Concrete_Strength_Assignment.ipynb

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        "<a href=\"https://colab.research.google.com/github/MuhammadRustamShomi/DL-Final-Solved-assigments-PIAIC146681/blob/main/Concrete_Strength_Assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Assignment: Compresive Strength Concrete Problem\n",
        "\n",
        "\n",
        "### Abstract: \n",
        "\n",
        "Concrete is the most important material in civil engineering. The concrete compressive strength (concrete strength to bear the load) is a highly nonlinear function of age and ingredients.  <br><br>\n",
        "\n",
        "<table border=\"1\"  cellpadding=\"6\" bordercolor=\"red\">\n",
        "\t<tbody>\n",
        "        <tr>\n",
        "\t\t<td bgcolor=\"#DDEEFF\"><p class=\"normal\"><b>Data Set Characteristics:&nbsp;&nbsp;</b></p></td>\n",
        "\t\t<td><p class=\"normal\">Multivariate</p></td>\n",
        "\t\t<td bgcolor=\"#DDEEFF\"><p class=\"normal\"><b>Number of Instances:</b></p></td>\n",
        "\t\t<td><p class=\"normal\">1030</p></td>\n",
        "\t\t<td bgcolor=\"#DDEEFF\"><p class=\"normal\"><b>Area:</b></p></td>\n",
        "\t\t<td><p class=\"normal\">Physical</p></td>\n",
        "        </tr>\n",
        "     </tbody>\n",
        "    </table>\n",
        "<table border=\"1\" cellpadding=\"6\">\n",
        "    <tbody>\n",
        "        <tr>\n",
        "            <td bgcolor=\"#DDEEFF\"><p class=\"normal\"><b>Attribute Characteristics:</b></p></td>\n",
        "            <td><p class=\"normal\">Real</p></td>\n",
        "            <td bgcolor=\"#DDEEFF\"><p class=\"normal\"><b>Number of Attributes:</b></p></td>\n",
        "            <td><p class=\"normal\">9</p></td>\n",
        "            <td bgcolor=\"#DDEEFF\"><p class=\"normal\"><b>Date Donated</b></p></td>\n",
        "            <td><p class=\"normal\">2007-08-03</p></td>\n",
        "        </tr>\n",
        "     </tbody>\n",
        "    </table>\n",
        "<table border=\"1\" cellpadding=\"6\">\t\n",
        "    <tbody>\n",
        "    <tr>\n",
        "\t\t<td bgcolor=\"#DDEEFF\"><p class=\"normal\"><b>Associated Tasks:</b></p></td>\n",
        "\t\t<td><p class=\"normal\">Regression</p></td>\n",
        "\t\t<td bgcolor=\"#DDEEFF\"><p class=\"normal\"><b>Missing Values?</b></p></td>\n",
        "\t\t<td><p class=\"normal\">N/A</p></td>\n",
        "\t\t<td bgcolor=\"#DDEEFF\"><p class=\"normal\"><b>Number of Web Hits:</b></p></td>\n",
        "\t\t<td><p class=\"normal\">231464</p></td>\n",
        "\t</tr>\n",
        "    </tbody>\n",
        "    </table>"
      ]
    },
    {
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      "metadata": {
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      "source": [
        "###  Description:\n",
        "| Features Name | Data Type | Measurement | Description |\n",
        "| -- | -- | -- | -- |\n",
        "Cement (component 1) | quantitative | kg in a m3 mixture | Input Variable\n",
        "Blast Furnace Slag (component 2) | quantitative | kg in a m3 mixture | Input Variable\n",
        "Fly Ash (component 3) | quantitative | kg in a m3 mixture | Input Variable\n",
        "Water (component 4) | quantitative | kg in a m3 mixture | Input Variable\n",
        "Superplasticizer (component 5) | quantitative | kg in a m3 mixture | Input Variable\n",
        "Coarse Aggregate (component 6) | quantitative | kg in a m3 mixture | Input Variable\n",
        "Fine Aggregate (component 7) | quantitative | kg in a m3 mixture | Input Variable\n",
        "Age | quantitative | Day (1~365) | Input Variable\n",
        "Concrete compressive strength | quantitative | MPa | Output Variable\n",
        "\n",
        "### WORKFLOW :\n",
        "- Load Data\n",
        "- Check Missing Values ( If Exist ; Fill each record with mean of its feature )\n",
