DM_FP_9731074.ipynb

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        "outputId": "84b54669-3e90-43dd-bb5a-4410b4ef7cf7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "# your code here\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "xWr_c-AuunSG"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "در ابتدا داده ها را به شکل دیتا فریم در می آوریم"
      ],
      "metadata": {
        "id": "l3Zv_An0O_WO"
      }
    },
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          "height": 352
        },
        "id": "r0P7IhREt9J1",
        "outputId": "447c0f38-3426-462f-ea27-ab6319de4af9"
      },
      "outputs": [
        {
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          "data": {
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              "   Unnamed: 0  Diabetes_binary  HighBP  High Cholesterol  Cholesterol Check  \\\n",
              "0           0              0.0     1.0               0.0                1.0   \n",
              "1           1              0.0     1.0               1.0                1.0   \n",
              "2           2              0.0     0.0               0.0                1.0   \n",
              "3           3              0.0     1.0               1.0                1.0   \n",
              "4           4              0.0     0.0               0.0                1.0   \n",
              "\n",
              "    BMI  Smoker  Stroke  HeartDiseaseorAttack  Physical Activity  ...  \\\n",
              "0  26.0     0.0     0.0                   0.0                1.0  ...   \n",
              "1  26.0     1.0     1.0                   0.0                0.0  ...   \n",
              "2  26.0     0.0     0.0                   0.0                1.0  ...   \n",
              "3  28.0     1.0     0.0                   0.0                1.0  ...   \n",
              "4  29.0     1.0     0.0                   0.0                1.0  ...   \n",
              "\n",
              "   Any Health Care  No Doctor because of Cost  General Health  Mental Health  \\\n",
              "0              1.0                        0.0          Medium            5.0   \n",
              "1              1.0                        0.0          Medium            0.0   \n",
              "2              1.0                        0.0            High            0.0   \n",
              "3              1.0                        0.0          Medium            0.0   \n",
              "4              1.0                        0.0            Good            0.0   \n",
              "\n",
              "   Physical Health Difficulty Walking     Sex   Age  Education Income  \n",
              "0             30.0                0.0    male   4.0       Cat6   Cat8  \n",
              "1              0.0                0.0    male  12.0       Cat6   Cat8  \n",
              "2             10.0                0.0    male  13.0       Cat6   Cat8  \n",
              "3              3.0                0.0    male  11.0       Cat6   Cat8  \n",
              "4              0.0                0.0  female   8.0       Cat5   Cat8  \n",
              "\n",
              "[5 rows x 23 columns]"
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              "      <td>Cat6</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "diabets_df = pd.read_csv(\"/content/gdrive/MyDrive/DM/diabetes.csv\" ,skipinitialspace=True)\n",
        "diabets_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# handle null values"
      ],
      "metadata": {
        "id": "XVG8ozEE7EbB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "حال اول مشاهده میکنیم که در چه ستونهایی مقدار نال وجود دارد"
      ],
      "metadata": {
        "id": "fTrMHBMTPIqk"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q15ucVEcwJCm",
        "outputId": "618d4a0f-6b48-44e7-b4b6-00917dbfab55"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0                   0\n",
              "Diabetes_binary              0\n",
              "HighBP                       0\n",
              "High Cholesterol             0\n",
              "Cholesterol Check            0\n",
              "BMI                          1\n",
              "Smoker                       0\n",
              "Stroke                       0\n",
              "HeartDiseaseorAttack         1\n",
              "Physical Activity            1\n",
              "Fruits                       2\n",
              "Veggies                      1\n",
              "Heavy Alcohol Consumption    1\n",
              "Any Health Care              2\n",
              "No Doctor because of Cost    2\n",
              "General Health               1\n",
              "Mental Health                1\n",
              "Physical Health              1\n",
              "Difficulty Walking           2\n",
              "Sex                          1\n",
              "Age                          1\n",
              "Education                    1\n",
              "Income                       1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "diabets_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "سپس این مقادیر را با مد جایگزین میکنیم\n",
        "چون بیشتر داده ها باینری هستند مد انتخاب بهتری از میانگین است"
      ],
      "metadata": {
        "id": "Kn2bWqKtPjqU"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cqNUfxShvm8r",
        "outputId": "d6ef8d1b-075c-45f8-dcbc-590f23c0c07c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0                   0\n",
              "Diabetes_binary              0\n",
              "HighBP                       0\n",
              "High Cholesterol             0\n",
              "Cholesterol Check            0\n",
              "BMI                          0\n",
              "Smoker                       0\n",
              "Stroke                       0\n",
              "HeartDiseaseorAttack         0\n",
              "Physical Activity            0\n",
              "Fruits                       0\n",
              "Veggies                      0\n",
              "Heavy Alcohol Consumption    0\n",
              "Any Health Care              0\n",
              "No Doctor because of Cost    0\n",
              "General Health               0\n",
              "Mental Health                0\n",
              "Physical Health              0\n",
              "Difficulty Walking           0\n",
              "Sex                          0\n",
              "Age                          0\n",
              "Education                    0\n",
              "Income                       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# replace null with mode\n",
        "for column in diabets_df.columns:\n",
        "    diabets_df[column].fillna(diabets_df[column].mode()[0], inplace=True)\n",
        "\n",
        "diabets_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "JjSSHEbuw4YV"
      },
      "outputs": [],
      "source": [
        "diabets_df = diabets_df.drop(['Unnamed: 0'], axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "سپس بررسی میکنیم هر ستون چه مقدارهایی را گرفته است تا مقدارهای بی ربط را حذف کنیم\n",
        "تنها مقدار بی ربط در ستون درآمد است که آن را حذف میکنیم"
      ],
      "metadata": {
        "id": "dJSFUntwPyif"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for column in diabets_df.columns:\n",
        "  print(column)\n",
        "  print(diabets_df[column].value_counts())\n",
        "  print(\"========================\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "13PdVF7B7VCn",
        "outputId": "2e3b2d3c-1191-4dc5-fa51-4cf261f0ddd4"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Diabetes_binary\n",
            "0.0    35346\n",
            "1.0    35346\n",
            "Name: Diabetes_binary, dtype: int64\n",
            "========================\n",
            "HighBP\n",
            "1.0    39832\n",
            "0.0    30860\n",
            "Name: HighBP, dtype: int64\n",
            "========================\n",
            "High Cholesterol\n",
            "1.0    37163\n",
            "0.0    33529\n",
            "Name: High Cholesterol, dtype: int64\n",
            "========================\n",
            "Cholesterol Check\n",
            "1.0    68943\n",
            "0.0     1749\n",
            "Name: Cholesterol Check, dtype: int64\n",
            "========================\n",
            "BMI\n",
            "27.0    6328\n",
            "26.0    4975\n",
            "28.0    4583\n",
