Diabetes_Tree_Classifier.ipynb

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   },
   "source": [
    "# Diabetes Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba58261d",
   "metadata": {
    "id": "ba58261d"
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
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   "execution_count": null,
   "id": "3d616f52",
   "metadata": {
    "id": "3d616f52"
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   "outputs": [],
   "source": [
    "df = pd.read_csv('diabetes.csv')"
   ]
  },
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       "      <th></th>\n",
       "      <th>Unnamed: 0</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",
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       "      <th>General Health</th>\n",
       "      <th>Mental Health</th>\n",
       "      <th>Physical Health</th>\n",
       "      <th>Difficulty Walking</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>female</td>\n",
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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]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c34b494",
   "metadata": {
    "id": "4c34b494"
   },
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cf031dc",
   "metadata": {
    "id": "6cf031dc"
   },
   "source": [
    "## Null"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jubmsd0eOXMr",
   "metadata": {
    "id": "jubmsd0eOXMr"
   },
   "source": [
    "<font color='blue'>\n",
    "    Find all different values present in each column\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b34478ee",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b34478ee",
    "outputId": "f8665f57-e02c-4002-ec35-70fdfa2bdc32"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0 \n",
      " [    0     1     2 ... 70689 70690 70691]\n",
      "\n",
      "Diabetes_binary \n",
      " [0. 1.]\n",
      "\n",
      "HighBP \n",
      " [1. 0.]\n",
      "\n",
      "High Cholesterol \n",
      " [0. 1.]\n",
      "\n",
      "Cholesterol Check \n",
      " [1. 0.]\n",
      "\n",
      "BMI \n",
      " [26. 28. 29. 18. 31. 32. 27. 24. 21. 58. 30. 20. 22. 38. 40. 25. 36. 47.\n",
      " 19. 37. 41. 23. 34. 35. 42. 17. 33. 44. 15. 52. 69. 56. 45. 39. 92. 53.\n",
      " 98. 50. 46. 79. 48. 16. 63. 72. 54. 49. 68. 43. 84. 73. 76. 55. 51. 75.\n",
      " 57. 60. 12. 77. 82. 67. 71. 61. 14. 81. 59. 86. 13. 87. 65. 95. 89. 62.\n",
      " 64. 66. 85. 70. 83. nan 80. 78. 74.]\n",
      "\n",
      "Smoker \n",
      " [0. 1.]\n",
      "\n",
      "Stroke \n",
      " [0. 1.]\n",
      "\n",
      "HeartDiseaseorAttack \n",
      " [ 0.  1. nan]\n",
      "\n",
      "Physical Activity \n",
      " [ 1.  0. nan]\n",
      "\n",
      "Fruits \n",
      " [ 0.  1. nan]\n",
      "\n",
      "Veggies \n",
      " [ 1.  0. nan]\n",
      "\n",
      "Heavy Alcohol Consumption \n",
      " [ 0.  1. nan]\n",
      "\n",
      "Any Health Care \n",
      " [ 1.  0. nan]\n",
      "\n",
      "No Doctor because of Cost \n",
      " [ 0.  1. nan]\n",
      "\n",
      "General Health \n",
      " ['Medium' 'High' 'Good' 'Low' 'Very Low' nan]\n",
      "\n",
      "Mental Health \n",
      " [ 5.  0.  7.  3.  4.  2. 30. 20.  1. 15. 10. 25. 14. 28.  6. 29. 26. 12.\n",
      " 16. 22. 13.  8.  9. 21. 18. 17. 27. nan 24. 23. 11. 19.]\n",
      "\n",
      "Physical Health \n",
      " [30.  0. 10.  3.  6.  4. 15.  1.  2. 14.  7. 25. 21. 20.  5.  8. 22. 23.\n",
      " 29. 12. 18. 28. 26. 24. 27. 11. 13. 16. 17.  9. 19. nan]\n",
      "\n",
      "Difficulty Walking \n",
      " [ 0.  1. nan]\n",
      "\n",
      "Sex \n",
      " ['male' 'female' nan]\n",
      "\n",
      "Age \n",
      " [ 4. 12. 13. 11.  8.  1.  6.  3.  7. 10.  9.  5.  2. nan]\n",
      "\n",
      "Education \n",
      " ['Cat6' 'Cat5' 'Cat4' 'Cat3' 'Cat2' 'Cat1' nan]\n",
      "\n",
      "Income \n",
      " ['Cat8' 'Cat7' 'Cat6' 'Cat3' 'Cat4' 'Cat1' 'Cat5' 'Cat2' nan 'Unknown']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in df:\n",
    "    print(f\"{col} \\n {df[col].unique()}\\n\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db40b2b6",
   "metadata": {
    "id": "db40b2b6"
   },
   "source": [
    "<font color='blue'>\n",
    "    Unify all missing types\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "709050fe",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "709050fe",
    "outputId": "b6a77f07-9c28-4b48-e437-66f2d50b3979"
   },
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.replace('Unknown', None, inplace=True)\n",
    "df.replace('-', None, inplace=True)\n",
    "df.replace('?', None, inplace=True)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36dce012",
   "metadata": {
    "id": "36dce012"
   },
   "source": [
    "<font color='blue'>\n",
    "    Remove rows containing more than 2 missing values\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "156fdbdc",
   "metadata": {
    "id": "156fdbdc"
   },
   "outputs": [],
   "source": [
    "df = df[df.isnull().sum(axis=1) <= 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6c06c56",
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    },
    "id": "e6c06c56",
    "outputId": "3f2bd500-b779-4f55-88c3-9d24e1e4956b"
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   "outputs": [
    {
     "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         0\n",
       "Physical Activity            0\n",
       "Fruits                       1\n",
       "Veggies                      0\n",
       "Heavy Alcohol Consumption    0\n",
       "Any Health Care              1\n",
       "No Doctor because of Cost    1\n",
       "General Health               0\n",
       "Mental Health                0\n",
       "Physical Health              0\n",
       "Difficulty Walking           1\n",
       "Sex                          0\n",
       "Age                          0\n",
       "Education                    0\n",
       "Income                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09e8a731",
   "metadata": {
    "id": "09e8a731"
   },
   "source": [
    "<font color='blue'>\n",
    "    Find NaNs and replace them by either the mean(numerical) or the mode(categorical and binary) of that column. <br>\n",
    "    By doing so we can keep and refine the dataset with out removing any data\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c8df567",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7c8df567",
    "outputId": "2d33afe3-0eda-4c4b-f759-403ae58c8718"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:6392: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return self._update_inplace(result)\n"
     ]
    }
   ],
   "source": [
    "df[\"BMI\"].fillna(df['BMI'].mean(), inplace=True)\n",
    "\n",
    "\n",
    "df[\"Fruits\"].fillna(df['Fruits'].value_counts().idxmax(), inplace=True)\n",
    "\n",
    "df[\"Any Health Care\"].fillna(df['Any Health Care'].value_counts().idxmax(), inplace=True)\n",
    "\n",
    "df[\"No Doctor because of Cost\"].fillna(df['No Doctor because of Cost'].value_counts().idxmax(), inplace=True)\n",
    "\n",
    "df[\"Difficulty Walking\"].fillna(df['Difficulty Walking'].value_counts().idxmax(), inplace=True)\n",
    "\n",
    "df[\"Income\"].fillna(df['Income'].value_counts().idxmax(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c84ed24f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c84ed24f",
