Breast_Cancer_Prediction.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":[],"machine_shape":"hm","authorship_tag":"ABX9TyMpBZG3P12EYV+DojleEiIm"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"standard"},"cells":[{"cell_type":"markdown","source":["### <center> Breast Cancer Prediction </center>\n","#### <center> Prepared by Orhun </center>"],"metadata":{"id":"VA00pxZhRu_s"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"51TcdbSnh2kU","executionInfo":{"status":"ok","timestamp":1665379168474,"user_tz":300,"elapsed":3,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"outputs":[],"source":["from pydrive.auth import GoogleAuth\n","from pydrive.drive import GoogleDrive\n","from google.colab import auth\n","from oauth2client.client import GoogleCredentials"]},{"cell_type":"code","source":["import numpy as np \n","import pandas as pd \n","from sklearn.metrics import classification_report"],"metadata":{"id":"5UPRbez42AIW","executionInfo":{"status":"ok","timestamp":1665379169731,"user_tz":300,"elapsed":385,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["auth.authenticate_user()\n","gauth = GoogleAuth()\n","gauth.credentials = GoogleCredentials.get_application_default()\n","drive = GoogleDrive(gauth)\n","\n","file_id = \"\" #You have to put breast cancer dataset's google drive id\n","downloaded = drive.CreateFile({'id':file_id})\n","downloaded.FetchMetadata(fetch_all=True)\n","downloaded.GetContentFile(downloaded.metadata['title'])"],"metadata":{"id":"oom6bbiosWut","executionInfo":{"status":"ok","timestamp":1665379180379,"user_tz":300,"elapsed":10650,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["df_cancer = pd.read_csv(\"breast-cancer-wisconsin.data.csv\")"],"metadata":{"id":"Lww9ApYqsfZ3","executionInfo":{"status":"ok","timestamp":1665379184022,"user_tz":300,"elapsed":2,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["df_cancer.columns =['Patient_ID_number', 'Clump_Thickness', 'Uniformity_of_Cell_Size', 'Uniformity_of_Cell_Shape', 'Marginal_Adhesion', 'Single_Epithelial_Cell_Size', \n","                    'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']"],"metadata":{"id":"HJaIRyjdLnJU","executionInfo":{"status":"ok","timestamp":1665379185088,"user_tz":300,"elapsed":168,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["df_cancer"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":488},"id":"2_uJkICD2Ij5","executionInfo":{"status":"ok","timestamp":1665379186115,"user_tz":300,"elapsed":4,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"1c05208e-cfa3-4971-aae5-2edf1576b496"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     Patient_ID_number  Clump_Thickness  Uniformity_of_Cell_Size  \\\n","0              1002945                5                        4   \n","1              1015425                3                        1   \n","2              1016277                6                        8   \n","3              1017023                4                        1   \n","4              1017122                8                       10   \n","..                 ...              ...                      ...   \n","693             776715                3                        1   \n","694             841769                2                        1   \n","695             888820                5                       10   \n","696             897471                4                        8   \n","697             897471                4                        8   \n","\n","     Uniformity_of_Cell_Shape  Marginal_Adhesion  Single_Epithelial_Cell_Size  \\\n","0                           4                  5                            7   \n","1                           1                  1                            2   \n","2                           8                  1                            3   \n","3                           1                  3                            2   \n","4                          10                  8                            7   \n","..                        ...                ...                          ...   \n","693                         1                  1                            3   \n","694                         1                  1                            2   \n","695                        10                  3                            7   \n","696                         6                  4                            3   \n","697                         8                  5                            4   \n","\n","     Bare_Nuclei  Bland_Chromatin  Normal_Nucleoli  Mitoses  Class  \n","0             10                3                2        1      2  \n","1              2                3                1        1      2  \n","2              4                3                7        1      2  \n","3              1                3                1        1      2  \n","4             10                9                7        1      4  \n","..           ...              ...              ...      ...    ...  \n","693            2                1                1        1      2  \n","694            1                1                1        1      2  \n","695            3                8               10        2      4  \n","696            4               10                6        1      4  \n","697            5               10                4        1      4  \n","\n","[698 rows x 11 columns]"],"text/html":["\n","  <div id=\"df-bccbdeab-f8e8-4244-8c38-1d6b86e0e049\">\n","    <div class=\"colab-df-container\">\n","      <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>Patient_ID_number</th>\n","      <th>Clump_Thickness</th>\n","      <th>Uniformity_of_Cell_Size</th>\n","      <th>Uniformity_of_Cell_Shape</th>\n","      <th>Marginal_Adhesion</th>\n","      <th>Single_Epithelial_Cell_Size</th>\n","      <th>Bare_Nuclei</th>\n","      <th>Bland_Chromatin</th>\n","      <th>Normal_Nucleoli</th>\n","      <th>Mitoses</th>\n","      <th>Class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1002945</td>\n","      <td>5</td>\n","      <td>4</td>\n","      <td>4</td>\n","      <td>5</td>\n","      <td>7</td>\n","      <td>10</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1015425</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1016277</td>\n","      <td>6</td>\n","      <td>8</td>\n","      <td>8</td>\n","      <td>1</td>\n","      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If so, perform data scaling"],"metadata":{"id":"KM0PNPYtS6ZK"}},{"cell_type":"code","source":["from sklearn.preprocessing import MinMaxScaler\n","from sklearn.preprocessing import StandardScaler"],"metadata":{"id":"T2dA6ggGSh6h","executionInfo":{"status":"ok","timestamp":1665379188056,"user_tz":300,"elapsed":3,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":["scaler = MinMaxScaler()\n","scaler2 = StandardScaler()"],"metadata":{"id":"Ucl9ufisTisj","executionInfo":{"status":"ok","timestamp":1665379188056,"user_tz":300,"elapsed":3,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":15,"outputs":[]},{"cell_type":"code","source":["X_train_scaled = scaler2.fit_transform(X_train)\n","X_test_scaled = scaler2.fit_transform(X_test)"],"metadata":{"id":"9bv3Z0TfTleL","executionInfo":{"status":"ok","timestamp":1665379188275,"user_tz":300,"elapsed":1,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":["##3) Define a sequential layered neural network architecture. Experiment with 2, 3, and 4 layers. You can decide on the number of neurons in each layer."],"metadata":{"id":"nVweEQ4bUjSX"}},{"cell_type":"code","source":["import keras\n","from keras.models import Sequential\n","from keras.layers import Dense\n","from keras.layers import Dropout\n","\n","import matplotlib.pyplot as plt"],"metadata":{"id":"RbLjMxLEUQtz","executionInfo":{"status":"ok","timestamp":1665379190675,"user_tz":300,"elapsed":1833,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":17,"outputs":[]},{"cell_type":"markdown","source":["### 3-1) \n","\n","#### Model 1 = 2 Layers with 3 Neurons\n","\n","#### Model 2 = 3 Layers with 8 Neurons\n","\n","#### Model 3 = 4 Layers with 20 Neurons"],"metadata":{"id":"Zja4gSqipTMH"}},{"cell_type":"code","source":["model1 = Sequential()\n","\n","model1.add(Dense(units = 3, activation = 'relu', input_dim = 9))\n","#model1.add(Dropout(rate = 0.1))\n","\n","model1.add(Dense(units = 3, activation = 'relu'))\n","#model1.add(Dropout(rate = 0.1))\n","\n","model1.add(Dense(units = 1, activation = 'sigmoid'))"],"metadata":{"id":"9OfbFVEnVCnM","executionInfo":{"status":"ok","timestamp":1665379193485,"user_tz":300,"elapsed":2812,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["model2 = Sequential()\n","\n","model2.add(Dense(units = 8, activation = 'relu', input_dim = 9))\n","#model2.add(Dropout(rate = 0.1))\n","\n","model2.add(Dense(units = 8, activation = 'relu'))\n","#model2.add(Dropout(rate = 0.1))\n","\n","model2.add(Dense(units = 8, activation = 'relu'))\n","#model2.add(Dropout(rate = 0.1))\n","\n","model2.add(Dense(units = 1, activation = 'sigmoid'))"],"metadata":{"id":"oMnqmxEOqQZx","executionInfo":{"status":"ok","timestamp":1665379193485,"user_tz":300,"elapsed":6,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":19,"outputs":[]},{"cell_type":"code","source":["model3 = Sequential()\n","\n","model3.add(Dense(units = 20, activation = 'relu', input_dim = 9))\n","model3.add(Dropout(rate = 0.1))\n","\n","model3.add(Dense(units = 20, activation = 'relu'))\n","model3.add(Dropout(rate = 0.1))\n","\n","model3.add(Dense(units = 20, activation = 'relu'))\n","model3.add(Dropout(rate = 0.1))\n","\n","model3.add(Dense(units = 20, activation = 'relu'))\n","model3.add(Dropout(rate = 0.1))\n","\n","model3.add(Dense(units = 1, activation = 'sigmoid'))"],"metadata":{"id":"DM_gmLU7qXkf","executionInfo":{"status":"ok","timestamp":1665379193485,"user_tz":300,"elapsed":4,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["model1.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['accuracy'])\n","model2.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['accuracy'])\n","model3.