boston_h.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"boston_h.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyMLkaWxPsyXLEaeYFGvasza"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"4E9eU-tBQ9v6"},"source":["# 보스턴 집값 예측\n"]},{"cell_type":"code","metadata":{"id":"-_X0jpqIQ2Vg","executionInfo":{"status":"ok","timestamp":1610080412799,"user_tz":-540,"elapsed":2981,"user":{"displayName":"Mean S","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg1UoDhl6mBqO0nWsSfE_af7bAkW_c7UbbXGnIT=s64","userId":"15566271537246085511"}}},"source":["# 라이브러리 사용\n","import tensorflow as tf\n","import pandas as pd"],"execution_count":1,"outputs":[]},{"cell_type":"code","metadata":{"id":"0G9MLA2KRAur","colab":{"base_uri":"https://localhost:8080/","height":248},"executionInfo":{"status":"ok","timestamp":1610080413785,"user_tz":-540,"elapsed":3948,"user":{"displayName":"Mean S","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg1UoDhl6mBqO0nWsSfE_af7bAkW_c7UbbXGnIT=s64","userId":"15566271537246085511"}},"outputId":"68f4d7a5-9745-4f2d-e442-0d367decf680"},"source":["# 1.과거의 데이터를 준비합니다.\n","파일경로 = 'https://raw.githubusercontent.com/blackdew/tensorflow1/master/csv/boston.csv'\n","보스턴 = pd.read_csv(파일경로)\n","print(보스턴.columns)\n","보스턴.head()"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Index(['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad', 'tax',\n","       'ptratio', 'b', 'lstat', 'medv'],\n","      dtype='object')\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>crim</th>\n","      <th>zn</th>\n","      <th>indus</th>\n","      <th>chas</th>\n","      <th>nox</th>\n","      <th>rm</th>\n","      <th>age</th>\n","      <th>dis</th>\n","      <th>rad</th>\n","      <th>tax</th>\n","      <th>ptratio</th>\n","      <th>b</th>\n","      <th>lstat</th>\n","      <th>medv</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.00632</td>\n","      <td>18.0</td>\n","      <td>2.31</td>\n","      <td>0</td>\n","      <td>0.538</td>\n","      <td>6.575</td>\n","      <td>65.2</td>\n","      <td>4.0900</td>\n","      <td>1</td>\n","      <td>296</td>\n","      <td>15.3</td>\n","      <td>396.90</td>\n","      <td>4.98</td>\n","      <td>24.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.02731</td>\n","      <td>0.0</td>\n","      <td>7.07</td>\n","      <td>0</td>\n","      <td>0.469</td>\n","      <td>6.421</td>\n","      <td>78.9</td>\n","      <td>4.9671</td>\n","      <td>2</td>\n","      <td>242</td>\n","      <td>17.8</td>\n","      <td>396.90</td>\n","      <td>9.14</td>\n","      <td>21.6</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.02729</td>\n","      <td>0.0</td>\n","      <td>7.07</td>\n","      <td>0</td>\n","      <td>0.469</td>\n","      <td>7.185</td>\n","      <td>61.1</td>\n","      <td>4.9671</td>\n","      <td>2</td>\n","      <td>242</td>\n","      <td>17.8</td>\n","      <td>392.83</td>\n","      <td>4.03</td>\n","      <td>34.7</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.03237</td>\n","      <td>0.0</td>\n","      <td>2.18</td>\n","      <td>0</td>\n","      <td>0.458</td>\n","      <td>6.998</td>\n","      <td>45.8</td>\n","      <td>6.0622</td>\n","      <td>3</td>\n","      <td>222</td>\n","      <td>18.7</td>\n","      <td>394.63</td>\n","      <td>2.94</td>\n","      <td>33.4</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.06905</td>\n","      <td>0.0</td>\n","      <td>2.18</td>\n","      <td>0</td>\n","      <td>0.458</td>\n","      <td>7.147</td>\n","      <td>54.2</td>\n","      <td>6.0622</td>\n","      <td>3</td>\n","      <td>222</td>\n","      <td>18.7</td>\n","      <td>396.90</td>\n","      <td>5.33</td>\n","      <td>36.2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      crim    zn  indus  chas    nox  ...  