diff --git a/.gitignore b/.gitignore
index 4cd40e4..d42017f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,3 +3,5 @@
 **/input
 **/model
 **/data
+**/logs
+**/model.h5
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 9a622da..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>63634</th>\n",
+       "      <th>10484</th>\n",
        "      <td>578.28934</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>108258</th>\n",
+       "      <th>15539</th>\n",
        "      <td>578.28934</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>82400</th>\n",
+       "      <th>189701</th>\n",
        "      <td>578.28934</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>229712</th>\n",
+       "      <th>124036</th>\n",
        "      <td>578.28934</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>239499</th>\n",
+       "      <th>30473</th>\n",
        "      <td>578.28934</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
@@ -2005,14 +2005,14 @@
       ],
       "text/plain": [
        "            Fraud  Normal\n",
-       "63634   578.28934     0.0\n",
-       "108258  578.28934     0.0\n",
-       "82400   578.28934     0.0\n",
-       "229712  578.28934     0.0\n",
-       "239499  578.28934     0.0"
+       "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>-0.001558</td>\n",
-       "      <td>0.000875</td>\n",
-       "      <td>-0.001892</td>\n",
-       "      <td>-0.001439</td>\n",
-       "      <td>-0.003383</td>\n",
-       "      <td>0.002990</td>\n",
-       "      <td>-0.002319</td>\n",
-       "      <td>-0.004030</td>\n",
-       "      <td>0.004065</td>\n",
-       "      <td>0.005675</td>\n",
-       "      <td>-0.003996</td>\n",
-       "      <td>0.004484</td>\n",
-       "      <td>-0.001110</td>\n",
-       "      <td>0.002187</td>\n",
-       "      <td>-0.002189</td>\n",
-       "      <td>0.001561</td>\n",
-       "      <td>0.000641</td>\n",
-       "      <td>0.002570</td>\n",
-       "      <td>-0.001781</td>\n",
-       "      <td>-0.005305</td>\n",
-       "      <td>0.003222</td>\n",
-       "      <td>-0.002413</td>\n",
-       "      <td>-0.002843</td>\n",
-       "      <td>-0.004712</td>\n",
-       "      <td>0.004207</td>\n",
-       "      <td>-0.002641</td>\n",
-       "      <td>-0.004198</td>\n",
-       "      <td>-0.001204</td>\n",
-       "      <td>-0.007545</td>\n",
-       "      <td>-0.000781</td>\n",
-       "      <td>0.004354</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>0.998500</td>\n",
-       "      <td>0.991199</td>\n",
-       "      <td>0.990627</td>\n",
-       "      <td>0.992018</td>\n",
-       "      <td>0.997557</td>\n",
-       "      <td>0.990920</td>\n",
-       "      <td>1.000773</td>\n",
-       "      <td>0.970588</td>\n",
-       "      <td>0.952178</td>\n",
-       "      <td>0.997451</td>\n",
-       "      <td>0.994908</td>\n",
-       "      <td>0.996820</td>\n",
-       "      <td>1.001257</td>\n",
-       "      <td>0.997821</td>\n",
-       "      <td>0.997179</td>\n",
-       "      <td>0.998505</td>\n",
-       "      <td>1.001794</td>\n",
-       "      <td>1.018943</td>\n",
-       "      <td>1.004320</td>\n",
-       "      <td>0.993911</td>\n",
-       "      <td>0.942704</td>\n",
-       "      <td>0.939417</td>\n",
-       "      <td>0.989367</td>\n",
-       "      <td>0.965734</td>\n",
-       "      <td>1.002527</td>\n",
-       "      <td>0.997197</td>\n",
-       "      <td>1.002805</td>\n",
-       "      <td>0.986277</td>\n",
-       "      <td>0.933587</td>\n",
-       "      <td>0.954577</td>\n",
-       "      <td>1.046103</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>-1.996495</td>\n",
-       "      <td>-17.189967</td>\n",
-       "      <td>-30.511117</td>\n",
-       "      <td>-20.154062</td>\n",
-       "      <td>-3.927001</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>-15.052954</td>\n",
-       "      <td>-25.218244</td>\n",
-       "      <td>-42.653531</td>\n",
-       "      <td>-8.613050</td>\n",
-       "      <td>-20.376630</td>\n",
-       "      <td>-4.700120</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>-4.027674</td>\n",
-       "      <td>-18.381718</td>\n",
-       "      <td>-4.178028</td>\n",
+       "      <td>-3.867219</td>\n",
+       "      <td>-18.829627</td>\n",
+       "      <td>-4.797586</td>\n",
        "      <td>-15.182703</td>\n",
-       "      <td>-26.619430</td>\n",
