Tensorflow house price predicting model.ipynb

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      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Twinkle0799/Predicton-of-House-prices-using-tensorflow/blob/master/Tensorflow%20house%20price%20predicting%20model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UupjK_ZYBCzM",
        "colab_type": "text"
      },
      "source": [
        " TASK 1 : **IMPORTING LIBRARIES**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2xaQqJNBQZv",
        "colab_type": "code",
        "outputId": "52c476af-cdb0-47e2-e940-5fa692018514",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "#from utils import *\n",
        "import sys\n",
        "sys.path.append('PATH_TO_TENSORFLOW_OBJECT_DETECTION_FOLDER')\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense,Dropout\n",
        "from tensorflow.keras.callbacks import EarlyStopping, LambdaCallback\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "\n",
        "\n",
        "%matplotlib inline\n",
        "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)\n",
        "\n",
        "print('Libraries Imported')"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Libraries Imported\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PF_6HK1b004H",
        "colab_type": "text"
      },
      "source": [
        "TASK 2 : **IMPORTING THE DATA**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8HdFM0Hg1a_Z",
        "colab_type": "code",
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        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 81
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "#To upload from your local drive"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-f8c57e8d-e5eb-4238-b760-b710f86b97ae\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-f8c57e8d-e5eb-4238-b760-b710f86b97ae\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving data.csv to data.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "70qMBwim2cd5",
        "colab_type": "code",
        "outputId": "31d2c521-4fb3-4a4b-aa70-b45de8b97393",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "df = pd.read_csv('data.csv')\n",
        "df.columns = [' ','data','age','distance','stores','latitude','longitude','price']\n",
        "df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "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></th>\n",
              "      <th>data</th>\n",
              "      <th>age</th>\n",
              "      <th>distance</th>\n",
              "      <th>stores</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2007</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>86</td>\n",
              "      <td>121</td>\n",
              "      <td>12032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2016</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>90</td>\n",
              "      <td>120</td>\n",
              "      <td>13560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2002</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>80</td>\n",
              "      <td>128</td>\n",
              "      <td>12029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2014</td>\n",
              "      <td>25</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>81</td>\n",
              "      <td>122</td>\n",
              "      <td>14157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2008</td>\n",
              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>88</td>\n",
              "      <td>126</td>\n",
              "      <td>14287</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      data  age  distance  stores  latitude  longitude  price\n",
              "0  1  2007    4         2       3        86        121  12032\n",
              "1  2  2016   18         3       7        90        120  13560\n",
              "2  3  2002   13         2       2        80        128  12029\n",
              "3  4  2014   25         5       8        81        122  14157\n",
              "4  5  2008   11         9       3        88        126  14287"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8TMF9LNk4g7M",
        "colab_type": "text"
      },
      "source": [
        "CHECK MISSING DATA : Its  good practice to check if the data has any missing values. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p92L83LF42jc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "outputId": "2d48d77f-0b1e-49ae-9743-8b3fd2be7873"
      },
      "source": [
        "df.isna().sum"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.sum of               data    age  distance  stores  latitude  longitude  price\n",
              "0     False  False  False     False   False     False      False  False\n",
              "1     False  False  False     False   False     False      False  False\n",
              "2     False  False  False     False   False     False      False  False\n",
              "3     False  False  False     False   False     False      False  False\n",
              "4     False  False  False     False   False     False      False  False\n",
              "...     ...    ...    ...       ...     ...       ...        ...    ...\n",
              "4994  False  False  False     False   False     False      False  False\n",
