D7_Python_MO_IST_SU_.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Bu uygulamanın veri seti: İBB Açık Veri Portalı, İlçe Bazında Su Tüketim Miktarı, Veri Seti Linki-->> https://data.ibb.gov.tr/dataset/ilce-bazinda-su-tuketim-miktari\n",
        "\n",
        "Bu uygulamada amaç: İBB İstanbulun İlçeleri Genelinde Günlük Su Tüketimlerinin Yapay Zeka Algoritmalarıyla İşlenerek Analizler Ortaya Çıkarılması\n",
        "\n",
        "https://github.com/acetinkaya/ai_igu/blob/main/ilcesuveriseti.csv\n",
        "\n",
        "Veri Seti Excel Dosyası İse bir önceki haftadaki kaynak kodu kullanarak *.excel dosyasından *.csv dosyasına çevrimi gerçekleştirilebilir...\n",
        "\n",
        "Öğr. Gör. Ali ÇETİNKAYA - 18.10.2024\n"
      ],
      "metadata": {
        "id": "EYAROy2Fdo7w"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QcOfustrdSmM",
        "outputId": "e657d931-f340-4efc-e1b2-217b5aba3f9d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selam ISTKA YZ Son Ders :DDD \n"
          ]
        }
      ],
      "source": [
        "print(\"Selam ISTKA YZ Son Ders :DDD \")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "print(sys.version)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ii4okbwjdXLU",
        "outputId": "3c36d431-c6c0-4e01-936e-166cb439cc50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bTexCI0pdYE5",
        "outputId": "b7d478ba-df74-4cec-b9bf-22cac7c8e4a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.chdir('/content/drive/My Drive/YAPAYZEKA_PYTHON')\n",
        "!pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7tIqOSbxdaH2",
        "outputId": "45ce6532-8754-44fd-b533-5a94d44c9539"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/My Drive/YAPAYZEKA_PYTHON\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "VeriSetiYolu = '/content/drive/My Drive/YAPAYZEKA_PYTHON/veriseti'\n",
        "\n",
        "csv_dosyalari = [dosya for dosya in os.listdir(VeriSetiYolu) if dosya.endswith('.csv')]\n",
        "\n",
        "csv_dosya_sayisi = len(csv_dosyalari)\n",
        "print(\"VeriSetiYolu dosyası içerisinde\", csv_dosya_sayisi, \"tane *.csv uzantılı dosyası bulunmaktadır.\")\n",
        "\n",
        "print(\"Bulunan CSV Dosyaları:\")\n",
        "for dosya in csv_dosyalari:\n",
        "    print(dosya)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RPt-Da3QddPW",
        "outputId": "14df1fe2-eea3-4377-8ec4-695e0f772856"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "VeriSetiYolu dosyası içerisinde 1 tane *.csv uzantılı dosyası bulunmaktadır.\n",
            "Bulunan CSV Dosyaları:\n",
            "ilcesuveriseti.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "dosya_yolu = '/content/drive/My Drive/YAPAYZEKA_PYTHON/veriseti/ilcesuveriseti.csv'\n",
        "csv_dosya = pd.read_csv(dosya_yolu)\n",
        "\n",
        "print(csv_dosya)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KcEE-zdsdlUH",
        "outputId": "e834863a-1a27-46b0-b928-b1392c87e1d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   ID;ILCELER;2015;2016;2017;2018;2019;2020;2021;2022\n",
            "0   1;ADALAR;1.432.494;1.472.276;1.366.581;1.399.1...\n",
            "1   2;ARNAVUTKOY;8.002.123;9.178.953;10.176.132;11...\n",
            "2   3;ATASEHIR;18.597.049;19.974.097;21.205.911;21...\n",
            "3   4;AVCILAR;16.220.476;17.052.253;17.558.403;18....\n",
            "4   5;BUYUKCEKMECE;9.915.435;10.788.924;11.172.787...\n",
            "5   6;BAHCELIEVLER;23.610.609;24.320.533;24.868.59...\n",
            "6   7;BAKIRKOY;11.510.117;11.793.169;12.509.301;15...\n",
            "7   8;BAYRAMPASA;11.545.682;11.914.393;12.516.185;...\n",
            "8   9;BAGCILAR;26.691.720;28.182.511;29.362.186;31...\n",
            "9   10;BASAKSEHIR;16.174.934;18.100.255;19.576.095...\n",
            "10  11;BEYKOZ;10.254.740;10.891.434;11.331.655;12....\n",
            "11  12;BEYLIKDUZU;12.346.365;13.630.387;14.948.006...\n",
            "12  13;BEYOGLU;11.167.382;11.311.755;11.420.559;12...\n",
            "13  14;BESIKTAS;11.976.186;12.385.216;12.609.953;1...\n",
            "14  15;ESENLER;15.854.265;16.584.976;17.162.651;17...\n",
            "15  16;ESENYURT;27.933.397;32.799.007;36.083.030;3...\n",
            "16  17;EYUPSULTAN;17.116.911;17.901.848;18.222.331...\n",
            "17  18;FATIH;22.358.121;21.781.211;21.983.686;23.8...\n",
            "18  19;GOP;17.730.853;18.637.751;18.940.716;19.614...\n",
            "19  20;GUNGOREN;18.994.573;12.600.044;12.769.886;1...\n",
            "20  21;KUCUKCEKMECE;29.188.561;31.086.665;31.994.3...\n",
