intelligent_transportation.ipynb

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      "name": "intelligent_transportation.ipynb",
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        "<a href=\"https://colab.research.google.com/github/MizanMustakim/BIT_Machine_Learning_Course_Assignments_2022/blob/main/intelligent_transportation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "jel1cG8_I_Kd"
      },
      "execution_count": null,
      "outputs": []
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        "id": "YkF4Uz-DGqjo"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import datetime\n",
        "import time\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split as tts\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.metrics import confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.read_csv(r\"/content/drive/MyDrive/intelligent_transportation_project/gy_link_info.txt\", delimiter=\";\")\n",
        "df1"
      ],
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        "df2 = pd.read_csv(\"/content/drive/MyDrive/intelligent_transportation_project/gy_link_top.txt\", delimiter=\";\")\n",
        "df2.head()"
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        "df2[\"in_links\"] = df2[\"in_links\"].fillna(\"0\")\n",
        "df2[\"out_links\"] = df2[\"out_links\"].fillna(\"0\")\n",
        "df2.head()"
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4377906284594800514</td>\n",
              "      <td>4377906284514600514</td>\n",
              "      <td>4377906285594800514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4377906289425800514</td>\n",
              "      <td>0</td>\n",
              "      <td>4377906284653600514</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4377906284525800514</td>\n",
              "      <td>4377906281234600514</td>\n",
              "      <td>4377906280334600514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4377906284422600514</td>\n",
              "      <td>3377906289434510514#4377906287959500514</td>\n",
              "      <td>4377906283422600514</td>\n",
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        "df2.info()"
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      "execution_count": null,
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        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 132 entries, 0 to 131\n",
            "Data columns (total 3 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   link_ID    132 non-null    uint64\n",
            " 1   in_links   132 non-null    object\n",
            " 2   out_links  132 non-null    object\n",
            "dtypes: object(2), uint64(1)\n",
            "memory usage: 3.2+ KB\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "df3 = pd.read_csv(\"/content/drive/MyDrive/intelligent_transportation_project/gy_link_travel_time_part2.txt\", delimiter=\";\")\n",
        "df3.head()"
      ],
      "metadata": {
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      "execution_count": 18,
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              "                linkID        date                              time_interval  \\\n",
              "0  4377906283422600514  2017-05-06  [2017-05-06 11:04:00,2017-05-06 11:06:00)   \n",
              "1  3377906289434510514  2017-05-06  [2017-05-06 10:42:00,2017-05-06 10:44:00)   \n",
              "2  3377906285934510514  2017-05-06  [2017-05-06 11:56:00,2017-05-06 11:58:00)   \n",
              "3  3377906285934510514  2017-05-06  [2017-05-06 17:46:00,2017-05-06 17:48:00)   \n",
              "4  3377906287934510514  2017-05-06  [2017-05-06 10:52:00,2017-05-06 10:54:00)   \n",
              "\n",
              "   travel_time  \n",
              "0          3.0  \n",
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        "fig, axes = plt.subplots(figsize=(10,8), nrows=2, ncols=1)\n",
        "fig.suptitle(\"\\nHist Plot of travel time data\\n\")\n",
        "df3[\"travel_time\"].hist(bins=100, ax=axes[0])\n",
        "df3[\"travel_time\"] = np.log1p(df3[\"travel_time\"])\n",
        "df3[\"travel_time\"].hist(bins=100, ax=axes[1])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
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        "id": "S3TRQhMIau5e",
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      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def quantile(data):\n",
        "    data[data < data.quantile(0.05)] = data.quantile(0.05)\n",
        "    data[data > data.quantile(0.95)] = data.quantile(0.95)\n",
        "\n",
        "    return data\n",
        "\n",
        "df3[\"travel_time\"] = df3.groupby([\"linkID\", \"date\"])[\"travel_time\"].transform(quantile)"
      ],
      "metadata": {
        "id": "Z8OLbH6pa0E0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RztmsE8pdkXW",
        "outputId": "207180fa-2c49-4651-ac5c-ad3f67268a59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7705175, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3[\"date\"] = pd.to_datetime(df3[\"date\"])\n",
        "df3[\"date\"].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "avk_v39ha8I5",
        "outputId": "b137c4f7-cf66-4dfc-bc95-067808188beb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count                 7705175\n",
              "unique                    122\n",
              "top       2017-05-05 00:00:00\n",
              "freq                    89149\n",
              "first     2017-03-01 00:00:00\n",
              "last      2017-06-30 00:00:00\n",
              "Name: date, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = df3.loc[df3[\"date\"] < \"2017-03-8\"].reset_index(drop=True)\n",
        "df3.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "gmWoOzzIa8LJ",
        "outputId": "7828f741-4992-4db3-dc23-2951b391a299"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                linkID       date                              time_interval  \\\n",
              "0  3377906285434510514 2017-03-04  [2017-03-04 17:12:00,2017-03-04 17:14:00)   \n",
              "1  3377906285934510514 2017-03-04  [2017-03-04 03:42:00,2017-03-04 03:44:00)   \n",
              "2  3377906287674510514 2017-03-04  [2017-03-04 07:22:00,2017-03-04 07:24:00)   \n",
              "3  3377906289674510514 2017-03-04  [2017-03-04 06:40:00,2017-03-04 06:42:00)   \n",
              "4  3377906284395510514 2017-03-04  [2017-03-04 12:28:00,2017-03-04 12:30:00)   \n",
              "\n",
              "   travel_time  \n",
              "0     0.741937  \n",
              "1     2.097962  \n",
              "2     1.609438  \n",
              "3     1.648659  \n",
              "4     1.547563  "
            ],
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              "          document.querySelector('#df-71e0a48a-444c-43c0-9453-d2bd2da09e47 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-71e0a48a-444c-43c0-9453-d2bd2da09e47');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3[\"Start_time\"] = [i.strip(\"[)\").split(\",\")[0].split(\" \")[1] for i in df3[\"time_interval\"]]\n",
        "df3 = df3.drop([\"time_interval\"], axis=1)\n",
        "df3.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pEMXeoxXfCvp",
        "outputId": "732925d1-1485-483a-a641-564ab6f9de77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                linkID       date  travel_time Start_time\n",
              "0  3377906285434510514 2017-03-04     0.741937   17:12:00\n",
              "1  3377906285934510514 2017-03-04     2.097962   03:42:00\n",
              "2  3377906287674510514 2017-03-04     1.609438   07:22:00\n",
              "3  3377906289674510514 2017-03-04     1.648659   06:40:00\n",
              "4  3377906284395510514 2017-03-04     1.547563   12:28:00"
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              "\n",
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              "      <th>linkID</th>\n",
              "      <th>date</th>\n",
              "      <th>travel_time</th>\n",
              "      <th>Start_time</th>\n",
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              "      <td>1.648659</td>\n",
              "      <td>06:40:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3377906284395510514</td>\n",
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              "\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-fe743a3a-b119-4f46-9032-b9bc687db30d');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# df3[\"Start_time\"] = pd.to_datetime(df3[\"Start_time\"]).dt.time\n",
        "# df3[\"Start_time\"].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5aWXwQy9fC0S",
        "outputId": "dfdc59cb-e388-49c1-852d-a692f2cb507c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      1100043\n",
              "unique         720\n",
              "top       18:38:00\n",
              "freq          1799\n",
