From 3608244e1177561c1c54d13de897c48589a44950 Mon Sep 17 00:00:00 2001
From: pmonormal <pmon2648@daum.net>
Date: Wed, 24 Oct 2018 14:15:22 +0900
Subject: [PATCH] Update Fraud detection Kernel as Korean about 100%
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

3.1 t-SNE 파트를 마무리 하였습니다.
이후 리뷰 과정이 필요합니다.

Fixes #34
---
 .../Fraud detection analysis with NN.ipynb    | 948 +++++++++---------
 1 file changed, 473 insertions(+), 475 deletions(-)

diff --git a/Korean/Fraud detection analysis with NN/Fraud detection analysis with NN.ipynb b/Korean/Fraud detection analysis with NN/Fraud detection analysis with NN.ipynb
index b1a13a3..42d5a85 100644
--- a/Korean/Fraud detection analysis with NN/Fraud detection analysis with NN.ipynb	
+++ b/Korean/Fraud detection analysis with NN/Fraud detection analysis with NN.ipynb	
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "_cell_guid": "38f6daf8-0c7e-4828-b7c7-9f360b74cba2",
     "_uuid": "7bce1457305f4af15a8906b5fbe0f86b122b7682"
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "_cell_guid": "7ba71260-0875-4985-b4c5-f2a2ec628b54",
     "_uuid": "286e20d11c5ead05833ca6ac9e00c5939c13d5fa"
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "_cell_guid": "c6a0171a-c2d9-4c72-baae-fba61d477c72",
     "_uuid": "a1c048a75eb006044448a22827ef7de59efc494d",
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "_cell_guid": "f08cc4ff-90bb-4c95-995d-07656f0d6a9d",
     "_uuid": "b0eb1ab69917df26b9ca2e2198f519b9db9052c7"
@@ -351,7 +351,7 @@
        "[5 rows x 31 columns]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -362,7 +362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "_cell_guid": "24fc466d-83cd-4879-8d06-1d0816fbfbe1",
     "_uuid": "f2e49b6bbdda52f693585a950f15aedfe503cd66"
@@ -664,7 +664,7 @@
        "[8 rows x 31 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -675,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "_cell_guid": "05265544-4b26-4a73-bda6-7b771dfde5ef",
     "_uuid": "440ba6636b61f618dba1b33ece3d1cb68a271a5d"
@@ -718,7 +718,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -740,7 +740,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "_cell_guid": "d18cfe2f-8ce3-4152-9d4d-d13a5aba98b5",
     "_uuid": "b46bef6e0c5bdd30d0925da9e4f4ab16abe1ba17"
@@ -789,7 +789,7 @@
        "1     492"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -813,7 +813,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "_cell_guid": "12b5b06a-628f-47ce-98fb-00605730beaa",
     "_uuid": "c847730cc5b27c8d435ae64b1c7301e029720c79"
@@ -857,7 +857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "_cell_guid": "3f3d814d-ada7-46dc-9212-50980c26c4aa",
     "_uuid": "73fc67c42c92b5114db71961a41316bb891d4917",
@@ -908,7 +908,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
     "_cell_guid": "60957c60-a4e4-432a-8f05-c7b8da45ea20",
     "_uuid": "763984c3387c17fe0266d3528bec6da18f7699d2"
@@ -952,7 +952,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {
     "_cell_guid": "d7b08ee9-d91a-48b4-a50f-efcbe3908204",
     "_uuid": "3b2da5d815af3bebe7fd44ff772cb7e80a731ed5"
@@ -999,7 +999,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "_cell_guid": "8cdd28b8-85b8-4995-b320-1e7540481184",
     "_uuid": "c40a05e151ddc7d17f3b5b8d5be1aa2023188ece"
@@ -1200,7 +1200,7 @@
        "[5 rows x 32 columns]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1222,7 +1222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {
     "_cell_guid": "9199eee3-ec1a-4928-bd89-979b05a41c51",
     "_uuid": "fcba2978e404c225eaac0de7329ba229b5a86342"
@@ -1268,7 +1268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "_cell_guid": "12d5d40d-3b59-4125-91e2-aebd23887e0c",
     "_uuid": "e7e926df5df4a997648b24235a1505cd3f698fcf"
@@ -1281,7 +1281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "_cell_guid": "24cf2a80-78ba-452c-aa9b-8fd1a76b0b4c",
     "_uuid": "760bae0b9f115df97241a5779a6f2e84bc00a08f",
@@ -1334,7 +1334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {
     "_cell_guid": "0dec79b2-4ec5-46a0-8143-1d5863df0dd7",
     "_uuid": "731020f2f6e2ae4d31b85ccfc04c4f63ce4915ad"
@@ -1358,7 +1358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "_cell_guid": "4bdcef2e-df1b-4cd5-8cee-a412a0f28256",
     "_uuid": "bf58d74592307afc3af5f9fb8462902deef3450b"
@@ -1370,7 +1370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "_cell_guid": "a82b8db0-fbfe-4261-b422-e95e058b5bfe",
     "_uuid": "d0226f19abfc030162c5d079033de0ce2f5a64de"
@@ -1408,7 +1408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "_cell_guid": "b1218f02-11d5-46e4-8df0-2f3ee4074f39",
     "_uuid": "3b37738dbc7f67be086a825963beafab5f185b90"
@@ -1692,7 +1692,7 @@
        "4   69.99      0                 0     1.0  "
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1713,7 +1713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "_cell_guid": "dc3439f2-f413-4a34-8739-5a7117841291",
     "_uuid": "8a9deb44f4e3e719c3e2cee9c6d86b8897ec14bb"
@@ -1735,7 +1735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "_cell_guid": "e689ca7a-3d9c-407f-9097-d69032a8ae05",
     "_uuid": "3415505256cf5f7de16d122338deead06e397284"
@@ -1755,7 +1755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {
     "_cell_guid": "36678aa1-f708-4b23-9c96-501360b516f7",
     "_uuid": "6c9a13a5194345124c85094be9cb8513b4e6de38"
@@ -1774,7 +1774,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {
     "_cell_guid": "93e91d1c-6a0c-4c08-9956-f89d9d23808e",
     "_uuid": "97c7e3be6512a087c939200fcf1cc1de83847590"
@@ -1793,7 +1793,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {
     "_cell_guid": "10624941-f8b8-45b8-9e34-43626da3164c",
     "_uuid": "b1c50dafe03fe0c0a0482d8b0feff37c0dffddd6"
@@ -1813,7 +1813,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {
     "_cell_guid": "f7ef7483-ddad-47a3-9a6d-8a5f31eca4b3",
     "_uuid": "9edf3188be449f28bf7ef913e736066ea46e97a6"
@@ -1853,7 +1853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {
     "_cell_guid": "34d61841-0158-4ab9-80b3-a15046ed13fe",
     "_uuid": "b67fcf530489d6c018a3765153aad8b22cc5d234"
@@ -1876,7 +1876,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {
     "_cell_guid": "a3b431a9-b85f-4b0b-b822-870328523a26",
     "_uuid": "1593dc3cabc20c66ecad2447fdde880391a46804"
@@ -1896,7 +1896,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {
     "_cell_guid": "5aece1f2-ee25-4c3d-8cbe-d112c0dd460f",
     "_uuid": "0b3a2881eadcdb252f5eef8721d76fdbf3aeeb52"
@@ -1930,7 +1930,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {
     "_cell_guid": "b0ec16a5-7365-48dd-828f-846cbd2e947d",
     "_uuid": "da4329f8146cb8aff30d13a27c07288669e22149"
@@ -1945,7 +1945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1975,27 +1975,27 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>46918</th>\n",
+       "      <th>10484</th>\n",
        "      <td>578.28934</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10891</th>\n",
+       "      <th>15539</th>\n",
        "      <td>578.28934</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15539</th>\n",
+       "      <th>189701</th>\n",
        "      <td>578.28934</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14338</th>\n",
+       "      <th>124036</th>\n",
        "      <td>578.28934</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>17480</th>\n",
+       "      <th>30473</th>\n",
        "      <td>578.28934</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
@@ -2004,15 +2004,15 @@
        "</div>"
       ],
       "text/plain": [
-       "           Fraud  Normal\n",
-       "46918  578.28934     0.0\n",
-       "10891  578.28934     0.0\n",
-       "15539  578.28934     0.0\n",
-       "14338  578.28934     0.0\n",
-       "17480  578.28934     0.0"
+       "            Fraud  Normal\n",
+       "10484   578.28934     0.0\n",
