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 ece030e..9a622da 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	
@@ -1975,27 +1975,27 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>16863</th>\n",
+       "      <th>63634</th>\n",
        "      <td>578.28934</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>241254</th>\n",
+       "      <th>108258</th>\n",
        "      <td>578.28934</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>76929</th>\n",
+       "      <th>82400</th>\n",
        "      <td>578.28934</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>235634</th>\n",
+       "      <th>229712</th>\n",
        "      <td>578.28934</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>23308</th>\n",
+       "      <th>239499</th>\n",
        "      <td>578.28934</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
@@ -2005,11 +2005,11 @@
       ],
       "text/plain": [
        "            Fraud  Normal\n",
-       "16863   578.28934     0.0\n",
-       "241254  578.28934     0.0\n",
-       "76929   578.28934     0.0\n",
-       "235634  578.28934     0.0\n",
-       "23308   578.28934     0.0"
+       "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"
       ]
      },
      "execution_count": 30,
@@ -2145,240 +2145,240 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>-0.001224</td>\n",
-       "      <td>-0.001042</td>\n",
-       "      <td>-0.002830</td>\n",
-       "      <td>-0.005544</td>\n",
-       "      <td>0.005997</td>\n",
-       "      <td>0.000293</td>\n",
-       "      <td>0.001331</td>\n",
-       "      <td>-0.005805</td>\n",
-       "      <td>-0.004224</td>\n",
-       "      <td>0.003585</td>\n",
-       "      <td>-0.003315</td>\n",
-       "      <td>0.006343</td>\n",
-       "      <td>0.014670</td>\n",
-       "      <td>-0.005487</td>\n",
-       "      <td>0.004601</td>\n",
-       "      <td>-0.002224</td>\n",
-       "      <td>-0.000504</td>\n",
-       "      <td>-0.001790</td>\n",
-       "      <td>-0.006839</td>\n",
-       "      <td>0.001810</td>\n",
-       "      <td>-0.002665</td>\n",
-       "      <td>-0.002930</td>\n",
-       "      <td>0.001825</td>\n",
-       "      <td>0.000312</td>\n",
-       "      <td>0.002653</td>\n",
-       "      <td>-0.001334</td>\n",
-       "      <td>0.001097</td>\n",
-       "      <td>0.000116</td>\n",
-       "      <td>0.000894</td>\n",
-       "      <td>0.001173</td>\n",
-       "      <td>-0.000229</td>\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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
-       "      <td>1.001920</td>\n",
-       "      <td>1.020089</td>\n",
-       "      <td>1.018808</td>\n",
-       "      <td>1.011348</td>\n",
-       "      <td>0.998416</td>\n",
-       "      <td>0.999303</td>\n",
-       "      <td>0.998207</td>\n",
-       "      <td>1.018658</td>\n",
-       "      <td>1.088381</td>\n",
-       "      <td>0.991733</td>\n",
-       "      <td>0.990113</td>\n",
-       "      <td>0.997706</td>\n",
-       "      <td>0.996597</td>\n",
-       "      <td>1.001690</td>\n",
-       "      <td>0.990480</td>\n",
-       "      <td>0.996792</td>\n",
-       "      <td>0.996392</td>\n",
-       "      <td>0.991946</td>\n",
-       "      <td>0.997001</td>\n",
-       "      <td>1.001586</td>\n",
-       "      <td>1.009827</td>\n",
-       "      <td>1.068501</td>\n",
-       "      <td>1.009975</td>\n",
-       "      <td>1.072639</td>\n",
-       "      <td>1.003264</td>\n",
-       "      <td>1.003527</td>\n",
-       "      <td>1.003375</td>\n",
-       "      <td>1.010746</td>\n",
-       "      <td>1.039948</td>\n",
-       "      <td>0.985931</td>\n",