            "21  22;KADIKOY;23.910.542;24.310.256;24.797.597;26...\n",
            "22  23;KARTAL;19.155.143;20.007.459;20.375.603;21....\n",
            "23  24;KAGITHANE;18.207.418;19.030.632;19.286.724;...\n",
            "24  25;MALTEPE;21.906.064;22.794.286;22.703.875;24...\n",
            "25  26;PENDIK;26.499.977;28.474.258;29.743.117;31....\n",
            "26  27;SANCAKTEPE;12.313.398;14.090.151;15.239.008...\n",
            "27  28;SARIYER;17.555.072;18.711.570;19.427.187;20...\n",
            "28  29;SULTANBEYLI;9.599.250;10.224.559;10.814.861...\n",
            "29  30;SALTANGAZI;17.142.236;18.219.393;19.163.693...\n",
            "30  31;SILIVRI;7.620.350;8.227.604;8.452.805;8.941...\n",
            "31  32;TUZLA;11.546.080;12.690.324;13.625.147;14.9...\n",
            "32  33;ZEYTINBURNU;12.576.945;13.068.037;13.469.51...\n",
            "33  34;CATALCA;2.931.018;3.222.450;3.311.210;3.665...\n",
            "34  35;CEKMEKOY;8.951.936;9.705.483;10.205.702;10....\n",
            "35  36;UMRANIYE;28.085.173;30.065.065;31.061.816;3...\n",
            "36  37;USKUDAR;25.142.059;26.110.542;26.353.986;27...\n",
            "37  38;SILE;1.835.302;2.098.928;2.196.246;2.522.41...\n",
            "38  39;SISLI;15.717.630;15.948.709;16.076.563;17.0...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Veri setinin yolunun gösterimi ve yüklemesi\n",
        "VeriSetiYolu = '/content/drive/My Drive/YAPAYZEKA_PYTHON/veriseti/ilcesuveriseti.csv'\n",
        "data = pd.read_csv(VeriSetiYolu, delimiter=';')\n",
        "\n",
        "# ; verilerini temizleyip , verisini dönüştürme işlemidir.\n",
        "for i in data.columns[2:]:\n",
        "    data[i] = data[i].str.replace('.', '').astype(int)\n",
        "\n",
        "print(data.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pr773XroeHr3",
        "outputId": "4271b929-4926-45fe-a48c-1c6db5866a8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   ID       ILCELER      2015      2016      2017      2018      2019  \\\n",
            "0   1        ADALAR   1432494   1472276   1366581   1399182   1371291   \n",
            "1   2    ARNAVUTKOY   8002123   9178953  10176132  11404878  13818204   \n",
            "2   3      ATASEHIR  18597049  19974097  21205911  21496185  22428468   \n",
            "3   4       AVCILAR  16220476  17052253  17558403  18312736  19485453   \n",
            "4   5  BUYUKCEKMECE   9915435  10788924  11172787  12222470  13606571   \n",
            "\n",
            "       2020      2021      2022  \n",
            "0   1513070   1435991   1543403  \n",
            "1  15570466  17102127  18891299  \n",
            "2  23737630  24281319  24377611  \n",
            "3  20824893  20690506  21556002  \n",
            "4  16015731  16167480  17412198  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as ticker\n",
        "\n",
        "data = pd.read_csv(VeriSetiYolu, delimiter=';')\n",
        "\n",
        "for i in data.columns[2:]:\n",
        "    data[i] = data[i].str.replace('.', '').astype(int)\n",
        "\n",
        "ilceler = [\"AVCILAR\", \"BEYLIKDUZU\", \"BUYUKCEKMECE\", \"KUCUKCEKMECE\", \"SILIVRI\", \"ESENYURT\"]\n",
        "filtre = data[data['ILCELER'].isin(ilceler)]\n",
        "\n",
        "plt.figure(figsize=(15, 8))\n",
        "\n",
        "for ilce in ilceler:\n",
        "    ilce_data = filtre[filtre['ILCELER'] == ilce]\n",
        "    plt.plot(data.columns[2:], ilce_data.iloc[0, 2:], label=ilce)\n",
        "\n",
        "plt.xlabel('Yıllar')\n",
        "plt.ylabel('Su Tüketimi (m3)')\n",
        "plt.title('Belirli İlçelerin Yıllara Göre Su Tüketim Miktarları')\n",
        "\n",
        "ax = plt.gca()\n",
        "ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{int(x):,}'))\n",
        "\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        },
        "id": "HK8K1qXceJ51",
        "outputId": "6e2c511c-6531-46da-9237-00271070eedd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Su veri seti üzerinin görselleştirilmesinde graph ve plot ekleme ile görselin detaylandırılması - plotly Kütüphane Kurulum İşlemi !pip install plotly\n"
      ],
      "metadata": {
        "id": "TugnB3pzeM1s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install plotly"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rbDzjCQ-eNT6",
        "outputId": "1e3d9cc7-c64e-40ba-8212-790e1d477751"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (5.24.1)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly) (9.0.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly) (24.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "data = pd.read_csv(VeriSetiYolu, delimiter=';')\n",