        "- Standardized the Input Variables. **Hint**: Centeralized the data\n",
        "- Split into 50% Training(Samples,Labels) , 30% Test(Samples,Labels) and 20% Validation Data(Samples,Labels).\n",
        "- Model : input Layer (No. of features ), 3 hidden layers including 10,8,6 unit & Output Layer with activation function relu/tanh (check by experiment).\n",
        "- Compilation Step (Note : Its a Regression problem , select loss , metrics according to it)\n",
        "- Train the Model with Epochs (100) and validate it\n",
        "- If the model gets overfit tune your model by changing the units , No. of layers , activation function , epochs , add dropout layer or add Regularizer according to the need .\n",
        "- Evaluation Step\n",
        "- Prediction\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8_lYngD2OZJN"
      },
      "source": [
        "# Load Data:\n",
        "[Click Here to Download DataSet](https://github.com/ramsha275/ML_Datasets/blob/main/compresive_strength_concrete.csv)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FgrSF5j_OZJO"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tensorflow.keras import models, layers , optimizers"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "thBGRKLPPHZY",
        "outputId": "c2d7eaec-7be9-46a0-8730-9fcf5eca4c0d"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H43i_gBFPe02"
      },
      "source": [
        "df= pd.read_csv(\"/content/gdrive/MyDrive/compresive_strength_concrete.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "oH6RIxSbQy3d",
        "outputId": "b2de4098-1bee-4fae-bbf1-08fad78068e4"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cement (component 1)(kg in a m^3 mixture)</th>\n",
              "      <th>Blast Furnace Slag (component 2)(kg in a m^3 mixture)</th>\n",
              "      <th>Fly Ash (component 3)(kg in a m^3 mixture)</th>\n",
              "      <th>Water  (component 4)(kg in a m^3 mixture)</th>\n",
              "      <th>Superplasticizer (component 5)(kg in a m^3 mixture)</th>\n",
              "      <th>Coarse Aggregate  (component 6)(kg in a m^3 mixture)</th>\n",
              "      <th>Fine Aggregate (component 7)(kg in a m^3 mixture)</th>\n",
              "      <th>Age (day)</th>\n",
              "      <th>Concrete compressive strength(MPa, megapascals)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>540.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>162.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1040.0</td>\n",
              "      <td>676.0</td>\n",
              "      <td>28</td>\n",
              "      <td>79.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>540.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>162.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1055.0</td>\n",
              "      <td>676.0</td>\n",
              "      <td>28</td>\n",
              "      <td>61.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>332.5</td>\n",
              "      <td>142.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>228.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>932.0</td>\n",
              "      <td>594.0</td>\n",
              "      <td>270</td>\n",
              "      <td>40.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>332.5</td>\n",
              "      <td>142.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>228.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>932.0</td>\n",
              "      <td>594.0</td>\n",
              "      <td>365</td>\n",
              "      <td>41.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>198.6</td>\n",
              "      <td>132.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>192.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>978.4</td>\n",
              "      <td>825.5</td>\n",
              "      <td>360</td>\n",