+       "15539   578.28934     0.0\n",
+       "189701  578.28934     0.0\n",
+       "124036  578.28934     0.0\n",
+       "30473   578.28934     0.0"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2033,7 +2033,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {
     "_cell_guid": "9f134b83-2b88-40d2-8f4a-525625b190dd",
     "_uuid": "850c8b36783631282f2f0163f5433e0358da9f9a"
@@ -2051,7 +2051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -2145,240 +2145,240 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>-0.001286</td>\n",
-       "      <td>-0.002631</td>\n",
-       "      <td>-0.001593</td>\n",
-       "      <td>0.003897</td>\n",
-       "      <td>0.005106</td>\n",
-       "      <td>0.001136</td>\n",
-       "      <td>0.003338</td>\n",
-       "      <td>0.001420</td>\n",
-       "      <td>0.005280</td>\n",
-       "      <td>-0.003772</td>\n",
-       "      <td>0.001565</td>\n",
-       "      <td>0.006694</td>\n",
-       "      <td>0.006557</td>\n",
-       "      <td>0.000992</td>\n",
-       "      <td>-0.004114</td>\n",
-       "      <td>-0.001910</td>\n",
-       "      <td>0.003052</td>\n",
-       "      <td>0.000119</td>\n",
-       "      <td>0.001319</td>\n",
-       "      <td>0.000586</td>\n",
-       "      <td>-0.001582</td>\n",
-       "      <td>-0.001935</td>\n",
-       "      <td>-0.001926</td>\n",
-       "      <td>-0.001921</td>\n",
-       "      <td>0.000102</td>\n",
-       "      <td>-0.005428</td>\n",
-       "      <td>-0.002525</td>\n",
-       "      <td>-0.001413</td>\n",
-       "      <td>-0.000401</td>\n",
-       "      <td>0.000211</td>\n",
-       "      <td>-0.000611</td>\n",
+       "      <td>-0.000981</td>\n",
+       "      <td>0.002513</td>\n",
+       "      <td>-0.005920</td>\n",
+       "      <td>0.004468</td>\n",
+       "      <td>0.003515</td>\n",
+       "      <td>0.000882</td>\n",
+       "      <td>-0.007049</td>\n",
+       "      <td>0.000640</td>\n",
+       "      <td>0.003547</td>\n",
+       "      <td>-0.003336</td>\n",
+       "      <td>0.001112</td>\n",
+       "      <td>0.001566</td>\n",
+       "      <td>-0.003003</td>\n",
+       "      <td>0.002476</td>\n",
+       "      <td>-0.001457</td>\n",
+       "      <td>0.004771</td>\n",
+       "      <td>-0.001298</td>\n",
+       "      <td>0.006475</td>\n",
+       "      <td>0.002145</td>\n",
+       "      <td>-0.003752</td>\n",
+       "      <td>0.005089</td>\n",
+       "      <td>0.002532</td>\n",
+       "      <td>-0.002266</td>\n",
+       "      <td>0.000694</td>\n",
+       "      <td>0.001255</td>\n",
+       "      <td>0.000230</td>\n",
+       "      <td>-0.003617</td>\n",
+       "      <td>-0.001128</td>\n",
+       "      <td>-0.005178</td>\n",
+       "      <td>0.000832</td>\n",
+       "      <td>-0.000229</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
-       "      <td>1.000527</td>\n",
-       "      <td>1.006693</td>\n",
-       "      <td>0.997540</td>\n",
-       "      <td>0.997086</td>\n",
-       "      <td>1.006792</td>\n",
-       "      <td>0.991543</td>\n",
-       "      <td>0.996668</td>\n",
-       "      <td>0.973116</td>\n",
-       "      <td>0.948871</td>\n",
-       "      <td>0.993364</td>\n",
-       "      <td>0.990888</td>\n",
-       "      <td>1.002877</td>\n",
-       "      <td>1.000311</td>\n",
-       "      <td>0.997226</td>\n",
-       "      <td>1.005559</td>\n",
-       "      <td>1.002712</td>\n",
-       "      <td>1.001343</td>\n",
-       "      <td>1.003773</td>\n",
-       "      <td>1.005842</td>\n",
-       "      <td>1.000361</td>\n",
-       "      <td>1.006151</td>\n",
-       "      <td>0.966084</td>\n",
-       "      <td>0.995486</td>\n",
-       "      <td>0.993314</td>\n",
-       "      <td>0.999420</td>\n",
-       "      <td>0.995134</td>\n",
-       "      <td>1.001346</td>\n",
-       "      <td>0.979838</td>\n",
-       "      <td>1.026780</td>\n",
-       "      <td>0.956109</td>\n",
-       "      <td>0.993369</td>\n",
+       "      <td>0.999579</td>\n",
+       "      <td>1.002597</td>\n",
+       "      <td>1.018439</td>\n",
+       "      <td>0.992005</td>\n",
+       "      <td>1.001664</td>\n",
+       "      <td>0.990564</td>\n",
+       "      <td>0.987920</td>\n",
+       "      <td>0.975891</td>\n",
+       "      <td>0.974153</td>\n",
+       "      <td>0.996545</td>\n",
+       "      <td>0.986738</td>\n",
+       "      <td>0.997945</td>\n",
+       "      <td>0.986894</td>\n",
+       "      <td>1.006684</td>\n",
+       "      <td>0.989983</td>\n",
+       "      <td>0.998620</td>\n",
+       "      <td>0.993258</td>\n",
+       "      <td>0.979525</td>\n",
+       "      <td>0.996698</td>\n",
+       "      <td>1.007079</td>\n",
+       "      <td>0.984642</td>\n",
+       "      <td>0.978265</td>\n",
+       "      <td>0.996165</td>\n",
+       "      <td>0.986243</td>\n",
+       "      <td>0.996716</td>\n",
+       "      <td>1.004727</td>\n",
+       "      <td>1.005400</td>\n",
+       "      <td>0.974566</td>\n",
+       "      <td>1.028555</td>\n",
+       "      <td>1.036437</td>\n",
+       "      <td>0.997525</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
-       "      <td>-1.996580</td>\n",
-       "      <td>-20.443469</td>\n",
-       "      <td>-23.276581</td>\n",
-       "      <td>-22.213271</td>\n",
-       "      <td>-4.013912</td>\n",
-       "      <td>-23.251009</td>\n",
-       "      <td>-15.288057</td>\n",
-       "      <td>-19.739398</td>\n",
-       "      <td>-33.230188</td>\n",
-       "      <td>-7.955047</td>\n",
-       "      <td>-16.780247</td>\n",
-       "      <td>-4.475685</td>\n",
-       "      <td>-18.698648</td>\n",
-       "      <td>-3.907070</td>\n",
-       "      <td>-20.044245</td>\n",
-       "      <td>-4.397796</td>\n",
+       "      <td>-1.996495</td>\n",
+       "      <td>-28.798504</td>\n",
+       "      <td>-44.035215</td>\n",
+       "      <td>-20.513492</td>\n",
+       "      <td>-3.653301</td>\n",
+       "      <td>-29.290217</td>\n",
+       "      <td>-19.636023</td>\n",
+       "      <td>-22.807581</td>\n",
+       "      <td>-32.877534</td>\n",
+       "      <td>-8.630238</td>\n",
+       "      <td>-19.239745</td>\n",
+       "      <td>-4.026853</td>\n",
+       "      <td>-18.062021</td>\n",
+       "      <td>-3.867219</td>\n",
+       "      <td>-18.829627</td>\n",
+       "      <td>-4.797586</td>\n",
        "      <td>-15.182703</td>\n",
-       "      <td>-29.626400</td>\n",
-       "      <td>-11.332636</td>\n",
-       "      <td>-6.059567</td>\n",
-       "      <td>-30.673398</td>\n",
-       "      <td>-25.367337</td>\n",
-       "      <td>-12.246104</td>\n",
-       "      <td>-37.187338</td>\n",
-       "      <td>-4.636193</td>\n",
-       "      <td>-9.458963</td>\n",
-       "      <td>-3.557744</td>\n",
-       "      <td>-24.515479</td>\n",
+       "      <td>-26.943365</td>\n",
+       "      <td>-11.081002</td>\n",
+       "      <td>-8.523283</td>\n",
+       "      <td>-26.675106</td>\n",
+       "      <td>-25.326726</td>\n",
+       "      <td>-13.089986</td>\n",
+       "      <td>-42.838782</td>\n",
+       "      <td>-4.660608</td>\n",
+       "      <td>-14.379544</td>\n",
+       "      <td>-5.401088</td>\n",
+       "      <td>-23.266164</td>\n",
        "      <td>-25.520958</td>\n",
        "      <td>-0.353229</td>\n",
        "      <td>-0.046062</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
-       "      <td>-0.855242</td>\n",
-       "      <td>-0.469524</td>\n",
-       "      <td>-0.358808</td>\n",
-       "      <td>-0.578933</td>\n",
-       "      <td>-0.596381</td>\n",
-       "      <td>-0.500065</td>\n",
-       "      <td>-0.571141</td>\n",