    "outputId": "f40d2c33-d316-40b3-927f-653ba64ecc1b"
   },
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a084a6f1",
   "metadata": {
    "id": "a084a6f1"
   },
   "source": [
    "## White space"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9428360d",
   "metadata": {
    "id": "9428360d"
   },
   "source": [
    "<font color='blue'>\n",
    "    Replace white spaces in column names\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "678c30cc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "678c30cc",
    "outputId": "427ae95e-9305-40ee-9783-109af9fba615"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed:_0', 'Diabetes_binary', 'HighBP', 'High_Cholesterol',\n",
       "       'Cholesterol_Check', 'BMI', 'Smoker', 'Stroke', 'HeartDiseaseorAttack',\n",
       "       'Physical_Activity', 'Fruits', 'Veggies', 'Heavy_Alcohol_Consumption',\n",
       "       'Any_Health_Care', 'No_Doctor_because_of_Cost', 'General_Health',\n",
       "       'Mental_Health', 'Physical_Health', 'Difficulty_Walking', 'Sex', 'Age',\n",
       "       'Education', 'Income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = df.columns.str.replace(' ','_')\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c3da50",
   "metadata": {
    "id": "90c3da50"
   },
   "source": [
    "<font color='blue'>\n",
    "    Replace white spaces in the whole dataframe\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b65e4932",
   "metadata": {
    "id": "b65e4932"
   },
   "outputs": [],
   "source": [
    "df.replace(' ', '_', regex=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f210fd",
   "metadata": {
    "id": "31f210fd"
   },
   "source": [
    "## Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "507a33bd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 364
    },
    "id": "507a33bd",
    "outputId": "33804024-d3b7-4fde-f3dc-4a0dd510bab7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-041f85fc-f4ce-44e8-afcc-3318da1effb4\">\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>Unnamed:_0</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>HeartDiseaseorAttack</th>\n",
       "      <th>Physical_Activity</th>\n",
       "      <th>Fruits</th>\n",
       "      <th>Veggies</th>\n",
       "      <th>Heavy_Alcohol_Consumption</th>\n",
       "      <th>Any_Health_Care</th>\n",
       "      <th>No_Doctor_because_of_Cost</th>\n",
       "      <th>Mental_Health</th>\n",
       "      <th>Physical_Health</th>\n",
       "      <th>Difficulty_Walking</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>35345.834618</td>\n",
       "      <td>0.500007</td>\n",
       "      <td>0.563466</td>\n",
       "      <td>0.525710</td>\n",
       "      <td>0.975259</td>\n",
       "      <td>29.856995</td>\n",
       "      <td>0.475280</td>\n",
       "      <td>0.062172</td>\n",
       "      <td>0.147812</td>\n",
       "      <td>0.703032</td>\n",
       "      <td>0.611789</td>\n",
       "      <td>0.788771</td>\n",
       "      <td>0.042721</td>\n",
       "      <td>0.954959</td>\n",
       "      <td>0.093916</td>\n",
       "      <td>3.752062</td>\n",
       "      <td>5.810499</td>\n",
       "      <td>0.252734</td>\n",
       "      <td>8.584105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>20407.117356</td>\n",
       "      <td>0.500004</td>\n",
       "      <td>0.495959</td>\n",
       "      <td>0.499342</td>\n",
       "      <td>0.155337</td>\n",
       "      <td>7.113991</td>\n",
       "      <td>0.499392</td>\n",
       "      <td>0.241469</td>\n",
       "      <td>0.354916</td>\n",
       "      <td>0.456926</td>\n",
       "      <td>0.487346</td>\n",
       "      <td>0.408183</td>\n",
       "      <td>0.202229</td>\n",
       "      <td>0.207396</td>\n",
       "      <td>0.291714</td>\n",
       "      <td>8.155682</td>\n",
       "      <td>10.062308</td>\n",
       "      <td>0.434583</td>\n",
       "      <td>2.852141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17673.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>35346.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>53018.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>70691.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-041f85fc-f4ce-44e8-afcc-3318da1effb4')\"\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",
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       "  </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",
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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",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
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       "\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-041f85fc-f4ce-44e8-afcc-3318da1effb4 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-041f85fc-f4ce-44e8-afcc-3318da1effb4');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "         Unnamed:_0  Diabetes_binary        HighBP  High_Cholesterol  \\\n",
       "count  70691.000000     70691.000000  70691.000000      70691.000000   \n",
       "mean   35345.834618         0.500007      0.563466          0.525710   \n",
       "std    20407.117356         0.500004      0.495959          0.499342   \n",
       "min        0.000000         0.000000      0.000000          0.000000   \n",
       "25%    17673.500000         0.000000      0.000000          0.000000   \n",
       "50%    35346.000000         1.000000      1.000000          1.000000   \n",
       "75%    53018.500000         1.000000      1.000000          1.000000   \n",
       "max    70691.000000         1.000000      1.000000          1.000000   \n",
       "\n",
       "       Cholesterol_Check           BMI        Smoker        Stroke  \\\n",
       "count       70691.000000  70691.000000  70691.000000  70691.000000   \n",
       "mean            0.975259     29.856995      0.475280      0.062172   \n",
       "std             0.155337      7.113991      0.499392      0.241469   \n",
       "min             0.000000     12.000000      0.000000      0.000000   \n",
       "25%             1.000000     25.000000      0.000000      0.000000   \n",
       "50%             1.000000     29.000000      0.000000      0.000000   \n",
       "75%             1.000000     33.000000      1.000000      0.000000   \n",
       "max             1.000000     98.000000      1.000000      1.000000   \n",
       "\n",
       "       HeartDiseaseorAttack  Physical_Activity        Fruits       Veggies  \\\n",
       "count          70691.000000       70691.000000  70691.000000  70691.000000   \n",
       "mean               0.147812           0.703032      0.611789      0.788771   \n",
       "std                0.354916           0.456926      0.487346      0.408183   \n",
       "min                0.000000           0.000000      0.000000      0.000000   \n",
       "25%                0.000000           0.000000      0.000000      1.000000   \n",
       "50%                0.000000           1.000000      1.000000      1.000000   \n",
       "75%                0.000000           1.000000      1.000000      1.000000   \n",
       "max                1.000000           1.000000      1.000000      1.000000   \n",
       "\n",
       "       Heavy_Alcohol_Consumption  Any_Health_Care  No_Doctor_because_of_Cost  \\\n",