              "      <td>44.30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "   Cement (component 1)(kg in a m^3 mixture)  ...  Concrete compressive strength(MPa, megapascals) \n",
              "0                                      540.0  ...                                             79.99\n",
              "1                                      540.0  ...                                             61.89\n",
              "2                                      332.5  ...                                             40.27\n",
              "3                                      332.5  ...                                             41.05\n",
              "4                                      198.6  ...                                             44.30\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
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    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q8S-QLsBR4Bd",
        "outputId": "0becc1de-2209-49d1-bb32-cb0094e3f596"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cement (component 1)(kg in a m^3 mixture)                0\n",
              "Blast Furnace Slag (component 2)(kg in a m^3 mixture)    0\n",
              "Fly Ash (component 3)(kg in a m^3 mixture)               0\n",
              "Water  (component 4)(kg in a m^3 mixture)                0\n",
              "Superplasticizer (component 5)(kg in a m^3 mixture)      0\n",
              "Coarse Aggregate  (component 6)(kg in a m^3 mixture)     0\n",
              "Fine Aggregate (component 7)(kg in a m^3 mixture)        0\n",
              "Age (day)                                                0\n",
              "Concrete compressive strength(MPa, megapascals)          0\n",
              "dtype: int64"
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          "execution_count": 5
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          "height": 348
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        "outputId": "c518539c-b105-4fcc-b264-a54e8f3e2a1e"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cement (component 1)(kg in a m^3 mixture)</th>\n",
              "      <th>Blast Furnace Slag (component 2)(kg in a m^3 mixture)</th>\n",
              "      <th>Fly Ash (component 3)(kg in a m^3 mixture)</th>\n",
              "      <th>Water  (component 4)(kg in a m^3 mixture)</th>\n",
              "      <th>Superplasticizer (component 5)(kg in a m^3 mixture)</th>\n",
              "      <th>Coarse Aggregate  (component 6)(kg in a m^3 mixture)</th>\n",
              "      <th>Fine Aggregate (component 7)(kg in a m^3 mixture)</th>\n",
              "      <th>Age (day)</th>\n",
              "      <th>Concrete compressive strength(MPa, megapascals)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1030.000000</td>\n",
              "      <td>1030.000000</td>\n",
              "      <td>1030.000000</td>\n",
              "      <td>1030.000000</td>\n",
              "      <td>1030.000000</td>\n",
              "      <td>1030.000000</td>\n",
              "      <td>1030.000000</td>\n",
              "      <td>1030.000000</td>\n",
              "      <td>1030.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>281.167864</td>\n",
              "      <td>73.895825</td>\n",
              "      <td>54.188350</td>\n",
              "      <td>181.567282</td>\n",
              "      <td>6.204660</td>\n",
              "      <td>972.918932</td>\n",
              "      <td>773.580485</td>\n",
              "      <td>45.662136</td>\n",
              "      <td>35.817961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>104.506364</td>\n",
              "      <td>86.279342</td>\n",
              "      <td>63.997004</td>\n",
              "      <td>21.354219</td>\n",
              "      <td>5.973841</td>\n",
              "      <td>77.753954</td>\n",
              "      <td>80.175980</td>\n",