            "24.0    4392\n",
            "30.0    4344\n",
            "        ... \n",
            "85.0       1\n",
            "83.0       1\n",
            "80.0       1\n",
            "78.0       1\n",
            "74.0       1\n",
            "Name: BMI, Length: 80, dtype: int64\n",
            "========================\n",
            "Smoker\n",
            "0.0    37094\n",
            "1.0    33598\n",
            "Name: Smoker, dtype: int64\n",
            "========================\n",
            "Stroke\n",
            "0.0    66297\n",
            "1.0     4395\n",
            "Name: Stroke, dtype: int64\n",
            "========================\n",
            "HeartDiseaseorAttack\n",
            "0.0    60243\n",
            "1.0    10449\n",
            "Name: HeartDiseaseorAttack, dtype: int64\n",
            "========================\n",
            "Physical Activity\n",
            "1.0    49699\n",
            "0.0    20993\n",
            "Name: Physical Activity, dtype: int64\n",
            "========================\n",
            "Fruits\n",
            "1.0    43249\n",
            "0.0    27443\n",
            "Name: Fruits, dtype: int64\n",
            "========================\n",
            "Veggies\n",
            "1.0    55760\n",
            "0.0    14932\n",
            "Name: Veggies, dtype: int64\n",
            "========================\n",
            "Heavy Alcohol Consumption\n",
            "0.0    67672\n",
            "1.0     3020\n",
            "Name: Heavy Alcohol Consumption, dtype: int64\n",
            "========================\n",
            "Any Health Care\n",
            "1.0    67508\n",
            "0.0     3184\n",
            "Name: Any Health Care, dtype: int64\n",
            "========================\n",
            "No Doctor because of Cost\n",
            "0.0    64053\n",
            "1.0     6639\n",
            "Name: No Doctor because of Cost, dtype: int64\n",
            "========================\n",
            "General Health\n",
            "Medium      23428\n",
            "Good        19871\n",
            "Low         13303\n",
            "High         8282\n",
            "Very Low     5808\n",
            "Name: General Health, dtype: int64\n",
            "========================\n",
            "Mental Health\n",
            "0.0     48092\n",
            "30.0     4320\n",
            "2.0      3266\n",
            "5.0      2519\n",
            "1.0      2051\n",
            "3.0      1967\n",
            "10.0     1924\n",
            "15.0     1767\n",
            "20.0     1125\n",
            "4.0       981\n",
            "7.0       825\n",
            "25.0      425\n",
            "14.0      353\n",
            "6.0       288\n",
            "8.0       198\n",
            "12.0      130\n",
            "28.0       99\n",
            "21.0       84\n",
            "29.0       53\n",
            "18.0       42\n",
            "16.0       28\n",
            "9.0        28\n",
            "22.0       22\n",
            "17.0       18\n",
            "26.0       17\n",
            "27.0       17\n",
            "13.0       15\n",
            "23.0       13\n",
            "24.0       10\n",
            "11.0        8\n",
            "19.0        7\n",
            "Name: Mental Health, dtype: int64\n",
            "========================\n",
            "Physical Health\n",
            "0.0     39915\n",
            "30.0     7953\n",
            "2.0      4102\n",
            "1.0      2853\n",
            "3.0      2438\n",
            "5.0      2332\n",
            "10.0     1980\n",
            "15.0     1913\n",
            "4.0      1376\n",
            "7.0      1326\n",
            "20.0     1292\n",
            "14.0      802\n",
            "25.0      557\n",
            "6.0       447\n",
            "8.0       276\n",
            "21.0      229\n",
            "28.0      211\n",
            "12.0      205\n",
            "29.0       95\n",
            "9.0        55\n",
            "18.0       50\n",
            "16.0       50\n",
            "17.0       35\n",
            "27.0       34\n",
            "22.0       31\n",
            "23.0       27\n",
            "26.0       26\n",
            "13.0       26\n",
            "24.0       24\n",
            "11.0       22\n",
            "19.0       10\n",
            "Name: Physical Health, dtype: int64\n",
            "========================\n",
            "Difficulty Walking\n",
            "0.0    52826\n",
            "1.0    17866\n",
            "Name: Difficulty Walking, dtype: int64\n",
            "========================\n",
            "Sex\n",
            "female    38386\n",
            "male      32306\n",
            "Name: Sex, dtype: int64\n",
            "========================\n",
            "Age\n",
            "10.0    10857\n",
            "9.0     10112\n",
            "8.0      8603\n",
            "11.0     8044\n",
            "7.0      6872\n",
            "13.0     5426\n",
            "12.0     5394\n",
            "6.0      4648\n",
            "5.0      3519\n",
            "4.0      2793\n",
            "3.0      2049\n",
            "2.0      1396\n",
            "1.0       979\n",
            "Name: Age, dtype: int64\n",
            "========================\n",
            "Education\n",
            "Cat6    26020\n",
            "Cat5    20030\n",
            "Cat4    19473\n",
            "Cat3     3447\n",
            "Cat2     1647\n",
            "Cat1       75\n",
            "Name: Education, dtype: int64\n",
            "========================\n",
            "Income\n",
            "Cat8       20646\n",
            "Cat7       11425\n",
            "Cat6       10287\n",
            "Cat5        8010\n",
            "Cat4        6658\n",
            "Cat3        5556\n",
            "Cat2        4498\n",
            "Cat1        3611\n",
            "Unknown        1\n",
            "Name: Income, dtype: int64\n",
            "========================\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#remove unknown value for Income\n",
        "\n",
        "diabets_df.drop(diabets_df[diabets_df.Income == 'Unknown'].index ,inplace=True)\n",
        "\n",
        "print(diabets_df['Income'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8JnagbYi9bc0",
        "outputId": "28891795-1a9a-45f9-9989-e3781ffd3275"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cat8    20646\n",
            "Cat7    11425\n",
            "Cat6    10287\n",
            "Cat5     8010\n",
            "Cat4     6658\n",
            "Cat3     5556\n",
            "Cat2     4498\n",
            "Cat1     3611\n",
            "Name: Income, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Rename columns"
      ],
      "metadata": {
        "id": "govYEivb7q23"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b6NFEabJwof3",
        "outputId": "0b58879f-a7b5-47a3-ba20-e3fb4aa15337"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Diabetes_binary', 'HighBP', 'High_Cholesterol', 'Cholesterol_Check',\n",
              "       'BMI', 'Smoker', 'Stroke', 'Heart_Diseaseor_Attack',\n",
              "       'Physical_Activity', 'Fruits', 'Veggies', 'Heavy_Alcohol_Consumption',\n",
              "       'Any_Health_Care', 'No_Doctor', 'General_Health', 'Mental_Health',\n",
              "       'Physical_Health', 'Difficulty_Walking', 'Sex', 'Age', 'Education',\n",
              "       'Income'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "diabets_df = diabets_df.rename(columns={\"High Cholesterol\": \"High_Cholesterol\", \"Cholesterol Check\": \"Cholesterol_Check\" ,\"HeartDiseaseorAttack\" :\"Heart_Diseaseor_Attack\" ,\"Physical Activity\":\"Physical_Activity\" , \"Heavy Alcohol Consumption\" :\"Heavy_Alcohol_Consumption\" ,\"Any Health Care\" : \"Any_Health_Care\" ,\"No Doctor because of Cost\" : \"No_Doctor\" ,\"General Health\" :\"General_Health\" ,\"Mental Health\" : \"Mental_Health\" ,\"Physical Health\" :\"Physical_Health\" ,\"Difficulty Walking\" :\"Difficulty_Walking\" })\n",
        "diabets_df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# One Hot Encode"
      ],
      "metadata": {
        "id": "yxQ6CyhA9jkY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "حال تایپ هر ستون را بررسی میکنیم تا آنهایی که تایپشان آبجکت هست را وان هات اینکود کنیم"
      ],
      "metadata": {
        "id": "qQIFxBcrQAdO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h1JUomshy-Qi",