       "count               70691.000000     70691.000000               70691.000000   \n",
       "mean                    0.042721         0.954959                   0.093916   \n",
       "std                     0.202229         0.207396                   0.291714   \n",
       "min                     0.000000         0.000000                   0.000000   \n",
       "25%                     0.000000         1.000000                   0.000000   \n",
       "50%                     0.000000         1.000000                   0.000000   \n",
       "75%                     0.000000         1.000000                   0.000000   \n",
       "max                     1.000000         1.000000                   1.000000   \n",
       "\n",
       "       Mental_Health  Physical_Health  Difficulty_Walking           Age  \n",
       "count   70691.000000     70691.000000        70691.000000  70691.000000  \n",
       "mean        3.752062         5.810499            0.252734      8.584105  \n",
       "std         8.155682        10.062308            0.434583      2.852141  \n",
       "min         0.000000         0.000000            0.000000      1.000000  \n",
       "25%         0.000000         0.000000            0.000000      7.000000  \n",
       "50%         0.000000         0.000000            0.000000      9.000000  \n",
       "75%         2.000000         6.000000            1.000000     11.000000  \n",
       "max        30.000000        30.000000            1.000000     13.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a86fe3e9",
   "metadata": {
    "id": "a86fe3e9"
   },
   "source": [
    "<font color='blue'>\n",
    "    BMI, Mental Health, Physical Health and Age need to me normalized due to their broad range\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a32bf4c0",
   "metadata": {
    "id": "a32bf4c0"
   },
   "source": [
    "### BMI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "iFfSl95DOxRD",
   "metadata": {
    "id": "iFfSl95DOxRD"
   },
   "source": [
    "<font color='blue'>\n",
    "    We categorize BMI into 7 sections based on the standard BMI categotization. By doing so we have a normalized range of vlues\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e2c6ac2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9e2c6ac2",
    "outputId": "df830e49-ea52-40e8-86e9-97e6041765f9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        4.0\n",
       "1        4.0\n",
       "2        4.0\n",
       "3        4.0\n",
       "4        4.0\n",
       "        ... \n",
       "70687    6.0\n",
       "70688    4.0\n",
       "70689    3.0\n",
       "70690    2.0\n",
       "70691    3.0\n",
       "Name: BMI, Length: 70691, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BMI'] = pd.cut(df['BMI'], (0, 16, 18.5, 25, 30, 35, 40, np.inf), labels=[1, 2, 3, 4, 5, 6, 7]).astype('float')\n",
    "df['BMI']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a448a30",
   "metadata": {
    "id": "6a448a30"
   },
   "source": [
    "### Mental Health, Physical Health, Age"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Sm2L0NckPMec",
   "metadata": {
    "id": "Sm2L0NckPMec"
   },
   "source": [
    "<font color='blue'>\n",
    "    These columns contain values ranging from 0 to 30 or 13 for Age. I used min-max normalization to change their range into 0-1 so that the whole dataset is unified.\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2721337",
   "metadata": {
    "id": "e2721337"
   },
   "outputs": [],
   "source": [
    "df['Mental_Health'] = (df['Mental_Health']-df['Mental_Health'].min())/(df['Mental_Health'].max()-df['Mental_Health'].min())\n",
    "df['Physical_Health'] = (df['Physical_Health']-df['Physical_Health'].min())/(df['Physical_Health'].max()-df['Physical_Health'].min())\n",
    "df['Age'] = (df['Age']-df['Age'].min())/(df['Age'].max()-df['Age'].min())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d198f06",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 364
    },
    "id": "5d198f06",
    "outputId": "9d7bbcb9-f90c-4b56-ff10-788f6a4faf29"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "      <div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed:_0</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>HeartDiseaseorAttack</th>\n",
       "      <th>Physical_Activity</th>\n",
       "      <th>Fruits</th>\n",
       "      <th>Veggies</th>\n",
       "      <th>Heavy_Alcohol_Consumption</th>\n",
       "      <th>Any_Health_Care</th>\n",
       "      <th>No_Doctor_because_of_Cost</th>\n",
       "      <th>Mental_Health</th>\n",
       "      <th>Physical_Health</th>\n",
       "      <th>Difficulty_Walking</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "      <td>70691.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>35345.834618</td>\n",
       "      <td>0.500007</td>\n",
       "      <td>0.563466</td>\n",
       "      <td>0.525710</td>\n",
       "      <td>0.975259</td>\n",
       "      <td>4.343495</td>\n",
       "      <td>0.475280</td>\n",
       "      <td>0.062172</td>\n",
       "      <td>0.147812</td>\n",
       "      <td>0.703032</td>\n",
       "      <td>0.611789</td>\n",
       "      <td>0.788771</td>\n",
       "      <td>0.042721</td>\n",
       "      <td>0.954959</td>\n",
       "      <td>0.093916</td>\n",
       "      <td>0.125069</td>\n",
       "      <td>0.193683</td>\n",
       "      <td>0.252734</td>\n",
       "      <td>0.632009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>20407.117356</td>\n",
       "      <td>0.500004</td>\n",
       "      <td>0.495959</td>\n",
       "      <td>0.499342</td>\n",
       "      <td>0.155337</td>\n",
       "      <td>1.200387</td>\n",
       "      <td>0.499392</td>\n",
       "      <td>0.241469</td>\n",
       "      <td>0.354916</td>\n",
       "      <td>0.456926</td>\n",
       "      <td>0.487346</td>\n",
       "      <td>0.408183</td>\n",
       "      <td>0.202229</td>\n",
       "      <td>0.207396</td>\n",
       "      <td>0.291714</td>\n",
       "      <td>0.271856</td>\n",
       "      <td>0.335410</td>\n",
       "      <td>0.434583</td>\n",
       "      <td>0.237678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17673.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>35346.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>53018.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.833333</td>\n",
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       "      <th>max</th>\n",
       "      <td>70691.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         Unnamed:_0  Diabetes_binary        HighBP  High_Cholesterol  \\\n",
       "count  70691.000000     70691.000000  70691.000000      70691.000000   \n",
       "mean   35345.834618         0.500007      0.563466          0.525710   \n",
       "std    20407.117356         0.500004      0.495959          0.499342   \n",
       "min        0.000000         0.000000      0.000000          0.000000   \n",
       "25%    17673.500000         0.000000      0.000000          0.000000   \n",
       "50%    35346.000000         1.000000      1.000000          1.000000   \n",
       "75%    53018.500000         1.000000      1.000000          1.000000   \n",
       "max    70691.000000         1.000000      1.000000          1.000000   \n",
