Read_CSV_data_and_analyse_with_using_Decision_Tree.ipynb

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      "mount_file_id": "14Yi2NEhA_60tesV9DQTFhuohIUcNxpEq",
      "authorship_tag": "ABX9TyOri1At6FiRUe1gxDcm7lxI",
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      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/masoudshahrian/DataScience-code/blob/master/Read_CSV_data_and_analyse_with_using_Decision_Tree.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeRegressor, export_text\n",
        "from sklearn.metrics import r2_score\n",
        "import pickle\n"
      ],
      "metadata": {
        "id": "KaAS3FPryFBq"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# مرحله 1: بارگذاری و پاک‌سازی داده‌ها\n",
        "data = pd.read_csv('/content/my_data10.csv')\n",
        "# data_cleaned = data.dropna()  # حذف ردیف‌های با داده‌های خالی\n"
      ],
      "metadata": {
        "id": "TAuD0TeoyIKR"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "id": "4Hbwkqujy_2x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeRegressor, export_text\n",
        "from sklearn.metrics import r2_score\n",
        "import pickle\n",
        "\n",
        "# مرحله 1: بارگذاری و پاک‌سازی داده‌ها\n",
        "# بارگذاری داده‌ها از فایل CSV\n",
        "data = pd.read_csv('/content/my_data10.csv')\n",
        "\n",
        "# نمایش نام ستون‌ها برای شناسایی ستون هدف\n",
        "print(\"نام ستون‌ها:\")\n",
        "print(data.columns)\n",
        "\n",
        "# نام ستون هدف (خروجی) را مشخص کنید\n",
        "target_column_name = 'target'  # نام صحیح ستون خروجی را وارد کنید\n",
        "\n",
        "# بررسی وجود ستون هدف\n",
        "if target_column_name not in data.columns:\n",
        "    raise ValueError(f\"ستون '{target_column_name}' در داده‌ها وجود ندارد.\")\n",
        "\n",
        "# حذف ردیف‌های با داده‌های خالی\n",
        "data_cleaned = data.dropna()\n",
        "\n",
        "# مرحله 2: محاسبه همبستگی\n",
        "correlation_matrix = data_cleaned.corr()\n",
        "target_correlation = correlation_matrix[target_column_name].abs().sort_values(ascending=False)\n",
        "\n",
        "# چاپ همبستگی‌ها\n",
        "print(\"همبستگی بین ویژگی‌ها و خروجی:\")\n",
        "print(target_correlation)\n",
        "\n",
        "# انتخاب پارامترهای مهم (با آستانه پایین‌تر)\n",
        "important_features = target_correlation[target_correlation > 0.2].index.tolist()  # آستانه 0.2\n",
        "important_features.remove(target_column_name)  # حذف ستون هدف از ویژگی‌ها\n",
        "\n",
        "print(\"پارامترهای مهم:\")\n",
        "print(important_features)\n",
        "\n",
        "# بررسی اینکه آیا ویژگی‌های مهم وجود دارد\n",
        "if len(important_features) == 0:\n",
        "    raise ValueError(\"هیچ ویژگی مهمی برای مدل وجود ندارد.\")\n",
        "\n",
        "# مرحله 3: تقسیم داده‌ها\n",
        "X = data_cleaned[important_features]  # ورودی‌ها\n",
        "y = data_cleaned[target_column_name]  # خروجی\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# آموزش مدل درخت تصمیم\n",
        "model = DecisionTreeRegressor(random_state=42)\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# پیش‌بینی و محاسبه R²\n",
        "y_pred = model.predict(X_test)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "if r2 > 0.75:\n",
        "    print(f'R²: {r2} - مدل موفق است.')\n",
        "\n",
        "    # مرحله 4: استخراج رابطه\n",
        "    tree_rules = export_text(model, feature_names=list(X.columns))\n",
        "    print(\"قوانین درخت تصمیم:\")\n",
        "    print(tree_rules)\n",
        "\n",
        "    # ذخیره مدل\n",
        "    with open('decision_tree_model.pkl', 'wb') as file:\n",
        "        pickle.dump(model, file)\n",
        "else:\n",
        "    print(f'R²: {r2} - مدل نیاز به بهبود دارد.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dBP-2H5dAodZ",
        "outputId": "4cba9765-781f-40c7-cb60-3593bdf42b1c"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "نام ستون‌ها:\n",
            "Index(['feature1', 'feature2', 'feature3', 'feature4', 'feature5', 'feature6',\n",
            "       'feature7', 'feature8', 'feature9', 'feature10', 'feature11',\n",
            "       'feature12', 'feature13', 'feature14', 'feature15', 'feature16',\n",
            "       'feature17', 'feature18', 'target'],\n",
            "      dtype='object')\n",
            "همبستگی بین ویژگی‌ها و خروجی:\n",
            "target       1.000000\n",
            "feature5     0.316375\n",
            "feature18    0.184349\n",
            "feature14    0.176963\n",
            "feature16    0.151777\n",
            "feature17    0.151777\n",
            "feature12    0.148044\n",
            "feature7     0.113013\n",
            "feature11    0.108649\n",
            "feature15    0.103262\n",
            "feature10    0.101930\n",
            "feature13    0.087286\n",
            "feature8     0.086045\n",
            "feature4     0.064139\n",
            "feature3     0.035998\n",
            "feature9     0.029226\n",
            "feature1     0.013248\n",
            "feature6     0.003696\n",