        "\n",
        "for i in data.columns[2:]:\n",
        "    data[i] = data[i].str.replace('.', '').astype(int)\n",
        "\n",
        "ilce_bilgileri = [\"AVCILAR\", \"BEYLIKDUZU\", \"BUYUKCEKMECE\", \"KUCUKCEKMECE\", \"SILIVRI\", \"ESENYURT\"]\n",
        "filtre = data[data['ILCELER'].isin(ilce_bilgileri)]\n",
        "\n",
        "fig = go.Figure()\n",
        "\n",
        "for ilce in ilce_bilgileri:\n",
        "    ilce_data = filtre[filtre['ILCELER'] == ilce]\n",
        "    fig.add_trace(go.Scatter(\n",
        "         x=data.columns[2:],\n",
        "         y=ilce_data.iloc[0, 2:],\n",
        "         mode='lines+markers',\n",
        "         line=dict(width=3),\n",
        "         name=ilce\n",
        "        ))\n",
        "\n",
        "fig.update_layout(\n",
        "        title='AVCILAR, BEYLIKDUZU, BUYUKCEKMECE, KUCUKCEKMECE, SILIVRI, ESENYURT İlçelerin Yıllara Göre Su Tüketim Miktarları',\n",
        "        xaxis_title='Yıllar',\n",
        "        yaxis_title='Su Tüketimi (m3)',\n",
        "        yaxis=dict(tickformat=','),\n",
        "        template='plotly_white'\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "G1RRWjpPePxv",
        "outputId": "6b11fd46-7111-498e-e266-baa3e7622f09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PRCIlh4jm6FD",
        "outputId": "8fb065f0-d3ac-430b-811e-7697a5fa6c6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.17.0)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.3.25)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.6.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: h5py>=3.10.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.11.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (18.1.1)\n",
            "Requirement already satisfied: ml-dtypes<0.5.0,>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.4.1)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.4.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.1)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.20.3)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.32.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (75.1.0)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.5.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.12.2)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.64.1)\n",
            "Requirement already satisfied: tensorboard<2.18,>=2.17 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.17.0)\n",
            "Requirement already satisfied: keras>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.4.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.37.1)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.26.4)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.44.0)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (13.9.2)\n",
            "Requirement already satisfied: namex in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (0.0.8)\n",
            "Requirement already satisfied: optree in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (0.13.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.4.0)\n",
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            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2024.8.30)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (3.7)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (3.0.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.18,>=2.17->tensorflow) (3.0.1)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow) (2.18.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.2.0->tensorflow) (0.1.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
        "\n",
        "data = pd.read_csv(VeriSetiYolu, delimiter=';')\n",
        "\n",
        "for col in data.columns[2:]:\n",
        "    data[col] = data[col].str.replace('.', '').astype(int)\n",
        "\n",
        "selected_ilces = [\"AVCILAR\", \"BEYLIKDUZU\", \"BUYUKCEKMECE\", \"KUCUKCEKMECE\", \"SILIVRI\", \"ESENYURT\"]\n",
        "filtered_data = data[data['ILCELER'].isin(selected_ilces)]\n",
        "\n",
        "ilce = 'AVCILAR'\n",
        "ilce_data = filtered_data[filtered_data['ILCELER'] == ilce].iloc[0, 2:].values.reshape(-1, 1)\n",
        "\n",
        "years = data.columns[2:].astype(int)\n",
        "\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "ilce_data_scaled = scaler.fit_transform(ilce_data)\n",
        "\n",
        "def create_dataset(dataset, tahmin_adimi=1):\n",
        "    dataX, dataY = [], []\n",
        "    for i in range(len(dataset) - tahmin_adimi):\n",
