diabetes-eda-missing-values.ipynb

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   "source": [
    "from pandas import read_csv, DataFrame\n",
    "from numpy import linspace\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier, GradientBoostingClassifier, RandomForestClassifier\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "amazing-xerox",
   "metadata": {
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   "source": [
    "pima = read_csv('../input/pima-indians-diabetes-database/diabetes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "varied-bicycle",
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
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       "      <th>1</th>\n",
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       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "id": "plain-archive",
   "metadata": {
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     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "pima.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "modified-wallet",
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     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</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>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pima.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "loving-airline",
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     "start_time": "2021-04-13T09:37:29.653613",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "chicken-valentine",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values 0: 5\n",
      "Outcome\n",
      "0    3\n",
      "1    2\n",
      "Name: Outcome, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('Values 0:', repr(pima[pima.Glucose == 0].shape[0]))\n",
    "print(pima[pima.Glucose == 0].groupby('Outcome')['Outcome'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "emotional-memory",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T09:37:29.817742Z",
     "iopub.status.busy": "2021-04-13T09:37:29.814510Z",
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     "status": "completed"
    },
    "tags": []
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   "outputs": [],
   "source": [
    "Glucose_0 = pima[(pima['Glucose']== 0)]\n",
    "pima[(pima['Glucose']== 0) & (pima['Outcome'] == 0)] = Glucose_0[Glucose_0['Outcome']== 0].replace(0, pima[(pima['Outcome']== 0)].mean())\n",
    "pima[(pima['Glucose']== 0) & (pima['Outcome'] == 1)] = Glucose_0[Glucose_0['Outcome']== 1].replace(0, pima[(pima['Outcome']== 1)].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "signed-jamaica",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T09:37:29.890517Z",
     "iopub.status.busy": "2021-04-13T09:37:29.889281Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values 0: 35\n",
      "Outcome\n",
      "0    19\n",
      "1    16\n",
      "Name: Outcome, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Values 0:\", repr(pima[pima.BloodPressure == 0].shape[0]))\n",
    "print(pima[pima.BloodPressure == 0].groupby('Outcome')['Outcome'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "narrow-storm",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "BloodPressure_0 = pima[(pima['BloodPressure']== 0)]\n",
    "pima[(pima['BloodPressure']== 0) & (pima['Outcome'] == 0)] = BloodPressure_0[BloodPressure_0['Outcome']== 0].replace(0, pima[(pima['Outcome']== 0)].mean())\n",
    "pima[(pima['BloodPressure']== 0) & (pima['Outcome'] == 1)] = BloodPressure_0[BloodPressure_0['Outcome']== 1].replace(0, pima[(pima['Outcome']== 1)].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dominant-casting",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T09:37:30.040744Z",
     "iopub.status.busy": "2021-04-13T09:37:30.039667Z",
     "iopub.status.idle": "2021-04-13T09:37:30.044870Z",
     "shell.execute_reply": "2021-04-13T09:37:30.044100Z"
    },
    "papermill": {
     "duration": 0.038376,
     "end_time": "2021-04-13T09:37:30.045044",
     "exception": false,
     "start_time": "2021-04-13T09:37:30.006668",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values 0: 194\n",
      "Outcome\n",
      "0    121\n",
      "1     73\n",
      "Name: Outcome, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Values 0: \" + repr(pima[pima.SkinThickness == 0].shape[0]))\n",
    "print(pima[pima.SkinThickness == 0].groupby('Outcome')['Outcome'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "interior-wisdom",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T09:37:30.125979Z",
