house_price_prediction.ipynb

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              "     Transaction date  House Age  Distance from nearest Metro station (km)  \\\n",
              "0         2012.916667       32.0                                  84.87882   \n",
              "1         2012.916667       19.5                                 306.59470   \n",
              "2         2013.583333       13.3                                 561.98450   \n",
              "3         2013.500000       13.3                                 561.98450   \n",
              "4         2012.833333        5.0                                 390.56840   \n",
              "..                ...        ...                                       ...   \n",
              "409       2013.000000       13.7                                4082.01500   \n",
              "410       2012.666667        5.6                                  90.45606   \n",
              "411       2013.250000       18.8                                 390.96960   \n",
              "412       2013.000000        8.1                                 104.81010   \n",
              "413       2013.500000        6.5                                  90.45606   \n",
              "\n",
              "     Number of convenience stores  latitude  longitude  Number of bedrooms  \\\n",
              "0                              10  24.98298  121.54024                   1   \n",
              "1                               9  24.98034  121.53951                   2   \n",
              "2                               5  24.98746  121.54391                   3   \n",
              "3                               5  24.98746  121.54391                   2   \n",
              "4                               5  24.97937  121.54245                   1   \n",
              "..                            ...       ...        ...                 ...   \n",
              "409                             0  24.94155  121.50381                   3   \n",
              "410                             9  24.97433  121.54310                   2   \n",
              "411                             7  24.97923  121.53986                   1   \n",
              "412                             5  24.96674  121.54067                   1   \n",
              "413                             9  24.97433  121.54310                   2   \n",
              "\n",
              "     House size (sqft)  House price of unit area  \n",
              "0                  575                      37.9  \n",
              "1                 1240                      42.2  \n",
              "2                 1060                      47.3  \n",
              "3                  875                      54.8  \n",
              "4                  491                      43.1  \n",
              "..                 ...                       ...  \n",
              "409                803                      15.4  \n",
              "410               1278                      50.0  \n",
              "411                503                      40.6  \n",
              "412                597                      52.5  \n",
              "413               1097                      63.9  \n",
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              "  "
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "data = pd.read_excel('/content/drive/MyDrive/dataset.xlsx')\n",
        "data"
      ]
    },
    {
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        "x = data.iloc[:,1:-1]\n",
        "y = data.iloc[:,-1:]\n",
        "y"
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      "execution_count": 3,
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              "     House price of unit area\n",
              "0                        37.9\n",
              "1                        42.2\n",
              "2                        47.3\n",
              "3                        54.8\n",
              "4                        43.1\n",
              "..                        ...\n",
              "409                      15.4\n",
              "410                      50.0\n",
              "411                      40.6\n",
              "412                      52.5\n",
              "413                      63.9\n",
              "\n",
              "[414 rows x 1 columns]"
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              "      <th>3</th>\n",
              "      <td>54.8</td>\n",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(x,y, test_size=0.3)\n",
        "\n",
        "scaler = StandardScaler()\n",
        "x_train = scaler.fit_transform(X_train)\n",
        "x_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "oEQiIEZCKPLM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec3b3459-9ea7-4ff0-b1d8-1ddd736252b2"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.45928565, -0.36147317,  0.99427101, ...,  0.17647241,\n",
              "         1.2451369 ,  0.47165543],\n",
              "       [ 1.73621813, -0.46788963, -1.35002998, ...,  0.06425209,\n",
              "         1.2451369 ,  0.69293078],\n",
              "       [-0.12442198,  0.96907763, -0.34532956, ..., -1.32176731,\n",
              "        -1.20278531, -1.38257602],\n",
              "       ...,\n",
              "       [ 0.03135254, -0.5613745 ,  1.32917115, ...,  0.46522645,\n",
              "        -1.20278531, -1.51422085],\n",
              "       [-1.18022706,  0.85666084, -0.34532956, ..., -1.37098675,\n",
              "        -1.20278531, -1.05766537],\n",
              "       [ 1.13908247, -0.55785584,  0.65937087, ...,  0.38778787,\n",
              "        -1.20278531, -1.34616362]])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using Statistics"
      ],
      "metadata": {
        "id": "34JYtE9TP90a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression  \n",