        "        a = dataset[i:(i + tahmin_adimi), 0]\n",
        "        dataX.append(a)\n",
        "        dataY.append(dataset[i + tahmin_adimi, 0])\n",
        "    return np.array(dataX), np.array(dataY)\n",
        "\n",
        "tahmin_adimi = 4\n",
        "trainX, trainY = create_dataset(ilce_data_scaled, tahmin_adimi)\n",
        "\n",
        "trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))\n",
        "\n",
        "model = Sequential()\n",
        "model.add(LSTM(50, return_sequences=True, input_shape=(tahmin_adimi, 1)))\n",
        "model.add(LSTM(50))\n",
        "model.add(Dense(1))\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "\n",
        "history = model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2, validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lf_9LnLEeeo4",
        "outputId": "814089fe-c44e-4705-b66c-db92c8048928"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/rnn/rnn.py:204: UserWarning:\n",
            "\n",
            "Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "3/3 - 8s - 3s/step - loss: 0.5898 - val_loss: 0.8800\n",
            "Epoch 2/100\n",
            "3/3 - 1s - 402ms/step - loss: 0.5173 - val_loss: 0.7657\n",
            "Epoch 3/100\n",
            "3/3 - 0s - 115ms/step - loss: 0.4612 - val_loss: 0.6421\n",
            "Epoch 4/100\n",
            "3/3 - 0s - 74ms/step - loss: 0.3833 - val_loss: 0.5153\n",
            "Epoch 5/100\n",
            "3/3 - 0s - 56ms/step - loss: 0.3138 - val_loss: 0.3789\n",
            "Epoch 6/100\n",
            "3/3 - 0s - 85ms/step - loss: 0.2389 - val_loss: 0.2379\n",
            "Epoch 7/100\n",
            "3/3 - 0s - 99ms/step - loss: 0.1512 - val_loss: 0.1092\n",
            "Epoch 8/100\n",
            "3/3 - 0s - 101ms/step - loss: 0.0738 - val_loss: 0.0188\n",
            "Epoch 9/100\n",
            "3/3 - 0s - 138ms/step - loss: 0.0267 - val_loss: 0.0089\n",
            "Epoch 10/100\n",
            "3/3 - 0s - 81ms/step - loss: 0.0046 - val_loss: 0.0904\n",
            "Epoch 11/100\n",
            "3/3 - 0s - 77ms/step - loss: 0.0253 - val_loss: 0.1389\n",
            "Epoch 12/100\n",
            "3/3 - 0s - 107ms/step - loss: 0.0392 - val_loss: 0.1092\n",
            "Epoch 13/100\n",
            "3/3 - 0s - 91ms/step - loss: 0.0217 - val_loss: 0.0533\n",
            "Epoch 14/100\n",
            "3/3 - 0s - 93ms/step - loss: 0.0143 - val_loss: 0.0113\n",
            "Epoch 15/100\n",
            "3/3 - 0s - 64ms/step - loss: 0.0088 - val_loss: 4.0091e-04\n",
            "Epoch 16/100\n",
            "3/3 - 0s - 98ms/step - loss: 0.0081 - val_loss: 1.5498e-04\n",
            "Epoch 17/100\n",
            "3/3 - 0s - 32ms/step - loss: 0.0116 - val_loss: 6.0318e-04\n",
            "Epoch 18/100\n",
            "3/3 - 0s - 41ms/step - loss: 0.0113 - val_loss: 1.2641e-07\n",
            "Epoch 19/100\n",
            "3/3 - 0s - 21ms/step - loss: 0.0084 - val_loss: 0.0011\n",
            "Epoch 20/100\n",
            "3/3 - 0s - 18ms/step - loss: 0.0055 - val_loss: 0.0041\n",
            "Epoch 21/100\n",
            "3/3 - 0s - 20ms/step - loss: 0.0049 - val_loss: 0.0089\n",
            "Epoch 22/100\n",
            "3/3 - 0s - 19ms/step - loss: 0.0052 - val_loss: 0.0123\n",
            "Epoch 23/100\n",
            "3/3 - 0s - 23ms/step - loss: 0.0062 - val_loss: 0.0147\n",
            "Epoch 24/100\n",
            "3/3 - 0s - 22ms/step - loss: 0.0061 - val_loss: 0.0131\n",
            "Epoch 25/100\n",
            "3/3 - 0s - 42ms/step - loss: 0.0056 - val_loss: 0.0100\n",
            "Epoch 26/100\n",
            "3/3 - 0s - 18ms/step - loss: 0.0051 - val_loss: 0.0058\n",
            "Epoch 27/100\n",
            "3/3 - 0s - 20ms/step - loss: 0.0049 - val_loss: 0.0032\n",
            "Epoch 28/100\n",
            "3/3 - 0s - 19ms/step - loss: 0.0054 - val_loss: 0.0017\n",
            "Epoch 29/100\n",
            "3/3 - 0s - 22ms/step - loss: 0.0055 - val_loss: 0.0013\n",
            "Epoch 30/100\n",
            "3/3 - 0s - 18ms/step - loss: 0.0056 - val_loss: 0.0025\n",
            "Epoch 31/100\n",
            "3/3 - 0s - 20ms/step - loss: 0.0050 - val_loss: 0.0026\n",
            "Epoch 32/100\n",
            "3/3 - 0s - 20ms/step - loss: 0.0053 - val_loss: 0.0042\n",
            "Epoch 33/100\n",
            "3/3 - 0s - 19ms/step - loss: 0.0049 - val_loss: 0.0040\n",
            "Epoch 34/100\n",
            "3/3 - 0s - 20ms/step - loss: 0.0050 - val_loss: 0.0047\n",
            "Epoch 35/100\n",
            "3/3 - 0s - 45ms/step - loss: 0.0051 - val_loss: 0.0037\n",
            "Epoch 36/100\n",