     "iopub.status.busy": "2021-04-13T09:37:30.119672Z",
     "iopub.status.idle": "2021-04-13T09:37:30.133910Z",
     "shell.execute_reply": "2021-04-13T09:37:30.133226Z"
    },
    "papermill": {
     "duration": 0.06719,
     "end_time": "2021-04-13T09:37:30.134112",
     "exception": false,
     "start_time": "2021-04-13T09:37:30.066922",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "SkinThickness_0 = pima[(pima['SkinThickness']== 0)]\n",
    "pima[(pima['SkinThickness']== 0) & (pima['Outcome'] == 0)] = SkinThickness_0[SkinThickness_0['Outcome']== 0].replace(0, pima[(pima['Outcome']== 0)].mean())\n",
    "pima[(pima['SkinThickness']== 0) & (pima['Outcome'] == 1)] = SkinThickness_0[SkinThickness_0['Outcome']== 1].replace(0, pima[(pima['Outcome']== 1)].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "uniform-texture",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T09:37:30.192311Z",
     "iopub.status.busy": "2021-04-13T09:37:30.191597Z",
     "iopub.status.idle": "2021-04-13T09:37:30.195479Z",
     "shell.execute_reply": "2021-04-13T09:37:30.196542Z"
    },
    "papermill": {
     "duration": 0.040996,
     "end_time": "2021-04-13T09:37:30.196906",
     "exception": false,
     "start_time": "2021-04-13T09:37:30.155910",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de casos anormais na espessura das dobras cutâneas: 2\n",
      "445    63.0\n",
      "579    99.0\n",
      "Name: SkinThickness, dtype: float64\n",
      "Outcome\n",
      "1    2\n",
      "Name: Outcome, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Número de casos anormais na espessura das dobras cutâneas: \" + repr(pima[pima.SkinThickness > 60].shape[0]))\n",
    "print(pima[pima.SkinThickness > 60]['SkinThickness'])\n",
    "print(pima[pima.SkinThickness > 60].groupby('Outcome')['Outcome'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "naked-korea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T09:37:30.246363Z",
     "iopub.status.busy": "2021-04-13T09:37:30.245695Z",
     "iopub.status.idle": "2021-04-13T09:37:30.253158Z",
     "shell.execute_reply": "2021-04-13T09:37:30.252561Z"
    },
    "papermill": {
     "duration": 0.033234,
     "end_time": "2021-04-13T09:37:30.253330",
     "exception": false,
     "start_time": "2021-04-13T09:37:30.220096",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pima['SkinThickness'].iloc[579] = pima['SkinThickness'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "wound-resort",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T09:37:30.301097Z",
     "iopub.status.busy": "2021-04-13T09:37:30.300324Z",
     "iopub.status.idle": "2021-04-13T09:37:30.313986Z",
     "shell.execute_reply": "2021-04-13T09:37:30.313242Z"
    },
    "papermill": {
     "duration": 0.039272,
     "end_time": "2021-04-13T09:37:30.314360",
     "exception": false,
     "start_time": "2021-04-13T09:37:30.275088",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Values: 141\n",
      "Outcome\n",
      "0    94\n",
      "1    47\n",
      "Name: Outcome, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Missing Values: \" + repr(pima[pima.Insulin == 0].shape[0]))\n",
    "print(pima[pima.Insulin == 0].groupby('Outcome')['Outcome'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "published-account",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T09:37:30.366944Z",
     "iopub.status.busy": "2021-04-13T09:37:30.365916Z",
     "iopub.status.idle": "2021-04-13T09:37:30.391830Z",
     "shell.execute_reply": "2021-04-13T09:37:30.392362Z"
    },
    "papermill": {
     "duration": 0.054759,
     "end_time": "2021-04-13T09:37:30.392602",
     "exception": false,
     "start_time": "2021-04-13T09:37:30.337843",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "Insulin_0 = pima[(pima['Insulin'] == 0)]\n",
    "pima[(pima['Insulin']== 0) & (pima['Outcome'] == 0)] = Insulin_0[Insulin_0['Outcome']== 0].replace(0, pima[(pima['Outcome']== 0)].mean())\n",
    "pima[(pima['Insulin']== 0) & (pima['Outcome'] == 1)] = Insulin_0[Insulin_0['Outcome']== 1].replace(0, pima[(pima['Outcome']== 1)].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "known-delta",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T09:37:30.448158Z",
     "iopub.status.busy": "2021-04-13T09:37:30.447395Z",
     "iopub.status.idle": "2021-04-13T09:37:30.451326Z",
     "shell.execute_reply": "2021-04-13T09:37:30.452221Z"
    },
    "papermill": {