              "Name: Start_time, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Phase distribution of day"
      ],
      "metadata": {
        "id": "1HpQYloir6bq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "empty_time_phase = np.zeros((df3.shape[0],720))\n",
        "empty_time_phase_df = pd.DataFrame(empty_time_phase, columns = [\"phase\"+str(i+1) for i in range(720)])\n",
        "df4 = pd.concat([df3, empty_time_phase_df], axis=1)\n",
        "\n",
        "df4.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "yqXT6rzwo5ch",
        "outputId": "240407b8-3591-403a-e3b6-2ee4e06684ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                linkID       date  travel_time Start_time  phase1  phase2  \\\n",
              "0  3377906285434510514 2017-03-04     0.741937   17:12:00     0.0     0.0   \n",
              "1  3377906285934510514 2017-03-04     2.097962   03:42:00     0.0     0.0   \n",
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              "4  3377906284395510514 2017-03-04     1.547563   12:28:00     0.0     0.0   \n",
              "\n",
              "   phase3  phase4  phase5  phase6  ...  phase711  phase712  phase713  \\\n",
              "0     0.0     0.0     0.0     0.0  ...       0.0       0.0       0.0   \n",
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              "4     0.0     0.0     0.0     0.0  ...       0.0       0.0       0.0   \n",
              "\n",
              "   phase714  phase715  phase716  phase717  phase718  phase719  phase720  \n",
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              "\n",
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              "    }\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",
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              "\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9770c216-32cd-4326-8329-d85b69643bba button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9770c216-32cd-4326-8329-d85b69643bba');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df4.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8tNXE1D1y0hp",
        "outputId": "ba5e3fae-0b9a-4503-e027-7aaf937299fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(550863, 724)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "minute = list(range(0, 59, 2))\n",
        "hour = list(range(0,24))\n",
        "hour_phase = list(range(1,25))\n",
        "minute_phase = list(range(1,31))\n",
        "df4[\"day\"] = np.zeros((df4.shape[0], 1))\n",
        "\n",
        "for i in range(0,df4.shape[0]):\n",
        "    a = hour.index(int(df4[\"Start_time\"][i].split(\":\")[0]))\n",
        "    b = minute.index(int(df4[\"Start_time\"][i].split(\":\")[1]))\n",
        "    phase_num = hour_phase[a] * minute_phase[b]\n",
        "    column_name = \"phase\"+str(phase_num)\n",
        "    df4[column_name][i] = 1\n",
        "\n",
        "    if df4[\"date\"][i].day % 7 != 0:\n",
        "        df4[\"day\"][i] = df4[\"date\"][i].day % 7\n",
        "    else:\n",
        "        df4[\"day\"][i] = 7\n",
        "\n",
        "df4.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6JVLVQxNr28Q",
        "outputId": "56cab4f8-b699-4626-fb31-01a928584a64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  if sys.path[0] == '':\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  from ipykernel import kernelapp as app\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(550863, 725)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "fRoiP1Wg0il6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df4 = df4.drop([\"date\"], axis=1)\n",
        "df4.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "E5lt2Pqm3TRM",
        "outputId": "97379e49-0dd2-466d-fde8-a4f55aa68635"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id = df1[\"link_ID\"].unique()\n",
        "index_data = pd.DataFrame(data=id, columns=[\"linkID\"])\n",
        "index_data[\"id_index\"] = range(1, len(id)+1)\n",
        "index_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "9YhItod0VL7i",
        "outputId": "d268f2f0-9a47-4ff8-9124-065d28e05a94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                linkID  id_index\n",
              "0  4377906289869500514         1\n",
              "1  4377906284594800514         2\n",
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        "final_data = pd.merge(df4,index_data, on=\"linkID\", how=\"inner\")\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "date = final_data[\"day\"].values.tolist()\n",
        "id = final_data[\"id_index\"].values.tolist()\n",
        "time_travel = final_data[\"travel_time\"].values.tolist()\n",
        "time = []\n",
        "for i in final_data[\"Start_time\"]:\n",
        "    hour = i.split(\":\")[0]\n",
        "    minute = i.split(\":\")[1]\n",
        "    time.append((int(hour) * 60) + int(minute))\n",
        "\n",
        "data = {\"road_id\":id,\n",
        "        \"day\":date,\n",
        "        \"time(minute)\":time,\n",
        "        \"travel_time\":time_travel\n",
        "        }\n",
        "\n",
        "d = pd.DataFrame(data=data)\n",
        "d"
      ],
      "metadata": {
        "colab": {
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          "height": 424
        },
        "id": "egW2Oe-n0ic6",
        "outputId": "0e8b42b8-efe7-4c20-d477-7855e466e3a7"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "        road_id  day  time(minute)  travel_time\n",
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              "      <td>716</td>\n",
              "      <td>0.832909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>119</td>\n",
              "      <td>4.0</td>\n",
              "      <td>720</td>\n",
              "      <td>0.832909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>119</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1162</td>\n",
              "      <td>1.098612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>550858</th>\n",
              "      <td>67</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1188</td>\n",
              "      <td>2.476538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>550859</th>\n",
              "      <td>67</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1246</td>\n",
              "      <td>2.370244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>550860</th>\n",
              "      <td>67</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1282</td>\n",
              "      <td>3.520461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>550861</th>\n",
              "      <td>67</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1392</td>\n",
              "      <td>2.302585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>550862</th>\n",
              "      <td>67</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1418</td>\n",
              "      <td>2.028148</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>550863 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-33b02d85-8c0a-44ff-8890-1e24bf70afd6')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-33b02d85-8c0a-44ff-8890-1e24bf70afd6 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-33b02d85-8c0a-44ff-8890-1e24bf70afd6');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = d.drop([\"travel_time\"], axis=1)\n",
        "Y = d[\"travel_time\"]\n",
        "\n",
        "X_train, X_test, y_train, y_test = tts(X, Y, test_size=0.20, shuffle=True)\n",
        "print(\"Shape of input value for the train: \", X_train.shape[0])\n",
        "print(\"Shape of input value for the test: \", X_test.shape[0])\n",
        "\n",
        "print(\"\\nShape of output value for the train: \", y_train.shape[0])\n",
        "print(\"Shape of output value for the test: \", y_test.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T4u9Aig02MtS",
        "outputId": "6e9b0f38-87eb-4289-b593-7577b28414cf"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of input value for the train:  440690\n",
            "Shape of input value for the test:  110173\n",
            "\n",
            "Shape of output value for the train:  440690\n",
            "Shape of output value for the test:  110173\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "skYYef-l2Mv_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jaRf8hzVr3Jk",
        "outputId": "6f51e84a-9483-444a-c1e8-b1a7458e154d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 550863 entries, 0 to 550862\n",
            "Columns: 725 entries, linkID to id_index\n",
            "dtypes: float64(722), int64(1), object(1), uint64(1)\n",
            "memory usage: 3.0+ GB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_data.to_csv(\"/content/drive/MyDrive/final_data.csv\", index=False)"
      ],
      "metadata": {
        "id": "QIvQ1mBWRr5r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_data = pd.read_csv(\"/content/drive/MyDrive/final_data.csv\")"
      ],
      "metadata": {