-       "      <td>-11.332636</td>\n",
-       "      <td>-5.576921</td>\n",
-       "      <td>-25.102071</td>\n",
-       "      <td>-30.857650</td>\n",
-       "      <td>-10.220648</td>\n",
-       "      <td>-43.582333</td>\n",
-       "      <td>-4.647752</td>\n",
-       "      <td>-11.098125</td>\n",
-       "      <td>-3.438550</td>\n",
-       "      <td>-21.900531</td>\n",
-       "      <td>-26.225413</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>-0.853642</td>\n",
-       "      <td>-0.466209</td>\n",
-       "      <td>-0.362183</td>\n",
-       "      <td>-0.585318</td>\n",
-       "      <td>-0.602321</td>\n",
-       "      <td>-0.501455</td>\n",
-       "      <td>-0.580411</td>\n",
-       "      <td>-0.452014</td>\n",
-       "      <td>-0.174859</td>\n",
-       "      <td>-0.575417</td>\n",
-       "      <td>-0.496465</td>\n",
-       "      <td>-0.742776</td>\n",
-       "      <td>-0.406047</td>\n",
-       "      <td>-0.646881</td>\n",
-       "      <td>-0.446149</td>\n",
-       "      <td>-0.632536</td>\n",
-       "      <td>-0.529489</td>\n",
-       "      <td>-0.569855</td>\n",
-       "      <td>-0.595161</td>\n",
-       "      <td>-0.567157</td>\n",
-       "      <td>-0.272174</td>\n",
-       "      <td>-0.312526</td>\n",
-       "      <td>-0.752276</td>\n",
-       "      <td>-0.260018</td>\n",
-       "      <td>-0.585197</td>\n",
-       "      <td>-0.610906</td>\n",
-       "      <td>-0.683196</td>\n",
-       "      <td>-0.174948</td>\n",
-       "      <td>-0.158387</td>\n",
-       "      <td>-0.330520</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>-0.215398</td>\n",
-       "      <td>0.010908</td>\n",
-       "      <td>0.039725</td>\n",
-       "      <td>0.114781</td>\n",
-       "      <td>-0.017499</td>\n",
-       "      <td>-0.039237</td>\n",
-       "      <td>-0.207896</td>\n",
-       "      <td>0.031778</td>\n",
-       "      <td>0.018143</td>\n",
-       "      <td>-0.044134</td>\n",
-       "      <td>-0.086174</td>\n",
-       "      <td>-0.028081</td>\n",
-       "      <td>0.139761</td>\n",
-       "      <td>-0.010723</td>\n",
-       "      <td>0.049833</td>\n",
-       "      <td>0.058610</td>\n",
-       "      <td>0.080505</td>\n",
-       "      <td>-0.074360</td>\n",
-       "      <td>-0.007117</td>\n",
-       "      <td>-0.000040</td>\n",
-       "      <td>-0.080443</td>\n",
-       "      <td>-0.041695</td>\n",
-       "      <td>0.003188</td>\n",
-       "      <td>-0.017968</td>\n",
-       "      <td>0.070837</td>\n",
-       "      <td>0.028794</td>\n",
-       "      <td>-0.115727</td>\n",
-       "      <td>0.003776</td>\n",
-       "      <td>0.034129</td>\n",
-       "      <td>-0.264951</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>0.935794</td>\n",
-       "      <td>0.671981</td>\n",
-       "      <td>0.486347</td>\n",
-       "      <td>0.674209</td>\n",
-       "      <td>0.520561</td>\n",
-       "      <td>0.441977</td>\n",
-       "      <td>0.296168</td>\n",
-       "      <td>0.458953</td>\n",
-       "      <td>0.275407</td>\n",
-       "      <td>0.549793</td>\n",
-       "      <td>0.412137</td>\n",
-       "      <td>0.730527</td>\n",
-       "      <td>0.619815</td>\n",
-       "      <td>0.665147</td>\n",
-       "      <td>0.515714</td>\n",
-       "      <td>0.713242</td>\n",
-       "      <td>0.596373</td>\n",
-       "      <td>0.476147</td>\n",
-       "      <td>0.592595</td>\n",
-       "      <td>0.559670</td>\n",
-       "      <td>0.175418</td>\n",
-       "      <td>0.252089</td>\n",
-       "      <td>0.728847</td>\n",
-       "      <td>0.234457</td>\n",
-       "      <td>0.735596</td>\n",
-       "      <td>0.668969</td>\n",
-       "      <td>0.500451</td>\n",
-       "      <td>0.229667</td>\n",
-       "      <td>0.236983</td>\n",
-       "      <td>-0.043378</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>1.641950</td>\n",
-       "      <td>1.240880</td>\n",
-       "      <td>9.990545</td>\n",
-       "      <td>2.761943</td>\n",
-       "      <td>8.351757</td>\n",
-       "      <td>21.022418</td>\n",
+       "      <td>1.642055</td>\n",
+       "      <td>1.253349</td>\n",
+       "      <td>10.121300</td>\n",
+       "      <td>2.690291</td>\n",
+       "      <td>11.918722</td>\n",
+       "      <td>25.214090</td>\n",
        "      <td>17.952680</td>\n",
        "      <td>35.611260</td>\n",
-       "      <td>16.400322</td>\n",
