RNN.ipynb

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      "cell_type": "markdown",
      "source": [
        "# RNN ile Havayolu Yolcu Sayısı Tahmini"
      ],
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      "source": [
        "# 1) Business Problem\n",
        "\n",
        "RNN yöntemini kullanarak Airline Passengers dataset üzerinden gelecek yıllarda havayolu şirketini kullanabilecek kişi sayısını tahmin etme.\n",
        "\n",
        "Month: Aylık olarak yer alan tarih değişkenidir.\n",
        "\n",
        "Passengers: İlgili işlem tarihindeki yolcu sayısını , yani aylık yolcu sayısını temsil etmektedir."
      ],
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      "cell_type": "markdown",
      "source": [
        "# 2) Data Understanding;"
      ],
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      "source": [
        "2.1) İmport Libraries"
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        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import datetime as dt\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, SimpleRNN, Dropout\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import os\n",
        "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
        "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)"
      ],
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      "execution_count": 1,
      "outputs": []
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      "source": [
        "2.2) Veri Setinin Yüklenmesi"
      ],
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        "id": "MgenhCuFK8nM"
      }
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      "source": [
        "df = pd.read_csv(\"/content/international-airline-passengers.csv\")"
      ],
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        "id": "NyguivoLJY7P"
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      "execution_count": 2,
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        "df.head()"
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              "     Month  \\\n",
              "0  1949-01   \n",
              "1  1949-02   \n",
              "2  1949-03   \n",
              "3  1949-04   \n",
              "4  1949-05   \n",
              "\n",
              "   International airline passengers: monthly totals in thousands. Jan 49 ? Dec 60  \n",
              "0                                              112.0                               \n",
              "1                                              118.0                               \n",
              "2                                              132.0                               \n",
              "3                                              129.0                               \n",
              "4                                              121.0                               "
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              "     Month  Passengers\n",
              "0  1949-01       112.0\n",
              "1  1949-02       118.0\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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7178ef42-1f3d-4b5f-aa23-cb9b09874877\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7178ef42-1f3d-4b5f-aa23-cb9b09874877')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7178ef42-1f3d-4b5f-aa23-cb9b09874877 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 145,\n  \"fields\": [\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 145,\n        \"samples\": [\n          \"1954-10\",\n          \"1960-09\",\n          \"1951-04\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Passengers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 119.9663169429432,\n        \"min\": 104.0,\n        \"max\": 622.0,\n        \"num_unique_values\": 118,\n        \"samples\": [\n          293.0,\n          340.0,\n          121.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PVOgH6xGF7US",
        "outputId": "9c390e5c-e9a9-4edc-98a4-d5be4e1fab94"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 Month  Passengers\n",
              "140                                            1960-09       508.0\n",
              "141                                            1960-10       461.0\n",
              "142                                            1960-11       390.0\n",
              "143                                            1960-12       432.0\n",
              "144  International airline passengers: monthly tota...         NaN"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-73e48e94-251a-4f49-a214-bedc7efd382d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <td>508.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>1960-10</td>\n",
              "      <td>461.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>1960-11</td>\n",
              "      <td>390.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>1960-12</td>\n",
              "      <td>432.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>International airline passengers: monthly tota...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-73e48e94-251a-4f49-a214-bedc7efd382d')\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      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",
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              "\n",
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              "      margin-bottom: 4px;\n",