        "id": "2oJVU_2CoMdM"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = final_data.drop([\"linkID\", \"Start_time\", \"travel_time\"], axis=1)\n",
        "Y = final_data[[\"travel_time\"]]\n",
        "\n",
        "X_train, X_test, y_train, y_test = tts(X, Y, test_size=0.40, shuffle=True)\n",
        "print(\"Shape of input value for the train: \", X_train.shape[0])\n",
        "print(\"Shape of input value for the test: \", X_test.shape[0])\n",
        "\n",
        "print(\"\\nShape of output value for the train: \", y_train.shape[0])\n",
        "print(\"Shape of output value for the test: \", y_test.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XZV_AoHqJFNv",
        "outputId": "e6a34841-776b-4890-e97c-a3bf73780bc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of input value for the train:  330517\n",
            "Shape of input value for the test:  220346\n",
            "\n",
            "Shape of output value for the train:  330517\n",
            "Shape of output value for the test:  220346\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential([tf.keras.layers.Dense(64, activation=\"relu\", input_shape=[X_train.shape[1]]),\n",
        "                                    tf.keras.layers.Dense(1)\n",
        "])\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TfhlaYCHr_g2",
        "outputId": "7bc85f6e-4e00-496b-c4cd-920c82a0858c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_10 (Dense)            (None, 64)                256       \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 321\n",
            "Trainable params: 321\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "opt = tf.keras.optimizers.RMSprop(learning_rate=0.00001)\n",
        "model.compile(\n",
        "    loss='mse',\n",
        "    optimizer = opt)\n",
        "\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(monitor='loss',\n",
        "                               min_delta=0,\n",
        "                               patience=2,\n",
        "                               verbose=0, mode='auto')\n",
        "\n",
        "            \n",
        "epoch = 100\n",
        "batch_size = X_train.shape[0] // epoch\n",
        "\n",
        "# Train the Model\n",
        "history = model.fit(X_train, y_train, epochs=epoch, batch_size=batch_size, validation_data=(X_test, y_test), callbacks=[early_stopping])\n",
        "\n",
        "model.save(\"/content/drive/MyDrive/intelligent_transportation_project/model.h5\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uhHvW0wBr_jt",
        "outputId": "80d5deb7-1723-45aa-9233-d40f27f72bc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "101/101 [==============================] - 1s 4ms/step - loss: 4062.5718 - val_loss: 3531.2976\n",
            "Epoch 2/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 3082.4988 - val_loss: 2631.1626\n",
            "Epoch 3/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 2246.8022 - val_loss: 1864.4674\n",
            "Epoch 4/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1546.8420 - val_loss: 1236.4595\n",
            "Epoch 5/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 982.1786 - val_loss: 739.8640\n",
            "Epoch 6/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 553.2123 - val_loss: 381.6969\n",
            "Epoch 7/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 258.5881 - val_loss: 154.1552\n",
            "Epoch 8/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 94.9574 - val_loss: 55.2715\n",
            "Epoch 9/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 46.6320 - val_loss: 43.3671\n",
            "Epoch 10/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 40.8681 - val_loss: 38.6310\n",
            "Epoch 11/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 36.2693 - val_loss: 34.1697\n",
            "Epoch 12/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 32.0409 - val_loss: 30.1308\n",
            "Epoch 13/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 28.1694 - val_loss: 26.4190\n",
            "Epoch 14/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 24.6965 - val_loss: 23.0976\n",
            "Epoch 15/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 21.5713 - val_loss: 20.2001\n",
            "Epoch 16/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 18.8088 - val_loss: 17.5958\n",
            "Epoch 17/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 16.3933 - val_loss: 15.3033\n",
            "Epoch 18/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 14.3207 - val_loss: 13.4688\n",
            "Epoch 19/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 12.5877 - val_loss: 11.8425\n",
            "Epoch 20/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 11.1330 - val_loss: 10.5219\n",
            "Epoch 21/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 9.9568 - val_loss: 9.5199\n",
            "Epoch 22/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 9.0278 - val_loss: 8.6800\n",
            "Epoch 23/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 8.3123 - val_loss: 8.0699\n",
            "Epoch 24/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 7.7177 - val_loss: 7.4401\n",
            "Epoch 25/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 7.1604 - val_loss: 6.9246\n",
            "Epoch 26/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 6.6414 - val_loss: 6.4347\n",
            "Epoch 27/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 6.1561 - val_loss: 5.9231\n",
            "Epoch 28/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 5.6918 - val_loss: 5.5220\n",
            "Epoch 29/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 5.2592 - val_loss: 5.0716\n",
            "Epoch 30/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 4.8529 - val_loss: 4.6572\n",
            "Epoch 31/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 4.4788 - val_loss: 4.3637\n",
            "Epoch 32/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 4.1356 - val_loss: 4.0048\n",
            "Epoch 33/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 3.8248 - val_loss: 3.6784\n",
            "Epoch 34/100\n",
            "101/101 [==============================] - 1s 7ms/step - loss: 3.5387 - val_loss: 3.4374\n",
            "Epoch 35/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 3.2900 - val_loss: 3.2707\n",
            "Epoch 36/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 3.0737 - val_loss: 3.0225\n",
            "Epoch 37/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 2.8826 - val_loss: 2.8266\n",
            "Epoch 38/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 2.7161 - val_loss: 2.6857\n",
            "Epoch 39/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 2.5728 - val_loss: 2.5197\n",
            "Epoch 40/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 2.4545 - val_loss: 2.4662\n",
            "Epoch 41/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 2.3545 - val_loss: 2.3408\n",
            "Epoch 42/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 2.2709 - val_loss: 2.2855\n",
            "Epoch 43/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 2.2022 - val_loss: 2.2236\n",
            "Epoch 44/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 2.1431 - val_loss: 2.1202\n",
            "Epoch 45/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 2.0863 - val_loss: 2.0986\n",
            "Epoch 46/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 2.0351 - val_loss: 2.0225\n",
            "Epoch 47/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.9874 - val_loss: 2.0092\n",
            "Epoch 48/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.9419 - val_loss: 1.9743\n",
            "Epoch 49/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.8996 - val_loss: 1.9365\n",
            "Epoch 50/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.8619 - val_loss: 1.9053\n",
            "Epoch 51/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.8247 - val_loss: 1.8275\n",
            "Epoch 52/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.7893 - val_loss: 1.8275\n",
            "Epoch 53/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.7572 - val_loss: 1.7909\n",
            "Epoch 54/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.7260 - val_loss: 1.7484\n",
            "Epoch 55/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.6974 - val_loss: 1.7191\n",
            "Epoch 56/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.6698 - val_loss: 1.6723\n",
            "Epoch 57/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.6438 - val_loss: 1.7340\n",
            "Epoch 58/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.6198 - val_loss: 1.6346\n",
            "Epoch 59/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.5959 - val_loss: 1.6187\n",
            "Epoch 60/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.5738 - val_loss: 1.6273\n",