-       "      <td>0.997525</td>\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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
-       "      <td>-1.996369</td>\n",
-       "      <td>-20.661780</td>\n",
-       "      <td>-28.722479</td>\n",
-       "      <td>-22.213271</td>\n",
-       "      <td>-3.717201</td>\n",
-       "      <td>-21.540062</td>\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>-29.290217</td>\n",
        "      <td>-15.052954</td>\n",
-       "      <td>-33.551864</td>\n",
-       "      <td>-61.302416</td>\n",
-       "      <td>-12.227994</td>\n",
-       "      <td>-22.411893</td>\n",
-       "      <td>-4.363472</td>\n",
-       "      <td>-17.242432</td>\n",
-       "      <td>-5.819382</td>\n",
-       "      <td>-19.292570</td>\n",
-       "      <td>-4.584886</td>\n",
-       "      <td>-14.141290</td>\n",
-       "      <td>-26.540290</td>\n",
-       "      <td>-10.846039</td>\n",
-       "      <td>-6.059567</td>\n",
-       "      <td>-29.624863</td>\n",
-       "      <td>-47.418984</td>\n",
-       "      <td>-12.246104</td>\n",
-       "      <td>-58.716303</td>\n",
-       "      <td>-4.584519</td>\n",
-       "      <td>-16.683278</td>\n",
-       "      <td>-3.847473</td>\n",
-       "      <td>-24.515479</td>\n",
-       "      <td>-25.260665</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>-18.062021</td>\n",
+       "      <td>-4.027674</td>\n",
+       "      <td>-18.381718</td>\n",
+       "      <td>-4.178028</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>-0.353229</td>\n",
        "      <td>-0.046062</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
-       "      <td>-0.858169</td>\n",
-       "      <td>-0.470222</td>\n",
-       "      <td>-0.358188</td>\n",
-       "      <td>-0.589043</td>\n",
-       "      <td>-0.592879</td>\n",
-       "      <td>-0.497053</td>\n",
-       "      <td>-0.575976</td>\n",
-       "      <td>-0.445508</td>\n",
-       "      <td>-0.175328</td>\n",
-       "      <td>-0.575156</td>\n",
-       "      <td>-0.487916</td>\n",
-       "      <td>-0.740248</td>\n",
-       "      <td>-0.391293</td>\n",
-       "      <td>-0.656250</td>\n",
-       "      <td>-0.439695</td>\n",
-       "      <td>-0.634752</td>\n",
-       "      <td>-0.535462</td>\n",
-       "      <td>-0.571714</td>\n",
-       "      <td>-0.596332</td>\n",
-       "      <td>-0.557152</td>\n",
-       "      <td>-0.272606</td>\n",
-       "      <td>-0.309250</td>\n",
-       "      <td>-0.744022</td>\n",
-       "      <td>-0.257902</td>\n",
-       "      <td>-0.579922</td>\n",
-       "      <td>-0.605941</td>\n",
-       "      <td>-0.679235</td>\n",
-       "      <td>-0.175332</td>\n",
-       "      <td>-0.159296</td>\n",
-       "      <td>-0.331279</td>\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>-0.046062</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
-       "      <td>-0.214577</td>\n",
-       "      <td>0.013992</td>\n",
-       "      <td>0.035383</td>\n",
-       "      <td>0.112973</td>\n",
-       "      <td>-0.000758</td>\n",
-       "      <td>-0.042363</td>\n",
-       "      <td>-0.203824</td>\n",
-       "      <td>0.030520</td>\n",
-       "      <td>0.020143</td>\n",
-       "      <td>-0.039460</td>\n",
-       "      <td>-0.085708</td>\n",
-       "      <td>-0.024836</td>\n",
-       "      <td>0.153613</td>\n",
-       "      <td>-0.018689</td>\n",
-       "      <td>0.055610</td>\n",
-       "      <td>0.047554</td>\n",
-       "      <td>0.077496</td>\n",
-       "      <td>-0.079770</td>\n",
-       "      <td>-0.010362</td>\n",
-       "      <td>0.005983</td>\n",
-       "      <td>-0.080698</td>\n",
-       "      <td>-0.041409</td>\n",