tax  ptratio       b  lstat  medv\n","0  0.00632  18.0   2.31     0  0.538  ...  296     15.3  396.90   4.98  24.0\n","1  0.02731   0.0   7.07     0  0.469  ...  242     17.8  396.90   9.14  21.6\n","2  0.02729   0.0   7.07     0  0.469  ...  242     17.8  392.83   4.03  34.7\n","3  0.03237   0.0   2.18     0  0.458  ...  222     18.7  394.63   2.94  33.4\n","4  0.06905   0.0   2.18     0  0.458  ...  222     18.7  396.90   5.33  36.2\n","\n","[5 rows x 14 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"code","metadata":{"id":"m72Pin04UMSJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1610080413788,"user_tz":-540,"elapsed":3940,"user":{"displayName":"Mean S","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg1UoDhl6mBqO0nWsSfE_af7bAkW_c7UbbXGnIT=s64","userId":"15566271537246085511"}},"outputId":"593ffcab-34b6-48e9-a33f-322ad4ab1def"},"source":["# 독립변수, 종속변수 분리 \n","독립 = 보스턴[['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad', 'tax',\n","            'ptratio', 'b', 'lstat']]\n","종속 = 보스턴[['medv']]\n","print(독립.shape, 종속.shape)"],"execution_count":3,"outputs":[{"output_type":"stream","text":["(506, 13) (506, 1)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"d-ZMjIQWRD1_","executionInfo":{"status":"ok","timestamp":1610080413792,"user_tz":-540,"elapsed":3938,"user":{"displayName":"Mean S","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg1UoDhl6mBqO0nWsSfE_af7bAkW_c7UbbXGnIT=s64","userId":"15566271537246085511"}}},"source":["# 2. 모델의 구조를 만듭니다\n","X = tf.keras.layers.Input(shape=[13])\n","H = tf.keras.layers.Dense(10, activation='swish')(X)\n","Y = tf.keras.layers.Dense(1)(H)\n","model = tf.keras.models.Model(X, Y)\n","model.compile(loss='mse')"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"51sKlSxIRy-v","executionInfo":{"status":"ok","timestamp":1610080535831,"user_tz":-540,"elapsed":825,"user":{"displayName":"Mean S","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg1UoDhl6mBqO0nWsSfE_af7bAkW_c7UbbXGnIT=s64","userId":"15566271537246085511"}},"outputId":"f9107cdf-29d3-4589-f9f6-b891abb7bff3"},"source":["model.summary()"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Model: \"model_1\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","input_1 (InputLayer)         [(None, 13)]              0         \n","_________________________________________________________________\n","dense (Dense)                (None, 10)                140       \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 1)                 11        \n","=================================================================\n","Total params: 151\n","Trainable params: 151\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"DkkQqg3yRFRR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1610080432048,"user_tz":-540,"elapsed":4161,"user":{"displayName":"Mean S","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg1UoDhl6mBqO0nWsSfE_af7bAkW_c7UbbXGnIT=s64","userId":"15566271537246085511"}},"outputId":"b6a2874c-e75f-493b-9348-bbcdc6fa4b9b"},"source":["# 3.데이터로 모델을 학습(FIT)합니다.