            "Epoch 61/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.5534 - val_loss: 1.6033\n",
            "Epoch 62/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.5336 - val_loss: 1.5653\n",
            "Epoch 63/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.5144 - val_loss: 1.5273\n",
            "Epoch 64/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.4966 - val_loss: 1.5702\n",
            "Epoch 65/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.4801 - val_loss: 1.5439\n",
            "Epoch 66/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.4637 - val_loss: 1.4618\n",
            "Epoch 67/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.4472 - val_loss: 1.4440\n",
            "Epoch 68/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.4322 - val_loss: 1.4335\n",
            "Epoch 69/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.4170 - val_loss: 1.4356\n",
            "Epoch 70/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.4032 - val_loss: 1.4090\n",
            "Epoch 71/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.3893 - val_loss: 1.4533\n",
            "Epoch 72/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.3762 - val_loss: 1.4257\n",
            "Epoch 73/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.3636 - val_loss: 1.4218\n",
            "Epoch 74/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.3509 - val_loss: 1.3486\n",
            "Epoch 75/100\n",
            "101/101 [==============================] - 0s 4ms/step - loss: 1.3385 - val_loss: 1.3782\n",
            "Epoch 76/100\n",
            "101/101 [==============================] - 0s 4ms/step - loss: 1.3266 - val_loss: 1.3656\n",
            "Epoch 77/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.3158 - val_loss: 1.3572\n",
            "Epoch 78/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.3047 - val_loss: 1.3684\n",
            "Epoch 79/100\n",
            "101/101 [==============================] - 0s 4ms/step - loss: 1.2941 - val_loss: 1.3717\n",
            "Epoch 80/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.2845 - val_loss: 1.3175\n",
            "Epoch 81/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.2742 - val_loss: 1.2780\n",
            "Epoch 82/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.2643 - val_loss: 1.3375\n",
            "Epoch 83/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.2560 - val_loss: 1.2570\n",
            "Epoch 84/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.2466 - val_loss: 1.2829\n",
            "Epoch 85/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.2384 - val_loss: 1.2399\n",
            "Epoch 86/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.2304 - val_loss: 1.2352\n",
            "Epoch 87/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.2225 - val_loss: 1.2432\n",
            "Epoch 88/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.2152 - val_loss: 1.2327\n",
            "Epoch 89/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.2079 - val_loss: 1.2376\n",
            "Epoch 90/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.2014 - val_loss: 1.2033\n",
            "Epoch 91/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.1951 - val_loss: 1.1974\n",
            "Epoch 92/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.1888 - val_loss: 1.2146\n",
            "Epoch 93/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.1834 - val_loss: 1.1863\n",
            "Epoch 94/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.1780 - val_loss: 1.1997\n",
            "Epoch 95/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.1728 - val_loss: 1.2434\n",
            "Epoch 96/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.1690 - val_loss: 1.1858\n",
            "Epoch 97/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.1633 - val_loss: 1.2026\n",
            "Epoch 98/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.1594 - val_loss: 1.2054\n",
            "Epoch 99/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.1556 - val_loss: 1.1869\n",
            "Epoch 100/100\n",
            "101/101 [==============================] - 0s 3ms/step - loss: 1.1518 - val_loss: 1.2026\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f, ax1 = plt.subplots(1, 1, figsize=(10, 4))\n",
        "t = f.suptitle('Traffic flow prediction loss value analysis', fontsize=12)\n",
        "f.subplots_adjust(top=0.85, wspace=0.3)\n",
        "\n",
        "epochs = range(1, len(history.history[\"loss\"])+1)\n",
        "\n",
        "ax1.plot(epochs, history.history['loss'], label='Train')\n",
        "ax1.plot(epochs, history.history['val_loss'], label='Validation')\n",
        "ax1.set_ylabel('Loss')\n",
        "ax1.set_xlabel('Epoch')\n",
        "ax1.set_title('MSE Loss')\n",
        "l1 = ax1.legend(loc=\"best\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "iLkBW7-pJFQE",
        "outputId": "6a5af841-ad1e-44df-b476-b5448322555d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ],
            "image/png": 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4JZV/I9VD0hTgQuAE4Gzgu5JKeyHuAxpTXcXaLbsKHYaZmVm/tXHjRqZNm8a0adM44ogjGDdu3L71PXv2HHDfefPm8Xd/93e9FGn+5HUwgaQ64F3AlcDfK2vzewvwoVTlJuBy4Brg/LQMcDvw7VT/fGBuRDQCL0haCswC/pDP2A9mTM0A1m7xKBgzM7N8GT58OAsWLADg8ssvZ/DgwfzjP/7jvu1NTU2UlbWfysycOZOZM2f2Spz5lO8WtW8CXwBa0vpwYHNENKX1BmBcWh4HrAJI27ek+vvK29lnH0kXS5onad6GDRt6+jpeY8zQKtZsdqJmZmbWmy666CI+8YlPcMopp/CFL3yBxx9/nNNOO43p06dz+umns2TJEgAeeughzjvvPCBL8j72sY9x5plnctRRR3H11VcX8hIOSd5a1CSdB6yPiPmSzszXeVpFxLXAtQAzZ86MfJ9vTE0VL29vZE9TCxVlHpNhZmb92xU/X8iiNVt79JhTxg7lsj874ZD3a2ho4Pe//z2lpaVs3bqVRx55hLKyMn7961/zpS99iTvuuOM1+zz33HM8+OCDbNu2jWOPPZZPfvKThzSfWaHks+vzDODdks4FqoChwLeAGkllqdWsDlid6q8G6oEGSWVANbAxp7xV7j4FM7Z6AADrtu6mvnZggaMxMzM7fLz//e+ntDS7XX3Lli3MmTOH559/Hkns3bu33X3e9a53UVlZSWVlJaNGjWLdunXU1dX1ZthdkrdELSIuBS4FSC1q/xgRH5b0f4ELgLnAHOBnaZe70vof0vbfRERIugv4kaSvA2OBycDj+Yq7s46orgJgzeZdTtTMzKzf60rLV74MGjRo3/K//Mu/cNZZZ/HTn/6UFStWcOaZZ7a7T2Vl5b7l0tJSmpqa2q1XbArxZIJ/AuZK+irwFHBdKr8OuDkNFthENtKTiFgo6TZgEdAEXBIRzb0f9v7G1mSJ2ktbfZ+amZlZoWzZsoVx47Jb12+88cbCBpMHvXJzVUQ8FBHnpeXlETErIo6OiPen0ZxExO60fnTavjxn/ysjYlJEHBsR9/ZGzAdzROr69IACMzOzwvnCF77ApZdeyvTp0/tMK9mhUETe77vvdTNnzox58+bl/TxTL7+P904fx1fOPzHv5zIzM+ttixcv5vjjjy90GP1Ke5+ppPkR0e5cIh6u2A1jqz2XmpmZmeWPE7VuOMJPJzAzM7M8cqLWDWNrqnjJLWpmZmaWJ07UumFM9QBe3r6HxqaCD0I1MzOzfsiJWje0zqXmVjUzMzPLBydq3dD6dAIPKDAzM7N8cKLWDWPSpLceUGBmZtbzzjrrLO677779yr75zW/yyU9+st36Z555Jq3Tc5177rls3rz5NXUuv/xyrrrqqgOe984772TRokX71v/1X/+VX//614cafo9wotYNY/Y9RsotamZmZj1t9uzZzJ07d7+yuXPnMnv27IPue88991BTU9Ol87ZN1L7yla/wtre9rUvH6i4nat0wsKKM6gHlvkfNzMwsDy644AJ+8YtfsGfPHgBWrFjBmjVruPXWW5k5cyYnnHACl112Wbv7TpgwgZdffhmAK6+8kmOOOYY3vOENLFmyZF+d733ve5x88smcdNJJ/Pmf/zk7d+7k97//PXfddRef//znmTZtGsuWLeOiiy7i9ttvB+CBBx5g+vTpTJ06lY997GM0NjbuO99ll13GjBkzmDp1Ks8991yPfAaFeNZnvzLGc6mZmdnh4N4vwkt/7NljHjEVzvlah5tra2uZNWsW9957L+effz5z587lAx/4AF/60peora2lubmZt771rTzzzDO87nWva/cY8+fPZ+7cuSxYsICmpiZmzJjB61//egDe97738dd//dcAfPnLX+a6667j05/+NO9+97s577zzuOCCC/Y71u7du7nooot44IEHOOaYY/jIRz7CNddcw2c/+1kARowYwZNPPsl3v/tdrrrqKr7//e93+yNyi1o3jamuctenmZlZnuR2f7Z2e952223MmDGD6dOns3Dhwv26Kdt65JFHeO9738vAgQMZOnQo7373u/dte/bZZ3njG9/I1KlTueWWW1i4cOEBY1myZAkTJ07kmGOOAWDOnDk8/PDD+7a/733vA+D1r389K1as6Ool78ctat00pmYATzdsKXQYZmZm+XWAlq98Ov/88/nc5z7Hk08+yc6dO6mtreWqq67iiSeeYNiwYVx00UXs3t21BpOLLrqIO++8k5NOOokbb7yRhx56qFuxVlZWAlBaWtpjD4h3i1o3jRlaxaYde9i915PempmZ9bTBgwdz1lln8bGPfYzZs2ezdetWBg0aRHV1NevWrePee+894P5vetObuPPOO9m1axfbtm3j5z//+b5t27ZtY8yYMezdu5dbbrllX/mQIUPYtm3ba4517LHHsmLFCpYuXQrAzTffzJvf/OYeutL25S1Rk1Ql6XFJT0taKOmKVH6jpBckLUivaalckq6WtFTSM5Jm5BxrjqTn02tOvmLuijE12VxqHlBgZmaWH7Nnz+bpp59m9uzZnHTSSUyfPp3jjjuOD33oQ5xxxhkH3HfGjBl88IMf5KSTTuKcc87h5JNP3rft3/7t3zjllFM444wzOO644/aVX3jhhfzXf/0X06dPZ9myZfvKq6qquOGGG3j/+9/P1KlTKSkp4ROf+ETPX3AORUR+DiwJGBQR2yWVA78DPgN8Arg7Im5vU/9c4NPAucApwLci4hRJtcA8YCYQwHzg9RHxSkfnnjlzZrTOo5IXETD/Rhgxmd83HceHvv8YP/rrUzh90oj8ndPMzKyXLV68mOOPP77QYfQr7X2mkuZHxMz26uetRS0y29NqeXodKCs8H/hB2u9RoEbSGOCdwP0RsSklZ/cDZ+cr7k6R4IEr4I+373uM1FoPKDAzM7Meltd71CSVSloArCdLth5Lm65M3ZvfkFSZysYBq3J2b0hlHZUXVnU9bFnFmPQYqZe2OlEzMzOznpXXRC0imiNiGlAHzJJ0InApcBxwMlAL/FNPnEvSxZLmSZq3YcOGnjjkgdWMh82rGFBRSs3ActZs9lxqZmbW/+TrFqnDUVc+y14Z9RkRm4EHgbMjYm3q3mwEbgBmpWqrgfqc3epSWUflbc9xbUTMjIiZI0eOzMdl7K9mPGxZBRGMqR7gB7ObmVm/U1VVxcaNG52s9YCIYOPGjVRVVR3SfnmbR03SSGBvRGyWNAB4O/AfksZExNo02OA9wLNpl7uAT0maSzaYYEuqdx/w75KGpXrvIGuVK6zqeti7E3ZuYmx1FWucqJmZWT9TV1dHQ0MDvdJTdRioqqqirq7ukPbJ54S3Y4CbJJWStdzdFhF3S/pNSuIELCAbBQpwD9mIz6XATuCjABGxSdK/AU+kel+JiE15jLtzalIj35aVHFFdxfyVHQ5CNTMz65PKy8uZOHFiocM4rOUtUYuIZ4Dp7ZS/pYP6AVzSwbbrget7NMDuqk6J2uaVjK05gc0797JrTzMDKkoLG5eZmZn1G34yQVfVjM/eN69iTOsUHX44u5mZmfUgJ2pdNWAYVAyGLatenUvN96mZmZlZD3Ki1lVS1v25eRVj01xqnqLDzMzMepITte6oqd83mAD8vE8zMzPrWU7UuiO1qFWVl1I7qMJTdJiZmVmPcqLWHTX1sHsz7N7KmOoqXvJgAjMzM+tBTtS6o3Xk55Zs5KcHE5iZmVlPcqLWHdW5U3QM8GACMzMz61FO1Lpj39MJVjGmpoqtu5vY0dhU2JjMzMys33Ci1h2DRkFpBWxemTPprbs/zczMrGc4UeuOkhKorkv3qGVzqfnpBGZmZtZTnKh1V834