-       "      <td>0.007294</td>\n",
-       "      <td>-0.017332</td>\n",
-       "      <td>0.072023</td>\n",
-       "      <td>0.028636</td>\n",
-       "      <td>-0.115717</td>\n",
-       "      <td>0.003963</td>\n",
-       "      <td>0.032194</td>\n",
-       "      <td>-0.263912</td>\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>-0.046062</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
-       "      <td>0.939522</td>\n",
-       "      <td>0.672490</td>\n",
-       "      <td>0.487033</td>\n",
-       "      <td>0.677709</td>\n",
-       "      <td>0.534019</td>\n",
-       "      <td>0.439335</td>\n",
-       "      <td>0.301598</td>\n",
-       "      <td>0.458236</td>\n",
-       "      <td>0.275742</td>\n",
-       "      <td>0.550495</td>\n",
-       "      <td>0.403495</td>\n",
-       "      <td>0.730971</td>\n",
-       "      <td>0.625753</td>\n",
-       "      <td>0.662847</td>\n",
-       "      <td>0.513317</td>\n",
-       "      <td>0.699394</td>\n",
-       "      <td>0.593329</td>\n",
-       "      <td>0.468597</td>\n",
-       "      <td>0.592829</td>\n",
-       "      <td>0.564164</td>\n",
-       "      <td>0.173084</td>\n",
-       "      <td>0.252904</td>\n",
-       "      <td>0.733052</td>\n",
-       "      <td>0.236988</td>\n",
-       "      <td>0.729891</td>\n",
-       "      <td>0.668831</td>\n",
-       "      <td>0.496408</td>\n",
-       "      <td>0.227238</td>\n",
-       "      <td>0.231837</td>\n",
-       "      <td>-0.039260</td>\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>-0.046062</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
-       "      <td>1.641929</td>\n",
-       "      <td>1.229413</td>\n",
-       "      <td>13.000116</td>\n",
-       "      <td>2.657487</td>\n",
-       "      <td>8.568820</td>\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>15.993545</td>\n",
-       "      <td>25.484930</td>\n",
-       "      <td>15.697933</td>\n",
-       "      <td>9.388014</td>\n",
-       "      <td>12.531076</td>\n",
-       "      <td>11.432405</td>\n",
-       "      <td>7.854665</td>\n",
-       "      <td>3.923102</td>\n",
-       "      <td>10.981446</td>\n",
-       "      <td>6.211952</td>\n",
-       "      <td>6.528161</td>\n",
-       "      <td>8.592238</td>\n",
-       "      <td>4.522099</td>\n",
-       "      <td>5.897892</td>\n",
-       "      <td>34.033648</td>\n",
-       "      <td>37.034649</td>\n",
-       "      <td>14.473016</td>\n",
-       "      <td>30.430985</td>\n",
-       "      <td>6.642261</td>\n",
-       "      <td>14.425293</td>\n",
-       "      <td>7.181774</td>\n",
-       "      <td>21.576490</td>\n",
-       "      <td>68.528383</td>\n",
-       "      <td>51.265692</td>\n",
+       "      <td>17.952680</td>\n",
+       "      <td>35.611260</td>\n",
+       "      <td>16.400322</td>\n",
+       "      <td>8.294456</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>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>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      -0.001224     -0.001042     -0.002830     -0.005544      0.005997   \n",
-       "std        1.001920      1.020089      1.018808      1.011348      0.998416   \n",
-       "min       -1.996369    -20.661780    -28.722479    -22.213271     -3.717201   \n",
-       "25%       -0.858169     -0.470222     -0.358188     -0.589043     -0.592879   \n",
-       "50%       -0.214577      0.013992      0.035383      0.112973     -0.000758   \n",
-       "75%        0.939522      0.672490      0.487033      0.677709      0.534019   \n",
-       "max        1.641929      1.229413     13.000116      2.657487      8.568820   \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",
        "\n",
        "                 V5            V6            V7            V8            V9  \\\n",
        "count  56961.000000  56961.000000  56961.000000  56961.000000  56961.000000   \n",
-       "mean       0.000293      0.001331     -0.005805     -0.004224      0.003585   \n",
-       "std        0.999303      0.998207      1.018658      1.088381      0.991733   \n",
-       "min      -21.540062    -15.052954    -33.551864    -61.302416    -12.227994   \n",
-       "25%       -0.497053     -0.575976     -0.445508     -0.175328     -0.575156   \n",
-       "50%       -0.042363     -0.203824      0.030520      0.020143     -0.039460   \n",
-       "75%        0.439335      0.301598      0.458236      0.275742      0.550495   \n",
-       "max       21.022418     15.993545     25.484930     15.697933      9.388014   \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",
        "\n",
        "                V10           V11           V12           V13           V14  \\\n",
        "count  56961.000000  56961.000000  56961.000000  56961.000000  56961.000000   \n",
-       "mean      -0.003315      0.006343      0.014670     -0.005487      0.004601   \n",
-       "std        0.990113      0.997706      0.996597      1.001690      0.990480   \n",
-       "min      -22.411893     -4.363472    -17.242432     -5.819382    -19.292570   \n",
-       "25%       -0.487916     -0.740248     -0.391293     -0.656250     -0.439695   \n",
-       "50%       -0.085708     -0.024836      0.153613     -0.018689      0.055610   \n",
-       "75%        0.403495      0.730971      0.625753      0.662847      0.513317   \n",
-       "max       12.531076     11.432405      7.854665      3.923102     10.981446   \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",
        "\n",
        "                V15           V16           V17           V18           V19  \\\n",
        "count  56961.000000  56961.000000  56961.000000  56961.000000  56961.000000   \n",
-       "mean      -0.002224     -0.000504     -0.001790     -0.006839      0.001810   \n",
-       "std        0.996792      0.996392      0.991946      0.997001      1.001586   \n",
-       "min       -4.584886    -14.141290    -26.540290    -10.846039     -6.059567   \n",
-       "25%       -0.634752     -0.535462     -0.571714     -0.596332     -0.557152   \n",
-       "50%        0.047554      0.077496     -0.079770     -0.010362      0.005983   \n",
-       "75%        0.699394      0.593329      0.468597      0.592829      0.564164   \n",
-       "max        6.211952      6.528161      8.592238      4.522099      5.897892   \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",
        "\n",
        "                V20           V21           V22           V23           V24  \\\n",
        "count  56961.000000  56961.000000  56961.000000  56961.000000  56961.000000   \n",
-       "mean      -0.002665     -0.002930      0.001825      0.000312      0.002653   \n",
-       "std        1.009827      1.068501      1.009975      1.072639      1.003264   \n",
-       "min      -29.624863    -47.418984    -12.246104    -58.716303     -4.584519   \n",
-       "25%       -0.272606     -0.309250     -0.744022     -0.257902     -0.579922   \n",
-       "50%       -0.080698     -0.041409      0.007294     -0.017332      0.072023   \n",
-       "75%        0.173084      0.252904      0.733052      0.236988      0.729891   \n",
-       "max       34.033648     37.034649     14.473016     30.430985      6.642261   \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",
        "\n",
        "                V25           V26           V27           V28        Amount  \\\n",