-       "      <td>-0.450554</td>\n",
-       "      <td>-0.172817</td>\n",
-       "      <td>-0.584409</td>\n",
-       "      <td>-0.493729</td>\n",
-       "      <td>-0.741852</td>\n",
-       "      <td>-0.396398</td>\n",
-       "      <td>-0.649772</td>\n",
-       "      <td>-0.449871</td>\n",
-       "      <td>-0.640807</td>\n",
-       "      <td>-0.531302</td>\n",
-       "      <td>-0.575388</td>\n",
-       "      <td>-0.595679</td>\n",
-       "      <td>-0.564300</td>\n",
-       "      <td>-0.272836</td>\n",
-       "      <td>-0.311325</td>\n",
-       "      <td>-0.747634</td>\n",
-       "      <td>-0.262140</td>\n",
-       "      <td>-0.584945</td>\n",
-       "      <td>-0.611699</td>\n",
-       "      <td>-0.681904</td>\n",
-       "      <td>-0.174783</td>\n",
-       "      <td>-0.161692</td>\n",
-       "      <td>-0.331159</td>\n",
+       "      <td>-0.848082</td>\n",
+       "      <td>-0.472047</td>\n",
+       "      <td>-0.362059</td>\n",
+       "      <td>-0.583160</td>\n",
+       "      <td>-0.597038</td>\n",
+       "      <td>-0.502249</td>\n",
+       "      <td>-0.579251</td>\n",
+       "      <td>-0.448915</td>\n",
+       "      <td>-0.174413</td>\n",
+       "      <td>-0.591501</td>\n",
+       "      <td>-0.492679</td>\n",
+       "      <td>-0.747584</td>\n",
+       "      <td>-0.413937</td>\n",
+       "      <td>-0.658836</td>\n",
+       "      <td>-0.444461</td>\n",
+       "      <td>-0.635628</td>\n",
+       "      <td>-0.536064</td>\n",
+       "      <td>-0.567924</td>\n",
+       "      <td>-0.593381</td>\n",
+       "      <td>-0.570330</td>\n",
+       "      <td>-0.275025</td>\n",
+       "      <td>-0.310829</td>\n",
+       "      <td>-0.748917</td>\n",
+       "      <td>-0.256147</td>\n",
+       "      <td>-0.581180</td>\n",
+       "      <td>-0.608956</td>\n",
+       "      <td>-0.679814</td>\n",
+       "      <td>-0.174964</td>\n",
+       "      <td>-0.161507</td>\n",
+       "      <td>-0.330120</td>\n",
        "      <td>-0.046062</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
-       "      <td>-0.216935</td>\n",
-       "      <td>0.002039</td>\n",
-       "      <td>0.042019</td>\n",
-       "      <td>0.121779</td>\n",
-       "      <td>-0.011481</td>\n",
-       "      <td>-0.041114</td>\n",
-       "      <td>-0.205355</td>\n",
-       "      <td>0.032774</td>\n",
-       "      <td>0.021144</td>\n",
-       "      <td>-0.049309</td>\n",
-       "      <td>-0.082977</td>\n",
-       "      <td>-0.021572</td>\n",
-       "      <td>0.147645</td>\n",
-       "      <td>-0.015403</td>\n",
-       "      <td>0.051120</td>\n",
-       "      <td>0.049099</td>\n",
-       "      <td>0.075530</td>\n",
-       "      <td>-0.077553</td>\n",
-       "      <td>0.000442</td>\n",
-       "      <td>0.006663</td>\n",
-       "      <td>-0.079186</td>\n",
-       "      <td>-0.041587</td>\n",
-       "      <td>0.008966</td>\n",
-       "      <td>-0.018311</td>\n",
-       "      <td>0.067176</td>\n",
-       "      <td>0.021756</td>\n",
-       "      <td>-0.106715</td>\n",
-       "      <td>0.003712</td>\n",
-       "      <td>0.033434</td>\n",
-       "      <td>-0.265271</td>\n",
+       "      <td>-0.216304</td>\n",
+       "      <td>0.008638</td>\n",
+       "      <td>0.036382</td>\n",
+       "      <td>0.115526</td>\n",
+       "      <td>-0.010671</td>\n",
+       "      <td>-0.040643</td>\n",
+       "      <td>-0.207725</td>\n",
+       "      <td>0.029786</td>\n",
+       "      <td>0.017563</td>\n",
+       "      <td>-0.046103</td>\n",
+       "      <td>-0.085007</td>\n",
+       "      <td>-0.026277</td>\n",
+       "      <td>0.135150</td>\n",
+       "      <td>-0.013610</td>\n",
+       "      <td>0.051565</td>\n",
+       "      <td>0.054417</td>\n",
+       "      <td>0.069225</td>\n",
+       "      <td>-0.071421</td>\n",
+       "      <td>-0.008159</td>\n",
+       "      <td>0.002786</td>\n",
+       "      <td>-0.080291</td>\n",
+       "      <td>-0.040014</td>\n",
+       "      <td>0.009542</td>\n",
+       "      <td>-0.018098</td>\n",
+       "      <td>0.070257</td>\n",
+       "      <td>0.034972</td>\n",
+       "      <td>-0.115678</td>\n",
+       "      <td>0.003000</td>\n",
+       "      <td>0.032948</td>\n",
+       "      <td>-0.265031</td>\n",
        "      <td>-0.046062</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
-       "      <td>0.935268</td>\n",
-       "      <td>0.669606</td>\n",
-       "      <td>0.487402</td>\n",
-       "      <td>0.684229</td>\n",
-       "      <td>0.532341</td>\n",
-       "      <td>0.446344</td>\n",
-       "      <td>0.303910</td>\n",
-       "      <td>0.462273</td>\n",
-       "      <td>0.277804</td>\n",
-       "      <td>0.538129</td>\n",
-       "      <td>0.419252</td>\n",
-       "      <td>0.734912</td>\n",
-       "      <td>0.625491</td>\n",
-       "      <td>0.667426</td>\n",
-       "      <td>0.513534</td>\n",
-       "      <td>0.709356</td>\n",
-       "      <td>0.602099</td>\n",
-       "      <td>0.472357</td>\n",
-       "      <td>0.597576</td>\n",
-       "      <td>0.569193</td>\n",
-       "      <td>0.173913</td>\n",
-       "      <td>0.250213</td>\n",
-       "      <td>0.719569</td>\n",
-       "      <td>0.241384</td>\n",
-       "      <td>0.726921</td>\n",
-       "      <td>0.664707</td>\n",
-       "      <td>0.497974</td>\n",
-       "      <td>0.224292</td>\n",
-       "      <td>0.236219</td>\n",
-       "      <td>-0.045377</td>\n",
+       "      <td>0.935563</td>\n",
+       "      <td>0.674915</td>\n",
+       "      <td>0.484174</td>\n",
+       "      <td>0.678346</td>\n",
+       "      <td>0.525606</td>\n",
+       "      <td>0.438875</td>\n",
+       "      <td>0.294860</td>\n",
+       "      <td>0.461690</td>\n",
+       "      <td>0.274024</td>\n",
+       "      <td>0.542844</td>\n",
+       "      <td>0.424213</td>\n",
+       "      <td>0.726922</td>\n",
+       "      <td>0.617056</td>\n",
+       "      <td>0.674697</td>\n",
+       "      <td>0.513263</td>\n",
+       "      <td>0.713854</td>\n",
+       "      <td>0.595188</td>\n",
+       "      <td>0.469295</td>\n",
+       "      <td>0.599208</td>\n",
+       "      <td>0.564558</td>\n",
+       "      <td>0.175128</td>\n",
+       "      <td>0.251457</td>\n",
+       "      <td>0.726915</td>\n",
+       "      <td>0.239267</td>\n",
+       "      <td>0.721670</td>\n",
+       "      <td>0.674138</td>\n",
+       "      <td>0.495060</td>\n",
+       "      <td>0.225670</td>\n",
+       "      <td>0.235973</td>\n",
+       "      <td>-0.045537</td>\n",
        "      <td>-0.046062</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
-       "      <td>1.641865</td>\n",
-       "      <td>1.249048</td>\n",
-       "      <td>11.446954</td>\n",
+       "      <td>1.642055</td>\n",
+       "      <td>1.253349</td>\n",
+       "      <td>10.121300</td>\n",
        "      <td>2.690291</td>\n",
-       "      <td>8.969436</td>\n",
-       "      <td>21.128231</td>\n",
-       "      <td>16.057594</td>\n",
-       "      <td>27.728845</td>\n",
-       "      <td>16.049132</td>\n",
-       "      <td>8.385903</td>\n",
-       "      <td>14.080677</td>\n",
-       "      <td>11.775017</td>\n",
+       "      <td>11.918722</td>\n",
+       "      <td>25.214090</td>\n",
+       "      <td>17.952680</td>\n",
+       "      <td>35.611260</td>\n",
+       "      <td>16.751505</td>\n",
+       "      <td>9.459845</td>\n",
+       "      <td>14.001643</td>\n",
+       "      <td>11.000614</td>\n",
        "      <td>4.409860</td>\n",
        "      <td>4.590704</td>\n",
-       "      <td>7.742518</td>\n",
-       "      <td>5.600456</td>\n",
-       "      <td>6.845356</td>\n",
-       "      <td>8.962121</td>\n",