\n","model.fit(독립, 종속, epochs=100)"],"execution_count":5,"outputs":[{"output_type":"stream","text":["Epoch 1/100\n","16/16 [==============================] - 1s 1ms/step - loss: 53769.3309\n","Epoch 2/100\n","16/16 [==============================] - 0s 1ms/step - loss: 29611.7711\n","Epoch 3/100\n","16/16 [==============================] - 0s 1ms/step - loss: 15863.1379\n","Epoch 4/100\n","16/16 [==============================] - 0s 1ms/step - loss: 6708.7527\n","Epoch 5/100\n","16/16 [==============================] - 0s 1ms/step - loss: 2231.9043\n","Epoch 6/100\n","16/16 [==============================] - 0s 1ms/step - loss: 730.1156\n","Epoch 7/100\n","16/16 [==============================] - 0s 1ms/step - loss: 382.8750\n","Epoch 8/100\n","16/16 [==============================] - 0s 1ms/step - loss: 275.4337\n","Epoch 9/100\n","16/16 [==============================] - 0s 1ms/step - loss: 219.7105\n","Epoch 10/100\n","16/16 [==============================] - 0s 2ms/step - loss: 195.0059\n","Epoch 11/100\n","16/16 [==============================] - 0s 1ms/step - loss: 159.4539\n","Epoch 12/100\n","16/16 [==============================] - 0s 2ms/step - loss: 134.2558\n","Epoch 13/100\n","16/16 [==============================] - 0s 1ms/step - loss: 130.2924\n","Epoch 14/100\n","16/16 [==============================] - 0s 1ms/step - loss: 115.1935\n","Epoch 15/100\n","16/16 [==============================] - 0s 1ms/step - loss: 119.8810\n","Epoch 16/100\n","16/16 [==============================] - 0s 1ms/step - loss: 103.4136\n","Epoch 17/100\n","16/16 [==============================] - 0s 1ms/step - loss: 94.8946\n","Epoch 18/100\n","16/16 [==============================] - 0s 1ms/step - loss: 88.0840\n","Epoch 19/100\n","16/16 [==============================] - 0s 2ms/step - loss: 80.9863\n","Epoch 20/100\n","16/16 [==============================] - 0s 2ms/step - loss: 85.3887\n","Epoch 21/100\n","16/16 [==============================] - 0s 1ms/step - loss: 71.2057\n","Epoch 22/100\n","16/16 [==============================] - 0s 1ms/step - loss: 85.3361\n","Epoch 23/100\n","16/16 [==============================] - 0s 1ms/step - loss: 72.0737\n","Epoch 24/100\n","16/16 [==============================] - 0s 2ms/step - loss: 65.7482\n","Epoch 25/100\n","16/16 [==============================] - 0s 1ms/step - loss: 60.5430\n","Epoch 26/100\n","16/16 [==============================] - 0s 1ms/step - loss: 66.8842\n","Epoch 27/100\n","16/16 [==============================] - 0s 1ms/step - loss: 72.4519\n","Epoch 28/100\n","16/16 [==============================] - 0s 1ms/step - loss: 63.1345\n","Epoch 29/100\n","16/16 [==============================] - 0s 1ms/step - loss: 62.5149\n","Epoch 30/100\n","16/16 [==============================] - 0s 1ms/step - loss: 67.4028\n","Epoch 31/100\n","16/16 [==============================] - 0s 1ms/step - loss: 62.4209\n","Epoch 32/100\n","16/16 [==============================] - 0s 2ms/step - loss: 52.0354\n","Epoch 33/100\n","16/16 [==============================] - 0s 1ms/step - loss: 55.1678\n","Epoch 34/100\n","16/16 [==============================] - 0s 1ms/step - loss: 58.9882\n","Epoch 35/100\n","16/16 [==============================] - 0s 1ms/step - loss: 52.9350\n","Epoch 36/100\n","16/16 [==============================] - 0s 1ms/step - loss: 56.8017\n","Epoch 37/100\n","16/16 [==============================] - 0s 1ms/step - loss: 59.3711\n","Epoch 38/100\n","16/16 [==============================] - 0s 1ms/step - loss: 56.7571\n","Epoch 39/100\n","16/16 [==============================] - 0s 1ms/step - loss: 49.7183\n","Epoch 40/100\n","16/16 [==============================] - 0s 1ms/step - loss: 51.3645\n","Epoch 41/100\n","16/16 [==============================] - 0s 1ms/step - loss: 53.6981\n","Epoch 42/100\n","16/16 [==============================] - 0s 1ms/step - loss: 55.2097\n","Epoch 43/100\n","16/16 [==============================] - 0s 1ms/step - loss: 47.2905\n","Epoch 44/100\n","16/16 [==============================] - 0s 1ms/step - loss: 52.1368\n","Epoch 45/100\n","16/16 [==============================] - 0s 1ms/step - loss: 46.7681\n","Epoch 46/100\n","16/16 [==============================] - 0s 1ms/step - loss: 52.8539\n","Epoch 47/100\n","16/16 [==============================] - 0s 1ms/step - loss: 51.7876\n","Epoch 48/100\n","16/16 [==============================] - 0s 1ms/step - loss: 51.7712\n","Epoch 49/100\n","16/16 [==============================] - 0s 1ms/step - loss: 43.8799\n","Epoch 50/100\n","16/16 [==============================] - 0s 1ms/step - loss: 40.8393\n","Epoch 51/100\n","16/16 [==============================] - 0s 1ms/step - loss: 46.2818\n","Epoch 52/100\n","16/16 [==============================] - 0s 2ms/step - loss: 45.0120\n","Epoch 53/100\n","16/16 [==============================] - 0s 2ms/step - loss: 40.7686\n","Epoch 54/100\n","16/16 [==============================] - 0s 1ms/step - loss: 47.1876\n","Epoch 55/100\n","16/16 [==============================] - 0s 1ms/step - loss: 45.7486\n","Epoch 56/100\n","16/16 [==============================] - 0s 1ms/step - loss: 39.2032\n","Epoch 57/100\n","16/16 [==============================] - 0s 1ms/step - loss: 40.9866\n","Epoch 58/100\n","16/16 [==============================] - 0s 1ms/step - loss: 34.1330\n","Epoch 59/100\n","16/16 [==============================] - 0s 1ms/step - loss: 43.8016\n","Epoch 60/100\n","16/16 [==============================] - 0s 1ms/step - loss: 41.0854\n","Epoch 61/100\n","16/16 [==============================] - 0s 2ms/step - loss: 40.7519\n","Epoch 62/100\n","16/16 [==============================] - 0s 1ms/step - loss: 39.6997\n","Epoch 63/100\n","16/16 [==============================] - 0s 1ms/step - loss: 36.2996\n","Epoch 64/100\n","16/16 [==============================] - 0s 2ms/step - loss: 40.0981\n","Epoch 65/100\n","16/16 [==============================] - 0s 1ms/step - loss: 35.9401\n","Epoch 66/100\n","16/16 [==============================] - 0s 2ms/step - loss: 36.0334\n","Epoch 67/100\n","16/16 [==============================] - 0s 2ms/step - loss: 37.6545\n","Epoch 68/100\n","16/16 [==============================] - 0s 2ms/step - loss: 32.4971\n","Epoch 69/100\n","16/16 [==============================] - 0s 1ms/step - loss: 37.1697\n","Epoch 70/100\n","16/16 [==============================] - 0s 2ms/step - loss: 41.2146\n","Epoch 71/100\n","16/16 [==============================] - 0s 1ms/step - loss: 39.1171\n","Epoch 72/100\n","16/16 [==============================] - 0s 1ms/step - loss: 40.9465\n","Epoch 73/100\n","16/16 [==============================] - 0s 1ms/step - loss: 33.9295\n","Epoch 74/100\n","16/16 [==============================] - 0s 1ms/step - loss: 36.3100\n","Epoch 75/100\n","16/16 [==============================] - 