        "count  56961.000000  56961.000000  56961.000000  56961.000000  56961.000000   \n",
-       "mean      -0.001334      0.001097      0.000116      0.000894      0.001173   \n",
-       "std        1.003527      1.003375      1.010746      1.039948      0.985931   \n",
-       "min      -16.683278     -3.847473    -24.515479    -25.260665     -0.353229   \n",
-       "25%       -0.605941     -0.679235     -0.175332     -0.159296     -0.331279   \n",
-       "50%        0.028636     -0.115717      0.003963      0.032194     -0.263912   \n",
-       "75%        0.668831      0.496408      0.227238      0.231837     -0.039260   \n",
-       "max       14.425293      7.181774     21.576490     68.528383     51.265692   \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",
        "\n",
        "       Amount_max_fraud  \n",
        "count      56961.000000  \n",
-       "mean          -0.000229  \n",
-       "std            0.997525  \n",
+       "mean           0.004354  \n",
+       "std            1.046103  \n",
        "min           -0.046062  \n",
        "25%           -0.046062  \n",
        "50%           -0.046062  \n",
@@ -2656,27 +2656,27 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch: 0 Acc = 0.98030 Cost = 70089.28125 Valid_Acc = 0.98034 Valid_Cost =  15254.80078\n",
-      "Epoch: 5 Acc = 0.98767 Cost = 58580.29297 Valid_Acc = 0.98750 Valid_Cost =  14394.41895\n",
-      "Epoch: 10 Acc = 0.98854 Cost = 48541.71875 Valid_Acc = 0.98883 Valid_Cost =  14806.13574\n",
-      "Epoch: 15 Acc = 0.98865 Cost = 39388.20703 Valid_Acc = 0.98894 Valid_Cost =  16517.65625\n",
-      "Epoch: 20 Acc = 0.99094 Cost = 31618.80859 Valid_Acc = 0.99122 Valid_Cost =  20146.30859\n",
-      "Epoch: 25 Acc = 0.99130 Cost = 22800.29883 Valid_Acc = 0.99136 Valid_Cost =  24841.26758\n",
-      "Epoch: 30 Acc = 0.99118 Cost = 18227.08789 Valid_Acc = 0.99143 Valid_Cost =  28714.44141\n",
-      "Epoch: 35 Acc = 0.99246 Cost = 11901.30176 Valid_Acc = 0.99305 Valid_Cost =  36806.41406\n",
-      "Epoch: 40 Acc = 0.99238 Cost = 14242.89941 Valid_Acc = 0.99238 Valid_Cost =  45021.91797\n",
-      "Epoch: 45 Acc = 0.99631 Cost = 9457.01465 Valid_Acc = 0.99586 Valid_Cost =  56201.80469\n",
-      "Epoch: 50 Acc = 0.99604 Cost = 10883.57422 Valid_Acc = 0.99582 Valid_Cost =  58257.39453\n",
-      "Epoch: 55 Acc = 0.99695 Cost = 6030.76562 Valid_Acc = 0.99666 Valid_Cost =  62535.69531\n",
-      "Epoch: 60 Acc = 0.99780 Cost = 6262.52979 Valid_Acc = 0.99758 Valid_Cost =  74222.21094\n",
-      "Epoch: 65 Acc = 0.99716 Cost = 4710.59668 Valid_Acc = 0.99673 Valid_Cost =  65601.07031\n",
-      "Epoch: 70 Acc = 0.99813 Cost = 4759.97021 Valid_Acc = 0.99726 Valid_Cost =  75777.33594\n",
-      "Epoch: 75 Acc = 0.99081 Cost = 6893.59326 Valid_Acc = 0.99119 Valid_Cost =  59490.25781\n",
-      "Epoch: 80 Acc = 0.99744 Cost = 2822.39600 Valid_Acc = 0.99695 Valid_Cost =  82329.80469\n",
-      "Epoch: 85 Acc = 0.99785 Cost = 2602.71289 Valid_Acc = 0.99740 Valid_Cost =  94100.67969\n",
-      "Epoch: 90 Acc = 0.99610 Cost = 3647.93262 Valid_Acc = 0.99582 Valid_Cost =  75909.85156\n",
-      "Epoch: 95 Acc = 0.99828 Cost = 3060.12622 Valid_Acc = 0.99775 Valid_Cost =  99243.14844\n",
-      "Epoch: 100 Acc = 0.99632 Cost = 3478.99805 Valid_Acc = 0.99558 Valid_Cost =  93043.17969\n",
+      "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",
       "\n",
       "Optimization Finished!\n",
       "\n",
@@ -2769,7 +2769,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 720x288 with 2 Axes>"
       ]
@@ -2818,7 +2818,7 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from ./model/model.ckpt\n",
-      "Testing Accuracy = 0.9961027\n"