       "\n",
       "       Cholesterol_Check           BMI        Smoker        Stroke  \\\n",
       "count       70691.000000  70691.000000  70691.000000  70691.000000   \n",
       "mean            0.975259      4.343495      0.475280      0.062172   \n",
       "std             0.155337      1.200387      0.499392      0.241469   \n",
       "min             0.000000      1.000000      0.000000      0.000000   \n",
       "25%             1.000000      3.000000      0.000000      0.000000   \n",
       "50%             1.000000      4.000000      0.000000      0.000000   \n",
       "75%             1.000000      5.000000      1.000000      0.000000   \n",
       "max             1.000000      7.000000      1.000000      1.000000   \n",
       "\n",
       "       HeartDiseaseorAttack  Physical_Activity        Fruits       Veggies  \\\n",
       "count          70691.000000       70691.000000  70691.000000  70691.000000   \n",
       "mean               0.147812           0.703032      0.611789      0.788771   \n",
       "std                0.354916           0.456926      0.487346      0.408183   \n",
       "min                0.000000           0.000000      0.000000      0.000000   \n",
       "25%                0.000000           0.000000      0.000000      1.000000   \n",
       "50%                0.000000           1.000000      1.000000      1.000000   \n",
       "75%                0.000000           1.000000      1.000000      1.000000   \n",
       "max                1.000000           1.000000      1.000000      1.000000   \n",
       "\n",
       "       Heavy_Alcohol_Consumption  Any_Health_Care  No_Doctor_because_of_Cost  \\\n",
       "count               70691.000000     70691.000000               70691.000000   \n",
       "mean                    0.042721         0.954959                   0.093916   \n",
       "std                     0.202229         0.207396                   0.291714   \n",
       "min                     0.000000         0.000000                   0.000000   \n",
       "25%                     0.000000         1.000000                   0.000000   \n",
       "50%                     0.000000         1.000000                   0.000000   \n",
       "75%                     0.000000         1.000000                   0.000000   \n",
       "max                     1.000000         1.000000                   1.000000   \n",
       "\n",
       "       Mental_Health  Physical_Health  Difficulty_Walking           Age  \n",
       "count   70691.000000     70691.000000        70691.000000  70691.000000  \n",
       "mean        0.125069         0.193683            0.252734      0.632009  \n",
       "std         0.271856         0.335410            0.434583      0.237678  \n",
       "min         0.000000         0.000000            0.000000      0.000000  \n",
       "25%         0.000000         0.000000            0.000000      0.500000  \n",
       "50%         0.000000         0.000000            0.000000      0.666667  \n",
       "75%         0.066667         0.200000            1.000000      0.833333  \n",
       "max         1.000000         1.000000            1.000000      1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5facb253",
   "metadata": {
    "id": "5facb253"
   },
   "source": [
    "## One-Hot-Encode Categorical Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b61e34d",
   "metadata": {
    "id": "2b61e34d"
   },
   "outputs": [],
   "source": [
    "categorical = ['General_Health','Sex', 'Education', 'Income']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "SGDMPyTtPlxm",
   "metadata": {
    "id": "SGDMPyTtPlxm"
   },
   "source": [
    "<font color='blue'>\n",
    "    These 4 columns appear in categorical forms. we have to change them into one-hot encoding\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bead25a",
   "metadata": {
    "id": "4bead25a"
   },
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, columns=categorical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2c0e75a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 488
    },
    "id": "e2c0e75a",
    "outputId": "478f6f1b-ea98-4a77-ca5d-460aaa34df98"
   },
   "outputs": [
    {
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       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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      ],
      "text/plain": [
       "       Unnamed:_0  Diabetes_binary  HighBP  High_Cholesterol  \\\n",
       "0               0              0.0     1.0               0.0   \n",
       "1               1              0.0     1.0               1.0   \n",
       "2               2              0.0     0.0               0.0   \n",
       "3               3              0.0     1.0               1.0   \n",
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       "...           ...              ...     ...               ...   \n",
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       "70688       70688              1.0     0.0               1.0   \n",
       "70689       70689              1.0     1.0               1.0   \n",
       "70690       70690              1.0     1.0               1.0   \n",
       "70691       70691              1.0     1.0               1.0   \n",
       "\n",
       "       Cholesterol_Check  BMI  Smoker  Stroke  HeartDiseaseorAttack  \\\n",
       "0                    1.0  4.0     0.0     0.0                   0.0   \n",
       "1                    1.0  4.0     1.0     1.0                   0.0   \n",
       "2                    1.0  4.0     0.0     0.0                   0.0   \n",
       "3                    1.0  4.0     1.0     0.0                   0.0   \n",
       "4                    1.0  4.0     1.0     0.0                   0.0   \n",
       "...                  ...  ...     ...     ...                   ...   \n",
       "70687                1.0  6.0     0.0     0.0                   0.0   \n",
       "70688                1.0  4.0     1.0     0.0                   1.0   \n",
       "70689                1.0  3.0     0.0     0.0                   1.0   \n",
       "70690                1.0  2.0     0.0     0.0                   0.0   \n",
       "70691                1.0  3.0     0.0     0.0                   1.0   \n",
       "\n",
       "       Physical_Activity  ...  Education_Cat5  Education_Cat6  Income_Cat1  \\\n",
       "0                    1.0  ...               0               1            0   \n",
       "1                    0.0  ...               0               1            0   \n",
       "2                    1.0  ...               0               1            0   \n",
       "3                    1.0  ...               0               1            0   \n",
       "4                    1.0  ...               1               0            0   \n",
       "...                  ...  ...             ...             ...          ...   \n",
       "70687                0.0  ...               0               0            1   \n",
       "70688                0.0  ...               0               0            0   \n",
       "70689                0.0  ...               0               1            0   \n",
       "70690                0.0  ...               0               0            0   \n",
       "70691                1.0  ...               0               1            0   \n",