        "outputId": "492b780a-64ad-4ca1-a446-a904043cb3a3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Diabetes_binary              float64\n",
              "HighBP                       float64\n",
              "High_Cholesterol             float64\n",
              "Cholesterol_Check            float64\n",
              "BMI                          float64\n",
              "Smoker                       float64\n",
              "Stroke                       float64\n",
              "Heart_Diseaseor_Attack       float64\n",
              "Physical_Activity            float64\n",
              "Fruits                       float64\n",
              "Veggies                      float64\n",
              "Heavy_Alcohol_Consumption    float64\n",
              "Any_Health_Care              float64\n",
              "No_Doctor                    float64\n",
              "General_Health                object\n",
              "Mental_Health                float64\n",
              "Physical_Health              float64\n",
              "Difficulty_Walking           float64\n",
              "Sex                           object\n",
              "Age                          float64\n",
              "Education                     object\n",
              "Income                        object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "diabets_df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "k-wgq1YN3s7Y"
      },
      "outputs": [],
      "source": [
        "import sklearn\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vqt5OuAZ5N2A",
        "outputId": "ee084d83-cc66-4e15-ae3a-d7a911a9b530"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "General health categroies :\n",
            "Medium      23428\n",
            "Good        19871\n",
            "Low         13302\n",
            "High         8282\n",
            "Very Low     5808\n",
            "Name: General_Health, dtype: int64\n",
            "==========================================\n",
            "Sex categroies :\n",
            "female    38385\n",
            "male      32306\n",
            "Name: Sex, dtype: int64\n",
            "==========================================\n",
            "Education categroies :\n",
            "Cat6    26020\n",
            "Cat5    20029\n",
            "Cat4    19473\n",
            "Cat3     3447\n",
            "Cat2     1647\n",
            "Cat1       75\n",
            "Name: Education, dtype: int64\n",
            "==========================================\n",
            "Income categroies :\n",
            "Cat8    20646\n",
            "Cat7    11425\n",
            "Cat6    10287\n",
            "Cat5     8010\n",
            "Cat4     6658\n",
            "Cat3     5556\n",
            "Cat2     4498\n",
            "Cat1     3611\n",
            "Name: Income, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "\n",
        "print(\"General health categroies :\")\n",
        "print(diabets_df['General_Health'].value_counts())\n",
        "print(\"==========================================\")\n",
        "\n",
        "print(\"Sex categroies :\")\n",
        "print(diabets_df['Sex'].value_counts())\n",
        "print(\"==========================================\")\n",
        "\n",
        "print(\"Education categroies :\")\n",
        "print(diabets_df['Education'].value_counts())\n",
        "print(\"==========================================\")\n",
        "\n",
        "print(\"Income categroies :\")\n",
        "print(diabets_df['Income'].value_counts())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "برای وان هات اینکود از روش دامیز استفاده میکنیم و مقدار اینکود شده به ستون های دیتاست اضافه میشود"
      ],
      "metadata": {
        "id": "9qKNy-s5QLcP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "XiVHruyXCkd_",
        "outputId": "98dd24cb-8900-4fe3-c489-f3a9b8dc9ec8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Diabetes_binary  HighBP  High_Cholesterol  Cholesterol_Check   BMI  Smoker  \\\n",
              "0              0.0     1.0               0.0                1.0  26.0     0.0   \n",
              "1              0.0     1.0               1.0                1.0  26.0     1.0   \n",
              "2              0.0     0.0               0.0                1.0  26.0     0.0   \n",
              "3              0.0     1.0               1.0                1.0  28.0     1.0   \n",
              "4              0.0     0.0               0.0                1.0  29.0     1.0   \n",
              "\n",
              "   Stroke  Heart_Diseaseor_Attack  Physical_Activity  Fruits  ...  \\\n",
              "0     0.0                     0.0                1.0     0.0  ...   \n",
              "1     1.0                     0.0                0.0     1.0  ...   \n",
              "2     0.0                     0.0                1.0     1.0  ...   \n",
              "3     0.0                     0.0                1.0     1.0  ...   \n",
              "4     0.0                     0.0                1.0     1.0  ...   \n",
              "\n",
              "   Education_Cat5  Education_Cat6  Income_Cat1  Income_Cat2  Income_Cat3  \\\n",
              "0               0               1            0            0            0   \n",
              "1               0               1            0            0            0   \n",
              "2               0               1            0            0            0   \n",
              "3               0               1            0            0            0   \n",
              "4               1               0            0            0            0   \n",
              "\n",
              "   Income_Cat4  Income_Cat5  Income_Cat6  Income_Cat7  Income_Cat8  \n",
              "0            0            0            0            0            1  \n",
              "1            0            0            0            0            1  \n",
              "2            0            0            0            0            1  \n",
              "3            0            0            0            0            1  \n",
              "4            0            0            0            0            1  \n",
              "\n",
              "[5 rows x 39 columns]"
            ],
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              "\n",
              "  <div id=\"df-01629b0e-2ec7-4316-b878-663ff6d82234\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>Diabetes_binary</th>\n",
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              "      <th>Cholesterol_Check</th>\n",
              "      <th>BMI</th>\n",
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              "      <th>Income_Cat3</th>\n",
              "      <th>Income_Cat4</th>\n",
              "      <th>Income_Cat5</th>\n",
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              "  </thead>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-01629b0e-2ec7-4316-b878-663ff6d82234 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-01629b0e-2ec7-4316-b878-663ff6d82234');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# one hot encode\n",
        "sex_types = ('female' ,'male')\n",
        "dum_df_sex = pd.get_dummies(diabets_df, columns=[\"Sex\"], prefix=[\"sex\"] )\n",
        "dum_df_general_health = pd.get_dummies(dum_df_sex, columns=[\"General_Health\"], prefix=[\"General_health\"] )\n",
        "dum_df_education = pd.get_dummies(dum_df_general_health, columns=[\"Education\"], prefix=[\"Education\"] )\n",
        "dum_df_income = pd.get_dummies(dum_df_education, columns=[\"Income\"], prefix=[\"Income\"] )\n",
        "one_hot_df = dum_df_income\n",
        "one_hot_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5kNT6KdhXIs0",
        "outputId": "b8315ddc-510a-4f63-f03a-9ee20d6a81b0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Diabetes_binary', 'HighBP', 'High_Cholesterol', 'Cholesterol_Check',\n",
              "       'BMI', 'Smoker', 'Stroke', 'Heart_Diseaseor_Attack',\n",
              "       'Physical_Activity', 'Fruits', 'Veggies', 'Heavy_Alcohol_Consumption',\n",
              "       'Any_Health_Care', 'No_Doctor', 'Mental_Health', 'Physical_Health',\n",
              "       'Difficulty_Walking', 'Age', 'sex_female', 'sex_male',\n",
              "       'General_health_Good', 'General_health_High', 'General_health_Low',\n",
              "       'General_health_Medium', 'General_health_Very Low', 'Education_Cat1',\n",