-       "      <td>4.857366</td>\n",
-       "      <td>6.869402</td>\n",
-       "      <td>22.234396</td>\n",
+       "      <td>7.546716</td>\n",
+       "      <td>6.319690</td>\n",
+       "      <td>6.528161</td>\n",
+       "      <td>10.052776</td>\n",
+       "      <td>5.622204</td>\n",
+       "      <td>6.422705</td>\n",
+       "      <td>49.443471</td>\n",
        "      <td>37.034649</td>\n",
-       "      <td>11.398946</td>\n",
-       "      <td>35.364210</td>\n",
-       "      <td>6.642261</td>\n",
-       "      <td>11.646088</td>\n",
-       "      <td>6.475679</td>\n",
-       "      <td>22.795198</td>\n",
-       "      <td>48.080214</td>\n",
-       "      <td>31.149316</td>\n",
+       "      <td>9.357086</td>\n",
+       "      <td>36.076612</td>\n",
+       "      <td>6.601689</td>\n",
+       "      <td>10.599127</td>\n",
+       "      <td>7.181774</td>\n",
+       "      <td>30.107589</td>\n",
+       "      <td>102.543241</td>\n",
+       "      <td>75.250448</td>\n",
        "      <td>21.709793</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -2388,68 +2388,68 @@
       "text/plain": [
        "               Time            V1            V2            V3            V4  \\\n",
        "count  56961.000000  56961.000000  56961.000000  56961.000000  56961.000000   \n",
-       "mean      -0.001286     -0.002631     -0.001593      0.003897      0.005106   \n",
-       "std        1.000527      1.006693      0.997540      0.997086      1.006792   \n",
-       "min       -1.996580    -20.443469    -23.276581    -22.213271     -4.013912   \n",
-       "25%       -0.855242     -0.469524     -0.358808     -0.578933     -0.596381   \n",
-       "50%       -0.216935      0.002039      0.042019      0.121779     -0.011481   \n",
-       "75%        0.935268      0.669606      0.487402      0.684229      0.532341   \n",
-       "max        1.641865      1.249048     11.446954      2.690291      8.969436   \n",
+       "mean      -0.000981      0.002513     -0.005920      0.004468      0.003515   \n",
+       "std        0.999579      1.002597      1.018439      0.992005      1.001664   \n",
+       "min       -1.996495    -28.798504    -44.035215    -20.513492     -3.653301   \n",
+       "25%       -0.848082     -0.472047     -0.362059     -0.583160     -0.597038   \n",
+       "50%       -0.216304      0.008638      0.036382      0.115526     -0.010671   \n",
+       "75%        0.935563      0.674915      0.484174      0.678346      0.525606   \n",
+       "max        1.642055      1.253349     10.121300      2.690291     11.918722   \n",
        "\n",
        "                 V5            V6            V7            V8            V9  \\\n",
        "count  56961.000000  56961.000000  56961.000000  56961.000000  56961.000000   \n",
-       "mean       0.001136      0.003338      0.001420      0.005280     -0.003772   \n",
-       "std        0.991543      0.996668      0.973116      0.948871      0.993364   \n",
-       "min      -23.251009    -15.288057    -19.739398    -33.230188     -7.955047   \n",
-       "25%       -0.500065     -0.571141     -0.450554     -0.172817     -0.584409   \n",
-       "50%       -0.041114     -0.205355      0.032774      0.021144     -0.049309   \n",
-       "75%        0.446344      0.303910      0.462273      0.277804      0.538129   \n",
-       "max       21.128231     16.057594     27.728845     16.049132      8.385903   \n",
+       "mean       0.000882     -0.007049      0.000640      0.003547     -0.003336   \n",
+       "std        0.990564      0.987920      0.975891      0.974153      0.996545   \n",
+       "min      -29.290217    -19.636023    -22.807581    -32.877534     -8.630238   \n",
+       "25%       -0.502249     -0.579251     -0.448915     -0.174413     -0.591501   \n",
+       "50%       -0.040643     -0.207725      0.029786      0.017563     -0.046103   \n",
+       "75%        0.438875      0.294860      0.461690      0.274024      0.542844   \n",
+       "max       25.214090     17.952680     35.611260     16.751505      9.459845   \n",
        "\n",
        "                V10           V11           V12           V13           V14  \\\n",
        "count  56961.000000  56961.000000  56961.000000  56961.000000  56961.000000   \n",
-       "mean       0.001565      0.006694      0.006557      0.000992     -0.004114   \n",
-       "std        0.990888      1.002877      1.000311      0.997226      1.005559   \n",
-       "min      -16.780247     -4.475685    -18.698648     -3.907070    -20.044245   \n",
-       "25%       -0.493729     -0.741852     -0.396398     -0.649772     -0.449871   \n",
-       "50%       -0.082977     -0.021572      0.147645     -0.015403      0.051120   \n",
-       "75%        0.419252      0.734912      0.625491      0.667426      0.513534   \n",
-       "max       14.080677     11.775017      4.409860      4.590704      7.742518   \n",
+       "mean       0.001112      0.001566     -0.003003      0.002476     -0.001457   \n",
+       "std        0.986738      0.997945      0.986894      1.006684      0.989983   \n",
+       "min      -19.239745     -4.026853    -18.062021     -3.867219    -18.829627   \n",
+       "25%       -0.492679     -0.747584     -0.413937     -0.658836     -0.444461   \n",
+       "50%       -0.085007     -0.026277      0.135150     -0.013610      0.051565   \n",
+       "75%        0.424213      0.726922      0.617056      0.674697      0.513263   \n",
+       "max       14.001643     11.000614      4.409860      4.590704      7.546716   \n",
        "\n",
        "                V15           V16           V17           V18           V19  \\\n",
        "count  56961.000000  56961.000000  56961.000000  56961.000000  56961.000000   \n",
-       "mean      -0.001910      0.003052      0.000119      0.001319      0.000586   \n",
-       "std        1.002712      1.001343      1.003773      1.005842      1.000361   \n",
-       "min       -4.397796    -15.182703    -29.626400    -11.332636     -6.059567   \n",
-       "25%       -0.640807     -0.531302     -0.575388     -0.595679     -0.564300   \n",
-       "50%        0.049099      0.075530     -0.077553      0.000442      0.006663   \n",
-       "75%        0.709356      0.602099      0.472357      0.597576      0.569193   \n",
-       "max        5.600456      6.845356      8.962121      4.857366      6.869402   \n",
+       "mean       0.004771     -0.001298      0.006475      0.002145     -0.003752   \n",
+       "std        0.998620      0.993258      0.979525      0.996698      1.007079   \n",
+       "min       -4.797586    -15.182703    -26.943365    -11.081002     -8.523283   \n",
+       "25%       -0.635628     -0.536064     -0.567924     -0.593381     -0.570330   \n",
+       "50%        0.054417      0.069225     -0.071421     -0.008159      0.002786   \n",
+       "75%        0.713854      0.595188      0.469295      0.599208      0.564558   \n",
+       "max        6.319690      6.528161     10.052776      5.622204      6.422705   \n",
        "\n",
        "                V20           V21           V22           V23           V24  \\\n",
        "count  56961.000000  56961.000000  56961.000000  56961.000000  56961.000000   \n",
-       "mean      -0.001582     -0.001935     -0.001926     -0.001921      0.000102   \n",
-       "std        1.006151      0.966084      0.995486      0.993314      0.999420   \n",
-       "min      -30.673398    -25.367337    -12.246104    -37.187338     -4.636193   \n",
-       "25%       -0.272836     -0.311325     -0.747634     -0.262140     -0.584945   \n",
-       "50%       -0.079186     -0.041587      0.008966     -0.018311      0.067176   \n",
-       "75%        0.173913      0.250213      0.719569      0.241384      0.726921   \n",