-       "      <td>8.294456</td>\n",
+       "      <td>16.751505</td>\n",
+       "      <td>9.459845</td>\n",
        "      <td>14.001643</td>\n",
-       "      <td>9.372968</td>\n",
-       "      <td>4.577738</td>\n",
-       "      <td>3.750653</td>\n",
-       "      <td>7.998916</td>\n",
-       "      <td>5.206414</td>\n",
-       "      <td>5.558168</td>\n",
-       "      <td>9.293592</td>\n",
-       "      <td>4.910137</td>\n",
-       "      <td>5.792269</td>\n",
-       "      <td>30.668893</td>\n",
-       "      <td>26.225771</td>\n",
-       "      <td>9.949211</td>\n",
+       "      <td>11.000614</td>\n",
+       "      <td>4.409860</td>\n",
+       "      <td>4.590704</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>9.357086</td>\n",
        "      <td>36.076612</td>\n",
        "      <td>6.601689</td>\n",
-       "      <td>10.500893</td>\n",
-       "      <td>6.475679</td>\n",
-       "      <td>20.039391</td>\n",
-       "      <td>48.865274</td>\n",
-       "      <td>40.424940</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      -0.001558      0.000875     -0.001892     -0.001439     -0.003383   \n",
-       "std        0.998500      0.991199      0.990627      0.992018      0.997557   \n",
-       "min       -1.996495    -17.189967    -30.511117    -20.154062     -3.927001   \n",
-       "25%       -0.853642     -0.466209     -0.362183     -0.585318     -0.602321   \n",
-       "50%       -0.215398      0.010908      0.039725      0.114781     -0.017499   \n",
-       "75%        0.935794      0.671981      0.486347      0.674209      0.520561   \n",
-       "max        1.641950      1.240880      9.990545      2.761943      8.351757   \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       0.002990     -0.002319     -0.004030      0.004065      0.005675   \n",
-       "std        0.990920      1.000773      0.970588      0.952178      0.997451   \n",
-       "min      -29.290217    -15.052954    -25.218244    -42.653531     -8.613050   \n",
-       "25%       -0.501455     -0.580411     -0.452014     -0.174859     -0.575417   \n",
-       "50%       -0.039237     -0.207896      0.031778      0.018143     -0.044134   \n",
-       "75%        0.441977      0.296168      0.458953      0.275407      0.549793   \n",
-       "max       21.022418     17.952680     35.611260     16.400322      8.294456   \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      -0.003996      0.004484     -0.001110      0.002187     -0.002189   \n",
-       "std        0.994908      0.996820      1.001257      0.997821      0.997179   \n",
-       "min      -20.376630     -4.700120    -18.062021     -4.027674    -18.381718   \n",
-       "25%       -0.496465     -0.742776     -0.406047     -0.646881     -0.446149   \n",
-       "50%       -0.086174     -0.028081      0.139761     -0.010723      0.049833   \n",
-       "75%        0.412137      0.730527      0.619815      0.665147      0.515714   \n",
-       "max       14.001643      9.372968      4.577738      3.750653      7.998916   \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       0.001561      0.000641      0.002570     -0.001781     -0.005305   \n",
-       "std        0.998505      1.001794      1.018943      1.004320      0.993911   \n",
-       "min       -4.178028    -15.182703    -26.619430    -11.332636     -5.576921   \n",
-       "25%       -0.632536     -0.529489     -0.569855     -0.595161     -0.567157   \n",
-       "50%        0.058610      0.080505     -0.074360     -0.007117     -0.000040   \n",
-       "75%        0.713242      0.596373      0.476147      0.592595      0.559670   \n",
-       "max        5.206414      5.558168      9.293592      4.910137      5.792269   \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       0.003222     -0.002413     -0.002843     -0.004712      0.004207   \n",
-       "std        0.942704      0.939417      0.989367      0.965734      1.002527   \n",
-       "min      -25.102071    -30.857650    -10.220648    -43.582333     -4.647752   \n",
-       "25%       -0.272174     -0.312526     -0.752276     -0.260018     -0.585197   \n",
-       "50%       -0.080443     -0.041695      0.003188     -0.017968      0.070837   \n",
-       "75%        0.175418      0.252089      0.728847      0.234457      0.735596   \n",
-       "max       30.668893     26.225771      9.949211     36.076612      6.601689   \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      -0.002641     -0.004198     -0.001204     -0.007545     -0.000781   \n",