            "feature2     0.000587\n",
            "Name: target, dtype: float64\n",
            "پارامترهای مهم:\n",
            "['feature5']\n",
            "R²: -0.7259471868870035 - مدل نیاز به بهبود دارد.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import r2_score\n",
        "import pickle\n",
        "\n",
        "# مرحله 1: بارگذاری و پاک‌سازی داده‌ها\n",
        "# بارگذاری داده‌ها از فایل CSV\n",
        "data = pd.read_csv('/content/my_data10.csv')\n",
        "\n",
        "# نمایش نام ستون‌ها برای شناسایی ستون هدف\n",
        "print(\"نام ستون‌ها:\")\n",
        "print(data.columns)\n",
        "\n",
        "# نام ستون هدف (خروجی) را مشخص کنید\n",
        "target_column_name = 'target'  # نام صحیح ستون خروجی را وارد کنید\n",
        "\n",
        "# بررسی وجود ستون هدف\n",
        "if target_column_name not in data.columns:\n",
        "    raise ValueError(f\"ستون '{target_column_name}' در داده‌ها وجود ندارد.\")\n",
        "\n",
        "# حذف ردیف‌های با داده‌های خالی\n",
        "data_cleaned = data.dropna()\n",
        "\n",
        "# مرحله 2: محاسبه همبستگی\n",
        "correlation_matrix = data_cleaned.corr()\n",
        "target_correlation = correlation_matrix[target_column_name].abs().sort_values(ascending=False)\n",
        "\n",
        "# انتخاب پارامترهای مهم (با آستانه پایین‌تر)\n",
        "important_features = target_correlation[target_correlation > 0.2].index.tolist()  # آستانه 0.2\n",
        "important_features.remove(target_column_name)  # حذف ستون هدف از ویژگی‌ها\n",
        "\n",
        "print(\"پارامترهای مهم:\")\n",
        "print(important_features)\n",
        "\n",
        "# بررسی اینکه آیا ویژگی‌های مهم وجود دارد\n",
        "if len(important_features) == 0:\n",
        "    raise ValueError(\"هیچ ویژگی مهمی برای مدل وجود ندارد.\")\n",
        "\n",
        "# مرحله 3: تقسیم داده‌ها\n",
        "X = data_cleaned[important_features]  # ورودی‌ها\n",
        "y = data_cleaned[target_column_name]  # خروجی\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# مرحله 4: آموزش مدل Random Forest\n",
        "# تنظیم هایپرپارامترها با استفاده از GridSearchCV\n",
        "param_grid = {\n",
        "    'n_estimators': [100, 200],\n",
        "    'max_depth': [None, 10, 20, 30],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4],\n",
        "    'max_features': ['auto', 'sqrt']\n",
        "}\n",
        "\n",
        "rf_model = RandomForestRegressor(random_state=42)\n",
        "grid_search = GridSearchCV(estimator=rf_model, param_grid=param_grid, cv=5, n_jobs=-1, verbose=2)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# بهترین مدل\n",
        "best_model = grid_search.best_estimator_\n",
        "\n",
        "# پیش‌بینی و محاسبه R²\n",
        "y_pred = best_model.predict(X_test)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "if r2 > 0.75:\n",
        "    print(f'R²: {r2} - مدل موفق است.')\n",
        "\n",
        "    # ذخیره مدل\n",
        "    with open('random_forest_model.pkl', 'wb') as file:\n",
        "        pickle.dump(best_model, file)\n",
        "else:\n",
        "    print(f'R²: {r2} - مدل نیاز به بهبود دارد.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hVISc0PsBVjT",
        "outputId": "a5ed38bd-f3b5-4a22-cb2e-6b256e162d84"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "نام ستون‌ها:\n",
            "Index(['feature1', 'feature2', 'feature3', 'feature4', 'feature5', 'feature6',\n",
            "       'feature7', 'feature8', 'feature9', 'feature10', 'feature11',\n",
            "       'feature12', 'feature13', 'feature14', 'feature15', 'feature16',\n",
            "       'feature17', 'feature18', 'target'],\n",
            "      dtype='object')\n",
            "پارامترهای مهم:\n",
            "['feature5']\n",
            "Fitting 5 folds for each of 144 candidates, totalling 720 fits\n",
            "R²: -0.04415884795213576 - مدل نیاز به بهبود دارد.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:540: FitFailedWarning: \n",
            "360 fits failed out of a total of 720.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "230 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 888, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1466, in wrapper\n",
            "    estimator._validate_params()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 666, in _validate_params\n",
            "    validate_parameter_constraints(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 95, in validate_parameter_constraints\n",
            "    raise InvalidParameterError(\n",
            "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "130 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 888, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1466, in wrapper\n",
            "    estimator._validate_params()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 666, in _validate_params\n",
            "    validate_parameter_constraints(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 95, in validate_parameter_constraints\n",
            "    raise InvalidParameterError(\n",