+      "Testing Accuracy = 0.9904498\n"
      ]
     }
    ],
@@ -2836,7 +2836,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "최종 저장되었던 모델을 불러와 **Test 셋으로 99.68% 의 정확도를 얻을 수 있습니다.**"
+    "최종 저장되었던 모델을 불러와 **Test 셋으로 99% 정도의 정확도를 얻을 수 있습니다.**"
    ]
   },
   {
@@ -3225,7 +3225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -3251,7 +3251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 48,
    "metadata": {
     "_cell_guid": "78dcafd6-c7e5-4396-b4f4-cba7ae30de9f",
     "_uuid": "e9eb5647ea9a1daf4f91f4577f3d4d0e8064f096"
@@ -3264,7 +3264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -3728,7 +3728,7 @@
        "130872 -0.560888  1354.98                 0  "
       ]
      },
-     "execution_count": 50,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3746,7 +3746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 50,
    "metadata": {
     "_cell_guid": "952ab8e5-cccd-4188-8690-c33e405545d4",
     "_uuid": "7ae50c4c16bf36e9b001f658909116004a86aed3"
@@ -3759,7 +3759,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 51,
    "metadata": {
     "_cell_guid": "5c03b27c-581a-452b-86dd-ae4ce2b37877",
     "_uuid": "92cce9a63827d1a9e11bc143b46f605f3879eeae"
@@ -3793,7 +3793,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 52,
    "metadata": {
     "_cell_guid": "94bb1a46-193f-47fd-bf1f-f0e917492298",
     "_uuid": "add388ac1620f9fb2eb1850b35a81411c30055a6"
@@ -3806,7 +3806,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -3816,7 +3816,7 @@
        "       [1., 0.]], dtype=float32)"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3834,7 +3834,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 54,
    "metadata": {
     "_cell_guid": "9136cfd3-7e69-4fdf-a658-d0677cb32e26",
     "_uuid": "94754afc163a673cfc20a64ae01032d0b6e60454"
@@ -3852,7 +3852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -4258,7 +4258,7 @@
        "max           21.709793  "
       ]
      },
-     "execution_count": 60,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4276,7 +4276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 56,
    "metadata": {
     "_cell_guid": "6c0f7416-412a-4415-94e8-362fa13948ea",
     "_uuid": "9c648e3b03efea588dea1d20893bf148151447f7"
@@ -4296,7 +4296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 57,
    "metadata": {
     "_cell_guid": "bbf4cf25-3052-4b8f-9ac7-700865de4905",
     "_uuid": "cf449dc135ffd673be7ef9b800df733c6c48831d"
@@ -4324,7 +4324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 58,
    "metadata": {
     "_cell_guid": "2dec00ca-e47c-4142-89bc-e7a445314ebc",
     "_uuid": "086c950d76895f638b313701200a144fdf3851b6"
@@ -4343,7 +4343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 59,
    "metadata": {
     "_cell_guid": "bbbd50fb-41e8-4f71-b497-f8e19038dad0",
     "_uuid": "239658b1175b942a7aa179f8ae8b71dccbcb8f6d"
@@ -4354,34 +4354,34 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/10\n",
-      "227845/227845 [==============================] - 5s 20us/step - loss: 0.9254 - acc: 0.8761\n",
+      "227845/227845 [==============================] - 1s 6us/step - loss: 0.9423 - acc: 0.8743\n",
       "Epoch 2/10\n",
-      "227845/227845 [==============================] - 3s 12us/step - loss: 0.1404 - acc: 0.9815\n",
+      "227845/227845 [==============================] - 1s 3us/step - loss: 0.1413 - acc: 0.9813\n",
       "Epoch 3/10\n",
-      "227845/227845 [==============================] - 3s 13us/step - loss: 0.0815 - acc: 0.9913\n",
+      "227845/227845 [==============================] - 1s 3us/step - loss: 0.0819 - acc: 0.9913\n",
       "Epoch 4/10\n",
-      "227845/227845 [==============================] - 3s 14us/step - loss: 0.0603 - acc: 0.9949\n",