-       "std        0.997197      1.002805      0.986277      0.933587      0.954577   \n",
-       "min      -11.098125     -3.438550    -21.900531    -26.225413     -0.353229   \n",
-       "25%       -0.610906     -0.683196     -0.174948     -0.158387     -0.330520   \n",
-       "50%        0.028794     -0.115727      0.003776      0.034129     -0.264951   \n",
-       "75%        0.668969      0.500451      0.229667      0.236983     -0.043378   \n",
-       "max       10.500893      6.475679     20.039391     48.865274     40.424940   \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           0.004354  \n",
-       "std            1.046103  \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 = 0.97776 Cost = 82759.57812 Valid_Acc = 0.97767 Valid_Cost =  8259.38965\n",
-      "Epoch: 5 Acc = 0.98852 Cost = 73093.75781 Valid_Acc = 0.98971 Valid_Cost =  7532.35059\n",
-      "Epoch: 10 Acc = 0.99023 Cost = 62864.16406 Valid_Acc = 0.99094 Valid_Cost =  7449.86719\n",
-      "Epoch: 15 Acc = 0.98994 Cost = 53767.84375 Valid_Acc = 0.99066 Valid_Cost =  7594.30762\n",
-      "Epoch: 20 Acc = 0.98749 Cost = 42582.04688 Valid_Acc = 0.98873 Valid_Cost =  7900.34814\n",
-      "Epoch: 25 Acc = 0.99368 Cost = 36059.70312 Valid_Acc = 0.99442 Valid_Cost =  9603.81641\n",
-      "Epoch: 30 Acc = 0.99562 Cost = 35505.75781 Valid_Acc = 0.99558 Valid_Cost =  13627.77832\n",
-      "Epoch: 35 Acc = 0.99511 Cost = 24553.49219 Valid_Acc = 0.99498 Valid_Cost =  16987.88281\n",
-      "Epoch: 40 Acc = 0.99403 Cost = 15877.84961 Valid_Acc = 0.99438 Valid_Cost =  17617.75195\n",
-      "Epoch: 45 Acc = 0.99708 Cost = 30019.80469 Valid_Acc = 0.99684 Valid_Cost =  21042.43359\n",
-      "Epoch: 50 Acc = 0.99434 Cost = 16480.59766 Valid_Acc = 0.99442 Valid_Cost =  26418.41992\n",
-      "Epoch: 55 Acc = 0.99558 Cost = 10637.00391 Valid_Acc = 0.99589 Valid_Cost =  27701.11328\n",
-      "Epoch: 60 Acc = 0.99739 Cost = 7652.20947 Valid_Acc = 0.99719 Valid_Cost =  35590.22266\n",
-      "Epoch: 65 Acc = 0.99384 Cost = 7286.31445 Valid_Acc = 0.99449 Valid_Cost =  28434.03320\n",
-      "Epoch: 70 Acc = 0.99685 Cost = 5848.18164 Valid_Acc = 0.99687 Valid_Cost =  36722.49219\n",
-      "Epoch: 75 Acc = 0.99669 Cost = 4516.80273 Valid_Acc = 0.99635 Valid_Cost =  39600.40234\n",
-      "Epoch: 80 Acc = 0.99617 Cost = 4747.16602 Valid_Acc = 0.99589 Valid_Cost =  40842.24609\n",
-      "Epoch: 85 Acc = 0.99625 Cost = 4744.38867 Valid_Acc = 0.99617 Valid_Cost =  37780.37500\n",
-      "Epoch: 90 Acc = 0.99820 Cost = 4898.35400 Valid_Acc = 0.99789 Valid_Cost =  49322.94531\n",
-      "Epoch: 95 Acc = 0.99793 Cost = 5422.48340 Valid_Acc = 0.99758 Valid_Cost =  50593.96094\n",
-      "Epoch: 100 Acc = 0.99080 Cost = 7493.91504 Valid_Acc = 0.99020 Valid_Cost =  40995.75781\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 = 0.9904498\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"
@@ -4364,24 +4364,24 @@
       "Epoch 5/10\n",
       "227845/227845 [==============================] - 1s 3us/step - loss: 0.0502 - acc: 0.9960\n",
       "Epoch 6/10\n",
-      "227845/227845 [==============================] - 1s 3us/step - loss: 0.0450 - acc: 0.9969\n",
+      "227845/227845 [==============================] - 1s 3us/step - loss: 0.0449 - acc: 0.9969\n",
       "Epoch 7/10\n",
-      "227845/227845 [==============================] - 1s 3us/step - loss: 0.0402 - acc: 0.9972\n",
+      "227845/227845 [==============================] - 1s 3us/step - loss: 0.0401 - acc: 0.9973\n",
       "Epoch 8/10\n",
-      "227845/227845 [==============================] - 1s 3us/step - loss: 0.0377 - 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: 0.0357 - acc: 0.9978\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 0x1f5b800d5f8>"
+       "<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 [==============================] - 2s 38us/step\n",
-      "Test score: 0.027562282392704363\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": 66,
+   "execution_count": 62,
    "metadata": {
     "_cell_guid": "1d565b39-10cf-48ee-8dad-98f585dc0b23",
     "_uuid": "c59e2bca8f631eb923808c7a7c845beae3265770"
@@ -4446,45 +4446,45 @@
      "text": [
       "Train on 227845 samples, validate on 56962 samples\n",
       "Epoch 1/20\n",