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['accuracy'])"],"metadata":{"id":"PU5ufrMtVbc6","executionInfo":{"status":"ok","timestamp":1665379193486,"user_tz":300,"elapsed":5,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["history1 = model1.fit(X_train_scaled, y_train, epochs=30, verbose=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"g4VeO3ETeC4q","outputId":"602321ca-1a6a-4da9-fd3c-b61ef643d5a9","executionInfo":{"status":"ok","timestamp":1665379198706,"user_tz":300,"elapsed":5225,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/30\n","16/16 [==============================] - 4s 3ms/step - loss: 0.7460 - accuracy: 0.3033\n","Epoch 2/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.7128 - accuracy: 0.4037\n","Epoch 3/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.6845 - accuracy: 0.5102\n","Epoch 4/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.6599 - accuracy: 0.6004\n","Epoch 5/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.6300 - accuracy: 0.6865\n","Epoch 6/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.5919 - accuracy: 0.7439\n","Epoch 7/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.5482 - accuracy: 0.7602\n","Epoch 8/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.5062 - accuracy: 0.7869\n","Epoch 9/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.4668 - accuracy: 0.7992\n","Epoch 10/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.4301 - accuracy: 0.8279\n","Epoch 11/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.3964 - accuracy: 0.8443\n","Epoch 12/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.3654 - accuracy: 0.8607\n","Epoch 13/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.3374 - accuracy: 0.8709\n","Epoch 14/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.3116 - accuracy: 0.8975\n","Epoch 15/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.2878 - accuracy: 0.9139\n","Epoch 16/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.2657 - accuracy: 0.9242\n","Epoch 17/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.2456 - accuracy: 0.9242\n","Epoch 18/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.2266 - accuracy: 0.9283\n","Epoch 19/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.2093 - accuracy: 0.9324\n","Epoch 20/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.1936 - accuracy: 0.9447\n","Epoch 21/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.1803 - accuracy: 0.9426\n","Epoch 22/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.1689 - accuracy: 0.9467\n","Epoch 23/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.1591 - accuracy: 0.9467\n","Epoch 24/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.1502 - accuracy: 0.9426\n","Epoch 25/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.1427 - accuracy: 0.9447\n","Epoch 26/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.1359 - accuracy: 0.9467\n","Epoch 27/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.1301 - accuracy: 0.9488\n","Epoch 28/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.1251 - accuracy: 0.9508\n","Epoch 29/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.1203 - accuracy: 0.9549\n","Epoch 30/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.1166 - accuracy: 0.9549\n"]}]},{"cell_type":"code","source":["predictions = model1.predict (X_test_scaled)\n","predictions = np.round(predictions).reshape(y_test.shape[0])\n","print(classification_report(y_test, predictions))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WZ307fQh4yLi","executionInfo":{"status":"ok","timestamp":1665379198892,"user_tz":300,"elapsed":190,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"b47c4778-5590-45a9-cf8b-e40cd34fb7ee"},"execution_count":23,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.95      0.93      0.94        75\n","           1       0.96      0.97      0.97       135\n","\n","    accuracy                           0.96       210\n","   macro avg       0.95      0.95      0.95       210\n","weighted avg       0.96      0.96      0.96       210\n","\n"]}]},{"cell_type":"code","source":["history2 = model2.fit(X_train_scaled, y_train, epochs=30, verbose=1)"],"metadata":{"id":"XIxBRIsWqoH_","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665379200869,"user_tz":300,"elapsed":1979,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"c2768ffb-2499-427b-d488-45a459648070"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.6771 - accuracy: 0.3299\n","Epoch 2/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.6215 - accuracy: 0.4078\n","Epoch 3/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.5788 - accuracy: 0.6906\n","Epoch 4/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.5451 - accuracy: 0.9180\n","Epoch 5/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.5179 - accuracy: 0.9549\n","Epoch 6/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.4946 - accuracy: 0.9590\n","Epoch 7/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.4731 - accuracy: 0.9611\n","Epoch 8/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.4518 - accuracy: 0.9631\n","Epoch 9/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.4272 - accuracy: 0.9631\n","Epoch 10/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.3941 - accuracy: 0.9672\n","Epoch 11/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.3470 - accuracy: 0.9672\n","Epoch 12/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.2900 - accuracy: 0.9652\n","Epoch 13/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.2320 - accuracy: 0.9652\n","Epoch 14/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.1836 - accuracy: 0.9652\n","Epoch 15/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.1485 - accuracy: 0.9652\n","Epoch 16/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.1265 - accuracy: 0.9652\n","Epoch 17/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.1129 - accuracy: 0.9652\n","Epoch 18/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.1042 - accuracy: 0.9652\n","Epoch 19/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0982 - accuracy: 0.9652\n","Epoch 20/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0941 - accuracy: 0.9652\n","Epoch 21/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.0913 - accuracy: 0.9652\n","Epoch 22/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.0888 - accuracy: 0.9693\n","Epoch 23/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0870 - accuracy: 0.9693\n","Epoch 24/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.0859 - accuracy: 0.9693\n","Epoch 25/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0842 - accuracy: 0.9693\n","Epoch 26/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.0835 - accuracy: 0.9693\n","Epoch 27/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.0822 - accuracy: 0.9693\n","Epoch 28/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.0812 - accuracy: 0.9713\n","Epoch 29/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.0805 - accuracy: 0.9713\n","Epoch 30/30\n","16/16 [==============================] - 0s 2ms/step - loss: 0.0797 - accuracy: 0.9713\n"]}]},{"cell_type":"code","source":["history3 = model3.fit(X_train_scaled, y_train, epochs=30, verbose=1)"],"metadata":{"id":"hTPkK6ZZqpWf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665379203087,"user_tz":300,"elapsed":2226,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"4ab4e4a3-81a4-4c47-bb8f-9ba7c5c39a25"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/30\n","16/16 [==============================] - 1s 3ms/step - loss: 0.6509 - accuracy: 0.6168\n","Epoch 2/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.4777 - accuracy: 0.8873\n","Epoch 3/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.3335 - accuracy: 0.9201\n","Epoch 4/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.2199 - accuracy: 0.9385\n","Epoch 5/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.1524 - accuracy: 0.9549\n","Epoch 6/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.1333 - accuracy: 0.9590\n","Epoch 7/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.1234 - accuracy: 0.9672\n","Epoch 8/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.1213 - accuracy: 0.9631\n","Epoch 9/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.1041 - accuracy: 0.9693\n","Epoch 10/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0997 - accuracy: 0.9754\n","Epoch 11/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.1058 - accuracy: 0.9734\n","Epoch 12/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.1147 - accuracy: 0.9652\n","Epoch 13/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.1051 - accuracy: 0.9693\n","Epoch 14/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0990 - accuracy: 0.9713\n","Epoch 15/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.1059 - accuracy: 0.9713\n","Epoch 16/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0975 - accuracy: 0.9734\n","Epoch 17/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0963 - accuracy: 0.9775\n","Epoch 18/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0865 - accuracy: 0.9734\n","Epoch 19/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0886 - accuracy: 0.9754\n","Epoch 20/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0953 - accuracy: 0.9754\n","Epoch 21/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0952 - accuracy: 0.9754\n","Epoch 22/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0878 - accuracy: 0.9754\n","Epoch 23/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0905 - accuracy: 0.9734\n","Epoch 24/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0745 - accuracy: 0.9775\n","Epoch 25/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0895 - accuracy: 0.9775\n","Epoch 26/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0808 - accuracy: 0.9775\n","Epoch 27/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0849 - accuracy: 0.9795\n","Epoch 28/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0840 - accuracy: 0.9816\n","Epoch 29/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0752 - accuracy: 0.9795\n","Epoch 30/30\n","16/16 [==============================] - 0s 3ms/step - loss: 0.0773 - accuracy: 0.9816\n"]}]},{"cell_type":"markdown","source":["#### 3-2) Plot Loss and Accuracy"],"metadata":{"id":"wcGgKi1_qtRi"}},{"cell_type":"markdown","source":["#### 3-2-1) Model 1 "],"metadata":{"id":"6ie-FHLdq4tN"}},{"cell_type":"code","source":["loss_train = history1.history['loss']\n","\n","plt.plot(loss_train, 'g', label='Training