/Sa9dYuamZmZ9RQnat1VXQ+bVzK6uhKAtZudqJmZmVnPcKLWXTXjYcd6KmMvIwZXuOvTzMzMeowTte6qbh352cCY6gGs9hQdZmZm1kPylqhJqpL0uKSnJS2UdEUqnyjpMUlLJf1YUkUqr0zrS9P2CTnHujSVL5H0znzF3CX7puhYyfjagTS84kTNzMzMekY+W9QagbdExEnANOBsSacC/wF8IyKOBl4BPp7qfxx4JZV/I9VD0hTgQuAE4Gzgu5JK8xj3oal5ddLbutoBNLyyk+aWKGxMZmZm1i/kLVGLzPa0Wp5eAbwFuD2V3wS8Jy2fn9ZJ298qSal8bkQ0RsQLwFJgVr7iPmRDxoJKYcsqxtcOZG9zsG6rBxSYmZlZ9+X1HjVJpZIWAOuB+4FlwOaIaJ2+vwEYl5bHAasA0vYtwPDc8nb2yT3XxZLmSZq3YcOGfFxO+0rLYOhY2Jx1fQKs3LSz985vZmZm/VZeE7WIaI6IaUAdWSvYcXk817URMTMiZo4cOTJfp2lfdT1sXkX9sCxRW+VEzczMzHpAr4z6jIjNwIPAaUCNpLK0qQ5YnZZXA/UAaXs1sDG3vJ19ikNNPWxZxdiaAZTIiZqZmZn1jHyO+hwpqSYtDwDeDiwmS9guSNXmAD9Ly3elddL230REpPIL06jQicBk4PF8xd0lNeNh6xoq1MKY6gHu+jQzM7MeUXbwKl02BrgpjdAsAW6LiLslLQLmSvoq8BRwXap/HXCzpKXAJrKRnkTEQkm3AYuAJuCSiGjOY9yHrroeohm2raG+dgCrPEWHmZmZ9YC8JWoR8QwwvZ3y5bQzajMidgPv7+BYVwJX9nSMPaZ1LrXNqxhfO5gHl/TiYAYzMzPrt/xkgp5Q3TqX2krqhw1kw7ZGdu0prkY/MzMz63ucqPWE6rrsfcsqxg/PRn42vOL71MzMzKx7nKj1hPIqGDwaNq+kbpjnUjMzM7Oe4UStp1TX73s6AXiKDjMzM+s+J2o9pSab9HbE4AoGlJeycpNHfpqZmVn3OFHrKdX1sKUBRaQpOtyiZmZmZt3jRK2n1IyH5kbYsZ7xtQPd9WlmZmbd5kStp9S0TtGxirphWaKWPVjBzMzMrGucqPWU6jTp7ZaVjK8dyI49zWzasaewMZmZmVmf5kStp+z3dAJP0WFmZmbd50Stp1QOgaoa2LKK+tYpOvzMTzMzM+sGJ2o9qWY8vPIi9bUDAM+lZmZmZt3jRK0n1R4Fm5YzsKKMEYMrnKiZmZlZtzhR60nDJ8HmF6G5ifragb5HzczMzLolb4mapHpJD0paJGmhpM+k8sslrZa0IL3OzdnnUklLJS2R9M6c8rNT2VJJX8xXzN1WexS0NO0b+elEzczMzLqjLI/HbgL+ISKelDQEmC/p/rTtGxFxVW5lSVOAC4ETgLHAryUdkzZ/B3g70AA8IemuiFiUx9i7pnZS9r5xOfXD6rn7mbXsbW6hvNQNl2ZmZnbo8pZBRMTaiHgyLW8DFgPjDrDL+cDciGiMiBeApcCs9FoaEcsjYg8wN9UtPsNTorZpOeNrB9LcEqzdvLuwMZmZmVmf1StNPZImANOBx1LRpyQ9I+l6ScNS2ThgVc5uDamso/LiM2gkVAyGTcuoax356Wd+mpmZWRflPVGTNBi4A/hsRGwFrgEmAdOAtcB/99B5LpY0T9K8DRs29MQhuxJEdp/axmWe9NbMzMy6La+JmqRysiTtloj4CUBErIuI5ohoAb5H1rUJsBqoz9m9LpV1VL6fiLg2ImZGxMyRI0f2/MV0VpqiY0z1AMpK5Ck6zMzMrMvyOepTwHXA4oj4ek75mJxq7wWeTct3ARdKqpQ0EZgMPA48AUyWNFFSBdmAg7vyFXe3pSk6SqOZccMGuEXNzMzMuiyfoz7PAP4S+KOkBansS8BsSdOAAFYAfwMQEQsl3QYsIhsxeklENANI+hRwH1AKXB8RC/MYd/fUTtpvig63qJmZmVlX5S1Ri4jfAWpn0z0H2OdK4Mp2yu850H5Fpfao7H3jcuqGjea+NS8VNh4zMzPrszzBV0/bN0VHNqBg0449bG9sKmxMZmZm1ic5Uetp+6boWL5v5Ke7P83MzKwrnKj1tJwpOurTXGoeUGBmZmZd4UQtH4ZPcouamZmZdZsTtXyoPQo2v0h1pRhSWeZEzczMzLrEiVo+pCk6tHkl9bUD3fVpZmZmXeJELR9ap+jY9ALjawfy4kYnamZmZnbonKjlQ84UHZNGDWLlpp3sbW4pbExmZmbW53QqUZM0SFJJWj5G0sl5N/cAABoPSURBVLvTczytPYNGQsUQ2LiMSSMH09QSblUzMzOzQ9bZFrWHgSpJ44BfkT0a6sZ8BdXnSVA7ETYtZ9LIwQAs27C9wEGZmZlZX9PZRE0RsRN4H/DdiHg/cEL+wuoHhk+CTcs4auQgwImamZmZHbpOJ2qSTgM+DPwilZXmJ6R+ovYo2LySIeUwemgly9bvKHREZmZm1sd0NlH7LHAp8NOIWCjpKODB/IXVD6QpOti8kkkjB7tFzczMzA5ZWWcqRcRvgd8CpEEFL0fE3+UzsD5v38jP5Rw9agw/fWo1EYGkwsZlZmZmfUZnR33+SNJQSYOAZ4FFkj6f39D6uH1zqWUDCrbtbmLDtsbCxmRmZmZ9Sme7PqdExFbgPcC9wESykZ8dklQv6UFJiyQtlPSZVF4r6X5Jz6f3Yalckq6WtFTSM5Jm5BxrTqr/vKQ5XbrS3tZmig6Ape7+NDMzs0PQ2UStPM2b9h7grojYC8RB9mkC/iEipgCnApdImgJ8EXggIiYDD6R1gHOAyel1MXANZIkdcBlwCjALuKw1uStquVN0jGod+ekBBWZmZtZ5nU3U/hdYAQwCHpZ0JLD1QDtExNqIeDItbwMWA+OA84GbUrWbyJI/UvkPIvMoUCNpDPBO4P6I2BQRrwD3A2d3Mu7CSlN0HDG0ioEVpSxb7xY1MzMz67xOJWoRcXVEjIuIc1Mi9SJwVmdPImkCMB14DBgdEWvTppeA0Wl5HLAqZ7eGVNZRedtzXCxpnqR5GzZs6Gxo+VV7FLzyImpp8shPMzMzO2SdHUxQLenrrYmQpP8ma13rzL6DgTuAz6b73PaJiODgXaidEhHXRsTMiJg5cuTInjhk99VOgmhOU3QMYrm7Ps3MzOwQdLbr83pgG/CB9NoK3HCwndJ9bXcAt0TET1LxutSlSXpfn8pXA/U5u9elso7Ki1/OFB2TRg5m9eZd7NzTVNiYzMzMrM/obKI2KSIui4jl6XUFcNSBdlA2Ydh1wOKI+HrOpruA1pGbc4Cf5ZR/JI3+PBXYkrpI7wPeIWlYGkTwjlRW/HKn6BiVjfx0q5qZmZl1VmcTtV2S3tC6IukMYNdB9jmDbAqPt0hakF7nAl8D3i7peeBtaR3gHmA5sBT4HvC3ABGxCfg34In0+koqK37tTNHh+9TMzMysszr1ZALgE8APJFWn9Vd4tVWsXRHxO6Cjafjf2k79AC7p4FjXk3W/9i37puhYxpHDB1IiT9FhZmZmndfZUZ9PR8RJwOuA10XEdOAteY2svxgxGV7+E1XlpdTXDvQUHWZmZtZpne36BCAituaM3Pz7PMTT/4w6HjavhMZtnqLDzMzMDskhJWpt+OninTHqhOx9/XMcPWowy1/eQXNLj8xIYmZmZv1cdxI1ZxudMer47H39QiaNHMSephZWv3KwcRhmZmZmB0nUJG2TtLWd1zZgbC/F2LfVHAnlg2D9Yo/8NDMzs0NywEQtIoZExNB2XkMiorMjRg9vJSVZq9q6hU7UzMzM7JB0p+vTOmvU8bB+EcMGllM7qMKJmpmZmXWKE7XeMPoE2LkRdmxg0shBLFvvudTMzMzs4Jyo9YZRU7L31P3pFjUzMzPrDCdqvWF06xQdi5g0cjAbd+zhlR17ChuTmZmZFT0nar1h0IjsuZ/rFjFp1CDAAwrMzMzs4Jyo9ZZRU/a1qIETNTMzMzs4J2q9ZfQJsOE56mqqqCgt8cPZzczM7KCcqPWWUcfD3p2Ubl7BxBGD/HB2MzMzO6i8JWqSrpe0XtKzOWWXS1otaUF6nZuz7VJJSyUtkfTOnPKzU9lSSV/MV7x5t++Zn4uZNGoQS931aWZmZgeRzxa1G4Gz2yn/RkRMS697ACRNAS4ETkj7fFdSqaRS4DvAOcAUYHaq2/eMOi57X7+IyaOGsHLTTnY0NhU2JjMzMytqeUvUIuJhYFMnq58PzI2Ixoh4AVgKzEqvpRGxPCL2AHNT3b6nYhAMmwDrFnLiuGoi4LmXthY6KjMzMytihbhH7VOSnkldo8NS2ThgVU6dhlTWUflrSLpY0jxJ8zZs2JCPuLtv1AmwfhEnjhsKwLOrnaiZmZlZx3o7UbsGmARMA9YC/91TB46IayNiZkTMHDlyZE8dtmeNngIbl3HEQKgdVMGzq7cUOiIzMzMrYmW9ebKIWNe6LOl7wN1pdTVQn1O1LpVxgPK+Z9TxEM3o5ec5YexQFq5xi5qZmZl1rFdb1CSNyVl9L9A6IvQu4EJJlZImApOBx4EngMmSJkqqIBtwcFdvxtyjRr36KKkTx1Xzp3XbaGxqLmxMZmZmVrTy1qIm6VbgTGCEpAbgMuBMSdOAAFYAfwMQEQsl3QYsApqASyKiOR3nU8B9QClwfUQszFfMeTd8EpRWZIna2DfT1BL86aXtTK2rLnRkZmZmVoTylqhFxOx2iq87QP0rgSvbKb8HuKcHQyuc0nIYcSysW8QJ09OAgjVbnKiZmZlZu/xkgt426nhYv4jxtQMZUlnGwjUeUGBmZmbtc6LW20ZPga2rKWncwpSxQz1Fh5mZmXXIiVpvy3mU1Injqlm8ditNzS2FjcnMzMyKkhO13jbq+Ox9/UJOGDuUxqYWlr+8o7AxmZmZWVFyotbbquugshrWZVN0AJ741szMzNrlRK23SWlAwWKOGjGIqvIS36dmZmZm7XKiVgijp8C6hZQJjjtiqEd+mpmZWbucqBXC2BnQuAU2LuXEcUNZtGYrLS1R6KjMzMysyDhRK4T6Wdl7w+OcOLaabY1NrNy0s7AxmZmZWdFxolYIwydDVTU0PPHqgAJ3f5qZmVkbTtQKoaQExs2EVU8wefRgykrEwjUeUGBmZmb7c6JWKHUnw/pFVDbv5JjRQzxFh5mZmb2GE7VCqT8ZCFg9nxPHDWXhmq1EeECBmZmZvcqJWqGMe332nu5T27RjDy9t3V3YmMzMzKyo5C1Rk3S9pPWSns0pq5V0v6Tn0/uwVC5JV0taKukZSTNy9pmT6j8vaU6+4u11A4bBiGNh1ROcMHYogCe+NTMzs/3ks0XtRuDsNmVfBB6IiMnAA2kd4BxgcnpdDFwDWWIHXAacAswCLmtN7vqFupOh4QmOP2IIkh8lZWZmZvvLW6IWEQ8Dm9oUnw/clJZvAt6TU/6DyDwK1EgaA7wTuD8iNkXEK8D9vDb567vqT4Zdmxi4fSWTRg72EwrMzMxsP719j9roiFibll8CRqflccCqnHoNqayj8v6h7uTsvSHr/vQUHWZmZparYIMJIhvi2GPDHCVdLGmepHkbNmzoqcPm18jjoGIIrHqcqeOqWbtlN+s8oMDMzMyS3k7U1qUuTdL7+lS+GqjPqVeXyjoqf42IuDYiZkbEzJEjR/Z44HlRUgrjZkDDE8yaWAvAo8s3FjgoMzMzKxa9najdBbSO3JwD/Cyn/CNp9OepwJbURXof8A5Jw9Iggneksv6jfhasW8gJI8oYUlXGH5Y5UTMzM7NMWb4OLOlW4ExghKQGstGbXwNuk/Rx4EXgA6n6PcC5wFJgJ/BRgIjYJOnfgCdSva9ERNsBCn1b3ckQzZS+tIBTJg7nD25RMzMzsyRviVpEzO5g01vbqRvAJR0c53rg+h4Mrbi0DihY9TinT3ovv168jtWbdzGuZkBh4zIzM7OC85MJCm1gLdROgoZ5nDZpOIC7P83MzAxwolYc6mdBw+McO2owtYMq+P2ylwsdkZmZmRUBJ2rFoG4m7NhAydaVnHpULY8u2+gHtJuZmZkTtaJQNyt7X/UEpx01nDVbdrNy087CxmRmZmYF50StGIyaAuWDoOEJTps0AoDf+z41MzOzw54TtWJQWpYmvn2cSSMHMXJIpQcUmJmZmRO1olE3E176I9q7k9MnZfOp+T41MzOzw5sTtWJx1JnQ0gTLHuS0o4azYVsjyzZsL3RUZmZmVkBO1IrFkWdAZTUsuZfT031q7v40MzM7vDlRKxal5TD57fCnX1JfU8G4mgEeUGBmZnaYc6JWTI49B3a+jFZnTyl4dPlGWlp8n5qZmdnhyolaMZn8digpgyX3cNpRw3ll516WrNtW6KjMzMysQJyoFZOqapjwBlhy777nfrr708zM7PDlRK3YHPsuePlPjG1azYThAz2gwMzM7DDmRK3YHHt29r7kHk6bNJzHXthIs+9TMzMzOywVJFGTtELSHyUtkDQvldVKul/S8+l9WCqXpKslLZX0jKQZhYi519SMh9FT903TsW13E0+ufKXQUZmZmVkBFLJF7ayImBYRM9P6F4EHImIy8EBaBzgHmJxeFwPX9Hqkve24c2HVo7xlfCkDK0r5yZMNhY7IzMzMCqCYuj7PB25KyzcB78kp/0FkHgVqJI0pRIC95thzIFoY9OIDnH3iEdz99Fp2720udFRmZmbWywqVqAXwK0nzJV2cykZHxNq0/BIwOi2PA1bl7NuQyvYj6WJJ8yTN27BhQ77i7h1jpsGQsfDcL7hgRh3bGpv41aJ1hY7KzMzMelmhErU3RMQMsm7NSyS9KXdjZE8jP6Q76CPi2oiYGREzR44c2YOhFoCUtaot+w2njh/EuJoB3D7f3Z9mZmaHm4IkahGxOr2vB34KzALWtXZppvf1qfpqoD5n97pU1r8dey7s3UnJikd434xx/O75DazburvQUZmZmVkv6vVETdIgSUNal4F3AM8CdwFzUrU5wM/S8l3AR9Loz1OBLTldpP3XxDdCxWBY8gveN6OOloCfPtX/81MzMzN7VSFa1EYDv5P0NPA48IuI+CXwNeDtkp4H3pbWAe4BlgNLge8Bf9v7IRdAWSUc/VZY8ksm1g7g9UcO4/b5DWS9wmZmZnY4KOvtE0bEcuCkdso3Am9tpzyAS3ohtOJz7Ltg0c9gxSNc8PqJXPqTP/JMwxZOqq8pdGRmZmbWC4ppeg5ra8r5MHg0PPLfvOt1Y6gsK+EOz6lmZmZ22HCiVszKq+D0T8MLv2XohgW844Qj+NmCNTQ2eU41MzOzw4ETtWL3+o/CgGHwyFVc8Po6tuzay28Wrz/4fmZmZtbnOVErdpWD4dRL4E+/5A2D1zJ6aKW7P83MzA4TTtT6gll/DZVDKf1/X+c908fx4JINbNjWWOiozMzMLM+cqPUFA2rg5L+ChXfyoaMaiQiuum9JoaMyMzOzPHOi1lecdgmUVXHkov/l4jdN4sfzVvHQEt+rZmZm1p85UesrBo2AmR+FZ37M52ZWMHnUYL54xx/ZsmtvoSMzMzOzPHGi1pec/mkoKaXysf/hqvefxIbtjXz17kWFjsrMzMzyxIlaXzJ0LEz7MDz1Q04a9AqfePNR/N/5DfzmuXWFjszMzMzywIlaX/OGz0FZFdxwLp95XRPHjh7CpT/5I1t2ugvUzMysv3Gi1tcMOxI+eg9ECxU3nct339jIy9v3cMXdCwsdmZmZmfUwJ2p90RFT4eO/gkEjmfTLv+DrU1fxkydX89W7F7Fm865CR2dmZmY9xIlaXzXsSPjYr2D0Cbz7T1/kPyfM5/r/9wJv/M8H+fStT/HUylcKHaGZmZl1kyKi0DF0iqSzgW8BpcD3I+JrHdWdOXNmzJs3r9diK6g9O+C2ObD0fpoGj2Fd1LJw+2AamodRWj2WkaNGU1o5iNKqIZRVDaZiwGDKBwyhcsAgyqsGUzlwMAMGDmZgZSVVFSVUlJYgqdBXZWZmdtiQND8iZra3ray3g+kKSaXAd4C3Aw3AE5LuigjPTVExCGbfCo/9L2XrFzFu62rGVK2mefMzlO/YBS907jCNUcZuKthMBY1U0qhK9qiSppIKmkoqaSqppLmkkpbS7BWllbSUVmQDG0oroawClVaisgpUVklJebZcUlZBaVklpeWVlJS3Lpen9wrKyisoLaugtKw8LZdRXlZBWXk5paWllJaWUFYiJ49mZnZY6hOJGjALWBoRywEkzQXOB5yoAZSWw+mf2rdaApREQONW2L2VPbu3sWvHdhp3bGX3zm3s3b2dpsadNO/eTsuenbTs2Ql7dkLTLti7GzXtpqR5FyVNjQxo2U1Zy05KW16hvGkP5dFIWeylIvZQzl4qaMrrpe2NUhopoZkSWiihmdJsWdl6CyVEem+RCEoItZYre+nVZSQg1QNCJWldRNo/k61DCSH21SEdo3U59h1PkOqxX1mWYO47rtoeA1AWS+6+7EtMc4/dcZ1XY2ktabsOIaFXD5Szvc37fknxIZblbs7ZGu3u086+7ZRGV5L0Tu6jds/d+fNFJ6u2Vy3aKW0/nk7qC/8z016MBY27uD6z7kTTpd9JXrWN51B777qzf3fPvb/SQbWc/J5Pd+sY3dFXErVxwKqc9QbglNwKki4GLgYYP35870VWrCSoqoaqaiqAinydp6UFmhuheQ9Ne3azp3E3ext3sbdxN01Ne2ja20jTnkZa9u6mae8eWpr20tLUmN730NLUCC1NtDQ3Ec1N0LKXaN4LLc1ESxO0NENLM2ppgpYmiBaIZtTSvG+ZaEHRkvPejAiIQLSWRyprASJne1ZHASW0oMgSz/22R7TZh+y4rf/U7jtOvLrvvv3I2bYvvWt339aU7tXjvHb91X07rkOwXzxt6TX7k1M3cuq13cZr9gMoUd+4fcLMrCteLKkHJ2rdFxHXAtdCdo9agcM5fJSUQMkAKB9AWVV1//mDsh4XLS2vLevgHtl4tUJ7pQfcv7P33bZfrZ3jdfR/4z18ng537kRLSbR0dLygc+00nbzuTsbT/im6/l3lQ/tfyyFc