-       "max       22.234396     37.034649     11.398946     35.364210      6.642261   \n",
+       "mean       0.005089      0.002532     -0.002266      0.000694      0.001255   \n",
+       "std        0.984642      0.978265      0.996165      0.986243      0.996716   \n",
+       "min      -26.675106    -25.326726    -13.089986    -42.838782     -4.660608   \n",
+       "25%       -0.275025     -0.310829     -0.748917     -0.256147     -0.581180   \n",
+       "50%       -0.080291     -0.040014      0.009542     -0.018098      0.070257   \n",
+       "75%        0.175128      0.251457      0.726915      0.239267      0.721670   \n",
+       "max       49.443471     37.034649      9.357086     36.076612      6.601689   \n",
        "\n",
        "                V25           V26           V27           V28        Amount  \\\n",
        "count  56961.000000  56961.000000  56961.000000  56961.000000  56961.000000   \n",
-       "mean      -0.005428     -0.002525     -0.001413     -0.000401      0.000211   \n",
-       "std        0.995134      1.001346      0.979838      1.026780      0.956109   \n",
-       "min       -9.458963     -3.557744    -24.515479    -25.520958     -0.353229   \n",
-       "25%       -0.611699     -0.681904     -0.174783     -0.161692     -0.331159   \n",
-       "50%        0.021756     -0.106715      0.003712      0.033434     -0.265271   \n",
-       "75%        0.664707      0.497974      0.224292      0.236219     -0.045377   \n",
-       "max       11.646088      6.475679     22.795198     48.080214     31.149316   \n",
+       "mean       0.000230     -0.003617     -0.001128     -0.005178      0.000832   \n",
+       "std        1.004727      1.005400      0.974566      1.028555      1.036437   \n",
+       "min      -14.379544     -5.401088    -23.266164    -25.520958     -0.353229   \n",
+       "25%       -0.608956     -0.679814     -0.174964     -0.161507     -0.330120   \n",
+       "50%        0.034972     -0.115678      0.003000      0.032948     -0.265031   \n",
+       "75%        0.674138      0.495060      0.225670      0.235973     -0.045537   \n",
+       "max       10.599127      7.181774     30.107589    102.543241     75.250448   \n",
        "\n",
        "       Amount_max_fraud  \n",
        "count      56961.000000  \n",
-       "mean          -0.000611  \n",
-       "std            0.993369  \n",
+       "mean          -0.000229  \n",
+       "std            0.997525  \n",
        "min           -0.046062  \n",
        "25%           -0.046062  \n",
        "50%           -0.046062  \n",
@@ -2457,7 +2457,7 @@
        "max           21.709793  "
       ]
      },
-     "execution_count": 32,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2478,7 +2478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {
     "_cell_guid": "3e0e5055-c2a2-4ae3-af2b-de125a639a95",
     "_uuid": "aa08adecd21ceb6f9a848781cda9d06c33228a51"
@@ -2497,7 +2497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -2545,7 +2545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {
     "_cell_guid": "b764085a-53cb-44b1-82f5-e1e55a006189",
     "_uuid": "90f9779d5395f8251c9fda66f04bbd478de7013d"
@@ -2569,7 +2569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {
     "_cell_guid": "f65afcbb-8b50-4341-ab1f-7e0c123228ac",
     "_uuid": "b705e2acc952ec62a1bd4ba471c832f71153d9e0"
@@ -2607,7 +2607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {
     "_cell_guid": "5f23426d-1a1f-444b-b283-d49e209fadfb",
     "_uuid": "19d65b6f8a52be7ed465746c8c5782f3af94b45f"
@@ -2625,7 +2625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {
     "_cell_guid": "7d907d74-1a9f-4846-8a28-36317e9f0c4e",
     "_uuid": "421e4fa26c569fedc5240bb3bdddd8f3e2f93aff"
@@ -2645,7 +2645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {
     "_cell_guid": "3f0b796a-8454-4cc9-b5ec-3075b9565bc3",
     "_uuid": "825805efc899b013c6bf7eaebdf10a68aee89d1a",
@@ -2656,27 +2656,27 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch: 0 Acc = 0.96045 Cost = 72040.01562 Valid_Acc = 0.96057 Valid_Cost =  5780.09863\n",
-      "Epoch: 5 Acc = 0.97615 Cost = 59136.39062 Valid_Acc = 0.97669 Valid_Cost =  4825.07178\n",
-      "Epoch: 10 Acc = 0.97558 Cost = 49881.53125 Valid_Acc = 0.97640 Valid_Cost =  5390.75293\n",
-      "Epoch: 15 Acc = 0.97695 Cost = 41524.66406 Valid_Acc = 0.97707 Valid_Cost =  6727.43359\n",
-      "Epoch: 20 Acc = 0.97956 Cost = 32224.70703 Valid_Acc = 0.97981 Valid_Cost =  9004.73535\n",
-      "Epoch: 25 Acc = 0.98736 Cost = 24203.04297 Valid_Acc = 0.98739 Valid_Cost =  13070.36719\n",
-      "Epoch: 30 Acc = 0.99214 Cost = 19842.75781 Valid_Acc = 0.99266 Valid_Cost =  18814.29102\n",
-      "Epoch: 35 Acc = 0.99255 Cost = 18297.86133 Valid_Acc = 0.99322 Valid_Cost =  25349.83008\n",
-      "Epoch: 40 Acc = 0.99368 Cost = 10427.76758 Valid_Acc = 0.99389 Valid_Cost =  29455.54688\n",
-      "Epoch: 45 Acc = 0.99122 Cost = 7588.53857 Valid_Acc = 0.99143 Valid_Cost =  32230.46875\n",
-      "Epoch: 50 Acc = 0.99425 Cost = 7408.01416 Valid_Acc = 0.99466 Valid_Cost =  38232.20312\n",
-      "Epoch: 55 Acc = 0.99285 Cost = 6066.94238 Valid_Acc = 0.99294 Valid_Cost =  40489.00000\n",
-      "Epoch: 60 Acc = 0.99652 Cost = 10733.87598 Valid_Acc = 0.99624 Valid_Cost =  46334.16797\n",
-      "Epoch: 65 Acc = 0.99609 Cost = 3727.55029 Valid_Acc = 0.99596 Valid_Cost =  48752.45312\n",
-      "Epoch: 70 Acc = 0.99247 Cost = 5904.94922 Valid_Acc = 0.99256 Valid_Cost =  44731.08594\n",
-      "Epoch: 75 Acc = 0.99488 Cost = 6603.65234 Valid_Acc = 0.99442 Valid_Cost =  50844.75000\n",
-      "Epoch: 80 Acc = 0.99630 Cost = 3647.98730 Valid_Acc = 0.99635 Valid_Cost =  51758.85156\n",
-      "Epoch: 85 Acc = 0.99706 Cost = 3211.94214 Valid_Acc = 0.99702 Valid_Cost =  60267.28906\n",
-      "Epoch: 90 Acc = 0.99712 Cost = 3631.59058 Valid_Acc = 0.99684 Valid_Cost =  62279.23047\n",
-      "Epoch: 95 Acc = 0.99730 Cost = 3395.90649 Valid_Acc = 0.99723 Valid_Cost =  63181.06250\n",
-      "Epoch: 100 Acc = 0.99285 Cost = 5376.84277 Valid_Acc = 0.99277 Valid_Cost =  47035.11719\n",
+      "Epoch: 0 Acc = 0.96798 Cost = 69768.75781 Valid_Acc = 0.96896 Valid_Cost =  6496.27637\n",
+      "Epoch: 5 Acc = 0.97706 Cost = 55160.21875 Valid_Acc = 0.97756 Valid_Cost =  6001.99268\n",
+      "Epoch: 10 Acc = 0.97528 Cost = 47303.35156 Valid_Acc = 0.97626 Valid_Cost =  6052.61523\n",
+      "Epoch: 15 Acc = 0.96992 Cost = 42592.11719 Valid_Acc = 0.96984 Valid_Cost =  6556.98730\n",
+      "Epoch: 20 Acc = 0.97315 Cost = 34016.53125 Valid_Acc = 0.97324 Valid_Cost =  7744.64990\n",
+      "Epoch: 25 Acc = 0.96592 Cost = 29393.52344 Valid_Acc = 0.96429 Valid_Cost =  10923.19238\n",
+      "Epoch: 30 Acc = 0.96947 Cost = 25339.57617 Valid_Acc = 0.96928 Valid_Cost =  14446.64453\n",
+      "Epoch: 35 Acc = 0.98453 Cost = 15805.66797 Valid_Acc = 0.98473 Valid_Cost =  18466.99609\n",
+      "Epoch: 40 Acc = 0.99003 Cost = 10293.99219 Valid_Acc = 0.98975 Valid_Cost =  23956.53125\n",
+      "Epoch: 45 Acc = 0.99058 Cost = 8710.39062 Valid_Acc = 0.99066 Valid_Cost =  26466.08008\n",
+      "Epoch: 50 Acc = 0.98655 Cost = 10369.94531 Valid_Acc = 0.98613 Valid_Cost =  27938.96680\n",
+      "Epoch: 55 Acc = 0.99605 Cost = 6647.59082 Valid_Acc = 0.99572 Valid_Cost =  34803.36328\n",
+      "Epoch: 60 Acc = 0.99304 Cost = 6389.05273 Valid_Acc = 0.99326 Valid_Cost =  29414.13672\n",
+      "Epoch: 65 Acc = 0.99607 Cost = 4425.70020 Valid_Acc = 0.99551 Valid_Cost =  40496.61719\n",
+      "Epoch: 70 Acc = 0.99449 Cost = 5236.49023 Valid_Acc = 0.99403 Valid_Cost =  41031.98047\n",
+      "Epoch: 75 Acc = 0.99723 Cost = 3715.36353 Valid_Acc = 0.99663 Valid_Cost =  45331.58594\n",
+      "Epoch: 80 Acc = 0.99494 Cost = 5643.76025 Valid_Acc = 0.99477 Valid_Cost =  37233.25781\n",