              "       'Education_Cat2', 'Education_Cat3', 'Education_Cat4', 'Education_Cat5',\n",
              "       'Education_Cat6', 'Income_Cat1', 'Income_Cat2', 'Income_Cat3',\n",
              "       'Income_Cat4', 'Income_Cat5', 'Income_Cat6', 'Income_Cat7',\n",
              "       'Income_Cat8'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# target = one_hot_df.loc[:,['Diabetes_binary']].values\n",
        "# one_hot_df = one_hot_df.drop(['Diabetes_binary'], axis=1)\n",
        "# features = one_hot_df.iloc[:,1:].values\n",
        "one_hot_df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Normalize Data"
      ],
      "metadata": {
        "id": "OpEIyH2c952O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "برای نرمال سازی مقیاس همه داده ها را با استفاده از استاندارد اسکالر عوض کرده و همه را به یک رنج میبریم"
      ],
      "metadata": {
        "id": "9GAcFKxtQY1x"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "-6bAmMDw4El9",
        "outputId": "43c88ef7-a58c-47ce-b5a0-3d80bb211ef1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Diabetes_binary    HighBP  High_Cholesterol  Cholesterol_Check       BMI  \\\n",
              "0              0.0  0.880212         -1.052784            0.15923 -0.542161   \n",
              "1              0.0  0.880212          0.949863            0.15923 -0.542161   \n",
              "2              0.0 -1.136090         -1.052784            0.15923 -0.542161   \n",
              "3              0.0  0.880212          0.949863            0.15923 -0.261024   \n",
              "4              0.0 -1.136090         -1.052784            0.15923 -0.120455   \n",
              "\n",
              "     Smoker    Stroke  Heart_Diseaseor_Attack  Physical_Activity    Fruits  \\\n",
              "0 -0.951696 -0.257444               -0.416474           0.649932 -1.255394   \n",
              "1  1.050755  3.884337               -0.416474          -1.538623  0.796562   \n",
              "2 -0.951696 -0.257444               -0.416474           0.649932  0.796562   \n",
              "3  1.050755 -0.257444               -0.416474           0.649932  0.796562   \n",
              "4  1.050755 -0.257444               -0.416474           0.649932  0.796562   \n",
              "\n",
              "   ...  Education_Cat5  Education_Cat6  Income_Cat1  Income_Cat2  Income_Cat3  \\\n",
              "0  ...       -0.628765        1.310265    -0.232016    -0.260678    -0.292061   \n",
              "1  ...       -0.628765        1.310265    -0.232016    -0.260678    -0.292061   \n",
              "2  ...       -0.628765        1.310265    -0.232016    -0.260678    -0.292061   \n",
              "3  ...       -0.628765        1.310265    -0.232016    -0.260678    -0.292061   \n",
              "4  ...        1.590419       -0.763204    -0.232016    -0.260678    -0.292061   \n",
              "\n",
              "   Income_Cat4  Income_Cat5  Income_Cat6  Income_Cat7  Income_Cat8  \n",
              "0    -0.322456    -0.357477    -0.412678    -0.439061     1.556906  \n",
              "1    -0.322456    -0.357477    -0.412678    -0.439061     1.556906  \n",
              "2    -0.322456    -0.357477    -0.412678    -0.439061     1.556906  \n",
              "3    -0.322456    -0.357477    -0.412678    -0.439061     1.556906  \n",
              "4    -0.322456    -0.357477    -0.412678    -0.439061     1.556906  \n",
              "\n",
              "[5 rows x 39 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ba91dc4e-ce03-4a31-8a9d-ec5abfc248e3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Diabetes_binary</th>\n",
              "      <th>HighBP</th>\n",
              "      <th>High_Cholesterol</th>\n",
              "      <th>Cholesterol_Check</th>\n",
              "      <th>BMI</th>\n",
              "      <th>Smoker</th>\n",
              "      <th>Stroke</th>\n",
              "      <th>Heart_Diseaseor_Attack</th>\n",
              "      <th>Physical_Activity</th>\n",
              "      <th>Fruits</th>\n",
              "      <th>...</th>\n",
              "      <th>Education_Cat5</th>\n",
              "      <th>Education_Cat6</th>\n",
              "      <th>Income_Cat1</th>\n",
              "      <th>Income_Cat2</th>\n",
              "      <th>Income_Cat3</th>\n",
              "      <th>Income_Cat4</th>\n",
              "      <th>Income_Cat5</th>\n",
              "      <th>Income_Cat6</th>\n",
              "      <th>Income_Cat7</th>\n",
              "      <th>Income_Cat8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>0.880212</td>\n",
              "      <td>-1.052784</td>\n",
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              "      <td>-0.357477</td>\n",
              "      <td>-0.412678</td>\n",
              "      <td>-0.439061</td>\n",
              "      <td>1.556906</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.880212</td>\n",
              "      <td>0.949863</td>\n",
              "      <td>0.15923</td>\n",
              "      <td>-0.542161</td>\n",
              "      <td>1.050755</td>\n",
              "      <td>3.884337</td>\n",
              "      <td>-0.416474</td>\n",
              "      <td>-1.538623</td>\n",
              "      <td>0.796562</td>\n",
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              "      <td>-0.628765</td>\n",
              "      <td>1.310265</td>\n",
              "      <td>-0.232016</td>\n",
              "      <td>-0.260678</td>\n",
              "      <td>-0.292061</td>\n",
              "      <td>-0.322456</td>\n",
              "      <td>-0.357477</td>\n",
              "      <td>-0.412678</td>\n",
              "      <td>-0.439061</td>\n",
              "      <td>1.556906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.136090</td>\n",
              "      <td>-1.052784</td>\n",
              "      <td>0.15923</td>\n",
              "      <td>-0.542161</td>\n",
              "      <td>-0.951696</td>\n",
              "      <td>-0.257444</td>\n",
              "      <td>-0.416474</td>\n",
              "      <td>0.649932</td>\n",
              "      <td>0.796562</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.628765</td>\n",
              "      <td>1.310265</td>\n",
              "      <td>-0.232016</td>\n",
              "      <td>-0.260678</td>\n",
              "      <td>-0.292061</td>\n",
              "      <td>-0.322456</td>\n",
              "      <td>-0.357477</td>\n",
              "      <td>-0.412678</td>\n",
              "      <td>-0.439061</td>\n",
              "      <td>1.556906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.880212</td>\n",
              "      <td>0.949863</td>\n",
              "      <td>0.15923</td>\n",
              "      <td>-0.261024</td>\n",
              "      <td>1.050755</td>\n",
              "      <td>-0.257444</td>\n",
              "      <td>-0.416474</td>\n",
              "      <td>0.649932</td>\n",
              "      <td>0.796562</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.628765</td>\n",
              "      <td>1.310265</td>\n",
              "      <td>-0.232016</td>\n",
              "      <td>-0.260678</td>\n",
              "      <td>-0.292061</td>\n",
              "      <td>-0.322456</td>\n",
              "      <td>-0.357477</td>\n",
              "      <td>-0.412678</td>\n",
              "      <td>-0.439061</td>\n",
              "      <td>1.556906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.136090</td>\n",
              "      <td>-1.052784</td>\n",
              "      <td>0.15923</td>\n",
              "      <td>-0.120455</td>\n",
              "      <td>1.050755</td>\n",
              "      <td>-0.257444</td>\n",
              "      <td>-0.416474</td>\n",
              "      <td>0.649932</td>\n",
              "      <td>0.796562</td>\n",
              "      <td>...</td>\n",
              "      <td>1.590419</td>\n",
              "      <td>-0.763204</td>\n",
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              "      <td>-0.292061</td>\n",
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              "      <td>-0.357477</td>\n",
              "      <td>-0.412678</td>\n",
              "      <td>-0.439061</td>\n",
