NY    12202.0         P   1664.87      1    1.000000  \n",
       "60031          NY    12202.0         P  24778.99      1    1.000000  \n",
       "60027          NY    12202.0         P   3058.56      1    1.000000  \n",
       "60004          NY    12202.0         P    173.19      1    1.000000  \n",
       "59998          NY    12202.0         P   9117.34      1    1.000000  \n",
       "59988          NY    12202.0         P   3437.71      1    1.000000  \n",
       "59933          NY    12202.0         P   1941.07      1    1.000000  \n",
       "59917          NY    12202.0         P   5454.92      1    1.000000  \n",
       "59914          NY    12202.0         P    324.50      1    1.000000  \n",
       "59911          NY    12202.0         P  10598.09      1    1.000000  \n",
       "59910          NY    12202.0         P   2096.08      1    1.000000  \n",
       "59902          NY    12202.0         P  11368.59      1    1.000000  \n",
       "59892          NY    12202.0         P   4764.70      1    1.000000  \n",
       "59878          NY    12202.0         P  24366.78      1    1.000000  \n",
       "59843          NY    12202.0         P  23382.48      1    1.000000  \n",
       "59817          NY    12202.0         P   7360.42      1    1.000000  \n",
       "59791          NY    12202.0         P   5376.42      1    1.000000  \n",
       "59749          NY    12202.0         P   6927.27      1    0.999999  \n",
       "59744          NY    12202.0         P  12193.36      1    0.999998  \n",
       "82562          NV    89109.0         P   1976.33      1    0.999997  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sorted = df.sort_values(by=['prediction'],ascending=False)\n",
    "df_sorted.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5142140316    46\n",
       "5142847398    45\n",
       "5142199009    45\n",
       "5142160778    41\n",
       "5142189341    41\n",
       "5142181728    39\n",
       "5142212038    39\n",
       "5142220919    38\n",
       "5142214614    37\n",
       "5142202847    37\n",
       "5142138135    36\n",
       "5142271065    34\n",
       "5142152857    32\n",
       "5142179617    32\n",
       "5142235211    32\n",
       "5142197711    32\n",
       "5142182128    31\n",
       "5142189113    30\n",
       "5142197563    30\n",
       "5142116864    28\n",
       "Name: Cardnum, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bads = df[df['Fraud']==1]\n",
    "bads['Cardnum'].value_counts().head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4353000719908    107\n",
       "930090121224      50\n",
       "8834000695423     46\n",
       "4503738417400     45\n",
       "4620009957157     39\n",
       "618901687330      36\n",
       "900009045549      36\n",
       "9108234610000     33\n",
       "253052983001      33\n",
       "938909877224      32\n",
       "4503082476300     31\n",
       "6006333528866     30\n",
       "997674930332      29\n",
       "6070095870009     27\n",
       "9900020006406     25\n",
       "Name: Merchnum, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bads['Merchnum'].value_counts().head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "107"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "card = 5142140316\n",
    "# card = 5142847398\n",
    "# card = 5142199009\n",
    "# card = 5142160778\n",
    "# card = 5142189341\n",
    "# card = 5142181728\n",
    "# card = 5142212038\n",
    "# card = 5142220919\n",
    "# card = 5142214614\n",
    "# card = 5142202847\n",
    "# card = 5142138135\n",
    "# card = 5142271065\n",
    "# card = 5142152857\n",
    "# card = 5142179617\n",
    "# card = 5142235211\n",
    "# card = 5142197711\n",
    "# card = 5142182128\n",
    "# card = 5142189113\n",
    "# card = 5142197563 \n",
    "\n",
    "merch = 4353000719908\n",
    "# merch = 930090121224  \n",
    "# merch = 48834000695423\n",
    "# merch = 44503738417400\n",
    "# merch = 44620009957157\n",
    "# merch = 4618901687330\n",
    "# merch = 4900009045549\n",
    "# merch = 49108234610000\n",
    "# merch = 4253052983001\n",
    "# merch = 4938909877224\n",
    "# merch = 44503082476300\n",
    "# merch = 46006333528866\n",
    "# merch = 4997674930332\n",
    "# merch = 46070095870009\n",
    "# merch = 49900020006406\n",
    "# sample = df[df['Cardnum'] == card]\n",
    "sample = df[df['Merchnum'] == str(merch)]\n",
    "sample['Fraud'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f83a6103a00>]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.xticks(rotation=90, ha='right')\n",