            "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:1103: UserWarning: One or more of the test scores are non-finite: [        nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            " -0.35205808 -0.34026996 -0.27000651 -0.25745138 -0.17021371 -0.16258492\n",
            " -0.18144979 -0.17879101 -0.17521168 -0.17490114 -0.11629659 -0.11497274\n",
            " -0.08547448 -0.0868928  -0.08547448 -0.0868928  -0.0748488  -0.07619733\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            " -0.29882975 -0.29173122 -0.24407275 -0.23552047 -0.16477914 -0.15805971\n",
            " -0.17326541 -0.16960309 -0.1686959  -0.16735993 -0.11523064 -0.11326157\n",
            " -0.08562277 -0.0864317  -0.08562277 -0.0864317  -0.07484652 -0.07588298\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            " -0.35210082 -0.3402896  -0.27000651 -0.25745138 -0.17021371 -0.16258492\n",
            " -0.18144979 -0.17879101 -0.17521168 -0.17490114 -0.11629659 -0.11497274\n",
            " -0.08547448 -0.0868928  -0.08547448 -0.0868928  -0.0748488  -0.07619733\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            " -0.35205808 -0.34026996 -0.27000651 -0.25745138 -0.17021371 -0.16258492\n",
            " -0.18144979 -0.17879101 -0.17521168 -0.17490114 -0.11629659 -0.11497274\n",
            " -0.08547448 -0.0868928  -0.08547448 -0.0868928  -0.0748488  -0.07619733]\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install numpy matplotlib"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kEvXvTeECd-5",
        "outputId": "ef0d9536-cca5-45a0-8b95-09824980ceab"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.26.4)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.54.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.7)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (10.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np  # وارد کردن numpy برای استفاده از np.argsort\n",
        "import matplotlib.pyplot as plt  # برای رسم نمودار\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import r2_score\n",
        "import pickle\n",
        "\n",
        "# مرحله 1: بارگذاری و پاک‌سازی داده‌ها\n",
        "# بارگذاری داده‌ها از فایل CSV\n",
        "data = pd.read_csv('/content/my_data10.csv')\n",
        "\n",
        "# نمایش نام ستون‌ها برای شناسایی ستون هدف\n",
        "print(\"نام ستون‌ها:\")\n",
        "print(data.columns)\n",
        "\n",
        "# نام ستون هدف (خروجی) را مشخص کنید\n",
        "target_column_name = 'target'  # نام صحیح ستون خروجی را وارد کنید\n",
        "\n",
        "# بررسی وجود ستون هدف\n",
        "if target_column_name not in data.columns:\n",
        "    raise ValueError(f\"ستون '{target_column_name}' در داده‌ها وجود ندارد.\")\n",
        "\n",
        "# حذف ردیف‌های با داده‌های خالی\n",
        "data_cleaned = data.dropna()\n",
        "\n",
        "# مرحله 2: محاسبه همبستگی\n",
        "correlation_matrix = data_cleaned.corr()\n",
        "target_correlation = correlation_matrix[target_column_name].abs().sort_values(ascending=False)\n",
        "\n",
        "# انتخاب پارامترهای مهم (با آستانه پایین‌تر)\n",
        "important_features = target_correlation[target_correlation > 0.2].index.tolist()  # آستانه 0.2\n",
        "important_features.remove(target_column_name)  # حذف ستون هدف از ویژگی‌ها\n",
        "\n",
        "print(\"پارامترهای مهم:\")\n",
        "print(important_features)\n",
        "\n",
        "# بررسی اینکه آیا ویژگی‌های مهم وجود دارد\n",
        "if len(important_features) == 0:\n",
        "    raise ValueError(\"هیچ ویژگی مهمی برای مدل وجود ندارد.\")\n",
        "\n",
        "# مرحله 3: تقسیم داده‌ها\n",
        "X = data_cleaned[important_features]  # ورودی‌ها\n",
        "y = data_cleaned[target_column_name]  # خروجی\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# مرحله 4: آموزش مدل Random Forest\n",
        "# تنظیم هایپرپارامترها با استفاده از GridSearchCV\n",
        "param_grid = {\n",
        "    'n_estimators': [100, 200],\n",
        "    'max_depth': [None, 10, 20, 30],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4],\n",
        "    'max_features': ['auto', 'sqrt']\n",
        "}\n",
        "\n",
        "rf_model = RandomForestRegressor(random_state=42)\n",
        "grid_search = GridSearchCV(estimator=rf_model, param_grid=param_grid, cv=5, n_jobs=-1, verbose=2)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# بهترین مدل\n",
        "best_model = grid_search.best_estimator_\n",
        "\n",
        "# پیش‌بینی و محاسبه R²\n",
        "y_pred = best_model.predict(X_test)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "if r2 > 0.75:\n",
        "    print(f'R²: {r2} - مدل موفق است.')\n",
        "\n",
        "    # مرحله 5: استخراج و رسم اهمیت ویژگی‌ها\n",
        "    importances = best_model.feature_importances_\n",
        "    indices = np.argsort(importances)[::-1]\n",
        "\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.title(\"اهمیت ویژگی‌ها\")\n",
        "    plt.bar(range(X.shape[1]), importances[indices], align=\"center\")\n",
        "    plt.xticks(range(X.shape[1]), np.array(important_features)[indices], rotation=90)\n",