0s 1ms/step - loss: 32.1203\n","Epoch 76/100\n","16/16 [==============================] - 0s 1ms/step - loss: 36.3937\n","Epoch 77/100\n","16/16 [==============================] - 0s 2ms/step - loss: 39.4882\n","Epoch 78/100\n","16/16 [==============================] - 0s 1ms/step - loss: 37.5905\n","Epoch 79/100\n","16/16 [==============================] - 0s 1ms/step - loss: 42.7099\n","Epoch 80/100\n","16/16 [==============================] - 0s 1ms/step - loss: 33.6329\n","Epoch 81/100\n","16/16 [==============================] - 0s 1ms/step - loss: 40.2339\n","Epoch 82/100\n","16/16 [==============================] - 0s 1ms/step - loss: 35.7130\n","Epoch 83/100\n","16/16 [==============================] - 0s 1ms/step - loss: 34.0176\n","Epoch 84/100\n","16/16 [==============================] - 0s 1ms/step - loss: 33.6158\n","Epoch 85/100\n","16/16 [==============================] - 0s 1ms/step - loss: 32.7258\n","Epoch 86/100\n","16/16 [==============================] - 0s 2ms/step - loss: 36.8856\n","Epoch 87/100\n","16/16 [==============================] - 0s 1ms/step - loss: 38.8724\n","Epoch 88/100\n","16/16 [==============================] - 0s 2ms/step - loss: 40.6241\n","Epoch 89/100\n","16/16 [==============================] - 0s 1ms/step - loss: 41.0187\n","Epoch 90/100\n","16/16 [==============================] - 0s 1ms/step - loss: 33.1467\n","Epoch 91/100\n","16/16 [==============================] - 0s 2ms/step - loss: 33.8324\n","Epoch 92/100\n","16/16 [==============================] - 0s 1ms/step - loss: 40.1845\n","Epoch 93/100\n","16/16 [==============================] - 0s 1ms/step - loss: 39.5939\n","Epoch 94/100\n","16/16 [==============================] - 0s 1ms/step - loss: 39.4691\n","Epoch 95/100\n","16/16 [==============================] - 0s 1ms/step - loss: 34.9946\n","Epoch 96/100\n","16/16 [==============================] - 0s 1ms/step - loss: 41.1535\n","Epoch 97/100\n","16/16 [==============================] - 0s 2ms/step - loss: 34.6964\n","Epoch 98/100\n","16/16 [==============================] - 0s 1ms/step - loss: 38.2763\n","Epoch 99/100\n","16/16 [==============================] - 0s 1ms/step - loss: 33.5579\n","Epoch 100/100\n","16/16 [==============================] - 0s 2ms/step - loss: 38.0221\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<tensorflow.python.keras.callbacks.History at 0x7f6643026278>"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XEVDQFfNRg4q","executionInfo":{"status":"ok","timestamp":1610080486737,"user_tz":-540,"elapsed":13956,"user":{"displayName":"Mean S","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg1UoDhl6mBqO0nWsSfE_af7bAkW_c7UbbXGnIT=s64","userId":"15566271537246085511"}},"outputId":"be82b605-b474-40f0-b1f1-cbb164cbb0c5"},"source":["# 3.데이터로 모델을 학습(FIT)합니다.\r\n","model.fit(독립, 종속, epochs=1000,verbose=0)\r\n","model.fit(독립, 종속, epochs=10)"],"execution_count":6,"outputs":[{"output_type":"stream","text":["Epoch 1/10\n","16/16 [==============================] - 0s 1ms/step - loss: 24.6290\n","Epoch 2/10\n","16/16 [==============================] - 0s 1ms/step - loss: 23.7569\n","Epoch 3/10\n","16/16 [==============================] - 0s 1ms/step - loss: 25.6683\n","Epoch 4/10\n","16/16 [==============================] - 0s 1ms/step - loss: 23.5669\n","Epoch 5/10\n","16/16 [==============================] - 0s 1ms/step - loss: 25.7836\n","Epoch 6/10\n","16/16 [==============================] - 0s 1ms/step - loss: 24.3838\n","Epoch 7/10\n","16/16 [==============================] - 0s 1ms/step - loss: 26.2743\n","Epoch 8/10\n","16/16 [==============================] - 0s 1ms/step - loss: 25.2677\n","Epoch 9/10\n","16/16 [==============================] - 0s 1ms/step - loss: 25.2419\n","Epoch 10/10\n","16/16 [==============================] - 0s 1ms/step - loss: 25.7028\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<tensorflow.python.keras.callbacks.History at 0x7f663bd42c18>"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ke3yeAKGRq4h","executionInfo":{"status":"ok","timestamp":1610080514987,"user_tz":-540,"elapsed":14729,"user":{"displayName":"Mean S","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg1UoDhl6mBqO0nWsSfE_af7bAkW_c7UbbXGnIT=s64","userId":"15566271537246085511"}},"outputId":"ef0bc89b-89e0-4314-b7b3-dad986cfb490"},"source":["# 3.데이터로 모델을 학습(FIT)합니다.\r\n","model.fit(독립, 종속, epochs=1000,verbose=0)\r\n","model.fit(독립, 종속, epochs=10)"],"execution_count":7,"outputs":[{"output_type":"stream","text":["Epoch 1/10\n","16/16 [==============================] - 0s 1ms/step - loss: 24.6776\n","Epoch 2/10\n","16/16 [==============================] - 0s 1ms/step - loss: 23.1708\n","Epoch 3/10\n","16/16 [==============================] - 0s 1ms/step - loss: 23.2286\n","Epoch 4/10\n","16/16 [==============================] - 0s 1ms/step - loss: 23.1201\n","Epoch 5/10\n","16/16 [==============================] - 0s 1ms/step - loss: 23.2839\n","Epoch 6/10\n","16/16 [==============================] - 0s 1ms/step - loss: 23.3746\n","Epoch 7/10\n","16/16 [==============================] - 0s 1ms/step - loss: 23.3036\n","Epoch 8/10\n","16/16 [==============================] - 0s 1ms/step - loss: 24.1416\n","Epoch 9/10\n","16/16 [==============================] - 0s 1ms/step - loss: 23.9470\n","Epoch 10/10\n","16/16 [==============================] - 0s 1ms/step - loss: 22.5948\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<tensorflow.python.keras.callbacks.History at 0x7f663bd50d68>"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"0ZeAwrqz-vUo"},"source":["loss가 22까지 줄어든 것을 확인하였습니다."]},{"cell_type":"code","metadata":{"id":"OATEnOjKRG_s","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1610080650625,"user_tz":-540,"elapsed":707,"user":{"displayName":"Mean S","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg1UoDhl6mBqO0nWsSfE_af7bAkW_c7UbbXGnIT=s64","userId":"15566271537246085511"}},"outputId":"fa4add33-7612-4cdf-d955-7c41daf073ac"},"source":["# 4. 모델을 이용합니다\n","print(model.predict(독립[5:10]))"],"execution_count":9,"outputs":[{"output_type":"stream","text":["[[29.03547 ]\n"," [23.734613]\n"," [20.383854]\n"," [12.635843]\n"," [19.984522]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GxQN_P-H_m-W","executionInfo":{"status":"ok","timestamp":1610080655999,"user_tz":-540,"elapsed":740,"user":{"displayName":"Mean S","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg1UoDhl6mBqO0nWsSfE_af7bAkW_c7UbbXGnIT=s64","userId":"15566271537246085511"}},"outputId":"d6f8340a-c1d5-4a12-aeaa-b031272c07c6"},"source":["# 종속변수 확인\r\n","print(종속[5:10])"],"execution_count":10,"outputs":[{"output_type":"stream","text":["   medv\n","5  28.7\n","6  22.9\n","7  27.1\n","8  16.5\n","9  18.9\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Ggwb8gS6_yKI"},"source":["값을 비교해보면 모델이 값을 잘 맞추고 있습니다.\r\n"]}]}