+      "Epoch: 85 Acc = 0.99803 Cost = 4176.42676 Valid_Acc = 0.99730 Valid_Cost =  53945.60938\n",
+      "Epoch: 90 Acc = 0.99724 Cost = 3877.90479 Valid_Acc = 0.99695 Valid_Cost =  54652.80859\n",
+      "Epoch: 95 Acc = 0.99828 Cost = 5205.10254 Valid_Acc = 0.99775 Valid_Cost =  59848.65234\n",
+      "Epoch: 100 Acc = 0.99771 Cost = 2827.92725 Valid_Acc = 0.99684 Valid_Cost =  62221.86328\n",
       "\n",
       "Optimization Finished!\n",
       "\n",
@@ -2761,7 +2761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {
     "_cell_guid": "c04813ad-3bbb-45c5-8098-06a78605d7b2",
     "_uuid": "2cc553c14ddcfcee1e3f833238da31fa18688c1e"
@@ -2769,7 +2769,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x288 with 2 Axes>"
       ]
@@ -2807,7 +2807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {
     "_cell_guid": "362ce01d-2582-47ea-9c04-3f2eb71dd87b",
     "_uuid": "a37daaf1fe266f2ee7840d08b9406bfa1b9ee841"
@@ -2818,7 +2818,7 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from ./model/model.ckpt\n",
-      "Testing Accuracy = 0.99227554\n"
+      "Testing Accuracy = 0.997156\n"
      ]
     }
    ],
@@ -2850,7 +2850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {
     "_cell_guid": "83d06e55-189a-4acb-bb42-934accebec99",
     "_uuid": "6918fa3e8198317d3b4e706e01366831ecc86c1e"
@@ -2873,7 +2873,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {
     "_cell_guid": "0e090edc-f544-41b6-8035-bf61c9e86205",
     "_uuid": "d0bdc4d26b0a9f23c9ed7df460d57e2f489ef164"
@@ -2885,7 +2885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {
     "_cell_guid": "5748a40a-3665-4ae6-8b21-36b0dedc2f83",
     "_uuid": "d800b296c079bf6e11d874cd482058c7fe9db931"
@@ -3163,7 +3163,7 @@
        "4   69.99      0                 0  "
       ]
      },
-     "execution_count": 44,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3184,7 +3184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {
     "_cell_guid": "b2967228-de84-4f0a-b1bb-502d3d45c28d",
     "_uuid": "87e7f5a83c48a98ae52cc55cf805cdf9a57d8426"
@@ -3196,7 +3196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {
     "_cell_guid": "0c2923f1-59a3-45b5-ab73-1ffc250ddf40",
     "_uuid": "0c430eecba7217f93d1ea970249fbbe85cd5f137",
@@ -3225,7 +3225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -3251,7 +3251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {
     "_cell_guid": "78dcafd6-c7e5-4396-b4f4-cba7ae30de9f",
     "_uuid": "e9eb5647ea9a1daf4f91f4577f3d4d0e8064f096"
@@ -3264,7 +3264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -3728,7 +3728,7 @@
        "130872 -0.560888  1354.98                 0  "
       ]
      },
-     "execution_count": 49,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3746,7 +3746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {
     "_cell_guid": "952ab8e5-cccd-4188-8690-c33e405545d4",
     "_uuid": "7ae50c4c16bf36e9b001f658909116004a86aed3"
@@ -3759,7 +3759,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {
     "_cell_guid": "5c03b27c-581a-452b-86dd-ae4ce2b37877",
     "_uuid": "92cce9a63827d1a9e11bc143b46f605f3879eeae"
@@ -3793,7 +3793,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "metadata": {
     "_cell_guid": "94bb1a46-193f-47fd-bf1f-f0e917492298",
     "_uuid": "add388ac1620f9fb2eb1850b35a81411c30055a6"
@@ -3806,7 +3806,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -3816,7 +3816,7 @@
        "       [1., 0.]], dtype=float32)"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3834,7 +3834,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "metadata": {
     "_cell_guid": "9136cfd3-7e69-4fdf-a658-d0677cb32e26",
     "_uuid": "94754afc163a673cfc20a64ae01032d0b6e60454"
@@ -3852,7 +3852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -4258,7 +4258,7 @@
        "max           21.709793  "
       ]
      },
-     "execution_count": 55,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4276,7 +4276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "metadata": {
     "_cell_guid": "6c0f7416-412a-4415-94e8-362fa13948ea",
     "_uuid": "9c648e3b03efea588dea1d20893bf148151447f7"
@@ -4296,7 +4296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {
     "_cell_guid": "bbf4cf25-3052-4b8f-9ac7-700865de4905",
     "_uuid": "cf449dc135ffd673be7ef9b800df733c6c48831d"
@@ -4324,7 +4324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 59,
    "metadata": {
     "_cell_guid": "2dec00ca-e47c-4142-89bc-e7a445314ebc",
     "_uuid": "086c950d76895f638b313701200a144fdf3851b6"
@@ -4343,7 +4343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "metadata": {
     "_cell_guid": "bbbd50fb-41e8-4f71-b497-f8e19038dad0",
     "_uuid": "239658b1175b942a7aa179f8ae8b71dccbcb8f6d"
@@ -4368,20 +4368,20 @@
       "Epoch 7/10\n",
       "227845/227845 [==============================] - 1s 3us/step - loss: 0.0401 - acc: 0.9973\n",
       "Epoch 8/10\n",
-      "227845/227845 [==============================] - 1s 3us/step - loss: 0.0375 - acc: 0.9976\n",
+      "227845/227845 [==============================] - 1s 3us/step - loss: 0.0376 - acc: 0.9976\n",
       "Epoch 9/10\n",
       "227845/227845 [==============================] - 1s 3us/step - loss: 0.0360 - acc: 0.9978\n",
       "Epoch 10/10\n",
-      "227845/227845 [==============================] - 1s 3us/step - loss: 0.0346 - acc: 0.9979\n"
+      "227845/227845 [==============================] - 1s 3us/step - loss: 0.0347 - acc: 0.9979\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<keras.callbacks.History at 0x14b01327d30>"
+       "<keras.callbacks.History at 0x2d12748f128>"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4394,7 +4394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 61,
    "metadata": {
     "_cell_guid": "e45c27c0-1e29-47cf-befe-bba155dd21cf",
     "_uuid": "a6790157aaf0370faa31223ebd4c1faeeaf84f9a"
@@ -4404,8 +4404,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "56962/56962 [==============================] - 3s 46us/step\n",
-      "Test score: 0.027564537810652604\n",
+      "56962/56962 [==============================] - 2s 40us/step\n",
+      "Test score: 0.027565599375151788\n",
       "Test accuracy: 0.9982795547909132\n"
      ]
     }
@@ -4434,7 +4434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
    "metadata": {
     "_cell_guid": "1d565b39-10cf-48ee-8dad-98f585dc0b23",
     "_uuid": "c59e2bca8f631eb923808c7a7c845beae3265770"
@@ -4446,43 +4446,43 @@
      "text": [
       "Train on 227845 samples, validate on 56962 samples\n",
       "Epoch 1/20\n",
-      " - 1s - loss: 0.0331 - acc: 0.9980 - val_loss: 0.0276 - val_acc: 0.9983\n",
+      " - 1s - loss: 0.0332 - acc: 0.9980 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 2/20\n",
-      " - 1s - loss: 0.0329 - acc: 0.9980 - val_loss: 0.0276 - val_acc: 0.9983\n",
+      " - 1s - loss: 0.0326 - acc: 0.9980 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 3/20\n",
-      " - 1s - loss: 0.0324 - acc: 0.9981 - val_loss: 0.0276 - val_acc: 0.9983\n",
+      " - 1s - loss: 0.0322 - acc: 0.9981 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 4/20\n",
-      " - 1s - loss: 0.0317 - acc: 0.9981 - val_loss: 0.0276 - val_acc: 0.9983\n",
+      " - 1s - loss: 0.0318 - acc: 0.9981 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 5/20\n",
-      " - 1s - loss: 0.0311 - acc: 0.9981 - val_loss: 0.0276 - val_acc: 0.9983\n",
+      " - 1s - loss: 0.0309 - acc: 0.9981 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 6/20\n",
-      " - 1s - loss: 0.0307 - acc: 0.9981 - val_loss: 0.0276 - val_acc: 0.9983\n",
+      " - 1s - loss: 0.0308 - acc: 0.9981 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 7/20\n",
-      " - 1s - loss: 0.0306 - acc: 0.9982 - val_loss: 0.0276 - val_acc: 0.9983\n",