        "    plt.xlim([-1, X.shape[1]])\n",
        "    plt.ylabel(\"اهمیت\")\n",
        "    plt.show()\n",
        "\n",
        "    # ذخیره مدل\n",
        "    with open('random_forest_model.pkl', 'wb') as file:\n",
        "        pickle.dump(best_model, file)\n",
        "else:\n",
        "    print(f'R²: {r2} - مدل نیاز به بهبود دارد.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AJmCeTlZCc-2",
        "outputId": "d1e436ad-9d45-4eac-982a-840de1d175c7"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "نام ستون‌ها:\n",
            "Index(['feature1', 'feature2', 'feature3', 'feature4', 'feature5', 'feature6',\n",
            "       'feature7', 'feature8', 'feature9', 'feature10', 'feature11',\n",
            "       'feature12', 'feature13', 'feature14', 'feature15', 'feature16',\n",
            "       'feature17', 'feature18', 'target'],\n",
            "      dtype='object')\n",
            "پارامترهای مهم:\n",
            "['feature5']\n",
            "Fitting 5 folds for each of 144 candidates, totalling 720 fits\n",
            "R²: -0.04415884795213576 - مدل نیاز به بهبود دارد.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:540: FitFailedWarning: \n",
            "360 fits failed out of a total of 720.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "208 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 888, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1466, in wrapper\n",
            "    estimator._validate_params()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 666, in _validate_params\n",
            "    validate_parameter_constraints(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 95, in validate_parameter_constraints\n",
            "    raise InvalidParameterError(\n",
            "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "152 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 888, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1466, in wrapper\n",
            "    estimator._validate_params()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 666, in _validate_params\n",
            "    validate_parameter_constraints(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 95, in validate_parameter_constraints\n",
            "    raise InvalidParameterError(\n",
            "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/ma/core.py:2820: RuntimeWarning: invalid value encountered in cast\n",
            "  _data = np.array(data, dtype=dtype, copy=copy,\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:1103: UserWarning: One or more of the test scores are non-finite: [        nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            " -0.35205808 -0.34026996 -0.27000651 -0.25745138 -0.17021371 -0.16258492\n",
            " -0.18144979 -0.17879101 -0.17521168 -0.17490114 -0.11629659 -0.11497274\n",
            " -0.08547448 -0.0868928  -0.08547448 -0.0868928  -0.0748488  -0.07619733\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            " -0.29882975 -0.29173122 -0.24407275 -0.23552047 -0.16477914 -0.15805971\n",
            " -0.17326541 -0.16960309 -0.1686959  -0.16735993 -0.11523064 -0.11326157\n",
            " -0.08562277 -0.0864317  -0.08562277 -0.0864317  -0.07484652 -0.07588298\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            " -0.35210082 -0.3402896  -0.27000651 -0.25745138 -0.17021371 -0.16258492\n",
            " -0.18144979 -0.17879101 -0.17521168 -0.17490114 -0.11629659 -0.11497274\n",
            " -0.08547448 -0.0868928  -0.08547448 -0.0868928  -0.0748488  -0.07619733\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            " -0.35205808 -0.34026996 -0.27000651 -0.25745138 -0.17021371 -0.16258492\n",
            " -0.18144979 -0.17879101 -0.17521168 -0.17490114 -0.11629659 -0.11497274\n",
            " -0.08547448 -0.0868928  -0.08547448 -0.0868928  -0.0748488  -0.07619733]\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# 1. نمودار پراکندگی\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(y_test, y_pred, alpha=0.7)\n",
        "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], color='red', linestyle='--')  # خط ایده‌آل\n",
        "plt.title('Real elements & Predictive elements')\n",
        "plt.xlabel('Real elements')\n",
        "plt.ylabel('Predictive elements')\n",
        "plt.grid()\n",
        "plt.show()\n",
        "\n",
        "# 2. نمودار خطای پیش‌بینی\n",
        "errors = y_test - y_pred\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(y_pred, errors, alpha=0.7)\n",
        "plt.axhline(0, color='red', linestyle='--')  # خط صفر\n",
        "plt.title('Erroe of prediction')\n",
        "plt.xlabel('Predictive elements')\n",
        "plt.ylabel('Error(real elements-predictive elements)')\n",
        "plt.grid()\n",
        "plt.show()\n",
        "\n",
        "# 3. نمودار اهمیت ویژگی‌ها\n",
        "importances = best_model.feature_importances_\n",
        "indices = np.argsort(importances)[::-1]\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.title('Priority of Elements')\n",
        "plt.bar(range(X.shape[1]), importances[indices], align='center')\n",