              "      <td>1.556906</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 39 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba91dc4e-ce03-4a31-8a9d-ec5abfc248e3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ba91dc4e-ce03-4a31-8a9d-ec5abfc248e3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ba91dc4e-ce03-4a31-8a9d-ec5abfc248e3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "scaler = StandardScaler()\n",
        "one_hot_df.iloc[:,1:] = scaler.fit_transform(one_hot_df.iloc[:,1:])\n",
        "one_hot_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Split train and test and labels"
      ],
      "metadata": {
        "id": "e9NBIHGa-aYf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "فیچرها و لیبل را جدا میکنیم و سپس داده های آموزش و تست را میسازیم"
      ],
      "metadata": {
        "id": "kRry-iRBejCe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "g2jUyTMP_gLD"
      },
      "outputs": [],
      "source": [
        "target = one_hot_df.loc[:,['Diabetes_binary']].values\n",
        "features = one_hot_df.iloc[:,1:].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e57wGa416EIZ",
        "outputId": "503c655f-c8c7-4fc9-f7af-a72d3f106f94"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(56552, 38)\n",
            "(14139, 38)\n",
            "(14138, 38)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split( features, target, test_size=0.2, random_state=1)\n",
        "_, X_val,_, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=1) \n",
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(X_val.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Make classifier"
      ],
      "metadata": {
        "id": "XQaD9GH0-mtn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ITslgoo17gHN",
        "outputId": "d7554d79-cfb7-49d1-df4d-3875b2d659ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.7/dist-packages (0.90)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from xgboost) (1.21.6)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from xgboost) (1.4.1)\n"
          ]
        }
      ],
      "source": [
        "!pip3 install xgboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "WDrGAVkj737z"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import accuracy_score ,precision_score , recall_score , plot_confusion_matrix, ConfusionMatrixDisplay, confusion_matrix , classification_report \n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from xgboost import XGBClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "مدل را با پارامترهای داده شده میسازیم و روی داده های آموزش فیت میکنیم"
      ],
      "metadata": {
        "id": "IynioURteo71"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "622SV8sq951Z"
      },
      "outputs": [],
      "source": [
        "model = XGBClassifier(learning_rate=0.1 , Max_depth=4 ,N_estimator=200 , Subsample=0.5 ,Colsample_bytree=1 , Random_seed=123 ,Eval_metric=\"auc\" , Verbosity=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FrMzVpHf-xML",
        "outputId": "6bd35435-5dc7-4911-fa68-6f858d60dbc3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tvalidation_0-error:0.299866\tvalidation_1-error:0.302589\n",
            "Multiple eval metrics have been passed: 'validation_1-error' will be used for early stopping.\n",
            "\n",
            "Will train until validation_1-error hasn't improved in 10 rounds.\n",
            "[1]\tvalidation_0-error:0.301457\tvalidation_1-error:0.30365\n",
            "[2]\tvalidation_0-error:0.280839\tvalidation_1-error:0.284128\n",
            "[3]\tvalidation_0-error:0.284039\tvalidation_1-error:0.287028\n",
            "[4]\tvalidation_0-error:0.283173\tvalidation_1-error:0.285967\n",
            "[5]\tvalidation_0-error:0.290228\tvalidation_1-error:0.293182\n",
            "[6]\tvalidation_0-error:0.282112\tvalidation_1-error:0.286108\n",
            "[7]\tvalidation_0-error:0.276666\tvalidation_1-error:0.280025\n",
            "[8]\tvalidation_0-error:0.275251\tvalidation_1-error:0.278964\n",
            "[9]\tvalidation_0-error:0.272652\tvalidation_1-error:0.276206\n",
            "[10]\tvalidation_0-error:0.268655\tvalidation_1-error:0.271891\n",
            "[11]\tvalidation_0-error:0.266374\tvalidation_1-error:0.268921\n",
            "[12]\tvalidation_0-error:0.261229\tvalidation_1-error:0.266091\n",
            "[13]\tvalidation_0-error:0.260468\tvalidation_1-error:0.26496\n",
            "[14]\tvalidation_0-error:0.259283\tvalidation_1-error:0.264111\n",
            "[15]\tvalidation_0-error:0.258718\tvalidation_1-error:0.26305\n",
            "[16]\tvalidation_0-error:0.258152\tvalidation_1-error:0.262272\n",
            "[17]\tvalidation_0-error:0.257533\tvalidation_1-error:0.261494\n",
            "[18]\tvalidation_0-error:0.256932\tvalidation_1-error:0.261352\n",
            "[19]\tvalidation_0-error:0.255959\tvalidation_1-error:0.261565\n",
            "[20]\tvalidation_0-error:0.255535\tvalidation_1-error:0.260716\n",
            "[21]\tvalidation_0-error:0.254403\tvalidation_1-error:0.259513\n",
            "[22]\tvalidation_0-error:0.253766\tvalidation_1-error:0.259938\n",
            "[23]\tvalidation_0-error:0.252369\tvalidation_1-error:0.258665\n",
            "[24]\tvalidation_0-error:0.252263\tvalidation_1-error:0.258028\n",
            "[25]\tvalidation_0-error:0.251751\tvalidation_1-error:0.257109\n",
            "[26]\tvalidation_0-error:0.25092\tvalidation_1-error:0.256543\n",
            "[27]\tvalidation_0-error:0.250849\tvalidation_1-error:0.25633\n",
            "[28]\tvalidation_0-error:0.250778\tvalidation_1-error:0.255977\n",
            "[29]\tvalidation_0-error:0.250407\tvalidation_1-error:0.256189\n",
            "[30]\tvalidation_0-error:0.250495\tvalidation_1-error:0.25633\n",
            "[31]\tvalidation_0-error:0.249929\tvalidation_1-error:0.255552\n",
            "[32]\tvalidation_0-error:0.24977\tvalidation_1-error:0.255057\n",
            "[33]\tvalidation_0-error:0.249487\tvalidation_1-error:0.255128\n",
            "[34]\tvalidation_0-error:0.249735\tvalidation_1-error:0.254562\n",
            "[35]\tvalidation_0-error:0.249416\tvalidation_1-error:0.254067\n",
            "[36]\tvalidation_0-error:0.249576\tvalidation_1-error:0.254704\n",
            "[37]\tvalidation_0-error:0.249416\tvalidation_1-error:0.254279\n",
            "[38]\tvalidation_0-error:0.249187\tvalidation_1-error:0.254704\n",
            "[39]\tvalidation_0-error:0.248868\tvalidation_1-error:0.253289\n",
            "[40]\tvalidation_0-error:0.248426\tvalidation_1-error:0.253643\n",
            "[41]\tvalidation_0-error:0.248214\tvalidation_1-error:0.253572\n",
            "[42]\tvalidation_0-error:0.248143\tvalidation_1-error:0.252865\n",
            "[43]\tvalidation_0-error:0.248232\tvalidation_1-error:0.252865\n",
            "[44]\tvalidation_0-error:0.248196\tvalidation_1-error:0.252865\n",
            "[45]\tvalidation_0-error:0.248055\tvalidation_1-error:0.252652\n",
            "[46]\tvalidation_0-error:0.248002\tvalidation_1-error:0.25244\n",
            "[47]\tvalidation_0-error:0.24779\tvalidation_1-error:0.252157\n",
            "[48]\tvalidation_0-error:0.247595\tvalidation_1-error:0.251238\n",
            "[49]\tvalidation_0-error:0.248073\tvalidation_1-error:0.251733\n",
            "[50]\tvalidation_0-error:0.24809\tvalidation_1-error:0.251662\n",
            "[51]\tvalidation_0-error:0.247825\tvalidation_1-error:0.251804\n",
            "[52]\tvalidation_0-error:0.247931\tvalidation_1-error:0.251874\n",
            "[53]\tvalidation_0-error:0.248179\tvalidation_1-error:0.252016\n",
            "[54]\tvalidation_0-error:0.24832\tvalidation_1-error:0.252157\n",
            "[55]\tvalidation_0-error:0.248161\tvalidation_1-error:0.251662\n",
            "[56]\tvalidation_0-error:0.248002\tvalidation_1-error:0.25145\n",
            "[57]\tvalidation_0-error:0.24809\tvalidation_1-error:0.251733\n",
            "[58]\tvalidation_0-error:0.247878\tvalidation_1-error:0.250248\n",
            "[59]\tvalidation_0-error:0.247843\tvalidation_1-error:0.250601\n",
            "[60]\tvalidation_0-error:0.247896\tvalidation_1-error:0.25053\n",
            "[61]\tvalidation_0-error:0.247737\tvalidation_1-error:0.250601\n",
            "[62]\tvalidation_0-error:0.248037\tvalidation_1-error:0.25046\n",
            "[63]\tvalidation_0-error:0.24832\tvalidation_1-error:0.250813\n",
            "[64]\tvalidation_0-error:0.247878\tvalidation_1-error:0.250177\n",
            "[65]\tvalidation_0-error:0.247737\tvalidation_1-error:0.249965\n",
            "[66]\tvalidation_0-error:0.247631\tvalidation_1-error:0.250743\n",
            "[67]\tvalidation_0-error:0.24756\tvalidation_1-error:0.250672\n",