            "3/3 - 0s - 20ms/step - loss: 0.0052 - val_loss: 0.0044\n",
            "Epoch 37/100\n",
            "3/3 - 0s - 22ms/step - loss: 0.0050 - val_loss: 0.0044\n",
            "Epoch 38/100\n",
            "3/3 - 0s - 48ms/step - loss: 0.0050 - val_loss: 0.0026\n",
            "Epoch 39/100\n",
            "3/3 - 0s - 19ms/step - loss: 0.0051 - val_loss: 0.0014\n",
            "Epoch 40/100\n",
            "3/3 - 0s - 20ms/step - loss: 0.0050 - val_loss: 0.0013\n",
            "Epoch 41/100\n",
            "3/3 - 0s - 20ms/step - loss: 0.0051 - val_loss: 0.0021\n",
            "Epoch 42/100\n",
            "3/3 - 0s - 19ms/step - loss: 0.0051 - val_loss: 0.0032\n",
            "Epoch 43/100\n",
            "3/3 - 0s - 18ms/step - loss: 0.0050 - val_loss: 0.0022\n",
            "Epoch 44/100\n",
            "3/3 - 0s - 18ms/step - loss: 0.0049 - val_loss: 0.0016\n",
            "Epoch 45/100\n",
            "3/3 - 0s - 17ms/step - loss: 0.0048 - val_loss: 0.0017\n",
            "Epoch 46/100\n",
            "3/3 - 0s - 21ms/step - loss: 0.0048 - val_loss: 0.0016\n",
            "Epoch 47/100\n",
            "3/3 - 0s - 19ms/step - loss: 0.0050 - val_loss: 0.0026\n",
            "Epoch 48/100\n",
            "3/3 - 0s - 17ms/step - loss: 0.0050 - val_loss: 0.0031\n",
            "Epoch 49/100\n",
            "3/3 - 0s - 19ms/step - loss: 0.0049 - val_loss: 0.0028\n",
            "Epoch 50/100\n",
            "3/3 - 0s - 20ms/step - loss: 0.0047 - val_loss: 0.0019\n",
            "Epoch 51/100\n",
            "3/3 - 0s - 22ms/step - loss: 0.0052 - val_loss: 5.9885e-04\n",
            "Epoch 52/100\n",
            "3/3 - 0s - 23ms/step - loss: 0.0049 - val_loss: 8.6468e-04\n",
            "Epoch 53/100\n",
            "3/3 - 0s - 21ms/step - loss: 0.0049 - val_loss: 0.0014\n",
            "Epoch 54/100\n",
            "3/3 - 0s - 22ms/step - loss: 0.0049 - val_loss: 8.0287e-04\n",
            "Epoch 55/100\n",
            "3/3 - 0s - 18ms/step - loss: 0.0048 - val_loss: 6.2690e-04\n",
            "Epoch 56/100\n",
            "3/3 - 0s - 19ms/step - loss: 0.0051 - val_loss: 0.0016\n",
            "Epoch 57/100\n",
            "3/3 - 0s - 18ms/step - loss: 0.0050 - val_loss: 0.0024\n",
            "Epoch 58/100\n",
            "3/3 - 0s - 20ms/step - loss: 0.0051 - val_loss: 0.0010\n",
            "Epoch 59/100\n",
            "3/3 - 0s - 19ms/step - loss: 0.0049 - val_loss: 5.0636e-04\n",
            "Epoch 60/100\n",
            "3/3 - 0s - 19ms/step - loss: 0.0048 - val_loss: 3.2975e-04\n",
            "Epoch 61/100\n",
            "3/3 - 0s - 18ms/step - loss: 0.0046 - val_loss: 4.6682e-04\n",
            "Epoch 62/100\n",
            "3/3 - 0s - 48ms/step - loss: 0.0052 - val_loss: 0.0018\n",
            "Epoch 63/100\n",
            "3/3 - 0s - 23ms/step - loss: 0.0047 - val_loss: 0.0021\n",
            "Epoch 64/100\n",
            "3/3 - 0s - 24ms/step - loss: 0.0047 - val_loss: 0.0015\n",
            "Epoch 65/100\n",
            "3/3 - 0s - 32ms/step - loss: 0.0049 - val_loss: 6.1290e-04\n",
            "Epoch 66/100\n",
            "3/3 - 0s - 40ms/step - loss: 0.0046 - val_loss: 4.8012e-04\n",
            "Epoch 67/100\n",
            "3/3 - 0s - 25ms/step - loss: 0.0048 - val_loss: 2.3092e-04\n",
            "Epoch 68/100\n",
            "3/3 - 0s - 24ms/step - loss: 0.0049 - val_loss: 8.3625e-04\n",
            "Epoch 69/100\n",
            "3/3 - 0s - 22ms/step - loss: 0.0046 - val_loss: 9.0283e-04\n",
            "Epoch 70/100\n",
            "3/3 - 0s - 25ms/step - loss: 0.0048 - val_loss: 0.0013\n",
            "Epoch 71/100\n",
            "3/3 - 0s - 43ms/step - loss: 0.0047 - val_loss: 0.0012\n",
            "Epoch 72/100\n",
            "3/3 - 0s - 22ms/step - loss: 0.0050 - val_loss: 2.7084e-04\n",
            "Epoch 73/100\n",
            "3/3 - 0s - 22ms/step - loss: 0.0047 - val_loss: 4.0885e-04\n",
            "Epoch 74/100\n",
            "3/3 - 0s - 19ms/step - loss: 0.0046 - val_loss: 3.0976e-04\n",
            "Epoch 75/100\n",
            "3/3 - 0s - 20ms/step - loss: 0.0046 - val_loss: 3.3232e-04\n",
            "Epoch 76/100\n",
            "3/3 - 0s - 19ms/step - loss: 0.0047 - val_loss: 1.5152e-04\n",
            "Epoch 77/100\n",
            "3/3 - 0s - 22ms/step - loss: 0.0047 - val_loss: 1.3134e-04\n",
            "Epoch 78/100\n",
            "3/3 - 0s - 24ms/step - loss: 0.0051 - val_loss: 9.5693e-04\n",
            "Epoch 79/100\n",
            "3/3 - 0s - 43ms/step - loss: 0.0048 - val_loss: 5.7441e-04\n",
            "Epoch 80/100\n",
            "3/3 - 0s - 47ms/step - loss: 0.0047 - val_loss: 3.8435e-04\n",