              "      <td>63.169912</td>\n",
              "      <td>16.705742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>102.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>121.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>801.000000</td>\n",
              "      <td>594.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.330000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>192.375000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>164.900000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>932.000000</td>\n",
              "      <td>730.950000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>23.710000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>272.900000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>6.400000</td>\n",
              "      <td>968.000000</td>\n",
              "      <td>779.500000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>34.445000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>350.000000</td>\n",
              "      <td>142.950000</td>\n",
              "      <td>118.300000</td>\n",
              "      <td>192.000000</td>\n",
              "      <td>10.200000</td>\n",
              "      <td>1029.400000</td>\n",
              "      <td>824.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>46.135000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>540.000000</td>\n",
              "      <td>359.400000</td>\n",
              "      <td>200.100000</td>\n",
              "      <td>247.000000</td>\n",
              "      <td>32.200000</td>\n",
              "      <td>1145.000000</td>\n",
              "      <td>992.600000</td>\n",
              "      <td>365.000000</td>\n",
              "      <td>82.600000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "       Cement (component 1)(kg in a m^3 mixture)  ...  Concrete compressive strength(MPa, megapascals) \n",
              "count                                1030.000000  ...                                       1030.000000\n",
              "mean                                  281.167864  ...                                         35.817961\n",
              "std                                   104.506364  ...                                         16.705742\n",
              "min                                   102.000000  ...                                          2.330000\n",
              "25%                                   192.375000  ...                                         23.710000\n",
              "50%                                   272.900000  ...                                         34.445000\n",
              "75%                                   350.000000  ...                                         46.135000\n",
              "max                                   540.000000  ...                                         82.600000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NHZ4H5GPSUz8",
        "outputId": "4506cf77-7d0e-4fd0-e016-fdf143aa4b92"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1030 entries, 0 to 1029\n",
            "Data columns (total 9 columns):\n",
            " #   Column                                                 Non-Null Count  Dtype  \n",
            "---  ------                                                 --------------  -----  \n",
            " 0   Cement (component 1)(kg in a m^3 mixture)              1030 non-null   float64\n",
            " 1   Blast Furnace Slag (component 2)(kg in a m^3 mixture)  1030 non-null   float64\n",
            " 2   Fly Ash (component 3)(kg in a m^3 mixture)             1030 non-null   float64\n",
            " 3   Water  (component 4)(kg in a m^3 mixture)              1030 non-null   float64\n",
            " 4   Superplasticizer (component 5)(kg in a m^3 mixture)    1030 non-null   float64\n",