     "duration": 0.037537,
     "end_time": "2021-04-13T09:37:30.452669",
     "exception": false,
     "start_time": "2021-04-13T09:37:30.415132",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Values: 1\n",
      "Outcome\n",
      "0    1\n",
      "Name: Outcome, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"Missing Values: \" + repr(pima[pima.BMI == 0].shape[0]))\n",
    "print(pima[pima.BMI == 0].groupby('Outcome')['Outcome'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "liberal-airfare",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T09:37:30.505688Z",
     "iopub.status.busy": "2021-04-13T09:37:30.504241Z",
     "iopub.status.idle": "2021-04-13T09:37:30.527167Z",
     "shell.execute_reply": "2021-04-13T09:37:30.527814Z"
    },
    "papermill": {
     "duration": 0.05094,
     "end_time": "2021-04-13T09:37:30.528038",
     "exception": false,
     "start_time": "2021-04-13T09:37:30.477098",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Substitua o valor zero pelo valor médio das classes\n",
    "BMI_0 = pima[(pima['BMI']== 0)] \n",
    "pima[(pima['BMI']== 0) & (pima['Outcome'] == 0)] = BMI_0[BMI_0['Outcome']== 0].replace(0, pima[(pima['Outcome']== 0)].mean())\n",
    "pima[(pima['BMI']== 0) & (pima['Outcome'] == 1)] = BMI_0[BMI_0['Outcome']== 1].replace(0, pima[(pima['Outcome']== 1)].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "friendly-daughter",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T09:37:30.583486Z",
     "iopub.status.busy": "2021-04-13T09:37:30.582637Z",
     "iopub.status.idle": "2021-04-13T09:37:31.315933Z",
     "shell.execute_reply": "2021-04-13T09:37:31.314999Z"
    },
    "papermill": {
     "duration": 0.761526,
     "end_time": "2021-04-13T09:37:31.316108",
     "exception": false,
     "start_time": "2021-04-13T09:37:30.554582",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,10))\n",
    "sns.heatmap(pima.corr(), annot=True, cmap=plt.cm.Blues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "stylish-duration",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T09:37:31.377272Z",
     "iopub.status.busy": "2021-04-13T09:37:31.376356Z",
     "iopub.status.idle": "2021-04-13T09:37:31.494408Z",
     "shell.execute_reply": "2021-04-13T09:37:31.493784Z"
    },
    "papermill": {
     "duration": 0.151616,
     "end_time": "2021-04-13T09:37:31.494572",
     "exception": false,
     "start_time": "2021-04-13T09:37:31.342956",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Outcome', ylabel='count'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+emybT1dVJbkWuKOqrgVIcj2whtGDCY8EvjQ6w8UBjB73IA3KUNBSdwNwynghyUHAEcA9PPho+sC9+HN/1N5/PDaeW14GPABcVlWv2Ys/U3pMnj7SUrcNeGqS06B/Pel7GX0N5M3AUUn2S7Ka0bfUzfm/JE9q48uBU5Mc1vZxaKv/O6OnzgK8FvjXPejrSuClSZ7T9vm0JM/d019O2lOGgpa0Gj0R8ncY/ad+E/CfwH2M7i76EvBfjI4mzgG+NrbpZuCaJBe3p8q+E/hCkm8A72tz3gScnuQa4PX85FrDJH3tBt4AfKhtfwXwvJ/295Qm5VNSJUmdRwqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSuv8HHGGod05G2B8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(pima['Outcome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fantastic-desperate",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T09:37:31.580643Z",
     "iopub.status.busy": "2021-04-13T09:37:31.555994Z",
     "iopub.status.idle": "2021-04-13T09:37:33.259534Z",
     "shell.execute_reply": "2021-04-13T09:37:33.258042Z"
    },
    "papermill": {
     "duration": 1.738293,
     "end_time": "2021-04-13T09:37:33.259751",
     "exception": false,
     "start_time": "2021-04-13T09:37:31.521458",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1080 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = pima.columns[:8]\n",
    "plt.subplots(figsize=(18,15))\n",
    "length = len(columns)\n",
    "\n",
    "for i,j in zip(columns,range(length)):\n",
    "    plt.subplot((length/2), 3, j+1)\n",
    "    plt.subplots_adjust(wspace=0.2, hspace=0.5)\n",
    "    pima[i].hist(bins=20, edgecolor='black')\n",
    "    plt.title(i)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "radical-mattress",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T09:37:33.326994Z",
     "iopub.status.busy": "2021-04-13T09:37:33.325822Z",
     "iopub.status.idle": "2021-04-13T09:37:34.342772Z",
     "shell.execute_reply": "2021-04-13T09:37:34.343740Z"
    },
    "papermill": {
     "duration": 1.055376,
     "end_time": "2021-04-13T09:37:34.343993",
     "exception": false,