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              "      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-73e48e94-251a-4f49-a214-bedc7efd382d 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-73e48e94-251a-4f49-a214-bedc7efd382d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-613cd3b3-781e-4bb5-bf2d-b7a18b408349\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-613cd3b3-781e-4bb5-bf2d-b7a18b408349')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-613cd3b3-781e-4bb5-bf2d-b7a18b408349 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"1960-10\",\n          \"International airline passengers: monthly totals in thousands. Jan 49 ? Dec 60\",\n          \"1960-11\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Passengers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6277811983033,\n        \"min\": 390.0,\n        \"max\": 508.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          461.0,\n          432.0,\n          508.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N3-gzvCJH28E",
        "outputId": "f4fc8a25-9b33-41ee-e979-ed840c541660"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(145, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CzTuAMHNH6nt",
        "outputId": "993e774b-0123-491a-cc86-7b9b42027e41"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Month          object\n",
              "Passengers    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hYeHaqKoH9za",
        "outputId": "8f5c0499-c7e7-4785-c590-02ef4de46416"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Month         0\n",
              "Passengers    1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "APem9ArNIIjs",
        "outputId": "5c7a9cd4-38b0-4bfc-99d4-7fa511f22344"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            count        mean         std    min    25%    50%    75%    max\n",
              "Passengers  144.0  280.298611  119.966317  104.0  180.0  265.5  360.5  622.0"
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              "\n",
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              "      background-color: #E2EBFA;\n",
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              "        document.querySelector('#df-53637702-427d-4a59-995c-37eb43b3fbdc 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-53637702-427d-4a59-995c-37eb43b3fbdc');\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",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 144.0,\n        \"max\": 144.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          144.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 280.2986111111111,\n        \"max\": 280.2986111111111,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          280.2986111111111\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 119.9663169429432,\n        \"max\": 119.9663169429432,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          119.9663169429432\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 104.0,\n        \"max\": 104.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          104.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 180.0,\n        \"max\": 180.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          180.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 265.5,\n        \"max\": 265.5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          265.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 360.5,\n        \"max\": 360.5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          360.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 622.0,\n        \"max\": 622.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          622.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3) Data Preparation"
      ],
      "metadata": {
        "id": "HcPqtnK2I1Wo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[:144]"
      ],
      "metadata": {
        "id": "39uLYz4HIUV7"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1cl225HAJALK",
        "outputId": "de0d18aa-e0b6-4e64-87f6-852051591e44"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Month  Passengers\n",
              "139  1960-08       606.0\n",
              "140  1960-09       508.0\n",
              "141  1960-10       461.0\n",
              "142  1960-11       390.0\n",
              "143  1960-12       432.0"
            ],
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-638e9eb8-8e85-4342-9a9a-c903b394764c');\n",
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              "                                                    [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",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-35aaf571-b701-4aa5-a048-54b85668562c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zj6TimoKJCAb",
        "outputId": "9e5361cb-3f17-466e-ed0c-b22e86d3cd91"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 144 entries, 0 to 143\n",
            "Data columns (total 2 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Month       144 non-null    object \n",
            " 1   Passengers  144 non-null    float64\n",
            "dtypes: float64(1), object(1)\n",
            "memory usage: 2.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Month\"] = pd.to_datetime(df[\"Month\"])"
      ],
      "metadata": {
        "id": "IJOKPwJ8JEQC"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5wZY4b1qJQF2",
        "outputId": "dc229346-14d7-4d96-ce1a-d85efedc5abf"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 144 entries, 0 to 143\n",