        "plt.xticks(range(X.shape[1]), [important_features[i] for i in indices], rotation=90)\n",
        "plt.xlim([-1, X.shape[1]])\n",
        "plt.xlabel('Features')\n",
        "plt.ylabel('Priority')\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iLS-q6T2CITR",
        "outputId": "05abdff2-482a-41f1-d432-7aa709a78efd"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "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 pandas as pd\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import r2_score\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pickle\n",
        "\n",
        "# مرحله 1: بارگذاری و پاک‌سازی داده‌ها\n",
        "data = pd.read_csv('/content/my_data10.csv')\n",
        "\n",
        "# نمایش نام ستون‌ها برای شناسایی ستون هدف\n",
        "print(\"نام ستون‌ها:\")\n",
        "print(data.columns)\n",
        "\n",
        "target_column_name = 'target'  # نام صحیح ستون خروجی را وارد کنید\n",
        "\n",
        "# حذف ردیف‌های با داده‌های خالی\n",
        "data_cleaned = data.dropna()\n",
        "\n",
        "# مرحله 2: محاسبه همبستگی\n",
        "correlation_matrix = data_cleaned.corr()\n",
        "target_correlation = correlation_matrix[target_column_name].abs().sort_values(ascending=False)\n",
        "\n",
        "# انتخاب پارامترهای مهم\n",
        "important_features = target_correlation[target_correlation > 0.2].index.tolist()  # آستانه 0.2\n",
        "important_features.remove(target_column_name)\n",
        "\n",
        "print(\"پارامترهای مهم:\")\n",
        "print(important_features)\n",
        "\n",
        "# مرحله 3: تقسیم داده‌ها\n",
        "X = data_cleaned[important_features]\n",
        "y = data_cleaned[target_column_name]\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# مرحله 4: آموزش مدل Random Forest\n",
        "param_grid = {\n",
        "    'n_estimators': [100, 200],\n",
        "    'max_depth': [None, 10, 20, 30],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4],\n",
        "    'max_features': ['auto', 'sqrt']\n",
        "}\n",
        "\n",
        "rf_model = RandomForestRegressor(random_state=42)\n",
        "grid_search = GridSearchCV(estimator=rf_model, param_grid=param_grid, cv=5, n_jobs=-1, verbose=2)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# بهترین مدل\n",
        "best_model = grid_search.best_estimator_\n",
        "\n",
        "# پیش‌بینی و محاسبه R²\n",
        "y_pred = best_model.predict(X_test)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(f'R²: {r2} - مدل موفق است.')\n",
        "\n",
        "# مرحله 5: استخراج قوانین درخت تصمیم\n",
        "# برای استخراج قوانین از درخت‌های تصمیم می‌توانیم از درخت‌های تصمیم موجود در جنگل استفاده کنیم\n",
        "for tree in best_model.estimators_[:3]:  # فقط سه درخت اول را نمایش می‌دهیم\n",
        "    from sklearn.tree import export_text\n",
        "    rules = export_text(tree, feature_names=list(X.columns))\n",
        "    print(\"Rules of Decesion tree\")\n",
        "    print(rules)\n",
        "\n",
        "# مرحله 6: تحلیل اهمیت ویژگی‌ها\n",
        "importances = best_model.feature_importances_\n",
        "indices = np.argsort(importances)[::-1]\n",
        "\n",
        "# نمایش اهمیت ویژگی‌ها\n",
        "plt.figure()\n",
        "plt.title(\" Priority of fetures\")\n",
        "plt.bar(range(X.shape[1]), importances[indices], align=\"center\")\n",
        "plt.xticks(range(X.shape[1]), [X.columns[i] for i in indices], rotation=90)\n",
        "plt.xlim([-1, X.shape[1]])\n",
        "plt.show()\n",
        "\n",
        "# مرحله 7: ذخیره مدل\n",
        "with open('random_forest_model.pkl', 'wb') as file:\n",
        "    pickle.dump(best_model, file)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-T9J-7vjEdC5",
        "outputId": "54a6c9a3-0dcc-4064-f016-eb5742e288eb"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "نام ستون‌ها:\n",
            "Index(['feature1', 'feature2', 'feature3', 'feature4', 'feature5', 'feature6',\n",
            "       'feature7', 'feature8', 'feature9', 'feature10', 'feature11',\n",
            "       'feature12', 'feature13', 'feature14', 'feature15', 'feature16',\n",
            "       'feature17', 'feature18', 'target'],\n",
            "      dtype='object')\n",
            "پارامترهای مهم:\n",
            "['feature5']\n",
            "Fitting 5 folds for each of 144 candidates, totalling 720 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:540: FitFailedWarning: \n",
            "360 fits failed out of a total of 720.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "42 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 888, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1466, in wrapper\n",
            "    estimator._validate_params()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 666, in _validate_params\n",
            "    validate_parameter_constraints(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 95, in validate_parameter_constraints\n",
            "    raise InvalidParameterError(\n",