36HE3qnv8LW/j76qw++1u59tZ/bv7r7t1BteUthxl33l39XVQH3Oel0qM7M+Qu38x67YOmvMzIpNX5me4wlgsqSJkiqAC4G7ChyTmZmZWV71iRa1iGiS9CngPrLpOa6PCE/Fb2ZmZv1an0jUACLiHuCeQsdhZmZm1lv6StenmZmZ2WHHiZqZmZlZkXKiZmZmZlaknKiZmZmZFak+81D2QyFpA/BiDx92BPByDx/Tus/fS/Hyd1Oc/L0UL383xak3vpcjI2Jkexv6ZaKWD5LmdfRkeyscfy/Fy99NcfL3Urz83RSnQn8v7vo0MzMzK1JO1MzMzMyKlBO1zru20AFYu/y9FC9/N8XJ30vx8ndTnAr6vfgeNTMzM7Mi5RY1MzMzsyLlRO0gJJ0taYmkpZK+WOh4DmeS6iU9KGmRpIWSPpPKayXdL+n59D6s0LEejiSVSnpK0t1pfaKkx9Jv58eSKgod4+FIUo2k2yU9J2mxpNP8myk8SZ9L/x17VtKtkqr8mykMSddLWi/p2Zyydn8jylydvqNnJM3Id3xO1A5AUinwHeAcYAowW9KUwkZ1WGsC/iEipgCnApek7+OLwAMRMRl4IK1b7/sMsDhn/T+Ab0TE0cArwMcLEpV9C/hlRBwHnET2Hfk3U0CSxgF/B8yMiBOBUuBC/JsplBuBs9uUdfQbOQeYnF4XA9fkOzgnagc2C1gaEcsjYg8wFzi/wDEdtiJibUQ8mZa3kf2DM47sO7kpVbsJeE9hIjx8SaoD3gV8P60LeAtwe6ri76UAJFUDbwKuA4iIPRGxGf9mikEZMEBSGTAQWIt/MwUREQ8Dm9oUd/QbOR/4QWQeBWokjclnfE7UDmwcsCpnvSGVWYFJmgBMBx4DRkfE2rTpJWB0gcI6nH0T+ALQktaHA5sjoimt+7dTGBOBDcANqVv6+5IG4d9MQUXEauAqYCVZgrYFmI9/M8Wko99Ir+cFTtSsz5E0GLgD+GxEbM3dFtkwZg9l7kWSzgPWR8T8Qsdir1EGzACuiYjpwA7adHP6N9P70v1O55Ml0mOBQby2682KRKF/I07UDmw1UJ+zXpfKrEAklZMlabdExE9S8brWpuf0vr5Q8R2mzgDeLWkF2e0BbyG7L6omdeuAfzuF0gA0RMRjaf12ssTNv5nCehvwQkRsiIi9wE/Ifkf+zRSPjn4jvZ4XOFE7sCeAyWkkTgXZzZ53FTimw1a67+k6YHFEfD1n013AnLQ8B/hZb8d2OIuISyOiLiImkP1GfhMRHwYeBC5I1fy9FEBEvASsknRsKnorsAj/ZgptJXCqpIHpv2ut34t/M8Wjo9/IXcBH0ujPU4EtOV2keeEJbw9C0rlk99+UAtdHxJUFDumwJekNwCPAH3n1Xqgvkd2ndhswHngR+EBEtL0x1HqBpDOBf4yI8yQdRdbCVgs8BfxFRDQWMr7DkaRpZIM8KoDlwEfJ/ifdv5kCknQF8EGy0exPAX9Fdq+TfzO9TNKtwJnACGAdcBlwJ+38RlJi/W2yruqdwEcjYl5e43OiZmZmZlac3PVpZmZmVqScqJmZmZkVKSdqZmZmZkXKiZqZmZlZkXKiZmZmZlaknKiZ2WFHUrOkBTmvHnsouaQJkp7tqeOZ2eGt7OBVzMz6nV0RMa3QQZiZHYxb1MzMEkkrJP2npD9KelzS0al8gqTfSHpG0gOSxqfy0ZJ+Kunp9Do9HapU0vckLZT0K0kDCnZRZtanOVEzs8PRgDZdnx/M2bYlIqaSzT7+zVT2P8BNEfE64Bbg6lR+NfDbiDiJ7BmaC1P5ZOA7EXECsBn48zxfj5n1U34ygZkddiRtj4jB7ZSvAN4SEcsllQMvRcRwSS8DYyJibypfGxEjJG0A6nIf8yNpAnB/RExO6/8ElEfEV/N/ZWbW37hFzcxsf9HB8qHIfT5jM74f2My6yImamdn+Ppjz/oe0/HvgwrT8YeCRtPwA8EkASaWSqnsrSDM7PPj/8szscDRA0oKc9V9GROsUHcMkPUPWKjY7lX0auEHS54ENwEdT+WeAayV9nKzl7JPA2rxHb2aHDd+jZmaWpHvUZkbEy4WOxcwM3PVpZmZmVrTcomZmZmZWpNyiZmZmZlaknKiZmZmZFSknamZmZmZFyomamZmZWZFyomZmZmZWpJyomZmZmRWp/x9VRAmMFL9qVwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def MAPE(Y_actual,Y_Predicted):\n",
        "    mape = np.mean(np.abs((Y_actual - Y_Predicted)/Y_actual))*100\n",
        "    return mape"
      ],
      "metadata": {
        "id": "DWnx1e-Eo5gB"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = XGBRegressor().fit(X_train, y_train)\n",
        "y_pred = model2.predict(X_test).reshape(-1,1)\n",
        "model2.save_model(\"/content/drive/MyDrive/XGBRegressor_modedl.h5\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h8pXSL4-o5id",
        "outputId": "a5c51f0a-543f-4875-fea2-4e27474dab86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[14:32:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = XGBRegressor()\n",
        "model2.load_model(\"/content/drive/MyDrive/XGBRegressor_modedl.h5\")\n",
        "y_pred = model2.predict(X_test).reshape(-1,1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23JF4UjiCuSK",
        "outputId": "97295eb9-f0b5-4a90-e4bf-b36341a7cce5"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[14:50:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "1pUmUgGwCuYj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a = MAPE(y_test, y_pred)[0]"
      ],
      "metadata": {
        "id": "_BMmP8z2-HB4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "b = MAPE(y_test, model.predict(X_test))"
      ],
      "metadata": {
        "id": "e8Wu64jIeRfI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "plt.bar(\"XGBRegressor Model\",a, label=\"XGBRegressor Model\", width=0.15)\n",
        "plt.bar(\"Neural network Model\",b, label=\"Neural network Model\", width=0.15)\n",
        "plt.title(\"\\nModel Analysis on intelligence traffic flow prediction\\n\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "ejuzVGmcOAw0",
        "outputId": "b3bcec45-86f0-41fc-b458-1afcda01d9ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R-VCvJBKzCvD",
        "outputId": "7d41ab33-a2e6-4b66-9bff-06d2d41ef176"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6886/6886 [==============================] - 13s 2ms/step - loss: 0.8584 - accuracy: 0.0000e+00\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8584083914756775, 0.0]"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2.score(X_test, y_test)"
      ],
      "metadata": {
        "id": "e0o4kwihzC0a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Jf0GkYk4w-S8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "afKRrk0xw-Va"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "hO3f22xWOAzB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cleaned_data = index_data.copy()\n",
        "cleaned_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Q-P1UKPCN07a",
        "outputId": "5f13d200-a533-4c7e-e453-026524b135d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               link_id  id_index\n",
              "0  4377906289869500514         1\n",
              "1  4377906284594800514         2\n",
              "2  4377906289425800514         3\n",
              "3  4377906284525800514         4\n",
              "4  4377906284422600514         5"
            ],
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>0</th>\n",
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              "      <td>4377906284594800514</td>\n",
              "      <td>2</td>\n",
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              "      <th>2</th>\n",
              "      <td>4377906289425800514</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4377906284525800514</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4377906284422600514</td>\n",
              "      <td>5</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b65d5d57-089c-4c97-9f80-5bc3ddd39c8a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      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",
              "    [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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b65d5d57-089c-4c97-9f80-5bc3ddd39c8a 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-b65d5d57-089c-4c97-9f80-5bc3ddd39c8a');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "gpB0fUOKOByt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "IJvvFuavOB4N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.zeros((132,132))\n",
        "empty_df = pd.DataFrame(x, columns=list(range(1, x.shape[0]+1)))\n",
        "cleaned_data = pd.concat([index_data, empty_df], axis=1)"
      ],
      "metadata": {
        "id": "fNHHv8uosnXT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(cleaned_data.shape[0]):\n",
        "    if \"#\" in df2[\"in_links\"][i]:\n",
        "        a = df2[\"in_links\"][i].split(\"#\")\n",
        "        for j in a:\n",
        "            column_id = cleaned_data[\"id_index\"][cleaned_data[\"link_id\"] == eval(j)].values[0]\n",
        "            row_index = cleaned_data[\"link_id\"][cleaned_data[\"link_id\"] == int(df2[\"link_ID\"][i])].index[0]\n",
        "            cleaned_data.loc[row_index, column_id] = 1  \n",
        "    elif eval(df2[\"in_links\"][i]) != 0:\n",
        "        column_id = cleaned_data[\"id_index\"][cleaned_data[\"link_id\"] == eval(df2[\"in_links\"][i])].values[0]     #column_id\n",
        "        row_index = cleaned_data[\"link_id\"][cleaned_data[\"link_id\"] == int(df2[\"link_ID\"][i])].index[0]         # row index\n",
        "        cleaned_data.loc[row_index, column_id] = 1\n",
        "\n",
        "    else:\n",
        "        pass"
      ],
      "metadata": {
        "id": "SjprPwIQUXk5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(cleaned_data.shape[0]):\n",
        "    if \"#\" in df2[\"out_links\"][i]:\n",
        "        a = df2[\"out_links\"][i].split(\"#\")\n",
        "        for j in a:\n",
        "            column_id = cleaned_data[\"id_index\"][cleaned_data[\"link_id\"] == eval(j)].values[0]\n",
        "            row_index = cleaned_data[\"link_id\"][cleaned_data[\"link_id\"] == int(df2[\"link_ID\"][i])].index[0]\n",
        "            cleaned_data.loc[row_index, column_id] = -1  \n",
        "    elif eval(df2[\"out_links\"][i]) != 0:\n",
        "        column_id = cleaned_data[\"id_index\"][cleaned_data[\"link_id\"] == eval(df2[\"out_links\"][i])].values[0]     #column_id\n",
        "        row_index = cleaned_data[\"link_id\"][cleaned_data[\"link_id\"] == int(df2[\"link_ID\"][i])].index[0]         # row index\n",
        "        cleaned_data.loc[row_index, column_id] = -1\n",
        "\n",
        "    else:\n",
        "        pass"
      ],
      "metadata": {
        "id": "VDhD4jjK5iov"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "cleaned_data.head()"
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        "colab": {
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        "id": "_n_aZmckTUAy",