            "Data columns (total 2 columns):\n",
            " #   Column      Non-Null Count  Dtype         \n",
            "---  ------      --------------  -----         \n",
            " 0   Month       144 non-null    datetime64[ns]\n",
            " 1   Passengers  144 non-null    float64       \n",
            "dtypes: datetime64[ns](1), float64(1)\n",
            "memory usage: 2.4 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Minimum Tarih:\", df[\"Month\"].min())\n",
        "print(\"Maksimum Tarih\", df[\"Month\"].max())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HZroIvo1JRg8",
        "outputId": "2f282d8b-fc40-4247-f12a-04220a8252d6"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum Tarih: 1949-01-01 00:00:00\n",
            "Maksimum Tarih 1960-12-01 00:00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.index = df[\"Month\"]"
      ],
      "metadata": {
        "id": "5Uxm45X8JnXs"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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        "colab": {
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        },
        "id": "gbFROjWLL6WY",
        "outputId": "ddbd6bdf-4a9d-415c-9bc4-3d23eb6905b9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Month  Passengers\n",
              "Month                            \n",
              "1949-01-01 1949-01-01       112.0\n",
              "1949-02-01 1949-02-01       118.0\n",
              "1949-03-01 1949-03-01       132.0\n",
              "1949-04-01 1949-04-01       129.0\n",
              "1949-05-01 1949-05-01       121.0"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "repr_error": "cannot insert Month, already exists"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(\"Month\", axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "Nj6hDTZKL7kz"
      },
      "execution_count": 19,
      "outputs": []
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        {
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          "data": {
            "text/plain": [
              "            Passengers\n",
              "Month                 \n",
              "1949-01-01       112.0\n",
              "1949-02-01       118.0\n",
              "1949-03-01       132.0\n",
              "1949-04-01       129.0\n",
              "1949-05-01       121.0"
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              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a3382678-f7b2-44e2-b55c-00d176baea14 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 144,\n  \"fields\": [\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1949-01-01 00:00:00\",\n        \"max\": \"1960-12-01 00:00:00\",\n        \"num_unique_values\": 144,\n        \"samples\": [\n          \"1958-10-01 00:00:00\",\n          \"1950-08-01 00:00:00\",\n          \"1955-11-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Passengers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 119.9663169429432,\n        \"min\": 104.0,\n        \"max\": 622.0,\n        \"num_unique_values\": 118,\n        \"samples\": [\n          293.0,\n          340.0,\n          121.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_df = df.copy()"
      ],
      "metadata": {
        "id": "AJa_Dd8MMQ9G"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot(figsize=(14, 8), title=\"Monthly Airline Passengers\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "sX18QgHUMWJV",
        "outputId": "9adcd2db-7ac0-4fae-d134-b678253e2f6f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = df[\"Passengers\"].values"
      ],
      "metadata": {
        "id": "caVmhgxVMhr9"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s4RROWLVNYGE",
        "outputId": "5862d243-9a77-466b-8462-9e2874dd271a"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([112., 118., 132., 129., 121.])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(data[0:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1AxINPknNaMF",
        "outputId": "3fecae4d-3b38-4df9-9565-685d072d0e17"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(data[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YYTnuXBTNd0r",
        "outputId": "9f24f34d-ab4d-43e8-c01b-cf569e09d1f1"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.float64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5QtrJhWpOdfQ",
        "outputId": "5e48f25a-32ef-4f70-e20e-6d34d1abd873"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(144,)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.reshape(-1, 1)"
      ],
      "metadata": {
        "id": "4JJsXqqEOfJX"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mHerqYg8OimE",
        "outputId": "39ae7bea-f952-444c-c4f8-c6ba854346dc"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(144, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def split_data(dataframe, test_size):\n",
        "  position = int(round(len(dataframe) * (1-test_size)))\n",
        "  train = dataframe[:position]\n",
        "  test = dataframe[position:]\n",
        "  return train, test, position"
      ],
      "metadata": {
        "id": "my0YT0JeOn96"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train, test, position = split_data(data, 0.33)\n"
      ],
      "metadata": {
        "id": "OQACsoztQB4N"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train.shape, test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FkUs8oKqQIgV",