+      "227845/227845 [==============================] - 1s 3us/step - loss: 0.0606 - acc: 0.9948\n",
       "Epoch 5/10\n",
-      "227845/227845 [==============================] - 3s 13us/step - loss: 0.0497 - acc: 0.9961: 1s\n",
+      "227845/227845 [==============================] - 1s 3us/step - loss: 0.0502 - acc: 0.9960\n",
       "Epoch 6/10\n",
-      "227845/227845 [==============================] - 3s 13us/step - loss: 0.0447 - acc: 0.9970\n",
+      "227845/227845 [==============================] - 1s 3us/step - loss: 0.0450 - acc: 0.9969\n",
       "Epoch 7/10\n",
-      "227845/227845 [==============================] - 3s 13us/step - loss: 0.0401 - acc: 0.9972\n",
+      "227845/227845 [==============================] - 1s 3us/step - loss: 0.0402 - acc: 0.9972\n",
       "Epoch 8/10\n",
-      "227845/227845 [==============================] - 3s 13us/step - loss: 0.0373 - acc: 0.9977\n",
+      "227845/227845 [==============================] - 1s 3us/step - loss: 0.0377 - acc: 0.9976\n",
       "Epoch 9/10\n",
-      "227845/227845 [==============================] - 3s 13us/step - loss: 0.0356 - acc: 0.9978\n",
+      "227845/227845 [==============================] - 1s 3us/step - loss: 0.0357 - acc: 0.9978\n",
       "Epoch 10/10\n",
-      "227845/227845 [==============================] - 3s 13us/step - loss: 0.0345 - acc: 0.9979\n"
+      "227845/227845 [==============================] - 1s 3us/step - loss: 0.0346 - acc: 0.9979\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<keras.callbacks.History at 0x2ea76744898>"
+       "<keras.callbacks.History at 0x1f5b800d5f8>"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4394,7 +4394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 60,
    "metadata": {
     "_cell_guid": "e45c27c0-1e29-47cf-befe-bba155dd21cf",
     "_uuid": "a6790157aaf0370faa31223ebd4c1faeeaf84f9a"
@@ -4404,8 +4404,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "56962/56962 [==============================] - 4s 67us/step\n",
-      "Test score: 0.027575257398611885\n",
+      "56962/56962 [==============================] - 2s 38us/step\n",
+      "Test score: 0.027562282392704363\n",
       "Test accuracy: 0.9982795547909132\n"
      ]
     }
@@ -4425,25 +4425,69 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "_cell_guid": "130799f9-6a91-4c74-b761-26c00c3bd8c6",
-    "_uuid": "343bbc820dd1043196daef8b36ec7b8da445eaec"
-   },
+   "metadata": {},
    "source": [
-    "### Training and testing accuracy and loss vs epoch:\n",
+    "### Training과 Testing, 그리고 loss vs epoch:\n",
     "\n",
-    "Let us plot train and test accuracy and loss vs. epoch collcting the history by running the model again:"
+    "일단 모델을 다시 실행하여 Train과 Test의 정확도와 epoch에 따른 loss를 살펴보도록 하겠습니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 66,
    "metadata": {
     "_cell_guid": "1d565b39-10cf-48ee-8dad-98f585dc0b23",
-    "_uuid": "c59e2bca8f631eb923808c7a7c845beae3265770",
-    "collapsed": true
+    "_uuid": "c59e2bca8f631eb923808c7a7c845beae3265770"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "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",
+      "Epoch 2/20\n",
+      " - 1s - loss: 0.0114 - acc: 0.9983 - val_loss: 0.0123 - 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",
+      "Epoch 4/20\n",
+      " - 1s - loss: 0.0112 - acc: 0.9983 - val_loss: 0.0119 - 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",
+      "Epoch 6/20\n",
+      " - 1s - loss: 0.0109 - acc: 0.9983 - val_loss: 0.0118 - 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",
+      "Epoch 8/20\n",
+      " - 1s - loss: 0.0108 - acc: 0.9983 - val_loss: 0.0118 - 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",
+      "Epoch 10/20\n",