            "[68]\tvalidation_0-error:0.247631\tvalidation_1-error:0.250601\n",
            "[69]\tvalidation_0-error:0.247241\tvalidation_1-error:0.250318\n",
            "[70]\tvalidation_0-error:0.247348\tvalidation_1-error:0.249752\n",
            "[71]\tvalidation_0-error:0.247206\tvalidation_1-error:0.249965\n",
            "[72]\tvalidation_0-error:0.247436\tvalidation_1-error:0.25046\n",
            "[73]\tvalidation_0-error:0.246976\tvalidation_1-error:0.249965\n",
            "[74]\tvalidation_0-error:0.246799\tvalidation_1-error:0.250177\n",
            "[75]\tvalidation_0-error:0.246711\tvalidation_1-error:0.250177\n",
            "[76]\tvalidation_0-error:0.246711\tvalidation_1-error:0.249752\n",
            "[77]\tvalidation_0-error:0.246693\tvalidation_1-error:0.249682\n",
            "[78]\tvalidation_0-error:0.246676\tvalidation_1-error:0.24954\n",
            "[79]\tvalidation_0-error:0.246676\tvalidation_1-error:0.249399\n",
            "[80]\tvalidation_0-error:0.246623\tvalidation_1-error:0.24954\n",
            "[81]\tvalidation_0-error:0.246888\tvalidation_1-error:0.249823\n",
            "[82]\tvalidation_0-error:0.24657\tvalidation_1-error:0.249611\n",
            "[83]\tvalidation_0-error:0.246764\tvalidation_1-error:0.250248\n",
            "[84]\tvalidation_0-error:0.246852\tvalidation_1-error:0.250177\n",
            "[85]\tvalidation_0-error:0.246729\tvalidation_1-error:0.250035\n",
            "[86]\tvalidation_0-error:0.246481\tvalidation_1-error:0.24954\n",
            "[87]\tvalidation_0-error:0.246428\tvalidation_1-error:0.248904\n",
            "[88]\tvalidation_0-error:0.246499\tvalidation_1-error:0.249116\n",
            "[89]\tvalidation_0-error:0.246446\tvalidation_1-error:0.249399\n",
            "[90]\tvalidation_0-error:0.246782\tvalidation_1-error:0.249682\n",
            "[91]\tvalidation_0-error:0.246693\tvalidation_1-error:0.249682\n",
            "[92]\tvalidation_0-error:0.246658\tvalidation_1-error:0.249328\n",
            "[93]\tvalidation_0-error:0.246481\tvalidation_1-error:0.24947\n",
            "[94]\tvalidation_0-error:0.246375\tvalidation_1-error:0.249682\n",
            "[95]\tvalidation_0-error:0.246269\tvalidation_1-error:0.249965\n",
            "[96]\tvalidation_0-error:0.246216\tvalidation_1-error:0.249965\n",
            "[97]\tvalidation_0-error:0.246304\tvalidation_1-error:0.249965\n",
            "Stopping. Best iteration:\n",
            "[87]\tvalidation_0-error:0.246428\tvalidation_1-error:0.248904\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(Colsample_bytree=1, Eval_metric='auc', Max_depth=4,\n",
              "              N_estimator=200, Random_seed=123, Subsample=0.5, Verbosity=1)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "import numpy as np\n",
        "model.fit(X_train, y_train ,eval_set=[(X_train , y_train),(X_val,y_val)],early_stopping_rounds=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "همانطور که مشاهده میشود دقت روی داده های آموزش 75 درصد است"
      ],
      "metadata": {
        "id": "BgYMBAWie_2V"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5AJ-WuEDBxUW",
        "outputId": "de560f68-508b-441f-e5f3-2f8e906e0313"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy_train :\n",
            "0.7535719337954448\n",
            "===========================================\n",
            "recall train :\n",
            "0.7965850560925704\n",
            "===========================================\n",
            "precision train :\n",
            "0.7343089430894308\n",
            "===========================================\n"
          ]
        }
      ],
      "source": [
        "y_pred_train = model.predict(X_train)\n",
        "accuracy_train = accuracy_score(y_train, y_pred_train)\n",
        "\n",
        "print(\"accuracy_train :\")\n",
        "print(accuracy_train)\n",
        "print(\"===========================================\")\n",
        "\n",
        "recall_train = recall_score(y_train, y_pred_train)\n",
        "print(\"recall train :\")\n",
        "print(recall_train)\n",
        "print(\"===========================================\")\n",
        "\n",
        "precision_train = precision_score(y_train, y_pred_train)\n",
        "print(\"precision train :\")\n",
        "print(precision_train)\n",
        "print(\"===========================================\")\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "تابع زیر برای رسم ماتریس در هم ریختگی نوشته شده است"
      ],
      "metadata": {
        "id": "ZvOVx9cPfMF5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "ADABV9JXEbHU"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "def conf_matrix(y_test, y_predicted):\n",
        "  cm = confusion_matrix(y_test, y_predicted)\n",
        "  plt.figure(figsize=(10,10))\n",
        "  plt.clf()\n",
        "  plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Wistia)\n",
        "  classNames = ['Negative','Positive']\n",
        "  plt.title('confusion Matrice')\n",
        "  plt.ylabel('True label')\n",
        "  plt.xlabel('Predicted label')\n",
        "  tick_marks = np.arange(len(classNames))\n",
        "  plt.xticks(tick_marks, classNames, rotation=45)\n",
        "  plt.yticks(tick_marks, classNames)\n",
        "  s = [['TN','FP'], ['FN', 'TP']]\n",
        "  \n",
        "  for i in range(2):\n",
        "      for j in range(2):\n",
        "          plt.text(j,i, str(s[i][j])+\" = \"+str(cm[i][j]))\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "با توجه به مقادیر به دست آمده دقت روی داده های تست 75 درصد و ریکال 79 و پرسیژن 73 درصد است\n",
        "\n",
        "ماتریس درهم ریختگی نیز نشان میدهد که تا حد خوبی مقادیر مثبت و منفی درست تشخیص داده شده اند هر چند اشتباهات قابل توجهی نیز داشته ایم"
      ],
      "metadata": {
        "id": "KU77Z-LufV0Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "conf_matrix(y_train,y_pred_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "id": "1zdtYSnhhc8x",
        "outputId": "e74b3f38-1f42-4ee7-a715-3a0e80c858eb"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "برای داده های تست نیز مشاهده میشود که دقت 75 درصد و ریکال 79 و پرسیژن 73 درصد است\n",
        "ماتریس در هم ریختگی نیز تا حد زیادی شبیه داده های آموزش است"
      ],
      "metadata": {
        "id": "wOQCf8dyi7Nk"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 806
        },
        "id": "1AfsN_CZCOiv",
        "outputId": "818be7b1-e383-4863-e5cf-ad240cb2a624"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test accuracy :\n",
            "0.7552867953886413\n",
            "===========================================\n",
            "recall test :\n",
            "0.794399199885698\n",
            "===========================================\n",
            "precision test :\n",
            "0.7334124785648332\n",
            "===========================================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "y_pred_test = model.predict(X_test)\n",
        "accuracy_test = accuracy_score(y_test, y_pred_test)\n",
        "print(\"test accuracy :\")\n",
        "print(accuracy_test)\n",
        "print(\"===========================================\")\n",
        "\n",
        "recall_test = recall_score(y_test, y_pred_test)\n",
        "print(\"recall test :\")\n",
        "print(recall_test)\n",
        "print(\"===========================================\")\n",
        "\n",
        "precision_test = precision_score(y_test, y_pred_test)\n",
        "print(\"precision test :\")\n",
        "print(precision_test)\n",
        "print(\"===========================================\")\n",
        "\n",
        "conf_matrix(y_test,y_pred_test)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tune Hyperparameter"
      ],
      "metadata": {
        "id": "9jm88vaf-srv"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "if_WD4BfEBol"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "def my_roc_auc_score(model, X, y): \n",
        "  return roc_auc_score(y, model.predict_proba(X)[:,1])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "با استفاده از گریدسرچ و پارامترهای گفته شده بهترین پارامترها را به دست می آوریم"
      ],
      "metadata": {
        "id": "lZfGutuPf1MG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x_EcrGdmkKTH",
        "outputId": "b37bb000-17a9-41df-c1bd-aa5ee44b2c1a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Colsample_bytree': 0.8,\n",
              " 'Max_depth': 2,\n",
              " 'N_estimator': 100,\n",