        "outputId": "cfbbed28-66f8-4edf-8c7f-4588636ec546"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(96, 1) (48, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler_train = MinMaxScaler(feature_range= (0, 1))"
      ],
      "metadata": {
        "id": "PnmZ5ZZpQO5Z"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train = scaler_train.fit_transform(train)"
      ],
      "metadata": {
        "id": "Dg1lGsPbSCnR"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler_test = MinMaxScaler(feature_range= (0, 1))"
      ],
      "metadata": {
        "id": "YJyJr8XLSYFJ"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test = scaler_test.fit_transform(test)"
      ],
      "metadata": {
        "id": "hJkDang_Sexo"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CB5lhLVoSmO7",
        "outputId": "0b1538c8-170b-4e8c-deb3-bf7d10691650"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.02588997],\n",
              "       [0.04530744],\n",
              "       [0.09061489],\n",
              "       [0.08090615],\n",
              "       [0.05501618]])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l3E6zXBxSrFU",
        "outputId": "292744fd-39a3-405e-8ac3-22922e980fd3"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.04361371],\n",
              "       [0.        ],\n",
              "       [0.17133956],\n",
              "       [0.14641745],\n",
              "       [0.1682243 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_features(data, lookback):\n",
        "  X, Y = [], []\n",
        "  for i in range(lookback, len(data)):\n",
        "    X.append(data[i-lookback:i,0])\n",
        "    Y.append(data[i,0])\n",
        "\n",
        "  return np.array(X), np.array(Y)"
      ],
      "metadata": {
        "id": "HukulwIoSs0L"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lookback = 1"
      ],
      "metadata": {
        "id": "o8q_88cayZz8"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train veri seti\n",
        "x_train, y_train = create_features(train, lookback)"
      ],
      "metadata": {
        "id": "8llsaQinypDS"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Test veri seti\n",
        "x_test, y_test = create_features(test, lookback)"
      ],
      "metadata": {
        "id": "Fx2w5brIyzsR"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LoJHjup1zCaX",
        "outputId": "b6065512-0b03-4c28-b794-7c21afd8d5b2"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(95, 1) (95,) (47, 1) (47,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = np.reshape(x_train, (x_train.shape[0], 1, x_train.shape[1]))\n",
        "x_test = np.reshape(x_test, (x_test.shape[0], 1, x_test.shape[1]))\n",
        "y_train = y_train.reshape(-1, 1)\n",
        "y_test = y_test.reshape(-1, 1)"
      ],
      "metadata": {
        "id": "tdt8GfpZzNgW"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s4JPCnX80nl2",
        "outputId": "b53323c8-7944-489a-ac10-163960a756c1"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(95, 1, 1) (95, 1) (47, 1, 1) (47, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4) Modeling"
      ],
      "metadata": {
        "id": "7KMnHgKZ12Ek"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(SimpleRNN(units=50,\n",
        "                    activation='relu',\n",
        "                    input_shape=(x_train.shape[1], lookback)))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(1))\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9oldGhbM0ybN",
        "outputId": "f483dbdb-10de-4c9a-9167-9425935c972b"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " simple_rnn (SimpleRNN)      (None, 50)                2600      \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 50)                0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 51        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2651 (10.36 KB)\n",
            "Trainable params: 2651 (10.36 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Optimizasyon ve Değerlendirme Metrikleri Ayarlanması\n",
        "\n",
        "model.compile(loss=\"mean_squared_error\", optimizer=\"adam\")"
      ],
      "metadata": {
        "id": "awg9oZg72pVI"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "callbacks = [EarlyStopping(monitor=\"val_loss\", patience=3, verbose=1, mode=\"min\"),\n",
        "             ModelCheckpoint(filepath=\"mymodel.h5\", monitor=\"val_loss\", mode=\"min\",\n",
        "                             save_best_only=True, save_weights_only= False, verbose=1)]"
      ],
      "metadata": {
        "id": "OxT_qJcv3O9V"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(x=x_train,\n",
        "                    y=y_train,\n",
        "                    epochs=50,\n",
        "                    batch_size=1,\n",
        "                    validation_data = (x_test, y_test),\n",
        "                    callbacks = callbacks,\n",
        "                    shuffle=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FxCjNoyp3657",
        "outputId": "0747fd70-4ced-44f9-b90a-13a5d4bcf8ca"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "88/95 [==========================>...] - ETA: 0s - loss: 0.0094\n",
            "Epoch 1: val_loss improved from inf to 0.03748, saving model to mymodel.h5\n",
            "95/95 [==============================] - 7s 15ms/step - loss: 0.0107 - val_loss: 0.0375\n",
            "Epoch 2/50\n",
            "95/95 [==============================] - ETA: 0s - loss: 0.0190\n",
            "Epoch 2: val_loss improved from 0.03748 to 0.02856, saving model to mymodel.h5\n",