       "\n",
       "       Income_Cat2  Income_Cat3  Income_Cat4  Income_Cat5  Income_Cat6  \\\n",
       "0                0            0            0            0            0   \n",
       "1                0            0            0            0            0   \n",
       "2                0            0            0            0            0   \n",
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       "4                0            0            0            0            0   \n",
       "...            ...          ...          ...          ...          ...   \n",
       "70687            0            0            0            0            0   \n",
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       "70690            0            0            1            0            0   \n",
       "70691            1            0            0            0            0   \n",
       "\n",
       "       Income_Cat7  Income_Cat8  \n",
       "0                0            1  \n",
       "1                0            1  \n",
       "2                0            1  \n",
       "3                0            1  \n",
       "4                0            1  \n",
       "...            ...          ...  \n",
       "70687            0            0  \n",
       "70688            0            0  \n",
       "70689            0            0  \n",
       "70690            0            0  \n",
       "70691            0            0  \n",
       "\n",
       "[70691 rows x 40 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4364f7f8",
   "metadata": {
    "id": "4364f7f8"
   },
   "source": [
    "# Seperate Label and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8eafe576",
   "metadata": {
    "id": "8eafe576"
   },
   "outputs": [],
   "source": [
    "Diabetes_binary = df[\"Diabetes_binary\"]\n",
    "df.drop([\"Unnamed:_0\", \"Diabetes_binary\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7662b641",
   "metadata": {
    "colab": {
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    },
    "id": "7662b641",
    "outputId": "ecc9438a-47e3-4336-877d-78020eca55ff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.0\n",
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  {
   "cell_type": "markdown",
   "id": "daa04d37",
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   "source": [
    "# Buildind the XGBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "285999b8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "285999b8",
    "outputId": "1634c621-fd61-4668-95df-593dad13922e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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": [
    "!pip install xgboost\n",
    "from sklearn.model_selection import train_test_split\n",
    "from xgboost import XGBClassifier\n",
    "import xgboost\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, log_loss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66c14004",
   "metadata": {
    "id": "66c14004"
   },
   "outputs": [],
   "source": [
    "X = df\n",
    "Y = Diabetes_binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "UGrfbWHiJrsm",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 488
    },
    "id": "UGrfbWHiJrsm",
    "outputId": "350b09a0-7cab-4190-b9fd-22c9606dcdde"
   },
   "outputs": [
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       "2                   0               1            0            0            0   \n",
       "3                   0               1            0            0            0   \n",
       "4                   1               0            0            0            0   \n",
       "...               ...             ...          ...          ...          ...   \n",
       "70687               0               0            1            0            0   \n",
       "70688               0               0            0            0            0   \n",
       "70689               0               1            0            0            0   \n",
       "70690               0               0            0            0            0   \n",
       "70691               0               1            0            1            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",
       "70687            0            0            0            0            0  \n",
       "70688            0            0            1            0            0  \n",
       "70689            1            0            0            0            0  \n",
       "70690            1            0            0            0            0  \n",
       "70691            0            0            0            0            0  \n",
       "\n",
       "[70691 rows x 38 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Thl6NHTPPzsb",
   "metadata": {
    "id": "Thl6NHTPPzsb"
   },
   "source": [
    "<font color='blue'>\n",
    "    80 % of data wil be used for training and 20 % for test\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f07fe5e",
   "metadata": {
    "id": "3f07fe5e"
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42, stratify=Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f16073d5",
   "metadata": {
    "id": "f16073d5"
   },
   "source": [
    "### Initiate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91568259",
   "metadata": {
    "id": "91568259"
   },
   "outputs": [],
   "source": [
    "model = XGBClassifier(\n",
    "            tree_method='gpu_hist',\n",
    "            Learning_rate=0.1,\n",
    "            Max_depth=4,\n",
    "            N_estimator=200,\n",
    "            Subsample=0.5,\n",
    "            Colsample_bytree=1,\n",
    "            Random_seed=123,\n",
    "            Eval_metric='auc',\n",
    "            Verbosity=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "287d65a7",
   "metadata": {
    "id": "287d65a7"
   },
   "source": [
    "### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2980cec",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e2980cec",
    "outputId": "aab73f0c-e85e-49b0-b59b-62774a623abb",
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "eval_set = [(x_test, y_test)]\n",
    "model.fit(x_train, y_train, early_stopping_rounds=10,  eval_set=eval_set, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t3AGuC8zLyAk",
   "metadata": {
    "id": "t3AGuC8zLyAk"
   },
   "source": [
    "### Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "z_lMMO_QMya5",
   "metadata": {
    "id": "z_lMMO_QMya5"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "G1ZPjHosLsl2",
   "metadata": {
    "id": "G1ZPjHosLsl2"
   },
   "outputs": [],
   "source": [
    "def show_res(actual, predicted, color):\n",
    "  acc = accuracy_score(actual, predicted)\n",
    "  recall = recall_score(actual, predicted)\n",
    "  precision = precision_score(actual, predicted)\n",
    "  print(f\" Accuracy => {acc}\\n Recall => {recall}\\n Precision => {precision}\")\n",
    "  conf_matrix = confusion_matrix(y_true=actual, y_pred=predicted)\n",
    "  \n",
    "  # Print the confusion matrix using Matplotlib\n",
    "  fig, ax = plt.subplots(figsize=(5, 5))\n",
    "  ax.matshow(conf_matrix, cmap=color, alpha=0.3)\n",
    "  for i in range(conf_matrix.shape[0]):\n",
    "      for j in range(conf_matrix.shape[1]):\n",
    "          ax.text(x=j, y=i,s=conf_matrix[i, j], va='center', ha='center', size='xx-large')\n",