              "4995  False  False  False     False   False     False      False  False\n",
              "4996  False  False  False     False   False     False      False  False\n",
              "4997  False  False  False     False   False     False      False  False\n",
              "4998  False  False  False     False   False     False      False  False\n",
              "\n",
              "[4999 rows x 8 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W-4N9ZH75Rd4",
        "colab_type": "text"
      },
      "source": [
        "TASK 3 : **DATA NORMALIZATION**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zvovTx_t5YE_",
        "colab_type": "code",
        "outputId": "a34d4557-f758-495d-f690-a2aedcc629f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "df = df.iloc[:, 1:]\n",
        "df_norm = (df - df.mean())/df.std()\n",
        "df_norm.head()\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "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>data</th>\n",
              "      <th>age</th>\n",
              "      <th>distance</th>\n",
              "      <th>stores</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.350447</td>\n",
              "      <td>-1.318954</td>\n",
              "      <td>-0.930413</td>\n",
              "      <td>-0.609189</td>\n",
              "      <td>0.325211</td>\n",
              "      <td>-1.261125</td>\n",
              "      <td>-1.836255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.298471</td>\n",
              "      <td>-0.083366</td>\n",
              "      <td>-0.617878</td>\n",
              "      <td>0.663413</td>\n",
              "      <td>1.590122</td>\n",
              "      <td>-1.576801</td>\n",
              "      <td>-0.339484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.266513</td>\n",
              "      <td>-0.524647</td>\n",
              "      <td>-0.930413</td>\n",
              "      <td>-0.927340</td>\n",
              "      <td>-1.572157</td>\n",
              "      <td>0.948607</td>\n",
              "      <td>-1.839193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.932045</td>\n",
              "      <td>0.534428</td>\n",
              "      <td>0.007190</td>\n",
              "      <td>0.981564</td>\n",
              "      <td>-1.255929</td>\n",
              "      <td>-0.945449</td>\n",
              "      <td>0.245315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.167234</td>\n",
              "      <td>-0.701160</td>\n",
              "      <td>1.257326</td>\n",
              "      <td>-0.609189</td>\n",
              "      <td>0.957666</td>\n",
              "      <td>0.317255</td>\n",
              "      <td>0.372658</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       data       age  distance    stores  latitude  longitude     price\n",
              "0 -0.350447 -1.318954 -0.930413 -0.609189  0.325211  -1.261125 -1.836255\n",
              "1  1.298471 -0.083366 -0.617878  0.663413  1.590122  -1.576801 -0.339484\n",
              "2 -1.266513 -0.524647 -0.930413 -0.927340 -1.572157   0.948607 -1.839193\n",
              "3  0.932045  0.534428  0.007190  0.981564 -1.255929  -0.945449  0.245315\n",
              "4 -0.167234 -0.701160  1.257326 -0.609189  0.957666   0.317255  0.372658"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UglCTm8w7NRt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "79a4377a-724c-429b-8672-a13ddaff5f89"
      },
      "source": [
        "#convert label values\n",
        "y_mean=df['price'].mean()\n",
        "y_std=df['price'].std()\n",
        "def convert_label_value(pred):\n",
        "  return int(pred*y_std+y_mean)\n",
        "print(convert_label_value(-1.836255))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12031\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oD67lfUUHyHG",
        "colab_type": "text"
      },
      "source": [
        "TASK 4 : **TRAINING AND TEST SET**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wRUa4WbaH8bv",
        "colab_type": "code",
        "outputId": "d9f6758c-34f5-4de0-9475-bdf037ce124a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "x = df_norm.iloc[:, :6]\n",
        "x.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "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>data</th>\n",
              "      <th>age</th>\n",
              "      <th>distance</th>\n",
              "      <th>stores</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.350447</td>\n",
              "      <td>-1.318954</td>\n",
              "      <td>-0.930413</td>\n",
              "      <td>-0.609189</td>\n",
              "      <td>0.325211</td>\n",
              "      <td>-1.261125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.298471</td>\n",
              "      <td>-0.083366</td>\n",
              "      <td>-0.617878</td>\n",
              "      <td>0.663413</td>\n",
              "      <td>1.590122</td>\n",
              "      <td>-1.576801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.266513</td>\n",
              "      <td>-0.524647</td>\n",
              "      <td>-0.930413</td>\n",
              "      <td>-0.927340</td>\n",
              "      <td>-1.572157</td>\n",