-      " - 1s - loss: 0.0118 - acc: 0.9983 - val_loss: 0.0123 - 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: 0.0114 - acc: 0.9983 - val_loss: 0.0123 - 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: 0.0116 - acc: 0.9983 - val_loss: 0.0121 - 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: 0.0112 - acc: 0.9983 - val_loss: 0.0119 - 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: 0.0115 - acc: 0.9983 - val_loss: 0.0118 - 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: 0.0109 - acc: 0.9983 - val_loss: 0.0118 - 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: 0.0111 - acc: 0.9983 - val_loss: 0.0117 - 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: 0.0108 - acc: 0.9983 - val_loss: 0.0118 - 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: 0.0109 - acc: 0.9983 - val_loss: 0.0119 - val_acc: 0.9983\n",
+      " - 1s - loss: 0.0296 - acc: 0.9982 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 10/20\n",
-      " - 1s - loss: 0.0106 - acc: 0.9983 - val_loss: 0.0118 - val_acc: 0.9983\n",
+      " - 1s - loss: 0.0298 - acc: 0.9982 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 11/20\n",
-      " - 1s - loss: 0.0111 - acc: 0.9983 - val_loss: 0.0117 - 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: 0.0106 - acc: 0.9983 - val_loss: 0.0117 - 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: 0.0106 - acc: 0.9983 - val_loss: 0.0116 - 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: 0.0104 - acc: 0.9983 - val_loss: 0.0115 - 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: 0.0106 - acc: 0.9983 - val_loss: 0.0117 - 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: 0.0106 - acc: 0.9983 - val_loss: 0.0116 - 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: 0.0100 - acc: 0.9983 - val_loss: 0.0115 - 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: 0.0102 - acc: 0.9983 - val_loss: 0.0115 - val_acc: 0.9983\n",
+      " - 1s - loss: 0.0270 - acc: 0.9983 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 19/20\n",
-      " - 1s - loss: 0.0101 - acc: 0.9983 - val_loss: 0.0114 - val_acc: 0.9983\n",
+      " - 1s - loss: 0.0268 - acc: 0.9983 - val_loss: 0.0276 - val_acc: 0.9983\n",
       "Epoch 20/20\n",
-      " - 1s - loss: 0.0104 - acc: 0.9983 - val_loss: 0.0114 - val_acc: 0.9983\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": 67,
+   "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": 67,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4519,7 +4519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 64,
    "metadata": {
     "_cell_guid": "ac30ce3a-89f9-40a4-a392-50f7d8562f9a",
     "_uuid": "0e03766581fb5a6519af77ac9b737869c7b0e28b",
@@ -4528,7 +4528,7 @@
    "outputs": [
     {
      "data": {
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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": 72,
+   "execution_count": 65,
    "metadata": {
     "_cell_guid": "52ed3913-ab3c-48e3-9de7-3810af65283b",
     "_uuid": "0df771cc4e8f0c117aaabd93d9778bec837851d4"
@@ -4606,7 +4606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 66,
    "metadata": {
     "_cell_guid": "fac6cd1f-36ee-4aaa-af29-f94e69db57ef",
     "_uuid": "3afc3624947e0f5454a3893f86163dea548a70f9"
@@ -4648,22 +4648,18 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "_cell_guid": "bbde0f17-8207-4182-b515-5272645e489e",
-    "_uuid": "81874f10f4453c3211185dc814351db71087a382"
-   },
+   "metadata": {},
    "source": [
-    "## 2.3 Autoencoder neural network with Keras\n",
+    "## 2.3 Keras Autoencoder 신경망\n",
     "\n",
-    "We have found out that standard neural network is not able to capture this highly skewed Fraud data (< 0.2%).  \n",
-    "So I am going to redo this problem by setting up a **Autoencoder network with Keras **.  By definition, Autoencoder network is such that we **develop a model to predict the input** i.e. we are going to optimize the weights and biases (W and b) so that the model gives f(x) = x. How do we do it in NN? We squeeze the network in the middle with input and output layers being the same. The loss or error in Autoencoder is called reconstruction error which is of course minimized during training.  \n",
+    "저흰 앞의 기본적인 신경망을 통해 심하게 편향된 사기 데이터 (< 0.2%)를 잡을 수 없다는 것을 알았습니다. 그래 이 문제를 소위 인풋을 예측하기위해 만들어진 **Autoencoder 네트워크**를 케라스로 구성하여 이 문제를 다시 바라보기로 하였습니다. 예를 들어, f(x) = x를 모델로 가지기 위해 Weights 와 Biases를 최적화 한다고 해보면, NN에서는 어떻게 할까요? input, output 층이 같아질 때 까지 중간에서 네트워크를 계속 짜낼 것입니다. Autoencoder의 loss와 error는 이러한 학습과정을 최소화 할 수 있도록 재구축 되었습니다.\n",
     "\n",
-    "Let us reload the packages."