            "Epoch 81/100\n",
            "3/3 - 0s - 20ms/step - loss: 0.0049 - val_loss: 9.3308e-04\n",
            "Epoch 82/100\n",
            "3/3 - 0s - 20ms/step - loss: 0.0048 - val_loss: 0.0011\n",
            "Epoch 83/100\n",
            "3/3 - 0s - 19ms/step - loss: 0.0046 - val_loss: 7.9985e-04\n",
            "Epoch 84/100\n",
            "3/3 - 0s - 21ms/step - loss: 0.0052 - val_loss: 2.3266e-05\n",
            "Epoch 85/100\n",
            "3/3 - 0s - 20ms/step - loss: 0.0050 - val_loss: 2.4735e-05\n",
            "Epoch 86/100\n",
            "3/3 - 0s - 21ms/step - loss: 0.0051 - val_loss: 1.4085e-04\n",
            "Epoch 87/100\n",
            "3/3 - 0s - 45ms/step - loss: 0.0050 - val_loss: 6.7305e-04\n",
            "Epoch 88/100\n",
            "3/3 - 0s - 20ms/step - loss: 0.0049 - val_loss: 0.0010\n",
            "Epoch 89/100\n",
            "3/3 - 0s - 29ms/step - loss: 0.0046 - val_loss: 3.8268e-04\n",
            "Epoch 90/100\n",
            "3/3 - 0s - 20ms/step - loss: 0.0046 - val_loss: 3.4698e-04\n",
            "Epoch 91/100\n",
            "3/3 - 0s - 18ms/step - loss: 0.0046 - val_loss: 5.5696e-05\n",
            "Epoch 92/100\n",
            "3/3 - 0s - 19ms/step - loss: 0.0047 - val_loss: 1.2606e-04\n",
            "Epoch 93/100\n",
            "3/3 - 0s - 29ms/step - loss: 0.0046 - val_loss: 1.8884e-04\n",
            "Epoch 94/100\n",
            "3/3 - 0s - 43ms/step - loss: 0.0046 - val_loss: 2.6779e-04\n",
            "Epoch 95/100\n",
            "3/3 - 0s - 18ms/step - loss: 0.0045 - val_loss: 2.3712e-04\n",
            "Epoch 96/100\n",
            "3/3 - 0s - 20ms/step - loss: 0.0050 - val_loss: 3.1936e-06\n",
            "Epoch 97/100\n",
            "3/3 - 0s - 21ms/step - loss: 0.0049 - val_loss: 4.8110e-05\n",
            "Epoch 98/100\n",
            "3/3 - 0s - 19ms/step - loss: 0.0051 - val_loss: 1.7156e-04\n",
            "Epoch 99/100\n",
            "3/3 - 0s - 18ms/step - loss: 0.0046 - val_loss: 4.8358e-04\n",
            "Epoch 100/100\n",
            "3/3 - 0s - 17ms/step - loss: 0.0047 - val_loss: 2.5930e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainPredict = model.predict(trainX)\n",
        "trainPredict = scaler.inverse_transform(trainPredict)\n",
        "trainY = scaler.inverse_transform([trainY])\n",
        "test_data = ilce_data_scaled[-tahmin_adimi:]\n",
        "test_data = np.reshape(test_data, (1, tahmin_adimi, 1))\n",
        "test_predict = model.predict(test_data)\n",
        "test_predict = scaler.inverse_transform(test_predict)\n",
        "test_y = ilce_data[-1:]\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(history.history['loss'], label='Eğitim Kaybı')\n",
        "plt.plot(history.history['val_loss'], label='Doğrulama Kaybı')\n",
        "plt.title('Model Eğitim ve Doğrulama Kaybı')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Kayıp')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "pwY9wMV_eg-y",
        "outputId": "55a16171-6cec-4c60-a0bd-3a5e85156535"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 387ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 298ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Modelin_kayip_degeri = [loss * 100 for loss in history.history['loss']]\n",
        "for epoch, loss in enumerate(Modelin_kayip_degeri, 1):\n",
        "    print(f\"Eğitim Adımı {epoch}: Kayıp Oranı = {loss:.2f}%\")\n",
        "Kayip_hesabi = np.mean(Modelin_kayip_degeri)\n",
        "print(\"Eğitim Modelinin Toplam Kayıp Oranı (Yüzdelik): \", Kayip_hesabi)\n",
        "print(\"Eğitim Modelinin Başarı Oranı (Yüzdelik): \", 100-Kayip_hesabi)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qgr_uVwFfGVe",
        "outputId": "ec7009dc-f46f-4c4f-da6e-769009239b07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Eğitim Adımı 1: Kayıp Oranı = 58.98%\n",
            "Eğitim Adımı 2: Kayıp Oranı = 51.73%\n",
            "Eğitim Adımı 3: Kayıp Oranı = 46.12%\n",
            "Eğitim Adımı 4: Kayıp Oranı = 38.33%\n",
            "Eğitim Adımı 5: Kayıp Oranı = 31.38%\n",
            "Eğitim Adımı 6: Kayıp Oranı = 23.89%\n",
            "Eğitim Adımı 7: Kayıp Oranı = 15.12%\n",
            "Eğitim Adımı 8: Kayıp Oranı = 7.38%\n",
            "Eğitim Adımı 9: Kayıp Oranı = 2.67%\n",
            "Eğitim Adımı 10: Kayıp Oranı = 0.46%\n",
            "Eğitim Adımı 11: Kayıp Oranı = 2.53%\n",
            "Eğitim Adımı 12: Kayıp Oranı = 3.92%\n",
            "Eğitim Adımı 13: Kayıp Oranı = 2.17%\n",
            "Eğitim Adımı 14: Kayıp Oranı = 1.43%\n",
            "Eğitim Adımı 15: Kayıp Oranı = 0.88%\n",