              " 'learning_rate': 0.1}"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "params = [{'learning_rate': [0.02, 0.05, 0.1, 0.3],\n",
        "         'Max_depth': [2, 3, 4],\n",
        "         'N_estimator': [100, 200, 300],\n",
        "         'Colsample_bytree' : [0.8, 1]\n",
        "         \n",
        "         }]\n",
        "kfold = StratifiedKFold(n_splits=3,shuffle=True, random_state=42)\n",
        "model2 = XGBClassifier(Eval_metric=\"auc\" ,Subsample=0.5)\n",
        "clf = GridSearchCV(estimator = model2, param_grid = params , scoring= my_roc_auc_score , cv=kfold)\n",
        "clf.fit(X_train , np.ravel(y_train))\n",
        "cv_results = pd.DataFrame(clf.cv_results_)\n",
        "clf.best_params_"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "مدل را به علت تایم زیاد برای آموزش برای آینده ذخیره میکنیم :)"
      ],
      "metadata": {
        "id": "OMpsU5w1gCmm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# save model for later\n",
        "import pickle\n",
        "filename = 'tune_model.sav'\n",
        "# pickle.dump(model, open(filename, 'wb'))\n",
        "\n",
        "# clf = pickle.load(open(filename, 'rb'))"
      ],
      "metadata": {
        "id": "90sDzbzEQBWK"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf.score(X_train , y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "13T8UjsQcwbV",
        "outputId": "1781830a-6e87-4f21-e7af-8a064d86e311"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8320732424893531"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "حال مدل دسته بندی خود را با بهترین پارامترها را میسازیم و دوباره روی داده های آموزش فیت میکنیم"
      ],
      "metadata": {
        "id": "2aReGk0bgJvu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "9zfMiGHwidwp"
      },
      "outputs": [],
      "source": [
        "model2 = XGBClassifier(learning_rate=0.1 , Max_depth=2 ,N_estimator=100 ,Colsample_bytree=0.8)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TSg9Lt_LQHDG",
        "outputId": "bdf99245-8e0e-4d32-9518-8c81ae448b8a"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(Colsample_bytree=0.8, Max_depth=2, N_estimator=100)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "با استفاده از پارامترهای تنظیم شده کمی دقت افزایش میابد ولی در کل تغییر خیلی محسوسی ایجاد نشده است"
      ],
      "metadata": {
        "id": "_-XDSm6-jTOH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_train2 = model2.predict(X_train)\n",
        "accuracy_train2 = accuracy_score(y_train, y_pred_train2)\n",
        "print(accuracy_train2)\n",
        "print(classification_report(y_train, y_pred_train2)) \n",
        "\n",
        "conf_matrix(y_train, y_pred_train2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 824
        },
        "id": "o2AOA3MGQViI",
        "outputId": "ae70845a-45eb-4e61-bc58-8deb94e3a273"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7541201018531617\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.78      0.71      0.74     28206\n",
            "         1.0       0.73      0.80      0.76     28346\n",
            "\n",
            "    accuracy                           0.75     56552\n",
            "   macro avg       0.76      0.75      0.75     56552\n",
            "weighted avg       0.76      0.75      0.75     56552\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_test2 = model2.predict(X_test)\n",
        "accuracy_test2 = accuracy_score(y_test, y_pred_test2)\n",
        "print(accuracy_test2)\n",
        "print(classification_report(y_test, y_pred_test2)) \n",
        "conf_matrix(y_test, y_pred_test2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 824
        },
        "id": "SNQsw_CeQmvY",
        "outputId": "01f1adbf-a7be-43ba-8ec8-703507c72045"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7545795317914987\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.78      0.72      0.75      7140\n",
            "         1.0       0.73      0.79      0.76      6999\n",
            "\n",
            "    accuracy                           0.75     14139\n",
            "   macro avg       0.76      0.75      0.75     14139\n",
            "weighted avg       0.76      0.75      0.75     14139\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_test2 = model2.predict(X_test)\n",
        "accuracy_test2 = accuracy_score(y_test, y_pred_test2)\n",
        "print(\"test accuracy :\")\n",
        "print(accuracy_test2)\n",
        "print(\"===========================================\")\n",
        "\n",
        "recall_test2 = recall_score(y_test, y_pred_test2)\n",
        "print(\"recall accuracy :\")\n",
        "print(recall_test2)\n",
        "print(\"===========================================\")\n",
        "\n",
        "precision_test2 = precision_score(y_test, y_pred_test2)\n",
        "print(\"precision accuracy :\")\n",
        "print(precision_test2)\n",
        "print(\"===========================================\")\n",
        "\n",
        "conf_matrix(y_test,y_pred_test2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 806
        },
        "id": "mjOtcAnUS1Tf",
        "outputId": "ff26bae7-a797-4a46-e070-3c232ab02a85"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test accuracy :\n",
            "0.7545795317914987\n",
            "===========================================\n",
            "recall accuracy :\n",
            "0.7939705672238891\n",
            "===========================================\n",
            "precision accuracy :\n",
            "0.7326301911667765\n",
            "===========================================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv_results.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "OVM16ejjTLS8",
        "outputId": "82830d02-a8bd-4bc3-b603-863452b07cb4"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
              "0       3.139454      0.036950         0.058873        0.000105   \n",
              "1       3.151464      0.040019         0.071445        0.005216   \n",
              "2       3.127419      0.034077         0.065845        0.000892   \n",
              "3       3.065390      0.017149         0.059925        0.001066   \n",
              "4       3.109126      0.011939         0.058662        0.000949   \n",
              "\n",
              "  param_Colsample_bytree param_Max_depth param_N_estimator  \\\n",
              "0                    0.8               2               100   \n",
              "1                    0.8               2               100   \n",
              "2                    0.8               2               100   \n",
              "3                    0.8               2               100   \n",
              "4                    0.8               2               200   \n",
              "\n",
              "  param_learning_rate                                             params  \\\n",
              "0                0.02  {'Colsample_bytree': 0.8, 'Max_depth': 2, 'N_e...   \n",
              "1                0.05  {'Colsample_bytree': 0.8, 'Max_depth': 2, 'N_e...   \n",
              "2                 0.1  {'Colsample_bytree': 0.8, 'Max_depth': 2, 'N_e...   \n",
              "3                 0.3  {'Colsample_bytree': 0.8, 'Max_depth': 2, 'N_e...   \n",
              "4                0.02  {'Colsample_bytree': 0.8, 'Max_depth': 2, 'N_e...   \n",
              "\n",
              "   split0_test_score  split1_test_score  split2_test_score  mean_test_score  \\\n",
              "0           0.819266           0.818662           0.814271         0.817400   \n",
              "1           0.827005           0.827548           0.823534         0.826029   \n",
              "2           0.828880           0.829651           0.825875         0.828135   \n",
              "3           0.828149           0.828905           0.825614         0.827556   \n",
              "4           0.819266           0.818662           0.814271         0.817400   \n",
              "\n",
              "   std_test_score  rank_test_score  \n",
              "0        0.002226               55  \n",
              "1        0.001778               37  \n",
              "2        0.001629                1  \n",
              "3        0.001408               19  \n",
              "4        0.002226               55  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-760470c7-b553-4241-8e01-b03d1f8a24fc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_Colsample_bytree</th>\n",
              "      <th>param_Max_depth</th>\n",
              "      <th>param_N_estimator</th>\n",