            " 5   Coarse Aggregate  (component 6)(kg in a m^3 mixture)   1030 non-null   float64\n",
            " 6   Fine Aggregate (component 7)(kg in a m^3 mixture)      1030 non-null   float64\n",
            " 7   Age (day)                                              1030 non-null   int64  \n",
            " 8   Concrete compressive strength(MPa, megapascals)        1030 non-null   float64\n",
            "dtypes: float64(8), int64(1)\n",
            "memory usage: 72.5 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1BOyIHonVu9W",
        "outputId": "157f8be5-2a12-45a1-cf4f-6fda531e18a9"
      },
      "source": [
        "df.keys()# to gets columns name"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Cement (component 1)(kg in a m^3 mixture)',\n",
              "       'Blast Furnace Slag (component 2)(kg in a m^3 mixture)',\n",
              "       'Fly Ash (component 3)(kg in a m^3 mixture)',\n",
              "       'Water  (component 4)(kg in a m^3 mixture)',\n",
              "       'Superplasticizer (component 5)(kg in a m^3 mixture)',\n",
              "       'Coarse Aggregate  (component 6)(kg in a m^3 mixture)',\n",
              "       'Fine Aggregate (component 7)(kg in a m^3 mixture)', 'Age (day)',\n",
              "       'Concrete compressive strength(MPa, megapascals) '],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0pI6EOmVSYA9"
      },
      "source": [
        "# split dataframe into input and output\n",
        "data=df.iloc[:,:8]\n",
        "\n",
        "targets=df.iloc[:,-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "VgcX-c4uV9H1",
        "outputId": "4bce68d7-6688-4839-d155-8ef96f63d999"
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('ggplot')\n",
        "pd.DataFrame.hist(data, figsize = [30,30], color=\"Green\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vzlCuZHa5kPXTTz/Znj17bPXq1Xb//fdbvXr1nOMPHTpkISEh1qhRI1uxYoXt2rXL5s6dawsWLDAzy9R+NdmFCxcsICDAOnXq5HKB1NWu3nZLliyxKVOm2NatWy0uLs769+9vuXLlculP8+bNrXTp0rZ48WLbunWrRUZGWt68ea9ZyMqXL59NnDjRZdhPP/1kHh4e1qNHD4uJibHt27fbpEmTnBc5ZDQXy2wubHb5ey5//vw2duxYi4uLszFjxpinp6fNnTvXzC7nyzVq1LCqVavaihUrbPPmzda6dWvLly+f84KV5APfKlWq2Pfff287duywyMhICwgIsISEBDO7nO+WL1/eWrZsadHR0bZz504bMmSI5c6d27Zt25bh+WzYsMEk2axZs+zQoUN25MiRdNf3lU6cOGG+vr4uF4uZXX5vSXLGkdZ2uvJkwu+//27lypWzJ5980s6ePWtmZqdOnbLChQvbI488Yps2bbJffvnF7r//fvPx8bmuEz/prYfUbN682caNG2cxMTG2a9cuGzt2rHl6etrixYvTnOZ6c61/6/jx4/bKK69YaGionTp1Ks1213uMc/VJuiNHjliRIkWsd+/eZmY2ZMgQCwoKsj/++MM5PrXjucyc+ClbtqzNnz/f9uzZY/v378/QsY+Z2YIFC8zT09OOHz9+/SsUgFvJTCHLzOzBBx+0yMhIlzbt27e3Jk2apLucCxcuWJEiRWz+/PlmZtakSRN75513XNo0b97cGjVqZBs3brS9e/fa4sWL7YsvvnCOv95zD+Qz2ZPPpHcuhXyGfIZ85tZHIctNrV271rnTTs+pU6fMx8cnxVWw06ZNs8DAQOfrEiVK2GOPPeZ8nZSUZAEBAfbII4+4DMuXL5/zyo0lS5aYt7d3ijtWnnvuOee8kneGo0aNco6/ePGi+fv720cffeQc5unpmeLK49Q89thj1rp1a5dhkyZNMm9vb+fO/moDBgyw0NBQl4JNcsU/+aqPQYMGmaenp8sdMF26dDEPDw+XL8Tu3bvbvffe63zdoUMH8/Pzc1kHP/74o0mynTt3mplZsWLFUlyB0LNnTwsLC3O+LlGihDVv3tylTZMmTaxNmzZmlrntmN58zFIWETLiWonk448/bk888YTzdWrbKfm9EBUVZU888YSVK1fOfv/9d+f4kydPWu7cuVMkfDVr1ryuQta11kNGzJ4923Lnzm1JSUlptqlfv75VrVrVZViFChWsUqVKLsOqVKni/LK8Xps3bzY/Pz/z9PS0PHnypDiRdLXkdX7lVVq//vqrSbKRI0c6hyUngVu2bHGZ7sqTtadOnbIKFSrYk08+aXfffbdLsdYs5cnaZBktZDVs2NClTYcOHeypp55yGXb27Fnz8fGxOXPmuAyvVq2avfbaa+msCQC3gg4dOpinp6f5+fmZn5+fORwO8/PzS5HrXOtqxvj4eJNkK1euNDOz999/30qXLp3mAdfgwYMzdBVn8r7Rx8fHGaOfn59NnTo1zbhSK2SVLl3a5Xslo7nS1ZL33clXDg8YMMAKFSqUZsEpM/vVK82ePdsKFChguXLlsnvvvde6d+9uP//8c4p5X6sAVaVKFRsyZIiZme3YscOZEyQ7f/68FStWLN35JN8hdvWFIXXq1LFmzZqlOV1Gc7HM5sJml7/n2rdv7zLvtm3bWp06dczMLCoqyiS5nCw6e/asFS5c2HmyLPnA98r3+uHDh02S/fDDD2Z2+QA6NDQ0xdWbDzzwgPXo0SPD87n6oDszOnbsaCVKlEhR7E1MTHRegWqW9nZKPpmwadMmK1q0qL3yyisun4VPPvkkRV69fft2k3RdJ37SWw8Z9eijj1qnTp3SHH+9udb1ev31183Pz88kWfny5Z3HG2m53mOcq0/8mJktXrzYPD097e233zYvL68U+43Ujucyc+LnyqvFM3rsY2a2adMmk2Rbt25Nd10AuHVcnRP6+flZmTJlnOOv3kfNmjXLfH19nXfDHDt2zHx8fFzu6E/N7NmzrVChQs7v1i+//NKKFy/u8t1UpUqVNO/WN7u+cw/kMzc/n8nIuRTyGfIZM/KZW92t92DOHMKu+sG8tMTGxurMmTNq1aqV/P39nX+dO3dWYmKi/v77b2fbqlWrOv/38PBQwYIFVaVKFZdhISEhOnLkiCQpOjpa58+fV2hoqMu8p0+frp07d7rEcffddzv/9/T0VEhIiP76669M9/vMmTPKkyePy7D169erQoUKKlasWJrr4L777lPu3Lld+hoYGKjY2FjnsNDQUJdnpxYuXFiFCxdWwYIFXYYl9z9ZhQoVFBgY6Hxdu3ZtSdK2bdt0/PhxHThwQPXq1XOZpn79+tq3b59Onz7tHHblOpKkokWLOtdRZrZjevO5UfLkyaMzZ844X6e2nZI999xz2rJli1auXKnixYs7h+/atUvnz5/Xfffd59L+/vvvv66Yrmc9zJ49W/Xq1VPRokXl7++vdu3a6fz58zp8+HC601352ZEuv0+u/OwkD7v6vZNZZcuWVUxMjKKjo/X222+rZ8+e+v7776853ZXxFS5cWJJc4ksell58vr6+mjlzpmbPnq0jR45o8uTJ19uNVNWoUcPldXR0tObMmePyfi9QoIDOnj2bYv9y9fsPwK2rZs2aiomJUUxMjNatW6euXbvq2Wef1bp169KcJiYmRo8//rjCwsIUEBDg/G75/fffJUmtW7fWhQsXVKJECUVGRurzzz/XiRMnrjvGKVOmOGNMXnZm3Hvvvak++/5audLSpUv10EMP6Y477lBAQIDq1Kkj6f/3c/369apVq1aavxmZmf3qlR5//HH9+eef+uGHH9SqVStt27ZNjRo1SvHM+yv9/fff6tKli8qVK6d8+fLJ399fsbGxzliTf9D5yu/8XLlyKTw8PM15SnLu61PLBR988MFUp8lMLpbZXDjZ1blK7dq1nTlmbGysChQooAoVKjjHe3t7q2bNmi55qOT6HihUqJA8PT2d74Ho6GgdPnzYuT6T/1asWJFu3n31fK5X3759NXfuXM2fP1/+/v4u45K3R/L2SWs7SZffG/Xq1dPTTz+tcePGuXwWtm3bpvLly7vk1cnvoeuR2fVw+vRp9e3bVxUrVlRQUJD8/f21cOFC5/s2PVmRa2VEnz59tHHjRi1ZskQlS5bU448/fs392fUe41ztgQceUO/evfX222+rU6dOWf5bNVfmgpk59rn6/Qfg9nBlThgTE6Mff/wxzbaPPvqoAgMDNWPGDEnS9OnTFRgYqObNm6e7jE8++UTt2rWTl5eXJKlFixZKTEx0Ocbu2bOn3nvvPdWsWVNvvPGGli9fnmI+mT33QD5z8/OZjJxLIZ8hn8kK5DPZyyu7A0DqSpcuLQ8PD23btk0tW7ZMs92lS5ckSf/3f/+nMmXKpBgfFBTk/D9Xrlwu4xwOR6rDkud56dIlBQYGKjo6OsV8rywapfb6yvlkRsGCBZWQkJDp6TIis/3Paumto8xsx6xa15mRkJCgIkWKOF+nt52aNWumTz/9VD/88IPatWuXYrzD4ciSmDK7HtauXasnn3xSb775pkaMGKH8+fNrzZo16tChg86fP5/usm7Weyd37twqVaqUJKlatWras2eP3nnnHTVt2jTD8SWv39SGXSu+lStXSpLzC/fK911aHA5HisJ7aj94efWJ10uXLumZZ55R3759U7QtUKCAy+uEhASX5AXArcvHx8e5n5Oke+65R/PmzdOYMWM0ffr0FO1Pnz6tBx98UHXq1NGUKVNUqFAhSVLFihWd++7Q0FD99ttvWrJkiRYvXqzBgwfrjTfe0Nq1a3XHHXdkOsbQ0FCXGJN5eHhc1/4uWXrfW3/88YcefvhhPfPMMxo4cKCCg4N14MABRUREXPM7Kllm9qtX8/b2VsOGDdWwYUO9+eabGjJkiN566y316dNHd955Z4r2kZGR+uOPPzR8+HCFhYXJx8dHbdq0SRFrZr/zg4OD5XA4ckwuKMklFyxfvrzmzJmToo2vr2+G55NZZqYePXroyy+/1M8//5ziQh1Jzu2R/F2c3nbKly+fqlSporlz56pHjx4pLkTLqjxQyvx66NOnj+bNm6f3339fZcuWlZ+fn3r37q3ExMRrLiurcq1rCQ4OVnBwsEqXLq1atWqpQIECmjFjhl566aUMxZYcy/W8r5OSkrRq1Sp5enpq9+7dMrNrbq/UivZp/fD5lfvGzBz7XP3+A3B7uDonTI+Xl5c6duyoiRMn6uWXX9akSZP03HPPOQtUqdm3b58WLVqkRYsW6X//+59zeFJSkj755BM1a9ZM0uULcZs0aaIffvhBS5YsUdOmTfX444+75KWZPfdAPnPz85mMnEshnyGfSQv5zK2DO7LcVFBQkJo2baoPPvgg1Z3RhQsXdOrUKVWsWFF58uTRnj17VKpUqRR/np6e1x1DeHi4/vnnH509ezbFfK+80yYjcufOraSkpGu2u+eee1Jc7XHvvfdq27ZtOnDgQKrTVKxYUWvWrHE5ebJp0yYlJiaqUqVKmYozNdu3b9fx48edr1evXi3p8p1aefPmVbFixVJctbNs2TKFhYWl+KJOS1Zux4yu68zYsmWLy1XUqW2nZO3atdO0adP0/PPPa9q0ac7hpUqVUu7cufXLL7+4tF+zZk2WxpqWlStXKjg4WEOGDFHNmjVVpkyZNN9T7uLSpUs6e/bsTVnW1q1b9eqrr2rSpEmKiIhQmzZtdO7cOef4tN5XISEhOnjwoPP1uXPnnFfipyc8PFybN2/WXXfdleL9nj9/fme706dPa/fu3de8ih/ArcvT0zPNK9O2b9+uv//+W++++64aNGig8uXL69ixYykKSt7e3mrSpImGDx+uLVu26PTp05o7d66krPtevHp/J0kbN2781/OVLl+9eubMGY0ZM0a1a9dW2bJlU1yJee+992r16tU6depUqvPI6H41I8qXLy9JLlcRXmn58uXq0qWLHn30UVWuXFlFihTRnj17nOOTr+a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            "text/plain": [
              "<Figure size 2160x2160 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "zNhK-R69vqFN",
        "outputId": "0c96d1c5-ef5e-4bad-c94c-b157af754bd0"
      },
      "source": [
        "plt.style.use('ggplot')\n",
        "targets.hist( figsize = [7,7], color=\"Blue\")\n",
        "plt.title(\"Actual Targets\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mel2k2Nwf0LI"
      },
      "source": [
        "# standardized or normalized the data (input variables)\n",
        "mean = data.mean(axis=0)\n",
        "data-= mean\n",
        "std = data.std(axis=0)\n",
        "data/= std"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6Q4b8UpXI8a"
      },
      "source": [
        "# data spliting using machine learning spliting method\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test=train_test_split(data,targets,random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cxld28AodAxY",