    "plt.plot(sample['Date'],sample['prediction'])\n",
    "plt.plot_date(sample['Date'],sample['Fraud'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>counter</th>\n",
       "      <th>index</th>\n",
       "      <th>Recnum</th>\n",
       "      <th>Cardnum</th>\n",
       "      <th>Date</th>\n",
       "      <th>Merchnum</th>\n",
       "      <th>Merch description</th>\n",
       "      <th>Merch state</th>\n",
       "      <th>Merch zip</th>\n",
       "      <th>Transtype</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Fraud</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>86387</td>\n",
       "      <td>86388</td>\n",
       "      <td>5142180432</td>\n",
       "      <td>2010-11-10</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>237.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>86406</td>\n",
       "      <td>86407</td>\n",
       "      <td>5142139483</td>\n",
       "      <td>2010-11-10</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>178.36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>86485</td>\n",
       "      <td>86486</td>\n",
       "      <td>5142178848</td>\n",
       "      <td>2010-11-10</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>15.11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>86802</td>\n",
       "      <td>86803</td>\n",
       "      <td>5142146217</td>\n",
       "      <td>2010-11-12</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>63.45</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>86907</td>\n",
       "      <td>86908</td>\n",
       "      <td>5142223659</td>\n",
       "      <td>2010-11-12</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>73.90</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000859</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   counter  index  Recnum     Cardnum       Date       Merchnum  \\\n",
       "0        1  86387   86388  5142180432 2010-11-10  4353000719908   \n",
       "1        2  86406   86407  5142139483 2010-11-10  4353000719908   \n",
       "2        3  86485   86486  5142178848 2010-11-10  4353000719908   \n",
       "3        4  86802   86803  5142146217 2010-11-12  4353000719908   \n",
       "4        5  86907   86908  5142223659 2010-11-12  4353000719908   \n",
       "\n",
       "    Merch description Merch state  Merch zip Transtype  Amount  Fraud  \\\n",
       "0  ACI*AMAZON.COM INC          WA    98101.0         P  237.60      0   \n",
       "1  ACI*AMAZON.COM INC          WA    98101.0         P  178.36      0   \n",
       "2  ACI*AMAZON.COM INC          WA    98101.0         P   15.11      0   \n",
       "3  ACI*AMAZON.COM INC          WA    98101.0         P   63.45      0   \n",
       "4  ACI*AMAZON.COM INC          WA    98101.0         P   73.90      0   \n",
       "\n",
       "   prediction  \n",
       "0    0.002190  \n",
       "1    0.001906  \n",
       "2    0.001160  \n",
       "3    0.000849  \n",
       "4    0.000859  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsample = sample[sample['Date'] > '2010-11-09']\n",
    "tsample = tsample[tsample['Date'] < '2010-12-01']\n",
    "tsample.reset_index(inplace=True)\n",
    "tsample.reset_index(inplace=True)\n",
    "tsample.rename(columns={'level_0':'counter'},inplace=True)\n",
    "tsample['counter'] = tsample['counter']+1\n",
    "tsample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f83a6146250>]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.xticks(rotation=90, ha='right')\n",
    "plt.plot(tsample['Date'],tsample['prediction'])\n",
    "plt.plot_date(tsample['Date'],tsample['Fraud'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f83a60c66d0>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.xticks(rotation=90, ha='right')\n",
    "plt.plot(tsample['counter'],tsample['prediction'])\n",
    "plt.scatter(tsample['counter'],tsample['Fraud'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>counter</th>\n",
       "      <th>index</th>\n",
       "      <th>Recnum</th>\n",
       "      <th>Cardnum</th>\n",
       "      <th>Date</th>\n",
       "      <th>Merchnum</th>\n",
       "      <th>Merch description</th>\n",
       "      <th>Merch state</th>\n",
       "      <th>Merch zip</th>\n",
       "      <th>Transtype</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Fraud</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>86387</td>\n",
       "      <td>86388</td>\n",
       "      <td>5142180432</td>\n",
       "      <td>2010-11-10</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>237.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>86406</td>\n",