    "  \n",
    "  plt.xlabel('Predictions', fontsize=18)\n",
    "  plt.ylabel('Actuals', fontsize=18)\n",
    "  plt.title('Confusion Matrix', fontsize=18)\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "E2CjHuurOBtS",
   "metadata": {
    "id": "E2CjHuurOBtS"
   },
   "source": [
    "### Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5fc3e3d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 410
    },
    "id": "c5fc3e3d",
    "outputId": "41f2e079-3105-4009-8c3c-e495388921c9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Accuracy => 0.7538015418346418\n",
      " Recall => 0.7963224893917963\n",
      " Precision => 0.7339329943944727\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_res(y_test, model.predict(x_test), 'Greens')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5lXaPhaSOEsH",
   "metadata": {
    "id": "5lXaPhaSOEsH"
   },
   "source": [
    "### Train Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d64edae",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 410
    },
    "id": "9d64edae",
    "outputId": "8f36ff2a-1f97-4496-8ecc-65c35b1f2c65"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Accuracy => 0.7504243881737163\n",
      " Recall => 0.793181496675626\n",
      " Precision => 0.7306965530722617\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_res(y_train, model.predict(x_train), 'Purples')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Dj9e36rFOUYS",
   "metadata": {
    "id": "Dj9e36rFOUYS"
   },
   "source": [
    "# Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7050cffa",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7050cffa",
    "outputId": "dde3f91e-fee8-47bc-b2dd-6d0f4d829fd5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'base_score': 0.5,\n",
       " 'booster': 'gbtree',\n",
       " 'colsample_bylevel': 1,\n",
       " 'colsample_bynode': 1,\n",
       " 'colsample_bytree': 1,\n",
       " 'gamma': 0,\n",
       " 'learning_rate': 0.1,\n",
       " 'max_delta_step': 0,\n",
       " 'max_depth': 3,\n",
       " 'min_child_weight': 1,\n",
       " 'missing': None,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': 1,\n",
       " 'nthread': None,\n",
       " 'objective': 'binary:logistic',\n",
       " 'random_state': 0,\n",
       " 'reg_alpha': 0,\n",
       " 'reg_lambda': 1,\n",
       " 'scale_pos_weight': 1,\n",
       " 'seed': None,\n",
       " 'silent': None,\n",
       " 'subsample': 1,\n",
       " 'verbosity': 1}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgboost.XGBClassifier().get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01fdd59d",
   "metadata": {
    "id": "01fdd59d"
   },
   "outputs": [],
   "source": [
    "params_dict={\n",
    "          'learning_rate_list' : [0.02, 0.05, 0.1, 0.3],\n",
    "          'max_depth_list' : [2, 3, 4], \n",
    "          'n_estimators_list' : [100, 200, 300], \n",
    "          'colsample_bytree' : [0.8, 1]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "C6gMjbFQO2JX",
   "metadata": {
    "id": "C6gMjbFQO2JX"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "uJ7X69uJOrYS",
   "metadata": {
    "id": "uJ7X69uJOrYS"
   },
   "outputs": [],
   "source": [
    "def my_roc_auc_score(model, X, y): \n",
    "  return roc_auc_score(y, model.predict_proba(X)[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "DehceMCRPwJT",
   "metadata": {
    "id": "DehceMCRPwJT"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold, cross_val_score, train_test_split, StratifiedKFold, GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9TOZmJKePivo",
   "metadata": {
    "id": "9TOZmJKePivo"
   },
   "outputs": [],
   "source": [
    "tuning_model = XGBClassifier(tree_method='gpu_hist',\n",
    "                             random_state = 42,\n",
    "                            eval_metric='auc',\n",
    "                            # use_label_encoder=False,\n",
    "                            Subsample=0.5\n",
    "                            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "wQ_LcVHhPSgB",
   "metadata": {
    "id": "wQ_LcVHhPSgB"
   },
   "outputs": [],
   "source": [
    "kfold = StratifiedKFold(n_splits=3,\n",
    "                        shuffle=True,\n",
    "                        random_state=42)\n",
    "\n",
    "grid_search = GridSearchCV(tuning_model,\n",
    "                            params_dict,\n",
    "                            scoring=my_roc_auc_score,\n",
    "                            cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2KqfDeFrQTtF",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2KqfDeFrQTtF",
    "outputId": "b72178a4-f0ad-45cb-c064-d2347dfce53f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.827831324881323 using {'colsample_bytree': 0.8, 'learning_rate_list': 0.02, 'max_depth_list': 2, 'n_estimators_list': 100}\n"
     ]
    }
   ],
   "source": [
    "grid_result = grid_search.fit(x_train, y_train)\n",
    "\n",
    "print(f\"Best: {grid_result.best_score_} using {grid_result.best_params_}\")\n",
    "best_params = grid_result.best_params_\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Iz2zirxoQDo_",
   "metadata": {
    "id": "Iz2zirxoQDo_"
   },
   "source": [
    "<font color='blue'>\n",
    "    Here we found the best parameters for our model\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Xs_nRbAeS4qO",
   "metadata": {
    "id": "Xs_nRbAeS4qO"
   },
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "GDILgACfS7eC",
   "metadata": {
    "id": "GDILgACfS7eC"
   },
   "outputs": [],
   "source": [
    "best_model = XGBClassifier( \n",
    "                            tree_method='gpu_hist',\n",
    "                            **grid_result.best_params_,\n",
    "                            Random_seed=123,\n",
    "                            eval_metric='auc',\n",
    "                            Subsample=0.5,\n",
    "                            )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "IcHQguqdTXeb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IcHQguqdTXeb",
    "outputId": "f4c2de1e-d332-4be7-a16d-be4720a186b5",
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "eval_set = [(x_test, y_test)]\n",
    "best_model.fit(x_train, y_train, early_stopping_rounds=10,  eval_set=eval_set, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "E4IZCcBGQ-fN",
   "metadata": {
    "id": "E4IZCcBGQ-fN"
   },
   "source": [
    "## Test Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "Dx1srePhQuIu",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 410
    },
    "id": "Dx1srePhQuIu",
    "outputId": "0c2ab3eb-0d5b-42fc-f1bc-c3de910fafb4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Accuracy => 0.7547917108706415\n",
      " Recall => 0.7900990099009901\n",
      " Precision => 0.7380103051922314\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_res(y_test, best_model.predict(x_test), 'Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4Wr4EmoVRBpy",
   "metadata": {
    "id": "4Wr4EmoVRBpy"
   },
   "source": [
    "## Test Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hbA1mYfvRDov",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 410
    },
    "id": "hbA1mYfvRDov",
    "outputId": "500374de-a693-40a8-f4e9-85fdb821a216"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Accuracy => 0.7524755976800113\n",