        "outputId": "95290955-35b4-45d7-df65-967dba140784"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               link_id  id_index\n",
              "0  4377906289869500514         1\n",
              "1  4377906284594800514         2\n",
              "2  4377906289425800514         3\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
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          "metadata": {},
          "execution_count": 10
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      "cell_type": "code",
      "source": [
        "cleaned_data.to_csv(\"/content/drive/MyDrive/intelligent_transportation_project/clean.csv\")"
      ],
      "metadata": {
        "id": "ljU6VFDQXyV6"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        ""
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      "metadata": {
        "id": "7d7DaHY3urIK"
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      "outputs": []
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        "df2.loc[df2[\"link_ID\"] == cleaned_data[\"link_id\"][92]]"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "SP5Sp9gbzIox",
        "outputId": "b626c1a1-6346-4830-f776-894e745111e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                link_ID                                 in_links  \\\n",
              "92  3377906282418510514  4377906280329500514#4377906286334600514   \n",
              "\n",
              "              out_links  \n",
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              "          const element = document.querySelector('#df-b3b5bb74-acff-48de-9613-3d43ac5516ab');\n",
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              "            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",
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              "  </div>\n",
              "  "
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          "metadata": {},
          "execution_count": 98
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      "execution_count": null,
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          },
          "metadata": {},
          "execution_count": 104
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleaned_data[\"link_id\"][cleaned_data[\"link_id\"] == int(df2[\"link_ID\"][0])].index[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "0ZVWXWsIlg99",
        "outputId": "8990abca-c9bf-411e-919b-c8d02a3d09da"
      },
      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "0"
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          },
          "metadata": {},
          "execution_count": 38
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    {
      "cell_type": "code",
      "source": [
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      "metadata": {
        "id": "g2455nQH8Ag_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "metadata": {
        "id": "XiMZnOQ9LN02"
      },
      "execution_count": null,
      "outputs": []
    },
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          "height": 488
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        "outputId": "2214cbce-05e3-4ba4-c419-f1d7af9b9ce4"
      },
      "execution_count": null,
      "outputs": [
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              "\n",
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              "          const element = document.querySelector('#df-971eaca9-5c41-442e-831b-1ef7d2339097');\n",
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              "            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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleaned_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fZvB9i-459mr",
        "outputId": "824b4076-f2de-447f-aab2-1e0d4dcc879b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 132 entries, 0 to 131\n",
            "Columns: 134 entries, linkID to 132\n",
            "dtypes: float64(132), int64(1), uint64(1)\n",
            "memory usage: 138.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = df3[[\"linkID\", \"date\", \"Start_time\", \"travel_time\"]]\n"
      ],
      "metadata": {
        "id": "RKrsO1Z07bfA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "int(data[\"time_interval\"][0].strip(\"[)\").split(\",\")[0].split(\" \")[0].split(\"-\")[-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LWF5GKtu7bh4",
        "outputId": "7a1e1272-2435-4686-ea66-6d70962ae590"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "int(data[\"time_interval\"][0].strip(\"[)\").split(\",\")[0].split(\" \")[1].split(\":\")[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VC15YXJ47box",
        "outputId": "1e5a61b0-e6eb-4a90-9168-603afc3a7d75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "day = [int(i.strip(\"[)\").split(\",\")[0].split(\" \")[0].split(\"-\")[-1]) for i in df3[\"time_interval\"]]\n",
        "start_hour = [int(i.strip(\"[)\").split(\",\")[0].split(\" \")[1].split(\":\")[0]) for i in df3[\"time_interval\"]]\n",
        "start_minute = [int(i.strip(\"[)\").split(\",\")[0].split(\" \")[1].split(\":\")[1]) for i in df3[\"time_interval\"]]\n",
        "month = [int(i.strip(\"[)\").split(\",\")[0].split(\" \")[0].split(\"-\")[1]) for i in df3[\"time_interval\"]]"
      ],
      "metadata": {
        "id": "3uARRgx5Cf8H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "X62w31Lqj_5f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"month\"] = month\n",
        "data[\"day\"] = day\n",
        "\n",
        "data[\"hour\"] = start_hour\n",
        "data[\"Start_minute\"] = start_minute\n",
        "\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YOnkXZrICf-V",
        "outputId": "a17fca74-5499-400f-b477-89da0c5e0c67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "                linkID  travel_time  month  day  hour  Start_minute\n",
              "0  4377906283422600514          3.0      5    6    11             4\n",
              "1  3377906289434510514          1.0      5    6    10            42\n",
              "2  3377906285934510514         35.2      5    6    11            56\n",
              "3  3377906285934510514         26.2      5    6    17            46\n",
              "4  3377906287934510514         10.4      5    6    10            52"
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        "end_minute = []\n",
        "for i in df3[\"time_interval\"]:\n",
        "    a = int(i.strip(\"[)\").split(\",\")[1].split(\" \")[1].split(\":\")[1])\n",
        "    if a == 0:\n",
        "        end_minute.append(60)\n",
        "    else:\n",
        "        end_minute.append(a)"
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        "data[\"End_minute\"] = end_minute\n",
        "\n",
        "data.head()"
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            "text/plain": [
              "                linkID  travel_time  month  day  hour  Start_minute  \\\n",
              "0  4377906283422600514          3.0      5    6    11             4   \n",
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              "4  3377906287934510514         10.4      5    6    10            52   \n",
              "\n",
              "   End_minute  \n",
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              "<p>5 rows × 137 columns</p>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = train_data[[\"month\", \"day\", \"hour\", \"Start_minute\", \"End_minute\", \"id_index\"]]\n",
        "Y = train_data[[\"travel_time\"]]"
      ],
      "metadata": {
        "id": "zDF0THvK6IWb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Gg_1awXm6I5R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.to_csv(\"/content/drive/MyDrive/intelligent_transportation_project/train.csv\", index=False)"
      ],
      "metadata": {
        "id": "liMAHnG7KePf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "msfCTuq3M81z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train = train_data[::10]\n",
        "validation = train_data[::50]\n",
        "test = train_data[10::50]"
      ],
      "metadata": {
        "id": "PP4USlI6XIWE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.to_csv(\"/content/drive/MyDrive/intelligent_transportation_project/train_data.csv\", index=False)"
      ],
      "metadata": {
        "id": "RcuVLbehuJ3Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "validation.to_csv(\"/content/drive/MyDrive/intelligent_transportation_project/validation_data.csv\", index=False)"
      ],
      "metadata": {
        "id": "zhcWHFLzuK41"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test.to_csv(\"/content/drive/MyDrive/intelligent_transportation_project/test_data.csv\", index=False)"
      ],
      "metadata": {
        "id": "Is50p4gQuK8Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "oKR4TQmBxLao"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = train.drop([\"linkID\", \"travel_time\", \"id_index\"], axis=1)\n",