            "95/95 [==============================] - 1s 8ms/step - loss: 0.0190 - val_loss: 0.0286\n",
            "Epoch 3/50\n",
            "78/95 [=======================>......] - ETA: 0s - loss: 0.0128\n",
            "Epoch 3: val_loss improved from 0.02856 to 0.02077, saving model to mymodel.h5\n",
            "95/95 [==============================] - 0s 5ms/step - loss: 0.0134 - val_loss: 0.0208\n",
            "Epoch 4/50\n",
            "87/95 [==========================>...] - ETA: 0s - loss: 0.0077\n",
            "Epoch 4: val_loss did not improve from 0.02077\n",
            "95/95 [==============================] - 0s 4ms/step - loss: 0.0106 - val_loss: 0.0224\n",
            "Epoch 5/50\n",
            "76/95 [=======================>......] - ETA: 0s - loss: 0.0060\n",
            "Epoch 5: val_loss did not improve from 0.02077\n",
            "95/95 [==============================] - 0s 3ms/step - loss: 0.0109 - val_loss: 0.0216\n",
            "Epoch 6/50\n",
            "77/95 [=======================>......] - ETA: 0s - loss: 0.0063\n",
            "Epoch 6: val_loss improved from 0.02077 to 0.02075, saving model to mymodel.h5\n",
            "95/95 [==============================] - 0s 4ms/step - loss: 0.0095 - val_loss: 0.0207\n",
            "Epoch 7/50\n",
            "82/95 [========================>.....] - ETA: 0s - loss: 0.0056\n",
            "Epoch 7: val_loss did not improve from 0.02075\n",
            "95/95 [==============================] - 0s 3ms/step - loss: 0.0081 - val_loss: 0.0214\n",
            "Epoch 8/50\n",
            "74/95 [======================>.......] - ETA: 0s - loss: 0.0048\n",
            "Epoch 8: val_loss did not improve from 0.02075\n",
            "95/95 [==============================] - 0s 3ms/step - loss: 0.0079 - val_loss: 0.0217\n",
            "Epoch 9/50\n",
            "93/95 [============================>.] - ETA: 0s - loss: 0.0084\n",
            "Epoch 9: val_loss did not improve from 0.02075\n",
            "95/95 [==============================] - 0s 3ms/step - loss: 0.0087 - val_loss: 0.0213\n",
            "Epoch 9: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 5))\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history[\"loss\"], label=\"Training Loss\")\n",
        "plt.plot(history.history[\"val_loss\"], label=\"Validation Loss\")\n",
        "plt.legend(loc=\"upper right\")\n",
        "plt.xlabel(\"Epoch\", fontsize=16)\n",
        "plt.ylabel(\"Loss\", fontsize=16)\n",
        "plt.ylim([0,max(plt.ylim())])\n",
        "plt.title(\"Training and Validation Loss\", fontsize=16)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "qqy6oVvU4WM5",
        "outputId": "5155eabf-eb2f-4e36-9a52-a8673d1d12a2"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Training and Validation Loss')"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5) Evaluation"
      ],
      "metadata": {
        "id": "e9vaKMjS6bjU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss = model.evaluate(x_test, y_test, batch_size=1)\n",
        "print(\"\\nTest loss: %.1f%%\" % (100.0 * loss))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y1M-3wdG6MP6",
        "outputId": "896485e1-7076-412c-d59e-4189320dd16e"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "47/47 [==============================] - 0s 2ms/step - loss: 0.0213\n",
            "\n",
            "Test loss: 2.1%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_predict = model.predict(x_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DZKJiBAt627r",
        "outputId": "d48e59b2-518f-4e2a-af5c-1403f3685578"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 0s 4ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_predict = model.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uXuvRXmm7ANm",
        "outputId": "459264b8-513b-4dc3-d648-bfc881b51d5e"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 5ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_predict = scaler_train.inverse_transform(y_train)\n",
        "test_predict = scaler_test.inverse_transform(y_test)"
      ],
      "metadata": {
        "id": "vKDtcdUs7EmP"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train veri setine ait RMSE değeri\n",
        "train_rmse = np.sqrt(mean_squared_error(y_train, train_predict))"
      ],
      "metadata": {
        "id": "HKgif8zB7Xs0"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Test veri setine ait RMSE değeri\n",
        "test_rmse = np.sqrt(mean_squared_error(y_test, test_predict))"
      ],
      "metadata": {
        "id": "VbLBhaD87lq2"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Train RMSE: {train_rmse}\")\n",
        "print(f\"Test RMSE: {test_rmse}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5hCQIGrs7weF",
        "outputId": "3c2c5412-20c4-48fc-f6ab-33d3250d46e1"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train RMSE: 225.84420760147808\n",
            "Test RMSE: 422.279348351012\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "s20-r-vk8BDQ",
        "outputId": "2ec850e4-8473-4514-e5ae-948907ec1f05"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            count        mean         std    min    25%    50%    75%    max\n",
              "Passengers  144.0  280.298611  119.966317  104.0  180.0  265.5  360.5  622.0"
            ],
            "text/html": [
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              "    <div>\n",
              "<style scoped>\n",
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              "      <th>mean</th>\n",
