ANN_CSPP_by_Syed_Habeeb_Haider.ipynb

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        "# **Introduction**\n",
        "\n",
        ">  As a vehicle salesperson, I would like to create a model that can estimate the overall amount that consumers would spend.\n",
        "\n",
        "###**Problem Statement**:\n",
        "To predict amount paid for a car\n",
        "\n",
        "### **Task type**:\n",
        "*Regression*\n",
        "\n",
        "### **Algorithm**:\n",
        "Deep Learning **Artificial Neural Network** (ANN)"
      ]
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
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              "      <th>customer name</th>\n",
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              "      <th>annual Salary</th>\n",
              "      <th>credit card debt</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Martina Avila</td>\n",
              "      <td>cubilia.Curae.Phasellus@quisaccumsanconvallis.edu</td>\n",
              "      <td>Bulgaria</td>\n",
              "      <td>0</td>\n",
              "      <td>41.851720</td>\n",
              "      <td>62812.09301</td>\n",
              "      <td>11609.380910</td>\n",
              "      <td>238961.2505</td>\n",
              "      <td>35321.45877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Harlan Barnes</td>\n",
              "      <td>eu.dolor@diam.co.uk</td>\n",
              "      <td>Belize</td>\n",
              "      <td>0</td>\n",
              "      <td>40.870623</td>\n",
              "      <td>66646.89292</td>\n",
              "      <td>9572.957136</td>\n",
              "      <td>530973.9078</td>\n",
              "      <td>45115.52566</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Naomi Rodriquez</td>\n",
              "      <td>vulputate.mauris.sagittis@ametconsectetueradip...</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>1</td>\n",
              "      <td>43.152897</td>\n",
              "      <td>53798.55112</td>\n",
              "      <td>11160.355060</td>\n",
              "      <td>638467.1773</td>\n",
              "      <td>42925.70921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Jade Cunningham</td>\n",
              "      <td>malesuada@dignissim.com</td>\n",
              "      <td>Cook Islands</td>\n",
              "      <td>1</td>\n",
              "      <td>58.271369</td>\n",
              "      <td>79370.03798</td>\n",
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              "      <td>548599.0524</td>\n",
              "      <td>67422.36313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cedric Leach</td>\n",
              "      <td>felis.ullamcorper.viverra@egetmollislectus.net</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>1</td>\n",
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            "text/plain": [
              "     customer name                                    customer e-mail  \\\n",
              "0    Martina Avila  cubilia.Curae.Phasellus@quisaccumsanconvallis.edu   \n",
              "1    Harlan Barnes                                eu.dolor@diam.co.uk   \n",
              "2  Naomi Rodriquez  vulputate.mauris.sagittis@ametconsectetueradip...   \n",
              "3  Jade Cunningham                            malesuada@dignissim.com   \n",
              "4     Cedric Leach     felis.ullamcorper.viverra@egetmollislectus.net   \n",
              "\n",
              "        country  gender        age  annual Salary  credit card debt  \\\n",
              "0      Bulgaria       0  41.851720    62812.09301      11609.380910   \n",
              "1        Belize       0  40.870623    66646.89292       9572.957136   \n",
              "2       Algeria       1  43.152897    53798.55112      11160.355060   \n",
              "3  Cook Islands       1  58.271369    79370.03798      14426.164850   \n",
              "4        Brazil       1  57.313749    59729.15130       5358.712177   \n",
              "\n",
              "     net worth  car purchase amount  \n",
              "0  238961.2505          35321.45877  \n",
              "1  530973.9078          45115.52566  \n",
              "2  638467.1773          42925.70921  \n",
              "3  548599.0524          67422.36313  \n",
              "4  560304.0671          55915.46248  "
            ]
          },
          "execution_count": 2,
          "metadata": {},
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      ],
      "source": [
        "git_url = 'https://raw.githubusercontent.com/SyedHabib14/ANN-CSPP/main/car_purchasing.csv'\n",
        "df = pd.read_csv(git_url, encoding='latin-1')\n",
        "df.head()"
      ]
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          "data": {
            "text/plain": [
              "(500, 9)"
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        "df.shape"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 500 entries, 0 to 499\n",
            "Data columns (total 9 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   customer name        500 non-null    object \n",
            " 1   customer e-mail      500 non-null    object \n",
            " 2   country              500 non-null    object \n",
            " 3   gender               500 non-null    int64  \n",
            " 4   age                  500 non-null    float64\n",
            " 5   annual Salary        500 non-null    float64\n",
            " 6   credit card debt     500 non-null    float64\n",
            " 7   net worth            500 non-null    float64\n",
            " 8   car purchase amount  500 non-null    float64\n",
            "dtypes: float64(5), int64(1), object(3)\n",
            "memory usage: 35.3+ KB\n"
          ]
        }
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      "source": [
        "df.info()"
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              "      <th>gender</th>\n",
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              "      <th>count</th>\n",
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              "      <td>500.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>500.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.506000</td>\n",
              "      <td>46.241674</td>\n",
              "      <td>62127.239608</td>\n",
              "      <td>9607.645049</td>\n",
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              "      <th>std</th>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>20000.000000</td>\n",
              "      <td>100.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.949969</td>\n",
              "      <td>54391.977195</td>\n",
              "      <td>7397.515792</td>\n",
              "      <td>299824.195900</td>\n",
              "      <td>37629.896040</td>\n",
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              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>46.049901</td>\n",
              "      <td>62915.497035</td>\n",
              "      <td>9655.035568</td>\n",
              "      <td>426750.120650</td>\n",
              "      <td>43997.783390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>51.612263</td>\n",
              "      <td>70117.862005</td>\n",
              "      <td>11798.867487</td>\n",
              "      <td>557324.478725</td>\n",