       "      <td>86407</td>\n",
       "      <td>5142139483</td>\n",
       "      <td>2010-11-10</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>178.36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>86485</td>\n",
       "      <td>86486</td>\n",
       "      <td>5142178848</td>\n",
       "      <td>2010-11-10</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>15.11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>86802</td>\n",
       "      <td>86803</td>\n",
       "      <td>5142146217</td>\n",
       "      <td>2010-11-12</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>63.45</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>86907</td>\n",
       "      <td>86908</td>\n",
       "      <td>5142223659</td>\n",
       "      <td>2010-11-12</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>73.90</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>86984</td>\n",
       "      <td>86985</td>\n",
       "      <td>5142137416</td>\n",
       "      <td>2010-11-13</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>35.90</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>87000</td>\n",
       "      <td>87001</td>\n",
       "      <td>5142236799</td>\n",
       "      <td>2010-11-14</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>98.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>87282</td>\n",
       "      <td>87283</td>\n",
       "      <td>5142221962</td>\n",
       "      <td>2010-11-16</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>32.28</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>87317</td>\n",
       "      <td>87318</td>\n",
       "      <td>5142226979</td>\n",
       "      <td>2010-11-16</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>35.06</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>87415</td>\n",
       "      <td>87416</td>\n",
       "      <td>5142221962</td>\n",
       "      <td>2010-11-16</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>234.54</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>87440</td>\n",
       "      <td>87441</td>\n",
       "      <td>5142226979</td>\n",
       "      <td>2010-11-16</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>55.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>87458</td>\n",
       "      <td>87459</td>\n",
       "      <td>5142178848</td>\n",
       "      <td>2010-11-16</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>87482</td>\n",
       "      <td>87483</td>\n",
       "      <td>5142223659</td>\n",
       "      <td>2010-11-16</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>52.84</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>87504</td>\n",
       "      <td>87505</td>\n",
       "      <td>5142223659</td>\n",
       "      <td>2010-11-16</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>70.90</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>87509</td>\n",
       "      <td>87510</td>\n",
       "      <td>5142226979</td>\n",
       "      <td>2010-11-16</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>179.32</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>87587</td>\n",
       "      <td>87588</td>\n",
       "      <td>5142206786</td>\n",
       "      <td>2010-11-17</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>AMAZON.COM  *SUPERSTOR</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>327.23</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>87637</td>\n",
       "      <td>87638</td>\n",
       "      <td>5142256802</td>\n",
       "      <td>2010-11-17</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>35.65</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>87686</td>\n",
       "      <td>87687</td>\n",
       "      <td>5142206786</td>\n",
       "      <td>2010-11-17</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>AMAZON.COM  *SUPERSTOR</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>108.93</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>87852</td>\n",
       "      <td>87853</td>\n",
       "      <td>5142211054</td>\n",
       "      <td>2010-11-17</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>97.95</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>88031</td>\n",
       "      <td>88032</td>\n",
       "      <td>5142126504</td>\n",
       "      <td>2010-11-18</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>88065</td>\n",
       "      <td>88066</td>\n",
       "      <td>5142260253</td>\n",