loss')\n","plt.title('Training Loss')\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.legend()\n","plt.show()\n","\n","acc_train = history1.history['accuracy']\n","\n","plt.plot(acc_train, 'g', label='Training Accuracy')\n","plt.title('Training Acc')\n","plt.xlabel('Epochs')\n","plt.ylabel('Acc')\n","plt.legend()\n","plt.show()\n","\n","print(\"Evaluation\")\n","model1.evaluate(X_test_scaled, y_test)\n"],"metadata":{"id":"00_KxAzNmkRp","colab":{"base_uri":"https://localhost:8080/","height":625},"executionInfo":{"status":"ok","timestamp":1665379203517,"user_tz":300,"elapsed":433,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"4d5a3edd-58d1-44c9-9b10-c7c00f50d488"},"execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Evaluation\n","7/7 [==============================] - 0s 2ms/step - loss: 0.1138 - accuracy: 0.9571\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.11378832906484604, 0.9571428298950195]"]},"metadata":{},"execution_count":26}]},{"cell_type":"markdown","source":["#### 3-2-1) Model 2"],"metadata":{"id":"pUzOEAKKq-j0"}},{"cell_type":"code","source":["loss_train2 = history2.history['loss']\n","\n","plt.plot(loss_train2, 'g', label='Training loss')\n","plt.title('Training Loss')\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.legend()\n","plt.show()\n","\n","acc_train2 = history2.history['accuracy']\n","\n","plt.plot(acc_train2, 'g', label='Training Accuracy')\n","plt.title('Training Acc')\n","plt.xlabel('Epochs')\n","plt.ylabel('Acc')\n","plt.legend()\n","plt.show()\n","\n","print(\"Evaluation\")\n","model2.evaluate(X_test_scaled, y_test)\n"],"metadata":{"id":"rPFldXW_f0Eb","colab":{"base_uri":"https://localhost:8080/","height":625},"executionInfo":{"status":"ok","timestamp":1665379203892,"user_tz":300,"elapsed":381,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"a5c62d55-d0ad-484f-da43-51f1d909c27d"},"execution_count":27,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYMAAAEWCAYAAACEz/viAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deZzNhf7H8ddnFsvYki1ZQilFmAwVLbRYWqibihtdNyVadEuL6t5Q3RbVrRTdKOreFlyV/MSlFNoZUbImy41Kg+zLzPD5/XEODc0Yxpz5zjnn/Xw8zmPO93u+33Pe3znM+3yX8/2auyMiIvEtIegAIiISPJWBiIioDERERGUgIiKoDEREBJWBiIigMpA4ZmaTzexPhT2tSDQyfc9AoomZbc0xmALsAnaHh29099eLPlXBmVlr4DV3rxl0FolvSUEHEDkc7l52730zWwlc7+4fHDidmSW5e3ZRZhOJZtpMJDHBzFqb2Wozu8fMfgZGmVlFM5toZhlm9mv4fs0c80w3s+vD93uY2Sdm9mR42hVm1qGA09Y1s5lmtsXMPjCzoWb2WgGW6eTw6240swVm1jHHYxeZ2cLwa6wxszvD4yuHl3OjmW0ws4/NTP/PJV/6RyKx5BjgaOA4oBehf9+jwsO1gR3A8weZ/3RgCVAZGAy8bGZWgGnfAGYBlYCBQPfDXRAzSwb+D5gKVAVuBV43s5PCk7xMaLNYOaAR8GF4fD9gNVAFqAbcB2hbsORLZSCxZA8wwN13ufsOd1/v7m+5+3Z33wL8HTj3IPOvcvcR7r4beBWoTugP6iFPa2a1gebAA+6e6e6fABMKsCxnAGWBx8LP8yEwEegafjwLOMXMyrv7r+7+VY7x1YHj3D3L3T927RiUQ6AykFiS4e479w6YWYqZvWhmq8xsMzATOMrMEvOY/+e9d9x9e/hu2cOc9lhgQ45xAD8c5nIQfp4f3H1PjnGrgBrh+1cAFwGrzGyGmZ0ZHv8EsAyYambLzax/AV5b4pDKQGLJgZ+A+wEnAae7e3ngnPD4vDb9FIafgKPNLCXHuFoFeJ4fgVoHbO+vDawBcPfZ7t6J0Cak8cDY8Pgt7t7P3esBHYE7zOz8Ary+xBmVgcSycoT2E2w0s6OBAZF+QXdfBaQDA82sRPgT+6X5zWdmpXLeCO1z2A7cbWbJ4UNQLwVGh5/3GjOr4O5ZwGZCm8gws0vM7ITw/otNhA673ZPri4rkoDKQWPYMUBpYB3wB/LeIXvca4ExgPfAwMIbQ9yHyUoNQaeW81SL0x78DofzDgGvdfXF4nu7AyvDmr97h1wSoD3wAbAU+B4a5+0eFtmQSs/SlM5EIM7MxwGJ3j/iaiUhBac1ApJCZWXMzO97MEsysPdCJ0HZ9kWJL30AWKXzHAG8T+p7BaqCPu88NNpLIwWkzkYiIaDORiIhEeDNReHvps0Ai8JK7P3bA408DbcKDKUBVdz/qYM9ZuXJlr1OnTgTSiojErjlz5qxz9yp5PR6xMgh/y3MocCGh7aazzWyCuy/cO427355j+luB1Pyet06dOqSnp0cgsYhI7DKzVQd7PJKbiVoAy9x9ubtnAqMJHVWRl67AmxHMIyIieYhkGdRg/3OyrOa386rsx8yOA+ry25kXD3y8l5mlm1l6RkZGoQcVEYl3xWUHchdgXPgMkL/j7sPdPc3d06pUyXOTl4iIFFAkdyCvYf8TdNUMj8tNF+DmCGYRkSKQlZXF6tWr2blzZ/4TS0SUKlWKmjVrkpycfFjzRbIMZgP1zawuoRLoAvzxwInMrAFQkdB5VEQkiq1evZpy5cpRp04d8r4ukESKu7N+/XpWr15N3bp1D2veiG0mCl9/9hZgCrAIGOvuC8zswZyX7yNUEqN1AQ6R6Ldz504qVaqkIgiImVGpUqUCrZlF9HsG7j4JmHTAuAcOGB4YyQwiUrRUBMEq6O+/uOxAjrhv1n5D/w/6oxUQEZHfi5symLFyBo9/+jgTl04MOoqIRMj69etp2rQpTZs25ZhjjqFGjRr7hjMzMw86b3p6On379s33NVq2bFkoWadPn84ll1xSKM9VGOLmrKW903ozLH0Y/ab2o90J7SiRWCLoSCJSyCpVqsS8efMAGDhwIGXLluXOO+/c93h2djZJSbn/2UtLSyMtLS3f1/jss88KJ2wxEzdrBsmJyTzV9im+2/AdQ2cNDTqOiBSRHj160Lt3b04//XTuvvtuZs2axZlnnklqaiotW7ZkyZIlwP6f1AcOHMh1111H69atqVevHkOGDNn3fGXLlt03fevWrencuTMNGjTgmmuu2bcZetKkSTRo0IBmzZrRt2/ffNcANmzYwGWXXUbjxo0544wz+OabbwCYMWPGvjWb1NRUtmzZwk8//cQ555xD06ZNadSoER9//HGh/J7iZs0AoMMJHWh3fDsGzRhE9ybdqZxSOehIIjHrL//9C/N+nleoz9n0mKY80/6Zw55v9erVfPbZZyQmJrJ582Y+/vhjkpKS+OCDD7jvvvt46623fjfP4sWL+eijj9iyZQsnnXQSffr0+d2x+3PnzmXBggUce+yxtGrVik8//ZS0tDRuvPFGZs6cSd26denatWu++QYMGEBqairjx4/nww8/5Nprr2XevHk8+eSTDB06lFatWrF161ZKlSrF8OHDadeuHffffz+7d+9m+/bth/37yE3crBlAaC/7U22fYmvmVgZ8pCsQisSLK6+8ksTERAA2bdrElVdeSaNGjbj99ttZsGBBrvNcfPHFlCxZksqVK1O1alXWrl37u2latGhBzZo1SUhIoGnTpqxcuZLFixdTr169fcf5H0oZfPLJJ3Tv3h2A8847j/Xr17N582ZatWrFHXfcwZAhQ9i4cSNJSUk0b96cUaNGMXDgQObPn0+5cuUK+mvZT1ytGQA0rNqQ3mm9eSH9Bfo070Ojqo2CjiQSkwryCT5SypQps+/+3/72N9q0acM777zDypUrad26da7zlCxZct/9xMREsrOzCzTNkejfvz8XX3wxkyZNolWrVkyZMoVzzjmHmTNn8t5779GjRw/uuOMOrr322iN+rbhaM9hrYOuBlC9Znjum3KFDTUXizKZNm6hRI3TOzFdeeaXQn/+kk05i+fLlrFy5EoAxY8bkO8/ZZ5/N66+/DoT2RVSuXJny5cvz/fffc+qpp3LPPffQvHlzFi9ezKpVq6hWrRo33HAD119/PV999VWh5I7LMqicUpkB5w7g/eXvM+m7SfnPICIx4+677+bee+8lNTW10D/JA5QuXZphw4bRvn17mjVrRrly5ahQocJB5xk4cCBz5syhcePG9O/fn1dffRWAZ555hkaNGtG4cWOSk5Pp0KED06dPp0mTJqSmpjJmzBhuu+22QskddddATktL88K4uE3m7kxOfeFUAL7t8y3JiYd3UicR+b1FixZx8sknBx0jcFu3bqVs2bK4OzfffDP169fn9ttvz3/GQpLb+2Bmc9w9z2Nn43LNAKBEYgmeavsUS9cvZdjsYUHHEZEYMmLECJo2bUrDhg3ZtGkTN954Y9CR8hW3awYQOsNfu9fakf5jOt/d+h2VUioVyvOKxCutGRQPWjM4TGbGP9r9g027NjFoxqCg44jEhGj7gBlrCvr7j+syAGhUtRE3NruRYbOHsShjUdBxRKJaqVKlWL9+vQohIHuvZ1CqVKnDnjeuNxPtlbEtg/rP1adlrZZMukZHF4kUlK50Fry8rnSW32aiuPvSWW6qlKnCA+c+QL+p/Zj83WQ61O8QdCSRqJScnHzYV9iS4iHuNxPtdUuLWzjh6BPoN7UfWbuzgo4jIlKkVAZhew81XbRuES/OeTHoOCIiRUplkMOlJ17K+XXPZ8D0AWzYsSHoOCIiRUZlkMPeQ0037tzIXVPvCjqOiEiRURkcoHG1xvRv1Z+R80byxvw3go4jIlIkVAa5GNRmEK1qteLGiTeydP3SoOOIiEScyiAXSQlJvHnFm5RILMHV465mZ7aOmRaR2KYyyEOtCrX412X/Yt7P8+g3pV/QcUREIiqiZWBm7c1siZktM7P+eUxzlZktNLMFZlasNtJffOLF9DuzH8PShzFu4big44iIREzEysDMEoGhQAfgFKCrmZ1ywDT1gXuBVu7eEPhLpPIU1CPnP8LpNU6n54SeLP91edBxREQiIpJrBi2AZe6+3N0zgdFApwOmuQEY6u6/Arj7LxHMUyAlEkswuvNoDKPLuC5k7s4MOpKISKGLZBnUAH7IMbw6PC6nE4ETzexTM/vCzNrn9kRm1svM0s0sPSMjI0Jx81bnqDqM6jSK2T/Opv8HuW7tEhGJakHvQE4C6gOtga7ACDM76sCJ3H24u6e5e1qVKlWKOGLI5Sdfzq0tbuXpL55mwpIJgWQQEYmUSJbBGqBWjuGa4XE5rQYmuHuWu68AlhIqh2LpiQuf4LTqp9FjfA/+t+l/QccRESk0kSyD2UB9M6trZiWALsCBH6nHE1orwMwqE9psVGz30pZMKsmYzmPI3pNN17e66uymIhIzIlYG7p4N3AJMARYBY919gZk9aGYdw5NNAdab2ULgI+Aud18fqUyF4YSjT2DEpSP47IfP+NtHfws6johIodCVzgqo98TevDjnRSZfM5n2J+S631tEpNjI70pnQe9AjlpPt3uaxtUa0+3tbixZtyToOCIiR0RlUEClk0sz7spxJCYkcuG/L9QOZRGJaiqDI1C/Un2mdpvK5l2bueBfF7B269qgI4mIFIjK4Ag1OaYJk66ZxJota2j7Wlt+3fFr0JFERA6byqAQtKzVkne7vMvidYu56I2L2Jq5NehIIiKHRWVQSC6odwFjOo9h9prZXDb6Ml0DQUSiisqgEF3W4DJGdRrFtBXT6DKui76UJiJRQ2VQyLo36c7zHZ7n3SXvct2E69jje4KOJCKSr6SgA8Sim1vczKZdm7j/w/spX6I8z1/0PGYWdCwRkTypDCLk3rPuZdPOTQz+bDAVSlXgkfMfCTqSiEieVAYRYmY8dsFjbNq1iUc/eZQKJStwz1n3BB1LRCRXKoMIMjOGXjSULZlb6D+tP0kJSdxx5h3aZCQixY7KIMISExJ5pdMrZO3O4s737+T7X79nSIchJCXoVy8ixYf+IhWB5MRkRnceTd0P6jL4s8Gs2LiCMZ3HUL5k+aCjiYgAOrS0yCRYAo9f+DjDLxnO+9+/z1kjz9LJ7USk2FAZFLEbmt3Af7v9l1WbVnH6S6eT/mPw12YQEVEZBOCCehfwec/PKZVUinNGncM7i94JOpKIxDmVQUBOqXIKX/T8gsbVGnPF2Ct48rMnibarzolI7FAZBKha2Wp89KeP6HxKZ+56/y76vNdH5zMSkUDoaKKAlU4uzejOoznhwxN49JNHWbFxBWM7j6VCqQpBRxOROKI1g2IgwRJ45PxHeLnjy3y44kNajmzJwoyFQccSkTiiMihGrku9jindppCxLYPmI5ozcu5I7UcQkSKhMihmzqt7HvN6z+OMmmfQc0JPur3Tjc27NgcdS0RinMqgGDq23LFM7TaVh9o8xOhvR9NseDPm/Dgn6FgiEsMiWgZm1t7MlpjZMjPrn8vjPcwsw8zmhW/XRzJPNElMSOSv5/yV6X+azs7snZz58pk888Uz2mwkIhERsTIws0RgKNABOAXoaman5DLpGHdvGr69FKk80ers485m3o3z6FC/A7dPuZ1Oozuxfvv6oGOJSIyJ5JpBC2CZuy9390xgNNApgq8XsyqlVGL81eN5tv2zTPl+Ck3+2YSPV30cdCwRiSGRLIMawA85hleHxx3oCjP7xszGmVmt3J7IzHqZWbqZpWdkZEQia7FnZvQ9vS+f9/yc0smlaf1qax6a8RC79+wOOpqIxICgdyD/H1DH3RsD7wOv5jaRuw939zR3T6tSpUqRBixuTqt+Gl/1+oqujbrywPQHaP1qa77f8H3QsUQkykWyDNYAOT/p1wyP28fd17v7rvDgS0CzCOaJGeVKluPfl/+bf132L+avnU/jfzZm2Oxh7PE9QUcTkSgVyTKYDdQ3s7pmVgLoAkzIOYGZVc8x2BFYFME8McXM6N6kO9/e9C1n1z6bmyfdTNt/t9U1EkSkQCJWBu6eDdwCTCH0R36suy8wswfNrGN4sr5mtsDMvgb6Aj0ilSdW1Sxfk8nXTGb4JcP5cs2XNBrWiJe/elmHoIrIYbFo+6ORlpbm6em6IExuVm5cyZ/f/TPTV07novoXMeLSERxb7tigY4lIMWBmc9w9La/Hg96BLIWozlF1mHbtNJ5t/ywfrfiIRsMa8fo3r2stQUTypTKIMQmWQN/T+zKv9zwaVG5At3e6ccXYK/hl2y9BRxORYkxlEKNOrHQiH//5YwZfMJj3vnuPhsMaMm7huKBjiUgxpTKIYYkJidzV6i6+6vUVx1U4jiv/cyVdxnXR6SxE5HdUBnGgYdWGfN7zcx5u8zBvL3qbhsMa8u7id4OOJSLFiMogTiQnJnP/OfeT3iud6uWqc9mYy+j+Tnc27NgQdDQRKQZUBnGmcbXGzLp+FgPPHcjob0fTaFgjJi6dGHQsEQmYyiAOJScmM6D1AGZdP4vKKZW59M1L+fO7f2bjzo1BRxORgKgM4lhq9VTSe6Xz17P/yr+//jenvnAqU5ZNCTqWiARAZRDnSiSW4KHzHuLznp9TvmR52r/enr6T+5K5OzPoaCJShFQGAkDzGs2Z02sOfzn9Lzw36zlav9Ka1ZtXBx1LRIqIykD2KZVUiqfbP83YzmOZ/8t8TnvxNKYtnxZ0LBEpAioD+Z0rG17J7BtmU6VMFdq+1pZHP35U10oQiXEqA8lVg8oN+PL6L7mq4VXc9+F9XD7mch1tJBLDVAaSp7IlyvLGH95gSPshTPpuEmnD0/j656+DjiUiEaAykIMyM249/VZm9JjBjuwdnPHyGbw6L9dLVYtIFFMZyCFpWaslc2+cy5k1z6THuz3oPbE3O7N3Bh1LRAqJykAOWdUyVZnafSr3tLqHF+e8yLmvnMuvO34NOpaIFAKVgRyWpIQkHrvgMd666i3m/TyPDq93YMuuLUHHEpEjpDKQAvnDyX9gbOexpP+YzqVvXsr2rO1BRxKRI6AykALr1KAT/77838xcNZPLx1zOruxdQUcSkQJSGcgR6XpqV17q+BJTv5/K1eOuJmt3VtCRRKQAVAZyxK5LvY7nOjzHu0ve5drx17J7z+6gI4nIYUoKOoDEhlta3MK2zG30n9aflKQURnQcQYLps4ZItIjo/1Yza29mS8xsmZn1P8h0V5iZm1laJPNIZN1z1j387Zy/MXLeSG6bfBvuHnQkETlEEVszMLNEYChwIbAamG1mE9x94QHTlQNuA76MVBYpOoNaD2Jb5jb+8cU/KFOiDI+e/yhmFnQsEcnHIZWBmZUBdrj7HjM7EWgATHb3g+0tbAEsc/fl4ecYDXQCFh4w3UPA48Bdhxteih8z48m2T7I9azuPf/o4ZZLL8Ldz/xZ0LBHJx6FuJpoJlDKzGsBUoDvwSj7z1AB+yDG8OjxuHzM7Dajl7u8d7InMrJeZpZtZekZGxiFGlqCYGUMvHsq1Ta7lgekP8NRnTwUdSUTycaibiczdt5tZT2CYuw82s3lH8sJmlgD8A+iR37TuPhwYDpCWlqYN0VEgwRJ4uePL7MjawZ3v30m1stXo1rhb0LFEJA+HumZgZnYmcA2w91N8Yj7zrAFq5RiuGR63VzmgETDdzFYCZwATtBM5diQlJPHaH17jnOPO4eZJN/PDph/yn0lEAnGoZfAX4F7gHXdfYGb1gI/ymWc2UN/M6ppZCaALMGHvg+6+yd0ru3sdd68DfAF0dPf0w14KKbZKJJZgVKdR7N6zm54TeuoII5Fi6pDKwN1nuHtHd388vHlnnbv3zWeebOAWYAqwCBgbLpIHzazjESeXqFGvYj2ebPsk7y9/n+FzhgcdR0RyYYfySc3M3gB6A7sJfeIvDzzr7k9ENt7vpaWleXq6Vh6ijbvT9rW2fP7D58zvM5+6FesGHUkkrpjZHHfPczP8oW4mOsXdNwOXAZOBuoSOKBI5JGbGyx1fJsESuG7CdezxPUFHEpEcDrUMks0smVAZTAh/v0Abf+Ww1K5Qm6fbPc30ldMZNntY0HFEJIdDLYMXgZVAGWCmmR0HbI5UKIld16VeR4cTOnDPB/ewbMOyoOOISNih7kAe4u413P0iD1kFtIlwNolBZsaIS0eQnJBMj/E9dIZTkWLikMrAzCqY2T/2fgvYzJ4itJYgcthqlK/BkA5D+PSHT3n2y2eDjiMiHPpmopHAFuCq8G0zMCpSoST2dW/cnY4ndeT+D+9n8brFQccRiXuHWgbHu/sAd18evg0C6kUymMQ2M+PFS14kJTmFHuN7kL0nO+hIInHtUMtgh5mdtXfAzFoBOyITSeLFMWWPYehFQ/lyzZc6mZ1IwA61DHoDQ81sZfg8Qs8DN0YslcSNqxtezRUnX8ED0x/g21++DTqOSNw61KOJvnb3JkBjoLG7pwLnRTSZxAUz44WLX6BCyQr0GN+DrN0Hu0SGiETKYV320t03h7+JDHBHBPJIHKpSpgovXPwCc36aw2OfPBZ0HJG4dCTXQNa1DKXQXHHKFXRt1JWHZj6kL6OJBOBIykCno5BC9VTbpyiRWIL+H/QPOopI3DloGZjZFjPbnMttC3BsEWWUOFG9XHXubnU3by16i0//92nQcUTiykHLwN3LuXv5XG7l3P1QL5kpcsj6ndmPY8sdS7+p/XQhHJEidCSbiUQKXZkSZXi4zcN8ueZLxi4YG3QckbihMpBi59om19K4WmP6T+vPzuydQccRiQsqAyl2EhMSeartU6zcuJLnZz0fdByRuKAykGLpgnoX0OGEDjw882HWbV8XdByRmKcykGLriQufYEvmFh6a8VDQUURinspAiq2GVRtyw2k3MCx9GEvXLw06jkhMUxlIsTao9SBKJZXSF9FEIkxlIMVatbLV6N+qP+8sfoeZq2YGHUckZqkMpNi7/czbqVGuBndOvZM9vifoOCIxKaJlYGbtzWyJmS0zs9+t55tZbzObb2bzzOwTMzslknkkOqUkp/DI+Y8w+8fZjP52dNBxRGJSxMrAzBKBoUAH4BSgay5/7N9w91PdvSkwGPhHpPJIdOvWuBupx6Ry77R79UU0kQiI5JpBC2BZ+JrJmcBooFPOCXJcGwGgDDoTquQhwRJ4su2T/G/T/3j2i2eDjiMScyJZBjWAH3IMrw6P24+Z3Wxm3xNaM+ib2xOZWS8zSzez9IyMjIiEleLvvLrnccmJl/DIJ4+QsU3/DkQKU+A7kN19qLsfD9wD/DWPaYa7e5q7p1WpUqVoA0qxMviCwWzL3MagGYOCjiISUyJZBmuAWjmGa4bH5WU0cFkE80gMOLnKyfRq1ot/pv+TxesWBx1HJGZEsgxmA/XNrK6ZlQC6ABNyTmBm9XMMXgx8F8E8EiMGth5ISnIK9067N+goIjEjYmXg7tnALcAUYBEw1t0XmNmDZtYxPNktZrbAzOYBdwB/ilQeiR1Vy1Sl/1n9Gb94PJ/875Og44jEBIu2q0mlpaV5enp60DEkYNuztlP/ufrUrlCbz677DDMLOpJIsWZmc9w9La/HA9+BLFIQKckpPNTmIb5Y/QVvL3o76DgiUU9lIFHrT03+RMMqDek/rT9Zu7OCjiMS1VQGErUSExIZfOFglm1YxvA5w4OOIxLVVAYS1Tqc0IE2ddowaMYgNu/anP8MIpIrlYFENTNj8IWDydiewROfPhF0HJGopTKQqJd2bBpdG3Xlqc+f4sctPwYdRyQqqQwkJvz9vL+TvSebAR8NCDqKSFRSGUhMqFuxLre0uIWR80ay4JcFQccRiToqA4kZ9599P+VKlKP/NF0vWeRwqQwkZlRKqcR9Z9/HxKUTmb5yetBxRKKKykBiyq0tbqVW+Vrc9f5dul6yyGFQGUhMKZ1cmofPe5j0H9P5z4L/BB1HJGqoDCTmXHPqNTSp1oR7p93LruxdQccRiQoqA4k5e09TsWLjCv6Z/s+g44hEBZWBxKS2x7flwnoX8tDMh9i4c2PQcUSKPZWBxKzHL3icDTs28PgnjwcdRaTYUxlIzEqtnkq3xt14+ounWbJuSdBxRIo1lYHEtMEXDiYlOYWeE3rqUFORg1AZSEw7puwxPNP+GT794VOGzhoadByRYktlIDGve+PutD+hPfdOu5cVv64IOo5IsaQykJhnZrx4yYskWAI3/N8NuHvQkUSKHZWBxIXaFWoz+MLBTFsxjZfnvhx0HJFiR2UgcaNXs160rtOaflP7sWbzmqDjiBQrKgOJGwmWwEuXvkTW7ix6v9dbm4tEcohoGZhZezNbYmbLzOx3J5k3szvMbKGZfWNm08zsuEjmETn+6OP5+3l/Z+LSibz57ZtBxxEpNiJWBmaWCAwFOgCnAF3N7JQDJpsLpLl7Y2AcMDhSeUT26nt6X86oeQa3Tr6VtVvXBh1HpFiI5JpBC2CZuy9390xgNNAp5wTu/pG7bw8PfgHUjGAeESB0IruRHUeyNXMrt06+Neg4IsVCJMugBvBDjuHV4XF56QlMzu0BM+tlZulmlp6RkVGIESVenVzlZAacO4D/LPwPby96O+g4IoErFjuQzawbkAY8kdvj7j7c3dPcPa1KlSpFG05i1l0t76LpMU256b2b2LBjQ9BxRAIVyTJYA9TKMVwzPG4/ZnYBcD/Q0d11JRIpMsmJyYzqNIr1O9Zzx5Q7go4jEqhIlsFsoL6Z1TWzEkAXYELOCcwsFXiRUBH8EsEsIrlqekxT+rfqz6tfv8rk73LdSikSFyJWBu6eDdwCTAEWAWPdfYGZPWhmHcOTPQGUBf5jZvPMbEIeTycSMX8956+cUuUUek3sxeZdm4OOIxIIi7Yv3qSlpXl6enrQMSTGfLn6S1qObEnHkzoy7spxJCYkBh1JpFCZ2Rx3T8vr8WKxA1kkaKfXPJ2n2z3N+MXjuem9m/TtZIk7SUEHECku+p7el5+3/syjnzxK9XLVGdh6YNCRRIqMykAkh7+f93d+3vozg2YMolqZavRp3ifoSCJFQmUgkoOZMfzS4WRsz+DmSTdTtUxVrjjliqBjiZm3y5EAAAyVSURBVESc9hmIHCApIYkxncdwRs0z+OPbf2T6yulBRxKJOJWBSC5SklOY+MeJHF/xeDqN7sTXP38ddCSRiFIZiOTh6NJHM6XbFMqXLE/719vr+skS01QGIgdRq0It/nvNf9mVvYt2r7UjY5tOlCixSWUgko+GVRsy8Y8TWb15NRe9cRFbM7cGHUmk0KkMRA5By1otGdN5DHN/mssVY68gc3dm0JFECpXKQOQQXXrSpYy4dARTv59K93e6sz1re/4ziUQJlYHIYfhz6p8ZfMFgxi4Yy2kvnkb6jzpPlsQGlYHIYbqr1V180P0DtmVt48yXz+TBGQ+SvSc76FgiR0RlIFIA59c7n/l95nN1w6sZMH0AZ408i6XrlwYdS6TAVAYiBXRUqaN47Q+vMabzGJauX0rqi6m8MPsFnfFUopLKQOQIXdXwKub3mc/Ztc/mpkk3cfEbF/PTlp+CjiVyWFQGIoWgRvkaTL5mMkMvGsr0ldNp9EIjxi0cF3QskUOmMhApJGbGTc1vYu6Nczm+4vFc+Z8r6f5Od31rWaKCykCkkJ1U+SQ+ve5TBp47kDfnv0mtp2vRY3wPvvrpq6CjieRJZSASAcmJyQxoPYBvb/qWnqk9GbdwHM2GN+OskWcxdsFYsnZnBR1RZD8WbUc+pKWleXq6vugj0WXTzk2MmjeK52Y9x/Jfl1OjXA36pPWhV7NeVClTJeh4EgfMbI67p+X5uMpApOjs8T1M/m4yQ2YNYer3UymRWIKujbpya4tbaXZss6DjSQxTGYgUU4vXLeb5Wc/zyrxX2Ja1jebHNuei+hfR7vh2NK/RnKQEXZVWCo/KQKSY27sJ6c1v32T2mtk4ToWSFTi/3vm0O74dbY9vS52j6gQdU6JcoGVgZu2BZ4FE4CV3f+yAx88BngEaA13cPd8Ds1UGEss27NjAtOXTmPL9FKZ8P4XVm1cDUP/o+vuKoU3dNpQtUTbgpBJtAisDM0sElgIXAquB2UBXd1+YY5o6QHngTmCCykDkN+7OkvVLmLJsClOXT2X6yulsz9pOUkISp1Y9ldRjUkmtnkrTY5rSpFoTypUsF3RkKcbyK4NIbpRsASxz9+XhIKOBTsC+MnD3leHH9kQwh0hUMjMaVG5Ag8oNuO2M29iVvYvPfviM95e/T/qP6UxYOoGR80aGpsU44egTQuVQrSmp1VNJPSaVamWrBbwUEi0iWQY1gB9yDK8GTi/IE5lZL6AXQO3atY88mUgUKplUkjZ129CmbhsgtObw45YfmfvzXOb+NJe5P89l1ppZjF0wdt88VVKqULdiXeoeFb5VrEudo+pQ96i61K5Qm5JJJYNaHClmouJwBXcfDgyH0GaigOOIFAtmRo3yNahRvgaXnHjJvvG/7viVr9d+zdyf5rIwYyErNq5g9o+zeWvRW/tdd8EIzb+3HI4tdyzVylSjWtlq+/2slFKJBNP3U2NdJMtgDVArx3DN8DgRiaCKpSvSuk5rWtdpvd/43Xt2s2bLGlb8uoKVG1eyYuMKVmwM3Z+xagY/bfmJrD2//2Z0oiVSpUyV/QqickplKqdUplLpSvvuV06pTKWUSlQqXYnkxOQiWlopLJEsg9lAfTOrS6gEugB/jODrichBJCYkUrtCbWpXqM25nPu7x92djTs3snbbWn7e+jNrt65l7ba1v/0M31+8bjHrt69nW9a2PF+rQskKVEqpxFGljqJCyQqUL1me8iXL77tfodT+48qWKEvp5NKUTir9u5+lkkphZpH81QgRLAN3zzazW4AphA4tHenuC8zsQSDd3SeYWXPgHaAicKmZDXL3hpHKJCJ5MzMqlq5IxdIVaVC5Qb7T78zeyfrt61m3fR3rd4R+rtu+bt+4dTvWsWnnJjbv2syKjSvYvGszm3dtZtPOTez23YeVrVRSKUonlSYlOYVyJcvtK5KcBXPg/bIlylIisQTJicmhnwmhnznH7b2pdPSlMxEpYu7Ojuwd+4pi867NbMncwo6sHezI3pHnz+1Z29mRtYMtmVt+K5ZdOZ5j1xacgv89M4yU5BRSklMoU6JM6Gdymd+NK5VYipJJJSmVVGq/W8nE/ccdWDy5ldHecSWTSu57LFL7Z4I8tFRE5HfMfvujW71c9UJ73j2+h62ZW/eVw9bMrWTtziJzdyaZuzPJ2hO6f+C4Xdm79pXN9qztbMvcxvbsHPeztpOxPYNVm1axPWs7u7J3sTN7577bkRRQbhItcb+1lpxlMeDcAXRp1KVQX28vlYGIxIQES9i3iaiouDtZe7L2K4e9t9yKKGcZ7R23K3vXfo/vve3K3vXb8J7Qz0qlK0VsWVQGIiIFZGb7Pr0XZQlFgg4eFhERlYGIiKgMREQElYGIiKAyEBERVAYiIoLKQEREUBmIiAhReG4iM8sAVhVw9srAukKMUxzE2jLF2vJA7C1TrC0PxN4y5bY8x7l7lbxmiLoyOBJmln6wEzVFo1hbplhbHoi9ZYq15YHYW6aCLI82E4mIiMpARETirwyGBx0gAmJtmWJteSD2linWlgdib5kOe3niap+BiIjkLt7WDEREJBcqAxERiZ8yMLP2ZrbEzJaZWf+g8xwpM1tpZvPNbJ6ZReVFoc1spJn9Ymbf5hh3tJm9b2bfhX9WDDLj4chjeQaa2Zrw+zTPzC4KMuPhMrNaZvaRmS00swVmdlt4fFS+TwdZnqh9n8yslJnNMrOvw8s0KDy+rpl9Gf6bN8bMShz0eeJhn4GZJQJLgQuB1cBsoKu7Lww02BEws5VAmrtH7RdlzOwcYCvwL3dvFB43GNjg7o+FS7uiu98TZM5DlcfyDAS2uvuTQWYrKDOrDlR396/MrBwwB7gM6EEUvk8HWZ6riNL3ycwMKOPuW80sGfgEuA24A3jb3Ueb2T+Br939hbyeJ17WDFoAy9x9ubtnAqOBTgFninvuPhPYcMDoTsCr4fuvEvqPGhXyWJ6o5u4/uftX4ftbgEVADaL0fTrI8kQtD9kaHkwO3xw4DxgXHp/vexQvZVAD+CHH8Gqi/B8AoTd7qpnNMbNeQYcpRNXc/afw/Z+BakGGKSS3mNk34c1IUbE5JTdmVgdIBb4kBt6nA5YHovh9MrNEM5sH/AK8D3wPbHT37PAk+f7Ni5cyiEVnuftpQAfg5vAmipjioW2Y0b4d8wXgeKAp8BPwVLBxCsbMygJvAX9x9805H4vG9ymX5Ynq98ndd7t7U6AmoS0hDQ73OeKlDNYAtXIM1wyPi1ruvib88xfgHUL/AGLB2vB23b3bd38JOM8Rcfe14f+oe4ARROH7FN4O/Rbwuru/HR4dte9TbssTC+8TgLtvBD4CzgSOMrOk8EP5/s2LlzKYDdQP710vAXQBJgScqcDMrEx45xdmVgZoC3x78LmixgTgT+H7fwLeDTDLEdv7BzPscqLsfQrvnHwZWOTu/8jxUFS+T3ktTzS/T2ZWxcyOCt8vTehAmUWESqFzeLJ836O4OJoIIHyo2DNAIjDS3f8ecKQCM7N6hNYGAJKAN6JxeczsTaA1odPtrgUGAOOBsUBtQqcqv8rdo2KnbB7L05rQpgcHVgI35tjWXuyZ2VnAx8B8YE949H2EtrNH3ft0kOXpSpS+T2bWmNAO4kRCH/DHuvuD4b8To4GjgblAN3fflefzxEsZiIhI3uJlM5GIiByEykBERFQGIiKiMhAREVQGIiKCykBkHzPbneOslfMK8+y2ZlYn59lMRYqbpPwnEYkbO8Jf6ReJO1ozEMlH+NoRg8PXj5hlZieEx9cxsw/DJzebZma1w+Ormdk74fPLf21mLcNPlWhmI8LnnJ8a/rYoZtY3fH79b8xsdECLKXFOZSDym9IHbCa6Osdjm9z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yUbCuLXNIsVNEoDAQkfQRZhisBSaa2QQzG0DsC39F643M7IvAcOCVEGsJxcadG8kbkEfBoIJklyIickhCCwN3bwCuB1YBHwCPuvs6M7vNzGbFbToHWObuHlYtYanYVcHY/LGYWbJLERE5JKF1LQVw95XAylbrftRqeWGYNYQpUhNhXP64ZJchInLI+soF5JQUiSoMRCQ9KAx6qLaulh17dzBumMJARFKfwqCHIjURALUMRCQtdBoGZjbYzPrFLfczs0HhltX3RaJBGKhlICJpoCstgz8D8V/+g4DnwikndVREKwA0WqmIpIWuhEGOu9c2LwSP1TKoiZDdL5sj8o5IdikiIoesK2HwmZl9qXnBzE4C9oZXUmqIRCOMGTpGQ1eLSFroyn0GC4A/mNkWwIBRwKWhVpUCItGIrheISNroNAzcfW0wZETz0Jyl7l4fbll9X6QmwpeP+nKyyxARSYiu9Ca6Dhjs7u+5+3tAnpn9S/il9V11jXVs2b1F3UpFJG105ZrB1e7eMh2lu+8Erg6vpL5v867NOK6eRCKSNroSBlkWNxJbMJ3lgPBK6vt0j4GIpJuuXEB+GnjEzH4dLF8D/Cm8kvo+3X0sIummK2HwPWAe8J1g+V1iPYoyVnPLYEz+mE62FBFJDZ2eJnL3JuA1oJzYJPfnEJufIGNFaiKMyhtFTnZOsksREUmIdlsGZjYJmBv8fAo8AuDuZ/dOaX1Xxa4KnSISkbTSUcvgQ2KtgAvc/Qx3/y+gzQnrM02kJqKeRCKSVjoKgwuBrcBqM7vXzM4ldgdyRmvyJiqiahmISHppNwzc/Ql3nwN8EVhNbFiKw8zsHjP7+94qsK/Z/tl29jfuV7dSEUkrXbmA/Jm7/97dvwYUAW8R62GUkdStVETSUbdmOnP3ne6+2N3PDaugvk43nIlIOtK0l93UPKmNWgYikk5CDQMzm2lmpWa23sxubmebfzCz981snZn9Psx6EiFSE2HowKHk5+QnuxQRkYTpyh3IPRKMYbQI+ApQCaw1sxXu/n7cNhOBW4Dp7r7TzA4Lq55EiUQjahWISNoJs2UwDVjv7hvcvQ5YBsxutc3VwKJgJFTcfXuI9SSEJrURkXQUZhiMBjbFLVcG6+JNAiaZ2V/N7FUzm9nWjsxsnpmVmFlJVVVVSOV2TaRGLQMRST/JvoCcDUwEZhAb9uJeMxvWeqOgB1OxuxcXFhb2comfi+6LEt0fVRiISNoJMww2A/HDehYF6+JVAivcvd7dNwJlxMKhT2rpSaTTRCKSZsIMg7XARDObYGYDgDnAilbbPEGsVYCZFRA7bbQhxJoOSfM9BhqXSETSTWhh4O4NwPXAKmJDXj/q7uvM7DYzmxVstgqoNrP3iQ158e/uXh1WTYdKdx+LSLoKrWspgLuvBFa2WvejuMcO3BT89HmRaIQBWQM4PO/wZJciIpJQyb6AnFIi0djQ1f1Mf20ikl70rdYNGrpaRNKVwqAbdI+BiKQrhUEX7W/Yz9barepJJCJpSWHQRZt2xW6m1j0GIpKOFAZdpG6lIpLOFAZdpLuPRSSdKQy6KBKNYBhFQ4uSXYqISMIpDLooEo1w5JAjGZA1INmliIgknMKgiyI1msdARNKXwqCLmu8+FhFJRwqDLmjyJjZFN6knkYikLYVBF2yr3UZ9U73CQETSlsKgC1ruMdA1AxFJUwqDLmie1EYtAxFJVwqDLmhuGegCsoikK4VBF0SiEYbnDGfIwCHJLkVEJBQKgy6oiFboeoGIpDWFQRdEoprHQETSm8KgE+6uSW1EJO0pDDpRs6+G3XW7dZpIRNJaqGFgZjPNrNTM1pvZzW08f4WZVZnZ28HPVWHW0xPqVioimSA7rB2bWRawCPgKUAmsNbMV7v5+q00fcffrw6rjUKlbqYhkgjBbBtOA9e6+wd3rgGXA7BDfLxSa1EZEMkGYYTAa2BS3XBmsa+0iM3vXzB4zszFt7cjM5plZiZmVVFVVhVFruyLRCLnZuRQOKuzV9xUR6U3JvoD8JDDe3U8AngUeaGsjd1/s7sXuXlxY2Ltfys1DV5tZr76viEhvCjMMNgPxv+kXBetauHu1u+8PFu8DTgqxnh7RpDYikgnCDIO1wEQzm2BmA4A5wIr4DczsiLjFWcAHIdbTI7rhTEQyQWi9idy9wcyuB1YBWcASd19nZrcBJe6+ArjBzGYBDcAO4Iqw6umJvfV72f7ZdvUkEpG0F1oYALj7SmBlq3U/int8C3BLmDUcik27Yte/1TIQkXSX7AvIfZomtRGRTKEw6IDuPhaRTKEw6ECkJkKWZTF6aFu3R4iIpA+FQQci0Qijh44mu1+ol1ZERJJOYdCBimiFehKJSEZQGHRA9xiISKZQGLSjsamRyl2VCgMRyQgKg3Zs2b2FhqYGdSsVkYygMGiHupWKSCZRGLRDN5yJSCZRGLSjeVKbMUPbnGJBRCStKAzaEYlGKBhUwOABg5NdiohI6BQG7VC3UhHJJAqDdmhSGxHJJAqDNri7WgYiklEUBm3YsXcHe+r3KAxEJGMoDNrQfI+BxiUSkUyhMGiD7jEQkUyjMGiD7j4WkUyjMGhDpCbC4P6DGZE7ItmliIj0CoVBGyLRWLdSM0t2KSIivUJh0IaKaIVOEYlIRgk1DMxsppmVmtl6M7u5g+0uMjM3s+Iw6+mKJm9iY81G9SQSkYwSWhiYWRawCDgPOBaYa2bHtrHdEGA+8FpYtXTHqvWr2LF3B2eOOzPZpYiI9JowWwbTgPXuvsHd64BlwOw2tvvfwP8B9oVYS5fd+dqdHDnkSC4+9uJklyIi0mvCDIPRwKa45cpgXQsz+xIwxt3/p6Mdmdk8Mysxs5KqqqrEVxpYt30dz3z8DNedfB0DsgaE9j4iIn1N0i4gm1k/4BfAv3W2rbsvdvdidy8uLCwMraa7X7ubnOwc5p00L7T3EBHpi8IMg81A/MwwRcG6ZkOAvwPWmFk5cCqwIlkXkav3VLP03aV884RvUjCoIBkliIgkTZhhsBaYaGYTzGwAMAdY0fyku0fdvcDdx7v7eOBVYJa7l4RYU7sWv7GYfQ37mH/K/GS8vYhIUoUWBu7eAFwPrAI+AB5193VmdpuZzQrrfXuivrGeX679JV856iscd9hxyS5HRKTXZYe5c3dfCaxste5H7Ww7I8xaOvLY+4+xZfcW7v3avckqQUQkqTL+DmR3545X72DSyEnMPHpmsssREUmKjA+DVytfZe2Wtcw/ZT79LOP/OkQkQ2X8t9+dr93JsJxhfGvKt5JdiohI0oR6zaCvq4hW8Pj7j3PTaTeRNyAv2eWIhKK+vp7Kykr27esTN/lLyHJycigqKqJ///7del1Gh8Gi1xcBcP2065NciUh4KisrGTJkCOPHj9ew7GnO3amurqayspIJEyZ067UZe5ros7rPWPzmYi485kKNUCppbd++fYwcOVJBkAHMjJEjR/aoFZixYbD0naXU7KthwakLkl2KSOgUBJmjp591RoZBkzdx12t3cfKRJ3Na0WnJLkdEJOkyMgxWrV9FaXUpC05doN+YREJWXV3N1KlTmTp1KqNGjWL06NEty3V1dR2+tqSkhBtuuKHT9zj99NMTVS4ACxYsYPTo0TQ1NSV0v31ZRl5A1pwFIr1n5MiRvP322wAsXLiQvLw8vvvd77Y839DQQHZ2219FxcXFFBd3Pnblyy+/nJhigaamJpYvX86YMWN44YUXOPvssxO273gdHXcy9J1KeknznAU/OecnmrNAMs6Cpxfw9ra3E7rPqaOmcufMO7v1miuuuIKcnBzeeustpk+fzpw5c5g