        "regressor= LinearRegression()  \n",
        "regressor.fit(x_train, Y_train)  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kO3NHQKHASbd",
        "outputId": "ab2307e9-227a-4865-be95-3c243763f379"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred= regressor.predict(x_test)  \n",
        "x_pred= regressor.predict(x_train)  "
      ],
      "metadata": {
        "id": "Mtsg5XflHHWM"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Train Score: ', regressor.score(x_train, Y_train))  \n",
        "print('Test Score: ', regressor.score(x_test, Y_test))  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l4ywUu5aHmCh",
        "outputId": "cceee7f5-06e0-481a-e32a-059729e52c46"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Score:  0.656965667112068\n",
            "Test Score:  0.42662265425260126\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using Tensorflow"
      ],
      "metadata": {
        "id": "yG471yjiP4Ni"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "model = keras.Sequential([\n",
        "      layers.Input(shape = (7,)),\n",
        "      layers.Dense(64, activation='relu'),\n",
        "      layers.Dense(64, activation='relu'),\n",
        "      layers.Dense(1)\n",
        "  ])\n",
        "\n",
        "model.compile(loss='mean_absolute_error',optimizer=tf.keras.optimizers.Adam(0.001))"
      ],
      "metadata": {
        "id": "HRXKA5yPKJm7"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r = model.fit(x_train, Y_train, validation_data=(x_test,Y_test), epochs = 200)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NMaPVNRuMBHi",
        "outputId": "ae1d343a-d36b-4734-dafa-0ba30dede555"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "10/10 [==============================] - 0s 22ms/step - loss: 4.0966 - val_loss: 6.1706\n",
            "Epoch 2/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 4.1730 - val_loss: 6.2808\n",
            "Epoch 3/200\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 4.0599 - val_loss: 6.2178\n",
            "Epoch 4/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 4.0458 - val_loss: 6.1459\n",
            "Epoch 5/200\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 4.0261 - val_loss: 6.2620\n",
            "Epoch 6/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 4.0870 - val_loss: 6.2167\n",
            "Epoch 7/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 4.0814 - val_loss: 6.2899\n",
            "Epoch 8/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 4.0329 - val_loss: 6.2566\n",
            "Epoch 9/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 4.0548 - val_loss: 6.2469\n",
            "Epoch 10/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 4.0364 - val_loss: 6.2795\n",
            "Epoch 11/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 4.0438 - val_loss: 6.2501\n",
            "Epoch 12/200\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 4.0568 - val_loss: 6.3047\n",
            "Epoch 13/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 4.0534 - val_loss: 6.2244\n",
            "Epoch 14/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 4.0003 - val_loss: 6.0694\n",
            "Epoch 15/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 4.0469 - val_loss: 6.0070\n",
            "Epoch 16/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 4.0435 - val_loss: 5.9979\n",
            "Epoch 17/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 4.0101 - val_loss: 6.0491\n",
            "Epoch 18/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 4.0064 - val_loss: 6.1014\n",
            "Epoch 19/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 4.0202 - val_loss: 6.1604\n",
            "Epoch 20/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.9981 - val_loss: 6.1689\n",
            "Epoch 21/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.9753 - val_loss: 6.2057\n",
            "Epoch 22/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.9956 - val_loss: 6.2712\n",
            "Epoch 23/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.9854 - val_loss: 6.1721\n",
            "Epoch 24/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 4.0629 - val_loss: 6.1355\n",
            "Epoch 25/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 4.0421 - val_loss: 6.0733\n",
            "Epoch 26/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.9649 - val_loss: 6.0347\n",
            "Epoch 27/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 4.0117 - val_loss: 6.0855\n",
            "Epoch 28/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 4.0625 - val_loss: 6.0729\n",
            "Epoch 29/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.9593 - val_loss: 6.1320\n",
            "Epoch 30/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 4.0075 - val_loss: 6.2050\n",
            "Epoch 31/200\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 3.9255 - val_loss: 6.1204\n",
            "Epoch 32/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.9530 - val_loss: 6.1726\n",
            "Epoch 33/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.9900 - val_loss: 6.2493\n",
            "Epoch 34/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 4.0077 - val_loss: 6.3570\n",
            "Epoch 35/200\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 4.0577 - val_loss: 6.2945\n",
            "Epoch 36/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.9760 - val_loss: 6.1840\n",
            "Epoch 37/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.9326 - val_loss: 6.0917\n",
            "Epoch 38/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.9950 - val_loss: 6.1585\n",
            "Epoch 39/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.9788 - val_loss: 6.1309\n",
            "Epoch 40/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.9284 - val_loss: 6.0432\n",