       "      <td>2010-11-18</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>53.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>88101</td>\n",
       "      <td>88102</td>\n",
       "      <td>5142216837</td>\n",
       "      <td>2010-11-18</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>69.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>88153</td>\n",
       "      <td>88154</td>\n",
       "      <td>5142264676</td>\n",
       "      <td>2010-11-18</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>182.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>88265</td>\n",
       "      <td>88266</td>\n",
       "      <td>5142212179</td>\n",
       "      <td>2010-11-19</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>118.95</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>88288</td>\n",
       "      <td>88289</td>\n",
       "      <td>5142126504</td>\n",
       "      <td>2010-11-19</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>70.76</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>88300</td>\n",
       "      <td>88301</td>\n",
       "      <td>5142121708</td>\n",
       "      <td>2010-11-19</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>24.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>88387</td>\n",
       "      <td>88388</td>\n",
       "      <td>5142237607</td>\n",
       "      <td>2010-11-19</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>81.88</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>88537</td>\n",
       "      <td>88538</td>\n",
       "      <td>5142215286</td>\n",
       "      <td>2010-11-22</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>18.95</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>88587</td>\n",
       "      <td>88588</td>\n",
       "      <td>5142272511</td>\n",
       "      <td>2010-11-22</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>48.90</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>88752</td>\n",
       "      <td>88753</td>\n",
       "      <td>5142215286</td>\n",
       "      <td>2010-11-22</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>88816</td>\n",
       "      <td>88817</td>\n",
       "      <td>5142283322</td>\n",
       "      <td>2010-11-23</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>15.15</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>88884</td>\n",
       "      <td>88885</td>\n",
       "      <td>5142177486</td>\n",
       "      <td>2010-11-23</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>67.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>88999</td>\n",
       "      <td>89000</td>\n",
       "      <td>5142283322</td>\n",
       "      <td>2010-11-23</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>43.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>89314</td>\n",
       "      <td>89315</td>\n",
       "      <td>5142177486</td>\n",
       "      <td>2010-11-24</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>35.78</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>89364</td>\n",
       "      <td>89365</td>\n",
       "      <td>5142235211</td>\n",
       "      <td>2010-11-25</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>AMAZON.COM  *SUPERSTOR</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>472.53</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>89365</td>\n",
       "      <td>89366</td>\n",
       "      <td>5142235211</td>\n",
       "      <td>2010-11-25</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>ACI*AMAZON.COM INC</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>1397.77</td>\n",
       "      <td>1</td>\n",
       "      <td>0.029239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>89366</td>\n",
       "      <td>89367</td>\n",
       "      <td>5142235211</td>\n",
       "      <td>2010-11-25</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>AMAZON.COM  *SUPERSTOR</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>497.03</td>\n",
       "      <td>1</td>\n",
       "      <td>0.046437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>89368</td>\n",
       "      <td>89369</td>\n",
       "      <td>5142235211</td>\n",
       "      <td>2010-11-25</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>AMAZON.COM  *SUPERSTOR</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>890.53</td>\n",
       "      <td>1</td>\n",
       "      <td>0.069032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>89374</td>\n",
       "      <td>89375</td>\n",
       "      <td>5142235211</td>\n",
       "      <td>2010-11-25</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>AMAZON.COM  *SUPERSTOR</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>1288.90</td>\n",