+      " - 1s - loss: 0.0106 - acc: 0.9983 - val_loss: 0.0118 - 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",
+      "Epoch 12/20\n",
+      " - 1s - loss: 0.0106 - acc: 0.9983 - val_loss: 0.0117 - 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",
+      "Epoch 14/20\n",
+      " - 1s - loss: 0.0104 - acc: 0.9983 - val_loss: 0.0115 - 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",
+      "Epoch 16/20\n",
+      " - 1s - loss: 0.0106 - acc: 0.9983 - val_loss: 0.0116 - 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",
+      "Epoch 18/20\n",
+      " - 1s - loss: 0.0102 - acc: 0.9983 - val_loss: 0.0115 - 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",
+      "Epoch 20/20\n",
+      " - 1s - loss: 0.0104 - acc: 0.9983 - val_loss: 0.0114 - val_acc: 0.9983\n"
+     ]
+    }
+   ],
    "source": [
     "history = model.fit(X_train, Y_train, batch_size = 2048, epochs = 20, \n",
     "         validation_data = (X_test, Y_test), verbose = 2)"
@@ -4451,13 +4495,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {
     "_cell_guid": "1970b306-ca29-4d69-bef8-78dd0380fc04",
-    "_uuid": "10b4e95a8da3549bac6c619f8e3a42a79523d11d",
-    "collapsed": true
+    "_uuid": "10b4e95a8da3549bac6c619f8e3a42a79523d11d"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Check the history keys\n",
     "history.history.keys()"
@@ -4465,13 +4519,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "metadata": {
     "_cell_guid": "ac30ce3a-89f9-40a4-a392-50f7d8562f9a",
     "_uuid": "0e03766581fb5a6519af77ac9b737869c7b0e28b",
-    "collapsed": true
+    "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, ax = plt.subplots(2,1)\n",
     "ax[0].plot(history.history['loss'], color='b', label=\"Training loss\")\n",
@@ -4491,21 +4558,20 @@
     "_uuid": "b3bf094cf456be8350a4d26c4a8d08d6ecf3b78a"
    },
    "source": [
-    "Within this small number of epochs, we see that the test dataset accuracy did not clearly change.\n",
+    "작은 epoch내에서 Test 데이터셋의 정확도의 변화를 잘 보이지 않음을 알 수 있습니다.\n",
     "\n",
     "### Confusion matrix\n",
     "\n",
-    "Let us have a look at the confusion matrix for this 2 classes.\n",
-    "I am using the below function to get the confusion matrix."
+    "이번엔 2 클래스에 대한 Confusion Matrix를 보도록 하겠습니다.  \n",
+    "Confusion matrix를 얻기위해 아래와 같은 함수를 사용하도록 하겠습니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 72,
    "metadata": {
     "_cell_guid": "52ed3913-ab3c-48e3-9de7-3810af65283b",
-    "_uuid": "0df771cc4e8f0c117aaabd93d9778bec837851d4",
-    "collapsed": true
+    "_uuid": "0df771cc4e8f0c117aaabd93d9778bec837851d4"
    },
    "outputs": [],
    "source": [
@@ -4540,13 +4606,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "metadata": {
     "_cell_guid": "fac6cd1f-36ee-4aaa-af29-f94e69db57ef",
-    "_uuid": "3afc3624947e0f5454a3893f86163dea548a70f9",
-    "collapsed": true
+    "_uuid": "3afc3624947e0f5454a3893f86163dea548a70f9"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Predict the values from the validation dataset\n",
     "Y_pred = model.predict(X_test)\n",
@@ -4561,6 +4639,13 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "기본적으로 **이 모델은 사기 거래를 정확히 예측 못하고 있었습니다.** 그래도 적어도, 보통 거래를 사기라고 예측하진 않았습니다."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -4568,8 +4653,6 @@
     "_uuid": "81874f10f4453c3211185dc814351db71087a382"
    },
    "source": [
-    "Basically **the model did not predict the Fraud transactions correctly**. However, it did not predict any Normal transaction as Fraud.\n",
-    "\n",
     "## 2.3 Autoencoder neural network with Keras\n",
     "\n",
     "We have found out that standard neural network is not able to capture this highly skewed Fraud data (< 0.2%).  \n",