            "Epoch 41/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.9599 - val_loss: 6.0477\n",
            "Epoch 42/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.9452 - val_loss: 6.1096\n",
            "Epoch 43/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.9445 - val_loss: 6.1590\n",
            "Epoch 44/200\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 3.9148 - val_loss: 6.0969\n",
            "Epoch 45/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 4.0051 - val_loss: 6.1139\n",
            "Epoch 46/200\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 3.9066 - val_loss: 6.0584\n",
            "Epoch 47/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.9220 - val_loss: 6.0483\n",
            "Epoch 48/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.9192 - val_loss: 6.1966\n",
            "Epoch 49/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.9401 - val_loss: 6.2071\n",
            "Epoch 50/200\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 3.9131 - val_loss: 6.1635\n",
            "Epoch 51/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8972 - val_loss: 6.1909\n",
            "Epoch 52/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8765 - val_loss: 6.2473\n",
            "Epoch 53/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 4.0083 - val_loss: 6.3410\n",
            "Epoch 54/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.9634 - val_loss: 6.3644\n",
            "Epoch 55/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 4.0761 - val_loss: 6.2515\n",
            "Epoch 56/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.9266 - val_loss: 6.1533\n",
            "Epoch 57/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.9270 - val_loss: 6.2991\n",
            "Epoch 58/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.9953 - val_loss: 6.3506\n",
            "Epoch 59/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.9411 - val_loss: 6.2186\n",
            "Epoch 60/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8765 - val_loss: 6.2204\n",
            "Epoch 61/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.9022 - val_loss: 6.2865\n",
            "Epoch 62/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.8913 - val_loss: 6.3460\n",
            "Epoch 63/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.9301 - val_loss: 6.3008\n",
            "Epoch 64/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.9388 - val_loss: 6.1125\n",
            "Epoch 65/200\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 3.9636 - val_loss: 6.1764\n",
            "Epoch 66/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.8689 - val_loss: 6.2355\n",
            "Epoch 67/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8585 - val_loss: 6.2629\n",
            "Epoch 68/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.9177 - val_loss: 6.1388\n",
            "Epoch 69/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8602 - val_loss: 6.1084\n",
            "Epoch 70/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8773 - val_loss: 6.1279\n",
            "Epoch 71/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.8792 - val_loss: 6.1800\n",
            "Epoch 72/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8580 - val_loss: 6.1966\n",
            "Epoch 73/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8312 - val_loss: 6.2675\n",
            "Epoch 74/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8139 - val_loss: 6.1867\n",
            "Epoch 75/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8131 - val_loss: 6.2501\n",
            "Epoch 76/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8958 - val_loss: 6.3473\n",
            "Epoch 77/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.9173 - val_loss: 6.3242\n",
            "Epoch 78/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8568 - val_loss: 6.1522\n",
            "Epoch 79/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.9434 - val_loss: 6.1536\n",
            "Epoch 80/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.8912 - val_loss: 6.1990\n",
            "Epoch 81/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8367 - val_loss: 6.1214\n",
            "Epoch 82/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.8476 - val_loss: 6.0953\n",
            "Epoch 83/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8300 - val_loss: 6.2057\n",
            "Epoch 84/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.8273 - val_loss: 6.1988\n",
            "Epoch 85/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8047 - val_loss: 6.2539\n",
            "Epoch 86/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8119 - val_loss: 6.3440\n",
            "Epoch 87/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8518 - val_loss: 6.3802\n",
            "Epoch 88/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8598 - val_loss: 6.3275\n",
            "Epoch 89/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.7639 - val_loss: 6.1287\n",
            "Epoch 90/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8740 - val_loss: 6.1328\n",
            "Epoch 91/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.8468 - val_loss: 6.1166\n",
            "Epoch 92/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.7887 - val_loss: 6.0941\n",
            "Epoch 93/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.9376 - val_loss: 6.2200\n",
            "Epoch 94/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8046 - val_loss: 6.2209\n",
            "Epoch 95/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.9677 - val_loss: 6.2221\n",
            "Epoch 96/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.8343 - val_loss: 6.2159\n",
            "Epoch 97/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.7794 - val_loss: 6.1663\n",
            "Epoch 98/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.7821 - val_loss: 6.1770\n",