        "outputId": "ef7039a6-c353-4953-d669-8d2c05f8892c"
      },
      "source": [
        "print(f\"Shape of training data :{X_train.shape},\\nShape of training targets:{y_train.shape}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of training data :(772, 8),\n",
            "Shape of training targets:(772,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XsIXmksBdKGl",
        "outputId": "90ffa9ff-5c1f-449e-c901-5cb05d73312c"
      },
      "source": [
        "print(f\"Shape of testing data :{X_test.shape},\\nShape of testing targets:{y_test.shape}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of testing data :(258, 8),\n",
            "Shape of testing targets:(258,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pJ4OdxP9g5Wa"
      },
      "source": [
        "X_train= np.asarray(X_train).astype('float32')\n",
        "X_test= np.asarray(X_test).astype('float32')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uTKLaC-_jgZC"
      },
      "source": [
        "val_X = X_train[:230]\n",
        "partial_X_train=X_train[230:]\n",
        "val_y=y_train[:230]\n",
        "partial_y_train=y_train[230:]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yI9PhkMyhH9Q"
      },
      "source": [
        "from keras import models, layers , optimizers"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FBHIpQiNhLsk"
      },
      "source": [
        "network=models.Sequential()\n",
        "network.add(tf.keras.layers.Dense(10,activation=\"relu\",input_shape=(X_train.shape[1],)))\n",
        "network.add(tf.keras.layers.Dense(8,activation=\"relu\"))\n",
        "network.add(tf.keras.layers.Dense(1))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vxNlTIfWihRB",
        "outputId": "2e4c5b43-8773-44da-afd0-960f2daee68e"
      },
      "source": [
        "network.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 10)                90        \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 8)                 88        \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 9         \n",
            "=================================================================\n",
            "Total params: 187\n",
            "Trainable params: 187\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DojP0-ZZipQE"
      },
      "source": [
        "network.compile(optimizer=optimizers.RMSprop(lr=0.001), loss=\"mse\",metrics=['mae'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KE-wc1irh7dL",
        "outputId": "4f222c88-ed81-4ac7-c35d-03d929697c6a"
      },
      "source": [
        "history=network.fit(partial_X_train,partial_y_train,epochs=10,validation_data=(val_X,val_y),batch_size=1,verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "542/542 [==============================] - 1s 2ms/step - loss: 228.2416 - mae: 11.9803 - val_loss: 231.3306 - val_mae: 12.3365\n",
            "Epoch 2/10\n",
            "542/542 [==============================] - 1s 1ms/step - loss: 194.4006 - mae: 11.1911 - val_loss: 208.5373 - val_mae: 11.6359\n",
            "Epoch 3/10\n",
            "542/542 [==============================] - 1s 1ms/step - loss: 179.4741 - mae: 10.6709 - val_loss: 192.1631 - val_mae: 11.2043\n",
            "Epoch 4/10\n",
            "542/542 [==============================] - 1s 1ms/step - loss: 166.0736 - mae: 10.2928 - val_loss: 174.8127 - val_mae: 10.5846\n",
            "Epoch 5/10\n",
            "542/542 [==============================] - 1s 1ms/step - loss: 161.2122 - mae: 10.0535 - val_loss: 163.8926 - val_mae: 10.1796\n",
            "Epoch 6/10\n",
            "542/542 [==============================] - 1s 1ms/step - loss: 154.9717 - mae: 9.7488 - val_loss: 156.5477 - val_mae: 10.0759\n",
            "Epoch 7/10\n",
            "542/542 [==============================] - 1s 1ms/step - loss: 148.6139 - mae: 9.5402 - val_loss: 149.1398 - val_mae: 9.8197\n",