            "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "318 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 888, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1466, in wrapper\n",
            "    estimator._validate_params()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 666, in _validate_params\n",
            "    validate_parameter_constraints(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 95, in validate_parameter_constraints\n",
            "    raise InvalidParameterError(\n",
            "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/ma/core.py:2820: RuntimeWarning: invalid value encountered in cast\n",
            "  _data = np.array(data, dtype=dtype, copy=copy,\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:1103: UserWarning: One or more of the test scores are non-finite: [        nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            " -0.35205808 -0.34026996 -0.27000651 -0.25745138 -0.17021371 -0.16258492\n",
            " -0.18144979 -0.17879101 -0.17521168 -0.17490114 -0.11629659 -0.11497274\n",
            " -0.08547448 -0.0868928  -0.08547448 -0.0868928  -0.0748488  -0.07619733\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            " -0.29882975 -0.29173122 -0.24407275 -0.23552047 -0.16477914 -0.15805971\n",
            " -0.17326541 -0.16960309 -0.1686959  -0.16735993 -0.11523064 -0.11326157\n",
            " -0.08562277 -0.0864317  -0.08562277 -0.0864317  -0.07484652 -0.07588298\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            " -0.35210082 -0.3402896  -0.27000651 -0.25745138 -0.17021371 -0.16258492\n",
            " -0.18144979 -0.17879101 -0.17521168 -0.17490114 -0.11629659 -0.11497274\n",
            " -0.08547448 -0.0868928  -0.08547448 -0.0868928  -0.0748488  -0.07619733\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            " -0.35205808 -0.34026996 -0.27000651 -0.25745138 -0.17021371 -0.16258492\n",
            " -0.18144979 -0.17879101 -0.17521168 -0.17490114 -0.11629659 -0.11497274\n",
            " -0.08547448 -0.0868928  -0.08547448 -0.0868928  -0.0748488  -0.07619733]\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R²: -0.04415884795213576 - مدل موفق است.\n",
            "Rules of Decesion tree\n",
            "|--- feature5 <= 0.18\n",
            "|   |--- feature5 <= 0.17\n",
            "|   |   |--- feature5 <= -0.09\n",
            "|   |   |   |--- feature5 <= -0.13\n",
            "|   |   |   |   |--- value: [333.84]\n",
            "|   |   |   |--- feature5 >  -0.13\n",
            "|   |   |   |   |--- value: [276.67]\n",
            "|   |   |--- feature5 >  -0.09\n",
            "|   |   |   |--- feature5 <= -0.06\n",
            "|   |   |   |   |--- feature5 <= -0.06\n",
            "|   |   |   |   |   |--- feature5 <= -0.08\n",
            "|   |   |   |   |   |   |--- value: [425.16]\n",
            "|   |   |   |   |   |--- feature5 >  -0.08\n",
            "|   |   |   |   |   |   |--- value: [318.19]\n",
            "|   |   |   |   |--- feature5 >  -0.06\n",
            "|   |   |   |   |   |--- value: [518.74]\n",
            "|   |   |   |--- feature5 >  -0.06\n",
            "|   |   |   |   |--- feature5 <= 0.09\n",
            "|   |   |   |   |   |--- feature5 <= 0.03\n",
            "|   |   |   |   |   |   |--- feature5 <= -0.01\n",
            "|   |   |   |   |   |   |   |--- feature5 <= -0.04\n",
            "|   |   |   |   |   |   |   |   |--- feature5 <= -0.04\n",
            "|   |   |   |   |   |   |   |   |   |--- feature5 <= -0.05\n",
            "|   |   |   |   |   |   |   |   |   |   |--- value: [329.90]\n",
            "|   |   |   |   |   |   |   |   |   |--- feature5 >  -0.05\n",
            "|   |   |   |   |   |   |   |   |   |   |--- value: [362.48]\n",
            "|   |   |   |   |   |   |   |   |--- feature5 >  -0.04\n",
            "|   |   |   |   |   |   |   |   |   |--- value: [458.24]\n",
            "|   |   |   |   |   |   |   |--- feature5 >  -0.04\n",
            "|   |   |   |   |   |   |   |   |--- feature5 <= -0.02\n",
            "|   |   |   |   |   |   |   |   |   |--- value: [286.31]\n",
            "|   |   |   |   |   |   |   |   |--- feature5 >  -0.02\n",
            "|   |   |   |   |   |   |   |   |   |--- value: [348.51]\n",
            "|   |   |   |   |   |   |--- feature5 >  -0.01\n",
            "|   |   |   |   |   |   |   |--- value: [403.51]\n",
            "|   |   |   |   |   |--- feature5 >  0.03\n",
            "|   |   |   |   |   |   |--- feature5 <= 0.07\n",
            "|   |   |   |   |   |   |   |--- feature5 <= 0.05\n",
            "|   |   |   |   |   |   |   |   |--- value: [338.64]\n",
            "|   |   |   |   |   |   |   |--- feature5 >  0.05\n",
            "|   |   |   |   |   |   |   |   |--- value: [259.06]\n",
            "|   |   |   |   |   |   |--- feature5 >  0.07\n",
            "|   |   |   |   |   |   |   |--- value: [360.80]\n",
            "|   |   |   |   |--- feature5 >  0.09\n",
            "|   |   |   |   |   |--- feature5 <= 0.13\n",
            "|   |   |   |   |   |   |--- value: [509.08]\n",
            "|   |   |   |   |   |--- feature5 >  0.13\n",
            "|   |   |   |   |   |   |--- value: [349.49]\n",