       "      <td>1</td>\n",
       "      <td>0.074207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>89377</td>\n",
       "      <td>89378</td>\n",
       "      <td>5142235211</td>\n",
       "      <td>2010-11-25</td>\n",
       "      <td>4353000719908</td>\n",
       "      <td>AMAZON.COM  *SUPERSTOR</td>\n",
       "      <td>WA</td>\n",
       "      <td>98101.0</td>\n",
       "      <td>P</td>\n",
       "      <td>3619.88</td>\n",
       "      <td>1</td>\n",
       "      <td>0.239189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    counter  index  Recnum     Cardnum       Date       Merchnum  \\\n",
       "0         1  86387   86388  5142180432 2010-11-10  4353000719908   \n",
       "1         2  86406   86407  5142139483 2010-11-10  4353000719908   \n",
       "2         3  86485   86486  5142178848 2010-11-10  4353000719908   \n",
       "3         4  86802   86803  5142146217 2010-11-12  4353000719908   \n",
       "4         5  86907   86908  5142223659 2010-11-12  4353000719908   \n",
       "5         6  86984   86985  5142137416 2010-11-13  4353000719908   \n",
       "6         7  87000   87001  5142236799 2010-11-14  4353000719908   \n",
       "7         8  87282   87283  5142221962 2010-11-16  4353000719908   \n",
       "8         9  87317   87318  5142226979 2010-11-16  4353000719908   \n",
       "9        10  87415   87416  5142221962 2010-11-16  4353000719908   \n",
       "10       11  87440   87441  5142226979 2010-11-16  4353000719908   \n",
       "11       12  87458   87459  5142178848 2010-11-16  4353000719908   \n",
       "12       13  87482   87483  5142223659 2010-11-16  4353000719908   \n",
       "13       14  87504   87505  5142223659 2010-11-16  4353000719908   \n",
       "14       15  87509   87510  5142226979 2010-11-16  4353000719908   \n",
       "15       16  87587   87588  5142206786 2010-11-17  4353000719908   \n",
       "16       17  87637   87638  5142256802 2010-11-17  4353000719908   \n",
       "17       18  87686   87687  5142206786 2010-11-17  4353000719908   \n",
       "18       19  87852   87853  5142211054 2010-11-17  4353000719908   \n",
       "19       20  88031   88032  5142126504 2010-11-18  4353000719908   \n",
       "20       21  88065   88066  5142260253 2010-11-18  4353000719908   \n",
       "21       22  88101   88102  5142216837 2010-11-18  4353000719908   \n",
       "22       23  88153   88154  5142264676 2010-11-18  4353000719908   \n",
       "23       24  88265   88266  5142212179 2010-11-19  4353000719908   \n",
       "24       25  88288   88289  5142126504 2010-11-19  4353000719908   \n",
       "25       26  88300   88301  5142121708 2010-11-19  4353000719908   \n",
       "26       27  88387   88388  5142237607 2010-11-19  4353000719908   \n",
       "27       28  88537   88538  5142215286 2010-11-22  4353000719908   \n",
       "28       29  88587   88588  5142272511 2010-11-22  4353000719908   \n",
       "29       30  88752   88753  5142215286 2010-11-22  4353000719908   \n",
       "30       31  88816   88817  5142283322 2010-11-23  4353000719908   \n",
       "31       32  88884   88885  5142177486 2010-11-23  4353000719908   \n",
       "32       33  88999   89000  5142283322 2010-11-23  4353000719908   \n",
       "33       34  89314   89315  5142177486 2010-11-24  4353000719908   \n",
       "34       35  89364   89365  5142235211 2010-11-25  4353000719908   \n",
       "35       36  89365   89366  5142235211 2010-11-25  4353000719908   \n",
       "36       37  89366   89367  5142235211 2010-11-25  4353000719908   \n",
       "37       38  89368   89369  5142235211 2010-11-25  4353000719908   \n",
       "38       39  89374   89375  5142235211 2010-11-25  4353000719908   \n",
       "39       40  89377   89378  5142235211 2010-11-25  4353000719908   \n",
       "\n",
       "         Merch description Merch state  Merch zip Transtype   Amount  Fraud  \\\n",
       "0       ACI*AMAZON.COM INC          WA    98101.0         P   237.60      0   \n",
       "1       ACI*AMAZON.COM INC          WA    98101.0         P   178.36      0   \n",
       "2       ACI*AMAZON.COM INC          WA    98101.0         P    15.11      0   \n",
       "3       ACI*AMAZON.COM INC          WA    98101.0         P    63.45      0   \n",
       "4       ACI*AMAZON.COM INC          WA    98101.0         P    73.90      0   \n",
       "5       ACI*AMAZON.COM INC          WA    98101.0         P    35.90      0   \n",