/fz779u0jNzeX+++/n8mTJ7NmzRpuv/12nnrqKRYuXEhFRQUbNmygoqKCBQsWtLQa8vLyqK2tZc2aNSxcuJCCggLee+89TjrpJH73u99hZqxcuZKbbrqJwYMHM336dDZs2MBTTz11UG1r1qzhuOOO49JLL+Xhhx9uCYNPPvmE73znO2zYsAGAe+65h9NPP52lS5dy++23Y2accMIJPPjgg1xxxRVccMEFXHzxxQfV98Mf/pDhw4fz4YcfUlZWxte//nU2bdrEvn37mD9/PvPmxYbQf/rpp7n11ltpbGykoKCAZ599lsmTJ/Pyyy9TWFhIU1MTkyZN4pVXXiERQ/tnXBhozgKRvqGyspKXX36ZrKwsdu3axUsvvUR2djbPPfcct956K48//vhBr/nwww9ZvXo1u3fvZvLkyVx77bUH9ad/6623WLduHUceeSTTp0/nr3/9K8XFxVxzzTW8+OKLTJgwgblz57Zb18MPP8zcuXOZPXs2t956K/X19fTv358bbriBs846i+XLl9PY2EhtbS3r1q3jxz/+MS+//DIFBQXs2LGj0+N+8803ee+991q6fi5ZsoQRI0awd+9eTj75ZC666CKampq4+uqrW+rdsWMH/fr147LLLuOhhx5iwYIFPPfcc0yZMiUhQQAZFgbVe6p58N0HNWeBZKzu/gYfpksuuYSsrCwAotEol19+OR999BFmRn19fZuvOf/88xk4cCADBw7ksMMO45NPPqGoqOiAbaZNm9ayburUqZSXl5OXl8dRRx3V8gU8d+5cFi9efND+6+rqWLlyJb/4xS8YMmQIp5xyCqtWreKCCy7g+eefZ+nSpQBkZWWRn5/P0qVLueSSSygoiH2fjBgxotPjnjZt2gH3ANx9990sX74cgE2bNvHRRx9RVVXFmWee2bJd836//e1vM3v2bBYsWMCSJUu48sorO32/rsqoMLj3zXvZ27BXcxaI9AGDBw9uefzDH/6Qs88+m+XLl1NeXs6MGTPafM3AgQNbHmdlZdHQ0NCjbdqzatUqampqOP744wHYs2cPubm5XHDBBV3eB0B2dnbLxeempqYDLpTHH/eaNWt47rnneOWVVxg0aBAzZszo8B6BMWPGcPjhh/P888/z+uuv89BDD3Wrro5kTG+i+sZ6fvn6L/nyUV/WnAUifUw0GmX06NgU6b/97W8Tvv/JkyezYcMGysvLAXjkkUfa3O7hhx/mvvvuo7y8nPLycjZu3Mizzz7Lnj17OPfcc7nnnnsAaGxsJBqNcs455/CHP/yB6upqgJbTROPHj+eNN94AYMWKFe22dKLRKMOHD2fQoEF8+OGHvPrqqwCceuqpvPjii2zcuPGA/QJcddVVXHbZZQe0rBIhY8Lg8Q8eZ/PuzSw4RTeZifQ1//Ef/8Ett9zCiSee2K3f5LsqNzeXX/3qV8ycOZOTTjqJIUOGkJ+ff8A2e/bs4emnn+b8889vWTd48GDOOOMMnnzySe666y5Wr17N8ccfz0knncT777/Pcccdx/e//33OOusspkyZwk033QTA1VdfzQsvvMCUKVN45ZVXDmgNxJs5cyYNDQ0cc8wx3HzzzZx66qkAFBYWsnjxYi688EKmTJnCpZde2vKaWbNmUVtbm9BTRADm7gndYdiKi4u9pKT7M2M+VfYU9715H3+89I8aqloyygcffMAxxxyT7DKSrra2lry8PNyd6667jokTJ3LjjTcmu6xuKykp4cYbb+Sll15qd5u2PnMze8Pd2+2nmzHfihdMuoAn5jyhIBDJUPfeey9Tp07luOOOIxqNcs011yS7pG772c9+xkUXXcRPf/rThO87Y1oGIplKLYPMo5aBiLQp1X7pk57r6WetMBBJczk5OVRXVysQMkDzfAY5OTndfm1G3WcgkomKioqorKykqqoq2aVIL2ie6ay7Qg0DM5sJ3AVkAfe5+89aPf8d4DqgEagF5rn7+2HWJJJp+vfv3+1ZryTzhHaayMyygEXAecCxwFwzO7bVZr939+PdfSrwc+AXYdUjIiLtC/OawTRgvbtvcPc6YBkwO34Dd98VtzgY0ElNEZEkCPM00WhgU9xyJXBK643M7DrgJmAAcE5bOzKzecA8gLFjNV+xiEiiJf0CsrsvAhaZ2T8CPwAub2ObxcBiADOrMrNID9+uAPi0p7X2Uel2TOl2PJB+x5RuxwPpd0xtHc+4jl4QZhhsBsbELRcF69qzDLins526e48H7zazko5uukhF6XZM6XY8kH7HlG7HA+l3TD05njCvGawFJprZBDMbAMwBVsRvYGYT4xbPBz4KsR4REWlHaC0Dd28ws+uBVcS6li5x95maSgkAAAVCSURBVHVmdhtQ4u4rgOvN7MtAPbCTNk4RiYhI+EK9ZuDuK4GVrdb9KO5xb88yc/DURqkv3Y4p3Y4H0u+Y0u14IP2OqdvHk3ID1YmISOJpbCIREVEYiIhIBoWBmc00s1IzW29mNye7nkNlZuVm9jcze9vMUnKCBzNbYmbbzey9uHUjzOxZM/so+HN4MmvsjnaOZ6GZbQ4+p7fN7KvJrLG7zGyMma02s/fNbJ2ZzQ/Wp+Tn1MHxpOznZGY5Zva6mb0THNP/CtZPMLPXgu+8R4Jene3vJxOuGQTjJJUBXyF2J/RaYG4qD4pnZuVAsbun7I0yZnYmsQEKl7r73wXrfg7scPefBaE93N2/l8w6u6qd41kI1Lr77cmsrafM7AjgCHd/08yGAG8AXweuIAU/pw6O5x9I0c/JzAwY7O61ZtYf+Aswn9jIDn9092Vm9t/AO+7e7r1cmdIy6HScJOl97v4isKPV6tnAA8HjB4j9R00J7RxPSnP3re7+ZvB4N/ABsaFmUvJz6uB4UpbH1AaL/YMfJza8z2PB+k4/o0wJg7bGSUrpfwDEPuxnzOyNYOymdHG4u28NHm8DDk9mMQlyvZm9G5xGSonTKW0xs/HAicBrpMHn1Op4IIU/JzPLMrO3ge3As8DHQI27NwSbdPqdlylhkI7OcPcvERsi/LrgFEVa8dg5zFQ/j3kP8AVgKrAV+L/JLadnzCwPeBxY0Gq04ZT8nNo4npT+nNy9MZgKoIjYmZAvdncfmRIG3R0nqc9z983Bn9uB5cT+AaSDT4Lzus3nd7cnuZ5D4u6fBP9Rm4B7ScHPKTgP/TjwkLv/MVidsp9TW8eTDp8TgLvXAKuB04BhZtZ8Y3Gn33mZEgadjpOUSsxscHDxCzMbDPw98F7Hr0oZK/h8WJLLgf+XxFoOWfMXZuAbpNjnFFyc/A3wgbvHTz6Vkp9Te8eTyp+TmRWa2bDgcS6xjjIfEAuFi4PNOv2MMqI3EUDQVexOPh8n6SdJLqnHzOwoYq0BiA0p8vtUPB4zexiYQWy43U+A/wSeAB4FxgIR4B/cPSUuyrZzPDOInXpwoBy4Ju5ce59nZmcALwF/A5qC1bcSO8+ecp9TB8czlxT9nMzsBGIXiLOI/YL/qLvfFnxPLANGAG8Bl7n7/nb3kylhICIi7cuU00QiItIBhYGIiCgMREREYSAiIigMREQEhYFICzNrjBu18u1Ejm5rZuPjRzMV6WtCnfZSJMXsDW7pF8k4ahmIdCKYO+LnwfwRr5vZ0cH68Wb2fDC42Z/NbGyw/nAzWx6ML/+OmZ0e7CrLzO4Nxpx/JrhbFDO7IRhf/10zW5akw5QMpzAQ+Vxuq9NEl8Y9F3X344FfEruTHeC/gAfc/QTgIeDuYP3dwAvuPgX4ErAuWD8RWOTuxwE1wEXB+puBE4P9fCesgxPpiO5AFgmYWa2757Wxvhw4x903BIOcbXP3kWb2KbGJUuqD9VvdvcDMqoCi+Fv/g+GSn3X3icHy94D+7v5jM3ua2KQ4TwBPxI1NL9Jr1DIQ6Rpv53F3xI8L08jn1+zOBxYRa0WsjRtpUqTXKAxEuubSuD9fCR6/TGwEXIB/IjYAGsCfgWuhZdKR/PZ2amb9gDHuvhr4HpAPHNQ6EQmbfgMR+VxuMFtUs6fdvbl76XAze5fYb/dzg3X/CtxvZv8OVAFXBuvnA4vN7J+JtQCuJTZhSluygN8FgWHA3cGY9CK9StcMRDoRXDModvdPk12LSFh0mkhERNQyEBERtQxERASFgYiIoDAQEREUBiIigsJARESA/w97xv0kqpN1FQAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Evaluation\n","7/7 [==============================] - 0s 2ms/step - loss: 0.0815 - accuracy: 0.9667\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.08154818415641785, 0.9666666388511658]"]},"metadata":{},"execution_count":27}]},{"cell_type":"markdown","source":["#### 3-2-1) Model 3"],"metadata":{"id":"-Io2cQn9rpC0"}},{"cell_type":"code","source":["loss_train3 = history3.history['loss']\n","\n","plt.plot(loss_train3, 'g', label='Training loss')\n","plt.title('Training Loss')\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.legend()\n","plt.show()\n","\n","acc_train3 = history3.history['accuracy']\n","\n","plt.plot(acc_train3, 'g', label='Training Accuracy')\n","plt.title('Training Acc')\n","plt.xlabel('Epochs')\n","plt.ylabel('Acc')\n","plt.legend()\n","plt.show()\n","\n","print(\"Evaluation\")\n","model3.evaluate(X_test_scaled, y_test)\n"],"metadata":{"id":"kAX9kQwGrnyP","colab":{"base_uri":"https://localhost:8080/","height":625},"executionInfo":{"status":"ok","timestamp":1665379204277,"user_tz":300,"elapsed":396,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"9c971c6e-7acc-4e1c-8bc4-71f0e300d2a3"},"execution_count":28,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Evaluation\n","7/7 [==============================] - 0s 2ms/step - loss: 0.0735 - accuracy: 0.9714\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.07354230433702469, 0.9714285731315613]"]},"metadata":{},"execution_count":28}]},{"cell_type":"markdown","source":["# 4) Train the model on the training data. Experiment with different number of epochs."],"metadata":{"id":"O4eOKpCYrziN"}},{"cell_type":"markdown","source":["### 4-1) Epochs=10"],"metadata":{"id":"z5tyJFCtr9TA"}},{"cell_type":"code","source":["history1_epoch10 = model3.fit(X_train_scaled, y_train,batch_size = 5, epochs=10, verbose=1)"],"metadata":{"id":"vVpypCTMryVv","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665379207228,"user_tz":300,"elapsed":2954,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"72ae8a88-7440-433d-b6d5-fdc8733efeb9"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0850 - accuracy: 0.9775\n","Epoch 2/10\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0812 - accuracy: 0.9795\n","Epoch 3/10\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0899 - accuracy: 0.9754\n","Epoch 4/10\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0695 - accuracy: 0.9795\n","Epoch 5/10\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0725 - accuracy: 0.9795\n","Epoch 6/10\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0717 - accuracy: 0.9816\n","Epoch 7/10\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0740 - accuracy: 0.9775\n","Epoch 8/10\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0651 - accuracy: 0.9816\n","Epoch 9/10\n","98/98 [==============================] - 0s 4ms/step - loss: 0.0647 - accuracy: 0.9816\n","Epoch 10/10\n","98/98 [==============================] - 0s 4ms/step - loss: 0.0643 - accuracy: 0.9795\n"]}]},{"cell_type":"code","source":["acc_epoch10 = history1_epoch10.history['accuracy']\n","print(max(acc_epoch10))"],"metadata":{"id":"VaYXIzfNvCi3","executionInfo":{"status":"ok","timestamp":1665379207228,"user_tz":300,"elapsed":7,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"208b7ed9-6850-4d4b-b0e4-9dc1891df3ed"},"execution_count":30,"outputs":[{"output_type":"stream","name":"stdout","text":["0.9815573692321777\n"]}]},{"cell_type":"markdown","source":["### 4-2) Epochs=20"],"metadata":{"id":"pvOvayQysDDc"}},{"cell_type":"code","source":["history2_epoch20 = model3.fit(X_train_scaled, y_train,batch_size = 5, epochs=20, verbose=1)"],"metadata":{"id":"XFNOSbbksEZH","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665379214218,"user_tz":300,"elapsed":6992,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"90b3a2e1-768b-4eef-bed6-3a711ac9f2df"},"execution_count":31,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","98/98 [==============================] - 0s 4ms/step - loss: 0.0616 - accuracy: 0.9795\n","Epoch 2/20\n","98/98 [==============================] - 0s 4ms/step - loss: 0.0487 - accuracy: 0.9795\n","Epoch 3/20\n","98/98 [==============================] - 0s 4ms/step - loss: 0.0561 - accuracy: 0.9795\n","Epoch 4/20\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0641 - accuracy: 0.9775\n","Epoch 5/20\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0590 - accuracy: 0.9836\n","Epoch 6/20\n","98/98 [==============================] - 0s 4ms/step - loss: 0.0594 - accuracy: 0.9816\n","Epoch 7/20\n","98/98 [==============================] - 0s 4ms/step - loss: 0.0637 - accuracy: 0.9775\n","Epoch 8/20\n","98/98 [==============================] - 0s 4ms/step - loss: 0.0455 - accuracy: 0.9857\n","Epoch 9/20\n","98/98 [==============================] - 0s 4ms/step - loss: 0.0546 - accuracy: 0.9836\n","Epoch 10/20\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0493 - accuracy: 0.9836\n","Epoch 11/20\n","98/98 [==============================] - 0s 4ms/step - loss: 0.0549 - accuracy: 0.9775\n","Epoch 12/20\n","98/98 [==============================] - 0s 4ms/step - loss: 0.0468 - accuracy: 0.9898\n","Epoch 13/20\n","98/98 [==============================] - 0s 4ms/step - loss: 0.0566 - accuracy: 0.9795\n","Epoch 14/20\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0589 - accuracy: 0.9816\n","Epoch 15/20\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0484 - accuracy: 0.9816\n","Epoch 16/20\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0508 - accuracy: 0.9836\n","Epoch 17/20\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0498 - accuracy: 0.9836\n","Epoch 18/20\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0399 - accuracy: 0.9877\n","Epoch 19/20\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0410 - accuracy: 0.9857\n","Epoch 20/20\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0587 - accuracy: 0.9795\n"]}]},{"cell_type":"code","source":["acc_epoch20 = history2_epoch20.history['accuracy']\n","print(max(acc_epoch20))"],"metadata":{"id":"rJAgHES9xqlo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665379214218,"user_tz":300,"elapsed":12,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"4e5dccec-e478-49f7-ed6c-43cc89bdc34d"},"execution_count":32,"outputs":[{"output_type":"stream","name":"stdout","text":["0.9897540807723999\n"]}]},{"cell_type":"markdown","source":["### 4-3) Epochs=60"],"metadata":{"id":"NQpuhGhQsFdw"}},{"cell_type":"code","source":["history3_epoch60 = model3.fit(X_train_scaled, y_train,batch_size = 5, epochs=60, verbose=1)"],"metadata":{"id":"7D5rptHosH2X","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665379230782,"user_tz":300,"elapsed":16571,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"68acffe2-d9e4-42cd-ddaf-f8dc7353c83b"},"execution_count":33,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0490 - accuracy: 0.9734\n","Epoch 2/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0489 - accuracy: 0.9836\n","Epoch 3/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0356 - accuracy: 0.9918\n","Epoch 4/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0406 - accuracy: 0.9836\n","Epoch 5/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0524 - accuracy: 0.9795\n","Epoch 6/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0444 - accuracy: 0.9857\n","Epoch 7/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0429 - accuracy: 0.9836\n","Epoch 8/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0377 - accuracy: 0.9857\n","Epoch 9/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0339 - accuracy: 0.9877\n","Epoch 10/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0323 - accuracy: 0.9898\n","Epoch 11/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0359 - accuracy: 0.9877\n","Epoch 12/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0345 - accuracy: 0.9836\n","Epoch 13/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0404 - accuracy: 0.9857\n","Epoch 14/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0301 - accuracy: 0.9877\n","Epoch 15/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0360 - accuracy: 0.9836\n","Epoch 16/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0253 - accuracy: 0.9898\n","Epoch 17/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0390 - accuracy: 0.9816\n","Epoch 18/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0389 - accuracy: 0.9898\n","Epoch 19/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0320 - accuracy: 0.9857\n","Epoch 20/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0246 - accuracy: 0.9898\n","Epoch 21/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0350 - accuracy: 0.9857\n","Epoch 22/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0400 - accuracy: 0.9795\n","Epoch 23/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0310 - accuracy: 0.9898\n","Epoch 24/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0232 - accuracy: 0.9918\n","Epoch 25/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0368 - accuracy: 0.9836\n","Epoch 26/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0262 - accuracy: 0.9898\n","Epoch 27/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0202 - accuracy: 0.9939\n","Epoch 28/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0300 - accuracy: 0.9898\n","Epoch 29/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0250 - accuracy: 0.9898\n","Epoch 30/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0221 - accuracy: 0.9877\n","Epoch 31/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0179 - accuracy: 0.9918\n","Epoch 32/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0172 - accuracy: 0.9918\n","Epoch 33/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0223 - accuracy: 0.9898\n","Epoch 34/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0143 - accuracy: 0.9959\n","Epoch 35/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0244 - accuracy: 0.9918\n","Epoch 36/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0306 - accuracy: 0.9877\n","Epoch 37/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0169 - accuracy: 0.9959\n","Epoch 38/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0286 - accuracy: 0.9857\n","Epoch 39/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0158 - accuracy: 0.9918\n","Epoch 40/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0278 - accuracy: 0.9857\n","Epoch 41/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0272 - accuracy: 0.9918\n","Epoch 42/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0237 - accuracy: 0.9877\n","Epoch 43/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0274 - accuracy: 0.9918\n","Epoch 44/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0239 - accuracy: 0.9918\n","Epoch 45/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0279 - accuracy: 0.9898\n","Epoch 46/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0176 - accuracy: 0.9918\n","Epoch 47/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0213 - accuracy: 0.9918\n","Epoch 48/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0222 - accuracy: 0.9939\n","Epoch 49/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0191 - accuracy: 0.9959\n","Epoch 50/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0246 - accuracy: 0.9959\n","Epoch 51/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0124 - accuracy: 0.9959\n","Epoch 52/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0089 - accuracy: 0.9980\n","Epoch 53/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0253 - accuracy: 0.9918\n","Epoch 54/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0139 - accuracy: 0.9980\n","Epoch 55/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0110 - accuracy: 0.9939\n","Epoch 56/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0291 - accuracy: 0.9918\n","Epoch 57/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0084 - accuracy: 1.0000\n","Epoch 58/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0201 - accuracy: 0.9918\n","Epoch 60/60\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0111 - accuracy: 0.9959\n"]}]},{"cell_type":"code","source":["acc_epoch60 = history3_epoch60.history['accuracy']\n","print(max(acc_epoch60))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TIMm3AZmxPge","executionInfo":{"status":"ok","timestamp":1665379230782,"user_tz":300,"elapsed":7,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"2f6777ec-9dc0-45a7-dcf9-ca5f4dd7bfd9"},"execution_count":34,"outputs":[{"output_type":"stream","name":"stdout","text":["1.0\n"]}]},{"cell_type":"markdown","source":["### 4-4) Epochs=100"],"metadata":{"id":"quhKeoFLsIXU"}},{"cell_type":"code","source":["history4_epoch100 = model3.fit(X_train_scaled, y_train,batch_size = 5, epochs=100, verbose=1)"],"metadata":{"id":"P2akPbJ9sJ6m","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665379259203,"user_tz":300,"elapsed":28424,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"58b797d9-b474-49f0-a855-d14eaaf47363"},"execution_count":35,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0350 - accuracy: 0.9877\n","Epoch 2/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0109 - accuracy: 0.9959\n","Epoch 3/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0111 - accuracy: 0.9959\n","Epoch 4/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0358 - accuracy: 0.9918\n","Epoch 5/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0114 - accuracy: 0.9980\n","Epoch 6/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0281 - accuracy: 0.9877\n","Epoch 7/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0153 - accuracy: 0.9959\n","Epoch 8/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0256 - accuracy: 0.9877\n","Epoch 9/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0396 - accuracy: 0.9877\n","Epoch 10/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0138 - accuracy: 0.9959\n","Epoch 11/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0096 - accuracy: 1.0000\n","Epoch 12/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0294 - accuracy: 0.9939\n","Epoch 13/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0126 - accuracy: 0.9980\n","Epoch 14/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0082 - accuracy: 0.9959\n","Epoch 15/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0269 - accuracy: 0.9918\n","Epoch 16/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0152 - accuracy: 0.9939\n","Epoch 17/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0166 - accuracy: 0.9959\n","Epoch 18/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0124 - accuracy: 0.9959\n","Epoch 19/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0184 - accuracy: 0.9918\n","Epoch 20/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0148 - accuracy: 0.9939\n","Epoch 21/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0171 - accuracy: 0.9959\n","Epoch 22/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0133 - accuracy: 0.9959\n","Epoch 23/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0075 - accuracy: 0.9980\n","Epoch 24/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0140 - accuracy: 0.9959\n","Epoch 25/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0113 - accuracy: 0.9959\n","Epoch 26/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0046 - accuracy: 1.0000\n","Epoch 27/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0079 - accuracy: 0.9959\n","Epoch 28/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0048 - accuracy: 1.0000\n","Epoch 29/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0346 - accuracy: 0.9939\n","Epoch 30/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0102 - accuracy: 0.9939\n","Epoch 31/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0106 - accuracy: 0.9959\n","Epoch 32/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0170 - accuracy: 0.9918\n","Epoch 33/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0102 - accuracy: 0.9939\n","Epoch 34/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0111 - accuracy: 0.9959\n","Epoch 35/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0099 - accuracy: 0.9939\n","Epoch 36/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0185 - accuracy: 0.9980\n","Epoch 37/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0062 - accuracy: 0.9980\n","Epoch 38/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0448 - accuracy: 0.9857\n","Epoch 39/100\n","98/98 [==============================] - 0s 