            "Epoch 99/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7907 - val_loss: 6.1600\n",
            "Epoch 100/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7996 - val_loss: 6.1359\n",
            "Epoch 101/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7951 - val_loss: 6.2186\n",
            "Epoch 102/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.7552 - val_loss: 6.2613\n",
            "Epoch 103/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8222 - val_loss: 6.2611\n",
            "Epoch 104/200\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 3.8368 - val_loss: 6.3250\n",
            "Epoch 105/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7902 - val_loss: 6.2525\n",
            "Epoch 106/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.8631 - val_loss: 6.2617\n",
            "Epoch 107/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8105 - val_loss: 6.1912\n",
            "Epoch 108/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8047 - val_loss: 6.1900\n",
            "Epoch 109/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7623 - val_loss: 6.2707\n",
            "Epoch 110/200\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 3.7273 - val_loss: 6.2123\n",
            "Epoch 111/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.9025 - val_loss: 6.3004\n",
            "Epoch 112/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8126 - val_loss: 6.2287\n",
            "Epoch 113/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.9143 - val_loss: 6.1795\n",
            "Epoch 114/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8800 - val_loss: 6.1194\n",
            "Epoch 115/200\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 3.8500 - val_loss: 6.1583\n",
            "Epoch 116/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8019 - val_loss: 6.2496\n",
            "Epoch 117/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.7320 - val_loss: 6.1550\n",
            "Epoch 118/200\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 3.7715 - val_loss: 6.1768\n",
            "Epoch 119/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.7810 - val_loss: 6.1364\n",
            "Epoch 120/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.7661 - val_loss: 6.1787\n",
            "Epoch 121/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8435 - val_loss: 6.2512\n",
            "Epoch 122/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7030 - val_loss: 6.2241\n",
            "Epoch 123/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7362 - val_loss: 6.1360\n",
            "Epoch 124/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.8220 - val_loss: 6.0320\n",
            "Epoch 125/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7645 - val_loss: 6.0954\n",
            "Epoch 126/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7576 - val_loss: 6.1486\n",
            "Epoch 127/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7131 - val_loss: 6.2093\n",
            "Epoch 128/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7268 - val_loss: 6.1172\n",
            "Epoch 129/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7492 - val_loss: 6.1085\n",
            "Epoch 130/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.7959 - val_loss: 6.0723\n",
            "Epoch 131/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.7827 - val_loss: 6.0693\n",
            "Epoch 132/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.8200 - val_loss: 6.0321\n",
            "Epoch 133/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.7272 - val_loss: 6.2138\n",
            "Epoch 134/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7320 - val_loss: 6.3013\n",
            "Epoch 135/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6992 - val_loss: 6.1482\n",
            "Epoch 136/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7417 - val_loss: 6.1747\n",
            "Epoch 137/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7700 - val_loss: 6.1689\n",
            "Epoch 138/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7081 - val_loss: 6.0962\n",
            "Epoch 139/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.7540 - val_loss: 6.1728\n",
            "Epoch 140/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.7488 - val_loss: 6.1387\n",
            "Epoch 141/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6810 - val_loss: 6.1874\n",
            "Epoch 142/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6830 - val_loss: 6.2812\n",
            "Epoch 143/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6741 - val_loss: 6.2377\n",
            "Epoch 144/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.7188 - val_loss: 6.2077\n",
            "Epoch 145/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.6898 - val_loss: 6.2247\n",
            "Epoch 146/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7302 - val_loss: 6.2734\n",
            "Epoch 147/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7086 - val_loss: 6.1740\n",
            "Epoch 148/200\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 3.6686 - val_loss: 6.2028\n",
            "Epoch 149/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.6814 - val_loss: 6.2632\n",
            "Epoch 150/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7472 - val_loss: 6.2839\n",
            "Epoch 151/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.6899 - val_loss: 6.2259\n",
            "Epoch 152/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.6902 - val_loss: 6.1464\n",
            "Epoch 153/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6552 - val_loss: 6.2067\n",
            "Epoch 154/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.6626 - val_loss: 6.2765\n",