              "      <td>51254.709517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>20000.000000</td>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>80000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7a8eda8d-a150-463e-bd66-3da9b7f3f608')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7a8eda8d-a150-463e-bd66-3da9b7f3f608 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7a8eda8d-a150-463e-bd66-3da9b7f3f608');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-69920cac-3689-4f38-8081-a3d4972f79ec\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-69920cac-3689-4f38-8081-a3d4972f79ec')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-69920cac-3689-4f38-8081-a3d4972f79ec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "           gender         age  annual Salary  credit card debt  \\\n",
              "count  500.000000  500.000000     500.000000        500.000000   \n",
              "mean     0.506000   46.241674   62127.239608       9607.645049   \n",
              "std      0.500465    7.978862   11703.378228       3489.187973   \n",
              "min      0.000000   20.000000   20000.000000        100.000000   \n",
              "25%      0.000000   40.949969   54391.977195       7397.515792   \n",
              "50%      1.000000   46.049901   62915.497035       9655.035568   \n",
              "75%      1.000000   51.612263   70117.862005      11798.867487   \n",
              "max      1.000000   70.000000  100000.000000      20000.000000   \n",
              "\n",
              "            net worth  car purchase amount  \n",
              "count      500.000000           500.000000  \n",
              "mean    431475.713625         44209.799218  \n",
              "std     173536.756340         10773.178744  \n",
              "min      20000.000000          9000.000000  \n",
              "25%     299824.195900         37629.896040  \n",
              "50%     426750.120650         43997.783390  \n",
              "75%     557324.478725         51254.709517  \n",
              "max    1000000.000000         80000.000000  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "K8D9P0itFCjp"
      },
      "outputs": [],
      "source": [
        "df[\"age\"] = df[\"age\"].astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "0icf66hwFTtN",
        "outputId": "c1471af4-d753-44d4-9cfd-5a32b1fce68b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "customer name          498\n",
              "customer e-mail        500\n",
              "country                211\n",
              "gender                   2\n",
              "age                     44\n",
              "annual Salary          500\n",
              "credit card debt       500\n",
              "net worth              500\n",
              "car purchase amount    500\n",
              "dtype: int64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "U1YjFMPSFYp6"
      },
      "outputs": [],
      "source": [
        "data = df.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "HzRwDy7jFm4A"
      },
      "outputs": [],
      "source": [
        "data.drop(columns = [\"customer name\" , \"customer e-mail\" , \"country\"], axis = 1 , inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "VBFm-sTxFxWf"
      },
      "outputs": [],
      "source": [
        "new_data =  [\"age\",\"annual Salary\",\"credit card debt\",\"net worth\",\"car purchase amount\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ArblmydxJZ34"
      },
      "source": [
        "# **Getting an Idea about data through Visualization.....**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rU8QisjkF3uG",
        "outputId": "a2e7400b-f470-4e99-c405-a9cb4cc8be1a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
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+eaumpKREQ4YM0ciRI/Xhhx9q0KBBGjRokLZs2fJd9gcAALAL0wj33nuvueqqq046HggETEJCgnn00UeteVVVVcbpdJo//elPxhhjtm3bZiSZ9evXWzVLly41DofD/OMf/zDGGPPss8+a5s2bm5qamqB1d+rUyfr8q1/9ymRlZQWtPy0tzfzmN7855e3x+XxGkvH5fKf8HQBnv8OHD5uMjAyTkZFhDh8+HOp2AJxhp3r8btSZnL/85S/q1auXfvnLX6pNmza67LLL9MILL1jjO3fulNfrVWZmpjUvLi5OaWlpKi0tlSSVlpYqPj5evXr1smoyMzMVFhamdevWWTVXX321oqKirBqPx6Py8nJ98803Vs3x66mvqV9PQ2pqauT3+4MmAABgT40KOX//+981a9YsdezYUcuXL9fo0aP129/+VnPmzJEkeb1eSZLb7Q76ntvttsa8Xq/atGkTNB4REaEWLVoE1TS0jOPXcbKa+vGGFBYWKi4uzpoSExMbs/kAAOAc0qiQEwgE1LNnTz3yyCO67LLLdPvtt+u2227T7Nmzv6/+zqgJEybI5/NZ065du0LdEgAA+J40KuS0bdtWKSkpQfO6dOmiiooKSVJCQoIkqbKyMqimsrLSGktISNDevXuDxo8dO6avv/46qKahZRy/jpPV1I83xOl0yuVyBU0AAMCeGhVyrrzySpWXlwfN++STT9ShQwdJUnJyshISElRcXGyN+/1+rVu3Tunp6ZKk9PR0VVVVqayszKpZuXKlAoGA0tLSrJo1a9bo6NGjVs2KFSvUqVMn60mu9PT0oPXU19SvBwAAnOcaczfz+++/byIiIszDDz9sduzYYebOnWuaNGliXn31Vatm6tSpJj4+3rzxxhvmo48+MgMHDjTJycnmyJEjVs11111nLrvsMrNu3Trz7rvvmo4dO5ohQ4ZY41VVVcbtdpthw4aZLVu2mPnz55smTZqY5557zqpZu3atiYiIMI899pjZvn27mTJliomMjDSbN28+5e3h6SrAnni6CrC3Uz1+NyrkGGPMm2++aX784x8bp9NpOnfubJ5//vmg8UAgYCZNmmTcbrdxOp3m2muvNeXl5UE1+/fvN0OGDDGxsbHG5XKZ4cOHmwMHDgTVbNq0yVx11VXG6XSaCy64wEydOvWEXhYuXGguueQSExUVZbp27WqWLFnSqG0h5AD2RMgB7O1Uj98OY4wJ7bmk0PH7/YqLi5PP5+P+HMBGjhw5ogEDBkiSli5dqpiYmBB3BOBMOtXjN++uAgAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAthQR6gZw+owxqq6uDnUbwFnn+P8u+G8EOFF0dLQcDkeo2/jeEXLOYdXV1RowYECo2wDOatdff32oWwDOOkuXLlVMTEyo2/jecbkKAADYEmdybOJgjyEyYfx1ApIkY6TAsX/+OSxCOg9OywP/jSNwTLEb/xTqNn5QjToqPvDAA/rd734XNK9Tp076+OOPJf3z8sldd92l+fPnq6amRh6PR88++6zcbrdVX1FRodGjR2vVqlWKjY1Vbm6uCgsLFRHx71beeecdFRQUaOvWrUpMTNTEiRN1yy23BK135syZevTRR+X1etW9e3c9/fTT6t27d2O33zZMWIQUHhnqNoCzSFSoGwDOKibUDYRAoy9Xde3aVV9++aU1vfvuu9ZYfn6+3nzzTS1atEirV6/Wnj17dMMNN1jjdXV1ysrKUm1trUpKSjRnzhwVFRVp8uTJVs3OnTuVlZWla665Rhs3btS4ceN06623avny5VbNggULVFBQoClTpmjDhg3q3r27PB6P9u7de7r7AQAA2EyjQ05ERIQSEhKsqVWrVpIkn8+nl156SdOnT1e/fv2Umpqql19+WSUlJXrvvfckSX/961+1bds2vfrqq+rRo4cGDBighx56SDNnzlRtba0kafbs2UpOTtbjjz+uLl26aMyYMbrxxhv1xBNPWD1Mnz5dt912m4YPH66UlBTNnj1bTZo00R/+8IczsU8AAIANNDrk7NixQ+3atdNFF12knJwcVVRUSJLKysp09OhRZWZmWrWdO3dWUlKSSktLJUmlpaXq1q1b0OUrj8cjv9+vrVu3WjXHL6O+pn4ZtbW1KisrC6oJCwtTZmamVXMyNTU18vv9QRMAALCnRoWctLQ0FRUVadmyZZo1a5Z27typvn376sCBA/J6vYqKilJ8fHzQd9xut7xeryTJ6/UGBZz68fqxb6vx+/06cuSI9u3bp7q6ugZr6pdxMoWFhYqLi7OmxMTExmw+AAA4hzTqxuPjf5Pl0ksvVVpamjp06KCFCxeeE8/bT5gwQQUFBdZnv99P0AEAwKa+0+/kxMfH65JLLtGnn36qhIQE1dbWqqqqKqimsrJSCQkJkqSEhARVVlaeMF4/9m01LpdLMTExatWqlcLDwxusqV/GyTidTrlcrqAJAADY03cKOQcPHtRnn32mtm3bKjU1VZGRkSouLrbGy8vLVVFRofT0dElSenq6Nm/eHPQU1IoVK+RyuZSSkmLVHL+M+pr6ZURFRSk1NTWoJhAIqLi42KoBAABoVMi5++67tXr1an3++ecqKSnR9ddfr/DwcA0ZMkRxcXEaOXKkCgoKtGrVKpWVlWn48OFKT09Xnz59JEn9+/dXSkqKhg0bpk2bNmn58uWaOHGi8vLy5HQ6JUmjRo3S3//+d40fP14ff/yxnn32WS1cuFD5+flWHwUFBXrhhRc0Z84cbd++XaNHj9ahQ4c0fPjwM7hrAADAuaxR9+Ts3r1bQ4YM0f79+9W6dWtdddVVeu+999S6dWtJ0hNPPKGwsDANHjw46McA64WHh2vx4sUaPXq00tPT1bRpU+Xm5urBBx+0apKTk7VkyRLl5+drxowZat++vV588UV5PB6rJjs7W1999ZUmT54sr9erHj16aNmyZSfcjAwAAM5fDmPM+fgjiJL+eeNxXFycfD7fOXl/zpEjR6ybwQ/0HMYvHgMATq7uqJpteEXSuf+CzlM9fvOCTgAAYEuEHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEuEHAAAYEsRoW4Ap88Y8+8PdUdD1wgA4Ox33HEi6PhhY4Scc1hNTY3152ab5oewEwDAuaSmpkZNmjQJdRvfOy5XAQAAW+JMzjnM6XRafz7Q/SYpPDKE3QAAzmp1R62z/scfP+yMkHMOczgc//4QHknIAQCckqDjh41xuQoAANgSIQcAANgSIQcAANgSIQcAANgSIQcAANgSIQcAANgSIQcAANgSIQcAANgSIQcAANgSIQcAANgSIQcAANgSIQcAANgSIQcAANjSdwo5U6dOlcPh0Lhx46x51dXVysvLU8uWLRUbG6vBgwersrIy6HsVFRXKyspSkyZN1KZNG91zzz06duxYUM0777yjnj17yul06uKLL1ZRUdEJ6585c6YuvPBCRUdHKy0tTe+///532RwAAGAjpx1y1q9fr+eee06XXnpp0Pz8/Hy9+eabWrRokVavXq09e/bohhtusMbr6uqUlZWl2tpalZSUaM6cOSoqKtLkyZOtmp07dyorK0vXXHONNm7cqHHjxunWW2/V8uXLrZoFCxaooKBAU6ZM0YYNG9S9e3d5PB7t3bv3dDcJAADYyGmFnIMHDyonJ0cvvPCCmjdvbs33+Xx66aWXNH36dPXr10+pqal6+eWXVVJSovfee0+S9Ne//lXbtm3Tq6++qh49emjAgAF66KGHNHPmTNXW1kqSZs+ereTkZD3++OPq0qWLxowZoxtvvFFPPPGEta7p06frtttu0/Dhw5WSkqLZs2erSZMm+sMf/nDSvmtqauT3+4MmAABgT6cVcvLy8pSVlaXMzMyg+WVlZTp69GjQ/M6dOyspKUmlpaWSpNLSUnXr1k1ut9uq8Xg88vv92rp1q1Xzn8v2eDzWMmpra1VWVhZUExYWpszMTKumIYWFhYqLi7OmxMTE09l8AABwDmh0yJk/f742bNigwsLCE8a8Xq+ioqIUHx8fNN/tdsvr9Vo1xwec+vH6sW+r8fv9OnLkiPbt26e6uroGa+qX0ZAJEybI5/NZ065du05towEAwDknojHFu3bt0tixY7VixQpFR0d/Xz19b5xOp5xOZ6jbAAAAP4BGnckpKyvT3r171bNnT0VERCgiIkKrV6/WU089pYiICLndbtXW1qqqqiroe5WVlUpISJAkJSQknPC0Vf3n/1bjcrkUExOjVq1aKTw8vMGa+mUAAIDzW6NCzrXXXqvNmzdr48aN1tSrVy/l5ORYf46MjFRxcbH1nfLyclVUVCg9PV2SlJ6ers2bNwc9BbVixQq5XC6lpKRYNccvo76mfhlRUVFKTU0NqgkEAiouLrZqAADA+a1Rl6uaNWumH//4x0HzmjZtqpYtW1rzR44cqYKCArVo0UIul0t33nmn0tPT1adPH0lS//79lZKSomHDhmnatGnyer2aOHGi8vLyrEtJo0aN0jPPPKPx48drxIgRWrlypRYuXKglS5ZY6y0oKFBubq569eql3r1768knn9ShQ4c0fPjw77RDAACAPTQq5JyKJ554QmFhYRo8eLBqamrk8Xj07LPPWuPh4eFavHixRo8erfT0dDVt2lS5ubl68MEHrZrk5GQtWbJE+fn5mjFjhtq3b68XX3xRHo/HqsnOztZXX32lyZMny+v1qkePHlq2bNkJNyMDAIDzk8MYY0LdRKj4/X7FxcXJ5/PJ5XKFup1GO3LkiAYMGCBJOtBzmBQeGeKOAABnrbqjarbhFUnS0qVLFRMTE+KGTt+pHr95dxUAALClM365CqHhCBzTeXtKDvhPxkiBf70PLyxCcjhC2w9wFnAEjv33Ipsh5NhE7MY/hboFAADOKlyuAgAAtsSZnHNYdHS0li5dGuo2gLNOdXW1rr/+eknSa6+9dk7+QjvwfTpf/psg5JzDHA7HOX13PPBDiI6O5r8T4DzF5SoAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLjQo5s2bN0qWXXiqXyyWXy6X09HQtXbrUGq+urlZeXp5atmyp2NhYDR48WJWVlUHLqKioUFZWlpo0aaI2bdronnvu0bFjx4Jq3nnnHfXs2VNOp1MXX3yxioqKTuhl5syZuvDCCxUdHa20tDS9//77jdkUAABgc40KOe3bt9fUqVNVVlamDz74QP369dPAgQO1detWSVJ+fr7efPNNLVq0SKtXr9aePXt0ww03WN+vq6tTVlaWamtrVVJSojlz5qioqEiTJ0+2anbu3KmsrCxdc8012rhxo8aNG6dbb71Vy5cvt2oWLFiggoICTZkyRRs2bFD37t3l8Xi0d+/e77o/AACAXZjvqHnz5ubFF180VVVVJjIy0ixatMga2759u5FkSktLjTHGvPXWWyYsLMx4vV6rZtasWcblcpmamhpjjDHjx483Xbt2DVpHdna28Xg81ufevXubvLw863NdXZ1p166dKSwsbFTvPp/PSDI+n69R3wNwdjt8+LDJyMgwGRkZ5vDhw6FuB8AZdqrH79O+J6eurk7z58/XoUOHlJ6errKyMh09elSZmZlWTefOnZWUlKTS0lJJUmlpqbp16ya3223VeDwe+f1+62xQaWlp0DLqa+qXUVtbq7KysqCasLAwZWZmWjUnU1NTI7/fHzQBAAB7anTI2bx5s2JjY+V0OjVq1Ci99tprSklJkdfrVVRUlOLj44Pq3W63vF6vJMnr9QYFnPrx+rFvq/H7/Tpy5Ij27dunurq6Bmvql3EyhYWFiouLs6bExMTGbj4AADhHNDrkdOrUSRs3btS6des0evRo5ebmatu2bd9Hb2fchAkT5PP5rGnXrl2hbgkAAHxPIhr7haioKF188cWSpNTUVK1fv14zZsxQdna2amtrVVVVFXQ2p7KyUgkJCZKkhISEE56Cqn/66via/3wiq7KyUi6XSzExMQoPD1d4eHiDNfXLOBmn0ymn09nYTQYAAOeg7/w7OYFAQDU1NUpNTVVkZKSKi4utsfLyclVUVCg9PV2SlJ6ers2bNwc9BbVixQq5XC6lpKRYNccvo76mfhlRUVFKTU0NqgkEAiouLrZqAAAAGnUmZ8KECRowYICSkpJ04MABzZs3T++8846WL1+uuLg4jRw5UgUFBWrRooVcLpfuvPNOpaenq0+fPpKk/v37KyUlRcOGDdO0adPk9Xo1ceJE5eXlWWdYRo0apWeeeUbjx4/XiBEjtHLlSi1cuFBLliyx+igoKFBubq569eql3r1768knn9ShQ4c0fPjwM7hrAADAuaxRIWfv3r26+eab9eWXXyouLk6XXnqpli9frp/+9KeSpCeeeEJhYWEaPHiwampq5PF49Oyzz1rfDw8P1+LFizV69Gilp6eradOmys3N1YMPPmjVJCcna8mSJcrPz9eMGTPUvn17vfjii/J4PFZNdna2vvrqK02ePFler1c9evTQsmXLTrgZGQAAnL8cxhgT6iZCxe/3Ky4uTj6fTy6XK9TtADhDjhw5ogEDBkiSli5dqpiYmBB3BOBMOtXjN++uAgAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAtkTIAQAAttSokFNYWKjLL79czZo1U5s2bTRo0CCVl5cH1VRXVysvL08tW7ZUbGysBg8erMrKyqCaiooKZWVlqUmTJmrTpo3uueceHTt2LKjmnXfeUc+ePeV0OnXxxRerqKjohH5mzpypCy+8UNHR0UpLS9P777/fmM0BAAA21qiQs3r1auXl5em9997TihUrdPToUfXv31+HDh2yavLz8/Xmm29q0aJFWr16tfbs2aMbbrjBGq+rq1NWVpZqa2tVUlKiOXPmqKioSJMnT7Zqdu7cqaysLF1zzTXauHGjxo0bp1tvvVXLly+3ahYsWKCCggJNmTJFGzZsUPfu3eXxeLR3797vsj8AAIBdmO9g7969RpJZvXq1McaYqqoqExkZaRYtWmTVbN++3UgypaWlxhhj3nrrLRMWFma8Xq9VM2vWLONyuUxNTY0xxpjx48ebrl27Bq0rOzvbeDwe63Pv3r1NXl6e9bmurs60a9fOFBYWnnL/Pp/PSDI+n68RWw3gbHf48GGTkZFhMjIyzOHDh0PdDoAz7FSP39/pnhyfzydJatGihSSprKxMR48eVWZmplXTuXNnJSUlqbS0VJJUWlqqbt26ye12WzUej0d+v19bt261ao5fRn1N/TJqa2tVVlYWVBMWFqbMzEyrpiE1NTXy+/1BEwAAsKfTDjmBQEDjxo3TlVdeqR//+MeSJK/Xq6ioKMXHxwfVut1ueb1eq+b4gFM/Xj/2bTV+v19HjhzRvn37VFdX12BN/TIaUlhYqLi4OGtKTExs/IYDAIBzwmmHnLy8PG3ZskXz588/k/18ryZMmCCfz2dNu3btCnVLAADgexJxOl8aM2aMFi9erDVr1qh9+/bW/ISEBNXW1qqqqirobE5lZaUSEhKsmv98Cqr+6avja/7ziazKykq5XC7FxMQoPDxc4eHhDdbUL6MhTqdTTqez8RsMAADOOY06k2OM0ZgxY/Taa69p5cqVSk5ODhpPTU1VZGSkiouLrXnl5eWqqKhQenq6JCk9PV2bN28OegpqxYoVcrlcSklJsWqOX0Z9Tf0yoqKilJqaGlQTCARUXFxs1QAAgPNbo87k5OXlad68eXrjjTfUrFkz6/6XuLg4xcTEKC4uTiNHjlRBQYFatGghl8ulO++8U+np6erTp48kqX///kpJSdGwYcM0bdo0eb1eTZw4UXl5edZZllGjRumZZ57R+PHjNWLECK1cuVILFy7UkiVLrF4KCgqUm5urXr16qXfv3nryySd16NAhDR8+/EztGwAAcC5rzCNbkhqcXn75ZavmyJEj5o477jDNmzc3TZo0Mddff7358ssvg5bz+eefmwEDBpiYmBjTqlUrc9ddd5mjR48G1axatcr06NHDREVFmYsuuihoHfWefvppk5SUZKKiokzv3r3Ne++915jN4RFywKZ4hBywt1M9fjuMMSZ0ESu0/H6/4uLi5PP55HK5Qt0OgDPkyJEjGjBggCRp6dKliomJCXFHAM6kUz1+8+4qAABgS4QcAABgS6f1CDmAYMYYVVdXh7oN/Mvxfxf8vZw9oqOj5XA4Qt0GziOEHOAMqK6utu4Bwdnl+uuvD3UL+Bfuj8IPjctVAADAljiTA5wB0dHRWrp0aajbwL8YY1RTUyPpn790ziWSs0N0dHSoW8B5hpADnAEOh4PT8GeZJk2ahLoFACHG5SoAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAAGBLhBwAtlRSUqLs7GyVlJSEuhUAIULIAWA71dXVmj59uiorKzV9+nRVV1eHuiUAIUDIAWA7c+fO1b59+yRJ+/bt07x580LcEYBQIOQAsJXdu3dr7ty5QfPmzp2r3bt3h6gjAKFCyAFgG8YYzZgxQ8aYoPmBQKDB+QDsjZADwDYqKiq0fv36E8KMMUbr169XRUVFiDoDEAqEHAC2kZiYKJfL1eCYy+VSYmLiD9wRgFAi5ACwjV27dsnv9zc45vf7tWvXrh+4IwChRMgBYBtJSUm6/PLL5XA4guY7HA717t1bSUlJIeoMQCgQcgDYhsPh0NixYxUWFvxPW3h4uMaOHXtC+AFgb4QcALbSvn17DR061Ao0DodDQ4cO1QUXXBDizgD80Ag5AGwnJydHLVu2lCS1atVKQ4cODXFHAEKBkAPAdqKjo1VQUCC32638/HxFR0eHuiUAIRAR6gYA4PtwxRVX6Iorrgh1GwBCiDM5AGyJt5ADIOQAsB3eQg5AOo2Qs2bNGv3iF79Qu3bt5HA49PrrrweNG2M0efJktW3bVjExMcrMzNSOHTuCar7++mvl5OTI5XIpPj5eI0eO1MGDB4NqPvroI/Xt21fR0dFKTEzUtGnTTuhl0aJF6ty5s6Kjo9WtWze99dZbjd0cADY0d+5c7d+/X5K0f/9+3kIOnKcaHXIOHTqk7t27a+bMmQ2OT5s2TU899ZRmz56tdevWqWnTpvJ4PEH/J5WTk6OtW7dqxYoVWrx4sdasWaPbb7/dGvf7/erfv786dOigsrIyPfroo3rggQf0/PPPWzUlJSUaMmSIRo4cqQ8//FCDBg3SoEGDtGXLlsZuEgAb2b17t+bNm2e9v8oYo3nz5vEWcuA85DDf4bW8DodDr732mgYNGiTpn/+YtGvXTnfddZfuvvtuSZLP55Pb7VZRUZFuuukmbd++XSkpKVq/fr169eolSVq2bJl+9rOfaffu3WrXrp1mzZql+++/X16vV1FRUZKk++67T6+//ro+/vhjSVJ2drYOHTqkxYsXW/306dNHPXr00OzZsxvst6amRjU1NdZnv9+vxMRE+Xy+k77vBsC5wxij8ePHa8OGDaqrq7Pmh4eHq2fPnpo2bRo/CAjYgN/vV1xc3H89fp/Re3J27twpr9erzMxMa15cXJzS0tJUWloqSSotLVV8fLwVcCQpMzNTYWFhWrdunVVz9dVXWwFHkjwej8rLy/XNN99YNcevp76mfj0NKSwsVFxcnDXxsj7AXurfQn58wJGkuro63kIOnIfOaMjxer2SJLfbHTTf7XZbY16vV23atAkaj4iIUIsWLYJqGlrG8es4WU39eEMmTJggn89nTbysD7CX+ndX/edrHcLCwnh3FXAeOq+ernI6nXK5XEETAPuof3fVf16FN8bw7irgPHRGQ05CQoIkqbKyMmh+ZWWlNZaQkKC9e/cGjR87dkxff/11UE1Dyzh+HSerqR8HgHoOh+OE4APA/s5oyElOTlZCQoKKi4uteX6/X+vWrVN6erokKT09XVVVVSorK7NqVq5cqUAgoLS0NKtmzZo1Onr0qFWzYsUKderUSc2bN7dqjl9PfU39egCcf4wxmjFjxgmXqxwOh2bMmEHQAc4zjQ45Bw8e1MaNG7Vx40ZJ/7zZeOPGjaqoqJDD4dC4ceP0v//7v/rLX/6izZs36+abb1a7du2sJ7C6dOmi6667Trfddpvef/99rV27VmPGjNFNN92kdu3aSZKGDh2qqKgojRw5Ulu3btWCBQs0Y8YMFRQUWH2MHTtWy5Yt0+OPP66PP/5YDzzwgD744AONGTPmu+8VAOckbjwGEMQ00qpVq4ykE6bc3FxjjDGBQMBMmjTJuN1u43Q6zbXXXmvKy8uDlrF//34zZMgQExsba1wulxk+fLg5cOBAUM2mTZvMVVddZZxOp7ngggvM1KlTT+hl4cKF5pJLLjFRUVGma9euZsmSJY3aFp/PZyQZn8/XuJ0A4KwUCATM3Xffbfr162cyMjKsqV+/fuaee+4xgUAg1C0COANO9fj9nX4n51x3qs/ZAzh37N69W7m5uUFncyIiIjRnzhxdcMEFIewMwJkSkt/JAYBQa9++vYYOHWo9SeVwODR06FACDnAeIuQAsJ2cnBy1bNlSktSqVSsNHTo0xB0BCAVCDgDbiY6OVkFBgdxut/Lz8xUdHR3qlgCEQESoGwCA78MVV1yhK664ItRtAAghzuQAAABbIuQAAABbIuQAAABbIuQAAABbIuQAAABbIuQAAABbIuQAAABbIuQAAABbIuQAAABbOq9/8bj+Bex+vz/EnQAAgFNVf9yuP46fzHkdcg4cOCBJSkxMDHEnAACgsQ4cOKC4uLiTjjvMf4tBNhYIBLRnzx41a9ZMDocj1O0AOIP8fr8SExO1a9cuuVyuULcD4AwyxujAgQNq166dwsJOfufNeR1yANiX3+9XXFycfD4fIQc4T3HjMQAAsCVCDgAAsCVCDgBbcjqdmjJlipxOZ6hbARAi3JMDAABsiTM5AADAlgg5AADAlgg5AADAlgg5AADAlgg5AGxn5syZuvDCCxUdHa20tDS9//77oW4JQAgQcgDYyoIFC1RQUKApU6Zow4YN6t69uzwej/bu3Rvq1gD8wHiEHICtpKWl6fLLL9czzzwj6Z/vqEtMTNSdd96p++67L8TdAfghcSYHgG3U1taqrKxMmZmZ1rywsDBlZmaqtLQ0hJ0BCAVCDgDb2Ldvn+rq6uR2u4Pmu91ueb3eEHUFIFQIOQAAwJYIOQBso1WrVgoPD1dlZWXQ/MrKSiUkJISoKwChQsgBYBtRUVFKTU1VcXGxNS8QCKi4uFjp6ekh7AxAKESEugEAOJMKCgqUm5urXr16qXfv3nryySd16NAhDR8+PNStAfiBEXIA2Ep2dra++uorTZ48WV6vVz169NCyZctOuBkZgP3xOzkAAMCWuCcHAADYEiEHAADYEiEHAADYEiEHAADYEiEHAADYEiEHAADYEiEHAADYEiEHAADYEiEHwFnrlltu0aBBg0LdBoBzFCEHAM4jn3/+uRwOhzZu3BjqVoDvHSEHgCSptrbW1usDcP4h5ADnqEAgoGnTpuniiy+W0+lUUlKSHn74YWv83nvv1SWXXKImTZrooosu0qRJk3T06FFr/IEHHlCPHj304osvKjk5WdHR0Q2up6ioSPHx8Xr99dfVsWNHRUdHy+PxaNeuXVZNQ5eVxo0bp5/85CfW55/85CcaM2aMxo0bp1atWsnj8UiStm7dqp///OdyuVxq1qyZ+vbtq88++yxoWY899pjatm2rli1bKi8vL2g7XnnlFfXq1UvNmjVTQkKChg4dqr1791rj33zzjXJyctS6dWvFxMSoY8eOevnll63xXbt26Ve/+pXi4+PVokULDRw4UJ9//vlJ93tdXZ1Gjhyp5ORkxcTEqFOnTpoxY0ZQTf3+eOSRR+R2uxUfH68HH3xQx44d0z333KMWLVqoffv2QX1I0ubNm9WvXz/FxMSoZcuWuv3223Xw4MGgfThu3Lig7wwaNEi33HKL9fnCCy/UI488ohEjRqhZs2ZKSkrS888/b40nJydLki677DI5HI6gvyPAbgg5wDlqwoQJmjp1qiZNmqRt27Zp3rx5QW/abtasmYqKirRt2zbNmDFDL7zwgp544omgZXz66af6v//7P/35z3/+1ssXhw8f1sMPP6w//vGPWrt2raqqqnTTTTc1uuc5c+YoKipKa9eu1ezZs/WPf/xDV199tZxOp1auXKmysjKNGDFCx44ds76zatUqffbZZ1q1apXmzJmjoqIiFRUVWeNHjx7VQw89pE2bNun111/X559/HnTQr98/S5cu1fbt2zVr1iy1atXK+q7H41GzZs30t7/9TWvXrlVsbKyuu+66k55pCgQCat++vRYtWqRt27Zp8uTJ+n//7/9p4cKFQXUrV67Unj17tGbNGk2fPl1TpkzRz3/+czVv3lzr1q3TqFGj9Jvf/Ea7d++WJB06dEgej0fNmzfX+vXrtWjRIr399tsaM2ZMo/fz448/rl69eunDDz/UHXfcodGjR6u8vFyS9P7770uS3n77bX355Zf685//3OjlA+cMA+Cc4/f7jdPpNC+88MIpf+fRRx81qamp1ucpU6aYyMhIs3fv3m/93ssvv2wkmffee8+at337diPJrFu3zhhjTG5urhk4cGDQ98aOHWsyMjKszxkZGeayyy4LqpkwYYJJTk42tbW1Da47NzfXdOjQwRw7dsya98tf/tJkZ2eftN/169cbSebAgQPGGGN+8YtfmOHDhzdY+8orr5hOnTqZQCBgzaupqTExMTFm+fLlJ13Hf8rLyzODBw8+oe+6ujprXqdOnUzfvn2tz8eOHTNNmzY1f/rTn4wxxjz//POmefPm5uDBg1bNkiVLTFhYmPF6vcaYf+7DsWPHBq174MCBJjc31/rcoUMH8+tf/9r6HAgETJs2bcysWbOMMcbs3LnTSDIffvjhKW8fcK7iTA5wDtq+fbtqamp07bXXnrRmwYIFuvLKK5WQkKDY2FhNnDhRFRUVQTUdOnRQ69at/+v6IiIidPnll1ufO3furPj4eG3fvr1RfaempgZ93rhxo/r27avIyMiTfqdr164KDw+3Prdt2zboclRZWZl+8YtfKCkpSc2aNVNGRoYkWds6evRozZ8/Xz169ND48eNVUlJifXfTpk369NNP1axZM8XGxio2NlYtWrRQdXX1CZfMjjdz5kylpqaqdevWio2N1fPPP3/Cvu3atavCwv79T6zb7Va3bt2sz+Hh4WrZsqW1Ldu3b1f37t3VtGlTq+bKK69UIBCwzsKcqksvvdT6s8PhUEJCQtA+A84XhBzgHBQTE/Ot46WlpcrJydHPfvYzLV68WB9++KHuv//+Ey7BHH9A/S7CwsJkjAmad/x9Mydb33/bDkknBCCHw6FAICDp35d4XC6X5s6dq/Xr1+u1116T9O8bmwcMGKAvvvhC+fn52rNnj6699lrdfffdkqSDBw8qNTVVGzduDJo++eQTDR06tMF+5s+fr7vvvlsjR47UX//6V23cuFHDhw8/Yd821Pe3bcupONX9/F3XA9gFIQc4B3Xs2FExMTEqLi5ucLykpEQdOnTQ/fffr169eqljx4764osvTnt9x44d0wcffGB9Li8vV1VVlbp06SJJat26tb788sug75zKI8qXXnqp/va3vzV4oD4VH3/8sfbv36+pU6eqb9++6ty5c4NnLFq3bq3c3Fy9+uqrevLJJ60bcXv27KkdO3aoTZs2uvjii4OmuLi4Bte5du1aXXHFFbrjjjt02WWX6eKLL/7Wsz6nqkuXLtq0aZMOHToUtK6wsDB16tTJ2o7j93NdXZ22bNnSqPVERUVZ3wXsjpADnIOio6N17733avz48frjH/+ozz77TO+9955eeuklSf8MQRUVFZo/f74+++wzPfXUU9YZjtMRGRmpO++8U+vWrVNZWZluueUW9enTR71795Yk9evXTx988IH++Mc/aseOHZoyZcopHXzHjBkjv9+vm266SR988IF27NihV1555ZQvzyQlJSkqKkpPP/20/v73v+svf/mLHnrooaCayZMn64033tCnn36qrVu3avHixVY4y8nJUatWrTRw4ED97W9/086dO/XOO+/ot7/9rXVD8H/q2LGjPvjgAy1fvlyffPKJJk2apPXr159Sv98mJydH0dHRys3N1ZYtW7Rq1SrdeeedGjZsmHVDeb9+/bRkyRItWbJEH3/8sUaPHq2qqqpGradNmzaKiYnRsmXLVFlZKZ/P9517B85WhBzgHDVp0iTdddddmjx5srp06aLs7GzrLMb//M//KD8/X2PGjFGPHj1UUlKiSZMmnfa6mjRponvvvVdDhw7VlVdeqdjYWC1YsMAa93g8mjRpksaPH6/LL79cBw4c0M033/xfl9uyZUutXLlSBw8eVEZGhlJTU/XCCy986z06x2vdurWKioq0aNEipaSkaOrUqXrssceCaqKiojRhwgRdeumluvrqqxUeHq758+db27VmzRolJSXphhtuUJcuXTRy5EhVV1fL5XI1uM7f/OY3uuGGG5Sdna20tDTt379fd9xxxyn1+22aNGmi5cuX6+uvv9bll1+uG2+8Uddee62eeeYZq2bEiBHKzc3VzTffrIyMDF100UW65pprGrWeiIgIPfXUU3ruuefUrl07DRw48Dv3DpytHOY/L/ACwHGKioo0bty4Rp8xAIBQ40wOAACwJUIOAACwJS5XAQAAW+JMDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsCVCDgAAsKX/D00s0Qpg6Qi8AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "for i in new_data:\n",
        "    sns.boxplot(df[i])\n",
        "    plt.xlabel(i)\n",
        "    plt.show()\n",
        "    print()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "vyPA0plZF_S1",
        "outputId": "ccdc358a-ba68-4d83-d54a-cf55d5287cf3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.scatterplot(data = data , x = \"net worth\" , y = \"car purchase amount\" , hue = \"net worth\" , palette = \"magma\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IaXySHs_HJLj",
        "outputId": "948b7703-bb4f-46b3-d2fd-5d85e4b6d17c"
      },
      "outputs": [
        {
          "data": {
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        document.querySelector('#df-4e3c1f53-2df5-4c9a-8a60-64b84a9b453d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   gender  age  annual Salary  credit card debt    net worth  \\\n",
              "0       0   41    62812.09301      11609.380910  238961.2505   \n",
              "1       0   40    66646.89292       9572.957136  530973.9078   \n",
              "2       1   43    53798.55112      11160.355060  638467.1773   \n",
              "3       1   58    79370.03798      14426.164850  548599.0524   \n",
              "4       1   57    59729.15130       5358.712177  560304.0671   \n",
              "\n",
              "   car purchase amount  \n",
              "0          35321.45877  \n",
              "1          45115.52566  \n",
              "2          42925.70921  \n",
              "3          67422.36313  \n",
              "4          55915.46248  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eijK29ILJQ9X"
      },
      "source": [
        "# **Splitting & Transforming....**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "GMSeOfToGspe"
      },
      "outputs": [],
      "source": [
        "X = data.drop(columns = [\"car purchase amount\"] , axis = 1)\n",
        "Y = data[\"car purchase amount\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "Q13ZpdKtHCVQ"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc = StandardScaler()\n",
        "\n",
        "X = sc.fit_transform(X)\n",
        "Y = sc.fit_transform(Y.values.reshape(-1,1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "0tBFEBywHofg",
        "outputId": "2843b3ff-cefe-499a-da78-3dd402b9f6c4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[-1.01207287 -0.59819619  0.05857619  0.57427133 -1.11046945]\n",
            " [-1.01207287 -0.72349912  0.38657041 -0.0099515   0.57392937]\n",
            " [ 0.98807114 -0.34759033 -0.71236095  0.4454518   1.19397625]\n",
            " ...\n",
            " [ 0.98807114  0.90543897  0.57832106  0.28802326  1.92114437]\n",
            " [ 0.98807114  1.65725655 -1.05333541  1.26384749 -0.54019026]\n",
            " [ 0.98807114  0.02831846 -0.06470949 -0.06205457  0.18153098]]\n"
          ]
        }
      ],
      "source": [
        "print(X)\n",
        "# shows transformed features of input"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "hnAHIwwYHtf1",
        "outputId": "9d59ad86-8c57-468f-f5ab-dba77b08bcbd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[-0.82586972]\n",
            "[0.08415655]\n",
            "[-0.11931261]\n",
            "[2.15682037]\n",
            "[1.08764431]\n",
            "[1.15236364]\n",
            "[-1.42013803]\n",
            "[0.29967139]\n",
            "[0.35343556]\n",
            "[-0.55938203]\n",
            "[1.37847633]\n",
            "[-0.17849074]\n"
          ]
        }
      ],
      "source": [
        "for i in range (12):\n",
        "  print(Y[i])\n",
        "# shows scaled output features upto 12"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "qrkiBvPvH_TH",
        "outputId": "b009c7ae-158c-46d4-f7d8-e1f7ecb0943c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(375, 5)\n",
            "(125, 5)\n",
            "(375, 1)\n",
            "(125, 1)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train , X_test , Y_train , Y_test = train_test_split(X , Y , test_size = 0.25 , random_state = 0)\n",
        "\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(Y_train.shape)\n",
        "print(Y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cuQ-s_TjJBmw"
      },
      "source": [
        "# **Finally, we gonna train the model <3**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "VeULbGfNI3oL"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import ReLU"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "2L4yTvIsJOFV"
      },
      "outputs": [],
      "source": [
        "classifier = Sequential()\n",
        "\n",
        "\n",
        "classifier.add(Dense(5 , activation = \"ReLU\"))\n",
        "\n",
        "classifier.add(Dense(units = 4 , activation = \"ReLU\"))\n",
        "\n",
        "classifier.add(Dense(units = 2 , activation = \"ReLU\"))\n",
        "\n",
        "classifier.add(Dense(1 , activation = \"linear\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "kA-Hsqu5Ju-Z"
      },
      "outputs": [],
      "source": [