      " Recall => 0.791342481256189\n",
      " Precision => 0.7342652753166634\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_res(y_train, best_model.predict(x_train), 'Reds')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "kzJz9wX4xa6k",
   "metadata": {
    "id": "kzJz9wX4xa6k"
   },
   "source": [
    "<font color='blue'>\n",
    "    We used our Best Parameters to do another classification based on them. ALthough the parameters are different the change in the accuracy and other metrics very minimal because the initial parameters were ok and also the data is well parametrized.\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "CC7c7fqjUlA0",
   "metadata": {
    "id": "CC7c7fqjUlA0"
   },
   "source": [
    "# Plotting Parameters Effects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "l3Wr1t7rVDFb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "l3Wr1t7rVDFb",
    "outputId": "d58f9c99-23c6-4202-85d4-47dec2140ccd",
    "tags": []
   },
   "outputs": [],
   "source": [
    "acc_dict = {}\n",
    "for key in params_dict.keys():\n",
    "  temp_list = []\n",
    "  parameters = {\n",
    "        'tree_method' : 'gpu_hist',\n",
    "        'colsample_bytree' : 0.8, \n",
    "        \"learning_rate_list\" : 0.02,\n",
    "        'max_depth_list': 2, \n",
    "        'n_estimators_list': 100,\n",
    "        'Random_seed':123,\n",
    "        'eval_metric':'auc',\n",
    "        'Subsample':0.5,\n",
    "    }\n",
    "  for val in params_dict[key]:\n",
    "    if key in parameters.keys():\n",
    "      parameters[key] = val\n",
    "    sample_model = XGBClassifier( \n",
    "                              **parameters\n",
    "                              )\n",
    "    \n",
    "    eval_set = [(x_test, y_test)]\n",
    "    sample_model.fit(x_train, y_train, early_stopping_rounds=10,  eval_set=eval_set, verbose=True)\n",
    "    temp_list.append(accuracy_score(y_test, sample_model.predict(x_test)))\n",
    "\n",
    "  acc_dict[key] = temp_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "il-A4pv0x2Ip",
   "metadata": {
    "id": "il-A4pv0x2Ip"
   },
   "source": [
    "### Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8aQVJkyUuF_",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "id": "a8aQVJkyUuF_",
    "outputId": "e1e1ce7c-b28b-4d0f-d719-6432bbf57ed4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(411)\n",
    "ax1.set_ylabel('accuracy')\n",
    "ax1.set_title('learning rate')\n",
    "\n",
    "line, = ax1.plot(params_dict['learning_rate_list'], acc_dict['learning_rate_list'])\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(412)\n",
    "ax2.set_ylabel('accuracy')\n",
    "ax2.set_title('max depth')\n",
    "\n",
    "line, = ax2.plot(params_dict['max_depth_list'], acc_dict['max_depth_list'])\n",
    "\n",
    "ax3 = fig.add_subplot(413)\n",
    "ax3.set_ylabel('accuracy')\n",
    "ax3.set_title('n estimators')\n",
    "\n",
    "line, = ax3.plot(params_dict['n_estimators_list'], acc_dict['n_estimators_list'])\n",
    "\n",
    "\n",
    "ax4 = fig.add_subplot(414)\n",
    "ax4.set_ylabel('accuracy')\n",
    "ax4.set_title('colsample bytree')\n",
    "\n",
    "line, = ax4.plot(params_dict['colsample_bytree'], acc_dict['colsample_bytree'])\n",
    "\n",
    "plt.subplots_adjust(left=0.1,\n",
    "                    bottom=0.1, \n",
    "                    right=0.9, \n",
    "                    top=0.9, \n",
    "                    wspace=0.4, \n",
    "                    hspace=0.4)\n",
    "\n",
    "fig.set_figheight(5)\n",
    "fig.set_figwidth(10)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Zq5vO3MLyAMx",
   "metadata": {
    "id": "Zq5vO3MLyAMx"
   },
   "source": [
    "<font color='blue'>\n",
    "    As stated above changing the parameters does not have a large shift in the accuracy or other metrics because the dataset is clean and well parametrized and not very complicated.\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5GbEUW9zx8fV",
   "metadata": {
    "id": "5GbEUW9zx8fV"
   },
   "source": [
    "### Mean Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "Maz-Hp_V5F0S",
   "metadata": {
    "id": "Maz-Hp_V5F0S"
   },
   "outputs": [],
   "source": [
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a-suuYcW9YJm",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a-suuYcW9YJm",
    "outputId": "0e569d5e-23cb-4b65-ff42-a7eb16200930",
    "tags": []
   },
   "outputs": [],
   "source": [
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8LRmfhGr9_N4",
   "metadata": {
    "id": "8LRmfhGr9_N4"
   },
   "outputs": [],
   "source": [
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2zeGj5fi9N_O",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2zeGj5fi9N_O",
    "outputId": "573790c9-37af-49fd-bdae-748a8a9edaee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0.02: 0.827831324881323,\n",
       " 0.05: 0.827831324881323,\n",
       " 0.1: 0.827831324881323,\n",
       " 0.3: 0.827831324881323}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_score = {}\n",
    "copy_res = copy.deepcopy(grid_result.best_params_)\n",
    "copy_res.pop('learning_rate_list')\n",
    "\n",
    "for i, value in zip(means, params):\n",
    "  copy_value = copy.deepcopy(value)\n",
    "  copy_value.pop('learning_rate_list')\n",
    "  if copy_res == copy_value:\n",
    "    lr_score[value['learning_rate_list']] = i\n",
    "\n",
    "lr_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "BKDmfHCc_xNi",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "BKDmfHCc_xNi",
    "outputId": "9ce226fa-fac8-423c-a5da-d60f56e87b7d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'learning_rate')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(list(lr_score.keys()), list(lr_score.values()))\n",
    "plt.ylabel('mean score')\n",
    "plt.xlabel('learning_rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "NBNnNmBx_XJU",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NBNnNmBx_XJU",
    "outputId": "0a439738-434a-4f57-ddb5-872286767494"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{100: 0.827831324881323, 200: 0.827831324881323, 300: 0.827831324881323}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ne_score = {}\n",
    "copy_res = copy.deepcopy(grid_result.best_params_)\n",
    "copy_res.pop('n_estimators_list')\n",
    "\n",
    "for i, value in zip(means, params):\n",
    "  copy_value = copy.deepcopy(value)\n",
    "  copy_value.pop('n_estimators_list')\n",
    "  if copy_res == copy_value:\n",
    "    ne_score[value['n_estimators_list']] = i\n",
    "\n",
    "ne_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7i1QIxtcAaHK",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "7i1QIxtcAaHK",
    "outputId": "51af59b2-3630-4997-f60e-81e161e65460"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f14405a4a10>]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j3XHmvhn1LOAyBvcP9gBXdMPmr6+5dXcF8C9zb+ZLdqJ3cSfxw+Dj1qPAdxlcy7qawTWrfwa+BHwKeHY9fYf7Jgbvzp8HemOua5rB9bX7up+5u+evBw52bfcCrx5zXbd383GgW0jPGxr/jm6+DgOvGmddXfttwO/MGzvO+XoZg8syB4b+3bZMeo0tUtdE19gidU10jR2rrkmvMeBFwP6uri/w9Ld+XsDgDW8a+FvgzK59Tbc/3fW/4ESP7X+BIEmNa+HSjSRpEQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJatz/A1JRMvYDe2VAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(list(ne_score.keys()), list(ne_score.values()))\n",
    "plt.ylabel('mean score')\n",