            "Epoch 155/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.6439 - val_loss: 6.2643\n",
            "Epoch 156/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.6773 - val_loss: 6.2101\n",
            "Epoch 157/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.7251 - val_loss: 6.1884\n",
            "Epoch 158/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7081 - val_loss: 6.2131\n",
            "Epoch 159/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6751 - val_loss: 6.2233\n",
            "Epoch 160/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.6273 - val_loss: 6.2395\n",
            "Epoch 161/200\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 3.6866 - val_loss: 6.2434\n",
            "Epoch 162/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6687 - val_loss: 6.1557\n",
            "Epoch 163/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6972 - val_loss: 6.0612\n",
            "Epoch 164/200\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 3.7179 - val_loss: 6.1070\n",
            "Epoch 165/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7434 - val_loss: 6.1843\n",
            "Epoch 166/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6812 - val_loss: 6.1187\n",
            "Epoch 167/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6980 - val_loss: 6.1539\n",
            "Epoch 168/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6651 - val_loss: 6.2193\n",
            "Epoch 169/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.6389 - val_loss: 6.1789\n",
            "Epoch 170/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7286 - val_loss: 6.1089\n",
            "Epoch 171/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7214 - val_loss: 6.0047\n",
            "Epoch 172/200\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 3.6998 - val_loss: 6.1037\n",
            "Epoch 173/200\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 3.6451 - val_loss: 6.2921\n",
            "Epoch 174/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.6557 - val_loss: 6.3319\n",
            "Epoch 175/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.6806 - val_loss: 6.2108\n",
            "Epoch 176/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.6822 - val_loss: 6.0976\n",
            "Epoch 177/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6476 - val_loss: 6.1775\n",
            "Epoch 178/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6167 - val_loss: 6.3721\n",
            "Epoch 179/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.6838 - val_loss: 6.3495\n",
            "Epoch 180/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.6625 - val_loss: 6.1475\n",
            "Epoch 181/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6237 - val_loss: 6.1360\n",
            "Epoch 182/200\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 3.6513 - val_loss: 6.1102\n",
            "Epoch 183/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6886 - val_loss: 6.1557\n",
            "Epoch 184/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.7138 - val_loss: 6.1658\n",
            "Epoch 185/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6260 - val_loss: 6.3339\n",
            "Epoch 186/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6482 - val_loss: 6.4361\n",
            "Epoch 187/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.6131 - val_loss: 6.2885\n",
            "Epoch 188/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6169 - val_loss: 6.1454\n",
            "Epoch 189/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.6593 - val_loss: 6.1819\n",
            "Epoch 190/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6209 - val_loss: 6.3135\n",
            "Epoch 191/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6383 - val_loss: 6.3254\n",
            "Epoch 192/200\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 3.6458 - val_loss: 6.3864\n",
            "Epoch 193/200\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 3.6532 - val_loss: 6.3658\n",
            "Epoch 194/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6494 - val_loss: 6.2766\n",
            "Epoch 195/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6427 - val_loss: 6.1841\n",
            "Epoch 196/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.5709 - val_loss: 6.2610\n",
            "Epoch 197/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.6982 - val_loss: 6.2852\n",
            "Epoch 198/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.6828 - val_loss: 6.3450\n",
            "Epoch 199/200\n",
            "10/10 [==============================] - 0s 5ms/step - loss: 3.5482 - val_loss: 6.1707\n",
            "Epoch 200/200\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 3.5755 - val_loss: 6.1627\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "p = model.predict(x_test)\n",
        "p = np.round(p).flatten()\n",
        "print('Predicted price:')\n",
        "print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c5isEhcUOLjJ",
        "outputId": "062159db-6d86-4460-8a07-ec6456612d2d"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 4ms/step\n",
            "Predicted price:\n",
            "[43. 50. 20. 42. 20. 46. 16. 48. 35. 18. 43. 49. 39. 24. 37. 52. 37. 22.\n",
            " 35. 23. 50. 36. 21. 46. 19. 33. 28. 47. 26. 26. 57. 45. 33. 26. 55. 44.\n",
            " 31. 51. 34. 22. 47. 33. 19. 44. 37. 47. 25. 47. 51. 32. 51. 47. 54. 47.\n",
            " 24. 22. 41. 33. 17. 41. 51. 26. 33. 46. 45. 47. 26. 37. 55. 35. 26. 53.\n",
            " 47. 18. 13. 44. 36. 38. 59. 55. 16. 51. 39. 36. 38. 43. 48. 37. 50. 46.\n",
            " 21. 48. 37. 24. 19. 46. 40. 41. 43. 16. 18. 47. 39. 34. 43. 51. 43. 42.\n",
            " 46. 23. 36. 39. 46. 49. 47. 17. 17. 41. 19. 51. 26. 44. 27. 28. 26.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o96rPNBEO2vX"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}