+    "패키지들을 재로딩 해보겠습니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {
     "_cell_guid": "46fd0dfc-0e84-45c2-9693-c07e6ec9fdb2",
     "_uuid": "43c44a4085b5376821650f4e520fddd7c44d10b4"
@@ -4693,38 +4689,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "metadata": {
     "_cell_guid": "4e21db4e-0eb1-46b0-a72c-72e4492291e0",
-    "_uuid": "0977481fa05b2f9189b4287a74d9f27e74e60af5",
-    "collapsed": true
+    "_uuid": "0977481fa05b2f9189b4287a74d9f27e74e60af5"
    },
    "outputs": [],
    "source": [
     "# Reload data and then drop time column and standardize Amount column\n",
     "# Also we follow the already done analysis on this dataset\n",
-    "df = pd.read_csv(\"../input/creditcard.csv\")\n",
+    "df = pd.read_csv(\"./input/creditcard.csv\")\n",
     "data = df.drop(['Time'], axis=1)\n",
     "data['Amount'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "_cell_guid": "3a80bb96-609e-48ac-85ba-327eeaed3a31",
-    "_uuid": "200f4154a82812172bc6750abd17182b0f46c2ea"
-   },
+   "metadata": {},
    "source": [
-    "Training the Autoencoder is somewhat different from what we typically do to. Here we do not specify the split betweeen Fraud and Normal transaction. We train the model on the normal transaction only (see how X_train is selected). "
+    "Autoencoder를 학습하는 것은 기존에 하던 것과는 조금 다릅니다. 일단 여기선 따로 사기(Fraud)인 거래와 보통(Normal) 거래를 따로 구분짓지 않습니다. 보통(Normal) 거래로만 모델을 학습 시키겠습니다. (아래 X_train 코드를 봐보세요!)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 69,
    "metadata": {
     "_cell_guid": "92fea18e-5583-4bb8-b6ba-c7a0c7d464cd",
-    "_uuid": "d38a1b8bb6933057053881c83df26893d3c2354e",
-    "collapsed": true
+    "_uuid": "d38a1b8bb6933057053881c83df26893d3c2354e"
    },
    "outputs": [],
    "source": [
@@ -4739,22 +4730,17 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "_cell_guid": "13e5058b-7011-4386-be66-a9717d24ed1f",
-    "_uuid": "e352475eb2cf97d07fda9b94df63c74aa6ee0fe1"
-   },
+   "metadata": {},
    "source": [
-    "Below I set up the AUtoencoder model. It has 4 fully connected layers after the input layer with 14, 7, 7 and 29 neurons respectively. First two layers are encoder and the last two layers are decoder. Input and output layers have same number of neurons. We are using L1 regularization.  \n",
-    "We can also build a different network in similar format."
+    "아래에 Autoencoder 모델을 구성하였습니다. 4 FC 층으로 input layer는 14, 7, 7, 29 뉴런으로 구성되어 있습니다. 처음 두 Layer는 Encoder(압축용)이고 마지막 두 Layer는 Decoder(해제용) 입니다. Input과 Output Layer는 같은 수의 뉴런을 가집니다. L1 regularization (Overfitting 방지를 위해) 을 적용합니다. 이런 비슷한 포맷으로 다른 네트워크도 형성할 수 있습니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 70,
    "metadata": {
     "_cell_guid": "a9efb494-f9ac-43ea-97e0-82dbf21bfb30",
-    "_uuid": "4fdf0d3c72e8337887142b87ac1d8b6ebee23644",
-    "collapsed": true
+    "_uuid": "4fdf0d3c72e8337887142b87ac1d8b6ebee23644"
    },
    "outputs": [],
    "source": [
@@ -4771,22 +4757,47 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "_cell_guid": "185fe5c1-aa05-4e7c-b963-0aa5504a8d57",
-    "_uuid": "b0a2e939953acff569d1cfe233e0aae6ae0334a9"
-   },
+   "metadata": {},
    "source": [
-    "Now we compile the autoencoder model and collect the history for 10 epochs with batch size of 1000."