        "classifier.compile(optimizer = \"adam\" , loss = \"mean_squared_error\" , metrics = [\"mean_absolute_error\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "2lrnFxdAJx3S"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "early = EarlyStopping(\n",
        "    monitor = \"val_mean_absolute_error\",\n",
        "    min_delta = 0.001,\n",
        "    patience = 10,\n",
        "    verbose = True,\n",
        "    mode = \"auto\",\n",
        "    restore_best_weights = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HTCh0DZBKEly",
        "outputId": "ee3fb8e6-19ad-4b0f-a58f-dea1d1073791"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/200\n",
            "29/29 [==============================] - 1s 17ms/step - loss: 0.9933 - mean_absolute_error: 0.7558 - val_loss: 1.0075 - val_mean_absolute_error: 0.8110\n",
            "Epoch 2/200\n",
            "29/29 [==============================] - 0s 6ms/step - loss: 0.9520 - mean_absolute_error: 0.7404 - val_loss: 0.9686 - val_mean_absolute_error: 0.7939\n",
            "Epoch 3/200\n",
            "29/29 [==============================] - 0s 5ms/step - loss: 0.9166 - mean_absolute_error: 0.7276 - val_loss: 0.9341 - val_mean_absolute_error: 0.7772\n",
            "Epoch 4/200\n",
            "29/29 [==============================] - 0s 5ms/step - loss: 0.8793 - mean_absolute_error: 0.7132 - val_loss: 0.8999 - val_mean_absolute_error: 0.7605\n",
            "Epoch 5/200\n",
            "29/29 [==============================] - 0s 5ms/step - loss: 0.8412 - mean_absolute_error: 0.6982 - val_loss: 0.8650 - val_mean_absolute_error: 0.7421\n",
            "Epoch 6/200\n",
            "29/29 [==============================] - 0s 5ms/step - loss: 0.7996 - mean_absolute_error: 0.6803 - val_loss: 0.8266 - val_mean_absolute_error: 0.7203\n",
            "Epoch 7/200\n",
            "29/29 [==============================] - 0s 4ms/step - loss: 0.7518 - mean_absolute_error: 0.6585 - val_loss: 0.7867 - val_mean_absolute_error: 0.6971\n",
            "Epoch 8/200\n",
            "29/29 [==============================] - 0s 8ms/step - loss: 0.7017 - mean_absolute_error: 0.6337 - val_loss: 0.7431 - val_mean_absolute_error: 0.6711\n",
            "Epoch 9/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.6465 - mean_absolute_error: 0.6043 - val_loss: 0.7009 - val_mean_absolute_error: 0.6442\n",
            "Epoch 10/200\n",
            "29/29 [==============================] - 0s 10ms/step - loss: 0.5904 - mean_absolute_error: 0.5730 - val_loss: 0.6591 - val_mean_absolute_error: 0.6176\n",
            "Epoch 11/200\n",
            "29/29 [==============================] - 0s 11ms/step - loss: 0.5359 - mean_absolute_error: 0.5386 - val_loss: 0.6203 - val_mean_absolute_error: 0.5919\n",
            "Epoch 12/200\n",
            "29/29 [==============================] - 0s 11ms/step - loss: 0.4869 - mean_absolute_error: 0.5039 - val_loss: 0.5853 - val_mean_absolute_error: 0.5641\n",
            "Epoch 13/200\n",
            "29/29 [==============================] - 0s 11ms/step - loss: 0.4431 - mean_absolute_error: 0.4675 - val_loss: 0.5542 - val_mean_absolute_error: 0.5363\n",
            "Epoch 14/200\n",
            "29/29 [==============================] - 0s 9ms/step - loss: 0.4069 - mean_absolute_error: 0.4363 - val_loss: 0.5222 - val_mean_absolute_error: 0.5101\n",
            "Epoch 15/200\n",
            "29/29 [==============================] - 0s 11ms/step - loss: 0.3782 - mean_absolute_error: 0.4047 - val_loss: 0.4964 - val_mean_absolute_error: 0.4866\n",
            "Epoch 16/200\n",
            "29/29 [==============================] - 0s 7ms/step - loss: 0.3529 - mean_absolute_error: 0.3770 - val_loss: 0.4700 - val_mean_absolute_error: 0.4641\n",
            "Epoch 17/200\n",
            "29/29 [==============================] - 0s 11ms/step - loss: 0.3322 - mean_absolute_error: 0.3559 - val_loss: 0.4433 - val_mean_absolute_error: 0.4432\n",
            "Epoch 18/200\n",
            "29/29 [==============================] - 0s 9ms/step - loss: 0.3137 - mean_absolute_error: 0.3384 - val_loss: 0.4198 - val_mean_absolute_error: 0.4230\n",
            "Epoch 19/200\n",
            "29/29 [==============================] - 0s 10ms/step - loss: 0.2981 - mean_absolute_error: 0.3219 - val_loss: 0.4011 - val_mean_absolute_error: 0.4061\n",
            "Epoch 20/200\n",
            "29/29 [==============================] - 0s 5ms/step - loss: 0.2848 - mean_absolute_error: 0.3076 - val_loss: 0.3837 - val_mean_absolute_error: 0.3927\n",
            "Epoch 21/200\n",
            "29/29 [==============================] - 0s 8ms/step - loss: 0.2725 - mean_absolute_error: 0.2959 - val_loss: 0.3664 - val_mean_absolute_error: 0.3787\n",
            "Epoch 22/200\n",
            "29/29 [==============================] - 0s 9ms/step - loss: 0.2607 - mean_absolute_error: 0.2876 - val_loss: 0.3487 - val_mean_absolute_error: 0.3678\n",
            "Epoch 23/200\n",
            "29/29 [==============================] - 0s 6ms/step - loss: 0.2495 - mean_absolute_error: 0.2808 - val_loss: 0.3349 - val_mean_absolute_error: 0.3571\n",
            "Epoch 24/200\n",
            "29/29 [==============================] - 0s 6ms/step - loss: 0.2397 - mean_absolute_error: 0.2753 - val_loss: 0.3193 - val_mean_absolute_error: 0.3454\n",
            "Epoch 25/200\n",
            "29/29 [==============================] - 0s 9ms/step - loss: 0.2299 - mean_absolute_error: 0.2706 - val_loss: 0.3041 - val_mean_absolute_error: 0.3337\n",
            "Epoch 26/200\n",
            "29/29 [==============================] - 0s 8ms/step - loss: 0.2200 - mean_absolute_error: 0.2642 - val_loss: 0.2904 - val_mean_absolute_error: 0.3231\n",
            "Epoch 27/200\n",
            "29/29 [==============================] - 0s 11ms/step - loss: 0.2106 - mean_absolute_error: 0.2607 - val_loss: 0.2753 - val_mean_absolute_error: 0.3138\n",
            "Epoch 28/200\n",
            "29/29 [==============================] - 0s 10ms/step - loss: 0.2007 - mean_absolute_error: 0.2543 - val_loss: 0.2621 - val_mean_absolute_error: 0.3042\n",
            "Epoch 29/200\n",
            "29/29 [==============================] - 0s 12ms/step - loss: 0.1922 - mean_absolute_error: 0.2494 - val_loss: 0.2485 - val_mean_absolute_error: 0.2972\n",
            "Epoch 30/200\n",
            "29/29 [==============================] - 0s 10ms/step - loss: 0.1841 - mean_absolute_error: 0.2499 - val_loss: 0.2337 - val_mean_absolute_error: 0.2869\n",
            "Epoch 31/200\n",
            "29/29 [==============================] - 0s 7ms/step - loss: 0.1754 - mean_absolute_error: 0.2432 - val_loss: 0.2227 - val_mean_absolute_error: 0.2787\n",
            "Epoch 32/200\n",
            "29/29 [==============================] - 0s 5ms/step - loss: 0.1686 - mean_absolute_error: 0.2360 - val_loss: 0.2135 - val_mean_absolute_error: 0.2719\n",
            "Epoch 33/200\n",
            "29/29 [==============================] - 0s 7ms/step - loss: 0.1623 - mean_absolute_error: 0.2359 - val_loss: 0.2017 - val_mean_absolute_error: 0.2644\n",
            "Epoch 34/200\n",
            "29/29 [==============================] - 0s 8ms/step - loss: 0.1557 - mean_absolute_error: 0.2304 - val_loss: 0.1930 - val_mean_absolute_error: 0.2580\n",
            "Epoch 35/200\n",
            "29/29 [==============================] - 0s 8ms/step - loss: 0.1498 - mean_absolute_error: 0.2258 - val_loss: 0.1838 - val_mean_absolute_error: 0.2510\n",
            "Epoch 36/200\n",
            "29/29 [==============================] - 0s 6ms/step - loss: 0.1438 - mean_absolute_error: 0.2225 - val_loss: 0.1758 - val_mean_absolute_error: 0.2435\n",
            "Epoch 37/200\n",
            "29/29 [==============================] - 0s 6ms/step - loss: 0.1379 - mean_absolute_error: 0.2170 - val_loss: 0.1676 - val_mean_absolute_error: 0.2374\n",