              "      <th>param_learning_rate</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.139454</td>\n",
              "      <td>0.036950</td>\n",
              "      <td>0.058873</td>\n",
              "      <td>0.000105</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "      <td>0.02</td>\n",
              "      <td>{'Colsample_bytree': 0.8, 'Max_depth': 2, 'N_e...</td>\n",
              "      <td>0.819266</td>\n",
              "      <td>0.818662</td>\n",
              "      <td>0.814271</td>\n",
              "      <td>0.817400</td>\n",
              "      <td>0.002226</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.151464</td>\n",
              "      <td>0.040019</td>\n",
              "      <td>0.071445</td>\n",
              "      <td>0.005216</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "      <td>0.05</td>\n",
              "      <td>{'Colsample_bytree': 0.8, 'Max_depth': 2, 'N_e...</td>\n",
              "      <td>0.827005</td>\n",
              "      <td>0.827548</td>\n",
              "      <td>0.823534</td>\n",
              "      <td>0.826029</td>\n",
              "      <td>0.001778</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.127419</td>\n",
              "      <td>0.034077</td>\n",
              "      <td>0.065845</td>\n",
              "      <td>0.000892</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "      <td>0.1</td>\n",
              "      <td>{'Colsample_bytree': 0.8, 'Max_depth': 2, 'N_e...</td>\n",
              "      <td>0.828880</td>\n",
              "      <td>0.829651</td>\n",
              "      <td>0.825875</td>\n",
              "      <td>0.828135</td>\n",
              "      <td>0.001629</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.065390</td>\n",
              "      <td>0.017149</td>\n",
              "      <td>0.059925</td>\n",
              "      <td>0.001066</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "      <td>0.3</td>\n",
              "      <td>{'Colsample_bytree': 0.8, 'Max_depth': 2, 'N_e...</td>\n",
              "      <td>0.828149</td>\n",
              "      <td>0.828905</td>\n",
              "      <td>0.825614</td>\n",
              "      <td>0.827556</td>\n",
              "      <td>0.001408</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.109126</td>\n",
              "      <td>0.011939</td>\n",
              "      <td>0.058662</td>\n",
              "      <td>0.000949</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>200</td>\n",
              "      <td>0.02</td>\n",
              "      <td>{'Colsample_bytree': 0.8, 'Max_depth': 2, 'N_e...</td>\n",
              "      <td>0.819266</td>\n",
              "      <td>0.818662</td>\n",
              "      <td>0.814271</td>\n",
              "      <td>0.817400</td>\n",
              "      <td>0.002226</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-760470c7-b553-4241-8e01-b03d1f8a24fc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-760470c7-b553-4241-8e01-b03d1f8a24fc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-760470c7-b553-4241-8e01-b03d1f8a24fc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.param_grid"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V8kaozY2oolT",
        "outputId": "ef4a7fcb-9233-4919-e07a-0a7011768a34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'Colsample_bytree': [0.8, 1],\n",
              "  'Max_depth': [2, 3, 4],\n",
              "  'N_estimator': [100, 200, 300],\n",
              "  'learning_rate': [0.02, 0.05, 0.1, 0.3]}]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualize Results"
      ],
      "metadata": {
        "id": "xa7f90GxAgxx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "پس از رسم تاثیر تغییر هر کدام از پارامترها روی عملکرد مدلها مشاهده میشود که بیشترین تاثیر مربوط به لرنینگ ریت بوده است و بقیه تاثیر چندانی نداشته اند"
      ],
      "metadata": {
        "id": "vHZLO1tMkAl4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "learning_rate = [0.02, 0.05, 0.1, 0.3]\n",
        "Max_depth = [2, 3, 4]\n",
        "N_estimator = [100, 200, 300]\n",
        "Colsample_bytree  = [0.8, 1]\n",
        "\n",
        "# plot results of learning rate \n",
        "\n",
        "best_md=clf.best_params_['Max_depth']\n",
        "best_cb = clf.best_params_['Colsample_bytree']\n",
        "best_ns = clf.best_params_['N_estimator']\n",
        "y = []\n",
        "for mean_score, params in zip(clf.cv_results_[\"mean_test_score\"], clf.cv_results_[\"params\"]):\n",
        "    if params[\"Max_depth\"]==best_md and params[\"Colsample_bytree\"]==best_cb and params[\"N_estimator\"]==best_ns :\n",
        "        \n",
        "        y.append(mean_score)\n",
        "\n",
        "plt.plot(learning_rate, y)\n",
        "\n",
        "plt.title('mean score for different learning rates')\n",
        "plt.xlabel('learning rate')\n",
        "plt.ylabel('mean score')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Q-Km71iXUl7H",
        "outputId": "dca4a845-e2a4-4be9-b3ba-ada2efad94b5"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learning_rate = [0.02, 0.05, 0.1, 0.3]\n",
        "Max_depth = [2, 3, 4]\n",
        "N_estimator = [100, 200, 300]\n",
        "Colsample_bytree  = [0.8, 1]\n",
        "\n",
        "# plot results of max_depth\n",
        "\n",
        "best_lr=clf.best_params_['learning_rate']\n",
        "best_cb = clf.best_params_['Colsample_bytree']\n",
        "best_ns = clf.best_params_['N_estimator']\n",
        "y = []\n",
        "for mean_score, params in zip(clf.cv_results_[\"mean_test_score\"], clf.cv_results_[\"params\"]):\n",
        "    if params[\"learning_rate\"]==best_lr and params[\"Colsample_bytree\"]==best_cb and params[\"N_estimator\"]==best_ns :\n",
        "        \n",
        "        y.append(mean_score)\n",
        "\n",
        "plt.plot(Max_depth, y)\n",
        "\n",
        "plt.title('mean score for different max depth')\n",
        "plt.xlabel('max depth')\n",
        "plt.ylabel('mean score')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "F7Mjq0p8ehoB",
        "outputId": "cd8ecb6d-de70-454c-cd05-9dee94969265"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learning_rate = [0.02, 0.05, 0.1, 0.3]\n",
        "Max_depth = [2, 3, 4]\n",
        "N_estimator = [100, 200, 300]\n",
        "Colsample_bytree  = [0.8, 1]\n",
        "\n",
        "# plot results of max_depth\n",
        "\n",
        "best_lr=clf.best_params_['learning_rate']\n",
        "best_cb = clf.best_params_['Colsample_bytree']\n",
        "best_md = clf.best_params_['Max_depth']\n",
        "y = []\n",
        "for mean_score, params in zip(clf.cv_results_[\"mean_test_score\"], clf.cv_results_[\"params\"]):\n",
        "    if params[\"learning_rate\"]==best_lr and params[\"Colsample_bytree\"]==best_cb and params[\"Max_depth\"]==best_md :\n",
        "        \n",
        "        y.append(mean_score)\n",
        "\n",
        "plt.plot(N_estimator, y)\n",
        "\n",
        "plt.title('mean score for different N_estimator')\n",
        "plt.xlabel('N_estimator')\n",
        "plt.ylabel('mean score')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "OVMQOXFhf7Il",
        "outputId": "36b72ac7-1548-4caf-d12b-ae7e0911c5b9"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learning_rate = [0.02, 0.05, 0.1, 0.3]\n",
        "Max_depth = [2, 3, 4]\n",
        "N_estimator = [100, 200, 300]\n",
        "Colsample_bytree  = [0.8, 1]\n",
        "\n",
        "# plot results of max_depth\n",
        "\n",
        "best_lr=clf.best_params_['learning_rate']\n",
        "best_ns = clf.best_params_['N_estimator']\n",
        "best_md = clf.best_params_['Max_depth']\n",
        "y = []\n",
        "for mean_score, params in zip(clf.cv_results_[\"mean_test_score\"], clf.cv_results_[\"params\"]):\n",
        "    if params[\"learning_rate\"]==best_lr and params[\"N_estimator\"]==best_ns and params[\"Max_depth\"]==best_md :\n",
        "        \n",
        "        y.append(mean_score)\n",
        "\n",
        "plt.plot(Colsample_bytree, y)\n",
        "\n",
        "plt.title('mean score for different Colsample_bytree')\n",
        "plt.xlabel('Colsample_bytree')\n",
        "plt.ylabel('mean score')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "7X3psoZBg33L",
        "outputId": "ddd8ebf2-d83f-4e93-ac3b-001754986352"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "DM Project.ipynb",
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}