    "plt.xlabel('n_estimators')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "Wm1ckCeQ_Xlu",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Wm1ckCeQ_Xlu",
    "outputId": "4a77df53-4959-4c02-a231-32629e8bf214"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2: 0.827831324881323, 3: 0.827831324881323, 4: 0.827831324881323}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md_score = {}\n",
    "copy_res = copy.deepcopy(grid_result.best_params_)\n",
    "copy_res.pop('max_depth_list')\n",
    "\n",
    "for i, value in zip(means, params):\n",
    "  copy_value = copy.deepcopy(value)\n",
    "  copy_value.pop('max_depth_list')\n",
    "  if copy_res == copy_value:\n",
    "    md_score[value['max_depth_list']] = i\n",
    "\n",
    "md_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1UppgP5RAc8e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "1UppgP5RAc8e",
    "outputId": "a68606c8-c60e-4415-9ed9-f7da15709f13"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1440511d10>]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAQAElEQVR4nO3cf7BcZX3H8ffHBIi/+KFJp0rAxBZHgsVK71BLbYtSaoxCdOwfQWmlpVJbYVqlVRwZi/zTXzPVaaV20HFQbMXU1k5UHKo1nXYUhBsgweBEY1AIdOoFf5VxCgW//WNP9LDe5O41u3uTx/drZuee8zzP2fPdJ2c/9+w5e5OqQpLUrsctdQGSpMky6CWpcQa9JDXOoJekxhn0ktS45UtdwLCVK1fWmjVrlroMSTqsbNu27f6qWjVf3yEX9GvWrGF2dnapy5Ckw0qSr+2vz0s3ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjRgr6JOuT7EqyO8ll8/SfmGRrktuS7Eiyodd3apIbk+xMckeSFeN8AZKkA1u+0IAky4CrgLOBvcAtSbZU1Z29YZcDm6vq3UnWAdcDa5IsBz4I/EZVbU/yVOD/xv4qJEn7NcoZ/enA7qraU1UPA9cBG4fGFHB0t3wMcF+3/GvAjqraDlBVD1TVowdftiRpVKME/fHAPb31vV1b3xXA+Un2Mjibv6RrfxZQSW5IcmuSN823gyQXJZlNMjs3N7eoFyBJOrBx3Yw9D7imqlYDG4BrkzyOwaWhFwCv7n6+IslZwxtX1dVVNVNVM6tWrRpTSZIkGC3o7wVO6K2v7tr6LgQ2A1TVjcAKYCWDs///qKr7q+q7DM72TzvYoiVJoxsl6G8BTkqyNsmRwCZgy9CYu4GzAJKczCDo54AbgJ9J8oTuxuyvAHciSZqaBb91U1WPJLmYQWgvA95XVTuTXAnMVtUW4FLgPUnewODG7AVVVcA3k/wVg18WBVxfVZ+Y1IuRJP2wDPL40DEzM1Ozs7NLXYYkHVaSbKuqmfn6/MtYSWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGjdS0CdZn2RXkt1JLpun/8QkW5PclmRHkg3z9D+Y5I/GVbgkaTQLBn2SZcBVwEuAdcB5SdYNDbsc2FxVzwM2AX871P9XwCcPvlxJ0mKNckZ/OrC7qvZU1cPAdcDGoTEFHN0tHwPct68jycuBu4CdB1+uJGmxRgn644F7eut7u7a+K4Dzk+wFrgcuAUjyJODNwNsPtIMkFyWZTTI7Nzc3YumSpFGM62bsecA1VbUa2ABcm+RxDH4BvKOqHjzQxlV1dVXNVNXMqlWrxlSSJAlg+Qhj7gVO6K2v7tr6LgTWA1TVjUlWACuBnwd+PclfAMcC30vyv1X1roOuXJI0klGC/hbgpCRrGQT8JuBVQ2PuBs4CrklyMrACmKuqX9o3IMkVwIOGvCRN14KXbqrqEeBi4Abgiwy+XbMzyZVJzu2GXQq8Nsl24EPABVVVkypakjS6HGp5PDMzU7Ozs0tdhiQdVpJsq6qZ+fr8y1hJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktS45UtdwDi9/WM7ufO+7yx1GZL0I1n39KP5k3NOGfvzekYvSY1r6ox+Er8JJelw5xm9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWrcSEGfZH2SXUl2J7lsnv4Tk2xNcluSHUk2dO1nJ9mW5I7u54vG/QIkSQe24P9Hn2QZcBVwNrAXuCXJlqq6szfscmBzVb07yTrgemANcD9wTlXdl+Q5wA3A8WN+DZKkAxjljP50YHdV7amqh4HrgI1DYwo4uls+BrgPoKpuq6r7uvadwOOTHHXwZUuSRjVK0B8P3NNb38sPn5VfAZyfZC+Ds/lL5nmeVwK3VtVDwx1JLkoym2R2bm5upMIlSaMZ183Y84Brqmo1sAG4Nsn3nzvJKcCfA78738ZVdXVVzVTVzKpVq8ZUkiQJRgv6e4ETeuuru7a+C4HNAFV1I7ACWAmQZDXwUeA3q+orB1uwJGlxRgn6W4CTkqxNciSwCdgyNOZu4CyAJCczCPq5JMcCnwAuq6rPjq9sSdKoFgz6qnoEuJjBN2a+yODbNTuTXJnk3G7YpcBrk2wHPgRcUFXVbffTwNuS3N49fmIir0SSNK8M8vjQMTMzU7Ozs0tdhiQdVpJsq6qZ+fr8y1hJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1LiRgj7J+iS7kuxOctk8/Scm2ZrktiQ7kmzo9b2l225XkhePs3hJ0sKWLzQgyTLgKuBsYC9wS5ItVXVnb9jlwOaqeneSdcD1wJpueRNwCvB04NNJnlVVj477hUiS5jfKGf3pwO6q2lNVDwPXARuHxhRwdLd8DHBft7wRuK6qHqqqu4Dd3fNJkqZklKA/Hrint763a+u7Ajg/yV4GZ/OXLGJbSdIEjetm7HnANVW1GtgAXJtk5OdOclGS2SSzc3NzYypJkgSjBf29wAm99dVdW9+FwGaAqroRWAGsHHFbqurqqpqpqplVq1aNXr0kaUGjBP0twElJ1iY5ksHN1S1DY+4GzgJIcjKDoJ/rxm1KclSStcBJwM3jKl6StLAFv3VTVY8kuRi4AVgGvK+qdia5Epitqi3ApcB7kryBwY3ZC6qqgJ1JNgN3Ao8Ar/cbN5I0XRnk8aFjZmamZmdnl7oMSTqsJNlWVTPz9fmXsZLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY1LVS11DY+RZA742kE8xUrg/jGVM07WtTjWtTjWtTgt1vWMqlo1X8chF/QHK8lsVc0sdR3DrGtxrGtxrGtxftzq8tKNJDXOoJekxrUY9FcvdQH7YV2LY12LY12L82NVV3PX6CVJj9XiGb0kqcegl6TGHRZBn+SEJFuT3JlkZ5I/mGdMkvx1kt1JdiQ5rdf3miRf7h6vmXJdr+7quSPJ55I8t9f31a799iSzU67rzCTf7vZ9e5K39frWJ9nVzeVlU67rj3s1fSHJo0me0vVNar5WJLk5yfaurrfPM+aoJB/u5uTzSdb0+t7Ste9K8uIp1/XGbj53JPm3JM/o9T3am8stU67rgiRzvf3/Tq9vUu/HUep6R6+mLyX5Vq9vIvPVe/5lSW5L8vF5+iZ7fFXVIf8Angac1i0/GfgSsG5ozAbgk0CA5wOf79qfAuzpfh7XLR83xbrO2Lc/4CX76urWvwqsXKL5OhP4+DzbLgO+AjwTOBLYPrztJOsaGn8O8JkpzFeAJ3XLRwCfB54/NOb3gb/rljcBH+6W13VzdBSwtpu7ZVOs64XAE7rl39tXV7f+4LjnahF1XQC8a55tJ/l+XLCuofGXAO+b9Hz1nv+NwD/s53030ePrsDijr6r/qqpbu+X/Ab4IHD80bCPwgRq4CTg2ydOAFwOfqqpvVNU3gU8B66dVV1V9rtsvwE3A6nHs+2DrOoDTgd1VtaeqHgauYzC3S1HXecCHxrHvBeqqqnqwWz2iewx/S2Ej8P5u+SPAWUnStV9XVQ9V1V3AbgZzOJW6qmprVX23W53W8TXKfO3PJN+Pi61rKscXQJLVwEuB9+5nyESPr8Mi6Pu6jzTPY/Dbuu944J7e+t6ubX/t06qr70IGnzr2KeBfk2xLctG4axqhrl/oPuZ+MskpXdshMV9JnsAgAP6p1zyx+eo+Vt8OfJ1BEO33+KqqR4BvA09lwvM1Ql19w8fXiiSzSW5K8vJx1bSIul7ZXVL6SJITurZDYr66S1xrgc/0mic2X8A7gTcB39tP/0SPr8Mq6JM8icEb/w+r6jtLXc8+o9SV5IUM3ohv7jW/oKpOY3BJ5/VJfnmKdd3K4P/GeC7wN8C/jHPfB1HXPucAn62qb/TaJjZfVfVoVf0sgzPi05M8Z1zPfTBGrSvJ+cAM8Je95mfU4M/pXwW8M8lPTbGujwFrqupUBmft7x9+jklYxL/jJuAjVfVor20i85XkZcDXq2rbOJ7vR3HYBH2SIxiEw99X1T/PM+Re4ITe+uqubX/t06qLJKcy+Mi2saoe2NdeVfd2P78OfJQxfeQfpa6q+s6+j7lVdT1wRJKVHALz1dnE0MfqSc5Xbx/fArbyw5cTvj8vSZYDxwAPMOH5GqEukvwq8Fbg3Kp6qLfNvvnaA/w7g09QU6mrqh7o1fJe4Oe65SWfr86Bjq9xz9cvAucm+SqDS6EvSvLBoTGTPb4We1F/KR4MbrJ8AHjnAca8lMfejL25fnDz5y4GN36O65afMsW6TmRwXe2MofYnAk/uLX8OWD/Fun6SH/zB3OnA3d12yxncIFvLD27GnjKturpxxwDfAJ44pflaBRzbLT8e+E/gZUNjXs9jb5Zt7pZP4bE3y/Ywvpuxo9T1PAY36E4aaj8OOKpbXgl8mfHdVB+lrqf1ll8B3NQtT/L9uGBdXd+zGdzYzzTma2jfZzL/zdiJHl9jfRGTegAvYHB9dgdwe/fYALwOeF03JsBV3UF/BzDT2/63GYTtbuC3plzXe4Fv9vpnu/Zndv+A24GdwFunXNfF3X63M7iJd0Zv+w0MvhHzlWnX1Y27gMENqP62k5yvU4Hburq+ALyta7+SwVkywArgH7tj6Gbgmb3t39rN1S7gJVOu69PAf/fmc0vXfkb3Ptje/bxwynX9ae/42go8u7f9pN6PC9bVrV8B/NnQthObr6H9nEkX9NM8vvwvECSpcYfNNXpJ0o/GoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+38ktRn0KgHjWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(list(md_score.keys()), list(md_score.values()))\n",
    "plt.ylabel('mean score')\n",
    "plt.xlabel('max_depth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0pqK0YTn_YCh",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0pqK0YTn_YCh",
    "outputId": "a88d681e-0087-4a7e-86a4-7e77faf9847e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0.8: 0.827831324881323, 1: 0.827794186241678}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cb_score = {}\n",
    "copy_res = copy.deepcopy(grid_result.best_params_)\n",
    "copy_res.pop('colsample_bytree')\n",
    "\n",
    "for i, value in zip(means, params):\n",
    "  copy_value = copy.deepcopy(value)\n",
    "  copy_value.pop('colsample_bytree')\n",
    "  if copy_res == copy_value:\n",
    "    cb_score[value['colsample_bytree']] = i\n",
    "\n",
    "cb_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6IcGIt-gAfVc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "6IcGIt-gAfVc",
    "outputId": "72d613b6-5d68-4783-bed4-ddd374c6ff2b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1440480fd0>]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(list(cb_score.keys()), list(cb_score.values()))\n",
    "plt.ylabel('mean score')\n",
    "plt.xlabel('max_depth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "KbO53cmbAjOd",
   "metadata": {
    "id": "KbO53cmbAjOd"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Diabetes_Tree_Classifier.ipynb",
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}