            "|   |--- feature5 >  0.17\n",
            "|   |   |--- value: [249.11]\n",
            "|--- feature5 >  0.18\n",
            "|   |--- feature5 <= 0.20\n",
            "|   |   |--- value: [519.86]\n",
            "|   |--- feature5 >  0.20\n",
            "|   |   |--- feature5 <= 0.28\n",
            "|   |   |   |--- feature5 <= 0.27\n",
            "|   |   |   |   |--- value: [373.24]\n",
            "|   |   |   |--- feature5 >  0.27\n",
            "|   |   |   |   |--- value: [348.50]\n",
            "|   |   |--- feature5 >  0.28\n",
            "|   |   |   |--- feature5 <= 0.32\n",
            "|   |   |   |   |--- value: [643.23]\n",
            "|   |   |   |--- feature5 >  0.32\n",
            "|   |   |   |   |--- feature5 <= 0.43\n",
            "|   |   |   |   |   |--- value: [393.94]\n",
            "|   |   |   |   |--- feature5 >  0.43\n",
            "|   |   |   |   |   |--- value: [429.75]\n",
            "\n",
            "Rules of Decesion tree\n",
            "|--- feature5 <= 0.18\n",
            "|   |--- feature5 <= -0.02\n",
            "|   |   |--- feature5 <= -0.08\n",
            "|   |   |   |--- value: [294.35]\n",
            "|   |   |--- feature5 >  -0.08\n",
            "|   |   |   |--- feature5 <= -0.04\n",
            "|   |   |   |   |--- feature5 <= -0.04\n",
            "|   |   |   |   |   |--- feature5 <= -0.07\n",
            "|   |   |   |   |   |   |--- value: [365.31]\n",
            "|   |   |   |   |   |--- feature5 >  -0.07\n",
            "|   |   |   |   |   |   |--- feature5 <= -0.06\n",
            "|   |   |   |   |   |   |   |--- value: [287.20]\n",
            "|   |   |   |   |   |   |--- feature5 >  -0.06\n",
            "|   |   |   |   |   |   |   |--- feature5 <= -0.06\n",
            "|   |   |   |   |   |   |   |   |--- value: [391.21]\n",
            "|   |   |   |   |   |   |   |--- feature5 >  -0.06\n",
            "|   |   |   |   |   |   |   |   |--- feature5 <= -0.05\n",
            "|   |   |   |   |   |   |   |   |   |--- value: [308.68]\n",
            "|   |   |   |   |   |   |   |   |--- feature5 >  -0.05\n",
            "|   |   |   |   |   |   |   |   |   |--- value: [339.83]\n",
            "|   |   |   |   |--- feature5 >  -0.04\n",
            "|   |   |   |   |   |--- value: [370.28]\n",
            "|   |   |   |--- feature5 >  -0.04\n",
            "|   |   |   |   |--- value: [299.26]\n",
            "|   |--- feature5 >  -0.02\n",
            "|   |   |--- feature5 <= 0.14\n",
            "|   |   |   |--- feature5 <= 0.10\n",
            "|   |   |   |   |--- feature5 <= 0.03\n",
            "|   |   |   |   |   |--- feature5 <= -0.00\n",
            "|   |   |   |   |   |   |--- value: [362.14]\n",
            "|   |   |   |   |   |--- feature5 >  -0.00\n",
            "|   |   |   |   |   |   |--- feature5 <= 0.01\n",
            "|   |   |   |   |   |   |   |--- value: [462.66]\n",
            "|   |   |   |   |   |   |--- feature5 >  0.01\n",
            "|   |   |   |   |   |   |   |--- value: [385.12]\n",
            "|   |   |   |   |--- feature5 >  0.03\n",
            "|   |   |   |   |   |--- feature5 <= 0.06\n",
            "|   |   |   |   |   |   |--- feature5 <= 0.05\n",
            "|   |   |   |   |   |   |   |--- value: [315.69]\n",
            "|   |   |   |   |   |   |--- feature5 >  0.05\n",
            "|   |   |   |   |   |   |   |--- value: [261.86]\n",
            "|   |   |   |   |   |--- feature5 >  0.06\n",
            "|   |   |   |   |   |   |--- value: [362.24]\n",
            "|   |   |   |--- feature5 >  0.10\n",
            "|   |   |   |   |--- value: [474.89]\n",
            "|   |   |--- feature5 >  0.14\n",
            "|   |   |   |--- value: [320.13]\n",
            "|--- feature5 >  0.18\n",
            "|   |--- feature5 <= 0.20\n",
            "|   |   |--- value: [575.17]\n",
            "|   |--- feature5 >  0.20\n",
            "|   |   |--- feature5 <= 0.32\n",
            "|   |   |   |--- feature5 <= 0.28\n",
            "|   |   |   |   |--- value: [394.42]\n",
            "|   |   |   |--- feature5 >  0.28\n",
            "|   |   |   |   |--- value: [595.53]\n",
            "|   |   |--- feature5 >  0.32\n",
            "|   |   |   |--- feature5 <= 0.43\n",
            "|   |   |   |   |--- value: [353.07]\n",
            "|   |   |   |--- feature5 >  0.43\n",
            "|   |   |   |   |--- value: [404.05]\n",
            "\n",
            "Rules of Decesion tree\n",
            "|--- feature5 <= 0.21\n",
            "|   |--- feature5 <= -0.08\n",
            "|   |   |--- feature5 <= -0.10\n",
            "|   |   |   |--- value: [312.42]\n",
            "|   |   |--- feature5 >  -0.10\n",
            "|   |   |   |--- value: [264.76]\n",
            "|   |--- feature5 >  -0.08\n",
            "|   |   |--- feature5 <= -0.06\n",
            "|   |   |   |--- value: [446.85]\n",
            "|   |   |--- feature5 >  -0.06\n",
            "|   |   |   |--- feature5 <= -0.05\n",
            "|   |   |   |   |--- feature5 <= -0.06\n",