       "6       ACI*AMAZON.COM INC          WA    98101.0         P    98.60      0   \n",
       "7       ACI*AMAZON.COM INC          WA    98101.0         P    32.28      0   \n",
       "8       ACI*AMAZON.COM INC          WA    98101.0         P    35.06      0   \n",
       "9       ACI*AMAZON.COM INC          WA    98101.0         P   234.54      0   \n",
       "10      ACI*AMAZON.COM INC          WA    98101.0         P    55.62      0   \n",
       "11      ACI*AMAZON.COM INC          WA    98101.0         P    11.93      0   \n",
       "12      ACI*AMAZON.COM INC          WA    98101.0         P    52.84      0   \n",
       "13      ACI*AMAZON.COM INC          WA    98101.0         P    70.90      0   \n",
       "14      ACI*AMAZON.COM INC          WA    98101.0         P   179.32      0   \n",
       "15  AMAZON.COM  *SUPERSTOR          WA    98101.0         P   327.23      1   \n",
       "16      ACI*AMAZON.COM INC          WA    98101.0         P    35.65      0   \n",
       "17  AMAZON.COM  *SUPERSTOR          WA    98101.0         P   108.93      1   \n",
       "18      ACI*AMAZON.COM INC          WA    98101.0         P    97.95      0   \n",
       "19      ACI*AMAZON.COM INC          WA    98101.0         P    14.00      0   \n",
       "20      ACI*AMAZON.COM INC          WA    98101.0         P    53.70      0   \n",
       "21      ACI*AMAZON.COM INC          WA    98101.0         P    69.86      0   \n",
       "22      ACI*AMAZON.COM INC          WA    98101.0         P   182.10      0   \n",
       "23      ACI*AMAZON.COM INC          WA    98101.0         P   118.95      0   \n",
       "24      ACI*AMAZON.COM INC          WA    98101.0         P    70.76      0   \n",
       "25      ACI*AMAZON.COM INC          WA    98101.0         P    24.92      0   \n",
       "26      ACI*AMAZON.COM INC          WA    98101.0         P    81.88      0   \n",
       "27      ACI*AMAZON.COM INC          WA    98101.0         P    18.95      0   \n",
       "28      ACI*AMAZON.COM INC          WA    98101.0         P    48.90      0   \n",
       "29      ACI*AMAZON.COM INC          WA    98101.0         P    17.02      0   \n",
       "30      ACI*AMAZON.COM INC          WA    98101.0         P    15.15      0   \n",
       "31      ACI*AMAZON.COM INC          WA    98101.0         P    67.62      0   \n",
       "32      ACI*AMAZON.COM INC          WA    98101.0         P    43.40      0   \n",
       "33      ACI*AMAZON.COM INC          WA    98101.0         P    35.78      0   \n",
       "34  AMAZON.COM  *SUPERSTOR          WA    98101.0         P   472.53      1   \n",
       "35      ACI*AMAZON.COM INC          WA    98101.0         P  1397.77      1   \n",
       "36  AMAZON.COM  *SUPERSTOR          WA    98101.0         P   497.03      1   \n",
       "37  AMAZON.COM  *SUPERSTOR          WA    98101.0         P   890.53      1   \n",
       "38  AMAZON.COM  *SUPERSTOR          WA    98101.0         P  1288.90      1   \n",
       "39  AMAZON.COM  *SUPERSTOR          WA    98101.0         P  3619.88      1   \n",
       "\n",
       "    prediction  \n",
       "0     0.002190  \n",
       "1     0.001906  \n",
       "2     0.001160  \n",
       "3     0.000849  \n",
       "4     0.000859  \n",
       "5     0.001019  \n",
       "6     0.000878  \n",
       "7     0.000859  \n",
       "8     0.000955  \n",
       "9     0.001364  \n",
       "10    0.001096  \n",
       "11    0.001064  \n",
       "12    0.001129  \n",
       "13    0.001509  \n",
       "14    0.001725  \n",
       "15    0.001403  \n",
       "16    0.001217  \n",
       "17    0.002228  \n",
       "18    0.000889  \n",
       "19    0.000949  \n",
       "20    0.000859  \n",
       "21    0.000862  \n",
       "22    0.000907  \n",
       "23    0.000879  \n",
       "24    0.001023  \n",
       "25    0.000850  \n",
       "26    0.000807  \n",
       "27    0.000864  \n",
       "28    0.000876  \n",
       "29    0.000932  \n",
       "30    0.000944  \n",
       "31    0.000875  \n",
       "32    0.001005  \n",
       "33    0.001067  \n",
       "34    0.001981  \n",
       "35    0.029239  \n",
       "36    0.046437  \n",
       "37    0.069032  \n",
       "38    0.074207  \n",
       "39    0.239189  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsample.head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duration:  0:27:08.262268\n"
     ]
    }
   ],
   "source": [
    "print(\"duration: \", datetime.now() - start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
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