4ms/step - loss: 0.0191 - accuracy: 0.9939\n","Epoch 40/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0086 - accuracy: 0.9959\n","Epoch 41/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0086 - accuracy: 0.9959\n","Epoch 42/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0088 - accuracy: 0.9959\n","Epoch 43/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0168 - accuracy: 0.9939\n","Epoch 44/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0131 - accuracy: 0.9939\n","Epoch 45/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0173 - accuracy: 0.9939\n","Epoch 46/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0287 - accuracy: 0.9959\n","Epoch 47/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0082 - accuracy: 0.9980\n","Epoch 48/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0082 - accuracy: 0.9959\n","Epoch 49/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0047 - accuracy: 1.0000\n","Epoch 50/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0104 - accuracy: 0.9980\n","Epoch 51/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0051 - accuracy: 0.9980\n","Epoch 52/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0189 - accuracy: 0.9918\n","Epoch 53/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0051 - accuracy: 0.9980\n","Epoch 54/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0043 - accuracy: 1.0000\n","Epoch 55/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0143 - accuracy: 0.9939\n","Epoch 56/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0113 - accuracy: 0.9959\n","Epoch 57/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0211 - accuracy: 0.9939\n","Epoch 58/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0100 - accuracy: 0.9959\n","Epoch 59/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0339 - accuracy: 0.9959\n","Epoch 60/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0259 - accuracy: 0.9980\n","Epoch 61/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0062 - accuracy: 0.9980\n","Epoch 62/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0081 - accuracy: 0.9980\n","Epoch 63/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0106 - accuracy: 0.9959\n","Epoch 64/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0050 - accuracy: 0.9980\n","Epoch 65/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0160 - accuracy: 0.9939\n","Epoch 66/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0038 - accuracy: 1.0000\n","Epoch 67/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0215 - accuracy: 0.9918\n","Epoch 68/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0103 - accuracy: 0.9959\n","Epoch 69/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0107 - accuracy: 0.9939\n","Epoch 70/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0179 - accuracy: 0.9918\n","Epoch 71/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0077 - accuracy: 0.9980\n","Epoch 72/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0123 - accuracy: 0.9959\n","Epoch 73/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0142 - accuracy: 0.9959\n","Epoch 74/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0056 - accuracy: 0.9980\n","Epoch 75/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0125 - accuracy: 0.9959\n","Epoch 76/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0080 - accuracy: 0.9980\n","Epoch 77/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0152 - accuracy: 0.9959\n","Epoch 78/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0058 - accuracy: 0.9980\n","Epoch 79/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0028 - accuracy: 1.0000\n","Epoch 80/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0083 - accuracy: 0.9959\n","Epoch 81/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0414 - accuracy: 0.9918\n","Epoch 82/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0197 - accuracy: 0.9939\n","Epoch 83/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0084 - accuracy: 0.9980\n","Epoch 84/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0070 - accuracy: 0.9980\n","Epoch 85/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0088 - accuracy: 0.9959\n","Epoch 86/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0082 - accuracy: 0.9980\n","Epoch 87/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0038 - accuracy: 1.0000\n","Epoch 88/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0149 - accuracy: 0.9939\n","Epoch 89/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0090 - accuracy: 0.9959\n","Epoch 90/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0030 - accuracy: 1.0000\n","Epoch 91/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0044 - accuracy: 1.0000\n","Epoch 92/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0173 - accuracy: 0.9939\n","Epoch 93/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0069 - accuracy: 0.9980\n","Epoch 94/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0043 - accuracy: 0.9980\n","Epoch 95/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0056 - accuracy: 1.0000\n","Epoch 96/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0036 - accuracy: 1.0000\n","Epoch 97/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0067 - accuracy: 0.9959\n","Epoch 98/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0061 - accuracy: 0.9959\n","Epoch 99/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0083 - accuracy: 0.9959\n","Epoch 100/100\n","98/98 [==============================] - 0s 3ms/step - loss: 0.0092 - accuracy: 0.9959\n"]}]},{"cell_type":"code","source":["acc_epoch100 = history4_epoch100.history['accuracy']\n","print(max(acc_epoch100))"],"metadata":{"id":"DOOXl3o0x5Za","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665379259203,"user_tz":300,"elapsed":8,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"35c808cb-ec2b-407d-a9c9-aa7bbc247399"},"execution_count":36,"outputs":[{"output_type":"stream","name":"stdout","text":["1.0\n"]}]},{"cell_type":"markdown","source":["# 5) What were the ’loss’ and accuracy values attained?"],"metadata":{"id":"vBGEqLlrsdjD"}},{"cell_type":"markdown","source":["Best\n","\n","<b>Train Loss</b> = 0.0001506\n","\n","<b>Train Accuracy</b> = %100\n","\n","\n","Best Loss and Accuracy values configurations\n","\n","---\n","Layers= 4\n","\n","Unit Numbers = 20\n","\n","Activation Function = Relu\n","\n","Last Layer Activation Function = Sigmoid\n","\n","Optimizer = Adam\n","\n","Epoch = 100 or higher\n","\n","Loss Function = Binary CrossEntropy\n","\n"],"metadata":{"id":"g4XKMqjssoue"}},{"cell_type":"markdown","source":["# 6) Use the trained model to make the predictions on the test data."],"metadata":{"id":"b2_EhQxWtQL9"}},{"cell_type":"code","source":["predictions = model3.predict (X_test_scaled)"],"metadata":{"id":"YE28oT4zNEnu","executionInfo":{"status":"ok","timestamp":1665379259415,"user_tz":300,"elapsed":215,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":37,"outputs":[]},{"cell_type":"code","source":["predictions = np.round(predictions).reshape(y_test.shape[0])"],"metadata":{"id":"msyHMxE6NKrT","executionInfo":{"status":"ok","timestamp":1665379259416,"user_tz":300,"elapsed":17,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":38,"outputs":[]},{"cell_type":"code","source":["predictions"],"metadata":{"id":"vCtlV98ANUTu","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665379259416,"user_tz":300,"elapsed":16,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"72f6e546-c375-46e6-c285-d37952b48f28"},"execution_count":39,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0., 1., 1., 1., 1., 0., 1., 1., 1., 0., 1., 0., 0., 1., 1., 0., 1.,\n","       1., 0., 0., 0., 0., 0., 1., 0., 1., 1., 0., 1., 0., 1., 1., 1., 1.,\n","       1., 1., 1., 0., 1., 1., 0., 0., 0., 1., 1., 1., 1., 0., 1., 0., 1.,\n","       1., 1., 1., 1., 1., 1., 0., 1., 1., 0., 1., 0., 1., 1., 0., 1., 0.,\n","       1., 1., 1., 0., 1., 1., 1., 1., 1., 0., 1., 0., 0., 1., 1., 0., 0.,\n","       0., 1., 1., 1., 1., 1., 0., 1., 1., 0., 1., 1., 1., 0., 0., 1., 1.,\n","       1., 1., 1., 1., 0., 1., 1., 0., 0., 1., 1., 1., 1., 0., 1., 1., 0.,\n","       1., 0., 1., 1., 1., 1., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n","       1., 1., 0., 0., 1., 0., 0., 1., 1., 1., 1., 1., 0., 1., 0., 0., 1.,\n","       1., 0., 0., 1., 1., 0., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 0., 1., 0., 1., 1., 0., 1., 1., 0., 1.,\n","       1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 0., 0., 0., 1., 0., 0.,\n","       0., 1., 1., 0., 0., 1.], dtype=float32)"]},"metadata":{},"execution_count":39}]},{"cell_type":"markdown","source":["#7) Show the confusion matrix corresponding to the test data."],"metadata":{"id":"7pyWjhUJM7GR"}},{"cell_type":"code","source":["from sklearn.metrics import confusion_matrix"],"metadata":{"id":"WvoKaeOfM18J","executionInfo":{"status":"ok","timestamp":1665379259416,"user_tz":300,"elapsed":13,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":40,"outputs":[]},{"cell_type":"code","source":["cm = confusion_matrix(y_test, predictions)"],"metadata":{"id":"9KKpSatMfFx-","executionInfo":{"status":"ok","timestamp":1665379259417,"user_tz":300,"elapsed":14,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":41,"outputs":[]},{"cell_type":"code","source":["cm"],"metadata":{"id":"a6Xw3sNOoOU_","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665379259417,"user_tz":300,"elapsed":13,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"ad1750b8-9de5-410c-85f4-a519428d9af5"},"execution_count":42,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 73,   2],\n","       [  3, 132]])"]},"metadata":{},"execution_count":42}]},{"cell_type":"markdown","source":["# 8) Show the classification report"],"metadata":{"id":"gaRch1SroYAb"}},{"cell_type":"code","source":["from sklearn.metrics import classification_report"],"metadata":{"id":"rmOoNvCxohgy","executionInfo":{"status":"ok","timestamp":1665379259417,"user_tz":300,"elapsed":9,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":43,"outputs":[]},{"cell_type":"code","source":["print(classification_report(y_test, predictions))"],"metadata":{"id":"v9SPHJe4oOtw","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665379259417,"user_tz":300,"elapsed":8,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"22153da8-edef-40eb-e5d3-9a0999c90205"},"execution_count":44,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.96      0.97      0.97        75\n","           1       0.99      0.98      0.98       135\n","\n","    accuracy                           0.98       210\n","   macro avg       0.97      0.98      0.97       210\n","weighted avg       0.98      0.98      0.98       210\n","\n"]}]},{"cell_type":"markdown","source":["# 9) Comment on the effect of using different number of layers and different number of neurons in layers on the accuracy of the predictions."],"metadata":{"id":"2j5fpqX7on13"}},{"cell_type":"markdown","source":["* As the number of layers and the number of neurons increase, the train loss convergence occurs faster. Likewise, accuracy convergence is also faster.