+      " - 1s - loss: 0.0305 - acc: 0.9982 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 8/20\n",
-      " - 1s - loss: 0.0295 - acc: 0.9981 - val_loss: 0.0276 - val_acc: 0.9983\n",
+      " - 1s - loss: 0.0297 - acc: 0.9982 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 9/20\n",
       " - 1s - loss: 0.0296 - acc: 0.9982 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 10/20\n",
       " - 1s - loss: 0.0298 - acc: 0.9982 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 11/20\n",
-      " - 1s - loss: 0.0288 - acc: 0.9982 - val_loss: 0.0276 - val_acc: 0.9983\n",
+      " - 1s - loss: 0.0287 - acc: 0.9982 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 12/20\n",
-      " - 1s - loss: 0.0292 - acc: 0.9982 - val_loss: 0.0276 - val_acc: 0.9983\n",
+      " - 1s - loss: 0.0293 - acc: 0.9982 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 13/20\n",
-      " - 1s - loss: 0.0286 - acc: 0.9983 - val_loss: 0.0276 - val_acc: 0.9983\n",
+      " - 1s - loss: 0.0284 - acc: 0.9982 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 14/20\n",
-      " - 1s - loss: 0.0288 - acc: 0.9982 - val_loss: 0.0276 - val_acc: 0.9983\n",
+      " - 1s - loss: 0.0285 - acc: 0.9982 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 15/20\n",
-      " - 1s - loss: 0.0286 - acc: 0.9982 - val_loss: 0.0276 - val_acc: 0.9983\n",
+      " - 1s - loss: 0.0283 - acc: 0.9982 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 16/20\n",
-      " - 1s - loss: 0.0279 - acc: 0.9982 - val_loss: 0.0276 - val_acc: 0.9983\n",
+      " - 1s - loss: 0.0275 - acc: 0.9982 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 17/20\n",
-      " - 1s - loss: 0.0275 - acc: 0.9983 - val_loss: 0.0276 - val_acc: 0.9983\n",
+      " - 1s - loss: 0.0274 - acc: 0.9983 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 18/20\n",
-      " - 1s - loss: 0.0272 - acc: 0.9983 - val_loss: 0.0276 - val_acc: 0.9983\n",
-      "Epoch 19/20\n",
       " - 1s - loss: 0.0270 - acc: 0.9983 - val_loss: 0.0276 - val_acc: 0.9983\n",
+      "Epoch 19/20\n",
+      " - 1s - loss: 0.0268 - acc: 0.9983 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 20/20\n",
       " - 1s - loss: 0.0262 - acc: 0.9983 - val_loss: 0.0275 - val_acc: 0.9983\n"
      ]
@@ -4495,7 +4495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 63,
    "metadata": {
     "_cell_guid": "1970b306-ca29-4d69-bef8-78dd0380fc04",
     "_uuid": "10b4e95a8da3549bac6c619f8e3a42a79523d11d"
@@ -4507,7 +4507,7 @@
        "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4519,7 +4519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 64,
    "metadata": {
     "_cell_guid": "ac30ce3a-89f9-40a4-a392-50f7d8562f9a",
     "_uuid": "0e03766581fb5a6519af77ac9b737869c7b0e28b",
@@ -4528,7 +4528,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -4568,7 +4568,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 65,
    "metadata": {
     "_cell_guid": "52ed3913-ab3c-48e3-9de7-3810af65283b",
     "_uuid": "0df771cc4e8f0c117aaabd93d9778bec837851d4"
@@ -4606,7 +4606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 66,
    "metadata": {
     "_cell_guid": "fac6cd1f-36ee-4aaa-af29-f94e69db57ef",
     "_uuid": "3afc3624947e0f5454a3893f86163dea548a70f9"
@@ -4659,7 +4659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 67,
    "metadata": {
     "_cell_guid": "46fd0dfc-0e84-45c2-9693-c07e6ec9fdb2",
     "_uuid": "43c44a4085b5376821650f4e520fddd7c44d10b4"
@@ -4689,7 +4689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 68,
    "metadata": {
     "_cell_guid": "4e21db4e-0eb1-46b0-a72c-72e4492291e0",
     "_uuid": "0977481fa05b2f9189b4287a74d9f27e74e60af5"
@@ -4712,7 +4712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 69,
    "metadata": {
     "_cell_guid": "92fea18e-5583-4bb8-b6ba-c7a0c7d464cd",
     "_uuid": "d38a1b8bb6933057053881c83df26893d3c2354e"
@@ -4737,7 +4737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 70,
    "metadata": {
     "_cell_guid": "a9efb494-f9ac-43ea-97e0-82dbf21bfb30",
     "_uuid": "4fdf0d3c72e8337887142b87ac1d8b6ebee23644"
@@ -4764,7 +4764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 71,
    "metadata": {
     "_cell_guid": "b6362ffe-6174-4194-b91c-eaea6c1e9905",
     "_uuid": "68953af8780d09095e1cccf95899587ef7fa54fc"
@@ -4818,7 +4818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 72,
    "metadata": {
     "_uuid": "e7eaba597a604fdf84e1c7c53a21ea5a273df289"
    },
@@ -4826,10 +4826,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x14b01e29fd0>"
+       "<matplotlib.legend.Legend at 0x2d12c359da0>"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -4857,7 +4857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 73,
    "metadata": {
     "_uuid": "40b643c218ef2e087703fe6e489d612b974ebd82"
    },
@@ -4895,7 +4895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 74,
    "metadata": {
     "_uuid": "85ef72f49fbb4b1aa012707166266317c9e58f63"
    },
@@ -4982,7 +4982,7 @@
        "max              254.158931      1.000000"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5003,7 +5003,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 75,
    "metadata": {
     "_uuid": "fb31d517e0af7ff89f1f2bbfaf30c805dc5fbee7"
    },
@@ -5030,7 +5030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 76,
    "metadata": {
     "_uuid": "f879d4001bc8784fd784c6f17af3af6ca8000b06"
    },
@@ -5071,20 +5071,9 @@
     "거래의 클래스를 예측하기 위해선, 해당 거래의 재구성된 에러를 추정해야합니다. **만약 예측된 에러가 threshold(임계값)보다 커진다면,** 이것은 사기(Fraud)라고 판단하고, 나머지는 보통(Normal) 거래로 판단합니다."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "_uuid": "dce49318cf93a3a156c2512a93ba48d4066e9bcf"
-   },
-   "source": [
-    "## Confusion Matrix with Autoencoder\n",
-    "\n",
-    "In order to predict the class of a transaction, we estimte the reconstruction error for that transaction. **If the predicted error is larger than a threshold** it is marked as Fraud and otherwise Normal. "
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 77,
    "metadata": {
     "_uuid": "c74ffebc84540fe12484e780deaa5974186db1b4"
    },
@@ -5097,7 +5086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 78,
    "metadata": {
     "_uuid": "ff271d2e1ade6d753e6389107b83983d15b2e371"
    },
@@ -5134,7 +5123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 79,
    "metadata": {
     "_uuid": "f7ff959efe78f6f49f44f9b6739db5408bd8ee02"
    },
@@ -5179,34 +5168,33 @@
     "collapsed": true
    },
    "source": [
-    "## 3. Visualizing the Data with t-SNE\n",
+    "## 3. t-SNE로 데이터 시각화\n",
     "\n",
-    "**t-Distributed Stochastic Neighbor Embedding (t-SNE)** is a technique ([wiki page](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding)) for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets. It was developed by Geoffrey Hinton and Laurens van der Maaten.  \n",
+    "**t-Distributed Stochastic Neighbor Embedding (t-분포 확률적 이웃 임베딩, t-SNE) [wiki page](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding)** 은 차원 축소 기술 중 하나입니다. 특히 높은 차원의 데이터를 시각화에 알맞도록(2, 3차원) 차원축소를 합니다. t-SNE는 Geoffrey Hinton 과 Laurens van der Maaten 이 개발하였습니다.\n",
     "\n",
-    "In this example, first we sue t-SNE on a original data sample (since the data is big and takes time to process) and then on the data used for training. If we see aclear contrast between fraud and normal in the scatter plot, then it indicates that the neural network and feature engineering works well for this prediction. "
+    "원본데이터를 다 사용하면 시간이 너무 많이 걸리기 때문에, 원본 데이터를 t-SNE를 위해 조금 샘플링 하도록 하겠습니다. 만약 t-SNE를 통한 산포도를 통해 사기 거래와 보통 거래를 확연히 대조된다면, 신경망과 특성공학을 통해 이 예측을 더 좋게 동작할 수 있을 것입니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 80,
    "metadata": {
     "_cell_guid": "666002dd-a494-44f8-8e5b-6edd1caac68b",
     "_uuid": "6efefcdc0b6a0e9a26dd603f3de470f09d1bf51c",
-    "collapsed": true