+    "이 Autoencoder 모델을 Compile하고, batch가 1000인 10 epoch의 기록들을 수집합니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 71,
    "metadata": {
     "_cell_guid": "b6362ffe-6174-4194-b91c-eaea6c1e9905",
     "_uuid": "68953af8780d09095e1cccf95899587ef7fa54fc"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 227451 samples, validate on 56962 samples\n",
+      "Epoch 1/10\n",
+      "227451/227451 [==============================] - 1s 6us/step - loss: 1.5176 - acc: 0.1459 - val_loss: 1.2206 - val_acc: 0.2074\n",
+      "Epoch 2/10\n",
+      "227451/227451 [==============================] - 1s 5us/step - loss: 1.0685 - acc: 0.2265 - val_loss: 1.0416 - val_acc: 0.2707\n",
+      "Epoch 3/10\n",
+      "227451/227451 [==============================] - 1s 5us/step - loss: 0.9830 - acc: 0.2880 - val_loss: 0.9903 - val_acc: 0.3025\n",
+      "Epoch 4/10\n",
+      "227451/227451 [==============================] - 1s 5us/step - loss: 0.9440 - acc: 0.3048 - val_loss: 0.9625 - val_acc: 0.3080\n",
+      "Epoch 5/10\n",
+      "227451/227451 [==============================] - 1s 5us/step - loss: 0.9232 - acc: 0.3085 - val_loss: 0.9468 - val_acc: 0.3102\n",
+      "Epoch 6/10\n",
+      "227451/227451 [==============================] - 1s 5us/step - loss: 0.9100 - acc: 0.3119 - val_loss: 0.9367 - val_acc: 0.3134\n",
+      "Epoch 7/10\n",
+      "227451/227451 [==============================] - 1s 5us/step - loss: 0.8998 - acc: 0.3156 - val_loss: 0.9275 - val_acc: 0.3178\n",
+      "Epoch 8/10\n",
+      "227451/227451 [==============================] - 1s 5us/step - loss: 0.8908 - acc: 0.3209 - val_loss: 0.9169 - val_acc: 0.3268\n",
+      "Epoch 9/10\n",
+      "227451/227451 [==============================] - 1s 5us/step - loss: 0.8722 - acc: 0.3474 - val_loss: 0.8963 - val_acc: 0.3654\n",
+      "Epoch 10/10\n",
+      "227451/227451 [==============================] - 1s 5us/step - loss: 0.8588 - acc: 0.3661 - val_loss: 0.8891 - val_acc: 0.3664\n"
+     ]
+    }
+   ],
    "source": [
     "nb_epoch = 10\n",
     "batch_size = 1000\n",
@@ -4800,21 +4811,39 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "_uuid": "24b27b01d17d99e3eb3198d3ae51c1df02c12ca0"
-   },
+   "metadata": {},
    "source": [
-    "The model is saved in model.h5 via Keras ModelCheckpoint. We can load the model we want.  \n",
-    "Now let us look at the model loss and accuracy during training and validation."
+    "이 모델은 케라스의 ModelCheckpoint를 통해 model.h5의 형태로 저장되었습니다. 그래서 우리가 원할때 해당 모델을 로딩할 수 있게 되었습니다. 이제 모델의 loss와 accuracy를 training과 validation할 동안 history 된 것을 기반으로 보도록 하겠습니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 72,
    "metadata": {
     "_uuid": "e7eaba597a604fdf84e1c7c53a21ea5a273df289"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x2d12c359da0>"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot model loss vs. epoch\n",
     "\n",
@@ -4828,11 +4857,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "metadata": {
     "_uuid": "40b643c218ef2e087703fe6e489d612b974ebd82"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot model Accuracy vs. epoch\n",
     "\n",
@@ -4846,22 +4886,107 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "_uuid": "94fa7a620b73096f72d3a9040e9685527359d8ad"
-   },
+   "metadata": {},
    "source": [
-    "## Prediction and reconstruction error with Autoencoder\n",
+    "## Autoencoder를 이용한 예측과 error 재구성\n",
     "\n",
-    "Let's do prediction on X_test based on the model and then look at the error (mean squared error) which is called the reconstruction error here."