            "Epoch 38/200\n",
            "29/29 [==============================] - 0s 8ms/step - loss: 0.1321 - mean_absolute_error: 0.2083 - val_loss: 0.1615 - val_mean_absolute_error: 0.2309\n",
            "Epoch 39/200\n",
            "29/29 [==============================] - 0s 6ms/step - loss: 0.1269 - mean_absolute_error: 0.2034 - val_loss: 0.1538 - val_mean_absolute_error: 0.2259\n",
            "Epoch 40/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.1213 - mean_absolute_error: 0.1972 - val_loss: 0.1470 - val_mean_absolute_error: 0.2195\n",
            "Epoch 41/200\n",
            "29/29 [==============================] - 0s 7ms/step - loss: 0.1161 - mean_absolute_error: 0.1900 - val_loss: 0.1407 - val_mean_absolute_error: 0.2145\n",
            "Epoch 42/200\n",
            "29/29 [==============================] - 0s 6ms/step - loss: 0.1119 - mean_absolute_error: 0.1898 - val_loss: 0.1340 - val_mean_absolute_error: 0.2077\n",
            "Epoch 43/200\n",
            "29/29 [==============================] - 0s 5ms/step - loss: 0.1063 - mean_absolute_error: 0.1796 - val_loss: 0.1280 - val_mean_absolute_error: 0.2019\n",
            "Epoch 44/200\n",
            "29/29 [==============================] - 0s 6ms/step - loss: 0.1016 - mean_absolute_error: 0.1766 - val_loss: 0.1214 - val_mean_absolute_error: 0.1962\n",
            "Epoch 45/200\n",
            "29/29 [==============================] - 0s 7ms/step - loss: 0.0974 - mean_absolute_error: 0.1699 - val_loss: 0.1166 - val_mean_absolute_error: 0.1907\n",
            "Epoch 46/200\n",
            "29/29 [==============================] - 0s 7ms/step - loss: 0.0928 - mean_absolute_error: 0.1681 - val_loss: 0.1098 - val_mean_absolute_error: 0.1849\n",
            "Epoch 47/200\n",
            "29/29 [==============================] - 0s 7ms/step - loss: 0.0882 - mean_absolute_error: 0.1623 - val_loss: 0.1040 - val_mean_absolute_error: 0.1775\n",
            "Epoch 48/200\n",
            "29/29 [==============================] - 0s 8ms/step - loss: 0.0839 - mean_absolute_error: 0.1566 - val_loss: 0.0984 - val_mean_absolute_error: 0.1730\n",
            "Epoch 49/200\n",
            "29/29 [==============================] - 0s 8ms/step - loss: 0.0799 - mean_absolute_error: 0.1524 - val_loss: 0.0940 - val_mean_absolute_error: 0.1661\n",
            "Epoch 50/200\n",
            "29/29 [==============================] - 0s 9ms/step - loss: 0.0763 - mean_absolute_error: 0.1473 - val_loss: 0.0887 - val_mean_absolute_error: 0.1601\n",
            "Epoch 51/200\n",
            "29/29 [==============================] - 0s 10ms/step - loss: 0.0722 - mean_absolute_error: 0.1405 - val_loss: 0.0846 - val_mean_absolute_error: 0.1562\n",
            "Epoch 52/200\n",
            "29/29 [==============================] - 0s 7ms/step - loss: 0.0673 - mean_absolute_error: 0.1396 - val_loss: 0.0766 - val_mean_absolute_error: 0.1486\n",
            "Epoch 53/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0637 - mean_absolute_error: 0.1341 - val_loss: 0.0729 - val_mean_absolute_error: 0.1457\n",
            "Epoch 54/200\n",
            "29/29 [==============================] - 0s 7ms/step - loss: 0.0603 - mean_absolute_error: 0.1255 - val_loss: 0.0693 - val_mean_absolute_error: 0.1401\n",
            "Epoch 55/200\n",
            "29/29 [==============================] - 0s 7ms/step - loss: 0.0572 - mean_absolute_error: 0.1202 - val_loss: 0.0651 - val_mean_absolute_error: 0.1373\n",
            "Epoch 56/200\n",
            "29/29 [==============================] - 0s 7ms/step - loss: 0.0543 - mean_absolute_error: 0.1160 - val_loss: 0.0620 - val_mean_absolute_error: 0.1354\n",
            "Epoch 57/200\n",
            "29/29 [==============================] - 0s 9ms/step - loss: 0.0509 - mean_absolute_error: 0.1147 - val_loss: 0.0572 - val_mean_absolute_error: 0.1309\n",
            "Epoch 58/200\n",
            "29/29 [==============================] - 0s 9ms/step - loss: 0.0479 - mean_absolute_error: 0.1108 - val_loss: 0.0535 - val_mean_absolute_error: 0.1299\n",
            "Epoch 59/200\n",
            "29/29 [==============================] - 0s 11ms/step - loss: 0.0452 - mean_absolute_error: 0.1062 - val_loss: 0.0501 - val_mean_absolute_error: 0.1225\n",
            "Epoch 60/200\n",
            "29/29 [==============================] - 0s 10ms/step - loss: 0.0427 - mean_absolute_error: 0.1015 - val_loss: 0.0470 - val_mean_absolute_error: 0.1211\n",
            "Epoch 61/200\n",
            "29/29 [==============================] - 0s 9ms/step - loss: 0.0401 - mean_absolute_error: 0.0964 - val_loss: 0.0443 - val_mean_absolute_error: 0.1185\n",
            "Epoch 62/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0378 - mean_absolute_error: 0.0948 - val_loss: 0.0416 - val_mean_absolute_error: 0.1177\n",
            "Epoch 63/200\n",
            "29/29 [==============================] - 0s 12ms/step - loss: 0.0357 - mean_absolute_error: 0.0921 - val_loss: 0.0393 - val_mean_absolute_error: 0.1134\n",
            "Epoch 64/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0334 - mean_absolute_error: 0.0885 - val_loss: 0.0368 - val_mean_absolute_error: 0.1119\n",
            "Epoch 65/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0315 - mean_absolute_error: 0.0874 - val_loss: 0.0352 - val_mean_absolute_error: 0.1109\n",
            "Epoch 66/200\n",
            "29/29 [==============================] - 0s 16ms/step - loss: 0.0299 - mean_absolute_error: 0.0853 - val_loss: 0.0324 - val_mean_absolute_error: 0.1075\n",
            "Epoch 67/200\n",
            "29/29 [==============================] - 1s 20ms/step - loss: 0.0280 - mean_absolute_error: 0.0833 - val_loss: 0.0304 - val_mean_absolute_error: 0.1064\n",
            "Epoch 68/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0264 - mean_absolute_error: 0.0792 - val_loss: 0.0285 - val_mean_absolute_error: 0.1033\n",
            "Epoch 69/200\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0251 - mean_absolute_error: 0.0783 - val_loss: 0.0268 - val_mean_absolute_error: 0.1010\n",
            "Epoch 70/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0232 - mean_absolute_error: 0.0736 - val_loss: 0.0248 - val_mean_absolute_error: 0.0973\n",
            "Epoch 71/200\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0220 - mean_absolute_error: 0.0740 - val_loss: 0.0232 - val_mean_absolute_error: 0.0941\n",
            "Epoch 72/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0204 - mean_absolute_error: 0.0706 - val_loss: 0.0215 - val_mean_absolute_error: 0.0899\n",
            "Epoch 73/200\n",
            "29/29 [==============================] - 0s 12ms/step - loss: 0.0190 - mean_absolute_error: 0.0676 - val_loss: 0.0200 - val_mean_absolute_error: 0.0871\n",
            "Epoch 74/200\n",
            "29/29 [==============================] - 0s 6ms/step - loss: 0.0178 - mean_absolute_error: 0.0692 - val_loss: 0.0180 - val_mean_absolute_error: 0.0843\n",
            "Epoch 75/200\n",
            "29/29 [==============================] - 0s 6ms/step - loss: 0.0161 - mean_absolute_error: 0.0672 - val_loss: 0.0166 - val_mean_absolute_error: 0.0820\n",
            "Epoch 76/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0148 - mean_absolute_error: 0.0623 - val_loss: 0.0161 - val_mean_absolute_error: 0.0805\n",
            "Epoch 77/200\n",
            "29/29 [==============================] - 0s 11ms/step - loss: 0.0140 - mean_absolute_error: 0.0616 - val_loss: 0.0150 - val_mean_absolute_error: 0.0793\n",
            "Epoch 78/200\n",
            "29/29 [==============================] - 0s 7ms/step - loss: 0.0129 - mean_absolute_error: 0.0582 - val_loss: 0.0135 - val_mean_absolute_error: 0.0721\n",
            "Epoch 79/200\n",