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              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>Passengers</th>\n",
              "      <td>144.0</td>\n",
              "      <td>280.298611</td>\n",
              "      <td>119.966317</td>\n",
              "      <td>104.0</td>\n",
              "      <td>180.0</td>\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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          "metadata": {},
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    {
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      "source": [
        "train_prediction_df= result_df[lookback:position]"
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      "execution_count": 60,
      "outputs": []
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    {
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      "source": [
        "train_prediction_df[\"Predicted\"] = train_predict"
      ],
      "metadata": {
        "id": "Xw8-eklN9VS-"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "execution_count": 64,
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            "text/plain": [
              "            Passengers  Predicted\n",
              "Month                            \n",
              "1949-02-01       118.0      118.0\n",
              "1949-03-01       132.0      132.0\n",
              "1949-04-01       129.0      129.0\n",
              "1949-05-01       121.0      121.0\n",
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              "variable_name": "train_prediction_df",
              "summary": "{\n  \"name\": \"train_prediction_df\",\n  \"rows\": 95,\n  \"fields\": [\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1949-02-01 00:00:00\",\n        \"max\": \"1956-12-01 00:00:00\",\n        \"num_unique_values\": 95,\n        \"samples\": [\n          \"1954-10-01 00:00:00\",\n          \"1950-12-01 00:00:00\",\n          \"1955-02-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Passengers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 71.52654304929258,\n        \"min\": 104.0,\n        \"max\": 413.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          180.0,\n          118.0,\n          163.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 71.52654304929258,\n        \"min\": 104.0,\n        \"max\": 413.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          180.00000000000003,\n          118.0,\n          163.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_prediction_df= result_df[position + lookback:]\n",
        "test_prediction_df[\"Predicted\"] = test_predict\n",
        "test_prediction_df.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 237
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        "id": "tJi8Xxz99x6c",
        "outputId": "98135f38-d1ab-4a0c-c5c9-6e26d587fe4e"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Passengers  Predicted\n",
              "Month                            \n",
              "1957-02-01       301.0      301.0\n",
              "1957-03-01       356.0      356.0\n",
              "1957-04-01       348.0      348.0\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-65a34e3d-7e00-446e-867c-5493205f9f92')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-65a34e3d-7e00-446e-867c-5493205f9f92 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-65a34e3d-7e00-446e-867c-5493205f9f92');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4512710c-6762-43e0-b705-af1ecc43fe81\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4512710c-6762-43e0-b705-af1ecc43fe81')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4512710c-6762-43e0-b705-af1ecc43fe81 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test_prediction_df",
              "summary": "{\n  \"name\": \"test_prediction_df\",\n  \"rows\": 47,\n  \"fields\": [\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1957-02-01 00:00:00\",\n        \"max\": \"1960-12-01 00:00:00\",\n        \"num_unique_values\": 47,\n        \"samples\": [\n          \"1959-05-01 00:00:00\",\n          \"1960-05-01 00:00:00\",\n          \"1959-04-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Passengers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77.97846544450898,\n        \"min\": 301.0,\n        \"max\": 622.0,\n        \"num_unique_values\": 42,\n        \"samples\": [\n          420.0,\n          362.0,\n          347.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77.97846544450898,\n        \"min\": 301.0,\n        \"max\": 622.0,\n        \"num_unique_values\": 42,\n        \"samples\": [\n          420.0,\n          362.0,\n          347.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train ve Test Tahminlerini ayrı ayrı görselleştirme işlemi\n",
        "plt.figure(figsize=(14, 5))\n",
        "plt.plot(result_df, label=\"Real number of passengers\")\n",
        "plt.plot(train_prediction_df[\"Predicted\"], color=\"blue\", label=\"Train Predicted Number of Passengers\")\n",
        "plt.plot(test_prediction_df[\"Predicted\"], color=\"red\", label=\"Test Predicted Number of Passengers\")\n",
        "plt.title(\"Number of Passengers Prediction\")\n",
        "plt.xlabel(\"Time\")\n",
        "plt.ylabel(\"Number of passengers\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "NxIu8JI8KFkx",
        "outputId": "d6b2f3ee-1ba2-4595-a32e-cb9905356177"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0heH7id2LXVv"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}