     "start_time": "2021-04-13T09:37:33.288617",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                    AxesSubplot(0.125,0.657941;0.227941x0.222059)\n",
       "Glucose                     AxesSubplot(0.398529,0.657941;0.227941x0.222059)\n",
       "BloodPressure               AxesSubplot(0.672059,0.657941;0.227941x0.222059)\n",
       "SkinThickness                  AxesSubplot(0.125,0.391471;0.227941x0.222059)\n",
       "Insulin                     AxesSubplot(0.398529,0.391471;0.227941x0.222059)\n",
       "BMI                         AxesSubplot(0.672059,0.391471;0.227941x0.222059)\n",
       "DiabetesPedigreeFunction          AxesSubplot(0.125,0.125;0.227941x0.222059)\n",
       "Age                            AxesSubplot(0.398529,0.125;0.227941x0.222059)\n",
       "Outcome                        AxesSubplot(0.672059,0.125;0.227941x0.222059)\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pima.plot(kind='box', subplots=True, layout=(3,3), figsize=(14,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "worst-italy",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T09:37:34.422712Z",
     "iopub.status.busy": "2021-04-13T09:37:34.418943Z",
     "iopub.status.idle": "2021-04-13T09:37:34.428288Z",
     "shell.execute_reply": "2021-04-13T09:37:34.427561Z"
    },
    "papermill": {
     "duration": 0.049265,
     "end_time": "2021-04-13T09:37:34.428450",
     "exception": false,
     "start_time": "2021-04-13T09:37:34.379185",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x = pima.drop(columns=['Outcome'])\n",
    "y = pima['Outcome']\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "x = scaler.fit_transform(x)\n",
    "\n",
    "x_treino, x_teste, y_treino, y_teste = train_test_split(x , y, test_size=0.20, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "civilian-baking",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T09:37:34.500130Z",
     "iopub.status.busy": "2021-04-13T09:37:34.499103Z",
     "iopub.status.idle": "2021-04-13T09:37:35.097046Z",
     "shell.execute_reply": "2021-04-13T09:37:35.096188Z"
    },
    "papermill": {
     "duration": 0.635959,
     "end_time": "2021-04-13T09:37:35.097284",
     "exception": false,
     "start_time": "2021-04-13T09:37:34.461325",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Classifier 0.8441558441558441\n",
      "ExtraTrees Classifier 0.8246753246753247\n",
      "Gradient Boosting Classifier 0.8961038961038961\n",
      "Random Forest Classifier 0.8766233766233766\n"
     ]
    }
   ],
   "source": [
    "modelos = []\n",
    "modelos.append(('Decision Tree Classifier',DecisionTreeClassifier()))\n",
    "modelos.append(('ExtraTrees Classifier',ExtraTreesClassifier()))\n",
    "modelos.append(('Gradient Boosting Classifier',GradientBoostingClassifier()))\n",
    "modelos.append(('Random Forest Classifier',RandomForestClassifier()))\n",
    "\n",
    "row_index = 0 \n",
    "\n",
    "for nome, modelo in modelos:\n",
    "    modelo.fit(x_treino, y_treino)\n",
    "    prev = modelo.predict(x_teste)    \n",
    "    acc = accuracy_score(y_teste, prev)\n",
    "    row_index += 1\n",
    "    print(nome, acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "charitable-wrapping",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T09:37:35.173568Z",
     "iopub.status.busy": "2021-04-13T09:37:35.172795Z",
     "iopub.status.idle": "2021-04-13T09:37:59.784211Z",
     "shell.execute_reply": "2021-04-13T09:37:59.783533Z"
    },
    "papermill": {
     "duration": 24.653924,
     "end_time": "2021-04-13T09:37:59.784420",
     "exception": false,
     "start_time": "2021-04-13T09:37:35.130496",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier 0.8961038961038961\n"
     ]
    }
   ],
   "source": [
    "params = {'n_estimators' : [1, 2, 4, 8, 16, 32, 64, 100, 200],\n",
    "          'learning_rate' : [0.1, 0.01],\n",
    "          'max_depth' : [3,4,5]}\n",
    "         \n",
    "model = GridSearchCV(GradientBoostingClassifier(), params, cv=10, n_jobs=-1).fit(x_treino, y_treino)\n",
    "\n",
    "prev = GradientBoostingClassifier(**model.best_params_).fit(x_treino, y_treino)\n",
    "prev = model.predict(x_teste)\n",
    "acc = accuracy_score(y_teste, prev)\n",
    "print(nome, acc)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.9"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 41.619851,
   "end_time": "2021-04-13T09:38:01.237007",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-04-13T09:37:19.617156",
   "version": "2.3.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}