            "Eğitim Adımı 16: Kayıp Oranı = 0.81%\n",
            "Eğitim Adımı 17: Kayıp Oranı = 1.16%\n",
            "Eğitim Adımı 18: Kayıp Oranı = 1.13%\n",
            "Eğitim Adımı 19: Kayıp Oranı = 0.84%\n",
            "Eğitim Adımı 20: Kayıp Oranı = 0.55%\n",
            "Eğitim Adımı 21: Kayıp Oranı = 0.49%\n",
            "Eğitim Adımı 22: Kayıp Oranı = 0.52%\n",
            "Eğitim Adımı 23: Kayıp Oranı = 0.62%\n",
            "Eğitim Adımı 24: Kayıp Oranı = 0.61%\n",
            "Eğitim Adımı 25: Kayıp Oranı = 0.56%\n",
            "Eğitim Adımı 26: Kayıp Oranı = 0.51%\n",
            "Eğitim Adımı 27: Kayıp Oranı = 0.49%\n",
            "Eğitim Adımı 28: Kayıp Oranı = 0.54%\n",
            "Eğitim Adımı 29: Kayıp Oranı = 0.55%\n",
            "Eğitim Adımı 30: Kayıp Oranı = 0.56%\n",
            "Eğitim Adımı 31: Kayıp Oranı = 0.50%\n",
            "Eğitim Adımı 32: Kayıp Oranı = 0.53%\n",
            "Eğitim Adımı 33: Kayıp Oranı = 0.49%\n",
            "Eğitim Adımı 34: Kayıp Oranı = 0.50%\n",
            "Eğitim Adımı 35: Kayıp Oranı = 0.51%\n",
            "Eğitim Adımı 36: Kayıp Oranı = 0.52%\n",
            "Eğitim Adımı 37: Kayıp Oranı = 0.50%\n",
            "Eğitim Adımı 38: Kayıp Oranı = 0.50%\n",
            "Eğitim Adımı 39: Kayıp Oranı = 0.51%\n",
            "Eğitim Adımı 40: Kayıp Oranı = 0.50%\n",
            "Eğitim Adımı 41: Kayıp Oranı = 0.51%\n",
            "Eğitim Adımı 42: Kayıp Oranı = 0.51%\n",
            "Eğitim Adımı 43: Kayıp Oranı = 0.50%\n",
            "Eğitim Adımı 44: Kayıp Oranı = 0.49%\n",
            "Eğitim Adımı 45: Kayıp Oranı = 0.48%\n",
            "Eğitim Adımı 46: Kayıp Oranı = 0.48%\n",
            "Eğitim Adımı 47: Kayıp Oranı = 0.50%\n",
            "Eğitim Adımı 48: Kayıp Oranı = 0.50%\n",
            "Eğitim Adımı 49: Kayıp Oranı = 0.49%\n",
            "Eğitim Adımı 50: Kayıp Oranı = 0.47%\n",
            "Eğitim Adımı 51: Kayıp Oranı = 0.52%\n",
            "Eğitim Adımı 52: Kayıp Oranı = 0.49%\n",
            "Eğitim Adımı 53: Kayıp Oranı = 0.49%\n",
            "Eğitim Adımı 54: Kayıp Oranı = 0.49%\n",
            "Eğitim Adımı 55: Kayıp Oranı = 0.48%\n",
            "Eğitim Adımı 56: Kayıp Oranı = 0.51%\n",
            "Eğitim Adımı 57: Kayıp Oranı = 0.50%\n",
            "Eğitim Adımı 58: Kayıp Oranı = 0.51%\n",
            "Eğitim Adımı 59: Kayıp Oranı = 0.49%\n",
            "Eğitim Adımı 60: Kayıp Oranı = 0.48%\n",
            "Eğitim Adımı 61: Kayıp Oranı = 0.46%\n",
            "Eğitim Adımı 62: Kayıp Oranı = 0.52%\n",
            "Eğitim Adımı 63: Kayıp Oranı = 0.47%\n",
            "Eğitim Adımı 64: Kayıp Oranı = 0.47%\n",
            "Eğitim Adımı 65: Kayıp Oranı = 0.49%\n",
            "Eğitim Adımı 66: Kayıp Oranı = 0.46%\n",
            "Eğitim Adımı 67: Kayıp Oranı = 0.48%\n",
            "Eğitim Adımı 68: Kayıp Oranı = 0.49%\n",
            "Eğitim Adımı 69: Kayıp Oranı = 0.46%\n",
            "Eğitim Adımı 70: Kayıp Oranı = 0.48%\n",
            "Eğitim Adımı 71: Kayıp Oranı = 0.47%\n",
            "Eğitim Adımı 72: Kayıp Oranı = 0.50%\n",
            "Eğitim Adımı 73: Kayıp Oranı = 0.47%\n",
            "Eğitim Adımı 74: Kayıp Oranı = 0.46%\n",
            "Eğitim Adımı 75: Kayıp Oranı = 0.46%\n",
            "Eğitim Adımı 76: Kayıp Oranı = 0.47%\n",
            "Eğitim Adımı 77: Kayıp Oranı = 0.47%\n",
            "Eğitim Adımı 78: Kayıp Oranı = 0.51%\n",
            "Eğitim Adımı 79: Kayıp Oranı = 0.48%\n",
            "Eğitim Adımı 80: Kayıp Oranı = 0.47%\n",
            "Eğitim Adımı 81: Kayıp Oranı = 0.49%\n",
            "Eğitim Adımı 82: Kayıp Oranı = 0.48%\n",
            "Eğitim Adımı 83: Kayıp Oranı = 0.46%\n",
            "Eğitim Adımı 84: Kayıp Oranı = 0.52%\n",
            "Eğitim Adımı 85: Kayıp Oranı = 0.50%\n",
            "Eğitim Adımı 86: Kayıp Oranı = 0.51%\n",
            "Eğitim Adımı 87: Kayıp Oranı = 0.50%\n",
            "Eğitim Adımı 88: Kayıp Oranı = 0.49%\n",
            "Eğitim Adımı 89: Kayıp Oranı = 0.46%\n",
            "Eğitim Adımı 90: Kayıp Oranı = 0.46%\n",
            "Eğitim Adımı 91: Kayıp Oranı = 0.46%\n",
            "Eğitim Adımı 92: Kayıp Oranı = 0.47%\n",
            "Eğitim Adımı 93: Kayıp Oranı = 0.46%\n",
            "Eğitim Adımı 94: Kayıp Oranı = 0.46%\n",
            "Eğitim Adımı 95: Kayıp Oranı = 0.45%\n",
            "Eğitim Adımı 96: Kayıp Oranı = 0.50%\n",
            "Eğitim Adımı 97: Kayıp Oranı = 0.49%\n",
            "Eğitim Adımı 98: Kayıp Oranı = 0.51%\n",
            "Eğitim Adımı 99: Kayıp Oranı = 0.46%\n",
            "Eğitim Adımı 100: Kayıp Oranı = 0.47%\n",