+    "scrolled": true
    },
    "outputs": [],
    "source": [
     "#reload the original dataset\n",
-    "tsne_data = pd.read_csv(\"../input/creditcard.csv\")"
+    "tsne_data = pd.read_csv(\"./input/creditcard.csv\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 81,
    "metadata": {
     "_cell_guid": "1ca3c3d5-7021-408b-9b8e-5b096ffdab70",
-    "_uuid": "9d16809b2e58203f07878e5f84d231627be0a3bd",
-    "collapsed": true
+    "_uuid": "9d16809b2e58203f07878e5f84d231627be0a3bd"
    },
    "outputs": [],
    "source": [
@@ -5217,11 +5205,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 82,
    "metadata": {
     "_cell_guid": "e2dff561-70c0-41db-b4fd-284e81ca937c",
-    "_uuid": "580710b043028b8174afa8f3a4966b3ee056f7e7",
-    "collapsed": true
+    "_uuid": "580710b043028b8174afa8f3a4966b3ee056f7e7"
    },
    "outputs": [],
    "source": [
@@ -5235,11 +5222,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 83,
    "metadata": {
     "_cell_guid": "4aef9135-c77f-4e0a-9778-d652c44642fa",
-    "_uuid": "7ea15c61745436c5d739b25a7d6a590a4000b638",
-    "collapsed": true
+    "_uuid": "7ea15c61745436c5d739b25a7d6a590a4000b638"
    },
    "outputs": [],
    "source": [
@@ -5249,13 +5235,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 84,
    "metadata": {
     "_cell_guid": "87a60d8e-1146-40fc-890f-7009ae9fc56b",
-    "_uuid": "e3467fb9d00627e3c343687a6c708c2bba2d2246",
-    "collapsed": true
+    "_uuid": "e3467fb9d00627e3c343687a6c708c2bba2d2246"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 1008x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#Build the scatter plot with the two types of transactions.\n",
     "color_map = {0:'red', 1:'blue'}\n",
@@ -5274,24 +5270,17 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "_cell_guid": "e30817e4-1f4d-410a-8de7-dff5ecd3f654",
-    "_uuid": "0749916bb5934b4c7177fc755a80c04226cb5f79",
-    "collapsed": true
-   },
+   "metadata": {},
    "source": [
-    "Most of the fraudulent transactions are well separated in the original dataset sample in this t-SNE plot, while some are mixed within the rest of the data.  \n",
-    "Just to note, this visualizayion may change from sample to sample and it may not be **exactly** representative for the whole dataset.\n",
-    "Now, let us look at t-SNE of the training dataset that was used in this analysis. It is expected to show good separation like the above one.  "
+    "대부분의 사기 거래들은 t-SNE 산점도에서 보시는 바와 같이 조금 섞여 있긴하지만 잘 분리되어 있습니다. 이 시각화는 전체 데이터를 완전히 대표하는게 아니고 샘플마다 다르게 나타날 수 있습니다. 지금, 우리가 분석에 사용했던 Training 셋을 t-SNE를 통해 살펴보도록 하겠습니다. 아마 위와 같이 분리가 잘되는 것을 볼 수 있을 것입니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 85,
    "metadata": {
     "_cell_guid": "f8aff8f9-74e4-4ae6-8b5a-2ebe4fa7159f",
-    "_uuid": "a687a2689a1bda5e00fbaf76d9cff4e5e0b40c05",
-    "collapsed": true
+    "_uuid": "a687a2689a1bda5e00fbaf76d9cff4e5e0b40c05"
    },
    "outputs": [],
    "source": [
@@ -5304,11 +5293,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 86,
    "metadata": {
     "_cell_guid": "409fe27f-1080-457f-81c2-0b56d72621a3",
-    "_uuid": "4525e5241602370a34859974b55aa29efec377e6",
-    "collapsed": true
+    "_uuid": "4525e5241602370a34859974b55aa29efec377e6"
    },
    "outputs": [],
    "source": [
@@ -5321,11 +5309,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 87,
    "metadata": {
     "_cell_guid": "7d7cec91-ed1a-4a7e-87e4-f64d19fd882b",
-    "_uuid": "76ea2a5da95e3b08893345196000cb5412ab386a",
-    "collapsed": true
+    "_uuid": "76ea2a5da95e3b08893345196000cb5412ab386a"
    },
    "outputs": [],
    "source": [
@@ -5334,13 +5321,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 88,
    "metadata": {
     "_cell_guid": "74fe3bcd-941a-4606-a661-6bf8b11a52c9",
-    "_uuid": "f7b4ece7e3296e94bfc6410fe2e90bd2ad3c2175",
-    "collapsed": true
+    "_uuid": "f7b4ece7e3296e94bfc6410fe2e90bd2ad3c2175"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "color_map = {1:'red', 0:'blue'}\n",
     "plt.figure()\n",
@@ -5358,42 +5355,43 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "_cell_guid": "0ff5c098-92f7-491b-94da-60b5853b95c0",
-    "_uuid": "07509dc372fe310a06ab2dfb09f221af1285f794"
-   },
+   "metadata": {},
    "source": [
-    "Here we see that fraud and normal transactions are almost clearly separated. This indicates that we should ne able to develop a model based on feature engineering based on traning subset and predict on the testing subset."
+    "여기에서 봤듯이 대부분 보통 거래들과 잘 분리됨을 볼 수 있습니다. 이는 우리가 특성 공학을 기반으로한 모델의 성능을 좋게 할 수 있음을 나타냅니다."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "_cell_guid": "48e41d3d-7ec6-42aa-bf8d-482de2a4502a",
-    "_uuid": "30885af43df07288dc8ad76027cf0436591e3ccf",
-    "collapsed": true
-   },
+   "metadata": {},
    "source": [
-    "### Conclusion\n",
+    "### 결론\n",
+    "\n",
+    "* EDA로 사기 거래와 보통 거래는 어떤 파라미터에서도 확연히 나눠지는 것을 볼 수 없었습니다.\n",
     "\n",
-    "* EDA did not show clear separation of fraud and normal transaction captured by any single parameter\n",
-    "* Fraud transactions are typically small. On a crude term, transaction with values more than maximum of  fraud transaction can safely be assumed as normal\n",
-    "* Both Tensorflow and Keras model built on the creditcard dataset showed very high accuracies (99.46% & 99.82%) however, failed to capture the Fraud transaction in this highly skewed data\n",
-    "* Autoencoder model with a small threshold for reconstruction error can capture most of Fraud transaction however, it also significantly misclassify Normal transaction as Fraud.\n",
-    "* t-SNE plot showed good separation between the normal and fraudalant transaction in the scatterplot suggesting prediction model to show good accuracy in model developed training and testing within the dataset.\n",
+    "* 사기 거래들은 매우 적었습니다. 다르게 말하면, 사기 거래의 최대 값보다 큰 거래들은 보통 거래라 판단할 수 있었습니다.\n",
     "\n",
-    "### How to improve on fraud detection?\n",
+    "* Tensorflow와 Keras로 만든 모델(FC)들은 매우 높은 정확도(99.46%, 99.82%)를 볼 수 있었습니다. 하지만, 매우 편향된 데이터라 사기 거래를 탐지하진 못했습니다.\n",
+    "\n",
+    "* 새로 재편성한 error계산 법으로 생성된 작은 임계값(threshold)를 가지는 Autoencoder 모델은 대부분의 사기 거래를 탐지할 수 있었습니다. 그러나, 보통거래를 상당히 사기거래로 잘못 탐지하였습니다.\n",
+    "\n",
+    "* t-SNE 산점도는 보통 거래와 사기데이터를 시각적으로 잘 구분됨을 보여주었습니다. 이는 특성 공학을 잘 하게 된다면 좋은 정확도를 가지는 모델을 만들 수 있다는 것을 보여주었습니다.\n",
+    "\n",
+    "\n",
+    "### 어떻게 사기 탐지 정확도를 향상시킬 수 있을까?\n",
+    "\n",
+    "* 물론, 데이터가 많은게 좋습니다. (특히 NN에서)\n",
+    "\n",
+    "* 더 큰 Autoencoder NN을 학습 시킵니다. (혹시 다른 방법이 있을까요?)\n",
     "\n",
-    "* Of course, more data is always better, particularly for NN\n",
-    "* Train a larger Autoencoder NN (any other method?)\n",
     "\n",
     "### Reference:\n",
     "\n",
-    "* Thanks to [tensoflow Kaggle notebook by Currie32](https://www.kaggle.com/currie32/predicting-fraud-with-tensorflow) that I directly took help from to exercise this analysis in implementing Tensorflow\n",
-    "* Documentation in [Keras model](https://keras.io/getting-started/sequential-model-guide/)\n",
-    "* Great Medium blog about applying NN [Autoencoder on Fraud Detection](https://medium.com/@curiousily/credit-card-fraud-detection-using-autoencoders-in-keras-tensorflow-for-hackers-part-vii-20e0c85301bd)\n",
+    "* [tensoflow Kaggle notebook by Currie32](https://www.kaggle.com/currie32/predicting-fraud-with-tensorflow) 는 Tensorflow로 개발된 이 분석을 하는 것을 도와줍니다. 이를 제공해 주셔서 감사합니다.\n",
+    "* [Keras model](https://keras.io/getting-started/sequential-model-guide/) 문서\n",
+    "* [Autoencoder on Fraud Detection](https://medium.com/@curiousily/credit-card-fraud-detection-using-autoencoders-in-keras-tensorflow-for-hackers-part-vii-20e0c85301bd)를 적용하는 블로그, Medium\n",
+    "\n",
     "\n",
-    "### If you have any suggestion, let me know!"
+    "### 만약 다른 제안이나 수정할 사항있으시면 알려주세요!"
    ]
   }
  ],