            "29/29 [==============================] - 0s 6ms/step - loss: 0.0119 - mean_absolute_error: 0.0556 - val_loss: 0.0129 - val_mean_absolute_error: 0.0714\n",
            "Epoch 80/200\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0113 - mean_absolute_error: 0.0561 - val_loss: 0.0120 - val_mean_absolute_error: 0.0676\n",
            "Epoch 81/200\n",
            "29/29 [==============================] - 0s 5ms/step - loss: 0.0108 - mean_absolute_error: 0.0558 - val_loss: 0.0112 - val_mean_absolute_error: 0.0659\n",
            "Epoch 82/200\n",
            "29/29 [==============================] - 0s 5ms/step - loss: 0.0096 - mean_absolute_error: 0.0504 - val_loss: 0.0105 - val_mean_absolute_error: 0.0643\n",
            "Epoch 83/200\n",
            "29/29 [==============================] - 0s 5ms/step - loss: 0.0091 - mean_absolute_error: 0.0480 - val_loss: 0.0099 - val_mean_absolute_error: 0.0610\n",
            "Epoch 84/200\n",
            "29/29 [==============================] - 0s 5ms/step - loss: 0.0088 - mean_absolute_error: 0.0493 - val_loss: 0.0093 - val_mean_absolute_error: 0.0597\n",
            "Epoch 85/200\n",
            "29/29 [==============================] - 0s 7ms/step - loss: 0.0079 - mean_absolute_error: 0.0464 - val_loss: 0.0088 - val_mean_absolute_error: 0.0577\n",
            "Epoch 86/200\n",
            "29/29 [==============================] - 0s 7ms/step - loss: 0.0075 - mean_absolute_error: 0.0465 - val_loss: 0.0085 - val_mean_absolute_error: 0.0563\n",
            "Epoch 87/200\n",
            "29/29 [==============================] - 0s 6ms/step - loss: 0.0071 - mean_absolute_error: 0.0434 - val_loss: 0.0080 - val_mean_absolute_error: 0.0541\n",
            "Epoch 88/200\n",
            "29/29 [==============================] - 0s 6ms/step - loss: 0.0067 - mean_absolute_error: 0.0435 - val_loss: 0.0078 - val_mean_absolute_error: 0.0544\n",
            "Epoch 89/200\n",
            "29/29 [==============================] - 0s 10ms/step - loss: 0.0063 - mean_absolute_error: 0.0422 - val_loss: 0.0074 - val_mean_absolute_error: 0.0517\n",
            "Epoch 90/200\n",
            "29/29 [==============================] - 0s 9ms/step - loss: 0.0062 - mean_absolute_error: 0.0418 - val_loss: 0.0073 - val_mean_absolute_error: 0.0532\n",
            "Epoch 91/200\n",
            "29/29 [==============================] - 0s 8ms/step - loss: 0.0058 - mean_absolute_error: 0.0403 - val_loss: 0.0069 - val_mean_absolute_error: 0.0495\n",
            "Epoch 92/200\n",
            "29/29 [==============================] - 0s 6ms/step - loss: 0.0054 - mean_absolute_error: 0.0399 - val_loss: 0.0068 - val_mean_absolute_error: 0.0495\n",
            "Epoch 93/200\n",
            "29/29 [==============================] - 0s 6ms/step - loss: 0.0052 - mean_absolute_error: 0.0394 - val_loss: 0.0069 - val_mean_absolute_error: 0.0533\n",
            "Epoch 94/200\n",
            "29/29 [==============================] - 0s 12ms/step - loss: 0.0050 - mean_absolute_error: 0.0388 - val_loss: 0.0064 - val_mean_absolute_error: 0.0483\n",
            "Epoch 95/200\n",
            "29/29 [==============================] - 0s 8ms/step - loss: 0.0051 - mean_absolute_error: 0.0389 - val_loss: 0.0066 - val_mean_absolute_error: 0.0537\n",
            "Epoch 96/200\n",
            "29/29 [==============================] - 0s 9ms/step - loss: 0.0047 - mean_absolute_error: 0.0374 - val_loss: 0.0060 - val_mean_absolute_error: 0.0464\n",
            "Epoch 97/200\n",
            "29/29 [==============================] - 0s 7ms/step - loss: 0.0044 - mean_absolute_error: 0.0362 - val_loss: 0.0060 - val_mean_absolute_error: 0.0473\n",
            "Epoch 98/200\n",
            "29/29 [==============================] - 0s 9ms/step - loss: 0.0042 - mean_absolute_error: 0.0362 - val_loss: 0.0058 - val_mean_absolute_error: 0.0459\n",
            "Epoch 99/200\n",
            "29/29 [==============================] - 0s 10ms/step - loss: 0.0042 - mean_absolute_error: 0.0365 - val_loss: 0.0056 - val_mean_absolute_error: 0.0445\n",
            "Epoch 100/200\n",
            "29/29 [==============================] - 0s 8ms/step - loss: 0.0041 - mean_absolute_error: 0.0362 - val_loss: 0.0056 - val_mean_absolute_error: 0.0452\n",
            "Epoch 101/200\n",
            "29/29 [==============================] - 0s 7ms/step - loss: 0.0039 - mean_absolute_error: 0.0349 - val_loss: 0.0054 - val_mean_absolute_error: 0.0439\n",
            "Epoch 102/200\n",
            "29/29 [==============================] - 0s 8ms/step - loss: 0.0038 - mean_absolute_error: 0.0345 - val_loss: 0.0056 - val_mean_absolute_error: 0.0478\n",
            "Epoch 103/200\n",
            "29/29 [==============================] - 0s 9ms/step - loss: 0.0040 - mean_absolute_error: 0.0364 - val_loss: 0.0053 - val_mean_absolute_error: 0.0426\n",
            "Epoch 104/200\n",
            "29/29 [==============================] - 0s 7ms/step - loss: 0.0035 - mean_absolute_error: 0.0332 - val_loss: 0.0052 - val_mean_absolute_error: 0.0422\n",
            "Epoch 105/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0035 - mean_absolute_error: 0.0336 - val_loss: 0.0050 - val_mean_absolute_error: 0.0411\n",
            "Epoch 106/200\n",
            "29/29 [==============================] - 0s 6ms/step - loss: 0.0034 - mean_absolute_error: 0.0332 - val_loss: 0.0048 - val_mean_absolute_error: 0.0389\n",
            "Epoch 107/200\n",
            "29/29 [==============================] - 0s 4ms/step - loss: 0.0033 - mean_absolute_error: 0.0330 - val_loss: 0.0047 - val_mean_absolute_error: 0.0398\n",
            "Epoch 108/200\n",
            "29/29 [==============================] - 0s 3ms/step - loss: 0.0032 - mean_absolute_error: 0.0322 - val_loss: 0.0046 - val_mean_absolute_error: 0.0382\n",
            "Epoch 109/200\n",
            "29/29 [==============================] - 0s 3ms/step - loss: 0.0031 - mean_absolute_error: 0.0318 - val_loss: 0.0044 - val_mean_absolute_error: 0.0369\n",
            "Epoch 110/200\n",
            "29/29 [==============================] - 0s 3ms/step - loss: 0.0030 - mean_absolute_error: 0.0314 - val_loss: 0.0044 - val_mean_absolute_error: 0.0369\n",
            "Epoch 111/200\n",
            "29/29 [==============================] - 0s 3ms/step - loss: 0.0030 - mean_absolute_error: 0.0319 - val_loss: 0.0043 - val_mean_absolute_error: 0.0371\n",
            "Epoch 112/200\n",
            "29/29 [==============================] - 0s 3ms/step - loss: 0.0028 - mean_absolute_error: 0.0312 - val_loss: 0.0041 - val_mean_absolute_error: 0.0364\n",
            "Epoch 113/200\n",
            "29/29 [==============================] - 0s 3ms/step - loss: 0.0027 - mean_absolute_error: 0.0299 - val_loss: 0.0041 - val_mean_absolute_error: 0.0358\n",
            "Epoch 114/200\n",
            "29/29 [==============================] - 0s 4ms/step - loss: 0.0026 - mean_absolute_error: 0.0302 - val_loss: 0.0040 - val_mean_absolute_error: 0.0352\n",
            "Epoch 115/200\n",
            "29/29 [==============================] - 0s 3ms/step - loss: 0.0026 - mean_absolute_error: 0.0306 - val_loss: 0.0040 - val_mean_absolute_error: 0.0359\n",
            "Epoch 116/200\n",
            "29/29 [==============================] - 0s 5ms/step - loss: 0.0025 - mean_absolute_error: 0.0293 - val_loss: 0.0038 - val_mean_absolute_error: 0.0341\n",
            "Epoch 117/200\n",
            "29/29 [==============================] - 0s 3ms/step - loss: 0.0026 - mean_absolute_error: 0.0311 - val_loss: 0.0037 - val_mean_absolute_error: 0.0339\n",
            "Epoch 118/200\n",
            "29/29 [==============================] - 0s 3ms/step - loss: 0.0024 - mean_absolute_error: 0.0292 - val_loss: 0.0037 - val_mean_absolute_error: 0.0338\n",