              "      <td>0.948607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.932045</td>\n",
              "      <td>0.534428</td>\n",
              "      <td>0.007190</td>\n",
              "      <td>0.981564</td>\n",
              "      <td>-1.255929</td>\n",
              "      <td>-0.945449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.167234</td>\n",
              "      <td>-0.701160</td>\n",
              "      <td>1.257326</td>\n",
              "      <td>-0.609189</td>\n",
              "      <td>0.957666</td>\n",
              "      <td>0.317255</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       data       age  distance    stores  latitude  longitude\n",
              "0 -0.350447 -1.318954 -0.930413 -0.609189  0.325211  -1.261125\n",
              "1  1.298471 -0.083366 -0.617878  0.663413  1.590122  -1.576801\n",
              "2 -1.266513 -0.524647 -0.930413 -0.927340 -1.572157   0.948607\n",
              "3  0.932045  0.534428  0.007190  0.981564 -1.255929  -0.945449\n",
              "4 -0.167234 -0.701160  1.257326 -0.609189  0.957666   0.317255"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SYYjWE7IIJ2L",
        "colab_type": "code",
        "outputId": "3508db31-5af5-4717-94f5-9902f91b32ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "y=df_norm.iloc[:,-1]\n",
        "y.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0   -1.836255\n",
              "1   -0.339484\n",
              "2   -1.839193\n",
              "3    0.245315\n",
              "4    0.372658\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_B73RWBmIiro",
        "colab_type": "code",
        "outputId": "e25ca80b-e7fd-4e3f-f5b0-21fb9b50b990",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "#feature and label values\n",
        "x_arr = x.values\n",
        "y_arr = y.values\n",
        "print('feature array shape:',x_arr.shape)\n",
        "print('label array shape: ',y_arr.shape)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "feature array shape: (4999, 6)\n",
            "label array shape:  (4999,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c52XEXK-KQ7r",
        "colab_type": "code",
        "outputId": "1cfd0e3c-800e-4229-d151-0d1cec516ad7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "#train and test split\n",
        "x_train,x_test,y_train,y_test = train_test_split(x_arr,y_arr,test_size=0.05,random_state=0)\n",
        "print('Training Set: ',x_train.shape,y_train.shape)\n",
        "print('Test Set :',x_test.shape,y_test.shape)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Set:  (4749, 6) (4749,)\n",
            "Test Set : (250, 6) (250,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EXFbVlqxUoBo",
        "colab_type": "text"
      },
      "source": [
        "TASK 5 : **CREATE THE MODEL**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CzNWMOd8UtTC",
        "colab_type": "code",
        "outputId": "922c3166-6544-4f40-ae71-21e36eb08bcc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "def get_model():\n",
        "  model=Sequential([Dense(10,input_shape=(6, ),activation = 'relu'),\n",
        "                    Dense(20,activation='relu'),Dense(1,activation='relu'),Dense(1)])\n",
        "  model.compile(loss='mse',optimizer ='adam')\n",
        "  return model\n",
        "get_model().summary()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 10)                70        \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 20)                220       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 21        \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 2         \n",
            "=================================================================\n",
            "Total params: 313\n",
            "Trainable params: 313\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vrUUnc1OKBOv",
        "colab_type": "text"
      },
      "source": [
        "TASK : 6 **MODEL TRAINING**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQ2caBTIKHbI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "es_cb = EarlyStopping(monitor= 'val_loss', patience = 5)\n",
        "model = get_model()\n",
        "preds_on_untrained = model.predict(x_test)\n",
        "history=model.fit(x_train,y_train,validation_data =(x_test,y_test), epochs = 100, callabacks = [es_cb])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FSpY4icTgd3U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plot_loss(history)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gPu71XzjC5au",
        "colab_type": "text"
      },
      "source": [
        "**PLOT RAW PREDICTIONS**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NPPxQjQi-_ox",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds_on_trained = model.predict(x_test)\n",
        "compare_predictions(preds_on_untrained,preds_on_trained,y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ySYwK6TCAduP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "price_untrained = [convert_label_value(y) for y in preds_on_untrained]\n",
        "price_trained=[convert_label_value(y) for y in preds_on_trained]\n",
        "price_test = [convert_label_value(y) for y in y_test]\n",
        "compare_predictions(price_untrained,price_trained,price_test)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}