            "Eğitim Modelinin Toplam Kayıp Oranı (Yüzdelik):  3.310552778188139\n",
            "Eğitim Modelinin Başarı Oranı (Yüzdelik):  96.68944722181186\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "Modelin_kayip_degeri = [loss * 100 for loss in history.history['loss']]\n",
        "Basari_orani = [100 - loss for loss in Modelin_kayip_degeri]\n",
        "epoch_sayisi = range(1, len(Basari_orani) + 1)\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(epoch_sayisi, Basari_orani, linestyle='-', color='b')\n",
        "plt.title(\"Eğitim Sürecinde Başarı Oranı\")\n",
        "plt.xlabel(\"Adım Sayısı\")\n",
        "plt.ylabel(\"Başarı Oranı (%)\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Hz2PeUcyjacS",
        "outputId": "5033c29a-7147-4577-ea01-3078557b9d20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "Modelin_kayip_degeri = [loss * 100 for loss in history.history['loss']]\n",
        "epoch_sayisi = range(1, len(Modelin_kayip_degeri) + 1)\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(epoch_sayisi, Modelin_kayip_degeri, linestyle='-', color='r') # marker='o'\n",
        "plt.title(\"Eğitim Sürecinde Hata Oranı\")\n",
        "plt.xlabel(\"Adım Sayısı\")\n",
        "plt.ylabel(\"Hata Oranı (%)\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "6UOUtfywjsdE",
        "outputId": "73eade88-0822-45d7-9c94-1bf4fb5d32ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "Modelin_kayip_degeri = [loss * 100 for loss in history.history['loss']]\n",
        "Basari_orani = [100 - loss for loss in Modelin_kayip_degeri]\n",
        "epoch_sayisi = range(1, len(Modelin_kayip_degeri) + 1)\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(epoch_sayisi, Modelin_kayip_degeri, linestyle='-', color='r', label='Hata Oranı (%)')\n",
        "plt.plot(epoch_sayisi, Basari_orani, linestyle='-', color='b', label='Başarı Oranı (%)')\n",
        "plt.title(\"Eğitim Sürecinde Başarı ve Hata Oranı\")\n",
        "plt.xlabel(\"Adım Sayısı\")\n",
        "plt.ylabel(\"Oran (%)\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "XizNateZj1fy",
        "outputId": "34464bdc-8a3e-4b02-e3fc-50807672a193"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "time_step = len(ilce_data) - len(trainY[0])\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(years[time_step:time_step + len(trainY[0])], trainY[0], label='Gerçek Değerler', color='blue')\n",
        "plt.plot(years[time_step:time_step + len(trainPredict)], trainPredict, label='Tahminler', color='red')\n",
        "plt.title('Avcılar İlçesi için Gerçek Su Kullanım Değerleri ve Tahmin Edilen Değerlerin Görselleştirilmesi')\n",
        "plt.xlabel('Yıl')\n",
        "plt.ylabel('Su Tüketimi x (10^6) m^3')\n",
        "plt.legend()\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "id": "EqIjV2BzfXBO",
        "outputId": "36c8ce71-9ef2-4b1c-c17d-a527fcb0d787"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "farklar = np.array(trainY[0]) - np.array(trainPredict)\n",
        "mutlak_farklar = np.abs(farklar)\n",
        "yuzdelik_hatalar = (mutlak_farklar / np.array(trainY[0])) * 100\n",
        "ortalama_yuzdelik_hata = np.mean(yuzdelik_hatalar)\n",
        "for year, gercek, tahmin, fark in zip(years[time_step:time_step + len(trainY[0])], trainY[0], trainPredict.flatten(), mutlak_farklar.flatten()):\n",
        "    print(\"Yıl:\", year ,\"Gerçek: \", gercek, \"Tahmin: \", tahmin, \"Mutlak Fark: \", fark)\n",
        "print(\"Ortalama Mutlak Hata: \", ortalama_yuzdelik_hata)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2RzGGfzkylc",
        "outputId": "920fef78-acb9-4700-a66b-602651a45529"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Yıl: 2019 Gerçek:  19485453.0 Tahmin:  19744604.0 Mutlak Fark:  259151.0\n",
            "Yıl: 2020 Gerçek:  20824893.0 Tahmin:  20335380.0 Mutlak Fark:  1080289.0\n",
            "Yıl: 2021 Gerçek:  20690506.0 Tahmin:  20946830.0 Mutlak Fark:  945902.0\n",
            "Yıl: 2022 Gerçek:  21556002.0 Tahmin:  21641920.0 Mutlak Fark:  1811398.0\n",
            "Ortalama Mutlak Hata:  4.107577637322954\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Sistemin_toplam_hata_payi = abs(Kayip_hesabi - ortalama_yuzdelik_hata)\n",
        "print(\"Geliştirilen Sistemin Yüzdelik oranıdaki hata payı=> %\",Sistemin_toplam_hata_payi)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kSQYtIVSnWmJ",
        "outputId": "edc7032b-76a9-4141-d38c-f8f4f94ae44d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Geliştirilen Sistemin Yüzdelik oranıdaki hata payı=> % 0.7970248591348144\n"
          ]
        }
      ]
    }
  ]
}