+    "한번 위의 모델 기반으로 X_test를 예측해보도록 하겠습니다. 그리고 여기서 재구성된 (mean squared) error를 살펴보도록 하겠습니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 74,
    "metadata": {
     "_uuid": "85ef72f49fbb4b1aa012707166266317c9e58f63"
    },
-   "outputs": [],
+   "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>reconstruction_error</th>\n",
+       "      <th>true_class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>56962.000000</td>\n",
+       "      <td>56962.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.871004</td>\n",
+       "      <td>0.001720</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.568457</td>\n",
+       "      <td>0.041443</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.089433</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.330827</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.502962</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.744535</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>254.158931</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       reconstruction_error    true_class\n",
+       "count          56962.000000  56962.000000\n",
+       "mean               0.871004      0.001720\n",
+       "std                3.568457      0.041443\n",
+       "min                0.089433      0.000000\n",
+       "25%                0.330827      0.000000\n",
+       "50%                0.502962      0.000000\n",
+       "75%                0.744535      0.000000\n",
+       "max              254.158931      1.000000"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "predictions = autoencoder.predict(X_test)\n",
     "mse = np.mean(np.power(X_test - predictions, 2), axis=1)\n",
@@ -4871,20 +4996,29 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "_uuid": "034da78bc51829cf6a38c6375dbc748bb5267c47"
-   },
+   "metadata": {},
    "source": [
-    "* Although true class is either 0 or 1, reconstruction error has much larger range. Let us look at this reconstruction error distribution for both Normal and Fraud classes in the test dataset."
+    "* 클래스가 0 이든 1 이든, 재구성된 error는 매우 넓은 범위를 가집니다. 이 error의 분포를 보통(Normal) 클래스와 사기(Fraud) 클래스로 나누어 살표 보도록 하겠습니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 75,
    "metadata": {
     "_uuid": "fb31d517e0af7ff89f1f2bbfaf30c805dc5fbee7"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Reconstruction error in normal class\n",
     "\n",
@@ -4896,11 +5030,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 76,
    "metadata": {
     "_uuid": "f879d4001bc8784fd784c6f17af3af6ca8000b06"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Reconstruction error in Fraud class\n",
     "\n",
@@ -4912,30 +5057,25 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "_uuid": "ec860edafaea08c950d528e2097c8dd58d50d2cf"
-   },
+   "metadata": {},
    "source": [
-    "We see that reconstruction error has larger tail (worse) in case of Fraud class."
+    "우린 재구성된 에러가 사기(Fraud) 클래스에서 넓은 꼬리를 가진다는 것을 볼 수 있습니다."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "_uuid": "dce49318cf93a3a156c2512a93ba48d4066e9bcf"
-   },
+   "metadata": {},
    "source": [
-    "## Confusion Matrix with Autoencoder\n",
+    "## Autoencoder의 Confusion Matrix\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. "
+    "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": "code",
-   "execution_count": null,
+   "execution_count": 77,
    "metadata": {
-    "_uuid": "c74ffebc84540fe12484e780deaa5974186db1b4",
-    "collapsed": true
+    "_uuid": "c74ffebc84540fe12484e780deaa5974186db1b4"
    },
    "outputs": [],
    "source": [
@@ -4946,11 +5086,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 78,
    "metadata": {
     "_uuid": "ff271d2e1ade6d753e6389107b83983d15b2e371"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Prediction with a threshold\n",
     "\n",
@@ -4972,11 +5123,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 79,
    "metadata": {
     "_uuid": "f7ff959efe78f6f49f44f9b6739db5408bd8ee02"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Confusion matrix\n",
     "\n",
@@ -4990,6 +5152,14 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Autoencoder 모델은 Fraud 거래를 매우 편향된 데이터 속에서도 탐지할 수 있습니다.**  \n",
+    "하지만 사기거래를 대부분 잡더라도, 보통(Normal) 거래도 사기로 12% 정도나 판단합니다! 더 큰 임계값(threshold)는 miss 분류를 줄일 수는 있겠으나, 사기 클래스의 탐지 정확도를 떨어트리게 됩니다."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -4998,37 +5168,33 @@
     "collapsed": true
    },
    "source": [
-    "** Autoencoder model is able to detect Fraud transaction in this highly skewed data!**  \n",
-    "Although the model catches most of Fraud transaction, it also classifies lot of normal transaction (12%!) as Fraud. Larger threshold will reduce misclassification of the Normal class but it will also reduce detection of the Fraud class.\n",
+    "## 3. t-SNE로 데이터 시각화\n",
     "\n",
-    "## 3. Visualizing the Data with t-SNE\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",
-    "**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",
-    "\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": [
@@ -5039,11 +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": [
@@ -5057,11 +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": [
@@ -5071,13 +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": {
+      "image/png": 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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",
@@ -5096,24 +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": [
@@ -5126,11 +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": [
@@ -5143,11 +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": [
@@ -5156,13 +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",
@@ -5180,42 +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",
+    "* 사기 거래들은 매우 적었습니다. 다르게 말하면, 사기 거래의 최대 값보다 큰 거래들은 보통 거래라 판단할 수 있었습니다.\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",
+    "* 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",
-    "### How to improve on fraud detection?\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!"
    ]
   }
  ],