            "Epoch 8/10\n",
            "542/542 [==============================] - 1s 1ms/step - loss: 142.0981 - mae: 9.3685 - val_loss: 144.5114 - val_mae: 9.5357\n",
            "Epoch 9/10\n",
            "542/542 [==============================] - 1s 1ms/step - loss: 141.5942 - mae: 9.2684 - val_loss: 138.0665 - val_mae: 9.3565\n",
            "Epoch 10/10\n",
            "542/542 [==============================] - 1s 1ms/step - loss: 139.9193 - mae: 9.1238 - val_loss: 133.2513 - val_mae: 9.1658\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "XY28DpqQlLYr",
        "outputId": "7c613781-6f23-4f2f-b11b-59b2dddd5166"
      },
      "source": [
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "425Sn4TpmDgv",
        "outputId": "3592aa9a-a833-4e20-8f4a-c37bafff623f"
      },
      "source": [
        "all_mae_histories=[]\n",
        "mae_history = history.history['val_mae']\n",
        "all_mae_histories.append(mae_history)\n",
        "print(all_mae_histories)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[12.336536407470703, 11.635929107666016, 11.204290390014648, 10.584555625915527, 10.179609298706055, 10.07591724395752, 9.819719314575195, 9.535701751708984, 9.356534004211426, 9.165823936462402]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYbwAuH9n0MS",
        "outputId": "70b750e4-22d1-489c-94c7-6ee7f5f5e190"
      },
      "source": [
        "average_mae_history = [np.mean([x[i] for x in all_mae_histories]) for i in range(2)]\n",
        "print(average_mae_history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[12.336536407470703, 11.635929107666016]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "_QIM2eiRmxTw",
        "outputId": "10566647-da8a-47f4-f5b7-cb8d653f4a26"
      },
      "source": [
        "plt.plot(range(1, len(average_mae_history) + 1), average_mae_history)\n",
        "plt.title('Validation MAE by Epochs')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Validation MAE')\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aedgvZfCoEqr",
        "outputId": "f95ca969-6576-4b34-bc2d-afe3fb9d08ff"
      },
      "source": [
        "test_mse_score, test_mae_score = network.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9/9 [==============================] - 0s 2ms/step - loss: 118.9785 - mae: 8.6969\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VGsxBThTqvjm",
        "outputId": "74c17c30-9632-4063-b141-054f31e56519"
      },
      "source": [
        " test_mae_score"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.696878433227539"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qepWbko8rXy7",
        "outputId": "40b36f3b-9be9-428e-a07d-81d52fb9b110"
      },
      "source": [
        "y_pred = network.predict(X_test)\n",
        "y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dBdthc-vsw4_",
        "outputId": "b39ee67c-0cb0-45b9-9f4a-2923869fbd43"
      },
      "source": [
        "y_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "747    26.06\n",
              "718    10.35\n",
              "175    79.30\n",
              "828    74.99\n",
              "713     9.69\n",
              "       ...  \n",
              "453    31.72\n",
              "906    39.42\n",
              "757    18.13\n",
              "875    18.28\n",
              "62      9.87\n",
              "Name: Concrete compressive strength(MPa, megapascals) , Length: 258, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "osFYrcPkrnnE",
        "outputId": "cc835415-fae9-4240-b891-d77efa986f39"
      },
      "source": [
        "plt.figure(figsize=(20,5))\n",
        "plt.subplot(1, 2, 1 )\n",
        "plt.hist(targets)\n",
        "plt.xlabel('original target value')\n",
        "plt.ylabel('count')\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.hist(y_pred)\n",
        "plt.xlabel('aggregated target value')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}