            "Epoch 119/200\n",
            "29/29 [==============================] - 0s 3ms/step - loss: 0.0023 - mean_absolute_error: 0.0285 - val_loss: 0.0037 - val_mean_absolute_error: 0.0337\n",
            "Epoch 120/200\n",
            "29/29 [==============================] - 0s 3ms/step - loss: 0.0025 - mean_absolute_error: 0.0308 - val_loss: 0.0036 - val_mean_absolute_error: 0.0333\n",
            "Epoch 121/200\n",
            "29/29 [==============================] - 0s 4ms/step - loss: 0.0023 - mean_absolute_error: 0.0300 - val_loss: 0.0037 - val_mean_absolute_error: 0.0343\n",
            "Epoch 122/200\n",
            "29/29 [==============================] - 0s 3ms/step - loss: 0.0021 - mean_absolute_error: 0.0284 - val_loss: 0.0036 - val_mean_absolute_error: 0.0341\n",
            "Epoch 123/200\n",
            "29/29 [==============================] - 0s 3ms/step - loss: 0.0022 - mean_absolute_error: 0.0294 - val_loss: 0.0036 - val_mean_absolute_error: 0.0339\n",
            "Epoch 124/200\n",
            "29/29 [==============================] - 0s 4ms/step - loss: 0.0022 - mean_absolute_error: 0.0297 - val_loss: 0.0034 - val_mean_absolute_error: 0.0323\n",
            "Epoch 125/200\n",
            "29/29 [==============================] - 0s 7ms/step - loss: 0.0020 - mean_absolute_error: 0.0283 - val_loss: 0.0034 - val_mean_absolute_error: 0.0325\n",
            "Epoch 126/200\n",
            "29/29 [==============================] - 0s 5ms/step - loss: 0.0019 - mean_absolute_error: 0.0273 - val_loss: 0.0034 - val_mean_absolute_error: 0.0325\n",
            "Epoch 127/200\n",
            "29/29 [==============================] - 0s 5ms/step - loss: 0.0019 - mean_absolute_error: 0.0276 - val_loss: 0.0034 - val_mean_absolute_error: 0.0332\n",
            "Epoch 128/200\n",
            "29/29 [==============================] - 0s 5ms/step - loss: 0.0019 - mean_absolute_error: 0.0277 - val_loss: 0.0033 - val_mean_absolute_error: 0.0320\n",
            "Epoch 129/200\n",
            "29/29 [==============================] - 0s 5ms/step - loss: 0.0018 - mean_absolute_error: 0.0275 - val_loss: 0.0033 - val_mean_absolute_error: 0.0325\n",
            "Epoch 130/200\n",
            "29/29 [==============================] - 0s 5ms/step - loss: 0.0018 - mean_absolute_error: 0.0268 - val_loss: 0.0033 - val_mean_absolute_error: 0.0314\n",
            "Epoch 131/200\n",
            "29/29 [==============================] - 0s 5ms/step - loss: 0.0017 - mean_absolute_error: 0.0271 - val_loss: 0.0032 - val_mean_absolute_error: 0.0320\n",
            "Epoch 132/200\n",
            "29/29 [==============================] - 0s 5ms/step - loss: 0.0017 - mean_absolute_error: 0.0268 - val_loss: 0.0032 - val_mean_absolute_error: 0.0318\n",
            "Epoch 133/200\n",
            "29/29 [==============================] - 0s 4ms/step - loss: 0.0018 - mean_absolute_error: 0.0281 - val_loss: 0.0032 - val_mean_absolute_error: 0.0328\n",
            "Epoch 134/200\n",
            "19/29 [==================>...........] - ETA: 0s - loss: 0.0013 - mean_absolute_error: 0.0261    Restoring model weights from the end of the best epoch: 124.\n",
            "29/29 [==============================] - 0s 6ms/step - loss: 0.0017 - mean_absolute_error: 0.0273 - val_loss: 0.0033 - val_mean_absolute_error: 0.0335\n",
            "Epoch 134: early stopping\n"
          ]
        }
      ],
      "source": [
        "model = classifier.fit(X_train , Y_train , validation_split = 0.25 , batch_size = 10 , epochs = 200 , callbacks = early)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2eDXJclyKk9t"
      },
      "source": [
        "##**Time to Predict on Test Data and Evaluate our ANN Classifier's performance!** ⚡"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-84alzvaKLU2",
        "outputId": "97e666e6-b003-462d-fb94-1dad493d29f6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 5)                 30        \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 4)                 24        \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 2)                 10        \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 3         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 67 (268.00 Byte)\n",
            "Trainable params: 67 (268.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "classifier.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t1UvRjmdKdoK",
        "outputId": "e889e034-670a-4758-8d61-17f75c9abad7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'mean_absolute_error', 'val_loss', 'val_mean_absolute_error'])"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.history.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "A2r65YQnLAgU",
        "outputId": "c3fa2a29-5bf3-4944-c60f-8a9dd02fd0ba"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(model.history[\"loss\"])\n",
        "plt.plot(model.history[\"val_loss\"])\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend([\"Train\" , \"Test\"])\n",
        "plt.title(\"Model Loss\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RcNRwQV9LDcA",
        "outputId": "7950d00e-66cb-45aa-ceb0-6be44fcd17d0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "4/4 [==============================] - 0s 3ms/step\n"
          ]
        }
      ],
      "source": [
        "Y_pred = classifier.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v0DM1dlFLQki",
        "outputId": "50979858-8a1b-46df-8f02-bcadf8fed69b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[-0.54017353]\n",
            "[-0.04867637]\n",
            "[-1.4263492]\n",
            "[0.71761286]\n",
            "[-0.5657053]\n"
          ]
        }
      ],
      "source": [
        "for j in range (5):\n",
        "  print(Y_pred[j])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KrNWJp4-LTET",
        "outputId": "7d6e2bbb-a932-4451-e59b-47c8872b1c83"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[-0.48823496]\n",
            "[-0.07509785]\n",
            "[-1.37442876]\n",
            "[0.73781113]\n",
            "[-0.55440579]\n"
          ]
        }
      ],
      "source": [
        "for k in range (5):\n",
        "  print(Y_test[k])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OUzXGBStMpDt"
      },
      "source": [
        "![Opera 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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kwhpQMCQLw4V",
        "outputId": "0c808f6a-138b-4ab4-a1d9-3488dcedc2d3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R2 Score= 0.9959548412130136\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import r2_score\n",
        "\n",
        "R2 = r2_score(Y_test, Y_pred)\n",
        "print(\"R2 Score=\",R2 )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hoIUYwgFKoCi",
        "outputId": "808178eb-2747-4f37-870e-67d9b317624a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean Squared Error: 0.004\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "mse = mean_squared_error(Y_test, Y_pred)\n",
        "print(\"Mean Squared Error:\", mse.round(3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DkuzEn_NJC4f",
        "outputId": "abefbd3c-f87f-4715-8cde-cc94ca07e3bb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 0.033\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "mae = mean_absolute_error(Y_test, Y_pred)\n",
        "print(\"Mean Absolute Error:\", mae.round(3))"
      ]
    }
  ],
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    "colab": {
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      "authorship_tag": "ABX9TyMjXVkPtGRGxm8PFaLkjY3B"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
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  "nbformat_minor": 0
}