        "y_train = train[[\"travel_time\"]]"
      ],
      "metadata": {
        "id": "lifROycZusFf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_validation = validation.drop([\"linkID\", \"travel_time\", \"id_index\"], axis=1)\n",
        "y_validation = validation[[\"travel_time\"]]"
      ],
      "metadata": {
        "id": "MqEUFE45usHe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = test.drop([\"linkID\", \"travel_time\", \"id_index\"], axis=1)\n",
        "y_test = test[[\"travel_time\"]]"
      ],
      "metadata": {
        "id": "sCniy0_6XIYs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "EM9mHF1qxLx1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# X_train, X_test, y_train, y_test = tts(X, Y, test_size=0.20, shuffle=True)\n",
        "print(\"Shape of input value for the train: \", X_train.shape[0])\n",
        "print(\"Shape of input value for the test: \", X_test.shape[0])\n",
        "print(\"Shape of input value for the validation: \", X_validation.shape[0])\n",
        "\n",
        "print(\"\\nShape of output value for the train: \", y_train.shape[0])\n",
        "print(\"Shape of output value for the test: \", y_test.shape[0])\n",
        "print(\"Shape of output value for the train: \", y_validation.shape[0])"
      ],
      "metadata": {
        "id": "JY6pZhZIfOon",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf3714a0-f3f7-443b-bd8c-05502b069bc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of input value for the train:  247566\n",
            "Shape of input value for the test:  49513\n",
            "Shape of input value for the validation:  49514\n",
            "\n",
            "Shape of output value for the train:  247566\n",
            "Shape of output value for the test:  49513\n",
            "Shape of output value for the train:  49514\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UVCGHzwDL_BS",
        "outputId": "06d79794-3e14-41e1-bf81-508458437fac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((247566, 134), (247566, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "4OD6ZisFTIJ9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n"
      ],
      "metadata": {
        "id": "xoI0RCFoxMSA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "axvHDRgBS9c3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Qo3co5ONS9fO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "x0qfqu6pZfPQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vYL6Xp2XsW5W",
        "outputId": "b682b775-5eea-48b7-b080-8f0048dbbde7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3852585, 137)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "7NLLsyOYr_mm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "MD-G8ikEr_pL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "NJiW6ylFr_u2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "MjEXuFnnpRgx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ota3R_vFpXhO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "qXdkQBrlpXkc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a = train_data[:3852585]\n",
        "1926295, 1926295\n",
        "a.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CzFD50-kXIbA",
        "outputId": "3977c12f-d041-451b-8828-856f3ea564ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b = df1.drop(df1.index[3::10], axis=0)\n",
        "b.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PHP2AGv4XIdo",
        "outputId": "5646a40b-9c7c-4df8-8431-aeeac74128b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(119, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "xgPA36FyXIgR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ZFOGWMJ4KeU-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_data[\"start_time\"] = np.zeros((final_data.shape[0],1))\n",
        "final_data[\"end_time\"] = np.zeros((final_data.shape[0],1))\n",
        "for i in range(final_data.shape[0]):\n",
        "\n",
        "    final_data[\"start_time\"][i] = time.mktime(datetime.datetime.strptime(final_data[\"time_interval\"][i].strip(\"[)\").split(\",\")[0], '%Y-%m-%d %H:%M:%S').timetuple())\n",
        "    final_data[\"end_time\"][i] = time.mktime(datetime.datetime.strptime(final_data[\"time_interval\"][i].strip(\"[)\").split(\",\")[1], '%Y-%m-%d %H:%M:%S').timetuple())\n",
        "\n",
        "final_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wRnaEzXEPnsl",
        "outputId": "8c83e0e4-9ad2-49bb-8082-db9f2c7c7e6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      linkID        date  \\\n",
              "0        4377906283422600514  2017-05-06   \n",
              "1        4377906283422600514  2017-05-06   \n",
              "2        4377906283422600514  2017-05-06   \n",
              "3        4377906283422600514  2017-05-06   \n",
              "4        4377906283422600514  2017-05-06   \n",
              "...                      ...         ...   \n",
              "7705170  9377906286615510514  2017-06-29   \n",
              "7705171  9377906286615510514  2017-06-29   \n",
              "7705172  9377906286615510514  2017-06-11   \n",
              "7705173  9377906286615510514  2017-06-11   \n",
              "7705174  9377906286615510514  2017-06-11   \n",
              "\n",
              "                                     time_interval  travel_time  id_index  \\\n",
              "0        [2017-05-06 11:04:00,2017-05-06 11:06:00)          3.0        19   \n",
              "1        [2017-05-06 17:58:00,2017-05-06 18:00:00)          5.0        19   \n",
              "2        [2017-05-06 14:10:00,2017-05-06 14:12:00)          6.8        19   \n",
              "3        [2017-05-06 09:52:00,2017-05-06 09:54:00)          1.7        19   \n",
              "4        [2017-05-06 10:42:00,2017-05-06 10:44:00)          2.3        19   \n",
              "...                                            ...          ...       ...   \n",
              "7705170  [2017-06-29 06:48:00,2017-06-29 06:50:00)          8.0       123   \n",
              "7705171  [2017-06-29 07:48:00,2017-06-29 07:50:00)         13.5       123   \n",
              "7705172  [2017-06-11 07:40:00,2017-06-11 07:42:00)          4.8       123   \n",
              "7705173  [2017-06-11 07:36:00,2017-06-11 07:38:00)          4.8       123   \n",
              "7705174  [2017-06-11 07:38:00,2017-06-11 07:40:00)          4.8       123   \n",
              "\n",
              "           1    2    3    4    5  ...  125  126  127  128  129  130  131  132  \\\n",
              "0        0.0  0.0  0.0  0.0  1.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "1        0.0  0.0  0.0  0.0  1.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "2        0.0  0.0  0.0  0.0  1.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "3        0.0  0.0  0.0  0.0  1.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "4        0.0  0.0  0.0  0.0  1.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "...      ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
              "7705170  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0   \n",
              "7705171  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0   \n",
              "7705172  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0   \n",
              "7705173  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0   \n",
              "7705174  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0   \n",
              "\n",
              "           start_time      end_time  \n",
              "0        1.494069e+09  1.494069e+09  \n",
              "1        1.494093e+09  1.494094e+09  \n",
              "2        1.494080e+09  1.494080e+09  \n",
              "3        1.494064e+09  1.494064e+09  \n",
              "4        1.494067e+09  1.494067e+09  \n",
              "...               ...           ...  \n",
              "7705170  1.498719e+09  1.498719e+09  \n",
              "7705171  1.498722e+09  1.498723e+09  \n",
              "7705172  1.497167e+09  1.497167e+09  \n",
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          "metadata": {},
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    {
      "cell_type": "code",
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        "final_data.to_csv(\"/content/drive/MyDrive/intelligent_transportation_project/final_data.csv\", index=False)"
      ],
      "metadata": {
        "id": "zuS3iuJ-3cC3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "time.mktime(datetime.datetime.strptime(final_data[\"start_interval\"][0], '%Y-%m-%d %H:%M:%S').timetuple())\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HpJGgSwFfvFP",
        "outputId": "f0f58afd-bf18-422f-f989-6f8a7ed30068"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1494068640.0"
            ]
          },
          "metadata": {},
          "execution_count": 53
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      ]
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}