            "|   |   |   |   |   |--- value: [338.14]\n",
            "|   |   |   |   |--- feature5 >  -0.06\n",
            "|   |   |   |   |   |--- value: [282.06]\n",
            "|   |   |   |--- feature5 >  -0.05\n",
            "|   |   |   |   |--- feature5 <= -0.04\n",
            "|   |   |   |   |   |--- value: [402.75]\n",
            "|   |   |   |   |--- feature5 >  -0.04\n",
            "|   |   |   |   |   |--- feature5 <= 0.14\n",
            "|   |   |   |   |   |   |--- feature5 <= 0.05\n",
            "|   |   |   |   |   |   |   |--- feature5 <= -0.00\n",
            "|   |   |   |   |   |   |   |   |--- feature5 <= -0.04\n",
            "|   |   |   |   |   |   |   |   |   |--- value: [365.80]\n",
            "|   |   |   |   |   |   |   |   |--- feature5 >  -0.04\n",
            "|   |   |   |   |   |   |   |   |   |--- feature5 <= -0.02\n",
            "|   |   |   |   |   |   |   |   |   |   |--- value: [281.12]\n",
            "|   |   |   |   |   |   |   |   |   |--- feature5 >  -0.02\n",
            "|   |   |   |   |   |   |   |   |   |   |--- value: [340.61]\n",
            "|   |   |   |   |   |   |   |--- feature5 >  -0.00\n",
            "|   |   |   |   |   |   |   |   |--- feature5 <= 0.03\n",
            "|   |   |   |   |   |   |   |   |   |--- feature5 <= 0.01\n",
            "|   |   |   |   |   |   |   |   |   |   |--- value: [380.81]\n",
            "|   |   |   |   |   |   |   |   |   |--- feature5 >  0.01\n",
            "|   |   |   |   |   |   |   |   |   |   |--- value: [365.26]\n",
            "|   |   |   |   |   |   |   |   |--- feature5 >  0.03\n",
            "|   |   |   |   |   |   |   |   |   |--- value: [334.60]\n",
            "|   |   |   |   |   |   |--- feature5 >  0.05\n",
            "|   |   |   |   |   |   |   |--- feature5 <= 0.07\n",
            "|   |   |   |   |   |   |   |   |--- value: [277.62]\n",
            "|   |   |   |   |   |   |   |--- feature5 >  0.07\n",
            "|   |   |   |   |   |   |   |   |--- feature5 <= 0.10\n",
            "|   |   |   |   |   |   |   |   |   |--- feature5 <= 0.09\n",
            "|   |   |   |   |   |   |   |   |   |   |--- value: [333.32]\n",
            "|   |   |   |   |   |   |   |   |   |--- feature5 >  0.09\n",
            "|   |   |   |   |   |   |   |   |   |   |--- value: [372.23]\n",
            "|   |   |   |   |   |   |   |   |--- feature5 >  0.10\n",
            "|   |   |   |   |   |   |   |   |   |--- value: [305.68]\n",
            "|   |   |   |   |   |--- feature5 >  0.14\n",
            "|   |   |   |   |   |   |--- feature5 <= 0.15\n",
            "|   |   |   |   |   |   |   |--- value: [458.62]\n",
            "|   |   |   |   |   |   |--- feature5 >  0.15\n",
            "|   |   |   |   |   |   |   |--- feature5 <= 0.19\n",
            "|   |   |   |   |   |   |   |   |--- value: [309.73]\n",
            "|   |   |   |   |   |   |   |--- feature5 >  0.19\n",
            "|   |   |   |   |   |   |   |   |--- value: [354.43]\n",
            "|--- feature5 >  0.21\n",
            "|   |--- feature5 <= 0.44\n",
            "|   |   |--- feature5 <= 0.22\n",
            "|   |   |   |--- value: [541.53]\n",
            "|   |   |--- feature5 >  0.22\n",
            "|   |   |   |--- feature5 <= 0.32\n",
            "|   |   |   |   |--- value: [447.63]\n",
            "|   |   |   |--- feature5 >  0.32\n",
            "|   |   |   |   |--- value: [384.42]\n",
            "|   |--- feature5 >  0.44\n",
            "|   |   |--- value: [573.68]\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# 1. نمودار پراکندگی\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(y_test, y_pred, alpha=0.7)\n",
        "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], color='red', linestyle='--')  # خط ایده‌آل\n",
        "plt.title('Real elements & Predictive elements')\n",
        "plt.xlabel('Real elements')\n",
        "plt.ylabel('Predictive elements')\n",
        "plt.grid()\n",
        "plt.show()\n",
        "\n",
        "# 2. نمودار خطای پیش‌بینی\n",
        "errors = y_test - y_pred\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(y_pred, errors, alpha=0.7)\n",
        "plt.axhline(0, color='red', linestyle='--')  # خط صفر\n",
        "plt.title('Erroe of prediction')\n",
        "plt.xlabel('Predictive elements')\n",
        "plt.ylabel('Error(real elements-predictive elements)')\n",
        "plt.grid()\n",
        "plt.show()\n",
        "\n",
        "# 3. نمودار اهمیت ویژگی‌ها\n",
        "importances = best_model.feature_importances_\n",
        "indices = np.argsort(importances)[::-1]\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.title('Priority of Elements')\n",
        "plt.bar(range(X.shape[1]), importances[indices], align='center')\n",
        "plt.xticks(range(X.shape[1]), [important_features[i] for i in indices], rotation=90)\n",
        "plt.xlim([-1, X.shape[1]])\n",
        "plt.xlabel('Features')\n",
        "plt.ylabel('Priority')\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0TzgiA-fEnMQ",
        "outputId": "1848f5f0-e031-48d1-f083-fb3911d781b1"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}