\n","* Accuracy in different number of layers is close to each other because of consistency of the dataSet and no noise data etc. As the number of layers and neurons increases, accuracy slightly goes up."],"metadata":{"id":"uBJX8gf00k2v"}},{"cell_type":"markdown","source":["#10) How does the accuracy change by using different splits of the data, such as 70%, 75%, and 80%?"],"metadata":{"id":"3lg_6Z8F1u25"}},{"cell_type":"code","source":["X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"],"metadata":{"id":"r_maXk-3opnY","executionInfo":{"status":"ok","timestamp":1665379259417,"user_tz":300,"elapsed":7,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":45,"outputs":[]},{"cell_type":"code","source":["X_train_scaled = scaler2.fit_transform(X_train)\n","X_test_scaled = scaler2.fit_transform(X_test)"],"metadata":{"id":"R2RvH-1U18kJ","executionInfo":{"status":"ok","timestamp":1665379259418,"user_tz":300,"elapsed":7,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":46,"outputs":[]},{"cell_type":"code","source":["history1 = model1.fit(X_train_scaled, y_train, epochs=30, verbose=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GxD9GWi32Csm","executionInfo":{"status":"ok","timestamp":1665379260998,"user_tz":300,"elapsed":1587,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"f498bc73-4312-43ab-db93-1cbcf7444b3f"},"execution_count":47,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/30\n","18/18 [==============================] - 0s 3ms/step - loss: 0.1140 - accuracy: 0.9552\n","Epoch 2/30\n","18/18 [==============================] - 0s 2ms/step - loss: 0.1102 - accuracy: 0.9570\n","Epoch 3/30\n","18/18 [==============================] - 0s 2ms/step - loss: 0.1064 - accuracy: 0.9624\n","Epoch 4/30\n","18/18 [==============================] - 0s 3ms/step - loss: 0.1034 - accuracy: 0.9624\n","Epoch 5/30\n","18/18 [==============================] - 0s 3ms/step - loss: 0.1009 - accuracy: 0.9624\n","Epoch 6/30\n","18/18 [==============================] - 0s 2ms/step - loss: 0.0986 - accuracy: 0.9624\n","Epoch 7/30\n","18/18 [==============================] - 0s 3ms/step - loss: 0.0967 - accuracy: 0.9624\n","Epoch 8/30\n","18/18 [==============================] - 0s 3ms/step - loss: 0.0947 - accuracy: 0.9659\n","Epoch 9/30\n","18/18 [==============================] - 0s 2ms/step - loss: 0.0932 - accuracy: 0.9659\n","Epoch 10/30\n","18/18 [==============================] - 0s 2ms/step - loss: 0.0919 - accuracy: 0.9659\n","Epoch 11/30\n","18/18 [==============================] - 0s 2ms/step - loss: 0.0908 - accuracy: 0.9659\n","Epoch 12/30\n","18/18 [==============================] - 0s 2ms/step - loss: 0.0897 - accuracy: 0.9659\n","Epoch 13/30\n","18/18 [==============================] - 0s 2ms/step - loss: 0.0889 - accuracy: 0.9659\n","Epoch 14/30\n","18/18 [==============================] - 0s 3ms/step - loss: 0.0880 - accuracy: 0.9659\n","Epoch 15/30\n","18/18 [==============================] - 0s 3ms/step - loss: 0.0873 - accuracy: 0.9659\n","Epoch 16/30\n","18/18 [==============================] - 0s 2ms/step - loss: 0.0866 - accuracy: 0.9659\n","Epoch 17/30\n","18/18 [==============================] - 0s 3ms/step - loss: 0.0860 - accuracy: 0.9659\n","Epoch 18/30\n","18/18 [==============================] - 0s 2ms/step - loss: 0.0855 - accuracy: 0.9677\n","Epoch 19/30\n","18/18 [==============================] - 0s 2ms/step - loss: 0.0850 - accuracy: 0.9677\n","Epoch 20/30\n","18/18 [==============================] - 0s 3ms/step - loss: 0.0845 - accuracy: 0.9677\n","Epoch 21/30\n","18/18 [==============================] - 0s 2ms/step - loss: 0.0841 - accuracy: 0.9677\n","Epoch 22/30\n","18/18 [==============================] - 0s 2ms/step - loss: 0.0837 - accuracy: 0.9677\n","Epoch 23/30\n","18/18 [==============================] - 0s 3ms/step - loss: 0.0835 - accuracy: 0.9677\n","Epoch 24/30\n","18/18 [==============================] - 0s 3ms/step - loss: 0.0830 - accuracy: 0.9677\n","Epoch 25/30\n","18/18 [==============================] - 0s 2ms/step - loss: 0.0827 - accuracy: 0.9677\n","Epoch 26/30\n","18/18 [==============================] - 0s 2ms/step - loss: 0.0824 - accuracy: 0.9677\n","Epoch 27/30\n","18/18 [==============================] - 0s 2ms/step - loss: 0.0821 - accuracy: 0.9677\n","Epoch 28/30\n","18/18 [==============================] - 0s 2ms/step - loss: 0.0818 - accuracy: 0.9677\n","Epoch 29/30\n","18/18 [==============================] - 0s 3ms/step - loss: 0.0815 - accuracy: 0.9677\n","Epoch 30/30\n","18/18 [==============================] - 0s 2ms/step - loss: 0.0813 - accuracy: 0.9677\n"]}]},{"cell_type":"code","source":["loss_train = history1.history['loss']\n","\n","plt.plot(loss_train, 'g', label='Training loss')\n","plt.title('Training Loss')\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.legend()\n","plt.show()\n","\n","acc_train = history1.history['accuracy']\n","\n","plt.plot(acc_train, 'g', label='Training Accuracy')\n","plt.title('Training Acc')\n","plt.xlabel('Epochs')\n","plt.ylabel('Acc')\n","plt.legend()\n","plt.show()\n","\n","print(\"Evaluation\")\n","model1.evaluate(X_test_scaled, y_test)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":625},"id":"JJiKanhl2LxF","executionInfo":{"status":"ok","timestamp":1665379261457,"user_tz":300,"elapsed":462,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"340e1b84-9991-403f-8561-501caeaa4ddf"},"execution_count":48,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Evaluation\n","5/5 [==============================] - 0s 3ms/step - loss: 0.0751 - accuracy: 0.9786\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.07510096579790115, 0.9785714149475098]"]},"metadata":{},"execution_count":48}]},{"cell_type":"code","source":["predictions = model1.predict (X_test_scaled)"],"metadata":{"id":"IRcYuLPS2Vtm","executionInfo":{"status":"ok","timestamp":1665379261457,"user_tz":300,"elapsed":5,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":49,"outputs":[]},{"cell_type":"code","source":["predictions = np.round(predictions).reshape(y_test.shape[0])"],"metadata":{"id":"-zzm8WOR2uMp","executionInfo":{"status":"ok","timestamp":1665379261457,"user_tz":300,"elapsed":4,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}}},"execution_count":50,"outputs":[]},{"cell_type":"code","source":["print(classification_report(y_test, predictions))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zbNThRQs2ufF","executionInfo":{"status":"ok","timestamp":1665379261458,"user_tz":300,"elapsed":5,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"07ad3e10-99a5-4fae-b126-89dbd414d72a"},"execution_count":51,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.94      1.00      0.97        51\n","           1       1.00      0.97      0.98        89\n","\n","    accuracy                           0.98       140\n","   macro avg       0.97      0.98      0.98       140\n","weighted avg       0.98      0.98      0.98       140\n","\n"]}]},{"cell_type":"markdown","source":["**Result:** All parameters were used the same, only the percentage of the train test split was used as 70% in one and 80% in the other. \n","\n","* Training accuracy is 98% in the dataset allocated as 80% train 30% test\n","* Training accuracy is 98% in the dataset allocated as 70% train 30% test\n"],"metadata":{"id":"RLq7_I-V5Ykm"}},{"cell_type":"code","source":["%%shell\n","jupyter nbconvert --to html /content/Assignment1 (1).ipynb"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"id":"veavRZokV30C","executionInfo":{"status":"error","timestamp":1665379405308,"user_tz":300,"elapsed":533,"user":{"displayName":"Orhun VURAL","userId":"11088504853201775697"}},"outputId":"730b345f-76d2-4766-d345-eadc4bd8cd78"},"execution_count":53,"outputs":[{"output_type":"stream","name":"stdout","text":["/bin/bash: -c: line 0: syntax error near unexpected token `('\n","/bin/bash: -c: line 0: `jupyter nbconvert --to html /content/Assignment1 (1).ipynb'\n"]},{"output_type":"error","ename":"CalledProcessError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mCalledProcessError\u001b[0m                        Traceback (most recent call last)","\u001b[0;32m<ipython-input-53-5efd4333e7af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'shell'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'jupyter nbconvert --to html /content/Assignment1 (1).ipynb\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2357\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2358\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2359\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2360\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/_system_commands.py\u001b[0m in \u001b[0;36m_shell_cell_magic\u001b[0;34m(args, cmd)\u001b[0m\n\u001b[1;32m    107\u001b[0m   \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_run_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclear_streamed_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mparsed_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mignore_errors\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m     \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_returncode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    110\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m 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pylint:disable=unused-argument\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mCalledProcessError\u001b[0m: Command 'jupyter nbconvert --to html /content/Assignment1 (1).ipynb\n' returned non-zero exit status 1."]}]}]}